1 January 2010 

2 EPA/600/R-09/019F 

Integrated Science Assessment for 

Carbon Monoxide 

National Center for Environmental Assessment-RTP Division 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 
January 2010 ii 

Disclaimer 

This document has been reviewed in accordance with U.S. Environmental Protection Agency 

policy and approved for publication. Mention of trade names or commercial products does not 

constitute endorsement or recommendation for use. 
January 2010 iii 

Table of Contents 

TABLE OF CONTENTS______________________________________________________ III

LIST OF TABLES ________________________________________________________ VIII

LIST OF FIGURES ________________________________________________________ XII

CO PROJECT TEAM_____________________________________________________ XVIII

AUTHORS, CONTRIBUTORS, AND REVIEWERS ___________________________________ XX

CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE CO NAAQS REVIEW PANEL ___________ XXIV

ACRONYMS AND ABBREVIATIONS ___________________________________________ XXVI

CHAPTER 1. INTRODUCTION _______________________________________________ 1-1

1.1. Legislative Requirements _______________________________________________________1-2

1.2. History of the NAAQS for CO____________________________________________________4538H1-3

1.3. ISA Development _____________________________________________________________4539H1-4

1.4. Document Organization ________________________________________________________450H1-6

1.5. Document Scope _____________________________________________________________451H1-7

1.6. EPA Framework for Causal Determination__________________________________________452H1-8

1.6.1. Scientific Evidence Used in Establishing Causality_____________________________453H1-8

1.6.2. Association and Causation _______________________________________________45H1-9

1.6.3. Evaluating Evidence for Inferring Causation __________________________________45H1-9

1.6.4. Application of Framework for Causal Determination___________________________456H1-12

1.6.5. Determination of Causality ______________________________________________457H1-13

1.6.5.1. Effects on Human Populations ____________________________________458H1-15

1.6.5.2. Effects on Ecosystems or Public Welfare____________________________459H1-16

1.6.6. Concepts in Evaluating Adversity of Health Effects____________________________450H1-16

1.7. Summary __________________________________________________________________451H1-17

References ____________________________________________________________________452H1-18

CHAPTER 2. INTEGRATIVE OVERVIEW ________________________________________ 453H2-1

2.1. Ambient CO Sources and Concentrations __________________________________________45H2-1

2.2. Climate Forcing Effects ________________________________________________________45H2-2

2.3. Exposure to Ambient CO _______________________________________________________456H2-3

2.4. Dosimetry, Pharmacokinetics, and Mode of Action ___________________________________457H2-4

2.4.1. Dosimetry and Pharmacokinetics __________________________________________458H2-4

2.4.2. Mode of Action_________________________________________________________459H2-4

2.5. Health Effects ________________________________________________________________4560H2-5

2.5.1. Cardiovascular Morbidity_________________________________________________4561H2-5

2.5.2. Central Nervous System Effects ___________________________________________4562H2-6
January 2010 iv 

2.5.3. Birth Outcomes and Developmental Effects __________________________________2-7

2.5.4. Respiratory Morbidity____________________________________________________2-8

2.5.5. Mortality ______________________________________________________________456H2-9

2.6. Policy-Relevant Considerations _________________________________________________456H2-10

2.6.1. Susceptible Populations ________________________________________________4567H2-10

2.6.2. Concentration- and Dose-Response Relationships ___________________________4568H2-12

2.7. Integration of CO Health Effects_________________________________________________4569H2-13

References ____________________________________________________________________4570H2-18

CHAPTER 3. SOURCE TO EXPOSURE _________________________________________ 4571H3-1

3.1. Introduction__________________________________________________________________4572H3-1

3.2. CO Sources, Emissions, and Chemistry ___________________________________________4573H3-1

3.2.1. Direct CO Emissions ____________________________________________________457H3-1

3.2.2. Secondary CO Emissions and Associated Chemistry___________________________457H3-9

3.3. CO Climate Forcing Effects ____________________________________________________4576H3-11

3.4. Ambient Measurements _______________________________________________________457H3-16

3.4.1. Ambient Measurement Instruments _______________________________________4578H3-16

3.4.2. Ambient Sampling Network Design________________________________________4579H3-19

3.4.2.1. Monitor Siting Requirements _____________________________________4580H3-19

3.4.2.2. Spatial and Temporal Coverage___________________________________4581H3-20

3.5. Environmental Concentrations __________________________________________________4582H3-28

3.5.1. Spatial Variability ______________________________________________________4583H3-28

3.5.1.1. National Scale ________________________________________________458H3-28

3.5.1.2. Urban Scale __________________________________________________458H3-37

3.5.1.3. Micro- to Neighborhood Scale and the Near-Road Environment__________4586H3-53

3.5.2. Temporal Variability ____________________________________________________4587H3-62

3.5.2.1. Multiyear Trends_______________________________________________458H3-62

3.5.2.2. Hourly Variation _______________________________________________4589H3-65

3.5.3. Associations with Copollutants ___________________________________________4590H3-68

3.5.4. Policy-Relevant Background _____________________________________________4591H3-72

3.5.4.1. Surface-Based Determinations ___________________________________4592H3-72

3.5.4.2. Limitations of Other Possible Methods______________________________4593H3-74

3.6. Issues in Exposure Assessment_________________________________________________459H3-76

3.6.1. Summary of Findings from 2000 CO AQCD _________________________________459H3-76

3.6.2. General Exposure Concepts _____________________________________________4596H3-76

3.6.3. Exposure Modeling ____________________________________________________4597H3-78

3.6.3.1. Stochastic Population-Based Time-Weighted Microenvironmental 

Exposure Models ______________________________________________4598H3-78

3.6.3.2. Using Spatial Models to Estimate Exposure _________________________459H3-79

3.6.4. Personal Exposure Monitors for CO _______________________________________460H3-82

3.6.5. Indoor Exposure to CO _________________________________________________4601H3-82

3.6.5.1. Infiltration of Ambient CO ________________________________________4602H3-82

3.6.5.2. Exposure to Nonambient CO _____________________________________4603H3-83

3.6.6. Exposure Assessment Studies at Different Spatial Scales ______________________460H3-85

3.6.6.1. Neighborhood to Urban Scale Studies of Ambient CO Exposure _________4605H3-85

3.6.6.2. Microscale Studies of Ambient CO Exposure: Near-Road and On-Road 

Exposures____________________________________________________460H3-87

3.6.7. Association between Personal CO Exposure and Copollutants __________________4607H3-90

3.6.8. Implications for Epidemiology ____________________________________________4608H3-90

3.6.8.1. Measurement Error_____________________________________________4609H3-91

3.6.8.2. Exposure Issues Related to Nonambient CO ________________________4610H3-92

3.6.8.3. Spatial Variability ______________________________________________461H3-92

3.6.8.4. Temporal Variability ____________________________________________4612H3-93
January 2010 v 

3.6.8.5. CO Exposure in Copollutant Mixtures ______________________________4613H3-94

3.6.8.6. Conclusions __________________________________________________461H3-94

3.7. Summary and Conclusions ____________________________________________________4615H3-95

3.7.1. CO Sources, Emissions, and Chemistry ____________________________________461H3-95

3.7.2. Climate Forcing Effects Related to CO _____________________________________4617H3-95

3.7.3. Ambient CO Measurements _____________________________________________4618H3-96

3.7.4. Environmental CO Concentrations ________________________________________4619H3-96

3.7.5. Exposure Assessment and Implications for Epidemiology ______________________4620H3-97

References ____________________________________________________________________4621H3-99

CHAPTER 4. DOSIMETRY AND PHARMACOKINETICS OF CARBON MONOXIDE_____________ 462H4-1

4.1. Introduction__________________________________________________________________4623H4-1

4.2. Carboxyhemoglobin Modeling ___________________________________________________462H4-1

4.2.1. The Coburn-Forster-Kane and Other Models _________________________________4625H4-1

4.2.2. Multicompartment Models ________________________________________________462H4-6

4.2.3. Model Comparison _____________________________________________________4627H4-8

4.2.4. Mathematical Model Usage_______________________________________________4628H4-8

4.3. Absorption, Distribution, and Elimination __________________________________________4629H4-11

4.3.1. Pulmonary Absorption __________________________________________________4630H4-11

4.3.1.1. Mass Transfer of Carbon Monoxide________________________________4631H4-12

4.3.1.2. Lung Diffusion of Carbon Monoxide________________________________4632H4-12

4.3.2. Tissue Uptake ________________________________________________________463H4-13

4.3.2.1. Respiratory Tract ______________________________________________463H4-13

4.3.2.2. Blood _______________________________________________________4635H4-13

4.3.2.3. Heart and Skeletal Muscle _______________________________________463H4-15

4.3.2.4. Other Tissues _________________________________________________4637H4-16

4.3.3. Pulmonary and Tissue Elimination ________________________________________4638H4-17

4.3.4. COHb Analysis Methods ________________________________________________4639H4-18

4.4. Conditions Affecting Uptake and Elimination _______________________________________460H4-19

4.4.1. Physical Activity _______________________________________________________461H4-19

4.4.2. Altitude______________________________________________________________462H4-19

4.4.3. Physical Characteristics ________________________________________________463H4-20

4.4.3.1. Fetal Pharmacokinetics _________________________________________46H4-20

4.4.4. Health Status _________________________________________________________465H4-21

4.5. Endogenous CO Production and Metabolism ______________________________________46H4-22

4.6. Summary and Conclusions ____________________________________________________467H4-26

References ____________________________________________________________________468H4-28

CHAPTER 5. INTEGRATED HEALTH EFFECTS ___________________________________ 469H5-1

5.1. Mode of Action of CO Toxicity ___________________________________________________4650H5-1

5.1.1. Introduction ___________________________________________________________4651H5-1

5.1.2. Hypoxic Mechanisms____________________________________________________4652H5-1

5.1.3. Nonhypoxic Mechanisms ________________________________________________4653H5-2

5.1.3.1. Nonhypoxic Mechanisms Reviewed in the 2000 CO AQCD ______________465H5-2

5.1.3.2. Recent Studies of Nonhypoxic Mechanisms __________________________465H5-3

5.1.3.3. Implications of Nonhypoxic Mechanisms _____________________________465H5-9

5.1.3.4. Summary ____________________________________________________4657H5-12

5.2. Cardiovascular Effects ________________________________________________________4658H5-12

5.2.1. Epidemiologic Studies with Short-Term Exposure_____________________________4659H5-12

5.2.1.1. Heart Rate and Heart Rate Variability ______________________________460H5-13

5.2.1.2. ECG Abnormalities Indicating Ischemia_____________________________461H5-16

5.2.1.3. Arrhythmia ___________________________________________________462H5-17
January 2010 vi 

5.2.1.4. Cardiac Arrest_________________________________________________463H5-18

5.2.1.5. Myocardial Infarction ___________________________________________46H5-19

5.2.1.6. Blood Pressure________________________________________________465H5-19

5.2.1.7. Vasomotor Function ____________________________________________46H5-19

5.2.1.8. Blood Markers of Coagulation and Inflammation ______________________467H5-20

5.2.1.9. Hospital Admissions and Emergency Department Visits ________________468H5-23

5.2.2. Epidemiologic Studies with Long-Term Exposure _____________________________469H5-40

5.2.3. Summary of Epidemiologic Studies of Exposure to CO and Cardiovascular 

Effects ______________________________________________________________4670H5-40

5.2.4. Controlled Human Exposure Studies ______________________________________4671H5-41

5.2.5. Toxicological Studies ___________________________________________________4672H5-44

5.2.5.1. Endothelial Dysfunction _________________________________________4673H5-44

5.2.5.2. Cardiac Remodeling Effects______________________________________467H5-46

5.2.5.3. Electrocardiographic Effects______________________________________4675H5-47

5.2.5.4. Summary of Cardiovascular Toxicology_____________________________467H5-47

5.2.6. Summary of Cardiovascular Effects _______________________________________467H5-47

5.2.6.1. Short-Term Exposure to CO______________________________________4678H5-47

5.2.6.2. Long-Term Exposure to CO ______________________________________4679H5-48

5.3. Central Nervous System Effects ________________________________________________4680H5-49

5.3.1. Controlled Human Exposure Studies ______________________________________4681H5-49

5.3.2. Toxicological Studies ___________________________________________________4682H5-49

5.3.3. Summary of Central Nervous System Effects ________________________________4683H5-49

5.4. Birth Outcomes and Developmental Effects________________________________________468H5-50

5.4.1. Epidemiologic Studies __________________________________________________4685H5-50

5.4.1.1. Preterm Birth _________________________________________________468H5-50

5.4.1.2. Birth Weight, Low Birth Weight, and Intrauterine Growth 

Restriction/Small for Gestational Age_______________________________4687H5-53

5.4.1.3. Congenital Anomalies __________________________________________468H5-60

5.4.1.4. Neonatal and Postneonatal Mortality _______________________________4689H5-61

5.4.1.5. Summary of Epidemiologic Studies of Birth Outcomes and 

Developmental Effects __________________________________________4690H5-63

5.4.2. Toxicological Studies of Birth Outcomes and Developmental Effects ______________4691H5-63

5.4.2.1. Birth Outcomes________________________________________________4692H5-63

5.4.2.2. Developmental Effects __________________________________________4693H5-68

5.4.3. Summary of Birth Outcomes and Developmental Effects _______________________469H5-79

5.5. Respiratory Effects ___________________________________________________________4695H5-81

5.5.1. Epidemiologic Studies with Short-Term Exposure_____________________________469H5-81

5.5.1.1. Pulmonary Function, Respiratory Symptoms, and Medication Use________4697H5-81

5.5.1.2. Respiratory Hospital Admissions, ED Visits and Physician Visits _________4698H5-88

5.5.2. Epidemiologic Studies with Long-Term Exposure _____________________________469H5-96

5.5.2.1. Pulmonary Function ____________________________________________470H5-96

5.5.2.2. Asthma and Asthma Symptoms ___________________________________4701H5-97

5.5.2.3. Respiratory Allergy and Other Allergic Responses ____________________4702H5-98

5.5.2.4. Summary of Associations between Long-Term Exposure to CO and 

Respiratory Morbidity ___________________________________________4703H5-98

5.5.3. Controlled Human Exposure Studies ______________________________________470H5-99

5.5.4. Toxicological Studies ___________________________________________________4705H5-99

5.5.5. Summary of Respiratory Health Effects ___________________________________4706H5-100

5.5.5.1. Short-Term Exposure to CO_____________________________________470H5-100

5.5.5.2. Long-Term Exposure to CO _____________________________________4708H5-101

5.6. Mortality __________________________________________________________________4709H5-101

5.6.1. Epidemiologic Studies with Short-Term Exposure to CO ______________________4710H5-101

5.6.1.1. Summary of Findings from 2000 CO AQCD ________________________471H5-101

5.6.1.2. Multicity Studies ______________________________________________4712H5-101

5.6.1.3. Meta-Analysis of All Criteria Pollutants ____________________________4713H5-106

5.6.1.4. Single-City Studies ____________________________________________471H5-107
January 2010 vii 

5.6.1.5. Summary of Mortality and Short-Term Exposure to CO _______________4715H5-109

5.6.2. Epidemiologic Studies with Long-Term Exposure to CO_______________________4716H5-109

5.6.2.1. U.S. Cohort Studies ___________________________________________471H5-110

5.6.2.2. U.S. Cross-Sectional Analysis ___________________________________4718H5-113

5.6.2.3. Summary of Mortality and Long-Term Exposure to CO ________________4719H5-114

5.7. Susceptible Populations______________________________________________________4720H5-114

5.7.1. Preexisting Disease___________________________________________________4721H5-116

5.7.1.1. Cardiovascular Disease ________________________________________472H5-116

5.7.1.2. Obstructive Lung Disease ______________________________________4723H5-117

5.7.1.3. Diabetes ____________________________________________________472H5-118

5.7.1.4. Anemia _____________________________________________________4725H5-118

5.7.2. Lifestage ___________________________________________________________4726H5-119

5.7.2.1. Older Adults _________________________________________________472H5-119

5.7.2.2. Gestational Development _______________________________________4728H5-120

5.7.3. Gender_____________________________________________________________4729H5-121

5.7.4. Altitude_____________________________________________________________4730H5-121

5.7.5. Exercise____________________________________________________________4731H5-122

5.7.6. Proximity to Roadways ________________________________________________4732H5-122

5.7.7. Medications and Other Substances ______________________________________473H5-123

5.7.8. Summary of Susceptible Populations _____________________________________473H5-123

5.8. Summary _________________________________________________________________4735H5-124

References ___________________________________________________________________4736H5-125

ANNEX A. ATMOSPHERIC SCIENCE __________________________________________ 473HA-1

ANNEX B. DOSIMETRY STUDIES ____________________________________________ 4738HB-1

References ____________________________________________________________________ 4739HB-6

ANNEX C. EPIDEMIOLOGY STUDIES __________________________________________ 470HC-1

References ___________________________________________________________________ 471HC-99

ANNEX D. CONTROLLED HUMAN EXPOSURE STUDIES ____________________________ 472HD-1

References ____________________________________________________________________ 473HD-4

ANNEX E. TOXICOLOGICAL STUDIES _________________________________________ 47HE-1

References ___________________________________________________________________ 475HE-23
January 2010 viii 

List of Tables 

0HTable 1-1. Aspects to aid in judging causality.__________________________________________________________476H1-13

1HTable 1-2. Weight of evidence for causal determination.__________________________________________________47H1-14

2HTable 2-1. Causal determinations for health effects categories. _____________________________________________478H2-5

3HTable 2-2. Range of mean and 99th percentile concentrations (ppm) in US and Canadian studies of 

short-term CO exposure and CVD hospitalizations._____________________________________________479H2-15

4HTable 3-1. Literature values for CO yields from hydrocarbons in per carbon units, except as noted. 

Specific hydrocarbons are noted in parentheses. ______________________________________________4750H3-10

5HTable 3-2. Performance specifications for analytical detection of CO, based on 40 CFR Part 53. __________________4751H3-18

6HTable 3-3. Counts of CO monitors by sampling scale meeting 75% completeness criteria for use in the 

U.S. during 2005-2007. __________________________________________________________________4752H3-21

7HTable 3-4. Proximity to CO monitors for the total population by city._________________________________________4753H3-27

8HTable 3-5. Proximity to CO monitors for adults aged 65 and older by city. ____________________________________475H3-28

9HTable 3-6. Distribution of 1-h avg CO concentration (ppm) derived from AQS data._____________________________475H3-31

10HTable 3-7. Distribution of 24-h avg CO concentration (ppm) derived from AQS data.____________________________4756H3-33

1HTable 3-8. Distribution of 1-h daily max CO concentration (ppm) derived from AQS data. ________________________475H3-34

12HTable 3-9. Distribution of 8-h daily max CO concentration (ppm) derived from AQS data. ________________________4758H3-35

13HTable 3-10. Table of intersampler 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 Denver, CO. ________________________________________________________________4759H3-40

14HTable 3-11. Table of intersampler 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 Los Angeles, CA. ____________________________________________________________4760H3-46

15HTable 3-12. National distribution of all hourly observations, 1-h daily max, 1-h daily average, and 8-h 

daily max concentration (ppm) derived from AQS data, based on monitor scale 

designations, 2005-2007. _________________________________________________________________4761H3-53

16HTable 3-13. Percentage of time exposed to ambient CO (adjusted to reflect the absence of nonambient 

CO from ETS and gas cooking), average CO exposures, and percentage of exposure 

estimated for the population. ______________________________________________________________4762H3-86

17HTable 4-1. Predicted COHb levels resulting from 1, 8, and 24 h CO exposures in a modeled human at 

rest ___________________________________________________________________________________4763H4-9

18HTable 4-2. CO concentration in pmol/mg wet weight tissue and fold tissue CO concentration changes 

(normalized to background tissue concentrations) – human.______________________________________476H4-16

19HTable 5-1. Responses to CO exposures at low and moderate concentrations. _________________________________4765H5-8

20HTable 5-2. Tissue concentration of CO following inhalation exposure. _______________________________________476H5-10

21HTable 5-3. Tissue concentration of CO following increased endogenous production.____________________________476H5-11

2HTable 5-4. Summary of studies investigating the effect of CO exposure on HRV parameters._____________________4768H5-16

23HTable 5-5. Summary of studies investigating the effect of CO exposure on cardiac arrhythmias. __________________4769H5-18
January 2010 ix 

24HTable 5-6. Summary of studies investigating the effect of CO exposure on blood markers of 

coagulation and inflammation. _____________________________________________________________470H5-23

25HTable 5-7. Summary of CHD hospital admission studies.a ________________________________________________471H5-28

26HTable 5-8. Summary of stroke hospital admission studies.a _______________________________________________472H5-31

27HTable 5-9. Summary of CHF hospital admission studies. _________________________________________________473H5-33

28HTable 5-10. Association of ambient air pollution levels and cardiovascular morbidity in visits with and 

without specific secondary conditions. _______________________________________________________47H5-34

29HTable 5-11. Summary of nonspecific CVD hospital admission studies.________________________________________475H5-37

30HTable 5-12. Brief summary of PTB studies._____________________________________________________________476H5-53

31HTable 5-13. Brief summary of birth weight studies. _______________________________________________________47H5-59

32HTable 5-14. Behavioral responses. ___________________________________________________________________478H5-69

3HTable 5-15. Neuronal responses. ____________________________________________________________________479H5-72

34HTable 5-16. Neurotransmitter changes. ________________________________________________________________4780H5-74

35HTable 5-17. Developing auditory system. ______________________________________________________________4781H5-76

36HTable 5-18. Cardiovascular and systemic developmental responses._________________________________________4782H5-78

37HTable 5-19. Range of CO concentrations reported in key respiratory morbidity studies that examined 

effects associated with short-term exposure to CO._____________________________________________4783H5-82

38HTable 5-20. Range of CO concentrations reported in key respiratory HA and ED visit studies that 

examine effects associated with short-term exposure to CO. _____________________________________478H5-89

39HTable 5-21. Range of CO concentrations reported in key respiratory morbidity studies that examined 

effects associated with long-term exposure to CO. _____________________________________________4785H5-96

40HTable 5-22. Range of CO concentrations reported in multicity studies that examine mortality effects 

associated with short-term exposure to CO. _________________________________________________4786H5-102

41HTable 5-23. Range of CO concentrations reported in single-city studies that examine mortality effects 

associated with short-term exposure to CO. _________________________________________________478H5-107

42HTable 5-24. Range of CO concentrations reported in U.S.-based studies that examine mortality effects 

associated with long-term exposure to CO. __________________________________________________478H5-110

43HTable 5-25. Range of definitions of “susceptible” and “vulnerable” in the CO literature. __________________________4789H5-115

4HTable 5-26. Adult U.S. population in 2007 with respiratory diseases and cardiovascular diseases._________________4790H5-117

45HTable A-1. Listing of all CO monitors currently in use, along with their limits of detection. ________________________ 4791HA-8

46HTable A-2. Microscale monitors meeting 75% completeness criteria, 2005-2007. ______________________________ 4792HA-9

47HTable A-3. Middle scale monitors meeting 75% completeness criteria, 2005-2007. ____________________________ 4793HA-11

48HTable A-4. Neighborhood scale monitors meeting 75% completeness criteria, 2005-2007. ______________________ 479HA-12

49HTable A-5. Urban scale monitors meeting 75% completeness criteria, 2005-2007. ____________________________ 4795HA-15

50HTable A-6. Regional scale monitors meeting 75% completeness criteria, 2005-2007. __________________________ 4796HA-15

51HTable A-7. Monitors meeting 75% completeness criteria, 2005-2007 with no scale delared. _____________________ 479HA-16

52HTable A-8. Numbers of high LOD and trace-level monitors in each state that met completeness criteria 

for 2005-2007. ________________________________________________________________________ 4798HA-18
January 2010 x 

53HTable A-9. 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. _______________________________________________________________________ 479HA-30

54HTable A-10. 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. _________________________________________________________________________________ 480HA-33

5HTable A-11. 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. _________________________________________________________________________________ 4801HA-36

56HTable A-12. 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.__________________________________________________________________________ 4802HA-39

57HTable A-13. 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._________________________________________________________________________ 4803HA-42

58HTable A-14. 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 

Phoenix, AZ.__________________________________________________________________________ 480HA-45

59HTable A-15. 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 

Pittsburgh, PA. ________________________________________________________________________ 4805HA-48

60HTable A-16. 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. ___________________________________________________________________________ 4806HA-53

61HTable A-17. Comparison of distributional data at different monitoring scales for hourly, 1-h daily max, 

24-h avg, and 8-h daily max data for Atlanta, GA. _____________________________________________ 4807HA-55

62HTable A-18. Comparison of distributional data at different monitoring scales for hourly, 1-h daily max, 

24-h avg, and 8-h daily max data for Boston, MA. _____________________________________________ 480HA-55

63HTable A-19. Comparison of distributional data at different monitoring scales for hourly, 1-h daily max, 

24-h avg, and 8-h daily max data for Denver, CO._____________________________________________ 4809HA-56

64HTable A-20. Comparison of distributional data at different monitoring scales for hourly, 1-h daily max, 

24-h avg, and 8-h daily max data for Houston, TX. ____________________________________________ 4810HA-56

65HTable A-21. Comparison of distributional data at different monitoring scales for hourly, 1-h daily max, 

24-h avg, and 8-h daily max data for Los Angeles, CA. _________________________________________ 481HA-57

6HTable A-22. Comparison of distributional data at different monitoring scales for hourly, 1-h daily max, 

24-h avg, and 8-h daily max data for New York City, NY. _______________________________________ 4812HA-58

67HTable A-23. Comparison of distributional data at different monitoring scales for hourly, 1-h daily max, 

24-h avg, and 8-h daily max data for Phoenix, AZ. ____________________________________________ 4813HA-59

68HTable A-24. Comparison of distributional data at different monitoring scales for hourly, 1-h daily max, 

24-h avg, and 8-h daily max data for Pittsburgh, PA.___________________________________________ 481HA-60

69HTable A-25. Comparison of distributional data for hourly, 1-h daily max, 24-h avg, and 8-h daily max 

data for Seattle, WA. ___________________________________________________________________ 4815HA-60

70HTable A-26. Comparison of distributional data for hourly, 1-h daily max, 24-h avg, and 8-h daily max 

data for St. Louis, MO. __________________________________________________________________ 4816HA-61

71HTable B-1. Recent studies related to CO dosimetry and pharmacokinetics. ___________________________________ 4817HB-1
January 2010 xi 

72HTable C-1. Studies of CO exposure and cardiovascular morbidity. __________________________________________ 481HC-1

73HTable C-2. Studies of CO exposure and cardiovascular hospital admissions and ED visits.______________________ 4819HC-17

74HTable C-3. Studies of CO exposure and neonatal and postneonatal outcomes. _______________________________ 4820HC-26

75HTable C-4. Studies of short-term CO exposure and respiratory morbidity ____________________________________ 4821HC-35

76HTable C-5. Studies of short-term CO exposure and respiratory hospital admissions and ED visits. ________________ 482HC-42

7HTable C-6. Studies of long-term CO exposure and respiratory morbidity. ____________________________________ 4823HC-65

78HTable C-7. Studies of short-term CO exposure and mortality. _____________________________________________ 482HC-70

79HTable C-8. Studies of long-term CO exposure and mortality.______________________________________________ 4825HC-94

80HTable D-1. Controlled human exposure studies. ________________________________________________________ 4826HD-1

81HTable E-1. Human and animal studies. _______________________________________________________________ 4827HE-1
January 2010 xii 

List of Figures 

82HFigure 1-1. Identification of studies for inclusion in the ISA. _________________________________________________482H1-5

83HFigure 2-1. Excess risk estimates from epidemiologic studies of short-term CO exposure and CVD 

hospitalizations along with author-reported mean and AQS-derived 99th percentile CO 

concentrations. _________________________________________________________________________4829H2-15

84HFigure 3-1. CO emissions (tons) in the U.S. by source sector in 2002 from the NEI and the BEIS.___________________4830H3-3

85HFigure 3-2. Trends in anthropogenic CO emissions (MT) in the U.S. by source category for 1990 and 

1996-2002. _____________________________________________________________________________4831H3-4

86HFigure 3-3. Surface air CO concentrations at Chebogue Point during the ICARTT campaign. ______________________4832H3-5

87HFigure 3-4. CO concentrations centered at ~3,000 m above sea level measured by the MOPITT sensor 

on the Terra satellite for the period July 15-23, 2004, during intense wildfires in Alaska and 

the Yukon. _____________________________________________________________________________483H3-7

8HFigure 3-5. Trends in subnational CO emissions in the 10 U.S. EPA Regions for 1990 and 1996-2002. ______________483H3-7

89HFigure 3-6. CO emissions density map and distributions for the state of Colorado and for selected 

counties in Colorado in 2002, from the NEI and the BEIS._________________________________________4835H3-8

90HFigure 3-7. Components of RF in 2005 resulting from emissions since 1750. (S) and (T) indicate 

stratospheric and tropospheric changes, respectively.___________________________________________4836H3-14

91HFigure 3-8. Integrated RF of year 2000 emissions over 20-yr and 100-yr time horizons.__________________________4837H3-15

92HFigure 3-9. Scatterplot comparing data from co-located monitors in Charlotte, NC. _____________________________483H3-19

93HFigure 3-10. Map of CO monitor locations in the U.S. in 2007. ______________________________________________4839H3-22

94HFigure 3-11. Map of CO monitor locations with respect to population density in the Denver, CO CSA, 

total population. ________________________________________________________________________480H3-23

95HFigure 3-12. Map of CO monitor locations with respect to population density in the Denver, CO CSA, 

age 65 and older. _______________________________________________________________________481H3-24

96HFigure 3-13. Map of CO monitor locations with respect to population density in the Los Angeles, CA 

CSA, total population.____________________________________________________________________482H3-25

97HFigure 3-14. Map of CO monitor locations with respect to population density in the Los Angeles, CA 

CSA, age 65 and older. __________________________________________________________________483H3-26

98HFigure 3-15. County-level map of second-highest 1-h avg CO concentrations in the U.S. in 2007.___________________48H3-29

9HFigure 3-16. County-level map of second-highest 8-h avg CO concentrations in the U.S. in 2007.___________________485H3-30

10HFigure 3-17. Seasonal plots showing the variability in correlations between 24-h avg CO concentration 

with 1-h daily max and 8-h daily max CO concentrations and between 1-h daily max and 

8-h daily max CO concentrations. __________________________________________________________486H3-36

10HFigure 3-18. Map of CO monitor locations and major highways for Denver, CO._________________________________487H3-38

102HFigure 3-19. Box plots illustrating the distribution of 2005-2007 hourly CO concentrations in Denver, CO. ____________48H3-39

103HFigure 3-20. Intersampler correlation versus distance for monitors located within the Denver CSA.__________________489H3-41

104HFigure 3-21. Map of CO monitor locations and major highways for Los Angeles, CA._____________________________4850H3-42

105HFigure 3-22. Box plots illustrating the distribution of 2005-2007 hourly CO concentrations in Los 

Angeles, CA.___________________________________________________________________________4851H3-43
January 2010 xiii 

106HFigure 3-23. Intersampler correlation versus distance for monitors located within the Los Angeles CSA.______________4852H3-48

107HFigure 3-24. Aerial view of the location of CO monitors A and B (marked by the red pins) in Denver, CO, 

depicting their proximity to the urban core.____________________________________________________4853H3-50

108HFigure 3-25. Aerial view of the location of CO monitor I (marked by the red pin) in Azusa, CA (Los 

Angeles CSA), depicting its proximity to mixed use land. Scale: 1 cm = 145 m. ______________________485H3-51

109HFigure 3-26. Aerial view of the location of CO monitor Q (marked by the red pin) in Pasadena, CA (Los 

Angeles CSA), depicting its proximity to a residential neighborhood. Scale: 1 cm = 145 m. ______________485H3-52

10HFigure 3-27. Distribution of hourly CO concentration data by city and monitoring scale. ___________________________4856H3-55

1HFigure 3-28. Distribution of 1-h daily max CO concentration data by city and monitoring scale. _____________________4857H3-56

12HFigure 3-29. Relative concentrations of CO and copollutants at various distances from the I-710 freeway 

in Los Angeles._________________________________________________________________________485H3-57

13HFigure 3-30. CO concentration time series 20 m and 300 m from the I-440 highway in Raleigh, NC. _________________4859H3-58

14HFigure 3-31. CO concentration profile 10 m from I-440 in Raleigh, NC, behind a noise barrier and in 

open terrain. ___________________________________________________________________________4860H3-59

15HFigure 3-32. Dimensionless tracer gas concentration on the windward and leeward sides of the canyon 

plotted against the elevation of the measurement ______________________________________________4861H3-60

16HFigure 3-33. Normalized difference between CO measurements taken at ground level and from the 39th 

floor of a building in a Phoenix, AZ street canyon as a function of bulk Richardson number 

(Ri).__________________________________________________________________________________4862H3-61

17HFigure 3-34. (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. _____________________________4863H3-63

18HFigure 3-35. 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. ________________________________________________________486H3-64

19HFigure 3-36. Diel plot generated from weekday hourly CO data (ppm) for the 11 CSAs and CBSAs, 

2005-2007. ____________________________________________________________________________4865H3-66

120HFigure 3-37. Diel plot generated from weekend hourly CO data (ppm) for the 11 CSAs and CBSAs, 

2005-2007. ____________________________________________________________________________486H3-67

12HFigure 3-38. Seasonal plots showing the variability in correlations between hourly CO concentration and 

co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations. __________________________________4867H3-68

12HFigure 3-39. Seasonal plots showing the variability in correlations between hourly CO concentration and 

co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations for Denver, CO.______________________486H3-69

123HFigure 3-40. Seasonal plots showing the variability in correlations between hourly CO concentration and 

co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations for Los Angeles, CA.__________________4869H3-70

124HFigure 3-41. Linear regression of n-butane and isopentane concentration as a function of CO 

concentration, Riverside, CA.______________________________________________________________4870H3-71

125HFigure 3-42. Map of the baseline monitor sites used in this assessment to compute PRB concentrations._____________4871H3-73

126HFigure 3-43. Monthly (circles) and annual (squares) average CO concentrations (ppb), 2005-2007. _________________4872H3-74

127HFigure 3-44. Distribution of time that the sample population spends in various environments, from the 

NHAPS. ______________________________________________________________________________4873H3-79

128HFigure 3-45. Hourly personal versus ambient CO concentrations obtained in Baltimore, MD,_______________________487H3-84

129HFigure 3-46. Box plots of the ratio of personal to ambient concentrations obtained in Baltimore, MD, ________________4875H3-85
January 2010 xiv 

130HFigure 3-47. Comparison of in-vehicle (solid line) and outside-the-vehicle (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. _______________________________________4876H3-89

13HFigure 4-1. Plot of fractional sensitivities of selected variables versus time of exposure. __________________________487H4-4

132HFigure 4-2. Simulated COHb formation for two 5-day workweeks.____________________________________________487H4-6

13HFigure 4-3. Overall structure of the Bruce and Bruce (2008, 193977) multicompartment model of 

storage and transport of CO. _______________________________________________________________4879H4-7

134HFigure 4-4. Predicted COHb levels in healthy commuters exposed to various CO concentrations over a 

60-min commute twice a day.______________________________________________________________480H4-10

135HFigure 4-5. Predicted COHb levels due to various endogenous CO production rates.____________________________481H4-11

136HFigure 4-6. Diagrammatic presentation of CO uptake and elimination pathways and CO body stores._______________482H4-12

137HFigure 4-7. O2Hb dissociation curve of normal human blood, of blood containing 50% COHb, and of 

blood with only 50% Hb because of anemia. __________________________________________________483H4-15

138HFigure 4-8. Changes in blood COHb after exposure to CO for a few minutes (A) or several hours (B), 

representing the biphasic nature of CO elimination._____________________________________________48H4-18

139HFigure 4-9. Predicted maternal and fetal COHb during periodic exposure to CO (50 ppm for 16 h 

followed by 0 ppm for 8 h ). _______________________________________________________________485H4-21

140HFigure 4-10. Representative estimates of endogenous CO production rates resulting from various 

conditions and diseases. _________________________________________________________________486H4-23

14HFigure 4-11. Representative COHb saturation resulting from various diseases and conditions. _____________________487H4-24

142HFigure 4-12. Representative exhaled CO concentrations (ppm) resulting from various conditions plotted 

as fold increases over healthy human controls from each study.___________________________________48H4-25

143HFigure 5-1. Direct effects of CO. _____________________________________________________________________489H5-12

14HFigure 5-2. Summary of effect estimates (95% confidence intervals) associated with hospital 

admissions for various forms of CHD. _______________________________________________________4890H5-27

145HFigure 5-3. Summary of effect estimates (95% confidence intervals) associated with ED visits and 

hospital admissions for stroke. _____________________________________________________________4891H5-30

146HFigure 5-4. Summary of effect estimates (95% confidence intervals) associated with hospital 

admissions for CHF._____________________________________________________________________4892H5-32

147HFigure 5-5. Summary of effect estimates (95% confidence intervals) associated with hospital 

admissions for CVD._____________________________________________________________________4893H5-36

148HFigure 5-6. Effect estimates from studies of ED visits and hospital admissions for CVD outcomes other 

than stroke from single pollutant (CO only: black circles) and particulate copollutant (CO + 

PM2.5: red triangles; CO + PM10 or TSP: purple triangles) models. _________________________________489H5-38

149HFigure 5-7. Effect estimates from studies of ED visits and HAs for CVD outcomes other than stroke 

from single pollutant (CO only: black circles) and gaseous copollutant models (CO + NO2, 

SO2 and O3= green, blue, and orange triangles, respectively). ____________________________________4895H5-39

150HFigure 5-8. Regression of the percent change in time to ST endpoint between the pre- and 

postexposure exercise tests ([postexposure–pre-exposure]/pre-exposure) and the 

measured blood COHb levels at the end of exercise for the 63 subjects combined. ____________________4896H5-42

15HFigure 5-9. Summary of effect estimates (95% confidence intervals) for PTB associated with maternal 

exposure to ambient CO. _________________________________________________________________4897H5-52
January 2010 xv 

152HFigure 5-10. Summary of change in birth weight (95% confidence intervals) associated with maternal 

exposure to ambient CO. _________________________________________________________________489H5-57

153HFigure 5-11. Summary of effect estimates (95% confidence intervals) for LBW associated with maternal 

exposure to ambient CO. _________________________________________________________________489H5-57

154HFigure 5-12. Summary of effect estimates (95% confidence intervals) for SGA associated with maternal 

exposure to ambient CO. _________________________________________________________________490H5-58

15HFigure 5-13. Estimated effect (95% confidence intervals) on pulmonary function due to a 10th to 90th 

percentile increment change in pollutant concentration in single-pollutant models._____________________4901H5-84

156HFigure 5-14. Summary of associations for short-term exposure to CO and asthma symptoms, respiratory 

symptoms and medication use in asthmatic individuals. _________________________________________4902H5-87

157HFigure 5-15. Summary of associations for short-term exposure to CO and respiratory hospital 

admissions., ___________________________________________________________________________4903H5-93

158HFigure 5-16. Summary of associations for short-term exposure to CO and respiratory ED visits. ____________________490H5-95

159HFigure 5-17. Posterior means and 95% posterior intervals of national average estimates for CO effects 

on total (nonaccidental) mortality at lags 0, 1, and 2 within sets of the 90 U.S. cities with 

available pollutant data. _________________________________________________________________4905H5-103

160HFigure 5-18. Summary of percent increase in total (nonaccidental) mortality for short-term exposure to 

CO from multicity studies.________________________________________________________________4906H5-106

16HFigure 5-19. Summary of mortality risk estimates for long-term exposure to CO. _______________________________4907H5-113

162HFigure A-1. CO emissions density map and distribution for the state of Alaska and for Yukon-Koyukuk 

County in Alaska. _______________________________________________________________________ 4908HA-1

163HFigure A-2. CO emissions density map and distribution for the state of Utah and for selected counties in 

Utah. _________________________________________________________________________________ 490HA-2

164HFigure A-3. CO emissions density map and distribution for the state of Massachusetts and for selected 

counties in Massachusetts. _______________________________________________________________ 4910HA-3

165HFigure A-4. CO emissions density map and distribution for the state of Georgia and for selected 

counties in Georgia (Figure 1 of 2). _________________________________________________________ 491HA-4

16HFigure A-5. CO emissions distribution for selected counties in Georgia (Figure 2 of 2).___________________________ 4912HA-5

167HFigure A-6. CO emissions density map and distribution for the state of California and for selected 

counties in California. ____________________________________________________________________ 4913HA-6

168HFigure A-7. CO emissions density map and distribution for the state of Alabama and for Jefferson 

County in Alabama. _____________________________________________________________________ 491HA-7

169HFigure A-8. Map of CO monitor locations with respect to population density in the Anchorage CBSA, 

total population. _______________________________________________________________________ 4915HA-20

170HFigure A-9. Map of CO monitor locations with respect to population density in the Anchorage CBSA, 

ages 65 yr and older. ___________________________________________________________________ 4916HA-20

17HFigure A-10. Map of CO monitor locations with respect to population density in the Atlanta CSA, total 

population. ___________________________________________________________________________ 4917HA-21

172HFigure A-11. Map of CO monitor locations with respect to population density in the Atlanta CSA, ages 65 

yr and older. __________________________________________________________________________ 4918HA-21

173HFigure A-12. Map of CO monitor locations with respect to population density in the Boston CSA, total 

population. ___________________________________________________________________________ 491HA-22
January 2010 xvi 

174HFigure A-13. Map of CO monitor locations with respect to population density in the Boston CSA, ages 65 

yr and older. __________________________________________________________________________ 4920HA-22

175HFigure A-14. Map of CO monitor locations with respect to population density in the Houston CSA, total 

population. ___________________________________________________________________________ 4921HA-23

176HFigure A-15. Map of CO monitor locations with respect to population density in the Houston CSA, ages 

65 yr and older.________________________________________________________________________ 492HA-23

17HFigure A-16. Map of CO monitor locations with respect to population density in the New York City CSA, 

total population. _______________________________________________________________________ 4923HA-24

178HFigure A-17. Map of CO monitor locations with respect to population density in the New York City CSA, 

ages 65 yr and older. ___________________________________________________________________ 492HA-24

179HFigure A-18. Map of CO monitor locations with respect to population density in the Phoenix CSA, total 

population. ___________________________________________________________________________ 4925HA-25

180HFigure A-19. Map of CO monitor locations with respect to population density in the Phoenix CSA, ages 

65 yr and older.________________________________________________________________________ 4926HA-25

18HFigure A-20. Map of CO monitor locations with respect to population density in the Pittsburgh CSA, total 

population. ___________________________________________________________________________ 4927HA-26

182HFigure A-21. Map of CO monitor locations with respect to population density in the Pittsburgh CSA, ages 

65 yr and older.________________________________________________________________________ 4928HA-26

183HFigure A-22. Map of CO monitor locations with respect to population density in the Seattle CSA, total 

population. ___________________________________________________________________________ 492HA-27

184HFigure A-23. Map of CO monitor locations with respect to population density in the Seattle CSA, ages 65 

yr and older. __________________________________________________________________________ 4930HA-27

185HFigure A-24. Map of CO monitor locations with respect to population density in the St. Louis CSA, total 

population. ___________________________________________________________________________ 4931HA-28

186HFigure A-25. Map of CO monitor locations with respect to population density in the St. Louis CSA, ages 

65 yr and older.________________________________________________________________________ 4932HA-28

187HFigure A-26. Map of CO monitor locations with AQS Site IDs for Anchorage, AK._______________________________ 493HA-29

18HFigure A-27. 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. ___________________________ 493HA-31

189HFigure A-28. Map of CO monitor locations with AQS Site IDs for Atlanta, GA.__________________________________ 4935HA-32

190HFigure A-29. Box plots illustrating the seasonal distribution of hourly CO concentrations in Atlanta, GA. _____________ 4936HA-34

19HFigure A-30. Map of CO monitor locations with AQS Site IDs for Boston, MA.__________________________________ 4937HA-35

192HFigure A-31. Box plots illustrating the seasonal distribution of hourly CO concentrations in Boston, MA. _____________ 4938HA-37

193HFigure A-32. Map of CO monitor locations with AQS Site IDs for Houston, TX. _________________________________ 493HA-38

194HFigure A-33. Box plots illustrating the seasonal distribution of hourly CO concentrations in Houston, TX. ____________ 490HA-40

195HFigure A-34. Map of CO monitor locations with AQS Site IDs for New York City, NY. ____________________________ 491HA-41

196HFigure A-35. Box plots illustrating the seasonal distribution of hourly CO concentrations in New York 

City, NY. _____________________________________________________________________________ 492HA-43

197HFigure A-36. Map of CO monitor locations with AQS Site IDs for Phoenix, AZ. _________________________________ 493HA-44

198HFigure A-37. Box plots illustrating the seasonal distribution of hourly CO concentrations in Phoenix, AZ._____________ 49HA-46

19HFigure A-38. Map of CO monitor locations with AQS Site IDs for Pittsburgh, PA. _______________________________ 495HA-47
January 2010 xvii 

20HFigure A-39. Box plots illustrating the seasonal distribution of hourly CO concentrations in Pittsburgh, 

PA. _________________________________________________________________________________ 496HA-49

201HFigure A-40. Map of CO monitor locations with AQS Site IDs for Seattle, WA. _________________________________ 497HA-50

20HFigure A-41. Box plots illustrating the seasonal distribution of hourly CO concentrations in Seattle, WA. _____________ 498HA-51

203HFigure A-42. Map of CO monitor locations with AQS Site IDs for St. Louis, MO. ________________________________ 49HA-52

204HFigure A-43. Box plots illustrating the seasonal distribution of hourly CO concentrations in St. Louis, MO.____________ 4950HA-54

205HFigure 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 Anchorage, AK. ____________________________ 4951HA-62

206HFigure 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 Atlanta, GA. _______________________________ 4952HA-63

207HFigure 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 Boston, MA. _______________________________ 4953HA-64

208HFigure 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 New York City, NY. _________________________ 495HA-65

209HFigure 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 Phoenix, AZ. ______________________________ 495HA-66

210HFigure 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 Seattle, WA._______________________________ 4956HA-67
January 2010 xviii 

CO Project Team 

Executive Direction 

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

Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle 

Park, NC 

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

Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle 

Park, NC 

Dr. Mary Ross (Branch Chief)—National Center for Environmental Assessment, Office of Research 

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

Scientific Staff 

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

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

Dr. Jeffrey Arnold—National Center for Environmental Assessment, Office of Research and 

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

Dr. Christal Bowman—National Center for Environmental Assessment, Office of Research and 

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

Dr. Barbara Buckley—National Center for Environmental Assessment, Office of Research and 

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

Mr. Allen Davis—National Center for Environmental Assessment, Office of Research and 

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

Dr. Steven J. Dutton—National Center for Environmental Assessment, Office of Research and 

Development, 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, Office of Research and Development, 

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

Dr. Erin Hines—National Center for Environmental Assessment, Office of Research and 

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

Dr. Douglas Johns—National Center for Environmental Assessment, Office of Research and 

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

Dr. Thomas Luben—National Center for Environmental Assessment, Office of Research and 

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

Dr. Elizabeth Oesterling Owens—National Center for Environmental Assessment, Office of 

Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 
January 2010 xix 

Dr. Joseph Pinto—National Center for Environmental Assessment, Office of Research and 

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

Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, Office of Research 

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

Mr. Jason Sacks—National Center for Environmental Assessment, Office of Research and 

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

Technical Support Staff 

Ms. Laeda Baston—Senior Environmental Employment Program, National Center for 

Environmental Assessment, Office of Research and Development, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Ellen Lorang—National Center for Environmental Assessment, Office of Research and 

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

Ms. Deborah Wales—National Center for Environmental Assessment, Office of Research and 

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

Ms. Barbara Wright—Senior Environmental Employment Program, National Center for 

Environmental Assessment, Office of Research and Development, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 
January 2010 xx 

Authors, Contributors, and Reviewers 

Authors 

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

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

Dr. Jeffrey Arnold—National Center for Environmental Assessment, Office of Research and 

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

Dr. Christal Bowman—National Center for Environmental Assessment, Office of Research and 

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

Dr. Barbara Buckley—National Center for Environmental Assessment, Office of Research and 

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

Mr. Allen Davis—National Center for Environmental Assessment, Office of Research and 

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

Dr. Steven J. Dutton—National Center for Environmental Assessment, Office of Research and 

Development, 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, Office of Research and Development, 

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

Dr. Erin Hines—National Center for Environmental Assessment, Office of Research and 

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

Dr. Douglas Johns—National Center for Environmental Assessment, Office of Research and 

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

Dr. Thomas Luben—National Center for Environmental Assessment, Office of Research and 

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

Dr. Elizabeth Oesterling Owens— National Center for Environmental Assessment, Office of 

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

Dr. Joseph Pinto— National Center for Environmental Assessment, Office of Research and 

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

Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, Office of Research 

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

Dr. Mary Ross—National Center for Environmental Assessment, Office of Research and 

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

Mr. Jason Sacks—National Center for Environmental Assessment, Office of Research and 

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

Dr. Matthew Campen—Lovelace Respiratory Research Institute, Albuquerque, NM 
January 2010 xxi 

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 

Contributors 

Dr. Richard Baldauf—National Risk Management Research Laboratory, Office of Research and 

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

Dr. Vernon Benignus—National Health and Environmental Effects Research Laboratory, Office of 

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

Mr. Antonio Fernandez—Office of Transportation and Air Quality, Office of Air and Radiation, 

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

Mr. Lance McCluney—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

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

Dr. Kris Novak—National Center for Environmental Assessment, Office of Research and 

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

Dr. Adam Reff—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

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

Ms. Kathryn Sargeant—Office of Transportation and Air Quality, Office of Air and Radiation, 

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

Mr. Mark Schmidt—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

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

Dr. Joseph H. Somers—Office of Transportation and Air Quality, Office of Air and Radiation, 

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

Ms. Rhonda Thompson—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

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

Reviewers 

Dr. Richard Baldauf—National Risk Management Research Laboratory, Office of Research and 

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

Dr. Vernon Benignus—National Health and Environmental Effects Research Laboratory, Office of 

Research and Development, 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 
January 2010 xxii 

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 Research Laboratory, Office of 

Research and Development, 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 

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, Office of Research and 

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

Dr. Deirdre Murphy—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

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 H. Somers—Office of Transportation and Air Quality, Office of Air and Radiation, 

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

Dr. John Vandenberg—National Center for Environmental Assessment, Office of Research and 

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

Dr. Alan Vette—National Exposure Research Laboratory, Office of Research and Development, 

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, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 
January 2010 xxiii 

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, Office of Research and Development, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 
January 2010 xxiv 

Clean Air Scientific Advisory Committee 

CO NAAQS Review Panel 

Chair of the Environmental Protection Agency’s Clean Air Scientific Advisory Committee 

Dr. Jonathan M. Samet*, Department of Preventive Medicine at the Keck School of Medicine, and 

Director of the Institute for Global Health at the University of Southern California, Los Angeles, CA 

Chair of the Carbon Monoxide Review Panel 

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

University, Boston, MA 

Members 

Dr. Paul Blanc, Department of Occupational Medicine, University of California-San Francisco, San 

Francisco, CA

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. Joel Kaufman, Department of Environmental & Occupational Health Sciences, University of 

Washington, Seattle, WA 

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

California-Irvine, Irvine, CA 

Dr. Francine Laden, Department of Environmental Health, Harvard School of Public Health, 

Harvard University, Boston, MA 

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 
January 2010 xxv 

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

of Technology, Atlanta, GA 

Dr. Anne Sweeney, Department of Epidemiology & Biostatistics, School of Rural Public Health, 

Texas A&M Health Science Center, College Station, TX 

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 

Science Advisory Board Staff 

Ms. Kyndall Barry, Designated Federal Officer, 1200 Pennsylvania Avenue, N.W., Washington, DC, 

20460, Phone: 202-343-9868, Fax: 202-233-0643, Email:21Hbarry.kyndall@epa.gov

Physical/Courier/FedEx Address: 

Ms. Kyndall Barry, U.S. EPA Science Advisory Board Staff Office, Mail Code 1400F, Ariel Rios 

Building, Room 3610A, 1025 F Street, N.W., Washington, DC 20004 
January 2010 xxvi 

Acronyms and Abbreviations 

α alpha, ambient exposure factor 

a air exchange rate of the microenvironment 

AA abdominal aorta(s) 

AADT annual average daily traffic 

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 

AMI acute myocardial infarction 

AMP adenosine monophosphate 

ANOVA analysis of variance 

APO E apolipoprotein E 

ARI acute respiratory infection 

AP action potential 

APD action potential duration 

APEX Air Pollution Exposure 

APHEA Air Pollution and Health: A European Approach 

APTT activated partial thromboplastin time 

AQ air quality 

AQCD Air Quality Criteria Document 

AQS Air Quality System 

AR gastronomy reared 

ARCO gastronomy reared + CO exposure 

ARIC Atherosclerosis Risk in Communities 

ARID gastronomy reared with iron deficient diet 

ARIDCO gastronomy reared with iron deficient diet + CO exposure 

ATP adenosine triphosphate 

ATS American Thoracic Society 

AVP aortic valve prosthesis 
January 2010 xxvii 

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 endothelin A (ETA) receptor antagonist 

BS black smoke 

BSP black smoke particles 

Ca ambient concentration 

CA cardiac arrhythmia 

Ca2+ calcium ion 

CAA Clean Air Act 

CAD coronary artery disease 

CALINE California Line Source Dispersion Model 

CAMP Childhood Asthma Management Program 

cAMP cyclic AMP 

CAP(s) concentrated ambient particles, compound action potential(s) 

CASAC Clean Air Scientific Advisory Committee 

CASN Cooperative Air Sampling Network 

CAth cardiac atherosclerosis 

CBSA Core-Based Statistical Area 

CCGG Carbon Cycle Greenhouse Gases Group 

CD cardiac dysrhthmias 

CD-1 mouse strain 

CDC Centers for Disease Control and Prevention 

CdCl2 cadmium chloride 

CFK Coburn-Forster-Kane 

CFR Code of Federal Regulations 

cGMP cyclic GMP 

CH2O formaldehyde 
January 2010 xxviii 

CH2O2 formic acid 

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 

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

blood)) 

COMb carboxymyoglobin 

CONUS contiguous U.S. 

COPD chronic obstructive pulmonary disease 

CPS II Cancer Prevention Study II 

C-R concentration-response 

CRC Coordinating Research Council 

CrMP collapsin response mediator protein 

CRP C-reactive protein 

CSA Combined Statistical Area 

CVD cardiovascular disease 

d straight-line distance between monitor pairs 

df degrees of freedom 

DL lung diffusing capacity 

DLCO lung diffusing capacity of CO 
January 2010 xxix 

DmCO capacity for diffusion of CO into the muscle 

DMT-1 divalent metal transporter-1 

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 

E exposure over some duration 

Ea exposure to pollutant of ambient origin 

EC elemental carbon 

ED emergency department 

EKG, ECG electrocardiogram 

Ena exposure to pollutant of non-ambient origin 

eNOS endothelial nitric oxide synthase 

EPA U.S. Environmental Protection Agency 

EPO erythropoietin 

EPR Electron Paramagnetic Resonance 

EPRI Electric Power Research Institute 

ESRL Earth System Research Laboratory 

ET-1 endothelin-1 

ETA endothelin A (ETA) receptor 

ETS environmental tobacco smoke 

EXPOLIS six-city European air pollution study 

FAS apoptosis stimulating fragment 

FC interference filter 

FEF forced expiratory flow (L/s) 

FEF25-75 forced expiratory flow between the times at which 25% and 75% of 

the vital capacity is reached 

FEM Federal equivalent method 

FEV1 forced expiratory volume in 1 second 

fi fraction of time spent indoors 

FICO fractional concentration of CO in ambient air 

Finf infiltration factor 
January 2010 xxx 

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 

FVII Factor VII 

FW fresh weight 

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 

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 

H2O2 hydrogen peroxide 

H9c2 rat embryonic cardiomyocytes 

Hb hemoglobin 

HC(s) hydrocarbon(s) 

HCFC(s) hydrochlorofluorocarbon(s) 

HCO formyl radical 

HEAPSS Health Effects of Air Pollution among Susceptible Subpopulations 

HEK293 human embryonic kidney cells (experimentally transformed cell line) 

Hep3B Human hepatocarcinoma cell line 

HF heart failure, high frequency (HRV parameter) 

HFLFR high frequency to low frequency ratio (HRV parameter) 
January 2010 xxxi 

HH hypobaric hypoxia 

HIF-1α hypoxia-inducible factor 

HO heme oxygenase 

HO2 hydroperoxy radical 

HO-1 inducible isoform of heme oxygenase 

HO-2 constitutively expressed isoform of heme-oxygenase 

HO/CO heme oxygenase/carbon monoxide system 

HR heart rate, hazard ratio 

H/R hypoxia followed by reoxygenation 

HRV heart rate variability 

HS hemorrhagic stroke 

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

hν photon 

IARC International Agency for Research on Cancer 

IC inferior colliculus 

ICAM-1 intercellular adhesion molecule 

ICD implantable cardioverter defibrillator(s) 

ICR Institute for Cancer Research 

IDW inverse-distance-weighted 

IHD ischemic heart disease 

IL-x interleukin-6, 8, etc. 

INDAIR Indoor Air Model 

IOM Institute of Medicine 

IQR interquartile range 

IR immunoreactivity 

IS ischemic stroke 

ISA Integrated Science Assessment 

ITA internal thoracic artery of the heart 

Ito transient outward current 

IUGR intrauterine growth restriction 

K+

 potassium ion 

k dissociation rate 

kCO dissociation rate of carbon monoxide from hemoglobin 

Km Michaelis Constant; Michaelis-Menten equation of enzyme kinetics 

kO2 Dissociation rate of oxygen from hemoglobin 
January 2010 xxxii 

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

LCA+ leucocyte common antigen cells 

LD lactational day 

LDH lactate dehydrogenase 

LDL low-density lipoprotein 

LF low frequency (HRV parameter) 

LH luetenizing hormone 

LOAEL lowest observed adverse effect level 

LOD limit of detection 

LOESS locally weighted scatterplot smoothing 

LPS lipopolysaccharide 

LTP long-term potentiation 

LUR land use regression 

LV left ventricle 

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 Haldane coefficient representing the CO chemical affinity for Hb [or 

Mb]), Reaction mediator. 

MAPK mitogen-activated protein kinase 

MAO-A monoamine oxidase A 

Mb myoglobin 

MC ultrafine particle mass concentration 

METs metabolic equivalent unit(s) 

MHC major histocompatibility complex 

MI myocardial infarction, “heart attack” 

min minute(s) 

MIP-2 macrophage inflammatory protein-2 

mitral E to A ratio mitral ratio of peak early to late diastolic filling velocity 

MMEF maximal midexpiratory flow 

MMP matrix metalloproteinase 

MOA(s) mode(s) of Action 

MOBILE6 Mobile source emission factor model 

MODIS Moderate Resolution Imaging Spectroradiometer 
January 2010 xxxiii 

MONICA Monitoring of Trends and Determinants in Cardiovascular Disease 

MOPITT Measurement of Pollution in the Troposphere 

MPO myeloperoxidase 

MPT mitochondrial permeability transition 

MR maternally reared 

mRNA messenger RNA 

MSA Metropolitan Statistical Area 

MSNA muscle sympathetic nerve activity 

MT million tons 

MVO2 myocardial oxygen consumption 

NAAQS National Ambient Air Quality Standards 

NADPH nicotinamide adenine dinucleotide phosphate 

NADH-TR nicotinamide adenine dinucleotide - tetrazolium reductase 

NAPAP National Acid Precipitation Assessment Program 

NARSTO 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 

NE norepinephrine 

NEI National Emissions Inventory 

NF-κB nuclear factor kappa B 

NIHL noise-induced hearing loss 

NMDA N-methyl-D-aspartate 

NMHC(s) nonmethane hydrocarbon(s) 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

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 radical 

NO2 nitrogen dioxide 

NOAA National Oceanic and Atmospheric Administration 

NOAEL no observed adverse effect level 

NO•

-Hb nitrosyl bound Hb 
January 2010 xxxiv 

NO•-Mb nitrosyl bound Mb 

NOX nitrogen oxides, oxides of nitrogen 

NRC National Research Council 

NTS nucleus of the solitary tract (in brainstem) 

O3 ozone 

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

O2Mb oxymyoglobin 

OAE otoacoustic emissions 

OAQPS Office of Air Quality Planning and Standards 

OC organic carbon 

OH, OH• hydroxyl group, hydroxyl radical 

OR odds ratio 

OS occlusive stroke 

OSPM Operational Street Pollution Model 

P penetration factor 

P, p probability 

P90 90th percentile of the absolute difference in concentrations 

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 

PB barometric pressure (in mmHg) 

PBN N-tert-butyl-alpha-phenylnitrone 

PC average partial pressure in lung capillaries 

pCO partial pressure of CO 

PCO2 average partial pressure of O2 in lung capillaries 

PDGF platelet derived growth factor 

PEE prediction equation estimates 
January 2010 xxxv 

PEF peak expiratory flow 

PEFD(s) Personal Exposures Frequency Distributions 

PEM(s) personal exposure monitor(s) 

PH2O saturation pressure of water vapor 

PHD pulmonary heart disease 

PI partial pressure of inhaled air 

Pi inorganic phosphate 

PI3K phosphoinositide 3-kinase 

PICO CO partial pressure in inhaled air 

PIH primary intracerebral hemorrhage 

PKB protein kinases B 

PM particulate matter 

PM2.5 particulate matter with a nominal mean aerodynamic diameter less 

than or equal to 2.5 μm (referred to as fine PM) 

PM10 particulate matter with a nominal mean aerodynamic diameter less 

than or equal to 10 μm 

PM10-2.5 particulate matter with a nominal mean aerodynamic diameter greater 

than 2.5 μm and less than or equal to 10 μm (referred to as thoracic 

coarse particulate matter or the course fraction of PM10). 

Concentration may be measured or calculated as the difference 

between measured PM10 and measured PM2.5 concentrations. 

PMN polymorphonuclear leukocytes 

PNC particle number concentration / count 

PND post natal day 

pNEM/CO probabilistic NAAQS Exposure Model for CO 

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 

pPRB policy-relevant background 

PT prothrombin time 

PTB preterm birth 

PVCD peripheral vascular and cerebrovascular disease 

PvO2 venous oxygen tension 

PVO2 peak oxygen consumption 

Q cardiac output 

QCP Quantitative Circulatory Physiology 
January 2010 xxxvi 

blood flow to other tissues 

RA radial artery of the heart 

RAW 264.7 mouse macrophage cell line 

RBC red blood cell 

RF radiative forcing 

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

Ri Richardson number 

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 

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 

SA sphinganine 

SAA serum amyloid A 

SAB Science Advisory Board 

SBP systolic blood pressure, spontaneous bacterial peritonitis 

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

between each QRS complex in the EKG 

sEng soluble endoglin 

SES socioeconomic status 

SF6 sulfur hexafluoride (tracer gas) 

sFlt soluble Fms-like tyrosine kinase-1 

SGA small for gestational age 

sGC soluble guanylate cyclase 

SHEDS Stochastic Human Exposure and Dose Simulation 

SHR Spontaneously hypertensive rat strain 
January 2010 xxxvii 

SIDS sudden infant death syndrome 

SIPs State Implementation Plan(s) 

siRNA small inhibitory RNA 

SLAMS State and Local Air Monitoring Stations 

SMC smooth muscle cell(s) 

SnMP tin-(IV)-mesoporphyrin 

SNP single-nucleotide polymorphism 

SnPP-IX tin protoporphyrin IX 

SO sphingosine 

SO2 sulfur dioxide 

SO4

2- sulfate 

SOD superoxide dismutase 

SOPHIA Study of Particles and Health in Atlanta 

STEMS Space-Time Exposure Modeling System 

STN Speciation Trends Network 

STPD standard temperature and pressure, dry 

SV stroke volume 

SVEB supraventricular (atrium or atrioventricular node) ectopic beats 

τ tau, photochemical lifetime 

T lymphocytes thymus-dependent lymphocytes 

TBARS thiobarbituric acid reactive substances 

TC total carbon 

TFAM mitochondrial transcription factor A 

Tg teragram(s) 

TH tyrosine hydroxylase 

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

macrophages) 

TIA transient ischemic attack 

TNF-α tissue necrosis factor alpha 

TPM total particulate matter 

TSP total suspended particles 

UFP ultrafine particle(s) 

ULTRA Exposure and Risk Assessment for Fine and Ultrafine Particles in 

Ambient Air (Study) 

URI upper respiratory infection 

URTI upper respiratory tract infection 
January 2010 xxxviii 

USC U.S. Code 

VA alveolar ventilation 

Vb blood volume 

VCO endogenous CO production rate 

VD Dead space volume 

VE ventilation rate 

VEGF vascular endothelial growth factor 

VLF very low energy frequency (HRV parameter) 

Vmax maximum velocity 

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

consumption, maximal oxygen uptake or aerobic capacity) 

VOC(s) volatile organic compound(s) 

VPB ventricular premature beat 

vWF von Willebrand factor 

W width 

WBC white blood cell 

WHI Women’s Health Initiative 

WKY Wistar-Kyoto rat strain 

ZnPP IX Zn protoporphyrin IX 
Chapter 1. Introduction 

The Integrated Science Assessment (ISA) is a concise evaluation and synthesis of the most 

policy-relevant 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. 

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” (42 U.S.C. 7408). 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 ISA thus serves to update and 

revise the evaluation of the scientific evidence available at the time of the previous review of the 

NAAQS for CO that was completed in 2000. 

The integrated Plan for Review of the National Ambient Air Quality Standards for Carbon 

Monoxide (U.S. EPA, 2008, 193995) identifies key policy-relevant questions that provide a 

framework for this assessment of the scientific evidence. These questions frame the entire review of 

the NAAQS for CO and thus are informed by both science and policy considerations. The ISA 

organizes, presents, and integrates the scientific evidence which is considered along with findings 

from risk analyses and policy considerations to help the U.S. Environmental Protection Agency 

(EPA) address these questions during the NAAQS review. In evaluating the health evidence, the 

focus of this assessment is on scientific evidence that is most relevant to the following questions 

taken directly from the Integrated Review Plan: 

 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 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, 

multiday distributed lags)? 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 1-1 
 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, the Act) govern the establishment and revision of the 

NAAQS. Section 108 of the Act (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 of the Act (42 U.S.C. 7409) 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 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.”2

 The requirement that primary standards include 

an adequate margin of safety was intended to address uncertainties associated with inconclusive 

scientific and technical information available at the time of standard setting. It was also intended to 

provide a reasonable degree of protection against hazards that research has not yet identified. See 

Lead Industries Association v. EPA, 647 F.2d 1130, 1154 (D.C. Cir 1980), cert. denied, 449 U.S. 

1042 (1980); American Petroleum Institute v. Costle, 665 F.2d 1176, 1186 (D.C. Cir. 1981) cert. 

denied, 455 U.S. 1034 (1982). 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 susceptible population(s), and the kind and degree of the 

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

 

1

 The legislative history of section 109 of the Clean Air Act indicates that a primary standard is to be set at “the maximum permissible 

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

be made to a representative sample of persons comprising the sensitive group rather than to a single person in such a group” [S. Rep. No. 

91-1196, 91st Cong., 2d Sess. 10 (1970)]. 

2

 Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] include, but are not limited to, “effects on soils, water, crops, vegetation, 

man-made materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to

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

January 2010 1-2 
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 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 for CO, 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 (1979, 017687), which updated the scientific criteria upon which the initial 

CO standards were based. A Staff Paper (U.S. EPA, 1979, 194665) was prepared and, along with the 

AQCD (1979, 017687), 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 

(1984, 012690) and an updated Staff Paper (1984, 012691). 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 subsequently released a revised 

AQCD (U.S. EPA, 1991, 017643) for CASAC and public review. In a “closure letter” (McClellan, 

1991, 194666) sent to the Administrator, the CASAC concluded that the AQCD (U.S. EPA, 1991, 

017643) “. . . 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, 194667) 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, 1991, 017643) and staff conclusions 

and recommendations contained in the revised Staff Paper (U.S. EPA, 1992, 084191), 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 1991 AQCD (U.S. EPA, 1991, 017643) were initiated. A workshop 

was held in September 1998 to review and discuss material contained in the revised draft 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, 000907) was 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 

January 2010 1-3 
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 (National Research Council, 2003, 042550) 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 NAAQS review which started in 1997. 

1.3. ISA 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. Literature searches were conducted routinely to identify studies published 

since the last review, focusing on publications from 1999 to May 2009. Search strategies were 

iteratively modified to optimize identification of pertinent publications. Additional papers were 

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

which relevant papers may be published; independent identification of relevant literature by expert 

authors; and identification by the public and CASAC during the external review process. 

Publications considered for inclusion in the ISA were added to the Health and Environmental 

Research Online (HERO) database recently developed by EPA (http://cfpub.epa.gov/ncea/hero/); 

note that all references in the ISA include a HERO ID that provides a link to the database. Typically, 

only information that had undergone scientific peer review and had been published or accepted for 

publication was considered, along with analyses conducted by EPA using publicly available data. 

This review has attempted to evaluate all relevant data published since the last review pertaining to 

the atmospheric science of CO, human exposure to ambient CO, and epidemiologic, controlled 

human exposure, and animal toxicological studies on CO, including those related to exposureresponse relationships, mode(s) of action (MOA), or susceptible populations. Added to the body of 

research on CO effects 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. An 

extensive literature search for data on the ecological effects of ambient CO did not identify any 

relevant information published since the review of the ecological effects evidence in the 1979 CO 

AQCD (U.S. EPA, 1979, 017687). 

In general, in assessing the scientific quality and relevance of health and environmental effects 

studies, the following considerations have been taken into account when selecting studies for 

inclusion in the ISA or its annexes. The selection process for studies included in this ISA is shown in 

Figure 1-1. 

 Are the study populations, subjects, or animal models adequately selected and are they 

sufficiently well defined to allow for meaningful comparisons between study or exposure 

groups? 

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

Are likely covariates adequately controlled or taken into account in the study design and 

statistical analysis? 

 Are the air quality data, exposure, or dose metrics of adequate quality and sufficiently 

representative of information regarding ambient CO? 

 Are the health or welfare effect measurements meaningful and reliable? 

January 2010 1-4 
Figure 1-1. Identification of studies for inclusion in the ISA. 

In selecting epidemiologic studies, EPA considered whether a given study presented 

information on associations with short- or long-term CO exposures at or near ambient levels of CO; 

considered approaches to evaluate issues related to potential confounding by other pollutants; 

assessed potential effect modifiers; addressed health endpoints and populations not previously 

extensively researched; and evaluated important methodologic issues (e.g., lag or time period 

between exposure and effects, model specifications, thresholds, mortality displacement) related to 

interpretation of the health evidence. Among the epidemiologic studies selected, particular emphasis 

was placed on those studies most relevant to the review of the NAAQS. Specifically, studies 

conducted in the United States (U.S.) or Canada were discussed in more detail than those from other 

geographical regions. Particular emphasis was placed on: (1) recent multicity studies that employ 

standardized analysis methods for evaluating effects of CO and that provide overall estimates for 

effects based on combined analyses of information pooled across multiple cities; (2) studies that help 

understand quantitative relationships between exposure concentrations and effects; (3) new studies 

that provide evidence on effects in susceptible populations; and (4) studies that consider and report 

CO as a component of a complex mixture of air pollutants. 

January 2010 1-5 
Criteria for the selection of research evaluating controlled human exposure or animal 

toxicological studies included a focus on studies conducted using relevant pollutant exposures. For 

both types of studies, relevant pollutant exposures are considered to be those generally within one or 

two orders of magnitude of ambient CO concentrations. Studies in which higher doses were used 

may also be considered if they provide information relevant to understanding MOAs or mechanisms, 

as noted below. 

Evaluation of controlled human exposure studies focused on those that approximated expected 

human exposure conditions in terms of concentration and duration. In the selection of controlled 

human exposure studies, emphasis is placed on studies that (1) investigate potentially susceptible 

populations such as people with cardiovascular diseases; (2) address issues such as concentrationresponse or time-course of responses; (3) include control exposures to filtered air; and (4) have 

sufficient statistical power to assess findings. 

Review of the animal toxicological evidence focused on studies that approximate expected 

human dose conditions, which will vary depending on the toxicokinetics and biological sensitivity of 

the particular laboratory animal species or strains studied. Due to resource constraints on exposure 

duration and numbers of animals tested, animal studies typically utilize high-concentration 

exposures to acquire data relating to mechanisms and assure a measureable response. Such studies 

were considered to the extent that they provided useful information to inform our understanding of 

interspecies differences and potential sensitivity differences between healthy and susceptible human 

populations. 

These criteria provide benchmarks for evaluating various studies and for focusing on the 

policy-relevant studies in assessing the body of health and welfare effects evidence. Detailed critical 

analysis of all CO health and welfare effects studies, especially in relation to the above 

considerations, is beyond the scope of this document. Of most relevance for evaluation of studies is 

whether they provide useful qualitative or quantitative information on exposure-effect or 

exposure-response relationships for effects associated with current ambient air concentrations of CO 

that can inform decisions on whether to retain or revise the standards. 

In developing the CO ISA, EPA began by reviewing and summarizing the evidence on 

atmospheric sciences and exposure and the health effects evidence from in vivo and in vitro 

toxicological studies, controlled human exposure studies, and epidemiologic studies. In November 

2008, EPA invited EPA staff and other researchers with expertise in CO to a teleconference to review 

the scientific content of preliminary draft materials for the draft ISA and the annexes. The purpose of 

the initial peer review teleconference was to ensure that the ISA is up to date and focused on the 

most policy-relevant findings, and to assist EPA with integration of evidence within and across 

disciplines. Subsequently, EPA addressed comments and completed the initial integration and 

synthesis of the evidence. 

The integration of evidence on health or welfare effects involves collaboration between 

scientists from various disciplines. As described in the section below, the ISA organization is based 

on health effect categories. As an example, an evaluation of health effects evidence would include 

summaries of findings from epidemiologic, controlled human exposure, and toxicological studies, 

and integration of the results to draw conclusions based on the causal framework described below. 

Using the causal framework described in Section 1.6, EPA scientists consider aspects such as 

strength, consistency, coherence and biological plausibility of the evidence, and develop draft 

causality judgments on the nature of the relationships. The draft integrative synthesis sections and 

conclusions are reviewed by EPA internal experts and, as appropriate, by outside expert authors. In 

practice, causality determinations often entail an iterative process of review and evaluation of the 

evidence. The draft ISA is released for review by the CASAC and the public, and comments received 

on the characterization of the science as well as the implementation of the causal framework are 

carefully considered in revising and completing the ISA. 

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, 

January 2010 1-6 
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 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 health effects related to short-term exposures (i.e., hours, days, or weeks) and longterm exposures (i.e., months or years) to CO, including cardiovascular and systemic effects, central 

nervous system (CNS) effects, birth outcomes and developmental effects, respiratory effects, and 

mortality. 

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 Annexes B through E, detailed information about methods and results of health studies 

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

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

relationships between effects and exposure to CO. As noted in the section above, the most pertinent 

results of this body of studies are brought into the ISA. 

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., CO 

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 CO poisoning 

(Ernst and Zibrak, 1998, 049822; Penney, 2007, 194668; Raub et al., 2000, 002180). In addition, 

results of studies investigating the relationship between blood COHb concentrations and health 

effects (e.g., Hedblad et al., 2006, 199512) may be informative regarding the biological plausibility 

of health effects associated with changes in COHb concentrations. However, the lack of data on 

ambient concentrations and the likely contribution of non-ambient CO to COHb in these studies 

complicates the interpretation of the results with respect to ambient CO exposure, and therefore these 

studies will not be discussed in this review. The possible influence of other atmospheric pollutants on 

the interpretation of the role of CO in health effects studies is considered in this assessment. 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. The 1979 CO AQCD (U.S. EPA, 1979, 017687) 

reviewed research on the effects of CO on vegetation and soil microflora, which showed that visible 

symptoms and effects on growth, yield, and reproduction were observed in some studies at very high 

CO concentrations (1,000-10,000 ppm or greater), while biochemical and physiological responses, 

including reduced nitrogen fixation, were observed at lower concentrations (1,000 ppm and below). 

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

January 2010 1-7 
information published since the 1979 CO AQCD (U.S. EPA, 1979, 017687) pertinent to ambient CO 

exposures; hence, no section on ecological effects appears in this assessment. The reader is referred 

to the 1979 CO AQCD (U.S. EPA, 1979, 017687) for a detailed discussion of the effects of high CO 

concentrations on plants and microorganisms. The definition of public welfare for the NAAQS 

includes considerations of climate. Thus, the climate forcing effects of CO are summarized in 

Chapter 2 and are discussed in detail in Chapter 3, where distinctions are drawn between globalscale conclusions related to climate and the strongly variable continental and regional climate 

forcing effects from CO. 

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, (2008, 156586) 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 clinical, and animal toxicological studies) have been formulated by a number of regulatory 

and science agencies, including the IOM of the NAS (2008, 156586), International Agency for 

Research on Cancer (2006, 093206), EPA Guidelines for Carcinogen Risk Assessment (2005, 

086237), Centers for Disease Control and Prevention (2004, 056384), and National Acid 

Precipitation Assessment Program (1991, 095894). These formalized approaches offer guidance for 

assessing causality. The frameworks are similar in nature, although adapted to different purposes, 

and have proven effective in providing a uniform structure and language for causal determinations. 

Moreover, these frameworks have supported decision-making 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. 

January 2010 1-8 
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 can help characterize effects of concern, exposure-response 

relationships, susceptible populations and MOAs. In the absence of controlled human exposure 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, 156586). 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 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, 093070; 

IOM, 2008, 156586). 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 the 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. 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 assessment 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 

evaluating 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 metaregression, may be used. 

Meta-analysis may be a valuable tool for evaluating evidence by combining results from a 

body of studies. Blair et al. (1995, 079190) observe that meta-analysis can enhance understanding of 

associations between exposures and effects that are not readily apparent in examination of individual 

study results and can be particularly useful for formally examining sources of heterogeneity. 

January 2010 1-9 
However, these authors note that meta-analysis may not be useful when the relationship between the 

exposure and outcome is obvious, when only a few studies are available for a particular exposureoutcome relationship, where there is limited access to data of sufficient quality, or where there is 

substantial variation in study design or population. In addition, important differences in effect 

estimates, exposure metrics, or other factors may limit or even preclude quantitative statistical 

combination of multiple studies. 

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 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 times. 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. Endogenous production of CO creates a body burden 

of CO that, together with personal exposure from nonambient sources, contributes to baseline COHb 

levels. Endogenous production rates vary within and among individuals, particularly for individuals 

with diseases such as hemolytic anemia or chronic inflammation. This body burden of CO and 

COHb limits the lower range of exposures that can be practically covered in controlled human 

exposure studies. 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 healthcompromised individuals such as those with 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 

populations. 

Epidemiologic studies provide important information on the associations between health 

effects and exposure of human populations to ambient air pollution. In the evaluation of 

epidemiologic evidence, one important consideration is potential confounding. Confounding is “. . . 

a confusion of effects. Specifically, 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)” (Rothman and Greenland, 1998, 086599). One approach to remove spurious 

associations due to possible confounders is to control for characteristics that may differ between 

exposed and unexposed persons; this is frequently termed “adjustment.” Scientific judgment is 

needed regarding likely sources and magnitude of confounding, together with consideration of how 

well the existing constellation of study designs, results, and analyses address this potential threat to 

inferential validity. One key consideration in this review is evaluation of the potential contribution of 

CO to health effects when it is a component of a complex air pollutant mixture. Reported CO effect 

estimates in epidemiologic studies may reflect independent CO effects on health outcomes. Ambient 

CO may also be serving as an indicator of complex ambient air pollution mixtures that share the 

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

effects of CO or CO may influence the toxicity of copollutants. 

Another important consideration in the evaluation of epidemiologic evidence is effect 

modification. “Effect-measure modification differs from confounding in several ways. The main 

difference is that, whereas confounding is a bias that the investigator hopes to prevent or remove 

from the effect estimate, effect-measure modification is a property of the effect under study . . . In 

epidemiologic analysis one tries to eliminate confounding but one tries to detect and estimate effectmeasure modification” (Rothman and Greenland, 1998, 086599). Examples of effect modifiers in 

some of the studies evaluated in this ISA include environmental variables, such as temperature or 

humidity, individual risk factors, such as education, cigarette smoking status, age in a prospective 

January 2010 1-10 
cohort study, and community factors, such as percent of population > 65 yr old. It is often possible to 

stratify the relationship between health outcome and exposure by one or more of these risk factor 

variables. For variables that modify the association, effect estimates in each stratum will be different 

from one another and different from the overall estimate, indicating a different exposure-response 

relationship may exist in populations represented by these variables. Effect modifiers may be 

encountered (a) within single-city time-series studies or (b) across cities in a two-stage hierarchical 

model or meta-analysis. 

Several statistical methods are available to detect and control for potential confounders, with 

none of them being completely satisfactory. 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. In addition, more 

than one pollutant may exert similar health effects, resulting in independently observed associations 

for multiple pollutants. For example, PM2.5 and NO2 have each been linked to cardiovascular effects 

in epidemiologic studies. Correlation between CO concentrations and various copollutants, such as 

PM2.5 and NO2, makes it difficult to quantitatively interpret associations between different pollutant 

exposures and health effects. Thus, results of models that attempt to distinguish CO effects from 

those of copollutants must be interpreted with caution. The number and degree of diversity of 

covariates, as well as their relevance to the potential confounders, remain matters of scientific 

judgment. Despite these limitations, the use of multipollutant models is still the prevailing approach 

employed in most air pollution epidemiologic studies and provides 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. 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 or health outcome 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. 

Adjustment for potential confounders can be influenced by differential exposure measurement 

error. There are several components that contribute to exposure measurement error in epidemiologic 

studies, including the difference between true and measured ambient concentrations, the difference 

between average personal exposure to ambient pollutants and ambient concentrations at central 

monitoring sites, and the use of average population exposure rather than individual exposure 

estimates. Consideration of issues important for evaluation of exposure to ambient CO include: (1) 

spatial variability of CO concentrations across urban areas, particularly with respect to highly 

traveled roadways; (2) location of CO monitors at varying distances from roads; and (3) the 

detection limit of instruments in the CO monitoring network. Previous AQCDs have examined the 

role of measurement error for non-reactive pollutants in time-series epidemiologic studies using 

simulated data and mathematical analyses and suggested that transfer of effects from the “causal” 

variable to the confounder would only occur under unusual circumstances (i.e., “true” predictors 

having high positive or negative correlation; substantial measurement error; or extremely negatively 

correlated measurement errors) (U.S. EPA, 2004, 056905). 

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, multicity studies 

which use a consistent method to analyze data from across locations with different levels of 

covariates can provide insight on potential confounding in associations. Intervention studies, because 

of their quasi-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 

January 2010 1-11 
extended to explore the effects of putative toxicants on human health, especially through the study of 

model systems in other species. These studies evaluate the effects of exposures to a variety of 

pollutants in a highly controlled laboratory setting and allow exploration of MOAs or mechanisms 

by which a pollutant may cause effects. 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. Consideration of evidence on the non-hypoxic effects of CO via cell signaling and 

alteration of heme protein function along with evidence on COHb-mediated hypoxic stress, provides 

a more complete understanding of the biological response to CO. There are, however, uncertainties 

associated with quantitative extrapolations between laboratory animals and humans on the 

pathophysiological effects of any pollutant. Animal species can differ from each other in 

fundamental aspects of physiology and anatomy (e.g., metabolism, airway branching, hormonal 

regulation) that may limit extrapolation. 

Interpretations of experimental studies of air pollution effects in laboratory 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 species-specific differences in transcriptional regulation. Given these 

molecular differences, at this time there are uncertainties associated with quantitative extrapolations 

between laboratory animals and humans of observed pollutant-induced pathophysiological 

alterations under the control of widely varying biochemical, endocrine, and neuronal factors. 

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. 

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 (1965, 071664) and have been widely used 

(CDC, 2004, 056384; IARC, 2006, 093206; IOM, 2008, 156586; U.S. EPA, 2005, 086237).These 

aspects (Hill, 1965, 071664) have been modified (Table 1-2) for use in causal determinations 

specific to health and welfare effects or pollutant exposures (U.S. EPA, 2009, 179916).2

 Some 

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 multietiologic 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. 

 

1

 The “aspects” described by Hill (1965, 071664) 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. 

January 2010 1-12 
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. 

Biological gradient 

(exposure-response 

relationship) 

A well characterized 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. 

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. 

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. 

Specificity of the observed 

association 

As originally intended, this refers to increased inference of causality if one cause is associated with a single effect or disease 

(Hill, 1965, 071664). Based on our current understanding, this is now considered one of the weaker guidelines for causality; for 

example, many agents cause respiratory disease and respiratory disease has multiple causes. At the scale of ecosystems, as 

in epidemiology, complexity is such that single agents causing single effects, and single effects following single causes, are 

extremely unlikely. The ability to demonstrate specificity under certain conditions remains, however, a powerful attribute of 

experimental studies. Thus, although the presence of specificity may support causality, its absence does not exclude it. 

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. 

 

Although 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, 071664). 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. In addition, 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 (See discussion in CDC, 2004, 056384). 

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 (2008, 156586), EPA’s Guidelines for Carcinogen Risk Assessment (2005, 086237), and the 

 

1

 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 used here to be consistent with the EPA Guidelines for Carcinogen Risk Assessment and to provide a more nuanced set of 

categories. 

January 2010 1-13 
U.S. Surgeon General’s smoking reports (CDC, 2004, 056384). In the ISA, EPA uses a series of five 

descriptors to characterize the weight of evidence for causality. 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 

with relevant pollutant exposures. That is, the pollutant has been 

shown to result in health effects in studies in which chance, bias, and 

confounding could be ruled out with reasonable confidence. For 

example: a) controlled human exposure studies that demonstrate 

consistent effects; or b) 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 

with relevant pollutant exposures. That is, the pollutant has been 

shown to result in effects in studies in which chance, bias, and 

confounding could be ruled out with reasonable confidence. 

Controlled exposure studies (laboratory or small- to medium-scale 

field studies) provide 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 with relevant pollutant exposures, but important uncertainties 

remain. That is, the pollutant has been shown to result in health 

effects in studies in which chance and bias can be ruled out with 

reasonable confidence but potential issues remain. For example: a) 

observational studies show an association, 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 toxicological evidence from multiple 

studies from different laboratories that demonstrate effects, 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 with relevant pollutant exposures. 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 with relevant pollutant 

exposures, but is limited because chance, bias and confounding 

cannot be ruled out. For example, at least one high-quality 

epidemiologic study shows an association with a given health 

outcome but the results of other studies are inconsistent. 

Evidence is suggestive of a causal relationship with relevant pollutant 

exposures, but chance, bias and confounding cannot be ruled out. 

For example, at least one high-quality study shows an effect, but the 

results of other studies are inconsistent. 

Inadequate to 

infer a causal 

relationship 

Evidence is inadequate to determine that a causal relationship exists 

with relevant pollutant exposures. The available studies are of 

insufficient quantity, quality, consistency or statistical power to permit 

a conclusion regarding the presence or absence of an effect. 

The available studies are of insufficient quality, consistency or 

statistical power to permit a conclusion regarding the presence or 

absence of an effect. 

Not likely to 

be a causal 

relationship 

Evidence is suggestive of no causal relationship with relevant 

pollutant exposures. Several adequate studies, covering the full 

range of levels of exposure that human beings are known to 

encounter and considering susceptible populations, are mutually 

consistent in not showing an effect at any level of exposure. 

Several adequate studies, examining relationships with relevant 

exposures, are consistent in failing to show an effect at any level of 

exposure. 

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 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. 

January 2010 1-14 
1.6.5.1. Effects on Human Populations 

Once a determination is made regarding the causal relationship between the pollutant and 

outcome category, important questions regarding quantitative relationships include: 

 What is the concentration-response, exposure-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 populations appear to be differentially affected (i.e., more susceptible to effects)? 

To address these questions, the entirety of policy-relevant quantitative evidence is evaluated to 

best quantify those concentration-response relationships that exist. This requires evaluation of 

pollutant concentrations and exposure durations at which effects were observed for exposed 

populations, including potentially susceptible populations. This integration of evidence resulted in 

identification of a study or set of studies that best approximated the concentration-response 

relationships between health outcomes and CO, given the current state of knowledge and the 

uncertainties that surrounded these estimates. To accomplish this, evidence is considered from 

multiple and diverse types of studies. To the extent available, the ISA evaluates results from across 

epidemiologic studies that use various methods to evaluate the form of relationships between CO 

and health outcomes and draws conclusions on the most well-supported shape of these relationships. 

Animal data may also inform evaluation of concentration-response relationships, particularly relative 

to MOAs and characteristics of susceptible populations. Chapter 2 presents the integrated findings 

informative for evaluation of population risks. 

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 uncertainty, such as the low data density in the lower 

concentration range, possible influence of exposure measurement error, and individual differences in 

susceptibility to air pollution health effects, tend to smooth and “linearize” the 

concentration-response function. In addition, 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, 156359; Crump et al., 1976, 

003192; Hoel, 1980, 156555). 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, 

086599). 

Publication bias is a source of uncertainty regarding the magnitude of health risk estimates. It 

is well understood that studies reporting non-null findings are more likely to be published than 

reports of null findings, and publication bias can also result in overestimation of effect estimate sizes 

(Ioannidis, 2008, 188317). For example, effect estimates from single-city epidemiologic studies have 

been found to be generally larger than those from multicity studies (Anderson et al., 2005, 087916) 

Although publication bias commonly exists for many research areas, it may be present to a lesser 

degree for epidemiologic studies on CO. In general, epidemiologic studies have focused on the 

effects of PM, and CO was largely considered as a potentially confounding copollutant of PM. Thus, 

CO effect estimates may have been presented in these studies regardless of the statistical significance 

of the results. 

Finally, identification of the susceptible population groups contributes to an understanding of 

the public health impact of pollutant exposures. In this ISA, the term “susceptible population” will 

be used as an overarching concept to encompass populations variously described as susceptible, 

January 2010 1-15 
vulnerable, or sensitive. “Susceptible populations” is defined here as those populations that have a 

greater likelihood of experiencing health effects related to exposure to an air pollutant (e.g., CO) due 

to a variety of factors including but not limited to: genetic or developmental factors, race, gender, 

lifestage, lifestyle (e.g., smoking status and nutrition) or preexisting disease; as well as populationlevel factors that can increase an individual's exposure to an air pollutant (e.g., CO) such as 

socioeconomic status [SES], which encompasses reduced access to health care, low educational 

attainment, residential location, and other factors. Epidemiologic studies can help identify 

susceptible populations by evaluating health responses in the study population. Examples include 

stratified analyses for subsets of the population under study or testing for interactions or effect 

modification by factors such as gender, age group, or health status. Experimental studies using 

animal models of susceptibility or disease can also inform the extent to which health risks are likely 

greater in specific population groups. Further discussion of these groups is presented in Section 5.7. 

1.6.5.2.

1.6.6. 

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 generally consider the probability of quantitative changes in 

ecological and welfare effects in response to exposure. A challenge to the quantification of exposureresponse relationships for ecological effects is the great regional and local variability in ecosystems. 

Thus, exposure-response relationships are often determined for a specific ecological system and 

scale, rather than at the 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. 

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. 

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? (2000, 011738). This statement updated the guidance for defining 

adverse respiratory health effects that had been published 15 years earlier (ATS, 1985, 006522), 

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 

January 2010 1-16 
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 MI and sudden death. 

1.7. Summary 

This ISA is a concise evaluation and synthesis of the most policy-relevant science for 

reviewing the NAAQS for CO, and it is the chief means for communicating the critical science 

judgments relevant to that NAAQS review. It reviews the most policy-relevant evidence from 

atmospheric science, exposure, and health and environmental effects studies and includes 

mechanistic evidence from basic biological science. This final ISA incorporates clarification and 

revisions based on public comments and advice and comments provided by EPA’s CASAC on the 

first and second draft ISAs (Brain and Samet, 2009, 194669; Brain and Samet, 2010, 202840). 

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 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. 

January 2010 1-17 
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Society. Am J Respir Crit Care Med, 161: 665-673. 011738

Blair A; Burg J; Foran J; Gibb H; Greenland S; Morris R; Raabe G; Savitz D; Teta J; Wartenberg D; Wong O; Zimmerman 

R (1995). Guidelines for application of meta-analysis in environmental epidemiology. Regul Toxicol Pharmacol, 

22: 189-197. 079190

Brain JD; Samet JM (2009). Review of EPA's Integrated Science Assessment for carbon monoxide (first external review 

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194669

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Prevention, U.S. Department of Health and Human Services. Washington, DC. 056384

Clewell HJ; Crump KS (2005). Quantitative estimates of risk for noncancer endpoints. Risk Anal, 25: 285-289. 156359

Crump KS; Hoel DG; Langley CH; Peto R (1976). Fundamental carcinogenic processes and their implications for low dose 

risk assessment. Cancer Res, 36: 2973-2979. 003192

Ernst A; Zibrak JD (1998). Carbon monoxide poisoning. N Engl J Med, 339: 1603-1608. 049822

Gee GC; Payne-Sturges DC (2004). Environmental health disparities: A framework integrating psychosocial and 

environmental concepts. Environ Health Perspect, 112: 1645-1653. 093070

Hedblad B; Engström G; Janzon E; Berglund G; Janzon L (2006). COHb% as a marker of cardiovascular risk in never 

smokers: results from a population-based cohort study. Scand J Public Health, 34: 609-615. 199512

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Hoel DG (1980). Incorporation of background in dose-response models. Fed Proc, 39: 73-75. 156555

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Ioannidis JPA (2008). Why most discovered true associations are inflated. Epidemiology, 19: 640-648. 188317

IOM (2008). Improving the Presumptive Disability Decision-Making Process for Veterans; Committee on Evaluation of the 

Presumptive Disability Decision-Making Process for Veterans, Board on Military and Veterans Health. Washington, 

DC: Institue of Medicine of the National Academies, National Academies Press. 156586

McClellan RO (1991). CASAC Closure on the 1991 AQCD for Carbon Monoxide. Clean Air Scientific Advisory 

Committee, Science Advisory Board, U.S. Environmental Protection Agency. Washington, DC. US EPA-SABCASAC-LTR-92-015. 194666

McClellan RO (1992). CASAC Closure on the OAQPS Staff Paper for Carbon Monoxide. Clean Air Scientific Advisory 

Committee, Science Advisory Board, U.S. Environmenal Protection Agency. Washington, DC. US EPA-SABCASACLTR-2-016. 194667

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 1-18 
NAPAP (1991). The experience and legacy of NAPAP report of the Oversight Review Board. National Acid Precipitation 

Assessment Program. Washington, DC. 095894

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areas. Washington, DC: National Research Council of the National Academies Press. 042550

Penney DG (2007). Carbon Monoxide Poisoning. Boca Raton, FL: CRC Press. 194668

Raub JA; Mathieu-Nolf M; Hampson NB; Thom SR (2000). Carbon monoxide poisoning--a public health perspective. 

Toxicology, 145: 1-14. 002180

Rothman KJ; Greenland S (1998). Modern epidemiology. Philadelphia, PA: Lippincott-Raven Publishers. 086599

U.S. EPA (1979). Air quality criteria for carbon monoxide. U.S. Environmental Protection Agency. Washington, DC. EPA600/8-79-022. 017687

U.S. EPA (1979). Assessment of adverse health effects from carbon monoxide and implications for possible modifications 

of the standard. U.S. EPA. Research Triangle Park, NC. 194665

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Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. Research Triangle Park, NC. 

EPA-450/5-84-004. 012691

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1979 EPA air quality criteria document for carbon monoxide. U.S. Environmental Protection Agency. Washington, 

DC. EPA-600/8-83-033F. 012690

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NC. EPA/600/8-90/045F. http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=3000554R.txt. 017643

U.S. EPA (1992). Review of the national ambient air quality standards for carbon monoxide: 1992 reassessment of 

scientific and technical information OAQPS staff paper. U.S. Environmental Protection Agency. Washington, DC. 

EPA-452/R-92-004. 084191

U.S. EPA (2000). Air quality criteria for carbon monoxide. National Center for Environmental Assessment, Office of 

Research and Development, U.S. Environmental Protection Agency. Research Triangle Park, NC. EPA 600/P99/001F. 000907

U.S. EPA (2004). Air quality criteria for particulate matter. U.S. Environmental Protection Agency. Research Triangle Park, 

NC. EPA/600/P-99/002aF-bF. 056905

U.S. EPA (2005). Guidelines for carcinogen risk assessment, Risk Assessment Forum Report. U.S. Environmental 

Protection Agency. Washington, DC. EPA/630/P-03/001B. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=116283. 086237

U.S. EPA (2008). Plan for review of the National Ambient Air Quality Standards for carbon monoxide. U.S. Environmental 

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Washington, DC. EPA/600/R-08/139. 179916

January 2010 1-19 
Chapter 2. Integrative Overview 

The subsequent chapters of this ISA present the most policy-relevant information related to 

this review of the NAAQS for CO, including a synthesis of the evidence presented in the 2000 CO 

AQCD (U.S. EPA, 2000, 000907), along with the assessment of more recent studies. This chapter 

integrates important findings from the disciplines evaluated in this current assessment of the CO 

scientific literature, which includes the atmospheric sciences, ambient air data analyses, climate 

forcing effects, exposure assessment, dosimetry, and health effects research (animal toxicological 

studies, controlled human exposure studies, 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 

concentrations and health effects. The EPA framework applied here employs a five-level hierarchy 

that classifies the weight of evidence for causation: 

 Causal relationship 

 Likely to be a causal relationship 

 Suggestive of a causal relationship 

 Inadequate to infer a causal relationship 

 Not likely to be a causal relationship 

This evaluation led to causal determinations for several health outcome categories and 

characterization of the magnitude of the response, including responses in susceptible populations, 

over a range of relevant concentrations. This integration of evidence also provides a basis for 

characterizing the concentration-response relationships of CO and adverse health outcomes for the 

U.S. population, given the current state of knowledge. 

This chapter summarizes and integrates the newly available scientific evidence that best 

informs consideration of the policy-relevant questions that frame this assessment, which are 

presented in Chapter 1. Section 2.1 discusses the trends in ambient concentrations and sources of 

CO. Section 2.2 provides an overview of climate forcing related directly and indirectly to CO. 

Section 2.3 provides a brief summary of factors influencing personal exposure to ambient CO. 

Section 2.4 summarizes CO dosimetry and pharmacokinetics and describes what is known regarding 

the modes of action of CO. Section 2.5 integrates the evidence from 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. Section 2.6 summarizes policy-relevant 

considerations associated with exposure to CO including evidence of effects in potentially 

susceptible populations and information on the shape of the concentration-response function. Finally, 

Section 2.7 presents an integrated summary of the health effects of CO, reports the levels at which 

effects are observed and discusses important uncertainties to consider in the interpretation of the 

scientific evidence. 

2.1. Ambient CO Sources and Concentrations 

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 ~61 of ~117 million tons (MT) of total CO emissions, based on the most 

recent publicly available data meeting data quality objectives from EPA’s National Emissions 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 2-1 
Inventory (NEI). In metropolitan areas in the U.S., as much as 75% of all CO emissions result from 

on-road vehicle exhaust. The majority of these on-road CO emissions are derived from gasolinepowered vehicles. When emissions from incomplete combustion of fuels powering nonroad 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 primary sources of CO include wildfires, controlled vegetation burning, residential 

biomass combustion, and industrial processes. While CO emissions from nonroad mobile sources, 

wild fires, and industry have remained fairly constant, on-road mobile source CO emissions have 

decreased by roughly 5% per year since the early 1990s. Secondary sources of CO include the 

oxidation of both anthropogenic and biogenic hydrocarbons, such as methane and isoprene and other 

carbon containing species including aldehydes and alcohols. During summer when biogenic 

emissions are at their peak, secondary sources of CO are estimated to be a significant fraction of 

total U.S. sources; however, secondary sources are dispersed over the entire country, while direct 

emissions are concentrated near primary sources, such as on-road mobile sources, which are mainly 

in urban areas. Although these 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, and these uncertainties vary by source category, season, 

and region. 

Significant reductions in ambient CO concentrations and in the number of NAAQS 

exceedances have been observed over the past 25 yr, a continuation of trends documented in the 

2000 CO AQCD (U.S. EPA, 2000, 000907). 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. Roughly one-third of the 1-h daily max data fell below the limit 

of detection (LOD) for the majority of CO monitors reporting to AQS. 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. Half of the 8-h daily max 

concentrations fell below the LOD for the majority of CO monitors in the field. 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. 

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 yr, a 1-h avg concentration in excess 

of 35 ppm was reported only once (39 ppm), and there were only 7 reported 8-h avg values 

nationwide in excess of 9 ppm in all 3 yr. 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 (Section 3.5). 

Policy-relevant background (PRB) concentrations include contributions from natural sources 

everywhere in the world and from anthropogenic sources outside the U.S., Canada, and Mexico. 

PRB concentrations of CO were estimated 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 Oceanic and Atmospheric 

Administration’s (NOAA) Global Monitoring Division (GMD) and are not part of the EPA national 

regulatory network. The 3-yr avg CO PRB averaged ~0.13 ppm in Alaska, ~0.10 ppm in Hawaii, and 

~0.13 ppm over the contiguous U.S. (CONUS). 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 being more representative of the CONUS PRB. Note that these seven sites are affected by 

anthropogenic emissions in North America to varying degrees. 

2.2. Climate Forcing Effects 

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, 000907) and subsequently confirmed in the recent global assessments of climate change by the 

Intergovernmental Panel on Climate Change (IPCC, 2001, 156587; IPCC, 2007, 092765). CO is a 

weak direct contributor to radiative forcing (RF) and greenhouse warming. Sinha and Toumi (1996, 

193747) estimated the direct RF of CO computed for all-sky conditions at the tropopause to be 

January 2010 2-2 
0.024 W/m2

 based on an assumed change in CO mean global concentrations from 25 to 100 ppb 

since preindustrial times. The direct RF attributed to CO over this time frame is ~1.5% of the direct 

RF for CO2 estimated by the IPCC (Forster et al., 2007, 092936). 

More importantly, CO can indirectly cause increased RF because it reacts with tropospheric 

OH and thus can increase the lifetime of trace gases in the atmosphere including the GHGs CH4 and 

O3. Additionally, the major pathway for removal of CO from the atmosphere is reaction with OH to 

produce CO2. CH4, O3, and CO2 absorb infrared radiation from the Earth’s surface and contribute to 

the greenhouse effect. Indirect RF attributed to 1750-2005 emissions of CO through changes in 

concentration of the GHGs O3, CH4, and CO2 was estimated by Forster et al. (2007, 092936) to be 

~0.2 W/m2, or ~12% of the direct RF of CO2 (Figure 3-7). The future direct and indirect integrated 

RF for year 2000 emissions of CO was estimated to be ~0.2 W/m2

⋅yr with ~50% uncertainty over 

both 20-yr and 100-yr time horizons (Figure 3-8). The RF related to short-lived CO is ~25% of that 

for CO2 for a 20-yr time horizon but only ~7% of that for longer-lived CO2 over a 100-yr horizon. 

Overall, the evidence reviewed in this assessment is sufficient to conclude that a causal 

relationship exists between current atmospheric concentrations of CO and effects on 

climate. 

2.3. Exposure to Ambient CO 

Very few recent exposure assessment studies involve ambient CO concentration data. The 

studies of personal exposure to ambient CO presented here generally found that the largest 

percentage of time in which an individual is exposed to ambient CO occurs indoors but that the 

highest ambient CO exposure levels occur in transit. In-vehicle CO concentrations are typically 

reported to be between 2 and 5 times higher than ambient concentrations, although peak in-vehicle 

concentrations more than an order of magnitude higher than corresponding ambient monitor 

concentrations have also been reported. Among commuters, exposures were higher for those 

traveling in automobiles in comparison with those traveling on buses and motorbikes and with those 

cycling or walking. Ambient CO exposure in automobiles has been demonstrated to vary with 

vehicle ventilation settings, and a very small portion of that exposure is thought to come from the 

vehicle in which the exposed person travels. High near-road CO concentrations can be important for 

those living in the near-road environment because virtually all of ambient CO infiltrates indoors. 

Hence, indoor exposure to ambient CO is determined by the CO concentration outside the building. 

CO concentration in the near-road environment has been shown to decrease sharply with downwind 

distance from a highway, wind direction, and emission source strength (e.g., number of vehicles on a 

highway); natural and urban topography also influence localized ambient CO concentrations. 

Recent exposure assessment studies support one of the main conclusions of the 2000 CO 

AQCD (U.S. EPA, 2000, 000907), that central site ambient CO monitors may overestimate or 

underestimate individuals’ personal exposure to ambient CO because ambient CO concentration is 

spatially variable, particularly when analyzing exposures in the near-road environment. Exposure 

error may occur when the ambient CO concentration measured at the central site monitor is used as 

an ambient exposure surrogate and differs from the actual ambient CO concentration outside a 

subject’s residence and/or worksite. For example, measurement at a “hot spot” could skew 

community exposure estimates upwards, and likewise measurement at a location with few CO 

sources could skew exposure estimates downwards. Correlations across CO monitors can vary 

widely within and between cities across the U.S. as a function of natural and urban topography, 

meteorology, source strength and proximity to sources. Typically, intersampler correlation ranges 

from 0.35 to 0.65 for monitors sited at different scales within a metropolitan area, although it can be 

greater than 0.8 in some areas. 

Health effects estimates from time-series epidemiologic studies are not biased by spatial 

variability in CO concentrations if concentrations at different locations are correlated in time. 

Exposure assessment in epidemiologic studies is also complicated by the existence of CO in 

multipollutant mixtures emitted by combustion processes, making it difficult to quantify the health 

effects related specifically to CO exposure compared with those related to another combustionrelated pollutant or mix of pollutants. In most circumstances, exposure error tends to bias a health 

effect estimate downward (Sheppard et al., 2005, 079176; Zeger et al., 2000, 001949). Spatial and 

temporal variability not fully captured by ambient monitors and correlation of CO with copollutants 

January 2010 2-3 
are examples of sources of uncertainty that could widen confidence intervals of health effects 

estimates. 

2.4. Dosimetry, Pharmacokinetics, and Mode of Action 

2.4.1. Dosimetry and Pharmacokinetics 

Upon inhalation, CO elicits various health effects by binding to and altering the function of a 

number of heme-containing 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 (U.S. EPA, 2000, 000907) has a detailed description of 

the well-established Coburn-Forster-Kane (CFK) equation, which has been used for many years to 

model COHb formation. Since then, models have been developed 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 pCO 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, such 

as health-compromised individuals, 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. 

2.4.2. Mode of Action 

The diverse effects of CO are dependent upon concentration, 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 other 

mechanisms such as 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 (see Section 5.1). 

As discussed in the 2000 CO AQCD (U.S. EPA, 2000, 000907), 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 against tissue hypoxia. Depending on the extent of CO exposure, these compensatory 

changes may be effective in people with a healthy cardiovascular system. However, hemodynamic 

responses following CO exposure may be insufficient in people with decrements in cardiovascular 

function, resulting in health effects, as described in Section 5.2. Binding of CO to Mb, as discussed 

in the 2000 CO AQCD (U.S. EPA, 2000, 000907) and in Section 4.3.2.3, can also impair the delivery 

of O2 to tissues. Mb has a high affinity for CO, about 25 times that of O2; however, pathophysiologic 

effects are seen only after high-dose exposures to CO, resulting in COMb concentrations far above 

baseline levels. 

Nonhypoxic mechanisms underlying the biological effects of CO have been the subject of 

recent research since the 2000 CO AQCD (U.S. EPA, 2000, 000907). 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 

January 2010 2-4 
cytochrome c oxidase, heme loss from proteins, disruption of iron homeostasis, alteration in cellular 

redox status, alteration in ion channel activity and modulation of protein kinase pathways. CO is a 

ubiquitous cell signaling molecule with numerous physiological functions. The endogenous 

generation and release of CO from heme by HO-1 and HO-2 is tightly controlled, as is any 

homeostatic process. However, exogenously-applied CO has the capacity to disrupt multiple hemebased 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 and on signaling 

pathways. 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 lead to adverse health effects through altered cell signaling is an open question for which there 

are no definitive answers at this time. However, experiments demonstrating oxidative/nitrosative 

stress, inflammation, mitochondrial alterations and endothelial dysfunction at concentrations of CO 

within one or two orders of magnitude higher than ambient concentrations suggest a potential role 

for such mechanisms in pathophysiologic responses. Furthermore, prolonged increases in 

endogenous CO resulting from chronic diseases may provide a basis for the enhanced sensitivity of 

susceptible populations to CO-mediated health effects such as is seen in individuals with coronary 

artery disease. 

2.5. Health Effects 

This assessment reviewed health effects evidence regarding the effect of CO on several 

categories of health outcomes. Table 2-1 presents the overall conclusions of the ISA regarding the 

presence of a causal relationship between short-term (i.e., hours, days, or weeks) or long-term (i.e., 

months or years) exposure to relevant CO concentrations (defined in Chapter 1 as generally within 

one or two orders of magnitude of ambient CO concentrations) and health outcome categories. 

Summaries of the evidence supporting each causal determination and considerations relevant to 

application of the causal framework are provided in the following subsections. 

Table 2-1. Causal determinations for health effects categories. 

Outcome Category Exposure Period Causality Determination 

Short-term Likely to be a causal relationship Cardiovascular morbidity Long-term Inadequate to infer 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 Not likely to be a causal relationship 

2.5.1. 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 coronary artery disease (CAD) (Section 5.2). These studies, described in the 

1991 (U.S. EPA, 1991, 017643) and 2000 (U.S. EPA, 2000, 000907) 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 2-6% (Section 5.2.4). No human clinical 

studies have been designed to evaluate the effect of controlled exposures to CO resulting in COHb 

concentrations lower than 2%. Human clinical studies published since the 2000 CO AQCD 

January 2010 2-5 
(U.S. EPA, 2000, 000907) have reported no association between CO and ST-segment changes or 

arrhythmia; however, none of these studies included individuals with diagnosed heart disease. 

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 may not be representative of the most sensitive individuals in the 

population. It is conceivable that the most sensitive individuals respond to levels of COHb lower 

than those evaluated in controlled human exposure studies. 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. Although endogenous COHb is generally <1% in healthy individuals, 

higher endogenous COHb levels are observed in individuals with certain medical conditions. 

Nonambient exposures to CO, such as exposure to environmental tobacco smoke (ETS), may 

increase COHb above endogenous levels, depending on the gradient of pCO. Ambient exposures 

may cause a further increase in COHb. Modeling results described in Chapter 4 indicate that 

increases of ~1% COHb are possible with exposures of several ppm CO depending on exposure 

duration and exercise level. 

Findings of epidemiologic studies conducted since the 2000 CO AQCD (U.S. EPA, 2000, 

000907) are coherent with results of the controlled human exposure studies. These recent studies 

observed associations between ambient CO concentration and emergency department (ED) visits and 

hospital admissions (HAs) for ischemic heart disease (IHD), congestive heart failure (CHF) and 

cardiovascular diseases (CVD) as a whole and were conducted in locations where the mean 24-h avg 

CO concentrations ranged from 0.5 ppm to 9.4 ppm (Table 5-7). All but one of these studies that 

evaluated CAD outcomes (IHD, MI, angina) reported positive associations (Figure 5-2). Although 

CO is often considered a marker for the effects of another traffic-related pollutant or mix of 

pollutants, evidence indicates that CO associations generally remain robust in copollutant models 

and supports a direct effect of short-term ambient CO exposure on CVD morbidity. These studies 

add to findings reported in the 2000 CO AQCD (U.S. EPA, 2000, 000907) that demonstrated 

associations between short-term variations in ambient CO concentrations and exacerbation of heart 

disease. 

The known role of CO in limiting O2 availability lends biological plausibility to ischemiarelated health outcomes following CO exposure. However, it is not clear whether the small changes 

in COHb associated with ambient CO exposures result in substantially reduced O2 delivery to 

tissues. Recent toxicological studies suggest that CO may also act through other mechanisms by 

initiating or disrupting cellular signaling. Studies in healthy animals demonstrated oxidative injury 

and inflammation in response to 50-100 ppm CO, while studies in animal models of disease 

demonstrated exacerbation of cardiomyopathy and increased vascular remodeling in response to 

50 ppm CO. Further investigations will be useful in determining whether altered cell signaling 

contributes to adverse health effects following ambient CO exposure. 

Given the consistent and coherent evidence from epidemiologic and human clinical studies, 

along with biological plausibility provided by CO’s role in limiting O2 availability, it is concluded 

that a causal relationship is likely to exist between relevant short-term exposures to CO 

and cardiovascular morbidity. 

Only two epidemiologic studies were identified that investigated the relationship between 

long-term exposure to CO and cardiovascular effects, and the results of these studies provide very 

limited evidence of an association (Section 5.2.2). Considering the lack of evidence from controlled 

human exposure studies and the very limited evidence from toxicological studies on cardiovascular 

effects following long-term exposure to CO, the available evidence is inadequate to conclude 

that a causal relationship exists between relevant long-term exposures to CO and 

cardiovascular morbidity.

2.5.2. Central Nervous System Effects 

Exposure to high levels of CO has long been known to adversely affect central nervous system 

(CNS) function, with symptoms following acute CO poisoning including headache, dizziness, 

January 2010 2-6 
cognitive difficulties, 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 (U.S. EPA, 2000, 

000907) reported inconsistent neural and behavioral effects following exposures resulting in COHb 

concentrations 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. 

Toxicological studies that were not discussed in the 2000 CO AQCD (U.S. EPA, 2000, 

000907) employed rodent models to show that 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 (75-150 ppm). In utero CO exposure (75 and 150 ppm) was associated with 

significant myelination decrements and neurotransmitter effects (up to 200 ppm). Finally, perinatal 

CO exposure has been shown to affect the developing auditory system of rodents, inducing 

permanent changes into adulthood (12.5-100 ppm), some of which appear to be reactive oxygen 

species mediated. 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 exposures to CO and central nervous system effects.

2.5.3. 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 (U.S. EPA, 2000, 000907), 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 positive associations with 

CO exposure during early pregnancy when 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. Human clinical studies also demonstrated the heart as a target for CO effects (Section 5.2). 

Animal toxicological studies provide additional evidence for cardiac effects with reported transient 

cardiomegaly at birth after continuous in utero CO exposure (60, 125, 250 and 500 ppm CO) and 

delayed myocardial electrophysiological maturation (150 ppm CO). Toxicological studies have also 

shown that continuous in utero CO exposure (250 ppm) induced teratogenicity in rodent offspring in 

a dose-dependent manner that was further affected by dietary protein (65 ppm CO) or zinc 

manipulation (500 ppm CO). Toxicological studies of 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 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 

January 2010 2-7 
in some subset of births. Toxicology studies have found associations between CO exposure in 

laboratory animals and decrements in birth weight (90-600 ppm), as well as reduced prenatal growth 

(65-500 ppm CO). 

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 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 neonatal 

CO exposure. Auditory functional testing using otoacoustic emissions testing (OAE at 50 ppm CO) 

and 8th cranial nerve action potential (AP) amplitude measurements (12, 25, 50, 100 ppm CO) in 

rodents exposed perinatally to CO showed auditory decrements at postnatal day (PND) 22 (OAE and 

AP) and permanent changes in AP into adulthood (50 ppm CO). Furthermore, exogenous CO may 

interact with or disrupt the normal physiological roles that endogenous CO plays in the body. There 

is evidence that CO plays a role in maintaining pregnancy, controlling vascular tone, regulating 

hormone balance, and sustaining normal ovarian follicular maturation. 

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 nonhypoxic mechanisms 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 relevant long-term exposures to CO and developmental effects and birth 

outcomes. 

2.5.4. Respiratory Morbidity 

New epidemiologic studies, supported by the body of literature summarized in the 2000 CO 

AQCD (U.S. EPA, 2000, 000907), provide evidence of positive associations between short-term 

exposure to CO and respiratory-related outcomes including pulmonary function, respiratory 

symptoms, medication use, hospital admissions, and ED visits. The majority of the studies evaluated 

have not conducted extensive analyses to examine the potential influence of model selection or effect 

modifiers on the association between CO and respiratory morbidity. A limited number of studies 

have examined the potential confounding effects of copollutants on CO risk estimates, and found 

that CO risk estimates were generally robust to the inclusion of O3, SO2, and PM in two-pollutant 

models, but were slightly attenuated in models with NO2. However, the limited amount of evidence 

from studies that have examined the effect of gaseous pollutants on CO-respiratory morbidity risk 

estimates in two-pollutant models, specifically NO2, has contributed to the inability to disentangle 

the effects attributed to CO from the larger complex air pollution mix (particularly motor vehicle 

emissions), and this limits interpretation of the results observed in the epidemiologic studies 

evaluated. A key uncertainty in interpreting the epidemiologic studies evaluated is the biological 

mechanism(s) that could explain the effect of CO on respiratory health. Animal toxicological studies, 

however, provide some evidence that short-term exposure to CO (50-100 ppm) can cause oxidative 

injury and inflammation and alter pulmonary vascular remodeling. Controlled human exposure 

studies have not extensively examined the effect of short-term exposure to CO on respiratory 

morbidity, with a very limited number of studies reporting inconsistent effects of CO on pulmonary 

function. Although these controlled human exposure studies do not provide evidence to support 

CO-related respiratory health effects, epidemiologic studies show positive associations for COinduced lung-related outcomes and animal toxicological studies demonstrate the potential for an 

underlying biological mechanism, which together provide evidence that is suggestive of a causal 

relationship between relevant short-term exposures to CO and respiratory morbidity. 

January 2010 2-8 
Currently, only a few studies have been conducted that examine the association between longterm exposure to CO and respiratory morbidity, including allergy. Although some studies did observe 

associations between long-term 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 copollutant analyses to disentangle the 

respiratory effects associated with CO due to its high correlation with NO2 and other combustionrelated pollutants. Overall, the evidence available is inadequate to conclude that a causal 

relationship exists between relevant long-term exposures to CO and respiratory 

morbidity.

2.5.5. Mortality 

The recently available multicity studies, which consist of larger sample sizes, along with the 

single-city studies, evaluated reported associations that are generally consistent with the results of 

the studies evaluated in the 2000 CO AQCD (U.S. EPA, 2000, 000907). However, to date the 

majority of the literature has not conducted extensive analyses to examine the potential influence of 

model selection, effect modifiers, or confounders on the association between CO and mortality. 

The multicity studies reported comparable CO mortality risk estimates for total 

(nonaccidental) mortality, with the APHEA2 European multicity study showing slightly higher 

estimates for cardiovascular mortality in single-pollutant models. However, when examining 

potential confounding by copollutants, these studies consistently showed that although CO mortality 

risk estimates remained positive, they 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 (e.g., traffic). 

Of the studies evaluated, only the APHEA2 study focused specifically on the CO-mortality 

association 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 sensitivity 

analysis indicated an approximate 50-80% difference in CO risk estimates from a reasonable range 

of alternative models, which suggests that some model uncertainty likely influences the range of CO 

mortality risk estimates obtained in the studies evaluated. The examination of the CO-mortality 

concentration-response relationship found very weak evidence for a CO threshold at 0.5 mg/m3

(0.43 ppm). Finally, when examining a variety of city-specific variables to identify potential effect 

modifiers of the CO-mortality relationship, the APHEA2 study found that geographic region 

explained most of the heterogeneity in CO mortality risk estimates. 

 The results from the single-city studies are generally consistent with the multicity 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 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 

cause-specific mortality outcomes associated with CO exposure. In addition, the attenuation of CO 

risk estimates which was often observed in copollutant models contributes to the uncertainty as to 

whether CO is acting alone or as an indicator for other combustion-related pollutants. Overall, the 

epidemiologic evidence is suggestive of a causal relationship between relevant short-term 

exposures to CO and mortality. 

The evaluation of new epidemiologic studies conducted since the 2000 CO AQCD (U.S. EPA, 

2000, 000907) 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 (U.S. EPA, 2000, 000907). The reanalysis of the American Cancer Society (ACS) 

data by Jerrett et al. (2003, 087380) found no association between long-term exposure to CO and 

mortality. Similar results were obtained in an updated analysis of the ACS data when using earlier 

(1980) CO data, but negative associations were found when using more recent (1982-1998) data. 

January 2010 2-9 
These results were further confirmed in an extended analysis of the ACS data. The Women’s Health 

Initiative (WHI) Study also found no association between CO and CVD events (including mortality) 

using the mortality 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. An additional study was identified that used a cross-sectional study design, 

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- 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 populations (i.e., post-menopausal women and hypertensive men) combined with the 

lack of evidence for respiratory and cardiovascular morbidity outcomes following long-term 

exposure to CO; and the absence of a proposed mechanism to explain the progression to mortality 

following long-term exposure to CO provide supportive evidence that there is not likely to be a 

causal relationship between relevant long-term exposures to CO and mortality.

2.6. Policy-Relevant Considerations 

2.6.1. Susceptible Populations 

The examination of populations potentially at greater risk for health effects due to CO 

exposure is an important consideration in setting NAAQS to provide an adequate margin of safety 

for both the general population and sensitive populations (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 of an individual to CO-related 

health effects. These types of studies were those that included stratified analyses, examined 

individuals with an underlying health condition, or used animal models of disease. 

The most important susceptibility characteristic for increased risk due to CO exposure is CAD, 

also known as coronary heart disease (CHD). As discussed in Section 5.7, there were approximately 

13.7 million individuals with CHD in the U.S. in 2007. Persons with a normal cardiovascular system 

can tolerate substantial concentrations of CO, if they vasodilate or increase cardiac output in 

response to the hypoxia 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 CAD, and several studies have 

found that controlled exposures to CO resulting in COHb concentrations of 2-6% result in significant 

decreases in time to onset of exercise-induced angina or ST-segment changes in patients with stable 

angina. Epidemiologic studies found limited evidence for increased hospital admissions for ischemic 

heart disease (IHD) in individuals with secondary diagnoses of dysrhythmias or congestive heart 

failure (CHF). This combined evidence from controlled human exposure and epidemiologic studies 

indicates that individuals with underlying cardiovascular disease, particularly CAD, are a large 

population that is susceptible to increased health effects in response to exposure to ambient CO. 

Additional evidence for increased CO-induced cardiovascular effects is provided by toxicological 

studies that observed altered cardiac outcomes in animal models of cardiovascular disease. 

Other medical conditions that have been linked to increased susceptibility to CO-induced 

health effects include COPD, diabetes, and anemia. Individuals with hypoxia resulting from COPD 

may be particularly sensitive to CO during submaximal exercise typical of normal daily activity. The 

results available from epidemiologic and controlled human exposure studies provide preliminary 

evidence that individuals with obstructive lung disease (e.g., COPD patients with underlying 

hypoxia, asthmatics) may be susceptible to cardiovascular or respiratory effects due to CO exposure. 

Diabetics are known to have elevated exhaled CO concentrations indicative of increased endogenous 

CO production rates. In addition, some recent epidemiologic studies provide preliminary evidence 

for increased associations between short-term CO exposure and ED visits and hospital admissions 

for cardiovascular disease (CVD) among diabetics compared to non-diabetics, as well as associations 

between short-term CO exposure and changes in HRV parameters among subjects with metabolic 

syndrome, but not among healthy subjects. Increased endogenous CO production and the potential 

January 2010 2-10 
for higher baseline COHb concentrations in individuals with diabetes, combined with the limited 

epidemiologic evidence showing cardiovascular effects, suggests that diabetics are potentially 

susceptible to short-term exposure to CO. Individuals with various forms of anemia experience 

lowered hematocrit or produce altered forms of hemoglobin, resulting in decreased arterial O2

content; in addition, individuals with hemolytic anemia exhibit increased endogenous CO production 

rates and COHb levels. This suggests that individuals with anemia who have diminished O2-carrying 

capacity and/or high baseline COHb levels may be more susceptible to health effects due to ambient 

CO exposure, although no studies were identified that evaluated specific CO-related health effects in 

anemic individuals. 

Aging alters physiological parameters that influence the uptake, distribution, and elimination 

of CO. The general impact of these changes over an individual’s lifetime increases the time required 

for both loading and elimination of CO from the blood. As noted in the 2000 CO AQCD (U.S. EPA, 

2000, 000907), changes in metabolism that occur with age, particularly declining maximal oxygen 

uptake, may make the aging population susceptible to the effects of CO via impaired oxygen 

delivery to the tissues. Some epidemiologic studies reported increases in IHD or myocardial 

infarction (MI) HAs among older adults as compared to all-age groups or younger adults in response 

to short-term exposure to CO. Older adults represent a large and growing fraction of the U.S. 

population and have a higher prevalence of CAD and other cardiovascular conditions than the 

general population; combined with the limited evidence available from epidemiologic studies, this 

indicates that older adults are a potentially susceptible population for increased health effects due to 

CO. 

During gestational exposure, fetal CO pharmacokinetics differ from maternal kinetics, in part 

because human fetal Hb has a higher CO affinity than adult Hb. At steady-state conditions, fetal 

COHb concentrations are up to 10-15% higher on a relative basis than maternal COHb levels, and 

these levels are maintained over a longer period since the half-life for fetal CO Hb is approximately 

twice that of maternal COHb (7.5 h versus 4 h). Some epidemiologic studies reported higher 

associations between short-term CO exposure and IHD or MI HAs among older adults as compared 

to all-age groups or younger adults. Epidemiologic studies provide some evidence that CO exposure 

during pregnancy is associated with changes in birth outcomes, including PTB, cardiac birth defects, 

reductions in birth weight, and infant mortality in the postneonatal period. Toxicological studies 

report effects in laboratory animals that lend biological plausibility to outcomes observed in 

epidemiologic studies, including decrements in birth weight, reduced prenatal growth, and effects on 

the heart. Toxicological evidence also exists for additional developmental outcomes which have not 

been examined in epidemiologic or human clinical studies, including behavioral abnormalities, 

learning and memory deficits, locomotor effects, neurotransmitter changes, and changes in the 

auditory system. This evidence suggests that critical developmental phases may be characterized by 

enhanced sensitivity to CO exposure. 

COHb concentrations are generally higher in males than in females, and the COHb half-life is 

longer in healthy men than in women of the same age. However, women experience fluctuating 

COHb levels through the menstrual cycle due to variations in the endogenous CO-production rate. 

Only a limited number of epidemiologic studies have examined gender differences, and found some 

evidence for larger effects in males compared to females when examining the association between 

short-term CO exposure and IHD HAs. The limited epidemiologic evidence combined with known 

gender-related differences in endogenous CO production do not provide sufficient basis for 

determining whether CO disproportionately affects males or females. 

Increased altitude induces a number of physiological changes as compensatory mechanisms to 

counteract the effects of decreased barometric pressure and the resulting altitude-induced hypobaric 

hypoxia (HH). These changes generally increase both CO uptake and elimination, with increased 

COHb levels observed in subjects at rest and decreased COHb observed in individuals exposed to 

CO during exercise. In addition, baseline COHb levels increase due to increased endogenous CO 

production. A controlled human exposure study observed an additive effect of CO exposure and 

simulated high altitude on the reduction in time to onset of angina among a group of individuals with 

CAD. Acclimatization occurs as the length of stay at high altitude increases, indicating that visitors 

to high-altitude locations may have an increased risk of health effects due to CO exposure and 

represent a potentially susceptible population. 

Physiological changes associated with exercise tend to increase both uptake and elimination of 

CO. In a controlled human exposure study, healthy subjects exposed to CO and achieving COHb 

levels of ~5% observed a significant decrement in exercise duration and maximal effort capability 

January 2010 2-11 
during heavy exercise. Due to the counterbalancing effects of increased COHb formation and 

elimination rates, it is unclear whether individuals engaging in light to moderate exercise represent a 

population potentially susceptible to ambient CO exposure. 

CO concentrations on and adjacent to heavily traveled roadways are several times higher than 

concentrations measured at fixed-site monitors not located adjacent to roadways. In addition, studies 

of commuters have shown that commuting time is an important determinant of CO exposure for 

those traveling by car, bicycle, public transportation, and walking. Census data indicate that 17.9 

million occupied homes nationwide (16.1%) are located within approximately 90 m of a freeway, 

railroad, or airport, and that 5.5 million U.S. workers (5%) commute 60 min or more to work in 

automobiles. This evidence for elevated on-road and near-road CO concentrations combined with 

residential and commuting data indicates that the large numbers of individuals who spend a 

substantial amount of time on or near heavily traveled roadways are an important population that is 

potentially susceptible to increased health risks due to ambient CO exposure. 

Endogenous CO production can be altered by medications or other substances, including 

nicotinic acid, allyl-containing compounds (acetamids and barbiturates), diphenylhydantoin, 

progesterone, contraceptives, and statins. One epidemiologic study observed an association between 

short-term CO exposure and an increase in SDNN for CAD patients not taking beta blockers; 

however, this association did not persist in CAD patients taking beta blockers. Other compounds 

such as carbon disulfide and sulfur-containing chemicals (parathion and phenylthiourea) increase CO 

following metabolism by cytochrome p450s. The p450 system may also cause large increases in CO 

produced from the metabolic degradation of dihalomethanes such as methylene chloride. Minor 

sources of endogenous CO include the auto-oxidation of phenols, photo-oxidation of organic 

compounds, and lipid peroxidation of cell membrane lipids. Taken together, this evidence indicates 

that individuals ingesting medications and other substances that enhance endogenous or metabolic 

CO production represent a population that is potentially susceptible to increased health effects due to 

additional exposure to ambient CO. 

Overall, the controlled human exposure, epidemiologic, and toxicological studies evaluated in 

this assessment provide evidence for increased susceptibility among multiple populations. Medical 

conditions that increase endogenous CO production rates may also contribute to increased 

susceptibility to health effects from ambient CO exposure. Although the weight of evidence varies 

depending on the factor being evaluated, the clearest evidence indicates that individuals with CAD 

are most susceptible to an increase in CO-induced health effects. 

2.6.2. Concentration- and Dose-Response Relationships 

Currently, very limited information is available in the human clinical and epidemiologic 

literature regarding the CO concentration- or dose-response (C-R, D-R) relationships and the 

potential existence of a CO threshold. Two human clinical studies described in the 1991 (U.S. EPA, 

1991, 017643) and 2000 (U.S. EPA, 2000, 000907) CO AQCDs have evaluated the D-R relationship 

between percent COHb (a measure of internal dose of CO) and onset of exercise-induced angina 

among individuals with CAD. Anderson et al. (1973, 023134) exposed 10 adult men with stable 

angina (5 smokers and 5 nonsmokers) for 4 h to CO concentrations of 50 and 100 ppm, which 

resulted in average COHb concentrations 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 

2 concentrations of CO (average exposure concentrations of 117 and 253 ppm) resulting in average 

COHb concentrations in the range of 2.0-2.4% and 3.9-4.7% (Allred et al., 1989, 013018; Allred et 

al., 1989, 012697; Allred et al., 1991, 011871). Relative to control (average COHb 0.6-0.7%), COHb 

concentrations of 2.0-2.4% and 3.9-4.7% were observed to decrease the time required to induce STsegment changes indicative of myocardial ischemia by 5.1% (p = 0.01) and 12.1% (p < 0.001), 

respectively. Increasing COHb concentration was similarly shown to decrease the time to onset of 

exercise-induced angina. As described in Allred et al. (1989, 013018; 1989, 012697; 1991, 011871), 

the observed dose-response relationship was further evaluated by regressing the percent change in 

time to ST-segment change or time to angina on actual COHb concentration (0.2% - 5.1%) using the 

three exposures (air control and two CO exposures) for each subject. Regression analyses were 

conducted separately for each individual and the averages of the intercepts and slopes across subjects 

were reported. This analysis demonstrated statistically significant decreases in time to angina and 

January 2010 2-12 
ST-segment change of approximately 1.9% and 3.9%, respectively, per 1% increase in COHb 

concentration, with no evidence of a measurable threshold. The findings of Allred et al. (1989, 

013018; 1989, 012697; 1991, 011871) provide evidence of a significant D-R relationship over a 

range of COHb concentrations relevant to the NAAQS. While several other laboratory studies have 

evaluated cardiovascular effects of CO exposure among adults with CAD, differences in study 

protocols and analytical methods do not allow for an informative pooled or quantitative metaanalysis of the D-R relationship across studies (Section 5.2.4). 

Two studies 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, 098420) 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, 098420) 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 7 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. In addition to the time-series analyses investigating the association of CO 

concentrations with hospital admissions due to CVD among Medicare enrollees, Bell et al. (2009, 

193780) performed subset analyses using datasets that included only days with CO levels below 

certain specified values, ranging from 1 to 10 ppm (in 1 ppm increments). When these various COlimit values were evaluated, there were positive associations between cardiovascular health effects 

and CO concentrations at each level investigated in this study, thus providing no evidence for the 

existence of a threshold. The investigators also estimated an exposure-response curve allowing a 

nonlinear relationship between CO concentration and risk of CVD hospital admissions, and reported 

no evidence of departure from a linear exposure-response curve. 

2.7. Integration of CO Health Effects 

This section summarizes the main conclusions of this assessment regarding the health effects 

of CO and the concentrations at which those effects are observed. It also discusses important 

uncertainties that were considered in interpreting the health effects evidence. The clearest evidence 

for health effects associated with short-term exposure to CO is provided by studies of cardiovascular 

morbidity. The combined health effects evidence supports a likely causal relationship for this 

outcome. Controlled human exposure studies provide strong evidence of independent effects of CO 

on cardiac function, with effects being observed in patients with CAD following short-term CO 

exposures resulting in 2.0-2.4% COHb. Epidemiologic studies of ED visits and hospital admissions 

for ischemic heart disease report consistent positive associations with additional preliminary 

evidence for an increase in cardiovascular-related mortality provided by a multicity study. This 

epidemiologic evidence is coherent with ischemia-related effects observed in controlled human 

exposure studies. Recent toxicological evidence suggests that other mechanisms involving altered 

cellular signaling may play a role in cardiovascular disease outcomes following CO exposure. 

Consistent decreases in time to onset of exercise-induced angina, along with ST-segment 

changes indicative of myocardial ischemia, were observed in individuals with CAD following 

controlled CO exposures resulting in COHb concentrations of 2-6%, with no evidence of a threshold 

at the lowest levels tested. Modeling results described in Chapter 4 indicated that increases of ~1% 

COHb are possible with exposures of several ppm CO, depending on exposure duration and exercise 

level. Baseline COHb levels are <1% in healthy individuals, with higher endogenous CO production 

observed in individuals with certain medical conditions. The volunteers who participated in these 

studies were diagnosed with moderate to severe CAD, although they may not be representative of 

the most sensitive individuals in the population. Variability in activity patterns and severity of 

January 2010 2-13 
disease combined with daily fluctuations in baseline COHb levels may influence the critical level of 

increased COHb which leads to adverse cardiovascular effects in a particular individual. In addition, 

arterial COHb is transiently higher than venous COHb for several minutes following a rapid increase 

in inhaled CO concentration. Transient increases in ambient CO have the potential to elevate COHb 

to higher levels in the coronary arteries than in other vascular beds, possibly increasing heart CO 

levels and cardiovascular symptoms in diseased individuals. Quantification of the magnitude of 

effects at ambient concentrations from the results of controlled human exposure studies is difficult 

due to the gap between ambient concentrations and the higher concentrations used in these studies 

(i.e., experimental studies have not been conducted at levels within the range of current maximum 

ambient concentrations). 

Epidemiologic studies consistently show associations between ambient CO concentrations and 

cardiovascular endpoints other than stroke, particularly hospitalizations and ED visits for ischemic 

heart disease, MI, and angina. These effects are robust to adjustment for copollutants. Since the 

heterogeneity of endpoints in these studies does not lend itself to a quantitative meta-analysis, a 

forest plot was used to summarize the results. Figure 2-1 presents unadjusted health effect estimates 

from U.S. and Canadian studies of short-term CO exposure and CVD hospitalizations, along with 

mean and 99th percentile concentrations during the study periods. Table 2-2 summarizes the range of 

mean and 99th percentile concentrations observed in the studies presented in Figure 2-1. This 

evidence for ischemia-related outcomes is coherent with effects observed in controlled human 

exposure studies, although uncertainty regarding the extent of reduced O2 delivery to tissues 

following exposure to ambient CO concentrations contributes to the uncertainty in quantitative 

interpretation of effect estimates. 

Study Location Mean*a,b Avg Time Lag Outcome/ 

Group 

Effect Estimate (95% CI) 

 Non-Stroke CVD Endpoint

Symons et al. (2006, 091258) Baltimore, MD 0.4 (2.3) 8-h max 0-3 CHF 

Szyszkowicz (2007, 193793) Montreal, Can 0.5 24-h avg 0 IHD 

0 IHD, 65+ 

Wellenius et al. (2005, 087483) Pittsburgh, PA 1.03 (1.6-3.9) 24-h avg 0 CHF 

Bell et al. (2009, 193780) 126 U.S. Counties 1.3 (1.2-22.1)c 1-h max 0 CVD, 65+ 

Fung et al. (2005, 074322) Windsor, Can 1.3 1-h max 0-2 CVD, <65 

0-2 CVD, 65+ 

Metzger et al. (2004, 044222) Atlanta, GA 1.8 (5.5-5.9) 1-h max 0-2 IHD 

0-2 CHF 

Tolbert et al. (2007, 090316) Atlanta, GA 1.6 (5.3-5.4) 1-h max 0-2 CVD 

Peel et al. (2007, 090442) Atlanta, GA 1.8 (5.5-5.9) 1-h max 0-2 IHD 

0-2 CHF 

Koken et al. (2003, 049466) Denver, CO 0.9 (1.3-2.0) 24-h avg 3 CHF 

0-3 IHD 

0-3 IHD, sCHF 

Mann et al. (2002, 036723) California, US 2.07 (1.3-15.9) 8-h max 

0-3 IHD, sARR 

0 MI 

0 CHF 

Linn et al. (2000, 002839) Los Angeles, CA 1.5 (1.1-8.3) 24-h avg 

0 CVD 

0-2 IS, 65+ Stroke

0-2 CIS, 65+ 

Villeneuve et al. (2006, 090191) Edmonton, Can 0.8 24-h avg 

0-2 HS, 65+ 

Wellenius et al. (2005, 088685) Multicity, US 1.02** (1.2-7.1) 24-h avg 0-2 IS, 65+ 

0-2 HS, 65+ 

Linn et al. (2000, 002839) Los Angeles, CA 1.5 (1.1-8.3) 24-h avg 0 IS 

*CO Mean and (99th percentile) concentrations in ppm. ** Median 

a

 Mean concentrations (ppm) are presented as reported in the references. Studies are arranged in approximate order of 

increasing mean concentration, with adjustment for different averaging times using a 4:3:2 ratio of 1-h max, 8-h max, 

and 24-h avg values based on relationships derived from the national AQS CO distribution. b

 99th percentile concentrations (ppm) were obtained from the AQS database for durations and locations chosen to match 

those of the U.S. studies. When multiple monitors were available at the study location, the range of monitor specific 99th 

percentile concentrations during the study period is presented. No 99th percentile data are presented for Canadian studies. c

 For the Bell et al. (2009, 193780) study, the concentration statistics represent the 1999-2005 average of daily county-specific 

values. The central estimate is the median county-average across the U.S. The 99th percentile values represent the 

counties with the lowest and highest 99th percentile concentrations. Additional cause-specific effect estimates adjusted for 

NO2 are presented in Section 5.2.1. 

 

 Excess Risk 

Figure 2-1. Excess risk estimates from epidemiologic studies of short-term CO exposure and 

CVD hospitalizations along with author-reported mean and AQS-derived 99th 

percentile CO concentrations. See the footnotes related to concentration data. 

January 2010 2-14 
Table 2-2. Range of mean and 99th percentile concentrations (ppm) in US and Canadian studies of 

short-term CO exposure and CVD hospitalizations. See the notes in Figure 2-1 for 

sources of concentration data. 

Metric 1-h daily max 8-h daily max 24-h avg 

Mean 1.3-1.8 0.4-2.07 0.5-1.5 

99th percentile 1.2-22.1 1.3-15.9 1.1-8.3 

 

Additional studies provide evidence for associations between CO exposure and other health 

outcomes, including CNS effects, birth outcomes and developmental effects, respiratory effects, and 

mortality. Although inconsistent results were reported in controlled human exposure studies on 

neural and behavioral effects, toxicological studies in rodents found that perinatal exposure to CO 

can have a range of effects on the adult nervous system. This combined evidence is suggestive of a 

causal relationship between both short- and long-term CO exposure and CNS effects. Differences in 

fetal pharmacokinetics from those of the mother result in fetal COHb levels that are up to 10-15% 

higher than maternal COHb levels. Epidemiologic studies provide some evidence that CO exposure 

during pregnancy is associated with changes in birth outcomes, including increased risk of PTB, 

cardiac birth defects, small reductions in birth weight, and infant mortality in the postneonatal 

period. This evidence, in conjunction with developmental effects observed in toxicological studies, is 

suggestive of a causal relationship between long-term exposure to CO and birth and developmental 

effects. 

Evidence regarding the effect of short-term exposure to CO on respiratory morbidity is 

suggestive of a causal relationship, based on associations observed in epidemiologic studies and 

animal toxicological studies which indicate the potential for an underlying biological mechanism, 

while the evidence on long-term exposure and respiratory morbidity is inadequate to infer the 

presence of a causal relationship. 

An evaluation of epidemiologic studies that examined the effect of short-term exposure to CO 

on mortality provides evidence that is suggestive of a causal relationship. Epidemiologic studies that 

examined mortality and long-term exposure to CO reported consistent null associations, which, 

combined with the lack of respiratory and cardiovascular morbidity or a proposed biological 

mechanism for mortality following long-term exposure, indicate that there is not likely to be a causal 

relationship between long-term exposure to CO and mortality. 

Issues such as exposure error and isolation of the independent effect of CO as a component of 

a complex air-pollutant mixture contribute to uncertainty in interpreting the results of epidemiologic 

studies. Studies published since the 2000 CO AQCD (U.S. EPA, 2000, 000907) have provided 

insight regarding the nature and magnitude of these uncertainties. Exposures in near-road and onroad microenvironments are likely to be higher than concentrations measured at community-oriented 

regulatory monitors, which may result in over- or underestimation of the magnitude of ambient 

exposure for some individuals. Individuals who are susceptible to CO-induced health effects, such as 

those with CAD, may be at additional risk when experiencing elevated on-road CO concentrations. 

However, as discussed in Section 2.3 and in more detail in Section 3.6, spatial variability in absolute 

concentration will not introduce error into time-series epidemiologic studies if the concentrations are 

correlated in time. A recent study by Sarnat et al. (2009, 180084) found that associations between 

CO and cardiovascular ED visits were similar when based on different monitors within an urban 

center, regardless of monitor location or distance to population, while an association was not 

observed when using a rural monitor outside the urban area. This may have been related to the 

similarity of driving patterns and peak rush-hour times in the urban center as compared to the area 

around the rural monitor, where the temporal driving patterns were different. Simulations of ambient 

and nonambient exposures to a nonreactive pollutant indicated that nonambient exposure has no 

effect on the association between ambient exposure and health outcomes for the case where ambient 

and nonambient concentrations are independent, although variability is introduced. Nonambient 

exposure to CO is not expected to be temporally correlated with ambient CO concentrations, and 

therefore nonambient CO will not act as a confounder in epidemiologic associations with ambient 

CO. Exposure error is not likely to affect the magnitude of the population-averaged effect estimates 

observed in epidemiologic studies, although it would tend to widen the confidence intervals. 

January 2010 2-15 
Epidemiologic studies consider the effects of CO as a component of a complex mixture of air 

pollutants that varies across space and time, with moderate to high correlations observed between 

CO concentrations and those of other combustion-related pollutants. On-road vehicle exhaust 

emissions are a nearly ubiquitous source of combustion pollutant mixtures that include CO, NO2, and PM2.5, and these emissions are the most important contributor to ambient CO in near-road 

locations. Correlations between CO and NO2 reported in epidemiologic studies of short-term 

exposure to CO generally ranged from 0.3 to 0.86, with correlations reported in US studies ranging 

from 0.55 to 0.86. Correlations between CO and PM2.5 reported in all studies ranged from 0.17 to 

0.74, with correlations in US studies ranging from 0.43 to 0.62. This complicates the quantitative 

interpretation of effect estimates in these studies to apportion the relative extent to which CO at 

ambient concentrations is independently associated with cardiovascular or other effects, and the 

extent to which CO acts as a marker for the effects of another combustion-related pollutant or mix of 

pollutants. 

As summarized in Tolbert et al. (2007, 090316), when toxicological or controlled human 

exposure studies of two correlated pollutants provide evidence that each exerts an independent health 

effect, two-pollutant models may be appropriate to adjust the effect estimate for each pollutant for 

confounding by the other pollutant. PM2.5 and NO2 have each been linked to cardiovascular health 

effects in epidemiologic studies. In two-pollutant models in which one of the pollutants is linked to 

the measured outcome and the other is a surrogate for the first pollutant, the copollutant model can 

help identify which is the better predictor of the effect, particularly if the etiologically linked 

pollutant is measured with more error than the second pollutant. Uncertainty is introduced in the size 

of the effect estimate and the portion of the effect size represented by each of the coefficients in the 

model by correlation between the two pollutants and by differential exposure measurement error. 

Since the spatial variability of CO is a larger contributor to measurement error than for other more 

homogenously distributed pollutants such as PM2.5, robustness of CO effect estimates indicates that 

CO is the better predictor of effects in copollutant models. Although this complicates quantitative 

interpretation of the effect estimates reported in epidemiologic studies, the epidemiologic evidence 

for cardiovascular morbidity summarized in this assessment indicates that CO associations generally 

remain robust in copollutant models (Figure 5-6 and Figure 5-7), which, combined with the 

consistency of effects observed across studies, the coherence of epidemiologic health outcomes with 

effects observed in controlled human exposure studies, and the emerging evidence on the potential 

role for cell signaling effects at low tissue CO concentrations, supports an independent effect of 

short-term CO exposure on cardiovascular morbidity. This combined evidence supports a 

determination that the relationship between CO and cardiovascular morbidity is likely causal, while 

still recognizing that CO is a component of a mixture of combustion-related pollutants. 

Evidence from controlled human exposure and epidemiologic studies indicates that individuals 

with underlying CVD, specifically CAD, are an important susceptible population at increased risk of 

health effects due to ambient CO. Potentially susceptible populations include those with other 

underlying diseases, including anemia, obstructive lung disease, or diabetes; older adults and fetuses 

during critical phases of development; commuters and those living near heavily traveled roadways; 

visitors to high-altitude locations; and individuals ingesting medications and other substances that 

enhance endogenous or metabolic CO production. Limited evidence is available from controlled 

human exposure studies of CAD patients indicating a statistically significant inverse relationship 

between COHb concentration and time to ST segment change or time to exercise-induced angina. 

Epidemiologic analyses investigating the exposure-response relationship for mortality and 

cardiovascular morbidity did not find evidence for a departure from linearity or a threshold for CO 

effects. 

The new evidence reviewed in this ISA builds upon the health-effects evidence summarized in 

the 2000 CO AQCD (U.S. EPA, 2000, 000907), with many new epidemiologic studies adding to the 

body of evidence showing associations between acute cardiovascular effects and CO measured at 

ambient monitors. Controlled human exposure studies reviewed both in this ISA and the 2000 CO 

AQCD (U.S. EPA, 2000, 000907) show definitive evidence of cardiovascular effects among 

individuals with CAD following short-term CO exposure, resulting in COHb concentrations as low 

as 2.0-2.4%. Emerging toxicological evidence points to the potential role for CO in modes of action 

not directly related to COHb’s role in O2 delivery. In evaluating the several epidemiologic studies 

available at the time that reported associations between ambient CO and cardiovascular effects, the 

2000 CO AQCD (U.S. EPA, 2000, 000907) considered those findings to be inconclusive for multiple 

reasons, including: questions regarding the consistency of the results among studies; the ability of 

January 2010 2-16 
community fixed-site monitors to represent spatially variable ambient CO concentrations and 

personal exposures; the small expected increase in COHb due to ambient CO concentrations; the 

lack of biological plausibility for health effects to occur at such COHb levels, even in diseased 

individuals; the potentially greater impact of non-ambient exposure on COHb; and the possibility 

that ambient CO is serving as a surrogate for a mixture of combustion-related pollutants. Some of 

these uncertainties remain and complicate the quantitative interpretation of the epidemiologic 

findings, particularly regarding the biological plausibility of health effects occurring at COHb levels 

resulting from exposures to ambient CO concentrations measured at AQS monitors. New research 

summarized in this assessment reduces several of the other uncertainties noted in the 2000 CO 

AQCD (U.S. EPA, 2000, 000907) and demonstrates the lack of influence of nonambient exposure on 

effect estimates in epidemiologic studies, the consistency of epidemiologic study results, their 

robustness in copollutant models, and the coherence of ischemia-related outcomes with evidence 

from controlled human exposure studies. This consistent and coherent evidence from epidemiologic 

and human clinical studies, along with biological plausibility provided by the role of CO in limiting 

O2 availability, is sufficient to conclude that a causal relationship is likely to exist between relevant 

short-term CO exposures and cardiovascular morbidity. 

January 2010 2-17 
References 

Allred EN; Bleecker ER; Chaitman BR; Dahms TE; Gottlieb SO; Hackney JD; Hayes D; Pagano M; Selvester RH; Walden 

SM; Warren J (1989). Acute effects of carbon monoxide exposure on individuals with coronary artery disease. 

Health Effects Institute. Boston, MA. 012697

Allred EN; Bleecker ER; Chaitman BR; Dahms TE; Gottlieb SO; Hackney JD; Pagano M; Selvester RH; Walden SM; 

Warren J (1989). Short-term effects of carbon monoxide exposure on the exercise performance of subjects with 

coronary artery disease. N Engl J Med, 321: 1426-1432. 013018

Allred EN; Bleecker ER; Chaitman BR; Dahms TE; Gottlieb SO; Hackney JD; Pagano M; Selvester RH; Walden SM; 

Warren J (1991). Effects of carbon monoxide on myocardial ischemia. Environ Health Perspect, 91: 89-132. 

011871

Anderson EW; Andelman RJ; Strauch JM; Fortuin NJ; Knelson JH (1973). Effect of low-level carbon monoxide exposure 

on onset and duration of angina pectoris: a study in ten patients with ischemic heart disease. Ann Intern Med, 79: 

46-50. 023134

Bell ML; Peng RD; Dominici F; Samet JM (2009). Emergency admissions for cardiovascular disease and ambient levels of 

carbon monoxide: Results for 126 U.S. urban counties, 1999-2005. Circulation, 120: 949-955. 193780

Forster P; Ramaswamy V; Artaxo P; Berntsen T; Betts R; Fahey DW; Haywood J; Lean J; Lowe DC; Myhre G; Nganga J; 

Prinn R; Raga G; Schultz M; Van Dorland R (2007). Changes in atmospheric constituents and in radiative forcing, 

Chapter 2. In Solomon S, Qin D; Manning M; Chen Z; Marquis M; Averyt KB; Tignor M; Miller HL (Ed.),IPCC 

Fourth Assessment Report (AR4): Climate Change 2007: Working Group I Report: The Physical Science Basis. 

(pp. 129-234). Cambridge, U.K. and New York, NY: Intergovernmental Panel on Climate Change; Cambridge 

University Press. 092936

Fung KY; Luginaah I; Gorey KM; Webster G (2005). Air pollution and daily hospital admissions for cardiovascular 

diseases in Windsor, Ontario. Can J Public Health, 96: 29-33. 074322

IPCC (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment 

Report (TAR) of the Intergovernmental Panel on Climate Change [Houghton, J.T.,Y. Ding, D.J. Griggs, M. Noguer, 

P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge, UK and New York, NY, USA: 

Intergovernmental Panel on Climate Change, Cambridge University Press. 156587

IPCC (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth 

Assessment Report (AR4) of the Intergovernmental Panel on Climate Change . Cambridge, UK and New York, NY: 

Intergovernmental Panel on Climate Change, Cambridge University Press. 092765

Jerrett M; Burnett RT; Willis A; Krewski D; Goldberg MS; DeLuca P; Finkelstein N (2003). Spatial analysis of the air 

pollution-mortality relationship in the context of ecologic confounders. J Toxicol Environ Health A Curr Iss, 66: 

1735-1777. 087380

Koken PJM; Piver WT; Ye F; Elixhauser A; Olsen LM; Portier CJ (2003). Temperature, air pollution, and hospitalization 

for cardiovascular diseases among elderly people in Denver. Environ Health Perspect, 111: 1312-1317. 049466

Linn WS; Szlachcic Y; Gong H Jr; Kinney PL; Berhane KT (2000). Air pollution and daily hospital admissions in 

metropolitan Los Angeles. Environ Health Perspect, 108: 427-434. 002839

Mann JK; Tager IB; Lurmann F; Segal M; Quesenberry CP Jr; Lugg MM; Shan J; Van den Eeden SK (2002). Air pollution 

and hospital admissions for ischemic heart disease in persons with congestive heart failure or arrhythmia. Environ 

Health Perspect, 110: 1247-1252. 036723

Metzger KB; Tolbert PE; Klein M; Peel JL; Flanders WD; Todd KH; Mulholland JA; Ryan PB; Frumkin H (2004). 

Ambient air pollution and cardiovascular emergency department visits. Epidemiology, 15: 46-56. 044222

Peel JL; Metzger KB; Klein M; Flanders WD; Mulholland JA; Tolbert PE (2007). Ambient air pollution and cardiovascular 

emergency department visits in potentially sensitive groups. Am J Epidemiol, 165: 625-633. 090442

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 2-18 
Samoli E; Touloumi G; Schwartz J; Anderson HR; Schindler C; Forsberg B; Vigotti MA; Vonk J; Kosnik M; Skorkovsky J; 

Katsouyanni K (2007). Short-term effects of carbon monoxide on mortality: An analysis within the APHEA project. 

Environ Health Perspect, 115: 1578-1583. 098420

Sarnat SE; Klein M; Sarnat JA; Flanders WD; Waller LA; Mulholland JA; Russell AG; Tolbert PE (2009). An examination 

of exposure measurement error from air pollutant spatial variability in time-series studies. J Expo Sci Environ 

Epidemiol, In Press: 1-12. 180084

Sheppard L; Slaughter JC; Schildcrout J; Liu L-JS; Lumley T (2005). Exposure and measurement contributions to 

estimates of acute air pollution effects. J Expo Sci Environ Epidemiol, 15: 366-376. 079176

Sinha A; Toumi R (1996). A comparison of climate forcings due to chloroflurocarbons and carbon monoxide. Geophys Res 

Lett, 23: 65-68. 193747

Symons JM; Wang L; Guallar E; Howell E; Dominici F; Schwab M; Ange BA; Samet J; Ondov J; Harrison D; Geyh A 

(2006). A case-crossover study of fine particulate matter air pollution and onset of congestive heart failure symptom 

exacerbation leading to hospitalization. Am J Epidemiol, 164: 421-433. 091258

Szyszkowicz M (2007). Air pollution and emergency department visits for ischemic heart disease in Montreal, Canada. Int 

J Occup Med Environ Health, 20: 167-173. 193793

Tolbert PE; Klein M; Peel JL; Sarnat SE; Sarnat JA (2007). Multipollutant modeling issues in a study of ambient air quality 

and emergency department visits in Atlanta. J Expo Sci Environ Epidemiol, 17: S29-S35. 090316

U.S. EPA (1991). Air quality criteria for carbon monoxide. U.S. Environmental Protection Agency. Research Triangle Park, 

NC. EPA/600/8-90/045F. http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=3000554R.txt. 017643

U.S. EPA (2000). Air quality criteria for carbon monoxide. National Center for Environmental Assessment, Office of 

Research and Development, U.S. Environmental Protection Agency. Research Triangle Park, NC. EPA 600/P99/001F. 000907

Villeneuve PJ; Chen L; Stieb D; Rowe BH (2006). Associations between outdoor air pollution and emergency department 

visits for stroke in Edmonton, Canada. Eur J Epidemiol, 21: 689-700. 090191

Wellenius GA; Bateson TF; Mittleman MA; Schwartz J (2005). Particulate air pollution and the rate of hospitalization for 

congestive heart failure among medicare beneficiaries in Pittsburgh, Pennsylvania. Am J Epidemiol, 161: 1030-

1036. 087483

Wellenius GA; Schwartz J; Mittleman MA (2005). Air pollution and hospital admissions for ischemic and hemorrhagic 

stroke among medicare beneficiaries. Stroke, 36: 2549-2553. 088685

Zeger SL; Thomas D; Dominici F; Samet JM; Schwartz J; Dockery D; Cohen A (2000). Exposure measurement error in 

time-series studies of air pollution: concepts and consequences. Environ Health Perspect, 108: 419-426. 001949

January 2010 2-19 
Chapter 3. 

3.2.1. 

Source to Exposure 

3.1. Introduction 

This chapter reviews concepts and findings in atmospheric sciences and exposure assessment 

that provide a foundation for the detailed presentation of evidence of CO-related health effects in 

subsequent chapters and for a causality finding regarding climate forcing effects of CO. Section 3.2

provides an overview of the primary and secondary sources of CO as well as the atmospheric 

chemistry involved in the production and removal of CO by oxidation processes. Section 3.3

provides a description of climate forcing caused directly and indirectly by CO. Descriptions of CO 

measurement methods, monitor siting requirements, and monitor locations are presented in Section 

3.4. Ambient CO concentrations and their spatial and temporal variability are characterized in 

Section 3.5. The background concentrations of CO useful for risk and policy assessments informing 

decisions about the NAAQS, referred to as policy-relevant background (PRB) concentrations, are 

also presented in Section 3.5. Factors related to human exposure to ambient CO, and their 

implications for epidemiologic studies, are discussed in Section 3.6. Finally, a summary and 

conclusions of the chapter are presented in Section 3.7. 

3.2. CO Sources, Emissions, and Chemistry 

Direct CO Emissions 

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. 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. 

Additionally, by allowing time for the furnace flue gases to mix with air and be oxidized by OH to 

CO2 in the hot gas stream before the OH concentrations drop as the flue gases cool, the CO-to-CO2

ratio in these emissions is shifted toward CO2. 

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 CO emissions data meeting EPA’s data quality 

assurance objectives. In the U.S., direct CO emissions data are tracked in the National Emissions 

Inventory (U.S. EPA, 2006, 157070), a composite of data from various sources including industries 

and state, tribal, and local air agencies, and from the Biogenic Emissions Inventory System (BEIS). 

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 empirical estimates 

and engineering calculations 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 where models (MOBILE6, MOVES, NONROAD) are available to 

calculate inventories (U.S. EPA, 2006, 157070). The NEI includes fires of anthropogenic and natural 

origin. Anthropogenic fires include structural fires, agricultural fires, prescribed burning, and slash 

burning; forest wildfires are considered to be of natural origin. Estimates of direct CO emission from 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 3-1 
soil are calculated by the EPA using the BEIS model. Although these 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. These uncertainties vary by 

source category, season, and region. Discussion of uncertainties is provided in subsequent 

paragraphs related to mobile sources, the largest source category. 

Nationally, on-road mobile sources in the NEI constituted more than half of total CO 

emissions in 2002, or ~60.6 MT of ~116.8 MT total, which includes anthropogenic and biogenic 

emissions reported in the NEI and the BEIS (http://www.epa.gov/ttnchie1/emch/biogenic). High 

concentrations of CO can often occur in areas of heavy traffic. In metropolitan areas in the U.S., for 

example, as much as 75% of all CO emissions came from on-road vehicle exhaust in the 2002 NEI 

(U.S. EPA, 2006, 157070). 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 (Figure 3-1). 

CO emissions from internal combustion engines vary substantially with ambient temperature 

and operating conditions. Substantial light-duty gasoline vehicle CO emissions occur during the cold 

start before the catalyst is warmed up. Most emission tests such as the Federal Test Procedure (FTP) 

which includes a cold start portion and is used to certify that vehicles meet EPA emission standards, 

are conducted at about 75oF. Lower ambient temperatures result in increased CO emissions because 

spark ignition engines are required to run richer air:fuel ratios for longer periods of time, and also 

because the time before the catalyst is warmed up increases compared to the time for catalyst warmup occurring at 75oF (U.S. EPA, 2006, 199897). Thus, in addition to the vehicle CO emissions 

standards EPA implemented starting with the 1968 model year, EPA has also implemented a cold 

temperature CO emission standard for light-duty gasoline vehicles and trucks at 20oF that phased in 

for 40% of the new fleet in the 1994 model year, 80% for the 1995 model year, and 100% with the 

1996 and succeeding model years. The emission standard of 10 g/mile results in a reduction of about 

20-30% in CO emissions at 20o F (57 FR 3188-31923 July 17, 1992). Increased vehicle CO 

emissions can also occur under conditions such as high rates of acceleration, rapid speed 

fluctuations, heavy-vehicle load demands (such as occur while pulling a trailer or going up a steep 

hill), and use of air-conditioning. Such driving conditions were not originally fully reflected in the 

FTP. EPA has issued a Supplemental Federal Test Procedure (SFTP) to control excess CO emissions 

under these conditions. These regulations were phased in for the 1998-2000 model years (61 FR 

54852-54906 October 22, 1996). Moreover, the gasoline-powered spark ignition engines that 

predominate in light-duty on-road vehicles have higher uncontrolled CO emission rates than other 

combustion sources because they typically operate closer to the stoichiometric air-to-fuel ratio, have 

relatively short residence times at peak combustion temperatures, and have very rapid cooling of 

cylinder exhaust gases. By contrast, the diesel-powered engines that predominate in heavy-duty onroad vehicles and in off-road and non-road fixed combustion sources have much lower engine-out 

CO emissions than do the spark-ignition engines because the diesels typically operate at very high 

air-to-fuel ratios, which promote mixing oxygen and fuel, thus improving carbon burn. 

January 2010 3-2 
60,600,000

22,700,000

5,820,000

18,500,000

3,550,000

1,590,000

1,270,000

987,000

657,000

490,000

357,000

284,000

118,000

25,600

1,660

0 20,000,000 40,000,000 60,000,000 80,000,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Source: U.S. EPA (2006, 157070) 

Figure 3-1. CO emissions (tons) in the U.S. by source sector in 2002 from the NEI and the 

BEIS. The “fires” category has been extracted from Tier 3 miscellaneous 

categories of the NEI, and biogenic fires are those attributed to forest wildfires. 

The “soils” category comprises the BEIS data. The “roadway vehicles” and “nonroad vehicles” categories have been renamed here for clarity. 

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 oil-fired power plants and industrial, commercial, and institutional sources, as well as residential 

heaters (e.g., wood-burning stoves) and boilers; (2) industrial processes, which include chemical 

production, petroleum refining, metals production, and industrial processes other than fuel 

combustion; (3) on-road vehicles, which include cars, trucks, buses, and motorcycles; and (4) nonroad vehicles and engines, such as farm and construction equipment, boats, ships, snowmobiles, 

aircraft, locomotive, and the two-stroke engines found in lawnmowers, chainsaws, and other small 

gasoline-powered equipment. 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. The trend 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 1996-2002 because only 1990 data have 

been updated to be comparable to the more recent inventories made since 1996.) 

January 2010 3-3 
Source: U.S. EPA (2008, 157076) 

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, 192377). 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 include Harley et al. (2001, 193922; 2005, 088154), Parrish et al. (2002, 052472), 

Parrish (2006, 090352), Pollack et al. (2004, 184461), and Mobley et al. (2005, 194008). The 

consistent conclusion from those investigations has been that annual average U.S. on-road vehicle 

CO emissions have decreased at a rate of ~5% per year since the early 1990s. This can be seen from 

Figure 3-2 as well. Additional analyses by Harley et al. (2005, 088154) and Parrish (2006, 090352) 

were also consistent with the suggestion in Pollack et al. (2004, 184461) that the EPA MOBILE6 

vehicle emissions model (http://www.epa.gov/otaq/m6.htm) now overestimates vehicle CO 

emissions by a factor of ~2. Field measurements by Bishop and Stedman (2008, 194670) were in 

accord with Parrish’s (2006, 090352) findings that the measured trends of CO and NOX

concentrations from mobile sources in the U.S. indicated that modeled CO emission estimates were 

substantially too high. Hudman et al. (2008, 191253) found that the NEI overestimated 

anthropogenic CO emissions by 60% for the eastern U.S. during the period July 1-August 15, 2004 

using aircraft observations of CO from the International Consortium for Atmospheric Research on 

Transport and Transformation (ICARTT) campaign (Fehsenfeld et al., 2006, 190531) and results 

from a tropospheric chemistry model (GEOS-Chem)(Figure 3-3). 

January 2010 3-4 
Source: Reprinted with Permission of the Amercian Geophysical Union from Hudman et al. (2008, 191253) 

Figure 3-3. Surface air CO concentrations at Chebogue Point during the ICARTT campaign. 

Observations (black) are compared to model results using the 1999 NEI 

anthropogenic emissions (green) and with these CO emissions reduced by 60% 

(blue). Yellow bands are periods of U.S. outflow diagnosed by Millet et al. (2006, 

195106). Overestimation near day 200 is due to model misplacement of a large 

Alaskan/Canadian biomass burning plume. 

Improvements in emissions technologies not correctly represented in MOBILE emissions 

models have been suggested as one cause for this discrepancy. For example, Pokharel et al. (2002, 

052473; 2003, 053740) demonstrated substantial decrements in the CO fraction of tailpipe exhaust in 

several U.S. cities, and Burgard et al. (2006, 193222) documented improvements in emissions from 

heavy-duty on-road diesel engines. The Motor Vehicle Emission Simulator (MOVES) model has 

been designed to address some of the largest errors in the MOBILE model. It was released in final 

form in December 2009 (http://www.epa.gov/otaq/models/moves/index.htm). 

Estimates of non-anthropogenic CO emissions are made using the BEIS model with data from 

the Biogenic Emissions Landcover Database (BELD) and annual meteorological data. National 

biogenic emissions, excluding fires, were estimated to contribute 5%, or ~5.8 MT, of total CO 

emissions from all sources in 2002. Biogenic wildfires in 2002 added another 12%, or ~14.1 MT, to 

the national CO emissions total and were responsible for 76.1% of all CO emissions estimates from 

fires. This is shown in Figure 3-1 using the NEI and BEIS data. 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, though on non-local scales the magnitude of their contribution is small relative to 

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, 193247). 

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, 029520). Soil uptake of CO is driven by 

anaerobic bacteria (Inman et al., 1971, 010972). 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, 002828). 

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 10-30% initiated by lightning 

January 2010 3-5 
(Andreae, 1991, 078147). However, because fire management practices suppress natural wildfires, 

the buildup of fire fuels increases the susceptibility of forests to more severe but less frequent fires in 

the future. Thus 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. 

Biomass burning also exhibits strong seasonality and interannual variability (van der Werf et 

al., 2006, 157084), 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 

(Figure 3-4). The high CO concentration measured by MOPITT coincided with the surface location 

of fires tracked using aerosol plumes identified by the Moderate Resolution Imaging 

Spectroradiometer (MODIS) also on Terra. Subsequent modeling by Pfister et al. (2005, 093009) 

showed that the CO contribution from these fires in July 2004 was 33.1 (± 5.5) MT 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, 029473) 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 10 administrative Regions in the U.S., depicted in Figure 3-5, 

show a more nuanced view of the national concentrations and trends described 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 and the smallest (1.3 MT) in 

Region 10. 

At state and local levels, CO emissions from on-road mobile sources or from fires can 

dominate in different locations across the U.S. Figure 3-6 illustrates this variability with CO statelevel emissions totals 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. Emissions sources varied strongly across counties, however, with urban Denver County 

dominated by on-road vehicle emissions at 71% and rural Garfield County dominated by fire 

emissions at 67%. 

January 2010 3-6 
Source: Reprinted with Permission of the American Meteorological Society from Fishman et al. (2008, 193927) 

Figure 3-4. CO concentrations centered at ~3,000 m above sea level measured by the 

MOPITT sensor on the Terra satellite for the period July 15-23, 2004, during 

intense wildfires in Alaska and the Yukon. 

Source: U.S. EPA (2008, 157076) 

Figure 3-5. Trends in subnational CO emissions in the 10 U.S. EPA Regions for 1990 and 

1996-2002. 

January 2010 3-7 
Colorado 

35

0

180

4

1,400

3,500

7,300

1,300

21,000

530

86,000

140,000

970,000

400,000

1,100,000

0 500,000 1,000,000 1,500,000

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PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

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METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Denver County

0

130,000

42,000

36

2,900

3

340

42

390

180

99

0

0

760

0

0 50,000 100,000 150,000

SOLVENT UTILIZATION

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OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Garfield County

0

0

0

0

75

11

45

0

2,200

0

3,200

3,700

65,000

4,600

20,000

0 25,000 50,000 75,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Denver

County

Garfield 

County

Source:: U.S. EPA (2006, 157070) 

Figure 3-6. CO emissions density map and distributions for the state of Colorado and for 

selected counties in Colorado in 2002, from the NEI and the BEIS. The “fires” 

category has been extracted from Tier 3 miscellaneous categories of the NEI, and 

biogenic fires are those attributed to wildfires. The “soils” category comprises 

the BEIS data. The “roadway vehicles” and “non-road vehicles” categories have 

been renamed here for clarity. 

January 2010 3-8 
3.2.2. Secondary CO Emissions and Associated Chemistry 

Oxidation of anthropogenic and biogenic VOCs constitute important secondary sources of CO. 

For example, Hudman et al. (2008, 191253) determined that oxidation of isoprene and other biogenic 

VOCs contributed 9.1 MT of atmospheric CO (with isoprenes contributing 7.1 MT), and oxidation 

of anthropogenic VOCs contributed another 2.0 MT of CO emissions during the period July 1-

August 15, 2004, for the eastern U.S. In contrast, direct anthropogenic CO emissions were estimated 

to be 5.1 MT for this time period and location. Hence, secondary biogenic formation was found to be 

a more important source of CO emissions than direct anthropogenic activities for the study period. 

Hudman et al. (2008, 191253) noted that biogenic CO emissions were highest in the southeastern 

U.S., where isoprene emissions are also greatest. These estimates were obtained using aircraft 

measurements from the ICARTT campaign (Fehsenfeld et al., 2006, 190531) and estimates from the 

GEOS-Chem model (Bey et al., 2001, 051218), configured as described by Hudman et al.(2007, 

089474). 

Secondary CO production occurs by photooxidation of methane (CH4) and other VOCs, 

including nonmethane hydrocarbons (NMHCs) in the atmosphere and organic molecules in surface 

waters and soils. CH4 oxidation is summarized in this reaction sequence: 

CH4 + OH → CH3 + H2O

CH3 + O2 (+ M) → CH3O2 (+ M) 

CH3O2 + NO → CH3O + NO2

CH3O2 + HO2 → CH3OOH + O2

CH3O + O2 → CH2O + HO2

CH2O + hν → H2 + CO

or CH2O + hν → HCO + H

or CH2O + OH → HCO + H2O

HCO + O2 → CO + HO2

Reaction 3-1 

where M is a reaction mediator that is neither created nor destroyed and stabilizes the reaction 

product. 

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; OH and HO2 together are termed HOX) and CO. 

Reaction of methyl peroxy radical (CH3O2) with HO2 radicals to form methyl hydroperoxide 

(CH3OOH) is also operative, especially in low oxides of nitrogen (NO+NO2=NOX) conditions. 

Heterogeneous removal of the partially water-soluble intermediate products, such as CH3OOH and 

CH2O, 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 

HO2. 

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 

January 2010 3-9 
with more than a few carbons is still unknown. 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. 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, 011760). Yields from 

other compounds range from <0.1 for anthropogenic alkanes (Altshuller, 1991, 192375) to ~0.9 for 

ethane; yields from other compounds are given in Table 3-1 taken from Kanakidou and Crutzen 

(1999, 011760). 

Table 3-1. Literature values for CO yields from hydrocarbons in per carbon units, except as noted. 

Specific hydrocarbons are noted in parentheses. 

Reference CO Yields 

Zimmerman et al. (1978, 010758) 0.3 (hydrocarbons) 

Brewer et al. (1984, 194402) 0.22-0.27 (isoprene) 

Hanst et al. (1980, 011988) According to chamber experiments, CO and CO2 yield: 

 ~0.85 (ethylene) 

 ~0.90 (ethane) 

 ~0.80 (propane) 

 ~0.58 (n-butane) 

 ~0.73 (isoprene) 

 ~0.30 (alpha-pinene) 

Crutzen (1987, 002848) 0.9 of CH4

Kanakidou et al. (1991, 029701) 0.39 (C2H6 and C3H8) 

Jacob and Wofsy (1990, 029668) @ low NOX: 0.2 (isoprene) 

@ high NOX: 0.6 (isoprene) 

Crutzen et al. (1985, 194403) =0.8 (isoprene + OH) 

Kirchhoff and Marinho (1990, 194406) Isoprene oxidation may form 10 ppbv CO/d over the Amazon (3 km deep boundary layer)

Altshuller (1991, 192375) Conversion factors of 19 (C2-C6) anthropogenic alkenes vary between 0.010 and 0.075 

Manning et al. (1997, 194401) CH4 in the SH: 0.7 

Kanakidou and Crutzen (1999, 011760) Annual tropospheric mean conversion factors: 

CH4: 0.9 

 Isoprene: 0.4 

Other nonmethane hydrocarbons: 0.7 

Source: Adapted with Permission of Elsevier Ltd. from Kanakidou and Crutzen (1999, 011760)

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, 2000, 055565). The mean tropospheric 

photochemical lifetime (τ) of CO in the northern hemisphere is ~57 days (Khalil and Rasmussen, 

1990, 012352; Thompson and Cicerone, 1986, 019374). 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. Because the CO τ is shorter than the ~1 yr characteristic time scale for mixing 

between the hemispheres and because northern hemisphere CO emissions are higher due to 

anthropogenic activity (Khalil and Rasmussen, 1990, 012443), a large gradient in concentrations 

January 2010 3-10 
exists between the hemispheres (Edwards et al., 2004, 199889). 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 time 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 typically 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 affected by indoor air reactions. 

3.3. CO Climate Forcing Effects 

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, 000907) and subsequently confirmed in the recent global assessments of climate change by the 

Intergovernmental Panel on Climate Change (IPCC) (2001, 156587; 2007, 092765). CO is a weak 

direct 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. Sinha and Toumi (1996, 

193747) estimated the direct RF of CO computed for all-sky conditions at the tropopause, which is 

the IPCC’s preferred form for the calculation (Forster et al., 2007, 092936), to be 0.024 W/m2

 based 

on an assumed change in CO mean global concentration from 25 to 100 ppb since preindustrial 

times. The direct RF value similarly projected by Sinha and Toumi (1996, 193747) if the mean 

global background concentration were to increase from 25 to 290 ppb was 0.057 W/m2. 

However, because reaction with CO is the major sink for OH on a global scale, increased 

concentrations of CO can lead to increased concentrations of other trace gases whose loss processes 

also involve OH chemistry. Some of those trace gases, CH4 and O3 for example, absorb infrared 

radiation from the Earth’s surface and contribute to the greenhouse effect directly. Others, including 

hydrochlorofluorocarbons (HCFCs), 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 

2006 O3 ISA (U.S. EPA, 2006, 088089). Because CO’s chemical lifetime is longer than those of the 

VOCs most important for O3 formation on urban and regional scales and because CO oxidation has 

one-to-one stoichiometry (whereby one molecule of CO converts only one molecule of NO to NO2), 

CO has a significantly lower O3 forming potential than other VOCs in the troposphere. Carter (1998, 

192380) computed a maximum incremental reactivity for CO of 0.06 g O3 for 1 g CO, as compared 

to reactivities of total on-road vehicle exhaust emissions typically in the range of 3 to 4 g O3 per 

g VOC. 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 significantly to O3

formation even though its photochemical processing is slow. Using data from instrumented models, 

including that of Jeffries (1995, 003055), the NRC (1999, 010614) estimated, for example, that CO 

can contribute 15-25% of the total O3 forming potential of gasoline exhaust emissions, although this 

estimate shows strong regionality. The contribution of CO to urban and regional O3 concentration is 

often <10% owing to its very slow reactivity on these scales and to locally variable radical 

concentration ratios. 

Emissions of CO and the other O3 precursors, nonmethane VOCs (NMVOCs) and NOX, affect 

the oxidizing capacity of the atmosphere largely by perturbing HOX concentrations. From a climate 

perspective, this HOX perturbation chiefly affects the CH4 τ and production of O3 in the troposphere. 

Changes in the concentration of O3 and hence in its RF occur mainly in the time of a few months. 

However, Prather (1996, 193195) showed that changes in CH4 concentration and its RF extend to the 

“primary mode” timescale of troposphere chemistry of about 14 yr (Derwent et al., 2001, 047912; 

Wild et al., 2001, 193196). The primary mode timescale of CH4 is in part determined by the positive 

feedbacks in the CH4-OH-CO system in which even low concentration additions of CH4 produce 

additional CO through oxidation by OH. That additional CO then further decreases atmospheric OH 

concentrations when OH oxidizes it to CO2. The resulting decreased OH concentration then further 

increases the CH4 τ (Daniel and Solomon, 1998, 193235; Isaksen and Hov, 1987, 019490). 

Atmospheric CH4 concentrations since 1750 have increased by more than a factor of 2, giving an RF 

January 2010 3-11 
of ~0.6 W/m2

 (Forster et al., 2007, 092936). Roughly 25% of the global mean tropospheric CO is 

produced by CH4 oxidation (Wuebbles and Hayhoe, 2002, 044159). Using a 2-D global model on a 

coarse grid, Wang and Prinn (1999, 011758) showed that increasing CO and CH4 concentrations 

leading to decreased OH concentrations can extend the CO τ as well as the CH4 τ. Wang and Prinn 

(1999, 011758) varied the CO emissions and other model inputs and parameters in a matrix of 

simulations that showed, with increased or even constant 20th century CO concentrations, the CO τ

was increased by more than 50% in 100 yr. However, Wang and Prinn (1999, 011758) stated that 

their simulation omitted NMHCs and therefore likely underestimated CO concentrations while 

under- or overestimating hydroxyl radical concentrations. Likewise, low spatial resolution of the 

model likely incurred additional error in the solution. 

CH4 is long-lived and, in general, well mixed in the atmosphere. However, the reaction of CH4

and OH, and hence the CH4 τ, is governed by the behavior and location of emissions of the shortlived gases, including CO, VOCs, and NOX. This produces high regional variability and uncertainty 

in the concentrations and RFs from CO and its related climate forcing gases (Berntsen et al., 2006, 

193244; Fuglestvedt et al., 1999, 047431). NOX, for example, can produce effects on the combined 

indirect RF opposite in direction to those of CH4 since under most global background conditions an 

increase in NOX increases the global average OH concentration and decreases CH4 τ and RF 

(Berntsen et al., 2005, 193241; Wild et al., 2001, 193196). Wild et al. (2001, 193196) also showed 

that emissions changes in CO have effects opposite in sign to those of NOX because increases in CO 

act to depress OH concentrations and that the combined effect of CO and NOX emissions yields a 

positive RF. The results of this study underscore the need to consider the combined effects of 

pollutants emitted from similar sources. 

Using the 3-D global chemistry model MOZART-2 (Horowitz et al., 2003, 057770), Naik 

et al. (2005, 193194) simulated changes in global tropospheric O3 concentrations and RF resulting 

from differing reductions in emissions of NOX alone or a combination of NOX, CO, and NMHCs in 

nine regions of the Earth. For the reductions in Europe, North America, and Southeast Asia, reducing 

CO and NMHCs in addition to reducing NOX lowered the spatial inhomogeneity of the O3

concentration and RF because CO has a longer lifetime than NOX. 

Wild et al. (2001, 193196) used the University of California-Irvine chemical transport model 

(CTM) (Wild and Prather, 2000, 052402) driven by the NASA GISS II general circulation model 

(Rind and Lerner, 1996, 193750) to compute changes in O3 concentrations and RF from regional 

emissions of NOX and CO. Changes in O3 and CH4 result from increases in global surface NOX

emissions alone and run for 10-yr periods produced negative net RFs, ranging from -0.2 W/m2

 in 

East Asia to -0.5 W/m2 in the Tropics owing to the long-term interdependencies in the CO-CH4-NOX

system described above. When global CO emissions were increased by an 11 MT pulse for 1 yr 

together with the same 1-yr pulsed NOX surface emissions and run again for a 10-yr period, the 

global net RF rose to 1.7 W/m2 with an estimated 20% uncertainty based on the spatial variability 

and short-term reactivity of O3 (Wild et al., 2001, 193196). 

Determining whether several species’ τ and RF will increase or decrease in response to pulsed 

or step-wise emissions of the short-lived O3 precursor species (NMVOC, CO, and NOX) is 

complicated by its global location with respect to the O3 production response surface. See the 

description of the O3 production response surface and its dependence on NOX and radical 

concentrations in the 2008 NOX ISA (U.S. EPA, 2008, 157073) for additional details. Fiore et al. 

(2002, 051221; 2008, 193749) found that O3 is closely coupled with CH4 and that their relationship 

is influenced by regional variation in NOX concentrations. Using the weighted average results from 

12 3-D global chemistry models exercised for the IPCC Third Assessment Report (2001, 156587), 

Wigley et al. (2002, 047883) confirmed that increases in CO and VOC emissions increased the O3

RF both directly and indirectly through the CH4 effects described above. Furthermore, Wigley et al. 

(2002, 047883) demonstrated that NOX emissions produced a mix of direct and indirect increases in 

RF, mostly dominated by the direct effects for all modeled scenarios. Wigley et al. (2002, 047883) 

concluded that tropospheric O3 RF influences were larger than CH4 influences and that the shortlived reactive gases produced 60-80% of that forcing, with the remainder coming from CH4. Given 

these chemical interdependencies, calculations of an indirect RF for any of these short-lived O3

precursor species are most often made for all of the most important ones together. 

Figure 3-7 illustrates model estimates of the combined RF of increased short-lived O3

precursor species relative to long-lived GHGs, aerosols, and other changes (Forster et al., 2007, 

092936). The combined effect of increased CH4, CO, NMVOC, and NOX emissions for the period 

1750-2005 has produced tropospheric O3 concentrations associated with a net RF of ~0.4 W/m2

 with 

January 2010 3-12 
±50% uncertainty based on Shindell et al. (2005, 080129). Indirect effects of CO through the GHGs 

O3, CH4, and CO2 were estimated to contribute RF ~0.2 W/m2

, which is more than a factor of 2 

larger than the indirect effect of the shorter-lived NMVOCs on the same three GHGs (Forster et al., 

2007, 092936). Of the indirect effects on these three GHGs from CO emissions, the O3-related 

component was the largest, accounting for approximately one-half of the forcing (Forster et al., 

2007, 092936). In comparison, CO2 contributed a direct forcing of 1.6 ± 0.2 W/m2

 over this time 

period. 

Integrated RF estimates over longer time horizons may indicate the future climate effects of 

present-day emissions. Modeled integrated 20-yr and 100-yr time horizon RFs are presented by 

Forster et al. (2007, 092936) in Figure 3-8 for year 2000 emissions of short-lived and long-lived 

GHGs. The integrated RF for CO was estimated to be ~0.2 W/m2

⋅yr with ~50% uncertainty. It can be 

seen that the integrated RF of CO2 is much smaller for the 20-yr horizon because the lifetime of CO2

perturbations is roughly 150 yr. As a result, the RF related to short-lived CO is ~25% of that for CO2

for the 20-yr horizon (~0.7 W/m2

⋅yr) but only ~7% of that for longer-lived CO2 over a 100-yr time 

horizon (~2.4 W/m2

⋅yr). This indirect forcing is just slightly lower than the RF of year 2000 black 

carbon emissions from fossil fuel and biomass burning on the same horizons according to this 

assessment. 

It is also possible to compute individual contributions to the integral RF from CO based on 

separate emissions sectors. Unger et al. (2009, 193238) used the NASA GISS model for Physical 

Understanding of Composition-Climate Interactions and Impacts (G-PUCCINI) (Shindell et al., 

2006, 193751). Unger et al. (2009, 193238) divided the 1995 global anthropogenic CO emissions 

total of 933.3 MT/yr into sectors for on-road transport (ORT) and power generation (PG), and then 

separated contributions from each of these sectors for the U.S. and other large geographic regions of 

the Earth. ORT CO emissions in the U.S. were 84.1 MT/yr; PG CO emissions were 0.55 MT/yr out 

of the total U.S. anthropogenic CO emissions of 112.5 MT/yr. Unger et al. (2009, 193238) concluded 

from analysis of 7-yr runs that the CO indirect CH4 effects (that is, the CO effects through CH4

changes as described above) in the 1995 emissions run were -0.004 W/m2

 for the global ORT and 

-0.022 W/m2 for the global PG. In the U.S., the indirect CH4 RF was positive at +0.009 W/m2

because the positive effects on CH4 τ from the CO emissions dominated over the negative effects 

from NOX through OH. This RF fraction from indirect CH4 is approximately the same as the direct 

O3 RF from ORT in the U.S., 0.010 W/m2. Because the PG sector emits NOX but less CO relative to 

the ORT, the indirect CH4 RF from the U.S. PG was not dominated by the positive CO effects and 

remained a net negative at -0.006 W/m2

 (Unger et al., 2009, 193238). The authors acknowledged 

some uncertainty in this relationship related to the influence of NOX emissions on CO 

transformation, but no quantification of uncertainty in these modeled estimates was provided. 

These gross emissions sectors can also be subdivided to demonstrate more clearly the 

localized chemical interdependencies of the CO-CH4-NOX system. Fuglestvedt et al. (2008, 193242) 

used the Oslo CTM2 model to simulate effects from all emissions and changes in all transportation 

subsectors from 1850-2000. Fuglestvedt et al. (2008, 193242) found that global transport has been 

responsible for ~15% of the total anthropogenic CO2 RF and ~15% of the total anthropogenic O3 RF. 

Of the total O3 RF, the largest contributor was the shipping sector at 0.03 W/m2

, because its high 

NOX-to-CO and NOX-to-VOC ratios produced OH increases and hence CH4 decreases in regions of 

naturally low NOX. For the shipping segment of the transport sector, the high NOX emissions there 

reduced the CH4 τ but increased O3. The global mean effect from these two was small and still 

smaller than the direct negative effect from SO4

2- aerosols. In the on-road segment of global 

transportation, emissions of CO and VOCs together with NOX produce an O3 RF larger than the 

negative RF from CH4. 

January 2010 3-13 
Source: Reprinted with Permission of Cambridge University Press from Forster et al. (2007, 092936) 

Figure 3-7. Components of RF in 2005 resulting from emissions since 1750. (S) and (T) 

indicate stratospheric and tropospheric changes, respectively. 

January 2010 3-14 
Source: Reprinted with Permission of Cambridge University Press from Forster (2007, 092936) 

Figure 3-8. Integrated RF of year 2000 emissions over 20-yr and 100-yr time horizons. The 

values provided refer to global annual emissions, but effects are expected to vary 

regionally for short-lived gases. 

Caution is warranted in interpreting RF estimates. RF values are global model calculations 

using the assumption that global climate sensitivities are equal for all forcing mechanisms, whether 

CO2, SO4

2- and other aerosols, or the short-lived gases like CO (Berntsen et al., 2005, 193241; 

Berntsen et al., 2006, 193244). That assumption is under challenge now by GCM results using 

regionalized RF values separately for different forcing mechanisms and with CO2, O3, and solar 

input changes (Joshi et al., 2003, 193752). Joshi et al. (2003, 193752) found that global climate 

system sensitivities from non-CO2 RF varied by ±30% compared to CO2 RF. Other GCM 

experiments by Lelieveld et al. (2002, 190361), Rotstayn and Penner (2001, 193754), Menon et al. 

(2002, 155978), and Kristjansson (2002, 045282) have indicated that regionally changing RF can 

induce changes in large-scale circulation patterns that control the regionalized cycles of flooding and 

drought through disruptions in regional temperature and hydrologic cycles. However, such 

regionalized patterns resulting from GCM experiments are so uncertain and so widely variable 

across models that even the sign of these regionalized changes can vary with model type and any of 

January 2010 3-15 
the models’ unconstrained assumptions (Berntsen et al., 2006, 193244). Even with such uncertainty 

and variability, though, the consensus of the climate community is that the climate effects of changes 

to emissions of the long- and especially the short-lived pollutants, including CO, very likely depend 

on location. 

Global warming potential (GWP) is a widely used relative measure of the potential effect of 

different emissions on climate, usually defined as the time-integrated RF from an instantaneous 

pulsed release of 1 kg of a trace gas relative to the effects from a pulsed release of 1 kg of CO2. 

Because the greenhouse warming effects from CO are nearly completely indirect and because CO 

concentrations are spatially heterogeneous, neither the IPCC nor EPA computes direct GWPs for 

CO, just as they do not for tropospheric O3, NO, NO2, or VOCs (U.S. EPA, 2008, 184463). IPCC 

does estimate indirect GWP for CO using the integrated indirect RF (Forster et al., 2007, 092936). 

However, the indirect GWP values evaluated and summarized by IPCC are global and cannot reflect 

effects of localized emissions or emissions changes, making the values for the short-lived species 

NMVOC, CO, and NOX more uncertain than the values for the long-lived, well-mixed species as a 

result of the OH chemistry described above. Moreover, 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 (Forster et al., 2007, 092936). 

Variation in the CO GWP range of estimates can be attributed to the unusually large heterogeneity in 

model type and form, pulsed or stepped emissions increase, time-horizon unit, and integral or 

differential indirect effects in several combinations (with or without NOX emissions changes, 

including or excluding CO2 effects). 

Even with such variability in methods and tools, the CO GWPs have been largely in agreement 

for approximately 10 yr. The IPCC estimated the indirect GWP of CO to be 1.9 (Forster et al., 2007, 

092936). Daniel and Solomon (1998, 193235) used a global box model for changes through CH4 and 

O3 effects from pulsed CO emissions and estimated a CO GWP exclusive of the effect through CO2

to be between 1 and 4.4. Using the STOCHEM CTM, Derwent et al. (2001, 047912) estimated a 

pulsed emissions CO GWP, again exclusive of effects through CO2, to be 1.5. Johnson and Derwent 

(1996, 193192) had previously computed and integrated GWP of 2.1 for the CH4 and O3 effect from 

a step-wise emissions change using a 2-D and a 100-yr time horizon. Derwent et al. (2001, 047912) 

and Collins et al. (2002, 044156) subsequently differentiated that integral for each effect and 

reported GWP for step-wise CO emissions changes on a 100-yr time horizon of 1.0, 0.6, and 1.6 

through the effects on CH4, O3, and CO2, respectively. Berntsen et al. (2005, 193241) used the model 

LMDz v3.3 (Hauglustaine et al., 2004, 193191) to compute 100-yr GWP values for pulsed CO 

emissions through all indirect effects to be 1.9 as resolved for Europe and 2.4 for Asia, 

demonstrating the strong regionality in the indirect effects from these short-lived precursors. Most 

recently, Shindell et al. (2009, 201599) compared GWPs separately for CO, CH4, and NOX with and 

without interactions with aerosols. When including direct and indirect radiative effects related to 

interaction of CO with aerosols, the GWP for CO was estimated to rise from a range of 1-3 to a 

range of 3-8. 

3.4. Ambient Measurements 

3.4.1. Ambient Measurement Instruments 

For 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 August 2009, 20 automated FRMs and no FEMs had been approved for CO 

(http://www.epa.gov/ttn/amtic/criteria.html).

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. NDIR is based on the physics of CO’s characteristic infrared 

January 2010 3-16 
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. 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, 000907), 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, 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 nitrogen gas 

(N2). Radiation transmitted through the CO is completely attenuated at the wavelengths where CO 

absorbs strongly. The radiation transmitted through the 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. 

Specifications for CO monitoring are designed to help states demonstrate whether they have 

met compliance criteria; operational parameters required under 40 CFR 53 are provided in Table 3-2. Given the 1-h level of the NAAQS of 35 ppm and the 8-h level of the NAAQS of 9 ppm, a 1.0 ppm 

limit of detection (LOD) is sufficient for demonstration of compliance, where the LOD is set at three 

times an instrument’s noise level when analyzing a zero air sample to ensure that reported signals are 

in response to actual ambient CO concentrations. However, with ambient CO levels now routinely at 

or below 1 ppm, there is greater uncertainty in the monitoring data because a large percentage is 

below the LOD. For this reason, a new generation of ambient CO monitors has been designed for 

measurements below 0.5 ppm, with LOD = 0.04 ppm. Additionally, CO measurements at 

concentrations below 0.5 ppm are needed to support additional objectives, such as validating the 

inputs to CTMs, improving estimates of low-concentration CO exposure, and assessing differences 

between CO levels in urban and rural areas, because background CO concentrations are on the order 

of 0.1 ppm. Effective LOD is influenced by instrumental noise and drift and by the amount of water 

vapor in the air. Recent improvements in the instruments’ optical components and dehumidification 

of the air stream help to reduce the amount of noise and drift in the CO measurements. Newer GFC 

instruments have been designed for automatic zeroing to minimize drift (U.S. EPA, 2000, 000907). 

January 2010 3-17 
Table 3-2. Performance specifications for analytical detection of CO, based on 40 CFR Part 53. 

Parameter Specification 

Range 0-50 ppm 

Noise 0.5 ppm 

LOD 1.0 ppm 

Interference equivalent 

Each interfering substance ±1.0 ppm 

Total interfering substances 1.5 ppm 

Zero drift 

12 h ±1.0 ppm 

24 h ±1.0 ppm 

Span drift, 24 h 

20% of upper range limit ±10.0% 

80% of upper range limit ±2.5% 

Lag time 10 min 

Rise time 5 min 

Fall time 5 min 

Precision 

20% of upper range limit 0.5 ppm 

80% of upper range limit 0.5 ppm 

 

Currently, 24 models of CO monitors are in use; the models are listed in Annex A, Table A-1. 

Among them, 20 are older NDIR instruments listed to have an LOD of 0.5 ppm, and 4 are GFC 

instruments listed to have an LOD of 0.04 ppm. States do not routinely report the operational LOD, 

precision, and accuracy of the monitors to EPA’s Air Quality System (AQS). When the monitored 

value is below the LOD, some states report the raw monitored data, while others report the 

concentration as 50% of the LOD (0.25 ppm for high-LOD instruments and 0.02 ppm for low-LOD 

instruments) when reported data are below the LOD. Among several of the older instruments still in 

use (Federal Reference Method codes 008, 012, 018, 033, 041, 050, 051, and 054), performance 

testing has shown effective LODs of 0.62-1.05 ppm, with 24-h drift ranging from 0.044-0.25 ppm 

and precision ranging from 0.022-0.067 ppm at 20% of the upper range limit of the instrument 

(Michie et al., 1983, 194043). Among newer GFC instruments, manufacturer-declared LODs range 

from 0.02-0.04 ppm, with 24-h zero drift varying between 0.5% within 1 ppm and 0.1 ppm and 

precision varying from 0.5% to 0.1 ppm. 

Comparison of older and newer monitors with LOD = 0.5 ppm and 0.04 ppm, respectively, 

calls attention to several data quality issues with the older monitors; Figure 3-9 illustrates this point 

with data from two co-located monitors with LODs of 0.5 ppm and 0.04 ppm, in Charlotte, NC. 

First, the data appearing below the LOD of 0.5 ppm for the older monitor comprise 58% of the data 

obtained by that monitor. In contrast, no data from the 0.04 ppm LOD monitor are reported below 

0.04 ppm. Below 0.5 ppm, observations obtained with the older monitor are on average more than 

five times higher than those from the newer monitor. Second, the data from the older monitor are 

reported in units of 0.1 ppm, as seen in the lower resolution of the data with respect to the x-axis. 

Last, it is possible from the data that the older monitor exhibits some upward drift, since newer 

models have automatic zeroing functions. Above 0.5 ppm, the slope of the scatterplot is 0.95, 

suggesting that readings from the older monitor are on average 5% higher than those from the newer 

monitor. The median data are 0.4 ppm for the older monitor and 0.24 ppm for the newer monitor. 

However the mean from the older monitor is 0.5 ppm, in contrast with 0.330 ppm for the newer 

monitor. The 99th percentile is 1.8 ppm for the older monitor, in contrast with the newer monitor, 

whose 99th percentile level is 1.485 ppm. 

January 2010 3-18 
0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5 2 2.5 3 3.5 4

Method 054 CO Concentration (ppm)

Method 593 CO Concentration (ppm)

Below Method 054 LOD

Above Method 054 LOD

Figure 3-9. Scatterplot comparing data from co-located monitors in Charlotte, NC. Data from 

Method 054 are from an older model (Thermo Electron Model 48C, Waltham, MA) 

with LOD = 0.5 ppm, while data from Method 593 are from a newer instrument 

(Teledyne API Model 300EU, San Diego, CA) with LOD = 0.04 ppm. Above the 

Method 054 LOD, the methods vary linearly as: [Method 593] = 0.95[Method 054] - 

0.20 (R2 = 0.88, n = 6990); below the Method 054 LOD, the regression changes to 

[Method 593] = 0.19[Method 054] + 0.16 (R2 = 0.07, n = 9856). 

3.4.2. 

3.4.2.1.

Ambient Sampling Network Design 

Monitor Siting Requirements 

Minimum monitoring requirements for CO were revoked in the 2006 revisions to ambient 

monitoring requirements (71 FR 61236, October 17, 2006). This action was made to allow for 

reductions in measurements of CO and some other pollutants (SO2, NO2, and Pb) where measured 

levels were well below the applicable NAAQS and air quality problems were not expected. CO 

monitoring activities have been maintained at some State and Local Air Monitoring Stations 

(SLAMS), and these measurements of CO using FRM are required to continue until discontinuation 

is approved by the EPA Regional Administrator. CO monitors are typically sited at the following 

spatial scales (40 CFR Part 58 Appendix D): 

 Microscale: Data represent concentrations within a 100 m radius of the monitor. For CO, 

microscale monitors are sited 2-10 m from a roadway. Measurements are intended to 

represent the near-road or street canyon environment. 

January 2010 3-19 
 Middle scale: Data represent concentrations averaged over areas defined by 100-500 m 

radii. Measurements are intended to represent several city blocks. 

 Neighborhood scale: Data represent concentrations averaged over areas defined by 

0.5-4.0 km radii. Measurements are intended to represent extended portions of a city. 

In 2007, there were 376 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 requirement is met with a monitor situated at the 

CFR-defined microscale distance 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 neighborhood 

scale monitoring, the minimum monitor distance from a major roadway is directly 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 neighborhood scale CO 

monitor from a roadway with an average daily traffic count of 15,000 vehicles per day is 25 m, while 

the minimum distance is 135 m for a roadway with an average daily traffic count of 50,000 vehicles 

per day. Occasionally, CO monitors are sited at urban (covering areas of 4-50 km) or regional 

(covering areas of tens to hundreds of km) scale. More detail on siting requirements can be found in 

40 CFR Part 58 Appendices D and E. 

In addition to monitoring for determining compliance with the NAAQS, EPA is currently in 

the process of implementing plans for a new network of multipollutant stations called National Core 

(NCore) that is intended to meet multiple monitoring objectives. A subset of the SLAMS network, 

NCore stations are intended to address integrated air quality management needs to support long-term 

trends analysis, model evaluation, health and ecosystem studies, as well as the more traditional 

objectives of NAAQS compliance and Air Quality Index reporting. The complete NCore network, 

required to be fully implemented by January 1, 2011, will consist of approximately 60 urban and 20 

rural stations and will include some existing SLAMS sites that have been modified for the additional 

measurements. Each state will contain at least one NCore station, and 46 of the states plus 

Washington, DC, will have at least one urban station. CO will be measured using 0.04 ppm LOD 

monitors at all sites, as will SO2, NO, and NOY

1

; surface meteorology will also be measured at 

NCore sites. The advantage to the NCore strategy is that time-resolved, simultaneous measurements 

of multiple pollutants will be obtained at each site. The disadvantage is that the NCore network will 

be sparse, and so spatial variability will be difficult to ascertain from the data obtained. 

3.4.2.2.

 

Spatial and Temporal Coverage 

Figure 3-10 depicts the distribution of the 376 regulatory CO monitors operating in the U.S. in 

2007. Data from 291 of the 376 CO monitors operating year-round at 290 sites in the years 

2005-2007 met the data completeness criteria for inclusion in the multiyear ambient data analyses 

for this assessment. Completeness criteria require that data be collected for 75% of the hours in a 

day, 75% of the days in a quarter, and 3 complete quarters for all 3 yr; criteria for Region 10 were 

relaxed to 2 complete quarters because it contains Alaska. The greatest density of monitors is in the 

CSAs for Los Angeles, and San Francisco, CA; and along the Mid-Atlantic seaboard. 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. The number of monitors per sampling 

scale is provided in Table 3-3, and locations of monitors with nearby roadway types and traffic 

counts are provided in Annex A, Tables A-2 through A-7, for each monitoring scale. Twenty-four 

percent of the monitors meeting completeness criteria are categorized as “Null”, meaning that no 

scale has been identified for those monitors. Furthermore, given the overlap between scales 

regarding the type of road at which the monitor is sited, it is possible that scale has been 

misclassified for some of the monitors. 

1

 NCore sites must measure, at a minimum, PM2.5 particle mass using continuous and integrated/filter-based samplers, speciated PM2.5, 

PM10-2.5 particle mass, speciated PM10–2.5, O3, SO2, CO, NO/NOY, wind speed, wind direction, relative humidity, and ambient temperature. 

January 2010 3-20 
Table 3-3. Counts of CO monitors by sampling scale meeting 75% completeness criteria for use in 

the U.S. during 2005-2007. 

Monitoring Scale Count 

Microscale 57 

Middle Scale 31 

Neighborhood Scale 119 

Urban Scale 11 

Regional Scale 2 

Null 71 

Figure 3-10 also shows the locations of the newer 0.04 ppm LOD CO monitors throughout the 

U.S in 2007, indicated by blue and purple triangles. The newer monitors included in the analysis are 

located in: Baton Rouge, LA; Boston, MA; Charlotte, NC; Dallas, TX; Decatur, GA; Houston, TX; 

Portland, OR; Presque Isle, ME; San Jose, CA; and rural locations within Georgia and South 

Carolina. Other 0.04 ppm LOD monitors not meeting completeness criteria for the 2005-2007 

analysis were located in: Beltsville, MD; Cedar Rapids, IA; Davenport, IA; Des Moines, IA; 

Nederland, TX; Northbrook, IL; Plant City, FL; Seattle, WA; Thomaston, CT; Tulsa, OK; Westport, 

CT; and rural locations in Maryland and Wisconsin. A listing of 0.04 ppm and 0.5 ppm LOD 

monitors meeting completeness criteria by state for 2005-2007 is provided in Annex A, Table A-8. 

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 region.1 As an example, Figure 3-11 through Figure 3-14 display CO 

monitor density with respect to population density (for total population and elderly adults aged 65 

and over) for the Denver and Los Angeles CSAs (Annex A, Figures A-7 through A-22 show 

analogous plots for the other nine metropolitan regions). Figure 3-18 and Figure 3-21 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 the examples shown for Denver and Los Angeles, the 

monitors were typically located near high population density neighborhoods within the CSA. The 

Los Angeles CSA monitors appear to be distributed fairly evenly across the city of Los Angeles, 

while the Denver CSA had three monitors in the city center and two in the suburbs of the Denver 

CSA. Regional background sites were not included on the maps unless they lay within the 

CSA/CBSA. 

 

1

 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. 

January 2010 3-21 
Figure 3-10. Map of CO monitor locations in the U.S. in 2007. Locations are indicated with 

triangles: blue and red triangles show locations of the sites used in data analysis 

for this assessment; purple and green 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. 

January 2010 3-22 
Figure 3-11. Map of CO monitor locations with respect to population density in the Denver, CO 

CSA, total population. The circles indicate 5 km buffers around the monitors. 

January 2010 3-23 
Figure 3-12. Map of CO monitor locations with respect to population density in the Denver, CO 

CSA, age 65 and older. The circles indicate 5 km buffers around the monitors. 

January 2010 3-24 
Figure 3-13. Map of CO monitor locations with respect to population density in the Los 

Angeles, CA CSA, total population. The circles indicate 5 km buffers around the 

monitors. 

January 2010 3-25 
Figure 3-14. Map of CO monitor locations with respect to population density in the Los 

Angeles, CA CSA, age 65 and older. The circles indicate 5 km buffers around the 

monitors. 

January 2010 3-26 
Ambient monitors for CO and other criteria pollutants are located to monitor compliance 

rather than population exposures. However, CO monitors submitting data to the AQS are often used 

for exposure assessment. For this reason, data are presented here to assess population density in the 

vicinity of CO monitors. Table 3-4 and Table 3-5 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. For 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; 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. In combination, these data illustrate that population coverage varies by 

monitor and across cities. 

Table 3-4. 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 29,068 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 

 

January 2010 3-27 
Table 3-5. 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 31,036 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 

 

3.5. Environmental Concentrations 

3.5.1. 

3.5.1.1.

Spatial Variability 

National Scale 

The current NAAQS designates that the level of the NAAQS is not to be exceeded more than 

once per year at a given monitoring site. Figure 3-15 and Figure 3-16 show the second-highest 1-h 

and second-highest 8-h county-average CO concentrations, respectively, over the U.S. along with 

estimates of the fraction of the 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. One-hour (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 (Figure 

3-15). Moreover, only two monitored locations, one in Weber County, UT and the other in Jefferson 

County, AL (including Birmingham, AL), reported second-highest 1-h CO concentrations between 

15.1 and 35.0 ppm. Figure 3-16 shows that only 5 counties reported second-highest 8-h CO 

concentrations above 5.0 ppm: Jefferson County, AL; Imperial County, CA; Weber County, UT; 

Philadelphia County., PA; and Anchorage Municipality, AK. 

January 2010 3-28 
Figure 3-15. 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 living in counties with 

CO concentrations in the range indicated. Note that approximately 150 million 

people live in counties with no CO monitors. 

January 2010 3-29 
Figure 3-16. 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 living in counties with 

CO concentrations in the range indicated. Note that approximately 150 million 

people live in counties with no CO monitors. 

January 2010 3-30 
Table 3-6. 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 

a

CO monitoring is only available for quarters 1 and 4; since monitoring data are not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

January 2010 3-31 
Table 3-6 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. Several monitors in EPA Region 10, including four in Alaska, did not meet the data 

completeness criteria since CO reporting was only required during the first and fourth quarters of 

each year at these sites. Anchorage was included in the table, however, for an approximate 

comparison with the other CSAs and CBSAs reporting year-round measurements to AQS. 

Anchorage and other partial-year monitors were not, however, included in the national statistics 

shown in the table. AQS site number 371190041, located in Charlotte, NC, was the only site with colocated monitors both meeting the data completeness criteria and, therefore, the nationwide data in 

the table was derived from 286 monitors located at 285 sites. In Section 3.5.1.3, the nationwide 1-h 

avg statistics shown in Table 3-6 (along with the nationwide 24-h avg, 1-h daily max and 8-h daily 

max statistics) are further divided by monitoring scale (microscale, middle scale, etc.) to address 

issues relating to the near-road environment. 

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-yr period. More than 50% of the data nationwide were below the LOD for the 

majority of monitors in use. The largest recorded second-highest 1-h concentration, 26.3 ppm, for 

this period was reported in 2006 in Birmingham, AL (AQS site ID: 010736004). The highest 1-h 

concentration, 39 ppm, between 2005 and 2007, was reported in Ogden, UT (AQS site ID: 

490570006) on August 28, 2007. An annual outdoor barbeque festival held in Ogden on that day 

resulted in a period of elevated CO concentrations. The seasonally stratified concentrations in Table 

3-6 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 rows of Table 3-6 and illustrate the 

distribution of the site average CO concentrations recorded at the 285 monitoring sites for 

2005-2007 (Figure 3-10). The site reporting the highest 3-yr pooled 1-h avg CO concentration, 

1.5 ppm, was located in San Juan, Puerto Rico (AQS site ID: 721270003). The 11 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 11 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. 

The 99th percentile 1-h avg concentration in Anchorage was 5.0 ppm; the other selected cities with 

year-round monitoring had 99th percentile concentrations ranging from 0.9 ppm to 2.5 ppm. 

January 2010 3-32 
Table 3-7. 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 0.8 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 

a

CO monitoring is only available for quarters 1 and 4; since monitoring data are not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

Table 3-7 contains the distribution of 24-h avg CO concentrations derived from the 1-h 

concentrations reported to AQS and summarized in Table 3-6. The nationwide mean, median, and 

interquartile range for 24-h avg values during 2005-2007 were 0.5, 0.4 and 0.3 ppm, respectively. 

These were similar to those for the 1-h values and showed more than half the data falling below the 

LOD for the majority of monitors in the field. The maximum 24-h avg concentration in these years, 

7 ppm, was reported in Birmingham, AL (AQS site ID: 010736004). The 99th percentile 24-h avg 

January 2010 3-33 
concentrations ranged from 0.9 ppm to 2.5 ppm in the selected cities with year-round monitoring; 

Anchorage had a 99th percentile concentration of 3.3 ppm. 

Table 3-8. 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 

a

CO monitoring is only available for quarters 1 and 4; since monitoring data are not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

Table 3-8 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. Roughly one-third of the 1-h daily max data fall 

below the LOD for the majority of CO monitors reporting to AQS. The 99th percentile 1-h daily max 

January 2010 3-34 
concentrations ranged from 2.0 ppm to 5.3 ppm in the selected cities with year-round monitoring; 

Anchorage had a 99th percentile concentration of 7.6 ppm. 

Table 3-9. 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 

a

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. 

Table 3-9 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 

January 2010 3-35 
concentrations reported for 2005-2007 were 0.7, 0.5, and 0.5 ppm, respectively. Half of the 8-h daily 

max concentrations fell below the LOD for the majority of CO monitors in the field. The highest 8-h 

daily max concentration, 10.9 ppm, was recorded at a monitor located 5 mi north of Newkirk, OK 

(AQS site ID: 400719010). The 99th percentile 8-h daily max concentrations ranged from 1.5 ppm to 

3.8 ppm in the selected cities with year-round monitoring; Anchorage had a 99th percentile 8-h daily 

max concentration of 5.0 ppm. 

Spring

daily avg v

Winter

s. d

 daily

s. d

aily max 1h 

daily avg vs. max 8h 

daily max 1h v aily max 8h 

correlation (r)

0.0 0.2 0.4 0.6 0.8 1.0

Fall 

daily avg daily max 1h 

daily avg vs. ly max 8h 

daily max 1h . daily max 8h 

 vs.

 dai

 vs

correlation (r)

0.0 0.2 0.4 0.6 0.8 1.0

daily avg vs. daily max 1h 

daily avg vs. daily max 8h 

daily max 1h vs. daily max 8h 

correlation (r)

0.0 0.2 0.4 0.6 0.8 1.0

Summer

daily avg vs. daily max 1h 

daily avg vs. daily max 8h 

daily max 1h vs. daily max 8h 

correlation (r)

0.0 0.2 0.4 0.6 0.8 1.0







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Figure 3-17. Seasonal plots showing the variability in correlations between 24-h avg CO 

concentration with 1-h daily max and 8-h daily max CO concentrations and 

between 1-h daily max and 8-h daily max CO concentrations. Red bars denote the 

median, green stars denote the arithmetic mean, the box incorporates the IQR 

and the whiskers extend to the 5th and 95th percentiles. Correlations outside the 

5th and 95th percentiles are shown as individual points. 

Table 3-7 through Table 3-9 show distributions of CO data based on the 24-h avg, 1-h daily 

max and 8-h daily max concentration. The current standards are based on 1-h and 8-h calculations. 

While the nationwide concentrations vary in absolute magnitude based on these three statistics, the 

shape of the distributions are quite similar up to the 99th percentile. The relative increase from the 

99th percentile to the max for the 1-h daily max is larger than for the 24-h or 8-h daily max. This is 

to be expected since this statistic is more sensitive to short-term (less than 8 h) increases in CO 

concentration. Box plots showing the range in Pearson correlation coefficients (r) between the 

different statistics are shown in Figure 3-17. Included are the correlation of the 24-h avg with the 1-h 

daily max and 8-h daily max, as well as the correlation between the 1-h daily max and 8-h daily max, 

all calculated using the same 2005-2007 data set stratified by season. Correlations are generally quite 

high across all seasons and all comparisons, with median r > 0.8. Correlations are higher on average 

in the wintertime compared to the summertime for the two comparisons involving the 1-h daily max 

January 2010 3-36 
statistic. The correlations between the 24-h avg and the 8-h daily max are the highest in all seasons, 

which is in agreement with the distributional similarities shown in the preceding tables. 

3.5.1.2. Urban Scale 

This section describes urban variability in CO concentrations reported to AQS at the individual 

CSA/CBSA level. Denver, CO, and Los Angeles, CA, 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 Denver 

CSA and the Los Angeles CSA shown in Figure 3-18 and Figure 3-21, respectively, illustrate the 

location of all CO monitors meeting the inclusion criteria described earlier. Letters on the maps 

identify the individual monitor locations and correspond with the letters provided in the 

accompanying concentration box plots (Figure 3-19 and Figure 3-22) and pair-wise monitor 

comparison tables (Table 3-10 and Table 3-11). 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 

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. Pearson correlation is also plotted as a function of distance for Denver and Los 

Angeles in Figure 3-20 and Figure 3-23, respectively. Similar maps, box plots, and comparison 

tables for the nine remaining CSAs/CBSAs are included in Annex A. 

The information contained in these figures and tables should be used with some caution since 

many of the reported concentrations for the years 2005-2007 are near or below the monitors’ stated 

LOD. Because ambient concentrations are now in large part very near or below the 0.5 ppm LOD for 

the majority of FRMs and the coarsely reported measurement resolution is 0.1 ppm, the comparison 

statistics shown in these tables might be biased to exhibit specious heterogeneity in the box plots. 

January 2010 3-37 
Figure 3-18. Map of CO monitor locations and major highways for Denver, CO. 

January 2010 3-38 
A B C D E 

Site ID 08-031-0002 08-031-

0019 

08-013-

0009 

08-123-

0010 

08-001-

3001 

Mean 0.65 0.52 0.42 0.55 0.52 

SD 0.42 0.46 0.38 0.46 0.36 

Obs 25959 25552 25559 26048 25920 

Scale Micro Micro Micro Neighborhood 

Neighborhood 

concentration (ppm)

0

1

2

3

season

1234 1234 1234 1234 1234

Figure 3-19. Box plots illustrating the distribution of 2005-2007 hourly CO concentrations in 

Denver, CO. The data are stratified by season along the x-axis where 1 = winter, 

2 = spring, 3 = summer, and 4 = fall. The box plots show the median and 

interquartile range with whiskers extending from the 5th to the 95th percentile. 

Identifiers and statistics for each site are shown at the top of the figure. 

January 2010 3-39 
Table 3-10. Table of intersampler 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 Denver, CO. The table is grouped and identified by monitoring scale. 

Micro Neighborhood 

A B C D E 

A 1.00 0.76 0.46 0.45 0.59 

0.0 0.5 0.7 0.7 0.6 

0.00 0.34 0.44 0.36 0.29 

0 1.3 46.9 78.3 10.1 

B 1.00 0.49 0.46 0.64 

0.0 0.7 0.7 0.5 

0.00 0.47 0.42 0.37 

0 47.0 79.0 10.9 

C 1.00 0.54 0.53 

0.0 0.6 0.6 

0.00 0.43 0.43 

Micro 

0 44.6 38.5 

D 1.00 0.52 

0.0 0.6 

0.00 0.34 

Legend 0 68.2 

E r 1.00 

P90 0.0 

COD 0.00 

Neighborhood 

d 0 

January 2010 3-40 
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 9

Distance (km)

Correlation

0

Figure 3-20. Intersampler correlation versus distance for monitors located within the Denver 

CSA. 

January 2010 3-41 
Figure 3-21. Map of CO monitor locations and major highways for Los Angeles, CA. 

January 2010 3-42 
A B C D E F G H 

Site ID 06-065-

1003 

06-059-

1003 

06-037-

9033 

06-037-

1301 

06-071-

9004 

06-065-

9001 

06-037-

5005 

06-059-

0007 

Mean 0.67 0.31 0.23 0.98 0.53 0.29 0.24 0.42 

SD 0.42 0.47 0.29 0.89 0.38 0.20 0.37 0.46 

Obs 24885 24760 24135 24825 24844 24792 24965 24264 

Scale Micro Middle Middle Middle Middle Neighborhood 

Neighborhood Urban 

concentration (ppm)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

season

1234 1234 1234 1234 1234 1234 1234 1234

Figure 3-22. Box plots illustrating the distribution of 2005-2007 hourly CO concentrations in 

Los Angeles, CA. The data are stratified by season along the x-axis where 

1 = winter, 2 = spring, 3 = summer, and 4 = fall. The box plots show the median 

and interquartile range with whiskers extending from the 5th to the 95th 

percentile. Identifiers and statistics for each site are shown at the top of the 

figure (monitors without scale designations in AQS are labeled Null). Part 1 of 3 

of Figure 3-22. See the next two pages for parts 2 and 3 of Figure 3-22. 

January 2010 3-43 
I J K L M N O P 

Site ID 06-037-

0002 

06-071-

0001 

06-037-

1002 

06-059-

5001 

06-037-

4002 

06-037-

1103 

06-059-

2022 

06-065-

5001 

Mean 0.42 0.17 0.66 0.62 0.69 0.56 0.26 0.25 

SD 0.27 0.17 0.59 0.55 0.56 0.50 0.25 0.14 

Obs 2,5001 24105 24892 24705 24259 24645 24831 24938 

Scale Null Null Null Null Null Null Null Null 

concentration (ppm)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

season

1234 1234 1234 1234 1234 1234 1234 1234

Part 2 of 3 for Figure 3-22 

January 2010 3-44 
Q R S T U V W X 

Site ID 06-037-

2005 

06-037-

1701 

06-037-

1201 

06-065-

8001 

06-037-

6012 

06-071-

1004 

06-071-

0306 06-037-0113 

Mean 0.72 0.69 0.57 0.60 0.30 0.59 0.30 0.41 

SD 0.48 0.45 0.54 0.46 0.25 0.32 0.28 0.36 

Obs 24804 24912 24281 24778 24860 24767 24796 24916 

Scale Null Null Null Null Null Null Null Null 

concentration (ppm)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

season

1234 1234 1234 1234 1234 1234 1234 1234

Part 3 of 3 for Figure 3-22 

January 2010 3-45 
Table 3-11. Table of intersampler 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 Los Angeles, CA. The table is grouped and identified by monitoring 

scale (monitors without scale designations in AQS are labeled Null). 

Micro Middle Neighbor

-hood 

Urban Null 

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 0.56 0.56 0.54 0.73 0.72 0.45 0.62 0.54 0.35 0.70 0.66 0.46 0.62 0.61 0.48 0.53 0.78 0.73 0.67 0.54 0.70 0.55 0.57 

0.0 0.8 0.9 1.1 0.5 0.7 0.9 0.7 0.7 1.0 0.6 0.6 0.8 0.6 0.8 0.9 0.7 0.4 0.6 0.6 0.8 0.5 0.8 0.7 

Micro 

0.00 0.66 0.67 0.30 0.30 0.46 0.73 0.46 0.33 0.68 0.29 0.24 0.39 0.37 0.58 0.47 0.35 0.19 0.29 0.37 0.53 0.20 0.54 0.42 

0 57.1 104.6 74.8 21.3 30.5 95.0 51.3 52.6 110.6 88.2 51.0 74.1 77.4 43.3 80.1 70.1 35.0 108.0 6.1 114.5 27.4 62.8 98.1 

B 1.00 0.55 0.67 0.50 0.46 0.60 0.75 0.14 0.28 0.70 0.72 0.64 0.58 0.62 0.34 0.50 0.60 0.57 0.40 0.25 0.36 0.55 0.47 

0.0 0.6 1.3 0.7 0.5 0.5 0.5 0.7 0.7 0.9 0.7 0.9 0.8 0.5 0.6 0.9 0.9 0.8 0.9 0.7 0.8 0.6 0.7 

0.00 0.66 0.70 0.64 0.59 0.69 0.55 0.64 0.69 0.63 0.62 0.64 0.63 0.56 0.59 0.68 0.66 0.62 0.67 0.67 0.65 0.62 0.59 

0 112.0 38.6 76.9 55.1 55.8 17.3 51.2 158.5 66.3 27.9 29.5 51.6 23.7 129.6 54.0 46.3 80.7 59.3 96.2 54.9 107.5 64.4 

C 1.00 0.55 0.50 0.50 0.39 0.56 0.27 0.45 0.59 0.63 0.43 0.53 0.51 0.41 0.45 0.61 0.53 0.41 0.40 0.49 0.67 0.42 

0.0 1.6 0.7 0.4 0.6 0.7 0.6 0.4 1.1 0.9 1.1 0.9 0.4 0.4 1.0 0.9 0.9 0.9 0.5 0.7 0.4 0.7 

0.00 0.72 0.64 0.57 0.74 0.60 0.62 0.62 0.65 0.64 0.69 0.63 0.61 0.56 0.70 0.66 0.62 0.67 0.62 0.65 0.57 0.60 

 0 82.5 100.4 132.5 84.4 94.7 62.2 104.0 57.4 84.2 94.0 67.5 122.7 171.9 59.6 75.4 64.0 99.2 48.4 77.9 75.4 74.9 

D 1.00 0.44 0.39 0.63 0.71 0.21 0.33 0.70 0.78 0.70 0.74 0.57 0.28 0.53 0.65 0.49 0.35 0.23 0.39 0.51 0.50 

 0.0 1.3 1.6 1.5 1.1 1.5 1.7 0.9 0.9 1.0 1.0 1.5 1.7 1.0 1.0 1.2 1.2 1.6 1.3 1.5 1.3 

 0.00 0.42 0.56 0.76 0.51 0.44 0.73 0.35 0.30 0.39 0.41 0.65 0.56 0.39 0.29 0.41 0.45 0.60 0.35 0.61 0.50 

 0 88.6 86.0 20.4 27.4 35.0 152.6 29.1 23.8 11.8 15.3 59.5 154.5 23.8 45.0 42.1 73.7 58.2 57.0 103.4 26.4 

E 1.00 0.69 0.43 0.55 0.48 0.31 0.65 0.55 0.39 0.58 0.51 0.46 0.52 0.68 0.71 0.64 0.50 0.64 0.51 0.53 

 0.0 0.6 0.8 0.6 0.6 0.8 0.7 0.7 0.9 0.6 0.6 0.7 0.7 0.6 0.6 0.6 0.6 0.5 0.6 0.6 

 0.00 0.42 0.72 0.46 0.35 0.65 0.35 0.33 0.46 0.39 0.56 0.43 0.41 0.31 0.32 0.41 0.51 0.29 0.52 0.41 

Middle 

 0 48.0 108.0 68.5 59.9 90.2 96.3 65.7 90.1 87.9 64.6 73.3 78.6 44.2 116.3 17.7 119.3 32.7 44.9 109.1

F 1.00 0.43 0.53 0.56 0.30 0.58 0.55 0.36 0.53 0.51 0.49 0.47 0.69 0.66 0.66 0.56 0.68 0.49 0.55 

0.0 0.5 0.6 0.4 0.4 1.0 0.8 1.1 0.8 0.3 0.3 0.9 0.8 0.8 0.8 0.3 0.6 0.4 0.5 

0.00 0.70 0.42 0.38 0.58 0.46 0.43 0.54 0.46 0.50 0.32 0.53 0.46 0.40 0.49 0.47 0.43 0.47 0.39 

0 106.1 58.7 74.8 137.8 106.5 63.7 81.1 93.4 32.4 75.7 89.2 58.1 125.1 36.6 135.3 54.8 92.3 112.0 

G 1.00 0.58 0.19 0.18 0.64 0.59 0.59 0.59 0.42 0.26 0.40 0.51 0.52 0.43 0.24 0.27 0.41 0.59 

0.0 0.6 0.7 0.6 1.0 0.8 1.0 0.8 0.5 0.5 1.0 1.0 0.9 0.9 0.6 0.9 0.6 0.6 

Neighborhood 

0.00 0.71 0.72 0.75 0.72 0.72 0.75 0.73 0.73 0.70 0.75 0.73 0.72 0.74 0.73 0.73 0.72 0.69 

0 47.4 51.0 166.0 27.0 44.2 26.4 22.7 78.3 174.8 34.4 63.9 29.1 93.7 48.7 75.8 118.6 11.4 

H 1.00 0.29 0.31 0.72 0.81 0.63 0.70 0.67 0.37 0.54 0.69 0.61 0.49 0.29 0.47 0.54 0.59 

0.0 0.6 0.8 0.7 0.5 0.8 0.6 0.5 0.7 0.8 0.7 0.7 0.8 0.7 0.7 0.6 0.5 

Legend r 

P90 

COD 

d 0.00 0.43 0.62 0.41 0.38 0.48 0.40 0.46 0.41 0.52 0.44 0.41 0.49 0.53 0.45 0.50 0.39 

Urban 

 0 33.9 144.7 51.8 10.5 23.2 37.3 32.9 129.2 37.7 31.4 68.3 51.7 81.8 41.6 93.7 53.7 

I 1.00 0.17 0.43 0.34 0.17 0.46 0.42 0.33 0.41 0.57 0.44 0.47 0.43 0.61 0.24 0.45 

0.0 0.6 1.0 0.8 1.1 0.8 0.5 0.5 0.8 0.8 0.7 0.8 0.5 0.5 0.6 0.5 

0.00 0.62 0.35 0.33 0.47 0.38 0.52 0.36 0.43 0.33 0.33 0.43 0.47 0.30 0.50 0.38 

0 117.7 36.5 23.6 42.4 29.0 60.6 131.4 18.7 17.7 56.5 49.2 61.9 27.4 68.4 50.0 

J 1.00 0.35 0.34 0.24 0.34 0.33 0.29 0.31 0.40 0.31 0.19 0.25 0.36 0.43 0.25 

0.0 1.2 1.0 1.3 1.0 0.4 0.3 1.1 1.1 1.0 1.1 0.5 0.8 0.4 0.7 

0.00 0.67 0.66 0.70 0.66 0.63 0.55 0.71 0.68 0.64 0.69 0.62 0.66 0.59 0.62 

0 142.7 137.1 159.7 143.4 152.3 123.6 131.7 113.3 158.2 105.4 148.8 103.7 51.0 161.1

K 1.00 0.75 0.62 0.84 0.69 0.40 0.67 0.78 0.74 0.52 0.39 0.59 0.58 0.69 

0.0 0.6 0.8 0.5 1.0 1.2 0.7 0.5 0.6 0.8 1.1 0.7 1.0 0.8 

0.00 0.26 0.41 0.29 0.56 0.46 0.39 0.26 0.28 0.42 0.52 0.29 0.53 0.38 

0 43.6 40.8 14.7 84.6 167.7 18.1 53.5 20.0 85.3 30.1 63.8 97.8 18.9 

L 1.00 0.62 0.74 0.67 0.40 0.58 0.77 0.61 0.50 0.34 0.54 0.58 0.59 

0.0 0.7 0.5 0.8 1.0 0.7 0.5 0.7 0.8 0.9 0.6 0.8 0.6 

0.00 0.37 0.31 0.54 0.42 0.37 0.20 0.29 0.38 0.52 0.25 0.51 0.37 

0 24.6 29.8 41.5 130.6 28.1 24.3 61.5 50.2 73.4 35.8 86.4 48.5 

M 1.00 0.60 0.48 0.24 0.41 0.52 0.44 0.31 0.15 0.28 0.43 0.43 

0.0 0.8 1.1 1.2 0.9 0.8 0.9 1.0 1.2 0.9 1.1 0.9 

0.00 0.45 0.58 0.53 0.46 0.38 0.44 0.48 0.60 0.41 0.59 0.48 

0 27.1 52.1 152.4 34.7 48.6 52.3 74.0 69.4 60.3 109.6 35.2 

N 1.00 0.63 0.32 0.67 0.77 0.64 0.49 0.34 0.57 0.51 0.71 

0.0 0.8 1.0 0.7 0.5 0.7 0.8 0.9 0.6 0.8 0.6 

0.00 0.55 0.46 0.44 0.33 0.34 0.45 0.52 0.36 0.53 0.38 

0 70.2 157.4 11.7 43.8 31.8 75.1 44.7 55.2 95.9 21.2 

O 1.00 0.44 0.55 0.70 0.55 0.43 0.28 0.57 0.45 0.57 

0.0 0.3 0.9 0.9 0.8 0.9 0.5 0.7 0.4 0.6 

0.00 0.47 0.62 0.58 0.54 0.59 0.59 0.56 0.56 0.50 

Null 

0 107.9 69.5 48.9 101.2 47.5 114.6 52.6 102.5 85.9 

January 2010 3-46 
Micro Middle Neighbor

-hood 

Urban Null 

P 1.00 0.39 0.47 0.42 0.40 0.40 0.47 0.38 0.35 

0.0 1.0 1.0 0.9 0.9 0.4 0.7 0.4 0.6 

0.00 0.54 0.47 0.40 0.50 0.45 0.43 0.44 0.38 

0 149.6 114.2 187.6 82.4 192.2 104.2 102.8 178.1

Q 1.00 0.65 0.54 0.39 0.32 0.53 0.46 0.49 

0.0 0.6 0.8 0.8 1.0 0.7 0.9 0.8 

0.00 0.34 0.42 0.46 0.58 0.35 0.59 0.49 

0 35.4 38.0 67.2 46.2 46.0 84.3 31.6 

R 1.00 0.70 0.60 0.47 0.78 0.58 0.63 

0.0 0.6 0.7 0.9 0.5 0.9 0.7 

0.00 0.30 0.38 0.53 0.18 0.54 0.41 

0 73.4 31.8 79.6 12.0 62.4 65.0 

S 1.00 0.62 0.53 0.58 0.55 0.64 

0.0 0.7 0.8 0.6 0.8 0.7 

0.00 0.40 0.49 0.30 0.50 0.34 

0 105.2 20.4 83.8 115.6 17.8 

T 1.00 0.46 0.54 0.38 0.55 

0.0 0.9 0.6 0.9 0.7 

0.00 0.56 0.37 0.58 0.46 

0 110.8 22.8 57.0 96.1 

U 1.00 0.53 0.39 0.36 

0.0 0.6 0.5 0.6 

0.00 0.51 0.54 0.50 

0 88.3 110.7 37.4 

V 1.00 0.47 0.50 

0.0 0.7 0.6 

0.00 0.52 0.40 

0 52.7 76.4 

W 1.00 0.41 

0.0 0.6 

0.00 0.50 

0 115.3 

X 1.00 

0.0 

0.00 

Null 

0 

 

January 2010 3-47 
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250

Distance (km)

Correlation

 Figure 3-23. Intersampler correlation versus distance for monitors located within the Los 

Angeles CSA. 

The Denver CSA in Figure 3-18 incorporates an area of 33,723 km2 with a maximum straightline distance between CO monitors of 79 km. Of the five CO monitors meeting the inclusion criteria, 

three were sited for microscale monitoring and two were sited for neighborhood scale monitoring. 

Sites A and B are located in downtown Denver while Site E is located in an industrial region north of 

town and surrounded on three sides by three heavily-traveled interstate highways. Sites C and D are 

located in two smaller towns (Longmont and Greeley, respectively) north of Denver. The means and 

seasonal patterns shown in Figure 3-19 are similar for all five monitors within this CSA. The highest 

annual mean concentration (0.7 ppm) was observed at Site A, a downtown microscale monitor, while 

the lowest annual mean concentration (0.4 ppm) was observed at Site C, a microscale monitor in 

Longmont. The step-wise nature of the box plots is attributed to the 0.1 ppm resolution of the CO 

monitors used in the Denver CSA. Because these monitors have LOD of 0.5 ppm, it is also likely 

that the means and statistical distributions are biased as well. 

The Los Angeles CSA in Figure 3-21 incorporates an area of 88,054 km2

 and a maximum 

straight-line distance between monitors of 192 km, making it more than twice the size of the Denver 

CSA. Of the 11 CSAs/CBSAs investigated, Los Angeles had the largest number of CO monitors (N 

= 24) meeting the inclusion criteria. One monitor was sited for microscale, four for middle scale, two 

for neighborhood scale, and one for urban scale. The remaining 16 monitors did not contain a siting 

classification in AQS. The monitors were evenly distributed around the Los Angeles and Riverside 

areas, with outlying monitors in Santa Clarita (Site U), Lancaster (Site C), Victorville (Site W), 

Barstow (Site J) and Palm Springs (Site P). A large amount of variability is present in the means and 

seasonal patterns displayed in Figure 3-22. Generally speaking, lower annual mean concentrations 

(<0.3 ppm) were measured in the outlying towns including those listed above as well as Lake 

Elsinore (Site F) and Mission Viejo (Site O). In addition, a neighborhood scale upwind background 

site (Site G) located on the grounds of the Los Angeles International Airport and 1.5 km from the 

January 2010 3-48 
Pacific Ocean reported a relatively low mean annual concentration of 0.2 ppm. The highest annual 

mean concentration (1.0 ppm) was observed at Site D, a middle scale maximum concentration site 

located 25 m from a busy surface street and adjacent to the Imperial Shopping Mall. This site is also 

180 m from a major highway intersection and 350 m from Interstate 105. The step-wise nature of the 

box plots is attributed to the 0.1 ppm resolution of the CO monitors used in the Los Angeles CSA. 

Because these monitors have LOD of 0.5 ppm, it is also likely that the means and statistical 

distributions are biased as well. 

The pair-wise comparisons for measurements at the monitors in each of the 11 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 (Table 3-10, Table 3-11 and Annex Tables A-9 through A-16). 

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. 

For the monitor comparisons within the Denver CSA (Table 3-10 and Figure 3-20), the 

correlations tend to be inversely related to the monitor separation distance, with the highest 

correlation (r = 0.76) for the two downtown Denver monitors (Sites A and B) separated by 1.3 km 

and the lowest correlations (r ≤ 0.46) between the downtown Denver monitors and the Greeley 

monitor (Site D) located roughly 80 km north. While Sites A and B have a high correlation, the 

comparative magnitudes of the concentrations measured at these two sites, as determined by the P90 

and COD, is comparable to comparisons with much less proximal monitors. This is likely caused by 

the location of these two monitors on opposite sides of downtown Denver, as illustrated by the aerial 

view of monitors A and B in Figure 3-24. While there is no prevailing wind direction in Denver, the 

wind comes from the south-southwest with a slightly higher frequency than other directions, making 

Site A downwind of the urban core more frequently than Site B. Assuming traffic within the urban 

core is a major source of CO, this would explain the higher mean concentrations measured at Site A 

relative to Site B despite their close proximity. 

Greater variability in the pair-wise comparison statistics is observed in the Los Angeles CSA 

compared to the Denver CSA, partially due to the greater number of monitors spread over a larger 

area. Factors other than the distance between monitors, however, can contribute substantially to 

concentration disparities observed between monitors. To illustrate this point, Site S (located in 

Reseda, a suburb in the Simi Valley northwest of Los Angeles) correlates well (r = 0.73) with Site A 

(located 108 km to the southeast in Riverside). In fact, Site S correlates well (r > 0.62) with Sites A, 

E, F and T, all east of Los Angeles and all over 100 km away. Site S is located in a densely populated 

urban area with a mixture of commercial and residential land whereas the other four sites are located 

in less densely populated regions with commercial, residential and undeveloped land. Sites S and T 

contain no monitoring scale information in AQS, but Sites A, E and F are classified as microscale, 

middle scale and neighborhood scale, respectively. In contrast to the above example, Sites I and Q 

are located only 19 km apart in Azusa and Pasadena, respectively, and they correlate less well 

(r = 0.41). While these two locations are relatively close in proximity with similar topography, the 

siting of the two monitors is quite different. Site I in Azusa is located 700 m from I-210 in a mixed 

use community containing warehouses, small industry, housing and a gravel operation (Figure 3-25) 

while Site Q in Pasadena is located between a large residential neighborhood and the California 

Institute of Technology campus (Figure 3-26). Neither of these sites has monitoring scale 

designations reported in AQS. The contrasting CO emission sources surrounding these two monitors 

result in disparate concentrations with poor correlations despite their close proximity. Topography 

and micrometeorology can also play an important role in the correlation between monitors. For 

example, Sites C and P are isolated from the other sites in the Los Angeles CSA by the San Gabriel 

Mountains and the San Bernardino Mountains, respectively, resulting in lower than average 

concentrations (Figure 3-22) and relatively low pair-wise correlations (Table 3-11) for these two 

sites. This analysis demonstrates that agreement between monitors on an urban scale is a complex 

function of monitor siting, location relative to sources, geography, and micrometeorology. 

January 2010 3-49 
Figure 3-24. Aerial view of the location of CO monitors A and B (marked by the red pins) in 

Denver, CO, depicting their proximity to the urban core. Scale: 1 cm = 145 m. 

January 2010 3-50 
Figure 3-25. Aerial view of the location of CO monitor I (marked by the red pin) in Azusa, CA 

(Los Angeles CSA), depicting its proximity to mixed use land. 

Scale: 1 cm = 145 m. 

January 2010 3-51 
 

Figure 3-26. Aerial view of the location of CO monitor Q (marked by the red pin) in Pasadena, 

CA (Los Angeles CSA), depicting its proximity to a residential neighborhood. 

Scale: 1 cm = 145 m. 

January 2010 3-52 
3.5.1.3. Micro- to Neighborhood Scale and the Near-Road Environment 

Table 3-12 shows the 2005-2007 nationwide distributional data for all hourly, 1-h daily max, 

1-h daily avg, and 8-h daily max CO concentrations broken down by spatial sampling scale. The 

different sampling scales included in the table (microscale, middle scale, neighborhood scale and 

urban scale) were defined in Section 3.4.2.1. While monitors classified under all four scales are used 

for highest concentration monitoring and regulatory compliance, individual monitors are classified 

by spatial scale to be used for addressing more particular monitoring objectives. Microscale, middle 

scale, and neighborhood scale monitors are used to quantify source impacts while neighborhood 

scale and urban scale monitors are used for population-oriented monitoring (40 CFR Part 58 

Appendix D). For CO, traffic is the major source in an urban setting and therefore microscale data 

are sited “to represent distributions within street canyons, over sidewalks, and near major roadways” 

with at least one monitor sited to capture maximum concentrations, while middle scale monitors are 

sited to represent “air quality along a commercially developed street or shopping plaza, freeway 

corridors, parking lots and feeder streets” (40 CFR Part 58 Appendix D). The data used to create 

Table 3-12 were subject to the same 75% completeness criteria described in Section 3.5.1.1. More 

than 50% of the reported hourly data fell below the reported LOD (reported as 0.5 ppm for the 

majority of monitors reporting to AQS). 

Table 3-12. National distribution of all hourly observations, 1-h daily max, 1-h daily average, and 8-h 

daily max concentration (ppm) derived from AQS data, based on monitor scale 

designations, 2005-2007. 

PERCENTILES 

Time Scale n Mean Min 1 5 10 25 50 75 90 95 99 Max 

ALL HOURLY 

Microscale 1,428,745 0.6 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.1 1.4 2.2 19.6 

Middle Scale 771,941 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 1.0 1.3 2.3 18.9 

Neighborhood Scale 2,878,993 0.4 0.0 0.0 0.0 0.0 0.2 0.3 0.5 0.8 1.1 2.1 35.3 

Urban Scale 279,311 0.3 0.0 0.0 0.0 0.0 0.1 0.3 0.5 0.7 0.9 1.6 10.8 

1-H DAILY MAX 

Microscale 59,905 1.2 0.0 0.2 0.3 0.4 0.7 1.0 1.5 2.1 2.5 3.9 19.6 

Middle Scale 32,659 1.0 0.0 0.1 0.2 0.3 0.5 0.8 1.2 2.0 2.5 4.0 18.9 

Neighborhood Scale 121,328 0.9 0.0 0.0 0.1 0.2 0.4 0.6 1.1 1.8 2.4 4.0 35.3 

Urban Scale 11,784 0.7 0.0 0.0 0.0 0.1 0.3 0.5 0.9 1.3 1.8 3.1 10.8 

1-H DAILY AVERAGE 

Microscale 59,905 0.6 0.0 0.0 0.1 0.2 0.4 0.5 0.8 1.0 1.2 1.7 4.0 

Middle Scale 32,659 0.5 0.0 0.0 0.1 0.1 0.3 0.4 0.6 0.9 1.2 1.9 5.5 

Neighborhood Scale 121,328 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.0 1.6 7.0 

Urban Scale 11,784 0.3 0.0 0.0 0.0 0.0 0.2 0.3 0.5 0.7 0.8 1.2 2.5 

8-H DAILY MAX 

Microscale 59,905 0.8 0.3 0.3 0.3 0.3 0.5 0.7 1.1 1.5 1.8 2.6 5.8 

Middle Scale 32,659 0.7 0.1 0.3 0.3 0.3 0.3 0.6 0.9 1.4 1.9 2.8 6.2 

Neighborhood Scale 121,328 0.6 0.0 0.3 0.3 0.3 0.3 0.4 0.8 1.2 1.6 2.7 10.9 

Urban Scale 11,784 0.5 0.0 0.2 0.3 0.3 0.3 0.4 0.7 1.0 1.3 2.1 4.0 

January 2010 3-53 
The median hourly CO concentration across the U.S. obtained at microscale monitors was 

25% higher than at middle scale and 67% higher than at neighborhood scale. However, 

measurements at or below the median hourly concentration were almost entirely below the LOD for 

all scales, thereby limiting the usefulness of hourly median comparisons. The upper percentiles (90% 

and above), however, were all above the LOD and reveal consistently lower hourly concentrations 

for the urban scale monitors relative to the other monitors. For example, the 99th percentile of 

reported hourly values was 2.2, 2.3, and 2.1 ppm for microscale, middle scale and neighborhood 

scale, respectively, compared to 1.6 ppm for urban scale. Similar patterns were present in the 1-h 

daily max, 1-h daily average, and 8-h daily max distributions. Overall, the urban scale nationwide 

distributions tended to have lower concentrations relative to neighborhood scale, middle scale and 

microscale distributions (Table 3-12). 

Distributions categorized by spatial scale and CSA/CBSA are provided in Figure 3-27 for 

hourly data and in Figure 3-28 for 1-h daily max data for the select CSAs/CBSAs where data were 

available at multiple scales (not all scales were reported by each CSA/CBSA studied). Tables A-17 

through A-26 of Annex A contain tabular distributions for all CSAs/CBSAs except Anchorage. On a 

city-by-city basis, there was considerable variability when comparing distributions at the available 

spatial scales. With a few exceptions, however, the distribution of microscale and middle scale 

monitors tended to be higher than those obtained from neighborhood and urban scale monitors. For 

example, in CSAs/CBSAs containing both microscale and neighborhood scale monitors (Boston, 

Denver, Houston, Los Angeles, New York and Phoenix), median hourly concentrations at monitors 

sited for microscale were 20-40% higher than for middle scale and 0-150% greater than those sited 

for neighborhood scales. At the 99th percentile, microscale concentrations ranged from 31% less 

than to 59% greater than middle scale concentrations and from 14% less than to 67% greater than 

neighborhood scale. For most cities, the median hourly data are near or below the 0.5 ppm LOD 

reported for most monitors in use. In general, these data suggest that CO concentrations measured 

with monitors sited at micro- and middle scales, typically near roads, were somewhat elevated 

compared with neighborhood and urban scale monitor locations. However, the magnitude of these 

differences varies by city and is difficult to discern given the predominance of CO concentrations 

near or below the LOD. 

Despite differences in concentrations observed at different scales (Figure 3-27 and Figure 

3-28), intersampler correlations do not follow a distinct trend with respect to spatial monitoring scale 

(Table 3-10 and Table 3-11). For instance, intersampler correlation in Denver ranged from 0.46 to 

0.76 among microscale monitors and was 0.52 for the correlation between the two neighborhood 

scale monitors (no monitors in Denver reporting to the AQS are sited at middle scale). Intersampler 

correlation in Los Angeles ranged from 0.44 to 0.73 for middle scale, and the one pair of 

neighborhood scale monitors had a correlation of 0.43. Only one monitor was sited each at 

microscale and urban scale, and 16 of the 24 CO monitors in Los Angeles are not declared to sample 

at any spatial scale (scale designation = “null”). In Denver, the distribution of hourly CO data 

obtained at microscale was nearly identical to that obtained at neighborhood scale. In Los Angeles, 

the microscale data was typically higher than middle, neighborhood, or urban scale data except at the 

upper end of the distribution, where middle scale data were higher for both hourly and 1-h daily max 

data (Figure 3-27 and Figure 3-28). 

January 2010 3-54 
Atlanta

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Boston

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Denver

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Houston

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Los Angeles

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

New York City

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Percentile

C/C99

Phoenix

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

Percentile

C/C99

0

Pittsburgh

0.0

0.2

0.4

0.6

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1.0

0 20 40 60 80 100

Percentile

C/C99

Figure 3-27. Distribution of hourly CO concentration data by city and 

monitoring scale. For comparison purposes, the y-axis has 

been scaled to the city-specific 99th percentile 

concentration. Note that Anchorage, Seattle, and St. Louis 

CSAs are not included here because these cities do not have 

monitors sited at different scales.

microscale

middle scale

neighborhood scale

urban scale

January 2010 3-55 
Atlanta

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microscale

middle scale

neighborhood scale

urban scale

Figure 3-28. Distribution of 1-h daily max CO concentration data by 

city and monitoring scale. For comparison purposes, the 

y-axis has been scaled to the city-specific 99th 

percentile concentration. Note that Anchorage, Seattle, 

and St. Louis CSAs are not included here because these 

cities do not have monitors sited at different scales.

January 2010 3-56 
The microscale and middle scale CO data reported here are consistent with hourly 

concentrations reported in the literature for the near-road environment within the U.S. Baldauf et al. 

(2008, 190239) reported CO concentrations obtained 20 m from an interstate highway in Raleigh, 

NC, to have a median around 0.25 ppm and with maximum concentration <4.0 ppm. Zhu et al. 

(2002, 041553) reported CO concentration of 1.9-2.6 ppm at a distance of 17 m from an interstate 

highway in Los Angeles, with concentration decreasing exponentially with distance from the 

highway. Zhu et al. (2002, 041553) observed on-road CO concentrations to be approximately 10 

times higher than at an upwind monitoring site, as shown in Figure 3-29. Concentrations continued 

to decrease and were still two times higher than upwind levels at a monitoring site 300 m away. 

Baldauf et al. (2008, 190239) also reported a drop in concentration at a monitoring site 300 m from 

the road compared with the 20 m site. Figure 3-30 illustrates the distribution of measurements taken 

throughout a day. In this plot, the near-road (20 m distance) CO concentrations tended to be 

significantly higher than those obtained at 300 m, and the daily variability in the CO concentration 

time series was greater at the 20 m site than at the 300 m site. The ratio of 20 m to 200 m 

concentrations was higher for the Zhu et al. (2002, 041553) paper. This was likely due to the fact that 

the 300 m site was always downwind in Zhu et al. (2002, 041553), whereas winds were more 

variable in Baldauf et al. (2008, 190239). Other near-road measurements reported in the literature are 

similar to those from the Zhu et al. (2002, 041553) and Baldauf et al. (2008, 190239) studies. Chang 

et al. (2000, 001276) reported near-road ambient CO measurements obtained in downtown Baltimore 

(distance to road not specified) in the range of 0.5-1.3 ppm. Riediker et al. (2003, 043761) reported 

measurements of CO concentration obtained near one of four heavily-trafficked roads in Wake 

County, NC, to average 1.1 ppm (range: 0.4-1.7 ppm). Neighborhood scale measurements reported 

in the literature were also consistent with if not slightly lower than those reported by AQS. Gentner 

et al. (2009, 194034) reported CO concentrations ranging from roughly 0.4-0.9 ppm in Riverside, 

CA, 1 km east of an interstate highway. Singh et al. (2006, 190136) reported 24-h avg CO 

concentrations, obtained with a 0.04 ppm LOD CO monitor in Long Beach, CA, within 0.5 km and 

1.5 km of two interstate highways, to range from 0.2-1.4 ppm. 

Source: Reprinted with Permission of Elsevier Ltd. from Zhu et al. (2002, 041553) 

Figure 3-29. Relative concentrations of CO and copollutants at various distances from the 

I-710 freeway in Los Angeles. 

January 2010 3-57 
Source: Reprinted with Permission of Air and Waste Management Association from Baldauf et al. (2008, 190239) 

Figure 3-30. CO concentration time series 20 m and 300 m from the I-440 highway in Raleigh, 

NC. Symbols denote the mean concentration, and whiskers denote standard 

deviations. 

Determinants of spatial variability in ambient CO concentration include roadway density, 

traffic counts, meteorology, and natural and urban topography. Mobile sources are the largest single 

source of CO, and their abundance and density affect the magnitude of CO production. Rodes et al. 

(1998, 010611) compared traffic volume, roadway type, and concentrations of CO and several 

copollutants in Los Angeles and Sacramento, CA, in a study of on-road traffic emissions. They noted 

that there was little difference in CO concentration between arterial roads and freeways for Los 

Angeles. Rodes et al. (1998, 010611) found that traffic was also much more congested throughout 

Los Angeles. This finding was not surprising given that Los Angeles is a much larger city with 

substantially higher traffic volumes than Sacramento. Under similar wind conditions, morning 

concentrations were much higher in Los Angeles than Sacramento. Rodes et al. (1998, 010611) 

observed that high afternoon winds ventilate Los Angeles, but Sacramento is not as well ventilated. 

As a result, Sacramento has nearly the same concentrations as Los Angeles in the afternoon. This 

observation is consistent with measurements by Gentner et al. (2009, 194034), showing that CO 

concentrations varied inversely with wind speed. 

Measured on-road and road-side CO concentrations may also relate to the traffic volume. 

Among the 291 active sites where monitors met completeness criteria during 2005-2007, 57 were 

declared by state agencies as microscale with average annual daily traffic (AADT) counts on the 

nearby roads ranging from 500 vehicles per day at one site in Denver, CO to 133,855 vehicles per 

day in Tampa, FL with a geometric mean of 17,462 vehicles per day and a geometric standard 

deviation of 2.5 (Table A-2 of Annex A). Within a geometric standard deviation, the data range from 

6,576-40,000 vehicles per day. Only two monitors were sited at roads with 100,000 vehicles per day 

or more. In contrast, the site where Zhu et al. (2002, 041553) collected data had 160,000-178,000 

vehicles per day in 2001 (CalTrans, 2009, 194036). Microscale sites near roads in the mid-range of 

the traffic count data may record data that are not substantially different from those obtained from 

neighborhood scale measurements, as indicated in Table 3-12. Likewise, with little microscale data 

at roads with AADT of more than 100,000 vehicles per day, there is still much uncertainty regarding 

the magnitude of concentrations in the near-road environment. 

Field measurements, computational modeling, and wind tunnel experiments have shown that 

roadway design, roadside structures and vegetation, and on-road traffic levels can affect 

concentrations of CO and other pollutant concentrations near roadways. Field measurements 

reported by Baldauf et al. (2008, 191017) indicated that noise barriers could reduce near-road 

pollutant concentrations by as much as 50%, although this effect was highly dependent on 

January 2010 3-58 
meteorological conditions; these results are illustrated in Figure 3-31. This study also showed that 

the presence of mature vegetation further reduced concentrations and flattened the concentration 

gradient away from the road. Urban dispersion and wind-field modeling by Bowker et al. (2007, 

149997) also demonstrated the influence of noise barriers and vegetation on the concentrations and 

spatial variability of nonreactive pollutants emitted from traffic sources. Heist et al. (2009, 194037) 

ran wind tunnel experiments using a model of a road with different roadside features and a tracer gas 

line source emitted from the simulated road to study how concentrations of gaseous traffic emissions 

vary spatially in the near-road environment. They demonstrated that noise barriers and roadway 

design characteristics, such as the presence of embankments and elevated roadway segments, can 

alter airflow and contaminant dispersion patterns in the near-road environment. For example, their 

results indicated that roadway design having below-grade sections of road and embankments 

reduced concentrations away from the road. These results showed similar concentrations as those of 

Zhu et al. (2002, 041553), both for roadway segments at-grade with no obstructions to air flow and 

for elevated roadway segments with different road fill conditions. Additionally, Khare et al. (2005, 

194016) illustrated in a wind tunnel study that vertical dispersion of a nonreactive gas increased with 

increasing simulated traffic volume; this effect was also sensitive to changes in approaching wind 

direction. These studies taken together suggest that localized turbulence induced by roadside 

structures, roadway design, and traffic provide some mixing and resulting dilution of the CO 

concentration in the near-road environment; the extent of mixing effects varies by meteorological 

conditions and the specific roadway design and traffic loading. 

 Source: Reprinted with Permission of Elsevier Ltd. from Baldauf et al. (2008, 191017) 

Figure 3-31. CO concentration profile 10 m from I-440 in Raleigh, NC, behind a noise barrier 

and in open terrain. 

The geometry of urban street canyons has a profound effect on the distribution of CO 

concentrations on a microscale. 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, 155697; Chang and Meroney, 2003, 

090298; Kastner-Klein and Plate, 1999, 001961; So et al., 2005, 110746; Xiaomin et al., 2006, 

156165). 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. 

Parameters influencing street canyon dispersion include canyon height to width ratio (H/W), source 

positioning, wind speed and direction, building shape, and upstream configuration of buildings. 

Figure 3-32 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, 156165). 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. 

January 2010 3-59 
When the canyon height was twice the street width (typical of higher-density cities) and background 

winds were somewhat higher, near ground-level 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 and are heavily influenced by wind speed and street canyon 

topography. The relationship between in-canyon concentration and wind speed and turbulence is 

well established with concentration varying inversely with the magnitude of wind speed and 

turbulence (Britter and Hanna, 2003, 090295). When studying the effect of wind direction on street 

canyon concentration levels for a continuous “line source” of traffic exhaust, concentration levels 

were at local maxima under two conditions: wind perpendicular to or parallel to the street canyon. 

Wind gusts at the turbulence interface at the top of the canyon or traffic-based turbulence can also 

cause dilution of the exhaust concentration within the canyon (Kastner-Klein et al., 2000, 194035). 

Source: Reprinted with Permission of Elsevier Ltd. from Xiaomin et al. (2006, 156165) 

Figure 3-32. Dimensionless tracer gas concentration on the windward and leeward sides of 

the canyon plotted against the elevation of the measurement (Z) scaled by 

building height (H) under two different H/W and wind speed conditions. Shown 

are measurements obtained in a wind tunnel (symbols) and model simulations 

using computational fluid dynamics (lines). 

Street canyon field studies support the computational and wind tunnel modeling results 

described above. In a multisite survey of curbside CO concentration in London, U.K., Croxford and 

Penn (1998, 087176) observed up to threefold differences in concentration related to the side of the 

January 2010 3-60 
street on which the monitor was positioned relative to the wind direction, with H/W varying between 

0.7 and 1.7 depending on position within the canyon. Bogo et al. (2001, 192378) measured CO 

concentrations in a street canyon with H/W of 1 in Buenos Aires, Argentina, using a continuous CO 

monitor. Similar to the Xiaomin et al. (2006, 156165) simulation results for H/W of 1, Bogo et al. 

(2001, 192378) 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. However, for the case of a deep street canyon (H/W of 5.7) in 

Naples, Italy, Murena et al. (2008, 194038) observed that the concentrations on two sides of the 

canyon differed by <15%, with wind direction varying between 10° and 80° from the street axis. 

Doran et al. (2003, 143352) measured CO concentration in a street canyon in Phoenix, AZ, during 

the morning hours and observed that CO concentration decreases with elevation above the ground if 

turbulent mixing is small, but that the difference between ground level and 39th-floor (50 m AGL) 

measurements of CO concentration decreases when turbulent mixing increases (with maximum 

measurements at any elevation not exceeding 2 ppm). As shown in Figure 3-33, the larger difference 

in concentration as a function of turbulent mixing can occur when there are meteorologically stable 

conditions in the lower boundary layer. These results support findings from the modeling studies that 

CO concentration can vary by several times within a street canyon and is greatly influenced by local 

meteorology and building topography. 

Source: Reprinted with Permission of Elsevier Ltd. From Doran et al. (2003, 143352) 

Figure 3-33. Normalized difference between CO measurements taken at ground level and from 

the 39th floor of a building in a Phoenix, AZ street canyon as a function of bulk 

Richardson number (Ri). Bulk Ri is a dimensionless number that describes the 

ratio of potential to kinetic energy, and it is used here as a measure of stability 

within the street canyon, with greater Ri corresponding to greater stability and 

values near or less than zero indicating greater mixing. 

Research by Kaur and Nieuwenhuijsen (2009, 194014) and Carslaw et al. (2007, 148210) 

suggests that CO exposures are related to traffic volume and fleet mix in the street-canyon 

environment. Kaur and Nieuwenhuijsen (2009, 194014) used multiple linear regression to model CO 

concentration data from central London as a function of mode of transport (broken down by vehicle 

type), traffic count, wind speed, and temperature. They added each variable successively and found 

traffic count, temperature, wind speed, and walking to be significant parameters in the model, with 

traffic count being the strongest determinant. Analysis of variance showed variability in traffic count 

to explain 78% of the variability in CO levels for these data, and variability in mode of transport 

explained 6% of the variability. Likewise, Carslaw et al. (2007, 148210) used a generalized additive 

model to determine how CO concentration data (log-transformed) obtained in central London varied 

as a function of light- and heavy-duty traffic counts, along-street and cross-street components of 

January 2010 3-61 
wind, temperature, year, and Julian day. Light-duty vehicle count was a more important determinant 

of CO concentration than heavy-duty (i.e., diesel) vehicle count in this study. They found that the CO 

declined steadily with year and that wind was the most significant covariate. In addition to showing 

meteorology to be an important determinant of concentration, these modeling exercises also suggest 

a linear or log-linear relationship between concentration and traffic. 

3.5.2. 

3.5.2.1. 

Temporal Variability 

Multiyear Trends 

Figure 3-34 (top) shows ambient CO concentrations in ppm from 1980 to 2006 based on 

continuous measurements averaged over 8-h time segments. Figure 3-34 (bottom) depicts trends in 

the annual second-highest 8-h CO concentrations for 144 sites in 102 counties nationwide having 

data either in the SLAMS network or from other special purpose monitors. 

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 recorded during the past 27 yr (Figure 

3-34 [top]). Since 1992, more than 90% of these sites have reported second highest CO 

concentrations below the 8-h NAAQS of 9 ppm. 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. 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 8-h CO concentrations 

above the level of the NAAQS declined to zero over the same period (Figure 3-34 [bottom]). 

Consistent with the nationwide trends in emissions and concentrations, CO concentrations in 

all 10 EPA Regions have steadily decreased since 1980, with reductions over this period ranging 

from 68% in Region 7 to 85% in Region 1 (Figure 3-35). This is also consistent with declining 

emissions seen in many regions of the U.S., shown in Figure 3-5. 

January 2010 3-62 
Source: U.S. EPA (2008, 157076) 

Figure 3-34. (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). 

January 2010 3-63 
Source: U.S. EPA (2008, 157076) 

Figure 3-35. 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. 

January 2010 3-64 
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 Figure 3-36 and Figure 3-37, respectively, for the 11 CSAs and CBSAs examined in this assessment. Since these figures represent 

the distribution of hourly observations over a 3-yr period, any fluctuations or changes in the timing 

of the daily peaks would result in a broadening of the curves shown in the diel plot compared to the 

actual daily temporal behavior on any specific day measured by an individual monitor. However, 

these figures are useful for comparing the general hourly variation in CO concentrations across cities 

and by day of the week (i.e., weekday versus weekend). The weekday data showed that the 

Anchorage mean, median, 5th and 10th percentile CO concentration curves exhibit 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 a.m. The Seattle plot shows a daytime plateau beginning around 5:00 a.m. 

and lasting until roughly 10:00 p.m., with higher concentrations during morning and afternoon rush 

hour. Differences in hourly variation among the 11 CSAs and CBSAs reflect city-to-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. 

Figure 3-37 illustrates weekend diel trends for the 11 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 City shows that the mean and 

median CO concentrations remain fairly constant throughout the day, with a slight reduction between 

2:00 and 7:00 a.m. The 90th and 95th percentile curves illustrate more diel variation. 

January 2010 3-65 
Figure 3-36. Diel plot generated from weekday hourly CO data (ppm) for the 11 CSAs and 

CBSAs, 2005-2007. Included are the number of monitor days (N) and the median, 

mean, 5th, 10th, 90th and 95th percentiles of composite CO concentrations 

plotted by time of day. Note that the y-axis of the Anchorage and Phoenix plots 

are scaled to 5.8 ppm while the other plots are scaled to 3.0 ppm. 

January 2010 3-66 
Figure 3-37. Diel plot generated from weekend hourly CO data (ppm) for the 11 CSAs and 

CBSAs, 2005-2007. Included are the number of monitor days (N) and the median, 

mean, 5th, 10th, 90th and 95th percentiles of composite CO concentrations 

plotted by time of day. Note that the y-axis of the Anchorage and Phoenix plots 

are scaled to 5.8 ppm while the other plots are scaled to 3.0 ppm. 

January 2010 3-67 
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 in Figure 3-38 using AQS data across the U.S. AQS data were 

obtained from all available co-located monitors across the U.S. after application of the 75% 

completeness criteria described earlier in Section 3.5.1.1. Pearson correlation coefficients (r) were 

calculated using 2005-2007 data stratified by season. Correlation plots analogous to Figure 3-38 for 

select individual cities are provided in Annex A, Figures A-43 to A-48. 

Spring

SO2 

NO2 

O3 

PM10 

PM2.5 

correlation (r) with CO

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Fall 

SO2 

NO2 

O3 

PM10 

PM2.5 

correlation (r) with CO

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Winter

SO2

NO2

O3

PM10

PM2.5

correlation (r) with CO

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Summer

SO2

NO2

O3

PM10

PM2.5

correlation (r) with CO

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0











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Figure 3-38. Seasonal plots showing the variability in correlations between hourly CO 

concentration and co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations. 

Red bars denote the median, green stars denote the arithmetic mean, the box 

incorporates the IQR, and the whiskers extend to the 5th and 95th percentiles. 

Correlations outside the 5th and 95th percentiles are shown as individual points. 

In all cases, a wide range of correlations existed between CO and copollutants as illustrated in 

Figure 3-38. The mean and median correlation between CO and copollutants were positive for NO2, PM10, and PM2.5; near zero for SO2; and negative for O3. These findings reflect common combustion 

sources for CO, NO2, and PM. CO is highly correlated with NO2 and PM2.5 because they are both 

emitted directly during incomplete combustion and because secondary nitrate PM comes from NOX, which is largely produced from mobile sources. 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 IQR of correlations with SO2 spanned from positive to negative 

for all seasons; SO2 would not be expected to correlate well with CO because SO2 emanates 

primarily from industrial sources. Correlations between CO and O3 were almost entirely negative for 

January 2010 3-68 
winter, when CO emissions tend to be high and O3 formation is low. During the other three seasons, 

most of the CO-O3 correlations were also negative. Given the role of CO in O3 chemistry, crosscorrelation functions were also computed by season for the CO-O3 relationship (Annex A, Figure 

A-50). The data showed negative correlations at all lags with minima at zero lag for winter, spring, 

and fall. During the summer, a small positive peak correlation (r = 0.071) was centered at a lag 

of -8 h and a minima occurred at a lag of 1 h, r = -0.272. It is not known whether the positive lagged 

correlations in summertime are related to interaction of CO with O3 through chemistry, coinciding 

independent effects such as peak times for rush hour CO emissions and afternoon O3 formation, or 

some combination of interactive and independent effects. However, given the very low magnitude of 

these correlations, it is clear that many other factors influence the O3 and CO time series. 

Within and between individual metropolitan areas, the distribution of copollutant correlations 

varied substantially. Figure 3-39 and Figure 3-40 illustrate the correlations between CO and 

copollutants for Denver, CO, and Los Angeles, CA, to exemplify these differences. Copollutant 

correlation plots are also shown for other cities in Annex A, Figures A-44 through A-49. For 

instance, correlations between CO and copollutants are all positive for SO2, NO2, PM10, and PM2.5

and are all negative for O3 in Denver. In contrast, the correlations in Los Angeles span from negative 

to positive for O3, PM10, and PM2.5, in various seasons. The larger span of correlations for Los 

Angeles in comparison with Denver could result from several factors. For example, more variation 

in meteorology, topography, or source distribution with respect to monitor placement in Los Angeles 

may cause the distribution of copollutant correlations to be wider. In addition, fewer co-located 

monitors in Denver compared with Los Angeles may be causing some of the observed differences. 

Figure 3-39. Seasonal plots showing the variability in correlations between hourly CO 

concentration and co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations 

for Denver, CO. 

January 2010 3-69 
Figure 3-40. Seasonal plots showing the variability in correlations between hourly CO 

concentration and co-located hourly SO2, NO2, O3, PM10 and PM2.5 concentrations 

for Los Angeles, CA. 

Several recent studies reported correlations between ambient CO and other pollutants. 

Reported relationships were generally consistent with the correlations shown above using AQS data. 

Sarnat et al. (2001, 019401) reported significant positive Spearman’s correlations between CO and 

NO2 (r = 0.76) and PM2.5 (r = 0.69) and significant negative correlations between CO and O3

(r = -0.67) in Baltimore (concentration averaging periods not specified). Correlation of CO with SO2

was insignificant (r = -0.12). The Sarnat et al. (2001, 019401) 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, 087531) also 

reported significant year-round association between CO and PM2.5 and significant associations 

between CO and SO4

2- aerosols. Kim et al. (2006, 089820) measured CO, NO2, and PM2.5 at ambient 

fixed sites in Toronto, Canada, and 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). Tolbert 

et al. (2007, 090316) 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

2- (r = 0.14). The higher correlations of CO with EC, OC, and TC are likely 

related to the fact that CO and carbonaceous PM are both emitted by mobile sources. Gentner et al. 

(2009, 194034) analyzed the relationship between ambient CO and VOC concentrations, serving as 

markers of gasoline vehicle emissions in Riverside, CA. Correlations of CO with two compounds, 

n-butane and isopentane, are shown in Figure 3-41 for summer and fall. Higher concentrations of 

n-butane per unit of CO were observed for fall, as well as higher correlation (fall: r = 0.88; summer: 

r = 0.65). For isopentane, the slopes of regression are much closer for fall and summer, with higher 

correlations between isopentane and CO (fall: r = 0.93; summer: r = 0.86). Gentner et al. (2009, 

194034) noted that isopentane vapor fraction was higher in summer than winter and that the n-butane 

vapor fraction increases in winter. This reflects the higher volatility of n-butane compared with 

isopentane. In this work, Gentner et al. (2009, 194034) used emissions modeling to estimate that 

overall VOC emissions from gasoline varies with CO emissions with a ratio of 0.086 with a 

correlation of r = 0.80 in summer. Gentner et al. (2009, 194034) suggest that the near-road slope of 

January 2010 3-70 
ambient VOC to CO concentration might be influenced by upwind CO concentration and secondary 

CO production by oxidation of VOCs. 

Source: Reprinted with Permission of ACS from Gentner et al. (2009, 194034) 

Figure 3-41. Linear regression of n-butane and isopentane concentration as a function of CO 

concentration, Riverside, CA. 

January 2010 3-71 
3.5.4. 

3.5.4.1.

Policy-Relevant Background 

Background concentrations of pollutants used for informing policy decisions about national 

standards in the U.S. are commonly referred to at EPA as PRB concentrations. In this assessment, 

PRB concentrations include contributions from natural sources everywhere in the world and from 

anthropogenic sources outside the U.S., Canada, and Mexico. 

Surface-Based Determinations 

For this assessment, PRB concentrations of CO were determined from the extensive and longrunning 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); see 

http://www.esrl.noaa.gov/gmd/ccgg/iadv. Surface-based CO measurements have been made for more 

than 10 yr with exceptionally high sensitivity and selectivity at locations significantly away from 

local sources. In this assessment, for example, CO data through December 2007 are available with 

extensive quality assurance and control information from the worldwide network of 72 stations 

active in December 2008. ESRL GMD uses the highly sensitive gas chromatography-mercury 

liberation photometric detection technique with precision to 1 ppb in 50 ppb or 2 ppb in 200 ppb and 

accuracy to 1.5 ppb in 500 ppb or 2 ppb in 200 ppb. 

In order to smooth interannually changing meteorological and emissions effects, data from 

2005-2007 at 12 remote sites in the U.S. were used to determine PRB. A map of these sites is shown 

in Figure 3-42; they are: 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. These sites are affected by anthropogenic 

emissions in North America to varying degrees. Average concentrations for each month and for each 

of the 3 yr are shown for each site in Figure 3-43. All sites demonstrate the well-known seasonality 

in background CO with minima in the summer and fall and maxima in the winter and spring in the 

Northern Hemisphere. Northern Hemisphere summer-time minima are related in large measure to 

the enhanced photochemical reaction of CO with OH, as described in Section 3.2.2. Analysis for 

North American PRB is made here by segregating the three Alaska sites (owing to their high 

latitude) and the two Hawaii sites (owing to their distance from the continent) and treating the 

remaining seven sites as being more representative of the CONUS surface-level background 

concentrations. Outside the defined CONUS domain used here, the 3-yr avg CO PRB in Alaska 

ranged from 127 to 135 ppb with an average of 130 ppb, and from 95.3 to 103.1 ppb with an average 

of 99.2 ppb in Hawaii. Over the CONUS domain the 3-yr avg CO PRB concentration ranged from 

118 to 146 ppb with an average of 132 ppb. 

January 2010 3-72 
Figure 3-42. Map of the baseline monitor sites used in this assessment to compute PRB 

concentrations. 

January 2010 3-73 
Figure 3-43. 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. 

3.5.4.2. Limitations of Other Possible Methods 

The significance of CO for surface-level air quality and for its indirect climate forcing effects 

through CH4, O3, and CO2 as described previously in this chapter, and its long τ relative to that of 

other primarily urban and regional pollutants make it an important species for measurement and 

evaluation on multiple spatial, temporal, and chemical scales. 

In addition to the ESRL GMD surface network used in this assessment’s determination of CO 

PRB, CO concentrations away from local sources can be measured from space. So, for example, CO 

has been observed from space by the Measurement of Air Pollution from Satellites (MAPS) 

instrument on Space Shuttle orbiter flights for three 10-day missions in 1984 and 1994 (Connors et 

al., 1994, 193755) and by the Measurement of Pollution in the Troposphere (MOPITT) on the Terra 

satellite since 2000 (Emmons et al., 2004, 193756). Surface spatial coverage with both space-based 

instruments was limited by the common problems of cloud cover, high surface albedo and 

emissivities, and image swath pattern and timing, with the result that much of the CONUS, for 

example, was missed some of the time. In addition, all of these satellite measurements were limited 

though somewhat differently in the vertical resolution of their total column CO concentration values. 

January 2010 3-74 
For a determination of a PRB-equivalent background concentration for 2008, the MAPS data 

would be of no use, except for comparisons on temporal trends, and even that is limited by the very 

few observations from MAPS. MOPITT data might seem more useful were it not for MOPITT’s 

very low precision and accuracy in the lowest few kilometers above the Earth’s surface of its 

integrated total column CO measurement by thermal infrared radiances (Shindell et al., 2005, 

193746). MOPITT CO profile sensitivities are so low at the surface that retrievals at the 850 hPa 

level, the lowest reported, do not capture the surface concentration accurately but actually represent 

a broad and deep vertical slice of the lower troposphere with an integral concentration that often 

peaks well above 850 hPa (Shindell et al., 2006, 091028). Error analysis by Emmons et al. (2004, 

193756), reported in Shindell et al. (2006, 091028) revealed that MOPITT concentration error in the 

lower troposphere was 7% and had greater bias over cleaner sites, which are of most interest when 

estimating a CONUS PRB. 

Since the integrated total column measurements of CO from space-borne instruments are 

dominated by CO in the mid- and upper troposphere, comparisons to surface measurements are 

subject to appreciable error. Using a subset of seven to nine of the ESRL GMD network stations in 

North America, for example, to compare to the MAPS and MOPITT data, Shindell et al. (2005, 

193746) found that the satellite data showed an increase of between 3 and 13 ppb CO while the 

surface data at these locations showed a decrease of 20 ppb in the years 2000-2002 relative to 1994. 

Mean global concentrations of CO were apparently decreasing before 2000, but that trend has now 

mostly ended (Duncan and Logan, 2008, 194042), so that the integrated column CO total measured 

from space may have indicated a false trend. 

CO concentrations can also be predicted with numerical CTMs on regional, continental, and 

global scales. Hence it would, in principle, be possible to predict CO PRB concentrations for the 

CONUS. The chief limitation to this method comes from the highly uncertain emissions of CO 

worldwide needed to drive the global CTMs, which in turn set the boundary conditions and chemical 

flow fields for the finer-scale models that might be used to compute PRB. Interannual variability in 

CO emissions from global biomass burning is very high, and the emissions source strength of this 

signal is a very strong component of the CONUS PRB given the CO τ of ~57 d. The long τ means 

that PRB monitoring sites are subject to contamination by regional pollution. Estimates of total 

global CO emissions used in recent forward and inverse model experiments range from 

<1,100 MT/yr to >3,300 MT/yr (Shindell et al., 2005, 193746). 

A comprehensive evaluation of 26 state-of-the-science atmospheric chemistry models 

exercised for present-day and future CO simulations was performed and reported by Shindell et al. 

(2006, 091028). They found substantial under-prediction of CO in the extra-tropical Northern 

Hemisphere compared to satellite and local surface observations and large variability among the 

models as well, even when using identical CH4 abundances and CO emissions. In North America, for 

example, the multimodel average underestimated the observations of lower troposphere CO by 60 

ppb or more, or by ~50% or more of the measured background concentration at many of the ESRL 

GMD sites. The Pearson r values for the multimodel average against MOPITT data globally for 

2000-2001 was 0.84 ± 0.08 for April at 850 hPa (as near to the surface as tested) but only 0.55 ± 0.11 

in October (Shindell et al., 2006, 091028). Shindell et al. (2006, 091028) proposed several reasons 

why this could contribute to pervasive underprediction: (1) the models do not adequately simulate 

CO build-up during the wintertime periods of lower OH flux; (2) the models have no seasonal CH4

cycle with build-up in the Northern Hemisphere winter; and (3) variability in the models’ OH 

concentrations, which accounted for ~80% of the CO intermodel variance (Shindell et al., 2006, 

091028). 

All of the above methods have their own advantages and disadvantages. The levels determined 

by the ESRL/GMD network show the temporal and spatial variability of CO levels. Although these 

sites are subject to North American pollution sources to varying degrees, these data could be used 

provided this caveat is borne in mind. Resulting errors in estimating excess risks will be very small 

because the concentrations are only a small fraction of the CO NAAQS. 

January 2010 3-75 
3.6. 

3.6.1. 

Issues in Exposure Assessment 

Summary of Findings from 2000 CO AQCD 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) 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 personal-microenvironmental-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 elevated compared to ambient levels and equal to or 

lower than in-vehicle levels (Ott et al., 1994, 076546; Rodes et al., 1998, 010611). A small portion of 

the CO concentrations inside a vehicle cabin comes from the vehicle itself, while a substantial 

fraction comes from roadway mobile source 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 obtained at fixed-site monitors within the cities 

studied. High-traffic volumes contributed to increased in-vehicle concentrations. 

Prior to the 2000 CO AQCD, it was well known that CO levels in residences may be elevated 

above ambient due to nonambient indoor sources, such as cooking, space heating, and smoking. 

Separation of indoor CO into ambient and nonambient components was found to be important for 

determining the effect of ambient CO concentrations, although this had not been done successfully in 

studies conducted to date. Two large studies performed 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 et al., 1985, 011618; Johnson, 1984, 024652). 

Nonambient 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. Nonambient exposures tend to obscure the relationship between ambient 

CO concentrations, as measured at ambient monitors, and total personal CO exposure. 

3.6.2. General Exposure Concepts 

A theoretical model of personal exposure is presented to highlight measurable quantities and 

the uncertainties that exist in this framework. An individual’s time-integrated total exposure to CO 

can be described based on a compartmentalization of the person’s activities throughout a given time 

period: 

∫ = CE dt T j

Equation 3-2 

where ET = total (T) exposure over a time-period of interest, Cj = airborne CO concentration at 

microenvironment j, and dt = portion of the time-period spent in microenvironment j. Equation 3-2

can be decomposed into a model that accounts for exposure to CO of ambient (Ea) and nonambient 

(Ena) origin of the form: 

= + EEE naaT

Equation 3-3 

Examples of ambient CO sources include industrial and mobile source emissions, biomass 

combustion, and agricultural processes. Examples of nonambient sources include environmental 

tobacco smoke (ETS), cooking, and home heating. CO concentrations generated by ambient and 

January 2010 3-76 
nonambient sources are subject to spatial and temporal variability that can affect estimates of 

exposure and resulting health effects. Exposure factors affecting interpretation of epidemiologic 

studies are discussed in detail in Section 3.6.8. 

This assessment focuses on the ambient component of exposure because this is more relevant 

to the NAAQS review. Ea can be expressed in terms of the fraction of time spent in various outdoor 

and indoor microenvironments (Wallace et al., 2006, 089190; Wilson et al., 2000, 010288): 

a ∑ oo += ∑ CFfCfE ,inf, ioii

Equation 3-4 

where f = fraction of the relevant time period (equivalent to dt in Equation 3-2), subscript o = index 

of outdoor microenvironments, subscript i = index of indoor microenvironments, subscript o,i = 

index of outdoor microenvironments adjacent to a given indoor microenvironment i, and Finf,i = 

infiltration factor for indoor microenvironment i. Equation 3-4 is subject to the constraint Σfo + 

Σfi = 1 to reflect the total exposure over a specified time period, and each term on the right hand side 

of the equation has a summation because it reflects various microenvironmental exposures. Here, 

“indoors” 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). “Outdoor” exposure can occur in parks or yards, on sidewalks, and on bicycles or motorcycles. 

Finf is a function of the building air exchange characteristics. 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. Studies of CO infiltration are reviewed in Section 3.6.5.1. 

In epidemiologic studies, Ca is often used in lieu of outdoor microenvironmental data to represent 

these exposures based on the availability of data. Thus it is often assumed that Co = Ca and that the 

fraction of time spent outdoors can be expressed cumulatively as fo; the indoor terms still retain a 

summation because infiltration differs among different microenvironments. If an epidemiologic 

study employs only Ca, then the assumed model of an individual’s exposure to ambient CO, first 

given in Equation 3-4, is re-expressed solely as a function of Ca: 

( ) oa += ∑ CPffE ai

Equation 3-5 

Meteorology, strength of CO sources, spatial variability of CO concentration, proximity of the 

study population to sources of CO, design of the epidemiologic study, and other factors determine 

whether or not Equation 3-5 is a reasonable approximation for Equation 3-4. Errors and uncertainties 

inherent in use of Equation 3-5 in lieu of Equation 3-4 are described in Section 3.6.8 with respect to 

implications for interpreting epidemiologic studies. Epidemiologic studies often use concentration 

measured at a central site monitor to represent ambient concentration; thus α, the ratio between 

personal exposure to ambient CO and the ambient concentration of CO, is defined as: 

a

a

C

E

α =

 Equation 3-6 

Combination of Equation 3-5 and Equation 3-6 yield: 

+= ∑ Pff α o i

Equation 3-7 

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 P. Wallace et al. (2006, 089190) note that time-activity data and 

corresponding estimates of P for each microenvironmental exposure are needed to compute an 

individual’s α with accuracy. If local sources and sinks exist and are significant but not captured by 

central site monitors, then the ambient component of the local outdoor concentration may be 

January 2010 3-77 
estimated using dispersion models, land use regression models, receptor models, fine scale CTMs or 

some combination of these techniques. These techniques are described in Section 3.6.3. 

3.6.3. 

3.6.3.1. 

Exposure Modeling 

Stochastic Population-Based Time-Weighted Microenvironmental Exposure 

Models 

Population-based methods, such as the Air Pollution Exposure (APEX) and Stochastic Human 

Exposure and Dose Simulation (SHEDS) models, involve stochastic treatment of the model inputs 

(Burke et al., 2001, 014050; U.S. EPA, 2009, 194009). These are described in detail in the 2008 NOX

ISA (U.S. EPA, 2008, 157073), in Annex AX 3.6.1. Stochastic models utilize distributions of 

pollutant-related and individual-level variables, such as ambient and local CO concentration source 

contributions and breathing rate respectively, to compute the distribution of individual exposures 

across the modeled population. The models also have the capability to estimate received dose 

through a dosimetry model. Using distributions of input parameters in the model framework rather 

than point estimates allows the models to incorporate uncertainty and variability explicitly into 

exposure estimates (Zidek et al., 2007, 190076). These models estimate time-weighted exposure for 

modeled individuals by summing exposure in each microenvironment visited during the exposure 

period. For example, Bruinen de Bruin et al. (2004, 190943) utilized the EXPOLIS (exposure in 

polis, or cities) model to predict CO population exposures in Milan, Italy, based on subjects’ timeactivity data broken into 15-min intervals. The simulation results showed that the U.S. 8-h NAAQS 

level was exceeded in 1 case out of 1,000. The model also showed that exposures exceeded 20 ppm 

in 1 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 initial set of input data for population exposure models is ambient air quality data, which 

may come from a monitoring network or model estimates. Estimates of concentrations in a set of 

microenvironments are generated either by mass balance methods or microenvironmental factors. 

Microenvironments modeled include indoor residences; other indoor locations, such as schools, 

offices, and public buildings; and vehicles. The sequence of microenvironments and exertion levels 

during the exposure period is determined from characteristics of each modeled individual. The 

APEX model does this by generating a profile for each simulated individual by sampling from 

distributions of demographic variables such as age, gender, and employment; physiological variables 

such as height and weight; and situational variables such as living in a house with a gas stove or air 

conditioning. Activity patterns from a database such as Consolidated Human Activity Database 

(CHAD) are assigned to the simulated individual using age, gender, and biometric characteristics 

(U.S. EPA, 2009, 194010). Breathing rates are calculated for each activity based on exertion level, 

and the corresponding received dose is then computed. For APEX, the CO dosimetry algorithm 

calculates venous COHb levels using the nonlinear CFK model, as described in Chapter 4. 

(U.S. EPA, 2008, 191775). Summaries of individual- and population-level metrics are produced, 

such as maximum exposure or dose, number of individuals exceeding a specified exposure/dose 

threshold, and number of person-days at or above benchmark exposure levels. The models also 

consider the nonambient contribution to total exposure. Nonambient source terms are added to the 

infiltration of ambient pollutants to calculate the total concentration in the microenvironment. Output 

from model runs with and without nonambient sources can be compared to estimate the ambient 

contribution to total exposure and dose. 

Recent larger-scale human activity databases, such as those developed for the CHAD or the 

National Human Activity Pattern Survey (NHAPS), have been designed to characterize exposure 

patterns among much larger population subsets than can be examined during individual panel studies 

(Klepeis et al., 2001, 002437; McCurdy et al., 2000, 000782). CHAD consists of a consolidation of 

human activity data obtained during several panel studies in which diary or retrospective activity 

data were obtained, while NHAPS acquired sample population time-activity data through surveys 

about human activity (Klepeis et al., 2001, 002437). The complex human activity patterns across the 

population (all ages) are illustrated in Figure 3-44 (Klepeis et al., 2001, 002437), which is presented 

to illustrate the diversity of daily activities among the entire population as well as the proportion of 

time spent in each microenvironment. Different patterns would be anticipated when breaking down 

January 2010 3-78 
activity patterns only for subgroups such as children or the elderly. Population exposures can be 

estimated using CO concentration data in each microenvironment. 

Source: Reprinted with Permission of Nature from Klepeis et al. (2001, 002437). 

Figure 3-44. Distribution of time that the sample population spends in various environments, 

from the NHAPS. 

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, 014737). To examine indoor concentrations of ambient CO, 

Dimitroulopoulou et al. (2006, 090302) 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 nonambient 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 nonsource indoor concentrations was attributed to the 

lack of CO loss mechanisms. In the Reducing Urban Pollution Exposure from Road Transport 

(RUPERT) study, Bell et al. (2004, 192376) 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, 193986). 

3.6.3.2. Using Spatial Models to Estimate Exposure 

Another set of approaches to improve exposure estimates in urban areas involves construction 

of a concentration surface over a geographic area, with concentration between monitors estimated 

using a model to compensate for missing data. The calculated CO concentration surface can then be 

used to estimate exposures outside residences, schools, workplaces, roadways, or other locations of 

January 2010 3-79 
interest. This technique does not estimate exposure directly because it does not account for activity 

patterns or concentrations in different microenvironments. 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-principle models, such as dispersion models and CTMs, 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. 

The STEMS model provides an example of an integrated-exposure modeling approach using a 

range of spatial inputs. STEMS maps exposures based on inputs for traffic levels, atmospheric 

dispersion, background concentrations, and geography. Gulliver and Briggs (2005, 191079) 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, 

191079) 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 microscales (Jerrett et al., 2005, 092864). Flachsbart (1999, 015857) tested 

numerous meteorological, traffic, and background CO input variables in a regression approach to 

predicting CO exposure among individuals 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. 

Dispersion Models 

Dispersion models have been used both for direct estimation of exposure and as inputs for 

stochastic modeling systems, as described above. Location-based exposures have been predicted 

using a model such as California Line Source Dispersion Model (CALINE), the American 

Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD), 

CALPUFF (long-range plume transport model created by the California Air Resources Board), or the 

Operational Street Pollution Model (OSPM) for estimation of street-level ambient CO exposure 

(e.g., Abdul-Wahab, 2004, 194011; Delfino et al., 2009, 190254; Zhou and Levy, 2008, 190091). 

CALINE, CALPUFF, and AERMOD utilize Gaussian dispersion models to describe pollutant 

transport, while OSPM is a semi-empirical model of airflow and pollutant transport within an infinite 

street canyon. Delfino et al. (2009, 190254) used CALINE (version 4) to model exposure in the nearroad environment for estimation of relative risks of asthma hospitalizations as a function of increases 

in ambient CO and NOX concentrations. The concentration at each subject’s home was computed 

with the dispersion model, and then the data were aggregated to estimate a population risk. Zhou and 

Levy (2008, 190091) used results from an OSPM simulation to compute intake fraction, defined as 

the fraction of emissions that are inhaled or ingested, for ambient CO and other copollutants. 

Daytime activity patterns were modeled using both CHAD and the American Community Survey 

(http://www.census.gov/acs/www) to model commuting behaviors that would affect both mobilesource emissions and population-based exposures. With an individualized exposure approach, the 

model is deterministic. However, population exposures were estimated by performing repeated 

simulations using various housing characteristics and then computing the probability distribution 

function for exposure. When comparing street-canyon exposure computed by OSPM with near-road 

exposure computed simply with a Gaussian dispersion model, Zhou and Levy (2008, 190091) 

estimated that the street-canyon exposures would be three times greater than those in the general 

community. Isakov et al. (2009, 191192) developed a methodology to link a chemical transport 

model, used to compute regional scale spatiotemporally-varying concentration in an urban area, with 

stochastic population-exposure models to predict annual and seasonal variation in population 

exposure within urban microenvironments. Although this approach was demonstrated for PM2.5, it is 

January 2010 3-80 
similar to the one used by Zhou and Levy (2008, 190091) for linking ambient CO concentrations 

with population activity pattern data to associate the spatial-concentration field with personal 

exposure to ambient CO. 

Land Use Regression Models 

Land use regression (LUR) models have also been developed to estimate pollution levels as a 

function of several land use factors, such as land use designation, traffic counts, home heating usage, 

point source strength, and population density (Briggs et al., 1997, 025950; Gilliland et al., 2005, 

098820; Ryan and LeMasters, 2007, 156063). LUR is a regression derived from monitored 

concentration values as a function of data from a combination of the land use factors. The regression 

is then used for predicting concentration at multiple locations based on the independent variables at 

those particular locations without monitors. At the census-tract level, a LUR is a multivariate 

description of pollution as a function of traffic, land use, and topographic variables (Briggs et al., 

1997, 025950). Like deterministic-dispersion models, LUR can be performed over wide areas to 

develop a probability density function of exposure at the urban scale. However, Hoek et al. (2008, 

195851) warn of several limitations of LUR, including distinguishing real associations between 

pollutants and covariates from those of correlated copollutants, limitations in spatial resolution from 

monitor data, applicability of the LUR model under changing temporal conditions, and introduction 

of confounding factors when LUR is used in epidemiologic studies. 

A GIS platform is typically used to organize the independent variable data and map the results 

from an LUR simulation. The GIS software creates numerous lattice points for the regression of 

concentration as a function of the covariates. For instance, Krewski et al. (2009, 191193) computed 

PM2.5 concentrations for the New York City and Los Angeles metropolitan areas. For the Los 

Angeles analysis, the LUR was estimated at 23 monitors and then applied to simulate PM2.5

concentration at 18,000 points in the simulation domain, and an inverse distance weighting (IDW) 

kriging method was applied to interpolate the predicted concentration. In New York City, the LUR 

was estimated at 49 monitors for a 3-yr model and at 36 monitors for a model of winter 2000 and 

then applied to simulate PM2.5 concentration at 5,600 locations in the 28-county domain; kriging was 

employed only for the purpose of visualizing the concentration between monitors. The models 

explained 69% and 66% of the variation in PM2.5 in Los Angeles and New York City, respectively. 

GIS-based spatial-smoothing models can be used to estimate concentrations where monitors 

are not located. Yanosky et al. (2008, 099467) described an approach to estimate PM concentrations, 

using a combination of reported AQS data and GIS-based and meteorological covariates. Temporally 

stationary covariates included distance to nearest road for different PM size fractions, urban land use, 

population density, point-source emissions within 1 and 10 km buffers, and elevation above sea 

level. Time-varying covariates included area-source emissions, precipitation, and wind speed. In this 

analysis, the GIS-based covariates were temporally stationary, while the meteorological and PM 

monitored concentration inputs were time varying. This approach was applied to estimate PM2.5,

PM10-2.5, and PM10 exposures for the Nurse’s Health Study and provided estimates of concentration at 

approximately 70,000 nodes with PM2.5 and/or PM10 data input from more than 900 AQS sites, with 

good validation of the PM2.5 and PM10 models (Paciorek et al., 2009, 190090; Yanosky et al., 2008, 

099467; Yanosky et al., 2009, 190114). 

Marshall et al. (2008, 193983) 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 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 sub-optimal and were excluded from further analysis. In the case of Method 

2, a single downtown high-concentration 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 

January 2010 3-81 
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. Brauer et al. (2008, 156292) 

also evaluated LUR and IDW-based spatial-interpolation models and suggested that LUR is 

appropriate for directly-emitted pollutants with high spatial variability; Brauer et al. (2008, 156292) 

used NO and BC as examples, but CO emitted from mobile sources would also fall in that category. 

These results highlight the variation in local concentration estimates with choice of estimation 

technique. 

Originally, LUR was used to model NO2 dispersion. It has been adapted for modeling 

exposures to other pollutants, although application of LUR to CO exposures has been performed in 

only a few studies. Findings related to other pollutants are provided because they are instructive in 

how LUR can be used to predict CO concentrations. Carslaw et al. (2007, 148210) used multipleregression modeling to examine the effects of traffic volumes, wind components, temperature, and 

time on concentrations of CO, NOX, NO2 (O3 was also a predictive variable for NO2), benzene, and 

butadiene at a single site. These results were used for forecasting concentrations at that site, but the 

study lacked the spatial resolution to predict concentrations at alternate sites. Cassidy et al. (2007, 

199975) applied LUR to analyze the effect of wind, temperature, traffic volume, roadway size, 

number of stories of surrounding buildings, other sources of pollution, terrain, and time of day on 

concentrations of CO, PM2.5, and PM10 at 30 street-level sites within Baguio City, Philippines. In this 

work, they found traffic volume was the only significant predictor of CO during rush-hour periods, 

while winds significantly predicted early morning concentrations of CO, PM2.5, and PM10. The 

model was not used for spatial interpolation in this case. Brauer et al. (2003, 155702) used LUR to 

analyze PM2.5 exposure at 40-42 sites each within Stockholm, Sweden, Munich, Germany, and 

throughout The Netherlands. This study found a measure of traffic density to be the most significant 

variable predicting PM2.5 exposure and used GIS to apply the model at home addresses of asthmatic 

subjects to estimate exposures. Ryan et al. (2008, 156064) reported on an LUR model developed 

from monitor and land-use data and then applied at the locations of children to assess their exposure 

to traffic-derived EC for the Cincinnati Allergy and Air Pollution Study. Ryan et al. (2008, 156064) 

found traffic to be the most important determinant of diesel exhaust particle exposure. In this case, 

wind direction was also factored into the model as a determinant of EC mixing. Although these 

studies differed in the number of sites and in the pollutants of focus, they are instructive in 

considering how LUR can be employed for estimating CO exposure. 

3.6.4. Personal Exposure Monitors for CO 

Portable monitors for measuring personal CO exposure include the Langan and Draeger 

monitors, both of which use electrochemical oxidation-reduction techniques (Langan, 1992, 

046120). These monitors continuously log CO concentrations, making them suitable for use in 

personal monitoring studies. Electrochemical CO sensors typically have an LOD of 1 ppm and a 

90% sensor response time (or the time required for the sensor to register 90% of a step change in CO 

concentration) of 20-60 s. The 2000 CO AQCD (U.S. EPA, 2000, 000907) provided detail on design 

updates of electrochemical CO sensors made during the 1990s. Commercially available personal CO 

exposure monitors are not designed to detect concentrations below 1 ppm. Electrochemical personal 

CO monitors are also typically sensitive to temperature changes, so that data correction is normally 

required. 

3.6.5. 

3.6.5.1. 

Indoor Exposure to CO 

Infiltration of Ambient CO 

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 calculated I/O for CO. 

Polidori et al. (2007, 156877) calculated 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, 026050) reported I/O using 

CO measurements in the absence of indoor sources in a school building in Athens, Greece, and 

January 2010 3-82 
found that I/O varied with season. During the summer, median I/O was reported to be 0.57 on 

weekdays, 0.91 on Saturdays, and 0.81 on Sundays. In winter, median I/O was reported to be 0.82 

during weekdays, 0.90 on Saturdays, and 0.74 on Sundays. In a related study, Chaloulakou et al. 

(2003, 190945) 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, 090302), 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 

measurements below the LOD (Williams et al., 2003, 053335). Ni Riain et al. (2003, 053792) 

examined the effects of mechanical ventilation and wind speed on I/O. In this study, the authors 

measured indoor and outdoor concentrations at two buildings located on a six-lane highway in 

central London with natural and mechanical ventilation. Ni Riain et al. (2003, 053792) found that 

outdoor concentrations for each building and ventilation condition ranged from 1.5 ± 0.1 ppm to 1.9 

± 0.1 ppm. Ni Riain et al. (2003, 053792) reported cumulative I/O approaching 0.9 within 30 min of 

sampling for the mechanical ventilation case and cumulative I/O varying between 0.65 and 0.8 for 

more than 70 h of sampling for the natural ventilation case. Ni Riain et al. (2003, 053792) found that 

wind speed and direction influenced the variation in I/O. 

Indoor air flow may affect CO exposure in the absence of indoor sources. Milner et al. (2006, 

123100) compared hourly CO concentration time series from different parts of a building (with a 

mix of natural and mechanical ventilation) located near a busy road and intersection in central 

London, U.K. They found that, within a given floor, CO concentration is greater in rooms that are 

closer to busy roads or an intersection. They noted that the correlation coefficient between indoor 

and outdoor CO concentrations also decreased within the building with distance from the road; the 

correlation coefficients were reported to be 0.80 for two time series obtained in rooms near the road, 

while they were reported to range between 0.46 and 0.55 on the sides of the building furthest from 

the road. The magnitude of the difference between CO concentrations in different rooms located 

nearer or further from the roads also depended on wind direction. Milner et al. (2006, 123100) noted 

that I/O tended to decrease with increasing wind speed, but Chaloulakou et al. (2003, 190945) also 

noted that indoor CO concentration varied inversely with wind speed. Chaloulakou et al. (2003, 

190945) attributed their observation to reduced concentrations related to dilution effects. Milner 

et al. (2006, 123100) stated that this relationship could be due to dilution of CO or to the tendency of 

people to keep windows closed on windy days. Additionally, CO concentrations were higher on 

lower floors of the building and varied over a given day throughout the building. These findings 

suggest that differences in exposure can occur within the same building as a result of differences in 

air exchange related to access to windows, mechanical ventilation, and outdoor meteorological 

conditions. 

3.6.5.2. Exposure to Nonambient CO 

Several papers have investigated the microenvironmental sources of total personal CO 

exposure. The CDC conducted a survey of ED visits for nonfatal CO poisoning, CO exposure, or 

potential CO exposure, and found that home heating was the largest known source of CO exposure, 

prompting 16.4% of CO-related ED visits, followed by motor vehicle exhaust exposure accounting 

for 8.1% of ED visits (Annest et al., 2008, 190236). Alm et al. (2000, 192374; 2001, 020237) studied 

factors that contributed to elevated CO exposures among preschool children and found that presence 

of a gas stove at home, ETS, natural ventilation, and living in a high-rise building all contributed to 

increased CO exposures. Time-activity diaries were linked to personal CO exposures in the 

EXPOLIS study. Here, Georgoulis et al. (2002, 025563) observed that geometric mean exposure 

among smokers ranged from 0.33 ppm in Helsinki, Finland, to 3.2 ppm in Athens, Greece, while 

among nonsmokers it ranged from 0.36 ppm in Helsinki to 1.7 ppm in Milan and ambient CO 

concentration ranged from 0.42 ppm in Helsinki to 3.2 ppm in Athens. Bruinen de Bruin (2004, 

190943) found, for a panel of 46 subjects in Milan, that indoor CO concentrations were 3.4 ppm in 

the presence of gas cooking and ETS, compared with 2.9 ppm in the presence of ETS only, 2.4 ppm 

in the presence of gas cooking only, and 1.8 ppm in the absence of indoor CO sources. Scotto di 

Marco et al. (2005, 144054) reported that average indoor CO increased in the presence of ETS from 

0.96-1.2 ppm for the home indoor environment and from 1.0-1.4 ppm for the work indoor 

January 2010 3-83 
environment. CO concentrations were measured to decrease from 1.5 to 1.3 ppm in other (not home 

or work) indoor environments, but those locations included garages, restaurants, and bars and could 

have been differently influenced by CO from cooking, indoor automobiles, or other sources. 

Personal CO concentrations can also be much more variable than ambient measurements. 

Figure 3-45 shows hourly versus personal CO concentration data obtained by Chang et al. (2000, 

001276) for a 1998-1999 multipollutant 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 

nonambient sources of CO. Figure 3-46 is a box plot of the personal-to-ambient CO concentration 

ratio for the same five microenvironments. Wide variability is seen in these plots, particularly during 

the summer. Much of that variability could be due to the influence of nonambient sources, which 

would then result in poor correlation between total personal exposure and ambient concentration. 

Source: Reprinted with Permission of the Air and Waste Management Association from Chang et al. (2000, 001276) 

Figure 3-45. Hourly personal versus ambient CO concentrations obtained in Baltimore, MD, 

during summer of 1998 in five settings: indoor residence, indoor other, outdoor 

near road, outdoor away from road, and in vehicle. 

January 2010 3-84 
Source: Adapted with Permission of the Air and Waste Management Association from Chang et al. (2000, 001276) 

Figure 3-46. Box plots of the ratio of personal to ambient concentrations obtained in 

Baltimore, MD, during summer of 1998 and winter of 1999 in five settings: indoor 

residence, indoor other, outdoor near road, outdoor away from road, and in 

vehicle. The grey line shows the mean, and the black mid-line shows the median. 

S = summer; W = winter. 

Vehicle self-pollution, defined by Behrentz et al. (2004, 155682) as the fraction of a vehicle’s 

own exhaust entering the vehicle microenvironment, is another potential nonambient source of CO 

exposure. This has been studied using inert tracer gases to evaluate exposures of children riding 

school buses. Behrentz et al. (2004, 155682) 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. 

3.6.6. 

3.6.6.1. 

Exposure Assessment Studies at Different Spatial Scales 

Neighborhood to Urban Scale Studies of Ambient CO Exposure 

Although several multipollutant exposure studies have been conducted recently in the U.S., 

(e.g., Sarnat et al., 2006, 089784), most have not included CO in the suite of pollutants, possibly due 

to high detection limits in personal monitors. A few studies conducted in Europe and Canada 

measured personal-ambient relationships for CO. This section summarizes CO exposure assessment 

studies that compare personal exposure measurements with ambient concentration measurements for 

the purpose of examining how well these measures correspond. 

The EXPOLIS study (Georgoulis et al., 2002, 025563) 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 and were all statistically significant (p ≤ 0.01 for all cities 

except Prague, where p = 0.05). The regression coefficient for Helsinki (>1) likely reflects 

nonambient sources that were not controlled in the study. The ambient concentration was the only 

variable that was statistically significantly associated with 48-h personal exposure for all five cities 

January 2010 3-85 
in this study, with correlations between personal CO exposure and ambient CO concentration 

ranging from 0.33 to 0.77. Georgoulis et al. (2002, 025563) reported that CO exposure in traffic 

ranged from 0.99 ppm in Helsinki to 4.2 ppm in Athens, while ambient CO concentration ranged 

from 0.42 ppm in Helsinki to 3.2 ppm in Athens. 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, 190943). 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, 190943) study, in the absence of nonambient CO 

from ETS and gas cooking, are shown in Table 3-13. The largest percentage of time-weighted 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, 144054) found similar 

results. Bruinen de Bruin et al. (2004, 190943) and Scotto di Marco et al. (2005, 144054) found that 

mobile source emissions were important contributors to personal exposure, as described in Section 

3.6.6.2. 

Table 3-13. Percentage of time exposed to ambient CO (adjusted to reflect the absence of 

nonambient 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: Reprinted with Permission of Nature from Bruinen de Bruin et al. (2004, 190943) 

. 

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, 192374; 2001, 020237) reported higher personal exposures than ambient concentrations for 

children aged 3-6 yr in Helsinki. Their mean 1-h daily max exposure was 5.2 ppm, compared to 

1.4 ppm measured at a fixed-site monitor. For the average of 8-h and 24-h daily max 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 only 12% of the variability in the 8-h max 

January 2010 3-86 
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, 089820) reported mean CO concentrations of 1.4 ppm for a panel of 28 

cardiac-compromised individuals in Toronto, Canada. Corresponding fixed-site monitor mean 

concentrations ranged from 0.5 to 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 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, 056811) 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. 

The studies presented above present mixed results regarding the association between ambient 

CO concentration measurements and personal CO exposures. Some personal CO measurements have 

been reported to be higher than ambient concentrations, while others are similar. Additionally, 

correlation between ambient CO concentration and personal exposure has varied in the literature. 

Nonambient (described in Section 3.6.5) and in-transit sources (described in Section 3.6.6.2) have 

been identified as important contributors to personal exposure. These observations raise questions 

about where and when ambient CO concentration can be used as a surrogate for personal CO 

exposure; these concepts are explored further in Section 3.6.8. 

3.6.6.2. Microscale Studies of Ambient CO Exposure: Near-Road and On-Road 

Exposures 

The 2007 American Housing Survey (AHS) (U.S. Census Bureau, 2008, 194013) reports that 

17.9 million occupied homes nationwide (16.1%) are within ~90 m (300 ft) of a “4-or-more-lane 

highway, railroad, or airport” and so are exposed to the near-road environment. Within city centers, 

6.2 million occupied homes (19.7% of those living in city centers) are within approximately 90 m of 

a highway, railroad, or airport; whereas in rural areas outside designated Metropolitan Statistical 

Areas (MSA), 1.4 million occupied homes (9.2% of those in rural areas outside MSAs) are near a 

highway, railroad, or airport. Those data can be put into context for exposure assessment in the nearroad environment; Section 3.5.1.3 describes near-road studies in which ambient CO was measured 

within the vicinity of a road and microscale AQS data obtained in the near-road environment. The 

AQS data suggest some spatial variability (20-40% difference between microscale and middle scale 

monitors, with the hourly microscale concentration having a median of 0.5 ppm and a 99th percentile 

value of 2.2 ppm), which was much lower than that reported by Zhu et al. (2002, 041553) for the 

near-road environment, in which the average concentration at 17 m from the road was 2.3 ppm 

(range 1.9-2.6 ppm) and a factor of about 12.5 lower for the monitoring site located 300 m from the 

road. The larger discrepancy observed between the Zhu et al. (2002, 041553) data and the AQS data 

might be attributed to the fact that the sampling equipment used by Zhu et al. (2002, 041553) were 

downwind of the freeway for the entire sampling period, while the hourly AQS data represents a 

range of wind speeds and directions that vary across different monitoring sites. For those living in 

the 16.1% of occupied homes situated in the near-road environment (within ~90 m), median hourly 

CO concentrations are typically higher than those further from the road, but the magnitude of the 

outdoor concentration is still in most circumstances measured to be below 2.2 ppm. 

Kaur and Nieuwenhuijsen (2009, 194014) and Carslaw et al. (2007, 148210) suggest that CO 

exposures are related to traffic volume and fleet mix in the street-canyon environment. In this 

research, Kaur and Nieuwenhuijsen (2009, 194014) developed a multiple linear regression of CO as 

a function of mode of traffic, broken down by vehicle type, wind speed, temperature, and traffic 

count for data obtained in central London as part of the DAPPLE study of traffic-related pollution. 

They added each variable successively and found traffic count, temperature, wind speed, and 

walking to be significant parameters in the model, with traffic count being the strongest determinant. 

Analysis of variance showed variability in traffic count to explain 78% of the variability in CO levels 

for these data, and variability in mode of transport explained 6% of the variability. Likewise, 

Carslaw et al. (2007, 148210) used a generalized additive model to determine how CO concentration 

January 2010 3-87 
(log-transformed) varies as a function of year, the along-street and cross-street components of wind, 

temperature, Julian day, light and heavy traffic counts, and temperature for data obtained in central 

London. Light-duty vehicle count was a more important determinant of CO concentration than was 

heavy-duty (i.e., diesel) vehicle count in this study, which is not surprising because gasolinepowered vehicles are known to emit more CO than diesel engines. They found that the CO 

concentration declined steadily with year and that wind was the most significant covariate. The 

decline in CO concentration with year, adjusted for all other covariates, was usually significantly 

different than the simple relationship between concentration and year, but the adjusted and 

unadjusted trends were similar. In addition to showing meteorology to be an important determinant 

of concentration, these modeling exercises also suggest a linear or log-linear relationship between 

concentration and traffic count. 

Findings regarding meteorology are consistent with in-vehicle CO concentration studies. 

Gómez-Perales et al. (2007, 138816) 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, 047196) made a similar observation in a study of urban 

commuters’ exposure within a vehicle. These observations are sensible given the influence of 

meteorology on near-road concentrations shown by Baldauf et al. (2008, 190239) and Gokhale and 

Khare et al (2007, 194015). 

A number of studies have focused on transit-time CO exposure, which can occur while in a 

vehicle or cycling (on-road) or while walking (near-road). Chang et al. (2000, 001276) showed that 

personal exposures in vehicles were on average 2.8 times higher than ambient concentrations during 

the summer and 4.1 times higher than ambient concentrations in the winter (Figure 3-46). For the 

other four microenvironments tested, the average ratio of personal exposure to ambient concentration 

was ~1. Kaur et al. (2005, 086504) 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, 044199) found that 

average in-transit exposures in Athens, Greece, 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), trolleys (winter: 9.6 ± 1.9 ppm; summer: 8.2 ± 3 ppm), and 

rail transit (winter: 4 ± 0.6 ppm; summer: 3.4 ± 0.7 ppm). Duci et al. (2003, 044199) did not provide 

fixed-site CO concentrations but stated that in-transit exposures were higher in each case. GómezPerales et al. (2004, 054418) measured CO exposures on buses, minibuses, and metro cars in Mexico 

City, Mexico, to be 12 ppm, 15 ppm, and 7 ppm, respectively. These values are much higher than 

CONUS measurements and those presented by Kaur et al. (2005, 086504), but the relative difference 

between the minibus and bus exposures in the Gómez-Perales et al. study are similar to those seen 

for the taxi-to-bus or car-to-bus comparisons in Kaur et al. (2005, 086504). These studies indicate 

that on-road exposures might be influenced by vehicle type, but that city-to-city differences are 

likely larger than differences between different modes of transport. 

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, 190943; Scotto Di Marco et al., 2005, 144054). Scotto di Marco et al. (2005, 144054) 

found that, for a panel of 201 adult Helsinki, Finland, residents (aged 25-55 yr), subjects spent 8.1% 

(1.9 h) of their time in transit, 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, 190943) found that, in the absence of nonambient sources, the subjects spent 

8.5% (2 h) 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 exposures than urban residents with short commute 

times. According to the 2007 AHS (U.S. Census Bureau, 2008, 194013), 110.1 million U.S. workers 

(87.8% of those working) commute to work in automobiles. 32.8% of U.S. workers work at home or 

commute less than 15 min to work, 32.1% commute 15-29 min to work, 15.1% commute 30-44 min 

to work, 5.7% commute 45-59 min to work, and 5.0% commute 1 h or longer to work. 

Vehicle ventilation can be an important determinant of in-vehicle concentrations. A study from 

Abi Esber and El Fadel (2008, 190939) in Beirut, Lebanon, is presented because the authors 

observed in-vehicle CO concentration time series under a range of ventilation conditions, although 

January 2010 3-88 
the in-vehicle CO concentrations measured are substantially higher than those typically observed in 

the U.S. Abi Esber and El Fadel (2008, 190939) reported results from CO concentration 

measurements taken directly outside and within an automobile in Beirut, Lebanon, during the 

morning commute period of 7:30-9:30 a.m under three different ventilation conditions. Figure 3-47

shows that the time series for the cabin and outdoor CO samples are very similar for the fresh air 

scenario. However, for the recirculating air ventilation scenario, the in-vehicle concentration 

increases and then reaches a plateau at a higher level. Abi Esber et al. (2007, 190941) stated that 

unaccounted sources of CO cause the build-up of in-cabin CO concentrations when the ventilation is 

set to recirculation mode. The correspondence between in-vehicle and outside-vehicle concentrations 

for the fresh air ventilation experiments, and lack thereof for the recirculation mode ventilation 

experiments, observed by Abi Esber and El Fadel (2008, 190939) suggests that in-vehicle 

concentrations of ambient CO are affected by mode of ventilation. 

Source: Reprinted with Permission of Elsevier Ltd. from Abi Esber and El Fadel (2008, 190939) 

Figure 3-47. Comparison of in-vehicle (solid line) and outside-the-vehicle (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. 

Substantial variability can occur over time within a vehicle. Riediker et al. (2003, 043761) 

measured CO concentrations inside highway patrol cars during shifts. Troopers recorded in a timeactivity 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. Riediker et al. (2003, 043761) 

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. Riediker et al. (2003, 043761) noted 

that within-shift variability was higher than between-shift variability, which underscores the 

variability in police officers’ activities during a given shift. Data were not segregated by ventilation 

settings, although the police officers typically operated the air conditioning continually because the 

study was performed during the summer. Alm et al. (1999, 047196) reported in-vehicle CO 

concentrations of 5.7 ppm in the morning and 3.1 ppm in the afternoon commute for Kuopio, 

Finland. These data indicate that within-shift variability observed by Riediker et al. (2003, 043761) 

might be related to time of day. Likewise, Rodes et al. (1998, 010611) reported CO concentrations in 

vehicles in Sacramento and Los Angeles under different driving conditions (arterial, freeway, highoccupancy-vehicle freeway lane, and “maximum” conditions at rush-hour and nonrush-hour times). 

They measured peak in-vehicle concentrations spanning 7-67 ppm on a freeway during rush hour, 

although the mean for each scenario was <6 ppm. In comparison, the peak roadside concentration 

ranged from 3 to 11 ppm and the peak ambient CO concentration was 1.3 ppm at the time of the 

measurements. The Rodes et al. (1998, 010611) data agree with results from the Riediker et al. 

January 2010 3-89 
(2003, 043761) and Alm et al. (1999, 047196) studies showing that substantial variability in CO 

concentration inside the cabin of a vehicle can occur during the course of a commute. 

In their review of roadway exposures to CO and PM, Kaur et al. (2007, 190070) listed a 

number of factors that may influence near-road or on-road exposure. Vertical CO concentration 

gradients have been documented in which concentrations decreased with height; lower breathing 

zone height among children may make them more likely to be exposed to higher CO tailpipe 

emissions. With respect to transportation, Kaur et al. (2007, 190070) suggested that vehicle 

ventilation, speed, position in traffic, and start/stop activity influence in-vehicle exposures. Abi Esber 

and El Fadel (2008, 190939) and Riediker et al. (2003, 043761) illustrated the effect of vehicle 

ventilation on in-vehicle concentrations. The influence of vehicle speed and start/stop activity is 

consistent with the turbulence research of Khare et al. (2005, 194016) and Gokhale and Khare (2007, 

194015) that suggested an increase in traffic volume and vehicle movement acts to dilute the on-road 

concentration of CO discussed in Section 3.5.1.3. 

3.6.7. Association between Personal CO Exposure and Copollutants 

Since incomplete combustion is the primary source of ambient CO in urban areas, exposure to 

ambient CO is accompanied by exposure to other combustion-related pollutants, such as NOX, PM, 

and VOCs. Thus, ambient 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 natural 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 consistently positive on a national basis; correlations spanned from negative to 

positive for ambient CO with ambient SO2 and ambient PM10, and ambient CO was negatively 

correlated with ambient O3. The correlation between ambient CO and specific ambient VOCs 

depends on parameters such as ambient temperature and the volatility of a specific compound. 

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, 

086504) found low Pearson’s correlation of total personal CO exposure with personal PM2.5

exposure (r = 0.23). Personal CO exposure had much better correlation with personal ultrafine 

particle (UFP) exposure (r = 0.68). Chang et al. (2000, 001276) 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 versus summer). In general, the correlations of personal CO with 

personal VOCs tended to be stronger in the winter. Chang et al. (2000, 001276) suggested that lower 

wintertime indoor air exchange rates could increase exposure to nonambient CO and VOC sources, 

such as ETS, and hence increase correlations between personal exposure of CO to VOCs. Significant 

associations of CO with benzene and toluene were also observed in vehicle microenvironments. 

3.6.8. Implications for Epidemiology 

Exposure error can be an important contributor to variability in epidemiologic study results. 

Community time-series studies may involve thousands or millions of people whose exposure and 

health status is estimated over the course of a few years using a short monitoring interval (hours to 

days). Community-averaged concentration is typically used as a surrogate for ambient exposure in 

community time-series studies. Exposures and health effects are spatially aggregated over the time 

intervals of interest because community time-series studies are designed to examine health effects 

and their potential causes at the community level (e.g., Bell et al., 2009, 194033). A longitudinal 

cohort epidemiology study typically involves hundreds or thousands of subjects followed over 

several years or decades. Concentrations are generally aggregated over time and by community to 

estimate exposures (e.g., Rosenlund et al., 2006, 089796). In addition, panel studies, which consist of 

a relatively small sample (typically tens) of study participants followed over a period of days to 

months, have been used to examine the health effects associated with exposure to ambient 

January 2010 3-90 
concentrations of air pollutants. Panel studies include time-activity diary studies (Akland et al., 1985, 

011618; Bruinen de Bruin et al., 2004, 190942; Scotto Di Marco et al., 2005, 144054). These studies 

may apply a microenvironmental model to represent exposure to an air pollutant. 

The importance of exposure misclassification varies with study design and is dependent on the 

spatial and temporal aspects of the design. For example, the use of a community-averaged CO 

concentration in a community time-series epidemiologic study may not allow for adequate 

examination of the role of spatial variability. Other factors that could influence exposure estimates 

include spatial and temporal variability related to source strength, topography of the natural and built 

environment, and meteorology; measurement errors; use of ambient CO concentration as a surrogate 

for ambient CO exposure; and the presence of CO in a mixture of combustion-related pollutants. The 

following sections will consider various sources of error and how they affect the interpretation of 

results from epidemiologic studies of different designs. 

3.6.8.1. Measurement Error 

Measurement Error at Community-Based Ambient Monitors and Exposure 

Assessment 

Because CO concentrations measured with community-based ambient monitors are often used 

as surrogates for ambient CO exposure in epidemiology studies, the limitations of the 

instrumentation are important to consider. As stated in Section 3.4.2, among the 291 monitors 

meeting completeness criteria for 2005-2007, only 8 were monitors with LOD = 0.04 ppm; the other 

monitors had LOD of 0.5 ppm. Among the nationwide AQS data for 2005-2007 from these 291 

monitors, more than 50% of the hourly CO concentration data were below the LOD of the 

instrumentation. Data below the LOD adds uncertainty to the association between CO exposure and 

health effects estimates. Additionally, many of the monitors are not sited for a specific measurement 

scale, and a given scale classification can represent a range of CO source conditions, as described in 

Section 3.5.1.3. These factors also contribute uncertainty in interpretation of measurements. 

Instrumental measurement error, other than that related to high LOD, is not expected to bias 

health effect estimates substantially in most circumstances. Because there will be some random 

component to instrumental measurement error, the correlation of the measured CO concentration 

with the true CO concentration will likely be <1. When analyzing the effect of instrument error for 

measuring nonreactive ambient pollutants, Zeger et al. (2000, 001949) stated that the instrument 

error for ambient measurements “is close to the Berkson type.” In the Berkson error model, the 

measured-exposure estimate is used instead of the true exposure, based on the assumption that the 

average measurement is the average of the true exposure. It is generally expected that the health 

effects estimate will not be biased by using measured values with error but may have more 

uncertainty than would an estimate based on the true-average exposure. In order for instrument error 

to cause substantial bias in health effects estimates, the error term (the difference between the true 

concentrations and the measured concentrations) must be strongly correlated with the measured 

concentrations. 

Measurement Error for Personal Exposure Monitors 

Personal electrochemical CO monitors are subject to interference and drift and have a 

relatively high LOD (~1 ppm) relative to current ambient concentrations. Previous studies in the 

1980s and 1990s, when ambient levels were higher, involved successful deployment of these 

monitors, but more recent exposure studies have avoided personal CO measurements because there 

are now a high percentage of nondetects. The lack of a suitable personal monitor for measuring lowlevel exposures (<1 ppm) has hampered field studies assessing personal exposure to ambient CO. 

Chang et al. (2001, 019216) evaluated the Langan CO monitor as part of an air quality sampling 

manifold. At relatively high (0.4-3.0 ppm) CO concentrations, the instrument correlated well (R2

 = 

0.93) with a reference NDIR 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

 = 0.40 in summer and R2 = 0.59 in winter, with 

January 2010 3-91 
the arithmetic average concentration underestimated by 47% in summer and by 63% in winter. 

Chang et al. (2001, 019216) pointed out the need for frequent instrument zeroing to minimize 

instrument drift. Abi Esber and El Fadel (2007, 190940) 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 related to nonambient CO sources. This large variation in personal measurements can 

result in high levels of classical measurement error (Sheppard et al., 2005, 079176). 

3.6.8.2. Exposure Issues Related to Nonambient CO 

The focus of the ISA is on ambient CO because that is relevant to the NAAQS. Uncertainty 

related to nonambient CO exposure may make it difficult to distinguish the effect of ambient CO on 

health effects. Wallace and Ziegenfus (1985, 011656) used NHANES II (1976-1980) data to evaluate 

the relationship between COHb levels and ambient CO concentration in 20 U.S. cities. They found a 

significant slope of 0.066% per 1 ppm increase of CO concentration. However, there was much 

scatter in the data, and variability in ambient CO concentration only accounted for 3% of the 

variation in COHb. The authors attributed this scatter to variability in nonambient sources such as 

gas cooking and ETS. This finding illustrates the importance of considering the relative role of 

ambient and nonambient CO in total personal exposure. 

Ambient and nonambient CO are chemically identical and so exert the same health effects. At 

the same time, ambient and nonambient sources are distinct and not correlated with each other 

(Wilson and Suh, 1997, 077408) and so would not confound the association between ambient CO 

exposure and the health effect (see also Sheppard et al., 2005, 079176). Zeger and Diggle (2001, 

026017) noted that, because ambient and nonambient CO exposures are uncorrelated, in a health 

effects model the regression coefficient of ambient concentration should be equal to the product of α

(the ratio of ambient exposure to ambient concentration) and the regression coefficient obtained 

when average personal exposure is used. The confidence intervals around the estimate obtained 

using total personal exposure would be wider because nonambient CO concentrations add variability. 

This is true even for the case when the chemical compound is the same for the ambient and 

nonambient pollutants, as in the case of CO. Likewise, Sheppard et al. (2005, 079176) simulated 

ambient and nonambient exposures to a nonreactive pollutant and observed that nonambient 

exposure has no effect on the association between ambient exposure and health outcomes for the 

case where ambient and nonambient concentrations were independent. Hence, the bias that will be 

introduced to epidemiologic models by using ambient CO concentration instead of personal 

exposure to ambient CO is given by the average α. Random variations in daily values of α would not 

change the health effects estimate but would also widen the confidence intervals around the health 

effect estimate. 

3.6.8.3. Spatial Variability 

CO concentration is known to be spatially heterogeneous, as evidenced by the near-road and 

in-vehicle studies cited in Sections 3.5.1.3 and 3.6.6.2, as well as the intraurban correlations 

provided in Section 3.5.1.2 and Tables A-9 through A-16 of Annex A. Results from Zhu et al. (2002, 

041553), which showed a large CO concentration gradient in the near-road environment, support the 

contention that CO exposures for those living in the near-road environment but far from a monitor 

might be underestimated. Conversely, exposures for those living away from roads might be 

overestimated by near-road CO concentration measurements. Exposure error may occur if the 

ambient CO concentration measured at the central site monitor is used as an ambient exposure 

surrogate and differs from the actual ambient CO concentration outside a subject’s residence and/or 

worksite (in the absence of indoor CO sources). Averaging data from a large number of samplers will 

dampen intersampler variability, and use of multiple monitors over smaller land areas may allow for 

January 2010 3-92 
more variability to be incorporated into an epidemiologic analysis. This is consistent with 

conclusions presented in the 2000 AQCD (U.S. EPA, 2000, 000907). 

Community exposure may not be well represented when monitors cover large areas with 

several subcommunities having different sources and topographies. The intersampler correlations of 

AQS data from monitors, presented in Section 3.5.1.2, reflect how well the time series of 

concentration data correspond across metropolitan areas. Overall, the data show moderate site-to-site 

correlation; for example, in the Los Angeles CSA the mean of the correlation was 0.50, and within 

one standard deviation of the mean, the range of correlations was 0.36-0.65. Bell et al. (2009, 

194033) tested the association between monitor density and 1-h max CO effect estimates for CVD 

hospitalizations for 126 U.S. counties and found an 8% increase in effect estimate size (95% CI: -7% 

to - 24%) with an IQR decrease in area covered by the monitor. This difference was not statistically 

significant but suggested that the magnitude of the effect estimate might be related to monitor 

coverage. Sarnat et al. (2009, 180084) studied the spatial variability of CO, along with NO2, O3, and 

PM2.5, in the Atlanta, GA, metropolitan area and how spatial variability affects interpretation of 

epidemiologic results, using time-series data for circulatory disease ED visits. Sensitivity to spatial 

variability was examined at slightly greater than neighborhood scale (8 km) in this study. 

Interestingly, Sarnat et al. (2009, 180084) found that relative risk varied with distance between the 

monitor and study population when comparing urban to rural locations, but distance of the study 

population to the monitor was not an important factor when comparing urban population groups. 

This suggests that, even for spatially heterogeneous CO, urban scale measures may produce results 

comparable to neighborhood-scale exposures in some circumstances. This may be due to 

comparability of sites throughout a city, for example, as a result of similar traffic patterns. However, 

Sarnat et al. (2009, 180084) caution that, because their study was limited to 8 km radii, it is not 

possible to interpret this work with respect to near-road and on-road microscale exposures. 

3.6.8.4. Temporal Variability 

Temporal Correlation 

Within a city, lack of correlation of relevant time series at various sites results in smoothing 

the exposure/surrogate concentration function over time and resulting loss of peak structure from the 

data series. At the same time if monitors are well correlated across a metropolitan area, even if the 

magnitude of concentration varies over space, time series analyses should provide comparable 

results across larger spatial areas. Such temporal correlation resulted in the small variation in relative 

risk estimates within the metropolitan region in Sarnat et al. (2009, 180084), where peak rush-hour 

times were similar throughout the city, in comparison with the rural area where temporal driving 

patterns were different. Burnett and Goldberg (2003, 042798) found that community time-series 

epidemiologic study results reflect actual population dynamics only when five conditions are met: 

environmental covariates are fixed spatially but vary temporally; the probability of the health effect 

estimate is small at any given time; each member of the population has the same probability of the 

health effect estimate at any given time after adjusting for risk factors; each member of the 

population is equally affected by environmental covariates; and, if risk factors are averaged across 

members of the population, they will exhibit smooth temporal variation. Note that for this study, 

Burnett and Goldberg (2003, 042798) analyzed mortality related to PM exposure, but the results are 

not specific to a given pollutant or health effect and thus are generalized here for time-series 

analysis. Dominici et al. (2000, 005828) noted that ensuring correlation between ambient and 

community average exposure time-series air pollutant data is made difficult by limitations in 

availability and duration of detailed ambient concentration and exposure time-series data, resulting 

in a source of uncertainty. If sufficient data are available and the time series of concentration data 

adequately represent population dynamics, then high temporal correlation between sampling sites 

should limit bias in health effects estimates, even if the magnitude of the concentrations differ. 

January 2010 3-93 
Seasonality 

Community time-series epidemiologic studies can be designed to investigate seasonal effects 

by incorporating seasonal interaction terms for the exposure surrogate and/or meteorology (e.g., 

Dominici et al., 2000, 005828). Sheppard et al. (2005, 079176) examined the role of seasonality on 

epidemiologic models. They found that α for the population will vary seasonally. This makes sense 

because α is a function of the amount of time spent indoors and outdoors and of indoor ventilation. 

Given that use of ambient CO concentration instead of ambient CO exposure biases the coefficient 

used in epidemiologic models by α, Sheppard et al. (2005, 079176) found that seasonal trends 

causing a change in α would contribute additional positive or negative bias, depending on the season 

and region of the country. However, several studies discussed in Chapter 5 investigated seasonal 

effects. No consistent seasonal pattern across health outcomes was observed in these studies. 

3.6.8.5. CO Exposure in Copollutant Mixtures 

Because CO exposures most often occur together with exposure to other combustion-related 

pollutants, especially in traffic, interpretation of health studies using ambient CO data can be a 

challenge, as discussed further in Chapter 5. Ambient CO concentrations from AQS data (Section 

3.5.3) have been shown to be correlated with ambient concentrations of NO2 and VOCs, and 

personal CO exposures have been correlated with personal PM and VOC exposures (Section 3.6.7). 

Correlation between factors is one condition for confounding, so it is possible that coexistence of CO 

with NO2 or VOCs could confound estimates of the health effects of ambient CO concentrations, and 

CO concentration could potentially confound estimates of the health effects of NO2 or VOCs. For 

this to be true, both CO and the copollutant would have to be correlated with the health outcome of 

interest. The moderately high correlations between ambient CO and copollutants make it difficult to 

discern the extent to which CO and other compounds are associated with a given health effect. 

It is also possible that the factor of interest may be the multipollutant mixture emitted from onroad or other combustion processes. The HEI Report on Traffic Related Pollutants (HEI, 2009, 

191009) suggests that ambient CO, NO2, and benzene could all be considered as surrogates for 

mobile source-related pollution, but none are ideal surrogates for mobile-source pollution because 

ambient CO concentration tends to decrease rapidly with distance from the source (e.g., Baldauf et 

al., 2008, 190239; Zhu et al., 2002, 041553), NO2 is reactive and benzene is volatile. Additionally, 

PM components of mobile source emissions change rapidly in size and composition from secondary 

formation and other atmospheric processing. Given that the mixture of mobile source-related 

emissions changes rapidly as a result of these factors, the ratio of CO to other components of mobilesource emissions also changes. Hence, even if CO is itself stable within the mixture of copollutants, 

the dynamic evolution of the mixture may change the representativeness of CO as an indicator of 

that mixture over time. Additionally, reductions in CO emissions over the past 30 yr have brought 

ambient CO concentrations down substantially, with more than half of hourly measurements below 

the LOD for most instruments (Section 3.5.1.1). Furthermore, CO and other copollutants found in 

mobile-source emissions have multiple anthropogenic and biogenic sources and, as a result, are 

difficult to attribute solely to mobile source pollution (Section 3.2). For all of these reasons, the 

representativeness of CO as an indicator of the multipollutant mixture of mobile-source emissions 

has not been clearly determined. 

3.6.8.6. Conclusions 

This section presents considerations for exposure assessment and the exposure errors and 

uncertainties that can potentially affect health effects estimates. These issues can be categorized into 

the following areas: measurement, nonambient sources, spatial variability, temporal variability, and 

CO in copollutant mixtures. Potential influences of each of these sources on health effect estimates 

derived from panel, time-series, and longitudinal epidemiologic studies are described above. 

Additionally, error sources have the potential to interact with each other. For example, CO 

concentrations have been shown to decrease rapidly with distance from a highway, and so spatial 

variability is an important issue in assessing CO exposure. Exposure error may occur if the ambient 

CO concentration measured at the central site monitor is used as an ambient exposure surrogate and 

differs from the actual ambient CO concentration outside a subject’s residence and/or worksite. 

January 2010 3-94 
However in time-series epidemiologic studies, spatial variability will only be an important source of 

error if the time series of CO concentration at different locations are not well correlated in time. The 

spatial variability of CO, in mixture with the dynamically changing group of mobile source 

pollutants, adds to the difficulty of quantifying the health effects related specifically to CO compared 

with those related to other combustion-related copollutants. In most circumstances, exposure error 

tends to bias a health effect estimate downward (Sheppard et al., 2005, 079176; Zeger et al., 2000, 

001949). Insufficient spatial or temporal resolution to capture true variability and correlation of CO 

with copollutants are examples of sources of uncertainty that could widen confidence intervals and 

so reduce the statistical significance of health effects estimates. 

3.7. Summary and Conclusions 

3.7.1. CO Sources, Emissions, and Chemistry 

In the U.S., on-road mobile sources constituted more than half, or ~61 MT out of ~117 MT, of 

total CO emissions in the 2002 NEI and BEIS, which are the most recent publicly available CO 

emission datasets meeting EPA’s data quality assurance objectives. In metropolitan areas in the U.S., 

for example, as much as 75% of all CO emissions can come from on-road vehicle exhaust 

(U.S. EPA, 2006, 157070). The majority of these on-road CO emissions derive from gasolinepowered 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%, or ~5.8 MT, of total CO emissions from all sources in 2002, and fires in 2002 

added another 16%, or ~18.5 MT, to the national CO emissions total. Although these estimates are 

generated using well-established approaches, uncertainties inherent in the emission factors and 

models used to represent sources for which emissions have not been directly measured and vary by 

source category, season, and region. 

In addition to being emitted directly by incomplete combustion, CO is produced by 

photooxidation of CH4 and other VOCs in the atmosphere, including NMHCs. 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. 

3.7.2. Climate Forcing Effects Related to CO 

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, 000907) and subsequently confirmed in the recent global assessments of climate change by the 

Intergovernmental Panel on Climate Change (IPCC, 2001, 156587; IPCC, 2007, 092765). CO is a 

weak direct contributor to RF and greenhouse warming. Sinha and Toumi (1996, 193747) estimated 

the direct RF of CO computed for all-sky conditions at the tropopause to be 0.024 W/m2

 based on an 

assumed change in CO mean global concentrations from 25 to 100 ppb since preindustrial times. The 

direct RF attributed to CO over this time-frame is ~1.5% of the direct RF for CO2 estimated by the 

IPCC (Forster et al., 2007, 092936). 

More importantly, CO can indirectly cause increased RF because it reacts with tropospheric 

OH and thus can increase the lifetime of trace gases in the atmosphere including the GHGs CH4 and 

O3. Additionally, the major pathway for removal of CO from the atmosphere is reaction with OH to 

produce CO2. CH4, O3, and CO2 absorb infrared radiation from the Earth’s surface and contribute to 

the greenhouse effect. Indirect RF attributed to 1750-2005 emissions of CO through changes in 

concentration of the GHGs O3, CH4, and CO2 was estimated by Forster et al. (2007, 092936) to be 

~0.2 W/m2

 or ~12% of the direct RF of CO2 (Figure 3-7). The future direct and indirect integrated 

January 2010 3-95 
RF for year 2000 emissions of CO was estimated to be ~0.2 W/m2

⋅yr with ~50% uncertainty over 

both 20-yr and 100-yr time horizons (Figure 3-8). The RF related to short-lived CO is ~25% of that 

for CO2 for a 20-yr time horizon, but only ~7% of that for longer-lived CO2 over a 100-yr time 

horizon. Overall, the evidence reviewed in this assessment is sufficient to conclude that a causal 

relationship exists between current atmospheric concentrations of CO and effects on 

climate. 

3.7.3. 

3.7.4. 

Ambient CO Measurements 

As of August 2009, 24 automated FRMs and no FEMs had been approved for monitoring CO. 

All EPA FRMs for CO operate on the principle of nondispersive infrared (NDIR) detection and can 

include gas filter correlation (GFC). Current specifications for CO monitoring are designed to help 

states demonstrate whether they have met compliance criteria, with requirements for an LOD of 

1 ppm. The reported LOD for 20 of the 24 FRMs is 0.5 ppm, and four models of FRMs are in 

operation with an LOD of 0.04 ppm. FRMs with higher LOD also are limited to a precision of 

0.1 ppm and are more subject to drift compared with newer monitors with automatic drift correction 

options. 

For 2005-2007, there were 291 CO monitors meeting the 75% completeness requirements and 

reporting values year-round to the AQS in the 50 states, plus the District of Columbia, Puerto Rico, 

and the Virgin Islands. 57 monitors across the U.S. have been sited at microscale to capture 

near-road concentrations, 31 have been sited at middle scale, and 119 are sited for neighborhood 

scale monitoring; among the remaining 84 monitors, states did not declare the spatial scale of 

monitoring for 71 monitors, and 13 are sited for monitoring urban or regional scale. For CO, traffic 

is the major source in an urban setting and therefore microscale data are sited “to represent 

distributions within street canyons, over sidewalks, and near major roadways” while middle scale 

monitors are sited to represent “air quality along a commercially developed street or shopping plaza, 

freeway corridors, parking lots and feeder streets” (40 CFR Part 58 Appendix D). At middle and 

neighborhood scales, required minimum monitor distance from a road is directly related to the road’s 

average daily traffic count to capture community averages. Ambient monitors for CO and other 

criteria pollutants are located to monitor compliance rather than population exposures. However, 

AQS monitors are often used for exposure assessment. When comparing CO monitor location with 

population density, it was observed that population coverage varies both within and between cities. 

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 and 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-yr period. More than 50% of the data nationwide were 

below the LOD for the majority of monitors in use. The nationwide mean, median, and interquartile 

range for 8-h daily max concentrations, reported between 2005 and 2007, were 0.7, 0.5, and 

0.5 ppm, respectively. Half of the 8-h daily max concentrations fell below the LOD for the majority 

of CO monitors in the field. 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 yr. 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 responses between monitors in each city and 

among the cities. 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. Anchorage, AK, had concentrations 

roughly twice those of the other metropolitan areas. Most of the CSAs/CBSAs examined here had 

January 2010 3-96 
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 0.5 ppm LOD for 

most instruments, coupled with the coarsely reported measurement resolution of 0.1 ppm, can 

artificially influence the comparison statistics shown in the tables and result in apparent 

heterogeneity in the box plots (Figure 3-19 and Figure 3-22). 

CO measurements obtained at different monitoring scales were compared to assess spatial 

variability of CO concentration. The median hourly CO concentration across the U.S. obtained at 

microscale monitors was 25% higher than at middle scale and 67% higher than at neighborhood 

scale. The microscale and middle scale CO data reported here are consistent with hourly 

concentrations reported in the literature for the near-road environment within the United States, with 

CO concentration decaying with downwind distance from the road. Determinants of spatial 

variability of ambient CO concentration within the near-road environment include roadway density, 

traffic counts, meteorology, and natural and urban topography. 

In all cases, a wide range of correlations existed between CO and copollutants computed from 

AQS data. The mean and median correlations between CO and copollutants were positive for NO2, PM10, and PM2.5; near zero for SO2; and negative for O3. These findings might reflect common 

combustion sources for CO, NO2, and PM. Among those copollutants with positive associations, 

NO2 had the highest mean and median correlations, followed by PM2.5 and PM10. Within and 

between individual metropolitan areas, the distribution of copollutant correlations varied 

substantially. Studies in the literature also found fairly high correlations of CO with EC and certain 

VOCs. 

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 wellknown seasonality in background CO, with minima in the summer and fall and maxima in the winter 

and spring. The 3-yr 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 ambient CO concentration data. The 

studies of personal exposure to ambient CO presented here generally found that the largest 

percentage of time in which an individual is exposed to ambient CO occurs indoors, but the highest 

ambient CO exposure levels occur in transit. In-vehicle CO concentrations are typically reported to 

be between 2 and 5 times higher than ambient concentrations, although peak in-vehicle 

concentrations more than an order of magnitude higher than corresponding ambient monitor 

concentrations have also been reported. Among commuters, exposures were higher for those 

traveling in automobiles in comparison with those traveling on buses and motorbikes and with those 

cycling or walking. Ambient CO exposure in automobiles has been demonstrated to vary with 

vehicle ventilation settings, and a very small portion of that exposure is thought to come from the 

vehicle in which the exposed person travels. High near-road CO concentrations can be important for 

those living in the near-road environment because virtually all of ambient CO infiltrates indoors. 

Hence, indoor exposure to ambient CO is determined by the CO concentration outside the building. 

Residents of the 17.9 million occupied homes located within approximately 90 m of a highway, 

railroad, or airport may be exposed to elevated ambient CO levels. However, CO concentration in 

the near-road environment has been shown to decrease sharply with downwind distance from a 

highway, wind direction, emission source strength (e.g., number of vehicles on a highway). Natural 

and urban topography also influence localized ambient CO levels. 

Recent exposure assessment studies support one of the main conclusions of the 2000 CO 

AQCD, that central-site ambient CO monitors may overestimate or underestimate individuals’ 

personal exposure to ambient CO because ambient CO concentration is spatially variable, 

particularly when analyzing exposures in the near-road environment. Exposure error may occur if the 

ambient CO concentration measured at the central-site monitor is used as an ambient exposure 

surrogate and differs from the actual ambient CO concentration outside a subject’s residence and/or 

worksite. For example, measurement at a “hot spot” could skew community exposure estimates 

upwards, and likewise measurement at a location with few nearby CO sources could skew exposure 

January 2010 3-97 
estimates downwards. Correlations across CO monitors 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. Typically, intersampler correlation ranges from 0.35 to 0.65 for monitors sited 

at different scales within a metropolitan area, although it can be greater than 0.8 in some areas. 

Health effects estimates from time-series epidemiologic studies are not biased by spatial variability 

in CO concentrations if concentrations at different locations are correlated in time. Additionally, 

exposure assessment is complicated by the existence of CO in multipollutant mixtures emitted by 

combustion processes. Because ambient CO exists in a mixture with volatile and reactive pollutants, 

the correlation between exposure to ambient CO and copollutants can vary substantially over time 

and across locations. For this reason, it is difficult to quantify the effects related specifically to CO 

exposure compared with those related to another combustion-related pollutant or mix of pollutants. 

In most circumstances, exposure error tends to bias a health effect estimate downward. Spatial and 

temporal variability not fully captured by ambient monitors and correlation of CO with copollutants 

are examples of sources of uncertainty that could widen confidence intervals of health effects 

estimates. 

January 2010 3-98 
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January 2010 3-108 
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 hemoglobin, resulting in COHb formation and 

consequent reduction in O2-carrying capacity of blood and impaired O2 delivery to tissues. Research 

on CO pharmacokinetics dates 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, 000907). Relevant new data are included when available. Recent models of 

Hb binding are characterized, as well as measurements of tissue CO concentrations using new 

methods of extraction. 

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, 010762; Tyuma et al., 

1981, 011226). 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. 

4.2. Carboxyhemoglobin Modeling 

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 observed COHb, with or without the 

inclusion of physiological predictors such as initial COHb levels and alveolar ventilation (VA). These 

methods were reviewed in depth in the previous AQCD (U.S. EPA, 2000, 000907). It is important to 

note that CO empirical regression 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, 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 4-1 
012416) and Ott and Mage (1978, 011124), as well as various others (Chung, 1988, 012749; Forbes 

et al., 1945, 012850; Selvakumar et al., 1992, 013750; Sharan et al., 1990, 003798; Singh et al., 

1991, 013583). Using a linear differential equation where ambient CO concentrations varied, 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- 

to 15-min periods (Ott and Mage, 1978, 011124). Other empirical models predict COHb as a 

function of exposure time (Sharan et al., 1990, 003798; Singh et al., 1991, 013583) or exposure time 

and altitude (Selvakumar et al., 1992, 013750). A comparison of empirical model predictions showed 

a wide disparity in predicted COHb values, highlighting the inaccuracy of these models outside of 

the conditions on which they were presented (Tikuisis, 1996, 080960). 

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, 011145). 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, 010696; 

Stewart et al., 1973, 012428). The CFK model describes a four-element, 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

O2HB

L

I

A

O2HB

L

2

0 C 2

CO t

b

V

PP

COD

1

COP

V

PP

COD

1

1

MHbO

OPCOHb

V

dt

COHbd V

Equation 4-1 

Vb blood volume in milliliters (mL) 

[COHb]t COHb concentration at time t in mL CO/mL blood, at standard temperature and pressure, dry (STPD) 

VCO endogenous CO production rate in mL/min, STPD 

[COHb]0 COHb concentration at time zero in mL CO/mL blood, STPD 

[O2Hb] O2Hb concentration in mL O2/mL blood, STPD 

PCO2 average partial pressure of O2 in lung capillaries in mmHg 

M Haldane coefficient representing the CO chemical affinity for Hb 

DLCO lung diffusing capacity of CO in mL/min/mmHg, STPD 

PB barometric pressure in mmHg 

PH2O saturation pressure of water vapor at body temperature in mmHg (47 mmHg) 

VA alveolar ventilation in mL/min, STPD 

PICO CO partial pressure in inhaled air in mmHg 

The linear CFK 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 extends the linear 

CFK equation to incorporate the interdependence of COHb and O2Hb levels since they are derived 

from the same pool of blood Hb. This interdependence can be modeled by substituting (1.38 Hb 

[COHb]) for O2Hb, where THb refers to the number of grams of Hb per mililiter of blood (Peterson 

and Stewart, 1975, 010696). The nonlinear CFK differential equation is as follows in Equation 4-2: 

January 2010 4-2 
⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎝

⎛

− +

+

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎝

⎛

− + − −=

• •

•

A

O2HB

L A

O2HB

L

b t

0 C 2 CO t

b

V

PP

COD

1

1

V

PP

COD

1

1

M)]COHb[]TH[38.1(

OP]COHb[ V

dt

]COHb[d V

Equation 4-2 

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, 013164). The linear equation prediction of 

COHb concentration at or below 6% will deviate by no more than ± 0.5% COHb 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, so that the 

relative importance of the CFK variables will change with the experimental conditions (McCartney, 

1990, 013162). 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. This analysis found that only FICO (shown in Figure 4-1) and VCO will not affect the rate 

at which equilibrium is reached (McCartney, 1990, 013162). 

January 2010 4-3 
Source: Adapted with Permission of the American Industrial Hygiene Association from McCartney (1990, 013162) 

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

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, 013162). 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 (667-7,500 ppm for 75 s to 5 min) were not accurately 

predicted by CFK modeling (Benignus et al., 1994, 013908; Tikuisis et al., 1987, 012219; Tikuisis et 

al., 1987, 012138). Consistently, the COHb value predicted by the nonlinear CFK overpredicted 

observed venous COHb (0.8-6%) and underpredicted arterial COHb (1.5-6.1%) and this disparity 

increased after exercise. Individual differences between arterial and venous COHb varied from 

2.3-12.1% COHb (mean, 6.2 ± 2.7% COHb), where the observed steady state COHb averaged ~14% 

and the observed arterial peak COHb averaged ~17.5% (Smith et al., 1994, 076564)(Benignus et al., 

1994, 013908). 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 

and venous blood and differences in cardiac output and lung wash-in. This discrepancy in predicted 

and observed COHb suggests that over a short period (<10 min) the arterial COHb levels that are 

delivered to tissues could be higher than what is predicted by the CFK equation. A modified CFK 

was created to adjust for these issues and produce a more accurate COHb prediction (Smith et al., 

1994, 076564). This expanded CFK model used multiple compartments to model the lung, arm 

circulation, and the rest of the body (quickly and slowly perfused tissues). This model was more 

January 2010 4-4 
accurate than the nonlinear CFK in predicting the individual peak or maximal values of arterial and 

venous COHb during CO uptake in the first 10 min after exposure. However, both the nonlinear 

CFK and this expansion produced accurate predictions several minutes after the 5-min exposure 

ended. The expanded model required the use of two parameters, VA and Vb, that were not measured 

individually or derived from the literature, and instead were estimated by adjustments between the 

simulations and experimental subject data. 

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, 

098272). The most recent adaptation to the CFK equation incorporates alveoli-blood and bloodtissue CO exchanges and mass conservation of CO at all times (Gosselin et al., 2009, 190946). This 

model has a single free parameter whose value is estimated from one data set; however, it better 

predicted COHb formation over a wide range of CO levels and several temporal scenarios (Stewart 

et al., 1970, 013972; Tikuisis et al., 1987, 012138; Tikuisis et al., 1987, 012219; Tikuisis et al., 1992, 

013592) compared to the linear CFK model. Like the linear CFK model, this modified model 

assumes a constant level of oxyhemoglobin. Sensitivity analysis of the model showed that the most 

important parameter influencing the level of COHb in this model is M, followed by PCO2 and VA. 

Ambient exposure scenarios were simulated with this model to determine the CO concentrations 

needed to reach certain COHb levels in humans from 3 months of age to 40-yr-old adults (Gosselin 

et al., 2009, 190946). The CO concentrations needed to achieve 2% COHb vary from 24.4-48.1 ppm 

for a 1-h exposure, from 11.1-13.1 ppm for an 8-h exposure, and from 9.8-10.1 ppm for a daily 

exposure. Infants (1 yr old) were most sensitive to CO concentrations, whereas newborns (3 mo old) 

required the highest CO concentration to reach 2% COHb. Newborns required a higher CO exposure 

partially because the values used in the model for the newborn blood Hb concentration (170 gHb/ 

Lblood) is higher than at infancy (115 gHb/Lblood) or adulthood (150 gHb/Lblood). The model was also 

used to simulate time profiles of COHb formation for two work week exposure scenarios in a 

healthy 40-yr-old man. Figure 4-2A represents a high exposure scenario where the work period is 

spent at 35 ppm and the rest of the time at 3 ppm. Figure 4-2B represents a lower exposure scenario 

where there is a constant 3 ppm exposure. Both figures consist of 5 days where 24 h are broken up 

into 3 consecutive 8-h periods: sleeping from 12 a.m. to 8 a.m.; working with light exercise from 8 

a.m. to 4 p.m.; and sitting from 4 p.m. to 12 a.m.. 

January 2010 4-5 
Source: Reprinted with Permission of Informa Healthcare from Gosselin et al. (2009, 190946) 

Figure 4-2. Simulated COHb formation for two 5-day workweeks. “The 24-h day consists of 3 

consecutive 8-h periods: sleeping from 12 a.m. to 8 a.m.; working (light exercise) 

from 8 a.m. to 4 p.m.; and sitting from 4 p.m. to 12 a.m. (A) High exposure: work 

period at 35 ppm and the rest of the time at 3 ppm. (B) Low daily exposure at 

3 ppm. The CO exposure periods are represented by dotted lines 

(----) and the COHb simulations by solid lines (—).” 

4.2.2. Multicompartment Models 

A third approach applied more recently to model COHb formation is the use of 

multicompartment or physiologically-based pharmacokinetic (PBPK) models. Cronenberger et al. 

(2008, 194085) described a two-compartment population-based model to describe and predict COHb 

pharmacokinetics from smoking. This model required a compartment for extravascular binding of 

January 2010 4-6 
CO to accurately predict COHb formation during multiple short and rapid inhalations followed by a 

period of no exposure, as occurs in smoking. 

A five-compartment PBPK 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, 193975; Bruce and Bruce, 2006, 

193980; Bruce et al., 2008, 193977). This model structure is illustrated in Figure 4-3 and includes 

the dynamics of CO storage in the lung and its dependence on ventilation and CO pressure of mixed 

venous blood, relaxes the assumption that 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 assumption that ventilation and average 

partial pressure of alveolar O2 (PAO2) are constant. Another limitation of this model is that some of 

the physiological parameters used in simulations are estimated through visual fits to the COHb 

profile and not from experimental or published data. 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, 

193975) compared the two models and found similar results for long-duration exposure settings 

(1,000 min); however, the multicompartment model predicted somewhat lower COHb levels 

compared to the CFK model during transient CO uptake conditions when using data taken from 

Peterson and Stewart (1970, 012416). 

Source: Adapted with Permission of Elsevier Science from Bruce and Bruce (2008, 193977) 

Figure 4-3. Overall structure of the Bruce and Bruce (2008, 193977) 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. 

January 2010 4-7 
A multicompartment model of the human respiratory system was developed using 

characteristics of the tissue representation of Bruce and Bruce (2003, 193975), and the lung 

representation described in Selvakumar et al. (1992, 013750) and Sharan (1999, 194673), which 

considered the exchanges of CO, O2, and CO2 (Neto et al., 2008, 194672). The model contains six 

compartments including: alveolar, pulmonary capillaries, arterial, venous, tissue capillary, and 

tissues (muscular and non-muscular). The model was applied to four simulated physical activity 

levels, resting, sitting, standing, and walking, in a healthy subject exposed to the urban atmosphere 

of a metropolitan area of Brazil. The highest and lowest COHb levels were simulated in the walking 

individual, suggesting that greater variability in COHb occurs at higher physical activity levels. 

4.2.3. Model Comparison 

A number of models have been presented which predict COHb formation over numerous 

exposure scenarios. These models are often compared to the CFK equation to determine the most 

accurate predictive model under certain exposure conditions. As was mentioned in Section 4.2.1, 

Tikuisis (1996, 080960) conducted a comparison of empirical model predictions that showed a wide 

disparity in predicted COHb values, highlighting the inaccuracy of these models outside of the 

conditions on which they were presented. Smith et al. (1990, 013164) compared the linear and 

nonlinear CFK equations and concluded that the linear CFK equation gives a good approximation 

(within 1%) to the nonlinear solution over a large range of values during CO uptake and over a 

somewhat smaller range during CO elimination. The linear equation prediction of COHb 

concentration at or below 6% will only differ ± 0.5% COHb from the nonlinear equation prediction. 

Additionally, the most recently modified CFK model (Gosselin et al., 2009, 190946) better predicted 

COHb formation over a wide range of CO levels (50-4,000 ppm) and several temporal scenarios 

(Stewart et al., 1970, 013972; Tikuisis et al., 1987, 012138; Tikuisis et al., 1987, 012219; Tikuisis et 

al., 1992, 013592) compared to the linear CFK model. Linear regression slopes between the 

simulated COHb values from Gosselin et al. (2009, 190946) and the observed experimental values 

were closer to 1 in all experimental scenarios, indicating a better fit to the observed data. When 

evaluating all validation studies the modified model had an estimated slope of 0.996 (95% CI: 

0.986-1.001) compared to 0.917 (95% CI: 0.906-0.927) using the linear CFK model. Bruce and 

Bruce (2003, 193975) compared their model to the CFK and found similar results for long-duration 

exposure settings (1,000 min [16.5 h]), however, their multicompartment model predicted somewhat 

lower COHb levels over transient CO uptake conditions when using data taken from Peterson and 

Stewart (1970, 012416). The Bruce and Bruce model better predicts However, there has not been a 

quantitative comparison of the recent multicompartment models (Bruce and Bruce, 2003, 193975; 

Neto et al., 2008, 194672) and the improved CFK equation models (Gosselin et al., 2009, 190946; 

Smith et al., 1994, 076564) to determine which is most accurate in predicting COHb levels under 

exposure scenarios that include occasional peak concentrations. The nonlinear and linear CFK 

equations remain the most extensively validated and applied models for COHb prediction. 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. However, there has not 

been a quantitative comparison of the recent multicompartment models (Bruce and Bruce, 2003, 

193975)(Neto et al., 2008, 194672) and the improved CFK equation models (Smith et al., 1994, 

076564)(Gosselin et al., 2009, 190946) to determine which is most accurate in predicting COHb 

levels under exposure scenarios that include occasional peak concentrations. The nonlinear and 

linear CFK equations remain the most extensively validated and applied models for COHb 

prediction. 

4.2.4. Mathematical Model Usage 

As no new data have become available on the distribution of COHb levels in the U.S. 

population since large-scale nationwide surveys – e.g., National Health and Nutrition Examination 

Survey II (NCHS; et al., 1982, 011442) – and human exposure field studies – e.g., Denver, CO, and 

Washington, DC (Akland et al., 1985, 011618) – were conducted in the 1970s and 1980s, 

mathematical models are used to predict the resulting COHb levels from various CO exposure 

scenarios. Table 4-1 illustrates the predictions of venous COHb after 1, 8, or 24 h of CO exposure at 

a range of concentrations in a healthy adult human at rest (VA = 6 L/min; DLCO = 20 

January 2010 4-8 
[mL/min]/mmHg), during light exercise (VA = 15 L/min; DLCO = 34 [mL/min]/mmHg), and during 

moderate exercise (VA = 22 L/min; DLCO = 43 [mL/min]/mmHg). 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, 193859; Benignus et al., 2006, 151344). This dynamic whole body model uses the nonlinear 

CFK equation with modifications presented in Smith et al. (1994, 076564). The contribution of 

alveolar ventilation and lung diffusion to the changes in COHb levels is discussed in Section 4.3.1.2. 

Increased ventilation leads to an increased rate of CO uptake, causing COHb levels to reach 

equilibrium earlier. Also, increased ventilation leads to a decrease in steady state COHb levels due to 

increased CO expiration. For example, 35 ppm CO exposure at moderate exercise (22 L/min) results 

in a lower 24-h COHb saturation (4.73%), compared to COHb saturation from 35 ppm CO at rest 

(5.03%) (Table 4-1). Whereas, after 1 h, COHb levels are still increasing following exposure at all 

levels of exercise and have not reached steady state, thus the greater uptake from increased 

ventilation leads to initially elevated COHb in higher ventilation situations. 

Endogenous CO production varies as described in Section 4.5 but generally results in <1% 

COHb, with a QCP modeled value of 0.27% at time zero. The rate of endogenous CO production 

was set at 0.007 mL/min for this simulation, whereas both higher and lower values have been 

reported (Coburn et al., 1966, 010984) (Section 4.5). Table 4-1 illustrates that 35 ppm CO for 1-h 

results in between 0.9-1.9% COHb and 9 ppm CO for 8-h results in between 1.1-1.3% COHb, 

depending upon activity level. Also, this table shows that low concentration CO exposure over 

several hours can result in equivalent COHb levels compared to higher concentration, acute 

exposure. For example, in a resting condition without additional baseline COHb, COHb resulting 

from 35 ppm for 1 h (0.89%) is approximately equivalent to 6 ppm for 8 h (0.83%) or 4 ppm for 24 h 

(0.82%). 

Table 4-1. Predicted COHb levels resulting from 1, 8, and 24 h CO exposures in a modeled human 

at rest (VA = 6 L/min; DLCO = 20 (mL/min)/mmHg; VCO = 0.007 mL/min; 

initial COHb = 0.27%; Hb = 0.15 g/mL), during light exercise (VA = 15 L/min; 

DLCO = 34 (mL/min)/mmHg), and during moderate exercise (VA = 22 L/min; DLCO = 

43 (mL/min)/mmHg). The QCP model used a dynamic nonlinear CFK with the Smith 

et al. (1994, 076564) COHb algorithm and affinity constant M = 218. 

1 h 8 h 24 h 

CO (ppm) 6 L/min 15 L/min 22 L/min 6 L/min 15 L/min 22 L/min 6 L/min 15 L/min 22 L/min 

2 0.30 0.30 0.29 0.45 0.38 0.35 0.54 0.40 0.36 

3 0.31 0.33 0.34 0.54 0.51 0.48 0.68 0.54 0.49 

4 0.33 0.36 0.62 0.64 0.64 0.62 0.82 0.69 0.63 

6 0.36 0.44 0.48 0.83 0.90 0.88 1.10 0.97 0.91 

9 0.42 0.55 0.63 1.12 1.29 1.27 1.52 1.39 1.31 

15 0.53 0.77 0.92 1.69 2.05 2.06 2.35 2.22 2.12 

24 0.70 1.10 1.35 2.55 3.19 3.22 3.57 3.45 3.31 

35 0.89 1.50 1.89 3.58 4.55 4.60 5.03 4.91 4.73 

The QCP model incorporating the Smith et al. (1994, 076564) COHb algorithm was also used 

to simulate population exposure scenarios including various commuting concentrations (Figure 4-4) 

and endogenous production rates (Figure 4-5). Commuting concentrations were modeled since the 

highest ambient CO exposure levels are generally observed during transit (Section 3.6.6.2). Figure 

4-4 presents simulated COHb levels in a healthy adult throughout the second of 5 modeled days 

containing a 60-min commute at various CO concentrations. The U.S. Census Bureau estimates that 

5% of the population commutes in automobiles for 60 or more minutes to work daily 

(U.S. Census Bureau, 2008, 194013) and exposure studies have reported in-vehicle transit 

concentrations up to 50 ppm (Abi-Esber and El-Fadel, 2008, 190939; Duci et al., 2003, 044199). 

However, U.S. studies have reported in-vehicle concentrations of <6 ppm, although peak 

January 2010 4-9 
concentrations in congested urban areas have been reported to be higher than 50 ppm (Rodes et al., 

1998, 010611)(Riediker et al., 2003, 043761). CO concentrations during commuting lead to spikes in 

COHb in this model scenario with a 1% COHb increase over the initial COHb (0.3%) after 50 ppm 

exposure. Figure 4-4 also illustrates that the COHb saturation after CO exposure from commuting is 

not fully eliminated by the next commuting period. Modeling successive days results in the same 

pattern and degree of COHb formation, indicating no accumulation of COHb over time. 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 360 720 1080 1440

Time (min)

COHb (%)

2 ppm 10 ppm 20 ppm 50 ppm

Figure 4-4. Predicted COHb levels in healthy commuters exposed to various CO 

concentrations over a 60-min commute twice a day. Ambient CO concentration 

not during commuting time was 1 ppm. The activity pattern simulated: (1) 

sleeping for 8 h; (2) standing and light exercise for 30 min; (3) sitting during a 

60-min commute; (4) light exercise for 8.5 h; (5) sitting during a second 60-min 

commute; (6) moderate exercise for 60 min; and (7) sitting for 4 h. The graph 

illustrates the second day simulated under these conditions.1

Figure 4-5 presents simulated COHb levels in adults with various endogenous CO production 

rates throughout the second of 5 modeled days containing a 60-min commute at 20 ppm CO. The 

normal endogenous rate of CO production in young adult males with an average COHb of 0.88% 

averages 0.007 mL/min (18.7 ± 0.8 μmol/h) (Coburn et al., 1963, 013971). However, a number of 

diseases and conditions described in Section 4.5 can affect this production rate. Patients with 

 

1

 Sleeping/lying human parameters: VA- 3.8 L/min, VT- 467 mL, VD- 147 mL, VCO- 0.007 mL/min, DLCO- 17.9 mL/min/mmHg, M- 218, 

initial. COHb- 0.27%. Sitting human parameters: VA- 5.2 L/min, VT- 560 mL, VD- 155 mL, VCO- 0.007 mL/min, DLCO- 18 

mL/min/mmHg. Standing human parameters: VA- 6.4 L/min, VT- 636 mL, VD- 161 mL, VCO- 0.007 mL/min, DLCO- 19.3 mL/min/mmHg. 

Light exercise (1 MPH, 32 W) human parameters: VA- 13.4 L/min, VT- 994 mL, VD- 218 mL, VCO- 0.007 mL/min, DLCO- 30.4 

mL/min/mmHg. Heavy exercise (3 MPH, 96 W) human parameters: VA- 31.4 L/min, VT- 1642 mL, VD- 241 mL, VCO- 0.007 mL/min, 

DLCO- 49.6 mL/min/mmHg. 

January 2010 4-10 
hemolytic anemia have endogenous CO production rates ranging from 0.012 to 0.053 mL/min 

(31-143 μmol/h) (Coburn et al., 1966, 010984). The venous COHb levels in these same patients 

ranged from 0.77 to 2.62%. 

0

0.5

1

1.5

2

2.5

3

3.5

0 360 720 1080 1440

Time (min)

COHb (%)

0.007 mL/min 0.02 mL/min 0.04 mL/min 0.06 mL/min

Figure 4-5. Predicted COHb levels due to various endogenous CO production rates. The 

activity pattern presented in Figure 4-4 was used. Ambient CO concentration not 

during commuting time was 1 ppm and commuting CO concentration was 

20 ppm. The graph illustrates the second day simulated under these conditions. 

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 representing the product of DLCO and the mean 

alveolar pressure (PACO). The importance of dead space volume, gas mixing and homogeneity, and 

January 2010 4-11 
ventilation/perfusion matching were discussed in depth in the 2000 CO AQCD (U.S. EPA, 2000, 

000907). 

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 molecular 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 et al., 1973, 014336). Then, CO diffuses across the air-blood interface into plasma 

and subsequently into red blood cells (RBC), binding 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, 193759; 

Gibson and Roughton, 1955, 193941; Reeves and Park, 1992, 193847; Roughton and Forster, 1957, 

193862). The formation rate and level of COHb depends upon pCO, pO2 in the air, time of exposure, 

and the ventilation rate (Roughton and Forster, 1957, 193862). 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, 1970, 013916). Considerable concentrations of CO have been measured in 

spleen, lung, kidney, liver, muscle, and heart (Vreman et al., 2005, 193786; Vreman et al., 2006, 

098272), whereas less CO is localized to fatty tissues, such as adipose and brain. 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, 1970, 013916). Transport pathways and body stores of CO are shown in Figure 4-6. 

Source: Adapted with Permission of Wiley-Blackwell from Coburn (1967, 011144) 

Figure 4-6. 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 alveolocapillary membrane, through the plasma, across the RBC membrane and into the RBC stroma, where 

January 2010 4-12 
CO binding to Hb rapidly occurs. Membrane and blood phase transfer are governed by physicochemical 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 gradient for CO is usually higher than the blood-air 

gradient; therefore, CO uptake will be a proportionately faster process than CO elimination. 

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, 180430; Hsia, 2002, 193857). DLCO consistently decreases after intense bouts of 

exercise, likely due to the redistribution of blood volume to the periphery (Hanel et al., 1997, 

193918; Manier et al., 1991, 193979). 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 Q increases, increased capillary hematocrit, and more homogeneous 

distribution of capillary erythrocytes (Hsia, 2002, 193857). 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, 012411). 

DLCO is also altered by a number of diseases. Decreased DLCO is evident in patients with 

restrictive lung disease (i.e., decreased lung volumes) since a loss of lung tissue leads to a loss of 

functional lung units. DLCO also shows a good correlation with the severity of restrictive lung 

disease (Arora et al., 2001, 186713). Conditions affecting DLCO vary and include chronic 

obstructive pulmonary disease (Terzano et al., 2009, 108046), ulcerative colitis (Marvisi et al., 2000, 

186703; Marvisi et al., 2007, 186702), severe gastroesophageal reflux (Schachter et al., 2003, 

186707), beta thalassemia (Arora et al., 2001, 186713), thoracic or abdominal aortic aneurysm 

(Sakamaki et al., 2002, 186706), pulmonary arterial hypertension (Proudman et al., 2007, 186705), 

and chemotherapy for breast cancer (Yerushalmi et al., 2009, 186711). Diseases affecting CO 

kinetics and DLCO are also discussed in Section 4.4.4. 

4.3.2. 

4.3.2.1.

Tissue Uptake 

Respiratory Tract 

The upper respiratory tract contributes little to the overall CO uptake. The lung has nearly 

constant exposure to CO; however, relatively little CO diffuses into the tissue except 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, 011196) or in the monkey oronasal cavity after high CO 

exposure (Schoenfisch et al., 1980, 011404). 

4.3.2.2. 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, 

193914; Rodkey et al., 1969, 008151; Roughton, 1970, 013931). This would happen when breathing 

air containing 21% O2 and 960 ppm CO. This concept was presented by Haldane and Smith (1895, 

010538) and later represented as the Haldane constant M (210-250) in the Haldane equation by 

Douglas, Haldane, and Haldane (1912, 013965). M is relatively unaffected by changes in 

physiological pH, CO2, temperature, or 2,3-diphosphoglycerate: 

( ) ÷ 2 = × ÷ pOpCOMHbOCOHb 2

Equation 4-3 

The Hb association rate for CO is 10% slower than O2 and occurs in a cooperative manner 

(Chakraborty et al., 2004, 193759; Sharma et al., 1976, 193766). Hb is composed of four globin 

January 2010 4-13 
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, 193867). 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, 

193759). The half-time of dissociation reaction is about 11 s at 37°C (Holland, 1970, 193856). 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, 1988, 012519). Also, 

COHb equilibrium within the blood stream is not instantaneous. Men exposed to brief (~5 min) 

high-dose CO had an initial delay of 1-2 min in the appearance of venous COHb after the start of CO 

inhalation (Benignus et al., 1994, 013908; Smith et al., 1994, 076564). Additionally, arterial COHb 

concentrations were considerably higher than venous concentrations during CO exposure; however, 

they converged within 2-10 min after the end of exposure, as venous and arterial blood mixed. 

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, 013931). 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 4-7 (as 

explained in the 2000 CO AQCD) illustrates that in 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, for 

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). 

January 2010 4-14 
Source: U.S. EPA (1991, 017643) 

Figure 4-7. 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, 012436). 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. 

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, 

013359). The association rate 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, 016289), causing CO to be retained and possibly stored in the muscle. CO levels have been 

measured in human muscle and heart tissues with <2% COHb concentrations at background levels 

January 2010 4-15 
(15 and 31 picomole [pmol] CO/mg ww, respectively) (Vreman et al., 2006, 098272) (Table 4-2). 

Under conditions of CO asphyxiation, tissue concentrations increased 17-18 fold (265 and 527 pmol 

CO/mg ww muscle and heart tissue, respectively); however, heart tissue concentrations varied 

widely between individuals. Mouse muscle did not show this increase after exogenous CO exposure 

(Vreman et al., 2005, 193786). This may be due to the fact that human muscle has a 15-fold higher 

concentration of myoglobin protein than mouse muscle (Weller et al., 1986, 187298). 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, 193975). COMb concentrations in the heart and skeletal muscle increase with work 

load, due to a higher relative rate of CO binding to Mb relative to Hb. This causes an increase in 

COMb/COHb that is not seen at rest (Sokal et al., 1984, 011591). 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, 037513). 

Table 4-2. CO concentration in pmol/mg wet weight tissue and fold tissue CO concentration 

changes (normalized to background tissue concentrations) – human. 

Exposure Adipose Brain Muscle Heart Kidney Lung Spleen Blood % COHb 

Background 3 ± 1 3 ± 3 15 ± 9 31 ± 23 23 ± 18 57 ± 59 79 ± 75 165 ± 143 1.5 ± 1.2 

Fire 5 ± 4 

[1.7] 

7 ± 5 

[2.3] 

24 ± 16 

[1.6] 

54 ± 33 

[1.7] 

27 ± 11 

[1.2] 

131 ± 127 

[2.3] 

95 ± 69 

[1.2] 

286 ± 127 

[1.7] 

3.8 ± 3.2 

[2.5] 

Fire + CO 18 ± 29 

[6.0] 

17 ± 14 

[5.7] 

168 ± 172 

[11.2] 

128 ± 63 

[4.1] 

721 ± 427 

[31.3] 

1097 ± 697 

[19.2] 

2290 ± 1409 

[29.0] 

3623 ± 1975 

[22.0] 

40.7 ± 28.8 

[27.1] 

CO asphyxiation 25 ± 27 

[8.3] 

72 ± 38 

[24.0] 

265 ± 157 

[17.7] 

527 ± 249 

[17.0] 

885 ± 271 

[38.5] 

2694 ± 1730 

[47.3] 

3455 ± 1347 

[43.7] 

5196 ± 2625 

[31.5] 

56.4 ± 28.9 

[37.6] 

Source: Reprinted with Permission of Wiley-Blackwell from Vreman et al. (2006, 098272)

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, 180418). This value is estimated to be 0.3-0.4% 

min-1 or 0.24 mL/min (Bruce and Bruce, 2003, 193975; Prommer and Schmidt, 2007, 180421). The 

equilibration rate from blood to tissue is uncertain. 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, 193980). Table 4-2

contains tissue CO concentrations from humans under different CO exposure conditions. The 

distribution of CO between the different human 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, 

098272). Consistently, the spleen, lung, and kidney had the highest measured CO concentration and 

the most dramatic increases over basal levels; the brain and adipose had the lowest CO 

concentrations. In addition to these fatty tissues, the muscular tissues including the heart and skeletal 

muscle had similarly low increases over background CO levels. This pattern was also found in 

rodents exposed to exogenous CO; however, increased endogenous CO produced after heme 

administration did not follow this pattern of uptake (Vreman et al., 2005, 193786). Increased 

endogenous CO production led to moderately increased CO present in the lung, heart, liver, and 

spleen and no change in CO concentration in the testes, intestine, muscle, brain, and kidney. The 

spleen and liver have an abundance of HO-1 expression and are involved in the catabolism of heme, 

thus it is expected to have elevated CO concentrations in these organs after heme treatment. Also, 

elevated CO in the lung is not surprising since it is the site of CO excretion. The tissues analyzed in 

these studies were blanched before analysis; however, contamination of the tissue sonicates with 

blood from the vessels within each organ is a possible source of error. The measurements were 

presented by the authors as minimum tissue CO concentrations, due to the possibility of rapid loss of 

CO from blood and tissue exposed to the atmosphere, light, and elevated temperature (Chace et al., 

1986, 012020; Ocak et al., 1985, 011641). These results are not consistent with older papers, 

January 2010 4-16 
suggesting that negligible retention of CO occurs in the liver or brain (Sokal et al., 1984, 011591; 

Topping, 1975, 193784). 

4.3.3. Pulmonary and Tissue Elimination 

Blood COHb concentrations are generally considered to have a monotonically decreasing, 

second-order (logarithmic or exponential) elimination rate from equilibrium. However, more recent 

reports have presented evidence for a biphasic washout curve, especially after brief CO exposure 

(Figure 4-8) (Bruce and Bruce, 2006, 193980; Shimazu et al., 2000, 016420; Wagner et al., 1975, 

010989). This event is modeled by a two-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, 010803). The biphasic curve is more obvious after shortduration CO exposure (<1 h), whereas longer CO exposure (≥ 5 h) 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, 016420). Differences in elimination kinetics could also be a result of the 

variation in CO exposure duration (Weaver et al., 2000, 016421). 

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 

inter-individual variability (Bruce and Bruce, 2006, 193980; Landaw, 1973, 010803; Shimazu, 2001, 

016331). 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, 080895). In addition, in a series of 

poisoning cases, 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, 016421). On the contrary, in 

modeling the nonlinear kinetics of CO, a subject with a higher initial COHb will detoxify and 

eliminate CO more rapidly (Gosselin et al., 2009, 190946). Similarly, it has been shown that the 

absolute elimination rates are associated positively with the initial concentration of COHb, however 

the relative rate of elimination, expressed as a percentage decline in COHb% after a measured time, 

is independent of the initial COHb concentration (Wagner et al., 1975, 010989). 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 min, but may be shorter in adult 

females. With inhalation of normobaric 40% O2, the half-life falls to 75 min and further to 21 min 

when breathing 100% O2 because of greater competition for Hb by O2 (Landaw, 1973, 010803). 

Another study reports the half-life falls to 74 min (mean) after breathing 100% O2, although the 

range in this particular study was 26-148 min (Weaver et al., 2000, 016421). In addition, COHb halflife will fall further after normocapnic hyperoxic hyperpnea (i.e., hyperventilation while maintaining 

normal CO2 pressure in high O2) (Takeuchi et al., 2000, 005675). 

January 2010 4-17 
1

10

100

0 30 60 90 120 150 180

Time (min)

COHb (%)

A

B

Source: Adapted with Permission of Lippincott Williams & Wilkins from Shimazu et al. (2000, 016420) 

Figure 4-8. Changes in blood COHb after exposure to CO for a few minutes (A) or several 

hours (B), representing the biphasic nature of CO elimination. Note: y-axis is logscale. 

4.3.4. COHb Analysis Methods 

Blood COHb saturation can be analyzed using numerous methods with various benefits and 

limitations. The most popular current techniques include gas chromatography (GC) and 

spectrophotometry, specifically using CO-oximeters. CO-oximeters are commonly used because they 

require little sample preparation and simultaneously measure COHb, O2Hb, methemoglobin, and 

total hemoglobin concentration. However, at low concentrations of COHb relevant to ambient 

exposure (<5%), CO-oximeters overestimate COHb levels determined by GC (Mahoney et al., 1993, 

013859; Widdop, 2002, 030493). Conversely, at higher COHb levels (>5%), CO-oximeters will 

underestimate COHb concentrations. In addition to the inaccuracy of the CO-oximeters, some 

studies report considerable imprecision in the results. Also, numerous substances or conditions can 

interfere with CO-oximeter measurements (i.e., temperature, bilirubin, fetal hemoglobin). 

Alternatively, GC is an accurate, precise, highly specific analysis method and is generally used as the 

reference method for COHb analysis. GC requires the CO incorporated into blood or tissue samples 

to first be released using a liberating agent such as potassium ferricyanide or sulfosalicylic acid 

(Vreman et al., 2005, 193786; Vreman et al., 2006, 098272), and then measured directly or 

indirectly. This methodology is more complex and time-consuming than spectrophotometry. In either 

analysis method, it is important to remember that COHb measured at one site in the body does not 

necessarily represent whole body CO distribution. 

CO can also be measured directly in air or breath samples by using an electrochemical sensor 

that depends on the electrical signal generated by the oxidation of CO. There are conflicting reports 

on the correlation of exhaled CO (COex) with COHb. Multiple reports present positive correlation 

coefficients (r) ranging from 0.92 and 0.98 in smoking subjects (Jarvis et al., 1980, 011813; Jarvis et 

al., 1986, 012043; Landaw, 1973, 010803). Positive linear correlations have also been shown in 

diseased patients with increased COHb (De las Heras et al., 2003, 194087). Others have reported no 

correlation between low level COHb and COex and have suggested less correlation exists at the 

lower levels of COex relevant to ambient exposures (Horvath et al., 1998, 087191; Scharte et al., 

2000, 194112). Finally, CO is endogenously produced in the nose and paranasal sinus which may 

contribute to COex concentrations (Andersson et al., 2000, 011836). 

January 2010 4-18 
4.4. Conditions Affecting Uptake and Elimination 

4.4.1. Physical Activity 

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. Also, exercise will improve the ventilation/perfusion ratio 

in the lung and mobilize RBC reserves from the spleen. The majority of these changes facilitate CO 

uptake and transport, by increasing gas exchange efficiency. Likewise, the COHb elimination rate 

increases with physical activity, causing a decrease in COHb half-life (Joumard et al., 1981, 011330). 

During a transition period from rest to exercise while exposed to CO (500 ppm/10 min), the 

diffusing capacity and CO uptake were reported to rise faster than O2 consumption for each exercise 

intensity (Kinker et al., 1992, 086328). The two physiological variables that are most influential in 

the formation of COHb are alveolar ventilation and cardiac output. However, exercise did not affect 

the ability of the CFK equation to predict COHb saturation as long as appropriate variables were 

used for model analysis (Tikuisis et al., 1992, 013592). 

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 CO 

AQCD and other documents (U.S. EPA, 1978, 086321). 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 hypobaric 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, 193759). Sea level residents exposed to high 

altitude (3,658-5,800 m) for short or long visits (<1 year) experience negligible or minor changes in 

DLCO, although these changes in DLCO can be accounted for by polycythemia or increased red 

blood cell count and by the increased rate of reaction of carbon monoxide with hemoglobin due to 

hypoxia (West, 1962, 199513)(Guleria et al., 1971, 199518). Breathing CO (9 ppm) at rest at altitude 

produced higher COHb compared to sea level (McGrath et al., 1993, 013865), whereas high altitude 

exposure with exercise caused a decrease in COHb levels versus similar exposure at sea level 

(Horvath et al., 1988, 012725). 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, 021096). 

As the length of stay increases at high altitude, acclimatization occurs, inducing 

hyperventilation, polycythemia, and increased tissue capillarity and Mb content in skeletal muscle, 

which could also favor increased CO uptake. The DLCO of sea level natives who are long-term 

residents at altitude (3,100 m) increases from sea level values (Cerny et al., 1973, 199736). 

Additionally, natives of high altitude (3,100-3,658 m) have increased DLCO compared to natives of 

sea level or sea level natives that stay at high altitude (DeGraff et al., 1970, 199737) (Guleria et al., 

1971, 199518). This has been attributed to high pulmonary capillary blood volume and membrane 

diffusing capacity, and altered lung structure. Most of the early adaptive changes gradually revert to 

sea level values after individuals return to sea level. However, differences in people raised at high 

altitude persist even after reacclimatization to sea level (Hsia, 2002, 193857). For example, altitude 

natives (3,658 m) staying at sea-level still have increased DLCO compared to sea level natives which 

suggests a permanent change in the lung structure resulting in a larger diffusing surface area (Guleria 

et al., 1971, 199518). 

January 2010 4-19 
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, 011330; Klasner et al., 1998, 087196). After infancy, the COHb half-life increases with age, 

nearly doubling between 2 and 70 yr (Joumard et al., 1981, 011330). The rate of this increase in CO 

elimination is very rapid in the growing years (2-16 yr of age), but slows beyond adolescence. 

Alveolar volume and DLCO increase with increasing body length of infants and toddlers (Castillo et 

al., 2006, 193234), 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, 079363). 

COHb concentrations are generally lower in female subjects than in male subjects (Horvath et 

al., 1988, 012725), 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, 011330). However, women do 

have a higher rate of endogenous production while in the progesterone phase of the menstrual cycle 

and during pregnancy (Section 4.5). 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, 079363). 

Women also tended to be more resistant to altitude hypoxia (Horvath et al., 1988, 012725). 

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 DLCO (Pesola et al., 2004, 193842; Pesola et al., 2006, 

193855). 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, 193923). 

4.4.3.1. Fetal Pharmacokinetics 

Inhaled CO by pregnant animals quickly passes the placental barriers and enters the fetal 

circulation (Longo, 1977, 012599). Fetal CO pharmacokinetics do not follow the same kinetics as 

maternal CO exposure, making it difficult to estimate fetal COHb based on maternal levels. Fetal 

COHb will vary as a function of maternal exposure but will also depend upon the rate of endogenous 

fetal CO production (Section 4.5), placental diffusing capacity of CO, the relative affinity of fetal Hb 

for CO compared to O2, and the affinity of fetal blood for O2 (Longo, 1970, 013922). Human fetal 

Hb has a higher affinity for CO than adult Hb, where the ratio of fetal COHb to maternal COHb at 

steady state in humans is approximately 1.11 (Longo, 1970, 013922)(Di Cera et al., 1989, 

193998)(Hayde et al., 2000, 201602). Maternal and fetal COHb concentrations have been modeled 

as a function of time using a modified CFK equation (Hill et al., 1977, 011315). At steady-state 

conditions, the fetal COHb is up to 10-15% 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 5.75%. The fetal CO 

uptake lags behind the maternal for the first few hours but later may overtake the maternal values 

(Figure 4-9). Fetal COHb equilibrium may not be reached for 36-48 h after exposure. Similarly, 

during washout, the fetal COHb levels are maintained for longer, with a half-life of around 7.5 h 

versus the maternal half-life of around 4 h (Longo and Hill, 1977, 010802). 

January 2010 4-20 
Source: Reprinted with Permission of the American Physiological Society from Hill et al. (1977, 011315) 

Figure 4-9. Predicted maternal and fetal COHb during periodic exposure to CO (50 ppm for 

16 h followed by 0 ppm for 8 h ). 

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. 

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-Ståhl et al., 1976, 086306; Cavallin-Ståhl et al., 1976, 193239). 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 (VA/Q) 

inequality (Marthan et al., 1985, 086334), which could slow both CO uptake and elimination. 

Patients with pulmonary sarcoidosis, a restrictive lung disease, may also have a decrease in lung 

volumes, a loss of DLCO, and gas exchange abnormalities during exercise, including decreased 

arterial oxygen pressure (PaO2) and increased alveolar-arterial oxygen pressure difference (Lamberto 

et al., 2004, 193845). 

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 

January 2010 4-21 
100 ppm CO, compared to clean air (Kleinman et al., 1998, 047186). Hyperlipidemic patients may 

have decreased CO diffusion capacity, a loss of VA/Q gradient, and a decrease in PaO2 (Enzi, 1976, 

195794) (Section 5.2). 

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 due to endogenous CO production from heme protein 

catabolism. In the normal degradation of RBC Hb, the porphyrin ring of heme is broken at the 

α-methene bridge by HO. HO is co-localized with NADPH-flavoprotein reductase and biliverdin 

reductase on the endoplasmic reticulum, where it catabolizes heme in an O2 and NADPH-dependent 

manner to biliverdin, ferrous iron, and CO. Biliverdin is then further broken down by biliverdin 

reductase into bilirubin, a powerful endogenous antioxidant. HO mediated metabolism functions as 

the rate-limiting enzyme step in heme degradation and endogenous CO production (Wu and Wang, 

2005, 180411). Three isoforms of HO exist, but HO-1 is the only inducible form (Maines and 

Kappas, 1974, 193976; Maines et al., 1986, 193978; McCoubrey et al., 1997, 016715). 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, 180411). 

The major site of heme catabolism, and thus the major organ of CO production, is the liver, 

followed by the spleen, brain, and erythropoietic system (Berk et al., 1976, 012603). 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 18.8 μmol/h (0.42 mL/h or 0.007 mL/min) and produces 

between 400-500 μmol CO per day (Coburn et al., 1963, 013971; Coburn et al., 1964, 013956; 

Coburn et al., 1966, 010984) (Figure 4-10). The endogenous rate of production varied somewhat 

within individuals measured on multiple days (±4.5 μmol/h and ±0.35% COHb) (Coburn et al., 

1966, 010984). However, these measurements of day-to-day CO production variability were 

comparable to the equipment measurement error reported (±3.1 μmol/h). The endogenous rate of CO 

formation varies between different tissues, ranging from 0.029 nmol/mg protein/h in chorionic villi 

of term human placentas to 0.28 nmol/mg protein/h in cultured rat olfactory receptor neurons and rat 

liver perfusate (Marks et al., 2002, 030616). However, these estimations are uncertain 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, 

011836). It is also important to note that increased endogenous CO production does not universally 

lead to an increase in COHb saturation. 

January 2010 4-22 
Study Condition 

Coburn et al. (1963, 013971) Adult male 

Delivoria-Papadopoulos et al. (1974, 086316) Female - Progesterone phase 

Delivoria-Papadopoulos et al. (1974, 086316) Female - Estrogen phase 

Longo (1970, 013922) Pregnancy 

Longo (1970, 013922) Fetal 

Longo (1970, 013922) Postpartum - 24 h 

Longo (1970, 013922) Postpartum - day 4 

Coburn et al. (1966, 010984) Hemolytic anemia min 

Coburn et al. (1966, 010984) Hemolytic anemia max 

Zegdi et al. (2002, 037461) Sepsis survivors 

Scharte et al. (2006, 194115) Cardiac disease patients max 

Scharte et al. (2006, 194115) Critically ill on dialysis max 

 0 0.01 0.02 0.03 0.04 0.05 0.06

Endogenous CO production (mL/min)

Figure 4-10. Representative estimates of endogenous CO production rates resulting from 

various conditions and diseases. 

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, 012603). All of these sources result in a normal blood COHb concentration 

between 0.3 and 1% (Coburn et al., 1965, 011145). The level of endogenous production can be 

changed by drugs or a number of physiological conditions that alter RBC destruction, other 

hemoprotein breakdown, or HO-1 expression and activity (Figure 4-10). Nicotinic acid (Lundh et al., 

1975, 086332), allyl-containing compounds (acetamids and barbiturates) (Mercke et al., 1975, 

086303), diphenylhydantoin (Coburn, 1970, 010625), progesterone (Delivoria-Papadopoulos et al., 

1974, 086316), contraceptives (Mercke et al., 1975, 086308), and statins (Muchova et al., 2007, 

194098) can increase CO production. Compounds such as carbon disulfide and sulfur-containing 

chemicals (parathion and phenylthiourea) will increase CO by acting on P450 system moieties 

(Landaw et al., 1970, 012605). The P450 system may also cause large increases in CO produced 

from the metabolic degradation of dihalomethanes, leading to very high (>10%) COHb levels (Bos 

et al., 2006, 194084; Manno et al., 1992, 013707) that can be further enhanced by prior exposure to 

hydrocarbons or ethanol (Pankow et al., 1991, 013551; Wirkner et al., 1997, 082642). Minor sources 

of endogenous CO include auto-oxidation of phenols, flavonoids, and halomethanes, photo-oxidation 

of organic compounds, and lipid peroxidation of cell membrane lipids (Rodgers et al., 1994, 

076440). 

Women experience fluctuating COHb levels throughout menstruation when endogenous CO 

production doubles in the progesterone phase (0.62 mL/h versus 0.32 mL/h in estrogen phase) 

(Delivoria-Papadopoulos et al., 1974, 086316; Mercke and Lundh, 1976, 086309). Similarly, 

endogenous CO production increases during pregnancy (0.92 mL/h) due to contributions from fetal 

endogenous CO production (0.036 mL/h) and altered hemoglobin metabolism (Hill et al., 1977, 

011315; Longo, 1970, 013922). 

Any disturbance in RBC hemostasis by accelerated destruction of hemoproteins will lead to an 

increased production of CO (Figure 4-11 and Figure 4-12). 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, 012386; Hampson and Weaver, 2007, 

190272; Meyer et al., 1998, 047530; Solanki et al., 1988, 012426; Sylvester et al., 2005, 191954). 

Patients with hemolytic anemia exhibit COHb levels at least two- to threefold higher than healthy 

individuals and CO production rates two- to eightfold higher (Coburn et al., 1966, 010984). Recent 

studies report COHb levels measured by CO-oximeter that are elevated to levels between 4.6% and 

9.7% due to drug-induced hemolytic anemia (Hampson and Weaver, 2007, 190272) and between 

3.9% and 6.7% due to an unstable hemoglobin disorder (Hb Zürich) (Zinkham et al., 1980, 011435). 

January 2010 4-23 
Endogenous CO production rate varied from 0.70 to 3.18 mL/h in anemic patients (Coburn et al., 

1966, 010984). 

Study Condition 

Hampson and Weaver (2007, 190272) Hemolytic anemia max 

Hampson and Weaver (2007, 190272) Hemolytic anemia min 

Zinkham et al. (1980, 011435) Hb Zürich min 

Zinkham et al. (1980, 011435) Hb Zürich min 

Coburn et al. (1966, 010984) Hemolytic anemia max 

Coburn et al. (1966, 010984) Hemolytic anemia min 

Tran et al. (2007, 194120) Liver transplant 

De Las Heras et al. (2003, 194087) Peritonitis (SBP) 

De Las Heras et al. (2003, 194087) Cirrhosis 

Yasuda et al. (2005, 191953) Exacerbated COPD 

Morimatsu et al. (2006, 194097) Critically ill 

De Las Heras et al. (2003, 194087) Healthy 

 02468

COHb (%)

10

Figure 4-11. Representative COHb saturation resulting from various diseases and conditions. 

Measurements of COHb taken using CO-oximeter, except in Coburn et al. (1966, 

010984), where COHb was measured using GC. SBP: Spontaneous bacterial 

peritonitis 

Critically ill patients exhale more CO and have higher endogenous CO production than 

healthy controls, likely due to both increased heme turnover as well as upregulation of the 

expression and activity of HO-1 (Morimatsu et al., 2006, 194097; Scharte et al., 2000, 194112; 

Scharte et al., 2006, 194115) (Figure 4-12). CO production weakly correlates with the multiple organ 

dysfunction score (MODS), which estimates severity of organ dysfunction; however, it did not 

correlate with the Acute Physiology and Chronic Health Evaluation II score (APACHE II) (Scharte 

et al., 2006, 194115) or the sequential organ failure assessment score (SOFA) (Morimatsu et al., 

2006, 194097). Critically ill patients that survived had a higher exhaled CO (COex) concentration 

than nonsurvivors (median 3.9 ppm versus 2.4 ppm) (Morimatsu et al., 2006, 194097). Similarly, 

patients that survived severe sepsis had a higher CO production than those that did not survive (14.7 

± 5.3 versus 8.5 ± 3.3 μl/kg/h) (Zegdi et al., 2002, 037461). 

January 2010 4-24 
Study Condition 

Yamaya et al. (1998, 047525), Zayasu et al. (1997, 084291) Smokers 

De Las Heras et al. (2003, 194087) Peritonitis (SBP) 

De Las Heras et al. (2003, 194087) Cirrhosis 

Sylvester et al. (2005, 191954) Sickle Cell Anemia 

Yamaya et al. (1998, 047525) URTI 

Zayasu et al. (1997, 084291) Asthma w/ steroids 

Zayasu et al. (1997, 084291) Asthma 

Paredi et al. (1999, 118798) Cystic fibrosis 

Horvath et al. (1998, 087190) Bronchiectasis 

Paredi et al. (1999, 118798) Type 2 diabetes 

Paredi et al. (1999, 118798) Type 1 diabetes 

Morimatsu et al. (2006, 194097) Critically ill 

Morimatsu et al. (2006, 194097) Healthy 

 0 3 6 9 12 15

Exhaled CO (average fold change from healthy)

Figure 4-12. Representative exhaled CO concentrations (ppm) resulting from various 

conditions plotted as fold increases over healthy human controls from each 

study. SBP: Spontaneous bacterial peritonitis; URTI: Upper respiratory tract 

infection 

Diseases involving inflammation and infection result in increased endogenous CO production. 

For example, patients with severe sepsis or septic shock have a higher COex and endogenous CO 

production compared to control patients, which was reduced with treatment of the disease (i.e., 

antibiotics, surgery) (Zegdi et al., 2002, 037461). Similarly, patients with pre-existing cardiac 

disease, as well as patients with renal failure who undergo dialysis, produced higher amounts of 

endogenous CO compared to other critically ill patients (Scharte et al., 2006, 194115). High plasma 

COHb levels measured by CO-oximeter were found in nonsmoking patients evaluated for liver 

transplantation (mean 2.1%); however, this increase was not correlated with the Model for End Stage 

Liver Disease (MELD) score or the Child Turcotte Pugh score, used to assess the degree of liver 

impairment (Tran et al., 2007, 194120). Further investigation in cirrhotic patients, with and without 

ascites, provided evidence for increased plasma CO concentrations, HO-1 activity in 

polymorphonuclear cells, exhaled CO, and blood COHb (De las Heras et al., 2003, 194087; Tarquini 

et al., 2009, 194117). COex, plasma CO, and COHb levels were correlated with the Child-Pugh 

score, and thus the severity of disease. These parameters were significantly higher in patients with 

ascites or with spontaneous bacterial peritonitis (SBP) (COHb, healthy: 0.6 ± 0.1%; cirrhosis: 1.0 ± 

0.1%; with ascites: 1.6 ± 0.2%; with SBP: 1.9 ± 0.2%; measured by CO-oximeter). Both COex and 

COHb levels decreased after resolution of the infection in patients with SBP, reaching values similar 

to noninfected patients within 1 mo (De las Heras et al., 2003, 194087). Endotoxin concentration 

was correlated with plasma CO levels, suggesting a link between systemic endotoxemia and 

increased activity or expression of the HO/CO system (Tarquini et al., 2009, 194117). COex 

concentrations are also elevated in patients with diabetes (Type 1: 4.0 ± 0.7 ppm; Type 2: 5.0 ± 

0.4 ppm; healthy: 2.9 ± 0.2 ppm), and correlated with blood glucose levels and duration of disease 

(Paredi et al., 1999, 194102). Likewise, obese Zucker rats, a model of metabolic syndrome with 

insulin resistance, have increased respiratory CO excretion and COHb levels compared to lean 

Zucker rats (3.9 ± 0.1% versus 3.0 ± 0.1% COHb), which is decreased by HO inhibition (Johnson et 

al., 2006, 193874). 

Endogenous CO is also increased in airway inflammatory diseases. Patients with upper 

respiratory tract infections exhaled higher CO concentrations than normal controls and this increase 

was attenuated after recovery (Yamaya et al., 1998, 047525). Arterial COHb levels have been related 

January 2010 4-25 
to disease severity in COPD patients (Yasuda et al., 2005, 191953). Bronchiectasis patients had 

higher COex; however, anti-inflammatory treatment did not decrease the CO levels (Horvath et al., 

1998, 087191). Patients with cystic fibrosis had higher COex than normal controls (6.7 ± 0.6 ppm 

versus 2.4 ± 0.4 ppm), and patients treated with steroids had a decrease in CO levels (8.4 ± 1.0 ppm 

versus 5.1 ± 0.5 ppm) (Paredi et al., 1999, 118798). Increased arterial COHb measured by COoximeter was reported in patients with bronchial asthma, pneumonia, idiopathic pulmonary fibrosis, 

pyelonephritis, and active rheumatoid arthritis (Yasuda et al., 2002, 035206; Yasuda et al., 2004, 

191955). Similarly, asthmatic patients exhibited an elevation of COex that decreased with 

corticosteroid therapy (nonsmoking controls: 1.5 ± 0.1 ppm; asthmatics without corticosteroids: 5.6 

± 0.6 ppm; with corticosteroids: 1.7 ± 0.1 ppm; smoking controls: 21.6 ± 2.8 ppm) (Zayasu et al., 

1997, 084291). These results were confirmed and associated with increased expression of HO-1 in 

airway macrophages (Horvath et al., 1998, 087190). Also, COex was increased in patients with 

allergic rhinitis during the pollen season; however, their COex was similar to control subject levels 

out of season (Monma et al., 1999, 180426). Similarly, endogenous CO production and HO-1 

expression in nasal mucosa was correlated with allergic rhinitis in guinea pigs as described in 

Section 5.1 (Shaoqing et al., 2008, 192384). 

Altitude is also positively associated with baseline COHb concentrations (McGrath, 1992, 

001005)(McGrath et al., 1993, 013865). This increase in COHb with altitude-induced hypoxia is 

associated in rats and cells with increases in the mRNA, protein, and activity of HO-1 leading to 

enhanced endogenous CO production (Carraway et al., 2002, 026018; Lee et al., 1997, 082641). 

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. 

Because of the sensitivity of COHb to changes in the metabolic state, ranges of endogenous 

COHb levels in the population are uncertain. However, baseline levels of COHb, which reflect 

exposure to ambient and non-ambient CO and endogenous production of CO, have been measured in 

the population. COHb levels measured by CO-oximeter in packed red blood cell units reserved for 

use between 2004 and 2005 averaged 0.78 ± 1.48%, with 10.3% of samples having COHb levels of 

1.5% or greater and a maximum measurement of 12% (Ehlers et al., 2009, 194089). This study 

reported a decrease from a study conducted in 1982-1983 in the number of units with elevated 

COHb; at that time, 49% of units had COHb levels >1.5% (Aronow et al., 1984, 194083) versus 

10.3% in 2004-2005. Another study calculated that 23% of donated blood units had COHb levels 

exceeding 1.5%, with the highest measurement being 7.2% (Aberg et al., 2009, 194082). Smoking is 

the main factor causing increased blood concentrations of CO. A dose-response relationship was 

shown to exist between COHb concentration and the number of cigarettes smoked a day 

(nonsmoker: 1.59 ± 1.72%; 1-5 cig/day: 2.31 ± 1.94%; 6-14 cig/day: 4.39 ± 2.48%; 15-24 cig/day: 

5.68 ± 2.64%; ≥ 25 cig/day: 6.02 ± 2.86% COHb). The mean baseline COHb value for former 

smokers was higher than that of never smokers in this prospective cohort study (1.96 ± 1.87 versus 

1.59 ± 1.72%) (Hart et al., 2006, 194092). 

Endogenous CO is removed from the body mainly by expiration and oxidation. CO diffuses 

across the alveolar-capillary membrane and is exhaled. This event has been used as a noninvasive 

measurement of both endogenous and body load CO (Stevenson et al., 1979, 193767). CO can also 

be oxidized to CO2 by cytochrome c oxidase in the mitochondria (Fenn, 1970, 010821; Young and 

Caughey, 1986, 012091). 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 (Luomanmäki and Coburn, 1969, 012319). 

4.6. Summary and Conclusions 

CO elicits various health effects by binding with and altering the function of a number of 

heme-containing 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. Venous COHb levels have been 

modeled mainly by the CFK equation, but more recent models have included venous and arterial 

blood mixing and Mb and extravascular storage compartments, as well as other dynamics of CO 

physiology. The CFK equation remains the most extensively validated and applied model for COHb 

prediction. Recent 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 

January 2010 4-26 
blood and alveolar air or tissues is controlled by diffusion down the pCO 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 compromised individuals and 

developing fetuses, are at a greater risk from COHb induced health effects due to altered CO 

kinetics, compromised cardiopulmonary function, and increased baseline hypoxia levels. Altitude 

may also significantly affect the kinetics of COHb formation. Compensatory mechanisms, such as 

increased cardiac output, compensate for 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. A number of diseases and conditions affect endogenous CO production, possibly causing a 

higher endogenous COHb level. Finally, CO is removed from the body by expiration or oxidation to 

CO2. 

January 2010 4-27 
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January 2010 4-36 
Chapter 5. 

5.1.1. 

5.1.2. 

Integrated Health Effects 

5.1. Mode of Action of CO Toxicity 

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 other mechanisms such as 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. 

Hypoxic Mechanisms 

As discussed in the 2000 CO AQCD (U.S. EPA, 2000, 000907), the most well-known 

pathophysiologic 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 against tissue hypoxia. Depending on the extent of CO exposure, these compensatory 

changes may be effective in people with a healthy cardiovascular system. However, hemodynamic 

responses following CO exposure may be insufficient in people with decrements in cardiovascular 

function, resulting in health effects as described in Section 5.2. 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) reported changes in vasodilation due to CO 

levels between 500-2,000 ppm (Kanten et al., 1983, 011333; MacMillan, 1975, 012909). In one 

study, the vasodilatory response to CO in cerebral blood vessels was attributed to decreased O2

availability (Koehler et al., 1982, 011341). In another study, exposure of rats to 1,000 ppm CO 

resulted in increased cerebral blood flow which was not triggered by tissue hypoxia since no changes 

in intramitochondrial NADH levels preceded vasodilation (Meilin et al., 1996, 079919). However, 

the response was blocked by the inhibition of NOS indicating a role for the free radical species NO 

in CO-mediated vasodilation (Meilin et al., 1996, 079919). 

Increased cardiac output was also discussed in the 2000 CO AQCD (U.S. EPA, 2000, 000907) 

as a compensatory response to CO-mediated tissue hypoxia. Findings of studies which measured 

hemodynamic alterations following CO exposure were equivocal and sometimes contradictory 

(Penney, 1988, 012519). 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 which resulted in the rapid rise in COHb levels to ~9% 

(Ayres et al., 1973, 193943). However, there was no increase in cardiac output following a more 

gradual increase in COHb levels to ~9% achieved by exposure to 0.1% CO over a longer period of 

time (Ayres et al., 1973, 193943). Increased heart rate and stroke volume (SV) were observed in 

response to CO exposure in one study (Stewart et al., 1973, 012428); however, some experiments 

found no change in SV in humans with 18-20% COHb (Vogel and Gleser, 1972, 010898) or 12.5% 

COHb (Klausen et al., 1968, 193936). The 2000 CO AQCD (U.S. EPA, 2000, 000907) reported that 

blood pressure was generally unchanged in human CO exposure studies, while a number of animal 

studies demonstrated CO-induced hypotension (Penney, 1988, 012519). No changes in forearm 

blood flow, blood pressure, or heart rate were reported in humans with approximately 8% COHb 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 5-1 
(Hausberg and Somers, 1997, 083450). However, high-concentration exposures (3,000-10,000 ppm) 

in animals resulted in diminished organ blood flow (Brown and Piantadosi, 1992, 013441). In-depth 

discussion of hemodynamic changes resulting from CO exposure in recent human clinical studies 

can be found in Section 5.2.4. Binding of CO to Mb, as discussed in the 2000 CO AQCD (U.S. EPA, 2000, 000907) and in 

Section 4.3.2.3, can also impair the delivery of O2 to tissues. Mb has a high affinity for CO, about 

25 times that of O2; however, pathophysiologic 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, 013909). Conversely, rat 

hearts perfused with solutions containing 6% CO (60,000 ppm) exhibited no change in high-energy 

phosphate production, respiration rate, or contractile function (Chung et al., 2006, 193987; Glabe et 

al., 1998, 086704). 

5.1.3. Nonhypoxic Mechanisms 

 Nonhypoxic mechanisms underlying the biological effects of CO were discussed in the 2000 

CO AQCD (U.S. EPA, 2000, 000907) and are summarized below. Most of these mechanisms are 

related to CO’s ability to bind heme-containing proteins other than Hb and Mb (Raub and Benignus, 

2002, 041616). 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 concentrations of CO. 

5.1.3.1. Nonhypoxic Mechanisms Reviewed in the 2000 CO AQCD 

Inhibition of heme-containing proteins such as cytochrome c oxidase and cytochrome P450 

reductases 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, 193764). 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 were above 50% (Brown and Piantadosi, 

1992, 013441). It is unlikely that this could arise under physiologic conditions or under conditions 

relevant to ambient exposures. 

A series of studies from the laboratory of Thom, Ischiropoulos and colleagues indicated that 

CO exposure produced a pro-oxidant cellular environment by liberation of NO. Exposure to CO 

concentrations of 10-20 ppm and above caused isolated rat platelets, as well as cultured bovine 

pulmonary endothelial cells, to release NO (Thom and Ischiropoulos, 1997, 085644). This response 

was blocked by treatment with an NOS inhibitor, indicating that the NO released was dependent on 

NOS activity. An increase in available NO was also seen in the lung and brain of CO-exposed rats 

(Ischiropoulos et al., 1996, 079491; Thom et al., 1999, 016757). Reaction of NO with superoxide to 

form the highly active oxidant species, peroxynitrite (Thom et al., 1997, 084337), was thought to 

lead to the activation and sequestration of leukocytes in brain vessels (Thom et al., 2001, 193779) 

and aorta (Thom et al., 1999, 016753), oxidation of plasma lipoproteins (Thom et al., 1999, 016753), 

and the formation of protein nitrotyrosine (Ischiropoulos et al., 1996, 079491; Thom et al., 1999, 

016757; Thom et al., 1999, 016753). NO release by CO was attributed to the displacement of NO 

from nitrosyl-bound heme proteins. The rate of this event was slow; however, it occurred at 

environmentally-relevant concentrations of CO (Thom et al., 1997, 084337). 

CO exposure also increased the production of other pro-oxidant species, including hydrogen 

peroxide (H2O2) and hydroxyl radical (OH). High-level CO exposure (2,500 ppm) increased OH in 

rat brain, and this response was distinct from tissue hypoxia (Piantadosi et al., 1997, 081326). The 

mechanism for enhanced H2O2 production was unclear. The release of H2O2 in the lung of 

CO-exposed rats was dependent upon the production of NO, as it was inhibited by pretreatment with 

an NOS inhibitor (Thom et al., 1999, 016757). It is possible that peroxynitrite formed after CO 

exposure inhibited electron transport at complexes I through III, or that cytochrome c oxidase 

inhibition led to mitochondrial dysfunction and ROS production. 

Evidence was presented for CO-mediated vasorelaxation by three different mechanisms. First, 

CO may inhibit the synthesis of vasoconstrictors by P450 heme proteins (Wang, 1998, 086074). 

January 2010 5-2 
Vasodilation in isolated vessels was demonstrated via this P450-dependent mechanism using high 

concentrations of CO (approximately 90,000 ppm) (Coceani et al., 1988, 040493). In the case of 

cytochrome P450 enzymes, tissue CO levels may need to be abnormally high to elicit a response 

since the 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, 037463). P450 

inhibition may 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, 

086074). CO also interfered with the metabolism of barbiturates and other drugs; however, this was 

probably due to the hypoxic actions of CO rather than to P450 inhibition (Roth and Rubin, 1976, 

012703; Roth and Rubin, 1976, 012420). 

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 reversibly ligates the heme core of sGC, and the resulting protoporphyrin IX 

intermediate triggers cGMP production (Ndisang et al., 2004, 180425). CO caused vascular 

relaxation, independent of the endothelium, in human arterial rings (Achouh et al., 2008, 179918), 

rat tail artery (Wang et al., 1997, 084341), and rat thoracic aorta (Lin and McGrath, 1988, 012773), 

but not in cerebral vessels (Andresen et al., 2006, 180449; Brian et al., 1994, 076283). Activation of 

sGC by CO has been linked to neurotransmission, vasodilation, bronchodilation, inhibition of 

platelet aggregation, and inhibition of smooth muscle proliferation (Brüne and Ullrich, 1987, 

016535; Cardell et al., 1998, 086700; Cardell et al., 1998, 011534; Morita et al., 1997, 085345; 

Verma et al., 1993, 193999). 

CO-mediated vasorelaxation can also be caused by activation of voltage- or Ca2+-activated 

potassium (K+

) channels in smooth muscle cells, which leads to membrane hyperpolarization, 

voltage-dependent Ca2+ channel closing, reduction of resting Ca2+ concentration and vascular tissue 

relaxation (Farrugia et al., 1993, 013826; Wang et al., 1997, 084341). This effect may be linked to 

sGC activity; however, it has also been reported to occur independently (Dubuis et al., 2003, 

180439; Naik and Walker, 2003, 193852). Developmental stage and tissue type will determine 

whether K+

 channels or the sGC/cGMP pathway play more of a role in vasorelaxation (Ndisang et 

al., 2004, 180425). 

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 ROS following cytochrome c oxidase inhibition would likely 

predispose towards cellular injury and death. The release of NO from sequestered stores could 

contribute to the pro-oxidant status if superoxide levels are simultaneously increased. Furthermore, 

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 1,000-fold more 

potent with respect to vasodilation and sGC activation (Stone and Marletta, 1994, 076455). CO 

could further contribute to endothelial dysfunction by this mechanism. Although the 2000 CO 

AQCD (U.S. EPA, 2000, 000907) made no definitive links between these nonhypoxic mechanisms 

of CO and CO-mediated health effects, it did document the potential for CO to interfere with basic 

cellular and molecular processes that could lead to dysfunction and/or disease. 

5.1.3.2. Recent Studies of Nonhypoxic Mechanisms 

Since the 2000 CO AQCD (U.S. EPA, 2000, 000907), new studies have provided additional 

evidence for nonhypoxic mechanisms of CO which involve the binding of CO to reduced iron in 

heme proteins. These mechanisms, which may be interrelated, are described below and include: 

 Alteration in NO signaling 

 Inhibition of cytochrome c oxidase 

 Heme loss from protein 

January 2010 5-3 
 Disruption of iron homeostasis 

 Alteration in cellular redox status 

Recent studies have also demonstrated nonhypoxic mechanisms of CO which are either 

indirectly linked to heme protein interactions or not yet understood. These mechanisms are described 

below and include: 

 Alteration in ion channel activity 

 Modulation of protein kinase pathways 

This assessment evaluates these nonhypoxic mechanisms in terms of their potential to 

contribute to health effects associated with environmentally-relevant CO exposures. As discussed 

above, CO at high concentrations may promote oxidative stress, cell injury and death, inflammation 

and endothelial dysfunction. Whether lower CO concentrations trigger these same processes is of 

key interest since they may potentially contribute to adverse health effects following ambient 

exposures. 

In addition, a large number of studies published since the 2000 CO AQCD (U.S. EPA, 2000, 

000907) have focused on the role of CO derived from HO-catalyzed heme metabolism as an 

endogenous signaling molecule and on the potential therapeutic effects of exogenous CO 

administered at high concentrations. This assessment addresses aspects of these topics pertaining to 

the evaluation of health effects associated with environmentally-relevant CO exposures. 

Alteration in NO Signaling 

Work by Thorup et al. (1999, 193782) demonstrated altered NO signaling in isolated rat renal 

resistance arteries. In one set of experiments, rapid release of NO was observed in response to 

exogenous CO. This response was biphasic, peaking at 100 nM CO in the perfusate and declining at 

higher concentrations. It was also NOS-dependent as it required L-arginine and was blocked by a 

NOS inhibitor. The authors attributed the effects of CO on NO release to either stimulated eNOS or 

to displacement of preformed NO from intracellular binding sites. These findings are similar to those 

of Thom and colleagues (Ischiropoulos et al., 1996, 079491; Thom and Ischiropoulos, 1997, 085644; 

Thom et al., 1994, 076459; Thom et al., 1997, 084337; Thom et al., 1999, 016753; Thom et al., 

1999, 016757; Thom et al., 2000, 011574; Thom et al., 2006, 098418) 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, 193782) 

demonstrated inhibition of agonist-stimulated NOS activity in isolated rat renal resistance arteries. 

Here rapid NOS-dependent release of NO following carbachol stimulation was blocked by 

pretreatment with 100 nM CO or by upregulation of intracellular HO-1. Additional experiments 

using blood-perfused isolated juxtamedullary afferent arterioles demonstrated a biphasic response to 

CO with rapid vasodilation observed at lower, but not higher, concentrations of CO. These same 

higher concentrations of CO inhibited agonist-stimulated vasodilation in the arterioles. In order to 

determine whether CO had a direct effect on the activity of NOS, which is a heme protein, purified 

recombinant eNOS was exposed in vitro to CO in the presence of the necessary substrates and 

cofactors. A dose-dependent inhibition of NOS by CO was observed, suggesting that CO-mediated 

NO release in the isolated vessels was not due to stimulated NOS activity. The authors concluded 

that CO effects on vascular tone were due to the liberation of NO from intracellular binding sites at 

lower concentrations and to the inhibition of NOS at higher concentrations. 

These findings illustrate the potential of CO to alter processes dependent on endogenous NO 

either by enhancing intracellular concentrations of free NO (increased vasodilatory influence) or by 

inhibiting agonist-induced NO production by NOS (decreased vasodilatory influence). In addition, 

CO may compete with NO for binding to sGC as discussed above. Since NO activates sGC to a 

greater extent than CO, NO-dependent vasodilation may be significantly impaired in the presence of 

CO. In fact, a recent study in transgenic mice demonstrated that chronic overexpression of HO-1 in 

vascular smooth muscle resulted in attenuated NO-mediated vasodilation and elevated blood 

pressure (Imai et al., 2001, 193864). Results of this study suggested that decreased sensitivity of sGC 

to NO contributed to the changes in vascular function. The considerations mentioned above, 

however, do not preclude an important role for CO in maintaining vasomotor tone in vessels where 

January 2010 5-4 
CO and NO do not compete for available heme sites on sGC. This could occur when both mediators 

are present at low concentrations compared with sGC or in situations where NOS does not colocalize with sGC, as discussed by Thorup et al. (1999, 193782). 

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 mitochondrial 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 a study by Alonso et al. (2003, 193882), 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, 193884). In this latter study, increased ROS were 

observed following exposure to 250 ppm CO, 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., 2006, 184462). Exposure of mice 

to 1,000 ppm CO for 3 h, resulting in COHb levels of 61%, decreased cytochrome c oxidase activity 

in heart mitochondria (Iheagwara et al., 2007, 193861). 

Heme Content Loss from Proteins 

In addition to decreasing the activity of cytochrome 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 in heart 

mitochondria (Iheagwara et al., 2007, 193861). CO-mediated heme release was also seen in a study 

by Cronje et al. (2004, 180440) 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, 096321). Furthermore in this same study, exposure of cultured human 

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, which was observed in other studies (Cronje et al., 2004, 

180440; Iheagwara et al., 2007, 193861), may also contribute to disruption of iron homeostasis. Iron 

homeostasis is critical for the sequestration of free iron and the prevention of iron-mediated redox 

cycling which leads 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 oxidative stress in cultured cells (Kim et al., 2008, 193961; Zuckerbraun et al., 2007, 

193884). A dose-dependent increase in dichlorofluorescein (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, 193884). 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, 193884). Recently, inhibition of cytochrome c oxidase was demonstrated in HEK-293 

cells transfected with HO-1 and in macrophages with induced HO-1; this effect was attributed to 

January 2010 5-5 
endogenously produced CO (D'Amico et al., 2006, 193992). 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, 193961). Significant 

increases in apoptosis were also observed in this model. Thus in this study, CO exposure led to 

uncoupled mitochondrial respiration and ROS-induced programmed cell death. 

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, 193961; Patel et al., 2003, 043155). 

In addition, mitochondrial redox stress was observed in livers of rats exposed to 50 ppm CO 

(Piantadosi et al., 2006, 180424). 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, 011574), and mitochondrial biogenesis was observed in hearts of mice 

exposed to 250 ppm CO for 1 h (Suliman et al., 2007, 193768). 

Several mechanisms could contribute to the cellular redox stress elicited by CO exposure. 

First, inhibition of cytochrome c oxidase could result in increased mitochondrial superoxide 

generation. Secondly, interactions of CO with heme proteins could lead to the release of heme and 

free iron and subsequently 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, 180424) and the cardiac mitochondrial biogenesis 

observed in mice exposed to CO (Suliman et al., 2007, 193768) were attributed specifically to 

nonhypoxic mechanisms of CO. 

Alteration in Ion Channel Activity 

Work by Dubuis et al. (Dubuis et al., 2002, 193911) demonstrated increased current through 

Ca2+-activated K+

 channels in smooth muscle cells from pulmonary arteries of rats exposed to 

530 ppm CO for 3 wk. These findings provide further evidence for non-cGMP-dependent 

vasodilatory actions of CO. 

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 

anti-proliferative effects (Durante et al., 2006, 193778; Ryter et al., 2006, 193765). These studies 

generally involved pretreatment with CO followed by exposure to another agent 12-24 h later. There 

is extensive literature on this topic as reviewed by Ryter et al. (2006, 193765), Durante et al. (2006, 

193778) 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, 037531). A second example is provided by the study of Kim et al. 

(2005, 193959) where 250 ppm CO inhibited PDGF- induced smooth muscle cell proliferation by 

upregulating p21Waf1/Cip1 and caveolin-1, and down-regulating cyclin A expression. In this case, 

effects were dependent upon cGMP and the p38 MAPK pathway (Kim et al., 2005, 193959). Thirdly, 

rat endothelial cells exposed to 15 ppm CO escaped anoxia/reoxygenation-induced 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, 184460). 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, 193961). While research focusing 

on therapeutic applications of CO generally involves high-level short-term exposure to CO (i.e., 250-

1,000 ppm for up to 24 h), some studies found effects below 20 ppm (Zhang et al., 2005, 184460). 

Few if any studies on the therapeutic effects of CO have explored the dose-response relationship 

January 2010 5-6 
between CO and pathway activation/deactivation, so it remains unclear how these effects may be 

related to environmentally-relevant exposures. 

Concentration-Response Relationships 

In many cases, the concentrations of exogenous CO required for these nonhypoxic effects 

were much higher (Alonso et al., 2003, 193882; Favory et al., 2006, 184462; Iheagwara et al., 2007, 

193861; Thorup et al., 1999, 193782) than concentrations of CO in ambient air. However, in some 

studies the effects were 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, 193992; Imai et al., 2001, 

193864; Thorup et al., 1999, 193782). For example, HO-1 upregulation or 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, 193864; Thorup et al., 1999, 193782). In the study by 

D’Amico et al. (2006, 193992), overexpression of HO-1 in cells inhibited cellular respiration by 

12% and decreased cytochrome c oxidase activity by 23%. It is not clear how comparable these 

conditions involving increased intracellular concentrations of endogenous CO are to increased 

intracellular concentrations of CO resulting from exogenous CO exposures. Neither is it clear what 

concentrations of intracellular CO are generated locally within cells as a result of HO-catalyzed 

heme metabolism. However, a small amount of a relatively high local concentration of endogenous 

CO produced in a regulated manner by HO-1 and HO-2 may be sufficient to react with local targets 

(e.g., heme proteins), while a larger amount of exogenous CO may be required to reach the same 

targets. This may be due to indiscriminate reactions of exogenous CO with other target proteins or to 

other issues related to compartmentalization. It is conceivable that acute or chronic exposures to 

ambient CO could “sensitize” (or “desensitize”) targets of endogenous cellular CO production, but 

there is no experimental evidence to support this mechanism. 

There is a growing appreciation that nonhypoxic mechanisms may contribute to the effects 

associated with CO toxicity and poisoning (Ischiropoulos et al., 1996, 079491; Thom et al., 1994, 

076459; Weaver et al., 2007, 193939). On the other hand, recent studies suggest that exogenous CO 

at lower concentrations may have beneficial anti-inflammatory, anti-proliferative and cytoprotective 

effects under certain circumstances (Durante et al., 2006, 193778; Ryter et al., 2006, 193765). 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” 

(50-250 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 nonhypoxic 

mechanisms were demonstrated at CO concentrations of 50 ppm and less. 

January 2010 5-7 
Table 5-1. Responses to CO exposures at low and moderate concentrations. 

Study Model System CO Exposure Response Notes 

IN VITRO 

Alonso et al. (2003, 

193882) 

Human muscle 

mitochondria 

50, 100, 500 ppm 

5 min Decreased cytochrome c oxidase activity 

Thom and Ischiropoulos 

(1997, 085644) 

Rat 

platelets 10 ppm Increased free NO 

Thom et al. (1997, 

084337) 

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, 

011574) 

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, 

037531) 

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, 

193959) 

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, 

193961) 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, 

184460) 

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, 193884) 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 

Ghio et al. (2008, 

096321) 

Human bronchial epithelial 

cells 

10-100 ppm 

24 h 

Dose-dependent decrease in cellular nonheme iron (effect at 10 ppm was significant, 

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 experiments in 

same paper 

IN VIVO 

Ghio et al. (2008, 

096321) 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 

Compare with in vitro experiments in 

same paper 

Thom et al. (1999, 

016753) 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. (1999, 

016757) 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, 

180414) Rats 200 ppm 

72 wk 

No changes in lung morphology 

No pulmonary hypertension 

No atherosclerotic lesions in systemic 

vessels 

Ventricular hypertrophy 

Loennechen et al. 

(1999, 011549) Rats 100 and 200 ppm 

1-2 wk 

Increased ET-1 mRNA in the heart 

ventricles, increased right and left 

ventricular weight 

12 and 23% COHb 

Favory et al. (2006, 

184462) 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, 

180424) Rats 

50 ppm CO or 

hypobaric hypoxia for 

1, 3, or 7 days 

Liver mitochondrial oxidative and nitrosative 

stress, altered mitochondrial permeability 

pore transition sensitivity 

CO effects not mimicked by hypobaric 

hypoxia 

Suliman et al. (2007, 

193768) Mice 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, 

087874) 

Rats 

Model of MI 

35 ppm 

1 h 

Decreased delayed ventricular beat 

frequency Altered arrhythmogenesis 

Wellenius et al. (2006, 

156152) 

Rats 

Model of MI 

35 ppm 

1 h Decreased supraventricular ectopic beats Altered arrhythmogenesis 

January 2010 5-8 
Study Model System CO Exposure Response Notes 

Carraway et al. (2002, 

026018) 

Rats 

Model of hypoxic 

pulmonary vascular 

remodeling 

Hypobaric hypoxia 

± 50 ppm CO 

3 wk 

CO promoted remodeling and increased 

pulmonary vascular resistance 

Gautier et al. (2007, 

096471) 

Rats 

Model of right ventricle 

hypertrophy secondary to 

chronic hypoxia 

3 wk of hypobaric 

hypoxia 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, 

193833) 

Rats 

Model of right ventricle 

hypertrophy secondary to 

chronic hypoxia 

50 ppm 

10 wk 

CO increased cardiac dilation and 

decreased left ventricular function 

Melin et al. (2002, 

037502) 

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 Nonhypoxic Mechanisms 

A key issue in understanding the biological effects of environmentally-relevant exposures to 

CO is whether the resulting partial pressures of CO (pCO) in cells and tissues can initiate cell 

signaling which is normally mediated by endogenously generated CO or perturb signaling which is 

normally mediated by other signaling molecules such as NO. 

Several aspects need to be considered. First of all, during a period of exogenous CO uptake, 

Hb acts as a buffer for most cells and tissues by limiting the availability of free CO. Nevertheless, 

COHb delivers CO to cells and tissues. This delivery involves CO’s dissociation from Hb followed 

by its diffusion down a pCO gradient. Hence, greater release of CO from COHb will occur under 

conditions of low cell/tissue pCO. Conversely, higher cell/tissue pCO in cells/tissues than in the 

blood will lead to the egress of CO from cells/tissues. 

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, 193992). NO, 

which also competes with O2 and CO for binding to heme proteins, may have a similar impact. 

A third consideration is whether certain cell types serve as primary targets for the effects of 

CO. Besides the blood cells (including leukocytes and platelets), the first cells encountering CO 

following its dissociation from Hb are the endothelial cells which line blood vessels. 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 pulmonary microvascular endothelial dysfunction, inflammatory cell 

activation, and respiratory epithelial injury may ensue as a result of preferential targeting of these 

cell types. 

Since there is potential for exogenous CO to affect endogenous pools of CO, the 

concentrations of CO in cells and tissues before and after exogenous exposures are of great interest. 

Table 5-2 summarizes findings from four recent studies relevant to this issue. It should be noted that 

exposure to 50 ppm CO resulted in a three- to fivefold increase in tissue CO concentration. 

January 2010 5-9 
Table 5-2. Tissue concentration of CO following inhalation exposure. 

Study CO Exposure Tissue CO Concentrations COHb Notes 

Cronje et al. (2004, 

180440) 

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, 

180424) 

Vreman et al. (2005, 

193786) 

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, 

180424) 

Rats 

50 ppm 

1-7 days 

Liver: 30-40 pmol/mg 

Control liver 10 pmol/mg 

4-5% 

Control 1% 

CO concentration reached a plateau after 1 day 

Vreman et al. (2005, 

193786) 

Mice 

50, 250 and 

1,250 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, 180440) 

Data is expressed as pmol CO/mg tissue wet weight 

Furthermore, endogenous CO production is known to be increased during inflammation, 

hypoxia, increased heme availability and other conditions of cellular stress where HO-1 or HO-2 

activity is increased. A few studies reported cell and tissue concentrations of CO along with 

accompanying COHb levels resulting from enhanced endogenous CO production; Table 5-3 summarizes these findings. Additional measurements of CO levels in cells and tissues following 

increased endogenous production and following inhalation of exogenous CO may provide further 

insight into the relationship between the CO tissue concentration and biological responses. 

January 2010 5-10 
Table 5-3. Tissue concentration of CO following increased endogenous production. 

Study Exposure Tissue CO COHb Notes 

Carraway et al. (2000, 

021096) 

Rats 

Hypobaric hypoxia for 21 days 

1.5-2.8% 

Control 0.5% 

COHb highest after days 1 and 21 

at three- to fourfold higher than controls 

Piantadosi et al. (2006, 

180424) 

Rats 

Hypobaric hypoxia 

1-7 days 

Liver: 5-12 pmol/mg 

Control liver 10 pmol/mg 

1-1.25% 

Control 1% 

CO concentration reached a plateau after 1 day 

Vreman et al. (2005, 

193786) 

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% 

Data is expressed as pmol CO/mg tissue wet weight 

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 et al., 2006, 180424), which could complicate interpretation of 

results from studies in which HO-1 and HO-2 activities are increased. In addition, indiscriminate 

reactions occurring in the case of exogenous CO would likely lead to less specific responses than 

those mediated by reactions of endogenously-produced CO with local targets. Hence, the situations 

of increased endogenous CO production and of exogenous CO exposure are not equivalent. 

A further consideration is that in the numerous conditions and disease states where HO-1 is 

induced, increased levels of endogenously produced CO may represent an adaptive response to stress 

(Durante et al., 2006, 193778; Piantadosi, 2008, 180423). These increases and the accompanying 

increases in COHb generally fall in the range of 1.5- to 4-fold, with the exception of some situations 

of hemolytic anemia and hemoglobin disorders (see Figure 4-11 for results in humans). The resulting 

excess endogenous CO may react intracellularly with heme proteins or diffuse into the blood 

according to the gradient of pCO in the cell/tissue and blood compartments. In many cases, 

beneficial effects or compensatory mechanisms may result as a result of short-term induction of 

HO-1, as reviewed by Ryter et al. (2006, 193765) and Durante et al. (2006, 193778). Longer term 

increases in HO-1 are sometimes associated with protective responses, as in the case of 

atherosclerosis (Cheng et al., 2009, 193775; Durante et al., 2006, 193778), and sometimes with 

pathophysiologic responses as demonstrated in hypoxic pulmonary vascular remodeling (Carraway 

et al., 2002, 026018) and models of salt-sensitive hypertension (Johnson et al., 2003, 193868; 

Johnson et al., 2004, 193870) and metabolic syndrome (Johnson et al., 2006, 193874). Increased 

endogenous CO in hearts of individuals with ischemic heart disease and in lungs of individuals with 

various forms of inflammatory lung disease might also be expected (Scharte et al., 2006, 194115; 

Yamaya et al., 1998, 047525; Yasuda et al., 2005, 191953) (Figure 4-12). It is conceivable that 

prolonged increases in endogenous CO production in chronic disease states may result in less of a 

reserve capacity to handle additional intracellular CO resulting from exogenous exposures, but there 

is no experimental evidence to support this mechanism. Perhaps these circumstances lead to 

dysregulated functions or toxicity. Thus, CO may be responsible for a continuum of effects from cell 

signaling to adaptive responses to cellular injury (Piantadosi, 2008, 180423), depending on 

intracellular concentrations of CO, heme proteins and molecules which modulate CO binding to 

heme proteins. 

January 2010 5-11 
5.1.3.4. Summary

CO is a ubiquitous cell signaling molecule with numerous physiological functions. The 

endogenous generation and release of CO from heme by HO-1 and HO-2 is tightly controlled, as is 

any homeostatic process. However, exogenously-applied CO has the capacity to disrupt multiple 

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 and on 

signaling pathways. 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 lead to adverse health effects through altered cell signaling is an open question for 

which there are no definitive answers at this time. However, experiments demonstrating 

oxidative/nitrosative stress, inflammation, mitochondrial alterations and endothelial dysfunction at 

concentrations of CO within one or two orders of magnitude higher than ambient concentrations 

suggest a potential role for such mechanisms in pathophysiologic responses. Furthermore, prolonged 

increases in endogenous CO resulting from chronic diseases may provide a basis for the enhanced 

sensitivity of susceptible populations to CO-mediated health effects such as is seen in individuals 

with coronary artery disease. 

Figure 5-1. Direct effects of CO. The dashed line refers to uptake of inhaled CO by 

respiratory epithelial cells and resident macrophages in the lung. The uptake of 

CO by all other cells and tissues is dependent on COHb. 

5.2. Cardiovascular Effects 

5.2.1. Epidemiologic Studies with Short-Term Exposure 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) examined the association between short-term 

variations in ambient CO concentrations and cardiovascular morbidity. While the results presented 

by these studies did provide suggestive evidence of ambient CO levels being associated with 

exacerbation of heart disease, the AQCD determined that the evidence was inconclusive. The reasons 

January 2010 5-12 
for this conclusion were given as: internal inconsistencies and lack of coherence of the reported 

results within and across studies; the degree to which average ambient CO levels derived from fixedsite monitors are representative of spatially heterogeneous ambient CO values or of personal 

exposures that often include nonambient CO; and the lack of biological plausibility for any harmful 

effects occurring with the very small changes in COHb levels (from near 0 up to 1.0%) over typical 

baseline levels (about 0.5%) that would be expected with the low average ambient CO 

concentrations reported in the epidemiologic studies (generally <5.0 ppm, 1-h daily max) (U.S. EPA, 

2000, 000907). These reasons were also cited in the discussion of the effects of short-term exposure 

to CO on mortality and other types of morbidity. The AQCD posited that ambient CO concentrations 

used as exposure indices in epidemiologic studies may be surrogates for ambient air mixtures 

produced by combustion sources and/or other constituents of such mixtures. In addition, the AQCD 

noted that the epidemiologic evidence was stimulating increased scientific interest regarding ambient 

CO exposures as a potential risk factor for exacerbation of heart disease and other health effects, 

although the epidemiologic studies were subject to considerable biological and statistical uncertainty. 

The following section reviews the literature since the 2000 CO AQCD, including numerous 

new studies on relevant cardiac endpoints and biomarkers and additional studies of daily hospital 

admissions for heart disease. New epidemiologic evidence addresses some of the aforementioned 

uncertainties, including consistency and coherence of results and the possibility that CO may be 

acting as a surrogate for other combustion-derived air pollutants. 

5.2.1.1. Heart Rate and Heart Rate Variability 

Heart rate variability (HRV) refers to the beat-to-beat alterations in the heart rate (HR) 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 (Deedwania et al., 2005, 195134; De Jong 

and Randall, 2005, 193996; Huikuri et al., 1999, 184464; Rajendra et al., 2006, 193787). 

Three 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 21 active 

residents aged 53-87 yr and performed up to 12 ECG assessments on each subject over a period of 4 

mo (summer 1997). Particles (PM10, PM2.5) and several gaseous pollutants (O3, NO2, and SO2) were 

monitored at fixed sites (up to 4.8 mi from the study site), while CO was monitored 0.25 mi from 

each participant's 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, 011432). 

A similar study by the same group of researchers 2 yr later involved 28 older subjects (aged 

61-89 yr) 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 wk, and HRV parameters 

(SDNN, r-MSSD, PNN50, LF/HF ratio) were measured for 30 min each session. Data for PM2.5, 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 between CO 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, 074317). 

 

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). 

January 2010 5-13 
An additional 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-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 ma 

were examined. The main pollutant effects were with PM2.5 and O3, while CO was not associated 

with HRV (Park et al., 2005, 057331). 

A study in Mexico City selected 30 subjects from the outpatient clinic at the National Institute 

of Cardiology and followed them for ~10 h (starting at 9:00 a.m.) (Riojas-Rodriguez et al., 2006, 

156913). 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 (HR, R-R, LF, HF) at 

various lags. In copollutant models with PM2.5, 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 an additional study conducted in Mexico City, 34 residents from a nursing home underwent 

HRV analysis every other day for 3 mo (Holguin et al., 2003, 057326). 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, 088453) 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, 099036) recruited 36 subjects with CAD 

from the Toronto Western Hospital’s noninvasive cardiac diagnostic unit. HR and HRV (SDNN, NN, HF, LF, HF/LH ratio) were assessed 1 day each week for up to 10 wk by a Holter monitoring 

system. Personal air sampling for PM2.5 and CO was carried out for the same 24-h period while 

subjects went about their usual daily activities for that period. Stratified results showed that among 

those not on beta-receptor-blockers, 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. 

Peters et al. (1999, 011554) examined HR among a sub-sample of the Monitoring of Trends 

and Determinants in Cardiovascular Disease (MONICA) study (n = 2,681) in Augsburg, Germany. 

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. 

Thirty-one subjects with CHF had their pulse rate recorded daily over a 2-mo period, and the 

correlation between pulse rate and air pollutants was examined (Goldberg et al., 2008, 180380). 

There was weak evidence for a decrease in pulse rate associated with the lag 1 SO2 concentration 

after adjustment for personal and meteorological factors and no evidence for an effect associated 

with any of the other air pollutants (adjusted mean difference for CO: 0.245 [95% CI -0.209 to 

0.700] lag 0-2). 

Liao et al (2004, 056590) investigated men and women aged 45-64 yr 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 

January 2010 5-14 
Helsinki, Finland; a panel of subjects with CAD was followed for 6 mo with biweekly clinical visits, 

which included an ECG reading to assess HRV (Timonen et al., 2006, 088747). 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), with PM2.5 in Amsterdam (r = 0.58), and 

more highly correlated 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- to 2-day avg lag and a 0- to 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, 087415). 

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, 053625). The patients were asked to follow their 

usual daily activities, but data were recorded only three times with 1-wk 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. 

A study in Taiwan recruited 83 patients (aged 40-75 yr) from the National Taiwan University 

Hospital, Taipei, and conducted ambulatory ECG readings using a Holter system (Chan et al., 2005, 

088988). Ambient air pollution exposures for PM10, NO2, SO2, and CO were derived from 12 fixedsite 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. 

Min et al. (2009, 199514) investigated the effects of CO on cardiac autonomic function by 

measuring HRV in patients with and without metabolic syndrome. Several criteria were used to 

classify metabolic syndrome, including waist circumference, triglycerides and cholesterol levels, 

blood pressure, and fasting glucose level. The group classified as having metabolic syndrome 

showed significant decreases in SDNN and HF, and those declines were significantly associated with 

CO exposure with a 1- to 2-day lag. Copollutant models with PM10 and NO2 gave similar results. 

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 presents a summary of the reviewed 

studies. Due to the heterogeneity of endpoints (see column “cardiac endpoint” in Table 5-4), these 

studies do not lend themselves to a quantitative meta-analysis or inclusion in a summary figure. 

January 2010 5-15 
Table 5-4. Summary of studies investigating the effect of CO exposure on HRV parameters. 

Study Location 

Sample Size Cardiac Endpoint

Upper CO 

Concentrations 

from AQSa

 in 

ppm 

CO Concentrations 

Reported by Study 

Authors in ppm 

Copollutants 

Gold et al. (2000, 011432) Boston, MA 

(n = 21) 

HR, SDNN, 

r-MSSD 

98th%: 0.80-2.48 

99th%: 0.89-2.57 

(24 h)t 

Mean: 0.47(24 h) 

Range: 0.12-0.82 

PM10, PM2.5, O3, NO2, 

SO2

Schwartz et al. (2005, 074317) Boston, MA 

(n = 28) 

SDNN, 

r-MSSD, PNN, LF/HF 

98th%: 0.95-2.14 

99th%: 0.96-2.60 

(24 h) 

25th, 50th, 75th 

percentiles: 

0.38, 0.45, 0.54 

PM2.5, BC, NO2, O3

Park et al. (2005, 057331) Boston, MA 

(n = 4 97) SDNN, LF, HF, LF/HF 

98th%: 0.92-1.45 

99th%: 0.99-1.66 

(24 h) 

Mean: 0.50 (24 h) 

Range: 0.13-1.8 

PM2.5, BC, O3, NO2, 

SO2

Riojas-Rodriguez et al. (2006, 

156913) 

Mexico City, Mexico 

(n = 30) HF, LF, VLF, HR, R-R NA Mean: 2.9 (11 h) Range: 0.1-18 PM2.5

Holguin et al. (2003, 057326) Mexico City, Mexico 

(n = 34) HF,LF, LF/HF NA Mean: 3.3(24 h) Range: 1.8-4.8 PM2.5, O3, NO2, SO2

Wheeler et al. (2006, 088453) Atlanta, GA 

(n = 30) 

SDNN, r-MSSD, PNN, 

LF, HF, LF/HF 

98th%: 2.8-3.1 

99th%: 2.9-3.8 

(8 h) 

Mean: 362 ppb (4h) 

25th, 50th, 75th 

percentiles: 

221.5, 304.3, 398.1 

PM2.5, O3, NO2, SO2

Dales (2004, 099036) Toronto, Canada 

(n = 36) 

SDNN, HF, LF, LF/HF, 

N-N NA Mean: 2.4b

Range: 0.4-16.5 PM2.5

Peters et al. (1999, 011554) Augsburg, Germany 

(n = 2681) HR NA Mean: 3.6 Range: 1.5-7.1 TSP, SO2

Goldberg et al. (2008, 180380) Montreal, Canada 

(n-31) Pulse rate NA NR; IQR: 1.8 ppm NO2, O3, SO2, PM2.5

Liao et al. (2004, 056590) 

Maryland, 

North Carolina, 

Minnesota, 

(n = 4899-6784) 

HR, SDNN, LF, HF 

98th%: 0.39-2.29 

99th%: 0.43-2.66 

(24 h) 

Mean: 0.65 (24 h) PM10, O3, NO2, SO2

Timonen et al. (2006, 088747) 

Amsterdam, 

The Netherlands; 

Erfurt, Germany; 

Helsinki, Finland 

(n = 131) 

SDNN, HF, LF/HF NA Mean: 0.35-0.52 

Range: 0.09-2.17 PM2.5, PM10, NO2

Ibald-Mulli et al. (2004, 

087415) 

Amsterdam, 

The Netherlands; 

Erfurt, Germany; 

Helsinki, Finland 

(n = 131) 

BP, HR NA Mean: 0.35-0.52 

Range: 0.09-2.17 

UFP, PM10, PM2.5, NO2, 

SO2

Tarkiainen et al. (2003, 

053625) 

Kuopio, Finland 

(n = 6) PNN, SDNN, r-MSSD NA Mean: 4.6 Range: 0.5-27.4 None 

Chan et al. (2005, 088988) Taipei, Taiwan 

(n = 83) 

SDNN, 

r-MSSD, LF NA Mean: 1.1 Range: 0.1-7.7 PM10, NO2, SO2

Min et al. (2009, 199514) South Korea 

(n=986) SDNN, HF, LF NA Mean: 0.45 Range: 0.10-7.20 PM10, NO2, SO2

NA: Not Available 

a Includes range across individual monitors in study site; AQS data available for U.S. studies only b 95th percentile of 24-h levels 

5.2.1.2. ECG Abnormalities Indicating Ischemia 

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, including ischemia. Gold 

et al. (2005, 087558) recruited a panel of 28 older adults living at or near an apartment complex 

located within 0.5 km of a monitoring site in Boston, MA. Each subject underwent weekly ECGs for 

12 wk in summer 1999 with the main outcome of interest being the ST-segment. Air pollution data in 

the form of PM2.5, 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- to 24-h, 12-h, and 24-h ma) before the ECG. 

The final analyses included 24 subjects with 269 observations, and results showed consistent 

negative associations of ST-segment change 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 

change; however, only BC remained significant in multipollutant models. 

The most recent study by this group of researchers utilized a repeated-measures study to 

investigate the associations between ambient air pollution and ST-segment level changes averaged 

over 30-min periods in patients with coronary artery disease (CAD) (Chuang et al., 2008, 155731). 

January 2010 5-16 
The authors reported that increases in mean PM2.5, BC, NO2 and SO2 concentrations predicted 

depression of 30-min averaged ST-segment levels. No association of ST-segment depression was 

observed with CO or O3. 

5.2.1.3. Arrhythmia 

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 

resulting from dysfunction of 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/min), while bradycardia refers to a 

slow heart beat (e.g., <60 beats/min). 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, 078995) 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 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., 2000, 011347). 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., 2006, 089814), Atlanta, GA (among 518 subjects) (Metzger et al., 2007, 092856), 

Boston, MA (among 203 subjects) (Rich et al., 2005, 079620), and Vancouver, Canada (Rich et al., 

2004, 055631; Vedal et al., 2004, 055630) (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 did not find an association with ambient CO (Rich et 

al., 2005, 079620). 

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 nonsmoking older adults for 24 wk during summer and fall 

(Sarnat et al., 2006, 090489). Ambient exposures for up to 5 days prior to the health assessment 

(based on a 5-day ma) 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-mo period (Berger et al., 2006, 098702). 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 

January 2010 5-17 
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, the studies that have examined associations between CO and the occurrence of 

cardiac arrhythmias provided little evidence of a CO effect on cardiac arrhythmias. While most 

studies analyzed data from ICDs, very few reported significant associations, which is similar to the 

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 

Upper CO 

Concentrations 

from AQSa in ppm

CO Concentrations 

Reported by Study 

Authors 

 in ppm 

Copollutants 

ARRHYTHMIAS (AMONG PATIENTS WITH ICDS) 

Dockery et al. (2005, 

078995) 

Boston, MA 

(n = 203) 

Ventricular 

tachycardia 

98th%: 0.89-2.33 

99th%: 0.99-2.55 

(24 h) 

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. (2000, 

011347) 

Massachusetts, US 

(n = 100) Ventricular 

fibrillation or 

tachycardia 

98th%: 1.60-2.58 

99th%: 1.75-2.71 

(24 h) 

Mean: 0.58 (24 h) 

Max: 1.66 

PM2.5, PM10, BC, O3, NO2, SO2, 

SO4

2-

Rich et al. (2006, 

089814) 

Boston, MA 

(n = 56) Ventricular 

arrhythmia 

98th%: 0.89-2.33 

99th%: 0.99-2.55 

(24 h) 

25th, 50th, 75th percentiles: 

0.4, 0.5, 0.6 (24 h) 

PM2.5, EC, O3, NO2, SO2

Metzger et al. (2007, 

092856) 

Atlanta, GA 

(n = 518) Ventricular 

tachycardia 

98th%: 5.0 

99th%: 5.6 

(1 h) 

Mean: 1.7 (1 h) 

Range: 0.1-7.7 

PM10, PM2.5, O3, NO2, SO2

Rich et al. (2005, 

079620) 

Boston, MA 

(n = 203) Atrial fibrillation 

98th%: 0.89-2.33 

99th%: 0.99-2.55 

(24 h) 

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, 

055631) 

Vancouver, 

Canada 

(n = 34) 

ICD discharge 

due to 

arrhythmia 

NA Mean: 0.55 (24 h) 

IQR: 0.16 

PM2.5, PM10, EC, O3, NO2, SO2, 

SO4

2–

Vedal et al. (2004, 

055630) 

Vancouver, Can 

(n = 50) 

ICD discharge 

due to 

arrhythmia 

NA Mean: 0.6 (24 h) 

Range: 0.3-1.6 

PM10, O3, NO2, SO2

ARRHYTHMIAS (VIA ECG) 

Sarnat et al. (2006, 

090489) 

Steubenville, OH 

(n = 32) 

Atrial or 

ventricular 

tachycardia 

98th%: 1.42 

99th%: 1.81 

(24 h) 

Mean: 0.2 (24 h) 

Range: 0.1, 1.5 

PM2.5, O3, NO2, SO2, SO4

2–, EC 

Berger et al. (2006, 

098702) 

Erfurt, Germany 

(n = 57) 

Atrial or 

ventricular 

tachycardia 

NA 

Mean: 0.45 (24 h) 

Min, Med, Max 

0.10, 0.38, 1.68 

PM10, PM2.5, NO2, NO, SO2, UF 

NA: Not Available 

a

 Includes range across individual monitors in study site; AQS data available for U.S. studies only. 

5.2.1.4. Cardiac Arrest 

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 

January 2010 5-18 
few studies have investigated the association between ambient CO exposure and the risk of cardiac 

arrest, and none reported a significant association between increased CO exposure and the 

occurrence of cardiac arrest. 

Two studies (Levy et al., 2001, 017171; Sullivan et al., 2003, 043156) were evaluated that 

examined the association between ambient CO and cardiac arrest. Both studies were conducted in 

Seattle, WA, using a case-crossover study design and found no association between short-term 

exposure to CO and cardiac arrest. 

 These studies examined air pollution exposures for black smoke particles (BSP), PM10, SO2, 

and CO. The correlation coefficient for PM10 and CO was 0.8 in both studies. The first of these 

studies examined paramedic-attended out-of-hospital primary cardiac arrests among 362 cases 

(1998-1994) in Seattle and King County, WA, whereby lags of 0-5 days were analyzed (Levy et al., 

2001, 017171). There was no indication of association between CO and out-of-hospital primary 

cardiac arrest (RR 0.99 [95% CI: 0.83-1.18]). The second of these studies examined out-of-hospital 

primary cardiac arrest for a 10-yr period (1985-1994) among subjects within a health organization 

database (the Group Health Cooperative of Puget Sound), whereby 0- through 2-day lags were 

analyzed (Sullivan et al., 2003, 043156). The relative risk of primary cardiac arrest was 0.95 

(95% CI: 0.85-1.05; lag 0). 

5.2.1.5. 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, 016546) 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 (2 h before the onset) (OR for CO: 1.22 [95% CI: 

0.89-1.67]). 

5.2.1.6. Blood Pressure 

Only two studies have investigated whether short-term exposure to CO influences BP. The 

earlier of these two studies examined BP among 2,607 men and women aged 25-64 yr who 

participated in the Augsburg, Germany, MONICA study (Ibald-Mulli et al., 2001, 016030). 

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., 2004, 087489). While results showed significant associations 

between increased BP and ambient PM2.5, SO2, O3, and BC, there was no significant effect for CO 

(results not presented quantitatively). 

5.2.1.7. Vasomotor Function 

Gaseous pollutants, including SO2, NO and CO, were found to affect large artery endothelial 

function among 40 healthy white male nonsmokers in Paris, France, whereas PM was found to 

exaggerate the dilatory response of small arteries to ischemia (Briet et al., 2007, 093049). Changes in 

amplitude of flow-mediated dilatation were highly dependent on changes in 5-day lag concentrations 

of SO2, NO and CO, but not NO2, PM2.5 or PM10. The effect attributed to CO (β coefficient: -0.68 

[95% CI: -1.22 to -0.15]) was the smallest in magnitude when compared to those for SO2 and NO, 

but overall the effect estimates were similar and all were statistically significant. Similarly, PM2.5, 

PM10, NO2 and CO were positively correlated with small artery reactive hyperemia, and the effect 

attributed to CO was the smallest in magnitude when compared to those for PM2.5, PM10, and NO2; 

but overall, the effect estimates were similar and all were statistically significant. 

January 2010 5-19 
5.2.1.8. 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, B-type natriuretic peptide (BNP), endothelial function, Factor VII, C-reactive protein 

(CRP), prothrombin, intercellular adhesion molecule (ICAM-1), and white blood cell count (WBC). 

Delfino et al. (2008, 156390) measured blood plasma biomarkers in a panel of 29 nonsmoking, 

elderly subjects with a history of CAD living in retirement communities in the Los Angeles, CA, air 

basin, in order to identify associations with systemic inflammation. The blood plasma biomarkers 

included CRP, fibrinogen, tumor necrosis factor-α (TNF- α) and its soluble receptor-II (sTNF-RII), 

interleukin-6 (IL-6) and its soluble receptor (IL-6sR), fibrin D-dimer, soluble platelet selectin 

(sP-selectin), soluble vascular cell adhesion molecule-1 (sVCAM-1), soluble ICAM-1, and 

myeloperoxidase (MPO). Overall, there were statistically significant associations for many of the 

biomarker and pollutant combinations, with some of the strongest effects for CRP, IL-6 and sTNFRII with indoor and outdoor concentrations of NO2 and CO. Only the outdoor concentrations 

indicated an effect of PM for these three biomarkers of inflammation. There was weak evidence for 

an effect of outdoor and indoor CO on the biomarker of platelet activation (sP-selectin), and for an 

effect of many of the air pollutants examined on fibrinogen, TNF- α, sVCAM-1, sICAM-1, and 

MPO. Parameter estimates for fibrin D-dimer were close to zero for most models. Overall, the 

results suggest that traffic related pollutants, including PM2.5, UFPs, OC and CO, lead to increases in 

systemic inflammation and platelet activation in elderly people with a history of CAD. 

Delfino et al. (2009, 200844) added a second year of data from 31 additional subjects to data 

used in their previous analysis of 29 subjects (Delfino et al., 2008, 156390). This updated panel 

study of 60 elderly individuals with CAD investigated the relationship of air pollutants to changes in 

circulating biomarkers of inflammation, platelet activation and antioxidant capacity. The updated 

analysis focused on the biomarkers that were most informative in the previous analysis (Delfino et 

al., 2008, 156390) and included IL-6, TNF-α, sTNF-RII, CRP, and sP-selectin. Additionally, frozenthawed erythrocyte lysates were assayed spectrophotometrically for activities of two antioxidant 

enzymes, glutathione peroxidase-1 (GPx-1) and copper-zinc superoxide dismutase (Cu,Zn-SOD). 

Hourly outdoor home-air pollutants were measured over 9 days before each blood draw. There was 

evidence for an association of CO with IL-6, P-selectin, TNF-RII and CRP, but not for TNF- α, 

Cu,Zn-SOD, or GPx-1. Many positive associations were found for IL-6, sP-selectin, sTNF-RII, 

TNF- α, and CRP with markers of traffic-related air pollution (EC, OC, BC, NOX, and CO), 

confirming the earlier finding that traffic related pollutants may lead to increases in systemic 

inflammation and platelet activation in elderly people with a history of CAD. 

Circulating levels of BNP are directly associated with cardiac hemodynamics and symptom 

severity in patients with heart failure and serve as a marker of functional status. Wellenius et al. 

(2007, 092830) examined the association between BNP levels and short-term changes in ambient air 

pollution levels among 28 patients with chronic stable heart failure and impaired systolic function. 

The authors reported no association between CO along with the other pollutants examined and 

measures of BNP at any lag. 

Pekkanen et al. (2000, 013250) examined the association between daily concentrations of air 

pollution and concentrations of plasma fibrinogen measured among 4,982 male and 2,223 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 of CO 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 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, 088677) 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 cardiovascular disease 

(CVD). CO was not associated with other hemostatic or inflammatory factors. 

January 2010 5-20 
In Israel, Steinvil et al. (2008, 188893) examined WBC, fibrinogen, and CRP among 3,659 

study subjects enrolled in the Tel-Aviv Sourasky Medical Center inflammation survey, in which 

subjects lived <11 km 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 correlation coefficients 

were high between CO and NO2 (r = 0.86) and PM10 (r = 0.75). Exposures for lag days 0-7 were 

analyzed, and ambient CO had a negative effect on fibrinogen only among males. Negative 

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 (Ruckerl et al., 2006, 088754) recruited 57 

nonsmoking 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, 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 to 5 days (lags 1-4) was calculated for each patient. For CRP, 

the odds of observing concentrations above the 90th percentile (8.5 mg/L) 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 (lag 0-3) was associated with a decrease in FVII. 

A similar study by Rückerl and colleagues (2007, 156931) was conducted among 1,003 MI 

survivors across six 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, 090733) 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 9 different study areas, and a network average for each pollutant was calculated 

across the available monitors within each of the 9 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. 

Rudez et al. (2009, 193783) collected 13 consecutive blood samples within a 1-yr period and 

measured light-transmittance platelet aggregometry, thrombin generation, fibrinogen and CRP in 

40 healthy individuals in Rotterdam, The Netherlands. In general, air pollution increased platelet 

aggregation as well as coagulation activity but had no clear effect on systemic inflammation. 

Specifically, there were notable associations between maximal aggregation and CO, NO and NO2

and between late aggregation and CO. The effects for CO were the highest in magnitude and 

persisted over most of the lag times investigated. There also was evidence of an increase in 

endogenous thrombin potential and peak thrombin generation associated with CO, NO, NO2 and O3, but no clear associations between PM10 and peak height or lag time of thrombin generation. In 

addition, there was no evidence for an effect of any of the air pollutants examined on CRP or 

fibrinogen levels. These prothrombotic effects may partly explain the relationship between air 

pollution and the risk of ischemic cardiovascular disease. 

January 2010 5-21 
Ljungman et al. (2009, 191983) investigated the effect of CO and NO2 on inflammation in 

certain genetic subpopulations of MI survivors. Specifically, they examined whether IL-6 and 

fibrinogen gene variants could affect plasma IL-6 response to CO or NO2. The study included 

955 MI survivors from 6 European cities. This study provides evidence of gene-environment 

interaction where IL-6 and fibrinogen gene polymorphisms modified the effects of CO and NO2 on 

IL-6 levels in this panel of subjects with existing cardiovascular disease. Subjects with the 

homozygous major allele genotypes for all 3 IL-6 polymorphisms examined showed larger IL-6 

responses to increased CO, and there was evidence of a genetic interaction with NO2 for one of the 

polymorphisms. Subjects with the homozygote minor allele genotype for one fibrinogen 

polymorphism showed both a larger and clearer effect modification for the IL-6 response to 

increased CO compared to the IL-6 polymorphisms. Similar magnitudes of effect modification were 

seen for NO2, but the effect modification pattern was not statistically significant. A second fibrinogen 

polymorphism did not modify the response to air pollution. Overall, this study provides evidence for 

the influence of CO on IL-6 levels in subjects with genetic polymorphisms of the IL-6 and 

fibrinogen genes. In this study, 16% of the subjects had a polymorphism combination that resulted in 

a statistically significant gene-gene-environment interaction potentially implicating a higher risk of 

health effects from air pollution in these patients with ischemic heart disease. 

In summary, a growing number of studies provide some evidence of a link between CO 

exposure and blood markers of coagulation and inflammation. Further studies are required to 

determine whether the prothrombotic effects characterized by many of the blood markers may partly 

explain the relationship between CO and the risk of ischemic cardiovascular disease. The results of a 

recent gene-gene-environment interaction study are particularly interesting. Table 5-6 summarizes 

the reviewed studies. Due to the heterogeneity of endpoints (see column “cardiac endpoint” in Table 

5-6), these studies do not lend themselves to a quantitative meta-analysis or inclusion in a summary 

figure. 

January 2010 5-22 
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 

Upper CO 

Concentrations 

from AQSa

 in 

ppm 

CO Concentrations 

Reported by Study 

Authors in ppm 

Copollutants 

Delfino et al. (2008, 156390) Los Angeles, CA 

(n=29) 

CRP, fibrinogen, TNFα, IL-6, fibrin D-dimer, 

sP-selectin, sVCAM-1, 

sICAM-1, MPO 

98th%: 2.9 

99th%: 3.1 

(1 h) 

Outdoor Mean: 0.71 (1 h) 

Indoor Mean: 0.78 (1 h) 

O3, NO2, EC, OC, BC, 

PM0.25, PM0.25-2.5, 

PM2.5-10

Delfino et al. (2009, 200844) Los Angeles, CA 

(n=60) 

CRP, TNF-α, IL-6, sPselectin, sTNF-RII, 

Cu,Zn-SOD, GPx-1 

NA Outdoor mean: 0.50-0.58 (1h) 

O3, NO2,NOX, EC, OC, BC, 

SOC, PN, PM0.25, PM0.25-2.5, 

PM2.5-10

Wellenius et al. (2007, 

092830) 

Boston, MA 

(n=28) 

BNP 

98th%: 0.75-2.22 

99th%: 0.92-2.48 

(24 h) 

Mean: 0.44 (24 h) PM2.5, SO2, NO2, O3, BC 

Pekkanen et al (2000, 013250) 

London, U.K. 

(n = 7205) Plasma fibrinogen NA 

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, 088677) 

US 

(n = 10.208) 

Fibrinogen, VII-C, 

WBC, albumin, vWF 

98th%: 0.39-2.29 

99th%: 0.43-2.66 

(24 h) 

Mean: 1.4 (24 h) PM10, O3, NO2, SO2

Steinvil et al (2008, 188893) 

Tel-Aviv, Israel 

(n = 3659) 

CRP, fibrinogen, 

WBC 

NA 

Mean: 0.8 

25th, 50th, 75th percentiles: 

0.7, 0.8, 1.0 

PM10, O3, NO2, SO2

Rückerl et al (2006, 088754) Erfurt, Germany 

(n = 57) 

CRP, SAA, cell 

adhesions and 

coagulation 

NA Mean: 0.45 (24 h) 

Range: 0.10, 1.68 

PM10, PM2.5, UFP, EC, NO2

Rückerl et al (2007, 156931) Six European cities 

(n = 1003) IL-6, CRP, fibrinogen NA Mean: 0.29-1.48 (24 h) PM10, PM2.5, O3, NO2, SO2

Baccarelli et al (2007, 090733) 

Lombardia Region, 

Italy 

(n = 1218) 

PT, APTT, fibrinogen, 

anticoagulants NA 

Mean: 1.14-3.11 

Max: 5.52-11.43 

PM10, O3, NO2, SO2

Rudez et al. (2009, 193783) 

Rotterdam, The 

Netherlands 

(n=40) 

Platelet aggregation, 

thrombin generation, 

fibrinogen, CRP 

NA Median: 0.29 (24 h) PM10, NO, NO2, O3

Ljungman et al. (2009, 

191983) 

Six European cities 

(n=955) 

IL-6 and fibrinogen 

polymorphisms NA Mean: 0.25-1.29 (24 h) NO2, PM10, PM2.5

NA: Not Available 

a

 Includes range across individual monitors in study site; AQS data available for U.S. studies only. 

5.2.1.9. Hospital Admissions and Emergency Department Visits 

Since the 2000 CO AQCD (U.S. EPA, 2000, 000907) there have been a number of studies that 

investigated the effect of ambient CO on hospital admissions and ED visits for CVD. Some of these 

studies have focused solely on one specific CVD outcome, and these studies are discussed first. The 

subsequent sections provide a discussion of the studies that investigated hospital admissions and ED 

visits for all CVD outcomes (e.g., nonspecific) or a variety of specific CVD outcomes. 

 

January 2010 5-23 
Coronary Heart Disease 

Ischemic heart disease (IHD), also known as CHD, is caused by inadequate circulation of the 

blood to the heart muscle, which is a result of the coronary arteries being blocked by cholesterol 

deposits or by vasospasm. CHD can lead to sudden episodes such as MI or death, as well as chronic 

conditions such as angina pectoris (chest pain). 

Ischemic Heart Disease 

A number of 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). A multicity time-series study was conducted to estimate the risk of CVD 

hospitalization associated with short-term CO exposure in 126 U.S. urban counties from 1999-2005 

for over 9 million Medicare enrollees 65 yr old and older (Bell et al., 2009, 193780). The analyses 

yielded positive associations between same-day CO concentration adjusted for NO2 concentration 

and increased risk of hospitalization for IHD 1.004 (95% PI: 1.001-1.007). Cause-specific effect 

estimates were not presented for CO alone (without adjustment for NO2). 

Mann and colleagues (2002, 036723) 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 1988-1995 in southern California. 

There were 54,863 visits analyzed and a 0.75 ppm increase in 8-h max CO 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) increase among persons 

without either secondary diagnosis. Of all the pollutants examined (PM10, NO2, O3, CO), only NO2

showed positive effects estimates similar in magnitude to CO. Although no multipollutant models 

were analyzed, a moderate to high correlation between CO and NO2 was found across the seven 

regions ranging from 0.64 to 0.86. This study indicated that people with IHD and underlying CHF 

and/or arrhythmia represent a potentially susceptible population relative 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, 193793). 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), resulting in 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 (2003, 095552) examined daily counts of hospital admissions for IHD in 

Seoul, Korea, for the period from December 1997 to December 1999. Single-day lags 0-5 were 

analyzed, and the lag period with the strongest association for each pollutant was presented by the 

authors. 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+ yr 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 copollutant model 

with PM10 the CO effect was somewhat attenuated (RR 1.04 [95% CI: 0.98-1.11]). 

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 1993-2000 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., 2004, 

044222) used a time-series design and analyzed a 3-day ma 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, 090442) examined the association of ambient air 

pollution levels and cardiovascular-related ED visits with and without specific secondary conditions 

January 2010 5-24 
(e.g., comorbidity). Within a time-stratified case-crossover design using the same lag structure 

previously mentioned, the main results showed that a 1 ppm increase in 1-h max CO concentration 

was associated with an 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, 089770) 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. A time-stratified case-crossover design was employed, and the age groups of 15-64 yr 

and ≥ 65 yr 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 among the elderly group (≥ 65 yr). No association was observed for 

the younger age group. 

The second of the Australian studies (Jalaludin et al., 2006, 189416) examined ED visits for 

CVD outcomes in the elderly (65+ yr) in Sydney for the period 1997-2001. 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). 

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 1996-2001 (Hosseinpoor et al., 

2005, 087413). Using a time-series approach, single-day lags of 0-3 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 (OR: 1.005 [95% CI: 1.001-1.008]). 

Myocardial Infarction 

Linn et al. (2000, 002839) 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 yr. 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 yr) across 11 hospitals (Koken et al., 2003, 049466). Data 

between July and August for the period 1993-1997 were analyzed. Single-day lags 0-4 were 

examined and CO showed no association with hospital admissions for MI (quantitative results were 

not reported). 

As part of the Health Effects of Air Pollution among Susceptible Subpopulations (HEAPSS) 

study, Lanki et al. (2006, 089788) 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 between CO and PM10 the range was 0.21 to 0.56. A total of 26,854 

hospital admissions were analyzed, and pooled estimates from all five cities showed that there was a 

weak positive association with AMI hospital admissions 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 cities (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 <75-yr age 

group (RR: 1.080 [95% CI: 1.017-1.144]) and with non-fatal AMI in the ≥ 75-yr age group (RR: 

1.044 [95% CI: 1.011-1.076]). 

Further analyses within the HEAPSS cohort were conducted using the event of cardiac 

readmission among the first MI survivors (n = 22, 006) (Von Klot et al., 2005, 088070). The 

readmissions of interest were those with primary diagnosis of AMI, angina pectoris, dysrhythmia, 

January 2010 5-25 
and heart failure that occurred at least 29 days after the index event. Single-day lags 0-3 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]); 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, 074311). 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 with 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 hospital admissions (Barnett et al., 

2006, 089770). 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 yr). Table 5-7 shows a summary of the CHD hospital admission 

studies that examined CO exposures. 

In summary, the majority of studies reported significant increases in the daily number of 

admissions for IHD, angina 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). Figure 5-2 shows the effect estimates 

associated with daily admissions for various forms of CHD from selected studies. 

January 2010 5-26 
Study Location Lag Age Group/Outcome Effect Estimate (95% CI) 

Metzger et al. (2004, 044222) Atlanta, GA 0-2 IHD

Peel et al. (2007, 090442) Atlanta, GA 0-2 

California, US 0-3 

California, US 0-3 sCHF 

Mann et al.(2002, 036723) 

California, US 0-3 sARR 

Barnett et al. (2006, 089770) Australia, New Zealand 0-1 15-64 yr 

Australia, New Zealand 0-1 65+ yr 

Jalaludin et al. (2006, 189416) Sydney, Australia 0-1 65+ yr 

Montreal, Canada 0 All ages 

Montreal, Canada 0 All ages Males 

Montreal, Canada 0 All ages Females 

Montreal, Canada 0 65+ yr Males and Females 

Montreal, Canada 0 65+ yr Males 

Szyszkowicz (2007, 193793) 

Montreal, Canada 0 65+ yr Females 

Lee et al. (2003, 095552) Seoul, Korea 5 All ages 

Seoul, Korea 5 64+ yr 

von Klot et al. (2005, 088070) Multicity, Europe 0 35+ yr Angina

Hosseinpoor et al. (2005, 087413) Tehran, Iran 1 All ages 

Linn et al. (Linn et al., 2000, 002839) Los Angeles, CA 0 All year MI

Barnett et al. (2006, 089770) Australia, New Zealand 0-1 15-64 yr 

Australia, New Zealand 0-1 65+ yr 

Multicity, Europe 0 35+ yr All cities 

Multicity, Europe 0 <75 yr Nonfatal 

Multicity, Europe 0 <75 yr Fatal 

Multicity, Europe 0 75+ yr Nonfatal 

Lanki et al. (2006, 089788) 

Multicity, Europe 0 75+ yr Fatal 

von Klot et al. (2005, 088070) Multicity, Europe 0 35+ yr 

Rome, Italy 0-2 18+ yr 

Rome, Italy 0-2 18-64 yr 

Rome, Italy 0-2 65-74 yr 

D'Ippoliti et al. (2003, 074311) 

Rome, Italy 0-2 75+ yr 

Relative Risk 

Figure 5-2. Summary of effect estimates (95% confidence intervals) associated with hospital 

admissions for various forms of CHD. 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. 

January 2010 5-27 
Table 5-7. Summary of CHD hospital admission studies.a 

Study Location Endpoints 

Examined Copollutants Lags Examined 

Upper CO 

Concentrations from 

AQSc in ppm 

CO Concentrations 

Reported by Study 

Authors in ppm 

STUDIES THAT FOCUSED SOLELY ON CHD 

Mann et al. (2002, 036723) 

Southern 

California 

(1988-1995) 

IHD PM10, NO2, O3 0,1,2, 2-4ma 

98th%: 1.0-13.8 

99th%: 1.3-15.9 (8 h) Mean: 2.07 (8h) 

Szyszkowicz (2007, 

193793) 

Montreal, Can 

(1997-2002) 

IHD NO2 0,1 NA Mean: 0.5 (24 h) 

Lee et al.(2003, 095552) 

Seoul, Korea 

(1997-1999) 

IHD PM10, NO2, SO2, 

O3 0,1,2,3,4,5 NA Mean: 1.8 

Lanki et al. (2006, 089788)

b 5 European cities 

(1992-2000) 

MI (first 

acute) 

PM10, NO2, O3, 

PNC 0,1,2,3 NA 

Highest city was Rome. 

25th = 1.5 

75th = 2.6 

von Klot et al. (2005, 

088070)

b

 

5 European cities 

(1992-2001) 

MI, Angina, 

Cardiaca PM10, NO2, O3, 

PNC 0,1,2,3 NA Mean: highest city was Rome: 1.9 (24 h) 

D’Ippoliti et al. (2003, 

074311)

b

Rome, Italy 

(1995-1997) 

MI TSP, NO2, SO2 0,1,2,3,4, 0-2 NA Mean: 3.8 (24 h) 

Hosseinpoor et al. (2005, 

087413)

b

Tehran, Iran 

(1996-2001) Angina PM10, NO2, SO2, 

O3

0,1,2,3 NA Mean: 9.4 (24 h) 

STUDIES THAT EXAMINED CHD AMONG OTHER CVDS 

Metzger et al.(2004, 

044222) 

Atlanta, GA 

(1993-2000) 

IHD, All 

CVD, CD, 

CHF, PVCD 

PM10, NO2, SO2, 

O3 0-2ma 

98th%: 5.0-5.1 

99th%: 5.5-5.9 (1 h) Mean: 1.8 (1 h) 

Peel et al. (2007, 090442) 

Atlanta, GA 

(1993-2000) 

IHD, All 

CVD, CD, 

CHF, PVCD 

PM10, NO2, SO2, 

O3 0-2ma 

98th%: 5.0-5.1 

99th%: 5.5-5.9 (1 h) Mean: 1.8 (1 h) 

Barnett et al. (2006, 

089770) 

Australia and 

New Zealand 

(1998-2001) 

IHD, MI, All 

CVD, CA, 

Stroke 

PM10, NO2, O3 Lag 0-1 NA Mean: (8 h) 

0.5- 2.1 

Jalaludin et al. (2006, 

189416) 

Sydney, 

Australia 

(1997-2001) 

IHD, All 

CVD, Stroke, 

Cardiac 

PM10, NO2, SO2, 

O3 0,1,2,3, 0-1 NA Mean: 0.82 (8 h) 

Linn et al. (2000, 002839) 

Los Angeles, 

CA 

(1992-1995) 

MI, All CVD, 

CHF, CA, OS PM10, NO2, O3 0 

98th%: 1.0-7.8 

99th%: 1.1-8.3 (24 h) 

Mean: (24 h) 

Winter 1.7, Spring 1.0, 

Summer 1.2, Fall 2.1 

Koken et al.(2003, 

049466) 

Denver, CO 

(1993-1997) 

MI, CAth, 

PHD, CD, 

CHF 

PM10, NO2, SO2, 

O3

0,1,2,3,4 

98th%: 1.2-2.0 

99th%: 1.3-2.0 (24 h) Mean: 0.9 ppm (24 h) 

a Cardiac = AMI, angina, dysrhythmia, or HF; CA = Cardiac arrhythmia; CAth = Cardiac atherosclerosis; CD = cardiac dysrhythmias; CHF = Congestive heart failure; PHD = Pulmonary heart disease; 

OS = Occlusive stroke; PVCD = peripheral vascular and cerebrovascular disease, ma = moving average. 

b

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 temperature. 

c Includes range across individual monitors in study site; AQS data available for U.S. studies only. 

NA: Not Available 

January 2010 5-28 
Stroke 

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 ministroke, which has the same 

early symptoms as a normal stroke but the symptoms disappear within 24 h, leaving no apparent 

deficits. A limited number of air pollution studies have investigated hospital admissions for the three 

main forms of stroke and generally report small, positive associations or no association with ambient 

CO concentrations measured during lag periods between 0 and 3 days. 

In the multicity time-series study conducted by Bell et al. (2009, 193780), the analyses yielded 

small, positive associations between same-day CO concentration adjusted for NO2 concentration and 

increased risk of hospitalization for cerebrovascular outcomes 1.005 (95% PI: 1.002-1.009). Causespecific effect estimates were not presented for CO alone (without adjustment for NO2). 

A U.S. study across 9 cities investigated hospital admissions for ischemic and hemorrhagic 

stroke among Medicare beneficiaries aged 65+ yr of age (155,503 ischemic and 19,314 hemorrhagic 

admissions from the ED) (Wellenius et al., 2005, 088685). Single-day lags 0-2 were examined and 

based on a pooled estimate, same-day CO (lag 0) was associated with an increase in ischemic stroke 

admissions of 1.98% (95% CI: 0.86-3.12) per 0.5 ppm increase in 24-h CO concentration), but not 

hemorrhagic stroke admissions (-1.14%, 95% CI: -3.40 to 1.18). All other pollutants examined 

(PM10, NO2, SO2) were also associated with an increase in ischemic stroke admissions but not 

hemorrhagic stroke admissions. 

Villeneuve and colleagues (2006, 090191) studied ED visits for hemorrhagic strokes, acute 

ischemic strokes and transient ischemic attacks among individuals 65+ yr of age at 5 hospitals within 

the Edmonton, Canada, area between April 1992 and March 2002 (12,422 visits). Within a timestratified case-crossover design, the analyses were stratified by two seasonal groups (October-March 

and April-September). CO was found to only have an effect on ischemic stroke during AprilSeptember (OR: 1.32 [95% CI 1.09-1.60] per a 0.5 ppm increase in 24-h CO concentration) for a 

3-day avg across lags 0-2. 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. 

In Kaohsiung City, Taiwan, CO averaged over lags 0-2 was associated with increased 

admissions for stroke across 63 hospitals (Tsai et al., 2003, 080133). From 1997 through 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. These results persisted in twopollutant 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, 002839). 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 yr. When the analyses were stratified by season, there was a 1.8% (p=0.017) increase during 

winter, a 4.55% (p=0.039) increase during summer, and a 1.6% (p=0.015) 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 1997-2002 (Chan et al., 2006, 090193). 

Single-day lags 0-3 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). 

A time-series study that focused specifically on stroke hospital admissions conducted in Dijon, 

France, did not report a significant association with ambient CO (Henrotin et al., 2007, 093270). 

Hospital admissions for different types of first-ever stroke (e.g., ischemic, hemorrhagic) among 

January 2010 5-29 
subjects >40 yr were analyzed for the period 1994-2004. A bidirectional case-crossover study design 

was employed where single-day lags between 0-3 days were examined and CO had no significant 

association for any lag. 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, 089770). A time-stratified casecrossover design was employed and the age groups of 15-64 yr and ≥ 65 yr 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+ yr) in 

Sydney for the period 1997-2001 (Jalaludin et al., 2006, 189416). Using a time-series approach, 

single-day lags of 0-3 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. 

In summary, there was limited evidence that increased ambient CO concentrations might be 

associated with hospital admissions for stroke. The largest positive effects came from the Taiwan 

study in Kaohsiung (Tsai et al., 2003, 080133), with slightly larger effects during the warmer period 

(>20°C). Similarly, in the Canadian study by Villeneuve and colleagues (2006, 090191), there was a 

stronger effect during the warmer period (April-September). Studies in France and Australia reported 

no association between ambient CO concentrations and increased hospital admissions or ED visits 

for stroke. Figure 5-3 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. 

Study Location Lag Outcome/Group Effect Estimate (95% CI) 

Wellenius et al. (2005, 088685) Multicity, US 0-2 65+ yr Ischemic Stroke

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 IS, 65+ yr, All Seasons 

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 IS, 65+ yr, Apr-Sep 

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 IS. 65+ yr, Oct-Mar 

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 CIS, 65+ yr, All Seasons 

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 CIS, 65+ yr, Apr-Sep 

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 CIS. 65+ yr, Oct-Mar 

Tsai et al. (2003, 080133) Kaohsiung, Taiwan 0-2 <20°C Temp 

Tsai et al. (2003, 080133) Kaohsiung, Taiwan 0-2 >20°C Temp 

Linn et al. (2000, 002839) Los Angeles, CA 0 

Chan et al. (2006, 090193) Taipei, Taiwan 1 

Henrotin et al. (2007, 093270) Dijon, France 3 

Wellenius et al. (2005, 088685) Multicity, US 0-2 65+ yr Hemorrhagic Stroke

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 65+ yr, All Seasons 

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 65+ yr, Apr-Sep 

Villeneuve et al. (2006, 090191) Edmonton, Can 0-2 65+ yr Oct-Mar 

Tsai et al. (2003, 080133) Kaohsiung, Taiwan 0-2 <20°C Temp 

Tsai et al. (2003, 080133) Kaohsiung, Taiwan 0-2 >20°C Temp 

Chan et al. (2006, 090193) Taipei, Taiwan 1 

Henrotin et al. (2007, 093270) Dijon, France 1 

Chan et al. (2006, 090193) Taipei, Taiwan 2 Stroke (nonspecific)

Jalaludin et al. (2006, 189416) Sydney, Australia 0-1 

 Relative Risk 

Figure 5-3. 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. 

IS=ischemic stroke, CIS=cerebral ischemic stroke. 

January 2010 5-30 
Table 5-8. Summary of stroke hospital admission studies.a 

Study Location 

Type Of 

Stroke 

Examined 

Copollutants Lags 

Examined

Upper CO 

Concentrations 

from AQSc in ppm 

CO Concentrations 

Reported by Study 

Authors in ppm 

STUDIES THAT FOCUSED SOLELY ON STROKE 

Wellenius et al. (2005, 088685) 

9 US cities 

(1993-1999) 

Isch, Hem PM10, NO2, SO2 0,1, 2 

98th%: 0.9-5.9 

99th%: 1.2-7.1 (24 h) 

25th, 50th, 75th 

percentiles: 

0.73, 1.02, 1.44 

Villeneuve et al. (2006, 090191) Edmonton, Can 

(1992-2002) Isch, Hem, TIA NO2, SO2, O3 0,1, 0-2 NA Mean: 0.8 (24 h) 

Tsai et al. (2003, 080133) Kaohsiung, Taiwan 

(1997-2000) 

Isch, Hem PM10, NO2, SO2, 

O3 0-2 NA Mean: 0.79 (24 h) 

Chan et al. (2006, 090193) Taipei, Taiwan 

(1997-2002) 

All, Isch, Hem PM10, NO2, SO2, 

O3 0,1,2,3 NA Mean: 1.7 (8h) 

Henrotin et al. (2007, 093270)

b Dijon, France 

(1994-2004) 

Isch, Hem PM10, NOX, SO2, 

O3 0,1,2,3 NA Mean: 0.59 (24 h) 

STUDIES THAT EXAMINED STROKE AMONG OTHER CVDS 

Linn et al. (2000, 002839) Los Angeles, CA 

(1992-1995) 

Isch PM10, NO2, O3 Lag 0 

98th%: 1.0-7.8 

99th%: 1.1-8.3 (24 h) 

Mean: (24 h) 

Winter 1.7, Spring 1.0, 

Summer 1.2, Fall 2.1 

Barnett et al. (2006, 089770) 

Australia and New 

Zealand 

(1998-2001) 

All PM10, NO2, O3 Lag 0-1 NA Mean: (8h) 

0.5-2.1 

Jalaludin et al. (2006, 189416) Sydney, Australia 

(1997-2001) 

All PM10, NO2, SO2, 

O3 0,1,2,3, 0-1 NA Mean: 0.82 (8h) 

a Isch = Ischemic; Hem = Hemorrhagic; TIA = transient ischemic attack 

b

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 

temperature. 

c

 Includes range across individual monitors in study site; AQS data available for U.S. studies only. 

NA: Not Available 

Congestive Heart Failure 

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. 

In the multicity time-series study conducted by Bell et al. (2009, 193780), the analyses yielded 

positive associations between same-day CO concentration adjusted for NO2 concentration and 

increased risk of hospitalization for HF (1.009 [95% PI: 1.005-1.012]). Cause-specific effect 

estimates were not presented for CO alone (without adjustment for NO2). 

Wellenius and colleagues (2005, 087483) examined the rate of hospitalization for CHF among 

55,019 Medicare recipients (≥ 65 yr) residing in Allegheny County, PA, during 1987-1999. A timestratified case-crossover design was employed and single-day lags of 0-3 were analyzed. A 0.5 ppm 

increase in 24-h avg CO concentration on the same-day (lag 0) was associated with a 4.1% 

(95% CI: 3.0-5.3) increase in the rate of hospitalization for CHF. This result persisted in copollutant 

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, 091258). The patients 

were interviewed after admission through the ED during their stays in overnight wards. The 

January 2010 5-31 
interview was designed to collect information about symptom onset, health conditions, and factors 

related to air pollution exposure. Various lag periods (single day and cumulative days 0-3) 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) (Linn et al., 2000, 002839). Using a time-series approach, a 

0.5 ppm increase in same-day 24-h avg CO concentration was associated with a 1.25% increase in 

CHF hospital admissions among people >30 yr. 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, CO, investigated daily admissions for various CVDs among 

older adults (>65 yr) across 11 hospitals (Koken et al., 2003, 049466). Single-day lags 0-4 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 1993-2000 were analyzed (Metzger et al., 2004, 044222). A time-series 

design was used and a 3-day ma 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). Peel et al. (2007, 090442) examined 

the same cardiovascular-related effects among those with and without specific secondary conditions 

(e.g., comorbidity) and found that 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). 

In Kaohsiung city, Taiwan, a study analyzed 13,475 admissions for CHF across 63 hospitals 

for the period 1996 through 2004 (Lee et al., 2007, 090707). 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 

(>25 ̊ 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. 

Study Location Lag Group Effect Estimate (95% CI) 

Metzger et al. (2004, 044222) Atlanta, GA 0-2 

Peel et al. (2007, 090442) Atlanta, GA 0-2 

Wellenius et al.(2005, 087483) Pittsburgh, PA 0 65+ yr 

Linn et al. (2000, 002839) Los Angeles, CA 0 

Koken et al. (2003, 049466) Denver, CO 3 65+ yr 

Symons et al. (2006, 091258) Baltimore, MD 0-3 

Lee et al. (2007, 090707) Kaohsiung, Taiwan 0-2 <25°C Temp 

Lee et al. (2007, 090707) Kaohsiung, Taiwan 0-2 >25°C Temp 

Yang (2008, 157160) Taipei, Taiwan 0-2 <20°C Temp 

Yang (2008, 157160) Taipei, Taiwan 0-2 >20°C Temp 

 Relative Risk 

Figure 5-4. 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. 

A case-crossover analysis was undertaken to examine the association between levels of 

ambient air pollutants and hospital admissions for CHF among individuals residing in Taipei, 

Taiwan, from 1996 through 2004 (Yang, 2008, 157160). During the 9 yr of the study, there were 

24,240 CHF hospital admissions for the 47 hospitals in Taipei. The analyses were stratified by 

temperature, either warm days (>20°C; n = 2325 days) or cool days (<20°C; n = 963 days). The 

number of CHF admissions was associated with concentrations of PM10, NO2, CO and O3 on warm 

days, however on cool days, the positive effects on increased CHF admissions remained positive, 

although the effects were diminished for NO2 and CO, and disappeared completely for PM10 and O3

concentrations. In two-pollutant models, CO remained statistically significant after the inclusion of 

PM10, SO2 or O3 on warm days. On cool days, the effects associated with CO remained positive, but 

January 2010 5-32 
were no longer statistically significant after the inclusion of PM10, SO2, or NO2, but became 

statistically significant and negative after the inclusion of O3 in the model (Figure 5-6). 

Figure 5-4 shows the effect estimates for associations between CO and daily admissions for 

CHF from selected studies; Table 5-9 summarizes the CHF 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 positive associations at lags of 

0-3 days. 

Table 5-9. Summary of CHF hospital admission studies. 

Study Location Endpoints 

Examined Copollutants Lags Examined

Upper CO 

Concentrations 

from AQSa in ppm 

CO Concentrations 

Reported by Study 

Authors in ppm 

STUDIES THAT FOCUSED SOLELY ON HF 

Wellenius et al. (2005, 

087483) 

Pittsburgh, PA 

(1987-1999) CHF PM10, NO2, SO2, 

O3

0,1,2,3 

98th%: 1.4-3.4 

99th%: 1.6-3.9 (24 h) Mean: 1.03 (24 h) 

Symons et al.(2006, 

091258) 

Baltimore, MD 

(2002) CHF PM2.5, NO2, O3 0,1,2,3 

98th%: 1.9-2.1 

99th%: 2.3 (8 h) Mean: 0.4 (8 h) 

Lee et al. (2007, 

090707) 

Kaohsiung, 

Taiwan 

(1996-2004) 

CHF PM10, NO2, SO2, 

O3

0-2 NA Mean: 0.76 (24 h) 

Yang (2008, 157160) Taipei, Taiwan 

(1996-2004) 

CHF PM10, NO2, SO2, 

O3 0-2 NA Mean: 1.26 (24 h) 

STUDIES THAT EXAMINED HF AMONG OTHER CVDS 

Linn et al. (2000, 

002839) 

Los Angeles, CA 

(1992-1995) 

CHF, MI, All 

CVD, CA, OS PM10, NO2, O3 0 

98th%: 1.0-7.8 

99th%: 1.1-8.3 (24 h) 

Mean: (24 h) 

Winter 1.7; Spring 1.0 

Summer 1.2; Fall 2.1 

Koken et al. (2003, 

049466) 

Denver, CO 

(1993-1997) 

CHF, MI, CAth, 

PHD, CD 

PM10, NO2, SO2, 

O3

0,1,2,3 

98th%: 1.2-2.0 

99th%: 1.3-2.0 (24 h) Mean: 0.9 (24 h) 

Metzger et al. (2004, 

044222) 

Atlanta, GA 

(1993-2000) 

CHF, IHD, All 

CVD, CD, 

PVCD 

PM10, NO2, SO2, 

O3

0-2ma 

98th%: 5.0-5.1 

99th%: 5.5-5.9 (1 h) Mean 1.8 (1 h) 

Peel et al. (2007, 

090442) 

Atlanta, GA 

(1993-2000) 

CHF, IHD, All 

CVD, CD, 

PVCD 

PM10, NO2, SO2, 

O3 0-2ma 

98th%: 5.0-5.1 

99th%: 5.5-5.9 (1 h) Mean 1.8 (1 h) 

Cardiac = AMI, angina, dysrhythmia, or HF; CA = Cardiac arrhythmia; CAth = Cardiac atherosclerosis; CD = cardiac dysrhythmias; CHF = Congestive heart failure; PHD = Pulmonary heart disease; 

OS = Occlusive stroke; PVCD = peripheral vascular and cerebrovascular disease, ma = moving average. 

NA: Not Available 

a

 Includes range across individual monitors in study site; AQS data available for U.S. studies only. 

Cardiovascular Diseases 

The following section reviews studies that have investigated the effect of CO on ED visits and 

hospital admissions for all CVD outcomes (e.g., nonspecific). Several of these studies also examined 

specific CVDs and were briefly discussed in previous sections. 

A multicity time-series study was conducted to estimate the risk of CVD hospitalization 

associated with short-term CO exposure in 126 U.S. urban counties from 1999-2005 for over 

9 million Medicare enrollees 65 yr old and older (Bell et al., 2009, 193780). The analyses yielded 

positive associations between same-day CO concentration and increased risk of hospitalization for 

total CVD outcomes, which remained positive and statistically significant but were attenuated with 

copollutant adjustment, especially with NO2 (Figure 5-6). Overall, a 1 ppm increase in same-day 1-h 

max CO was associated with a 1.010 (95% PI: 1.008-1.011) increase in risk of CVD admissions. 

January 2010 5-33 
After adjustment for NO2, the estimate was attenuated to 1.005 (95% PI: 1.004-1.007). For most 

cause-specific CVD hospitalizations, associations were positive and statistically significant for same 

day CO concentration adjusted for same-day NO2 (IHD 1.004 [95% PI: 1.001-1.007], heart rhythm 

1.006 [95% PI: 1.001-1.011], HF 1.009 [95% PI: 1.005-1.012], and cerebrovascular 1.005 [95% PI: 

1.002-1.009]). Cause-specific effect estimates were not presented for CO alone (without adjustment 

for NO2). 

As discussed earlier, a study was conducted in Atlanta, GA where over 4 million ED visits 

from 31 hospitals for the period 1993-2000 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 ma over single-day lags 

0-2 as the a priori lag structure (Metzger et al., 2004, 044222). 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, 

090442). Within a time-stratified case-crossover design, a 3-day ma 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 comorbidity 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 comorbidities, 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. 

Table 5-10. Association of ambient air pollution levels and cardiovascular morbidity in visits with 

and without specific secondary conditions. 

Co-morbidity IHD Dysrhythmias 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) - 

DYSRHYTHMIAS 

- 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: Reprinted with Permission of Oxford Journals from Peel et al. (2007, 090442)

The third study utilizing the SOPHIA data extended the time period to include 1993 through 

2004 (Tolbert et al., 2007, 090316) and focused on two large outcome groups: a respiratory diseases 

group and a cardiovascular diseases group. The combined cardiovascular case group included the 

January 2010 5-34 
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 two-pollutant combinations of NO2, CO and total carbon, 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 >30 yr (Linn et al., 2000, 002839). When the analyses were stratified by season, the strongest 

CO effect occurred during the winter (1.9% increase) followed by the 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 1-3 days) and an increase in the number of daily cardiac hospital 

admissions (quantitative results not reported) (Slaughter et al., 2005, 073854). Similarly, a timeseries 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, 074322). A total of 

11,632 cardiac admissions were analyzed for the period 1995-2000. 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-yr age group 

(lag 0) was -2.6 (95% CI: -6.2 to 3.3); and for the 65+ yr 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 1997-2001 were analyzed (Chang et al., 2005, 080086). 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., 2004, 094376). 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 CVD 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, 089770). 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+ yr. Among those aged 

15-64 yr 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+ yr) in Sydney for the period 

1997-2001 (Jalaludin et al., 2006, 189416). 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, 013257) analyzed 

emergency hospital admissions in Valencia, Spain, for the period 1994-1996. The mean daily number 

of CVD admissions was 7, and 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, 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). 

January 2010 5-35 
Ballester et al. (2006, 088746) extended this research to include data from 14 Spanish cities 

for the period 1995-1999. 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 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. A study was carried out to evaluate the association between air pollution cardiovascular ED 

visits in subjects with and without diabetes in Sao Paulo, Brazil (Pereira Filho et al., 2008, 190260). 

From January 2001 to July 2003, 45,000 ED visits were registered due to CVDs, of which 700 were 

registered due to CVDs in diabetic patients. SO2 and NO2 were positively and statistically 

significantly associated with CVD ED visits among diabetics and nondiabetics, while CO was only 

positive and statistically significant among non-diabetic patients. PM10 and O3 were not positively 

associated with ED admissions among either group. 

Table 5-11 summarizes the non-specific CVD hospital admission studies that examined CO 

exposures. Due to the heterogeneity of endpoints, these studies do not lend themselves to a 

quantitative meta-analysis, and a forest plot was used to summarize the results of the studies on all 

CVD outcomes. Figure 5-5 shows the effect estimates associated with daily admissions for 

nonspecific 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 small yet precise 

positive 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. 

Study Location Lag Group Effect Estimate (95% CI) 

Bell et al. (2009, 193780) 126 US counties 0 65+ yr 

Tolbert et al. (2007, 090316) Atlanta, GA 0-2 

Linn et al. (2000, 002839) Los Angeles, CA 0 All Year 

Linn et al. (2000, 002839) Los Angeles, CA 0 Spring 

Linn et al. (2000, 002839) Los Angeles, CA 0 Summer 

Linn et al. (2000, 002839) Los Angeles, CA 0 Fall 

Linn et al. (2000, 002839) Los Angeles, CA 0 Winter 

Fung et al. (2005, 074322) Windsor, Can 0-2 <65 yr 

Fung et al. (2005, 074322) Windsor, Can 0-2 65+ yr 

Barnett et al. (2006, 089770) Australia, New Zealand 0-1 15-64 yr 

Barnett et al. (2006, 089770) Australia, New Zealand 0-1 65+ yr 

Jalaludin et al. (2006, 189416) Sydney, Australia 0-1 65+ yr 

Ballester et al. (2006, 088746) Multicity, Spain 0-1 

Pereira Filho et al. (2008, 190260) Sao Paulo, Brazil 0 Diabetic 

Pereira Filho et al. (2008, 190260) Sao Paulo, Brazil 0 Nondiabetic 

Chang et al. (2005, 080086) Taipei, Taiwan 0-2 >20°C Temp 

Chang et al. (2005, 080086) Taipei, Taiwan 0-2 <20°C Temp 

Yang et al. (2004, 094376) Kaohsiung, Taiwan 0-2 <20°C Temp 

Yang et al. (2004, 094376) Kaohsiung, Taiwan 0-2 >20°C Temp 

Relative Risk 

Figure 5-5. 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. 

January 2010 5-36 
Table 5-11. Summary of nonspecific CVD hospital admission studies. 

Study Location CVD Codes Copollutants Lags 

Examined 

Upper CO 

Concentrations from 

AQSb in ppm 

CO Concentrations 

Reported by Study 

Authors in ppm 

Bell et al. (2009, 193780) 

126 urban US 

counties 

(1999-2005) 

Total CVD PM2.5, NO2, EC 0, 1, 2 

98th%: 1.1-19.1 

99th%: 1.2-22.1 (1 h) 

Median: 1.3 (1 h) 

Median: 0.5 (24 h) 

Metzger et al. (2004, 044222) 

Atlanta, GA 

(1993-2000) 

All CVD PM10, NO2, SO2, O3 0-2ma 

98th%: 5.0-5.1 

99th%: 5.5-5.9 (1 h) Mean: 1.8 (1 h) 

Peel et al. (2007, 090442) 

Atlanta, GA 

(1993-2000) 

All CVD PM10, NO2, 

SO2, O3 0-2ma 

98th%: 5.0-5.1 

99th%: 5.5-5.9 (1 h) Mean 1.8 (1 h) 

Tolbert et al. (2007, 090316) 

Atlanta, GA 

(1993-2004) 

All CVD PM10, NO2, 

SO2, O3 0-2ma 

98th%: 4.7-4.9 

99th%: 5.3-5.4 (1 h) Mean 1.6 (1 h) 

Linn et al. (2000, 002839) Los Angeles, CA 

(1992-1995) 

All CVD PM10, NO2, O3 0 

98th%: 1.0-7.8 

99th%: 1.1-8.3 (24 h) 

Mean: (24 h) 

Winter 1.7; Spring 1.0;

Summer 1.2; Fall 2.1 

Slaughter et al. (2005, 073854) Spokane, WA 

(1995-2001) 

All CVD 

(ICD9: 390-

459) 

PM10, PM2.5, 

CO 1,2,3 

98th%: 1.5-4.6 

99th%: 1.7-5.0 (24 h) 

Mean: range across 5 

monitors 0.42-1.82 

(24 h) 

Fung et al. (2005, 074322) 

Windsor, Can 

(1995-2000) 

All CVD (HF, 

IHF, or 

Dysrhythmia) 

PM10, NO2, 

SO2, O3

0, 0-1, 0-2 NA Mean: 1.3 (1 h) 

Chang et al. (2005, 080086) Taipei, Taiwan 

(1997-2001) 

All CVD 

(ICD9: 410-

429) 

PM10, NO2, 

SO2, O3 0-2 NA Mean: 1.37 (24 h) 

Yang et al. (2004, 094376) Kaohsiung, Taiwan 

(1997-2000) 

All CVD 

(ICD9: 410-

429) 

PM10, NO2, 

SO2, O3 0-2 NA Mean: 0.79 (24 h) 

Barnett et al. (2006, 089770) 

Australia and New 

Zealand 

(1998-2001) 

All CVD 

(ICD9: 390-

459) 

PM10, NO2, O3 0-1 NA Mean: (8h) 

0.5-2.1 

Jalaludin et al. (2006, 189416) Sydney, Australia 

(1997-2001) 

All CVD 

(ICD9: 390-

459) 

PM10, NO2, 

SO2, O3 0,1,2,3, 0-1 NA Mean: 0.82 (8h) 

Ballester et al. (2001, 013257)

a Valencia, Spain 

(1994-1996) 

All CVD 

(ICD9: 390-

459) 

BS, NO2, SO2, 

O3 1,2,3,4,5 NA Mean: 0.54 (24 h) 

Ballester et al. (2006, 088746)

a Multicity, Spain 

(1995-1999) 

All CVD 

(ICD9: 390-

459) 

BS, PM10, TSP, 

NO2, SO2, O3

0-1 NA 

Mean: range across 14 

cities 

0.12-0.24 (8h) 

Pereira Filho et al. (2008, 

190260) 

Sao Paulo, Brazil 

(2001-2003) 

All CVD PM10, NO2, 

SO2, O3

0, 1, 2, 0-1, 

0-2, 0-3 NA Mean: 2.7 (8 h) 

a

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 

temperature. 

b

 Includes range across individual monitors in study site; AQS data available for U.S. studies only.

January 2010 5-37 
Figure 5-6 and Figure 5-7 summarize 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 two instances – Lee et 

al. (2007, 090707) (<25°C adjusted for NO2) and Yang (2008, 157160) (<20°C adjusted for O3) – 

when the single pollutant effect estimate was positive for CO, it remained positive after the addition 

of any of the copollutants investigated. 

Study Outcome Lag Pollutant Group Effect Estimate (95% CI) 

Wellenius et al. (2005, 087483) CHF 0 CO Alone PM10

Wellenius et al. (2005, 087483) CHF 0 CO + PM10

Chan et al. (2006, 090193) CD 2 CO Alone 

Chan et al. (2006, 090193) CD 2 CO + PM10

Lee et al. (2007, 090707) CHF 0 CO Alone >25°C 

Lee et al. (2007, 090707) CHF 0 CO + PM10

D’Ippoliti et al. (2003, 074311) IHD 0-2 CO Alone 

D’Ippoliti et al. (2003, 074311) IHD 0-2 CO + TSP 

Chang et al. (2005, 080086) CVD 0 CO Alone >20°C 

Chang et al. (2005, 080086) CVD 0 CO + PM10

Chang et al. (2005, 080086) CVD 0 CO Alone <20°C 

Chang et al. (2005, 080086) CVD 1 CO + PM10

Lee et al. (2003, 095552) IHD 0 CO Alone 64+ yr 

Lee et al. (2003, 095552) IHD 0 CO + PM10

Ballester et al. (2006, 088746) CVD 0-1 CO Alone 

Ballester et al. (2006, 088746) CVD 0-1 CO + PM10

von Klot et al. (2005, 088070) CVD 0 CO Alone 

von Klot et al. (2005, 088070) CVD 0 CO + PM10

Lee et al. (2007, 090707) CHF 0 CO Alone <25°C 

Lee et al. (2007, 090707) CHF 0 CO + PM10

Yang (2008, 157160) CHF 0-2 CO Alone >20°C 

Yang (2008, 157160) CHF 0-2 CO + PM10

Yang (2008, 157160) CHF 0-2 CO Alone <20°C 

Yang (2008, 157160) CHF 0-2 CO + PM10

Bell et al. (2009, 193780) CVD 0 CO Alone PM2.5

Bell et al. (2009, 193780) CVD 0 CO + PM2.5

Tolbert et al. (2007, 090316) CVD 0-2 CO Alone 

Tolbert et al. (2007, 090316) CVD 0-2 CO + PM2.5

Chan et al. (2006, 090193) CD 2 CO Alone 

Chan et al. (2006, 090193) CD 2 CO + PM2.5

 Relative Risk 

Figure 5-6. Effect estimates from studies of ED visits and hospital admissions for CVD 

outcomes other than stroke from single pollutant (CO only: black circles) and 

particulate copollutant (CO + PM2.5: red triangles; CO + PM10 or TSP: purple 

triangles) models. 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. 

January 2010 5-38 
Study Outcome Lag Pollutant Label Effect Estimate (95% CI) 

Bell et al. (2009, 193780) CVD 0 CO Alone NO2

Bell et al. (2009, 193780) CVD 0 CO + NO2

Wellenius et al. (2005, 087483) CHF 0 CO Alone 

Wellenius et al. (2005, 087483) CHF 0 CO + NO2

Tolbert et al. (2007, 090316) CVD 0-2 CO Alone 

Tolbert et al. (2007, 090316) CVD 0-2 CO + NO2

Ballester et al. (2006, 088746) CVD 0-1 CO Alone 

Ballester et al. (2006, 088746) CVD 0-1 CO + NO2

Chang et al. (2005, 080086) CVD 0 CO Alone >20°C 

Chang et al. (2005, 080086) CVD 0 CO + NO2

Chang et al. (2005, 080086) CVD 0 CO Alone <20°C 

Chang et al. (2005, 080086) CVD 3 CO + NO2

Lee et al. (2007, 090707) CHF 0 CO Alone >25°C 

Lee et al. (2007, 090707) CHF 0 CO + NO2

Lee et al. (2007, 090707) CHF 0 CO Alone <25°C 

Lee et al. (2007, 090707) CHF 0 CO + NO2

Yang (2008, 157160) CHF 0-2 CO Alone >20°C 

Yang (2008, 157160) CHF 0-2 CO + NO2

Yang (2008, 157160) CHF 0-2 CO Alone <20°C 

Yang (2008, 157160) CHF 0-2 CO + NO2

Wellenius et al. (2005, 087483) CHF 0 CO Alone SO2

Wellenius et al. (2005, 087483) CHF 0 CO + SO2

Ballester et al. (2006, 088746) CVD 0-1 CO Alone 

Ballester et al. (2006, 088746) CVD 0-1 CO + SO2

Chang et al. (2005, 080086) CVD 0 CO Alone >20°C 

Chang et al. (2005, 080086) CVD 0 CO + SO2

Chang et al. (2005, 080086) CVD 0 CO Alone <20°C 

Chang et al. (2005, 080086) CVD 2 CO + SO2

Lee et al. (2007, 090707) CHF 0 CO Alone >25°C 

Lee et al. (2007, 090707) CHF 0 CO + SO2

Lee et al. (2007, 090707) CHF 0 CO Alone <25°C 

Lee et al. (2007, 090707) CHF 0 CO + SO2

Yang (2008, 157160) CHF 0-2 CO Alone >20°C 

Yang (2008, 157160) CHF 0-2 CO + SO2

Yang (2008, 157160) CHF 0-2 CO Alone <20°C 

Yang (2008, 157160) CHF 0-2 CO + SO2

Wellenius et al. (2005, 087483) CHF 0 CO Alone O3

Wellenius et al. (2005, 087483) CHF 0 CO + O3

Chan et al. (2006, 090193) CD 2 CO Alone 

Chan et al. (2006, 090193) CD 2 CO + O3

Ballester et al. (2006, 088746) CVD 0-1 CO Alone 

Ballester et al. (2006, 088746) CVD 0-1 CO + O3

Chang et al. (2005, 080086) CVD 0 CO Alone >20°C 

Chang et al. (2005, 080086) CVD 0 CO + O3

Chang et al. (2005, 080086) CVD 0 CO Alone <20°C 

Chang et al. (2005, 080086) CVD 4 CO + O3

von Klot et al. (2005, 088070) Cardiac 0 CO Alone 

von Klot et al. (2005, 088070) Cardiac 0 CO + O3

Lee et al. (2007, 090707) CHF 0 CO Alone >25°C 

Lee et al. (2007, 090707) CHF 0 CO + O3

Lee et al. (2007, 090707) CHF 0 CO Alone <25°C 

Lee et al. (2007, 090707) CHF 0 CO + O3

Yang (2008, 157160) CHF 0-2 CO Alone >20°C 

Yang (2008, 157160) CHF 0-2 CO + O3

Yang (2008, 157160) CHF 0-2 CO Alone <20°C 

Yang (2008, 157160) CHF 0-2 CO + O3

 Relative Risk 

Figure 5-7. Effect estimates from studies of ED visits and HAs for CVD outcomes other than 

stroke from single pollutant (CO only: black circles) and gaseous copollutant 

models (CO + NO2, SO2 and O3= green, blue, and orange triangles, respectively). 

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. 

January 2010 5-39 
5.2.2. Epidemiologic Studies with Long-Term Exposure 

Two studies examined CVD outcomes in association with long-term exposure to CO. 

Rosenlund et al. (2006, 089796) investigated long-term exposure (30 yr) to urban air pollution and 

the risk of MI in Sweden. The study included 2,246 cases and 3,206 controls aged 45-70 yr and 

residing in Stockholm County during 1992-1993. A detailed postal questionnaire was completed by 

4,067 subjects, and all addresses inhabited during more than 2 yr since 1960 were geocoded. The 

exposures were then derived from dispersion calculations based on emissions data for each decade 

since 1960. These calculations were estimates of annual mean levels of traffic-generated NOX, NO2, CO, PM10, and PM2.5, with the addition of SO2 from heating sources. The analyses were stratified by 

all cases, nonfatal cases, fatal cases, in-hospital death, and out-of-hospital death. Based on a 30-yr 

avg exposure all pollutants were not associated with overall MI incidence. However, increased CO 

was associated with out-of-hospital death from MI (OR: 1.81 [95% CI: 1.02-3.23] per 0.5 ppm 

increase in 30-yr avg CO concentration). Similar results were reported for NO2. The correlation 

between the 30-yr NO2 and CO exposures was reasonably strong (r = 0.74) and multipollutant 

models with both these pollutants included (NO2, CO) were not examined. No other pollutants were 

significantly associated with all other MI outcomes. The study period was extended to include 

43,275 cases of MI during 1985-1996 and 507,000 controls (Rosenlund et al., 2009, 190309). Fiveyear average exposures to NO2, PM10 and CO were associated with incidence of MI, especially with 

fatal disease; when examining only nonfatal disease, no association was observed. The effect 

estimate for CO (OR: 1.03 [95%C CI: 1.02-1.04] per 0.5 ppm increase in 5-yr avg) was similar in 

magnitude to those for NO2 and PM10. When the analysis was restricted to the group that did not 

move between population censuses (the least expected misclassification of true individual exposure), 

the effect estimate for CO increased to 1.17 (95% CI: 1.11-1.24) per 0.5 ppm increase in 5-yr avg, 

and although the effect estimates for NO2 and PM10 remained similar to the estimate for CO, in this 

analysis the effect estimate for CO was slightly greater in magnitude than the effect estimate for 

PM10. 

A small-area ecologic study analyzed mortality and hospital admissions for stroke across 1,030 

census districts in Sheffield, U.K. (Maheswaran et al., 2005, 088683). 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]). 

5.2.3. Summary of Epidemiologic Studies of Exposure to CO and 

Cardiovascular Effects 

A substantial number of epidemiologic studies have examined the potential association 

between exposure to CO and various relevant cardiac endpoints or biomarkers. Overall, despite some 

mixed results reported among panel and retrospective cohort studies, there was evidence that 

exposure to CO has an effect on HR, various HRV parameters, and blood markers of coagulation and 

inflammation. Conversely, based on results from panel studies, there was little evidence of a link 

between CO and cardiac arrhythmia, cardiac arrest, the occurrence of MI, and increased BP. 

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. Nearly all of the studies include 

same day (lag 0) or next day (lag1) lag periods, which are consistent with the proposed mechanism 

and biological plausibility of these CVD outcomes. When categorized by specific cardiovascular 

outcome, the evidence is consistent. Studies of hospital admissions and ED visits for IHD provide 

the strongest evidence of ambient CO being associated with adverse CVD outcomes. The effect 

estimates for this outcome are nearly all positive, many are statistically significant, and the 

magnitude of effect is similar among the studies. Though not as consistent as the IHD effects, the 

effects for all CVD hospital admissions (which include IHD admissions) and CHF hospital 

admissions also provide evidence for an association with ambient CO concentrations. There is very 

limited evidence that ambient CO is associated with ischemic stroke. It is difficult to determine from 

this group of studies the extent to which CO is independently associated with CVD outcomes or if 

January 2010 5-40 
CO is a marker for the effects of another traffic-related 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 and supports a direct effect of shortterm ambient CO exposure on CVD morbidity. 

5.2.4. Controlled Human Exposure Studies 

Controlled human exposure studies provide valuable information related to the health effects 

of short-term exposure to air pollutants. Results of controlled human exposure studies can be used to 

provide coherence with the evidence from epidemiologic studies by expanding the understanding of 

potential mechanisms for the observed health outcomes. However, they may also provide 

information that can be used directly in quantitatively characterizing the exposure concentrationhealth response relationships at ambient or near-ambient concentrations. 

Several human clinical studies cited in the 2000 CO AQCD (U.S. EPA, 2000, 000907) 

observed changes in measures of cardiovascular function among individuals with CAD, following 

short-term exposures to CO. Principal among these is a large multilaboratory study of men with 

stable angina (n = 63), designed to evaluate the effect of CO exposure resulting in COHb 

concentrations of 2% and 4% on exercise-induced angina and ST-segment changes indicative of 

myocardial ischemia (Allred et al., 1989, 013018; Allred et al., 1989, 012697; Allred et al., 1991, 

011871). The majority of subjects were following an anti-ischemic medication regimen (e.g., beta 

blockers, nitrates, or calcium channel antagonists) which was maintained throughout the study. On 

three separate occasions, subjects underwent an initial graded exercise treadmill test, followed by 50- 

to 70-min exposures under resting conditions to average CO concentrations of 0.7 ppm (room air 

concentration range 0-2 ppm), 117 ppm (range 42-202 ppm) and 253 ppm (range 143-357 ppm). 

After the 50- to 70-min exposures, subjects underwent a second graded exercise treadmill test, and 

the percent change in time to onset of angina and time to ST endpoint between the first and second 

exercise tests was determined. The investigators conducted two exercise tests on exposure days (preversus postexposure) to control for day-to-day variability in the endpoints of interest. The effect of 

CO was evaluated by comparing the percent change in time to onset of angina or ST-segment change 

between the CO and clean air exposure days. The order of the three exposures was randomly 

determined and counterbalanced across subjects. For the CO exposure sessions, postexposure target 

COHb concentrations were set at values 10% greater than the post-exercise targets (i.e., 2.2% and 

4.4%) to compensate for the elimination of CO during exercise testing in clean air following 

exposure. CO uptake constants were determined for each subject individually during a qualifying 

visit and were used to compute the inhaled concentration required to attain the target COHb 

concentrations. Although CO-oximetry was used at each center to rapidly provide approximate 

concentrations of COHb during the actual exposure, COHb concentrations determined by a gas 

chromatographic technique were used in the statistical analyses as this method is known to be more 

accurate than CO-oximetry and other spectrophotometric methods, particularly for samples 

containing COHb concentrations <5%. For the two CO exposures, the average postexposure COHb 

concentrations were reported as 2.4% and 4.7% (3.2% and 5.6% using CO-oximetry), and the 

average post-exercise COHb concentrations were reported as 2.0% and 3.9% (2.7% and 4.7% using 

CO-oximetry). While the average COHb concentrations during the exercise tests were clearly 

between the concentrations measured in postexposure and post-exercise blood samples, the study 

authors noted that the samples at the end of the exercise test represented the COHb concentrations at 

the approximate time of onset of myocardial ischemia as indicated by angina and ST segment 

changes. Relative to clean air exposure (COHb ≈ 0.6-0.7%), exposures to CO resulting in postexercise COHb concentrations of 2.0% and 3.9% were shown to decrease the time required to induce 

ST-segment changes by 5.1% (p = 0.01) and 12.1% (p < 0.001), respectively. These changes were 

well correlated with the onset of exercise-induced angina. The observed dose-response relationship 

was further evaluated by regressing the percent change in time to ST-segment change or time to 

angina on actual post-exercise COHb concentration (0.2-5.1%) using the three exposures (air control 

and two CO exposures) for each subject. Regression analyses were conducted separately for each 

individual and the averages of the intercepts and slopes across subjects were reported. This analysis 

demonstrated significant decreases in time to angina and ST-segment change of approximately 1.9% 

and 3.9%, respectively, per 1% increase in COHb concentration, with no evidence of a measurable 

January 2010 5-41 
threshold. The relationship between percent change in time to ST-segment endpoint and postexercise COHb concentration is illustrated in Figure 5-8. 

Source: Reprinted with Permission of HEI from Allred et al. (1989, 012697) 

Figure 5-8. Regression of the percent change in time to ST endpoint between the pre- and 

postexposure exercise tests ([postexposure–pre-exposure]/pre-exposure) and 

the measured blood COHb levels at the end of exercise for the 63 subjects 

combined. The line represents the average of individual regressions. 

In addition to the work of Allred et al. (1989, 013018; 1989, 012697; 1991, 011871) 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%, measured using spectrophotometric methods (Adams et al., 1988, 

012692; Anderson et al., 1973, 023134; Kleinman et al., 1989, 012696; Kleinman et al., 1998, 

047186). However, Sheps et al. (1987, 012212) 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, 

013286). 

While cardiovascular effects of CO have consistently been observed in studies of controlled 

human exposure among individuals with CAD at COHb concentrations between 2 and 6%, a 

quantitative meta-analysis of these studies is of limited value considering differences in the methods 

used. For example, variation in exercise protocols resulted in substantial differences between studies 

in total exercise time. More importantly, only Allred et al. (1989, 013018; 1989, 012697; 1991, 

January 2010 5-42 
011871) analyzed COHb concentration using gas chromatography. Although all studies measured 

COHb using spectrophotometric methods, these methods are only accurate within approximately 1% 

COHb of the true value at COHb concentrations < 5% (U.S. EPA, 1991, 017643). Therefore, a 

quantitative evaluation of changes in cardiovascular response with small increases in COHb 

concentration (< 1%) as measured using CO-oximetry is neither appropriate nor informative, 

particularly at low COHb concentrations. 

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 moderate to severe, with the majority either having a history of MI or having ≥ 70% occlusion 

of one or more of the coronary arteries. The 2000 CO AQCD (U.S. EPA, 2000, 000907) presented 

very little evidence of CO-induced changes in cardiovascular function in healthy adults. Davies and 

Smith (1980, 011288) 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 (U.S. EPA, 2000, 000907), 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, 

001026) 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 postexposure exercise. However, CO was 

demonstrated to decrease the postexposure 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 (U.S. EPA, 2000, 000907) which observed decreases in exercise 

duration and maximal aerobic capacity among healthy adults at COHb levels ≥ 3% (Drinkwater et 

al., 1974, 041332; Ekblom and Huot, 1972, 010886; Horvath et al., 1975, 010887; Raven et al., 

1974, 041340). 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. 

Kizakevich et al.(2000, 052691) 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, 021120) 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, 

193915) 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 and Somers (1997, 083450) 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, 193853). This protocol resulted in COHb 

concentrations of 5.6% and 9.4% following exposures of 30 and 60 min, respectively, with 

January 2010 5-43 
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, 097980). 

In summary, controlled human exposures to CO among individuals with CAD have been 

shown to consistently increase markers of myocardial ischemia at COHb concentrations between 2 

and 6%. 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 as low as 5%, this effect has not been observed consistently across studies. 

5.2.5. Toxicological Studies 

While there was no toxicological research reported in the 2000 CO AQCD (U.S. EPA, 2000, 

000907) that involved CO exposures at or below the NAAQS levels, adverse cardiovascular effects 

were reported for higher CO concentrations. The lowest observed effect levels for cardiovascular 

effects in experimental animals included 50 ppm (6-wk exposure, 2.6% COHb) for cardiac rhythm 

effects, 100 ppm (46 days, 9.3% COHb) for hematology effects, 150 ppm (30 min, 7.5% COHb) for 

hemodynamic effects, 200 ppm (30 days, 15.8% COHb) for cardiomegaly, and 250 ppm (10 wk, 

20% COHb) for atherosclerosis and thrombosis (Table 6-11 in the 2000 CO AQCD) (U.S. EPA, 

2000, 000907). Conflicting experimental data relating to the role of CO in promoting atherosclerotic 

vessel disease were discussed. While some animal studies have linked chronic CO exposure with 

atherosclerosis development resulting from increased fatty streaking and cellular lipid loading 

(Davies et al., 1976, 010660; Thomsen, 1974, 010704; Turner et al., 1979, 012328), other studies 

have failed to see this association (Penn et al., 1992, 013728; Stupfel and Bouley, 1970, 010557). 

Vascular insults due to acute exposure to CO concentrations of 50 ppm and higher were also reported 

(Ischiropoulos et al., 1996, 079491; Thom, 1993, 013895; Thom et al., 1998, 016750; Thom et al., 

1999, 016757; Thom et al., 1999, 016753). In addition, chronic CO exposure has been shown to 

result in ventricular hypertrophy (Penney et al., 1984, 011567; Penney et al., 1988, 012521). 

The following sections describe recent studies dealing with toxicity of low to moderate 

concentrations (35-250 ppm) 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 side-stream 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 

concentrations, has pathophysiologic effects on the cardiovascular system and on relatively 

ubiquitous cellular pathways. In evaluating these studies, it should be kept in mind that the 

traditional concept of CO pathophysiology resulting from reduced O2 delivery is likely to be more 

relevant for higher concentrations of CO than are currently found in the ambient environment. 

CO exposure at environmentally-relevant levels is unlikely to cause overt toxicity in a healthy 

cell; however, susceptibility may be rendered by disease or developmental stage. A common theme 

appears to be the vulnerability of vascular cells, especially the endothelium, which could be 

considered the first organ of contact once CO is taken up into the circulation. While relatively little 

research has been conducted since the 2000 CO AQCD (U.S. EPA, 2000, 000907), several key 

studies conducted at environmentally-relevant CO levels provide important clues to the potential 

public health implications of ambient CO exposure. 

5.2.5.1. Endothelial Dysfunction 

While the preferential binding to heme and effective displacement of O2 by CO has been well 

established for over a century, new information from various fields of study are beginning to 

elucidate nonhypoxic mechanisms that may lead to cardiovascular abnormalities associated with CO 

exposure. Research by Thom, Ischiropoulos, and colleagues (Ischiropoulos et al., 1996, 079491; 

Thom and Ischiropoulos, 1997, 085644; Thom et al., 1994, 076459; Thom et al., 1997, 084337; 

January 2010 5-44 
Thom et al., 1999, 016753; Thom et al., 1999, 016757), some of which was reported in the 2000 CO 

AQCD (U.S. EPA, 2000, 000907), has focused on CO-mediated displacement of NO from hemebinding sites. Some of this work demonstrates a specific pathway by which severe CO poisoning can 

lead to the release of NO from platelets with subsequent neutrophil activation and vascular injury 

(Ischiropoulos et al., 1996, 079491; Thom et al., 2006, 098418). The steps include: (1) peroxynitrite 

generation from the reaction of NO from platelets with neutrophil-derived superoxide; followed by 

(2) stimulation of intravascular neutrophil degranulation; that can result in (3) myeloperoxidase 

deposition along the vascular lining. Products from myeloperoxidase-mediated reactions can cause 

endothelial cell activation (Thom et al., 2006, 098418) and can lead to endothelial dysfunction. The 

concentrations used in these studies are greatly in excess of the NAAQS levels but certainly within 

the range of accidental or occupational exposures. Research by these same investigators at more 

environmentally-relevant CO levels was partially reviewed in the 2000 CO AQCD (U.S. EPA, 2000, 

000907). The release of free NO was noted in isolated rat platelets exposed to 10-20 ppm CO (Thom 

and Ischiropoulos, 1997, 085644). Increased nitrotyrosine content of the aorta was observed in rats 

exposed to 50 ppm CO for 1 h (Thom et al., 1999, 016757; Thom et al., 1999, 016753). Furthermore, 

in this same study, a 1-h exposure to 100 ppm CO led to albumin efflux from skeletal muscle 

microvasculature at 3 h and leukocyte sequestration in the aorta at 18 h; LDL oxidation was also 

reported. These effects were dependent on NOS but not on neutrophils or platelets. A second study 

demonstrated NO-dependent effects of 50-100 ppm CO in lungs and is described in Section 5.5.4

(Thom et al., 1999, 016757). Studies in cultured endothelial cells were also conducted using buffer 

saturated with 10-100 ppm CO (Thom et al., 1997, 084337). These experiments were designed to 

mimic conditions where blood COHb levels were between 3.8 and 28%, resulting in exposure of 

endothelial cells to 11-110 nM CO. CO stimulated the release of NO from endothelial cells along 

with formation of peroxynitrite; delayed cell death was observed at CO concentrations of 22 nM and 

higher (Thom et al., 1997, 084337). A more recent study demonstrated adaptive responses in 

endothelial cells exposed to this same range of CO concentrations (Thom et al., 2000, 011574). 

Specifically, 1-h exposure to 11 nM CO resulted in MnSOD and HO-1 induction and resistance to 

the apoptotic effects of 110 nM CO. These protective effects of CO were mediated by NO, as 

demonstrated using an inhibitor of NOS and a scavenger of peroxynitrite. Collectively, these 

experiments demonstrated oxidative and nitrosative stress, the initiation of inflammation, increased 

microvascular permeability and altered cell signaling in animals and isolated 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, 097088). In some cases CO promotes vasoconstriction, which is thought to be 

mediated by inhibition of endothelial NOS (Johnson and Johnson, 2003, 053611; Thorup et al., 1999, 

193782) 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 and Johnson, 2003, 053611; Johnson et al., 2006, 193874; Teran et 

al., 2005, 193770). Additional studies will be useful in determining whether environmentallyrelevant concentrations of CO have detrimental effects on preexisting conditions such as 

hypertension, metabolic syndrome or pregnancy. 

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, 191987) and gasoline engine emissions (6 h/day for 1, 

3, and 7 days at 80 ppm) (Lund et al., 2009, 180257). Furthermore, evidence of vascular ROS 

following gasoline emissions has been shown in certain animal models (6 h/day for 50 days at 

8-80 ppm) (Lund et al., 2007, 125741). While none of these studies examined the potential 

independent role of CO, it is clearly a common factor in the various combustion atmospheres, and 

future work will be needed to reveal its importance on vascular health. 

January 2010 5-45 
5.2.5.2. Cardiac Remodeling Effects 

Cardiomyopathy, or abnormal growth of the cardiac muscle, can manifest in different ways, 

depending on the nature of the insult. The adverse effects of cardiac hypertrophy are due to reduction 

of ventricular chamber volume and a diminishing efficiency of the heart. Such concentric 

hypertrophy typically occurs in response to chronic increases in load, as occurs with hypertension. 

Ischemia of the cardiac tissue can also lead to cardiac remodeling and myopathy. During and after an 

acute infarction or obstruction of major coronary vessels, downstream tissues can suffer severe 

regional ischemia that leads to significant necrosis. Such regions will lose the ability to contract, and 

surrounding tissue will show deficits in contractility. Decreased contractility is often a result of 

structural thinning of the ventricular wall, as well as metabolic impairments. Chronic ischemia, such 

as may result from CAD, may similarly impair cardiomyocyte function and cause decreased 

contractility and remodeling. However, ultimately cardiomyopathies are of a complex origin 

involving mismanagement of fluid balance, abnormal hormonal influences (epinephrine, 

angiotensin), and insufficient perfusion/nutrition. Assessing the role of exogenous CO in altering 

pathways leading to cardiomyopathy is a relatively new endeavor, and several new findings are of 

great interest. 

The heart is a known target for CO toxicity, potentially due to its high rate of O2 consumption. 

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, 180414) demonstrated cardiac hypertrophy in rats 

exposed for 72 wk to 200 ppm CO. COHb levels were reported to be 14.7%. Neither structural signs 

of hypertension in the pulmonary arteries nor atherosclerotic lesions in the systemic arteries were 

observed. A follow-up study by the same investigators (Bye et al., 2008, 193777) found reduced 

aerobic capacity and contractile function leading to pathologic cardiac hypertrophy in rats exposed 

for 18 mo to 200 ppm CO. Cardiac hypertrophy was also demonstrated in rats exposed to 

100-200 ppm CO for 1-2 wk (Loennechen et al., 1999, 011549). This response was accompanied by 

an increase in endothelin-1 expression. COHb levels were reported to be 12-23% in this latter study. 

Effects of CO on the healthy heart have also been demonstrated following short-term 

exposures. In a study by Favory et al. (2006, 184462) 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 h of recovery, while COHb values 

decreased from 11% to 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 was 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. 

Several studies examined the impact of lower levels (50 ppm) on preexisting 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, 096471). 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 wk 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 histopathologic 

findings. The authors cited previous work demonstrating that exogenous CO can inhibit NOS 

(Thorup et al., 1999, 193782), 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, 037502; 2005, 193833), Dark Agouti rats were exposed 

for 10 wk 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 (Gautier et al., 

2007, 096471) indicated that CO may interfere with normal homeostatic responses to hypoxia. This 

January 2010 5-46 
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, 026018) exposed rats to HH (380 torr) with or 

without co-exposure 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 studies of Gautier et al. (2007, 

096471) and Melin et al. (2002, 037502; 2005, 193833), suggested that the combined effect of low 

levels of CO with hypoxia is an enhanced right ventricle workload and an exacerbated 

cardiomyopathy related 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. 

5.2.5.3. Electrocardiographic Effects 

In two related studies, Wellenius et al. (2004, 087874; 2006, 156152) examined the effects of 

CO in an animal model of post-infarction myocardial sensitivity (Wellenius et al., 2002, 025405). In 

a previous study, ECG changes were observed during exposure to residual oil fly ash (ROFA) 

particles in anesthetized post-MI Sprague Dawley rats (Wellenius et al., 2002, 025405). Using this 

model, Wellenius and colleagues tested the effects of 35 ppm CO (1-h exposure) on the induction of 

spontaneous arrhythmias (Wellenius et al., 2004, 087874). CO exposure caused a statistically 

significant decrease (60.4%) in ventricular premature beat (VPB) frequency during the exposure 

period in rats with a high number of pre-exposure VPB. No interaction was observed with coexposure to carbon concentrated particles, which independently reduced VPB frequency during the 

postexposure period when administered alone. In a follow-up publication, results from the analysis 

of supraventricular ectopic beats (SVEB) were provided (Wellenius et al., 2006, 156152). A decrease 

in the number of SVEB was observed with CO (average concentration 37.9 ppm) compared to 

filtered air. While the authors concluded that CO exposure did not increase risk of SVEB in this 

particular rodent model of coronary occlusion, the fact that cardiac electrophysiological dynamics 

are significantly altered by short-term exposure to low-level CO may be of concern for other models 

of susceptibility. 

5.2.5.4.

5.2.6. 

5.2.6.1.

Summary of Cardiovascular Toxicology 

Experimental studies demonstrated that short-term exposure to 50-100 ppm CO resulted in 

aortic injury as measured by increased nitrotyrosine and the sequestration of activated leukocytes in 

healthy rats. In addition, skeletal muscle microvascular permeability was increased. Short-term 

exposure to 35 ppm CO altered cardiac electrophysiology in a rat model of arrhythmia. Furthermore, 

short-term exposure to 50 ppm CO exacerbated cardiac pathology and impaired function in an 

animal model of hypertrophic cardiomyopathy and enhanced vascular remodeling and increased 

pulmonary vascular resistance in an animal model of pulmonary hypertension. Ventricular 

hypertrophy was observed in healthy rats in response to chronic exposures of 100-200 ppm CO. 

These studies provide some support for the development of adverse health effects resulting from 

exposures to CO at environmentally-relevant concentrations. 

Summary of Cardiovascular Effects 

Short-Term Exposure to CO 

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. These studies, described in the 1991 (U.S. EPA, 1991, 017643) and 

January 2010 5-47 
2000 (U.S. EPA, 2000, 000907) 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 2-6% (Section 5.2.4). No human clinical studies have been designed to evaluate the effect of 

controlled exposures to CO resulting in COHb concentrations lower than 2%. Human clinical studies 

published since the 2000 CO AQCD (U.S. EPA, 2000, 000907) have reported no association 

between CO and ST-segment changes or arrhythmia; however, none of these studies included 

individuals with diagnosed heart disease. 

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 may not be representative of the most sensitive individuals in the 

population. It is conceivable that the most sensitive individuals respond to COHb concentrations 

lower than those evaluated in studies of controlled human exposures. 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. Although endogenous COHb is generally <1% in healthy individuals, 

higher endogenous COHb levels are observed in individuals with certain medical conditions. 

Nonambient exposures to CO, such as exposure to ETS, may increase COHb above endogenous 

levels, depending on the gradient of pCO. Ambient exposures may cause a further increase in COHb. 

Modeling results described in Chapter 4 indicate that increases of ~1% COHb are possible with 

exposures of several ppm CO, depending on exposure duration and exercise level. 

Findings of epidemiologic studies conducted since the 2000 CO AQCD (U.S. EPA, 2000, 

000907) are coherent with results of the controlled human exposure studies. These recent studies 

observed associations between ambient CO concentration and ED visits and hospital admissions for 

IHD, CHF and cardiovascular disease as a whole and were conducted in locations where the mean 

24-h avg CO concentrations ranged from 0.5 ppm to 9.4 ppm (Table 5-7). All but one of these 

studies that evaluated CAD outcomes (IHD, MI, angina) reported positive associations (Figure 5-2). 

Although CO is often considered a marker for the effects of another traffic-related pollutant or mix 

of pollutants, evidence indicates that CO associations generally remain robust in copollutant models 

and supports a direct effect of short-term ambient CO exposure on CVD morbidity. These studies 

add to findings reported in the 2000 CO AQCD (U.S. EPA, 2000, 000907) that demonstrated 

associations between short-term variations in ambient CO concentrations and exacerbation of heart 

disease. 

The known role of CO in limiting O2 availability lends biological plausibility to ischemiarelated health outcomes following CO exposure. However, it is not clear whether the small changes 

in COHb associated with ambient CO exposures results in substantially reduced O2 delivery to 

tissues. Recent toxicological studies suggest that CO may also act through other mechanisms by 

initiating or disrupting cellular signaling. Studies in healthy animals demonstrated oxidative injury 

and inflammation in response to 50-100 ppm CO, while studies in animal models of disease 

demonstrated exacerbation of cardiomyopathy and increased vascular remodeling in response to 

50 ppm CO. Further investigations will be useful in determining whether altered cell signaling 

contributes to adverse health effects following ambient CO exposure. 

Given the consistent and coherent evidence from epidemiologic and human clinical studies, 

along with biological plausibility provided by CO’s role in limiting O2 availability, it is concluded 

that a causal relationship is likely to exist between relevant short-term exposures to CO 

and cardiovascular morbidity. 

5.2.6.2. Long-Term Exposure to CO 

Only two epidemiologic studies were identified that investigated the relationship between 

long-term exposure to CO and cardiovascular effects, and the results of these studies provide very 

limited evidence of an association. Considering the lack of evidence from controlled human 

exposure studies and the very limited evidence from toxicological studies on cardiovascular effects 

following long-term exposure to CO, the available evidence is inadequate to conclude that a 

causal relationship exists between relevant long-term exposures to CO and 

cardiovascular morbidity.

January 2010 5-48 
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 

laboratories, and these studies were summarized in the 2000 CO AQCD (U.S. EPA, 2000, 000907). 

Briefly, decreases in visual tracking as well as visual and auditory vigilance were observed following 

exposures to CO resulting in COHb levels between 5% and 20% (Benignus et al., 1987, 012250; 

Fodor and Winneke, 1972, 011041; Horvath et al., 1971, 011075; Putz et al., 1979, 023137). One 

study reported similar behavioral effects (time discrimination) among a group of healthy volunteers 

with COHb levels <3% (Beard and Wertheim, 1967, 011015), though subsequent studies were 

unable to replicate these findings at such low exposure concentrations (Otto et al., 1979, 010863; 

Stewart et al., 1973, 093412). These outcomes represent a potentially important adverse effect of CO 

exposure resulting in COHb levels ≥ 5%, although it is important to note that these findings have not 

been consistent across studies. Similarly, some studies demonstrated decreases in reaction time as 

well as decrements in cognitive function and fine motor skills following controlled exposures to CO; 

however, these studies were not typically conducted using double-blind procedures, which may 

significantly affect the outcome of behavioral studies (Benignus, 1993, 013645). It should be noted 

that all behavioral studies of controlled CO exposure were conducted in normal, healthy adults. No 

new human clinical studies have 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 utero or perinatal exposure involving relatively low to relatively high concentrations of CO 

(12.5-750 ppm). Affected endpoints from this early, developmental CO exposure include behavior, 

memory, learning, locomotor ability, peripheral nervous system myelination, auditory decrements, 

and neurotransmitter changes. These data are addressed in detail in the Birth Outcomes and 

Developmental Effects section of the ISA (Section 5.4.2). Further, a group of studies have found that 

exposure to high concentrations of CO (500-1,200 ppm) can result in CO-dependent ototoxicity, 

specifically loss of threshold of cochlear compound action potentials (CAP) and potentiation of 

noise-induced hearing loss (NIHL) (Chen et al., 2001, 193985; Fechter et al., 1997, 081322; Fechter 

et al., 2002, 193926; Liu and Fechter, 1995, 076524). Proposed mechanisms for these effects include 

ROS generation and glutamate release. 

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 

symptoms following acute CO poisoning including headache, dizziness, cognitive difficulties, 

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 (U.S. EPA, 2000, 000907) 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. 

Toxicological studies that were not discussed in the 2000 CO AQCD (U.S. EPA, 2000, 

000907) employed rodent models to show that low to moderate 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 5.4). 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), nonspatial 

memory (75 and 150 ppm CO), spatial learning (endogenous CO inhibition), homing behavior 

January 2010 5-49 
(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. 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 by 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 these changes have been proposed to be mediated by ROS. Functional tests 

of the auditory system of rodents exposed neonatally to CO using OAE testing (50 ppm) and action 

potential amplitude measurements of the 8th cranial nerve (12, 25, 50, 100 ppm), revealed 

decrements in auditory function at PND22 and permanent changes into adulthood using action 

potential (AP) testing (50 ppm). Additionally, exposure to high concentrations of CO has been 

shown to result in CO-dependent ototoxicity in adult animals, possibly through glutamate and ROSdependent mechanisms. 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 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 exposures to CO and central nervous system effects. 

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 

limited when compared to the numerous studies that have examined the more well-established health 

effects of air pollution. Among this small number of studies, various dichotomized measures of birth 

weight, such as low birth weight (LBW), small for gestational age (SGA), and intrauterine growth 

restriction (IUGR), have received more attention in air pollution research while preterm birth 

(<37 wk gestation; [PTB]), congenital malformations, and infant mortality are less studied. 

In the 2000 CO AQCD (U.S. EPA, 2000, 000907), 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, 012243; Ritz and Yu, 1999, 086976). At that time this 

area of research was in its infancy; however, there has since been increasing interest. 

5.4.1.1. Preterm Birth 

A small number of air pollution-birth outcome studies have investigated the possible 

association between PTB and maternal exposure to CO, with the majority of U.S. studies conducted 

in southern California. The earliest of these studies examined exposures to ambient CO during the 

first month of pregnancy and the last 6 wk prior to birth among a cohort of 97,158 births in southern 

California between 1989 and 1993 (Ritz et al., 2000, 012068). The exposure assessment within this 

study was based on data from fixed-site monitors that fell within a 2-mi radius of the mother’s ZIP 

code area. The crude relative risks for PTB associated with a 1 ppm increase in 3-h avg CO 

concentration (6:00-9:00 a.m.) during the last 6 wk prior to birth and the first month of pregnancy 

were 1.04 (95% CI: 1.03-1.5) and 1.01 (95% CI: 1.00-1.03), respectively. However, when the 

authors controlled for other risk factors, only the effect associated with CO during the last 6 wk prior 

to birth persisted (RR: 1.02 [95% CI: 1.01-1.03]). Furthermore, when the analyses included variables 

January 2010 5-50 
for either season or other pollutants, the CO effect estimates generally were reduced such that they 

remained positive but were no longer statistically significant. 

Expanding on this research, Wilhelm and Ritz (2005, 088668) 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 wk prior to birth. Among women living within a 1-mi 

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 wk 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 (2007, 096146) were able to 

analyze detailed maternal information from a survey of 2,543 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 wk 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, 24-h avg 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 versus 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 were wider due to the smaller sample. Any possible association between CO 

and PTB was less evident during the last 6 wk 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. 

In contrast to the Los Angeles studies, a case-control study of PTB across California for the 

period 1999 through 2000 found a positive association with 24-h CO concentration during the entire 

pregnancy (OR: 1.03 [95% CI: 0.98-1.09] per 0.5 ppm increase) and the first month of gestation 

(OR: 1.05 [95% CI: 0.99-1.10] per 0.5 ppm increase), but no association during the last 2 wk of 

gestation (OR: 1.00 [95% CI: 0.96-1.04] per 0.5 ppm increase) (Huynh et al., 2006, 091240). 

Although there was an indication of an effect during early pregnancy, the small sample size (when 

compared to other studies) may not have provided sufficient power to detect 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, 089548). 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 and 2002, a kriging technique 

was used to assign the maternal exposures to CO. Kriging 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. CO concentrations during the first trimester were 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, 089828). 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]). 

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 and 2000 and found no association between PTB and CO (Jalaludin et 

January 2010 5-51 
al., 2007, 156601). 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 3 mo of 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 null for the last 3 mo 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. 

Figure 5-9 shows the odds 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, 012068; Ritz et al., 2007, 096146; Wilhelm and Ritz, 2005, 088668). 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. 

Study Location Exposure Period Exposure 

Indicator Effect Estimate (95% CI) 

Ritz et al. (2000, 012068) California, US First month <2 mi of monitor 

Ritz et al. (2000, 012068) California, US Last 6 weeks <2 mi of monitor 

Wilhelm & Ritz (2005, 088668) Los Angeles, CA First trimester ZIP code level 

Wilhelm & Ritz (2005, 088668) Los Angeles, CA Last 6 weeks ZIP code level 

Huynh et al. (2006, 091240) California, US First month County level 

Huynh et al. (2006, 091240) California, US Last 2 weeks County level 

Huynh et al. (2006, 091240) California, US Entire pregnancy County level 

Liu et al. (2003, 089548) Vancouver, Can First month City wide 

Liu et al. (2003, 089548) Vancouver, Can Last month City wide 

Jalaludin et al. (2007, 156601) Sydney, Australia First month City wide 

Jalaludin et al. (2007, 156601) Sydney, Australia First trimester City wide 

Jalaludin et al. (2007, 156601) Sydney, Australia Last month City wide 

 Odds Ratio 

Figure 5-9. 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. 

January 2010 5-52 
Table 5-12. Brief summary of PTB studies. 

Study Location 

Sample Size 

Mean CO 

(ppm) Exposure Assessment Exposure Window 

Ritz et al. (2000, 012068) California, US 

(n = 97,158) 2.7 (6-9 a.m.) <2 mi of monitor First mo 

Last 6 wk 

Wilhelm and Ritz (2005, 088668) Los Angeles, CA 

(n = 106,483) 1.4 (24 h) Varying distances to monitor Last 6 wk 

Ritz et al.(2007, 096146) Los Angeles, CA 

(n = 58,316) 0.87 (24 h) Nearest monitor to ZIP code 

Entire pregnancy 

First Trimester

Last 6 wk 

Huynh et al. (2006, 091240) California, US 

(n = 42,692) 0.8 (24 h) County level 

Entire pregnancy 

First mo 

Last 2 wk 

Liu et al. (2003, 089548) Vancouver, Can 

(n = 229,085) 1.0 (24 h) City-wide avg First mo 

Last mo 

Leem et al. (2006, 089828) Incheon, Korea 

(n = 52,113) 0.9 (24 h) Residential address within Dong-based on kriging 

First trimester 

Last trimester 

Jalaludin et al. (2007, 156601) Sydney, Australia 

(n = 123,840) 0.9 (8 h) City-wide avg and 

<5 km from monitor 

First mo 

First trimester 

Last trimester 

Last mo 

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 being a powerful predictor of infant 

mortality, it is the most studied outcome within air pollution-birth outcome research. Air pollution 

researchers have analyzed birth weight as a continuous variable and/or as a dichotomized variable in 

the forms of LBW (<2,500 g [5 lbs, 8 oz]) and SGA. 

It should be noted that the terms SGA, which is defined as a birth weight <10th percentile for 

gestational age (and often sex), and IUGR are used interchangeably. However, this definition of SGA 

does have limitations. For example, using it for IUGR may overestimate the percentage of “growthrestricted” neonates as it is unlikely that 10% of neonates have growth restriction (Wollmann, 1998, 

193812). 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, 193795). Nevertheless, it 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. Thus, all IUGR neonates will be SGA, but not all SGA neonates will 

be IUGR (Wollmann, 1998, 193812). In the following sections the terms SGA and IUGR are referred 

to as each cited study used the terms. 

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, 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 (U.S. EPA, 2000, 000907) and is briefly summarized here. Ritz and Yu 

(1999, 086976) 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, 088668) reported similar results when 

extending this study to include 136,134 births for the period 1994–2000. Exposure to ambient CO 

January 2010 5-53 
during each trimester was based on data recorded at monitoring stations of varying proximities to the 

mother’s residence. For women residing within 1 mi 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 with third-trimester 

exposure above the 75th percentile when compared to women below the 75th percentile. 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-mi radius of a station. However, when 

the analyses included women within a 1- to 2-mi or 2- to 4-mi 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 (quantitative results not reported 

by authors). 

Further validation in association with exposure times was observed in an analysis using a 

subset of participants in the Children’s Health Study. Salam and colleagues (2005, 087885) 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., 1999, 087243) who were born in California between 1975 and 1987 (n = 3,901). 

The study examined term births with a gestational age between 37 and 44 wk. Exposures in this 

study were based on CO data from up to the 3 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 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 association with LBW (OR: 1.00 

[95% CI: 0.88-1.16]). 

In contrast to the previous studies, another California study of 18,247 singleton births born at 

40-wk 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 mi 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, 087462). 

Two similar studies were conducted in the northeastern 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 and 1996 found that exposure to ambient 24-h avg 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, 016624). 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- to 44-wk gestation between 1999 and 

2002 in Connecticut and Massachusetts (Bell et al., 2007, 091059), and 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] per 0.5 ppm increase in CO). 

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 27.2 and 23.3 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. In contrast, in a previously cited California study by 

Huynh and colleagues (2006, 091240) 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. 

January 2010 5-54 
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- and 42-wk gestation) in Vancouver, 

BC, with monthly and trimester exposures to CO investigated in relation to LBW and IUGR (Liu et 

al., 2003, 089548). 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 two studies 

examined 386,202 singleton term births (37- to 42-wk gestation) in Calgary, Edmonton, and 

Montreal, between 1986 and 2000 (Liu et al., 2007, 090429). 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. 

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, 055613). The second of these studies examined 

311,735 singleton births (37- to 41-wk gestation) between 1998 and 2000 and reported a 6.0 g (95 % 

CI: 7.75-4.1) 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, 089824). 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. Ha and colleagues (2001, 019390) 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 

(2003, 043202) extended this study to include singleton term births for the period 1996-1998, 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 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 two through five of pregnancy. 

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 and 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, 087895). However, this result did not persist after 

controlling for other pollutants (PM10, NO2) and was only statistically 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) per 0.75 ppm 

increase in 8-h CO, 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 for each 

gestational age group. 

January 2010 5-55 
Of all studies reviewed, only two did not find an association between maternal exposure to CO 

and birth weight variables. In northern Nevada, Chen and colleagues (2002, 024945) examined CO, 

PM10, and O3 exposures among a cohort of 39,338 term births (37- to 44-wk 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- to 44-wk gestation) in 

Taipei and Kaoshiung, Taiwan, between 1995 and 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 contributing 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 included only those 

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., 2004, 089827). 

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 5-10 shows the change in birth weight (grams), Figure 5-11 shows the effect estimates for 

LBW, and Figure 5-12 shows the effect estimates for SGA. In general the reported reductions in 

birth weight are small (~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. 

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 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 no increase in risk of 

LBW or SGA. However, a measurable change, even if only a small one, at the population level is 

different than an effect observed for 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. 

January 2010 5-56 
Study Location Exposure 

 Period 

Exposure 

 Indicator Effect Estimate (95% CI) 

Bell et al. (2007, 091059) CT & MA, US Entire pregnancy County wide 

Salam et al. (2005, 087885) California, US First trimester ZIP code level 

Salam et al. (2005, 087885) California, US Second trimester ZIP code level 

Salam et al. (2005, 087885) California, US Third trimester ZIP code level 

Salam et al. (2005, 087885) California, US Entire pregnancy ZIP code level 

Chen et al. (2002, 024945) Northern NV, US First trimester County level 

Chen et al. (2002, 024945) Northern NV, US Second trimester County level 

Chen et al. (2002, 024945) Northern NV, US Third trimester County level 

Chen et al. (2002, 024945) Northern NV, US Entire pregnancy County level 

Mannes et al. (2005, 087895) Sydney, Australia First trimester City level 

Mannes et al. (2005, 087895) Sydney, Australia Second trimester City level 

Mannes et al. (2005, 087895) Sydney, Australia Third trimester City level 

Mannes et al. (2005, 087895) Sydney, Australia Last month City level 

Gouveia et al. (2004, 055613) Sao Paulo, Brazil First trimester City level 

Gouveia et al. (2004, 055613) Sao Paulo, Brazil Second trimester City level 

Gouveia et al. (2004, 055613) Sao Paulo, Brazil Third trimester City level 

Medeiros and Gouveia (2005, 089824) Sao Paulo, Brazil First trimester City level 

Medeiros and Gouveia (2005, 089824) Sao Paulo, Brazil Second trimester City level 

Medeiros and Gouveia (2005, 089824) Sao Paulo, Brazil Third trimester City level 

Change in birth weight (grams) 

Figure 5-10. 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. 

Study Location Exposure 

Period 

Exposure 

Indicator 

Effect Estimate (95% CI) 

Bell et al. (2007, 091059) CT & MA, US Entire pregnancy County level 

Maisonet et al. (2001, 016624) Northeastern US First trimester City level 

Maisonet et al. (2001, 016624) Northeastern US Second trimester City level 

Maisonet et al. (2001, 016624) Northeastern US Third trimester City level 

Wilhelm & Ritz (2005, 088668) LA County, CA Third trimester ZIP code level 

Salam et al. (2005, 087885) California, US First trimester ZIP code level 

Salam et al. (2005, 087885) California, US Second trimester ZIP code level 

Salam et al. (2005, 087885) California, US Third trimester ZIP code level 

Salam et al. (2005, 087885) California, US Entire pregnancy ZIP code level 

Liu et al. (2003, 089548) Vancouver, Can First month City level 

Liu et al. (2003, 089548) Vancouver, Can Second month City level 

Ha et al. (2001, 019390) Seoul, Korea First trimester City level 

Ha et al. (2001, 019390) Seoul, Korea Third trimester City level 

Lee et al. (2003, 043202) Seoul, Korea First trimester City level 

Lee et al. (2003, 043202) Seoul, Korea Second trimester City level 

Lee et al. (2003, 043202) Seoul, Korea Third trimester City level 

Lee et al. (2003, 043202) Seoul, Korea Entire pregnancy City level 

 Odds Ratio 

Figure 5-11. 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. 

January 2010 5-57 
Study Location Exposure Period Exposure 

Indicator Effect Estimate (95% CI) 

Salam et al. (2005, 087885) California, US First trimester ZIP code level 

Salam et al. (2005, 087885) California, US Second trimester ZIP code level 

Salam et al. (2005, 087885) California, US Third trimester ZIP code level 

Salam et al. (2005, 087885) California, US Entire pregnancy ZIP code level 

Liu et al. (2003, 089548) Vancouver, Can First month City level 

Liu et al. (2003, 089548) Vancouver, Can First trimester City level 

Liu et al. (2003, 089548) Vancouver, Can Second trimester City level 

Liu et al. (2003, 089548) Vancouver, Can Third trimester City level 

Liu et al. (2003, 089548) Vancouver, Can Last month City level 

Liu et al. (2007, 090429) Multicity, Can First trimester City level 

Liu et al. (2007, 090429) Multicity, Can Second trimester City level 

Liu et al. (2007, 090429) Multicity, Can Third trimester City level 

Mannes et al. (2005, 087895) Sydney, Australia First trimester City level 

Mannes et al. (2005, 087895) Sydney, Australia Second trimester City level 

Mannes et al. (2005, 087895) Sydney, Australia Third trimester City level 

Mannes et al. (2005, 087895) Sydney, Australia Last month City level 

 Odds Ratio 

Figure 5-12. 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. 

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, 087895; Parker et al., 2005, 087462; Wilhelm and Ritz, 2005, 088668), while in some 

studies it did persist (Bell et al., 2007, 091059; Gouveia et al., 2004, 055613; Liu et al., 2003, 

089548), and other studies did not report results from multipollutant models (Ha et al., 2001, 

019390; Lee et al., 2003, 043202; Maisonet et al., 2001, 016624; Medeiros and Gouveia, 2005, 

089824). 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 AfricanAmericans but not among whites and Hispanics (Maisonet et al., 2001, 016624). 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, 091059). Parker and colleagues (2005, 087462) also tested for interactions 

between race and found no significant association. 

January 2010 5-58 
Table 5-13. Brief summary of birth weight studies. 

Study Outcomes 

Examined 

Location 

Sample Size Mean CO (ppm) Exposure Assessment Exposure Windows 

UNITED STATES 

Ritz and Yu (1999, 086976) LBW Los Angeles, CA 

(n = 125, 573) 2.6 (6-9 a.m.) <2 mi of monitor Trimester 3 

Wilhelm and Ritz (2005, 

088668) LBW Los Angeles County, CA

(n = 136, 134) 1.4 (24 h) Varying distances from monitor Trimesters 1, 2, 3 

Salam et al. (2005, 087885) 

Birth weight 

LBW 

IUGR 

California, US 

(n = 3901) 1.8 (24 h) ZIP code level Entire pregnancy 

 Trimesters 1, 2, 3 

Parker et al. (2005, 087462) Birth weight 

SGA 

California, US 

(n = 18,247) 0.75 (8 h) <5 mi from monitor 

 Entire pregnancy 

 Trimesters 1, 2, 3 

 

Maisonet et al. (2001, 016624) LBW 

Boston, MA

Hartford, CT 

Philadelphia & 

Pittsburg, PA

Washington DC 

(n = 103,465) 

1.1 (24 h) City-wide avg Trimesters 1, 2, 3 

Bell et al. (2007, 091059) Birth weight 

LBW 

CT & MA, US 

(n = 358,504) 0.6 (24 h) County-level avg Entire pregnancy 

 Trimesters 1, 3 

Chen et al. (2002, 024945) Birth weight 

LBW 

Northern Nevada, US 

(n = 36,305) 0.9 (8 h) County level Trimesters 1, 2, 3 

CANADA 

Liu et al. (2003, 089548) 

LBW 

IUGR 

Vancouver, Can 

(n = 229,085) 1.0 (24 h) City-wide avg 

 Trimester 1 

Liu et al. (2007, 090429) IUGR 

Calgary, Edmonton, & 

Montreal, Can 

(n = 386,202) 

1.1 (24 h) City-wide avg Trimesters 1, 2, 3 

SOUTH AMERICA 

Gouveia et al. (2004, 055613) Birth weight 

LBW 

Sao Paulo, Brazil 

(n = 179,460) 3.7 (8 h) City-wide avg Trimesters 1, 2, 3 

Medeiros and Gouveia (2005, 

089824) 

Birth weight 

LBW 

Sao Paulo, Brazil 

(n = 311,735) 

3.0 (24 h) 

(Presented in 

graph) 

City-wide avg Trimesters 1, 2, 3 

AUSTRALIA/ASIA 

Ha et al. (2001, 019390) Birth weight 

LBW 

Seoul, Korea 

(n = 276,763) 1.2 (24 h) City-wide avg Trimesters 1 and 3 

Lee et al. (2003, 043202) LBW Seoul, Korea 

(n = 388,105) 1.2 (24 h) City-wide avg Entire pregnancy 

Trimesters 1, 2, 3 

Mannes et al. (2005, 087895) Birth weight 

SGA 

Sydney, Australia 

(n = 138,056) 0.8 (8 h) City-wide avg and 

<5 km from monitor 

Trimesters 1, 2, 3 

Last 30 days 

Lin et al. (2004, 089827) LBW 

Taipei, Kaoshiung, 

Taiwan 

(n = 92,288) 

Taipei 1.1, 

Kaoshiung 8.1 

<3 km of monitor Entire pregnancy 

Trimesters 1, 2, 3 

 

January 2010 5-59 
5.4.1.3. Congenital Anomalies 

Despite the growing evidence of an association between ambient air pollution and various 

adverse birth outcomes, few studies have investigated the effect of temporal variations in ambient air 

pollution on congenital anomalies. Heart defects have been the focus of the majority of these recent 

air pollution studies, given the higher prevalence than other congenital anomalies and associated 

mortality. Another study’s focus was cleft lip/palate. 

The earliest of these studies was conducted in southern California (Ritz et al., 2002, 023227). 

Exposure to ambient CO, NO2, O3 and PM10 during each of the first 3 mo 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. 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 higher 

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. 

Another study was conducted in Texas (Gilboa et al., 2005, 087892), where exposure to 

ambient CO, NO2, SO2, O3 and PM10 during the 3rd-8th week of gestation was examined among 

births between 1997 and 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 

to 14.2 km, with maximum distances ranging from 35.5 to 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 (Ritz et al., 2002, 023227), 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). 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. 

A study conducted in Atlanta, GA, investigated the associations between ambient air pollution 

concentrations during weeks 3-7 of pregnancy and risks of cardiovascular malformations among a 

cohort of pregnancies conceived during 1986-2003 (Strickland et al., 2009, 190324). The mean 24-h 

CO concentration during this period was 0.75 ppm. The authors did not report any statistically 

significant associations with ambient CO concentrations and cardiac malformations, though there 

were elevated risk ratios for ambient CO concentration and patent ductus arteriosus, Tetralogy of 

Fallot, and right ventricular outflow tract defect. These results remained consistently positive in five 

sensitivity analyses conducted and were closer to achieving statistical significance in these 

sensitivity analyses. The only statistically significant results were for the association between PM10

and patent ductus arteriosus. 

The last of these studies was a case-control study that examined maternal exposure to various 

air pollutants during the first 3 mo 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 3 mo of pregnancy. 

The mean 8-h avg CO concentration was 0.69 ppm. Interestingly, of all the pollutants examined, 

only O3 during the first 2 mo 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, 

193794). 

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 

January 2010 5-60 
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. The Atlanta study (Strickland et al., 

2009, 190324) found positive, though not statistically significant associations for patent ductus 

arteriosus, Tetralogy of Fallot, and right ventricular outflow tract defect. The elevated risk ratio for 

Tetralogy of Fallot is consistent with the result observed in Texas (Gilboa et al., 2005, 087892). 

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 and with patent 

ductus arteriosus in Atlanta, GA; 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, 087892) provided little discussion toward 

the inconsistent results with the southern California study. One suggestion was 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, 087892) also suggested the inconsistency was due to 

different exposure periods. In Texas the exposures were averaged over the 3rd-8th wk 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 some 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. 

5.4.1.4. Neonatal and Postneonatal Mortality 

A handful of studies examined the effect of ambient air pollution on neonatal and postneonatal 

mortality, with the former the least studied. These studies varied somewhat with regard to the 

outcomes and exposure periods examined and study designs employed. 

Neonatal 

In Sao Paulo, Brazil, a time-series study examined daily counts of neonatal (up to 28 days 

after birth) deaths for the period 1998-2000 in association with concurrent-day exposure to SO2, CO, 

O3, and PM10. Moving averages from 27 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., 2004, 095787). 

In another study of neonatal death, Hajat et al. (2007, 093276) created a daily time-series of air 

pollution and all infant deaths between 1990 and 2000 in 10 major cities in England. The mean daily 

CO concentration across the 10 cites was 0.57 ppm. This study provided no evidence for an 

association between ambient CO concentration and neonatal deaths. 

Postneonatal 

Two studies in the U.S. examined the potential association between ambient CO and 

postneonatal (from 28 days to 1 yr 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 of a monitoring 

site within the South Coast Air Basin of California. Exposures for 2-wk, 1-mo, 2-mo, and 6-mo 

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, 089819). Ambient 

January 2010 5-61 
1-h max CO concentrations averaged over the 2 mo 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 was not 

statistically significant. Based on exposure from 2 wk before death, CO was associated with an 

increased risk of respiratory related postneonatal deaths occurring 28 days to 1 yr after birth (OR: 

1.14 [95% CI: 1.03-1.25] per 1 ppm increase) and 28 days to 3 mo after birth (OR: 1.20 

[95% CI: 1.02-1.40]); but no effect was observed for respiratory related deaths occurring 4-12 mo 

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/normal-weight infants” and “preterm 

and/or LBW infants.” When these two strata were analyzed, CO was 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, 089819). 

Another study examined 3,583,495 births, including 6,639 postneonatal deaths occurring in 

96 counties throughout the U.S. (in counties with >250,000 residents) between 1989 and 2000 

(Woodruff et al., 2008, 098386). Only exposure during the first 2 mo of life was examined, and this 

was 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 2 mo 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 

postneonatal 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. 

In a study that included 10 major cities in England, Hajat et al. (2007, 093276) created a daily 

time-series of air pollution and all infant deaths between 1990 and 2000. While there was no 

evidence for an association with neonatal deaths and ambient CO concentrations, there was a strong 

adverse effect of CO in postneonatal deaths, although the confidence intervals were wide due to a 

small sample size (RR 1.09, 95% CI: 0.94-1.25). 

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 postneonatal deaths and 

ambient air pollutants, including CO. The data analyzed were from the cities of Taipei (Yang et al., 

2006, 090760) and Kaohsiung (Tsai et al., 2006, 090709), with ambient CO concentrations being 

1.6 ppm and 0.8 ppm, respectively. Both studies examined deaths for the period 1994-2000 and 

employed a case-crossover design that compared air pollution levels 1 wk before and after each 

infant’s death. 

Similarly, another study in South Korea examined postneonatal mortality for the period 

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 

postneonatal 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, 042552). An additional study examined 

the relationship between air pollution and postneonatal mortality for all causes in Seoul, Korea. This 

study used both case-crossover and time-series analyses for all firstborn infants during 1999-2003. 

The mean 8-h max CO concentration during this time period was 1.01 ppm. The association between 

ambient CO concentration and postneonatal mortality was the strongest in magnitude for CO when 

compared to the other criteria pollutants, though the confidence intervals were wide (RR: 1.02 

[95% CI: 0.87-1.20] for case-crossover analysis; RR: 1.23 [1.06-1.44] for time-series analysis per 

0.75 ppm increase in 8-h max CO concentration). 

In general, the inconsistent exposure periods examined among these studies restricts direct 

comparison and interpretation. Nevertheless, there is limited evidence that CO is associated with an 

increased risk of infant mortality during the postneonatal period. The exposure periods examined 

varied from the same-day CO to lag periods up to a 6-mo period prior to birth, with one study 

alternatively exploring exposures averaged over the first 2 mo 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, neonatal, and postneonatal exposures, and the possible interaction that birth weight and 

January 2010 5-62 
gestational age may have on the results yielded from these examined exposure periods needs further 

attention within this field of research. 

5.4.1.5.

5.4.2. 

5.4.2.1.

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 

associated with an increased risk of PTB. Additionally, there is evidence of ambient CO during 

pregnancy having a negative effect on fetal growth. In general, the reviewed studies (Figure 5-10

through Figure 5-12) reported small reductions in birth weight (~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. 

Three studies examined the effects of CO on cardiac birth defects and found maternal 

exposure to CO to be associated with an increased risk of cardiac birth defects. Human clinical 

studies also demonstrated the heart as a target for CO effects (Section 5.2). In general, there is 

limited evidence that CO is associated with an increased risk of infant mortality during the 

postneonatal period. 

Toxicological Studies of Birth Outcomes and Developmental 

Effects 

The brief overview of the reproductive and development toxicology of CO that follows is not 

limited to the past 10 yr as are other areas discussed in this document. This is because reproductive 

and developmental toxicology endpoints have not been covered in previous CO AQCDs. Effects of 

both exogenous CO exposure and endogenous production of CO are discussed since exposure to 

exogenous CO could possibly alter pathways normally regulated by endogenous CO production. 

This document details how in utero or perinatal CO exposure in pregnant dams or pups affects 

outcomes in the offspring, including postnatal mortality, skeletal development, the ability of the 

developing fetus to tolerate maternal dietary manipulation, behavioral outcomes, neurotransmitters, 

brain development, the auditory system, myocardial development, and immune system development. 

Similarly, endogenous CO is discussed in relation to pregnancy maintenance, vascular tone during 

gestation, the placenta, the ovaries, the anterior pituitary axis, and lactation. Together, this 

toxicological summary documents the importance of CO in reproductive and developmental 

toxicology in laboratory animal models. 

Birth Outcomes 

Decreased Birth Weight 

Multiple reports have been published associating CO exposure in laboratory animals and 

decrements in birth weight (90-600 ppm); some of these studies also noted reduced growth evident 

in the prenatal period (65-500 ppm CO). Significant decreases in fetal body weight at GD21 after 

21 days of continuous CO exposure (125, 250, or 500 ppm) in pregnant Wistar rats have been 

reported (Prigge and Hochrainer, 1977, 012326). This decrease was not found in rats exposed to 

60 ppm CO. Penney et al. (1983, 011385) exposed pregnant rats to CO (200 ppm) for the final 

17 days of prenatal development and also found significant decreases in near-term fetal rat weight at 

GD20-GD21; gestation in rats is ~ 22 days. Penney et al.(1982, 011387) continued to find decreased 

January 2010 5-63 
body weight to PND210 after postnatal CO exposure (500 ppm, PND1-PND32) and to a larger 

extent in male pups when compared to female pups. Singh et al. (1984, 011409; 1993, 013892) 

found significant decreases in fetal weight in gestationally CO-exposed mouse pups (65, 125, 250 or 

500 ppm) in two studies. Near-term fetal body weight was decreased at GD18 in mice exposed from 

GD7-GD18 to 125, 250, and 500 ppm CO but not 65 ppm CO (Singh and Scott, 1984, 011409). 

However, a second study found decreased fetal weight at GD18 with all CO exposures (65-500 ppm) 

from GD8-GD18 (Singh et al., 1993, 013892). 

A number of studies have found decreases in birth weight after CO exposure. A decrease in 

body weight at birth was seen in neonates of pregnant rats exposed to 157, 166, and 200 ppm CO 

over GD6-GD19 (Penney et al., 1983, 011385). Singh (2006, 190512) showed decreases in birth 

weight of mouse pups gestationally exposed for 6 h/day for the first 2 wk of pregnancy to 125 ppm 

but not 65 ppm CO. Carmines and Rajendran (2008, 188440) exposed Sprague Dawley rats to 

~600 ppm CO (dam COHb 30%) 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. Maternal body weight was unchanged through 

gestation, but corrected terminal body weight (body weight minus uterine weight) was significantly 

elevated in CO-exposed dams at term, indicating a decrease in uterine weight. Other studies have not 

found decreases in birth weight after gestational CO exposure (Carratu et al., 2000, 015839; Mereu 

et al., 2000, 193838). 

Other animal models have been used to examine decreased birth weight resulting from CO 

exposure. Astrup et al. (1972, 011121) found significant decreases (11 and 20%, respectively) in 

birth weight of rabbits exposed to either 90 or 180 ppm CO continuously over the duration of 

gestation. Tolcos et al. (2000, 015997) found significant decreases in body, brain, and liver weights, 

and crown-to-rump length in guinea pig fetuses after exposure 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). In other studies, there was no significant differences in birth weight of guinea pig pups after 

a similar exposure (200 ppm from GD23-GD25 to term, fetal and maternal COHb levels of 13% and 

8.5%, respectively) (McGregor et al., 1998, 085342; Tolcos et al., 2000, 010468) or in Long Evans 

rats (150 ppm CO continuous exposure over all of gestation) (Fechter and Annau, 1977, 010688). 

Fetal mouse weight was significantly greater than control in the 7 h/day exposures and significantly 

less than control animals in the 24 h/day (250 ppm CO, GD6-GD15) exposure groups, with 

corresponding significant differences in crown-to-rump length in the two groups (Schwetz et al., 

1979, 011855). However, animals that showed no decrement in birth weight were significantly 

smaller at PND4 compared to control guinea pigs (McGregor et al., 1998, 085342), with dam and 

fetal COHb levels of 13% and 8.5%, respectively, during pregnancy. 

Pregnancy Loss and Perinatal Death 

Two studies have provided evidence for CO-induced pregnancy loss and perinatal death at CO 

concentrations between 90 and 250 ppm. Schwetz et al. (1979, 011855) exposed CF-1 mice and New 

Zealand rabbits to 250 ppm CO over GD6-GD15 (mice) or GD6-GD18 (rabbits) for either 7 h/day or 

24 h/day, yielding 4 exposure paradigms. The fetuses were then collected at the termination of 

exposure, 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 CO for 7 h/day but not 24 h/day had a significant increase in the number of resorbed pups. Rabbits 

were less affected by CO exposure, manifesting no significant perinatal death or pregnancy loss. 

Astrup et al. (1972, 011121) studied the effect of CO on fetal development after continuous CO 

exposure (90 or 180 ppm CO, COHb 8-9% and 16-18%, respectively) over the duration of gestation 

in rabbits. 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 h 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 80 ppm CO exposure level. Gestation length was unchanged with CO 

exposure. Conversely, Fechter and Annau (1977, 010688) 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 at PND1. 

January 2010 5-64 
Effect of Maternal Diet 

As mentioned above, CO induced offspring mortality after prenatal exposure. Alterations in 

maternal dietary protein and zinc further altered offspring mortality and teratogenicity caused by CO 

(65-500 ppm). 

Maternal Protein Intake and Neonatal Mouse Mortality and Teratogenicity

Pregnant CD-1 mice were exposed intermittently (6 h/day for first 2 wk 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 dietary protein in modulating CO 

effects on neonatal mortality at 1 wk of age (Singh, 2006, 190512). 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 wk of age, pup mortality was significantly increased by CO exposure as well as 

dietary protein restriction; all pups in the 4% protein diet died by 1 wk of age. CO exposure (65 ppm 

only) combined with a normal protein diet (16%) and CO exposure (65 and 125 ppm) with a 

supplemental protein diet (27%) significantly increased pup mortality at 1 wk versus control air pups 

(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 wk of age versus control (0 ppm CO). In 

summary, these data show that in utero CO exposure induced increased neonatal mouse deaths at 

1 wk in supplemental protein and normal protein diet exposure groups and increased perinatal 

mortality when combined with supplemental or restricted protein. 

The role of diet as a contributor to teratogenicity of CO (0, 65, 125, or 250 ppm CO) in CD-1 

mice given various protein diets (4, 8, 16, or 27% protein) during pregnancy was explored by Singh 

et al. (1993, 013892). Timed-pregnant CD-1 mice were exposed continuously to CO from 

GD8-GD18, at which point animals were sacrificed and fetuses collected. 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, 2003, 053624; Singh et al., 1992, 013759). As expected, all levels of CO and the 

lowest protein diet (4 or 8% protein) given to the dams during gestation resulted in significantly 

decreased near-term 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 fetal malformations including jaw changes. All concentrations of CO exposure within each 

maternal dietary protein level significantly increased the percentage of litters with malformations in 

a dose-dependent manner. 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 increased 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. 

Maternal Zinc and Protein Intake and Neonatal Mortality and Teratogenicity

Singh (2003, 053624) explored how teratogenicity and fetal mortality were affected by zinc 

(Zn) modulation in CO-exposed (500 ppm from GD8-GD18) pregnant dams (CD-1 mouse) given 

protein-insufficient diets. CO exposure in low-protein conditions (9% protein) decreased the mean 

implants per litter as compared to air exposure. CO exposure also increased the near-term fetal 

mortality over all groups, and to a larger extent in the low-protein groups, both Zn normal (57% 

versus 6% mortality) and Zn deficient groups (86.6% versus 70.9% mortality). Under low-protein 

conditions, CO exposure increased the incidence of malformations (9.4% versus 0%) when Zn levels 

were normal and increased the incidence of gastroschisis (5% versus 0%) when Zn levels were low. 

Joint protein and Zn deficiency led to 60% of litters with gastroschisis. Conversely, CO exposure 

under Zn deficiency decreased the incidence of other malformations such as exencephaly, jaw, 

syndactyly, and tail malformations. 

Further studies by Neggers and Singh (2006, 193964) only partially confirmed these findings. 

As before, diets deficient in both Zn and protein had significant detrimental influence on both fetal 

malformations and mortality. Exposure to 500 ppm CO increased fetal mortality and malformation 

January 2010 5-65 
rates under deficient protein (9%) and supplemental Zn (3.3 g/kg diet) conditions; however, CO had 

a negligible effect on these endpoints under deficient protein and deficient or normal Zn conditions. 

Role of Endogenous CO 

CO is produced endogenously from heme protein catabolism by heme oxygenases, HO-1, 

HO-2, and HO-3. CO has recently been recognized as a second messenger signaling molecule, 

similar to NO, with a number of normal physiological roles in the body. Some of these roles are 

played in maintaining pregnancy, controlling vascular tone, regulating hormone balance, and 

sustaining normal ovarian follicular maturation. These areas could be potential areas of interaction of 

exogenous CO. 

Pregnancy Maintenance 

HO-1 is known to protect organs from rejection (Kotsch et al., 2006, 193899) and thus, HO 

may also protect the developing fetus from rejection by the non-self maternal immune system. 

Idiopathic spontaneous abortions are more frequent in women with HO-1 polymorphisms (GT)n 

microsatellite polymorphisms associated with altered HO-1 transcription in their genome (Denschlag 

et al., 2004, 193894). Similarly, administering HO-inhibitors to pregnant rodents induced total litter 

loss, possibly due to vasoconstriction and associated ischemia of the placental vascular bed 

(Alexandreanu and Lawson, 2002, 192373). Also, mice overexpressing HO-1 had a significantly 

decreased rate of spontaneous abortion (Zenclussen et al., 2006, 193873). Various pathologies of 

pregnancy, including IUGR and pre-eclampsia, are associated with significant decreases in placental 

HO activity (Denschlag et al., 2004, 193894; McLaughlin et al., 2003, 193827). Oxygenation is 

important in early pregnancy and triggers trophoblast invasion of the spiral arteries (Kingdom and 

Kaufmann, 1997, 193897). Women living at high altitude have an increased risk of adverse 

pregnancy outcomes versus women living at lower altitudes (Zamudio et al., 1995, 193908). Also, 

women living at high altitude, 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, 193891; Lyall et al., 

2000, 193902). Thus, the HO/CO system is crucial for the developing fetus, helps in maintaining 

pregnancy, and plays a role in spontaneous abortions. 

Vascular Control 

During pregnancy, there is increased blood volume without a concurrent increase in systemic 

BP, which is accomplished by a decrease in total peripheral vascular resistance (Zhao et al., 2008, 

193883). CO through the production of soluble guanylate cyclase is able to stimulate the relaxation 

of vascular smooth muscle (Villamor et al., 2000, 015838) and relaxation of pregnant rat tail artery 

and aortic rings (Longo et al., 1999, 011548). Further, the administration of the HO inhibitor SnMP 

increased maternal BP (systolic, diastolic, and mean arterial pressure) and significantly increased 

uterine artery blood flow velocity during pregnancy in mice (Zhao et al., 2008, 193883). Zhao et al. 

also showed pregnancy induced increased total body CO exhalation and that this increased CO 

production could be significantly decreased by SnMP administration. Abdominal aortas (AA) of 

pregnant dams are significantly dilated with pregnancy, and SnMP treatment leads to AA 

vasoconstriction to levels similar to nonpregnant mice. Isolated human placenta exposed to solutions 

containing CO demonstrated a concentration-dependent decrease in perfusion pressure (Bainbridge 

et al., 2002, 043161), further demonstrating the role of CO in maintaining basal vasculature tone. 

However, the addition of exogenous CO to isolated human and rat uterine tissue during pregnancy 

failed to induce relaxation and quiet the spontaneous contractility of rat or human myometrium 

(uterine smooth muscle)(Longo et al., 1999, 011548). CO is not able to relax all types of vascular 

smooth muscle (Brian et al., 1994, 076283), and pregnancy appears to modulate the response of 

tissues to CO (Katoue et al., 2005, 193896). 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. 

January 2010 5-66 
Hormone Regulation 

Endogenous CO has been shown to regulate neuroendocrine functions. Disruption of normal 

CO signaling causes changes in the cycles of a number of hormones involved in pregnancy. HO 

inhibition in rats significantly decreased ovarian production of gonadotrophin-induced 

androstenedione and progesterone without affecting estradiol levels (Alexandreanu and Lawson, 

2002, 192373). However, treatment with the HO-inducer, hemin, caused androstenedione and 

estradiol production from rat ovaries in vitro. CO also has been shown to have a stimulatory effect 

on gonadotropin-releasing hormone (GnRH) release from rat hypothalamic explants in vitro (Lamar 

et al., 1996, 078819), while in vivo CO appears not to influence GnRH secretion (Kohsaka et al., 

1999, 191000). HO-1 induction and HO concentration have been shown to be regulated by estrogen 

in the rat uterus (Cella et al., 2006, 193240) during pregnancy and in nongravid rats. This agrees 

with work by Tschugguel et al. (2001, 193785) in which CO was generated by primary endothelial 

cells from human umbilical veins and uterine arteries after exogenous 17-β estradiol administration. 

HO inhibition by CrMP decreased time in estrous in a dose-dependent manner (Alexandreanu and 

Lawson, 2002, 192373). 

HO-1 and HO-2 are expressed in rat anterior pituitary, and the secretion of gonadotropins and 

prolactin is affected by HO-inhibitor and HO-substrate administration (Alexandreanu and Lawson, 

2003, 193871). The estrogen-induced afternoon surge of luteinizing hormone (LH) was advanced 

forward in time by HO inhibition, and this advance could be reversed by concomitant administration 

of hemin. The serum follicle stimulating hormone (FSH) surge was unaffected by HO inhibition 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 HO 

inhibition and significantly decreased prolactin release. In vitro studies using pituitary explants 

showed that LH release was significantly increased by HO inhibition. HO inhibition also decreased 

litter weight gain during lactation, which the authors attributed to decreased maternal milk 

production or milk ejection problems as cross-fostered pups regained weight that was lost during 

nursing on HO-inhibited dams (Alexandreanu and Lawson, 2002, 192373). The lactational effects 

seen in this model may be explained by changes in prolactin (Alexandreanu and Lawson, 2003, 

193871). It is possible that HO inhibition by CrMP may also inhibit NO production, a mechanism 

that is distinct from CO-dependent effects. 

Ovarian Follicular Atresia 

As a part of normal follicular maturation in the ovaries, the majority of follicles undergo 

atresia via apoptosis prior to ovulation. Harada et al. (2004, 193920) 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 HOinhibitor 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. 

Summary of Toxicological Studies on Birth Outcomes 

There is some evidence that CO exposure leads to altered birth outcomes, including decreased 

birth and near-term body weight, increased pregnancy loss and perinatal death, and increased 

malformations. These events occurred at levels as low as 65 ppm for fetal body weight decrements 

and 90 ppm for changes in birth weight and perinatal death. Pregnancy loss was seen after exposure 

to 250 ppm CO, whereas skeletal malformations were present after 180 ppm CO. Dietary protein and 

zinc modifications exacerbated these CO-induced effects on birth outcomes. Maternal protein 

restriction and CO had a synergistic effect on peri- and postnatal mortality and an additive effect on 

malformations. Dietary zinc alterations resulted in inconsistent changes to CO-induced 

malformations and fetal mortality. 

 Endogenous CO is recognized as a second messenger signaling molecule with normal 

physiological roles in maintaining pregnancy and for proper fetal and postnatal development. The 

endogenous HO/CO system is also involved in controlling vascular tone, follicular maturation, 

ovarian steroidogenesis, secretion of gonadotropin and prolactin by the anterior pituitary, lactation, 

January 2010 5-67 
and estrous cyclicity in rodent studies. These areas could be potential points of interaction of 

exogenous CO with endogenous HO/CO. 

5.4.2.2. Developmental Effects 

Congenital Abnormalities 

Studies by Schwetz et al. (1979, 011855) found that gestational CO exposure (250 ppm) in 

CF-1 mice for 7 or 24 h/day over GD6-GD15 resulted in minor fetal skeletal alterations in the form 

of extra lumbar ribs and spurs (dam gestational COHb 10-15% for 7h/day exposure, 24 h/day dam 

COHb not measured). Similarly exposed rabbits did not exhibit these changes. 

Astrup et al. (1972, 011121) 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 in the 180 ppm CO group (n = 123) had deformities in 

their extremities at birth, whereas no control and no 90 ppm CO-exposed animals manifested with 

this malformation. 

Further skeletal malformations were seen after gestational CO exposure in mice as described 

above (“Effect of Maternal Diet”) (Singh et al., 1993, 013892). Briefly, pregnant CD-1 mice were 

exposed intermittently to CO (65-250 ppm; GD8-GD18) in combination with protein modified diets 

(27% [supplemental protein], 16% [control], 8% [low], or 4% [very low protein]) to assess the role 

of dietary protein in modulating CO effects on neonates at 1 wk of age. Maternal dietary protein 

restriction additively compounded the CO-induced skeletal malformations. Further, dietary 

restriction in Zn and protein led to increased teratogenicity, specifically increased incidence of 

gastroschisis (Singh, 2003, 053624). Conversely, Carmines and Rajendran (2008, 188440) did not 

find evidence of external malformations (teratogenicity) in rats after exposure to ~600 ppm CO from 

GD6-GD19. 

CNS Developmental Effects 

Behavioral 

Investigators have used animal models to study the effects of moderate CO exposure 

(65-150 ppm) during gestation on behavioral outcomes after birth, including active avoidance, 

learning and memory, homing, and motor activity. These studies generally found decrements in 

behavior in early life after in utero exposure to CO concentrations >125 ppm and in some cases as 

low as 65 ppm. Table 5-14 shows results of behavioral response studies with CO exposure 

≤ 150 ppm. 

January 2010 5-68 
Table 5-14. Behavioral responses. 

Study Model System CO Exposure Response Notes 

BEHAVIORAL RESPONSES 

De Salvia et al. (1995, 

079441) Rats 

75 and 150 ppm 

continuous 

GD0-GD20 

Impaired acquisition (3 and 18 mo) and reacquisition (18 mo) of 

avoidance behavior at 150 ppm, not 75 ppm 

Mactutus and Fechter 

(1985, 011536) Rats 

150 ppm 

continuous 

GD0-GD20 

Delayed acquisition of active avoidance (PND120) and disrupted 

retention (PND360) COHb 15.6 ± 1.1% 

Di Giovanni et al. 

(1993, 013822) Rats 

75 and 150 ppm 

continuous 

GD0-GD20 

CO (150 ppm) reduced the minimum frequency of ultrasonic calls as 

well as decreased responsiveness to a challenge dose of diazepam. 

There was no change in locomotion; however, CO impaired learning 

in a two-way active avoidance task. 

Mactutus and Fechter 

(1984, 011355) Rats 150 ppm Acquisition did not improve with age/maturation, failure to learn; 

impaired reacquisition (PND31), failure to retain COHb 15% 

Giustino et al. (1999, 

011538) Rats 

75 and 150 ppm 

continuous 

GD0-GD20 

Decreased exploration, habituation, nonspatial working memory 

COHb: 1.6 ± 0.1% 

(0 ppm); 7.36 ± 0.2% 

(75 ppm); 16.1 ± 0.9% 

(150 ppm) 

Zhuo et al. (1993, 

013905) 

Mouse 

hippocampal brain 

sections 

ZnPPIX (HO 

inhibitor) and 

0.1-1.0 μM CO 

HO inhibition blocked long-term potentiation and CO evoked 

synaptic potentials and long-term enhancement 

Stevens and Wang 

(1993, 188458) 

Mouse and rat 

hippocampal brain 

slices 

ZnPPIX (5-15 μM) HO inhibition blocked long-term potentiation but not long-term 

depression. 

Mereu (2000, 193838) Rat hippocampal 

brain sections 

150 ppm 

GD0-GD20 Impaired long-term potentiation maintenance 

Fechter and Annau 

(1980, 011295) Rats 

150 ppm 

continuous 

GD0-GD20 

Delayed homing behavior and poor reflexive response 

Fechter and Annau 

(1977, 010688) Rats 

150 ppm 

continuous 

GD0-GD20 

Decreased locomotor activity at PND1, PND4, and PND14, but not 

PND21 COHb 15% 

Singh (1986, 012827) Mice 

65 and 125 ppm 

continuous 

GD7-GD18 

Impaired aerial righting score at PND14 (65 and 125 ppm), impaired 

negative geotaxis at PND10 and righting reflex on PND1 (125 ppm) 

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, 

079441). 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, 011536), 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, 079441) found there were no 

significant behavioral impairments following exposure to 75 ppm CO. However, animals exposed to 

the 150 ppm in utero had significantly impaired acquisition (at 3 and 18 mo of age) and reacquisition 

(at 18 mo of age) of conditioned avoidance behavior. This impaired learning was also seen in 

gestationally CO (150 ppm, trend seen at 75 ppm) exposed rats at PND90 (Di Giovanni et al., 1993, 

013822). 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 moderate CO exposure in utero can lead to permanent 

behavioral changes in male offspring. 

Mactutus and Fechter (1984, 011355) also found that acquisition in a two-way conditioned 

avoidance test (flashing light warnings followed by mild footshock) failed to improve with age of in 

January 2010 5-69 
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. Overall, prenatal CO 

exposure (150 ppm, not 75 ppm) induced learning and memory deficits in male and female 

offspring. 

Habituation, Memory, and Learning. Giustino et al. (1999, 011538) exposed primiparious 

pregnant Wistar rats to CO (0, 75 or 150 ppm) by inhalation from GD0-GD20. 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). Male offspring at age 40 days were given two habituation 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. Results were quantified three ways. Exploration 

activity was defined as the time exploring both objects during each trial. Global habituation was 

quantified as a comparison of the time spent exploring the two objects in T1 to the 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 the time spent exploring the new object. 

These recognition sessions test for the preference that rats have for investigating novel objects in lieu 

of familiar objects and are a measurement of nonspatial working memory. The results of this study 

showed 40 day old animals that were gestationally exposed to CO (both 75 and 150 ppm) spent less 

time exploring novel objects when compared to control animals. Control rabbits habituated or 

learned after a second exposure to a previously explored object (T2<T1), but T2 and T1 were not 

significantly different with CO exposure (150 ppm). Results for rats exposed to 75 ppm were 

inconsistent in that significantly different exploratory times were found using one statistical method 

(Wilcoxon paired signed-rank test) and not found using another method (Kruskal-Wallis ANOVA). 

Finally, the decreased time spent with a familiar object by control rats was not seen in CO-exposed 

animals (75 or 150 ppm). 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 (Fried et al., 1998, 190210; Fried et al., 2003, 

190209). Nonetheless, CO is just one of many constituents of cigarette smoke. The results from 

these animal toxicology studies showed that in utero exposure to CO affects nonspatial working 

memory in young adult male rats. 

Studies have shown that endogenous and exogenous CO may be involved in the generation of 

the hippocampal long-term potentiation (LTP), which is believed to correlate with learning and 

memory (Hawkins et al., 1994, 076503; Mereu et al., 2000, 193838; Stevens and Wang, 1993, 

188458; Zhuo et al., 1993, 013905). It is possible that CO can act as a retrograde synaptic signaling 

messenger, allowing a signal to travel from a postsynaptic to presynaptic neuron. Treatment of 

mouse or rat hippocampal brain sections with ZnPPIX, an HO-inhibitor, blocked induction of the 

LTP but not long-term depression (Stevens and Wang, 1993, 188458; Zhuo et al., 1993, 013905). 

Exogenous CO exposure (0.1-1.0 μM) also evoked long-term enhancement and evoked synaptic 

potentials (Zhuo et al., 1993, 013905). Similarly, hippocampal slices from gestationally CO-exposed 

(150 ppm from GD0-20) Wistar rats exhibited an impaired ability to maintain LTP over time and a 

modest reduction in post-tetanic potentiation (Mereu et al., 2000, 193838). Conversely, other studies 

have found no correlation between CO and LTP using step-through, step-down, and water-maze tests 

(Bing et al., 1995, 079418; Toyoda et al., 1996, 079945). Thus, distinct types of learning may be 

differentially regulated by CO exposure, and endogenous CO, as modulated by HO-inhibitors, may 

manifest with different outcomes when compared to outcomes seen for exogenous CO. 

Homing and Locomotor Effects. Fechter and Annau (1977, 010688; 1980, 011295) 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 activity measured across 10-min intervals for a 1-h 

period. CO-exposed pups showed consistently less activity than air-exposed controls through the 

preweaning window, with significantly reduced activity seen at PND1 and PND4 (both after 

subcutaneous L-DOPA administration to induce movement) and at PND14 but not at PND21. 

However, the PND14 rats only showed decreased activity after 30 min of testing. Di Giovanni et al. 

(1993, 013822) found that prenatal CO (75 and 150 ppm) did not significantly affect locomotor 

activity or D-amphetamine induced hyperactivity at PND14 or PND21, but the rats were only 

subjected to a 30-min session. This study may have overlooked the later window of possible 

decreased activity. 

January 2010 5-70 
Under analogous exposure conditions, Fechter and Annau (1980, 011295) found that the 

development of homing behavior, orientation by the rat toward its home cage, was significantly 

delayed in rats prenatally exposed to 150 ppm CO. Also, exposed offspring manifested with poorer 

than normal performance on the negative geotaxis test, a reflexive response that results in a 

directional movement with or against gravity. Similarly, continuous prenatal CO exposure (125 ppm, 

GD7-GD18) in CD-1 mice impaired negative geotaxis at PND10 (Singh, 1986, 012827). 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, 193944). 

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) at PND14 

(Singh, 1986, 012827). The same trend of impaired righting reflex was seen in gestationally CO 

(150 ppm) exposed rats (Fechter and Annau, 1980, 011295). These behavioral tests indicated 

neuromuscular, vestibular, or postural effects in the CO-exposed neonate. 

Conversely, no gross impairment of motor activity was found as measured by infrared 

movement monitoring in Wistar rats treated in utero (GD0-GD20) with 0, 75 or 100 ppm CO 

(Carratu et al., 2000, 015839). Monitoring was done at PND40 and PND90 and may have been too 

late to detect CO-dependent changes. Earlier studies by Fechter and Annau (1977, 010688) identified 

an early window of sensitivity for CO-dependent motor activity deficits of PND1-PND14, with 

recovery by PND21. 

Emotionality. In utero CO exposure caused subtle alterations in the ontogeny of emotionality 

measured by the ultrasonic vocalization emitted by rat pups removed from their nest. Prenatal CO 

exposure (150 ppm) caused a reduction in the minimum frequency of ultrasonic calls emitted by 

PND5 pups (Di Giovanni et al., 1993, 013822). The rate of calling, maximum frequency, and 

duration and sound pressure level were not affected by prenatal CO. However, the rate of calling and 

responsiveness to a challenge dose of diazepam was decreased by prenatal CO exposure. Pup 

vocalization is mediated by the GABAergic neuron function which is altered by CO exposure (see 

below). 

Neuronal 

Since behavioral changes have been caused by CO exposure, studies have investigated 

whether CO exposure results in changes to neuronal structures and electrical excitability. Moderate 

levels of CO (75 -150 ppm) decrease peripheral nervous system (PNS) myelination due to impaired 

sphingomyelin homeostasis and can reversibly delay the rate of ion channel development after 

gestational exposure. In utero CO exposure also results in irreversible changes in sodium equilibrium 

potential. Further details of these studies are given below in Table 5-15. 

January 2010 5-71 
Table 5-15. Neuronal responses. 

Study Model 

System CO Exposure Response Notes 

NEURONAL RESPONSES 

Carratu et al. (2000, 

015839) Rats 

75 and 150 ppm 

continuous 

GD0-GD20 

Decreased peripheral nerve fiber myelin sheath thickness 

COHb: 0 ppm (GD10: 0.97 ± 

0.02; GD20: 1.62 ± 0.1.), 

75 ppm (GD10: 7.20 ± 0.12; 

GD20: 7.43 ± 0.62), and 

150 ppm (GD10: 14.42 ± 0.52; 

GD20: 16.08 ± 0.88) 

Carratu et al. (2000, 

015935) Rats 

150 ppm 

continuous 

GD0-GD20 

Impaired sphingomyelin homeostasis by increasing sphingosine 

Carratu et al. (1993, 

013812) Rats 

75 and 150 ppm 

continuous 

GD0-GD20 

Produced partly reversible changes in membrane excitability through 

delayed inward current inactivation and decreased inward current 

reversal potential 

COHb: 15% at 150 ppm 

De Luca et al. (1996, 

080911) Rats 

75 and 150 ppm 

continuous 

GD0-GD20 

Delayed development of the ion channels responsible for passive and 

active membrane electrical properties of skeletal muscle 

Montagnani et al. (1996, 

080902) Rats 75 or 150 ppm 

GD0-GD20 

CO (150 ppm) increased the tetrodotoxin-inhibition of PNS-evoked 

vasoconstriction at PND5-7. CO exposure caused the relaxant effect by 

ACh to appear earlier and the contractile response to disappear earlier 

(vasodilator effects). 

Dyer et al. (1979, 

190994) Rats 150 ppm 

GD0-GD21 

Increased early components (P1-N1 and N1-P1) of the cortical flash 

evoked potential peak-to-peak amplitudes at PND65 in female rats Maternal COHb: 15% 

Peripheral Nerve Myelination. The effect of in utero exposure (GD0-GD20) to 0, 75 or 150 ppm 

CO on sciatic nerve myelination in male offspring was studied in Wistar rats (Carratu et al., 2000, 

015839). 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), and 

150 ppm (GD10: 14.42 ± 0.52; GD20: 16.08 ± 0.88). The myelin sheath thickness of the peripheral 

nerve fibers was significantly decreased in CO-exposed animals (75 and 150 ppm); however, axon 

diameter was not affected. As mentioned above, even though CO affected myelination, it did not 

significantly affect motor activity of CO-exposed mice at 40 and 90 days. It is possible that these 

deficits in PNS myelination are due to impaired sphingomyelin homeostasis. In utero exposure 

(GD0-GD20) of Wistar rats to CO (150 ppm) caused a twofold increase in sphingosine (SO) but not 

sphinganine (SA) in the sciatic nerve at 90 days of age (Carratu et al., 2000, 015935). SO is an 

intermediate in sphingolipid turnover and SA is an intermediate of de novo sphingolipid 

biosynthesis. Hypoxia has been shown to induce sphingomyelin changes which could lead to 

impaired myelination and motor activity decrements (Ueda et al., 1998, 195136; Yoshimura et al., 

1999, 195135). Prenatal CO exposure had no effect on brain SA or SO levels in male offspring at 

90 days of age. These results demonstrate prenatal CO exposure could interrupt sphingolipid 

homeostasis in the PNS but not CNS, causing a decrease in nerve myelination without changes in 

motor activity. 

Electrophysiological Changes. 

Gestational exposure of Wistar rats to continuous CO (75 or 150 ppm (15% COHb at 

150 ppm) yielded electrophysiological changes in the PNS (Carratu et al., 1993, 013812). Changes 

were noticeable in voltage- and time-dependent properties of sodium channels in the sciatic nerve 

after in utero CO exposure. Changes in sodium channel inactivation kinetics were reversible (present 

at PND40 and absent at PND270) but changes in the sodium equilibrium potential were irreversible. 

In utero CO exposure (150 ppm) also delayed the development of the resting chloride conductance 

(GCl) and resting potassium conductance (GK), with levels matching the control by PND80 and 

PND60, respectively (De Luca et al., 1996, 080911). CO exposure (75 and 150 ppm) also altered the 

pharmacological properties of the chloride channel and excitability parameters of skeletal muscle 

fibers. These changes in the nerve electrophysiological properties could account for increased 

January 2010 5-72 
tetrodotoxin-inhibition of the vasoconstriction evoked by the PNS in 5- to 7-day-old prenatally 

exposed pups (Montagnani et al., 1996, 080902). Finally, gestational CO exposure increased early 

components (P1-N1 and N1-P1) of the cortical flash evoked potential peak-to-peak amplitudes at 

65 days postexposure (PND65) in female, not male, rats (Dyer et al., 1979, 190994). The early 

waves of the cortical evoked potential, an indicator of visual cortical functioning, generally indicate 

activity in the retinogeniculostriate system. These studies showed that in utero CO exposure had both 

reversible and irreversible effects on sodium and potassium channels, which are essential for proper 

electrophysiological function of the muscles and PNS. 

 Neurotransmitter Changes 

The developing nervous system is extremely sensitive to decreased oxygen availability. 

Virtually all neurotransmitter systems are present at birth but require further maturation. The studies 

listed below in Table 5-16 have shown that prenatal exposure to CO alters a number of 

neurotransmitters and their pathways at concentrations ranging from 75 to 300 ppm, both transiently 

and permanently. 

Medullar Neurotransmitters. SIDS is a complex syndrome that involves the aberrant 

development of brain stem nuclei controlling respiratory, cardiovascular, and arousal activity. To 

investigate changes in the structure and neurochemistry of the brain stem, Tolcos et al. (2000, 

015997) exposed pregnant guinea pigs to CO (200 ppm) over the last 60% of gestation. Guinea pigs 

and humans both have the majority of CNS development in utero. CO-exposed pups were found to 

have significant decrements in body, brain, and liver weights, crown-to-rump length, and medullar 

volume when compared to control pups. Neurotransmitter systems were also affected after CO 

exposure. Specifically, the brain stem displayed significant decreases in protein and 

immunoreactivity for tyrosine hydroxylase (TH), an enzyme necessary for catecholamine 

production, which is likely due to decreased cell number in specific medullar regions responsible for 

cardiorespiratory control. This was consistent with earlier work showing that prenatal CO exposure 

leads to aberrant respiratory responses to asphyxia and CO2 (McGregor et al., 1998, 085342). The 

cholinergic system was also affected by prenatal CO exposure with significant increases in choline 

acetyl-transferase (ChAT) immunoreactivity of the medulla; however, no changes in muscarinic 

acetylcholine receptor were seen. This is in contrast to human infants with SIDS who show 

decreased brain stem muscarinic receptor binding (Kinney et al., 1995, 193898). ChAT changes in 

this study (Tolcos et al., 2000, 015997) were from areas of the medulla associated with tongue 

innervation, which is crucial to swallowing, possibly in relation to breathing. 

A second risk factor for SIDS is hyperthermia. To explore the interaction of hyperthermia and 

CO-induced hypoxia, pregnant guinea pigs were exposed to CO (0 or 200 ppm) for 10 h/day for the 

last 60% of gestation (Tolcos et al., 2000, 010468). At PND4 male pups were exposed to 

hyperthermia or ambient temperature as a control. Brains were then collected at 1 and 8 wk of age. 

In utero CO exposure sensitized some areas of the brain to future hyperthermic insults. Specifically, 

CO plus hyperthermia induced significant increases in serotonin in multiple brain regions (NTS, 

DMV, and hypoglossal nucleus) at 1 wk of age; this change was no longer evident at 8 wk of age. 

Hyperthermia exposure alone induced decreased met-enkephalin neurotransmitter immunoreactivity 

at 1 wk of age that was absent at 8 wk 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 wk 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, lea ding to generation of necrotic lesions in the brain and changes in 

neurotransmitter levels. 

January 2010 5-73 
Table 5-16. Neurotransmitter changes. 

Study Model 

System CO Exposure Response Notes 

NEUROTRANSMITTER CHANGES 

Tolcos et al. (2000, 

015997) Guinea pigs 

200 ppm 

10h/day 

GD23-GD25 to 

GD61-GD63 

CO affected catecholaminergic system in brain stem by reducing tyrosine 

hydroxylase. Affected cholinergic system by increasing choline acetyltransferase. 

Fetal COHb: 13% 

Maternal COHb: 8.5% 

Tolcos et al. (2000, 

010468) Guinea pigs 

200 ppm 

10h/day 

GD23-GD25 to 

birth 

Hyperthermia on 

PND4 

CO sensitizes the brain to the effects of a short period of hyperthermia on 

PND4. The exposure combination resulted in lesions in the brain, as well 

as increased serotonin and glial fibrillary acidic protein. The exposure also 

caused reactive astrogliosis. 

Fetal COHb: 13% 

Maternal COHb: 8.5% 

McGregor et al. (1998, 

085342) Guinea pigs 

200 ppm 

10h/day 

GD23-GD25 to 

birth 

CO increased tidal volume during steady state hypercapnia and 

progressive asphyxia, due to increased ventilation. 

Fetal COHb: 13% 

Maternal COHb: 8.5% 

Cagiano et al. (1998, 

087170) Rats 75 and 150 ppm 

GD0-GD20 

In utero CO (150 ppm) exposure increased mount/intromission latency , 

decreased mount/intromission frequency, and induced ejaculatory 

abnormalities. CO also blunted the amphetamine-induced increase in 

dopamine. 

Maternal COHb: 

GD10: 1, 7, and 15%; 

GD20: 1.5, 7, and 16% (0, 

75, and 150 ppm CO, 

respectively) 

Hermans et al. (1993, 

190510) Rats 

Hypoxia 

(10.5% O2) 

GD15-GD21 

Hypoxia caused delayed initiation latencies of male sexual behavior and 

decreased number of ejaculations. 

Fechter et al. (1987, 

012259) Rats 

75, 150, and 

300 ppm 

GD0-GD20 or 

PND10 

Prenatal CO exposure continuing to PND10 leads to increased 

concentrations of dopamine but not dopamine metabolites in striatal tissue. 

Maternal COHb: 2.5 ± 

0.1%, 11.4 ± 0.3%, 18.5 ± 

0.5%, 26.8 ± 1.1% (0, 75, 

150, and 300 ppm, 

respectively) 

Storm and Fechter 

(1985, 011653) Rats 150 ppm 

GD0-GD20 

Prenatal CO exposure increased mean and total cerebellar norepinephrine 

concentration from PND14 to PND42, but not in the cortex. 

Storm and Fechter 

(1985, 011652) Rats 

75, 150, and 

300 ppm 

GD0-GD20 

CO transiently decreased 5HT and NE in the pons/medulla. CO increased 

NE in the cortex and hippocampus at PND42. CO dose-dependently 

reduced cerebellum wet weight. 

Maternal COHb: 2.5%, 

11.5%, 18.5%, and 26.8% 

(0, 75, 150, and 300 ppm, 

respectively) 

Storm et al. (1986, 

012136) Rats 

75, 150, and 

300 ppm 

GD0-PND10 

CO decreased cerebellar weight (150-300 ppm at PND10, 75-300 ppm at 

PND21) and decreased total cerebellar GABA (150-300 ppm at PND10 

and PND21). CO (300 ppm) exposed cerebella has fewer fissures. 

Maternal COHb: 2.5%, 

11.5%, 18.5%, and 26.8% 

(0, 75, 150, and 300 ppm, 

respectively) 

Benagiano et al. (2005, 

180445) Rats 75 ppm 

GD0-GD20 

CO reduced the number of GABA and GAD 65/67 positive neuronal bodies 

and axon terminals in the cerebellar cortex. 

Benagiano (2007, 

193892) Rats 75 ppm 

GD5-GD20 

Adult offspring exposed prenatally to CO exhibited decreased GABA and 

GAD in the molecular layer and Purkinje neuron layers of the cerebellar 

cortex 

Antonelli (2006, 194960) Rats 75 ppm 

GD5-GD20 

CO decreased cortical glutamatergic transmission both at rest and after a 

chemical depolarizing stimulus. 

Dopaminergic Effects. Dopamine is a catecholamine neurotransmitter that plays an important 

role in the regulation of male rat sexual behavior. Experiments assessing sexual behavior and 

mesolimbic dopaminergic function were conducted on adult (5 and 10 mo of age) male offspring 

gestationally exposed to CO (0, 75 or 150 ppm) (Cagiano et al., 1998, 087170). Maternal COHb at 

GD10 was 1, 7, and 15% and 1.5, 7, and 16% at GD20 (0, 75, and 150 ppm CO, respectively). At 

5 mo 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. Further, administration of amphetamine, which stimulates 

copulatory activity, did not alter CO-induced changes in mount-to-intromission latency or 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. Rats followed to 10 mo of age showed 

no significant changes in copulatory activity or neurochemical parameters after CO exposure, 

January 2010 5-74 
indicating recovery from earlier decrements. This altered male sexual behavior in CO-exposed 

offspring paralleled earlier studies of mice exposed gestationally to hypoxia (Hermans et al., 1993, 

190510). In summary, in utero exposure to CO delayed copulatory sexual behavior in male offspring 

with accompanying changes in the mesolimbic dopaminergic system. 

A second study also found no change in dopamine metabolite levels after prenatal exposure to 

CO; however, it did find an elevation in dopamine concentration in rats exposed both pre- and 

postnatally to CO. 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, 012259).

Noradrenergic and Serotonergic Changes. Other monoamine neurotransmitters, norepinephrine 

(NE) and serotonin (5HT), were tested for sensitivity to CO during development. Long Evans rats 

exposed to CO (75, 150, or 300 ppm) over the duration of gestation yielded a dose-dependent 

reduction in cerebellum wet weight (significant at 150 and 300 ppm) at PND21, with increases in NE 

concentration found in the cortex and hippocampus at PND42 but not PND21 (Storm and Fechter, 

1985, 011652). In a separate experiment, CO-exposed (150 ppm) animals presented with increased 

mean and total NE concentrations in the cerebellum but not cortex when monitored from PND14 to 

PND42 (Storm and Fechter, 1985, 011653). Also, NE concentration in the pons/medulla decreased 

linearly with increasing CO exposure at PND21 but not at PND42. A transitory decrease in 5HT 

concentration was also shown in the pons/medulla after gestational CO exposure (Storm and Fechter, 

1985, 011652). Thus, in these studies, it appeared that CO both transiently and permanently altered 

the pattern of postnatal neurotransmitter development in a region-specific manner and stunted 

postnatal growth of the cerebellum. 

Glutamatergic System. Glutamate is an abundant excitatory neurotransmitter that serves as a 

precursor for the synthesis of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) catalyzed 

by glutamic acid decarboxylase (GAD). Primary cell cultures obtained from the cerebral cortex of 

offspring (PND1) gestationally (GD5-GD20) exposed to CO (75 ppm) had decreased extracellular 

glutamate (basal and K+

-evoked) levels versus air-exposed controls (Antonelli et al., 2006, 194960). 

Similarly, 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 et al., 1986, 012136). It is possible this decrease in GABA 

content is due to a diminished activity of GAD. Rats exposed to CO (75 ppm) in utero (GD0-GD20) 

exhibited decreased GABA and GAD in the molecular layer and Purkinje neuron layer of the 

vermian cerebellar cortex (Benagiano et al., 2005, 180445; Benagiano et al., 2007, 193892). This 

alteration may functionally impair cortical glutamatergic transmission in CO-exposed offspring, 

possibly affecting learning and memory. 

The Developing Auditory System 

 The developing auditory system of rodents has recently been investigated as a target of CO 

exposure at levels as low as 12 ppm. The rat brain and auditory system go through extensive cell 

division and multicellular organization during a major growth spurt in the postnatal period (PND7-

PND20), making it a probable target for CO-induced effects. These studies showed that exposure to 

low concentrations of CO during development can lead to permanent changes in the auditory system 

that persist into adulthood. Similarly, prenatal exposure to tobacco smoke can cause auditory system 

deficits as seen in animal tests for auditory responsiveness, habituation, and auditory arousal. Term 

human infants born to smoking mothers have impaired cochlear development, albeit mild, with 

decreased amplitudes of transient evoked otoacoustic emissions (OAE) at the highest test frequency 

(4 kHz) versus newborns born to nonsmokers (Korres et al., 2007, 190908); CO is one of many 

potential affective components of cigarette smoke. 

January 2010 5-75 
Table 5-17. Developing auditory system. 

Study Model 

System CO Exposure Response Notes 

DEVELOPING AUDITORY SYSTEM 

Stockard-Sullivan et al. 

(2003, 190947) Rats 

12-100 ppm 

22 h/day 

PND6 to PND21-PND23 

CO (50 ppm) reduced otoacoustic emissions (preneural cochlear 

function) at 7.13 and 8.01 kHz. CO persistently attenuated the 

amplitude of the action potential of the eighth cranial nerve (12-

50 ppm), persisting to PND73. No functional impairment in the Morris 

Water Maze after CO exposure. 

COHb: 10.2% 

(100 ppm); 5.5% (AR); 

4.1% (MR) 

Lopez et al. (2003, 

193901) Rats 12 and 25 ppm 

PND8-PND22 

CO (25 ppm) led to swelling and mild vacuolization of nerve terminals 

innervating inner hair cells and the fibers of the 8th cranial nerve. CO 

(25 ppm) decreased expression of neurofilament and myelin basic 

proteins, cytochrome oxidase, NADH-TR, and calcium ATPase. 

Webber et al. (2003, 

190515) Rats 12.5, 25, 50 ppm 

PND8 to PND20-PND22 

CO decreased c-Fos immunoreactivity in the central inferior colliculus 

at both PND27 and PND75-PND77 over all dose groups (12.5, 25, or 

50 ppm CO) 

Webber et al. (2005, 

190514) Rats 25 and 100 ppm 

PND9-PND24 

CO exposure (25 and 100 ppm) decreased neurofilament proteins, 

decreased c-Fos expression in the central IC, and increased CuZnSOD 

in the spiral ganglion neurons. Iron deficiency ablated these responses. 

Lopez et al. (2008, 

097343) Rats 

25 ppm 

10-18 h/day 

GD5-20 or GD5-GD20 and 

PND5-PND20 

Prenatal CO exposure led to increased oxidative stress in the cochlear 

vasculature (high HO-1, SOD-1, iNOS, and nitrotyrosine) and 

decreased neurofilament proteins and synapsin-1. CO caused 

morphological deterioration of putative afferent terminals and mild 

deterioration in the inner hair cells at the basal region of the cochlea. 

Studies on the developing auditory system have used an artificial feeding system where pups 

were removed from their respective dams and fed a milk substitute comparable to natural rat milk 

via intragastric cannulation. This allowed nursing pups to be exposed to CO without possible 

confounding by lactational and maternal CO co-exposure. However, this invasive rat model does 

cause decreased brain, cerebellum, and lung weight at PND16 in normal air controls. A summary of 

these studies and others are presented in the above table (Table 5-17). 

Using this model, Stockard-Sullivan et al. (2003, 190947) examined Sprague Dawley rat pups 

receiving low-dose CO (12, 25, or 50 ppm) to determine how perinatal CO exposure (PND6 to 

PND21-PND23) functionally affected hearing in the developing rat. Rodent pups were either 

maternally reared (MR), nutritionally supported with the artificial feeding system (AR), or received 

AR plus CO exposure (ARCO). CO (50 ppm, not 25 ppm) exposure caused significant reductions in 

distortion product otoacoustic emissions (DPOAE) levels at certain frequencies (7.13 and 8.01 kHz), 

a measure of preneural cochlear function and thus not affected by eighth cranial nerve function. 

However, the frequency range where significant CO results were seen is very narrow and low 

compared to the normal rat audiogram. The eighth cranial nerve, or vestibulocochlear nerve, is 

responsible for transmitting sound from the inner ear to the brain. This study also found significant 

attenuation of the action potential (AP) of the eighth cranial nerve with ARCO exposure (12, 25, and 

50 ppm CO) versus AR controls at PND22. This is complicated by the finding that AR control 

animals had significant attenuation of the eighth cranial nerve AP versus MR control animals, 

implying that artificial rearing contributes to AP changes before CO was introduced. Nonetheless, 

the CO-dependent attenuation of the eighth cranial nerve AP (versus AR control) was permanent, 

persisting until adulthood in the 50 ppm CO exposure group (the only CO group monitored). 

Auditory brain stem response (ABR) conduction time was not affected in CO-exposed animals (12, 

25, 50, 100 ppm).These functional tests reported that neonatal exposure to low concentrations of CO 

can induce auditory functional changes in rodents. 

Further studies have investigated physiological changes in cochlear development resulting 

from chronic CO inhalation. Sprague Dawley rats exposed to low concentrations of CO (12 or 

25 ppm, ARCO) from PND6 to PND27 had no evidence of damage to the inner or outer hair cells 

(Lopez et al., 2003, 193901). However, CO (25 ppm) caused atrophy or vacuolization of the nerve 

cells that innervate the inner (not outer) hair cells. Also, fibers of the eighth cranial nerve at the level 

of the internal auditory canal had distorted myelination and vacuolization of the axoplasm after 

25 ppm CO exposure. Energy production markers in the organ of corti and spiral ganglion neurons 

January 2010 5-76 
including cytochrome oxidase (electron transport chain complex IV) and NADH-TR (marker of 

complex I reductase activity) were significantly decreased after inhalation of 25 ppm (not 12 ppm) 

CO versus control (AR and MR). Reduced energy production likely led to the decreased expression 

of the calcium-mediated myosin ATPase and neurofilament proteins in the organ of corti and spiral 

ganglion neurons (25 ppm CO). Since no changes in body weight were found after CO exposure in 

these experiments (Stockard-Sullivan et al., 2003, 190947), it is likely that the decreased electron 

transport chain enzymes are specific to vulnerable areas such as the cochlea. 

Further analysis focused attention on CO-induced changes in the inferior colliculus (IC), an 

auditory integrative section of the midbrain. Low concentrations of CO (12.5, 25, or 50 ppm) inhaled 

over PND8-PND22 decreased c-Fos immunoreactivity in the central IC at both PND27 and 

PND75-PND77; immunostaining of other subregions of the IC were not affected by CO (Webber et 

al., 2003, 190515). c-Fos is an immediate early gene whose tonotopic expression corresponds to 

neuronal activation in the auditory system. The same decrease in c-Fos expression was seen in rats 

exposed to 25 or 100 ppm CO from PND9 to PND24 (Webber et al., 2005, 190514). These COexposed rats also exhibited decreased neurofilament proteins and increased Cu-Zn superoxide 

dismutase (SOD1) in the spiral ganglion neurons. This response could be ablated by dietary iron 

restriction, suggesting an ROS-dependent contribution to the auditory changes seen after CO 

exposure. These authors postulated that CO creates a persistent oxidative stress condition where 

ROS generated via the interaction of peroxide and iron (via the Fenton reaction or Haber Weiss 

chemistry) leads to impaired cochlear development; decreasing the available iron 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. 

A recent study has found comparable auditory system responses after prenatal (GD5-GD20) 

exposure to CO with postnatal exposure (GD5-PND20,) similar to the studies described above 

(Lopez et al., 2008, 097343). Prenatal CO (25 ppm) exposure led to high levels of the oxidative 

stress markers HO-1, SOD-1, iNOS, and nitrotyrosine in cochlea vasculature and stria vascularis at 

PND12; however, unlike postnatally exposed pups, HO-1 and SOD1 levels returned to normal at 

PND20. Both groups of CO-exposed rats exhibited spiral ganglion cytoplasmic vacuolization, a 

decrease in type I spiral ganglion neuron neurofilament proteins, thinning and damage in the cells of 

the stria vascularis, and mild deterioration of the innervation of the inner hair cells. These nerve 

terminals also had a persistent decrease in synapsin-1, a regulatory neuronal phosphoprotein. These 

studies suggest that mild chronic CO exposure disrupts the developing auditory system, more often 

at the IHC innervation and the eighth cranial nerve of the spiral ganglion, possibly by creating an 

oxidative stress that may be reflected as hearing impairment. 

Summary of Toxicological Studies on Developmental Central Nervous System Effects 

Toxicological studies employing rodent models have shown that exposure to low 

concentrations of CO during the in utero or perinatal 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 (150 ppm), 

habituation (75 and 150 ppm), nonspatial memory (75 and 150 ppm), and emotionality (150 ppm). 

These behavioral changes could be due to neuronal changes or altered neurotransmitter signaling. In 

utero CO exposure (75 and 150 ppm) was associated with PNS myelination decrements from 

impaired sphingolipid homeostasis (150 ppm CO). These neuronal changes were also accompanied 

by electrophysiological changes such as reversible delays in ion channel development and 

irreversible changes in sodium equilibrium potential (150 ppm). Also, multiple studies demonstrated 

that in utero CO exposure affected cholinergic (200 ppm), catecholaminergic (200 ppm), 

noradrenergic (150 ppm), serotonergic (75 ppm), dopaminergic (75 ppm) and glutamatergic 

(75 ppm), neurotransmitter levels or transmission in exposed 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, perinatal CO exposure has 

been shown to affect the developing auditory system of rodents, inducing permanent changes into 

adulthood at concentrations as low as 12 ppm. Together, these animal studies demonstrate that in 

utero or perinatal exposure to CO can adversely affect adult behavior, neuronal function, 

neurotransmission, and the auditory system in rodents. 

January 2010 5-77 
Cardiovascular and Systemic Developmental Effects 

In utero exposure to moderate to high concentrations of CO (60, 125, 150, 250, or 500 ppm) is 

able to induce transient changes in cardiac morphology, cardiac action potentials, and systemic 

immunity that may make a CO-exposed animal more susceptible to other outside stressors during the 

immediate neonatal period. Studies of cardiovascular and systemic developmental responses to CO 

levels of 500 ppm and less are presented below in Table 5-18. 

Table 5-18. Cardiovascular and systemic developmental responses. 

Study Model 

System CO Exposure Response Notes 

CARDIOVASCULAR AND SYSTEMIC DEVELOPMENTAL RESPONSES 

Sartiani et al. (2004, 

190898) Rats 150 ppm 

GD0-GD20 

CO delayed action potential duration shortening, decreased the density 

of Ito channels and increased the density of ICa,L channels. 

Prigge and Hochrainer 

(1977, 012326) Rats 60, 125, 250, and 500 ppm 

GD0-GD21 

CO depressed fetal hemoglobin (250 and 500 ppm), reduced fetal 

weight (125, 250, and 500 ppm), decreased hematocrit (250 and 

500 ppm), and increased heart weight (60-500 ppm). 

Fechter et al. (1980, 

011294) Rats 150 ppm 

GD0-GD20 

CO transiently increased wet heart weight. There was no increase in 

dry heart weight. COHb: 15% 

Penney et al. (1982, 

011387) Rats 500 ppm 

PND1-PND32 

CO increased heart weight to body weight ratio, which remained high to 

PND107. Right ventricular weight was high through PND217. 

Hydroxyproline and cardiac cytochrome c was depressed but only 

during CO exposure. Neither lactate dehydrogenase nor myoglobin 

was altered by CO. 

Styka and Penney 

(1978, 011166) Rats 

400 or 500 ppm increased to 

1,100 ppm 

Adult 

6 wk 

CO caused increased heart weight to body weight that regressed within 

a couple of months after CO exposure. 

COHb: 

400 ppm-35%; 

1,100 ppm-58% 

Giustino et al. (1993, 

013833) Rats 75 and 150 ppm 

GD0-GD20 

CO decreased splenic macrophage killing (75 and 150 ppm), 

phagocytosis (150 ppm), and superoxide release (150 ppm). These 

alterations were reversible, not seen at PND60. 

Giustino et al. (1994, 

076343) Rats 75 and 150 ppm 

GD0-GD20 

CO (150 ppm) decreased the frequency of splenic leukocyte common 

antigen (LCA+) cells at PND21 but not PND15 or PND540 

COHb: 

150 ppm-15% 

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, 190898). Specifically, 

at 4 wk of age, the action potential duration (APD) of isolated cardiac myocytes from CO-exposed 

animals failed to shorten or mature as the APD of control animals did. 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 wk of age. These CO-dependent changes were resolved by 8 wk of age, reflecting 

a delayed maturation. 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. 

Heart Morphological Changes after In Utero or Perinatal CO Exposure 

Multiple authors have reported cardiomegaly following in utero exposure to low 

concentrations of CO. Prigge and Hochrainer (1977, 012326) 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, 011294) found that prenatal exposure to CO affected cardiac 

development in exposed offspring. Long Evans rats that were exposed to CO continuously 

January 2010 5-78 
(150 ppm) during gestation manifested with significant elevations in wet heart weight, as well as 

heart weight in relation to body weight at PND1 but not at PND4, PND14, or PND21. 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, 011387) studied CO-dependent (500 ppm) cardiomegaly in 

neonates (continuous CO exposure for 32 days starting at PND1). Other studies of adult male 

Charles River-derived rats exposed to CO for 6 wk (at 400 or 500 to 1,100 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, 011166). 

Systemic Immune Toxicology after In Utero CO Exposure 

In utero exposure (GD0-GD20) of male Wistar rats to moderate CO (0, 75, or 150 ppm) 

concentrations induced reversible changes in macrophage function (Giustino et al., 1993, 013833). 

The killing of Candida albicans (yeast) by splenic macrophages was significantly decreased at 

PND15 in gestationally CO-exposed male offspring (75 and 150 ppm) but recovered function by 

PND21. Macrophage phagocytosis of C. 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 concentration-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 rats to CO 

(150 ppm) induced a subtle decrease in the frequency of splenic immunocompetent cells (leukocyte 

common antigen [LCA+] cells) in a population of splenic immune cells at PND21 but not PND15 or 

PND540 (Giustino et al., 1994, 076343). Specific LCA+ cell subpopulations, including 

macrophages, Major Histocompatibility (MHC) II cells, T and B lymphocytes, showed a decreasing 

trend but were not significant with CO exposure. 

Summary of Toxicological Studies of Cardiovascular and Systemic Development 

In utero CO exposure is associated with various adverse, albeit nonpersistent, cardiac 

aberrations. Exposure to 150 ppm induced a delayed maturation of the cardiac action potential in 

CO-exposed offspring. In other studies, continuous in utero CO exposure (60-500 ppm) induced 

cardiomegaly at PND1, which was transient and regressed by PND4. CO (75 and 150 ppm) also 

affects nonspecific immunity, shown through a reversible and dose-dependent loss of function of 

splenic macrophages, with decreased killing ability, decreased phagocytosis, and decreased 

macrophage ROS production (150 ppm). Also, the distribution of splenic immunocompetent cells 

was slightly 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 moderate doses of CO 

(60-500 ppm) is able to induce transient changes in cardiac morphology, cardiac action potentials, 

and systemic nonspecific immunity. 

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 PTB and cardiac birth defects. These outcomes were not addressed in the 2000 CO AQCD 

(U.S. EPA, 2000, 000907), which included only two studies that examined the effect of ambient CO 

on 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 positive associations with 

CO exposure during early pregnancy when 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. 

January 2010 5-79 
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. Human clinical studies also demonstrated the heart as a target for CO effects (Section 5.2). 

Animal toxicological studies provided additional evidence for cardiac effects with reported transient 

cardiomegaly at birth after continuous in utero CO exposure (60, 125, 250 and 500 ppm CO) and 

delayed myocardial electrophysiological maturation (150 ppm CO). Toxicological studies have also 

shown that continuous in utero CO exposure (250 ppm) induced teratogenicity in rodent offspring in 

a dose-dependent manner that was further affected by dietary protein (65 ppm CO) or zinc 

manipulation (500 ppm CO). Toxicological studies of 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 Figure 5-10

through Figure 5-12, reported small reductions in birth weight (~5-20 g). Several studies examined 

various combinations of birth weight, LBW, and SGA/IUGR, and inconsistent results were 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 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. Toxicology studies have 

found associations between CO exposure in laboratory animals and decrements in birth weight 

(90-600 ppm), as well as reduced prenatal growth (65-500 ppm CO). 

In general, there is limited epidemiologic evidence that CO is associated with an increased risk 

of infant mortality during the neonatal or postneonatal periods. In support of this limited evidence, 

animal toxicological studies provided 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 neonatal 

CO exposure. Auditory functional testing using otoacoustic emissions testing (OAE at 50 ppm CO) 

and 8th cranial nerve action potential (AP) amplitude measurements (12, 25, 50, 100 ppm CO) in 

rodents exposed perinatally to CO showed auditory decrements at PND22 (OAE and AP) and 

permanent changes in AP into adulthood (50 ppm CO). Furthermore, exogenous CO may interact 

with or disrupt the normal physiological roles that endogenous CO plays in the body. There is 

evidence that CO plays a role in maintaining pregnancy, controlling vascular tone, regulating 

hormone balance, and sustaining normal ovarian follicular maturation. 

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 nonhypoxic mechanisms 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 relevant long-term exposures to CO and developmental effects and birth 

outcomes. 

January 2010 5-80 
5.5. Respiratory Effects 

5.5.1. Epidemiologic Studies with Short-Term Exposure 

This section evaluates the key epidemiologic studies published since the 2000 CO AQCD 

(U.S. EPA, 2000, 000907) that further examine the association between short-term exposure to CO 

and respiratory morbidity. Although the number of studies that have specifically examined the COrespiratory health relationship have increased, there are still considerably less than that for the other 

criteria air pollutants (e.g., PM and O3). The epidemiologic studies discussed below represent those 

studies which (1) were conducted in locations with ambient CO concentrations similar to those in the 

U.S.; (2) determined to use a reasonable study design and analytical methods; and (3) adequately 

adjusted for confounding using accepted methods. If limitations in the design or analytical methods 

used in a study were identified, they were noted. It is recognized that each of the studies evaluated 

has a varying degree of exposure measurement error due to (1) the number of monitors used within 

the study, the geographic size of the study area; (2) the spatial variability of CO; and (3) differences 

in personal exposure distributions in the population; (Section 3.6.8) all of which could influence the 

associations observed. As a result, in some instances specific details of a study are mentioned to 

address any potential bias in the reported CO associations. Finally, the issue of confounding by 

measured or unmeasured copollutants was evaluated, if possible, for each study, through the 

interpretation of copollutant models. The results from copollutant models were used as an attempt to 

disentangle the effect of CO from other pollutants while recognizing the high correlation between 

CO and other combustion-related pollutants. 

5.5.1.1. Pulmonary Function, Respiratory Symptoms, and Medication Use 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) briefly discussed the potential acute 

respiratory health effects associated with short-term exposure to CO. An evaluation of the 

epidemiologic literature at the time did not find any evidence of an association between short-term 

exposure to CO and lung function, respiratory symptoms, or respiratory disease. As a result, the 2000 

CO AQCD (U.S. EPA, 2000, 000907) did not conclude that a causal association exists between 

short-term exposure to CO and respiratory health effects. Multiple uncertainties were identified in 

the epidemiologic literature that contributed to this conclusion, which are discussed in Section 5.2.1. 

The following section evaluates the current literature that examines the potential association between 

short-term exposure to CO and respiratory health effects. Table 5-19 lists the studies evaluated in this 

section along with the respiratory health outcomes examined and CO concentrations reported. 

January 2010 5-81 
Table 5-19. Range of CO concentrations reported in key respiratory morbidity studies that examined 

effects associated with short-term exposure to CO. 

Study Location

Sample Size Years Health Outcome Metric 

Mean 

Concentration 

(ppm) 

Middle/Upper Percentile 

Concentrations (ppm) 

O’Connor et al. 

(2008, 156818)

b 7 U.S. cities 8/1998-7/2001 

Pulmonary 

function; 

Respiratory 

symptoms 

8-h max 

24-h avg NR 

8-h max: 

50th: 1.2 

75th 1.8 

99th 3.8 

24-h avg: 

50th: 0.7 

75th 0.9 

99th: 1.8 

Rabinovitch et al. 

(2004, 096753) 

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, 

087471) 

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, 025731)

 a

The Netherlands 

(n = 68) March - Aprilc Pulmonary 

function 24-h avg 0.80 Max: 1.34 

Ranzi et al. (2004, 

089500)

 a

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, 089800)

 a

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, 030335)

 a

Helsinki, Finland 

(n = 57) 

11/1996-4/1997 Pulmonary 

function 24-h avg NR 

50th: 0.35 

75th: 0.43 

Maximum: 0.96 

Timonen et al. 

(2002, 025653)

 a

Kuopio, Finland 

(n = 33) 

2/1994-4/1994 Pulmonary 

function 24-h avg 0.52 Maximum: 2.43 

Chen et al. (1999, 

011149) 

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, 050460) 

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, 086294) 

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, 

013254) 

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, 089812) 

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 

von Klot et al. 

(2002, 034706)

 a

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 

January 2010 5-82 
Study Location

Sample Size Years Health Outcome Metric 

Mean 

Concentration 

(ppm) 

Middle/Upper Percentile 

Concentrations (ppm) 

Park et al. (2005, 

088673) 

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, 092842) 

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, 001061)

 a

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 

a

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 temperature. b

This study did not present air quality statistics quantitatively, as a result, the air quality statistics presented were estimated from a figure. c

This study did not provide the year(s) in which air quality data was collected. 

Pulmonary Function 

As part of the Inner-City Asthma Study (ICAS), O’Connor et al. (2008, 156818) examined the 

effect of air pollutants (i.e., PM2.5, O3, NO2, CO, and SO2) on lung function in a population of 861 

children (5-12 yr old) with persistent asthma in 7 urban U.S. communities. Throughout the study, 

percent predicted forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) were 

examined for each subject during 2-wk periods twice daily every 6 mo for 2 yr. Lung function was 

examined in single pollutant models using both same-day (lag 0) and 5-day (lag 0-4) ma pollutant 

concentrations (Figure 5-13). CO was not found to be associated with percent predicted FEV1 at lag 

0, but there was some evidence for a reduction in percent predicted FEV1 when using the 5-day ma 

(-0.32 [95% CI: -0.75 to 0.11] per 0.5 ppm increase in 24-h avg CO concentrations). When 

examining percent predicted PEF, a small reduction was observed at lag 0 (not reported 

quantitatively), but the effect was found to be slightly larger at lag 0-4 (-0.28 [95% CI: -0.71 to 

0.15]). In this study, CO was found to be moderately correlated with other combustion related 

pollutants (e.g., PM2.5 [r = 0.44] and NO2 [r = 0.54]); however, CO was not included in the 

multipollutant models examined, limiting the interpretation of the small reductions in lung function 

observed. Although the observed reductions in lung function did not reach statistical significance, the 

results do provide some evidence for a potential effect of CO on lung function at relatively low CO 

concentrations (99th percentile max 8-h avg concentrations: ~ 3.8 ppm). 

January 2010 5-83 
 Source: Reprinted with Permission of Elsevier Ltd. from O’Connor et al. (2008, 156818) 

Figure 5-13. Estimated effect (95% confidence intervals) on pulmonary function due to a 10th 

to 90th percentile increment change in pollutant concentration in single-pollutant 

models. The estimates shown are from models that included either a 1-day or 

5-day avg of pollutant concentration. Effect estimates were adjusted for site, 

month, site-by-month interaction, temperature, and intervention group in mixed 

models. Panel A: percent predicted FEV1 as outcome variable; Panel B: percent 

predicted PEFR as outcome variable. 

The remaining U.S.-based studies evaluated consisted of single-city studies conducted in 

Denver, CO. Rabinovitch et al. (2004, 096753) examined the association between exposure to 

ambient air pollutants and asthma exacerbation in a panel of urban minority children, 6-12 yr old, 

with moderate to severe asthma over three winters. The investigators examined pulmonary function 

by measuring FEV1 and PEF in the morning on school days and also at night on weekends or other 

nonschool days. Using a 3-day ma (lag 0-2) for all pollutants, Rabinovitch et al. (2004, 096753) did 

not find an association between CO and either lung function parameter during the morning or at 

night. Silkoff et al. (2005, 087471) also examined lung function during the winter months, but in a 

panel of former smokers that were at least 40 yr old and had been diagnosed with COPD. In this 

study, CO concentrations were similar to those reported in Rabinovitch et al. (2004, 096753). 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, 087471) found contradictory 

results when examining the effects of CO for each of the winter periods (1999-2000 and 2000-2001) 

separately. During the analysis of the first winter (i.e., 1999-2000), CO was not found to be 

associated with lung function decrements in the morning at any lag, but there was some evidence for 

lung function decrements during the evening at lag 0. Of note is the increase in FEV1 during the 

morning that was observed at lag 1 during this time period. 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 and a moderate negative association with PEF at lag 0 in the morning and lag 2 in the 

evening. Silkoff et al. (2005, 087471) postulated that the difference in the FEV1 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 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, 087471). 

In the recent literature, the majority of studies that examined the association between shortterm exposure to CO and lung function have been conducted in Europe and Asia. These studies 

provide some evidence for CO-induced changes in lung function. 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, 030335) examined the association between CO and lung 

function in a panel consisting of 57 nonsmoking 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 

January 2010 5-84 
afternoon (β = -1.52, SE = 0.29) and evening (β = -1.81, SE = 0.27) for a 5-day avg. 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 the two-pollutant model at lag 0. In this study, moderate correlations 

between UFPs and other traffic-generated pollutants (e.g., CO [r = 0.44], NO [r = 0.60], and NO2

[r = 0.44]) make it difficult to attribute the observed respiratory effects to a specific pollutant. 

Lagorio et al. (2006, 089800) also conducted a study that examined the association between 

CO and lung function in adults. In this study, three panels of subjects with underlying asthma, 

COPD, or IHD, who resided in Rome, Italy, were selected. The ages of the subjects varied depending 

on the panel, but overall the subjects ranged from 18-80 yr old. In single-pollutant models with CO, 

a reduction in forced vital capacity (FVC) and FEV1 was observed 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, 089800) did observe a relatively high 

correlation between CO and PM2.5 but not NO2 (r = 0.05). Copollutant models were not conducted in 

this analysis to identify whether the CO associations observed are potentially confounded by other 

pollutants. 

Studies that focused on alterations in lung function in asthmatic children reported results 

consistent with those observed in adult asthmatics. Timonen et al. (2002, 025653) examined the 

effect of CO on bronchial responsiveness and pulmonary function (i.e., FVC, FEV1, MMEF, and 

AEFV) at rest and after exercise in a panel of children, 7-12 yr 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 (i.e., lung function measured prior to exercise) 

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. CO was not found to be 

associated with exercise-induced changes in lung function or bronchial responsiveness. Overall, 

Timonen et al. (2002, 025653) found that increased concentrations of combustion-related byproducts 

(i.e., BS, PM10, particle numbers, NO2, and CO) was associated with impairment in baseline lung 

function. These associations, along with the high correlation between CO and combustion-related 

pollutants (e.g., PM10 [r = 0.64]; NO2 [r = 0.88]), 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. 

Chen et al. (1999, 011149) examined the effect of CO on lung function in 941 8- to 13-yr-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 8:00 a.m. to 6:00 p.m.) in a single-pollutant model. However, the authors found a high 

correlation between CO and NO2 concentrations (r = 0.86-0.98), and did not conduct copollutant 

analyses. 

An additional study, Fischer et al. (2002, 025731), examined the association between CO and 

respiratory health, specifically lung function in a cohort study of 68 children ages 10-11 yr who lived 

in an urban environment (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, 025731) found negative 

associations between CO and FEV1, PEF, and MMEF at both lags 1 and 2, as well as an association 

between CO and an increase in eNO at lag 1. However, the study did not provide pollutant 

correlations or examine copollutant models, limiting the interpretation of these results. 

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 associations were 

observed across studies. The studies evaluated that included children enrolled in the Childhood 

Asthma Management Program (CAMP) study found that CO was positively associated with asthma 

symptoms. Yu et al. (2000, 013254) found a 1.14-fold increase in asthma symptoms 

([95% CI: 1.05-1.23] per 0.5 ppm increase in 24-h avg CO) at lag 1 in a population of 5- to 13-yr-old 

asthmatic children (n = 133) in Seattle, WA. Similar effects were observed at lag 0 and lag 2. These 

effects persisted when controlling for previous day’s asthma symptoms at all lags, with the largest 

effect at lag 1 (OR=1.12 [95% CI: 1.05-1.19]) and in multipollutant models with PM1.0 and SO2. 

January 2010 5-85 
Using the same population of children, Slaughter et al. (2003, 086294) found an 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] 

per 0.5 ppm increase in 24-h avg CO, respectively). However, this study only examined the effect of 

copollutant models on PM risk estimates, not CO. Schildcrout et al. (2006, 089812) examined the 

association between air pollutants and asthma symptoms in 990 children ages 5-12 yr in 8 North 

American cities. The authors found a positive association between short-term 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. 

As previously mentioned, O’Connor et al. (2008, 156818) conducted an additional multicity 

study to examine the effect of air pollutants (i.e., PM2.5, O3, NO2, CO, and SO2) on respiratory health 

in a population of 861 children (5-12 yr) with persistent asthma in 7 U.S. urban communities. The 

authors collected information on asthma symptoms every 2 mo and examined the association 

between a 2-wk recall of the asthma symptoms and each air pollutant. O’Connor et al. (2008, 

156818) used a 19-day lag, which encompassed the 14 days of the symptom recall period and the 

5-day lag period proceeding the symptom recall period. In a single-pollutant model, CO was 

significantly associated with number of days with a wheeze-cough (14% [95% CI: 2-29]), number of 

nights with asthma symptoms (i.e., nighttime asthma) (19% [95% CI: 4-36]), and number of days a 

child slowed down or stopped play (15% [95% CI: 2-30]) per 0.5 ppm increase in 24-h avg CO 

concentrations during the 2-wk recall period. In this study, CO effects were not examined in a 

copollutant model. 

U.S.-based single-city studies also found positive associations between CO and asthma 

symptoms (Delfino et al., 2003, 050460; Rabinovitch et al., 2004, 096753). Rabinovitch et al. (2004, 

096753) found evidence for an increase in asthma exacerbations in response to 24-h avg CO 

concentrations for a 3-day ma (lag 0-2) (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, 050460) also reported evidence of a positive association between CO and 

asthma symptoms (based on symptoms that interfere with daily activities) using a population of 

Hispanic children with asthma in a Los Angeles, CA, community. However, Delfino et al. (2003, 

050460) only found positive associations at 1-day lags when using either the 1-h max (OR=1.05 

[95% CI: 0.88-1.26] per 1 ppm increase in 1-h max CO concentrations) or max 8-h avg (OR=1.09 

[95% CI: 0.80-1.50] per 0.75 ppm increase in max 8-h avg CO concentrations) CO concentration as 

the exposure metric. It should be noted that in comparison to Rabinovitch et al. (2004, 096753) and 

the other respiratory symptoms studies discussed above, the mean ambient concentrations for 1-h 

max and max 8-h avg reported by Delfino et al. (2003, 050460) 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 addition to the U.S.-based studies presented above, international studies were evaluated 

that examined the association between short-term exposure to CO and asthma symptoms in study 

populations that included adults. Figure 5-14 summarizes the results from studies that provided 

comparable quantitative results and examined the association between short-term exposure to CO 

and asthma or respiratory symptoms in asthmatic individuals. A panel study consisting of 53 adults 

with asthma or asthma symptoms in Germany (Von et al., 2002, 034706) 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 UFPs (r = 0.66), complicating the 

interpretation of the associations observed. Additionally, Park et al. (2005, 088673) in a panel study 

of individuals 16-75 yr old in Incheon, Korea, with bronchial asthma, did not find an association 

between CO and nighttime asthma symptoms or cough. 

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 pollutant 

concentrations. The majority of U.S.-based studies (i.e., Rabinovitch et al., 2004, 096753; 

Schildcrout et al., 2006, 089812; Slaughter et al., 2003, 086294) focused on rescue inhaler use in 

children with ages ranging from 5 to13 yr. Rabinovitch et al. (2004, 096753) 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- to 12-yr old urban minority children with moderate to severe asthma 

January 2010 5-86 
in the winter in Denver, CO. In a population of 5- to 12-yr-old children with asthma in Seattle, WA, 

Slaughter et al. (2003, 086294) found a stronger 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 multicity study conducted by 

Schildcrout et al. (2006, 089812) which analyzed rescue inhaler use in 990 children ages 5-13 yr 

with asthma in 8 North American cities. Schildcrout et al. (2006, 089812) 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 a simultaneous increase in CO and other pollutants 

(i.e., NO2, PM10, and SO2) in joint models. Overall, Slaughter et al. (2003, 086294) and Schildcrout 

et al. (2006, 089812) question the associations observed due to the lack of biological plausibility for 

CO-induced respiratory effects and the high correlation between CO and NO2 (which suggests that 

other pollutants from mobile sources are driving the associations observed), respectively. Additional 

studies (Park et al., 2005, 088673; Silkoff et al., 2005, 087471; Von et al., 2002, 034706) conducted 

in Denver, CO; Erfurt, Germany; and Incheon, Korea, respectively, found associations between CO 

and medication use that are consistent with those previously reported, but in populations with 

combined ages ranging from 16 to 77 yr. Figure 5-14 presents the risk estimates from studies that 

examined the association between short-term exposure to CO and medication use in asthmatic 

individuals. 

Study Location Lag Age Effect Estimate (95% CI) 

Schildcrout et al. (2006, 089812) 8 N Amer cities 0-2a 5-12 Asthma Symptoms Children 

O’Connor et al. (2008, 156818) 7 US cities 0-18 5-12 Wheeze-cough 

O’Connor et al. (2008, 156818) 7 US cities 0-18 5-12 Nighttime asthma 

Yu et al. (2000, 013254) Seattle, WA 1 5-13 Asthma Symptoms 

Rabinovitch et al. (2004, 096753) Denver, CO 0-2 6-12 Asthma Exacerbation 

Delfino et al. (2003, 050460) LA, CA 1 10-16 Symp Score >2 (1-h max) 

Delfino et al. (2003, 050460) LA, CA 1 10-16 Symp Score >2 (max 8-h avg) 

Respiratory 

Symptoms 

von Klot et al. (2002, 034706) Erfurt, Germany 0-5 37-77 Wheeze Adults 

Schildcrout et al. (2006, 089812) 8 N. Amer cities 0-2a 5-12 Inhaler Use Children 

Slaughter et al. (2003, 086294) Seattle, WA 1 5-13 Inhaler Useb

Slaughter et al. (2003, 086294) Seattle, WA 1 5-13 Inhaler Usec

Rabinovitch et al. (2004, 096753) Denver, CO 0-2 6-12 Inhaler Use 

Medication 

von Klot et al. (2002, 034706) Erfurt, Germany 0-5 37-77 Inhaler Use (β2-agonist) Adults

Use 

von Klot et al. (2002, 034706) Erfurt, Germany 0-5 37-77 Inhaler Use (Corticosteroid) 

a

0-2 lag represents a 3-day moving sum 

b

Estimate presented is from a model that did not control for previous day’s asthma severity. 

c

Estimate presented is from a model that controlled for previous day’s asthma severity. Odds Ratio 

Figure 5-14. Summary of associations for short-term exposure to CO and asthma symptoms, 

respiratory symptoms and medication use in asthmatic individuals.1 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 Nonasthmatic Individuals 

In addition to examining the association between short-term exposure to CO and respiratory 

symptoms (e.g., cough, wheeze, shortness of breath) in asthmatic populations, some studies 

examined respiratory effects in individuals classified as nonasthmatics. Rodriguez et al. (2007, 

092842) examined the effect of CO on respiratory symptoms in a panel of 263 children 0- to 5-yr old 

at high risk for developing asthma in Perth, Australia. Rodriguez et al. (2007, 092842) 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 this study, copollutant models were not examined, CO correlations 

with other pollutants were not presented, and additional analyses were not conducted to further 

characterize the associations observed. 

 

1

 Effect estimates from Park et al. (2005, 088673) were not included in this figure because the study did not provide the increment at 

which the effect estimates were calculated. Additionally, estimates for Silkoff et al. (2005, 087471) were not included in the figure 

because results were not presented quantitatively. 

January 2010 5-87 
In a panel of individuals ≥ 50 yr of age with CHD in three European locations (Amsterdam, 

The Netherlands; Erfurt, Germany; and Helsinki, Finland) during the winter, de Hartog et al. (2003, 

001061) observed some marginal associations, specifically between CO concentration and the 

incidence of the respiratory symptoms shortness of breath and phlegm at lag 3, OR=1.17 (95% CI: 

0.96-1.40) and OR=1.22 (95% CI: 0.93-1.57), respectively, per 0.5 ppm increase in 24-h avg CO 

concentrations. However, the authors found that the associations between air-pollution exposure and 

respiratory symptoms were stronger for PM2.5 than for gaseous air pollutants. Overall, the 

associations observed in this study should be viewed with caution because the panel consisted of 

individuals on a variety of daily medications (i.e., beta blockers, ACE inhibitor + AT blocker, 

calcium antagonist, aspirin, digitalis, inhaled beta-agonist, and nitroglycerin). 

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 endpoints. Specifically, among asthmatics, the studies reviewed 

generally found positive associations between short-term exposure to CO and respiratory-related 

health effects (i.e., decrements in lung function, respiratory symptoms, and medication use). 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, 

which is evident by the high correlations reported between CO and other combustion-related 

pollutants (i.e., NO2 and PM). However, the limited number of copollutant analyses among this 

group of studies complicates the efforts to disentangle the health effects attributed to CO from the 

larger traffic-related pollutant mix. Additional uncertainty exists as to a biologically plausible 

mechanism that 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, 000907) evaluated a limited amount of literature that 

examined the association between short-term exposure to CO and respiratory hospital admissions 

(HAs), ED visits, and physician visits in the U.S. (i.e., Seattle, WA; Reno, NV; and Anchorage, AK) 

and Europe (i.e., Barcelona, Spain). From these studies, the 2000 CO AQCD (U.S. EPA, 2000, 

000907) concluded that positive associations were observed for short-term exposure to CO with 

several respiratory outcomes, including asthma and COPD. However, the lack of a biologically 

plausible mechanism for CO-induced respiratory morbidity at that time brought into question 

whether the results observed could be attributed to CO independently of other pollutants in the air 

pollutant mixture. Additional uncertainties were identified in the epidemiologic literature that 

contributed to this conclusion, which were discussed in Section 5.2.1. This section evaluates those studies published since the 2000 CO AQCD (U.S. EPA, 2000, 

000907) that examined the association between short-term exposure to CO at ambient concentrations 

similar to those found in the U.S. and respiratory-related HAs (Figure 5-15), ED visits (Figure 5-16), 

and physician visits. Unlike previous sections, which also evaluated studies conducted outside of 

North America, the expansive number of studies conducted in the U.S. and Canada provide adequate 

evidence to examine the association between short-term exposure to CO and respiratory HAs and 

ED visits. Although not discussed in this section, collectively, the studies conducted outside of the 

U.S. observed associations that are consistent with those observed in the U.S.- and Canadian-based 

studies evaluated below (see Annex C for results from the international studies evaluated). 

Overall, this section focuses on respiratory-related HAs because the majority of the literature 

examines HAs as opposed to ED visits or physician visits (Table 5-20 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 HAs, 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 HAs, ED visit, and physician visit studies, each outcome is evaluated in 

individual sections. In addition, each section presents results separately for respiratory health 

January 2010 5-88 
outcomes which include 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 5-20. Range of CO concentrations reported in key respiratory HA and ED visit studies that 

examine effects associated with short-term exposure to CO. 

Study Location Type of Visit (ICD9) Metric Mean Concentration (ppm) Middle/Upper Percentile 

Concentrations (ppm) 

Cakmak et al. 

(2006, 093272) 

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, 002839) 

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; 

Slaughter et al. 

(2005, 073854) 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, 093439) 

Toronto, ON, 

Can 

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, 055621) 

Vancouver, BC, 

Can 

Hospital Admissions: Respiratory 

diseases 24-h avg 0.98 50th: 0.82; 75th: 1.16 Maximum: 4.90 

Lin et al. (2003, 

042549) 

Toronto, ON, 

Can Hospital Admissions: Asthma 24-h avg 1.18 50th: 1.10; 75th: 1.40 Maximum: 6.10 

Lin et al. (2004, 

055600) 

Vancouver, BC, 

Can Hospital Admissions: Asthma 24-h avg 0.96 50th: 0.80; 75th: 1.12 Maximum: 4.90 

Moolgavkar 

(2003, 042864) 

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 

Yang et al. 

(2005, 090184) 

Vancouver, BC, 

Can Hospital Admissions: COPD 24-h avg 0.71 50th: 0.64 Maximum: 2.48 

Karr et al. 

(2006, 088751) 

South Coast Air 

Basin, CA 

Hospital Admissions: Acute 

bronchiolitis 24-h avg 

Lag 1: 

Index: 1.730 

Referent: 1.750 

Lag 4: 

Index: 1.760 

Referent: 1.790 

Lag 1: 

Index: 

50th: 1.52; 75th: 2.26; 

90th: 3.16 

Maximum: 9.60 

Referent: 

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 

Referent: 

50th: 1.55; 75th: 2.35; 

90th: 3.30 

Maximum: 9.60 

January 2010 5-89 
Study Location Type of Visit (ICD9) Metric Mean Concentration (ppm) Middle/Upper Percentile 

Concentrations (ppm) 

Karr et al. 

(2007, 090719) 

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 

Zanobetti and 

Schwartz (2006, 

090195) 

Boston, MA Hospital Admissions: Pneumonia 24-h avg NR 50th: 0.48; 75th: 0.60; 

95th: 0.88 

Lin et al. (2005, 

087828) 

Toronto, ON, 

Canada 

Hospital Admissions: Respiratory 

infections 24-h avg 1.16 50th: 1.05; 75th: 1.37 Maximum: 2.45 

Peel et al. 

(2005, 056305) Atlanta, GA ED Visits: All respiratory; asthma; 

COPD; URI; pneumonia 1-h max 1.8 90th: 3.4 

Tolbert et al. 

(2007, 090316) 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, 

156594) New York, NY ED Visits: Asthma 8-h max 1.31 50th: 1.23; 75th: 1.52; 

95th: 2.11 

Villeneuve et al. 

(2006, 091179) 

Toronto, ON, 

Canada 

Physicians Visits: 

Allergic rhinitis 24-h avg 1.1 Maximum: 2.2 

Sinclair et al. 

(2004, 088696) Atlanta, GA Urgent Care Visits: 

Asthma; respiratory infections 

1-h max 1.3 NR 

Hospital Admissions 

Respiratory Disease 

The majority of studies from North America that examined the association between short-term 

exposure to CO and HAs for all respiratory diseases were conducted in Canada, and only one of 

these studies presented results from a combined analysis of multiple cities (Cakmak et al., 2006, 

093272). In a study of 10 of the largest Canadian cities, Cakmak et al. (2006, 093272) examined 

respiratory HAs (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 cities1

). However, Cakmak et al. (2006, 093272) only examined the 

potential confounding effects of gaseous pollutants (i.e., NO2, SO2, and O3) on the CO risk estimate 

in a multipollutant model and did not provide correlation coefficients, limiting the interpretation of 

the effects observed in the single-pollutant model. U.S.-based studies (Los Angeles and Spokane) 

that examined HAs for all respiratory diseases reported similarly weak or null associations with CO 

(Linn et al., 2000, 002839; Slaughter et al., 2005, 073854). But two single-city studies conducted in 

Canada reported stronger associations, primarily through evidence from copollutant models, between 

short-term exposure to CO and respiratory disease HAs (Burnett et al., 2001, 093439; Yang et al., 

2003, 055621). In a study conducted in Toronto, Canada, for the time period 1980-1994, Burnett 

et al. (2001, 093439) reported a relatively strong association between 1-h max CO and respiratory 

disease HAs in children <2 yr 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 HAs for a 2-day avg (lag 0-1) per 1 ppm increase in 1-h max CO. In the two-pollutant 

model analysis with O3, the estimate for CO remained elevated (7.29% [95% CI: 1.75-13.1]), but CO 

 

1

 To determine the lag for the combined estimate across all 10 cities, Cakmak et al. (2006, 093272) averaged the strongest associations 

from lags 0-5 days from each city. 

January 2010 5-90 
was not found to be highly correlated with O3 (r = 0.24). Yang et al. (2003, 055621) 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 yr of age) respiratory disease (ICD-9: 460-519) HAs in Vancouver for the time period 

1986-1998. Yang et al. (2003, 055621) also reported elevated associations with 24-h avg CO and 

respiratory HAs (ICD-9: codes 460-519) for ages 65 yr 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 CO 

risk estimates remained the same when O3 was included in the model, which could be attributed to 

the lack of collinearity between CO and O3 due to their negative correlation (r = -0.52). 

Asthma 

Some studies that examined the effect of short-term exposure to CO on asthma HAs conducted 

all age and age-stratified analyses, specifically to examine effects in children. In a few studies 

conducted in Canada, evidence was observed for increased pediatric (ages 6-12 yr) asthma HAs 

(ICD-9: 493) in boys but not girls (Lin et al., 2003, 042549; Lin et al., 2004, 055600); however, a 

biological explanation was not provided which could explain this difference. Lin et al. (2003, 

042549) used a bidirectional case-crossover analysis in Toronto, Canada, for the years 1981-1993. 

The authors 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 an HA. 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 

this study, CO levels were moderately correlated with NO2 (r = 0.55) and PM2.5 (r = 0.45), and 

weakly correlated with SO2 (r = 0.37). In this study, copollutant analyses were not conducted to 

examine the potential confounding effect of different PM size fractions or gaseous pollutants on CO 

risk estimates. It should be noted that this study used a bidirectional case-crossover analysis, which 

may bias the results in either direction (Levy et al., 2001, 017172). 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, 017172). Lin et al. 

(2004, 055600) also examined the association between air pollutants and asthma HAs in children, 

but using a time-series study design in Vancouver during the years 1987-1998. In this study, the 

authors stratified results by socioeconomic status (SES) and found some evidence for an association 

between CO and asthma HAs for both girls and boys, of both high and low SES at lag 1 

(RR=1.01-1.06 per 0.5 ppm increase in 24-h avg CO); but overall, the evidence was less consistent 

for a greater effect in boys versus girls compared to Lin et al. (2003, 042549). In a study that 

examined asthma HAs for all ages and genders combined, Slaughter et al. (2005, 073854) observed 

some evidence for an increase in asthma HAs (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 CO 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 HAs. Moolgavkar (2003, 042864) (a reanalysis of Moolgavkar, 2000, 

010274) examined HAs 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 increases in HAs of 0.53-1.20% for all ages in Los Angeles County and 0.17-1.41% for 

ages ≥ 65 yr in Cook County, for a 0.5 ppm increase in 24-h avg CO and lags ranging from 0 to 

5 days. However, CO was found to be highly correlated with NO2 in both Cook County (r = 0.63) 

and Los Angeles County (r = 0.80), but Moolgavkar (2003, 042864) did not examine the influence of 

copollutants on CO risk estimates. Yang et al. (2005, 090184) reported similar results for COPD HAs 

(ICD-9 490-492, 494, 496) in Vancouver for ages ≥ 65 yr for the years 1994-1998 for a ma of 0- to 

6-day lags (RR = 1.14 [95% CI: 1.03-1.23] per 0.5 ppm increase in 24-h avg CO). In this study, CO 

concentrations were moderately correlated with NO2, SO2, and PM10, and moderately negatively 

correlated with O3. In copollutant models, Yang et al. (2005, 090184) found that risk estimates for 

CO and COPD HAs remained elevated with O3 (RR=1.19 [95% CI: 1.07-1.32]) or SO2 (RR=1.19 

[95% CI: 1.02-1.39]), but were attenuated when adjusting for NO2 (RR=1.07 [95% CI: 0.92-1.24]) 

or PM10 (RR=1.03 [95% CI: 0.89-1.21]). Contrary to Moolgavkar (2003, 042864) and Yang et al. 

January 2010 5-91 
(2005, 090184), Slaughter et al. (2005, 073854) found no association between short-term exposure to 

CO and COPD HAs (ICD-9 491, 492, 494, 496) in 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. However, this study did not examine correlations between CO and other gaseous 

pollutants or conduct copollutant analyses. 

Acute Bronchiolitis in Infants 

Karr et al. (2006, 088751; 2007, 090719) examined both short-term (lag 0 or 1) and longer 

term levels of CO in relation to acute bronchiolitis (ICD-9: 466) HAs during the first year of life 

from 1995-2000 in the South Coast Air Basin in California. Karr et al. (2006, 088751) found no 

evidence of a short-term association between ambient CO concentrations and HAs 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, 090719), which examined longer term exposures (average in the month 

prior to a HA and lifetime average) in a matched case-control study, did not provide any evidence of 

an association with CO. Neither of these studies examined the correlation between CO and other 

pollutants nor conducted copollutant analyses. 

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, 090195) examined pneumonia HAs (ICD-9 480-487) in Boston, MA, 

for the years 1995-1999 for individuals ages 65 yr and older, using a time-stratified case-crossover 

analysis. The authors reported an increase in pneumonia HAs 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, 090195) 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 

HAs separately, as was done by Zanobetti and Schwartz (2006, 090195), Lin et al. (2005, 087828) 

presented results for the overall effect of CO on respiratory infection HAs (ICD-9: 464, 466, 480-

487). In this analysis, Lin et al. (2005, 087828) examined the potential increase in respiratory HAs in 

children <15 yr of age in Toronto, Canada, for 1998-2001, using a bidirectional case-crossover 

approach. The authors reported elevated estimates for boys (OR=1.17 [95% CI: 1.03-1.32] 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: 0.91-1.23]). In multipollutant models with both PM2.5

and PM10-2.5 the CO risk estimates were slightly attenuated but remained positive (boys: OR=1.10 

[95% CI: 0.96-1.26]; girls: OR=1.03 [95% CI: 0.88-1.06]). Lin et al. (2005, 087828) did not provide 

an explanation as to why the estimates were stronger for boys than for girls. It should be noted that 

this study used a bidirectional case-crossover analysis, which, as discussed previously, may bias the 

results in either direction (Levy et al., 2001, 017172). 

January 2010 5-92 
Study Location Age Lag Effect Estimate (95% CI) 

Burnett et al. (2001, 093439) Toronto, Can <2 0-1 All Respiratory 

Yang et al. (2003, 055621) Vancouver, Can <3 1 

Linn et al. (2000, 002839) Los Angeles, CA 30+ 0 

Yang et al. (2003, 055621) Vancouver, Can 65+ 1 

Cakmak et al. (2006, 093272) 10 Canadian Cities All 2.8c 

Slaughter et al. (2005, 073854) Spokane, WA All 2 

Lin et al. (2003, 042549) Toronto, Can 6-12a 1 Asthma 

Lin et al. (2003, 042549) Toronto, Can 6-12b 1 

Slaughter et al. (2005, 073854) Spokane, WA All 2 

Slaughter et al. (2005, 073854) Spokane, WA 15+ 2 COPD 

Yang et al. (2005, 090184) Vancouver, Can 65+ 0-6 

Zanobetti & Schwartz (2006, 090195) Boston, MA 65+ 0 Pneumonia 

Karr et al. (2006, 088751) SCAB, CA <1 1 Acute Bronchiolitis 

Lin et al. (2005, 087828) Toronto, Can <15a 0-5 Respiratory Infection 

Lin et al. (2005, 087828) Toronto, Can <15b 0-5 a Boys 

b

Girls 

c

To determine the lag for the combined estimate across all 10 cities, Cakmak et al. (2006, 093272) 

averaged the strongest associations from lags 0-5 days from each city. 

 

 Relative Risk / Odds Ratio 

Figure 5-15. Summary of associations for 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, 056305) conducted a large single-city respiratory disease ED visit study in 

Atlanta, GA, which included data from 31 hospitals for the time period 1993–2000. In this study, 

results were reported for various respiratory-related visits (ICD-9 460-466, 477, 480-486, 491-493, 

496, 786.09). In an all-ages analysis, the authors found a RR=1.01 (95% CI: 1.00-1.02) for all 

respiratory disease ED visits for a 3-day avg (lag 0-2) per 1 ppm increase in 1-h max CO 

concentration. Tolbert et al. (2007, 090316) expanded the time period used in the Peel et al. (2005, 

056305) study to include ED visits through 2004 and reported similar results for all respiratory 

disease 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 (results not presented quantitatively), which could potentially be explained by the 

high correlations reported in Tolbert et al. (2007, 090316) between CO and NO2 (r = 0.70) and EC 

(r = 0.66) and the moderate correlation with PM2.5 (r = 0.51). One additional ED-visits study that 

also examined respiratory disease (Slaughter et al., 2005, 073854) presented essentially null results 

at lag 1 and 2 but found similar results to Peel et al. (2005, 056305) and Tolbert et al. (2007, 090316) 

at lag 3 (RR=1.02 [95% CI: 1.00-1.03] per 0.5 ppm increase in 24-h avg CO). Slaughter et al. (2005, 

073854) reported a weak to moderate correlation between CO and various PM size fractions but did 

not report the correlation between CO and gaseous pollutants, limiting the comparison of this study 

with Peel et al. (2005, 056305) and Tolbert et al. (2007, 090316). 

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, 056305). In this study, the authors reported 

 

1

 Risk estimates from Moolgavkar (2003, 042864) 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. 

2

 Risk estimates from Lin et al. (2004, 055600) 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, 042549). 

January 2010 5-93 
results from distributed lag models including lags 0-13 in addition to a ma 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 ma CO concentrations (RR = 1.01 

[95% CI: 0.99-1.02] for lags 0-2 compared to RR=1.08 [95% CI: 1.05-1.11] 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 yr) (RR=1.02 [95% CI: 1.00-1.04] 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, 056305). Slaughter et al. (2005, 

073854), 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, 

156594) also provide evidence of increased ED visits for asthma (ICD-9 493) for all ages in New 

York City for 1999-2002. Using three different models that adjusted for weather variables via 

different degrees of smoothing and/or a different number of weather variables, the authors found that 

CO effect estimates remained elevated in both an all-year analysis and in analyses stratified by warm 

(i.e., April to August) and cold (i.e., November to March) months. Using Model C, which adjusted 

for temporal trends using 8 degrees of freedom (df) and included variables to adjust for weather and 

day of the week, an all-year RR of 1.03 (95% CI: 1.01-1.06) per 0.75 ppm increase in maximum 8-h 

avg CO concentrations was reported. Ito et al. (2007, 156594) also examined copollutant models 

using Model C but only during the warm season. In this model CO effect estimates were robust to 

the inclusion of PM2.5 (RR = 1.06 [95% CI: 1.00-1.11]), O3 (RR=1.10 [95% CI: 1.05-1.15]), and SO2

(RR=1.04 [95% CI: 0.99, 1.09]) in the model, but the CO risk estimate was attenuated, resulting in a 

negative effect estimate when including NO2 (RR=0.97 [95% CI: 0.92-1.03]) in the model. 

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, 056305) 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 lag 0-2 ma) with similar 

results for the distributed lag model (RR=1.03 [95% CI: 0.98-1.09). However, results from Slaughter 

et al. (2005, 073854) from Spokane, WA, 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 HA analysis conducted by Zanobetti and Schwartz (2006, 090195) discussed 

above, Peel et al. (2005, 056305) 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 3-day ma (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 model (days 0-13), Peel et al. 

(2005, 056305) observed evidence of an association (RR=1.045 [95% CI: 1.01-1.08]). An 

examination of upper respiratory infection (URI) ED visits, the largest of the respiratory ED groups, 

found slightly increased risk estimates for both the 3-day ma (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 (not reported quantitatively). Upon conducting 

an age-stratified analysis, Peel et al. (2005, 056305) also found that infant (<1 yr of age) and 

pediatric (ages 2-18 yr) URI ED visit CO risk estimates were substantially stronger than the all-age 

risk estimates. 

January 2010 5-94 
Study Location Age Lag Effect Estimate (95% CI) 

Slaughter et al. (2005, 073854) Spokane, WA All 3 All Respiratory

Peel et al. (2005, 056305) Atlanta, GA All 0-2 

Tolbert et al. (2007, 090316) Atlanta, GA All 0-2 

Peel et al. (2005, 056305) Atlanta, GA 2-18 0-2 Asthma

Peel et al. (2005, 056305) Atlanta, GA All 0-2 

Peel et al. (2005, 056305) Atlanta, GA All 0-13a

Slaughter et al. (2005, 073854) Spokane, WA All 3 

Ito et al. (2007, 156594) NYCity, NY All 0-1 

Slaughter et al. (2005, 073854) Spokane, WA 15+ 1 COPD 

Peel et al. (2005, 056305) Atlanta, GA All 0-2 

Peel et al. (2005, 056305) Atlanta, GA All 0-13a

Peel et al. (2005, 056305) Atlanta, GA All 0-2 Pneumonia

Peel et al. (2005, 056305) Atlanta, GA All 0-13a

Peel et al. (2005, 056305) Atlanta, GA All 0-2 Respiratory Infection

Peel et al. (2005, 056305) Atlanta, GA All 0-13a

a

Unconstrained distributed lag 

 Relative Risk 

Figure 5-16. Summary of associations for 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 HAs 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. (2006, 091179) examined the effect of CO on physician visits for allergic rhinitis in 

individuals 65 yr 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., 2006, 091179). In an additional study, Sinclair et al. (2004, 088696) 

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. 

Summary of Associations between Short-Term Exposure to CO and Respiratory 

Hospital Admissions, ED Visits, and Physicians Visits 

Compared to other criteria air pollutants (e.g., O3 and PM), relatively few studies evaluated the 

association between short-term exposure to ambient CO and HAs and ED visits for various 

respiratory outcomes. Although evidence for consistent positive associations (Figure 5-15 and Figure 

5-16) has been found across the studies evaluated, there remains uncertainty as to a biologically 

plausible mechanism which could explain the association between CO exposure and respiratoryrelated health effects. As observed in the preceding section, several authors suggest that the observed 

associations are due to CO acting as an indicator of combustion-related pollution (e.g., traffic). The 

interpretation of the associations observed in the studies evaluated is further complicated by the 

moderate to high correlations reported between CO and other traffic-related pollutants such as NO2, PM2.5, EC, or BC. Only a few studies examined potential confounding of CO risk estimates by 

copollutants, and these studies found that CO risk estimates were generally robust to the inclusion of 

O3, SO2, and PM in two-pollutant models. However, those studies that examined two-pollutant 

models with NO2 found that CO risk estimates, although positive, were slightly attenuated. 

January 2010 5-95 
5.5.2. Epidemiologic Studies with Long-Term Exposure 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) did not evaluate any studies that examined the 

effect of long-term exposure to CO on respiratory health. The following section discusses those 

studies that analyze the effect of long-term exposure to CO on pulmonary function, asthma/asthma 

symptoms, and allergic rhinitis. Table 5-21 lists the studies evaluated in this section along with the 

respiratory health outcomes examined and CO concentrations reported. 

Table 5-21. Range of CO concentrations reported in key respiratory morbidity studies that examined 

effects associated with long-term exposure to CO. 

Studya Location 

(Sample Size) Year(s) Health Outcome Metric 

Mean 

Concentration 

(ppm) 

Middle/Upper 

Percentile 

Concentrations (ppm)

Mortimer et al. (2008, 

122163) 

San Joaquin 

Valley, CA 

(n=232) 

1989-2000 Pulmonary 

function 

Monthly mean 

of max 8-h 

avg 

NR NR 

Meng et al. (2007, 093275) 

Los Angeles and 

San Diego 

counties, CA 

11/2000-9/2001 Asthma symptoms Annual mean 

of 1-h avg NR NR 

Wilhelm et al. (2008, 

191912) 

Los Angeles and 

San Diego 

Counties, CA 

(n=612) 

1999-2001 Asthma symptoms Annual mean 

of 1-h avg 1.0 Maximum: 1.8 

Goss et al. (2004, 055624) 

U.S. 

2000 

Pulmonary 

function; Asthma 

symptoms 

Annual mean 

of 1-h avg 0.69 

25th: 0.48 

50th: 0.59 

75th: 0.83 

Hirsch et al. (1999, 003537) Dresden, Germany 9/1995-6/1996 Respiratory 

symptoms 

Annual mean 

of 0.5-h avg 0.60 75th: 0.76 

Maximum: 1.34 

Guo et al. (1999, 010937) 

Taiwan 

1994 Asthma; Asthma 

symptoms 

Annual mean 

of monthly avg 0.85 

50th: 0.84 

75th: 1.00 

Maximum: 1.61 

Wang et al. (1999, 008105) Kaohsiung and 

Pintong, Taiwan 1996 Asthma Annual avg NR 50th: 0.80 

Hwang et al. (2005, 089454) Taiwan 2000 Asthma Annual mean 

of monthly avg 0.66 

50th: 0.65 

75th: 0.75 

Maximum: 0.96 

Hwang et al. (2006, 088971) Taiwan 2000 Allergic rhinitis Annual mean 

of monthly avg 0.66 

50th: 0.65 

75th: 0.75 

Maximum: 0.96 

Lee et al. (2003, 049201) Taiwan 1994 Allergic rhinitis Annual avg 0.85 

50th: 0.84 

75th: 1.00 

Maximum: 1.61 

Arnedo-Pena et al. (2009, 

190238) 7 Spanish cities 2000 

Asthma, allergic 

rhinitis, atopic 

eczema 

Annual avg 

50th: 0.61 

75th: 0.78 

Maximum: 1.04 

Mortimer et al. (2008, 

187280) 

Fresno, CA 

(n=170) 

11/2000-4/2005 Allergic 

sensitization 

Monthly mean 

of 24-h avg NRb NRb

 

a

The number of individuals included in the study population was only provided for those studies that included less than 1,000 participants. b

This study only presented air quality data graphically. 

5.5.2.1. Pulmonary Function 

Mortimer et al. (2008, 122163) examined the effect of prenatal and lifetime exposures to air 

pollutants on pulmonary function in 232 asthmatic children who resided in the San Joaquin Valley of 

California. The strong temporal correlation between pollutants and pollutant metrics for different 

time periods in the study area contributed to the inability to draw conclusions about the effect of 

January 2010 5-96 
individual pollutant metrics on pulmonary function (Mortimer et al., 2008, 122163). The authors 

used a newly developed Deletion/Substitution/Addition (DSA) algorithm “to identify which 

pollutant metrics were most predictive of pulmonary function” (Mortimer et al., 2008, 122163). This 

methodology uses an exploratory process to identify the best predictive model for each outcome of 

interest. Focusing specifically on the exposure durations after birth, using this approach, Mortimer 

et al. (2008, 122163) found that exposure to CO early in life, ages 0-3 yr, was negatively associated 

with FEV1/FVC, resulting in an effect size of -2.5% per IQR increase in CO.1

 Additional negative 

associations were observed between exposure to CO during the first 6 yr of life and FEF25 (-6.7%) 

and FEF25-75/FVC (-4.8%) in children diagnosed with asthma prior to 2 yr of age. Overall, Mortimer 

et al. (2008, 122163) found that these effects were limited to subgroups, including AfricanAmericans and individuals diagnosed with asthma before the age of 2 yr. It must be noted that 

research still needs to be conducted to validate the aforementioned results obtained using the DSA 

algorithm and the subsequent calculation of effect estimates using GEE because the current model 

could underestimate the uncertainty surrounding the associations reported (Mortimer et al., 2008, 

122163). Although the authors did find associations between long-term exposure to CO and 

decrements in pulmonary function, they also observed high correlations between CO and NO2, which together are markers for pollutants generated by urban combustion sources (e.g., mobile 

sources) (Mortimer et al., 2008, 122163). 

Goss et al. (2004, 055624) also examined the effect of long-term exposure to CO on 

pulmonary function in a cohort of cystic fibrosis patients >6 yr of age enrolled in the Cystic Fibrosis 

National Patient Registry in 1999 and 2000. When examined cross-sectionally in 2000 using a 

multiple linear regression model, the authors found no association between CO and a reduction in 

FEV1. However, Goss et al. (2004, 055624) recognize that the CO results could be influenced by 

measurement error and subsequently exposure misclassification. 

5.5.2.2. Asthma and Asthma Symptoms 

U.S.-based studies consistently reported no association between long-term exposure to CO and 

asthma and asthma symptoms. Wilhelm et al. (2008, 191912) and Meng et al. (2007, 093275) both 

examined the association between long-term exposure to air pollutants and asthma symptoms in 

respondents to the 2001 California Health Interview Survey (CHIS) in populations consisting of 

children (0-17 yr) and adults (≥ 18 yr), respectively, who resided in Los Angeles and San Diego 

counties. Using a cross-sectional study design, Meng et al. (2007, 093275) found no association 

between long-term exposure to CO and poorly controlled asthma in adults, while Wilhelm et al. 

(2008, 191912) reported no associations between long-term exposure CO and asthma symptoms or 

asthma HA and ED visits in children during the study period (i.e., 2000-2001). In an additional 

U.S.-based study, Goss et al. (2004, 055624) found no association (OR=1.01 [95% CI: 0.92-1.10] 

per 0.5 ppm increase in annual average CO concentrations) between long-term exposure to CO and 

pulmonary exacerbations in a national cohort of individuals with cystic fibrosis >6 yr of age. 

Among studies conducted in other countries, a study conducted in Germany (Hirsch et al., 

1999, 003537) and studies conducted in Taiwan (Guo et al., 1999, 010937; Hwang et al., 2005, 

089454; Wang et al., 1999, 008105), all found positive associations between long-term exposure to 

CO and asthma or asthma symptoms in populations ranging from 6 to 16 yr old. In these studies, the 

authors addressed the observed associations differently. Guo et al. (1999, 010937) and Hwang et al. 

(2005, 089454) both concluded that it is unlikely CO directly affects the respiratory system; Hirsch 

et al. (1999, 003537) 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, 008105) did not 

interpret the association observed between long-term exposure to CO and adolescent asthma. Only 

Hwang et al. (2005, 089454) conducted a copollutant analysis and found that the asthma effects 

observed were robust to the inclusion of PM10, SO2 and O3 in the model. However, this study did not 

include NOX in a copollutant model, which is notable because NOX was found to be highly 

correlated with CO (r = 0.88). 

 

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. 

January 2010 5-97 
5.5.2.3. Respiratory Allergy and Other Allergic Responses 

Allergy is a major contributor to asthma and upper respiratory symptoms; as a result, studies 

have examined the effect of air pollutants on allergic outcomes. The studies evaluated that examined 

the association between long-term exposure to CO and allergic outcomes were primarily conducted 

outside of the U.S. and Canada. A multicity study conducted in 7 Spanish cities found that the annual 

average concentration of CO was associated with a higher prevalence of allergic rhinitis, 

rhinoconjunctivitis, and atopic eczema in 6- to 7-yr-old children (Arnedo-Pena et al., 2009, 190238). 

NO2 was also examined and found to be positively associated with allergic rhinitis, but, unlike CO, 

was negatively associated with eczema and rhinoconjunctivitis. It should be noted that in this data 

set CO and NO2 concentrations were negatively correlated (r = -0.55). Additionally, SO2 was 

positively associated with all allergic outcomes, while TSP matter was inversely associated with 

rhinitis and rhinoconjunctivitis. Hwang et al. (2006, 088971) and Lee et al. (2003, 049201) both 

examined the effect of long-term exposure to air pollutants on the prevalence of allergic rhinitis in a 

population of schoolchildren in Taiwan. Both studies found an association between allergic rhinitis 

prevalence and CO, but they also observed an association with NOX. As a result, although Hwang 

et al. (2006, 088971) and Lee et al. (2003, 049201) observed an increase in the prevalence of allergic 

rhinitis in response to an increase in long-term CO levels, they concluded that the combination of an 

association being observed for both CO and NOX can be attributed to the complex mixture of trafficrelated pollutants and not necessarily CO alone. Although questions surround the associations 

observed between long-term exposure to CO and allergic outcomes, the results are consistent with 

those presented in a multicity study that examined the association between short-term exposure to 

CO and allergic symptoms. Moon et al. (2009, 190297) observed associations between short-term 

CO exposure and allergic symptoms in children in South Korea. However, allergic symptoms were 

also associated with other pollutants, including PM10, SO2, and NO2, and the study did not present 

correlation coefficients to allow for further analysis of the results. It should be noted that 

toxicological experiments suggest that endogenously produced CO may play an integral part in the 

pathogenesis of allergic rhinitis, resulting in an additional potential pathway for CO-induced allergic 

outcomes (Shaoqing et al., 2008, 192384). 

Allergic symptoms such as rhinitis are a direct result of allergic sensitization, which is 

commonly measured by skin prick testing or IgE antibody measurement. Hirsch et al. (1999, 

003537), in a single-city study conducted in Dresden, Germany, observed no associations between 

annual average concentrations of CO, NO2, SO2, or O3 and allergy assessed by skin prick testing or 

serum IgE measurement in schoolchildren. However, prenatal exposure to CO was associated with 

allergic sensitization in a cohort of 6- to 11-year-old asthmatic children in California (Mortimer et 

al., 2008, 187280). Skin prick tests indicated higher levels of sensitization to indoor and outdoor 

allergens with an increase in CO exposure during the prenatal period; the association with 

sensitization to outdoor allergens remained after adjustment for effect modifiers, copollutants, and 

other potential confounders. Mortimer et al. (2008, 187280) also found that PM10 exposure was 

associated with sensitization to indoor allergens but was not significant after adjustment. 

Additionally, despite strong correlations between CO and NO2, no associations were reported with 

NO2. It should be noted that these results were produced using the DSA algorithm and, as discussed 

previously, additional research is still needed to evaluate the use of this method in air pollution 

epidemiology (Mortimer et al., 2008, 122163). 

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 longterm exposure to CO and respiratory morbidity. Although studies have reported positive associations 

for various respiratory outcomes, the limited evidence available, the new analytical methods 

employed, and the lack of studies that examined potential confounders of the CO-respiratory 

morbidity relationship, especially due to the high correlation between CO and other traffic-related 

pollutants, makes it difficult to attribute the associations observed to CO independent of other air 

pollutants. 

January 2010 5-98 
5.5.3. Controlled Human Exposure Studies 

Human clinical studies provide very little and inconsistent evidence of changes in pulmonary 

function following exposure to CO. In one older study, Chevalier et al. (1966, 010641) observed a 

significant decrease in total lung capacity following a short-term exposure to 5,000 ppm resulting in 

a COHb level of 4%. However, a similar study conducted at a higher CO concentration resulting in 

COHb levels of 17-19% found no CO-induced changes in lung volume or mechanics (Fisher et al., 

1969, 012381). The 2000 CO AQCD (U.S. EPA, 2000, 000907) reported no evidence of CO-induced 

changes in exercise ventilation at COHb levels <15% during submaximal exercise (Koike et al., 

1991, 013500). In two recent human clinical studies, exposure to CO (COHb ≈ 10%) was not found 

to significantly affect resting pulmonary ventilation compared with exposure to clean air under either 

hypoxic or hyperoxic exposure conditions (Ren et al., 2001, 193850; Vesely et al., 2004, 194000). 

The results of these studies demonstrate that the hypoxia- and CO2-induced increases in pulmonary 

ventilation are not affected by CO. One recent study evaluated the potential anti-inflammatory 

effects of controlled exposures to CO in the airways of 19 individuals with COPD (Bathoorn et al., 

2007, 193963). Subjects were exposed to both CO at concentrations of 100-125 ppm as well as room 

air for 2 h on each of 4 consecutive days. The authors reported a small decrease in sputum 

eosinophils, as well as a slight increase in the provocative concentration of methacholine required to 

cause a 20% reduction in FEV1 following exposure to CO. Although this study appears to 

demonstrate some evidence of an anti-inflammatory 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. A similar study found no evidence of systemic anti-inflammatory effects following 

exposure to higher CO concentrations (500 ppm for 1 h) in a group of healthy adults (Mayr et al., 

2005, 193984). 

5.5.4. Toxicological Studies 

As discussed in Section 5.2.5, the work of Thom, Ischiropoulos and colleagues (Ischiropoulos 

et al., 1996, 079491; Thom and Ischiropoulos, 1997, 085644; Thom et al., 1997, 084337; Thom et 

al., 1999, 016753; Thom et al., 1999, 016757) focused on CO-mediated displacement of NO from 

heme-binding sites. Although the concentrations of CO used in many of their studies were far higher 

than ambient levels, some of this research involved more environmentally-relevant CO levels. In one 

study (Thom et al., 1999, 016757), 1-h exposure of rats to 50 ppm CO resulted in increased lung 

capillary leakage 18 h later. Increased NO was observed in the lungs by electron paramagnetic 

resonance during 1-h exposure to 100 ppm CO and was accompanied by increases in H2O2 and 

nitrotyrosine. All of these effects were blocked by inhibition of NOS. These results, which were 

partially discussed in the 2000 CO AQCD (U.S. EPA, 2000, 000907), demonstrate the potential for 

exogenous CO to interact with NO-mediated pathways and to lead to pathophysiological effects in 

the lung. 

Recent work by Ghio et al. (2008, 096321) showed a disruption of cellular iron homeostasis 

following exposure to a low level of CO (50 ppm for 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 but significant neutrophilia in the 

lungs compared to air-exposed rats. This finding is contrary to the concept that CO acts as an antiinflammatory 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 nonspecific application of 

exogenous CO would have little capacity to discriminate between excessive and/or inappropriate 

iron which catalyzes oxidative stress and iron which may be required for normal homeostasis. 

January 2010 5-99 
A chronic inhalation study by Sorhaug et al. (2006, 180414) demonstrated no alterations in 

lung morphology in Wistar rats exposed to 200 ppm CO for 72 wk. COHb levels were reported to be 

14.7%, and morphological changes were noted in the heart as described in Section 5.2.5.2. 

A recent study by Carraway et al. (2002, 026018) 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. 

One new study found an association between increased endogenous CO and the development 

of allergic rhinitis (Shaoqing et al., 2008, 192384). In this model, guinea pigs which were sensitized 

and challenged with ovalbumin exhibited high immunoreactivity of HO-1 in the nasal mucosa and a 

more than doubling of blood COHb levels (measured by gas chromatography). It is not known 

whether the observed increase in endogenous CO resulting from ovalbumin-mediated 

inflammation/oxidative stress plays a role in the development of allergic rhinitis but suggests a 

potential mechanism by which exogenous CO could impact an allergic phenotype. 

In summary, one older study (Thom et al., 1999, 016757) and two new studies (Carraway et 

al., 2002, 026018; Ghio et al., 2008, 096321) 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, 180414). 

5.5.5. 

5.5.5.1.

Summary of Respiratory Health Effects 

Short-Term Exposure to CO 

New epidemiologic studies, supported by the body of literature summarized in the 2000 CO 

AQCD (U.S. EPA, 2000, 000907), provide evidence of positive associations between short-term 

exposure to CO and respiratory-related outcomes including pulmonary function, respiratory 

symptoms, medication use, HAs, and ED visits. The majority of the studies evaluated did not 

conduct extensive analyses to examine the potential influence of model selection or effect modifiers 

on the association between CO and respiratory morbidity. A limited number of studies examined the 

potential confounding effects of copollutants on CO risk estimates and found that CO risk estimates 

were generally robust to the inclusion of O3, SO2, and PM in two-pollutant models but were slightly 

attenuated in models with NO2. However, the limited amount of evidence from studies that examined 

the effect of gaseous pollutants on CO-respiratory morbidity risk estimates in two-pollutant models, 

specifically NO2, has contributed to the inability to disentangle the effects attributed to CO from the 

larger complex air pollution mix (particularly motor vehicle emissions), and this limits interpretation 

of the results observed in the epidemiologic studies evaluated. A key uncertainty in interpreting the 

epidemiologic studies evaluated is the biological mechanism(s) that could explain the effect of CO 

on respiratory health. Animal toxicological studies, however, provide some evidence that short-term 

exposure to CO (50-100 ppm) can cause oxidative injury and inflammation and alter pulmonary 

vascular remodeling. Controlled human exposure studies have not extensively examined the effect of 

short-term exposure to CO on respiratory morbidity, with a very limited number of studies reporting 

inconsistent effects of CO on pulmonary function. Although these controlled human exposure studies 

do not provide evidence to support CO-related respiratory health effects, epidemiologic studies show 

positive associations for CO-induced lung-related outcomes and animal toxicological studies 

demonstrate the potential for an underlying biological mechanism, which together provide evidence 

that is suggestive of a causal relationship between relevant short-term exposures to CO 

and respiratory morbidity. 

January 2010 5-100 
5.5.5.2.

5.6.1. 

Long-Term Exposure to CO 

Currently, only a few studies have been conducted that examine the association between longterm exposure to CO and respiratory morbidity including allergy. Although some studies did observe 

associations between long-term 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 copollutant analyses to disentangle the 

respiratory effects associated with CO due to its high correlation with NO2 and other combustionrelated pollutants. Overall, the evidence available is inadequate to conclude that a causal 

relationship exists between relevant long-term exposures to CO and respiratory 

morbidity. 

5.6. Mortality 

Epidemiologic Studies with Short-Term Exposure to CO 

Epidemiologic studies have traditionally focused on mortality effects associated with exposure 

to PM and O3, resulting in a limited number of studies that have conducted extended analysis to 

examine the potential influence of model selection, effect modifiers, or confounders on the 

association between CO and mortality. This has contributed to the inability to formulate a clear 

understanding of the association between short-term exposure to CO and mortality. This section 

summarizes the main findings of the 2000 CO AQCD (U.S. EPA, 2000, 000907) and evaluates the 

newly available information on the relationship between short-term exposure to CO and daily 

mortality in an effort to disentangle the CO-mortality effect from those effects attributed to other 

criteria air pollutants. 

5.6.1.1. Summary of Findings from 2000 CO AQCD 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) examined the association between short-term 

exposure to CO and mortality through the analysis of primarily single-city time-series studies, with 

additional evidence from one multicity study which included 11 Canadian cities. While the results 

presented by these studies did provide suggestive evidence that an association exists between CO 

and mortality, the AQCD concluded that inadequate evidence existed to infer a causal association 

between mortality and short-term exposure to ambient concentrations of CO. Multiple uncertainties 

were identified in the epidemiologic literature that contributed to this conclusion, which were 

discussed in Section 5.2.1. 

The majority of the recent time-series mortality studies, as mentioned previously, have not 

extensively examined the CO-mortality relationship. As such, CO has usually been considered as one 

of the potential confounding copollutants in air pollution epidemiologic studies. Given the limitation 

that most of these studies were not conducted to examine CO, the goal of this review is to evaluate 

the CO-mortality association and specifically the consistency of associations across studies, along 

with evidence of confounding and effect modification. 

5.6.1.2. Multicity Studies 

The following sections evaluate the recent literature that examined the association between 

short-term exposure to CO and mortality, and, in addition, discuss newly available information with 

regard to the issues specific to CO mentioned above. This evaluation focuses primarily on multicity 

studies because they provide a more representative sample of potential CO-related mortality effects 

and especially useful information by analyzing data from multiple cities using a consistent method, 

January 2010 5-101 
and thus avoiding potential publication bias.1 Table 5-22 details the multicity studies evaluated along 

with the mean CO concentrations reported in each study. 

Table 5-22. Range of CO concentrations reported in multicity studies that examine mortality effects 

associated with short-term exposure to CO. 

Study Location Years Averaging 

Time 

Mean 

Concentration 

(ppm) 

Range of Mean 

Concentrations 

Across Cities (ppm) 

Dominici et al. (2003, 056116; 2005, 

087912); Reanalysis of Samet et al. (2000, 

156939) 

82 US citiesa

(NMMAPS) 

1987-1994 24-h avg 1.02 Baton Rouge = 0.43 

Spokane = 2.19 

Burnett et al. (2004, 086247) 12 Canadian cities 1981-1999 24-h avg 1.02 Winnipeg = 0.58 

Toronto = 1.31 

Samoli et al. (2007, 098420)

b 19 European cities 

(APHEA2) 1990-1997c 8-h max 2.12 Basel = 0.52 

Athens = 5.3 

a

The study actually consisted of 90 U.S. cities, but only 82 had CO data. 

b

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. 

c

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 90 largest U.S. cities (82 cities for CO) in the National 

Morbidity, Mortality, and Air Pollution Study (NMMAPS) (Dominici et al., 2003, 056116; Dominici 

et al., 2005, 087912; a reanalysis of Samet et al., 2000, 156939) is by far the largest multicity study 

conducted to date to investigate the mortality effects of air pollution; however, the study primarily 

focused on PM10. The range in 24-h avg CO concentrations in a subset of 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 observed in the 

estimates generated using GAM models with default convergence criteria (Dominici et al., 2002, 

030458), Dominici et al. (2003, 056116; 2005, 087912)(reanalysis of Samet et al. (2000, 156939) 

conducted a reanalysis of the original data using GAM with stringent convergence criteria as well as 

GLM. 

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 between CO, SO2, or NO2, and total (nonaccidental) 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 5-17 presents the total mortality risk estimates 

for CO from Dominici et al. (2003, 056116). 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 intervals were wider in the multipollutant models; however, 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 cities2

) 

were available for the CO multipollutant analysis with PM10 and NO2 compared to the singlepollutant CO analysis (82 cities). However, most of the 32 cities that were excluded due to the lack 

of NO2 data 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 

 

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. 

2

 One city was excluded from the multipollutant analysis because it contained NO2 data but did not contain CO data. 

January 2010 5-102 
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. 

Source: Reprinted with Permission of HEI from Dominici et al.(2003, 056116) 

Figure 5-17. Posterior means and 95% posterior intervals of national average estimates for 

CO effects on total (nonaccidental) 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 Multicity Studies 

Since the 2000 CO AQCD (U.S. EPA, 2000, 000907), two Canadian multicity 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, 010273); 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, 086247). The 2000 study (Burnett and 

Goldberg, 2003, 042798) utilized GAM with default convergence criteria and, upon reanalysis, only 

examined PM indices. 

Burnett et al. (2004, 086247) is the most extensive Canadian multicity study conducted to 

date, both in terms of the length of the study and the number of cities covered. This study focused 

primarily on NO2-mortality associations because it was found to be the best predictor of fluctuations 

in mortality among the air pollutants examined (NO2, O3, SO2, CO, PM2.5, and PM10-2.5); however, 

the study did present single- and copollutant results for all pollutants included in the analysis. The 

mean CO concentrations reported by Burnett et al. (2004, 086247) are similar to those reported in 

NMMAPS (Table 5-22). 

Burnett et al. (2004, 086247) examined the effect of short-term exposure to CO on total 

(nonaccidental) 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, the strongest association was for the 3-day ma (i.e., average of 0-, 1-, and 2-day lags), and for O3, it 

was the 2-day ma. In this study, Burnett et al. (2004, 086247) used 24-h avg pollutant concentrations 

because these values showed stronger associations with mortality 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 (nonaccidental) mortality was 0.33% (95% CI: 0.12-0.54) per 0.5 ppm increase in 

24-h avg CO at lag 1. 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 

January 2010 5-103 
and PM was not presented. The results presented in this Canadian multicity study and NMMAPS are 

similar in that the 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. 

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, 019008) and NO2. Gryparis et al. (2004, 057276) 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 multicity studies), 

the Samoli et al. (2007, 098420) analysis focused specifically on CO. 

Samoli et al. (2007, 098420) investigated the effect of short-term exposure to CO on total 

(nonaccidental) and cardiovascular mortality in 19 European cities participating in the APHEA2 

project by using a two-stage 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 across all cities in the APHEA2 study of 2.12 ppm is 

higher than the estimated max 8-h avg CO concentrations reported for the U.S. cities examined in 

Dominici et al. (2003, 056116; 2005, 087912) and the Canadian cities examined in Burnett et al. 

(2004, 086247) 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 CO and 1-h max NO2

(r = 0.03-0.68). 

To examine the CO-mortality relationship, Samoli et al. (2007, 098420) conducted a timeseries 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 temporal confounding. These methods consisted of: (1) confining the 

extent of smoothing to 8 df/yr; and (2) selecting the appropriate extent of smoothing through 

minimization of the absolute value of the sum of partial autocorrelation functions (PACF) of the 

residuals, which resulted in the analysis using on average 5 df/yr for total (nonaccidental) 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, 098420) examined 

heterogeneity in CO risk estimates between cities by regressing risk estimates from individual cities 

on potential effect modifiers including: (1) the air pollution level and mix in each city (i.e., mean 

levels of pollutants, ratio PM10/NO2); (2) the exposure (number of CO monitors, correlation between 

monitors’ measurements); (3) variables describing the health status of the population (e.g., crude 

mortality rate);(4) the geographic area (northern, western, and central-eastern European cities); and 

(5) climatic conditions (mean temperature and relative humidity levels). 

Samoli et al. (2007, 098420) found that CO was associated with total (nonaccidental) 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 

(nonaccidental) 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 (nonaccidental) 

mortality and 0.70% (95% CI: 0.31-1.09) for cardiovascular mortality. In the sensitivity analysis, 

 

1

 The max 8-h avg concentration for the Dominici et al. (2003, 056116) and Burnett et al. (2004, 086247) 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. 

January 2010 5-104 
Samoli et al. (2007, 098420) used 8 or 12 df/yr to adjust for temporal confounding. Both approaches 

resulted in similar risk estimates, but using PACF to choose the extent of smoothing separately in 

each city generally resulted in larger CO risk estimates (by ~50-80%). This can be attributed to the 

smaller number of df/yr used in the model (on average 5 df/yr for total [nonaccidental] mortality and 

4 df/yr for cardiovascular mortality), which increases the magnitude of the effect and the amount of 

observed heterogeneity (Samoli et al., 2007, 098420). 

During the examination of the 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 

(nonaccidental) 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). CO risk estimates were reduced by a similar 

magnitude 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 (Dominici et al., 

2003, 056116) and the Canadian multicity study (Burnett et al., 2004, 086247). 

In the second-stage model, Samoli et al. (2007, 098420) 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 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, 098420) 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, 098420) 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 very 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., 0.43 ppm) in this data set is challenging because in 7 of the 

19 European cities examined, the lowest 10% of the CO distribution was at or above 2 mg/m3

(1.74 ppm). 

In summary, the APHEA2 analysis of CO in 19 cities found an association between CO and 

total (nonaccidental) 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 NMMAPS 

and the Canadian 12-city study. In addition, Samoli et al. (2007, 098420) 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 very weak evidence of a CO threshold, which requires further investigation. 

Other European Multicity Studies 

An additional European multicity study was conducted by Biggeri et al. (2005, 087395) 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, all of the pollutants showed 

positive associations with the mortality endpoints examined. However, copollutant models were not 

examined, and the correlations among the pollutants were not presented; therefore, it is unclear if the 

observed associations are shared or confounded. 

Summary of Multicity 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 

January 2010 5-105 
APHEA2 study being slightly larger (0.57%) (Figure 5-18). In both the NMMAPS and Canadian 

studies, a 1-day lag showed the strongest association; however, the APHEA2 study used an a priori 

exposure window (i.e., average of 0- and 1-day lags), which has been found to be the exposure 

window most strongly associated with mortality in PM analyses. 

The APHEA2 risk estimates presented in Figure 5-18 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 

50-80% 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-city 

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. 

Study Pollutant Effect Estimate (95% CI)

CO Alone 82 cities 

CO + PM10 

 82 cities 

Dominici et al. (2003, 056116; 2005, 087912) 

(reanalysis of Samet et al. (2000, 156939)) 

NMMAPS, lag 1 

CO + PM10 and NO2 

 57 cities 

CO Alone 

Burnett et al. (1998, 086427) 

12 Canadian cities, lag 1 

CO + NO2

a

CO Alone 

CO + PM10

CO + BS 

Samoli et al. (2007, 098420) 

APHEA2 (19 European cities, lag 0-1) 

CO + NO2 

a

NO2 is the average of 0-day, 1-day, and 2-day lags 

 Percent Increase 

Figure 5-18. Summary of percent increase in total (nonaccidental) mortality for short-term 

exposure to CO from multicity 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, 025205) reviewed the time-series mortality studies published between 1985 

and 2000 and conducted a meta-analysis to estimate combined effects for PM10, CO, NO2, O3, and 

SO2. Because many of the studies reviewed in the 2000 analysis used GAM with default 

convergence criteria, Stieb et al. (2003, 056908) updated the estimates from the meta-analysis by 

separating the GAM versus non-GAM studies. In this meta-analysis, the authors also presented 

separate combined estimates for single- and multipollutant models. Overall, there were more GAM 

estimates than non-GAM estimates for all of the pollutants except SO2. For CO, 4 single-pollutant 

model risk estimates were identified, resulting in a combined estimate of 3.18% (95% CI: 0.76-5.66) 

per 0.5 ppm increase in 24-h avg CO, and only 1 multipollutant model risk estimate (0.00% 

[95% CI: -1.71 to 1.74]) from the non-GAM studies. Thus, for CO, this study did not provide useful 

meta-estimates because the number of studies that contributed to the combined estimates for CO was 

small. 

January 2010 5-106 
5.6.1.4. Single-City Studies 

In addition to the multicity studies discussed above, there have also been several single-city 

U.S.- and Canadian-based time-series mortality studies that examined CO. The single-city studies, 

similar to the multicity studies, often focused on the PM-mortality association but also provided 

additional information that is not available in the multicity studies. Because the sample size used in 

each single-city study is small and subsequently results in wide confidence intervals, a quantitative 

comparison of the results from single- and multicity studies is difficult. In addition, some studies do 

not present CO results quantitatively, adding to the inability to adequately compare studies. Table 

5-23 lists the single-city studies evaluated along with the mean CO concentrations reported in each 

study. 

Table 5-23. Range of CO concentrations reported in single-city studies that examine mortality 

effects associated with short-term exposure to CO. 

Study Location Years Averaging 

Time 

Mean Concentration 

(ppm) 

Upper Percentile 

Concentrations 

(ppm) 

De Leon et al. (2003, 055688) New York, NY 1985-1994 24-h avg 2.45 95th: 4.04 

Klemm et al. (2004, 056585) Atlanta, GA 1998-2000 1-h max 1.31 Max: 7.40 

75th: 1.66 

Vedal et al. (2003, 039044)

a Vancouver, BC, Can 1994-1996 24-h avg 0.5 Max: 1.9 

90th: 0.9 

Villeneuve et al. (2003, 055051) Vancouver, BC, Can 1986-1999 24-h avg 1.0 Max: 4.9 

90th: 1.6 

Goldberg et al. (2003, 035202) Montreal, Quebec, Can 1984-1993 24-h avg 0.8 Max: 5.1 

75th: 1.0 

Hoek et al. (2000, 010350; 2001, 

016550); Reanalyzed by Hoek (2003, 

042818) 

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 

a

Study reported median CO concentrations. 

Single-City Studies Conducted in the United States 

De Leon et al. (2003, 055688) focused on the role of contributing respiratory diseases on the 

association between air pollution (i.e., PM10, O3, NO2, SO2, and CO) and primary nonrespiratory 

mortality (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 singlepollutant models, PM10, CO, SO2, and NO2 all showed the same pattern of association with 

circulatory mortality for individuals ≥ 75 yr, 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, 056585) analyzed 15 air pollutants for their associations with mortality in 

Atlanta, GA, for a 2-yr 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 singlepollutant models for nonaccidental 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 

nonaccidental mortality (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 yr), and these associations remained when PM2.5 was 

included in the model (results were presented graphically). 

January 2010 5-107 
Single-City Studies Conducted in Canada 

Vedal et al. (2003, 039044) examined the association between short-term exposure to “low 

levels” of air pollution (i.e., PM10, O3, NO2, SO2, and CO) and daily mortality 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, 039044) 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 multicity study 

conducted by Burnett et al. (2004, 086247). 

Villeneuve et al. (2003, 055051) 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 (nonaccidental), cardiovascular, and respiratory mortality. When 

examining the association between gaseous pollutants and all-cause (nonaccidental) mortality in this 

data set, NO2 and SO2 showed the strongest associations, while the association between CO and allcause 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 intervals for these categories and 

the smaller daily counts make it difficult to assess CO associations with cause-specific mortality 

outcomes. 

Goldberg et al. (2003, 035202) contrasted associations between air pollution and mortality in 

individuals with underlying CHF versus mortality in individuals who were identified as having CHF 

1 yr 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, 035202) 

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, 035202) found positive associations between air pollution and 

mortality in individuals diagnosed with CHF 1 yr prior to death. Of the air pollutants examined, 

CoH, NO2, and SO2 were most consistently associated with mortality for ages 65 yr 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, 098420) conducted a multicity 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, 010350) (reanalyzed by Hoek) (2003, 042818) examined associations 

between air pollution and all-cause (nonaccidental), 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 1986-1994. 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 nonaccidental 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, 010350). 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, 010350) study is somewhat in 

January 2010 5-108 
contrast to the result from the Samoli et al. (2007, 098420) multicity study in that in the Hoek et al. 

(2000, 010350) 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, 016550) (reanalysis by Hoek) (2003, 042818) 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. Thus, the results suggested larger impacts of air pollution on more 

specific cardiovascular causes; however, it is difficult to distinguish the effects of each pollutant 

from the larger air-pollution mixture. 

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

The recently available multicity studies, which consist of larger sample sizes, along with the 

single-city studies evaluated reported associations that are generally consistent with the results of the 

studies evaluated in the 2000 CO AQCD (U.S. EPA, 2000, 000907). However, to date the majority 

of the literature has not conducted extensive analyses to examine the potential influence of model 

selection, effect modifiers, or confounders on the association between CO and mortality. 

The multicity studies reported comparable CO mortality risk estimates for total 

(nonaccidental) mortality, with the APHEA2 European multicity study (Samoli et al., 2007, 098420) 

showing slightly higher estimates for cardiovascular mortality in single-pollutant models. However, 

when examining potential confounding by copollutants these studies consistently showed that, 

although CO mortality risk estimates remained positive, they 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 (e.g., traffic). 

Of the studies evaluated, only the APHEA2 study focused specifically on the CO-mortality 

association (Samoli et al., 2007, 098420), and, in the process, examined: (1) model sensitivity; (2) 

the CO-mortality concentration-response (C-R) relationship; and (3) potential effect modifiers of CO 

mortality risk estimates. The sensitivity analysis indicated an approximate 50-80% difference in CO 

risk estimates from a reasonable range of alternative models, which suggests that some model 

uncertainty likely influences the range of CO mortality risk estimates obtained in the studies 

evaluated. The examination of the CO-mortality concentration-response relationship found very 

weak evidence for a CO threshold at 0.5 mg/m3

 (0.43 ppm). Finally, when examining a variety of 

city-specific variables to identify potential effect modifiers of the CO-mortality relationship, the 

APHEA2 study found that geographic region explained most of the heterogeneity in CO mortality 

risk estimates. 

 The results from the single-city studies are generally consistent with the multicity 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 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 cause-specific mortality outcomes associated with CO exposure. In addition, the attenuation 

of CO risk estimates which was often observed in copollutant models contributes to the uncertainty 

as to whether CO is acting alone or as an indicator for other combustion-related pollutants. Overall, 

the epidemiologic evidence is suggestive of a causal relationship between relevant shortterm exposures to CO and mortality. 

5.6.2. Epidemiologic Studies with Long-Term Exposure to CO 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) did not evaluate the association between longterm exposure to CO and mortality because there were no studies at the time that examined this 

relationship. Since then there have been several new studies that examined the association between 

long-term exposure to CO and mortality. It should be noted, however, that these studies focused 

January 2010 5-109 
primarily on PM, and that CO was only considered in these studies as a potential confounder. 

Therefore, the information available from these new long-term exposure studies is somewhat 

limited, especially in comparison to that for PM. Table 5-24 lists the U.S.-based studies evaluated 

that examined the association between long-term exposure to CO and mortality, along with the mean 

CO concentrations reported in each study. 

Table 5-24. Range of CO concentrations reported in U.S.-based studies that examine mortality 

effects associated with long-term exposure to CO. 

Study Location Years Averaging 

Time 

Mean Concentration 

(ppm) 

Upper Percentile 

Concentrations 

(ppm) 

Jerrett et al. (2003, 087380) 107 US cities 1980 Annual avg 1.56 Maximum: 3.95 

Pope et al. (2002, 024689) 

1980: 113 US cities 

1982-1998: 122 US 

cities 

1980 

1982-1998 Annual avg 1980: 1.7 1982-1998: 1.1 NR 

Krewski et al. (2009, 191193) 108 US cities 1980 Annual avg 1.68 

75th: 2.13 

90th: 2.58 

95th: 3.05 

Maximum: 3.95 

Miller et al. (2007, 090130) 36 US cities 2000 Annual avg NR NR 

Lipfert et al. (2000, 004087) US 

1960-1974 

1975-1981 

1982-1988 

1989-1996 

Mean annual 95th 

percentile of 

hourly CO values 

1960-1974: 10.82 

1975-1981: 7.64 

1982-1988: 3.42 

1989-1996: 2.36 

1960-1974 

50th: 9.31 

Maximum: 35.3 

1975-1981 

50th: 7.04 

Maximum: 22.4 

1982-1988 

50th: 3.33 

Maximum: 15.20 

1989-1996 

50th: 2.30 

Maximum: 7.10 

Lipfert et al. (2006, 088756) US 1999-2001 

Mean annual 95th 

percentile of 

hourly CO values 

1.63 Maximum: 6.7 

Lipfert et al. (2006, 088218) US 

1976-1981 

1982-1988 

1989-1996 

1997-2001 

Mean annual 95th 

percentile of 

hourly CO values 

1976-1981: 7.6 

1982-1988: 3.4 

1989-1996: 2.4 

1997-2001: 1.6 

NR 

Lipfert and Morris (2002, 019217) 

1960-1969: 44 US 

counties 

1970-1974: 206 US 

counties 

1979-1981: 272 US 

counties 

1989-1991: 246 US 

counties 

1995-1997: 261 US 

counties 

1960-1969 

1970-1974 

1979-1981 

1989-1991 

1995-1997 

Mean annual 95th 

percentile of 

hourly CO values 

1960-1969: 13.8 

1970-1974: 9.64 

1979-1981: 5.90 

1989-1991: 2.69 

1995-1997: 1.72 

NR 

 

5.6.2.1. U.S. Cohort Studies 

American Cancer Society Cohort Studies 

Pope et al. (1995, 045159) 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 (nonaccidental), cardiopulmonary, and lung cancer 

mortality, but not with mortality for all other causes (i.e., nonaccidental minus cardiopulmonary and 

lung cancer). Gaseous pollutants were not analyzed in Pope et al. (1995, 045159). Jerrett et al. (2003, 

January 2010 5-110 
087380), using data from Krewski et al. (2000, 012281), conducted an extensive sensitivity analysis 

of the Pope et al. (1995, 045159) 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. 

Pope et al. (2002, 024689) 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. (2002, 

024689) 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 all-cause, 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) (Figure 5-19) 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 evidence from other 

mortality studies that examined the association between long-term exposure to CO and mortality do 

not suggest that CO elicits a protective effect. 

Krewski et al. (2009, 191193) further analyzed the ACS cohort by adding two additional years 

of mortality data (total period 1982-2000). This study extended the range of the analysis to 

incorporate sophisticated adjustment for bias and confounding as well as intra-urban analyses. 

However, the CO analysis was limited to using (1) nationwide data; (2) only 1980 CO 

concentrations; and (3) the standard Cox proportional hazards model. In addition to the death 

categories examined in Pope et al. (2002, 024689), this analysis also examined IHD mortality. As 

was the case with the Pope et al. (2002, 024689) analysis, Krewski et al. (2009, 191193) found that 

1980 CO data was not associated with any of the mortality categories examined: all-cause mortality 

HR=1.00 (95%CI: 0.99-1.01); cardio-pulmonary mortality, HR=1.00 (95% CI: 0.99-1.00); and IHD 

mortality, HR=1.00 (95% CI: 0.99-1.01) per 0.5 ppm increase in CO. 

Women’s Health Initiative Cohort Study 

Miller et al. (2007, 090130) studied 65,893 postmenopausal women between the ages of 50 

and 79 yr without previous CVD in 36 U.S. metropolitan areas from 1994 to 1998. The authors 

examined the association between one or more fatal or nonfatal cardiovascular events and airpollutant concentrations. Exposures to air pollution were estimated by assigning the year 2000 mean 

concentration 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 (0-100 m).” 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 event, including 261 cardiovascular-related deaths. Hazard ratios for the initial 

cardiovascular event were estimated. The following results are for models that included only subjects 

with nonmissing 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 

 

1

 These results were presented graphically in Pope et al. (2002, 024689) and were estimated for . Figure 5-19

January 2010 5-111 
SO2 (HR = 1.07 [95% CI: 0.95-1.20] per 5 ppb increase in the annual average). For CO the singlepollutant 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. Overall this study found that PM2.5 was clearly 

the best predictor of cardiovascular events. 

The Washington University-EPRI Veterans’ Cohort Mortality Studies 

Lipfert et al. (2000, 004087) 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 yr (up to 1996). Demographically, 35% of the cohort consisted of AfricanAmerican 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 longterm exposure to multiple pollutants, including, PM2.5, PM10, PM10-2.5, TSP, SO4

2-, CO, O3, NO2, SO2, and Pb. Lipfert et al. (2000, 004087) estimated exposures by indentifying the county of residence at 

the time of entry to the study. Four exposure periods (1960-1974, 1975-1981, 1982-1988, and 

1989-1996) were defined, and deaths during each of the three most recent exposure periods were 

considered. 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. (2000, 004087) 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. 

Lipfert et al. (2006, 088756) examined associations between traffic density and mortality in 

the same Veterans’ Cohort; however, 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 yr) 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. (2006, 088218) noted 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. (2006, 088756) 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 nonnegative effects 

observed. 

Lipfert et al. (2006, 088218) 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 1999-2001. As in the other Lipfert et al. (2006, 

088218) 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 values and mortality (RR = 0.995 [95% CI: 0.988-1.001] per 1 ppm increase 

January 2010 5-112 
in CO concentration) in a single-pollutant model. The study did not present copollutant model results 

for CO. 

Cause-specific 

Mortality Study Cohort 

Years of 

Mortality 

Data 

Year(s) 

of AQ 

Data 

Effect Estimate (95% CI)d 

Pope et al. (2002, 024689)

a ACS 1982-1998 1980 

Krewski et al. (2009, 191193) ACS 1982-2000 1980 

Pope et al. (2002, 024689)

a ACS 1982-1998 1982-98 

Lipfert et al. (2006, 088218) Veterans 1976-2001 1989-96 

Lipfert et al. (2006, 088218) Veterans 1976-2001 1989-96 + ln(VKTA)e

Lipfert et al. (2006, 088756) Veterans 1997-2001 1999-01 

Jerrett et al. (2003, 087380) ACS 1982-1998 1982c

All-cause 

Jerrett et al. (2003, 087380) ACS 1982-1998 1982c + SO4

2-

Pope et al. (2002, 024689)

a ACS 1982-1998 1980 

Cardiopulmonary Krewski et al. (2009, 191193) ACS 1982-2000 1980 

Pope et al. (2002, 024689)

a ACS 1982-1998 1982-98 

Miller et al. (2007, 090130)

b WHI 1994-1998 2000 Cardiovascular Miller et al. (2007, 090130)

b WHI 1994-1998 2000 Multipollutantf

IHD Krewski et al. (2009, 191193) ACS 1982-2000 1980 

Lung Cancer Krewski et al. (2009, 191193) ACS 1982-2000 1980 

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, 024689). 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

Unless otherwise specified, all results represent single pollutant models 

e

Natural log of Vehicle-km Traveled variable. 

f

Multipollutant model consisted of CO + PM2.5, PM10-2.5, SO2, NO2, O3. 

Relative Risk / Hazard Ratio 

Figure 5-19. 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. 

5.6.2.2. U.S. Cross-Sectional Analysis 

An ecological cross-sectional analysis involves regressing county- (or city-) average health 

outcome values on county-average explanatory variables such as air pollution and census statistics. 

Unlike the cohort studies described above, to the extent that individual level confounders are not 

adjusted for, the cross-sectional study design is considered to be subject to ecologic confounding. 

However, all of the cohort studies described above are also semi-ecologic in that the air-pollution 

exposure variables are ecologic (Kunzli and Tager, 1997, 086180). In this sense, cross-sectional 

studies may be useful in evaluating the correlation among exposure variables. 

Lipfert and Morris (2002, 019217) 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+ yr) 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 and 1984 and for 1999. In response to the varying 

number of counties with valid air pollution data by pollutant and time, Lipfert and Morris (2002, 

019217) 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. Many results were presented because of the large number of age groups, 

lagged-exposure time windows, and mortality study periods examined in the study; overall, the 

results were similar to those presented in the ACS cohort studies (i.e., PM2.5 and SO4

2- were found to 

be consistently and positively associated with mortality). Lipfert and Morris (2002, 019217) 

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). 

January 2010 5-113 
Also, consistent with the Lipfert et al. (2000, 012281) and the Pope et al. (2002, 024689) 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 yr) often showed a positive association with CO, but considering the small 

number of deaths attributed to this age group (<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. 

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 (U.S. EPA, 

2000, 000907) 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 (U.S. EPA, 2000, 000907). The reanalysis of the ACS data (Pope et al., 1995, 

045159) by Jerrett et al. (2003, 087380) found no association between long-term exposure to CO and 

mortality. Similar results were obtained in an updated analysis of the ACS data (Pope et al., 2002, 

024689) when using earlier (1980) CO data; however, negative associations were found when using 

more recent (1982-1998) data. These results were further confirmed in an extended analysis of the 

ACS data (Krewski et al., 2009, 191193). The Women’s Health Initiative Study also found no 

association between CO and CVD events (including mortality) using the mortality data from recent 

years (1994-1998) (Miller et al., 2007, 090130), 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 (Lipfert et al., 2006, 088218; Lipfert et al., 2006, 088756). An additional study (Lipfert and 

Morris, 2002, 019217) was identified that used a cross-sectional study design, which reported results 

for a study of U.S. counties that were generally consistent with the cohort studies: positive 

associations between long-term exposure to PM2.5 and 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 

populations (i.e., postmenopausal women and hypertensive men) combined with the lack of evidence 

for respiratory and cardiovascular morbidity outcomes following long-term exposure to CO; and the 

absence of a proposed mechanism to explain the progression to mortality following long-term 

exposure to CO provide supportive evidence that there is not likely to be a causal relationship 

between relevant long-term exposures to CO and mortality. 

5.7. Susceptible Populations 

Interindividual variation in human responses indicates that some populations are at increased 

risk for the detrimental effects of ambient exposure to an air pollutant (Kleeberger and Ohtsuka, 

2005, 130489). The NAAQS are intended to provide an adequate margin of safety for both general 

populations and sensitive subgroups, or those individuals potentially at increased risk for health 

effects in response to ambient air pollution (Section 1.1). To facilitate the identification of 

populations at the greatest risk for CO-related health effects, studies have evaluated factors that 

contribute to the susceptibility and/or vulnerability of an individual to CO. The definition for both of 

these terms varies across studies, but in most instances “susceptibility” refers to biological or 

intrinsic factors (e.g., lifestage, gender) while “vulnerability” refers to nonbiological or extrinsic 

factors (e.g., visiting a high-altitude location, medication use) (Table 5-25). Additionally, in some 

cases, the terms “at-risk” and sensitive populations have been used to encompass these concepts 

more generally. However, in many cases a factor that increases an individual's risk for morbidity or 

mortality effects from exposure to an air pollutant (e.g., CO) can not be easily categorized as a 

susceptibility or vulnerability factor. For example, a population that is characterized as having low 

SES, traditionally defined as a vulnerability factor, may have less access to healthcare resulting in 

the manifestation of disease (i.e., a susceptibility factor). But they may also reside in a location that 

results in exposure to higher concentrations of an air pollutant, increasing their vulnerability. 

Therefore, the terms “susceptibility” and “vulnerability” are intertwined and at times cannot be 

distinguished from one another. 

January 2010 5-114 
As a result of the inconsistencies in the definitions of “susceptibility” and “vulnerability” 

presented in the literature as well as the inability to clearly delineate whether an identified factor 

increases an individual's susceptibility or vulnerability to an air pollutant, in this ISA, the term 

“susceptible population” will be used as a blanket term and defined as follows: 

Populations that have a greater likelihood of experiencing health effects related to 

exposure to an air pollutant (e.g., CO) due to a variety of factors including, but not limited to: 

genetic or developmental factors, race, gender, lifestage, lifestyle (e.g., smoking status and 

nutrition) orpreexisting disease, as well as population-level factors that can increase an 

individual's exposure to an air pollutant (e.g., CO) such as socioeconomic status [SES], which 

encompasses reduced access to health care, low educational attainment, residential location, 

and other factors. 

Table 5-25. Range of definitions of “susceptible” and “vulnerable” in the CO literature. 

Definition Reference 

Susceptible: predisposed to develop a noninfectious disease 

Vulnerable: capable of being hurt; susceptible to injury or disease Merriam-Webster (2009, 192146) 

Susceptible: greater likelihood of an adverse outcome given a specific exposure, in comparison with the general 

population. Includes both host and environmental factors (e.g., genetics, diet, physiologic state, age, gender, social, 

economic, and geographic attributes). 

Vulnerable: periods during an individual’s life when they are more susceptible to environmental exposures. 

American Lung Association (2001, 016626) 

Susceptible: those who are more likely to experience adverse effects of CO exposure than normal healthy adults 

(e.g., persons with cardiovascular disease, COPD, reduced or abnormal hemoglobin, older adults, neonates). U.S. EPA (2008, 193995) 

Susceptible: greater or lesser biological response to exposure. 

Vulnerable: more or less exposed. U.S. EPA (2009, 192149) 

Vulnerable: to be susceptible to harm or neglect, that is, acts of commission or omission on the part of others that 

can wound. Aday (2001, 192150) 

Susceptible: may be those who are significantly more liable than the general population to be affected by a stressor 

due to life stage (e.g., children, the elderly, or pregnant women), genetic polymorphisms (e.g., the small but 

significant percentage of the population who have genetic susceptibilities), prior immune reactions (e.g., individuals 

who have been “sensitized” to a particular chemical), disease state (e.g., asthmatics), or prior damage to cells or 

systems (e.g., individuals with damaged ear structures due to prior exposure to toluene, making them more sensitive 

to damage by high noise levels). 

Vulnerable: differential exposure and differential preparedness (e.g., immunization). 

U.S. EPA (2003, 192145) 

Susceptible: intrinsic (e.g., age, gender, preexisting disease (e.g., asthma) and genetics) and extrinsic (previous 

exposure and nutritional status) factors. Kleeberger and Ohtsuka (2005, 130489) 

To examine whether air pollutants (e.g., CO) differentially affect certain populations, 

epidemiologic studies conduct stratified analyses to identify the presence or absence of effect 

modification. A thorough evaluation of potential effect modifiers may help identify populations that 

are more susceptible to an air pollutant (e.g., CO). Although the design of toxicological and 

controlled human exposure studies does not allow for an extensive examination of effect modifiers, 

the use of animal models of disease and the study of individuals with underlying disease or genetic 

polymorphisms do allow for comparisons between subgroups. Therefore, the results from these 

studies, combined with those results obtained through stratified analyses in epidemiologic studies, 

contribute to the overall weight of evidence for the increased susceptibility of specific populations to 

an air pollutant (e.g., CO). 

The remainder of this section discusses the epidemiologic, controlled human exposure, and 

toxicological studies evaluated in previous sections of Chapter 5 that provide information on 

potentially susceptible populations. The studies highlighted include only those studies that presented 

stratified results (e.g., males versus females or <65 yr versus ≥ 65 yr). This approach allows for a 

direct comparison between populations exposed to similar CO concentrations and within the same 

study design to determine whether a factor increases the susceptibility of a population to CO-related 

health effects. In addition, numerous studies that focus on only one potentially susceptible 

population can provide supporting evidence on susceptibility and are described in Sections 5.2

January 2010 5-115 
through 5.6; however, these studies are not discussed in detail in this section. It is recognized that by 

using this approach to identify potentially susceptible populations, some individuals with underlying 

medical conditions (i.e., reduced O2-carrying capacity or elevated COHb levels) or lifestyle 

characteristics may not be identified due to the lack of studies focusing on these populations. 

Discussion of conditions affecting CO uptake and elimination as well as endogenous CO production 

is presented in Sections 4.4 and 4.5, respectively. 

5.7.1. Preexisting Disease 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) identified certain populations within the 

general population that may be more susceptible to the effects of CO exposure, including individuals 

(particularly older adults) with CHD and other vascular diseases, anemia, or COPD. As discussed in 

the 2000 CO AQCD (U.S. EPA, 2000, 000907) and reviewed in Section 4.5 of this assessment, 

diseases that cause inflammation and systemic stress are known to increase endogenous CO 

production, which could potentially increase the susceptibility of individuals with such conditions to 

health effects induced by ambient CO exposure. The level of COHb that results in the manifestation 

of health effects varies depending on health outcome and disease state of individuals. The following 

sections summarize the evidence presented in the 2000 CO AQCD (U.S. EPA, 2000, 000907) along 

with new evidence which identifies populations with various preexisting diseases that may be 

susceptible to CO-induced health effects. 

5.7.1.1. Cardiovascular Disease 

Controlled exposures to CO resulting in COHb concentrations of 2-6% have been shown to 

affect cardiovascular function among individuals with CAD. Several studies have reported 

significant decreases in the time to onset of exercise-induced angina or ST-segment changes 

following CO exposure in patients with stable angina. In the largest such study (Allred et al., 1989, 

013018; Allred et al., 1989, 012697; Allred et al., 1991, 011871), COHb concentrations as low as 

2.0-2.4% were observed to significantly decrease the time required to induce ST-segment changes 

indicating myocardial ischemia (p = 0.01) (Section 5.2.4). In addition to the effects of CO on 

myocardial ischemia, there is some evidence to suggest that CO may provoke cardiac arrhythmia in 

patients with CAD; however, this has not been observed at COHb concentrations below 6% (Sheps 

et al., 1990, 013286). While healthy adults have been shown to experience a decrease in exercise 

performance following or during exposure to CO, no changes in cardiac rhythm or ECG parameters 

have been demonstrated. 

Evidence of CO-induced health effects in individuals with CAD is coherent with results from 

epidemiologic studies that examined the effect of preexisting cardiovascular conditions, through 

either secondary diagnoses or underlying comorbidities, on associations between CO and ED visits 

and HAs. Mann et al. (2002, 036723) found increased associations between CO and IHD HAs in 

individuals with secondary diagnoses of either CHF or dysrhythmia in southern California. Peel et 

al. (2007, 090442) also examined the effect of underlying cardiovascular conditions on 

cardiovascular-related HAs in response to short-term exposure to air pollutants, including CO in 

Atlanta, GA. Individuals with underlying dysrhythmia were found to have increased HAs for IHD, 

but unlike Mann et al. (2002, 036723), underlying CHF was not found to increase IHD HAs. Peel et 

al. (2007, 090442) also examined other underlying conditions and found increased HAs for 

additional cardiovascular effects not specifically related to IHD, including: dysrhythmia, PVCD, and 

CHF in individuals with underlying hypertension; dysrhythmia and PVCD in individuals with 

underlying CHF; and PVCD in individuals with underlying dysrhythmia. Although there is no 

evidence for a clear pattern of CO-induced cardiovascular effects among individuals without CAD 

across the epidemiologic studies evaluated, the available evidence suggests that underlying 

dysrhythmia increases IHD HAs in response to short-term exposure to CO. 

Additional evidence for increased CO-induced cardiovascular effects not specifically related to 

IHD is provided by toxicological studies that used animal models of cardiovascular disease. These 

studies have demonstrated that short-term exposure to 50 ppm CO in rats exacerbates 

cardiomyopathy and vascular remodeling related to pulmonary hypertension (Carraway et al., 2002, 

026018; Gautier et al., 2007, 096471; Melin et al., 2002, 037502; 2005, 193833). Although the 

population at risk for primary pulmonary hypertension is low, secondary pulmonary hypertension is 

January 2010 5-116 
a frequent complication of COPD (Section 5.7.1.2) and certain forms of heart failure. These studies 

demonstrate the potential for short-term exposure to CO to adversely affect individuals with 

underlying cardiovascular conditions. 

The combined evidence from controlled human exposure and epidemiologic studies provides 

coherence and biological plausibility for the association between CO and cardiovascular morbidity 

in individuals with CAD, particularly those with IHD. Approximately 13.7 million people in the U.S. 

have been diagnosed with CAD (also known as CHD), some fraction of whom have IHD (Table 

5-26). These individuals, therefore, represent a large population that may be more susceptible to 

ambient CO exposure than the general population. In addition, the continuous nature of the 

progression of CAD and its close relationship with other forms of cardiovascular disease suggest that 

a larger population than just those individuals with a prior diagnosis of CAD may be susceptible to 

health effects from CO exposure. 

Table 5-26. Adult U.S. population in 2007 with respiratory diseases and cardiovascular diseases. 

Chronic Condition/ 

Disease Adults (18+) Percentage of U.S. Adult Population by Age Percentage by Region 

COPDa (Millions) All (18+) 18-44 45-64 65-74 75+ NE MW S W 

 Chronic bronchitis 7.6 3.4 2.3 4.2 5.5 4.8 2.8 3.2 4.0 2.9 

 Emphysema 3.7 1.6 0.2 2.3 4.5 5.2 1.1 1.8 1.8 1.6 

Cardiovascular Diseasesb (Millions) All (18+) 18-44 45-64 65-74 75+ NE MW S W 

 All heart diseasec 25.1 11.2 4.1 12.2 27.1 35.8 10.6 12.3 11.3 10.2 

 Coronary heart diseased 13.7 6.1 0.9 6.7 18.6 23.6 5.3 6.7 6.4 5.5 

 Hypertension 52.9 23.2 8.2 32.1 50.9 57.4 21.3 23.4 25.1 21.0 

 Stroke 5.4 2.4 0.3 2.8 6.3 10.6 2.2 2.3 2.7 2.2 

a Respondents were asked if they had ever been told by a doctor or other health professional that they had emphysema. In a separate question, respondents were asked if they had been told by a doctor or 

other health professional in the last 12 mo that they had bronchitis. A person may be represented in more than one row. 

b

 In separate questions, respondents were asked if they had ever been told by a doctor or other health professional that they had: hypertension (or high blood pressure), coronary heart disease, angina (or 

angina pectoris), heart attack (or myocardial infarction), any other heart condition or disease not already mentioned, or a stroke. A person may be represented in more than one row. c

 Heart disease includes coronary heart disease, angina pectoris, heart attack, or any other heart condition or disease. d

 Coronary heart disease includes coronary heart disease, angina pectoris, or heart attack. 

Source: National Health Interview Survey, 2007, Tables 1-4 (Pleis and Lucas, 2009, 202833).

5.7.1.2. Obstructive Lung Disease 

COPD is a progressive disease resulting in decreased air flow to the lungs and which is 

especially prevalent among smokers. O2 limitation resulting from this reduction in air flow may 

exacerbate CO-related O2 limitation and subsequent cardiovascular or respiratory effects in 

individuals with COPD. The national prevalence of chronic bronchitis and emphysema, the two main 

forms of COPD, was estimated to be 7.6 million and 3.7 million people in 2007, respectively (Table 

5-26), although there could be overlap among these two populations. The 2000 CO AQCD 

(U.S. EPA, 2000, 000907) identified individuals with obstructive lung diseases, such as COPD, as a 

susceptible population due to a majority of COPD patients having exercise limitations as 

demonstrated by a decrease in O2 saturation during mild to moderate exercise. This may heighten the 

sensitivity of these individuals to CO during submaximal exercise typical of normal daily activity. In 

addition, COPD patients who are smokers may have elevated baseline COHb levels of 4-8% 

(U.S. EPA, 2000, 000907). COPD is often accompanied by a number of changes in gas exchange, 

including increased VD and VA/Q inequality (Marthan et al., 1985, 086334), which could slow both 

CO uptake and elimination. 

The few epidemiologic studies of cardiovascular effects in individuals with underlying COPD 

show weak positive associations between ambient CO and increased CVD HAs or ED visits. For 

example, Peel et al. (2007, 090442) found that associations between short-term CO exposure and 

HAs for PVCD or CHF were increased in individuals with a secondary diagnosis of COPD. 

However, underlying COPD was not associated with increased IHD or dysrhythmia HAs. As 

January 2010 5-117 
described in Section 5.7.1.1, animal toxicological studies demonstrate CO-induced exacerbation of 

vascular remodeling related to pulmonary hypertension, a form of which is a frequent complication 

of COPD. 

A controlled human exposure study of respiratory effects in individuals with COPD (Bathoorn 

et al., 2007, 193963), 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. Although the majority of the evidence for CO-induced effects comes from studies that 

focus on individuals with COPD, epidemiologic studies also report weak positive associations for 

asthmatics (Section 5.5.2.2) who can also experience exercise-induced airflow limitation. In 

addition, preliminary evidence from a recent animal toxicological study indicates mild pulmonary 

inflammation in response to 50 ppm CO (Ghio et al., 2008, 096321). Since pulmonary inflammation 

plays an important role in the exacerbation of COPD and asthma, it may serve as a mechanism 

underlying CO-induced respiratory effects; however, additional research is needed to confirm these 

results. Taken together, the limited evidence from epidemiologic and controlled human exposure 

studies and some preliminary evidence from toxicological studies suggests that individuals with 

obstructive lung disease (e.g., COPD patients with underlying hypoxia, asthmatics) may be 

susceptible to cardiovascular or respiratory effects due to CO exposure. 

5.7.1.3. Diabetes 

Exhaled CO concentrations are elevated in individuals with diabetes and are correlated with 

blood glucose levels and duration of disease, indicating increased endogenous CO production 

(Section 4.5). As a result, it has been speculated that elevated baseline COHb levels in diabetic 

individuals could increase the susceptibility of diabetics to CO-induced health effects in response to 

ambient CO exposures. Epidemiologic studies provide evidence which suggests that diabetics are at 

increased risk for cardiovascular ED visits and HAs compared to nondiabetics in response to shortterm exposure to CO (Pereira Filho et al., 2008, 190260; Zanobetti and Schwartz, 2001, 016710). 

This is consistent with results reported by Peel et al. (2007, 090442), who observed an increase in 

cardiovascular-related ED visits for dysrhythmias and PVCD in individuals with diabetes but not for 

IHD or CHF ED visits. The results from Peel et al. (2007, 090442) that indicate an increase in 

dysrhythmia ED visits for individuals with diabetes are consistent with results from a panel study 

conducted by Min et al. (2009, 199514) to investigate the relationship between CO and HRV in 

individuals with metabolic syndrome. Metabolic syndrome is characterized by risk factors for both 

diabetes and CVD, including elevations in blood pressure, fasting blood glucose, triglycerides, and 

waist circumference, as well as low levels of HDL cholesterol. Min et al. (2009, 199514) observed 

associations between short-term exposure to CO and changes in HRV parameters among subjects 

with metabolic syndrome but not among healthy subjects. In addition, the observed associations 

were robust in copollutant models with either PM10 or NO2. In an analysis of individual risk factors, 

the CO effects were stronger among subjects with higher levels of fasting blood glucose or 

triglycerides. Although no evidence was identified from controlled human exposure or toxicological 

studies regarding CO exposure and diabetes, vascular dysfunction was demonstrated in an animal 

model of metabolic syndrome and was attributed to increased endogenous CO production (Johnson 

et al., 2006, 193874). Thus, increased endogenous CO production and the potential for higher 

baseline COHb concentrations, combined with the limited epidemiologic evidence showing 

cardiovascular effects, suggests that diabetics are potentially susceptible to short-term exposure to 

CO. 

5.7.1.4. Anemia 

Although no controlled human exposure or epidemiologic studies were identified that 

specifically examined CO-related health effects in individuals with anemia, the 2000 CO AQCD 

(U.S. EPA, 2000, 000907) suggested that conditions such as anemia that produce tissue hypoxia by 

lowering the blood O2 carrying capacity or content will result in a greater risk of effects from COHbinduced hypoxia due to the combined effects of both sources of hypoxia. As discussed in Section 

4.4.4 of this ISA, anemias are a group of diseases that lower hematocrit and result in reduced arterial 

O2 content due to Hb deficiency through hemolysis, hemorrhage, or reduced hematopoiesis. 

Hereditary hemoglobinopathies such as sickle cell anemia and thalassemia also reduce the O2-

carrying capacity of the blood. Anemia may also result from pathologic conditions characterized by 

January 2010 5-118 
chronic inflammation such as malignant tumors or chronic infections (Cavallin-Ståhl et al., 1976, 

086306; Cavallin-Ståhl et al., 1976, 193239). The cardiovascular system of people with anemia 

compensates for the reduction in O2 carrying capacity by increasing cardiac output as both heart rate 

and stroke volume increase. One of the most prevalent forms of anemia arises from a single-point 

mutation in the Hb gene, resulting in sickle cell diseases. The affinity of Hb for O2 and its 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 effects due to CO-mediated hypoxia. Other hereditary hemoglobinopathies, 

such as thalassemia, also reduce the O2-carrying capacity of the blood due to the production of an 

abnormal form of hemoglobin. Overall, lowered hematocrit due to anemia may result in increased 

susceptibility and a greater response to inhalation of ambient CO. 

Anemia may also increase the susceptibility of an individual to CO exposure in a different 

manner through the increased production of endogenous CO as a result of the disturbance of RBC 

hemostasis by accelerated destruction of hemoproteins (Section 4.5). Pathologic conditions such as 

hemolytic anemias, hematomas, thalassemia, Gilbert’s syndrome with hemolysis, and other 

hematological diseases and illness will accelerate endogenous CO production (Berk et al., 1974, 

012386; Hampson and Weaver, 2007, 190272; Meyer et al., 1998, 047530; Solanki et al., 1988, 

012426; Sylvester et al., 2005, 191954). Patients with hemolytic anemia exhibit COHb levels at least 

two- to threefold higher than healthy individuals and CO production rates two- to eightfold higher 

(Coburn et al., 1966, 010984). Recent studies report elevated COHb levels of 4.6-9.7% due to druginduced hemolytic anemia (Hampson and Weaver, 2007, 190272) and between 3.9% and 6.7% due 

to an unstable hemoglobin disorder (Hb Zürich) (Zinkham et al., 1980, 011435). Taken together, this 

evidence suggests that individuals with anemia who have diminished O2-carrying capacity and/or 

high baseline COHb levels may be more susceptible to health effects due to ambient CO exposure, 

although no studies were identified that evaluated specific CO-related health effects in anemic 

individuals. 

5.7.2. Lifestage 

Age alters the variables that influence the uptake, distribution, and elimination of CO 

(Section 4.4.3). COHb levels decline more rapidly in young children compared to adults after CO 

exposure (Joumard et al., 1981, 011330; Klasner et al., 1998, 087196). After infancy, the COHb halflife increases with age, practically doubling between the ages of 2 and 70 yr (Joumard et al., 1981, 

011330). However, it should be noted that the rate of this reduction in CO elimination is very rapid 

in the growing years (2-16 yr of age) but slows beyond adolescence. An increase in alveolar volume 

and DLCO were observed with increasing body length of infants and toddlers (Castillo et al., 2006, 

193234); these changes suggest faster CO uptake due to more advanced lung development. After 

infancy, increasing age decreases DLCO and increases VA/Q mismatch, resulting in a longer duration 

for both loading and elimination of CO from the blood (Neas and Schwartz, 1996, 079363). 

5.7.2.1. Older Adults 

The 2000 CO AQCD (U.S. EPA, 2000, 000907) noted that changes in metabolism that occur 

with age, particularly declining maximal oxygen uptake, may make the aging population susceptible 

to the effects of CO via impaired oxygen delivery to the tissues. Several epidemiologic studies 

compared cardiovascular outcomes in older and younger adults, although no such studies were 

conducted in the U.S. In a study in Australia and New Zealand, Barnett et al. (2006, 089770) found 

an increase in IHD and MI HAs among individuals ≥ 65 yr of age compared with individuals aged 

15-64 yr in response to short-term exposure to CO. Lee et al. (2003, 095552) also found an 

association with IHD HAs in Seoul, Korea, for individuals ≥ 65 yr of age but not when all 

individuals were included in the analysis. Lanki et al. (2006, 089788) found an association with HAs 

for nonfatal MI in a multicity European study among those aged ≥ 75 yr but not for those <75 yr old. 

In contrast, D’Ippoliti et al. (2003, 074311) observed higher associations for MI hospital admissions 

in Rome among 18- to 64-yr olds than among either 65- to 74-yr olds or those 75 yr and over. 

Szyszkowicz (2007, 193793) found slightly lower associations for IHD hospital admissions in 

Montreal, Canada among those >64 yr of age than for the all-age group. Another Canadian study 

(Fung et al., 2005, 074322) conducted in Windsor, Ontario, found some evidence of increased 

January 2010 5-119 
associations for between CO and CVDs (defined as HF, IHD, or dysrhythmia) among individuals 

≥ 65 yr of age compared with the <65-yr age group. No controlled human exposure studies or 

toxicological studies were identified that compared CO effects among older and younger adults or 

animal models of senescence, respectively. Overall, the epidemiologic studies evaluated provide 

limited evidence that older adults may be susceptible to CO exposure. 

A combination of factors may be responsible for increased susceptibility to CO-related health 

effects among older adults. One important factor which may contribute to the observed increases in 

CO-induced cardiovascular effects is the much higher prevalence of CAD and other cardiovascular 

conditions in older adults compared with the general population. As shown in Table 5-26,18.6% of 

adults aged 65-74 yr and 23.6% of adults aged 75 yr and over reported having CHD, as compared 

with 6.1% of the population as a whole. Both the higher prevalence of CAD and the gradual decline 

in physiological processes associated with aging (U.S. EPA, 2006, 192082) may contribute to 

increased health effects in response to CO in this population. Older adults represent a large and 

growing fraction of the U.S. population, from 12.4% or 35 million people in 2000 to a projected 

19.3% or 72 million people in 2030 (U.S. Census, 2000, 157064), and, as a result, are a large 

population that is potentially susceptible to CO-induced health effects. 

5.7.2.2. Gestational Development 

CO inhaled by pregnant animals quickly crosses the placental barriers and enters fetal 

circulation. Effects of ambient CO may be enhanced during gestation because fetal CO 

pharmacokinetics do not follow the same kinetics as maternal CO exposure; this contributes to the 

difficulty in estimating fetal COHb based on maternal levels. It has been demonstrated that human 

fetal Hb has a higher affinity for CO than adult Hb (Di Cera et al., 1989, 193998). Maternal and fetal 

COHb concentrations have been modeled as a function of time using a modified CFK equation (Hill 

et al., 1977, 011315). At steady-state conditions, fetal COHb has been found to be 10-15% higher 

than maternal COHb levels. For example, exposure to 30 ppm CO results in a steady-state maternal 

COHb of 5% and a fetal COHb of 5.75%. Fetal CO uptake lags behind maternal uptake for the first 

few hours but later may overtake the maternal values. Similarly, during washout, fetal COHb levels 

are maintained for longer, with a half-life of around 7.5 h versus the maternal half-life of around 4 h 

(Longo and Hill, 1977, 010802). In addition, maternal endogenous CO production increases during 

pregnancy (0.92 mL/h) due to contributions from fetal endogenous CO production (0.036 mL/h) and 

altered hemoglobin metabolism (Hill et al., 1977, 011315; Longo, 1970, 013922). 

Epidemiologic studies provide limited evidence that in utero CO exposure is associated with 

changes in various birth outcomes (Section 5.4.1). CO exposure during early pregnancy was 

associated with an increased risk of PTB. In the studies that examined associations between CO and 

birth defects, maternal CO exposure was associated with an increased risk of cardiac birth defects, 

which is also coherent with evidence in Section 5.2 identifying the heart as a target organ for CO. 

There is also evidence for small reductions in birth weight (10-20 g) associated with CO exposure, 

generally in the first or third trimester, although the decrease does not generally translate to an 

increased risk of LBW or SGA. It is therefore difficult to conclude if CO is related to a small change 

in birth weight across all births or a marked effect in some subset of births. In addition, there is 

limited evidence that prenatal CO exposure is associated with an increased risk of infant mortality in 

the post-neonatal period. 

Toxicological studies lend biological plausibility to the CO-related developmental outcomes 

observed in epidemiologic studies (Section 5.4.2). Associations have been observed between CO 

exposure in laboratory animals and decrements in birth weight as well as reduced prenatal growth. 

Animal toxicological studies also provide evidence for effects on the heart, including transient 

cardiomegaly at birth after continuous in utero CO exposure and delayed myocardial 

electrophysiological maturation. 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. Furthermore, exogenous CO may interact or disrupt the normal 

physiological roles of endogenous CO in the body. There is evidence that CO plays a role in 

maintaining pregnancy, controlling vascular tone, regulating hormone balance, and sustaining 

normal follicular maturation. 

The developmental outcomes examined in the epidemiologic studies evaluated affect a 

substantial portion of the U.S. population. PTB and LBW have been established as strong predictors 

January 2010 5-120 
of infant mortality and morbidity (Barker et al., 2002, 193960; Berkowitz and Papiernik, 1993, 

055466; Li et al., 2003, 193965; McIntire et al., 1999, 015310). In 2004, 36.5% of all infant deaths 

in the U.S. were preterm-related (MacDorman et al., 2007, 193973). 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., 2007, 193982). Data from the 

Metropolitan Atlanta Congenital Defects Program (MACDP), which is one of the most 

comprehensive birth defect registries in the U.S., have shown that the prevalence of congenital heart 

defects increased between 1968 and 1997. During 1995-1997 the rate was 90.2 per 10,000 births 

(0.9%) and this was an increase of 58.7 per 10,000 births above the rate during 1968-1972 (Botto et 

al., 2001, 192379). Cardiovascular defects are the single largest contributor to infant mortality 

attributable to birth defects (CDC, 1998, 193243). Between 1995 and 1997, 1 in 13 infant deaths 

(7.4%) was due to a congenital heart defect (Boneva et al., 2001, 193972). The combined evidence 

from epidemiologic and toxicological studies, along with the increasing prevalence of PTB, LBW, 

and cardiac birth defects in the U.S. population, indicates that critical developmental phases may be 

characterized by enhanced sensitivity to CO exposure. 

5.7.3. Gender

COHb concentrations are generally higher in male subjects than in female subjects (Horvath et 

al., 1988, 012725). In addition, 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, 011330). The rate of decline of 

DLCO with age is lower in middle-aged women than in men; however, it is similar in older adults 

(Neas and Schwartz, 1996, 079363). Lower rates of decline in lung diffusing capacity are consistent 

with the observation that women tend to be more resistant than men to altitude hypoxia (Horvath et 

al., 1988, 012725). Women also experience fluctuating COHb levels through the menstrual cycle 

when endogenous CO production doubles in the progesterone phase (0.62 mL/h versus 0.32 mL/h in 

estrogen phase) (Delivoria-Papadopoulos et al., 1974, 086316; Mercke and Lundh, 1976, 086309). 

Similarly, endogenous CO production increases during pregnancy due to contributions from fetal CO 

production and altered hemoglobin metabolism as described above. In an epidemiologic study 

investigating the association between short-term CO exposure and IHD hospital admissions 

(Szyszkowicz, 2007, 193793), males had higher associations than females in both the all-ages group 

and in those >64 yr of age. However, this limited epidemiologic evidence combined with known 

gender-related differences in endogenous CO production do not provide sufficient basis for 

determining whether CO disproportionately affects males or females. 

5.7.4. Altitude 

Higher altitude results in changes in 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 (U.S. EPA, 2000, 000907) and other documents (U.S. EPA, 1978, 086321) 

and is reviewed in Section 4.4.2 of this ISA. 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 hypobaric hypoxia. These changes, which include increases 

in BP and cardiac output and redistribution of blood from skin to organs and from blood to 

extravascular compartments, generally will favor increased CO uptake and COHb formation, as well 

as CO elimination. It has been demonstrated that breathing CO (9 ppm) at rest at altitude produces 

higher COHb compared to sea level (McGrath et al., 1993, 013865), whereas high-altitude exposure 

in combination with exercise causes a decrease in COHb levels versus similar exposure at sea level 

(Horvath et al., 1988, 012725). This decrease could be a shift in CO storage or suppression of COHb 

formation, or both. In a controlled human exposure study on the health effects of CO at altitude, 

Kleinman et al. (1998, 047186) observed that CO exposure and simulated high altitude reduced the 

time to onset of angina relative to clean-air exposure at sea level by 9% and 11%, respectively, 

among a group of individuals with CAD. In this study, the combined effects of altitude and CO 

exposure were observed to be additive, with subjects experiencing, on average, an 18% decrease in 

the time to onset of angina following exposure to CO and simulated altitude relative to clean air 

January 2010 5-121 
exposure at sea level. No epidemiologic studies were identified that specifically examined the effect 

of altitude on health effects due to CO exposure. 

Altitude also increases the baseline COHb levels by inducing endogenous CO production and 

has been shown to be positively associated with baseline COHb concentrations (McGrath, 1992, 

001005; McGrath et al., 1993, 013865). This increase in COHb with altitude-induced hypoxia has 

also been associated with increases in mRNA, protein, and activity of HO-1 in rats and cells leading 

to enhanced endogenous CO production (Carraway et al., 2002, 026018; Chin et al., 2007, 190601). 

Early HH has been found to increase lung HO-1 protein and activity, whereas chronic HH induced 

endogenous CO production in nonpulmonary sites (Section 4.5) (Carraway et al., 2000, 021096). 

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 in increasing COHb 

concentrations at high altitudes has not been explored. 

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, these adaptive changes persist in 

people raised at high altitude even after reacclimatization to sea level (Hsia, 2002, 193857). This 

evidence indicates that visitors to high altitude locations may represent a potentially susceptible 

population for increased risk of health effects due to CO exposure. 

5.7.5. Exercise 

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. Likewise, the COHb elimination rate increases with 

physical activity, causing a decrease in COHb half-life (Joumard et al., 1981, 011330). The potential 

effects of CO on exercising individuals was demonstrated in a controlled human exposure study 

where healthy subjects exposed to CO achieved COHb levels of approximately 5%, which resulted 

in a significant decrement in exercise duration and maximal effort capability (measured by metabolic 

equivalent units) (Adir et al., 1999, 001026). These effects could be attributed to CO lowering the 

anaerobic threshold, allowing earlier fatigue of the skeletal muscles and decreasing maximal effort 

capability during heavy exercise. Due to the counterbalancing effects of increased rates of COHb 

formation and elimination, it is unclear whether individuals engaging in light to moderate exercise 

are a potentially susceptible population for increased health effects due to ambient CO exposure. 

5.7.6. 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 be exposed to elevated CO 

concentrations, as discussed in Chapter 3. Targeted sampling studies have found CO concentrations 

measured at the roadside to be several-fold higher than concentrations measured a few hundred 

meters downwind (Baldauf et al., 2008, 191017; Zhu et al., 2002, 041553), with the shape of the 

concentration profile dependent on wind speed and direction. AQS monitoring data aggregated 

across multiple sites with no adjustment for wind conditions show somewhat higher concentrations 

for microscale (near-road) monitors relative to middle-scale monitors, although the ratio is lower 

than that observed in the roadside studies. Elevated near-road concentrations are important for 

residents of the estimated 17.9 million occupied homes nationwide (16.1%) that are within 

approximately 90 m of a freeway, railroad, or airport, according to the 2007 American Housing 

Survey (2008, 194013). 

Studies of commuters have shown that commuting time is an important determinant of CO 

exposure for those traveling by car, bicycle, public transportation, and walking (Bruinen de Bruin et 

al., 2004, 190943; Kaur et al., 2005, 086504; Scotto Di Marco et al., 2005, 144054). In-vehicle 

concentrations have been measured to be several times higher than concentrations measured at fixedsite monitors not located adjacent to roadways (Bruinen de Bruin et al., 2004, 190943; Chang et al., 

2000, 001276; Kaur et al., 2005, 086504; Riediker et al., 2003, 043761; Scotto Di Marco et al., 2005, 

January 2010 5-122 
144054). Commuting is likely to be an important contributor to CO exposure for the 5.5 million U.S. 

worker (5%) who drive 60 min or more to work (U.S. Census Bureau, 2008, 194013). This evidence 

for elevated on-road and near-road CO concentrations combined with residential and commuting 

data indicates that the large numbers of individuals who spend a substantial amount of time on or 

near heavily traveled roadways are an important population that is potentially susceptible to 

increased health risks due to ambient CO exposure. 

5.7.7. Medications and Other Substances 

Endogenous CO production can be altered by medications or a number of physiological 

conditions that increase RBC destruction, the breakdown of hemoproteins other than Hb, and the 

production of bilirubin (Section 4.5). Nicotinic acid, allyl-containing compounds (acetamids and 

barbiturates), diphenylhydantoin, progesterone, contraceptives, and statins increase CO production. 

One epidemiologic study (Dales, 2004, 099036) investigated the effect of medication use on the 

relationship between ambient CO and HRV in individuals with CAD. The authors observed an 

association between short-term CO exposure and an increase in SDNN for CAD patients not taking 

beta blockers; however, this association did not persist in CAD patients taking beta blockers. 

Compounds such as carbon disulfide and sulfur-containing chemicals (parathion and 

phenylthiourea) increase CO following metabolism by cytochrome p450s. The P450 system may 

also cause large increases in CO produced from the metabolic degradation of dihalomethanes such as 

methylene chloride leading to very high (>10%) COHb levels, which can be further enhanced by 

prior exposure to HCs or ethanol. Minor sources of endogenous CO include the auto-oxidation of 

phenols, photo-oxidation of organic compounds, and lipid peroxidation of cell membrane lipids. 

Taken together, this evidence indicates that individuals ingesting medications and other substances 

that enhance endogenous or metabolic CO production represent a population that is potentially 

susceptible to increased health effects due to additional exposure to ambient CO. 

5.7.8. Summary of Susceptible Populations 

Individuals with CAD represent the population most susceptible to increased risk of COinduced health effects, based on evidence of significant decreases in the time to onset of exerciseinduced angina or ST-segment changes observed in controlled human exposure studies of individuals 

with CAD. This is coherent with the results from epidemiologic studies that observed associations 

between short-term CO exposure and ED visits and HAs for IHD and related outcomes. The limited 

evidence from stratified analyses in epidemiologic studies, which indicates that secondary diagnoses 

of CHF or dysrhythmia modify associations between short-term CO exposure and IHD HAs, 

provides further support that individuals with cardiovascular disease represent a potentially 

susceptible population. Additional evidence is provided by toxicological studies that demonstrated 

exacerbation of cardiomyopathy and increased vascular remodeling in animal models of 

cardiovascular disease. Although it is not clear whether the small changes in COHb associated with 

ambient CO exposures result in substantially diminished O2 delivery to tissues, the known role of 

CO in limiting O2 availability provides biological plausibility for ischemia-related health outcomes 

following CO exposure. The continuous nature of the progression of CAD and its close relationship 

with other forms of cardiovascular disease suggest that a larger population than just those individuals 

with a prior diagnosis of CAD may be susceptible to health effects from CO exposure. 

Populations potentially susceptible to CO-induced health effects also include individuals with 

other preexisting diseases, such as COPD or diabetes. Preliminary evidence available from 

controlled human exposure and epidemiologic studies suggests that individuals with obstructive lung 

disease may be susceptible to increased cardiovascular or respiratory effects due to CO exposure. 

Increased endogenous CO production and the potential for higher baseline COHb concentrations in 

individuals with diabetes, combined with the limited epidemiologic evidence showing cardiovascular 

effects, suggests that diabetics are potentially susceptible to short-term exposure to CO. Individuals 

with various types of anemia who have diminished O2-carrying capacity and/or high baseline COHb 

levels may be more susceptible to health effects due to ambient CO exposure, although no studies 

were identified that evaluated specific CO-related health effects in individuals with anemia. 

There is also evidence that older adults and the developing young represent populations 

potentially susceptible to CO-induced health effects. Epidemiologic studies provide limited evidence 

January 2010 5-123 
from stratified analyses indicating that associations between short-term CO exposure and hospital 

admissions for CAD are higher among those ≥ 65 yr old than for those <65 yr. The older adult 

population also has a much higher prevalence of CAD than the general population as a whole, which 

may contribute to their increased susceptibility. Recent studies on birth outcomes have provided 

limited evidence of associations between in utero CO exposure and PTB, LBW and cardiac birth 

defects. Toxicological studies provide evidence of effects on birth weight and growth as well as 

development of the cardiovascular and nervous systems following prenatal exposure to CO. This 

evidence, combined with differences between fetal and maternal CO pharmacokinetics, indicates that 

critical developmental phases may be characterized by enhanced sensitivity to CO exposure. 

Visitors to high-altitude locations may represent a potentially susceptible population due to 

changes in factors which affect the uptake and elimination of CO, although acclimatization occurs as 

length of stay increases. Individuals with substantial exposure to mobile source emissions, such as 

commuters and those living near heavily traveled roadways, represent an important population 

potentially susceptible to increased risk of CO-induced health effects due to elevated on-road and 

roadside CO concentrations. 

Overall, the controlled human exposure, epidemiologic, and toxicological studies evaluated in 

this assessment provide evidence for increased susceptibility among multiple populations. Medical 

conditions that increase endogenous CO production rates may also contribute to increased 

susceptibility to health effects from ambient CO exposure. Although the weight of evidence varies 

depending on the factor being evaluated, the clearest evidence indicates that individuals with CAD 

are most susceptible to an increase in CO-induced health effects. 

5.8. Summary 

The evidence reviewed in this chapter describes recent findings regarding the health effects of 

ambient CO. Section 5.1 presents evidence on the mode of action of CO, including its role in 

limiting O2 availability as well as its role in altered cell signaling. Evidence is presented in 

subsequent sections on the effect of short- and long-term exposure to CO on cardiovascular 

morbidity (Section 5.2), the central nervous system (Section 5.3), birth outcomes and developmental 

effects (Section 5.4), respiratory morbidity (Section 5.5), and mortality (Section 5.6). Potentially 

susceptible populations at increased risk of CO-induced health effects are discussed in Section 5.7. 

Table 2-1 summarizes causal determinations for the health outcome categories reviewed in this 

assessment. An integrative overview of the health effects of ambient CO and uncertainties associated 

with interpretation of the evidence is provided in Chapter 2. The strongest evidence regarding COinduced health effects relates to cardiovascular morbidity, and the combined evidence from 

controlled human exposure studies and epidemiologic studies indicates that a causal relationship is 

likely to exist between relevant short-term CO exposures and cardiovascular morbidity, particularly 

in individuals with CAD. The evidence is suggestive of a causal relationship between short-term 

exposure to CO and respiratory morbidity as well as between short-term CO exposure and mortality. 

The evidence is also suggestive of a causal relationship for birth outcomes and developmental effects 

following long-term exposure to CO, and for central nervous system effects linked to short- and 

long-term exposure to CO. The evidence indicates that there is not likely to be a causal relationship 

between long-term exposure to CO and mortality. For respiratory morbidity following long-term 

exposure to CO, the evidence was inadequate to infer a causal relationship. 

January 2010 5-124 
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January 2010 5-148 
Annex A. Atmospheric Science 

YukonKoyukuk

Alaska

0

0

0

670

1,458

92

6,613

0

19,136

145,340

88,239

23,049

1,697

8,403,258

0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Yukon-Koyukuk Census Area

0

0

2,494

3,758

406

52

0

0

0

0

8,095,364

0

0

0

0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

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

January 2010 A-1 
Weber

Utah

Grand

Utah

14

91

13

19

1,312

37,877

4,252

2,314

3,733

153

41,697

271,971

183,533

764,676

136,584

0 250,000 500,000 750,000 1,000,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Weber County

2

8

0

0

25

2,038

73

2

37

0

1,521

985

932

11,842

45,690

0 25,000 50,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Utah County

0

12

0

1

48

38

1

1,258

369

2

3,599

88,391

20,120

3,019

4,769

0 25,000 50,000 75,000 100,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Grand County

0

0

0

0

0

5

0

0

1

0

992

7,219

2,596

6,596

3,939

0 2,500 5,000 7,500 10,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

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

counties in Utah. 

January 2010 A-2 
Suffolk

Middlesex

Norfolk

Massachusetts

11

68

17

78

29

870

11,569

105

21,970

3,861

119,337

703

441,814

960,011

10,807

0 250,000 500,000 750,000 1,000,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Middlesex County

224,697

92,849

2

10

0

0

1

179

492

31

5,465

321

19,297

73

1,024

0 25,000 50,000 75,000 100,000 125,000 150,000 175,000 200,000 225,000 250,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Norfolk County

160,676

42,121

54

9,700

285

2,161

0

189

71

18

76

0

4

2

670

0 50,000 100,000 150,000 200,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Suffolk County 

30,901

66,491

0

0

0

0

11

114

225

0

1,295

1

3,022

99

254

0 25,000 50,000 75,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

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

for selected counties in Massachusetts. 

January 2010 A-3 
Fulton

Dekalb

Chatham

Liberty

Glynn

Georgia 

11

68

17

960,011

441,814

119,337

3,861

21,970

105

11,569

870

29

78

182,591

703

0 250,000 500,000 750,000 1,000,000 1,250,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Fulton County 

0

28

0

0

0

0

233

41

1,384

279

3,729

221,739

65,982

67

825

0 50,000 100,000 150,000 200,000 250,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Dekalb County 

62,735

141,889

0

0

5

1

0

102

0

988

254

2,938

0

812

1

0 25,000 50,000 75,000 100,000 125,000 150,000 175,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Figure A-4. CO emissions density map and distribution for the state of Georgia and for 

selected counties in Georgia (Figure 1 of 2). 

January 2010 A-4 
Chatham County

24,908

54,182

0

0

0

18

0

1,831

189

1,635

73

1,168

0

1,315

561

0 25,000 50,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Liberty County

0

3,423

15,423

11,018

256

55,427

52

0

0

373

0

12

0

0

1,626

0 25,000 50,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

` 

Glynn County

0

0

0

0

0

6,619

377

0

3,002

1,129

188

1,504

390

10,619

21,140

0 5,000 10,000 15,000 20,000 25,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

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

January 2010 A-5 
Los 

Angeles

San 

Diego

Orange

California

3,436,059

1,170,637

736,797

410,414

321,342

22,261

85,727

498

28,707

16,763

7,225

351

277

14,322

290

0 1,000,000 2,000,000 3,000,000 4,000,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Los Angeles County

2

86

1

12

2,425

500

9,814

79

24,537

142

10,927

8,085

63,164

212,892

824,676

0 250,000 500,000 750,000 1,000,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

San Diego County

0

5,260

0

0

132

98

895

0

4,935

162

25,768

10,580

62,392

101,515

283,597

0 100,000 200,000 300,000 400,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Orange County

1

18

0

0

17

46

655

0

3,761

107

3,593

2,374

1,145

103,377

220,477

0 100,000 200,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Figure A-6. CO emissions density map and distribution for the state of California and for 

selected counties in California. 

January 2010 A-6 
Jefferson

Alabama

216

128

174

5,721

13,619

48,004

11,212

38,247

67,132

104,914

71,052

146,582

308,000

389,081

1,237,459

0 500,000 1,000,000 1,500,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Jefferson County

181,846

47,932

546

2,655

2,888

2,176

38,088

27,010

3,360

13,592

145

5

54

19

8

0 50,000 100,000 150,000 200,000

SOLVENT UTILIZATION

MISCELLANEOUS

STORAGE & TRANSPORT

CHEMICAL & ALLIED PRODUCT MFG

PETROLEUM & RELATED INDUSTRIES

OTHER INDUSTRIAL PROCESSES

FUEL COMB. ELEC. UTIL.

METALS PROCESSING

FUEL COMB. INDUSTRIAL

WASTE DISPOSAL & RECYCLING

FUEL COMB. OTHER

SOILS

FIRES

NON-ROAD EQUIPMENT

ON-ROAD VEHICLES

Emissions (tons)

Anthropogenic

Biogenic

Figure A-7. CO emissions density map and distribution for the state of Alabama and for 

Jefferson County in Alabama. 

January 2010 A-7 
Table A-1. Listing of all CO monitors currently in use, along with their limits of detection. 

Method Code Method Description Reference Method Id Fed MDL (ppm) 

008 BENDIX 8501-5CA RFCA-0276-008 0.50000 

012 BECKMAN 866 RFCA-0876-012 0.50000 

018 MSA 202S RFCA-0177-018 0.50000 

033 HORIBA AQM-10--11--12 RFCA-1278-033 0.50000 

041 MONITOR LABS 8310 RFCA-0979-041 0.50000 

048 HORIBA 300E/300SE RFCA-1180-048 0.50000 

050 MASS-CO 1 (MASSACHUSETTS) RFCA-1280-050 0.50000 

051 DASIBI 3003 RFCA-0381-051 0.50000 

054 THERMO ELECTRON 48, 48C RFCA-0981-054 0.50000 

055 Gas Filter Correlation Thermo Electron 48C-TL N/A 0.04000 

066 MONITOR LABS 8830 RFCA-0388-066 0.50000 

067 DASIBI 3008 RFCA-0488-067 0.50000 

088 LEAR SIEGLER MODEL ML 9830 RFCA-0992-088 0.50000 

093 API MODEL 300 GAS FILTER RFCA-1093-093 0.50000 

106 HORIBA INSTR. MODEL APMA-360 RFCA-0895-106 0.50000 

108 ENVIRONMENT SA MODEL CO11M RFCA-0995-108 0.50000 

147 Environnement S.A. Model CO12M Co Analyzer RFCA-0206-147 0.50000 

158 HORIBA INSTR. MODEL APMA-370 RFCA-0506-158 0.50000 

167 DKK-TOA Cork Mode GFC-311E RFCA-0907-167 0.50000 

172 SIR S.A. Model S5006 RFCA-0708-172 0.50000 

554 Gas Filter Correlation Thermo Electron 48C-TLE N/A 0.04000 

588 Ecotech EC9830T RFCA-0992-088 0.04000 

593 API Model 300 EU RFCA-1093-093 0.04000 

January 2010 A-8 
Table A-2. Microscale monitors meeting 75% completeness criteria, 2005-2007. 

Monitor Code State Name City Name Traffic Count Road Type 

02-090-0002-42101-1 Alaska Fairbanks NR NR 

04-013-0016-42101-1 Arizona Phoenix 50,000 ARTERIAL 

04-019-1014-42101-1 Arizona Tucson 41,200 MAJ ST OR HY 

06-065-1003-42101-1 California Riverside 40,000 FREEWAY 

06-073-0007-42101-1 California San Diego 6,000 THRU ST OR HY 

08-013-0009-42101-1 Colorado Longmont 20,000 MAJ ST OR HY 

08-031-0002-42101-2 Colorado Denver 17,200 MAJ ST OR HY 

08-031-0019-42101-1 Colorado Denver 500 MAJ ST OR HY 

08-041-0015-42101-1 Colorado Colorado Springs 44,200 MAJ ST OR HY 

08-077-0018-42101-1 Colorado Grand Junction 13,525 THRU ST OR HY 

09-003-0017-42101-1 Connecticut Hartford 10,000 THRU ST OR HY 

11-001-023-42101-1 District Of Columbia Washington 30,000 THRU ST OR HY 

12-057-1070-42101-1 Florida Tampa 133,855 ARTERIAL 

12-086-4002-42101-1 Florida Miami 5,000 LOCAL ST OR HY 

12-095-1005-42101-1 Florida Orlando 30,000 MAJ ST OR HY 

12-103-0024-42101-1 Florida Saint Petersburg 35,000 MAJ ST OR HY 

12-103-2008-42101-1 Florida Clearwater 67,751 MAJ ST OR HY 

12-115-1004-42101-1 Florida Sarasota 31,000 MAJ ST OR HY 

13-121-0099-42101-1 Georgia Atlanta 44,000 MAJ ST OR HY 

17-031-0063-42101-1 Illinois Chicago 5,000 LOCAL ST OR HY 

17-031-6004-42101-1 Illinois Maywood NR NR 

17-143-0036-42101-1 Illinois Peoria 18,500 ARTERIAL 

17-167-0008-42101-1 Illinois Springfield 16,400 MAJ ST OR HY 

17-201-0011-42101-1 Illinois Rockford 11,400 ARTERIAL 

18-003-0011-42101-1 Indiana Fort Wayne 30430 MAJ ST OR HY 

18-089-0015-42101-1 Indiana East Chicago NR NR 

18-097-0072-42101-1 Indiana Indianapolis 21,237 MAJ ST OR HY 

18-163-0019-42101-1 Indiana Evansville 24,498 LOCAL ST OR HY 

21-111-1019-42101-1 Kentucky Louisville 22,000 MAJ ST OR HY 

27-053-0954-42101-1 Minnesota Minneapolis 29,352 MAJ ST OR HY 

27-123-0050-42101-1 Minnesota St. Paul NR NR 

27-137-0018-42101-1 Minnesota Duluth 12,000 MAJ ST OR HY 

27-145-3048-42101-1 Minnesota St. Cloud NR NR 

30-029-0010-42101-1 Montana Kalispell NR THRU ST OR HY 

30-031-0013-42101-1 Montana Not in a city 2,000 THRU ST OR HY 

33-011-1009-42101-1 New Hampshire Nashua 40,000 MAJ ST OR HY 

34-005-1001-42101-1 New Jersey Burlington 8,000 THRU ST OR HY 

34-017-1002-42101-1 New Jersey Jersey City 25,000 THRU ST OR HY 

37-067-0023-42101-1 North Carolina Winston-Salem 22,000 MAJ ST OR HY 

39-035-0048-42101-1 Ohio Cleveland 24,300 THRU ST OR HY 

January 2010 A-9 
Monitor Code State Name City Name Traffic Count Road Type 

39-035-0051-42101-1 Ohio Cleveland 16,150 MAJ ST OR HY 

39-035-0053-42101-1 Ohio Cleveland 19,550 MAJ ST OR HY 

39-049-0036-42101-1 Ohio Columbus 16,800 MAJ ST OR HY 

39-061-0021-42101-1 Ohio Cincinnati 17,250 LOCAL ST OR HY 

39-085-0006-42101-1 Ohio Mentor 25,240 MAJ ST OR HY 

39-113-0034-42101-1 Ohio Dayton 7,100 THRU ST OR HY 

39-153-0022-42101-1 Ohio Akron 13,150 MAJ ST OR HY 

41-029-0018-42101-1 Oregon Medford NR NR 

41-039-0013-42101-1 Oregon Eugene 17,500 MAJ ST OR HY 

41-051-0087-42101-1 Oregon Portland 4,150 LOCAL ST OR HY 

45-079-0020-42101-1 South Carolina Columbia 31,500 MAJ ST OR HY 

47-037-0021-42101-1 Tennessee Nashville 15,000 MAJ ST OR HY 

47-157-0036-42101-1 Tennessee Memphis 25,000 THRU ST OR HY 

48-029-0046-42101-1 Texas San Antonio 5,820 MAJ ST OR HY 

48-201-0075-42101-1 Texas Houston 6,576 LOCAL ST OR HY 

53-033-0019-42101-1 Washington Bellevue 100,000 MAJ ST OR HY 

53-063-0049-42101-1 Washington Spokane 10,000 MAJ ST OR HY 

“NR” denotes that the value was not reported. 

January 2010 A-10 
Table A-3. Middle scale monitors meeting 75% completeness criteria, 2005-2007. 

Monitor Code State Name City Name Traffic Count Road Type 

04-013-3010-42101-1 Arizona Phoenix 18,500 ARTERIAL 

06-029-0010-42101-1 California Bakersfield 30,300 ARTERIAL 

06-037-1301-42101-1 California Lynwood 35,000 ARTERIAL 

06-037-9033-42101-1 California Lancaster 2,320 LOCAL ST OR HY 

06-059-1003-42101-1 California Costa Mesa 1,000 LOCAL ST OR HY 

06-071-9004-42101-1 California San Bernardino 21,900 THRU ST OR HY 

06-085-0005-42101-1 California San Jose NR LOCAL ST OR HY 

12-0011-0010-42101-1 Florida Fort Lauderdale 1,000 LOCAL ST OR HY 

12-031-0080-42101-1 Florida Jacksonville 1,000 LOCAL ST OR HY 

12-031-0084-42101-1 Florida Jacksonville 500 LOCAL ST OR HY 

12-099-1004-42101-1 Florida Palm Beach 30,000 MAJ ST OR HY 

12-103-2006-42101-1 Florida Clearwater 23,400 MAJ ST OR HY 

17-031-3103-42101-1 Illinois Schiller Park 47,900 ARTERIAL 

20-209-0021-42101-1 Kansas Kansas City 7,720 MAJ ST OR HY 

24-510-0040-42101-1 Maryland Baltimore 15,300 THRU ST OR HY 

32-031-0022-42101-1 Nevada Reno NR NR 

34-003-0004-42101-1 New Jersey Fort Lee 250,000 ARTERIAL 

36-061-0056-42101-1 New York New York 45,000 MAJ ST OR HY 

39-049-0005-42101-1 Ohio Columbus 36,600 FREEWAY 

39-081-1001-42101-1 Ohio Mingo Junction 2,500 LOCAL ST OR HY 

39-151-0020-42101-1 Ohio Canton 11,000 MAJ ST OR HY 

40-143-0191-42101-1 Oklahoma Tulsa 50,800 FREEWAY 

42-003-0038-42101-1 Pennsylvania Pittsburgh 15,000 MAJ ST OR HY 

42-101-0047-42101-1 Pennsylvania Philadelphia NR NR 

45-019-0046-42101-1 South Carolina Not in a city NR LOCAL ST OR HY 

45-045-0008-42101-1 South Carolina Greenville NR LOCAL ST OR HY 

45-045-0009-42101-1 South Carolina Taylors 9,500 LOCAL ST OR HY 

47-163-0007-42101-1 Tennessee Kingsport NR NR 

48-439-1002-42101-1 Texas Fort Worth 100 LOCAL ST OR HY 

50-007-0014-42101-1 Vermont Burlington NR MAJ ST OR HY 

72-127-0003-42101-1 Puerto Rico San Juan 64,000 MAJ ST OR HY 

“NR” denotes that the value was not reported. 

January 2010 A-11 
Table A-4. Neighborhood scale monitors meeting 75% completeness criteria, 2005-2007. 

Monitor Code State Name City Name Traffic Count Road Type 

01-073-1003-42101-1 Alabama Fairfield 5,000 LOCAL ST OR HY 

01-073-6004-42101-1 Alabama Birmingham NR NR 

02-020-0018-42101-1 Alaska Anchorage NR NR 

02-020-0048-42101-1 Alaska Anchorage 5,000 LOCAL ST OR HY 

02-090-0020-42101-1 Alaska Fairbanks NR NR 

04-013-0019-42101-1 Arizona Phoenix NR LOCAL ST OR HY 

04-013-3002-42101-1 Arizona Phoenix 24,000 ARTERIAL 

04-019-0002-42101-1 Arizona Tucson 37,400 MAJ ST OR HY 

04-019-1011-42101-1 Arizona Tucson 47,000 MAJ ST OR HY 

04-019-1028-42101-1 Arizona Tucson 52,900 MAJ ST OR HY 

06-001-1001-42101-1 California Fremont (Centerville) 500 LOCAL ST OR HY 

06-013-0002-42101-1 California Concord 41,218 MAJ ST OR HY 

06-037-5005-42101-1 California Los Angeles 1,252 LOCAL ST OR HY 

06-053-1003-42101-1 California Salinas 33,193 THRU ST OR HY 

06-065-9001-42101-1 California Lake Elsinore NR NR 

06-067-0007-42101-1 California Sacramento 20,000 THRU ST OR HY 

06-073-0001-42101-1 California Chula Vista 5,000 LOCAL ST OR HY 

06-073-1002-42101-1 California Escondido NR NR 

06-073-2007-42101-1 California Otay Mesa 18,000 LOCAL ST OR HY 

06-083-1025-42101-1 California Capitan NR NR 

06-083-2004-42101-1 California Lompoc NR NR 

06-083-2011-42101-1 California Goleta 5,000 THRU ST OR HY 

06-083-4003-42101-1 California Vandenberg Air Force Base NR NR 

08-01-3001-42101-1 Colorado Welby 500 EXPRESSWAY 

08-067-7001-42101-1 Colorado Not in a city 2,436 LOCAL ST OR HY 

08-069-1004-42101-1 Colorado Fort Collins 5,000 THRU ST OR HY 

08-123-0010-42101-1 Colorado Greeley 6,650 THRU ST OR HY 

11-001-0041-42101-1 District Of Columbia Washington 540 LOCAL ST OR HY 

12-011-2004-42101-1 Florida Pompano Beach 1,000 LOCAL ST OR HY 

12-011-3002-42101-1 Florida Hollywood 1,000 LOCAL ST OR HY 

12-031-0083-42101-1 Florida Jacksonville 10,000 LOCAL ST OR HY 

12-086-0031-42101-1 Florida Miami 62,000 MAJ ST OR HY 

12-086-1019-42101-1 Florida Miami 8,000 MAJ ST OR HY 

12-095-2002-42101-1 Florida Winter Park 7,000 MAJ ST OR HY 

12-103-0018-42101-1 Florida Saint Petersburg 2,000 MAJ ST OR HY 

17-031-4002-42101-1 Illinois Cicero NR NR 

17-163-0010-42101-1 Illinois East Saint Louis 8,900 LOCAL ST OR HY 

18-097-0073-42101-1 Indiana Indianapolis (Remainder) 11,261 THRU ST OR HY 

20-173-0010-42101-1 Kansas Wichita 6,884 LOCAL ST OR HY 

21-111-0046-42101-1 Kentucky Louisville 6,500 THRU ST OR HY 

January 2010 A-12 
Monitor Code State Name City Name Traffic Count Road Type 

22-033-0009-42101-1 Louisiana Baton Rouge 5,000 LOCAL ST OR HY 

25-013-0016-42101-1 Massachusetts Springfield 5,000 LOCAL ST OR HY 

25-017-0007-42101-1 Massachusetts Lowell 15,000 THRU ST OR HY 

25-025-0042-42101-1 Massachusetts Boston 12,785 LOCAL ST OR HY 

27-03-0600-42101-1 Minnesota Fridley 1,400 LOCAL ST OR HY 

27-037-0020-42101-1 Minnesota Rosemount NR NR 

27-037-0423-42101-1 Minnesota Inver Grove Heights (RR name Inver 

Grove) NR NR 

29-510-0086-42101-1 Missouri St. Louis 81,850 MAJ ST OR HY 

30-111-0085-42101-1 Montana Billings 5,700 THRU ST OR HY 

31-055-0035-42101-1 Nebraska Omaha 2,900 LOCAL ST OR HY 

32-003-0538-42101-1 Nevada Las Vegas 20,000 LOCAL ST OR HY 

32-003-0539-42101-1 Nevada Las Vegas 21,000 MAJ ST OR HY 

32-003-0561-42101-1 Nevada Las Vegas 28,400 MAJ ST OR HY 

32-003-1021-42101-1 Nevada Las Vegas NR NR 

32-003-2002-42101-1 Nevada Las Vegas 6,750 THRU ST OR HY 

32-031-0016-42101-1 Nevada Reno 22,700 LOCAL ST OR HY 

32-031-0020-42101-1 Nevada Reno NR NR 

32-031-0025-42101-1 Nevada Reno NR NR 

32-031-1005-42101-1 Nevada Sparks 2,600 LOCAL ST OR HY 

32-031-2009-42101-1 Nevada Lemmon Valley-Golden Valley NR NR 

32-510-0004-42101-1 Nevada Carson City 1 LOCAL ST OR HY 

33-011-0020-42101-1 New Hampshire Manchester 500 LOCAL ST OR HY 

34-003-5001-42101-1 New Jersey Hackensack 15,000 THRU ST OR HY 

34-007-0003-42101-1 New Jersey Camden 45,000 MAJ ST OR HY 

35-001-019-42101-1 New Mexico Albuquerque 1 ARTERIAL 

35-001-0023-42101-1 New Mexico Albuquerque 41,200 MAJ ST OR HY 

35-001-0024-42101-1 New Mexico Albuquerque 15,500 MAJ ST OR HY 

35-001-0028-42101-1 New Mexico Albuquerque 2,0600 THRU ST OR HY 

35-001-1014-42101-1 New Mexico Albuquerque 8,000 THRU ST OR HY 

35-043-9004-42101-1 New Mexico Not in a city 100 LOCAL ST OR HY 

36-063-2008-42101-1 New York Niagara Falls 5,000 LOCAL ST OR HY 

37-119-0041-42101-1 North Carolina Charlotte 16,400 MAJ ST OR HY 

37-119-0041-42101-3 North Carolina Charlotte 16,400 MAJ ST OR HY 

39-035-0070-42101-1 Ohio Cleveland 100 LOCAL ST OR HY 

39-113-0028-42101-1 Ohio Dayton 5,100 LOCAL ST OR HY 

39-153-0020-42101-1 Ohio Akron 200 LOCAL ST OR HY 

40-021-9002-42101-1 Oklahoma Park Hill 10,300 LOCAL ST OR HY 

40-071-9010-42101-1 Oklahoma Not in a city 300 LOCAL ST OR HY 

40-109-0047-42101-1 Oklahoma Oklahoma City 27,000 MAJ ST OR HY 

41-051-0080-42101-1 Oregon Portland 5,000 LOCAL ST OR HY 

42-003-0031-42101-1 Pennsylvania Pittsburgh 4,562 THRU ST OR HY 

42-013-0801-42101-1 Pennsylvania Altoona 100 LOCAL ST OR HY 

January 2010 A-13 
Monitor Code State Name City Name Traffic Count Road Type 

42-017-0012-42101-1 Pennsylvania Bristol 500 LOCAL ST OR HY 

42-021-0011-42101-1 Pennsylvania Johnstown 6,000 LOCAL ST OR HY 

42-049-0003-42101-1 Pennsylvania Erie 1,000 LOCAL ST OR HY 

42-071-0007-42101-1 Pennsylvania Lancaster 2,000 THRU ST OR HY 

42-073-0015-42101-1 Pennsylvania New Castle 4,500 LOCAL ST OR HY 

42-091-0013-42101-1 Pennsylvania Norristown 8,500 MAJ ST OR HY 

42-095-0025-42101-1 Pennsylvania Freemansburg 100 LOCAL ST OR HY 

42-101-0004-42101-1 Pennsylvania Philadelphia 13800 MAJ ST OR HY 

42-101-0027-42101-1 Pennsylvania Philadelphia 46000 MAJ ST OR HY 

42-107-0003-42101-1 Pennsylvania Shenandoah 100 LOCAL ST OR HY 

42-125-0005-42101-1 Pennsylvania Charleroi NR NR 

44-007-1010-42101-1 Rhode Island East Providence 100,000 FREEWAY 

48-061-0006-42101-1 Texas Brownsville 30 LOCAL ST OR HY 

48-113-0069-42101-2 Texas Dallas 1,000 LOCAL ST OR HY 

48-141-0002-42101-1 Texas El Paso 7,270 THRU ST OR HY 

48-141-0029-42101-1 Texas El Paso 2,790 LOCAL ST OR HY 

48-141-0037-42101-1 Texas El Paso 5,000 LOCAL ST OR HY 

48-141-0044-42101-1 Texas El Paso 15,200 ARTERIAL 

48-141-0053-42101-1 Texas El Paso 1,992 FREEWAY 

48-141-0057-42101-1 Texas Socorro 500 LOCAL ST OR HY 

48-141-0058-42101-1 Texas El Paso 1,080 LOCAL ST OR HY 

48-201-0024-42101-1 Texas Not in a city 5,300 MAJ ST OR HY 

48-201-0047-42101-1 Texas Houston 5,860 MAJ ST OR HY 

48-201-1035-42101-1 Texas Houston 13,440 MAJ ST OR HY 

48-201-1039-42101-1 Texas Deer Park 16010 MAJ ST OR HY 

48-439-3011-42101-1 Texas Arlington 10,573 LOCAL ST OR HY 

48-453-0014-42101-1 Texas Austin 3,420 LOCAL ST OR HY 

48-479-0017-42101-1 Texas Laredo 30,380 ARTERIAL 

49-035-0003-42101-1 Utah Not in a city 16,500 THRU ST OR HY 

50-021-0002-42101-1 Vermont Rutland NR NR 

51-059-0005-42101-1 Virginia Not in a city 25 LOCAL ST OR HY 

51-650-0004-42101-2 Virginia Hampton 2,000 LOCAL ST OR HY 

51-760-0024-42101-1 Virginia Richmond 7,591 THRU ST OR HY 

51-770-0015-42101-1 Virginia Roanoke NR NR 

54-009-0011-42101-1 West Virginia Weirton NR NR 

54-029-0009-42101-1 West Virginia Weirton NR NR 

54-029-1004-42101-1 West Virginia Weirton 50 LOCAL ST OR HY 

“NR” denotes that the value was not reported. 

January 2010 A-14 
Table A-5. Urban scale monitors meeting 75% completeness criteria, 2005-2007. 

Monitor Code State Name City Name Traffic Count Road Type 

06-059-0007-42101-1 California Anaheim 1,000 LOCAL ST OR HY 

13-089-0002-42101-1 Georgia Decatur 9,250 LOCAL ST OR HY 

13-223-0003-42101-1 Georgia Not in a city 6 LOCAL ST OR HY 

25-027-0023-42101-1 Massachusetts Worcester NR LOCAL ST OR HY 

34-007-1001-42101-1 New Jersey Not in a city 4,000 THRU ST OR HY 

42-003-0010-42101-1 Pennsylvania Pittsburgh 1,000 MAJ ST OR HY 

42-007-0014-42101-1 Pennsylvania Beaver Falls NR NR 

42-129-0008-42101-1 Pennsylvania Greensburg 100 THRU ST OR HY 

42-133-0008-42101-1 Pennsylvania York 8,400 THRU ST OR HY 

48-141-0055-42101-1 Texas El Paso 2450 LOCAL ST OR HY 

51-059-0030-42101-1 Virginia Franconia 200 LOCAL ST OR HY 

“NR” denotes that the value was not reported. 

Table A-6. Regional scale monitors meeting 75% completeness criteria, 2005-2007. 

Monitor Code State Name City Name Traffic Count Road Type 

23-009-0103-42101-1 Maine Not in a city 3,500 LOCAL ST OR HY 

35-001-0029-42101-1 New Mexico South Valley 8,800 LOCAL ST OR HY 

“NR” denotes that the value was not reported. 

January 2010 A-15 
Table A-7. Monitors meeting 75% completeness criteria, 2005-2007 with no scale delared. 

Monitor Code State Name City Name Traffic Count Road Type 

04-013-9997-42101-1 Arizona Phoenix 250 LOCAL ST OR HY 

06-001-0007-42101-1 California Livermore 2,400 LOCAL ST OR HY 

06-007-0002-42101-1 California Chico 44,000 LOCAL ST OR HY 

06-013-1002-42101-1 California Bethel Island NR NR 

06-013-1004-42101-1 California San Pablo NR THRU ST OR HY 

06-013-3001-42101-1 California Pittsburg 9,600 THRU ST OR HY 

06-019-0007-42101-1 California Fresno 500 LOCAL ST OR HY 

06-019-0008-42101-1 California Fresno 20,000 MAJ ST OR HY 

06-019-0242-42101-1 California Fresno 500 LOCAL ST OR HY 

06-019-5001-42101-1 California Clovis 16,461 THRU ST OR HY 

06-025-0005-42101-1 California Calexico 7,000 LOCAL ST OR HY 

06-025-0006-42101-1 California Calexico 10 THRU ST OR HY 

06-025-1003-42101-1 California El Centro NR NR 

06-037-0002-42101-1 California Azusa 600 THRU ST OR HY 

06-037-0113-42101-1 California West Los Angeles NR NR 

06-037-1002-42101-1 California Burbank 2,400 LOCAL ST OR HY 

06-037-1103-42101-1 California Los Angeles 9,000 THRU ST OR HY 

06-037-1201-42101-1 California Reseda NR NR 

06-037-1701-42101-1 California Pomona NR NR 

06-037-2005-42101-1 California Pasadena 18,000 THRU ST OR HY 

06-037-4002-42101-1 California Long Beach 24,000 LOCAL ST OR HY 

06-037-6012-42101-1 California Santa Clarita 4,395 LOCAL ST OR HY 

06-041-0001-42101-1 California San Rafael 15,000 MAJ ST OR HY 

06-045-0008-42101-1 California Ukiah 12,000 LOCAL ST OR HY 

06-045-0009-42101-1 California Willits 18,000 MAJ ST OR HY 

06-055-0003-42101-1 California Napa NR NR 

06-059-2022-42101-1 California Mission Viejo 42,400 MAJ ST OR HY 

06-059-5001-42101-1 California La Habra NR NR 

06-065-5001-42101-1 California Palm Springs NR NR 

06-065-8001-42101-1 California Rubidoux (West Riverside) 18,000 THRU ST OR HY 

06-067-0002-42101-1 California North Highlands NR NR 

06-067-0006-42101-1 California Sacramento 10,000 LOCAL ST OR HY 

06-067-0013-42101-1 California Sacramento 100 LOCAL ST OR HY 

06-071-0001-42101-1 California Barstow NR NR 

06-071-0306-42101-1 California Victorville 454 LOCAL ST OR HY 

06-071-1004-42101-1 California Upland 15,000 THRU ST OR HY 

06-075-0005-42101-1 California San Francisco 240,700 FREEWAY 

06-077-1002-42101-1 California Stockton 6,000 LOCAL ST OR HY 

06-081-1001-42101-1 California Redwood City 1,000 LOCAL ST OR HY 

06-087-0003-42101-1 California Davenport NR NR 

January 2010 A-16 
Monitor Code State Name City Name Traffic Count Road Type 

06-095-0004-42101-1 California Vallejo 9,350 THRU ST OR HY 

06-097-0003-42101-1 California Santa Rosa 2,608 THRU ST OR HY 

06-099-0005-42101-1 California Modesto NR NR 

06-099-0006-42101-1 California Turlock 500 LOCAL ST OR HY 

09-003-1003-42101-1 Connecticut East Hartford 800 LOCAL ST OR HY 

10-003-1008-42101-1 Delaware Not in a city NR NR 

10-003-2004-42101-1 Delaware Wilmington 28,046 MAJ ST OR HY 

15-003-0010-42101-1 Hawaii Ewa Beach NR NR 

18-063-0002-42101-1 Indiana Pittsboro 500 LOCAL ST OR HY 

25-025-0002-42101-1 Massachusetts Boston 35,000 MAJ ST OR HY 

29-077-0032-42101-1 Missouri Springfield 1,000 LOCAL ST OR HY 

29-189-0004-42101-1 Missouri Sunset Hills 33,300 MAJ ST OR HY 

30-013-0001-42101-1 Montana Great Falls 26,155 MAJ ST OR HY 

31-109-0018-42101-1 Nebraska Lincoln NR NR 

34-023-2003-42101-1 New Jersey Perth Amboy 14,000 LOCAL ST OR HY 

34-025-2001-42101-1 New Jersey Freehold NR NR 

34-027-0003-42101-1 New Jersey Morristown NR NR 

36-001-0012-42101-1 New York Albany 12,000 MAJ ST OR HY 

36-029-0005-42101-1 New York Buffalo 26,000 ARTERIAL 

36-055-1007-42101-1 New York Rochester NR NR 

36-067-0017-42101-1 New York Syracuse NR NR 

36-081-0124-42101-1 New York New York 10,000 EXPRESSWAY 

36-093-0003-42101-1 New York Schenectady 37,000 EXPRESSWAY 

36-103-0009-42101-2 New York Holtsville 10,000 THRU ST OR HY 

48-479-0016-42101-1 Texas Laredo 16,180 MAJ ST OR HY 

49-057-0006-42101-1 Utah Ogden 38,000 ARTERIAL 

51-013-0020-42101-1 Virginia Not in a city 6,000 MAJ ST OR HY 

51-059-1005-42101-1 Virginia Annandale 24,000 MAJ ST OR HY 

51-059-5001-42101-1 Virginia McLean 36,845 MAJ ST OR HY 

51-510-0009-42101-1 Virginia Alexandria 3,974 LOCAL ST OR HY 

56-039-1012-42101-1 Wyoming Not in a city NR NR 

“NR” denotes that the value was not reported. 

January 2010 A-17 
Table A-8. Numbers of high LOD and trace-level monitors in each state that met completeness 

criteria for 2005-2007. 

State Number of high LOD monitors Number of trace-level monitors 

Alabama 2 0 

Alaska 4 0 

Arizona 9 0 

Arkansas 0 0 

California 65 0 

Colorado 9 0 

Connecticut 2 0 

Delaware 2 0 

District of Columbia 2 0 

Florida 18 0 

Georgia 3 0 

Hawaii 1 0 

Idaho 0 0 

Illinois 8 0 

Indiana 6 0 

Iowa 0 0 

Kansas 2 0 

Kentucky 2 0 

Louisiana 0 1 

Maine 0 1 

Maryland 1 0 

Massachusetts 4 1 

Michigan 0 0 

Minnesota 7 0 

Mississippi 0 0 

Missouri 3 0 

Montana 4 0 

Nebraska 2 0 

Nevada 12 0 

New Hampshire 2 0 

New Jersey 9 0 

New Mexico 7 0 

New York 9 0 

North Carolina 2 1 

North Dakota 0 0 

Ohio 14 0 

Oklahoma 4 0 

Oregon 3 1 

Pennsylvania 19 0 

Puerto Rico 1 0 

January 2010 A-18 
State Number of high LOD monitors Number of trace-level monitors 

Rhode Island 1 0 

South Carolina 3 1 

South Dakota 0 0 

Tennessee 3 0 

Texas 19 2 

Utah 2 0 

Vermont 2 0 

Virginia 9 0 

Washington 2 0 

West Virginia 3 0 

Wisconsin 0 0 

Wyoming 1 0 

January 2010 A-19 
Figure A-8. Map of CO monitor locations with respect to population density in the Anchorage 

CBSA, total population. 

Figure A-9. Map of CO monitor locations with respect to population density in the Anchorage 

CBSA, ages 65 yr and older. 

January 2010 A-20 
Figure A-10. Map of CO monitor locations with respect to population density in the Atlanta 

CSA, total population. 

Figure A-11. Map of CO monitor locations with respect to population density in the Atlanta 

CSA, ages 65 yr and older. 

January 2010 A-21 
Figure A-12. Map of CO monitor locations with respect to population density in the Boston 

CSA, total population. 

Figure A-13. Map of CO monitor locations with respect to population density in the Boston 

CSA, ages 65 yr and older. 

January 2010 A-22 
Figure A-14. Map of CO monitor locations with respect to population density in the Houston 

CSA, total population. 

Figure A-15. Map of CO monitor locations with respect to population density in the Houston 

CSA, ages 65 yr and older. 

January 2010 A-23 
Figure A-16. Map of CO monitor locations with respect to population density in the New York 

City CSA, total population. 

Figure A-17. Map of CO monitor locations with respect to population density in the New York 

City CSA, ages 65 yr and older. 

January 2010 A-24 
Figure A-18. Map of CO monitor locations with respect to population density in the Phoenix 

CSA, total population. 

Figure A-19. Map of CO monitor locations with respect to population density in the Phoenix 

CSA, ages 65 yr and older. 

January 2010 A-25 
Figure A-20. Map of CO monitor locations with respect to population density in the Pittsburgh 

CSA, total population. 

Figure A-21. Map of CO monitor locations with respect to population density in the Pittsburgh 

CSA, ages 65 yr and older. 

January 2010 A-26 
Figure A-22. Map of CO monitor locations with respect to population density in the Seattle 

CSA, total population. 

Figure A-23. Map of CO monitor locations with respect to population density in the Seattle 

CSA, ages 65 yr and older. 

January 2010 A-27 
Figure A-24. Map of CO monitor locations with respect to population density in the St. Louis 

CSA, total population. 

Figure A-25. Map of CO monitor locations with respect to population density in the St. Louis 

CSA, ages 65 yr and older. 

January 2010 A-28 
Figure A-26. Map of CO monitor locations with AQS Site IDs for Anchorage, AK. 

January 2010 A-29 
Table A-9. 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. 

 Neighborhood 

 A B 

A 1.00 0.73 

 0.0 1.1 

 0.00 0.32 

 0 9.0 

B Legend 1.00 

 r 0.0 

 P90 0.00 

Neighborhood 

 COD 0 

 d 

 

January 2010 A-30 
A B 

Site ID 02-020-0018 02-020-0048 

Mean 1.04 1.10 

SD 0.94 1.04 

Obs 12969 12703 

Figure A-27. 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. 

January 2010 A-31 
Figure A-28. Map of CO monitor locations with AQS Site IDs for Atlanta, GA. 

January 2010 A-32 
Table A-10. 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. 

 Micro Urban 

 A B C 

A 1.00 0.60 0.10 

 0.0 0.5 0.7 

Micro 

 0.00 0.27 0.38 

 0 22.5 61.7 

B 1.00 0.12 

 0.0 0.7 

 0.00 0.37 

 0 74.7 

C Legend 1.00 

 r 0.0 

 P90 0.00 

Urban 

 COD 0 

d 

 

January 2010 A-33 
1 

A B C 

Site ID 13-121- 0099 

13-089- 0002 13-223- 0003 

Mean 0.58 0.53 0.30 

SD 0.30 0.35 0.13 

Obs 25440 25531 25712 concentration (ppm)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

season

1234 1234 1234

Figure A-29. 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. 

January 2010 A-34 
Figure A-30. Map of CO monitor locations with AQS Site IDs for Boston, MA. 

January 2010 A-35 
Table A-11. 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. 

 Micro Neighborhood Urban Null 

 A B C D E F G 

A 1.00 0.50 0.38 0.49 0.43 0.46 0.35 

 0.0 0.6 0.6 0.5 0.6 0.5 0.7 

Micro 

 0.00 0.44 0.46 0.30 0.39 0.25 0.60 

 0 18.3 57.5 26.1 102.6 61.5 55.1 

B 1.00 0.50 0.41 0.40 0.49 0.35 

 0.0 0.4 0.4 0.4 0.5 0.4 

 0.00 0.48 0.41 0.40 0.42 0.58 

 0 39.7 41.3 89.1 57.9 37.2 

C 1.00 0.26 0.36 0.37 0.52 

 0.0 0.5 0.4 0.5 0.4 

 0.00 0.45 0.47 0.45 0.56 

 0 80.7 58.7 58.9 2.5 

D 1.00 0.29 0.40 0.27 

 0.0 0.4 0.4 0.5 

 0.00 0.37 0.28 0.58 

 Legend 0 128.6 85.8 78.2 

E r 1.00 0.34 0.34 

 P90 0.0 0.5 0.4 

 COD 0.00 0.39 0.55 

Neighborhood 

 d 0 58.9 60.2 

F 1.00 0.34 

 0.0 0.6 

Urban 

 0.00 0.59 

 0 58.0 

G 1.00 

 0.0 

 0.00 

Null 

 0 

January 2010 A-36 
A B C D E F G 

Site ID 33-011- 1009 

25-017- 0007 25-025- 0042 33-011- 0020 44-007- 1010 25-027- 0023 25-025- 0002 

Mean 0.60 0.33 0.36 0.45 0.34 0.53 0.26 

SD 0.37 0.22 0.26 0.27 0.22 0.23 0.24 

Obs 25869 24362 24260 25197 23707 24446 24134 concentration (ppm)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

season

1234 1234 1234 1234 1234 1234 1234

Figure A-31. 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. 

 

January 2010 A-37 
Figure A-32. Map of CO monitor locations with AQS Site IDs for Houston, TX. 

January 2010 A-38 
Table A-12. 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. 

 Micro Neighborhood 

 A B C D E 

 1.00 0.45 0.56 0.53 0.43 

A 0.0 0.4 0.4 0.5 0.4 

 0.00 0.47 0.47 0.74 0.47 

Micro 

 0.0 16.7 16.3 9.3 23.5 

 1.00 0.72 0.56 0.68 

B 0.0 0.3 0.5 0.3 

 0.00 0.29 0.73 0.24 

 0.0 17.5 19.8 32.2 

 1.00 0.65 0.63 

C 0.0 0.5 0.4 

 0.00 0.73 0.29 

 0.0 25.2 39.7 

 1.00 0.57 

D 0.0 0.4 

 0.00 0.72 

 Legend 0.0 14.5 

 r 1.00 

E P90 0.0 

 COD 0.00 

Neighborhood 

 d 0.0 

January 2010 A-39 
1 

A B C D E 

Site ID 48-201- 0075 

48-201- 0024 48-201- 0047 48-201- 1035 48-201- 1039 

Mean 0.35 0.42 0.39 0.14 0.33 

SD 0.26 0.27 0.33 0.22 0.16 

Obs 24922 23997 25241 25285 24480 concentration (ppm)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

season

1234 1234 1234 1234 1234

Figure A-33. 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. 

 

January 2010 A-40 
Figure A-34. Map of CO monitor locations with AQS Site IDs for New York City, NY. 

January 2010 A-41 
Table A-13. 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. 

 Micro Middle Neighborhood Null 

 A B C D E F G H I 

A 1.00 0.65 0.52 0.64 0.54 0.32 0.48 0.43 0.31 

 0.0 0.7 0.7 0.8 0.9 0.9 0.9 0.9 1.3 

Micro 

 0.00 0.28 0.24 0.29 0.35 0.34 0.34 0.35 0.81 

 0 15.9 8.9 16.8 29.9 55.0 35.7 20.5 85.5 

B 1.00 0.56 0.58 0.55 0.40 0.56 0.41 0.30 

 0.0 0.4 0.4 0.4 0.4 0.4 0.5 0.8 

 0.00 0.23 0.22 0.25 0.25 0.24 0.28 0.75 

 0 10.5 7.0 45.8 70.6 43.7 17.8 76.5 

C 1.00 0.54 0.41 0.33 0.41 0.46 0.29 

 0.0 0.4 0.4 0.4 0.4 0.4 0.7 

 0.00 0.23 0.28 0.25 0.26 0.26 0.77 

Middle 0 15.0 37.5 61.0 43.6 12.3 76.8 

D 1.00 0.55 0.35 0.54 0.59 0.49 

 0.0 0.4 0.5 0.4 0.4 0.7 

 0.00 0.23 0.26 0.23 0.23 0.74 

Neighborhood 

 0 45.4 71.5 38.1 24.5 82.9 

E 1.00 0.50 0.57 0.46 0.33 

 0.0 0.4 0.4 0.4 0.7 

 0.00 0.24 0.23 0.27 0.72 

 0 27.5 36.7 45.1 107.8 

F 1.00 0.47 0.33 0.32 

 0.0 0.4 0.4 0.6 

 0.00 0.23 0.27 0.73 

 0 61.9 65.0 120.3 

G Legend 1.00 0.34 0.31 

 r 0.0 0.4 0.7 

 P90 0.00 0.27 0.72 

 COD 0 55.8 119.7 

H d 1.00 0.43 

 0.0 0.6 

 0.00 0.73 

 0 65.1 

I 1.00 

 0.0

 0.00 

Null 0

 

January 2010 A-42 
A B C D E F G H I 

Site ID 34-017- 1002 

34-003- 0004 36-061- 0056 34-003- 5001 34-023- 2003 34-025- 2001 34-027- 0003 36-081- 0124 36-103- 0009 

Mean 0.85 0.55 0.62 0.52 0.48 0.50 0.49 0.47 0.12 

SD 0.43 0.27 0.21 0.30 0.27 0.24 0.25 0.23 0.17 

Obs 25646 23113 25547 25150 25028 25727 25691 25022 25749 concentration (ppm)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0

season

1234 1234 1234 1234 1234 1234 1234 1234 1234

Figure A-35. 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 xaxis. 

January 2010 A-43 
Figure A-36. Map of CO monitor locations with AQS Site IDs for Phoenix, AZ. 

January 2010 A-44 
Table A-14. 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 

Phoenix, AZ. 

 Micro Middle Neighborhood Null 

 A B C D E 

A 1.00 0.86 0.89 0.80 0.84 

 0.0 0.8 0.7 1.1 0.9 

Micro 

 0.00 0.39 0.37 0.43 0.37 

 0.0 3.9 1.6 8.9 3.5 

B 1.00 0.88 0.81 0.83 

 0.0 0.6 0.7 0.6 

Middle 

 0.00 0.34 0.41 0.33 

 0.0 3.4 6.6 5.2 

C 1.00 0.81 0.89 

 0.0 0.9 0.7 

 0.00 0.38 0.24 

 Legend 0.0 9.4 4.9 

D r 1.00 0.85 

 P90 0.0 0.6 

 COD 0.00 0.36 

Neighborhood 

 d 0.0 6.8 

E 1.00 

 0.0 

 0.00 

Null 

 0.0 

January 2010 A-45 
A B C D E 

Site ID 04-013- 0016 

04-013- 3010 04-013- 0019 04-013- 3002 04-013- 9997 

Mean 0.93 0.76 0.84 0.58 0.79 

SD 0.95 0.72 0.88 0.64 0.64 

Obs 25382 25414 25589 25657 25435 concentration (ppm)

0

1

2

3

4

5

season

1234 1234 1234 1234 1234

Figure A-37. Box plots illustrating the seasonal distribution of hourly CO concentrations in 

Phoenix, AZ. Note: 1 = winter, 2 = spring, 3 = summer, and 4 = fall on the x-axis. 

January 2010 A-46 
Figure A-38. Map of CO monitor locations with AQS Site IDs for Pittsburgh, PA. 

January 2010 A-47 
Table A-15. 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 

Pittsburgh, PA. 

 Middle Neighborhood Urban 

 A B C D E F G 

A 1.00 0.25 0.39 0.73 0.20 0.30 0.43 

 0.0 0.7 0.6 0.4 0.7 0.8 0.6 

Middle 

 0.00 0.65 0.51 0.39 0.56 0.88 0.68 

 0 33.3 68.2 0.7 43.4 44.1 1.8 

B 1.00 0.26 0.29 0.09 0.09 0.42 

 0.0 0.5 0.5 0.6 0.5 0.5 

 0.00 0.68 0.62 0.69 0.90 0.73 

 0 101.0 33.6 75.0 37.8 34.4 

C 1.00 0.42 0.16 0.21 0.11 

 0.0 0.4 0.6 0.6 0.6 

 0.00 0.51 0.57 0.87 0.72 

 0 68.0 27.5 104.1 66.8 

D 1.00 0.30 0.35 0.52 

 0.0 0.5 0.5 0.5 

 0.00 0.54 0.86 0.69 

Neighborhood 

 0 43.4 43.7 2.2 

E 1.00 0.02 0.05 

 0.0 0.7 0.7 

 0.00 0.87 0.74 

 0 84.1 41.9 

F 1.00 0.18 

 0.0 0.7 

 0.00 0.88 

 Legend 0 45.8 

G r 1.00 

 P90 0.0 

 COD 0.00 

Urban 

 d 0 

January 2010 A-48 
A B C D E F G 

SiteID 42-003- 0038 

42-125- 0005 42-073- 0015 42-003- 0031 42-007- 0014 42-129- 0008 42-003- 0010 

Mean 0.47 0.21 0.32 0.32 0.28 0.07 0.28 

SD 0.33 0.23 0.26 0.26 0.27 0.15 0.32 

Obs 25818 25319 25745 25936 25500 25785 25655 concentration (ppm)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

season

1234 1234 1234 1234 1234 1234 1234

Figure A-39. Box plots illustrating the seasonal distribution of hourly CO concentrations in 

Pittsburgh, PA. Note: 1 = winter, 2 = spring, 3 = summer, and 4 = fall on the x-axis. 

January 2010 A-49 
Figure A-40. Map of CO monitor locations with AQS Site IDs for Seattle, WA. 

January 2010 A-50 
A 

Site ID 53-033-0019 

Mean 0.75 

SD 0.49 

Obs 25818 

Figure A-41. 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. 

January 2010 A-51 
Figure A-42. Map of CO monitor locations with AQS Site IDs for St. Louis, MO. 

January 2010 A-52 
Table A-16. 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. 

 Neighborhood Null 

 A B C 

A 1.00 0.60 0.19 

 0.0 0.3 0.5 

 0.00 0.24 0.40 

 0 9.5 21.2 

B 1.00 0.19 

 0.0 0.5 

 0.00 0.42 

Neighborhood 

 0 19.8 

C Legend 1.00 

 r 0.0 

 P90 0.00 

Null 

 COD 0 

 d 

 

January 2010 A-53 
A B C 

Site ID 17-163- 0010 

29-510- 0086 29-189-0004

Mean 0.44 0.42 0.43 

SD 0.25 0.29 0.25 

Obs 25325 25938 25879 concentration (ppm)

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

season

1234 1234 1234

Figure A-43. 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. 

January 2010 A-54 
Table A-17. Comparison of distributional data at different monitoring scales for hourly, 1-h daily 

max, 24-h avg, and 8-h daily max data for Atlanta, GA. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Microscale 25,440 0.6 0.0 0.2 0.2 0.3 0.4 0.4 0.5 0.7 0.7 1.0 1.2 

Urban Scale 51,243 0.4 0.0 0.0 0.1 0.2 0.3 0.3 0.3 0.4 0.5 0.7 1.0 

1-H DAILY MAX 

Microscale 1,075 1.0 0.2 0.3 0.4 0.6 0.7 0.8 1.0 1.2 1.2 1.6 1.9 

Urban Scale 2,154 0.7 0.0 0.2 0.2 0.3 0.3 0.4 0.5 0.8 0.9 1.3 1.5 

1-H DAILY AVG 

Microscale 1,075 0.6 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.7 0.8 1.0 

Urban Scale 2,154 0.4 0.0 0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.7 0.9 

8-H DAILY MAX 

Microscale 1,075 0.8 0.3 0.3 0.3 0.4 0.5 0.6 0.7 0.9 0.9 1.2 1.3 

Urban Scale 2,154 0.5 0.0 0.1 0.2 0.3 0.3 0.3 0.4 0.6 0.7 1.0 1.3 

 

Table A-18. Comparison of distributional data at different monitoring scales for hourly, 1-h daily 

max, 24-h avg, and 8-h daily max data for Boston, MA. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Microscale 25,869 0.6 0.0 0.1 0.2 0.3 0.4 0.4 0.5 0.7 0.7 1.0 1.2 

Neighborhood Scale 97,526 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.3 0.4 0.5 0.6 0.8 

Urban Scale 24,446 0.5 0.0 0.1 0.3 0.3 0.4 0.4 0.5 0.6 0.6 0.8 0.9 

1-H DAILY MAX 

Microscale 1,080 1.2 0.2 0.4 0.5 0.6 0.7 0.8 0.9 1.2 1.4 2.0 2.5 

Neighborhood Scale 4,212 0.6 0.0 0.1 0.2 0.3 0.4 0.4 0.5 0.7 0.7 1.1 1.4 

Urban Scale 1,086 0.8 0.0 0.3 0.4 0.5 0.6 0.6 0.8 0.9 1.0 1.2 1.4 

1-H DAILY AVG 

Microscale 1,080 0.6 0.1 0.2 0.3 0.3 0.4 0.5 0.6 0.7 0.7 0.9 1.1 

Neighborhood Scale 4,212 0.4 0.0 0.0 0.1 0.1 0.2 0.3 0.4 0.4 0.5 0.6 0.7 

Urban Scale 1,086 0.5 0.0 0.1 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.8 

8-H DAILY MAX 

Microscale 1,080 0.8 0.3 0.3 0.3 0.4 0.6 0.6 0.7 0.9 1.0 1.4 1.7 

Neighborhood Scale 4,212 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.6 0.6 0.8 1.0 

Urban Scale 1,086 0.7 0.3 0.3 0.3 0.3 0.5 0.5 0.6 0.8 0.8 1.0 1.1 

 

January 2010 A-55 
Table A-19. Comparison of distributional data at different monitoring scales for hourly, 1-h daily 

max, 24-h avg, and 8-h daily max data for Denver, CO. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Microscale 77,070 0.5 0.0 0.0 0.1 0.1 0.3 0.3 0.4 0.6 0.7 1.0 1.3 

Neighborhood Scale 51,968 0.5 0.0 0.0 0.1 0.2 0.3 0.3 0.4 0.6 0.6 1.0 1.3 

1-H DAILY MAX 

Microscale 3,190 1.2 0.1 0.3 0.4 0.5 0.7 0.8 1.0 1.4 1.5 2.2 2.7 

Neighborhood Scale 2,173 1.1 0.1 0.2 0.3 0.4 0.6 0.6 0.9 1.3 1.5 2.1 2.6 

1-H DAILY AVG 

Microscale 3,190 0.5 0.0 0.1 0.2 0.2 0.3 0.4 0.5 0.6 0.6 0.9 1.0 

Neighborhood Scale 2,173 0.5 0.0 0.1 0.2 0.3 0.3 0.4 0.5 0.6 0.6 0.9 1.1 

8-H DAILY MAX 

Microscale 3,190 0.8 0.3 0.3 0.3 0.4 0.5 0.5 0.7 0.9 1.0 1.4 1.8 

Neighborhood Scale 2,173 0.8 0.3 0.3 0.3 0.3 0.4 0.5 0.7 0.9 1.0 1.5 1.8 

 

Table A-20. Comparison of distributional data at different monitoring scales for hourly, 1-h daily 

max, 24-h avg, and 8-h daily max data for Houston, TX. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Microscale 24,922 0.3 0.0 0.0 0.0 0.0 0.2 0.2 0.3 0.4 0.5 0.6 0.8 

Neighborhood Scale 99,003 0.3 0.0 0.0 0.0 0.0 0.2 0.2 0.3 0.4 0.4 0.6 0.8 

1-H DAILY MAX 

Microscale 1,043 0.7 0.0 0.0 0.2 0.3 0.4 0.5 0.6 0.8 0.9 1.2 1.4 

Neighborhood Scale 4,145 0.7 0.0 0.0 0.1 0.2 0.4 0.4 0.5 0.8 0.8 1.3 1.7 

1-H DAILY AVG 

Microscale 1,043 0.3 0.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.5 0.6 0.6 

Neighborhood Scale 4,145 0.3 0.0 0.0 0.0 0.1 0.2 0.2 0.3 0.4 0.4 0.5 0.6 

8-H DAILY MAX 

Microscale 1,043 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.6 0.6 0.8 1.0 

Neighborhood Scale 4,145 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.5 0.6 0.9 1.1 

 

January 2010 A-56 
Table A-21. Comparison of distributional data at different monitoring scales for hourly, 1-h daily 

max, 24-h avg, and 8-h daily max data for Los Angeles, CA. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Microscale 24,885 0.7 0.0 0.2 0.3 0.3 0.4 0.4 0.5 0.7 0.8 1.2 1.6 

Middle Scale 98,564 0.5 0.0 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.7 1.1 1.6 

Neighborhood Scale 49,757 0.3 0.0 0.0 0.0 0.0 0.1 0.1 0.2 0.3 0.3 0.6 0.8 

Urban Scale 24,264 0.4 0.0 0.0 0.1 0.1 0.1 0.2 0.3 0.4 0.5 1.0 1.4 

1-H DAILY MAX 

Microscale 1,080 1.3 0.2 0.4 0.5 0.6 0.8 0.8 1.1 1.6 1.7 2.3 2.7 

Middle Scale 4,299 1.2 0.0 0.1 0.1 0.2 0.5 0.6 0.9 1.3 1.5 2.5 3.7 

Neighborhood Scale 2,164 0.7 0.0 0.0 0.0 0.1 0.3 0.3 0.5 0.8 0.9 1.3 1.7 

Urban Scale 1,053 1.0 0.0 0.1 0.2 0.3 0.4 0.4 0.7 1.3 1.5 2.2 2.6 

1-H DAILY AVG 

Microscale 1,080 0.7 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.7 0.8 1.1 1.2 

Middle Scale 4,299 0.5 0.0 0.0 0.0 0.1 0.2 0.2 0.4 0.6 0.7 1.1 1.5 

Neighborhood Scale 2,164 0.3 0.0 0.0 0.0 0.0 0.1 0.2 0.2 0.3 0.4 0.5 0.6 

Urban Scale 1,053 0.4 0.0 0.0 0.1 0.1 0.2 0.2 0.3 0.5 0.6 0.9 1.1 

8-H DAILY MAX 

Microscale 1,080 0.9 0.3 0.3 0.4 0.4 0.6 0.6 0.8 1.1 1.2 1.6 1.8 

Middle Scale 4,299 0.8 0.3 0.3 0.3 0.3 0.3 0.3 0.6 0.9 1.0 1.8 2.4 

Neighborhood Scale 2,164 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.6 0.9 1.2 

Urban Scale 1,053 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.8 0.9 1.5 1.8 

 

January 2010 A-57 
Table A-22. Comparison of distributional data at different monitoring scales for hourly, 1-h daily 

max, 24-h avg, and 8-h daily max data for New York City, NY. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Microscale 25,646 0.8 0.0 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.1 1.4 1.6 

Middle Scale 48,660 0.6 0.0 0.1 0.3 0.3 0.4 0.5 0.6 0.7 0.7 0.9 1.0 

Neighborhood Scale 25,150 0.5 0.0 0.2 0.2 0.3 0.3 0.4 0.4 0.6 0.6 0.9 1.1 

1-H DAILY MAX 

Microscale 1,077 1.4 0.3 0.4 0.6 0.8 1.0 1.1 1.4 1.7 1.8 2.1 2.4 

Middle Scale 2,053 0.9 0.2 0.4 0.5 0.6 0.7 0.7 0.8 1.0 1.1 1.3 1.5 

Neighborhood Scale 1,053 0.9 0.2 0.3 0.4 0.4 0.6 0.6 0.8 1.0 1.1 1.5 1.9 

1-H DAILY AVG 

Microscale 1,077 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8 1.0 1.0 1.3 1.4 

Middle Scale 2,053 0.6 0.0 0.2 0.3 0.4 0.5 0.5 0.6 0.7 0.7 0.8 0.9 

Neighborhood Scale 1,053 0.5 0.1 0.2 0.3 0.3 0.4 0.4 0.5 0.6 0.6 0.8 1.0 

8-H DAILY MAX 

Microscale 1,077 1.2 0.3 0.4 0.6 0.7 0.9 0.9 1.1 1.4 1.4 1.7 1.9 

Middle Scale 2,053 0.7 0.3 0.3 0.4 0.4 0.6 0.6 0.7 0.8 0.9 1.0 1.2 

Neighborhood Scale 1,053 0.7 0.3 0.3 0.3 0.3 0.4 0.5 0.6 0.8 0.8 1.2 1.5 

 

January 2010 A-58 
Table A-23. Comparison of distributional data at different monitoring scales for hourly, 1-h daily 

max, 24-h avg, and 8-h daily max data for Phoenix, AZ. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Microscale 25,382 0.9 0.0 0.0 0.1 0.1 0.3 0.3 0.6 1.1 1.3 2.3 3.0 

Middle Scale 25,414 0.8 0.0 0.0 0.1 0.1 0.3 0.3 0.5 0.9 1.0 1.8 2.3 

Neighborhood Scale 51,246 0.7 0.0 0.0 0.1 0.1 0.2 0.3 0.4 0.7 0.8 1.8 2.4 

1-H DAILY MAX 

Microscale 1,063 2.2 0.0 0.2 0.5 0.7 1.1 1.2 1.9 2.8 3.1 4.2 4.7 

Middle Scale 1,066 1.8 0.1 0.3 0.5 0.7 1.0 1.1 1.6 2.2 2.4 3.2 3.8 

Neighborhood Scale 2,156 1.8 0.1 0.2 0.4 0.5 0.8 0.9 1.5 2.3 2.6 3.6 4.2 

1-H DAILY AVG 

Microscale 1,063 0.9 0.0 0.0 0.2 0.2 0.4 0.4 0.7 1.2 1.3 2.0 2.3 

Middle Scale 1,066 0.8 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.9 1.0 1.5 1.7 

Neighborhood Scale 2,156 0.7 0.0 0.1 0.2 0.2 0.3 0.4 0.5 0.9 0.9 1.5 1.8 

8-H DAILY MAX 

Microscale 1,063 1.5 0.3 0.3 0.3 0.4 0.6 0.7 1.2 2.0 2.2 3.1 3.5 

Middle Scale 1,066 1.2 0.3 0.3 0.3 0.4 0.7 0.7 1.0 1.5 1.7 2.3 2.7 

Neighborhood Scale 2,156 1.2 0.3 0.3 0.3 0.3 0.5 0.6 0.9 1.5 1.7 2.5 3.0 

 

January 2010 A-59 
Table A-24. Comparison of distributional data at different monitoring scales for hourly, 1-h daily 

max, 24-h avg, and 8-h daily max data for Pittsburgh, PA. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Middle Scale 25,818 0.5 0.0 0.0 0.1 0.1 0.3 0.3 0.4 0.5 0.6 0.8 1.1 

Neighborhood Scale 77,000 0.3 0.0 0.0 0.0 0.0 0.1 0.1 0.2 0.3 0.4 0.6 0.8 

Urban Scale 76,940 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.3 0.6 0.8 

1-H DAILY MAX 

Middle Scale 1,079 0.9 0.0 0.2 0.4 0.4 0.6 0.6 0.8 1.1 1.1 1.6 1.9 

Neighborhood Scale 3,210 0.6 0.0 0.0 0.1 0.2 0.3 0.3 0.5 0.7 0.7 1.1 1.3 

Urban Scale 3,208 0.4 0.0 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.7 1.0 1.2 

1-H DAILY AVG 

Middle Scale 1,079 0.5 0.0 0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.6 0.8 0.9 

Neighborhood Scale 3,210 0.3 0.0 0.0 0.0 0.0 0.1 0.2 0.3 0.3 0.4 0.6 0.7 

Urban Scale 3,208 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.3 0.6 0.7 

8-H DAILY MAX 

Middle Scale 1,079 0.7 0.3 0.3 0.3 0.3 0.4 0.4 0.6 0.7 0.8 1.1 1.3 

Neighborhood Scale 3,210 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.8 1.0 

Urban Scale 3,208 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.5 0.8 1.0 

 

Table A-25. Comparison of distributional data for hourly, 1-h daily max, 24-h avg, and 8-h daily max 

data for Seattle, WA. Microscale was the only scale at which monitoring was 

performed in Seattle, WA. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Microscale 25,818 0.8 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.9 0.9 1.3 1.6 

1-H DAILY MAX 

Microscale 1,079 1.5 0.2 0.4 0.5 0.7 0.9 1.0 1.3 1.7 1.8 2.4 2.9 

1-H DAILY AVG 

Microscale 1,079 0.8 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.9 0.9 1.2 1.4 

8-H DAILY MAX 

Microscale 1,079 1.1 0.3 0.3 0.4 0.5 0.7 0.8 1.0 1.3 1.4 1.8 2.2 

 

January 2010 A-60 
Table A-26. Comparison of distributional data for hourly, 1-h daily max, 24-h avg, and 8-h daily max 

data for St. Louis, MO. Neighborhood scale was the only scale at which monitoring 

was performed in St. Louis, MO. 

PERCENTILES 

Time Scale N Mean Min 1 5 10 25 50 75 90 95 99 max 

ALL HOURLY 

Neighborhood Scale 51,263 0.4 0.0 0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.8 

1-H DAILY MAX 

Neighborhood Scale 2,138 0.8 0.1 0.2 0.3 0.4 0.5 0.5 0.6 0.9 1.0 1.5 2.0 

1-H DAILY AVG 

Neighborhood Scale 2,138 0.4 0.0 0.1 0.2 0.3 0.3 0.3 0.4 0.5 0.5 0.6 0.7 

8-H DAILY MAX 

Neighborhood Scale 2,138 0.6 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.6 0.7 1.0 1.3 

 

January 2010 A-61 
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 Anchorage, AK. 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. Refer the numbers in this caption to those on the yaxis of each seasonal plot. Note that the data are not obtained for Anchorage 

during the summer, and so are not presented here. 

January 2010 A-62 
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 Atlanta, GA. 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. Refer the numbers in this caption to those on the yaxis of each seasonal plot. 

January 2010 A-63 
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 Boston, MA. 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. Refer the numbers in this caption to those on the 

y-axis of each seasonal plot. 

January 2010 A-64 
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 New York City, NY. 

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. Refer the numbers in this caption to those 

on the y-axis of each seasonal plot. 

January 2010 A-65 
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 Phoenix, AZ. 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. Refer the numbers in this caption to those on the 

y-axis of each seasonal plot. 

January 2010 A-66 
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 Seattle, WA. 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. Refer the numbers in this caption to those on the 

y-axis of each seasonal plot. 

January 2010 A-67 
Figure A-50. Cross-correlation functions for each season combined across sites where CO 

and O3 monitors were co-located in Atlanta, Boston, Denver, Los Angeles, New 

York City, and Phoenix. 

January 2010 A-68 
Annex B. Dosimetry Studies 

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

Reference Purpose Findings 

Aberg et al. (2009, 194082) To investigate CO concentrations in blood donors in 

Sweden. 

The mean CO concentration in blood donors was 84.5 μmol/L. 

Concentrations over 130 μmol/L were found in 6% of blood, and the 

highest concentration was 561 μmol/L . By using a calculation, 23% of 

banked blood bags could exceed 1.5% COHb, with a highest fraction of 

7.2% COHb. 

Abram et al. (2007, 193859) To present the Quantitative Circulatory Physiology 

(QCP) model as a teaching module in the practice of 

medicine. 

QCP is a dynamic mathematical model based on published models and 

parameters of biological interactions. 

Alcantara et al. (2007, 193867) 

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, 001026) 

To determine if low concentrations of CO would affect 

exercise performance and myocardial perfusion in 

young healthy men. 

Men with COHb levels between 4 and 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. 

Anderson et al. (2000, 011836) 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. 

Arora et al. (2001, 186713) To evaluate the effect of multiple transfusion recipient 

thalassemics on pulmonary function. 

DLCO was decreased in all the patients with restrictive lung disease and 

fall in DLCO showed a good correlation with the severity of restrictive 

disease. Thalassemics had a decrease in lung volume and a 

proportional decrease in flow rate. 

Benignus et al. (2006, 151344) To adapt and use a human model for toluene uptake 

and elimination including a brain compartment. 

The QCP 2004 model was used to construct simulations of scenarios of 

toxicant exposure and human activities. QCP accurately predicted 

toluene blood concentrations from inhaled exposure. 

Bos et al. (2006, 194084) To use a PBPK model to set AEGL for methylene 

chloride. 

This model adequately predicted COHb levels formed by various 

methylene chloride concentrations, specifically in nonconjugators 

lacking the GSTT-1 enzyme, and proposed AEGL values. 

Bruce and Bruce (2003, 193975) 

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, 193980) 

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 nonmuscle 

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 and Bruce (2008, 193977) 

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 intratissue pathways 

and diffusion of blood gases into tissues from precapillary vessels like arterioles. 

The former model of Bruce and Bruce (2006, 193980) 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 nonmuscle tissue. CO uptake from blood 

by muscle is much slower than O2, thus COHb% will fall rapidly while 

COMb% could remain high. 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 B-1 
Reference Purpose Findings 

Carraway et al. (2000, 021096) 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. 

Castillo et al. (2006, 193234) 

To describe a new method for measurement of CO 

DLCO and VA in sleeping infants (6-22 mo old), using a 

single 4-s 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. 

Chakraborty et al. (2004, 193759) 

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. 

Cronenberger et al. (2008, 

194085) 

To develop a population-based model to describe and 

predict the pharmacokinetics of COHb in adult 

smokers. 

This two-compartment model included zero-order input and first-order 

elimination and required a compartment for extravascular binding of CO 

to accurately predict COHb formation during multiple short and rapid 

inhalations, followed by a period of no exposure, as occurs in smoking. 

Smokers' COHb ranged from 0.8 to 11.1%. 

Cronje et al. (2004, 180440) 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 concentration dissociate 

during CO inhalation, but CO concentration does not follow blood CO 

concentration or 1/pO2 as in the Warburg theory during intake or 

elimination. Tissue CO concentration 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. 

De las Heras et al. (2003, 

194087) 

To assess production of CO (venous COHb measured 

by CO-oximeter and exhaled CO) in patients with 

cirrhosis with and without spontaneous bacterial 

peritonitis. 

Patients with SBP had higher CO production than noninfected cirrhotic 

patients and both groups of patients had higher CO production 

compared to healthy controls. CO production decreased slowly after 

resolution of the disease. 

Dutton et al. (2001, 021307) 

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 averaged 0.36 ppm over 4 h. PAH 4-h 

time avg reached 35 ng/m3. 

Ehlers et al. (2009, 194089) To determine the level of COHb found in banked blood 

in the Albany, NY region. 

The avg COHb level was 0.78%. The highest recorded COHb level was 

12%, and 10.3% of packed red blood cell units had levels of 1.5% 

COHb or higher. 

Gosselin et al. (2009, 190946) 

To develop a variant of the CFK model that links COHb 

levels in humans to ambient CO levels under various 

environmental or occupational exposure conditions. 

The model adds alveoli-blood and blood-tissue CO exchanges and 

mass conservation of CO at all times to the CFK equation. The model 

better predicted COHb formation over a wide range of CO levels and 

scenarios with linear regression analysis of predicted vs observed 

values generating a slope of 0.996 (95% CI: 0.986-1.001) compared to 

0.917 (95% CI: 0.906-0.927) using the CFK model 

Hampson and Weaver (2007, 

190272) 

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%. 

Hart et al. (2006, 194092) To investigate the relationship between COHb and 

smoking habit and mortality. 

COHb was related to self-reported smoking in a dose-dependent 

manner. COHb was positively associated with all causes of mortality 

analyzed including CHD, COPD, stroke, and lung cancer. Mean COHb 

levels ranged from 1.59% in never-smokers to 6.02% in the most often 

smoking group. 

Hsia (2002, 193857) 

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, and cardiac output. 

Johnson et al. (2006, 193874) 

To test that heme-derived CO formation is increased 

and contributes to hypertension and arteriolar 

endothelial dysfunction in obese Zucker rats. 

Obese Zucker rats showed increased respiratory CO excretion that was 

lowered by HO inhibition. Skeletal muscle arterioles of obese rats had 

attenuated ACh and flow responses that were abolished by HO 

inhibition (HO inhibition enhanced dilation). 

Lamberto et al. (2004, 193845) 

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. 

January 2010 B-2 
Reference Purpose Findings 

Levesque et al. (2000, 011886) 

To describe the results of air quality monitoring in an 

indoor ice skating rink during Monster Truck and car 

demolition exhibitions. 

Maximum time-weighted avg levels of CO were 100 ppm, with several 

peaks exceeding 200 ppm (max: 1,600 ppm). 

Lim et al. (2000, 126969) 

To investigate the expression of HO-1 and HO-2 in 

bronchial biopsies obtained from patients with mild 

asthma compared with that of subjects without asthma. 

HO-1 and HO-2 expression is widely distributed equally in healthy 

subjects and subjects with asthma and is not modulated by inhaled 

corticosteroid therapy. 

Mahoney et al. (1993, 013859) To compare CO-oximeter measurements of COHb 

against a gas chromatography reference method. 

In general, the 5 CO-oximeters that were tested underestimated COHb 

concentrations for COHb >2.5% and overestimated COHb concentration 

for COHb ≤ 2.5%, when compared to reference gas chromatography 

method. 

Marks et al. (2002, 030616) 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. 

Marvisi et al. (2007, 186702) 

To evaluate DLCO impairment and microalbuminuria in 

patients with active ulcerative colitis (UC) and to assess 

whether these tests correlate with intestinal 

inflammation. 

Reduced DLCO was present in 67% of patients. Microalbuminuria was 

present in 63% of patients with ulcerative colitis. 

Merx et al. (2001, 002006) 

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. 

Monma et al. (1999, 180426). To study whether exhaled CO levels were increased in 

seasonal allergic rhinitis. 

Exhaled CO concentrations were higher in allergic rhinitis patients 

during cedar pollen season (3.6 ppm; SD 0.3 ppm) that out (1.2 ppm; 

SD 0.1 ppm). 

Morimatsu et al. (2006, 194097) To examine exhaled CO, arterial COHb, and bilirubin 

IXα levels in critically ill patients. 

Exhaled CO concentrations were significantly higher in critically ill 

patients compared to controls. There was a significant correlation 

between exhaled CO and COHb or bilirubin. There was no correlation 

between exhaled CO and disease severity or degree of inflammation. 

There was higher exhaled CO in survivors compared to nonsurvivors. 

Muchova et al. (2007, 194098) 

To determine if long-term use of statins affects HO 

activity and blood and organ CO and bilirubin in FvB 

mice (6-8 wk). 

Rosuvastatin and atorvastatin treatment increased COHb, plasma 

bilirubin, and heart tissue CO content. Both statins caused an increase 

in HO activity in heart tissue, whereas no changes were seen in brain or 

lung. Liver HO activity was inconsistent over time and between statins. 

Both statins decreased the heart antioxidant capacity, and changes in 

HO activity and antioxidant capacity can be reversed by HO inhibitor 

treatment. 

Neto et al. (2008, 194672) 

To develop a model of the respiratory system to 

analyze CO transport in the human body submitted to 

several physical activity levels. 

The model contains 6 compartments including: alveolar, pulmonary 

capillaries, arterial, venous, tissue capillary, and tissues (muscular and 

nonmuscular). The highest and lowest COHb levels were simulated in 

the walking individual, suggesting that greater variability in COHb 

occurs in higher physical activity levels. 

Pelham et al. (2002, 025716) 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. 

Paredi et al. (1999, 194102) To investigate the level of exhaled CO produced by 

diabetic patients. 

Diabetic patients (types 1 and 2) had higher levels of exhaled CO than 

healthy subjects. Exhaled CO levels correlated with the incidence of 

glycemia and the duration of diabetes. 

Paredi et al. (1999, 118798) 

To investigate whether cystic fibrosis patients have 

higher exhaled levels of CO and if this is reduced by 

corticosteroid therapy. 

Cystic fibrosis patients had higher exhaled CO concentrations compared 

to healthy controls. Patients receiving corticosteroid therapy had lower 

exhaled CO concentrations. 

Pesola et al. (2004, 193842) 

To determine if healthy African Americans may be 

misdiagnosed as 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, 193855) 

To determine if healthy Asians may be misdiagnosed as 

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. 

January 2010 B-3 
Reference Purpose Findings 

Prommer and Schmidt (2007, 

180421) 

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). A 2.3% 

increase in total Hb mass would be found if CO diffusion was not 

included. 

Proudman et al. (2007, 186705) 

To review the signs of pulmonary arterial hypertension, 

including a drop in DLCO in patients with systemic 

sclerosis. 

Richardson et al. (2002, 037513) 

To combine invasive vascular measures of arterial and 

venous blood and muscle blood flow with noninvasive 

magnetic spectroscopy of deoxy-myoglobin 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.

Sakamaki et al. (2002, 186706) To evaluate the association of patients with aortic 

aneurysm to the prevalence obstructive airway disease.

Patients with AA had lower FEV1 and DLCO than controls. Presence of 

AA and male gender were associated with a higher risk of airway 

obstruction. 

Scharte et al. (2000, 194112) To investigate whether exhaled CO concentrations are 

increased in critically ill patients. 

Critically ill patients had higher exhaled CO concentrations and higher 

total CO production rates compared to healthy controls. No correlation 

was found between exhaled CO concentration and venous or arterial 

COHb. 

Scharte et al. (2006, 194115) 

To investigate the relationship between the severity of 

illness and endogenous CO production in critically ill 

patients. 

CO production rates weakly correlated with the multiple organ 

dysfunction score (R=0.27). Cardiac disease patients and patients 

undergoing dialysis produced higher amounts of CO compared to 

critically ill control patients. 

Schachter et al. (2003, 186707) To evaluate the association between severe 

gastroesophageal reflux and lung function. 

Patients with severe gastroesophageal reflux had reduced DLCO, 

remaining significant after adjusting for age, gender, BMI, and smoking. 

Shimazu et al. (2000, 016420) 

To study the effects of short-term (min) or long-term 

(several h) 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, 016331) To discuss the findings of Weaver et al. (2000, 016421) 

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, 016421) paper. 

Sylvester et al. (2005, 191954) To assess the usage of end tidal CO levels in children 

with sickle cell disease for measurement of hemolysis. 

Children with sickle cell disease had higher exhaled CO levels (4.9 ppm; 

SD 1.7 ppm) compared to healthy controls (1.3 ppm; SD 0.4 ppm). A 

positive correlation existed between end-tidal CO levels and COHb and 

bilirubin. 

Takeuchi et al. (2000, 005675) 

To examine the relationship between min ventilation 

and rate of COHb reduction during breathing 100% O2

and during normocapnic hyperoxic hyperpnea. 

Patients were exposed to 400-1,000 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. 

Tarquini et al. (2009, 194117) To measure plasma CO levels in patients with liver 

cirrhosis and portal hypertension. 

Plasma CO was higher in ascetic patients than nonascitic patients and 

both were higher than healthy controls. HO activity was higher in 

cirrhotic patients than healthy subjects and highest in patients with 

ascites. 

Terzano et al. (2009, 108046) To investigate the effect of postural changes on gas 

exchange in patients with COPD and healthy subjects. 

DLCO increased in healthy individuals from upright to supine position 

and upright to prone position. DLCO did not significantly change in 

COPD patients from upright to prone position. This is explained by 

homogeneous perfusion in healthy individuals and increased rigidity of 

lung capillaries due to COPD. 

Tran et al. (2007, 194120) To assess the correlation of COHb to severity of liver 

disease. 

No correlation was found with the Model for End Stage Liver Disease 

score, Child Turcotte Pugh score, or other biochemical or clinical 

measures of disease severity, such as spleen size, bilirubin, disease 

duration, or AST/ALT. The mean COHb was 2.1%. 

Vreman et al. (2005, 193786) 

To develop a sensitive and reproducible method of CO 

quantification in rodent (mouse and rat) tissue pre- and 

postexposure 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). 

Vreman et al. (2006, 098272) 

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. 

January 2010 B-4 
Reference Purpose Findings 

Weaver et al. (2000, 016421) 

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. 

Whincup et al. (2006, 195129) 

To report COHb levels from a population-based study in 

men aged 60-79 yr during the 20-yr follow-up of the 

British Regional Heart Study cohort. 

Mean COHb: 0.46%; Median COHb: 0.5% 

9.2% of men had COHb levels of 2.5% or greater (93% were smokers) 

0.1% of men had COHb levels of 7.5% or greater 

Smoking is the highest influence on COHb levels; however, other 

factors independently related were season, region, gas cooking and 

central heating, and active smoking 

Widdop (2002, 030493) To review carbon monoxide analysis methods, 

including CO-oximeters and gas chromatography. 

Wu and Wang (2005, 180411) 

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 and the potential 

pharmacological and therapeutic applications for CO. 

Yamaya et al. (1998, 047525) To determine whether upper respiratory tract infections 

increase exhaled CO concentrations. 

Exhaled CO increased in patients at the time of upper respiratory tract 

infection symptoms but decreased to nonsmoking healthy control levels 

during recovery. 

Yamaya et al. (2001, 180130) To determine whether the level of CO is related to the 

severity of asthma. 

Severe asthmatics exhaled more CO than nonsmoking controls. 

Exhaled CO concentrations in unstable severe asthmatics were higher 

than in stable severe asthmatics. Mild and moderate asthmatics did not 

differ from controls. Exhaled CO was correlated with FEV1 in all 

asthmatics. 

Yasuda et al. (2002, 035206) To determine whether arterial COHb is increased in 

patients with inflammatory pulmonary diseases. 

Arterial COHb concentrations are increased in patients with 

inflammatory pulmonary diseases, including exacerbated bronchial 

asthma (1.05%), pneumonia (1.08%), and idiopathic pulmonary fibrosis 

(1.03%) over controls (0.6%). 

Yasuda et al. (2004, 191955) 

To determine if COHb levels in the venous blood and 

arteriovenous COHb (a-vCOHb) differences are 

increased in patients with inflammatory pulmonary 

diseases compared to patients with extrapulmonary 

inflammation and control subjects. 

Patients with inflammatory pulmonary diseases, including bronchial 

asthma and pneumonia, had a large a-vCOHb difference. Both arterial 

and venous blood COHb increased in patients with inflammatory 

pulmonary disease, such as bronchial asthma, pneumonia, 

pyelonephritis and active rheumatoid arthritis. 

Yasuda et al. (2005, 102183) To study the relationship between COHb and disease 

severity in patients with COPD. 

COHb concentrations increased in patients with COPD at a stable 

condition over controls and patients with COPD with exacerbations were 

further increased. 

Yerushalmi et al. (2009, 186711) 

To evaluate the association of dose-dense 

chemotherapy in breast cancer patients with pulmonary 

dysfunction. 

Patients receiving dose-dense chemotherapy for breast cancer had a 

significant reduction in DLCO. 

Zegdi et al. (2002, 037461) 

To compare endogenous CO production in 

mechanically ventilated critically ill adult patients with 

and without severe sepsis. 

CO production was higher in septic patients during the first 3 days of 

treatment compared to controls. Survivors of sepsis had a significantly 

higher CO production compared to nonsurvivors. 

January 2010 B-5 
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January 2010 B-9 
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, 088988) 

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 yr 

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: Chuang (2008, 

155731) 

Period of Study: NR 

Location: Boston, MA 

Health Outcome: HRV (changes in STsegment) 

Study Design: Panel 

Statistical Analyses: Linear additive 

models; Additive mixed logistic regression 

models 

Age Groups Analyzed: 43-75

Sample Description: 48 patients with 

documented CAD who had undergone 

percutaneous coronary intervention for acute 

coronary syndrome (acute MI or unstable 

angina pectoris) or who had worsened CAD 

Averaging Time: 12 h, 24 h

Mean (SD) unit: 12 h: 

0.48ppm, 24 h: 0.46ppm

Range (Min, Max): 12-h: 

25th percentile- 0.35, 75th 

percentile- 0.62, Max- 1.88; 

24 h: 25th percentile- 0.37, 

75th percentile- 0.62, Max1.56 

Copollutant: NR 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined: NR 

Estimated RR for ST-segment depression ≥0.1 

mm (ppm): 12-h: 0.70 (0.58-0.84) 

24 h: 0.84 (0.68-1.03) 

Estimated ST-segment change, mm (ppm): 12-h 

mean: 0.013 (0.003-0.024) 

24 h mean: 0.007 (-0.004-0.019) 

CO not significantly associated with ST-segment 

depression.

Author: Dales et al. 

(2004, 099036) 

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 yr (mean 65 yr) 

Sample Description: 36 subjects with preexisting 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) 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 C-1 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Gold et al. 

(2000, 011432) 

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 yr 

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, 087558) 

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 yr 

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: Goldberg et al. 

(2008, 180380) 

Period of Study: 

July 2002-October 2003 

Location: 

Montreal, Quebec 

Health Outcome: Oxygen saturation and 

heart rate 

Study Design: Panel 

Statistical Analyses: Mixed regression 

models 

Age Groups Analyzed: 50-85 yr 

Sample Description: 31 subjects with CHF 

and limits in physical functioning in the Heart 

Failure and Heart Transplant Center at the 

McGill University Health Center 

Averaging Time: 24 h 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

PM2.5: r = 0.72 

NO2: r = 0.84

SO2 and NO2: r = 0.43 

Increment: NR 

Adjusted Mean Difference [Lower CI, Upper CI] 

Lags examined: 0, 1, 2 

Oxygen Saturation:

Lag 0: 0.004 ppm (-0.060, 0.067) 

Lag 1: -0.001 ppm (-0.066, 0.065) 

3-day: -0.005 ppm (-0.098. 0.088)

Pulse Rate: 

Lag 0: 0.011 ppm (-0.290, 0.312) 

Lag 1: 0.227 ppm (-0.080,0.535) 

3-day: 0.245 ppm (-0.209, 0.700)

Author: Holguin et al. 

(2003, 057326) 

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 yr (mean age 79 yr) 

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) 

January 2010 C-2 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Ibald-Mulli et al. 

(2004, 087415) 

Period of Study:

1998-1999 

Location: Helsinki, 

Finland 

Erfurt, Germany 

Amsterdam, Netherlands 

Health Outcome: BP and HR via ECG 

Study Design: Panel 

Statistical Analyses: Linear regression 

Age Groups Analyzed: ≥ 50 yr 

Sample Description: 131 nonsmokers with 

coronary heart disease 

Averaging Time: 24 h 

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: 

Amsterdam

PM2.5: r = 0.58 μg/m3 

NO2: r = 0.76 μg/m3

SO2: r = 0.50 mg/m3

UFP: r = 0.22 n/cm3 

ACP: r = 0.60 n/cm3

Erfurt

PM2.5: r = 0.77 μg/m3 

NO2: r = 0.86 μg/m3

SO2: r = 0.68 mg/m3

UFP: r = 0.72 n/cm3 

ACP: r = 0.78 n/cm3

Helsinki

PM2.5: r = 0.40 μg/m3 

NO2: r = 0.32 μg/m3

SO2: r = 0.19 mg/m3

UFP: r = 0.35 n/cm3 

ACP: r = 0.51 n/cm3

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined: 0, 1, 2, 3 

Results presented graphically 

Author: Liao et al. 

(2004, 056590) 

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 yr (mean 62 yr) 

Sample Description:

6,784 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 

January 2010 C-3 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Min (2009, 

199514) 

Period of Study:

December 2003 – 

January 2004 

Location: Tae-in island 

community in South 

Korea 

Health Outcome: HRV 

Study Design: Panel 

Statistical Analyses: Time-lag model 

Age Groups Analyzed: 20-87

Sample Description: 986 subjects, 367 with 

metabolic syndrome (MetS), 619 without 

MetS 

Averaging Time: 8 h 

Mean (SD) unit: 0.454 ppm 

(0.560) 

Range (Min, Max): 0.100, 

7.200 ppm 

Copollutant: PM10

Increment: NR 

Estimated % Increase in subjects with MetS

[Lower CI, Upper CI] 

Lags examined: 0-1, 1-2, 2-3, 3-4, 4-5, 5-6 

Single pollutant:

Lag 0-1:

Log(SDNN): -0.29 (-0.59, 0.00), p < 0.1 

Log(LF): -0.34 (-1.02, 0.33) 

Log(HF): -0.67 (-1.41, 0.08), p < 0.1

Lag 1-2:

Log(SDNN): -0.45 (-0.81, -0.10), p < 0.05 

Log(LF): -0.65 (-1.46, 0.17)

Log(HF): -1.04 (-1.94, -0.14), p < 0.05

Lag 2-3:

Log(SDNN): -0.28 (-0.57, 0.02), p < 0.1

Log(LF): -0.19 (-0.87, 0.48)

Log(HF): -0.82 (-1.57, -0.07), p < 0.05

Lag 3-4:

Log(SDNN): -0.18 (-0.47, 0.10)

Log(LF): -0.14 (-0.80, 0.51)

Log(HF): -0.46 (-1.19, 0.27) 

Lag 4-5:

Log(SDNN): -0.20 (-0.49, 0.09)

Log(LF): -0.36 (-1.04, 0.31)

Log(HF): -0.42 (-1.17, 0.33) 

Lag 5-6:

Log(SDNN): 0.13 (-0.18, 0.44)

Log(LF): 0.50 (-0.21, 1.20)

Log(HF): -0.03 (-0.81, 0.76) 

Co-pollutant (with PM10):

Lag 0-1: 

Log(SDNN): -0.25 (-0.56, 0.05)

Log(LF): -0.35 (-1.04, 0.31)

Log(HF): -0.67 (-1.44, 0.10), p<0.1 

Lag 1-2:

Log(SDNN): -0.48 (-0.88, -0.09), p<0.05; Log(LF): 

-0.72 (-1.63, 0.18); Log(HF): -1.09 (-2.09, -0.09), 

p<0.05 

Lag 2-3: 

Log(SDNN): -0.35 (-0.67, -0.03), p < 0.05

Log(LF): -0.17 (-0.90, 0.56) 

Log(HF): -0.78 (-1.59, 0.03), p < 0.1 

Lag 3-4: Log(SDNN): -0.22 (-0.55, 0.11)

Log(LF): -0.11 (-0.86, 0.63)

Log(HF): -0.34 (-1.17, 0.49) 

Lag 4-5:

Log(SDNN): -0.18 (-0.48, 0.12); Log(LF): -0.21 

(-0.89, 0.48); Log(HF): -0.37 (-1.14, 0.40) 

Lag 5-6: 

Log(SDNN): 0.17 (-0.14, 0.49)

Log(LF): 0.54 (-0.18, 1.25)

Log(HF): 0.00 (-0.80, 0.80) 

No significant results for subjects without MetS.

January 2010 C-4 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Park et al. 

(2005, 057331) 

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 yr 

Sample Description:

497 men from the normative aging study in 

Greater Boston area 

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. 

(1999, 011554) 

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 yr 

Sample Description:

2681 men and 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

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: RiojasRodriguez et al. (2006, 

156913) 

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 yr (mean 55 yr) 

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 min): 5, 10 

Lag 5 min: 

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, 074317) 

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 yr 

Sample Description:

28 subjects living at or near an apartment 

complex located on the same street as 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 to10.5) 

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) 

January 2010 C-5 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Tarkiainen et al. 

(2003, 053625) 

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: 

55-68 yr 

Sample Description: 6 male patients with 

angiographically- verified CAD 

Averaging Time: 24 h 

Mean (SD) unit:

4.6 ppm (max of CO episode) 

(personal monitoring) 

Range (Min, Max): 0.5, 27.4 

(max of CO episode) 

Copollutant: NR 

 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined: 5 min prior to CO episode, 5 min 

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, 088747) 

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 yr. 

Sample Description:

131 subjects with stable CAD followed for 6 

mo with biweekly 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, 088453) 

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 yr;IQR 55-73 yr 

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. 

January 2010 C-6 
Study Design Concentrations CO Effect Estimates (95% CI) 

ONSET OF CARDIAC ARRHYTHMIA 

Author: Berger et al. 

(2006, 098702) 

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 yr (mean 76 yr) 

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) 

Author: Dockery et al. 

(2005, 078995) 

Period of Study: 

1995-2002 

Location:

Boston, MA 

Health Outcome:

Tachyarrhythmias: 

Study Design: Panel 

Statistical Analyses: Logistic regression 

(GEE) 

Age Groups Analyzed: 

19-90 yr; mean 64 yr 

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 2-day 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, 092856) 

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 yr 

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) 

January 2010 C-7 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Peters et al. 

(2000, 011347) 

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 62 yr 

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. 

(2004, 055631) 

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 yr 

Sample Description:

34 patients who experienced at least 1 ICD 

discharge (8,201 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, 079620) 

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 and 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) 

Author: Rich et al. 

(2006, 089814) 

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 to 23-h ma: 

0- to 23-h ma: 0.99 (0.80-1.21) 

January 2010 C-8 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Rich et al. 

(2006, 088427) 

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

and 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: Sari et al. 

(2008, 190315) 

Period of Study: June 

2007 

Location: 

Gaziantep, Turkey 

Health Outcome: P-wave dispersion 

(predictors of atrial fibrillation, ventricular 

arrhythmias and sudden death) via ECG 

Study Design: Case control 

Statistical Analyses: Pearson correlation 

analysis 

Age Groups Analyzed: 

Barbecue workers mean age: 33.66 ± 9.43 yr

Control group mean age: 35.15 ± 6.78 yr 

Sample Description: 48 healthy males 

working at various indoor barbecue 

restaurants for at least 3 yr (avg:15.6 ± 7.1 

yr), 51 age-matched healthy men for control 

group 

Averaging Time: NR 

Mean (SD) unit: COHb% 

Indoor barbecue workers: 

6.48% ± 1.43 

Control Group: 

2.19% ± 1.30 

Range (Min, Max): NR 

Copollutant: NR 

 

Increment: NR 

Correlation coefficient for COHb [p-value] 

Lags examined: NR 

Pmin: -0.132 (0.245) 

Pmax: 0.215 (0.057) 

Pd: 0.315 (0.005) 

QTmin: 0.080 (0.454) 

QTmax: 0.402 (<0.001) 

QTd: 0.573 (<0.001) 

cQTd: 0.615 (<0.001) 

Author: Sarnat et al. 

(2006, 090489) 

Period of Study: 

24 wk 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 yr (mean age 71) 

Sample Description:

32 nonsmoking 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, 055630) 

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 yr (mean age 53 yr) 

Sample Description:

50 patients who experienced 1 or more 

arrhythmia event during the 4yr 

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) 

January 2010 C-9 
Study Design Concentrations CO Effect Estimates (95% CI) 

CARDIAC ARREST 

Author: Levy et al. 

(2001, 017171) 

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 yr 

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, 043156) 

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) 

MYOCARDIAL INFARCTION 

Author: Peters et al. 

(2001, 016546) 

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, 089796) 

Period of Study: 

1992-1994 

Location:

Stockholm, Sweden 

Health Outcome: MI 

Study Design: Case control 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: 

45-70 yr 

Sample Description:

1,397 cases;1,870 controls 

Averaging Time: 

Mean (SD) unit: 

66.8 μg/m3

 

(Estimated 30-yr 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) 

Nonfatal 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) 

Author: Rosenlund et 

al. (2009, 190309) 

Period of Study: NR 

Location: Stockholm 

County, Sweden 

Health Outcome: Fatal and nonfatal MI 

Study Design: Case control 

Statistical Analyses: Various multiple 

regression models 

Age Groups Analyzed: 15-79 yr 

Sample Description: 43,275 MI cases 

during 1985-1996; 511,065 controls 

Averaging Time: 1 yr 

Mean (SD) unit: 

Cases: 64.2 μg/m3

Controls: 55.8 μg/m3

Range (percentiles):

Cases: 5th = 7.3; 95th =267.4

Controls: 5th =6.1;95th=261.8

Copollutant: PM10, NO2

Increment: NR 

OR Estimate [Lower CI, Upper CI] 

5-yr avg exposure 

All subjects (n = 301,273) 

All cases: 1.01 (0.97-1.05) 

Nonfatal cases: 0.94 (0.89-1.00) 

Fatal cases: 1.14 (1.07-1.21) 

In-hospital death: 1.00 (0.91-1.10) 

Out-of-hospital death: 1.23 (1.14-1.32) 

Restriction to subjects who did not move between 

population census ( n = 80,155) 

All cases: 1.04 (0.94-1.14) 

Nonfatal cases: 0.96 (0.87-1.06) 

Fatal cases: 2.03 (1.59-2.60) 

In-hospital death: 2.04 (1.35-3.08) 

Out-of-hospital death: 2.03 (1.50-2.74) 

January 2010 C-10 
Study Design Concentrations CO Effect Estimates (95% CI) 

CHANGES IN BLOOD PRESSURE 

Author: 

Ibalde-Mulli et al. (2001, 

016030) 

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 yr 

Sample Description: 

2,607 men and women 25-64 yr 

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. 

(2004, 087489) 

Period of Study: 

1999-2001 

Location:

Boston, MA 

Health Outcome: BP 

Study Design: Cohort/Panel 

Statistical Analyses: Random effects 

Age Groups Analyzed: 39-90 yr 

Sample Description:

62 subjects with 631 total visits 

Averaging Time: 

1 h and 120 h avg 

Mean (SD) unit:

Same h: 0.81 ppm 

120-h avg: 0.66 ppm 

Range (Min, Max):

Same h: 

10th = 0.48; 90th = 1.22 

120-h avg: 

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, 090733) 

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 yr (mean 43 yr) 

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, or free protein S for all lag 

periods. 

January 2010 C-11 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Delfino et al. 

(2008, 156390) 

Period of Study:

2005-2006 

Location: Los Angeles, 

CA 

Health Outcome: Biomarkers of systemic 

inflammation 

Study Design: Panel 

Statistical Analyses: 

Linear mixed-effects models 

Age Groups Analyzed: 

≥ 65 yr (mean 85.7 yr) 

Sample Description: 29 nonsmoking 

subjects with history of CAD living in 

retirement communities 

Averaging Time: 24 h 

Mean (SD) unit:

0.78 ± 0.30 ppb

Range (Min, Max): 

0.22, 1.97

Copollutant (Outdoor): 

EC: r = 0.84

OC: r = 0.69

OCprimary: r = 0.73

NO2: r = 0.78

O3: r = -0.35

PM0.25: r = 0.84

PM0.25-2.5: r = 0.14

PM2.5-10: r = 0.51

Increment: NR 

Estimated coefficient 

Relationship to outdoor air pollutants: 

CRP (ng/mL): Lag 0: 847.52; 3-day avg: 728.79; 9-

day avg: 236.51 

IL-6 (pg/mL): Lag 0: 0.52; 3-day avg: 0.51; 9-day 

avg: 0.50 sTNF-RII (pg/mL): Lag 0: 154.05; 3-day 

avg: 139.45; 9-day avg: 225.60 

Relationship to indoor air pollutants: 

CRP (ng/mL): Lag 0: 695.39; 3-day avg: 527.37; 9-

day avg: 760.15 

IL-6 (pg/mL): Lag 0: 0.54; 3-day avg: 0.47; 9-day 

avg: 0.77 sTNF-RII (pg/mL): Lag 0: 114.22; 3-day 

avg: 107.95; 9-day avg: 273.38 

Relationship of sP-selction (ng/mL) to: 

Indoor air pollutants: Lag 0: 0.77; 5-day avg: 1.40; 

9-day avg: 2.19 

Outdoor air pollutants: Lag 0: 0.84; 5-day avg: 1.23; 

9-day avg: 4.29

Relationship of Cu, Zn-SOD (U/g Hb) to: 

Indoor air pollutants: Lag 0: -145.54; 5-day avg:

 -238.72; 9-day avg: -70.10 

Outdoor air pollutants: Lag 0: -105.73; 5-day avg: 

-176.72; 9-day avg: -41.92

January 2010 C-12 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Delfino et al.

(2009, 200844) 

Period of Study: JulmidOct and midOct-Feb 

of 2005-2006 and 

2006-2007 

Location: Los Angeles, 

CA

Health Outcome: Biomarkers of 

inflammation 

Study Design: Panel 

Statistical Analyses: Linear mixed effects 

models adjusted for confounders 

Age Groups: 65+ (84.1 ± 5.60) yr

Sample Description: 60 subjects with 

confirmed CAD history, nonsmoker, 

unexposed to environmental tobacco smoke

Averaging Time: 24 h

Mean (SD) unit: 0.50 (0.25) 

ppm 

Range (min, max): 0.11, 

1.30 

Copollutant: NO2, NOX, O3, 

PM0.25, PM0.25-2.5, PM2.5-10, EC, 

OC, BC, OCpri, SOC, PN/cm3

Increment: NR 

Regression coefficients (95% CI) 

Subjects with positive responses: 

Cu,Zn-SOD (U/g Hb): 1-day avg: 1441 (97, 2786), 

3-day avg: 2634 (1416, 3854), 5-day avg: 4227 

(2078, 6376), 7-day avg: 3474 (914, 6034), 9-day 

avg: 2954 (737, 5172) 

GPx-1 (U/g HB): 1-day avg: -0.97 (-4.45, 2.50), 

3-day avg: -2.21 (-6.48, 2.06), 5-day avg: 4.71 

(-2.90, 12.33), 7-day avg: 4.20 (-3.29, 11.68), 9-day 

avg: 4.76 (-1.58, 11.10) 

Subjects with negative responses: 

Cu,Zn-SOD (U/g Hb): 1-day avg: -195 (-338, -52), 

3-day avg: -242 (-399, -85), 5-day avg: -242 (-440, 

-44), 7-day avg: -315 (-664, 34), 9-day avg: -176 

(-508, 156) 

GPx-1 (U/g HB): 1-day avg: -0.82 (-1.55, -0.08), 

3-day avg: -0.85 (-1.66, -0.04), 5-day avg: -0.84 

(-1.88, 0.21), 7-day avg: -1.04 (-2.85, 0.78), 9-day 

avg: -0.47 (-2.19, 1.26) 

All subjects: 

IL-6 (pg/mL): 1-day avg.: 0.35 (0.17, 0.54), 3-day 

avg.: 0.40 (0.20, 0.61), 5-day avg.: 0.54 (0.27, 0.80), 

7-day avg.: 0.34 (-0.06, 0.74), 9-day avg.: 0.31 (-

0.07, 0.70) 

P-selectin (ng/mL): 1-day avg.: 3.33 (0.94, 5.73), 

3-day avg.: 3.65 (1.02, 6.29), 5-day avg.: 5.28 (1.86, 

8.70), 7-day avg.: 11.2 (5.39, 17.0), 9-day avg.: 10.4 

(4.83, 16.0) 

TNF-RII (pg/mL): 1-day avg: 112 (13, 211), 3-day 

avg: 136 (29, 243), 5-day avg: 229 (88, 371), 7-day 

avg: 132 (-86, 349), 9-day avg: 220 (19, 421) 

TNF-α (pg/mL): 1-day avg: 0.05 (-0.05, 0.16), 3-day 

avg: 0.09 (-0.03, 0.20), 5-day avg: 0.14 (-0.01, 0.29), 

7-day avg: 0.07 (-0.19, 0.33), 9-day avg: 0.14 (-0.11, 

0.39) 

CRP (ng/mL): 1-day avg: 780 (343, 1217), 3-day 

avg: 739 (255, 1222), 5-day avg: 1117 (485, 1749), 

7-day avg: 126 (-800, 1052), 9-day avg: 41 (-840, 

923) 

SOD (U/g Hb): 1-day avg: -62 (-231, 108), 3-day 

avg: -53 (-244, 138), 5-day avg: -37 (-285, 211), 

7-day avg: 98 (-314, 509), 9-day avg: 208 (-173, 

590) 

GPx-1 (U/g Hb): 1-day avg: -0.69 (-1.41, 0.03), 3-day 

avg: -0.69 (-1.48, 0.11), 5-day avg: -0.56 (-1.60, 

0.48), 7-day avg: -0.56 (-2.34, 1.21), 9-day avg: 0.05 

(-1.63, 1.72) 

Effect modification by medication use: 

TNF-RII (pg/mL): 1-day avg: All subjects: 125 (11, 

239), Statins: 48 (-105, 201), No Statins: 199 (47, 

352); 3-day avg: All subjects: 161 (39, 283), Statins: 

1 (-170, 171), No Statins: 306 (141, 472) ; 5-day avg: 

All subjects: 257 (100, 413), Statins: 15 (-210, 240), 

No Statins: 445 (240, 649); 7-dayay avg: All subjects: 

176 (-68, 419), Statins: 43 (-297, 382), No Statins: 

283 (-23, 589); 9-dayay avg: 265 (41, 489), Statins: 

160 (-158, 478), No Statins: 355 (65, 646) 

sP-selectin (ng/mL): 1-day avg: All subjects: 1.84 

(-0.62, 4.30), Clopidogrel: 0.00 (-2.80, 2.81), No 

Clopidogrel: 1.72 (-0.42, 3.86); 3-day avg: All 

subjects: 1.90 (-0.79, 4.60), Clopidogrel: -0.67 (-3.95, 

2.60), No Clopidogrel: 1.60 (-0.76, 3.96); 5-day avg: 

All subjects: 2.97 (-0.47, 6.41), Clopidogrel: -0.18 

January 2010 C-13 
Study Design Concentrations CO Effect Estimates (95% CI) 

(-4.38, 4.01), No Clopidogrel: 3.04 (0.06, 6.01); 

7-day avg: All subjects: 6.74 (0.75, 12.73), 

Clopidogrel: 2.24 (-4.22, 8.71), No Clopidogrel: 6.78 

(1.60, 3.96); 9-day avg: All subjects: 6.96 (1.20, 

12.72), Clopidogrel: 2.0 (-4.40, 8.48), No 

Clopidogrel: 5.54 (0.46, 10.6) 

Author: Liao et al. 

(2005, 088677) 

Period of Study: 

1996-1998 

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 yr 

Sample Description:

10,208 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 

Author: Ljungman et al. 

(2009, 191983) 

Period of Study:

May 2003-July 2004 

Location: Athens, 

Greece; Augsberg, 

Germany; Barcelona, 

Spain; Helsinki, Finland; 

Rome, Italy; Stockholm, 

Sweden 

Health Outcome: Plasma Interleukin-6 (IL6), Fibrinogen 

Study Design: Panel/Field 

Statistical Analyses: Linear Mixed Effects 

Model 

Age Groups Analyzed: 35-80 yr 

(mean = 62.2 yr) 

Sample Description: 955 subjects who had 

experienced MI between 4 mo and 6 yr 

before start of the study 

Averaging Time: 24 h 

Mean (SD) unit: 

Individual cities: 

0.29-1.48 mg/m3

 

Mean for all cities: 

0.78 mg/m3

Range (percentiles): 

25th = 0.56; 75th = 0.90 (for 

mean of all cities) 

Copollutant: 

(mean for all cities) 

NO2: r = 0.69 

PM10: r = 0.47 

PM2.5: r = 0.55 

PNC: r = 0.67 

Increment: 0.34 mg/m3

Change of IL-6 

% of overall mean per IQ range increase 

Genotypes: 1 1, 1 2, 2 2 

IL6 rs2069832 

1 1: 2.0 (0.3, 3.6); 1 2: -0.2 (-1.7, 1.3); 2 2: -2.0 

(-4.7, 0.8); p-value: 0.03 

IL6 rs2069840 

1 1: 2.0 (0.3, 3.8); 1 2: 0.4 (-0.9, 1.7); 2 2: -1.2 

(-3.4, 1.1); p-value: 0.04 

IL6 rs2069845 

1 1: 1.9 (0.2, 3.5); 1 2: -0.1 (-1.5, 1.4); 2 2: -1.6 

(-4.3, 1.2); p-value: 0.31 

FGA rs2070011 

1 1: 1.0 (-0.7, 2.7); 1 2: 0.7 (0.6, 2.0); 2 2: 0.4 

(-1.9, 2.7); p-value: 0.64 

FGB rs1800790 

1 1: -0.2 (-1.8, 1.3); 1 2: 2.1 (0.4, 3.8); 2 2: 4.5 

(1.1, 8.0); p-value: 0.02 

Author: Pekkanen et al. 

(2000, 013250) 

Period of Study: 

1991-1993 

Location:

London, England 

Health Outcome: Fibrinogen 

Study Design: Cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: 

35-55 yr 

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) 

January 2010 C-14 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Ruckerl et al. 

(2006, 088754) 

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 yr (mean = 66 yr) 

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, 156931) 

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 yr 

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) 

January 2010 C-15 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Rudez et al. 

(2009, 193783) 

Period of Study:

January 2005-December 

2006 

Location: Rotterdam, 

the Netherlands 

Health Outcome: Platelet aggregation, 

thrombin generation, Fibrinogen, C-reactive 

protein 

Study Design: Panel 

Statistical Analyses: Linear regression 

Age Groups Analyzed: Mean = 41 yr 

Sample Description: 40 healthy individuals 

Averaging Time: 24 h 

Median (SD) unit: 333 μg/m3

Range (percentiles): 

25th = 276; 75th = 412 

Copollutant: 

PM10: r >0.6 

NO: r >0.6 

NO2: r >0.6 

O3: -0.4 ≥ r ≥ -0.6 

Increment: NR 

Estimated Changes [Lower CI, Upper CI] 

Platelet Aggregation Parameters 

Maximal Platelat Aggregation: 

D0-6: -3.6 (-9.3, 2.1); D0-12: -4.7 (-11.0, 1.5); 

D0-24: -2.6 (-7.9, 2.7); I24-48: -1.1 (-7.2, 4.9); 

I48-72: 8.4 (2.5, 14.3); I72-96: -0.1 (-5.1, 5.0); 

D+I0-96: 9.5 (1.6, 17.4) 

Late Aggregation: 

D0-6: 10.5 (0.8, 20.3); D0-12: 11.6 (1.2, 21.9); 

D0-24: 11.2 (1.4, 21.0); I24-48: 7.5 (-2.2, 17.1); 

I48-72: 18.1 (8.4, 27.8); I72-96: 4.2 (-5.5, 13.9); 

D+I0-96: 20.4 (8.4, 32.4) 

Thrombin Generation 

ETP 

D0-6: -1.51 (-3.7, 0.80); D0-12: -1.1 (-3.4, 1.1); 

D0-24: -1.5 (-3.9, 0.9); I24-48: -0.7 (-3.4, 2.0); 

I48-72: 0.8 (-1.9, 3.4); I72-96: 3.5 (0.8, 6.2); 

D+I0-96: 0.8 (-2.7, 4.3) 

Peak 

D0-6: -2.5 (-6.3, 1.3) D0-12: -1.9, (-5.7, 1.9); 

D0-24: -3.3 (-7.3, 0.7); I24-48; -1.3 (-6.1, 3.6); 

I48-72: -0.5 (-5.0, 4.0) I72-96: 3.8 (-0.8, 8.4) 

D+I0-96: -1.7 (-7.5, 4.2) 

Lag Time 

D0-6: 1.0 (-0.5, 2.5); D0-12: 1.0 (-0.5, 2.5); 

D0-24: 1.6 (0.1, 3.1); I24-48; 0.4 (-1.3, 2.2); 

I48-72: -1.0 (-2.7, 0.7); I72-96: -1.5 (-3.2, 0.2); 

D+I0-96: 0.1 (-2.1, 2.2) 

Inflammatory Markers 

Fibrinogen 

I24-48; 0.0 (-1.7, 1.8); I48-72: 0.0 (-1.8, 1.9) I72-96: 

-0.1 (-1.9, 1.7) 

CRP 

I24-48; 3.2 (-6.4, 12.8); I48-72: -1.9 (-12.5, 8.7); 

I72-96: -4.5 (-15.3, 6.3) 

Author: Steinvil et al. 

(2008, 188893) 

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 = 46 yr 

Sample Description: 3,659 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 wk 

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 wk 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. 

VARIOUS MEASURES OF CARDIOVASCULAR HEALTH 

Author: Briet et al. 

(2007, 093049) 

Period of Study: NR 

Location: Paris, France 

Health Outcome: Endothelial function, 

Reactive Hyperemia 

Study Design: Case-crossover 

Statistical Analyses: Multiple regression 

models 

Age Groups Analyzed: 18-35 yr 

Sample Description: 40 healthy white male 

nonsmokers 

Averaging Time: 24 h 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

PM2.5, PM10, NO, NO2, SO2

Increment: NR 

β-Coefficient [Lower CI, Upper CI] 

Flow-mediated Brachial Artery Dilatation: 

-0.68 (-1.22, -0.15) 

Small Artery Reactive Hyperemia: 

10.46 (1.73, 19.31) 

January 2010 C-16 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Nautiyal et al. 

(2007, 190301) 

Period of Study:

August 1999-May 2000 

Location: 

Mandi Gobindgarh, India 

Morinda, India 

Health Outcome: Various measures of 

cardiovascular health via ECG (Minnesota 

Code) 

Study Design: Cross-sectional 

Statistical Analyses: NR 

Age Groups Analyzed: +15 yr 

Sample Description: 

 200 total survey participants (100/town) 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): 

Morinda 

Pure residential Site: 0-1 ppm 

GT Road Site: 2-3 ppm 

Mandi Gobindgarh 

Mixed Habitat Site: 0-3 ppm 

GT Road Site: 1-3 ppm 

Copollutant: 

 PM2.5, PM10, NOX, SOX

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined: NR 

No quantitative results presented 

Author: Wellenius et al. 

(2007, 092830) 

Period of Study:

February 2002-March 

2003 

Location: Boston, MA 

Health Outcome: Congestive heart failure 

Study Design: Cohort (retrospective) 

Statistical Analyses: Linear mixed models 

Age Groups Analyzed: 33-88 yr. 

Tai Chi Group mean age (n=14): 66 ± 13 yr. 

Control Group mean age (n=14): 63 ± 14 yr. 

Sample Description: 28 patients with CHF 

and impaired systolic function 

Averaging Time: 24 h 

Mean (SD) unit: 0.44 ppm 

Range (IQ): 0.20 ppm 

Copollutant: 

PM2.5: r = 0.35 

NO2, SO2, O3, BC 

 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined: 0, 1, 2, 3 

Results presented graphically 

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, 

090193) 

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, 

093270) 

Period of Study: 1994-2004 

Location:

Dijon, France 

Health Outcome (ICD9 or ICD10): 

Stroke (Ischemic & Hemorrhagic) 

Study Design: Bidirectional case 

crossover 

Statistical Analyses: Conditional logistic regression 

Age Groups Analyzed: ≥ 40 yr 

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. 

January 2010 C-17 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Maheswaran et al. 

(2005, 090769) 

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 yr 

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. (2003, 

080133) 

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

Author: Villeneuve et al. (2006, 

090191) 

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+ yr 

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. (2005, 

088685) 

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 yr 

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) 

January 2010 C-18 
Study Design Concentrations CO Effect Estimates (95% CI) 

ISCHEMIC HEART DISEASE 

Author: D’Ippoliti et al. (2003, 

074311) 

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+ yr 

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, 087413) 

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) 

Author: Lanki et al. (2006, 

089788) 

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+ yr 

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) 

January 2010 C-19 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Lee et al. (2003, 

095552) 

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+ yr 

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 yr: 

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. 

(2005, 090769) 

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+ yr 

Sample Description:

11,407 Emergency Hospital Admissions 

for CHD in patients 45+ yr (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) 

Author: Mann et al. (2002, 

036723) 

Period of Study: 1988-1995 

Location:

Southern California 

Health Outcome (ICD9): IHD (IHD) 

(410-414); 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, 

193793) 

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: 4,979 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 

January 2010 C-20 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: von Klot et al. (2005, 

088070) 

Period of Study: 1992-2001 

Location:

5 European cities: 

Augsburg, Germany 

Barcelona, Spain 

Helsinki, Finland 

Rome, Italy 

Stockholm, Sweden 

Health Outcome: Hospital cardiac (mi), 

angina, dysrythmia, heart failure) readmissions 

Study Design: 

Prospective Cohort 

Statistical Analyses: Poisson 

regression 

Age Groups Analyzed: All 

Sample Description: 22,006 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. (2007, 

090707) 

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:

13,475 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) 

Author: Symons et al. (2006, 

091258) 

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. (2005, 

087483) 

Period of Study: 1987-1999 

Location:

Pittsburgh, PA 

Hospital Admissions 

Health Outcome (ICD9): CHF 

(428, 428.1) 

Study Design: Case-crossover 

Statistical Analyses: Conditional 

logistic regression 

Age Groups Analyzed: 65+ yr 

Sample Description:

54,019 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) 

January 2010 C-21 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Yang (2008, 157160) 

Period of Study: 1996-2004 

Location: Taipei, Taiwan 

Hospital Admissions 

Health Outcome: CHF 

Study Design: Case-crossover 

Statistical Analyses: NR 

Age Groups Analyzed: NR 

Sample Description: 24,240 CHF HA 

from 47 hospitals 

Averaging Time: 24 h 

Mean (SD) unit: 1.26 ppm 

Range (Min, Max): 0.12, 3.66 

Copollutant: PM10, NO2, O3, SO2

 

Increment: NR 

OR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2 

Single Pollutant Model 

Warm days (>20o C): 1.24 (1.16, 1.33) 

Cool days (<20o C): 1.05 (0.96, 1.15) 

Two Pollutant Models 

Warm days (≥20°C) 

Adjusted for PM10: 1.16 (1.08, 1.26) 

Adjusted for NO2: 1.02 (0.92, 1.13) 

Adjusted for O3: 1.25 (1.17, 1.34) 

Adjusted for SO2: 1.32 (1.22, 1.42) 

Cool days (<20°C) 

Adjusted for PM10: 1.09 (0.97, 1.21) 

Adjusted for NO2: 1.07 (0.92, 1.25) 

Adjusted for O3: 0.89 (0.80, 0.99) 

Adjusted for SO2: 1.03 (0.92, 1.16) 

CARDIOVASCULAR DISEASES – NON-SPECIFIC 

Author: Ballester et al. (2001, 

013257) 

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, 

088746) 

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, 

089770) 

Period of Study: 1998-2001 

Location:

Brisbane, Canberra, 

Melbourne, Perth, Sydney 

Australia 

Auckland & Christchurch, New 

Zealand 

Hospital Admissions with 

CVDs 

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 yr & ≥ 65 yr 

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 yr 

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 yr 

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) 

January 2010 C-22 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Bell et al. (2009, 

193780) 

Period of Study: 1999-2005 

Location:

126 U.S. urban counties 

Hospital Admissions with 

CVDs 

Health Outcome (ICD9): Cardiac failure 

(428); cerebrovascular events (430-

438); heart rhythm disturbances (426-

427); ihd (410-414,429); peripheral 

vascular disease (440-448) 

Study Design: Time series 

Statistical Analyses: Log-linear overdispersed Poisson regression 

Age Groups Analyzed: ≥ 65 yr 

Sample Description: 

 >9.3 million Medicare subjects 

Averaging Time: 1 h 

Mean (SD) unit: 1.6 ppm 

Median (SD) unit: 1.3 ppm 

Median Range (Min, Max): 

 0.2, 9.7 

Copollutant: 

PM2.5: r = 0.26 

NO2: r = 0.56 

EC: r = 0.48 

Increment: 1 ppm 

% Change [Lower CI, Upper CI] 

Lags examined (days): 0-2 

Lag 0: 

Single pollutant model: 0.96 (0.79-1.12) 

Adjusted for PM2.5: 0.76 (0.57-0.96) 

Adjusted for NO2: 0.55 (0.36-0.74) 

Adjusted for EC: 0.97 (0.38-1.57) 

Author: Chang et al. (2005, 

080086) 

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: Filhol. (2008, 190260) 

Period of Study:

January 2001-July 2003 

Location:

Sao Paulo, Brazil 

ED Visits 

Health Outcome (ICD10): Hypertension 

and Cardiac Ischemic Disease (I10-I25) 

Study Design: Time series 

Statistical Analyses: Linear Poisson 

regression models 

Age Groups Analyzed: >18 yr 

Sample Description: 45,000 

Cardiovascular emergency room visits 

from diabetic and non-diabetic patients 

(tertiary referral teaching hospital) 

Averaging Time: 8 h 

Mean (SD) unit: 2.7 ppm 

Range (Min, Max): 0.7, 12.1 

Copollutant: correlation 

PM10: r = 0.69 

NO2: r = 0.58 

SO2: r = 0.52 

O3: r = 0.07 

Increment: 1.2 ppm 

Regression Coefficients [SEM] 

Lags examined (days): 0, 1, 2 

CVD Visits/Diabetes: 

Lag 0: 0.0575 (0.0410) 

Lag 1: - 0.0056 (0.0418) 

Lag 2: -0.0324 (0.0426) 

2-day moving avg: 0.0324 (0.0470) 

3-day moving avg: 0.0074 (0.0528) 

4-day moving avg: -0.0025 (0.0582) 

CVD Visits/Non-Diabetes: 

Lag 0: 0.0286 (0.0095) 

Lag 1: 0.0098 (0.0091) 

Lag 2: 0.0102 (0.0089) 

2-day moving avg: 0.0271 (0.0108) 

3-day moving avg: 0.0281 (0.0120) 

4-day moving avg: 0.0306 (0.0131) 

Author: Fung et al. (2005, 

074322) 

Period of Study: 1995-2000 

Location:

Windsor, Ontario, 

Canada 

Hospital Admissions of 

CVDs 

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 yr 

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 yr 

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) 

January 2010 C-23 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Jalaludin et al. (2006, 

189416) 

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+ yr 

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 CVD: 

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, 

049466) 

Period of Study: 1993-1997 

Location:

Denver, CO 

Hospital Admissions for CVD 

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 yr 

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. 

Author: Linn et al. (2000, 

002839) 

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 yr 

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) 

MI 

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 

January 2010 C-24 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Metzger et al. (2004, 

044222) 

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, 

090442) 

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, 

073854) 

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. 

Author: Tolbert et al. (2007, 

090316) 

Period of Study: 1993-2004 

Location:

Atlanta, GA 

ED Visits (from 41 hospitals) 

Health Outcome (ICD9): IHD (410-

414), cardiac dysrhythmias (427), CHF 

(428), peripheral vascular and 

cerebrovascular diseases (433-437, 

440, 443-445 and 451-453) 

Study Design: Time series 

Statistical Analyses: Poisson 

generalized linear model 

Age Groups Analyzed: NR 

Sample Description: 10,234,490 ED 

Visits (238,360 CVD group) 

Averaging Time: 1 h 

Mean (SD) unit: 1.6 ppm 

Range (Min, Max): 0.1, 7.7 

Copollutant: 

PM10: r = 0.51 

NO2: r =0.70 

SO2: r =0.28 

O3: r =0.27 

PM2.5: r = 0.47 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined (days): 1, 2, 3 

Single-Pollutant Model 

3-day ma: 1.020 (1.010, 1.030) 

Results for multi-pollutant models presented 

graphically 

January 2010 C-25 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Yang et al. (2004, 

094376) 

Period of Study: 1997-2000 

Location:

Kaohsiung City, Taiwan 

Health Outcome (ICD9: 

CVDs (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, 

091059) 

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 wk) 

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, 

156292) 

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:

70,249 live singleton births 

Averaging Time: LUR 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, 

024945) 

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 wk) 

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 

January 2010 C-26 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Conceicao et al. 

(2001, 016628) 

Period of Study: 1994-1997 

Location:

Sao Paulo, Brazil 

Health Outcome: Child 

mortality, under 5 yr 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 yr of age [SE] ; 

Lags examined: 0, 1, 2, 3 

Lag 2: 0.0306 (0.0076) (p < 0.01) 

Lag chosen for best fitting model 

Author: Gilboa et al. (2005, 

087892) 

Period of Study: 1997-2000 

Location:

Texas 

Health Outcome: 

Birth defects (heart defects 

and 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: wk 3 to 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 and valve, atrial septal, 

pulmonary artery and valve, ventricular septal, 

endocardial cushion and mitral valve , cleft lip, cleft 

palate, aortic valve stenosis, coarctation of the 

aorta, ostium secundum. 

Author: Gouveia et al. (2004, 

055613) 

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 wk) 

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, 

019390) 

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 wk) 

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, 

042552) 

Period of Study: 1995-1999 

Location:

Seoul, South Korea 

Health Outcome:

 Post-neonatal mortality 

(1 mo-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 mo-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) 

January 2010 C-27 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Hajat et al. (2007, 

093276) 

Period of Study: NR 

Location: 

Birmingham, Bristol, Leeds, 

Liverpool, London, 

Manchester, Middlesbrough, 

Newcastle, Nottingham, 

Sheffield 

England 

Health Outcome: Neonatal 

and postneonatal mortality 

Study Design: Time series 

Statistical Analyses:

Poisson regression (GLM) 

Age Groups Analyzed: NA 

Sample Description:

22,288 total infant deaths 

between 1990 and 2000 

Averaging Time: 3 days 

Mean (SD) unit: (mg/m3) 

Birmingham: 0.64; Bristol: 1.01; Leeds: 

0.73; Liverpool: 0.51; London: 0.77; 

Manchester: 0.63; Middlesbrough: 0.37; 

Newcastle: 0.67; Nottingham: 0.62; 

Sheffield: 0.60 

Range (Min, Max):

Birmingham: 0.4, 0.8; Bristol: 0.6, 1.2; 

Leeds: 0.5, 0.9; Liverpool: 0.3, 0.6; 

London: 0.5, 0.9; Manchester: 0.4, 0.7; 

Middlesbrough: 0.2, 0.4; Newcastle: 0.5, 

0.8; Nottingham: 0.4, 0.7; Sheffield: 0.3, 

0.7 

Copollutant: SO2, NO2, NO, O3, PM10 

Increment: 1 mg/m3

RR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2 

All infant deaths: 1.02 (0.96, 1.09) 

Neonatal deaths: 0.99 (0.92, 1.07) 

Post-neonatal deaths: 1.09 (0.94, 1.25) 

City-specific results of all infant mortality displayed 

graphically 

Author: Huynh et al. (2006, 

091240) 

Period of Study: 1999-2000 

Location:

California 

Health Outcome:

PTB (24-36 wk gestation) 

Study Design: Case-control

Statistical Analyses:

Conditional Logistic 

regression 

Age Groups Analyzed: 

Cases = 24- to 36-wk 

gestation; Controls = 39- to 

44-wk 

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 mo 

and last two wk 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 wk of 

pregnancy were similar. 

OR for PTB [Lower CI, Upper CI] 

First mo 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 wk 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 

Author: Hwang and Jaakkola 

(2008, 193794) 

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, 156601) 

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 wk) 

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 mo: 

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 mo prior to birth: 

All of Sydney: 0.96 (0.88-1.04) 

Within 5km of site: 1.00 (0.86-1.15) 

3 mo prior to birth: 

All of Sydney: 0.99 (0.90-1.09) 

Within 5km of site: 1.11 (0.94-1.31) 

January 2010 C-28 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Lee et al. (2003, 

043202) 

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 wk) 

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, 

089828) 

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. (2004, 

095787) 

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) 

Author: Lin et al. (2004, 

089503) 

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: 

92,288 full-term live 

singleton births (>37 wk) 

within 3 km 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. 

January 2010 C-29 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Liu et al. (2003, 

089548) 

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 mo: 1.01 (0.93-1.09) 

Last mo: 0.96 (0.88-1.04) 

OR for PTB [Lower CI, Upper CI] 

First mo: 0.95 (0.89-1.01) 

Last mo: 1.08 (1.01-1.15) 

OR for IUGR [Lower CI- Upper CI] 

First mo: 1.06 (1.01-1.10) 

Last mo: 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, 

090429) 

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 mo of pregnancy. 

(All results presented in Figures) 

Author: Maisonet et al. 

(2001, 016624) 

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 wk) 

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 

Author: Mannes 

et al. (2005, 087895) 

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-wk 

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 mo 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 trimeste: -22.88 (-44.58 to -1.18) 

One mo 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 mo 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 mo prior to birth: 1.10 (0.96-1.27) 

January 2010 C-30 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Medeiros et al. 

(2005, 089824) 

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 wk) 

Averaging Time: 24 h 

Mean (SD) unit:

Daily mean shown in Figure (see paper)

Range (Min, Max): NR 

Copollutant: NR 

Increment: 1 ppm 

Regression coefficient 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: Mortimer et al. 

(2008, 187280) 

Period of Study: 

November 2000-April 2005 

Location: Central Valley of 

Californinia 

Health Outcome: Allergic 

sensitization 

Study Design: Cohort 

Statistical Analyses: Chisquare tests 

Age Groups Analyzed: 

6-11 yrs. 

Sample Description: 170 

children with asthma from 

the FACES-LiTE study 

Averaging Time: 8 h 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

Entire Prenatal: 

PM10: r = 0.32 

NO2: r = 0.74 

O3: r = -0.40 

Trimester 2: 

PM10: r = 0.32 

NO2: r = 0.68 

O3: r = -0.26 

 

Increment: NR 

Trimester specific results presented graphically 

Single-pollutant Model for “sensitized to at least 

one outdoor allergen” 

OR adjusted for yr of birth and sex [Lower CI, 

Upper CI] 

Entire Pregnancy 

24-h avg: 1.45 (1.02, 2.07) 

Daily max: 1.53 (1.01, 2.33) 

8-h max: 1.55 (1.01, 2.37) 

2nd Trimester 

24-h avg: 1.52 (0.93, 2.47) 

Daily max: 1.50 (0.92, 2.45) 

8-h max: 1.45 (0.90, 2.35) 

Coefficient adjusted for yr of birth and sex [SE] 

Entire Pregnancy 

24-h avg: 1.33 (0.68) 

Daily max:0.54 (0.27) 

8-h max: 0.84 (0.42) 

2nd Trimester 

24-h avg: 0.57 (0.34) 

Daily max: 0.21 (0.13) 

8-h max: 0.32 (0.21) 

Author: Parker et al. (2005, 

087462) 

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 wk) 

within 5 mi 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) 

January 2010 C-31 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Ritz et al. (2000, 

012068) 

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- to 

44-wk 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 wk prior to birth: 1.04 (0.99-1.10) 

1st mo of pregnancy: 1.04 (0.99-1.09) 

Adjusted for various risk factors 

6 wk prior to birth: 1.06 (1.02-1.10) 

1st mo of pregnancy: 1.01 (0.97-1.04) 

Author: Ritz et al. (2002, 

023227) 

Period of Study: 1987-1993 

Location:

Southern California 

Health Outcome:

Birth defects (heart defects 

and 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 mo of pregnancy. 

Aortic artery and 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 and 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, 

089819) 

Period of Study: 1989-2000 

Location:

Southern California 

Health Outcome:

Postneonatal 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 wk prior to death, 1 mo prior to 

death, 2 mo prior to death, 6 mo prior to death 

All causes: 

2 wk prior to death: 1.14 (1.03-1.25) 

2 mo prior to death: 1.11 (1.06-1.16) 

SIDS: 

2 mo prior to death: 1.19 (1.10-1.28) 

Term/normal weight births 

2 mo 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 mo 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) 

January 2010 C-32 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Ritz et al. (2007, 

096146) 

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 wk 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, 

087885) 

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) 

Author: Son et al. (2008, 

190323) 

Period of Study: NR 

Location: Seoul, Korea 

Health Outcome:

Postneonatal mortality from 

all causes 

Study Design: Case 

crossover and time series 

Statistical Analyses:

Conditional logistic 

regression 

Age Groups Analyzed: NA 

Sample Description: 1,286 

first-born birth and infant 

death records from 

1999-2003 (only 

postneonatal deaths) 

Averaging Time: 8 h 

Mean (SD) unit: 1.01 ppm 

Range (Min, Max): 0.29, 3.54 

Copollutant: 

PM10, NO2, O3, SO2 

 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0-7 

Time Series: 1.323 (1.077, 1.625) 

Case-crossover(1:6): 1.029 (0.833, 1.271) 

CLR Analyses using different control selection 

schemes 

1:2: 1.076 (0.839, 1.379) 

1:4: 0.981 (0.784, 1.228) 

1:6: 1.029 (0.833, 1.271) 

Author: Strickland et al. 

(2009, 190324) 

Period of Study: NR 

Location: Atlanta, GA 

Health Outcome:

Cardiovascular 

malformations 

Study Design:

Retrospective cohort 

Statistical Analyses:

Poisson GLM 

Age Groups Analyzed: NA 

Sample Description:

Pregnancies reaching at 

least 20-wk gestation that 

were conceived during 

January 1, 1986-March 12, 

2003 

Averaging Time: 24-h 

Mean (SD) unit: 

By season of conception: 

March-May: 0.9 ppm 

June-August: 0.8 ppm 

Sept.-Nov.: 0.9 ppm 

Dec.-Feb.: 0.7ppm 

By yr of conception: 

1986-1991: 0.7 ppm 

1992-1997: 0.8 ppm 

1998-2003: 0.7 ppm 

Range (IQR): 0.3 

Copollutant: 

PM10 (24 h): r = 0.32 

NO2 (24 h): r = 0.41 

O3 (8 h): r = 0.07 

SO2 (24 h): r = 0.23 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Atrial septal defect, secundum: 1.16 (0.67, 2.00) 

Coarctation of the aorta: 1.15 (0.65, 2.06) 

Hypoplastic left heart syndrome: 0.82 (0.37, 1.84) 

Patent ductus arteriosus: 1.39 (0.72, 2.68) 

Pulmonary stenosis, valvar: 0.97 (0.53, 1.75) 

Tetralogy of Fallot: 1.09 (0.59, 2.00) 

Transposition of the great arteries: 1.29 (0.58, 2.85)

Ventricular septal defect, muscular: 

1.08 (0.77, 1.50) 

Ventricular septal defect, perimembranous: 

1.06 (0.67, 1.68) 

 Conotruncal defect: 1.22 (0.81, 1.85) 

Left ventricular outflow tract defect: 

1.09 (0.70, 1.68) 

Right ventricular outflow tract defects: 

0.73 (0.44, 1.22) 

January 2010 C-33 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Tsai et al. (2006, 

090709) 

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, 

088668) 

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 mi 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 wk 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: 

<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 wk prior to birth: 

<: 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: 

<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, 098386) 

Period of Study: 1999-2002 

Location:

U.S. counties with >250,000 

residents 

Health Outcome:

Postneonatal deaths 

all causes; respiratory; 

SIDS; ill-defined + 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 mo 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. (2004, 

094376) 

Period of Study: 1994-2000 

Location:

Taipei, Taiwan 

Health Outcome: 

Postneonatal 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) 

January 2010 C-34 
Table C-4. Studies of short-term CO exposure and respiratory morbidity 

Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Andersen et al. 

(2008, 096150) 

Period of Study: 

Dec 1998-Dec 2004 

Location:

Copenhagen, Denmark 

Health Outcome: Wheezing 

symptoms 

Study Design: Panel 

Statistical Analyses: Logistic 

regression (GEE) 

Age Groups Analyzed: 0-3 yrs 

Sample Description:

205 children of mothers with asthma 

Averaging Time: 24h 

Mean (SD) unit: 0.29 (0.10) ppm 

Range (percentiles):

25th = 0.22; 75th = 0.34 

Copollutant: correlation 

PM10: r = 0.45 

PM2.5: r = 0.45 

UFPNC: r = 0.52 

NO2: r = 0.75 

NOX: r = 0.74 

O3: r = -0.63 

Increment: NR 

OR Estimate [Lower CI, Upper CI] ; lag: 

Lags examined: 0, 1, 2, 3, 4, 2-4 

Lag 0: 0.96 (0.80, 1.15) 

Lag 1: 0.92 (0.77, 1.10) 

Lag 2: 1.08 (0.92, 1.28) 

Lag 3: 1.07 (0.90, 1.26) 

Lag 4: 1.02 (0.84, 1.23) 

3d mean: 1.07 (0.87, 1.32) 

Author: Bhattacharyya et 

al. (2009, 180154) 

Period of Study: 1997-

2006 

Location: NR (National 

Health Interview Survey as 

aggregated in the 

Integrated Health Interview 

Series served as data 

source) 

Health Outcome: Respiratory 

morbidity 

Study Design: Cross-sectional study 

Statistical Analyses: SPSS version 

14.0, univariate linear regression 

analysis 

Age Groups Analyzed: 18+ yr (avg: 

45.2 yr) 

Sample Description: Hay fever, 

weak/failing kidneys, sinusitis all in 

past 12 mo 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): 2.209-4.157ppm 

(decreased with increasing yr) 

Copollutant: NR 

 

Increment: NR 

Linear regression analysis for disease 

condition prevalence: Hayfever: Standardized 

B- 0.012, p-value- <0.001; Sinusitis: 

Standardized B- 0.027, p-value- <0.001; 

Kidney Weak/Failin: Standardized B- -0.001, pvalue- <0.001 

Lags examined: NR 

Author: Chen et al. (1999, 

011149) 

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 yr 

Pollutant: CO 

Averaging Time: 1-h max; 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): 

1-h max: (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 max 

-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 max 

1.2 (19.48); 1 

-1.44 (18.57); 2 

20.96 (15.67); 7 

Author: Chen et al. (2000, 

011931) 

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 max 

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 

January 2010 C-35 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: de Hartog et al. 

(2003, 001061) 

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: 

Nonsmoking individuals with CHD: 

Amsterdam: 37 

Erfurt: 47 

Helsinki: 47 

Age Groups Analyzed: ≥ 50 yr 

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 

Author: Delfino et al. 

(2003, 050460) 

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 yr 

Pollutant: CO 

Averaging Time: 

1-h max; 8-h max 

Mean (SD) unit: 

1-h max: 7.7 (3.1) ppb 

8-h max: 5.0 (2.0) ppb 

Range (Min, Max):

1-h max: (2, 17) 

8-h max: (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-max 

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 max 

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, 099124) 

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 yr 

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) 

January 2010 C-36 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Fischer et al. 

(2002, 025731) 

Period of Study: NR 

Location: 

Utrecht, Netherlands 

Health Outcome: 

Lung function (FVC, FEV1, PEF, 

MMEF) 

Study Design: Panel study 

Statistical Analyses: 

Restricted max 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 

Author: Ho et al. (2007, 

093265) 

Period of Study: 

Oct 1995-Mar 1996 

Location:

Taipei, Taiwan 

Health Outcome: Asthma 

Study Design: Panel 

Statistical Analyses: Logistic 

regression (GEE) 

Age Groups Analyzed: 10-17 yr 

Sample Description:

A stratified cluster random sample of 

students (n=69,367) from 1,139,452 

students sampled nationwide 

Averaging Time: 8 h 

Mean (SD) unit: NR 

Range (min, max): NR 

Copollutant: 

NO, NO2, NOX, O3, SO2, PM10, PSI 

Increment: very high, high, med, low, very low 

OR Estimate [Lower CI, Upper CI] ; lag: 

Lags examined: NR 

Females: 1.984 (1.536, 2.561) 

Males: 1.780 (1.377, 2.302) 

Monthly attack rate vs single air pollutant 

concentrations 

Estimate (p-value): 0.0750 (0.3336) 

Author: Lagorio et al. 

(2006, 089800) 

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 yr 

Asthma panel: 18-64 yr 

IHD panel: 40-64 yr 

Notes: Asthma panel was restricted to 

never smokers, while COPD and IHD 

panels include former smokers if 

smoking cessation occurred at least 

1 yr 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: Moon et al. (2009, 

190297) 

Period of Study: Apr 2003-May 2003 

Location:

Seoul, Incheon, Busan, & 

Jeju, Korea 

Health Outcome: Respiratory 

symptoms 

Study Design: Panel 

Statistical Analyses: Logistic 

regression (GEE) 

Age Groups Analyzed: < 13 yr 

Sample Description: 696 children 

Averaging Time: 24h 

Mean (SD) unit: NR 

IQ Range: 0.12ppm 

Copollutant: 

PM10, SO2, NO2, O3

Increment: 0.12 ppm (IQR) 

OR Estimate [Lower CI, Upper CI] ; lag: 

Lags examined: lag days 0-3 

 Lower resp. symptoms: 

1.005 (1.003, 1.008), lag 0 

Upper resp. symptoms: 

1.006 (1.003, 1.008), lag 0-2 

Irritation symptoms: 

1.004 (1.001, 1.006), lag 1-3 

January 2010 C-37 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Mortimer et al. 

(2008, 187280) 

Period of Study: 

Nov 2000-Apr 2005 

Location:

Fresno, California 

Health Outcome: Allergic sensitization

Study Design: Panel 

Statistical Analyses: Multistep 

modeling 

Age Groups Analyzed: 6-11 yr 

Sample Description: 170 children 

with physician diagnosed asthma 

Averaging Time: 24-h avg, 24-h 

max, 8-h max 

Mean (SD) unit: NR 

IQ Range (24-h avg, 24-h max, 8-h 

max): 0.28, 0.79, 0.52 

Copollutant: entire prenatal 

correlation 

NO2: r = 0.74 

O3: r = -0.40 

PM10: r = 0.32 

Increment: IQR 

OR Estimate [Lower CI, Upper CI] ; lag: 

Lags examined: NR 

Entire Pregnancy: 

CO 24-h avg: 1.45 (1.02, 2.07) 

CO 24-h max: 1.53 (1.01, 2.33) 

CO 24-h avg: 1.55 (1.01, 2.37) 

Author: Nkwocha et al. 

(2008, 190304) 

Period of Study: 

Feb 2005-Jul 2006 

Location:

Port Harcourt, Nigeria 

Health Outcome: Respiratory 

symptoms 

Study Design: Panel 

Statistical Analyses: Mixed Effects 

models 

Age Groups Analyzed: 0-5 yr 

Sample Description: 250 children 

Averaging Time: 8 h 

Mean (SD) unit: NR 

Range (min, max):

1.3 μg/m3, 1.83 μg/m3

Copollutant: NO2, SO2, PM10

Increment: NR 

Lags examined: NR 

R Estimate: 

Dry season: 0.13 

Wet season: 0.25 

Author: O’Connor et al. 

(2008, 156818) 

Period of Study: 

Aug 1998-Jul 2001 

Location:

Boston, MA; the Bronx, 

NY; Chicago, IL; Dallas, 

TX; New York, NY; Seattle, 

WA; Tuscon, AZ 

Health Outcome: respiratory 

symptoms 

Study Design: panel 

Statistical Analyses: Mixed Effects 

Models 

Age Groups Analyzed: 5-12 yr 

Sample Description:

861 children with persistent asthma 

and atopy living in low-income census 

tracts 

Averaging Time: 8 h 

Mean (SD) unit: NR 

Range (10th-90th): 872.1 ppb 

Copollutant: 

PM10, SO2, NO2, O3

Increment: 872.1 ppb 

Lags examined: NR 

Change Estimate [Lower CI, Upper CI]: 

FEV1: -0.56 (-1.31, 0.20) 

PEFR: -0.49 (-1.24, 0.27) 

Pollution Impact*[Lower CI, Upper CI]: 

Wheeze-cough: 1.26 (1.03, 1.55) 

Nighttime asthma: 1.35 (1.07, 1.71) 

Slow play: 1.28 (1.04, 1.59) 

OR [Lower CI, Upper CI]: 

Missed School: 1.08 (0.76, 1.53) 

*Coefficients from the negative binomial model 

and indicate the multiplicative effect per unit 

change 

Author: Park et al. (2002, 

093798) 

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. (2005, 

088673) 

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 yr 

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) 

January 2010 C-38 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Penttinen et al. 

(2001, 030335) 

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 nonsmoking 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, 096753) 

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 yr 

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, 

089500) 

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 yr 

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, 092842) 

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 yr 

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 

January 2010 C-39 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Schildcrout et al. 

(2006, 089812) 

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 yr 

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, 087471) 

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 yr 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 yr 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 yr 

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. 

January 2010 C-40 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Slaughter et al. 

(2003, 086294) 

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 yr 

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, 017157) 

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 max likelihood linear model 

Population: 126 children 

Age Groups Analyzed: 8-13 yr 

Notes: The study was only conducted 

for a two mo 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, 025653) 

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 yr 

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 

January 2010 C-41 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: von Klot et al. 

(2002, 034706) 

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 yr 

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, 

013254) 

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 yr 

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: Abe et al. 

(2009, 190536) 

Period of Study:

January 1-December 

31, 2005 

Location: Tokyo, 

Japan 

ED Visits 

Health Outcome: Asthma 

Study Design: Time-series 

Statistical Analyses: Bivariate 

Pearson correlation 

coefficitnes, ARIMA model 

Age Groups Analyzed:

Children: ≤14 yr, Adults: ≤ 15 yr 

Sample Description: Data 

from daily number of 

ambulance transports to ED for 

asthma 

Averaging Time: NR 

Mean (SD) unit: 11.5ppm 

Range (Min, Max): 3-44ppm 

Copollutant: NR 

 

Increment: 0.1ppm 

ARIMA model for ambulance transports to ED for asthma 

exacerbation among adults: β coefficient: 0.151, SE: 

0.098, t statistic: 1.537, P value: .125 

ARIMA model for ambulance transports to ED for asthma 

exacerbation among children: β coefficient: 0.019, SE: 

0.034, t statistic: 0.549, P value: 0.583 

Lags examined: 0 

On the day with the highest CO the number of transports 

was 25. The number of transports for adults and CO had 

significant bivariate correlations. The fitted ARIMA model 

had no significant associations. 

January 2010 C-42 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Anderson et 

al. (2001, 017033) 

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 quasilikelihood approach and GAM 

Age Groups Analyzed: 

All ages 

0-14 yr 

15-64 yr 

≥ 65 yr 

Pollutant: CO 

Averaging Time: Max 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 

Author: Andersen et 

al. (2007, 093201) 

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 

yr; ≥ 65 yr 

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, 007882) 

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 yr 

15-64 yr 

≥ 65 yr 

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 

January 2010 C-43 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Bedeschi et 

al. (2007, 090712) 

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: <5 yr 

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. 

Author: Bell et al. 

(2008, 091268) 

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 

January 2010 C-44 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Bellini et al. 

(2007, 097787) 

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, 016275) 

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 yr 

3-5 yr 

6-13 yr 

14-19 yr 

0-19 yr 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Mean (SD) unit: 4.8 (2.3) ppm 

Range (Min, Max): (0.6, 19.1) 

Copollutant: 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 

Author: Burnett et al. 

(1999, 017269) 

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: 

Multipollutant 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: 

Multipollutant 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, 093439) 

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 yr 

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 

January 2010 C-45 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Cakmak et al. 

(2006, 093272) 

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, 093034) 

Period of Study: 

1996-2004 

Location: 

Kaohsiung, Taiwan 

Hospital Admissions 

Health Outcome (ICD9): 

Pneumonia (480-486) 

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: 0.76 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 

Author: Chiu et al. 

(2009, 190249) 

Period of Study: 

1996-2004 

Location:

Taipei, Taiwan 

Hospital Admissions 

Health Outcome: pneumonia 

HA 

Study Design: case-crossover 

Statistical Analyses:

Conditional Logistic regression 

Age Groups Analyzed: 

All ages 

Sample Description:

152,594 HA for 47 hospitals in 

Taipei city 

Averaging Time: 24h 

Mean (SD) unit: 1.26 ppm 

Range (min, max): 0.12, 3.66 

Copollutant: correlation 

PM10: r = 0.34 

SO2: r = 0.57 

NO2: r = 0.69 

O3: r = -0.31 

Increment: 0.57 ppm (IQR) 

OR Estimate [Lower CI, Upper CI] ; lag: 

Lags examined: one wk before to one wk after 

CO: 

≥23°C: 1.25 (1.21, 1.29) 

<23°C: 1.12 (1.09, 1.15) 

CO + PM10: 

≥23°C: 1.23 (1.19, 1.27) 

<23°C: 1.05 (1.02, 1.09) 

CO + SO2: 

≥23°C: 1.25 (1.21, 1.30) 

<23°C: 1.27 (1.22,1.31) 

CO + NO2: 

≥23°C: 0.97 (0.93, 1.02) 

<23°C: 1.14 (1.09, 1.20) 

CO + O3: 

≥23°C: 1.24 (1.20, 1.28) 

<23°C: 1.21 (1.17, 1.24) 

January 2010 C-46 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Cho et al. 

(2000, 099051) 

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: Delfino et al. 

(2008, 156390) 

Period of Study:

January 1, 

2000-December 31, 

2003 

Location: Orange 

County, California 

ED Visits 

Health Outcome: Asthma 

Study Design: Longitudinal, 

Cohort 

Statistical Analyses:

Proportional hazards models in 

SAS version 9.2 

Age Groups Analyzed: 0-18 yr 

Sample Description: Various 

gender, race, insurance status, 

income, poverty level, 

residence distance to treating 

hospital 

Averaging Time: NR 

Mean (SD) unit: Cool season:

0.114 (0.052), Warm season: 0.103 

(0.048) 

Range (Min, Max): 

Cool season: 0.014 -0.378, 

Warm season: 0.013-0.482 

Copollutant: NOX

 

Increment: 0.056 ppm 

HR (95% CI): Unadjusted: 1.072 (1.016 – 1.131), 

Adjusted: 1.073 (1.013 – 1.137), Male: 1.054 (0.978 – 

1.137), Female: 1.100 (1.011 – 1.197), 0 yr: 1.158 (1.041 – 

1.289), 1-5 yr: 1.021 (0.933 – 1.117), 6-18 yr: 1.076 (0.972 

– 1.191), Median or less poverty: 1.054 (0.979 – 1.134), 

Greater than the median poverty: 1.094 (1.006 – 1.190), 

Greater than the median income: 1.120 (1.034 – 1.213), 

Median or less income: 1.041 (0.959 – 1.129), Private 

insurance: 1.102 (1.006 – 1.206), Government sponsored 

or self-pay insurance: 1.061 (0.989 – 1.138), Unknown 

insurance: 0.913 (0.591 – 1.412), White: 1.113 (1.027 – 

1.205), Hispanic: 1.081 (0.996 – 1.173), Non-Hispanic 

nonwhite: 0.804 (0.601 – 1.074) 

Lags examined: NR 

The point estimates for CO are stronger in girls than in boy 

and in infants than in older children. There is little 

difference in coefficients between adjusted and unadjusted 

CO models. There were significant increased risks of 

repeated hospital encounters of 7% to 10% per IQR 

increase in traffic-related CO exposure. 

Author: Farhat et al. 

(2005, 089461) 

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: Max 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 

January 2010 C-47 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Fung et al. 

(2006, 089789) 

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. Bidirectional case-crossover 

Statistical Analyses: 

1. Dewanji and Moolgavkar 

2. Poisson 

3. Conditional logistic 

regression 

Age Groups Analyzed: ≥ 65 yr 

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 

Bidirectional 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 

Author: Fusco et al. 

(2001, 020631) 

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 yr 

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 

January 2010 C-48 
Study Design Concentrations Effect Estimates (95% CI) 

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 (2000, 

010436) 

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 yr; <5 yr 

Pollutant: CO 

Averaging Time: Max 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 

Author: Hajat et al. 

(1999, 000924) 

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 yr 

15-64 yr 

≥ 65 yr 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

All yr: 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 

January 2010 C-49 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Hajat et al. 

(2002, 030358) 

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 yr 

15-64 yr 

≥ 65 yr 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

All yr: 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 

Author: Hapcioglu et 

al. (2006, 093263) 

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, 088976) 

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: Max 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, 023222) 

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 yr 

15-64 yr 

≥ 65 yr 

Pollutant: CO 

Averaging Time: Max 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 

January 2010 C-50 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Ito et al. 

(2007, 091262) 

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: Max 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 

Author: Jayaraman et 

al. (2008, 180352) 

Period of Study: 

2004-2005 

Location:

New Delhi, India 

Hospital Admissions 

Health Outcome: respiratory 

Study Design: time series 

Statistical Analyses: Poisson 

regression (GAM) 

Age Groups Analyzed: 

All ages 

Sample Description: 

daily HA for respiratory unit of 

Safdarjung hospital 

Averaging Time: 24-h 

Mean (SD) unit: 

2,379.14 (1,289.18) μg/m3

Range (min, max): 588, 8458 

Copollutant: 

SO2: r = 0.217* 

NO2: r = 0.204* 

SPM: r = 0.071 

RSPM: r = 0.120 

O3: r = 0.063 

*p < 0.05 

Increment: 10 μg/m3

RR Estimate [Lower CI, Upper CI] ; lag: 

Lags examined: lag days 0-3 

 Single Pollutant: 0.9989 (0.985, 2.715), 2 

Multi-pollutant: 0.998 (0.993, 1.004), 2 

Winter, all ages: 1.027 (1.004, 1.051), 2 

Winter, males 50-69: 2.625 (1.048, 1.158) 

Author: Karr et al. 

(2007, 090719) 

Period of Study: 

1995-2000 

Location:

South Coast Air Basin, 

CA 

Hospital Admissions 

Health Outcome (ICD9): 

Acute bronchiolitis (466.1) 

Study Design: Matched case 

control 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

Infants: 3 wk to 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, 088751) 

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 wk to1 yr 

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 wk and in the same mo 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 wk: 0.86 (0.68-1.1); 1 

29 1/7 – 34 wk: 1 (0.86-1.15); 1 

34 1/7 – 37 wk: 0.95 (0.87-1.04); 1 

37 1/7 – 44 wk: 1 (0.97-1.03); 1 

Increment: 1400 ppb 

Age Group: 

Overall: 0.97 (0.94-1); 4 

25-29 wk: 0.93 (0.72-1.2); 4 

29 1/7 – 34 wk: 0.89 (0.77-1.03); 4 

34 1/7 – 37 wk: 0.98 (0.90-1.08); 4 

37 1/7 – 44 wk: 0.97 (0.94-1); 4 

January 2010 C-51 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Kim et al. 

(2007, 092837) 

Period of Study: 

2002 

Location: 

Seoul, 

Korea 

Hospital Admissions 

Health Outcome (ICD10):

Asthma (J45 and J46) 

Study Design: 

Bidirectional case crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

All Ages 

Pollutant: CO 

Averaging Time: Max 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 

Author: Kontos et al. 

(1999, 011326) 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 yr 

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, 034826) 

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: <5 yr 

Pollutant: CO 

Averaging Time: 1-h max 

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 yr 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 

January 2010 C-52 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lee et al. 

(2006, 098248) 

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 yr 

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. 

(2007, 090707) 

Period of Study: 

1996-2003 

Location: 

Kaohsiung, Taiwan 

Hospital Admissions 

Health Outcome (ICD9): 

COPD (490-492, 494, 496) 

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: 0.77 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 

Author: Lin et al. 

(1999, 040437) 

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: <3 yr 

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 

January 2010 C-53 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lin et al. 

(2003, 042549) 

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 yr 

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. 

(2004, 055600) 

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 yr 

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 

Author: Lin et al. 

(2005, 087828) 

Period of Study: 

1998-2001 

Location: 

Toronto, 

Canada 

Hospital Admissions 

Health Outcome (ICD9):

Respiratory infections (464, 

466, and 480-487) 

Study Design: 

Bidirectional case crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: <5 yr 

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 

January 2010 C-54 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Linn et al. 

(2000, 002839) 

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 yr; ≥ 30 yr 

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 

January 2010 C-55 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Luginaah et 

al. (2005, 057327) 

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 yr 

15-64 yr 

≥ 65 yr 

Pollutant: CO 

Averaging Time: 1-h max 

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 

January 2010 C-56 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Martins et al. 

(2002, 035059) 

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 yr 

Pollutant: CO 

Averaging Time: Max 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, 052100) 

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 step-wise 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, 030325) 

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 yr; >64 yr 

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, 193772) 

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 yr 

15-64 yr 

>64 yr 

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) 

January 2010 C-57 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Moolgavkar 

(2000, 010274) 

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 yr 

20-64 yr 

≥ 65 yr 

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 

(2003, 042864) 

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 yr 

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 

January 2010 C-58 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Neidell et al. 

(2004, 057330) 

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 yr 

1-3 yr 

3-6 yr 

6-12 yr 

12-18 yr 

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, 040774) 

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: <8 yr 

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, 056305) 

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 yr 

Pollutant: CO 

Averaging Time: 1-h max 

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 

January 2010 C-59 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Sauerzapf et 

al. (2009, 180082) 

Period of Study: 

Jan 2006-Feb 2007 

Location:

Norfolk county, 

England 

Hospital Admissions 

Health Outcome: COPD 

Study Design: Case crossover 

Statistical Analyses: Logistic 

Regression 

Age Groups Analyzed: 

18+ yr (90% of patients 60+ yr) 

Sample Description:

1,050 COPD admissions 

Averaging Time: 24 h 

Mean (SD) unit: 

Control days: 194.46 (80.93) 

Case days: 204.73 (119.97) 

Range (min, max):

Control days: 105.20, 408.10 

Case days: 108.70, 432.20 

Copollutant: 

NO, NO2, NOX, O3

 * Control days = 7 days prior to 

admission; Case days = day of 

admission 

Increment: 10 μg/m3

Lags examined: 0-8 

OR Estimate [Lower CI, Upper CI]; lag: 

Unadjusted: 1.010 (1.001, 1.019); lag 0-7 

Adjusted: 1.015 (1.005, 1.025); lag 0-7 

Unadjusted: 1.013 (1.001, 1.025); lag 1-8 

Adjusted: 1.018 (1.005, 1.031); lag 1-8 

Author: Sheppard et 

al. (1999, 086921) 

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 yr 

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 

Author: Slaughter et 

al. (2005, 073854) 

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 

January 2010 C-60 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Stieb et al. 

(2000, 011675) 

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 max 

Mean (SD) unit:

All yr: 0.5 (0.3) ppm 

May-September: 0.6 (0.3) ppm 

All yr: 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 

Al% Increase (Lower CI, Upper CI); lag: 

Respiratory Illnesses 

Increment: 0.5 ppm 

All Year: -3.40; 7 

Increment: 1.7 ppm 

May- September: -5.70 

Author: Sun et al. 

(2006, 090768) 

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 yr; 16-55 yr 

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 

Author: Tenias et al. 

(2002, 026077) 

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 yr 

Pollutant: CO 

Averaging Time: 

24-h avg 

1-h max 

Mean (SD) unit:

24-h avg 

All yr: 3.1 mg/m3

Warm Months: 2.5 mg/m3

 

Cold Months: 3.7 mg/m3

1-h avg 

All yr: 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 max: (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 max 

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, 073513) 

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. 

January 2010 C-61 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Tolbert et al. 

(2007, 090316) 

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 max 

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, 180127) 

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 

Author: Tsai et al. 

(2006, 089768) 

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, 090711) 

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 yr; 

>65 yr 

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 

January 2010 C-62 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Villeneuve et 

al. (2006, 091179) 

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 yr 

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, 093267) 

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 yr; <2 yr; 2-5 yr; >5 yr 

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, 092848) 

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 

Author: Yang et al. 

(2007, 092847) 

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 

January 2010 C-63 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Yang et al. 

(2005, 090184) 

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 yr 

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, 055621) 

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 yr; ≥ 65 yr 

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. 

(2004, 087488) 

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 yr 

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, 

090195) 

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 

January 2010 C-64 
Table C-6. Studies of long-term CO exposure and respiratory morbidity. 

Study Design Concentrations Effect Estimates (95% CI) 

Author: Goss et al. 

(2004, 055624) 

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 yr 

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, 010937) 

Period of Study:

10/1995-5/1996 

Location: Taiwan 

Health Outcome: Asthma 

Study Design: Cohort 

Statistical Analyses: 

Logistic regression 

Population: 

331,686 nonsmoking children 

Age Groups Analyzed: 

Middle-school children 

(mean age = 13.8 yr) 

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) 

January 2010 C-65 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Hirsch et al. 

(1999, 003537) 

Period of Study:

Population: 

9/1995-6/1996 

Air: 

4/1994-4/1995 

Location:

Dresden, Germany 

Health Outcome: 

Asthma symptoms in the past 

12 mo (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 yr 

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 mo: 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 mo: 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 Children were defined as those children with 

specific IgE to aeroallergens ≤ 0.7 kU-L-1. 

Author: Hwang et al. 

(2006, 088971) 

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 yr 

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) 

January 2010 C-66 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Hwang et al. 

(2005, 089454) 

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 yr 

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. 

(2003, 049201) 

Period of Study:

10/1995-5/1996 

Location: Taiwan 

Health Outcome: Allergic rhinitis 

Study Design: Cohort 

Statistical Analyses: 

Multiple logistic regression 

Population: 

331,686 nonsmoking children 

Age Groups Analyzed: 12-14 yr 

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, 093275) 

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 yr 

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. 

January 2010 C-67 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Mortimer et 

al. (2008, 122163) 

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 yr 

Pollutant: CO 

Averaging Time:

8-h max monthly mean 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant; correlation: 

Lifetime 

NO2 (24-h avg): r = 0.68 

O3 (8-h max): r = -0.40 

PM10 (24-h avg): r = 0.05 

Prenatal 

CO (8-h max): r = 0.52 

NO2 (24-h avg): r = 0.37 

O3 (8-h max): 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 max 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 yr old 

-4.80% (0.0446) 

FEF25 

24-h avg CO exposure during ages 0 to 6 and 

diagnosed with asthma <2 yr 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 max CO exposure: 0.1711 (0.0653) 

Lifetime 1-h max CO exposure: -0.3242 (0.0919) 

24-h avg CO exposure during ages 0-3 and diagnosed 

with asthma <2 yr old: -0.1814 (0.0599) 

FEF25 

24-h avg CO exposure during ages 0-6 and diagnosed 

with asthma <2 yr old: -1.0460 (0.1953) 

FEF75 

Lifetime 8-h max CO exposure: -0.4214 (0.1423) 

Author: Singh et al. 

(2003, 052686) 

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 yr 

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. 

January 2010 C-68 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Sole et al. 

(2007, 090706) 

Period of Study: 

Location:

Sao Paulo West, Sao 

Paulo South, Santo 

Andre, Curitba, & 

Porto Alegre, Brazil 

Health Outcome: Symptoms of 

asthma, rhinitis, and eczema 

Study Design: Panel 

Statistical Analyses: Logistic 

Regression 

Age Groups Analyzed: 

13-14 yr 

Averaging Time: Annual 

Mean (SD) unit: 

Sao Paulo West: 7.70 ppm 

Sao Paulo South: 7.50 ppm 

Santo Andre: 9.80 ppm 

Curitba: 7.90 ppm 

Porto Alegre: 1.51 ppm 

Range (min, max): NR 

Copollutant: NO2, SO2, O3

Increment: Risk in relation to center w/ lowest annual 

mean (Porto Alegre = ref) 

OR Estimate [Lower CI, Upper CI]: 

Lags examined: NR 

Current Wheezing: 

Sao Paulo West: 1.26 (1.11, 1.42) 

Sao Paulo South: 1.03 (0.91, 1.18) 

Santo Andre: 1.36 (1.20, 1.56) 

Curitba: 1.05 (0.93, 1.19) 

Severe Asthma: 

Sao Paulo West: 1.20 (0.95, 1.50) 

Sao Paulo South: 0.59 (0.45, 0.78) 

Santo Andre: 0.62 (0.48, 0.81) 

Curitba: 0.64 (0.50, 0.82) 

Nighttime Coughing: 

Sao Paulo West: 1.06 (0.95, 1.17) 

Sao Paulo South: 0.93 (0.84, 1.03) 

Santo Andre: 0.91 (0.82, 1.02) 

Curitba: 0.99 (0.89, 1.10) 

Rhinoconjunctivitis: 

Sao Paulo West: 1.31 (1.15, 1.15) 

Sao Paulo South: 0.73 (0.64, 0.85) 

Santo Andre: 0.85 (0.74, 0.97) 

Curitba: 1.10 (0.96, 1.25) 

Severe Rhinits: 

Sao Paulo West: 1.01 (0.91, 1.49) 

Sao Paulo South: 0.68 (0.59, 0.77) 

Santo Andre: 0.73 (0.64, 0.83) 

Curitba: 1.03 (0.91, 1.16) 

Eczema: 

Sao Paulo West: 1.45 (1.20, 1.74) 

Sao Paulo South: 1.03 (0.85, 1.25) 

Santo Andre: 1.03 (0.85, 1.25) 

Curitba: 0.90 (0.75, 1.10) 

Flexural Eczema: 

Sao Paulo West: 1.42 (1.15, 1.76) 

Sao Paulo South: 0.71 (0.56, 0.91) 

Santo Andre: 0.68 (0.53, 0.87) 

Curitba: 0.73 (0.57, 0.92) 

Severe Eczema: 

Sao Paulo West: 1.08 (0.86, 1.35) 

Sao Paulo South: 0.42 (0.31, 0.56) 

Santo Andre: 0.38 (0.28, 0.51) 

Curitba: 0.30 (0.22, 0.41) 

Author: Wang et al. 

(1999, 008105) 

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 yr 

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) 

Author: Wilhelm et al. 

(2008, 191912) 

Period of Study: 

2000-2001 

Location:

Los Angeles County 

or San Diego County, 

California 

Health Outcome: Asthma 

symptoms/ED visit/HA 

Study Design: Panel 

Statistical Analyses: Logistic 

regression 

Age Groups Analyzed: 0-17 yr 

Sample Description:

612 children who reported a 

physician diagnosis of asthma at 

some point in their lives 

Averaging Time: annual 

Mean (SD) unit: 1.0 ppm 

Range (min, max): 0.34, 1.8 

Copollutant: correlation 

O3: r= -0.67 

PM10: r= 0.41 

PM2.5: r= 0.60 

NO2: r= 0.57 

traffic density: r= 0.02 

Increment: NR 

OR Estimate [Lower CI, Upper CI] ; lag: 

Lags examined: NR 

No associations observed between asthma symptom 

outcome measures (no results shown) 

January 2010 C-69 
Table C-7. Studies of short-term CO exposure and mortality. 

Study Design Concentrations Effect Estimates (95% CI) 

Author: Anderson et al. 

(2001, 017033) 

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: Max 8-h ma 

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, 

097787) 

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: Berglind et al. 

(2009, 190068) 

Period of Study: 1992-2002 

Location: Augsburg, 

Germany; Barcelona, Spain; 

Helsinki, Finland; Rome, 

Italy; Stockholm, Sweden 

Health Outcome: Mortality 

Study Design: Cohort 

Statistical Analyses: Poisson 

regression analysis 

Age Groups Analyzed: ≥ 35 yr 

Sample Description: Firsttime MI patients 

Averaging Time: 24 h 

Mean (SD) unit: Median calculated 

from daily 24-h means: 

Augsburg: 0.85 

Barcelona: 0.75 

Helsinki: 0.36 

Rome: 1.66 

Stockholm: 0.38 

Range (IQR): Augsburg: 0.43 

Barcelona: 0.75 

Helsinki: 0.36 

Rome: 1.11 

Stockholm: 0.38 

Copollutant: NR 

Increment: 0.2 mg/m3

% Change in Daily Nontrauma Deaths [Lower CI, 

Upper CI]: Mean of Lag 0 and 1: 2.61 (-0.26-5.56) 

Mean of Lag 0-4: 3.82 (1.00-6.72) 

Mean of Lag 0-14: 4.92 (2.11-7.81) 

Lags examined: 0, 1, 4, 14 

CO had a trend towards or positive associations with 

all cities for 2-day mean effects on daily mortality. 

CO was associated with risk for the 5-day avg. The 

strongest association was observed for the 15-day 

avg. 

January 2010 C-70 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Biggeri et al. (2005, 

087395) 

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: Max 8-h ma 

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, 

011922) 

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 yr

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) 

Author: Bremner et al. 

(1999, 007601) 

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 yr 

≥ 65 yr 

65-74 yr 

≥ 75 yr 

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 

January 2010 C-71 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Burnett et al. (2000, 

010273) 

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 nonaccidental 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, 

086247) 

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 

Author: Cakmak et al. 

(2007, 091170) 

Period of Study:

1/1997-12/2003 

Location:

Chile-7 cities 

Health Outcome (ICD9):

Mortality: All-cause 

(nonaccidental) (<800); CVDs 

(390-459); respiratory diseases 

(460-519) 

Study Design: Time series 

Statistical Analyses: Poisson; 

Random effects regression 

model 

Age Groups Analyzed: 

All ages 

≤ 64 yr 

65-74 yr 

75-84 yr 

≥ 85 yr 

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: 

Nonaccidental: 

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 

Author: Chock et al. (2000, 

010407) 

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 yr 

>75 yr 

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 

January 2010 C-72 
Study Design Concentrations Effect Estimates (95% CI) 

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 

January 2010 C-73 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Cifuentes et al. 

(2000, 010351) 

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 yr: 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, 016628) 

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 yr 

Pollutant: CO 

Averaging Time: Max 8-h ma 

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, 055688) 

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 yr 

>75 yr 

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.

January 2010 C-74 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Dominici et al. 

(2003, 056116) 

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, 

000896) 

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; Max 8-h avg 

Median (SD) unit:

24-h avg: 1.4 (1.0) ppm 

Max 8-h avg: 2.1 (1.6) ppm 

Range (Min, Max): 

24-h avg: (0.0, 7.6) 

Max 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, 

043739) 

Period of Study:

1986-1994 

Location: The Netherlands 

Health Outcome (ICD9):

Mortality: Nonaccidental 

(<800); pneumonia 

(480-486); COPD (490-496); 

cardiovascular (390-448) 

Study Design: Time series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: 

<45 yr 

45-64 yr 

65-74 yr 

≥ 75 yr 

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, 086323) 

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 yr 

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 

January 2010 C-75 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Forastiere et al. 

(2007, 090720) 

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 yr 

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, 016548) 

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 yr; ≥ 65 yr 

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, 035202) 

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 yr 

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, 088641) 

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 yr

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. 

(2000, 012132) 

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 yr 

<5 yr 

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 

January 2010 C-76 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Gwynn et al. (2000, 

074109) 

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 nonaccidental 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, 

016550) 

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, 

010350) 

Period of Study:

1986-1994 

Location: The Netherlands 

Health Outcome (ICD9):

Mortality: Pneumonia 

(480-486); COPD (490-496); 

CVDs (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, 

193774) 

Period of Study:

1976-1990 

Location:

Tokyo, Japan 

Health Outcome (ICD9):

Mortality: 

Total (nonaccidental) (<800) 

Study Design: Time series 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

≥ 65 yr 

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) 

January 2010 C-77 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Hong et al. (2002, 

035060) 

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 

Author: Hong et al. (1999, 

011195) 

Period of Study:

1/1995-12/1995 

Location:

Inchon, Korea 

Health Outcome (ICD9):

Mortality: Cardiovascular (400-

440); respiratory 

(460-519); nonaccidental 

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. (2002, 

024690) 

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. (1999, 

008087) 

Period of Study:

1/1995-8/1996 

Location:

Inchon, South Korea 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<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, 017063) 

Period of Study:

1976-1995 

Location: 

London, England 

Health Outcome (ICD9):

Mortality: Nonaccidental 

causes (<800) 

Study Design: Time series 

Statistical Analyses: Singleand 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.

January 2010 C-78 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Kettunen et al. 

(2007, 091242) 

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 yr 

Pollutant: CO 

Averaging Time: Max 8-h ma 

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 

Author: Klemm et al. (2004, 

056585) 

Period of Study:

8/1998-7/2000 

Location:

Fulton County and DeKalb 

County, GA (ARIES) 

Health Outcome (ICD9):

Mortality: Nonaccidental 

(<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 yr; ≥ 65 yr 

Pollutant: CO 

Averaging Time: 1-h max 

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: Knox et al. (2008, 

193776) 

Period of Study: 1996-2004 

Location: 352 English local 

authorities 

Health Outcome: Mortality 

Study Design: Cross sectional

Statistical Analyses: Linear 

regression 

Age Groups Analyzed: NR 

Sample Description: Data 

from Oxford Cancer 

Intelligence Unit 

Averaging Time: NR 

Meuan (SD) nit: NR 

Range (Min, Max): NR 

Copollutant: NR 

 

Increment: NR 

Significant (p<0.01) correlations (r) between CO and 

diseases: Lung cancer: 0.28, Stomach cancer: 0.20, 

Oesophagus cancer: -0.20, Prostate cancer: -0.25, 

Brain cancer: -0.24, Melanoma: -0.24, Hodgkin’s: -

0.19, Peripheral vascular disease: 0.15, Stroke: 

0.16, Rheumatic heart disease: 0.27, Peptic ulcer: 

0.28, Diabetes: 0.17, COPD: 0.25, Asthma: 0.14, 

Pneumonia: 0.44, Multiple sclerosis: -0.16, 

Motorneurone disease: -0.24, Parkinsons disease: -

0.15 

Significant (p<0.01) socially standardized 

correlations between diseases and exposures: Lung 

cancer: 0.25, Stomach cancer: 0.18, RHD: 0.19, 

Pneumonia: 0.37, COPD: 0.17, Peptic ulcer: 0.16 

Lags examined: NR 

January 2010 C-79 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Kwon et al. (2001, 

016699) 

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 yr 

55-64 yr 

65-74 yr 

75-84 yr 

≥ 85 yr 

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 wk: 1.088 (0.907-1.306); 0 

>4 wk: 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. (2007, 

093042) 

Period of Study:

1/2000-12/2004 

Location:

Seoul, Korea 

Health Outcome (ICD10):

Mortality: Nonaccidental (A00-

R99) 

Study Design: Time series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: Max 8-h ma 

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 

Author: Lipfert et al. (2000, 

004088) 

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:

Step-wise regression 

Age Groups Analyzed:

<65 yr 

≥ 65 yr 

Pollutant: CO 

Averaging Time: 24-h avg; 1-h max 

Mean (SD) unit:

Camden: 

24-h avg: 0.75 (0.40) ppm 

Philadelphia: 

24-h avg: 0.63 (0.40) ppm 

1-h max: 1.44 (1.04) 

Range (Min, Max):

Camden: (0.10, 3.8) 

Philadelphia: 

24-h avg: (0.10, 3.3) 

1-h max: (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 

January 2010 C-80 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lippmann et al. 

(2000, 011938) 

Period of Study:

1985-1990 

1992-1994 

Location:

Detroit, MI and Windsor, ON 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<800); circulatory (390-459); 

respiratory (460-519) 

Study Design: Time series 

Statistical Analyses:

Poisson GLM 

Age Groups Analyzed: 

≥ 65 yr 

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 

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 

January 2010 C-81 
Study Design Concentrations Effect Estimates (95% CI) 

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. 

(2005, 090769) 

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 yr 

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 

January 2010 C-82 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Maheswaran et al. 

(2005, 088683) 

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 yr 

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, 

001760) 

Period of Study:

1995-1997 

Location:

Phoenix, AZ 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<800); cardiovascular (390-

449) 

Study Design: Time series 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

>65 yr 

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 

January 2010 C-83 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Moolgavkar et al. 

(2000, 012054) 

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: 

Cook: r = -0.28; 

LA: r = -0.52; 

Maricopa: r = -0.61 

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; 

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. 

January 2010 C-84 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Moolgavkar et al. 

(2003, 051316) 

Period of Study: 1987-1995 

Location: 

Cook County, Illinois & Los 

Angeles County, California 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<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 

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 

January 2010 C-85 
Study Design Concentrations Effect Estimates (95% CI) 

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, 

006610) 

Period of Study:

1989-1992 

Location:

Coachella Valley, California 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<800); respiratory (460-519); 

cardiovascular (393-440) 

Study Design: Time series 

Statistical Analyses: Poisson 

GAM; LOESS 

Age Groups Analyzed: >50 yr 

Pollutant: CO 

Averaging Time: 1-h max 

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 

Author: Penttinen et al. 

(2004, 087432) 

Period of Study:

1988-1996 

Location:

Helsinki, Finland 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<800); respiratory (460-519); 

cardiovascular (393-440) 

Study Design: Time series 

Statistical Analyses: Poisson 

GAM, LOESS 

Age Groups Analyzed: 

All ages 

15-64 yr 

65-74 yr 

≥ 75 

Pollutant: CO 

Averaging Time: Max 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 Mortality 

-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. (2000, 

001756) 

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 

CVD 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 

January 2010 C-86 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Rainham et al. 

(2003, 053202) 

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, 019391) 

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 

January 2010 C-87 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Samet et al. (2000, 

013132) 

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 (nonaccidental) (<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, 

098420) 

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) 

Increment: 1 mg/m3

% Increase (Lower CI, Upper CI); lag:

Non-accidental mortality 

8 Degrees of Freedom per yr 

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 

January 2010 C-88 
Study Design Concentrations Effect Estimates (95% CI) 

Rome: (2.5, 5.9) 

Stockholm: (0.5, 1.2) 

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 

January 2010 C-89 
Study Design Concentrations Effect Estimates (95% CI) 

Population >75 yr of age (%): 

25th Percentile: 0.58% (0.25-0.92); 0-1 

75th Percentile: 0.94% (0.64-1.24); 0-1 

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 yr 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, 017915) 

Period of Study:

1989-1995 

Location:

Spokane, WA 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<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, 156976) 

Period of Study:

1996-1998 

Location:

Sao Paulo, Brazil 

Health Outcome (ICD10):

Mortality: MI (I.21) 

Study Design: Time series 

Statistical Analyses:

Poisson GAM, LOESS 

Age Groups Analyzed:

35-109 yr 

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, 073854) 

Period of Study:

1/1995-6/2001 

Location:

Spokane, WA 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<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.

January 2010 C-90 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Stieb et al. (2003, 

056908) 

Period of Study: 

1985-2000 

Location: All locations 

Health Outcome (ICD9):

Mortality: Nonaccidental 

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. (2007, 

091374) 

Period of Study:

9/1995-8/2001 

Location:

Erfurt, Germany 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<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, 

019367) 

Period of Study:

1990-1995 

Location:

Barcelona, Spain 

Health Outcome (ICD9):

Mortality: COPD (491, 492, 

494, 496) 

Study Design: 

Bidirectional case crossover 

Statistical Analyses:

Conditional logistic regression 

Age Groups Analyzed: >35 yr

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, 

034835) 

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 yr

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. (2003, 

050480) 

Period of Study:

1994-2000 

Location:

Kaohsiung, Taiwan 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<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 (nonaccidental): 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. (2006, 

090709) 

Period of Study:

1994-2000 

Location:

Kaohsiung, Taiwan 

Health Outcome (ICD9): 

Mortality: Total (nonaccidental) 

(<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 

January 2010 C-91 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Vedal et al. (2003, 

039044) 

Period of Study:

1/1994-12/1996 

Location:

Vancouver, BC, Canada 

Health Outcome (ICD9):

Mortality: Total (nonaccidental) 

(<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.

Author: Villeneuve et al. 

(2003, 055051) 

Period of Study:

1986-1999 

Location:

Vancouver, BC, Canada 

Health Outcome (ICD9): 

Mortality: Nonaccidental 

(<800); cardiovascular 

(401-440); respiratory 

(460-519); cancer (140-239) 

Study Design: Time series 

Statistical Analyses:

Poisson, natural splines 

Age Groups Analyzed: ≥ 65 yr

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: Wang et al. (2008, 

179974) 

Period of Study: Daily CO 

content: 2000-2005 (data 

from Beijing Environment 

Protection Bureau), Death 

rate: 2000-2003 

Location: Beijing, China 

Health Outcome: Mortality 

Study Design: Time series, 

Granger causality, Back 

propagation neural network 

model, MIV 

Statistical Analyses: Eviews 

3.1, SAS 9.0, Matlab 7.0 

Age Groups Analyzed: NR 

Sample Description: Death 

rate of respiratory diseases in 

Beijing from China Centers for 

Disease Control and 

Prevention 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

 

Increment: NR 

Granger causality: Acute respiratory diseases 

probability: 0.03122 

COPD probability: 0.00047 

Change of death rate of acute respiratory diseases: 

Increasing 10%: +0.437, Decreasing 10%: -0.386 

Change of death rate of COPD: Increasing 10%: 

+0.181, Decreasing 10%: -0.316 

Lags examined: 10 

January 2010 C-92 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Wichmann et al. 

(2000, 013912) 

Period of Study:

9/1995-12/1998 

Location:

Erfurt, Germany 

Health Outcome (ICD9):

Mortality: Nonaccidental 

(<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. (2004, 

055603) 

Period of Study:

1994-1998 

Location:

Taipei, Taiwan 

Health Outcome (ICD9):

Mortality: Nonaccidental 

(<800); circulatory (390-459); 

respiratory (460-519) 

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: 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 

January 2010 C-93 
Table C-8. Studies of long-term CO exposure and mortality. 

Study Design Concentrations Effect Estimates (95% CI) 

Author: Krewski et al. 

(2009, 191193) 

Period of Study: 

1983-2000 

Location:

United States 

Health Outcome: Mortality 

Study Design: Cohort 

Statistical Analyses: Random 

effects Cox model 

Age Groups Analyzed: 30+ yrs 

Sample Description:

508,538 adults living in large US 

cities 

Averaging Time: 1980 annual avg 

Mean (SD) unit: 1.68 (0.66) ppm 

Range (min, max):

0.19, 3.95 

Copollutant: 

PM15, PM2.5, SO2, SO4, TSP, O3, NO2

Increment: 1ppm 

HR Estimate [Lower CI, Upper CI]: 

Lags examined: NR 

All Causes: 1.00 (0.99, 1.01) 

Cardiopulmonary: 1.00 (0.99, 1.01) 

IHD: 1.01 (0.99, 1.03) 

Lung Cancer: 0.99 (0.97, 1.03) 

All Other Causes: 0.99 (0.98, 1.01) 

Author: Lipfert et al. 

(2000, 004087) 

Period of Study:

1975-1996 

Location:

32 Veterans Hospitals, 

USA 

Mortality 

Health Outcome (ICD9):

Nonaccidental 

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. 

January 2010 C-94 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lipfert and 

Morris (2002, 019217) 

Period of Study:

1960-1997 

Location:

U.S. counties 

Mortality 

Health Outcome (ICD9): 

Nonaccidental 

Study Design: 

Ecological/ cross sectional 

Statistical Analyses:

Staged regression 

Age Groups Analyzed: 

15-44 yr 

45-64 yr 

65-74 yr 

75-84 yr 

≥ 85 yr 

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) 

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 

January 2010 C-95 
Study Design Concentrations Effect Estimates (95% CI) 

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 

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) 

January 2010 C-96 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lipfert et al. 

(2006, 088218) 

Period of Study:

1976-2001 

Location:

32 Veterans Hospitals, 

USA 

Mortality 

Health Outcome (ICD9):

Nonaccidental 

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) 

Author: Lipfert et al. 

(2006, 088756) 

Period of Study:

1997-2002 

Location:

32 Veterans Hospitals, 

USA 

Mortality 

Health Outcome (ICD9):

Nonaccidental 

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, 087380) 

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 CVD 

Statistical Analyses:

Cox proportional-hazards model 

Age Groups Analyzed: ≥ 30 yr 

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) 

January 2010 C-97 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Miller et al. 

(2007, 090130) 

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 CVD 

Statistical Analyses:

Cox proportional-hazards model 

Age Groups Analyzed: 50-79 yr 

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, 024689) 

Period of Study:

1980-1998 

Location:

All 50 States, Washington 

DC, and Puerto Rico 

(ACS-CPS-II) 

Mortality 

Health Outcome (ICD9): 

Total (nonaccidental) (<800); lung 

cancer (162); cardiopulmonary 

(401-440, 460-519) 

Study Design: Prospective cohort 

Statistical Analyses:

Cox proportional hazards model 

Age Groups Analyzed: ≥ 30 yr 

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. 

January 2010 C-98 
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Yang Q; Chen Y; Shi Y; Burnett RT; McGrail KM; Krewski D (2003). Association between ozone and respiratory 

admissions among children and the elderly in Vancouver, Canada. Inhal Toxicol, 15: 1297-1308. 055621

Yu O; Sheppard L; Lumley T; Koenig JQ; S (2000). Effects of ambient air pollution on symptoms of asthma in Seattle-area 

children enrolled in the CAMP study. Environ Health Perspect, 108: 1209-1214. 013254

January 2010 C-112 
Zanobetti A; Canner MJ; Stone PH; Schwartz J; Sher D; Eagan-Bengston E; Gates KA; Hartley LH; Suh H; Gold DR 

(2004). Ambient pollution and blood pressure in cardiac rehabilitation patients. Circulation, 110: 2184-2189. 

087489

Zanobetti A; Schwartz J (2006). Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health, 

60: 890-895. 090195

January 2010 C-113 
Annex D. Controlled Human 

Exposure Studies 

Table D-1. Controlled human exposure studies. 

Study Subjects Exposure Findings 

Adir et al. (1999, 

001026) 

15 healthy nonsmokers 

Gender: M 

Age: 22-34 yr 

Inhaled Concentration: Not provided 

Exposure Duration: 3 min 45 s 

COHb Concentration: 4-6% 

COHb Analysis: CO-oximeter (IL-282) 

Exposures to CO and room air were separated 

by 1 mo, with the order of exposure randomly 

assigned. 

Exposure to CO resulted in a decrease in postexposure 

exercise duration (Bruce protocol) relative to clean air 

exposure in 13 out of 15 subjects (p = 0.0012). Statistically 

significant decreases in METs were also reported following CO 

exposure (p = 0.0001). No CO-induced changes in HR, BP, 

ECG parameters, or myocardial perfusion were observed. 

Bathoorn et al. (2007, 

193963) 

19 former smokers with 

COPD 

Gender: 18 M/1 F 

Age: 66-70 yr 

Inhaled Concentration: 100 ppm (9 subjects) or 

125 ppm (10 subjects) 

Exposure Duration: 2 h on each of 4 

consecutive days 

COHb Concentration: 2.7% (following 4th day 

exposure) 

COHb Analysis: Not provided 

Exposures to CO and room air conducted were 

separated by at least 1 wk, using a randomized 

crossover design. 

Following the 4th 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 C-reactive 

protein (CRP) were observed. Although this study appears to 

demonstrate some evidence of an anti-inflammatory effect of 

CO among subjects with COPD, it must be noted that 2 of 

these patients experienced exacerbations of COPD during or 

following CO exposure, with 1 patient requiring hospitalization 

2 mo after exposure (initial symptoms first experienced 1 wk 

postexposure). 

Hanada et al. (2003, 

193915) 

20 healthy adults 

Gender: M 

Age: 26 ± 1 yr 

Inhaled Concentration: Not provided 

Exposure Duration: 20 min 

COHb Concentration: 20-24% 

COHb Analysis: CO-oximeter (OSM-3) 

15 subjects exposed for 20 min (10 min rest, 

5 min handgrip exercise, 2 min postexercise 

ischemia, 3 min recovery) under the following 4 

conditions: (1) normoxia (inspiratory O2 fraction 

21.4%); (2) hypoxia (inspiratory O2 fraction 

10.3%); (3) CO + normoxia; and (4) CO + 

hyperoxia (inspiratory O2 fraction 95.9%). Trials 

involving exposure to CO were conducted last in 

this sequence. Each of the 4 conditions was 

separated from the next by 20 min of rest. 

5 subjects served as controls (4 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. 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 D-1 
Study Subjects Exposure Findings 

Kizakevich et al. (2000, 

052691) 

16 healthy nonsmokers 

Gender: M 

Age: 18-29 yr 

Inhaled Concentration: Initial short term 

(4-6 min) exposure to 1,000 or 3,000 pap, 

followed by exposures to 27, 55, 83, or 100 ppm 

to maintain COHb concentration. 

Exposure Duration: 4-6 min at 1,000 or 

3,000 pap, followed by 20 min at 27, 55, 83, or 

100 ppm. 

Target COHb Concentrations: 5, 10, 15, and 

20% 

COHb Analysis: CO-oximeter (IL-282) 

Subjects exposed on 4 separate days to 

increasing CO concentrations during either 

upper-body exercise (hand-crank) or lower-body 

exercise (treadmill). Targeted COHb 

concentrations were initially attained using shortterm (4-6 min) exposures to CO at 

concentrations of 1,000 or 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%. 

Mayr et al. (2005, 

193984) 

13 healthy nonsmokers 

Gender: M 

Age: 18-38 yr 

Inhaled Concentration: 500 ppm 

Exposure Duration: 1 h 

COHb Concentration: 7% 

COHb Analysis: CO-oximeter (AVL 912) 

Subjects exposed to both CO and clean air with 

exposures separated by a 6-wk period. 

Immediately following exposure, subjects were 

administered an intravenous bolus dose 

(2 ng/kg) of lipopolysaccharide (LPS). 

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, 

097980) 

12 healthy nonsmokers 

Gender: M 

Age: 25 ± 2.9 yr 

Inhaled Concentration: 3,000 ppm 

Exposure Duration: 3-8 min 

COHb Concentration: 6.2% 

COHb Analysis: Electrochemical sensor 

(Smokerlyzer) measuring CO in exhaled breath 

Exposures conducted on2 separate occasions to 

room air (6 min) and CO. Subjects were exposed 

to CO until COHb reached 6% (3- to 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). 

Ren et al. (2001, 

193850) 

12 healthy adults 

(10 nonsmokers and 1 

smoker) 

Gender: 9 M/3 F 

Age: 20-32 yr 

Inhaled Concentration: 0.4% (4,000 ppm) 

Exposure Duration: 10-30 min at 0.4% followed 

by ~ 8-h with periodic exposure to maintain 

COHb concentration 

COHb Concentration: 10% 

COHb Analysis: Not provided 

Each subject underwent 4 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. 

January 2010 D-2 
Study Subjects Exposure Findings 

Resch et al. (2005, 

193853) 

15 healthy nonsmokers 

Gender: M 

Age: 27 ± 4 yr 

Inhaled Concentration: 500 ppm 

Exposure Duration: 1 h 

COHb Concentration: ~ 10% 

COHb Analysis: CO-oximeter (AVL 912) 

Exposures to CO and synthetic air control were 

separated by a period of at least 1 wk. 

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, 

194000) 

10 healthy nonsmokers 

Gender: M 

Age: 22-52 yr 

Inhaled Concentration: 1,200 ppm 

Exposure Duration: 30-45 min 

COHb Concentration: 10% 

COHb Analysis: CO-oximeter (OSM-3) 

Prior to and following exposure, subjects 

performed hypoxic and hyperoxic rebreathing 

tests. Four subjects were exposed to hypoxic 

conditions first, while 6 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, 

021120) 

12 healthy smokers 

Gender: M 

Age: 27-47 yr 

Inhaled Concentration: 1,200-1,500 ppm 

Exposure Duration: 10 min each h, 16 h each 

day, over 7 days 

COHb Concentration: 5-6% 

COHb Analysis: CO-oximeter (Ciba Corning 

2500) 

Exposures were conducted over 21 consecutive 

days under3 different protocols, with each 

protocol lasting 7 days. In 1 protocol, subjects 

smoked 20 cigarettes per day, 1 every 45 min. In 

the other 2 protocols, every 45 min (20 times per 

day) subjects breathed either air or CO from a 

1-liter bag once per min for 10 min at a time. 

Subjects completed all 3 protocols, with 6 

subjects exposed sequentially to CO, smoking, 

then air, and the other 6 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 3 exposures. 

January 2010 D-3 
References 

Adir Y; Merdler A; Haim SB; Front A; Harduf R; Bitterman H (1999). Effects of exposure to low concentrations of carbon 

monoxide on exercise performance and myocardial perfusion in young healthy men. Occup Environ Med, 56: 535-

538. 001026

Bathoorn E; Slebos DJ; Postma DS; Koeter GH; van Oosterhout AJ; van der Toorn M; Boezen HM; Kerstjens HA (2007). 

Anti-inflammatory effects of inhaled carbon monoxide in patients with COPD: A pilot study. Eur Respir J, 30: 

1131-1137. 193963

Hanada A; Sander M; González-Alonso J (2003). Human skeletal muscle sympathetic nerve activity, heart rate and limb 

haemodynamics with reduced blood oxygenation and exercise. J Physiol, 551: 635-647. 193915

Kizakevich PN; McCartney ML; Hazucha MJ; Sleet LH; Jochem WJ; Hackney AC; Bolick K (2000). Noninvasive 

ambulatory assessment of cardiac function in healthy men exposed to carbon monoxide during upper and lower 

body exercise. Eur J Appl Physiol, 83: 7-16. 052691

Mayr FB; Spiel A; Leitner J; Marsik C; Germann P; Ullrich R; Wagner O; Jilma B (2005). Effects of carbon monoxide 

inhalation during experimental endotoxemia in humans. Am J Respir Crit Care Med, 171: 354-360. 193984

Morse CI; Pritchard LJ; Wust RC; Jones DA; Degens H (2008). Carbon monoxide inhalation reduces skeletal muscle 

fatigue resistance. Eur Phys J A, 192: 397-401. 097980

Ren X; Dorrington KL; Robbins PA (2001). Respiratory control in humans after 8 h of lowered arterial PO2, hemodilution, 

or carboxyhemoglobinemia. J Appl Physiol, 90: 1189-1195. 193850

Resch H; Zawinka C; Weigert G; Schmetterer L; Garhofer G (2005). Inhaled carbon monoxide increases retinal and 

choroidal blood flow in healthy humans. Invest Ophthalmol Vis Sci, 46: 4275-4280. 193853

Vesely AE; Somogyi RB; Sasano H; Sasano N; Fisher JA; Duffin J (2004). The effects of carbon monoxide on respiratory 

chemoreflexes in humans. Environ Res, 94: 227-233. 194000

Zevin S; Saunders S; Gourlay SG; Jacob P III; Benowitz NL (2001). Cardiovascular effects of carbon monoxide and 

cigarette smoking. J Am Coll Cardiol, 38: 1633-1638. 021120

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 D-4 
Annex E. Toxicological Studies 

Table E-1. Human and animal studies. 

Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Acevedo and Ahmed 

(1998, 016003) 

Human pregnant 

myometrium 

HO-1 and HO-2 (mRNA and protein) were upregulated in 

pregnant myometrium when compared to nonpregnant 

myometrium. The HO activator hemin inhibited spontaneous and 

oxytocin-induced contractility of the myometrium. Progesterone 

induced HO-1 and HO-2 mRNA expression. 

Achouha et al. (2008, 

179918) 

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. 

Ahmed et al. (2000, 

193863) 

Human placenta 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 vs 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, 

193865) 

Human placental 

cotyledons 

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, 

193869) 

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, 192373) 

Rat 

Sprague Dawley 

Female 

The role of the HO/CO system in estrous cyclicity, pregnancy and 

lactation was evaluated using HO inhibitors and substrates. The 

HO inhibitor CrMP decreased time in estrous. Administering HOinhibitors 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 ((2003, 

193871) 

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 (2003, 

193876) 

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, 

193882) 

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 3 other electron transport proteins. 

Andersen et al. (2006, 

180449) 

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. 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 E-1 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Antonelli et al. (2006, 

194960) 

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, 193935) 

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. 

Ashfaq et al. (2003, 

194002) 

Human placenta 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 et al. (1972, 

011121) 

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, 043161) 

Human placenta 72–3369 nM 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, 193949) 

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 h, 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- and CO-exposed 

placental tissue had decreased apoptosis and decreased PARP 

(a protein marker of apoptosis) vs control H/R-exposed cells. 

Secondary necrosis of the placental tissue post H/R was inhibited 

by CO treatment. 

Bainbridge and Smith 

(2005, 193946) 

Human placenta The role of HO in the placenta and during pregnancy is reviewed 

in this article. The conflicting data on the activity, localization and 

expression of HO in the placentas of pre-eclamptic women are 

presented. 

Bamberger et al. 

(2001, 016271) 

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, 

193953) 

Human myometrium HO and NOS did not maintain human uterine quiescence during 

pregnancy. 

Barber et al. (2001, 

193891) 

Human placenta Women who had pregnancies with fetal growth restrictions (FGR) 

produced term placenta with significant decreases in HO-2 vs 

healthy pregnancies. 

Baum et al. (2000, 

016435) 

Human End-tidal CO measurements in women with pregnancy-induced 

hypertension and pre-eclampsia were significantly lower than in 

normotensive pregnant women. 

Benagiano et al. 

(2005, 180445) 

Rat 

Wistar 

Female 

GD0-GD20 75 ppm CO caused a significant reduction in glutamic acid decarboxylase 

and GABA immunoreactivities in the cerebellar cortex of adult 

rats prenatally exposed to CO (number of positive neuronal 

bodies and axon terminals and the area they covered). No 

difference was found in the microscopic structure of the 

cerebellar cortex or distribution patterns of GAD or GABA. 

Benagiano (2007, 

193892) 

Rat 

Wistar 

Female 

GD5-GD20 75 ppm Prenatal CO reduced GAD and GABA immunoreactivities. There 

were no structural alterations of the cerebellar cortex. 

Bergeron et al. (1998, 

193967) 

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. 

January 2010 E-2 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Bing et al. (1995, 

079418) 

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, 099998) 

Human 

Mouse 

Nb had a high oxygen affinity similar to Mb, and thus may 

increase the availability of O2 to brain tissue. 

Bye et al. (2008, 

193777) 

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, 

087170) 

Rat 

Wistar 

Female 

GD0-GD20 75 or 150 ppm At 5 mo of age, CO-exposed male offspring showed decrements 

in sexual behavior, including an increase in mount- tointromission 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 in CO-exposed rats. 

Carmines and 

Rajendran (2008, 

188440) 

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. (1993, 

013812) 

Rat 

Wistar 

Male pups 

GD0-GD20 75 or 150 ppm Prenatal CO exposure slowed the inactivation kinetics of 

transient sodium current in the sciatic nerve fibers of 40-day-old 

male rats. The maximum number of activatable Na channels at 

normal resting potential was increased in CO exposed rats, and 

the voltage-current relationship showed a negative shift of 

sodium equilibrium potential. 

Carratu et al. (1995, 

079427) 

Rat 

Wistar 

150 ppm Sphingolipid homeostasis was disrupted in male offspring of 

prenatally exposed rats, without a disruption in motor function. 

Carratu et al. (2000, 

015935) 

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, and 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, 

015839) 

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, 

026018) 

Rat model of hypoxic 

pulmonary vascular 

remodeling 

(Strain of rat not stated) 

3 wk 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, 

193240) 

Rat 

Sprague Dawley 

HO-1 production and HO concentration were shown to be 

regulated by estrogen in the rat uterus. 

Chen (2001, 193985) Rat 

Long Evans 

Male 

2 mo 

3.5 h 1201 ± 18 ppm CO potentiates-noise induced hearing loss. The NMDA inhibitor 

(+)-MK-801 did not block the potentiation of the NIHL by CO. 

Cheng et al. (2009, 

193775) 

Human atherectomy 

biopsy (clinical carotid 

artery disease) 

Mouse model of 

vulnerable plaque 

ApoE-/- mouse 

HO-1 expression correlated with features of vulnerable human 

atheromatous plaque.HO-1 expression was upregulated in 

vulnerable lesions in the mouse model. Induction of HO-1 in the 

mouse impeded lesion progression into vulnerable plaques. 

Inhibition of HO-1 augmented plaque vulnerability. 

Overexpression of HO-1 resulted in plaque stabilization. It was 

concluded that HO-1 induction was atheroprotective. 

January 2010 E-3 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Chung et al. (2006, 

193987) 

Rat 

Sprague Dawley 

Male 

3-6% CO inactivation of Mb did not induce any change in the 

respiration rate, contractile function or high-energy phosphate 

levels in perfused rat hearts. 

Cronje et al. (2004, 

180440) 

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, 180424) 

Cudmore et al. (2007, 

193991) 

Human placenta 

Human (HUVEC) 

Mouse 

(HO-1 deficient mouse on 

129/SV × 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 preeclampsia. 

D'Amico et al. (2006, 

193992) 

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, 

193994) 

Human 

(neonatal blood) 

CO was lower at birth and 48-72 h postpartum in infants born by 

elective C-section and higher in vaginally born infants. 

De Luca et al. (1996, 

080911) 

Rat 

Wistar 

Female 

Male pups 

GD0-GD20 75 or 150 ppm Prenatal CO (150 ppm) delayed development of the ion channels 

responsible for passive and active membrane electrical 

properties of skeletal muscle. CO-induced lower values of resting 

chloride conductance was reversed at PND80. CO-induced 

delayed developmental reduction of resting potassium 

conductance was reversed at PND60. 

De Salvia et al. (1995, 

079441) 

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 mo) and 

reacquisition (at 18 mo) of conditioned avoidance behavior. 

Denschlag et al. 

(2004, 193894) 

Human Genetic polymorphisms in human HO-1 are linked to idiopathic 

recurrent miscarriages. 

Dewilde et al. (2001, 

019318) 

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. 

Di Giovanni et al. 

(1993, 013822) 

Rat 

Wistar 

Female 

GD0-GD20 75 and 150 ppm CO (150 ppm) reduced the minimum frequency of ultrasonic calls 

as well as decreased responsiveness to a challenge dose of 

diazepam. There was no change in locomotion; however CO 

impaired learning in a two-way active avoidance task. 

Dubois et al. (2002, 

193911) 

Rat 

Wistar 

Adult female 

250 g 

3 wk 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. 

January 2010 E-4 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Dubois et al. (2005, 

180435) 

Rat 

Wistar 

Male 

21 days 50 ppm CO attenuated PAHT by activating BKca channels in PA myocytes 

and reduced hemodynamic changes of PAHT. 

Dubois et al. (2003, 

180439) 

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. 

Durante et al. (2006, 

193778) 

Reviews the role of CO in cardiovascular function. 

Favory et al. (2006, 

184462) 

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, 010688) 

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 vs 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 vs air-exposed control dams. 

Fechter et al. (1980, 

011294) 

Rat 

Long Evans 

Continuous CO 

exposure 

throughout 

pregnancy 

150 ppm CO-exposed animals had cardiomegaly at birth (wet heart 

weight) that dissipated by PND4. 

Fechter and Annau 

(1980, 011295) 

Rat 

Long Evans 

Continuous 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, 

012194) 

Rat 

Long-Evans 

Male 

1-4 mL/100 g BW (ip) High-dose CO led to dose-dependent, reversible loss of the 

compound action potential sensitivity for high frequency tone 

bursts. Also, CO produced a dose-dependent elevation in the 

cochlear blood flow. 

Fechter et al.(1987, 

012259) 

Rat 

Long Evans

Male 

Continuous CO 

exposure 

throughout 

pregnancy or from 

GD0 to PND10 

75, 150, or 300 ppm The neostriatum of each PND21 rat brains was 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). 

Fechter et al. (1997, 

081322) 

Guinea pigs 35 ml/kg gas (ip) 

40% COHb 

CO impairs high-frequency auditory sensitivity, shown by 

increased compound action potential threshold at higher test 

frequencies. Free radical inhibitors blocked this response. 

Fechter et al. (1986, 

012030) 

Reviews the effects of carbon monoxide on brain development. 

January 2010 E-5 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Garofolo et al. (2002, 

193930) 

Human infants 

Rat Rat: PND2-PND5 

Human infants who die from SIDS showed decreased brainstem 

muscarinic receptor binding vs 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. 

Gautier et al. (2007, 

096471) 

Rat 

Wistar 

Adult male 

Model of right ventricular 

hypertrophy secondary to 

chronic hypoxia 

3 wk of HH ± CO 

in final wk 

Or 1 wk 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 an LVfilling 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-wk 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. 

Gaworski et al. (2004, 

193933) 

Rat 

Sprague Dawley 

2 h/day, 7 days/wk 

by nose-only 

inhalation 

Males: 4 wk prior 

to and during 

mating; and 

Females: 2 wk 

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, 

096321) 

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, 

011538) 

Rat 

Wistar 

Male and pregnant female 

GD0-GD20 75 or 150 ppm This study showed that CO- exposed (75 and 150 ppm) 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). 

January 2010 E-6 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Giustino et al. (1993, 

013833) 

Rat 

Wistar 

GD0-GD20 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. 

Giustino et al. (1994, 

076343) 

Rat 

Wistar 

Male pups 

GD0-GD20 75 or 150 ppm CO (150 ppm) decreased the number of leukocyte common 

antigen (LCA+) cells at PND21. This was reversed by PND540. 

CO (75 ppm), and other measures of immunological changes 

showed trends toward reduction (macrophages, T cells, B cells, 

and MHC II cells). 

Glabe et al. (1998, 

086704) 

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, 

010465) 

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, 

037497) 

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, 

193920) 

Pig 

Granulosa cells 

In this porcine model, HO was able to augment granulosa cell 

apoptosis allowing for proper follicular maturation. 

Hendler and Baum 

(2004, 193925) 

Human End-tidal breath CO measurements in pregnant women with 

contractions (term and pre-term) were lower than those 

measurements in noncontracting women. 

Hofmann and Brittain 

(1998, 052019) 

Human Partitioning of O2 and CO in the human embryonic Hb is 

discussed. 

Iheagwara et al. 

(2007, 193861) 

Mouse 

C57BL/6 

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, 

193864) 

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. 

Ischiropoulos et al. 

(1996, 079491) 

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. 

Johnson and Johnson 

(2003, 053611) 

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. Pretreatment 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. 

January 2010 E-7 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Johnson et al. (2003, 

193868) 

Rat 

Dahl/Rapp salt-resistant 

and salt-sensitive 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. 

Johnson et al. (2004, 

193870) 

Rat 

Sprague Dawley 

Male 

Deoxycorticosterone 

acetate (DOCA)-salt 

hypertension model 

Rats

WKY 

Rats 

Spontaneously 

hypertensive (SHR) 

 Salt-sensitive DOCA rats, but not SHR, had elevated aortic HO-1 

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 an 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. (2006, 

193874) 

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, 

193896) 

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, 

193954) 

Rat 

Wistar 

Pregnancy-induced modulation of calcium mobilization and 

downregulation of Rho-kinase expression contributed to 

attenuated vasopressin-induced contraction of the rat aorta. 

Khan et al. (2006, 

193955) 

Nb overexpressing BDF × 

CD1 mice 

Cerebral and myocardial infarcts were decreased in neuroglobin 

overexpressing mice, decreasing ischemic injury. 

Kim et al. (2005, 

193959) 

Primary rat pulmonary 

artery smooth muscle 

cells 

Rat 

Inbred LEW 

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. 

January 2010 E-8 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Kim et al. (2008, 

193961) 

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 respirationdeficient 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 oxidant-dependent 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, 

188447) 

Sheep 

Gravid and nongravid 

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, 

191987) 

Mouse 4 h Diesel emissions: 

350 μg/m3 Diesel exhaust enhanced vasoconstriction in veins but not arteries. It were suggested that this is through the uncoupling of 

eNOS. 

Korres et al. (2007, 

190908) 

Human Transient evoked otoacoustic emissions response and amplitude 

at 4,000 Hz was lower in neonates with prenatal exposure to 

cigarette smoke. There was no dose-dependent change in 

response depending on the amount cigarettes per day that was 

smoked. 

Kreiser et al. (2004, 

193948) 

Human End-tidal CO concentrations were lower in pregnant women with 

gestational hypertension and pre-eclampsia than normotensive 

women. 

Lash et al. (2003, 

193849) 

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, 

187003) 

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 

upregulation post-maternal LPS treatment. 

Liu and Fechter (1995, 

076524) 

Guinea pig 

Male 

35 mL/kg (ip) CO increased the compound action potential threshold at high 

frequencies. This could be blocked by inhibition of the glutamate 

receptor. 

Loennechen et al. 

(1999, 011549) 

Rat 

Sprague Dawley 

Female 

220-240g 

1 wk 

1 wk 100 ppm and 

1 wk 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-wk exposure, and by 

43% and 12% in the left and right ventricle, respectively, during 

the 1-wk exposure. Right ventricular to body weight ratio was 

increased by 18% and 16% in the 2-wk and 1-wk exposure 

groups, respectively. COHb levels were 23% and 12% in the 

2-wk and 1-wk exposure groups, respectively. 

Longo et al. (1999, 

011548) 

Rat uterine tissue and tail 

artery rings 

Sprague Dawley 

Human uterine biopsies 

10-4 M 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. 

January 2010 E-9 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Lopez et al. (2008, 

097343) 

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 showed vacuolization of 

afferent terminals at the base of the cochlea. At PND3, group A 

showed decreased synapsin-1 staining of the efferent nerve 

terminals. At PND20, groups A and B showed decreased 

neurofilament-IR (staining) in type I spiral ganglia neurons and 

afferent nerve fibers. At PND12 and PND20, group B showed increased HO-1 and SOD-1-IR in blood vessels of the stria 

vasularis; group A was similar to controls. From PND3-PND20, 

there was increased iNOS and increased nitrotyrosine-IR in 

blood vessels of the cochlea. 

Lopez et al. (2003, 

193901) 

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 vs 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 vs controls. 

Lund et al. (2007, 

125741) 

Mouse 

ApoE-/-

Male 

High-fat diet 

6 h/day, 7days/wk, 

7 wk 

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-gasoline and filtered-gasoline 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. (2009, 

180257) 

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. 

Lyall and Myatt (2002, 

193971) 

Human Women with pre-eclampsia produced term placenta with 

significant decreases in HO-2 vs women with healthy 

pregnancies. 

Lyall et al. (2000, 

193902) 

Human 

(placentas from 8-to19- 

wk 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, 011355) 

Rat 

Long Evans 

Continuous 

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 et al. (1998, 

085342) 

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 

that this may be related to changes in the brainstem. COHb was 

measuerd in maternal (8.53 ± 0.6% vs 0.25 ± 0.1%) and fetal 

blood (13.0 ± 0.4% vs 1.6 ± 0.1%) from CO-treated vs controls. 

January 2010 E-10 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

McLaughlin et al. 

(2001, 193823) 

Human placenta 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, 015815) 

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, 193827) 

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 vs normotensive pregnancies. 

McLean et al. (2000, 

016269) 

Human placenta HO activity was highest in the placenta near term. 

Melin et al. (2002, 

037502) 

Rat 

Dark Agouti 

Male 

Model of right ventricle 

hypertrophy secondary to 

chronic hypoxia (HH 

10 wk) 

10 wk 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. (2005, 

193833) 

Rat 

Dark Agouti 

Male and female 

Model of right ventricle 

hypertrophy secondary to 

chronic hypoxia (HH, 

10 wk) 

Half of the animals were 

exercise trained to induce 

LV hypertrophy 

10 wk 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, 

193838) 

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, 015842) 

Human 

Adult males aged 65-75 yr 

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. 

Montagnani et al. 

(1996, 080902) 

Rat 

Wistar 

Male pups 

GD0-GD20 75 or 150 ppm CO caused an increase in tetrodotoxin-induced inhibition of 

perivascular nerve stimulation PNS-evoked vasoconstriction, 

increased the time to NO-related relaxant effect by ACh, and 

decreased the contractile response evoked by ACh on resting 

tone. 

January 2010 E-11 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Naik and Walker 

(2003, 193852) 

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 large-conductance Ca2+-activated K+

 channels. However, exogenous CO 

vasodilation was cGMP dependent. 

Ndisang et al. (2004, 

180425) 

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, 193964) 

Mouse 

CD-1 

GD8-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 vs 8% 

and 16% protein groups. Further, dietary restriction of both 

protein and zinc with CO exposure to during gestation increased 

the incidence of pup mortality and malformations including 

gastroschisis. Zinc supplementation to a protein-deficient diet in 

CO-exposed mice decreased fetal mortality and malformation. 

Newby et al. (2005, 

193966) 

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, 

193958) 

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, 

193841) 

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 trigger apoptosis 

of pre-meiotic germ cells and modulate spermatogenesis under 

CdCl2 dependent oxidative stress. 

Patel et al. (2003, 

043155) 

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. 

Penney et al. (1983, 

011385) 

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. 

Penney et al. (1982, 

011387) 

Rat 

COBS 

GD0-GD32 350 ppm PND1-PND3, 

then 425 ppm PND4-

PND7, then 500 ppm 

PND8-PND32 

Postnatal CO exposure decreased body weight, to a greater 

extent in male pups. The heart to body weight ratio and left 

ventricle plus interventricular septum and right ventricle weight 

increased after birth in CO exposed pups. This persistent 

cardiomegaly was not explained by increasing in DNA or 

hydroxyproline. 

Piantadosi (2002, 

037463) 

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, 

180423) 

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. 

January 2010 E-12 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Piantadosi et al. 

(2006, 180424) 

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 

concluded that CO caused mitochondrial pore stress 

independently of its hypoxic effects 

Prigge and Hochrainer 

(1977, 012326) 

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, 041616) 

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, 037513) 

Human 

Male 

20% COHb 20% COHb did not influence O2Mb binding indicated by unaltered 

deoxy-myoglobin 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, 

193765) 

Reviews the basic science of exogenous and endogenous CO 

including HO-1 regulation. It also reviews some therapeutic 

applications for CO. 

Sartiani et al. (2004, 

190898) 

Rat 

Wistar 

In utero inhalation

exposure 

150 ppm At 4 wk 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 wk of age. All of these CO-dependent changes 

were no longer different from controls at 8 wk of age, showing a 

delayed maturation. 

Schwetz et al. (1979, 

011855) 

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 vs control. Increased birth weight 

in mice exposed to 7 h/day CO vs controls. No similar effects 

were seen in rabbits. 

Singh et al. (1992, 

013759) 

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 had a 

synergistic effect on offspring survival and an additive effect on 

malformations. 

Singh (2006, 190512) Mouse 

CD-1 

6 h/day during 

the first 

2nd wk 

of pregnancy 

65 or 125 ppm Modulating dam protein intake during in utero CO exposure 

altered pup mortality. 

Singh et al. (1993, 

013892) 

Mouse 

Albino CD-1 

GD8-GD18 65, 125, 250, or 500 ppm Mice were given various protein diets (4, 8, 16, or 27% protein) 

during pregnancy, along with CO exposure. All concentrations of 

CO exposure within each maternal dietary protein level 

significantly increased the percentage of litters with 

malformations in a dose-dependent manner. 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. 

January 2010 E-13 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Singh (2003, 053624) Mouse 

Albino CD-1 

GD8-GD18 500 ppm CO decreased the mean implants per litter and increased the 

incidence of fetal mortality. Under low protein conditions, CO 

exposure increased the incidence of malformations (9.4% vs 0%) 

when Zn levels were normal and increased the incidence of 

gastroschisis (5% vs 0%) when Zn levels were low. 

Singh and Scott 

(1984, 011409) 

Mouse 

Albino CD-1 

GD7-GD18 65, 125, 250, or 500 ppm All concentration of CO decreased fetal weight in mouse pups. 

Near-term fetal body weight was decreased at GD18 in mice 

exposed from GD7-GD18 to 125, 250, and 500 ppm CO but not 

at 65 ppm CO. 

Singh (1986, 012827) Mouse 

Albino CD-1 

GD7-GD18 65 or 125 ppm Impaired aerial righting score at PND14 (65 and 125 ppm), 

impaired negative geotaxis at PND10 and righting reflex on 

PND1 (125 ppm) 

Sitdikova et al. (2007, 

180417) 

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, 

037531) 

Human 

Primary human airway 

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, 

180414) 

Rat 

Wistar 

Female 

169 ± 4.5 g 

20 h/day, 

x 5 days/wk, 

x 72 wk 

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. 

Stevens and Wang 

(1993, 188458) 

Mouse 

C57/Bl-6J 

Rat 

Sprague Dawley 

Hippocampal brain slices 

HO inhibition blocked long-term potentiation but not long-term 

depression. 

Stockard-Sullivan et 

al. (2003, 190947) 

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) vs controls at 

PND22. 

Storm and Fechter 

(1985, 011653) 

Rat 

Long Evans 

GD0-parturition 150 ppm Prenatal CO exposure increased mean and total cerebellar 

norepinephrine concentration from PND14-PND42 but not in the 

cortex. 

Storm and Fechter 

(1985, 011652) 

Rat 

Long Evans 

GD0-GD20 75, 150, and 300 ppm CO transiently decreased 5HT and NE in the pons/medulla and 

increased NE in the cortex and hippocampus at PND42. CO 

dose-dependently reduced cerebellum wet weight. Maternal 

COHb: 2.5%, 11.5%, 18.5%, and 26.8% (0, 75, 150, and 300 

ppm, respectively). 

Storm et al. (1986, 

012136) 

Rat 

Long Evans 

GD0-PND10 

75, 150, and 300 ppm 

CO decreased cerebellar weight (150-300 ppm at PND10, 

75-300 ppm at PND21) and decreased total cerebellar GABA 

(150-300 ppm at PND10 and PND21). CO- exposed (300 ppm) 

cerebella had fewer fissures. 

Styka and Penney 

(1978, 011166) 

Rat 

Charles River 

Male 

6 wk 400 ppm or gradual 

increase from 500 to 

1,100 ppm 

CO caused increased heart weight to body weight that regressed 

within a couple of mo after CO exposure. COHb: 400 ppm – 35%; 

1,100 ppm – 58% 

January 2010 E-14 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Suliman et al. (2007, 

193768) 

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 

One-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 two- to threefold 24 h after exposure. CO 

activated gene expression of these proteins involved in cardiac 

mitochondrial biogenesis beginning at 2 h postexposure for PGC1α, 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, 

026022) 

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, 

011557) 

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 mo 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. 

Telfer et al. (2001, 

193769) 

Human 

Myometrium tissue 

obtained from gravid (preterm [25- to 34 wk 

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. 

Teran et al. (2005, 

193770) 

Rat 

Dahl/Rapp 

salt-sensitive rats 

Male 

 100 μM A high-salt diet for 1-4 wk 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 wk. 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 wk and then 

treated with a HO inhibitor. The authors concluded that the HOderived CO contributed to the development of hypertension and 

the impairment of endothelium-dependent vasodilator responses 

in this model. 

January 2010 E-15 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Thom et al. (1994, 

076459) 

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 and 

Ischiropoulos (1997, 

085644) 

Ra 

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 howNOwas measured (electrode vs 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. 

January 2010 E-16 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Thom et al. (1997, 

084337) 

Bovine pulmonary artery 

endothelial cells 

30 min-4 h 10-100 ppm (11-110 nM) One-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 blocked 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 

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 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 pretreated 

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 NOderived oxidants in response to CO. A delayed cell death 

occurred following exposures to 22 nM and higher concentrations 

of CO. 

Thom et al. (1999, 

016753) 

Rat 

Wistar 

Male 

200-290 g 

Some rats were 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 six-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. 

January 2010 E-17 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Thom et al. (1999, 

016757) 

Rat 

Wistar 

Male 

200-290 g 

 1 h 50-1,000 ppm Leakage of albumin into lung parenchyma occurred 18 h after 

rats were exposed to 100 ppm CO for 1 h. This response was not 

observed at earlier timepoints following CO exposure. 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 lungs 18 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, 

011574) 

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. (2001, 

193779) 

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, 

098418) 

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. 

January 2010 E-18 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Thorup et al. (1999, 

193782) 

Rat 

Sprague Dawley 

Male 

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 carbacholdependent 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. (2000, 

015997) 

Guinea pig 10 h/day over the 

last 60% of 

gestation 

200 ppm Fetal nd 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. (2000, 

010468) 

Guinea pig 10 h/day for the 

last 60% of 

gestation 

200 ppm Brains were collected at 1 and 8 wk 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. 

Toyada et al. (1996, 

079945) 

Tschugguel et al. 

(2001, 193785) 

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, 

193993) 

Mouse protein The authors presented the X-ray structure of CO-bound ferrous 

murine Nb. When CO binds, the heme group slides deeper into 

the protein crevice. 

Villamor et al. (2000, 

015838) 

Vreman et al. (2000, 

096915) 

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 3 times greater HO than the heart and 0.2 times of the 

liver. HO activity in rat vasculature was 3 times 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 

vasculature (only 50% effective). The activity of HO in the 

umbilical vessels may provide a role for CO in control of 

vasculature tone during pregnancy. 

January 2010 E-19 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Vreman et al. (2005, 

193786) 

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 threefold increase in CO 

excretion, reaching a maximum 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, 

193939) 

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, 

190515) 

Rat 

(Strain not stated) 

 PND8-PND22 12.5, 25, or 50 ppm Immunostaining 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. 

January 2010 E-20 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Webber et al. (2005, 

190514) 

Rat 

(Strain not stated) 

PND9-PND24 25 or 100 ppm Neurofilament loss from the spiral ganglion 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). 

Wellenius et al. (2004, 

087874) 

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. 

Wellenius et al. (2006, 

156152) 

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. 

Yoshiki et al.(2001, 

193790) 

Human HO localization in human endometrium and its changes in 

expression over the menstrual cycle were explored in this study. 

HO-1 was constitutively expressed throughout the menstrual 

cycle, and HO-2 was greater in the secretory than the 

proliferative phase of the menstrual 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. 

Yu et al. (2008, 

192384) 

Guinea pig 

Allergic rhinitis model 

using nasal ovalbumin 

sensitization 

Indicators of allergic rhinitis were enhanced by treatment with a 

HO-1 inducer and decreased by treatment with a HO-1 inhibitor. 

Immunoreactivity for HO-1 was shown in the lamina of mucosa of 

sensitized guinea pigs. Endogenous CO may play a role in the 

inflammation process of allergic rhinitis. 

Zamudio et al. (1995, 

193908) 

Human Women living at high altitude had an increased risk of adverse 

pregnancy outcomes vs women living at lower altitudes. 

Zenclussen et al. 

(2006, 193873) 

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, 

184460) 

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. 

January 2010 E-21 
Reference Species / Model Exposure 

Duration 

CO Concentration Findings 

Zhang et al. (2007, 

193879) 

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 

postLPS treatment. Pretreatment of mice with the spin trap agent 

PBN or the TNF α inhibitor pentoxifylline prevented the LPSdependent HO-1 upregulation. Thus ROS may mediate the LPSdependent upregulation of HO-1. 

Zhao et al. (2008, 

193883) 

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, 

013905) 

Guinea pig 

Adult male 

 Hippocampal LTP of brain sections is significantly affected by CO 

exposure with ZnPP IX, a HO inhibitor, blocking hippocampal 

LTP. 

Zuckerbraun et al. 

(2007, 193884) 

Macrophages 

RAW 264.7 

THP-1 cells, wild-type and 

respiration-deficient 

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 dose dependent 

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 LPSstimulated cells. These effects were diminished by pretreatment 

with antioxidants. The source of CO-derived oxidants was 

determined to be mitochondrial since respiration-deficient 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. 

January 2010 E-22 
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