# March 2009 1 2 EPA/600/R-09/019



# Integrated Science Assessment for Carbon Monoxide – First External Review Draft

National Center for Environmental Assessment-RTP Division Office of Research and Development U.S. Environmental Protection Agency Research Triangle Park, NC

March 2009 i DRAFT—DO NOT QUOTE OR CITE

### March 2009 ii DRAFT—DO NOT QUOTE OR CITE

# Disclaimer

This document is the first external review dr aft for review purposes only and does not constitute U.S. Environmental Protection Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

March 2009 iii DRAFT—DO NOT QUOTE OR CITE

### March 2009 iv DRAFT—DO NOT QUOTE OR CITE

# Table of Contents

Table of Contents__________________________________________________________________________________________________ v

List of Figures ___________________________________________________________________________________________________ xi

Acronyms and A bbreviations______________________________________________________________________________________ xxii

Chapter 1. Intr oduction ___________________________________________________________________________________________ 1-1 1.1. Legislative Requirements ____________________________________________________________________________ 1-2 1.2. History of t he NAAQS fo r CO _________________________________________________________________________ 1-4 1.3. Document Development _____________________________________________________________________________ 1-5 1.4. Document Organization______________________________________________________________________________ 1-5 1.5. Document Scope ___________________________________________________________________________________ 1-6 1.6. EPA Framework for C ausal Determination _______________________________________________________________ 1-7 1.6.1. Scientific Evidence Used in Establishing Causality____________________________________________________ 1-8 1.6.2. Association and Causation ______________________________________________________________________ 1-8 1.6.3. Evaluating Evidence for Inferring Causation _________________________________________________________ 1-9 1.6.4. Application of Framework for Causal Determination __________________________________________________ 1-13 1.6.5. Determination of Causality _____________________________________________________________________ 1-14 1.6.6. Concepts in Evaluating Adversity of Health Effects __________________________________________________ 1-18 1.7. Summary ________________________________________________________________________________________ 1-18

Chapter 2. Integrative Heal th Effects O verview _______________________________________________________________________ 2-1 2.1. Ambient Concentrations, Sources, and Exposure__________________________________________________________ 2-2 2.2. Dosimetry, Pharmacokineti cs, and Mode of Action_________________________________________________________ 2-4 2.2.1. Dosimetry and Pharmacokinetics _________________________________________________________________ 2-4 2.2.2. Mode of Action________________________________________________________________________________ 2-4 2.3. Health Effects _____________________________________________________________________________________ 2-6 2.3.1. Cardiovascula r Morbidity ________________________________________________________________________ 2-6 2.3.2. Central Nervous System Effects __________________________________________________________________ 2-7 2.3.3. Birth Outcomes and Developmental Effects _________________________________________________________ 2-9 2.3.4. Respiratory Morbidity__________________________________________________________________________ 2-10 2.3.5. Mortality ____________________________________________________________________________________ 2-11 2.4. Public Health Impacts ______________________________________________________________________________ 2-13 2.4.1. Concentration-Resp onse Relationship ____________________________________________________________ 2-13 2.4.2. Potentially Susceptible and Vulnerable Subpopulations _______________________________________________ 2-14

Chapter 3. Source to Exposure ____________________________________________________________________________________ 3-1 3.1. Introduction _______________________________________________________________________________________ 3-1 3.2. Sources and Emissions of CO_________________________________________________________________________ 3-1 3.3. Physics and Chemistry of Atmospheric CO_______________________________________________________________ 3-8 3.4. Ambient Measurements______________________________________________________________________________ 3-9 3.4.1. Ambient Measurement Instruments_______________________________________________________________ 3-10 3.4.2. Ambient Sampling Network Design _______________________________________________________________ 3-12 3.4.2.1. Monitor Siting Requirements _____________________________________________________________ 3-12 3.4.2.2. Spatial and Temporal Coverage __________________________________________________________ 3-12 3.5. Environmental Concentrations________________________________________________________________________ 3-20

March 2009 v DRAFT—DO NOT QUOTE OR CITE

### 3.5.1. Spatial Variability _____________________________________________________________________________ 3-20 3.5.1.1. National Scale ________________________________________________________________________ 3-20 3.5.1.2. Urban Scale __________________________________________________________________________ 3-28 3.5.1.3. Neighborhood Scale____________________________________________________________________ 3-39 3.5.2. Temporal Variability___________________________________________________________________________ 3-44 3.5.2.1. Multi-year Trends ______________________________________________________________________ 3-44 3.5.2.2. Hourly Variation _______________________________________________________________________ 3-46 3.5.3. Associations with Copollutants __________________________________________________________________ 3-50 3.5.4. Policy-Relev ant Background ____________________________________________________________________ 3-52 3.6. Issues in Exposure Assessment ______________________________________________________________________ 3-54 3.6.1. Summary of Findings from 2000 CO AQCD ________________________________________________________ 3-54 3.6.2. General Expos ure Concepts ____________________________________________________________________ 3-55 3.6.3. Monitoring Issues Associated with Exposure Assessment _____________________________________________ 3-58 3.6.3.1. Exposure Assessment using Community-Based Ambient Monitors _______________________________ 3-58 3.6.3.2. Personal Exposure Monitors _____________________________________________________________ 3-58 3.6.4. Indoor/Outdoor Relati onships and Infiltration _______________________________________________________ 3-59 3.6.5. Personal/Ambient Relationships _________________________________________________________________ 3-60 3.6.5.1. Panel and Population Exposure Studies ____________________________________________________ 3-60 3.6.5.2. Commuting Time CO Exposure Studies ____________________________________________________ 3-62 3.6.5.3. CO Exposure Assessment Variability and Error ______________________________________________ 3-65 3.6.6. Multi-pollutant Exposures ______________________________________________________________________ 3-67 3.6.7. Exposure Modeling ___________________________________________________________________________ 3-68 3.7. Summary and Conclusions __________________________________________________________________________ 3-71 3.7.1. Source s of CO _______________________________________________________________________________ 3-71 3.7.2. Physics and Chemistr y of Atmospheric CO_________________________________________________________ 3-71 3.7.3. Ambient CO Measurements ____________________________________________________________________ 3-72 3.7.4. Environmental CO Concentrations _______________________________________________________________ 3-72 3.7.5. Exposure Assessment and Implications for Epidemiology _____________________________________________ 3-73

Chapter 4. Dosimetry and Pharmacokinetics of Car bon Monoxide _______________________________________________________ 4-1 4.1. Introduction _______________________________________________________________________________________ 4-1 4.2. Carboxyhemoglobin Formation ________________________________________________________________________ 4-2 4.2.1. The Coburn-Forster-K ane and Other Models ________________________________________________________ 4-2 4.2.2. Multicompartment Model ________________________________________________________________________ 4-5 4.2.3. Mathematical Model Usage ______________________________________________________________________ 4-6 4.3. Absorption, Distribution, and Elimination_________________________________________________________________ 4-7 4.3.1. Pulmonary Absorption __________________________________________________________________________ 4-7 4.3.1.1. Mass Transfer of Carbon Monoxide_________________________________________________________ 4-8 4.3.1.2. Lung Diffusion of Carbon Monoxide_________________________________________________________ 4-9 4.3.2. Tissue Uptake _______________________________________________________________________________ 4-10 4.3.2.1. The Respir atory Tract___________________________________________________________________ 4-10 4.3.2.2. The Blood ____________________________________________________________________________ 4-10 4.3.2.3. Heart and Skeletal Muscle _______________________________________________________________ 4-12 4.3.2.4. Other Tissues _________________________________________________________________________ 4-13 4.3.3. Pulmonary and Tissue Elimination _______________________________________________________________ 4-13 4.4. Conditions Affecting Uptake and Elimination_____________________________________________________________ 4-15 4.4.1. Environment and Activity _______________________________________________________________________ 4-15 4.4.2. Altitude_____________________________________________________________________________________ 4-16 4.4.3. Physical Char acteristics________________________________________________________________________ 4-17 4.4.4. Health Status ________________________________________________________________________________ 4-18 4.5. Endogenous CO Production and Metabolism ____________________________________________________________ 4-18 4.6. Summary and Conclusions __________________________________________________________________________ 4-20

Chapter 5. Integrated Health Eff ects ________________________________________________________________________________ 5-1 5.1. Mode of Action of CO Toxicity _________________________________________________________________________ 5-1 5.1.1. Introduction __________________________________________________________________________________ 5-1 5.1.2. Hypoxic Mechanisms___________________________________________________________________________ 5-1

March 2009 vi DRAFT—DO NOT QUOTE OR CITE

### 5.1.3. Non-Hypoxic Mechanisms_______________________________________________________________________ 5-2 5.1.3.1. Non-Hypoxic Mechanisms Re viewed in the 20 00 CO AQ CD _____________________________________ 5-2 5.1.3.2. Recent Studies of Non-Hypoxic Mechanisms _________________________________________________ 5-4 5.1.3.3. Implications of Non-Hypoxic Mechamisms __________________________________________________ 5-11 5.2. Cardiovascular Effects______________________________________________________________________________ 5-13 5.2.1. Epidemiologic Studies _________________________________________________________________________ 5-13 5.2.1.1. Epidemiologic Studies with Short-Term Exposure_____________________________________________ 5-14 5.2.1.2. Epidemiologic Studies with Long-Term Exposure _____________________________________________ 5-44 5.2.1.3. Summary of Epidemiologic Studies of Exposure to CO and Cardiovascular Effects __________________ 5-44 5.2.2. Controlled Huma n Exposure Studies _____________________________________________________________ 5-45 5.2.3. Toxicological Studies__________________________________________________________________________ 5-48 5.2.3.1. Endothelial Dysfunction _________________________________________________________________ 5-48 5.2.3.2. Cardiac Remodeling Effects______________________________________________________________ 5-50 5.2.3.3. Electrocardiographic Effects _____________________________________________________________ 5-52 5.2.3.4. Summary of Card iovascular Toxicology_____________________________________________________ 5-52 5.2.4. Summary of Card iovascular Effects ______________________________________________________________ 5-53 5.3. Central Nervous System Effects ______________________________________________________________________ 5-54 5.3.1. Controlled Huma n Exposure Studies _____________________________________________________________ 5-54 5.3.2. Toxicological Studies__________________________________________________________________________ 5-55 5.3.3. Summary of Central Ne rvous System Effects _______________________________________________________ 5-55 5.4. Birth Outcomes and Developmental Effects _____________________________________________________________ 5-56 5.4.1. Epidemiologic Studies _________________________________________________________________________ 5-56 5.4.1.1. Preterm Birth _________________________________________________________________________ 5-57 5.4.1.2. Birth Weight, Low Birth Weight, and Intrauterine Growth Restriction/Small for Gestational Age _________ 5-61 5.4.1.3. Congenital Anomalies __________________________________________________________________ 5-71 5.4.1.4. Neonatal and Post-Neonatal Mortality ______________________________________________________ 5-73 5.4.1.5. Summary of Epidemiologic Studies of Birth Outcomes and Developmental Effects ___________________ 5-75 5.4.2. Toxicological Studies of Birth Outcomes and Developmental Effects_____________________________________ 5-75 5.4.2.1. Birth Outcomes________________________________________________________________________ 5-75 5.4.2.2. Developmental Effects __________________________________________________________________ 5-83 5.4.3. Summary of Birth Outcomes and Developmental Effects ______________________________________________ 5-94 5.5. Respiratory Effects ________________________________________________________________________________ 5-95 5.5.1. Epidemiologic Studies wi th Short-Term Exposure ___________________________________________________ 5-95 5.5.1.1. Pulmonary Function, Respiratory Symptoms, and Medication Use________________________________ 5-95 5.5.1.2. Respiratory Hospital Admissions, ED Visits and Physician Visits ________________________________ 5-103 5.5.2. Epidemiologic Studies wi th Long-Term Exposure___________________________________________________ 5-113 5.5.2.1. Pulmonary Function ___________________________________________________________________ 5-113 5.5.2.2. Asthma and Asthma Symptoms__________________________________________________________ 5-114 5.5.2.3. Allergic Rhinitis_______________________________________________________________________ 5-115 5.5.2.4. Summary of Associations between Long-Term Exposure to CO and Respiratory Morbidity ___________ 5-115 5.5.3. Controlled Huma n Exposure Studies ____________________________________________________________ 5-115 5.5.4. Toxicological Studies_________________________________________________________________________ 5-116 5.5.5. Summary of Respirat ory Health Effects __________________________________________________________ 5-117 5.5.5.1. Short-Term Exposure to CO ____________________________________________________________ 5-117 5.5.5.2. Long-Term Exposure to CO _____________________________________________________________ 5-118 5.6. Mortality ________________________________________________________________________________________ 5-119 5.6.1. Epidemiologic Studies with Short-Term Exposur e to CO _____________________________________________ 5-119 5.6.1.1. Summary of Findings from 2000 CO AQCD ________________________________________________ 5-119 5.6.1.2. Multicity Studies ______________________________________________________________________ 5-120 5.6.1.3. Meta-Analysis of A ll Criteria Pollutants ____________________________________________________ 5-127 5.6.1.4. Single-City Studies____________________________________________________________________ 5-128 5.6.1.5. Summary of Mortality and Short-Term Exposure to CO _______________________________________ 5-131 5.6.2. Epidemiologic Studies with Long-Term Exposure to CO _____________________________________________ 5-132 5.6.2.1. U.S. Coho rt Studies ___________________________________________________________________ 5-133 5.6.2.2. U.S. Cross-Se ctional Analysis ___________________________________________________________ 5-137 5.6.2.3. Summary of Mortality and Long-Term Exposure to CO________________________________________ 5-138 5.7. Public Health Impacts _____________________________________________________________________________ 5-138 5.7.1. Concentration-Respon se Relationship ___________________________________________________________ 5-139 5.7.2. Potentially Susceptible and Vulnerable Populations _________________________________________________ 5-140

March 2009 vii DRAFT—DO NOT QUOTE OR CITE

### 5.7.2.1. Susceptibility Characteristics ____________________________________________________________ 5-141 5.7.2.2. Vulnerability Characteristics_____________________________________________________________ 5-144

Annex A. Atmosphe ric Science ____________________________________________________________________________________ A-1

Annex B. Dosime try Studies______________________________________________________________________________________ B-51

Annex C. Epidemio logy Studies____________________________________________________________________________________ C-1

Annex D. Controlled Huma n Exposure Studies _______________________________________________________________________ D-1

Annex E. Toxicologi cal Studies ____________________________________________________________________________________ E-1

References _______________________________________________________________________________________________________ 1

March 2009 viii DRAFT—DO NOT QUOTE OR CITE

# List of Tables

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

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

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

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

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

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

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

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

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

Table 3-7. Table of inter-sampler comparison statistics, as defined in the text, including Pearson r, P90 (ppm), COD and d (km) for each pair of hourly CO monitors reporting to AQS for 2005-2007 in Phoenix, AZ. _______________________ 3-31

Table 3-8. Table of inter-sampler comparison statistics, as defined in the text, including Pearson r, P90 (ppm), COD and d (km) for each pair of hourly CO monitors reporting to AQS for 2005-2007 in Pittsburgh, PA. ______________________ 3-34

Table 3-9. Percentage of time exposed to ambient CO (adjusted to reflect the absence of non-ambient CO from ETS and gas cooking), average CO exposures, and percentage of exposure estimated for the population. ______________________ 3-61

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

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

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

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

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

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

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

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

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

### ____________________________________________________________ 5-29

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

### __________________________________________________________ 5-34

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

Table 5-10. Association of ambient air pollution levels and cardiovascular morbidity in visits with and without specific secondary conditions. ______________________________________________________________________________ 5-38

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

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

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

Table 5-14. Range of CO concentrations reported in key respiratory morbidity studies that examined effects associated with short-term exposure to CO. __________________________________________________________________________ 5-96

March 2009 ix DRAFT—DO NOT QUOTE OR CITE

### Table 5-15. Range of CO concentrations reported in key respiratory hospital admission and ED visit studies that examine effects associated with short-term exposure to CO. ______________________________________________________ 5-104

Table 5-16. Range of CO concentrations reported in multi-city studies that examine mortality effects associated with short-term exposure to CO. __________________________________________________________________________________ 5-120

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

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

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

Table A-1. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Anchorage, AK. ___________________________________ A-17

Table A-2. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Atlanta, GA. ______________________________________ A-19

Table A-3. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Boston, MA. _____________________________________ A-22

Table A-4. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Denver, CO. _____________________________________ A-25

Table A-5. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Houston, TX. _____________________________________ A-28

Table A-6. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Los Angeles, CA. _________________________________ A-31

Table A-7. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in New York City, NY. ________________________________ A-37

Table A-8. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in St. Louis, MO. ____________________________________ A-41

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

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

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

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

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

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

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

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

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

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

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

March 2009 x DRAFT—DO NOT QUOTE OR CITE

# List of Figures

Figure 3-1. CO emissions (MT) in the U.S. by source sector in 2002. ___________________________________________________ 3-2 Figure 3-2. Trends in anthropogenic CO emissions (MT) in the U.S. by source category for 1990 and 1996-2002. ________________ 3-3 Figure 3-3. CO concentrations measured by satellite at the 700 hectoPascal level (~10,000 feet above sea level) from MOPITT for the period 15-23 July 2004 during intense wildfires in Alaska and Yukon. _____________________________ 3-6 Figure 3-4. Trends in sub-national CO emissions in the 10 U.S. EPA Regions for 1990 and 1996 to 2002. ______________________ 3-6 Figure 3-5. CO emissions density map and distribution for the state of Colorado, and for selected counties in Colorado. ___________ 3-7 Figure 3-6. Map of ~385 CO monitor locations in the U.S. in 2007. ____________________________________________________ 3-14 Figure 3-7. Map of CO monitor locations with respect to population density in the Phoenix, AZ CBSA, total population. ___________ 3-15 Figure 3-8. Map of CO monitor locations with respect to populati on density in the Phoenix, AZ CBSA, age 65 and older. __________ 3-16 Figure 3-9. Map of CO monitor locations with respect to population density in the Pittsburgh, PA CSA, total population. ___________ 3-17 Figure 3-10. Map of CO monitor locations with respect to population density in the Pittsburgh, PA CSA, age 65 and older. _________ 3-18 Figure 3-11. County-level map of second-highest 1-h avg CO concentrations in the U.S. in 2007. _____________________________ 3-21 Figure 3-12. County-level map of second-highest 8-h avg CO concentrations in the U.S. in 2007. _____________________________ 3-22 Figure 3-13. Map of CO monitor locations with AQS Site IDs for Phoenix, AZ. ____________________________________________ 3-29 Figure 3-14. Box plots illustrating the seasonal distribution of 2005-2007 hourly CO concentrations in Phoenix, AZ. Note: 1 = winter, 2 = spring, 3 = summer, and 4 = fall on the x-axis. __________________________________________________ 3-30 Figure 3-15. Map of CO monitor locations with AQS Site IDs for Pittsburgh, PA. ___________________________________________ 3-32 Figure 3-16. Box plots illustrating the seasonal distribution of 2005-2007 hourly CO concentrations in Pittsburgh, PA. _____________ 3-33 Figure 3-17. Aerial view of the location of CO monitor C. _____________________________________________________________ 3-36 Figure 3-18. Aerial view of the location of CO monitor E. _____________________________________________________________ 3-37 Figure 3-19. Aerial view of the location of CO monitors A, B, and C. ____________________________________________________ 3-38 Figure 3-20. Aerial view of the location of CO monitor F. _____________________________________________________________ 3-39 Figure 3-21. Relative concentrations of CO and copollutants at various distances from the 710 freeway in Los Angeles. ___________ 3-41 Figure 3-22. Dimensionless tracer gas concentration as a function of elevation at windward and leeward locations and street canyon aspect ratios (H/W). _________________________________________________________________________ 3-42 Figure 3-23. Inter-sampler correlations as a function of distance between CO monitors for samplers located within 4 km (neighborhood scale) for Boston, Denver, Phoenix, and Pittsburgh. __________________________________________ 3-43 Figure 3-24. (Top) Trends in ambient CO in the U.S., 1980-2006, reported as the annual second highest 8-h concentrations (ppm) for the mean, median, 10% and 90% values. (Bottom) Trends in ambient CO in the U.S., 1980-2006, reported as the number of trend sites (y-axis) with annual second-highest 8-h concentrations above the level of the NAAQS (9 ppm). __________________________________________________________________________________ 3-44 Figure 3-25. Trends in ambient CO in the U.S., 1980-2005, reported as the annual second highest 8-h concentrations (ppm) for the EPA Regions 1 through 10, along with a depiction of the geographic extent of those Regions ___________________ 3-46 Figure 3-26. Diel plot generated from weekday hourly CO data (ppm) for the elev en CSAs and CBSAs 2005-2007. _______________ 3-48

March 2009 xi DRAFT—DO NOT QUOTE OR CITE

### Figure 3-27. Diel plot generated from weekend hourly CO data (ppm) for the elev en CSAs and CBSAs 2005-2007. _______________ 3-49 Figure 3-28. Seasonal plots of nationwide correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations. _________________________________________________________________ 3-50 Figure 3-29. Map of the baseline monitor sites used in this assessment to compute policy-relevant background concentrations. _____ 3-53 Figures 3-30. Monthly (circles) and annual (squares) average CO concentrations (ppb), 2005-2007 . ___________________________ 3-54 Figure 3-31. Distribution of time that a sample population spends in various environments, from the National Human Activity Pattern Survey. ___________________________________________________________________________________ 3-58 Figure 3-32. Comparison of in-vehicle (solid line) and engine (dotted line) results for (left) driving with windows closed and air conditioner in recirculating air mode, and (right) driving with windows closed and air conditioner in fresh air mode. _____ 3-63 Figure 3-33. Hourly personal vs. ambient CO concentrations obtained in Baltimore, MD. ____________________________________ 3-66 Figure 3-34. Box plots of the ratio of personal to ambient concentrations obtained in Baltimore, MD. ___________________________ 3-66 Figure 4-1. Plot of fractional sensitivities of selected variables versus time of exposure. _____________________________________ 4-4 Figure 4-2. Overall structure of the multicompartment model of storage and transport of CO. _________________________________ 4-6 Figure 4-3. Predicted COHb increments over endogenous levels resulting from 1, 8, 12, and 24 h CO exposures in a modeled healthy human at rest. _______________________________________________________________________________ 4-7 Figure 4-4. Diagrammatic presentation of CO uptake and elimination pathways and CO body stores. __________________________ 4-9 Figure 4-5. O 2 

### Hb dissociation curve of normal human blood, of blood containing 50% COHb, and of blood with only 50% Hb because of anemia. ________________________________________________________________________________ 4-12 Figure 4-6. Changes in blood COHb after short-term and long-term ex posure to CO, representing the biphasic nature of CO elimination. _______________________________________________________________________________________ 4-15 Figure 5-1. Summary of effect estimates (95% confidence intervals) associated with hospital admissions for various froms of IHD. ____________________________________________________________________________________________ 5-28 Figure 5-2. Summary of effect estimates (95% confidence intervals) associated with ED visits and hospital admissions for stroke. __________________________________________________________________________________________ 5-33 Figure 5.3. Summary of effect estimates (95% confidence intervals) associated with hospital admissions for CHF. _______________ 5-36 Figure 5-4. Summary of effect estimates (95% confidence intervals) associated with hospital admissions for CVD. ______________ 5-41 Figure 5-5. Effect estimates from studies of ED visits and hospital admissions for CVD outcomes other than stroke from single pollutant (CO only, closed circles) and copollutant (CO plus PM, NO 2 

### , SO 2 

### and O 3 

### , open circles) models. ____________ 5-43 Figure 5-6. Summary of effect estimates (95% confidence intervals) for PTB associated with maternal exposure to ambient CO. _____________________________________________________________________________________________ 5-60 Figure 5-7. Summary of change in birth weight (95% confidence intervals) associated with maternal exposure to ambient CO. _____ 5-67 Figure 5-8. Summary of effect estimates (95% confidence intervals) for LBW associated with maternal exposure to ambient CO. _____________________________________________________________________________________________ 5-68 Figure 5-9. Summary of effect estimates (95% confidence intervals) for SGA associated with maternal exposure to ambient CO. _____________________________________________________________________________________________ 5-69 Figure 5-10. Estimates for FEV 1 

### change expressed per SD change of the individual pollutants _______________________________ 5-98 Figure 5-11. Asthma symptoms, respiratory symptoms and medication use in asthmatic individuals associated with short-term exposure to CO. __________________________________________________________________________________ 5-102 Figure 5-12. Summary of associations between short-term ex posure to CO and respiratory hospital admissions. , 

### ________________ 5-109 Figure 5-13. Summary of associations between short-term exposure to CO and respiratory ED visits. _________________________ 5-112

March 2009 xii DRAFT—DO NOT QUOTE OR CITE

### Figure 5-14. Posterior means and 95% posterior intervals of national average estimates for CO effects on total (non-accidental) mortality at lags 0, 1, and 2 within sets of the 90 U.S. cities with available pollutant data. _________________________ 5-122 Figure 5-15. Summary of mortality risk estimates for short-term exposure to CO from multi-city studies. _______________________ 5-127 Figure 5-16. Summary of mortality risk estimates for long-term exposure to CO. __________________________________________ 5-136 Figure A-1. CO emissions density map and distribution for the state of Alaska and for Yukon-Koyukuk County in Alaska. ___________ A-1 Figure A-2. CO emissions density map and distribution for the state of Utah and for selected counties in Utah. ___________________ A-2 Figure A-3. CO emissions density map and distribution for the state of Massachusetts and for selected counties in Massachusetts. ____________________________________________________________________________________ A-3 Figure A-4a. CO emissions density map and distribution for the state of Georgia and for selected counties in Georgia (1 of 2) ________ A-4 Figure A-4b. CO emissions distribution for selected counties in Georgia (2 of 2) ____________________________________________ A-5 Figure A-5. CO emissions density map and distribution for the state of California and for selected counties in California. ___________ A-6 Figure A-6. CO emissions density map and distribution for the state of Alabama and for Jefferson County in Alabama. ____________ A-7 Figure A-7. Map of CO monitor locations with respect to populat ion density in the Anchorage CBSA, total population. _____________ A-8 Figure A-8. Map of CO monitor locations with respect to populat ion density in the Anchora ge CBSA, ages 65 and older. ___________ A-8 Figure A-9. Map of CO monitor locations with respect to population density in the Atlanta CSA, total population. __________________ A-9 Figure A-10. Map of CO monitor locations with respect to population density in the Atlanta CSA, ages 65 and older. _______________ A-9 Figure A-11. Map of CO monitor locations with respect to population density in the Boston CSA, total population. _________________ A-10 Figure A-12. Map of CO monitor locations with respect to population density in the Boston CSA, ages 65 and older. ______________ A-10 Figure A-13. Map of CO monitor locations with respect to population density in the Denver CSA, total population. ________________ A-11 Figure A-14. Map of CO monitor locations with respect to population density in the Denver CSA, ages 65 and older. ______________ A-11 Figure A-15. Map of CO monitor locations with respect to population density in the Houston CSA, total population. _______________ A-12 Figure A-16. Map of CO monitor locations with respect to population density in the Houston CSA, ages 65 and older. _____________ A-12 Figure A-17. Map of CO monitor locations with respect to population density in the Los Angeles CSA, total population. ____________ A-13 Figure A-18. Map of CO monitor locations with respect to population density in the Los Angeles CSA, ages 65 and older. __________ A-13 Figure A-19. Map of CO monitor locations with respect to population density in the New York City CSA, total population. ___________ A-14 Figure A-20. Map of CO monitor locations with respect to population density in the New York City CSA, ages 65 and older. ________ A-14 Figure A-21. Map of CO monitor locations with respect to population density in the Seattle CSA, total population. _________________ A-15 Figure A-22. Map of CO monitor locations with respect to population density in the Seattle CSA, ages 65 and older. ______________ A-15 Figure A-23. Map of CO monitor locations with respect to population density in the St. Louis CSA, total population. _______________ A-16 Figure A-24. Map of CO monitor locations with respect to population density in the St. Louis CSA, ages 65 and older. _____________ A-16 Figure A-25. Map of CO monitor locations with AQS Site IDs for Anchorage, AK. __________________________________________ A-17 Figure A-26. Box plots illustrating the seasonal distribution of hourly CO concentrations in Anchorage, AK. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-18 Figure A-27. Map of CO monitor locations with AQS Site IDs for Atlanta, GA. _____________________________________________ A-19

March 2009 xiii DRAFT—DO NOT QUOTE OR CITE

### Figure A-28. Box plots illustrating the seasonal distribution of hourly CO concentrations in Atlanta, GA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-20 Figure A-29. Map of CO monitor locations with AQS Site IDs for Boston, MA. _____________________________________________ A-21 Figure A-30. Box plots illustrating the seasonal distribution of hourly CO concentrations in Boston, MA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-23 Figure A-31. Map of CO monitor locations with AQS Site IDs for Denver, CO. _____________________________________________ A-24 Figure A-32. Box plots illustrating the seasonal distribution of hourly CO concentrations in Denver, CO. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-26 Figure A-33. Map of CO monitor locations with AQS Site IDs for Houston, TX. ____________________________________________ A-27 Figure A-34. Box plots illustrating the seasonal distribution of hourly CO concentrations in Houston, TX. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-29 Figure A-35. Map of CO monitor locations with AQS Site IDs for Los Angeles, CA. _________________________________________ A-30 Figure A-36a. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. _________________________________________________________ A-33 Figure A-36b. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. _________________________________________________________ A-34 Figure A-36c. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. _________________________________________________________ A-35 Figure A-37. Map of CO monitor locations with AQ S Site IDs for New York City, NY. _______________________________________ A-36 Figure A-38. Box plots illustrating the seasonal distribution of hourly CO concentrations in New York City, NY. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. _______________________________________________________ A-38 Figure A-39. Map of CO monitor locations with AQS Site IDs for Seattle, WA. _____________________________________________ A-39 Figure A-40. Box plots illustrating the seasonal distribution of hourly CO concentrations in Seattle, WA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-40 Figure A-41. Map of CO monitor locaitons with AQS Site IDs for St. Louis, MO. ___________________________________________ A-41 Figure A-42. Box plots illustrating the seasonal distribution of hourly CO concentrations in St. Louis, MO. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-42 Figure A-43. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations for Anchorage, AK. Also shown are correlations between 24-h average CO concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Note that the data are not obtained for Anchorage during the summer, and so are not presented here. _________________________ A-43 Figure A-44. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations for Atlanta, GA. Also shown are correlations between 24-h average CO concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) ___________________ A-44 Figure A-45. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations for Boston, MA. Also shown are correlations between 24-h average CO concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Red bars denote the median, and green stars denote the arithmetic mean. __________________________________________________ A-45

March 2009 xiv DRAFT—DO NOT QUOTE OR CITE

### Figure A-46. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations for Denver, CO. Also shown are correlations between 24-h average CO concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Red bars denote the median, and green stars denote the arithmetic mean. ____________________________________________ A-46 Figure A-47. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations for Los Angeles, CA. Also shown are correlations between 24-h average CO concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) _____ A-47 Figure A-48. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations for New York City, NY. Also shown are correlations between 24-h average CO concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) _____ A-48 Figure A-49. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations for Phoenix, AZ. Also shown are correlations between 24-h average CO concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) ________________ A-49 Figure A-50.

________________ A-50 Seasonal plots of correlations between hourly CO concentration with hourly (1) SO 2 

### , (2) NO 2 

### , (3) O 3 

### , (4) PM 10 

### , and (5) PM 2.5 

### concentrations for Seattle, WA. Also shown are correlations between 24-h average CO concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.)

March 2009 xv DRAFT—DO NOT QUOTE OR CITE

# Authors and Contributors

Authors Dr. Thomas Long (CO Team Leader)—Nationa l Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jeffrey Arnold—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Barbara Buckley—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Mr. Allen Davis—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Steven J. Dutton—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Craig Hansen— Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Dr. Erin Hines—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Douglas Johns—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Dr. Thomas Luben—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Elizabeth Oesterling Owens— National Center fo r Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Mary Ross—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Matthew Campen—Lovelace Respirator y Research Institute, Albuquerque, NM Dr. Kazuhiko Ito—Department of Environmental Medi cine, New York University School of Medicine, Tuxedo, NY Dr. Jennifer Peel—Department of Environmental an d Radiological Health Sciences, Colorado State University, Fort Collins, CO

March 2009 xvi DRAFT—DO NOT QUOTE OR CITE

## Contributors Dr. Richard Baldauf—National Risk Management R esearch Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Vernon Benignus—National Health and Environmental Effects Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Lance McCluney—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC

Dr. Kris Novak—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Adam Reff—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Mark Schmidt—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Rhonda Thompson—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC

Reviewers Dr. Richard Baldauf—National Risk Management R esearch Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Vernon Benignus—National Health and Environmental Effects Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Souad Benromdhane—Office of Air Quality Planni ng and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Philip Bromberg—School of Medicine, Univer sity of North Carolina, Chapel Hill, NC Dr. Matthew Campen—Lovelace Respirator y Research Institute, Albuquerque, NM Dr. Daniel Costa—National Program Director for Air, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Andrew Ghio—National Health and Environmental Effects Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Kazuhiko Ito—Department of Environmental Medi cine, New York University School of Medicine, Tuxedo, NY

Dr. Petros Koutrakis—Harvard School of Public Health, Harvard University, Cambridge, MA Mr. John Langstaff—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Barry Lefer—Department of Geosciences, University of Houston, Houston, TX Dr. Karen Martin—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC

March 2009 xvii DRAFT—DO NOT QUOTE OR CITE

## Dr. Dave McKee—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC

Ms. Connie Meacham—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Ines Pagan—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC

Dr. Jennifer Parker—National Center for Health St atistics, Centers for Disease Control, Atlanta, GA Dr. Jennifer Peel—Department of Environmental an d Radiological Health Sciences, Colorado State University, Fort Collins, CO Dr. Pradeep Rajan—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Harvey Richmond—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Joseph Somers—Office of Trans portation and Air Quality Planning an d Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Ann Arbor, MI Dr. John Vandenberg—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Alan Vette—National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. William Vizuete—Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC Ms. Debra Walsh—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Lin Weaver—Department of Internal Medicine, LDS Hospital, Salt Lake City, UT Dr. Lewis Weinstock—Office of Air Quality Planning a nd Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Ron Williams—National Exposure Research Labo ratory, U.S. Environmental Protection Agency, Research Triangle Park, NC CO Project Team

Executive Direction Dr. John Vandenberg (Director)—National Center fo r Environmental Assessment-RTP Division, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Debra Walsh (Deputy Director)—National Cent er for Environmental Assessment-RTP Division, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Mary Ross (Branch Chief)—National Center fo r Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

March 2009 xviii DRAFT—DO NOT QUOTE OR CITE

## Scientific Staff Dr. Thomas Long (CO Team Leader)—National Ce nter for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jeffrey Arnold—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Barbara Buckley—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Mr. Allen Davis—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Steven J. Dutton—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Craig Hansen— Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Dr. Erin Hines—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Douglas Johns—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Dr. Thomas Luben—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Elizabeth Oesterling Owens— National Center fo r Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Technical Support Staff Ms. Ellen Lorang— National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Ms. Connie Meacham—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

Ms. Deborah Wales—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC

March 2009 xix DRAFT—DO NOT QUOTE OR CITE

# Clean Air Scientific Advisory Committee CO NAAQS Review Panel

Chairperson Dr. Joseph Brain*, Department of Environmnetal Health, Harvard School of Public Health, Harvard University, Boston, MA

Members Dr. James Crapo * , Department of Medicine, National Jewish Medical and Research Center, Denver, CO Dr. Douglas Crawford-Brown, Department of Environm ental Sciences and Engineering, University of North Carolina, Chapel Hill, NC Dr. Thomas Dahms, Department of Anesthesiology R esearch and Critical Care, St. Louis University School of Medicine, St. Louis, MO Dr. Russell R. Dickerson, Department of Meteorolog y, University of Maryland, College Park, MD Dr. Laurence Fechter, Research Service, Departme nt 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, Cent er for Environmental Medicine, Asthma and Lung Biology, University of North Carolina, Chapel Hill, NC Dr. Michael T. Kleinman, Department of Commun ity & Environmental Medicine, University of California-Irvine, Irvine, CA Dr. Arthur Penn, Department of Comparative Biomedi cal Sciences, Louisiana State University School of Veterinary Medicine, Baton Rouge, LA Dr. Beate Ritz, School of Public Health, Epidemiolo gy, University of California at Los Angeles, Los Angeles, CA Dr. Paul Roberts, Sonoma Technology, Inc., Petaluma, CA Dr. Armistead (Ted) Russell * , Department of Civil and Environmenta l Engineering, Georgia Institute of Technology, Atlanta, GA Dr. Stephen R. Thom, Institute for Environmental Me dicine, University of Pennsylvania, Philadelphia, PA * Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA Administrator

March 2009 xx DRAFT—DO NOT QUOTE OR CITE

## Science Advisory Board Staff Dr. Ellen Rubin, Designated Federal Officer 1200 Pennsylvania Avenue, N.W. Washington, DC, 20460 Phone: 202-343-9975 Fax: 202-233-0643 Email: rubin.ellen@epa.gov Physical/Courier/FedEx Address: Dr. Ellen Rubin, EPA Science Advisory Board Staff Office Mail Code 1400F Woodies Building, Room 3610E 1025 F Street, N.W. Washington, DC 20004

March 2009 xxi DRAFT—DO NOT QUOTE OR CITE

# Acronyms and Abbreviations

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

a air exchange rate of the microenvironment AA abdominal aorta(s) ABR auditory brainstem response ACS American Cancer Society ACS-CPS-II ACS Cancer Prevention Study II ADP adenosine diphosphate

AEFV area under the expira tory flow-volume curve

AGL above ground level Akt Akt cell signaling pathway - protei n family (of protein kinases B [PKB[) AM, a.m. morning hours

AMI acute myocardial infarction AMP adenosine monophosphate

ANOVA analysis of variance

APO E apolipoprotein E

APO E -/- 

### mouse strain used as a model of athe rosclerosis

ARI acute respiratory infection AP action potential APD action poten tial duration

APEX Air Pollution Exposure (population-based model)

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

APHEA2 extended analysis of APHEA APTT activated partial thromboplastin time, (blood coagulability endpoint) AQ air quality AQCD Air Quality Cr iteria Document AQS (EPA) Air Quality System (database)

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

ARCO gastronomy reared + CO exposure

ARIC Atherosclerosis Risk in Communities (study)

ARID gastronomy reared w ith iron deficient diet

ARIDCO gastronomy reared with ir on deficient diet + CO exposure ATP adenosine triphosphate ATS American Thoracic Society

avg average AVP aortic valve prosthesis (surgica lly implantable biological prosthesis)

March 2009 xxii DRAFT—DO NOT QUOTE OR CITE

### β beta (beta coefficient, slope) B lymphocytes bursa-dependent lymphocytes BALF bronchoalveolar lavage fluid BC black carbon BEAS-2B human bronchial epithelial cell line BEIS Biogenic Emissions Inventory System BELD Biogenic Emissions Landcover Database BHR bronchial hyper-responsiveness

BK Ca 

### voltage and Ca 2+ 

### -activated K + 

### channel(s) BP blood pressure BQ-123 a selective endothelin A (ET A 

### ) receptor antagonist BS black smoke

BSP black smoke particles C carbon

C a 

### ambient concentration CA cardiac arrhythmia

Ca 2+ 

### calcium ion

CAA Clean Air Act

CAD coronary artery disease

CALINE California Line Source Dispersion Model CALPUFF Gaussian puff modeling system to simulate air quality dispersion and accesses long range transport of pollutants. Distributed by TRC Solutions.

CAMP Childhood Asthma Management Program (study) cAMP cyclic AMP CAP(s) compound action potential(s)

CASAC Clean Air Scientific Advisory Committee CASN Cooperative Ai r Sampling Network CAth cardiac atherosclerosis

CBSA Core-Based Statistical Area (containing at least one urba n area of 10,000 people [as determined by the 2000 census]; replaces the older MSA definition from the 1990 census).

CCGG Carbon Cycle Greenhouse Gases Group (within ESRL) CD cardiac dysrhthmias Cd cadmium

CD-1 mouse strain

CDC Centers for Disease Control and Prevention CdCl 2 

### cadmium chloride

CFK Coburn-Forster-Kane (equation or modeling)

CFR Code of Federal Regulations cGMP cyclic GMP

CH 2 

### O formaldehyde

CH 2 

### O 2 

### formic acid

March 2009 xxiii DRAFT—DO NOT QUOTE OR CITE

### CH 3 

### methyl groups CH 3 

### CHO acetaldehyde

CH 3 

### CO acetyl radical(s)

CH 3 

### CO 3 

### NO 2 

### PAN, peroxyacetyl nitrate

CH 3 

### O 2 

### methyl peroxy radical

CH 3 

### OOH methyl hydroperoxide

CH 4 

### methane ChAT choline acetyl-transferase

CHD coronary heart disease CHF congestive heart failure CI confidence interval(s)

CIS cerebral ischemic stroke

C

j 

### airborne concentration at location j CL/P cleft lip with or without palate

CNS central nervous system CO carbon monoxide

CO 2 

### carbon dioxide

COD coefficient of divergence CoH, COH coefficient of haze (a measurement of visibility interference in the atmosphere, as the quantity of dust and smoke in a theo retical 1,000 linear feet of air). COHb carboxyhemoglobin (% concentra tion measured in (mL CO/mL blood)) [COHb] 0 

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

### carboxyhemoglobin concentration at time t COMb carboxymyoglobin Complex IV mitochondrial cytochrome c oxidase complex assembly, respiratory chain complex IV in the mitochondrial inner membrane. Cong U.S. Congress

CONUS contiguous U.S. (United States of America) COPD chronic obstructi ve pulmonary disease

CPS II Cancer Prevention Study II C-R concentration-re sponse (relationship)

CRC Coordinating Research Council

CrMP collapsin response mediator protein CRP C-reactive protein

CSA Combined Statistical Area (an aggregate of adjacent CBSAs tied by specific commuting behaviors), as defined by the 2000 census Cu copper (element)

CVD cardiovascular disease

Δ delta, change, difference d straight-line distance between monitor pairs Dahl/Rapp salt-sensitive model of hyperten sion (substrain of Sprague Dawley rat)

D.C. District of Columbia

March 2009 xxiv DRAFT—DO NOT QUOTE OR CITE

### df degrees of freedom df/yr degrees of freedom per year

D L 

### lung diffusing capacity

D L 

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

D m 

### CO capacity for diffusion of CO into the muscle

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

DMV dorsal motor nucleus of the vagus nerve DNA deoxyribonucleic acid DOCA Deoxycorticosterone acetate dP/dt LV 

### left ventricular maximal and mini mal first derived pressure (+dP/dt LV 

### , -dP/dt LV 

### ) dP/dt RV 

### right ventricular maximal and mini mal first derived pressure (+dP/dt RV 

### , -dP/dt RV 

### ) DSA deletion/substitution/addition dt time spent in each location

Σ sigma, sum of a series

E exposure over some duration

E a 

### exposure to pollutant of ambient origin E/A mitral ratio of peak early to late diastolic filling velocity (E/A). EC elemental carbon ED emergency department EKG, ECG electrocardiogram; electrical activit y of the heart over time, measured by an electrocardiograph

E na 

### exposure to pollutant of non-ambient origin eNOS endothelial nitr ic oxide synthase

EPA U.S. Environmental Protection Agency EPO erythropoietin (stimulates production of new red blood cells)

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

EPRI Electric Power Research Institute ERK1/2 ERK-1 (MAPK p42) and ERK-2 (MAPK p44) (extracellular signal-re gulated kinases [in cell signaling pathway])

ESRL Earth System Research Laboratory (within GMD of NOAA)

ET-1 endothelin-1; vasoconstrictor that me diates regulation of vascular tone

ET A 

### endothelin A (ET A 

### ) receptor subtype

ETS environmental tobacco smoke

EXPOLIS six-city European air pollution study

Factor VII FVII, a vitamin K-dependent serine protease glycoprotein (also known as stable factor or proconvertin), initiates the process of coagulation. Factor VIIIC blood-coagulation factor VIII. (antih emophilic factor, part of factor VIII/von Willebrand factor complex; acts in the intr insic pathway of blood coagulation FAS apoptosis stim ulating fragment FC interference filter Fe iron

March 2009 xxv DRAFT—DO NOT QUOTE OR CITE

### Fe 2+ 

### ferrous iron

Fe 3+ 

### ferric iron

FEF forced expiratory flow (Liters/second)

FEF 25-75 

### determined from the time in seconds at wh ich 25% and 75% of the vital capacity is reached. The volume of air exhaled between these two times is the FEF 25–75 

### . This value reflects the status of medium and small sized airways. FEM Federal equi valent method

FEV 1 

### forced expiratory volume in 1 second (volume of air exhaled into the spirometer mouthpiece in one second , using maximum effort)

f i 

### fraction of time spent indoors

F I 

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

F inf 

### infiltration factor

f o 

### fraction of time spent outdoors FR Federal Register

FGR fetal growth restriction(s)

FRM Federal reference method

FSH follicle stimulating hormone

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

FVII Factor VII, a vitamin K-dependent seri ne protease glycoprotein (also known as stable factor or proconvertin), initiates the process of coagulation FW fresh weight G0/G1 phase Phase of Cell Cycle in whic h cell commits to mitosis (division)

g, mg, μ g gram(s), milligram(s), microgram(s) g/mole unit of molar mass (grams/mole) GAM generalized additive model(s) GD gestational day GEE generalized estimating equations

GEM gas extraction monitor

GFAP glial fibrillary acidic protein

GFC gas filter correlation GLM generalized linear models

GLMM generalized linear mixed models

GMD Global Monitoring Division (of NOAA) GMP guanosine monophosphate GSH glutathione GSSG oxidized glutathione GTP guanosine triphosphate GWP(s) global warming potential(s)

H atomic hydrogen, hydrogen radical, height h hour

H 2 

### molecular hydrogen

March 2009 xxvi DRAFT—DO NOT QUOTE OR CITE

### H 2 

### O water

H 2 

### O 2 

### hydrogen peroxide

H9c2 rat embryonic cardiomyocytes (cell line) Hb hemoglobin HC(s) hydrocarbon(s) HCFC(s) hydrochlorofluorocarbon(s) HCO formyl radical

HEAPSS Health Effects of Air Pollution among Susceptible Subpopulations (study) hectoPascal atmospheric level measured by atmospheric pressure (e.g., 700 hectoPascals ≈ 10,000 feet above sea level)

HEK293 human embryonic kidn ey cells (experimentally transformed cell line) Hem hemorrhagic (stroke) Hep3B Human hepatoca rcinoma cell line HF heart failure

HF high energy frequency (HRV parameter)

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

HH hypobaric hypoxia (measured in torr)

HIF-1 α oxygen-responsive component of the hy poxia-inducible factor (HIF) 1 complex his-Fe 2+ 

### -his iron binding scheme in neuroglobin (Nb); hexacoordinated deoxy ferrous (Fe 2+ 

### ) form of Nb, bound to histidine

HO heme oxygenase (microsomal enzyme that degrades heme protein to form endogenous CO and iron (Fe 2+ 

### ))

HO 2 

### hydroperoxy radical, hydrogen dioxide

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

HO-2 constitutively expressed isoform of heme-oxygenase HO/CO heme oxygenase/carbon monoxi de system (signaling pathway)

HR heart rate (beats per minute), hazard ratio H/R hypoxia followed by reoxygenation

HRV heart rate variability (beat-to-beat alterations in the heart determined by analyses of time and frequency domains in ECG(s). HUVEC(s) human umbilical ve in endothelial cell(s)

h

ν photon H/W height to width ratio I electrical current IARC International Agency for Research on Cancer

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

I ca,L 

### electrical current (I) through the L-type Ca 2+ 

### channel

ICAM-1 intercellular adhesion molecule ICD implantable cardioverter defibrillator(s)

ICR substrain of CD1 mouse, In stitute for Cancer Research IDW inverse-distance-weighted

IHD ischemic heart disease

March 2009 xxvii DRAFT—DO NOT QUOTE OR CITE

### IL-6 interleukin-6 IL-8 interleukin-8

INDAIR Indoor Air Model I/O indoor to outdoor concentration ratio

IOM Institute of Medicine i.p. intraperitoneal injection IQR interquartile range IR immunoreactivity IS ischemic stroke

ISA Integrated Science Assessment Isch ischemic (stroke) ITA internal thoracic artery of the heart (coronary artery bypass surgery graft)

I to 

### transient outward current i.v. intravenous (injection)

IUGR intrauterine growth restriction

K + 

### potassium ion

k dissociation rate, rate of CO loss from the microenvironment

k CO 

### dissociation rate of carbon monoxide from hemoglobin

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

K

m 

### Michaelis Constant; the substrat e concentration which gives 1⁄2 V max 

### (in the MichaelisMenten equation of enzyme kinetics) km kilometer(s)

K

m 

### /V max 

### slope of Michaelis-Menten equation; measures the efficency of an enzyme

k O2 

### Dissociation rate of oxygen from hemoglobin L, dL, mL, μ L Liter, deciLiter, milliLiter, microLiter lag 0 same day as the hospital, ED, clinic, physician visit lag 0-3 the three previous days before the hospital, ED, clinic, physician visit

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

LCA+ leucocyte common antigen cells LD lactational day

LDH lactate dehydrogenase (release of LDH is an indicator of cell membrane damage) LDL low-density lipoprotein leeward downwind

LF low energy frequency (HRV parameter) LF/HF low energy frequency to high en ergy frequency ratio (HRV parameter) LH luetenizing hormone LOAEL lowest observed adverse effect level

LOD limit of detection

LOESS locally weighted s catterplot smoothing (modeling method); aka LOWESS LPS lipopolysaccharide LTP long-term potenti ation (hippocampal)

March 2009 xxviii DRAFT—DO NOT QUOTE OR CITE

### LUR land use regression (model[s])

LV left ventricle (of heart) LV+S left ventricu lar plus septum

LVDP left ventricular developed pressure LVESP left ventricular end diastolic pressure

LVSF left ventricular shortening fraction LVW left ventricular work

m, cm, μ m, nm meter(s), centimeter, micrometer(s) = micron(s), nanometer(s) M, mM, μ M, nM, pM Molar, milliMolar, microMolar, nanoMolar, picoMolar

M Haldane coefficient representing the CO chemical affinity for Hb [or Mb]), Reaction mediator. ma moving average MAPK mitogen-activated protei n kinase(s), MAP kinase(s)

MAO-A monoamine oxidase A (mitochondrial enzy me; deaminates amine- neurotransmitters) max maximum Mb myoglobin

MC ultrafine particle mass concentration METs metabolic equivalent unit(s) mg/m 3 

### unit of chemical concentration in air (milligrams per cubic meter) MHC major histocompatibility complex

MI myocardial infarction, “heart attack” Miller PEE Miller equation prediction equation estimates min minute(s)

MIP-2 macrophage inflammatory protein-2 mitral E to A ratio mitral ratio of peak early to late diastolic filling velocity (E/A). MMEF maximal midexpiratory flow mmHg millimeters of mercury (unit of pressure) MMP matrix metalloproteinase MOA(s) mode(s) of Action

MOBILE6 (EPA) Mobile sour ce emission factor model MODIS Moderate Resolution Imaging Spectroradiometer mol, nmol mole, nanomole

MONICA Monitoring of Trends and Determin ants in Cardiovascular Disease (study)

MOPITT Measurement of Pollution in the Troposphere (instrument)

MPO myeloperoxidase (indicative of neutrophil degranulation) MPT mitochondrial perm eability transition

MR maternally reared; rodent pups reared with their respective dams mRNA messenger RNA ms millisecond(s)

MSA Metropolitan Statistical Area

MSNA muscle sympathetic nerve activity

March 2009 xxix DRAFT—DO NOT QUOTE OR CITE

### MT million tons MV O2 

### myocardial oxygen consumption n, N sample size

N number of monitor days

N 2 

### nitrogen gas NAAQS National Ambient Air Quality Standards

NADPH nicotinamide adenine dinucleotide phosphate NADH-TR nicotinamide adenin e dinucleotide - tetrazolium reductase ; histochemical st ain of muscle tissue - the space between the myofibrils.

NAPAP National Acid Precipitation Assessment Program

NARSTO (formerly) North American Rese arch Strategy for Tropospheric Ozone NAS National Academ y of Sciences NASA National Aeronautics and Space Administration Nb neuroglobin NC ultrafine particle number concentration

NDIR nondispersive infrared (detection) NE norepinephrine

NEI (EPA) National Emissions Inventory

NF- κ B nuclear factor kappa B Ni nickel (element) NIHL noise-induced hearing loss NMDA N-methyl-D-aspartate NMHC(s) nonmethane hydrocarbon(s)

NMMAPS National Morbidity, Mortality, and Air Pollution Study nmol nanomole

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

nNOS neuronal nitric oxide synthase (NOS) NO nitric oxide

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

NO 2 

### nitrogen dioxide

NOAA National Oceanic and At mospheric Administration

NOAEL no observed adverse effect level

NO • -Hb nitrosyl bound Hb

NO • -Mb nitrosyl bound Mb

NO X 

### nitrogen oxides, oxides of nitrogen

NRC National Research Council

NTS nucleus of the solitary tract (in brainstem)

O 2 

### oxygen O 3 

### ozone O 2 

### Hb oxyhemoglobin (% concentration in mL O 2 

### / mL blood)

O 2 

### Mb oxymyoglobin

March 2009 xxx DRAFT—DO NOT QUOTE OR CITE

### OAE otoacoustic emissions (testing) OAQPS Office of Air Quality Planning and Standards OC organic carbon OH, OH OH • hydroxyl group, hydroxyl radical(s) OR odds ratio(s) OS occlusive stroke

OSPM Operational Street Pollution Model

P penetration (of pollutant) P, p probability

P number of paired hourly observations

P 1 

### CO CO partial pressure in inhaled air (in mmHg) P13K phosphoinositide 3-kinase p21 Waf1/Cip1 

### inhibitor of CDK(s) (cyclin-dep endent kinases) - regulates cell cycle arrest following DNA damage. p38 p38 MAP kinase

P90 90th percentile of the absolute difference in concentrations P450 cytochrome P450

P A 

### alveolar pressure PA pulmonary artery (myocytes) PACF partial auto-correlation functions

P A 

### CO alveolar pressure for carbon monoxide PAF platelet activating factor

PAH polycyclic aromatic hydrocarbon PAHT pulmonary artery hypertension

PAN peroxyacetyl nitrate (CH 3 

### CO 3 

### NO 2 

### )

P A 

### O 2 

### alveolar pressure for oxygen

P a 

### O 2 

### arterial oxygen pressure PARP poly(ADP-ribose) polymeras e; (involved in DNA repair; cleaved by caspases during early apoptosis) Pb lead

PB barometric pressure (in mmHg)

PBN the free radical inhibitors PBN (a spin trap) or N-tert-butyl-alpha-phenylnitrone (free radical inhibitor) an organic spin trap agent de signed specifically to form "stable" adducts with free radicals in electr on spin resonance studies.

C 

# P average partial pressure in lung capillaries pCO partial pressure of CO in lung capillaries (in mmHg)

P CO 

### partial pressure of CO

2 C 

# OP average partial pressure of O 2 

### in lung capillaries (mmHg)

PDGF platelet derived growth factor PEE prediction e quation estimates

PEF peak expiratory flow (L/min) PEFD(s) Personal Exposures Frequency Distributions

March 2009 xxxi DRAFT—DO NOT QUOTE OR CITE

### PEM(s) personal exposure monitor(s) % percent

PHD pulmonary heart disease

P I 

### partial pressure of inhaled air

Pi inorganic phosphate

PIH primary intracerebral hemorrhage PKB protein kinases B

PM particulate matter p.m. afternoon/evening hours

PM 2.5 

### fine particulate matter, particles with a nominal mean aerodynamic diameter less than or equal to 2.5 μ m PM 10 

### particles with a mean aerodynamic diameter less than or equal to 10 μ m PM 10-2.5 

### coarse particulate matter, coarse fraction of PM 10 

### (referred to as thoracic coarse particles or coarse-fraction particles; generally including particles with a nominal mean aerodynamic diameter greater than 2.5 μ m and less than or equal to 10 μ m). PMN polymorphonuclear leukocytes pmol picomole PNC particle number c oncentration / count

PND post natal day pNEM/CO probabilistic NAAQS Exposure Model for CO (preceded APEX)

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

PNN 50 

### proportion of interval differences of successi ve normal-beat intervals greater than 50 ms in EKG

PNS peripheral nervous system pO 2 

### partial pressure of oxygen in lung capillaries (in mmHg) ppb parts per billion

ppm parts per million PRB policy-relevant background

PT prothrombin time (blood coagulability endpoint)

PTB preterm birth (birth after the 20th week, but before the 38th week of human pregnancy) PVCD peripheral vascular a nd cerebrovascular disease

PvO 2 

### venous oxygen tension

PVO 2 

### peak oxygen consumption

•

•

• 

## Q cardiac output QCP Quantitative Circulatory Physiology (model)

M 

## Q blood flow to muscle

OT 

## Q blood flow to other tissues

r correlation coefficient

R 2 

### coefficient of determination

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

RA radial artery of the heart

RAW 264.7 mouse macr ophage cell line

March 2009 xxxii DRAFT—DO NOT QUOTE OR CITE

### RBC red blood cell redox status ratio of interconvertible reduced/oxidized forms of a molecule rho(0) rho(0) cells (cells lacking mitochondrial DNA)

rMSSD mean squared diffe rences 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 fl y ash (particles) ROS reactive oxygen species

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

RUPERT Reducing Urban Pollution Exposure from Road Transport (study)

RV right ventricle (of heart) RVEDP right ventricular end diastolic pressure RVESP right ventricular end-systolic pressure

RVSF right ventricular shortening fraction RVW right ventricular work S second(s) SA sphinganine

SAA serum amyloid A

SAB (EPA) Science Advisory Board

SBP systolic blood pressure

SDNN standard deviation normal-to-normal ( NN or RR) time interval between each QRS complex in the EKG Se selenium

SEM standard error of mean sEng soluble endoglin SES socioeconomic status Sess Session (of Congress)

SF 6 

### sulfur hexafluoride (tracer gas) sFlt soluble Fms-like tyrosine kinase-1

SGA small for gestational age

sGC soluble guanylate cyclase

SHEDS (EPA) Stochastic Human Expos ure and Dose Simulation (model)

SHR Spontaneously hypertensive rat strain

SIDS sudden infant death syndrome

SIPs State Implementation Plan(s) siRNA small inhibitory RNA (silencing RNA)

SLAMS State and Local Ai r Monitoring Stations

SMC smooth muscle cell(s) Sn tin

March 2009 xxxiii DRAFT—DO NOT QUOTE OR CITE

### snMP tin-(IV)-mesoporphyrin (HO inhibitor) SNP single-nucleot ide polymorphism

SnPP-IX tin protoporphyrin IX, (HO inhibitor) SO sphingosine

SO 2 

### sulfur dioxide

SO 4 2- 

### sulfate SOD superoxide dismutase SOD1 cytoplasmic s uperoxide dismutase SOD2 mitochondrial su peroxide dismutase

SOPHIA Study of Particles and Health in Atlanta ST-segment segment of EKG betwee n QRS complex, and T wave. ST-s egment elevation may indicate myocardial infarction STAT1 / STAT3 signal transducers and activators of transcription (trans cription factors involved in cell signaling)

STEMS Space-Time Exposure Modeling System

STN (EPA) Speciation Trends Network

STPD standard temperature and pressure, dry SV stroke volume

SVEB supraventricular (atrium or atrioventricular node) ectopic beats

τ photochemical lifetime T trial T lymphocytes thymus-dependent lymphocytes

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

TBARS thiobarbituric acid reactive substances TC total carbon TFAM mitochondrial tran scription factor A Tg teragram(s) TH tyrosine hydroxylase

TH b 

### total blood concentration of hemoglobin (in g Hb/mL blood) THP-1 human monocyte-derived cell line, (can differentiate into macrophages)

TIA transient ischemic attack (mini-stroke)

TNF- α tissue necrosis factor alpha (WBC protei n boosts immune system , too much can cause inflammation)

torr unit of pressure, equal to 0.001316 (1/760) atmosphere TPM total particulate matter TSP total suspended particles UFP ultrafine particle(s) U.K. United Kingdom

ULTRA (Exposure and Risk Assessment for Fine a nd )Ultrafine Particles in Ambeint Air (Study) URI upper respiratory infection

U.S. United States of America

USC U.S. Code

March 2009 xxxiv DRAFT—DO NOT QUOTE OR CITE

### UV ultraviolet V vanadium

• 

# V minute ventilation

A 

# V • 

### alveolar ventilation (in mL/min at STPD)

V A 

### alveolar volume, a mesuremnt of lung size

V b 

### blood volume ( in mL)

V CO 

### CO uptake rate (the product of D L 

### CO and the mean P A 

### CO).

• CO 

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

V D 

### Dead space volume

V E 

### ventilation rate VEGF vascular endothelial growth factor VLF very low energy fre quency (HRV parameter)

V max 

### maximum velocity (catalyzed by a fixed enzyme concentration, in the Michaelis-Menten equation of enzyme kinetics)

VO 2 

### max maximum volume per time, of oxygen (max imal oxygen consumption, maximal oxygen uptake or aerobic capacity) VOC(s) volatile organic compound(s) vol volume

VPB ventricular premature beat V/Q ventilation-perfusion ratio

vWF von Willebrand factor (part of factor VIII/ von Willebrand factor complex; acts in the intrinsic pathway of blood coagulation) W width WBC white blood cell

WHI Women’s Health Initiative (Study) windward upwind

WKY Wistar-Kyoto rat strain

ww wet weight

X ij 

### , X ik 

### observed hourly concentrations for time period i at sites j and k

yr year Z/H elevation of the measurement (Z) scaled by height (H) ZIP (code) Zone Improvement Plan (syste m of postal codes used in the U.S.) Zn zinc

ZnPP IX Zn protoporphyrin IX, HO inhibitor

March 2009 xxxv DRAFT—DO NOT QUOTE OR CITE

# Chapter 1. Introduction

The Integrated Science Assessment (ISA) is a c oncise evaluation and synthesis of the most policyrelevant science for reviewing the national ambien t 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. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27

28 29 30 The ISA accurately reflects “the latest scientific knowledge useful in indicating the kind and extent of identifiable effects on public health which may be expected from the presence of [a] pollutant in ambient air” (U.S. Code, 1970). Ke y information and judgments formerly contained in the Air Quality Criteria Document (AQCD) for CO are incorporated in this assessment. Additional details of the pertinent scientific literature published since the last review, as well as selected older studies of particular interest, are included in a series of annexes. This first exte rnal draft ISA thus serves to update and revise the information available at the time of the previous AQCD for CO in 2000. The Integrated Plan for Review of the NAAQS fo r CO (U.S. EPA, 2008b) identifies key policyrelevant questions that provide a framework for this review of the scientific evidence. These questions frame the entire review of the NAAQS and thus are informed by both science and policy considerations. The ISA organizes and presents the scientific evidence su ch that it, when considered along with findings from risk analyses and policy considerations, will he lp the U.S. Environmental Protection Agency (EPA) address these questions during the NAAQS review:

 Has new information altered the scientific s upport 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 sen sitive to CO exposures? Is there new or emerging evidence on health effects beyond car diovascular and respiratory endpoints (e.g., systemic effects, developmental effect s, 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 expos ures due to other transportation sources, such

March 2009 1-1 DRAFT—DO NOT QUOTE OR CITE

# 1 2

3

4 5 6 7 8

9 10 11

12 13

14 15

16 17 18

19 20 21 22 23 24 25 26 27 28 29 as shipping, port operations, and recreational vehi cles? 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 beco ming 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) e xposures? To what extent is critical research becoming available that could improve our unde rstanding of the relationship between various health endpoints and different lag periods (e .g., single day, multi-day distributed lags)?

 To what extent does the evidence suggest that alternate dose indicators other than carboxyhemoglobin (COHb) levels (e.g., tissue oxygenation) should be evaluated to characterize the biological effect?

 Has new information altered conclusions from pr evious 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 a ltered the scientific conclusions regarding the occurrence of direct (or indirect) welfare eff ects associated with levels of CO found in the ambient air?



# 1.1. Legislative Requirements

Two sections of the Clean Air Act (CAA) govern the establishment and revision of the NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify and list “air pollutants” that “in [her] judgment, may reasonably be anticipated to endange r public health and welfare” and whose “presence ... in the ambient air results from numerous or diverse mobile or stationary sour ces” and to issue air quality criteria for those that are listed (42 U.S.C. 7408). Air quality criteria are intended to “accurately reflect the latest scientific knowledge useful in indicating the kind and extent of identifiable effects on public health or welfare which may be expected from the presence of [a] pollutant in ambient air...” 42 U.S.C. 7408(b). Section 109 (42 U.S.C. 7409) of the Clean Air Act directs the EPA Administrator to propose and promulgate “primary” and “secondary” National Ambi ent Air Quality Standards (NAAQS) for pollutants listed under section 108. Sec tion 109(b)(1) defines a primary standard as one “the attainment and maintenance of which in the judgment of the Admini strator, based on such criteria and allowing an

March 2009 1-2 DRAFT—DO NOT QUOTE OR CITE

## 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 associ ated with the presence of [the] pollutant in the ambient air.” 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 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 le vels 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 seeki ng not only to prevent pollution levels that have been demonstrated to be harmful but also to prevent lowe r 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 consider s such factors as the nature and severity of the health effects involved, the size of sensitive population(s) at risk, and the kind and degree of the uncertainties that must be addressed. The selection of an y particular approach to providing an adequate margin of safety is a policy choice left speci fically 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 ar e 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 thor ough review of the criteria published under section 108 and the national ambient air quality standards...and sh all 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 comp lete a review of the criteria...and the national primary and secondary ambient air quality standards...and shall recommend to the Administrator any

1 The legislative history of section 109 of the Clean Air Act indicat es that a primary standard is to be set at “the maximum per missible ambient air

level . . . which will protect the health of any [sensitive] group of the population, ” and that for this purpose “reference sho uld be made to a representative sample of persons comprising the sensitive group rath er 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, cr ops, vegetation, manmade materials, animals, wildlife, weather, visibility and climat e, damage to and deterioration of property, and hazards to tra nsportation, as well as effects on economic values and on personal comfort and well-being.”



### March 2009 1-3 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 new...standards and revisions of existing criteria and standards as may be appropriate...” Since the early 1980s, this independent review function has been pe rformed 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 a vg and 35 ppm, 1-h avg, neither to be exceeded more than once per year (36 FR 8186). In 1979, EPA published the Air Quality Criteria Document for Carbon Monoxide (U.S. EPA, 1979a), which updated the scientific criteria upon which the initial CO standards were based. A Staff Paper (U.S. EPA, 1979b) was prep ared and, along with the AQCD, served as the basis for development of proposed rulemaking (45 FR 550 66) 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 substa ntial reexamination of the scientific data, EPA prepared an Addendum to the 1979 AQCD (U.S. EPA, 1984b) and an updated Staff Paper (U.S. EPA, 1984a). Following review by CASAC, EPA announced its final decision (50 FR 37484) not to revise the existing primary standard and to revoke the secondary standard for CO on September 13, 1985, due to a lack of evidence of direct effects on public welfare at ambient concentrations. In 1987, EPA initiated action to revise the criteri a for CO and released a revised AQCD for CASAC and public review. In a “closure letter” (McClella n, 1991) sent to the Administrator, the CASAC concluded that the AQCD (U.S. EPA, 2000) “. . . provides a scientifically balanced and defensible summary of current knowledge of the effects of this pollutant and provides an adequate basis for the EPA to make a decision as to the appropriate primary NAA QS for CO.” A revised Staff Paper subsequently was reviewed by CASAC and the public, and in a “closure letter” (McClellan, 1992) sent to the Administrator, CASAC stated “. . . that a standard of the present form and with a numerical value similar to that of the present standard would be supported by the present scientific data on health effects of exposure to carbon monoxide.” Based on the revised AQCD (U.S. EPA, 2000) and staff conclusions and recommendations contained in the revised Staff Pape r (U.S. EPA, 1992), the Administrator announced the final decision (59 FR 38906) on August 1, 1994, that revision of the primary NAAQS for CO was not appropriate at that time. In 1997, revisions to the AQCD were initiated. A workshop was held in September 1998 to review and discuss material contained in the revised AQCD. On June 9, 1999, CASAC held a public meeting to review the draft AQCD and a draft exposure an alysis methodology document. Comments from CASAC and the public were considered in a second draft AQCD, which was reviewed at a CASAC meeting, held on November 18, 1999. After revision of the second draft AQCD, the final AQCD (U.S. EPA, 2000) was

March 2009 1-4 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27 28 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 re gions of the U.S. In response, the NRC convened the committee on Carbon Monoxide Episodes in Meteorol ogical and Topographical Problem Areas, which focused on Fairbanks, Alaska as a case study in an inte rim report, which was completed in 2002. A final report, Managing Carbon Monoxide Pollution in Meteorological and Topographical Problem Are as, was published in 2003 (NRC, 2003) and offered a wide ra nge of recommendations on management of CO air pollution, cold start emissions standards, oxygenated fuels, and CO monitoring. EPA did not complete the review which started in 1997.



# 1.3. Document Development

EPA initiated the current review of the NAAQS for CO on September 13, 2007 with a call for information from the public (72 FR 52369). In additi on to the call for information, publications were identified through an ongoing literature search proces s that includes extensive computer database mining on specific topics. Additional publications were identifie d by EPA scientists in a variety of disciplines by combing through peer-reviewed scientific literature obtained through these ongoing literature searches, reviewing previous EPA reports, and a review of refe rence lists from important publications. All relevantepidemiologic, human clinical, and animal toxicological studies, including those related to exposureresponse relationships, mode(s) of action (MOA), or susceptible subpopulations published since the last review were considered. Added to the body of resear ch were EPA’s analyses of air quality and emissions data, studies on atmospheric chemistry, transport, and fate of these emissions, as well as issues related to exposure to CO. A literature search conducted on the ec ological effects of ambient CO did not identify any relevant information. Further information was acquired from consultation with scientific experts and the public.



# 1.4. Document Organization

The ISA is composed of five chapters. This introductory chapter presents background information, and provides an overview of EPA’s framework for ma king causal judgments. Chapter 2 is an integrated summary of key findings and conclusions regardi ng the source to dose paradigm, MOA, and important health effects of CO, including cardiovascular, nervou s system, perinatal/developmental, respiratory, and mortality outcomes. Chapter 3 highlights key con cepts and evidence relevant to understanding the sources, ambient concentrations, atmospheric behavior , and exposure to ambient CO. Chapter 4 describes

March 2009 1-5 DRAFT—DO NOT QUOTE OR CITE

## 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 th e health effects of CO, including cardiovascular and systemic effects, central nervous system (CNS) ef fects, birth outcomes and developmental effects, respiratory effects, and mortality. 

# 1 2 3 4 5 6 7 8 9 10

11

12

13 14

15

16

20 21 22 23 24 25 26 27 28 29 30 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 s hort- and long-term exposure to CO (Annex C)

 controlled human exposure studies of health e ffects related to exposure to CO (Annex D); and

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

Within the Annexes, detailed information about methods and results of health studies is summarized in tabular format, and generally includ es information about concentrations of CO and 

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

# 18 relationships between effects and exposure to CO. 

# 19



# 1.5. Document Scope

For the current review of the primary CO standa rds, relevant scientific information on human exposures and health effects associated with exposur e to ambient CO has been assessed. Health effects resulting from accidental exposures to very high concentrations of non-ambient CO (i.e., carbon monoxide poisoning) are not directly relevant to ambient exposures, and as such, a discussion of these effects has deliberately been excluded from this docum ent. For a detailed review of the effects of high level exposures to CO, the reader is referred to the extensive body of literature related to carbon monoxide poisoning (Ernst and Zibrak, 1998; Penney, 2007; Raub et al., 2000). The possible influence of other atmospheric pollutants on the interpretation of the role of CO in health effects studies is considered. This includes other pollutants with the potential to co-occur in the environment (e.g., nitrogen dioxide [NO 2 

## ], sulfur dioxide [SO 2 

## ], ozone [O 3 

## ], and particulate matter [PM]). The review also assesses relevant scientific information associated with known or anticip ated public welfare effects that may be identified.

March 2009 1-6 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3 4

5 6 7 8 9 10 11 12 13 14 15 16

17

18 19

20

21

22 23

24 As discussed in Section 1.3, a literature review of the ecological effects of ambient CO identified no relevant information, and thus these effects are not assessed. With regard to climate effects this review includes updates and additional data available since the 2000 CO AQCD on the interaction of largely urban CO emissions and hydroxyl ra dical concentrations, and on background concentrations in the U.S.



# 1.6. EPA Framework for Causal Determination

The EPA has developed a consistent and transparen t 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 specific ity to the findings. This standardized language was drawn from across the federal government and wide r scientific community, especially from the recent National Academy of Sciences (NAS) Institute of Medicine (IOM) document, Improving the Presumptive Disability Decision-Making Process for Veterans (IOM, 2008), the most recent comprehensive work on evaluating causality. This introductory section focuses on the evaluation of health effects evid ence. While focusing on human health outcomes, the concepts are also genera lly 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 r each 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 character izing 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 b een formulated by a number of regulatory and science 

# 25 agencies, including the IOM of the NAS (IOM, 200 8), International Agency for Research on Cancer 

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

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

# 28 1991). These formalized approaches offer guidance for assessing causality. The frameworks are similar in 

# 29

March 2009 1-7 DRAFT—DO NOT QUOTE OR CITE

## nature, although adapted to differen t purposes, and have prove n effective in providing a uniform structure 

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

# 2 under conditions of uncertainty. 

# 3



# 1.6.1. Scientific Evidence Used in Establishing Causality

Causality determinations are based on the ev aluation 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 evid ence of a causal relationship between pollutant exposures and human health effects comes from human clin ical studies. This type of study experimentally evaluates the health effects of administered exposures in human volunteers under highly-controlled laboratory conditions. 

# 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

20 21 22 23 24 25 26 27 28 29 In epidemiologic or observational studies of humans, the investigator does not control exposures or intervene with the study population. Broadly, ob servational studies can describe associations between exposures and effects. These studies fall into several cat egories: cross-sectional, prospective cohort, and time-series studies. “Natural experiments” offer the oppor tunity to investigate changes in health with a change in exposure; these include comparisons of hea lth effects before and after a change in population exposures, such as closure of a pollution source. Experimental animal data complement the clin ical and observational data; these studies can help characterize effects of concern, exposure-response relationships, susceptible subpopulations and MOAs. In the absence of clinical or epidem iologic data, animal data alone may be sufficient to support a likely causal determination, assuming that humans res pond 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 asso ciation, a causal claim supports the creation of counterfactual claims; that is, a claim about what the world would have been like under different or changed circumstances (IOM, 2008). Much of the newl y available health information evaluated in this ISA comes from epidemiologic studies that report a st atistical association between ambient exposure and health outcome. Many of the health and environmental outcomes re ported in these studies have complex etiologies. Diseases such as asthma, coronary heart disease (CHD) or cancer are typically initiated by multiple

March 2009 1-8 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3 4 5

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 agents. Outcomes depend on a variety of factors, su ch as age, genetic susceptibility, nutritional status, immune competence, and social factors (Gee and Payne-Sturges, 2004; IOM, 2008). Effects on ecosystems are often also multifactorial with a co mplex web of causation. Further, exposure to a combination of agents could cause synergistic or anta gonistic effects. Thus, the observed risk represents the net effect of many actions and counteractions.



# 1.6.3. Evaluating Evidence for Inferring Causation

Moving from association to causation involves elimination of alternative explanations for the association. In estimating the causal influence of an exposure on health or environmental effects, it is recognized that scientific findings incorporate uncertain ty. Uncertainty can be defined as a state of having limited knowledge where it is impossible to exactly descr ibe an existing state or future outcome; e.g., the lack of knowledge about the correct value for a specifi c 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 sp ecific data, estimates, scenarios, etc. The uncertainty assessment is more quantitative. The process begins with simpler measures (e.g., ranges) and simpler analytical techniques and progresses, to the extent needed to support the decision for which the assessment is conducted, to more complex measures a nd techniques. Data will not be available for all aspects of an assessment and those data that are avai lable may be of questionable or unknown quality. In these situations, evaluation of uncertainty can in clude professional judgment or inferences based on analogy with similar situations. The net result is that the assessments will be based on a number of assumptions with varying degrees of uncertainty. Uncertainties commonly encountered in evaluating he alth evidence for the criteria air pollutants are outlined below for epidemiologic and experimental studies. Various approaches to characterizing uncertainty include classical statistical methods, sensitiv ity 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 stud ies; in some situations quantitative analysis approaches, such as meta-regression may be used. Controlled human exposure studies evaluate the effect s of exposures to a variety of pollutants in a highly controlled labor atory setting. Also referred to as human clinical studies, these experiments allow investigators to expose subjects to known concentra tions of air pollutants under carefully regulated environmental conditions and activity levels. In so me instances, controlled human exposure studies can also be used to characterize concen tration-response relationships at pollu tant concentrations relevant to ambient conditions. Controlled human exposures are t ypically conducted using a randomized crossover

March 2009 1-9 DRAFT—DO NOT QUOTE OR CITE

## design with subjects exposed both to CO and a clean air control. In this way, subjects serve as their own controls, effectively controlling for many potential confounders. However, human clinical studies are limited by a number of factors including a small sample size and short exposure time. The repetitive nature of ambient CO exposures at levels that can vary widely may lead to cumulative health effects, but this type of exposure is not practical to replicate in a laboratory setting. In a ddition, although subjects do serve as their own controls, personal exposure to pollu tants in the hours and days preceding the controlled exposures may vary significantly between and within i ndividuals. Finally, human clinical studies require investigators to adhere to stringent health criteria fo r a subject to be included in the study, and therefore the results cannot necessarily be generalized to an entire population. Although some human clinical studies have included health-compromised individuals such as asthmatics or individuals with chronic obstructive pulmonary disease (COPD) or coronary artery disease (CAD), these individuals must also be relatively healthy and do not represent the most sensitiv e individuals in the popu lation. Thus, a lack of observation of effects from human clinical studies does not necessarily mean that a causal relationship does not exist. While human clinical studies provide important information on the biological plausibility of associations observed between air pollutant exposure and health outcomes in epidemiologic studies, observed effects in these studies may underestimate the response in certain subpopulations. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Epidemiologic studies provide important informa tion on the associations between health effects and exposure of human populations to ambient ai r pollution. These studies also help to identify susceptible or vulnerable subgroups and associated risk factors. There are important methodological issues to be considered in evaluating results fro m air pollution epidemiologic studies, especially the potential for confounding and/or effect modi fication; and exposure measurement error. Scientific judgment is needed regarding s ources and magnitude of potential confounding by covariates, together with judgment about how well th e existing constellation of study designs, results, and analyses address this potential threat to inferential va lidity. One key consideration is evaluation of the potential contribution of CO to health effects when it is a component of a complex air pollutant mixture. There are multiple ways by which CO might cause or be associated with adverse health effects. First, the reported CO effect estimates in epidemiologic studies may reflect independent CO effects on health. Second, ambient CO may be serving as an indicator of complex ambient air pollution mixtures that share the same source as CO (e.g., motor vehicle emissions). Finally, copollutants may mediate the effects of CO, or CO may influence the toxicity of copollutants. Epidemiologists use the term “interaction” or “ef fect modification” to denote the departure from the observed joint risk from what might be expected based on the separate effects of the factors. In addition, confounding can result in the production of an association between adverse health effects and CO that is actually attributable to another factor that is associated with CO in a particular study. Multivariate models are the most widely used stra tegy to address confounding in epidemiologic studies,

March 2009 1-10 DRAFT—DO NOT QUOTE OR CITE

## but such models are not always easily interpreted when assessing effects of covarying pollutants such as fine particulate matter (PM 2.5 

## ) and NO 2 

## . 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Inferring causation requires consideration of poten tial confounders. In confounding, the apparent effect of the exposure of interest is distorted because th e effect of an extraneous factor is mistaken for or mixed with the actual exposure effect, which may be null. When associations are found in epidemiologic studies, one approach to remove spurious associa tions from possible confounders is to control for characteristics that may differ between exposed a nd unexposed persons; this is frequently termed “adjustment.” Multivariable regression models constitute one tool for estimating the association between exposure and outcome after adjusting for characteristic s of participants that might confound the results. The use of multipollutant regression models has been the prevailing approach for controlling potential confounding by copollutants in air pollution health effects studies. Finding the likely causal pollutant from multipollutant regression models is made difficult by the possibility that one or more air pollutants may be acting as a surrogate for an unmeasured or poorly -measured pollutant or for a particular mixture of pollutants. Further, the correlation between the ai r pollutant of interest and various copollutants may show temporal and spatial discongruities that can infl uence exposures and health effects. Thus, results of models that attempt to distinguish gaseous and partic le effects must be interpreted with caution. Despite these limitations, the use of multipollutant models is still the prevailing approach employed in most air pollution epidemiologic studies and may provide some insight into the potential for confounding or interaction among pollutants. Another way to adjust for potential confounding is through stratified analysis, i.e., examining the association within homogeneous groups with respect to the confounding variable. Stratified analysis can also be used to examine potential effect modifica tion. 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 ca usal association. Appropriate statistical adjustment for confounders requires identifying and measuring all reasonably expected confounders. Deciding which variables to control for in a statistical analysis of the association between exposure and disease depends on knowledge about possible mechanisms and the dist ributions of these factors in the population under study. Identifying these mechanisms makes it possible to control for potential sources that may result in a spurious association. Measurement error is another problem encountered when adjusting for spurious associations. Controlling for confounders, whether by adjustment or stratification, is only successful when the confounder is well-measured. Considered together, the effects of a well-measured covariate may be overestimated in contrast to a covariate measured wi th greater error. There are several components that contribute to exposure measurement error in these studies, including the difference between true and measured ambient concentrations, the difference betw een average personal exposure to ambient pollutants

March 2009 1-11 DRAFT—DO NOT QUOTE OR CITE

## and ambient concentrations at central monitoring sit es, and the use of average population exposure rather than individual exposure estimates. Previous assessments have examined the role of measurement error in time-series epidemiologic studies using simulated data and mathematical analyses and suggested that “transfer of effects” would only occur under unusual circumstances (e.g., “true” predictors having high positive or negative correlation; substantial measur ement error; or extremely negatively correlated measurement errors) (U.S. EPA, 2004). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Confidence that unmeasured confounders are not pr oducing the findings is increased when multiple studies are conducted in various settings using different subjects or exposures; each of which might eliminate another source of confounding from consideration. Thus, multi-city studies which use a consistent method to analyze data from across locations with different levels of covariates can provide insight on potential confounding in associations. Th e number and degree of diversity of covariates, as well as their relevance to the potential confounders, re main matters of scientific judgment. Intervention studies, because of their experimental nature, can be particularly useful in characterizing causation. In addition to clinical and epidemiologic studies, the tools of experimental biology have been valuable for developing insights into human physiol ogy and pathology. Laboratory tools have been extended to explore the effects of putative toxicants on human health, especially through the study of model systems in other species. Background knowledge of the biological mechanisms by which an exposure might or might not cause disease can prove cruc ial in establishing, or negating, a causal claim. Testable hypotheses about the causal nature of proposed mechanisms or modes of action are central to utilizing experimental data in causal determinations. Interpretations of experimental studies of air po llution effects in animals, as in the case of environmental comparative toxicology studies, are af fected by limitations associated with extrapolation models. The differences between humans and rodents w ith regard to pollutant absorption and distribution profiles based on metabolism, hormonal regulation, breathing pattern, exposure dose, and differences in lung structure and anatomy all have to be taken into consideration. Also, in spite of a high degree of homology and the existence of a high percentage of orthologous genes across humans and rodents(particularly mice), extrapolation of molecular alterations at the gene level is complicated by speciesspecific differences in transcriptional regulation. Given these molecular differences, there are uncertainties associated with quantitative extrapolations at this time between laboratory animals and humans of observed pollutant-induced pathophysiological alterations under the control of widely varying biochemical, endocrine, and neuronal factors.

March 2009 1-12 DRAFT—DO NOT QUOTE OR CITE

# 1.6.4. Application of Framework for Causal Determination

EPA uses a two-step approach to evaluate the scien tific 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 relati onships and the loads or levels, duration and pattern of exposures at which effects are observed. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 To aid judgment, various “aspects” 1 

## of causality have been discussed by many philosophers and scientists. The most widely cited aspects of causality in epidemiology, and public health, in general, were articulated by Sir Austin Bradford Hill in 1965 and have been widely used (CDC, 2004; IARC, 2006; IOM, 2008; U.S. EPA, 2005, 2008d). Several adaptations of the Hill aspects have been used in aiding causality judgments in the ecological sciences (Adams, 2003; Buck et al ., 2000; Collier, 2003; Fox, 1991; Gerritsen et al., 1998). These aspects (Hill, 1965) ha ve been modified (Table 1-1) for use in causal determinations specific to health and welfare effects or pollutant exposures (U.S. EPA, 2008f). 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 multi-etiologic health outcomes, such as asthma or cardiovascular disease, or ecological effects related to acidification. Aspects that usually play a larger role in dete rmination of causality are consistency of results across studies, coherence of effects observed in different study types or di sciplines, biological plausibility, exposure-response relationship, and evidence from “natural” experiments.



# Table 1-1. Aspects to aid in judging causality.

Consistency of the observed association An inference of causality is strengthened when a pattern of elevated risks is observed across several independent studies. The reproducibility of findings constitutes one of the strongest arguments fo r causality. If there ar e discordant results among investigations, possible reasons such as differences in exposure, c onfounding factors, and the power of the study are considere d. Coherence An inference of causality from epidemiolo gic associations may be str engthened by other lines of evidence (e.g., clinical and an imal studies) that support a cause-and-effect interpretation of the association. Evidence on ecologica l or welfare effects may be dr awn from a variety of experimental approaches (e.g., greenhouse, laboratory, and field) and subdisciplines of ecology (e.g., commun ity ecology, biogeochemistry and paleologi cal/historical reconstructions). The coherence of evidence from various fields greatly ad ds to the strength of an inference of causality. The absence of other lines of evidence, however, is not a reason to reject causal ity. Biological plausibility

. An inference of causality tends to be strengthened by consist ency with data from experimental studies or other sources demonstrating plausible biological mechani sms. A proposed mechanistic linking between an effect and exposure to the agent is an important source of support for causality, especially when dat a establishing the existence and functioning of those mechanistic links are available. A lack of biol ogic understanding, however, is not a reason to reject causality.

1 The “aspects” described by Hill (1965) have become, in the subsequent literature, mo re commonly described as “criteria.” The o riginal 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 interp retation of epidemiologic results. They have been modified here for use with a broad er array of data,

i.e., epidemiologic, controlled human exposur e, and animal toxicological studies, as we ll as in vitro data, and to be more cons istent with EPA’s Guidelines for Carcinogen Risk Assessment.



### March 2009 1-13 DRAFT—DO NOT QUOTE OR CITEStrength of the observed association The finding of large, precise risks increases confidence that the association is not likely due to chance, bias, or other facto rs. However, given a truly causal agent, a small magnitude in the effe ct could follow from a lower level of exposure, a lower poten cy, or the prevalence of other agents causing similar effects. While large effects support causality, modest effects therefore do n ot preclude it. Biological gradient (exposure-response relationship) A clear exposure-response relationship (e.g ., increasing effects associat ed with greater exposure) strongly suggests cause and effect, especially when such relationships are also observed fo r duration of exposure (e.g., in creasing effects observed follow ing longer exposure times). There ar e, 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 causalit y, the absence of an exposure-response relationship does not exclude a causal relationship. Experimental evidence. The strongest evidence for causality can be provided when a change in exposure brings about a change in occurrence or frequency of health or welfare effects. Temporal relationship of the observed association Evidence of a temporal sequence between the introduction of an agent, and appearance of the effect, constitutes another argument in favor of causality. Analogy Structure activity relationships and information on the agent’s st ructural analogs can provide in sight into whether an associat ion is causal. Similarly, information on mode of action for a chemical, as one of many structural analogs , can inform decisions regard ing likely causality.



## While these aspects provide a framework for assessing the evidence, they do not lend themselves to being considered in terms of simple formulas or fi xed rules of evidence leading to conclusions about causality (Hill, 1965). For example, one cannot simply count the number of studies reporting statistically significant results or statistically nonsignificant resu lts 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 weighi ng alternative views on controversial issues. Additionally, it is important to note that the aspects in Table 1-1 cannot be used as a strict checklist, but rather to determine the weight of the evidence for inferring causality. In particular, not meeting one or more of the principles does not automatically preclude a determination of causality (e.g., see discussion in (CDC, 2004). 

# 1 2 3 4 5 6 7 8 9 10 11

12 13 14 15 16 17 18 19 20 

# 1.6.5. Determination of Causality

In the ISA, EPA assesses the results of recen t 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 fo r causation, not just association 1 

## ; 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 wo rk of previous evaluations, most prominently the IOM’s Improving the Presumptive Disability Decision-Making Process for Veterans (IOM, 2008), EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005), and the U.S. Surgeon General’s smoking reports (CDC, 2004). In the ISA, EPA uses a series of five descriptors to characterize the weight of

1 It should be noted that the CDC and IOM frameworks use a four-category hierarchy fo r the strength of the evidence. A five-leve l hierarchy is

used here to be consistent with the EPA Guidelines for Carcinoge n Risk Assessment and to provide a more nuanced set of categori es.



### March 2009 1-14 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3 4 evidence for causality (U.S. EPA, 20 08d, f). This weight of evidence evaluation is based on various lines of evidence from across the health and environmenta l effects disciplines. These separate judgments are integrated into a qualitative statement about the ove rall weight of the evidence and causality. The five descriptors for causal determinati on are described in Table 1-2.



# Table 1-2. Weight of evidence for causal determination.

Health Effects Ecological and Welfare Effects

Causal relationship Evidence is sufficient to conclude that there is a causal relationship between relevant pollutant exposures and the health outcome. That is, a positive association has been observed between the pollutant and the outcome in studies in which chance, bias, and confounding could be ruled out with reasonable confidence. Evidence includes, for example, controlled human exposure studies; or observational studies that cannot be explained by plausible alternatives or are supported by other lines of evidence (e.g., animal studies or m ode of action information). Evidence includes replicated and consistent high-quality studies by multiple investigators. Evidence is sufficient to conclude that there is a causal relationship between relevant pollutant exposure and the outcome. Causality is supported when an association has been observed between the pollutant and the outcome in studies in which chance, bias, and confounding could be ruled out with reasonable confidence. Controlled exposure (laboratory or sm all- to medium-scale field studies) provides the strongest evidence for causality, but the scope of inference may be limited. Generally, determination is based on multiple studies conducted by multiple research groups, and evidence that is considered sufficient to infer a c ausal relationship is usually obtained from the joint consideration of many lines of evidence that reinforce each other. Likely to be a causal relationship Evidence is sufficient to conclude that a causal relationship is likely to exist between relevant pollutant exposures and health outcome but important uncertainties remain. That is, a positive association has been observed between the pollutant and the outcome in studies in which chance and bias can be ruled out with reasonable confidence but potential issues remain. For example: a) observational studies show positive associations but copollutant exposures are di fficult to address and/or other lines of evidence (cont rolled human exposure, animal, or mode of action information) are limit ed or inconsistent; or b) animal evidence from multiple studies, sex, or species is positive but limited or no human data are available. Evidenc e generally includes replicated and high-quality studies by multiple investigators. Evidence is sufficient to conclude that there is a likely causal association between relevant pollutant exposures and the outcome. That is, an association has been observed between the pollutant and the outcome in studies in which chance, bias and confounding are minimized, but uncertainties remain. For example, field studies show a relationship, but suspected interacting factors cannot be controlled, and other lines of evidence are limited or inconsistent. Generally, determination is based on multiple studies in multiple research groups.

Suggestive of a causal relationship Evidence is suggestive of a causal relationship between relevant pollutant exposures and the health outcome, but is limited because chance, bias and confounding cannot be ru led out. For example, at least one high-quality study shows a positive association but the results of other studies are inconsistent. Evidence is suggestive of an association between relevant pollutant exposures and the outcome, but chance, bias and confounding cannot be ruled out. For example, at least one high-quality study shows an association, but the results of other studies are inconsistent.

Inadequate to infer a causal relationship Evidence is inadequate to determine that a causal relationship exists between relevant pollutant exposures and health outcome. The available studies are of insufficient quantity, quality, consistency or statistical power to permit a conc lusion regarding the pres ence or absence of an association between relevant pollutant exposure and the outcome. The available studies are of insuff icient quality, consistency or statistical power to permit a c onclusion regarding the presence or absence of an association between relevant pollutant exposure and the outcome.

Suggestive of no causal relationship Evidence is suggestive of no causal relationship between relevant pollutant exposures and health outcome. Several adequate studies, covering the full range of levels of exposure that human beings are known to encounter and consideri ng susceptible or vulnerable subpopulations, are mutually consistent in not showing a positive association between exposure and the outcome at any level of exposure. Several adequate studies, examining relationships between relevant exposures and outcomes, are cons istent in failing to show an association between exposure and the outcome at any level of exposure.

Source: U.S. EPA (2008f)



# 5 6 7 8 9 10 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 evalua ted for health outcome categories, such as cardiovascular effects, and then conclusions were drawn based upon th e 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, eviden ce from multiple and various types of studies was

March 2009 1-15 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14

15 16

17

18

19 20

21 22 23 24 25 26 27 28 29 30 31 32 considered. Response was evaluated over a range of ob servations 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 concen tration to emphasize the relationship apparent at relevant pollutant exposures. Concen trations generally within an orde r of magnitude or two of ambient pollutant measurements are considered to be re levant 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 evaluati on of quantitative relationships for health and environmental effects are summarized below. 

# Effects on Human Populations

Important questions regarding quan titative relationships include:

 What is the concentration-response or dose -response relationship in the human population?

 What is the interrelationship between incidence and severity of effect?

 What exposure conditions (dose or exposur e, duration and pattern) are important?

 What subpopulations appear to be differentially affected i.e., more susceptible or vulnerable to effects?

Addressing these questions requires evaluating the en tirety of policy-relevant quantitative evidence regarding the concentration-response relationships incl uding levels of pollutant and exposure durations at which effects were observed, and subpopulations th at differ in susceptibility or vulnerability from the general population. This integration of evidence resulted in identification of a study or set of studies that best approximated the concentration response relationshi p for the U.S. population, given the current state of knowledge and the uncertainties that surrounded these estimates. An important consideration in characterizing the public health impacts associated with exposure to a pollutant is whether the concentration-response rela tionship 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 le vel, however, various sources of variability and

March 2009 1-16 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3 4 5 6 7 8 9 10 11

12 13 14 15

16 17

18

19 20 21 22 23 24 25 26 27 28 uncertainty (such as the low data density in the lower concentration range, possible influence of measurement error, and individual differences in susceptibility to air pollution health effects) tend to smooth and “linearize” the concentration-response func tion. Additionally, many chemicals and agents may act by perturbing naturally occurring backgroun d processes that lead to disease, which also linearizes population concentration-response relationships (C lewell and Crump, 2005; Crump et al., 1976; Hoel, 1980). These attributes of population dose-response may ex plain why the available human data at ambient concentrations for some environmental pollutants (e.g., PM, O 3 

## , 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 so me key events. These attributes of human population dose-response relationships have been extensively discussed in the broader epidemiologic literature (Rothman and Greenland, 1998). 

# Effects on Ecosystems or Public Welfare

Key questions for understanding the quantitative re lationships 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., t ypes, regions, taxonomic groups, populations, functions, etc.) appear to be affected, or are more sensitive to effects?

 Under what exposure conditions (amount deposited or concentr ation, duration and pattern) are effects seen?

 What is the shape of the concentration-response or exposure-response relationship?

Evaluations of causality typically characterize how the probability of ecological and welfare effects change in response to exposure. A challenge to the quantification of exposure-response relationships for ecological effects is the variability across ecosystems. Ecological responses are evaluated within the range of observations, so a quantitative rela tionship may be determined for a given ecological system and scale. There is great regional and local variability in eco systems, thus an exposure-response relationship generally cannot be determined at the larger national or even regional scale. Quantitative relationships therefore are available site by site. For example, an ecological response to deposition of a given pollutant can differ greatly between ecosystems. Where results from greenhouse or animal ecotoxicological studies are available, they may be used to aid in character izing exposure-response relati ons, particularly relative to mechanisms of action, and ch aracteristics of sensitive biota.

March 2009 1-17 DRAFT—DO NOT QUOTE OR CITE

# 1.6.6. Concepts in Evaluating Adversity of Health Effects

In evaluating the health evidence, a number of factors can be considered in determining the extent to which health effects are “adverse” for health outcomes such as changes in lung function or in cardiovascular health measures. Some health outcome events, such as hospitalization for respiratory or cardiovascular diseases, are clearly considered adverse; wh at is more difficult is determining the extent of change in the more subtle health me asures 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 potentia lly adverse in more susceptible individuals. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

28 29 30 For example, the extent to which changes in l ung function are adverse has been discussed by the American Thoracic Society (ATS) in an official statement titled What Constitutes an Adverse Health Effect of Air Pollution? (American Thoracic Society, 2000). This statement updated the guidance for defining adverse respiratory health effects that had been published 15 years earlier (American Thoracic Society, 1985), taking into account new investigative approach es 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. Expo sure 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 identifiabl e individual has a level associated with significant impairment. Exposure to air pollution could shift the distribution such that no identifiable individual experiences clinically relevant effects; this shif t toward decreased lung function, however, would be considered adverse because individuals within the population would have dimi nished 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 polluti on has been shown to affect markers of transient myocardial ischemia such as ST-segment abnormalitie s and onset of exertional angina. In some cases, these effects are silent yet may still increase the risk of a number of cardiac events, including myocardial infarction and sudden death.



# 1.7. Summary

This first external review draft ISA is a concise evaluation and synthesis of the most policy-relevant science for reviewing the NAAQS , and it communicates critical science judgments relevant to the NAAQS review. It reviews the most policy-relevant evidence from health and

March 2009 1-18 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3 4 5 6 7 environmental effects studies, including mechanistic evidence from basic biological science. Annexes to the ISA provide additional details of the literature published since the last review. A framework for making critical judgments concerning causality was presented in this chapter. It relies on a widely accepted set of principles and standardized language to express evaluation of the evidence. This approach can bring rigor and clarity to the current and futu re 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.

March 2009 1-19 DRAFT—DO NOT QUOTE OR CITE

# Chapter 2. Integrative Health Effects Overview

The subsequent chapters of this ISA present the most policy-relevant information related to the review of the NAAQS for CO, including a synth esis of the evidence presented in the 2000 CO AQCD with the results of more recent studies. This ch apter integrates important findings from the disciplines evaluated in the assessment of the CO scientific lite rature, which include atmospheric sciences, ambient air data analyses, exposure assessment, dosimetry, an d health effects research (animal toxicology, controlled human exposure, and epidemiologic studies). The EPA framework for causal determinations described in Chapter 1 has been app lied to the body of evidence evaluate d in this assessment in order to characterize the relationship between exposure to CO at relevant pollutant exposures and health effects. The EPA framework applied here employs a five -level hierarchy for causal determination: 

# 1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

20 21 22 23 24 25 26 27  Causal relationship

 Likely to be a causal relationship

 Suggestive of a causal relationship

 Inadequate to infer a causal relationship

 Suggestive of no causal relationship

This evaluation led to causal determinations for a range of health outcomes, and characterization of the magnitude of these responses, including responses in susceptible or vulnerable subpopulations, over a 

# 16 range of relevant concentrations. Th is integration of evidence also pr ovides a basis for characterizing the 

# 17 concentration-response relationships of CO and adverse health outcomes for the U.S. population, given 

# 18 the current state of knowledge. 

# 19 This chapter summarizes and inte grates the recently available scientific evidence along with key findings and conclusions from the 2000 CO AQCD that b est informs consideration of the policy-relevant questions that frame this assessment as presented in Ch apter 1. Section 2.1 discusses the trends in ambient concentrations and sources of CO and provides a brie f summary of factors influencing personal exposure to ambient CO. Section 2.2 summarizes CO dosimetry and pharmacokinetics and describes what is known regarding the hypoxic and non-hypoxic modes of action of CO. Section 2.3 integrates the evidence for studies that examined health effects related to short- and long-term exposure to CO and discusses important uncertainties identified in the interpretation of the scientific evidence. Finally, Section 2.4

March 2009 2-1 DRAFT—DO NOT QUOTE OR CITE

# 1 2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 presents the public health impacts a ssociated with exposure to CO, and includes evidence of effects in potentially susceptible and vulnerable populations from CO exposure.



# 2.1. Ambient Concentrations, Sources, and Exposure

CO is formed by incomplete combustion of carbon-containing fuels and by photochemical reactions in the atmosphere. Nationally, on-road mobile sources constituted more than half of total CO emissions in 2002, or ~63 of ~109 million tons (MT) of total CO emissions, based on the most recent publicly available data from EPA’s National Emissions Inventory (NEI). On-road mobile source emissions decreased by 16% since 1997, which is the most recent year covered in the 2000 CO AQCD. In metropolitan areas in the U.S., as much as 70-75% of all CO emissions result from on-road vehicle exhaust. The majority of these on-road CO emissi ons are derived from gasoline-powered vehicles since diesel vehicles emit relatively little CO. When emi ssions from incomplete combustion of fuels powering non-road mobile sources, such as farm and cons truction equipment, lawnmowers, boats, ships, snowmobiles, and aircraft are included, all mobile sources accounted for ~80% of total CO emissions in the U.S. in 2002. Other sources of CO include wild fires, controlled vegetation burning, residential biomass combustion, and industrial processes. While CO emissions from non-road mobile sources, fire, and industry have remained fairly constant, on-road mobile source CO emissions have decreased by roughly 5% per year since the early 1990s. Significant reductions in ambient CO concentra tions and in the number of NAAQS exceedances have been observed over the past 25 years, a conti nuation of trends documented in the 2000 CO AQCD. Nationwide ambient CO data from the EPA Air Qual ity System (AQS), for the years 2005-2007, show that the median 1-h daily maximum (max) concentr ation across the U.S. was 0.7 ppm; the mean was 0.9 ppm; the 95th percentile was 2.4 ppm; and the 99th percentile was 3.8 ppm. The median 8-h daily max ambient CO concentration for the years 2005-2007 was 0.5 ppm; the mean was 0.7 ppm; the 95th percentile was 1.7 ppm; and the 99th percentile wa s 2.6 ppm. The current CO NAAQS are 35 ppm (1-h avg) and 9 ppm (8-h avg), not to be exceeded more than once per year (yr). During the years 2005-2007, 1-h and 8-h CO concentrations did not exceed the NAAQS level more than once per year at any monitoring site. Moreover, in these 3 years, a 1-h avg concentration in excess of 35 ppm was reported only once, in 2007, in Ogden, UT (39 ppm), and ther e were only 7 reported 8-h avg values nationwide in excess of 9 ppm in all 3 years. Seasonally divi ded box plots of data from 2005-2007 compiled for spatially diverse urban metropolitan areas illustrate th e tendency for higher medi an CO concentrations and wider variations in concentrations in the wi nter and fall compared with the spring and summer. Policy-relevant background (PRB) concentrations of CO were computed for this assessment using data for the years 2005-2007 collected at 12 remote si tes in the U.S. which are part of the National

March 2009 2-2 DRAFT—DO NOT QUOTE OR CITE

## Oceanic and Atmospheric Administration’s (NOAA) Global Monitoring Division (GMD) and are not part of the EPA national regulatory network. The 3-year avg CO PRB averaged ~0.13 ppm in Alaska, ~0.10 ppm in Hawaii, and ~0.13 ppm over the contig uous U.S. (CONUS). (Note that the analysis for North American PRB in this assessment was made by segregating the three Alaska sites based on their high latitude and the two Hawaii sit es based on their distance from the continent and then treating the remaining seven sites as representative of the CONUS PRB.) 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 A person’s total personal exposure to CO is a co mbination of ambient and non-ambient exposures. Ambient exposure can be further broken down into direct exposure while outdoors and exposure to ambient CO which has infiltrated into buildings a nd vehicles. Several studies have shown that in the absence of indoor sources such as ETS or gas stoves, CO concentrations are highest in automobiles and near highly traveled roadways. Specific concentrations vary as a function of vehicle ventilation rate and CO emissions source strength and are related to, fo r example, on-road vehicle numbers and speed. Invehicle CO concentrations are typically reported to be between 2 and 5 times higher than ambient concentrations measured at the roadside, but have been reported to be as much as 25 times higher. A portion of these high concentrations comes from the vehicle’s own engine emissions entrained with the ambient concentrations from the roadway, although th e exact fraction of this self-pollution in the invehicle total varies with body integrity of the vehicle and is not known with certainty. Concentrations of CO, like those of other on-road vehicle pollutants, often display an inverse relationship with distance from the roadway. For CO, this fall-off with distan ce can be very sharp up to ~70 meters (m), beyond which the concentration increment from roadway emissions is largely indistinguishable from local ambient concentrations. The siting a nd location instructions for federal regulatory CO monitors explicitly recognize the need to measure at di stances close to mobile sources with a requirement for monitored areas that at least one monitor be sited to measure max concentration. This is often accomplished with a monitor situated at the CFR-defined microscale of 2-10 m from the roadway; these microscale monitors also have sample inlets mounted at 3 ± 0.5 m a bove ground level (AGL), unlike those at larger scale distances whose inlet heights can vary between 2 and 15 m AGL. In 2007, there were at least 70 CO monitors described as microscale reporting to AQS. Although the correlations across CO monitors sited to sample at different scales can be greater than 0.8 in some areas, they also can vary widely from within and between cities across the U.S. as a function of natural and urban topography, meteorology, and st rength and proximity to sources. At the same time, personal exposure monitoring captures both ambient and non-ambient CO concentrations. Because non-ambient CO exposure is not expected to be co rrelated with ambient CO concentrations at the monitor, the non-ambient contribution to total personal CO exposure complicates interpretation of health effects relationships observed in epidemiologic studies. For the general U.S. population, exposure error analysis for epidemiologic studies, as summarized in Chapter 3, indicates that fixed-site measured ambient CO concentration is generally a good indicator of ambient exposure to CO.

March 2009 2-3 DRAFT—DO NOT QUOTE OR CITE

# 2.2. Dosimetry, Pharmacokinetics, and Mode of Action

2.2.1. Dosimetry and Pharmacokinetics

CO elicits various health effects by binding to and altering the function of a number of hemecontaining molecules, mainly hemoglobin (Hb) . The formation of COHb reduces the oxygen (O 2 

## )-carrying capacity of blood and impairs the release of O 2 

## from oxyhemoglobin (O 2 

## Hb) to the tissues. The 2000 CO AQCD has a detailed description of the well-established Coburn-Forster-Kane (CFK) equation, which has been used for many years to m odel COHb formation. More recent work since then has developed models that include myoglobin (Mb) and extravascular storage compartments, as well as other dynamics of physiology relevant to CO uptake and elimination. These models have indicated that CO has a biphasic elimination curve, due to in itial washout from the blood followed by a slower flux from the tissues. The flow of CO between the blood a nd alveolar air or tissues is controlled by diffusion down the CO concentration gradient. The uptake of CO is governed not only by this CO pressure differential, but also by physiological parameters , such as minute ventilation and lung diffusing capacity, that can, in turn, be affected by factors such as ex ercise, age, and medical conditions (e.g., obstructive lung disease). Susceptible populations, including health compromised individuals and developing fetuses, are at a greater risk from COHb induced health effects due to altered CO kinetics, compromised cardiopulmonary processes, and increased baseline hypoxia levels. Altitude also may have a substantial effect on the kinetics of COHb formation, especially for visitors to high altitude areas. Compensatory mechanisms, such as increased cardiac output, combat the decrease in barometric pressure. Altitude also increases the endogenous production of CO through upregulation of heme oxygenase (HO). CO is considered a second messenger and is endogenously produced from the catabolism of heme proteins by enzymes such as HO-1 (the inducible form of heme oxygenase) and through endogenous lipid peroxidation. Finally, CO is removed from the body by expiration and oxidation to CO 2 

## . 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

22 23 24 25 26 27 

# 2.2.2. Mode of Action

The diverse effects of CO are dependent upon dose, duration of exposure, and the cell types and tissues involved. Responses to CO are not necessarily due to a single process and may instead be mediated by a combination of effects including C OHb-mediated hypoxic stress and mechanisms unrelated to tissue hypoxia including free ra dical production and the initiation of cell signaling. However binding of CO to reduced iron in heme proteins with subsequent alteration of heme protein function is the underlying mechanism for both the hypoxic and non-hypoxic biological responses to CO.

March 2009 2-4 DRAFT—DO NOT QUOTE OR CITE

## As discussed in the 2000 CO AQCD, the most well-known pathophysiological effect of CO is tissue hypoxia caused by binding of CO to Hb. Not only does the formation of COHb reduce the O 2 

## -carrying capacity of blood, but it also impairs the release of O 2 

## from O 2 

## Hb. Compensatory alterations in hemodynamics, such as vasodilation and incr eased cardiac output, protect from tissue hypoxia. At ambient CO concentrations, these compensatory changes are slight and likely tolerable in people with a healthy cardiovascular system. However, people with cardiovascular detriments may be unable to endure these small changes in hemodynamics which may lead to the presentation of health effects as described in Sections 5.2.1 and 5.2. 2. Binding of CO to Mb, as discussed in the 2000 CO AQCD and in Section 4.3.2.3, can also impair the delivery of O 2 

## to tissues. Mb has a high affinity for CO; however, physiological effects are seen only after high dose expo sures to CO, resulting in COMb concentrations far above baseline levels. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Non-hypoxic mechanisms underlying the biological effects of CO have been the subject of a substantial amount of recent research since the 2000 CO AQCD. Most of these mech anisms are related to CO’s ability to bind heme-containing proteins othe r than Hb and Mb. These mechanisms, which may be interrelated, include alteration in nitric ox ide (NO) signaling, inhibition of cytochrome c oxidase, heme loss from proteins, disruption of iron homeostasis, and alteration in cellular redox status. CO is a ubiquitous cell signaling molecule and the physiologi cal functions of HO-derived CO are numerous. The endogenous generation and release of CO from HO-1 and HO-2 is tightly controlled, as is any homeostatic process. Thus, exogenously-applied CO has the capacity to disr upt myriad heme-based signaling pathways due to its nonspecific nature. On ly a limited amount of information is available regarding the impact of exogenous CO on tissue and cellular levels of CO. However recent animal studies demonstrated increased tissue CO levels and biological responses following exposure to 50 ppm CO. Whether or not environmentally relevant exposures to CO can affect endogenous CO signaling pathways and lead to adverse health effects is an open ques tion for which there are no definitive answers at this time.

March 2009 2-5 DRAFT—DO NOT QUOTE OR CITE

# 2.3. Health Effects

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

Outcome Category Exposure Period Causality Determination Cardiovascular morbidity Short-term Likely to be a causal relationship Central nervous system effects Short- and long-term Suggestive of a causal relationship Birth outcomes and Developmental effects Long-te rm Suggestive of a causal relationship Short-term Suggestive of a causal relationship Respiratory morbidity Long-term Inadequate to infer a causal relationship Short-term Suggestive of a causal relationship Mortality Long-term Suggestive of no causal relationship



# 2.3.1. Cardiovascular Morbidity

The most compelling evidence of a CO-induced effect on the cardiovascular system at COHb levels relevant to the current NAAQS comes from a series of controlled human exposure studies among individuals with CAD (see Section 5.2). These st udies, described in the 1991 and 2000 CO AQCDs, demonstrate consistent decreases in the time to onset of exercise-induced angina and ST-segment changes following CO exposures resulting in COHb levels of 3-6%, with one multicenter study reporting similar effects at COHb levels as low as 2.4%. No human clinical studies have evaluated the effect of controlled exposures to CO resulting in COHb levels lower than 2.4%. Human clinical studies published since the 2000 CO AQCD have reported no asso ciation between CO and ST-segment changes or arrhythmia; however, none of these studies included indi viduals with diagnoses of heart disease. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 While the exact physiological significance of the observed ST-segment changes among individuals with CAD is unclear, ST-segment depression is a known indicator of myocardial ischemia. It is also important to note that the individuals with CAD who participated in these controlled exposure studies were not representative of the most sensitive individuals in the population. In fact, the most sensitive individuals may respond to levels of COHb lower than 2.4%. Variability in activity patterns and severity of disease among individuals with CAD is likely to infl uence the critical level of COHb which leads to adverse cardiovascular effects. The degree of ambient CO exposure which leads to a ttainment of critical levels of COHb will also vary between individuals. First of all, endogenous CO production varies as described in Section 4.5, but generally results in less than 1% COHb. Secondly, nonambient exposures to CO, such as exposure to ETS, can increase COHb above baseline levels. Ambien t exposures will result in an additive increase in

March 2009 2-6 DRAFT—DO NOT QUOTE OR CITE

## COHb. Using mathematical modeling to predict changes in COHb in h ealthy inactive adults (Quantitative Circulatory Physiology [QCP] model, Section 4.2.3), it can be estimated that exposure to 35 ppm CO for 1 h results in an increase of 0.6% COHb over baselin e and exposure to 9 ppm CO for 8 h results in an increase of 0.8% COHb over baseline. Furthermore, 24-h exposure to 3 ppm CO results in an increase of 0.4% COHb above baseline which can also be obtained following 1-h exposure to 30 ppm CO. Consequently, exposure to CO at concentrations relevant to the NAAQS has the potential to increase COHb to levels associated with adverse cardi ovascular health effects in some individuals. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

31 32 Findings of controlled human exposure studies are c oherent with findings of recent epidemiologic studies conducted since the 2000 CO AQCD, whic h observed associations between ambient CO concentration and emergency department (ED) vis its and hospital admissions for ischemic heart disease (IHD), congestive heart failure (CHF) and all-cause cardiovascular disease (CVD). All but one of these epidemiologic studies were conducted in locations wh ere the entire distribution of CO concentrations were at or below the level of the current NAAQS, with mean 24-h avg concentrations ranging from 0.5 ppm (Montreal, Canada) to 9.4 ppm (Tehran, Ira n) (Table 5-7). A single study reported a negative association between CO concentration and hospital ad missions and ED visits for IHD among all ages; all other associations were positive, with increases in hospital admissions and ED visits for IHD between 0.2% and 19.8% per standardized increase in CO co ncentration (Figure 5-1). These recent studies build upon the conclusions of the 2000 CO AQCD that short-term variations in ambient CO concentrations are associated with daily hospital admissions for heart disease. These health outcomes are consistent with a role for CO in limiting O 2 

## availability (i.e., hypoxic mechanisms) in individuals with CAD. However, rece nt toxicological studies suggested that CO may also act through non-hypoxic mechanisms by disrupting cellular signaling. Studies in healthy animals demonstrated oxidative injury and inflammation in response to 50-100 ppm CO while studies in disease models demonstrate effects on heart rhythm and ex acerbation of cardiomyopath y and vascular remodeling in response to 35-50 ppm CO. Furthermore, in utero exposure to 150 ppm CO alters postnatal elecrophysiological maturation in rat cardiomyocytes. Furt her investigations will be useful in determining the importance of non-hypoxic mechanisms followi ng environmentally-relevant CO exposures. Taken together, the evidence from epidemiologic, hum an clinical, and toxicological studies is 

# sufficient to conclude that a causal relationship is likely to exist between relevant short-term CO exposures and cardiovascular morbidity.



# 2.3.2. Central Nervous System Effects

Exposure to high levels of CO has long been known to adversely affect CNS function, with symptoms following acute CO poisoning including headache, dizziness, cognitive difficulties,

March 2009 2-7 DRAFT—DO NOT QUOTE OR CITE

## 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 repor ted 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 neurol ogical impairment owing to compensatory responses including increased cardiac output and cerebral bl ood flow. However, these compensatory mechanisms are likely impaired among certain potentially susceptible groups including individuals with reduced cardiovascular function. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Toxicological studies that were not discussed in the 2000 CO AQCD employed rodent models to show that low level CO exposure during the in uter o period can adversely affect adult outcomes including behavior, neuronal myelinati on, neurotransmitter levels or function, and the auditory system (discussed in Section 5.3). In utero CO exposure, including both in termittent and continuous exposure, has been shown to impair multiple behavioral outcomes in offsprin g including active avoidance behavior (150 ppm CO), non-spatial memory (75 and 150 ppm CO), spatial le arning (endogenous CO inhi bition), homing behavior (150 ppm CO), locomotor movement (150 ppm CO), and negative geotaxis (125 and 150 ppm). In two separate studies, in utero CO exposure (75 and 150 ppm) was associated with significant myelination decrements without associated chang es in motor activity in adult animals. Multiple studies demonstrated that in utero CO exposure affected glutamatergic, cholinergic, catecholaminergic, and dopaminergic neurotransmitter levels or transmission in exposed male rodents. Possible or demonstrated adverse outcomes from the CO-mediated aberrant neurotransm itter levels or transmission include respiratory dysfunction (200 ppm CO), impaired sexual behavi or (150 ppm CO), and an adverse response to hyperthermic insults resulting in neuronal damage (200 ppm). Finally, perinatal CO exposure has been shown to affect the developing auditory system of rodents, inducing permanent changes into adulthood. This is manifested with atrophy of cochlear cells i nnervating the inner hair cells (25 ppm CO), decreased immunostaining associated with impaired neuronal ac tivation (12.5 ppm CO), impaired myelination of auditory associated nerves (25 ppm CO), decreased energy production in the sensory cell organ of the inner ear or the organ of corti (25 ppm CO), some of which is mechanistically proposed to be mediated by reactive oxygen species (ROS) (25 ppm CO). Functional tests of the auditory system of neonatally, low level CO-exposed rodents, using otoacoustic em issions (OAE) testing (50 ppm CO) and amplitude measurements of the 8th cranial nerve action potential (12, 25, 50, 100 ppm CO), revealed decrements in auditory function at PND22 and permanent changes into adulthood using AP testing (50 ppm CO). Together, these animal studies demonstrate that in utero exposure to CO can adversely affect adult behavior, neuronal myelination, ne urotransmission, and the auditory system in adult male rodents. Considering the combined evidence from controlled human exposure and toxicological studies, the

March 2009 2-8 DRAFT—DO NOT QUOTE OR CITE

## evidence is 

# suggestive of a causal relationship between relevant short- and long-term CO exposures and central nervous system effects. 1 2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 

# 2.3.3. Birth Outcomes and Developmental Effects

The most compelling evidence for a CO-induced eff ect on birth and developmental outcomes is for preterm birth (PTB) and cardiac birth defects. These outcomes were not addressed in the 2000 CO AQCD, which included only two studies that examined the effect of ambient CO on low birth weight (LBW). Since then, a number of studies have been conducted looking at varied outcomes, including PTB, birth defects, fetal growth (including LBW), and infant mortality. There is limited epidemiologic evidence that CO during early pregnancy (e.g., first month and first trimester) is associated with an increased risk of PTB. The only U.S. studies to investigate the PTB outcome were conducted in California, and these reporte d consistent results whereby all studies reported a significant association with CO exposure during ear ly pregnancy, and exposures were assigned from monitors within close proximity of the mother’s r esidential address. Additional studies conducted outside of the U.S. provide supportive, though less cons istent, 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 associat ed with an increased risk of cardiac birth defects. This insult to the heart is coherent with results of human clinical studies demonstrating the heart as a target for CO effects (Section 5.2). Animal toxicol ogical studies provide additional evidence for such an insult to the heart, and reported transient cardiomegaly at birth after continuous in utero CO exposure (60, 125, 250 and 500 ppm CO), delayed myocardial el ectrophysiolgical maturation (150 ppm CO), or systemic splenic immunocompromise (75 or 150 ppm CO). Toxicological studies have also shown that exogenous continuous in utero CO exposure (250 ppm) induced teratogeni city in rodent offspring in a dose-dependent manner that was further exacerbated by dietary protein restriction (65 ppm CO) or zinc depletion (500 ppm CO). Toxicological studies of ex ogenous 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 durin g pregnancy having a negative effect on fetal growth in epidemiologic studies. In general, the re viewed studies, summarized in Figures 5-7 through 5-9, reported small reductions in birth weight (rangi ng ~5-20 g). Several studies examined various combinations of birth weight, LBW, and small for ge stational 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

March 2009 2-9 DRAFT—DO NOT QUOTE OR CITE

## susceptible births and increasing the risk of IUGR/LBW/SGA. It is difficult to conc lude 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. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

27 28 29 30 31 32 In general, there is limited epidemiologic evidence that CO is associated with an increased risk of infant mortality during the neonatal or post-neonatal periods. In support of this limited evidence, animal toxicological studies do provide some evidence that exogenous CO exposure to pups in utero significantly increased postnatal mortality (7 h/day and 24 h/day, 250 ppm CO; 24 h/day, 90 or 180 ppm CO) and prenatal mortality (7 h/day, 250 ppm CO). Evidence exists for additional developmental outcom es which have been examined in toxicological studies, but not epidemiologic or human clinical studies, including behavioral abnormalities, learning and memory deficits, locomotor effects, neurotransmitte r changes, and changes in the auditory system. Structural aberrations of the cochlea involving neuronal activation (12.5, 25 and 50 ppm CO) and auditory related nerves (25 ppm CO) were seen in pups after in neonatal CO exposure. Auditory functional testing using OAE (50 ppm CO) and AB R (12, 25, 50, 100 ppm CO) on rodents exposed perinataly to CO showed that CO-exposed neonates had auditory decrements at PND22 (OAE and ABR) and permanent changes into adulthood with ABR (50 ppm CO). Overall, there is limited, though positive, epidem iologic evidence for a CO-induced effect on PTB and birth defects, and weak evidence for a decrease in birth weight, other measures of fetal growth, and infant mortality. Animal toxicological studies provide support and coherence for these effects. Both hypoxic and non-hypoxic mechanisms that could lead to such effects have been proposed in the toxicological literature (Section 5.1), though a clear understanding of the mechanisms underlying reproductive and developmental effects is still lacking. Taking into consideration the positive evidence for some birth and developmental outcomes from epidemio logic studies and the resulting coherence for these associations in animal toxicological studies, the evidence is 

# suggestive of a causal relationship between long-term exposures to relevant CO con centrations and developmen tal effects and birth outcomes.



# 2.3.4. Respiratory Morbidity

New epidemiologic studies, supported by the b ody of literature summarized in the 2000 CO AQCD, provide evidence of positive associations betw een short-term exposure to CO and respiratoryrelated outcomes including pulmonary function, r espiratory symptoms, medication use, hospital admissions, and ED visits (discussed in Section 5.5) . However, the interpretation of the results from epidemiologic studies is difficult due to the lack of extensive copollutant analyses along with the moderate to high correlation between CO and other combustion/traffic ge nerated pollutants. To date the

March 2009 2-10 DRAFT—DO NOT QUOTE OR CITE

## majority of the literature has not extensively ex amined the association between CO and respiratory morbidity due to studies focusing primarily on eff ects associated with exposure to other criteria pollutants, namely PM and O 3 

## . This has contributed to the inability to disentangle the effects attributed to CO from the larger complex air pollution mix. In a ddition, uncertainty as to a biological mechanism to explain the respiratory-related effects observed in the epidemiologic literature further complicates the interpretation of these results, especially considering the low ambient CO concentrations reported (24-h avg: 0.35–2.1 ppm). However, animal toxicological studies do provide some evidence that short-term exposure to CO (50-100 ppm) can cause oxidative in jury and inflammation and alter pulmonary vascular remodeling. Human clinical studies have not extensivel y examined the effect of short-term exposure to CO on respiratory morbidity, specifically pulmonary f unction. The limited number of clinical studies that have been conducted prior to and since the 2000 CO AQCD provide very little evidence of any adverse effect of CO on the respiratory system at COHb le vels <10%. Although human clinical studies have not provided evidence to support CO-related respiratory health effects, the epidemiologic studies that examined the effects of short-term exposure to CO and lung-related outcomes show positive associations and animal toxicological studies demonstrate the poten tial for an underlying biological mechanism, which together provide evidence that is 

# suggestive of a causal relationshi p between short-term exposure to relevant CO concentrations and respiratory morbidity. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

29 30 31 32 Currently, only a few studies have been conducted that examine the association between long-term exposure to CO and respiratory morbidity. Although some studies did observe associations between longterm exposure to CO and respiratory health outco mes key uncertainties still exist. These uncertainties include: the lack of replication and validati on studies to evaluate new methodologies (i.e., Deletion/Substitution/Addition (DSA) algorithm) that ha ve been used to examin e the association between long-term exposure to CO and respiratory health eff ects; whether the respiratory health effects observed in response to long-term exposure to CO can be e xplained by the proposed biological mechanisms; and the lack of co-pollutant analyses to disentangle the r espiratory effects associated with CO due to its high correlation with NO 2 

## and other combustion-related pollutants. Overall, the evidence available is 

# inadequate to conclude that a causal relations hip exists between long-te rm exposure to relevant CO concentrations and respiratory morbidity.



# 2.3.5. Mortality

Among the gaseous pollutants examined in time-series mortality studies, CO is the least frequently studied criteria air pollutant. Because CO was mostly treated as a potential confounder in these studies, the information available regarding the nature of th e association between short-term exposure to CO and mortality is limited compared to the other pollutants. However, the recently available multi-city studies,

March 2009 2-11 DRAFT—DO NOT QUOTE OR CITE

## which consist of larger sample sizes, and single-city studies generally confirmed the findings reported in the 2000 CO AQCD (see Section 5.6). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 The multi-city studies report comparable CO mort ality risk estimates for total (non-accidental) mortality with the APHEA2 European multi-city study showing slightly higher estimates for cardiovascular mortality in single-pollutant models. However, when examining potential confounding by copollutants these studies consistently showed that CO mortality risk estimates were reduced when NO 2 

## was included in the model, but this observation may not be “confounding” in the usual sense in that NO 2 

## may also be a surrogate marker of other po llutants or pollution sources (i.e., traffic). Only one of the multi-city studies focused sp ecifically on the CO-mortality association (the APHEA study), examining: (1) model sensitivity; (2 ) the CO-mortality concentration-response (C-R) relationship; and (3) potential effect modifiers of CO mortality risk estimates. The APHEA2 study performed a sensitivity analysis, which indicated an approximate 50 - 80% difference in CO risk estimates from a reasonable range of alternative models . In addition, the study examined the CO-mortality C-R relationship through a grid search of varying threshold points, and found only weak evidence of a CO threshold at 0.5 mg/m 3 

## (0.43 ppm), but this result is complicated by the lowest 10% of the CO distribution for seven of the 19 cities examined being at or above 2 mg/m 3 

## (1.74 ppm). The examination of a variety of city-specific variables to identify potential effect modifiers of the CO-mortality relationship found that geographic region explained most of the heteroge neity in CO mortality risk estimates with the CO-mortality associations being stronger in western and southern European cities than eastern cities. A similar pattern has been reported for black smoke (BS) and SO 2 

## in previous APHEA studies, but the geographic variability observed does not provide specific information which could be used to evaluate the CO-mortality association. The results from the single-city studies evaluated are generally consistent with the multi-city studies in that some evidence of a positive associatio n was found for mortality upon short-term exposure to CO. However, the CO-mortality associations were often, but not always, attenuated when other copollutants were included in the regression models . In addition, limited ev idence was available to identify cause-specific mortality outcomes (e.g., cardi ovascular causes of death) associated with shortterm exposure to CO. The new multi- and single-city studies evaluate d provide evidence that an association between short-term exposure to CO and mo rtality exists, but limited evidence is available to evaluate causespecific mortality outcomes associated with CO exposure and it is unclear if CO is acting alone or as a surrogate for other combustion-related pollutants. In a ddition, the results also underscore the limitation of current analytical methods to disentangle the health ef fects associated with one pollutant in the complex air pollution mixture. Overall, the epidemiologic evidence is 

# suggestive of a causal relationship between short-term exposure to releva nt CO concentrations and mortality.

March 2009 2-12 DRAFT—DO NOT QUOTE OR CITE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

20 21 22 23 24 25 26 27 28 29 The evaluation of new epidemiologic studies conduc ted since the 2000 CO AQCD that investigated the association between long-term exposure to CO and mortality consistent ly found null or negative mortality risk estimates. No such studies were discussed in the 2000 CO AQCD. The re-analysis of the American Cancer Society (ACS) data by Jerrett et al. (2003) found no association between long-term exposure to CO and mortality. Similar results were obtained in an updated analysis conducted by the original ACS investigators when using earlier (1980) CO data, but negative associations were found when using more recent (1982-1998) data. The Women’ s Health Initiative (WHI) Study also found no association between CO and CVD events (including mortality) using the data from recent years (1994-1998), while the series of Veterans Cohort stud ies found no association or a negative association between mean annual 95th percentile of hourly CO values and mortality. In addition, a cross-sectional study of U.S. counties reported results that are ge nerally consistent with the cohort studies: positive associations between long-term exposure to PM 2.5 

## and sulfate (SO 4 2- 

## ) and mortality, and generally negative associations with CO. Overall, the consis tent null and negative associations observed across epidemiologic studies which included cohort pop ulations encompassing potentially susceptible subpopulations (i.e., post-menopausal women and hypertensive men) combined with (1) the lack of evidence for respiratory and cardiovascular morbidit y outcomes following long-term exposure to CO; and (2) the absence of a proposed mech anism to explain the progression to mortality following long-term exposure to CO provide supportive evidence that is 

# suggestive of no causal relationship between longterm exposure to relevant CO concentrations and mortality

.



# 2.4. Public Health Impacts

2.4.1. Concentration-Response Relationship

Currently, very limited information is available in the human clinical and epidemiologic literature regarding the CO C-R relationship and the potential presence of a CO threshold. Two human clinical studies described in the 2000 CO AQCD have evaluate d the C-R relationship between CO and onset of exercise-induced angina among individuals with CAD, but at the high end of CO concentrations (i.e., CO levels above the current NAAQS). Anderson et al. (1973) exposed 10 adult men with stable angina for 4 h to CO concentrations of 50 and 100 ppm, which r esulted in average COHb levels of 2.9% and 4.5%, respectively. Both exposures significantly decreased the time to onset of exercise-induced angina relative to room air control (1.6% COHb). However, th ere was no difference in response between the two exposure concentrations of CO. In a much larger study, 63 adults with stable angina were exposed for 1 h to two concentrations of CO (average exposure concentrations of 1 17 and 253 ppm) resulting in average

March 2009 2-13 DRAFT—DO NOT QUOTE OR CITE

## pre-exercise COHb levels of 2.4% and 4.7% (Allr ed et al., 1989). Relative to control (average COHb 0.7%), COHb levels of 2.4% and 4. 7% were observed to decrease the time to onset of angina by 4.2% (p = 0.054) and 7.1% (p = 0.004), respectively. In a ddition, these investigators reported a statistically significant decrease in the time to exercise-induced ST-segment depression with increasing COHb levels. These findings provide some evidence of a significant C-R relationship at COHb concentrations between 2.4 and 4.7%. However, the human clinical literature h as yet to evaluate the C-R relationship at lower CO concentrations or COHb levels. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27 28 29 30 31 32 One study in the epidemiologic litera ture attempted to examine the C-R relationship at the low end of CO concentrations through a threshold analysis. Samoli et al. (2007) in their examination of the association between short-term exposure to CO and mort ality conducted an ancillary analysis to examine the potential presence of a CO threshold. In this analys is the authors compared city-specific models to the threshold model, which consiste d of thresholds at 0.5 mg/m 3 

## (0.43 ppm) increments. Samoli et al. (2007) then computed the deviance between the two mode ls and summed the deviances for a given threshold over all cities. While the minimum deviance suggested a potential threshold of 0.43 ppm (the lowestthreshold examined), the comparison with the linear no-threshold model indicated weak evidence (pvalue >0.9) for a threshold. However, determining the presence of a threshold at the very low range of CO concentrations (i.e., at 0.43 ppm) in this data set is challenging, because, in seven of the 19 European cities examined, the lowest 10% of the CO distribution was at or above 2 mg/m 3 

## (1.74 ppm). By only using the 12 cities in the analysis that had minimum CO concentrations approaching 0.5 mg/m 3 

## (0.43 ppm), a limited number of observations were examined around the threshold of interest, which subsequently contributed to the inability to draw conclusions regarding the potential presence of a threshold with any certainty.



# 2.4.2. Potentially Susceptible and Vulnerable Subpopulations

The examination of both susceptible and vulnerable subpopulations to CO exposure allows for the NAAQS to provide an adequate margin of saf ety for both the general population and sensitive subpopulations (see Section 5.7 for a more deta iled discussion). During the evaluation of the CO literature, numerous studies were identified that ex amined whether underlying factors increased the susceptibility or vulnerability of an i ndividual to CO-related health effect s. In this ISA, a susceptible subpopulation is defined as those individuals with intrin sic biological characteristics that might exhibit an adverse health effect to a pollutant at concentrations lower than those needed to elicit the same response in the general population or those individuals that mi ght elicit a more adverse health effect at the same concentration. A vulnerable subpopul ation is defined as those individuals with external, nonbiological factors that increase the risk of adverse health effect s, such as differential exposure or living at altitude.

March 2009 2-14 DRAFT—DO NOT QUOTE OR CITE

## The most important susceptibility characteristic for increased risk due to CO exposure is CAD. Individuals with heart disease may be at a greater risk from CO exposure since they may already have compromised O 2 

## delivery. CO is notable among air pollutants because it is especially harmful in individuals with impaired cardiovascular systems. Persons with a normal cardiovascular system can tolerate substantial concentrations of CO, if they vasodilate in response to the hypoxemia produced by CO. In contrast, individuals unable to vasodilate in response to CO exposure may show evidence of ischemia at low concentrations of COHb. Many of the controlled human exposure studies have focused on individuals with IHD. Other medical conditions th at confer increased susceptibility include obstructive lung disease, which impairs airflow to the lungs, and conditions such as anemia that alter the blood O 2 

## carrying capacity or content and result in a greater risk from COHb induced hypoxia and decreased tissue O 2 

## delivery. Age is important for susceptibility in olde r adults, who have an increased elimination time for COHb. Newborns can also be considered a su sceptible subpopulation due to critical phases of development and differences between fetal a nd maternal CO pharmacokinetics in utero. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Subpopulations considered vulnerable to CO-rela ted health effects include people who spend a considerable amount of time in or near traffic, liv e at high altitude, exercise and/or use medications which may increase endogenous CO production. Individuals that spend a substantial amount of time on or near heavily traveled roadways, such as commuters and those living or working near freeways, are likely to experience elevated CO concentrations and theref ore constitute a potentially vulnerable subpopulation due to differential exposure. Vuln erable subpopulations also include individuals at high altitude, who undergo physiologic changes that favor increased CO uptake and COHb formation. Exercising individuals are also considered vulnerable becau se exercise facilitates CO uptake and transport by increasing gas exchange efficiency and the COHb el imination rate decreases with physical activity. Individuals who use certain medications which ma y increase endogenous CO pr oduction and the baseline level of COHb are also considered vul nerable to CO-related health effects.

March 2009 2-15 DRAFT—DO NOT QUOTE OR CITE

# Chapter 3. Source to Exposure

3.1. Introduction

This chapter contains basic information about c oncepts and findings in atmospheric sciences and exposure assessment pertaining to CO levels rele vant for establishing a foundation for the detailed discussions of health effects data in subsequent ch apters. Section 3.2 provides an overview of the sources of CO and examples of their spatial distribution. Atmospheric chemistry involved in the production and removal of CO by oxidation processes is discussed in Section 3.3. Descriptions of CO measurement methods and monitor siting requirements and locations ar e presented in Section 3.4. Data for ambient CO concentrations are characterized in Section 3.5. Policy relevant backgrou nd concentrations of CO, i.e., those concentrations defined to result from uncontrollable emissions, are also presented in Section 3.5. Finally, factors related to human exposure to CO are discussed in Section 3.6. 

# 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 

# 3.2. Sources and Emissions of CO

CO is a colorless, odorless, tasteless gas consis ting of one carbon (C) atom covalently bonded to one O 2 

## atom; its molar mass is 28.0101 g/mol. CO is formed primarily by incomplete combustion of carbon-containing fuels and photochemical reactions in the atmosphere. In general, any increase in fuel O 2 

## content, burn temperature, or mixing time in the combustion zone will tend to decrease production of CO relative to CO 2 

## ; hence CO emissions from large fossil-fueled power plants are typically very low since the boilers at these plants are tuned for highly efficient combustion with the lowest possible fuel consumption. Internal combustion engines used in mobile sources, by contrast, have widely varying operating conditions and, thus, inherently higher and varying CO formation. Figure 3-1 lists CO emissions totals in tons segrega ted by individual source sectors in the U.S. for 2002, which is the most recent publicly available data. In the U.S., CO emissions data are tracked in the National Emissions Inventory (U.S. EPA, 2006a), a composite of data from various sources including industries and state, tribal, and local air agencies. NEI data are collected for a ll states, the District of Columbia, the U.S. territories of Puerto Rico and Virg in Islands, and some of the territories of federally recognized American Indian nations. Different data s ources use different data collection methods, most of which are based on engineering calculations and es timates 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 mob ile source emitters (U.S. EPA, 2007). Although these

March 2009 3-1 DRAFT – DO NOT CITE OR QUOTE

## estimates are generated using well-established appro aches, uncertainties are inherent in the emission factors and models used to represent sources for wh ich emissions have not been directly measured. 

# 1 2 3 4 5 6 7 8 9 10 Nationally, on-road mobile sources in the NEI constitu ted more than half of total CO emissions in 2002, or ~63 MT of ~109 MT total. For this reason, high concentrations of CO can often occur in areas of heavy traffic. In metropolitan areas in the U.S., fo r example, as much as 70-75% of all CO emissions came from on-road vehicle exhaust in the 2002 NEI (U.S. EPA, 2006a). The majority of these on-road CO emissions derive from gasoline-powered vehicles since the O 2 

## content, pressure, and temperature required for diesel fuel ignition result in much less CO production. When the emissions from incomplete combustion of fuels powering non-road mobile sourc es were included, all mobile sources accounted for ~80% of total CO emissions in the U.S. in 2002; see Figure 3-1.

Nationwide Emissions 2002

0 0 2

2 1 1 3 22 15 63

0 100 

### Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (MT)

Source: U.S. EPA (2006a) 

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

Figure 3-2 shows present and historical CO emissions from the traditionally inventoried anthropogenic source categories: (1) fuel combustion, which includes emissions from coal-, gas-, and oilfired power plants and industrial, commercial, and institutional sources, as well as residential heaters (e.g., wood-burning stoves) and boilers; (2) industr ial processes, which includes chemical production, 

# 11 12 13 14

March 2009 3-2 DRAFT – DO NOT CITE OR QUOTE

## petroleum refining, metals productio n, and industrial processes other than fuel combustion; (3) on-road vehicles, which includes cars, trucks, buses, and motorc ycles; and (4) non-road vehicles and engines, such as farm and construction equipment, lawnmowers, chainsaws, boats, ships, snowmobiles, aircraft, locomotive, and others. Using these NEI data, trends in the national CO emissions can be computed and compared over time. So, for example, the national-scal e estimated anthropogenic CO emissions decreased 35% between 1990 and 2002; see Figure 3- 2. The trend plot in Figure 3-2 demonstrates that controls in the on-road vehicle sector have produced nearly all the national-level CO reductions since 1990. Data are presented here for 1990 and from 1996 to 2002 becau se only the 1990 data has been updated to be comparable to the more recent inventories made since 1996. 

# 1 2 3 4 5 6 7 8 9

Source: U.S. EPA (2006a, 2008c) 

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

With the exception of this downward trend resulti ng from emissions controls, anthropogenic CO emissions demonstrate less intera nnual variability than biogenic emi ssions (Bergamaschi et al., 2000). Several recent reports using bot h ambient concentrations and fuel-based emissions estimates have explored this annual-to-decadal emissions decrease in anthropogenic CO in finer detail. They included Harley et al. (2001; 2005), Parrish et al. (2002; 2006), Pollack et al. (2004), and Mobley et al. (2005). The consistent conclusion from those investigations has b een that annual average U.S. on-road vehicle CO emissions decreased at a rate of ~5% per year since the early 1990s. Additional analyses by Harley et al. (2005), Parrish et al. (2002) and Parrish (2006) were al so consistent with the suggestion in Pollack et al. (2004) that the EPA MOBILE6 vehicle emissions model ( 

# 10 11 12 13 14 15 16 17 http://www.epa.gov/otaq/m6.htm ) now overestimates vehicle CO emissions by a factor of ~2. 

# 18 19

March 2009 3-3 DRAFT – DO NOT CITE OR QUOTE

## Biogenic emissions can vary widely from year to year (Bergamaschi et al., 2000). CO isotopes, most notably 14 

## CO, have been especially useful in helping refine emissions estimates and partition them between combustion and other sources. Weinstock and Niki (1972) analyzed measurements of the 14 

## C content of CO in Tonawanda, NY (a suburb of Buff alo) obtained by MacKay et al. (1963) during the winter of 1960 and found at that time that a large proprotion of the CO was produced by sources containing modern carbon and not fossil carbon. Th e main source of this modern carbon CO was conjectured by Weinstock and Niki (1972) to have been from photochemical oxidation of CH 4 

## ; however, CH 4 

## has a long atmospheric life time (about 8 year s) but does oxidize to compounds like methyl radical and also is a contributor to O 3 

## formation. Other work suggested that CO sources such as isoprene oxidation and biomass burning were also important. Fo r example, Conny (1998) reviewed studies using measurements of 14 

## CO to characterize winter sources of CO in Las Vegas, NV, and Albuquerque, NM. These studies concluded that the contribution from residential wood burning could have been as high as 30% in the samples collected. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Estimates of non-anthropogenic CO emissions ar e made using the Biogenic Emissions Inventory System (BEIS) model with data from the Biogenic Emissions Landcover Database (BELD) and annual meteorological data; see http://www.epa.gov/ttnchie1/emch/biogenic . National biogenic emissions, excluding fires, were estimated to contribute ~5% of total CO emissions from all sources in 2002; fires in 2002 added another 13%, or ~14.5 MT, to the nationa l CO emissions total. Geogenic emissions of CO also included in this inve ntory, 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 va riability and infrequent though often violent release mean geogenic CO measurem ents are very difficult to make with precision. On a global scale, however, the magnitude of their contribution is small relative to all anthropogenic sources. Photodecomposition of organic matter in ocea ns, rivers, lakes, and other surface waters, and from soil surfaces also releases CO (Goldstein and Galba lly, 2007). However, soils can act as a CO source or a sink depending on soil moisture, UV flux reaching the soil surface, and soil temperature (Conrad and Seiler, 1985). Soil uptake of CO is driven by anaer obic bacteria (Inman et al., 1971). Emissions of CO from soils appear to occur by ab iotic 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 th e release of CO (King, 1999). 

# 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Biomass burning consists of wildfires and the in tentional 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. Gl obally, most wildfires may be ignited directly as the result of human activities leaving only 10 to 30% initiated by lightning (Andreae, 1991). However, because fire management practices suppress natural wild fires, the buildup of fire fuels increases the

March 2009 3-4 DRAFT – DO NOT CITE OR QUOTE

## susceptibility of forests to more severe but less frequent fires in the future. There is considerable uncertainty in attributing the fraction of wildfire emissions to human activities because the emissions from naturally occurring fires that would have been present in the absence of fire suppression practices are not known. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Biomass burning also exhibits strong seasonality an d interannual variability (van der Werf et al., 2006), with most biomass burned during the local dry seas on. This is true for both prescribed burns and wildfire. The unusually warm and dry weather in central Alaska and western Yukon in the summer of 2004, for example, contributed to the burning of 11 million acres there. These fires, the largest on record for this region, produced CO emissions easily tracked by the Measurement of Pollution in the Troposphere (MOPITT) instrument on NASA’s Terra satellite; see Figure 3-3. The high CO concentration measured by MOPITT coincided with the surface locati on of fires using aerosol plumes identified by the Moderate Resolution Imaging Spectroradiometer (MOD IS) also on Terra. Subsequent modeling by Pfister et al. (2005) showed that the CO contribution from these fires in July 2004 was 30 (±5) teragrams (Tg) that summer, or in the range of the total U.S. anthropogenic CO emissions during the same time. The smoldering phase of combustion yields higher CO emissions than the flaming phase. Using controlled combustion chamber experiments Lobert et al. (1991) found that with a wide variety of vegetation types, on average, 84% of the CO fro m biomass fires was produced during the smoldering phase and 16% during the flaming phase of combustion. CO emissions data for EPA’s ten administrative Regi ons in the U.S. depicted in Figure 3-4 show a more nuanced view of the national concentrations a nd trends described just above. Net anthropogenic CO emissions were estimated to have declined in all EPA Regions between 1990 and 2002 with the largest decrease (10.8 MT) occurring in Region 9. On still finer scales, CO emissions from on-road mobile sources or from fires can dominate in different places across the U.S. Figure 3-5 illustrates th is variability with CO state-level emissions total and selected county totals in 2002 for Colorado. (Annex A includes analogous data for Alaska, Utah, Massachusetts, Georgia, California, and Alabama. ) In Colorado, emissions from fires and on-road vehicles were nearly equal: ~0.9 MT from fires a nd ~1.1 MT from on-road vehicles, though emissions varied strongly across counties with urban Denver C ounty dominated by on-road vehicle emissions at 71% and rural Garfield County dominated by fire emissions at 67%.

March 2009 3-5 DRAFT – DO NOT CITE OR QUOTESource: Fishman et al. (2008) 

# Figure 3-3. CO concentrations measured by satellite at the 700 hectoPascal l evel (~10,000 feet above sea level) from MOPITT for the peri od 15-23 July 2004 during intense wildfires in Alaska and Yukon.

Source: U.S. EPA (2006a, 2008c) 

# Figure 3-4. Trends in sub-national CO emissi ons in the 10 U.S. EPA Regions for 1990 and 1996 to 2002.

March 2009 3-6 DRAFT – DO NOT CITE OR QUOTEDenver County

Garfield County



# Colorado State Emissions 2002

53 334 527 6,838 7,290 25,743

78,308 385,460 966,816 1,110,980

0 1,400,000 

## Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Denver County Emissions 2002

0 36 5 305 391 905 1,864 48,658 0 129,554

0 200,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Garfield County Emissions 2002

0 3 0 135 45 2,239 3,121 5,607 64,816

20,309

0 200,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) 

# Figure 3-5. CO emissions density map and dist ribution for the state of Colorado, and for selected counties in Colorado.

March 2009 3-7 DRAFT – DO NOT CITE OR QUOTE

# 3.3. Physics and Chemistry of Atmospheric CO

In addition to being emitted directly by combustion sources, CO is produced by photooxidation of methane (CH 4 

## ) and other VOCs including nonmethane hydrocarbons (NMHCs) in the atmosphere, and of organic molecules in surface waters and soils. CH 4 

## oxidation is summarized in the following reaction

sequence: 

# 1 2 3 4

2 2 2 2 2 2 2 2 2 2 3 2 3 23 23 23 23 4 

# )( )(

HOCOOHCO OHHCO OHOCHor HHCOhOCHor COHhOCH HOOCHOOCH NOOCHNOOCH MOCHMOCH OHCHOHCH

+→+ +→+ +→+ +→+ +→+

+→+ + →++ + → +

ν ν

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 where M is a reaction mediator providing collisional energy but is neither created nor destroyed. Photolysis of formaldehyde (CH 2 

## O) proceeds by two pathways. The first produces molecular hydrogen (H 2 

## ) 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 O 2 

## to form hydroperoxy radical (HO 2 

## ) and CO. Reaction of methyl peroxy radical (CH 3 

## O 2 

## ) with HO 2 

## radicals (reaction not shown) to form methyl hydroperoxide (CH 3 

## OOH) is also operative, especially in low oxides of nitrogen (NO X 

## ) conditions. Heterogeneous removal of the water-soluble intermediate products CH 3 

## OOH, CH 2 

## O, and radicals will decrease CO yields from CH 4 

## oxidation. While oxidation of CH 2 

## O nearly always produces CO and some small quantities of formic acid (CH 2 

## O 2 

## ) in the reaction of CH 2 

## O with HO 2 

## (not shown here), oxidation of acetaldehyde (CH 3 

## CHO) does not always yield two CO molecules. Reaction of CH 3 

## CHO with OH can yield acetyl radicals (CH 3 

## CO) which then will participate with O 2 

## in a termolecular recombination reaction to form peroxyacyl radicals, which then can react with nitric oxide (NO) to form CH 3 

## and CO 2 

## ; or the peroxyacyl radicals can react with NO 2 

## to form peroxyacetyl nitrate (PAN), CH 3 

## CO 3 

## NO 2 

## . In this way, one carbon atom is oxidized directly to CO 2 

## without passing through CO. The yield of CO from these pathways depends on the OH concentration and the photolysis rate of CH 3 

## CHO, as well as on the abundance of NO, since peroxyacyl radicals also will react with other odd hydrogen radicals like OH. Estimating the CO yield from oxidation of hydrocarbons (HCs) larger than CH 4 

## requires computing the yields of CH 2 

## O, CH 3 

## CHO, CH 3 

## CO, 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-ph ase kinetics for many HCs with more than a few

March 2009 3-8 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 carbons is still unknown, and this is especially the case for several im portant classes of VOCs including the aromatics, biogenic HCs including isoprene, and their intermediate oxidation products like epoxides, nitrates, and carbonyls. It has long been known that as much as 30% of the carbon in HCs in many urban areas is in the form of aromatics largely from mob ile sources since gasoline contains significant quantities of aromatics (Grosjean and Fung, 1984; Seila et al., 1989). Yet mass balance analyses performed on irradiated smog chamber mixtures of aromatic HCs indicate that only about one-half of the carbon is in the form of compounds that can be identified. In a ddition, 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 CH 4 

## , for example, is ~0.9 on a per carbon basis (Kanakidou and Crutzen, 1999). Yields from other compounds range from less than 0.1 for anthropogenic alkanes (Altshulle r, 1991) to ~0.7 for some other non-CH 4 

## HCs; see Kanakidou and Crutzen (1999) for CO yields from other HCs. The major pathway for removal of CO from the atmosphere is reaction with OH to produce CO 2 

## and H radicals that rapidly combine with O 2 

## to form HO 2 

## radicals with a rate constant at 1 atm in air of ~2.4 × 10 -13 

## cm 3 

## /molecule/s (Finlayson-Pitts and Pitts Jr). The mean tropospheric photochemical lifetime ( τ ) of CO in the northern hemisphere is ~57 days (Khalil and Rasmussen, 1990; Thompson and Cicerone, 1986). Owing to variation in atmospheric water vapor, OH concentration, and insolation, shorter τ are found nearer the tropics and longer ones at higher latitud es. During winter at high latitudes CO has nearly no photochemical reactivity on urban and regional scales. The CO τ is shorter than the characteristic time scale for mixing between the hemispheres of ~1 year; he nce a large gradient in concentrations can exist between the hemispheres. In addition, the CO τ at high latitudes is long enough to result in much smaller gradients between 30° latitude and the pole of either hemisphere. The typical residence times of CO in urban areas when assuming a diel-average OH concentration of 3 × 10 6 

## /cm 3 

## in urban areas is ~16 days, so CO will not be destroyed in urban areas where it is emitted and will likely be mixed on continental and larger scales. OH concentrations are orders of magn itude lower in indoor environments and so CO will generally not be destroyed by indoor air reactions. Recent data do not alter the current well-establishe d understanding of the role of urban and regional CO in continental and global-scale chemistry outlined in the 2000 CO AQCD (U.S. EPA, 2000), and subsequently confirmed in the global assessments of climate change by the Intergovernmental Panel on Climate Change (IPCC, 2001, 2007). CO is a we ak contributor to greenhouse warming because its fundamental absorption band near 4.63 μm is fa r from the spectral maximu m of earth’s longwave radiation at ~10 μm. However, because reaction with CO is also the major sink for OH, changes in CO concentrations can lead to chang es in the concentrations of other trace gases whose loss processes involve OH attack. Some of these trace gases, CH 4 

## , for example, absorb infrared radiation from the earth’s surface and contribute to the greenhouse effect directly; othe rs, including the hydrochlorofluorocarbons (HCFCs)

March 2009 3-9 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27 and methyl chloride and methyl bromide, can deplete stratospheric O 3 

## , increasing the surface-incident UV flux. This indirect effect of CO on stratospheric O 3 

## concentrations is opposite in sign to the effect of CO on O 3 

## in the troposphere where CO reacts in a manne r similar to other VOCs in the presence of NO X 

## and UV to create O 3 

## . (See the detailed description of O 3 

## formation from VOCs and NO X 

## in the 2008 NO X 

## ISA (U.S. EPA, 2008e). The long chemical lifetime and one-to-one stoichiometry of CO oxidation (whereby one molecule of CO converts only one molecule of NO to NO 2 

## ) means that CO has significantly lower O 3 

## forming potential than other VOCs in the troposphere. Carter (1998) computed a maximum incremental reactivity for CO of 0.07 g O 3 

## for 1 g CO, as compared to reactivities of total on-road vehicle exhaust emissions in the range of 3 to 4. However, because th e total mass of CO emissions is substantially greater than those of the other VOCs with higher carbon numbers and faster reactivities, CO can contribute greatly to O 3 

## formation even though its photochemical pro cessing is slow. Using data from instrumented models including that of Jeffries (1995), NRC (1999) estimated, for example, that CO can contribute 15- 25% of the total O 3 

## forming potential of gasoline exhaust emissions; this figure varies from region to region. The contribution of CO to urban and regional O 3 

## concentration is often less than 10% owing to its very slow reactivity on these scales and to lo cally variable radical concentration ratios. Because the greenhouse warming effects from CO ar e nearly completely indirect, and because CO concentrations are spatially hete rogeneous, neither the IPCC nor EPA computes global warming potentials (GWPs) for CO, just as they do not for tropospheric O 3 

## , NO, NO 2 

## , or VOCs (U.S. EPA, 2008a). Additionally, urban and regiona l-scale oxidation of CO to CO 2 

## under current atmospheric conditions proceeds very slowly and IPCC considers production of CO 2 

## through this pathway to be double counting of CO effects (IPCC, 2007).



# 3.4. Ambient Measurements

3.4.1. Ambient Measurement Instruments

To promote uniform enforcement of the air quality standards set forth under the Clean Air Act, EPA has established provisions in the Code of Federal Regulations (CFR) under which analytical methods can be designated as federal reference methods or federal equivalent me thods (FRM or FEM, respectively). Measurements for determinations of NAAQS compliance must be made with FRMs or FEMs. As of December 2008, 19 automated FRMs and no FEMs had been approved for CO ( http://www.epa.gov/ttn/amtic/criteria.html ). 

# 28

March 2009 3-10 DRAFT – DO NOT CITE OR QUOTE

## All EPA FRMs for CO operate on the principle of nondispersive infrared (NDIR) detection and can include the gas filter correlation (GFC) methodology. NDIR is an automated and continuous method based on the specific absorption of infrared radiation by the CO molecule. Most commercially available analyzers incorporate a gas filter to minimize interferences from other gases and operate near atmospheric pressure. The most sensitive trace-level versions of these instruments can detect minimum CO concentrations of ~0.04 ppm; the required lower detection limit for FRMs in the EPA network is 1.0 ppm (40 CFR 53.20 Table B-1). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 NDIR is based on the physics of CO’s characteristic infrared absorption near 4.63 μ m. 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 chem icals, are reasonably independent of ambient air temperature changes, are sensitive over wide concentra tion ranges, and have fast response times. Earlier concerns over zero-drift and nonlinear span r esponses have been addressed. An extensive and comprehensive review of NDIR, GFC, and alterna tive, non-FRM techniques for CO detection including tunable diode laser spectroscopy, gas chromatograp hy, mercury liberation, and resonance fluorescence was made for the 2000 CO AQCD (U.S. EPA, 2000), a nd 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 fre quently 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 CO 2 

## , and very small interference from water vapor. During operation, air flows continuously through a sample cell. Radiation from the infrared source is directed by optical transfer elements through two main optical subsystems: (1) the rotating gas filter and (2) the optical multipass (sample) cell. The beam exits the sample cell through an interference filter (FC), which limits the spectral passband to a few of the strongest CO absorption lines. Detection of the transmitted radiation occurs at the infrared detector . The gas correlation cell is constructed with two compartments, one filled with 0.5 atm CO, and a second with pure N 2 

## . Radiation transmitted through the CO is completely attenuated at the wavelengths wh ere CO absorbs strongly. The radiation transmitted through the nitrogen gas (N 2 

## ) is reduced by coating the exit window of the cell with a neutral attenuator so that the amounts of radiation transmitted by th e two cells are made approximately equal in the passband that reaches the detector. In operation, radi ation 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 appreciab ly changed, whereas that through the N 2 

## cell is changed. This imbalance is linearly related to CO concentrations in ambient air.

March 2009 3-11 DRAFT – DO NOT CITE OR QUOTE

# 3.4.2. Ambient Sampling Network Design

3.4.2.1. Monitor Siting Requirements

CO monitoring is included at all active NCore s ites and State and Local Air Monitoring Stations (SLAMS) where continued measurements of CO using FRM are required until discontinuation is approved by the EPA Regional Administrator. Where SLA MS CO monitoring is required, at least one of the sites must be a max concentration site for that specific area. In 2007, there were ~385 CO monitors reporting values to the EPA Air Quality System (AQS ) database. Where CO monitoring is ongoing, 40 CFR Part 58 requires at least one CO monitor to capture maximum levels in a give n region. This is often done with a monitor situated at the CFR-defined micr oscale distance (2-10 m) from the side of a roadway for CO. Microscale monitor locations also have sample inlets mounted at 3 ± 0.5 m above ground level, unlike the monitors sampling for larger scales, whose in let heights can vary between 2 and 15 m. For the CFR-defined middle (up to 500 m) and neighborhood (~500 m-4 km) scale monitoring, the minimum monitor distance from a major roadway is inversely re lated to the average daily traffic counts on that roadway to ensure that measurements are not substantially influenced by any one roadway. For example, the minimum distance of a middle scale CO monitor fro m a roadway with an average daily traffic count of 50,000 vehicles per day is 135 m. More detail on s iting requirements can be found in 40 CFR Part 58 Appendices D through E. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

16 17 18 19 20 21 22 23 24 25 26 

# 3.4.2.2. Spatial and Temporal Coverage

Figure 3-6 depicts the distribution of the ~385 regulatory CO monitors operating in the U.S. in 2007. Data from 285 of the ~385 CO monitors operati ng year-round in the years 2005-2007 met the 75% data completeness criterion for inclusion in the mu lti-year ambient data analyses for this assessment. The greatest density of monitors is in the CSAs for Los Angeles, CA and San Francisco, CA, and along the Mid-Atlantic sea board. Monitors are also located in regions where biomass burning is more prevalent, such as Anchorage, AK, but not all of these monito rs report values from all seasons of all years. Eleven metropolitan regions were chosen for closer investigation of monitor siting based on their relevance to the health studies assessed in subsequent chapters of this ISA and to demonstrate specific points about geospatial distributions of CO emissions and concentrations. These regions were: Anchorage, AK; Atlanta, GA; Boston, MA; Denver, CO; Houston, TX; Los Angeles, CA; New York City, NY; Phoenix, AZ; Pittsburgh, PA; Seattle, WA; and St. Loui s, 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 hen ce which monitors, to include for each metropolitan 

# 27 28

March 2009 3-12 DRAFT – DO NOT CITE OR QUOTE

## region. 1 

## As an example, Figures 3-7 through 3-10 display CO monitor density with respect to population density (for total population and el derly adults aged 65 and over) for Phoenix and Pittsburgh. (Annex A includes analogous plots for the other nine metropolitan regions.) 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Although ambient monitors for CO and other criteri a pollutants are explicitly not located in order to monitor population exposures, it is instructive to test the utility of the current network for characterizing this exposure. Tables 3-1 and 3-2 show the population density around CO monitors for the total population and for elderly adults aged 65 and over for each CSA/CBSA. The percentage of population within specific radii of the monitors for each city was, for the most part, similar between the total and elderly populations. In the cases of Anchorage, Denver, Phoenix, and St. Louis however, the percentage of the elderly population within given radii of the monitors was considerably different compared with the total population. Between-city disparities in population density were larger. Los Angeles, with 85%, and Denver, with 68%, had the largest proportion of the total population within 15 km of a monitor. Seattle, with 18%, had the lowest population coverage in large part because ambient CO concentrations there require only a single CO monitor. As concerns the elderly population, Los Angeles, at 83%, Anchorage, at 73%, and Denver, at 70%, had the greatest population coverage within 15 km of a monitor, whereas Seattle, at 18%, again, had the lowest coverage. Proximity to monitoring stations is considered further in sections 3.5 and 3.6 regarding spatial variability within cities. Figures 3-7 through 3- 10 show that multiple CO monitors in Phoenix and Pitts burgh were in the city center, and that Pittsburgh also had monitors in outlying areas of moderate to low population density. The CO monitors in Phoenix appear to provide good coverage of areas of the highest total population density, while areas of high elderly population density we re located at greater distances from the monitors. CO monitors in Pittsburgh appear to sample areas of high population densit y well for both the total and elderly populations. Figures 3-13 and 3-15 in section 3.5 and additional figures in Annex A show the locations of CO monitors for the 11 CSAs/CBSAs in relation to ma jor roadways, including Interstate highways, U.S. highways, state highways, and other major roadways required for traffic network connectivity. In most cases, the monitors were concentrat ed near the center of the CSA/CBSA. 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 dat a and

replaces the older Metropolitan Statistical Area (MSA) definition fr om 1990. The CSA represents an aggregate of adjacent CBSAs tied by specific commuting behaviors. The broader CS A definition was used when selecting mon itors for the cities listed above with the exception of Anchorage and Phoenix, which are not contai ned within a CSA. Therefore, the smaller CBSA definition was used for these metropol itan areas.



### March 2009 3-13 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-6. Map of ~385 CO monitor locations in the U.S. in 2007. Locations are indicated with triangles: filled triangles show locations of the 285 monito rs used in data analysis for this assessment; open triangles are at lo cations with monitors which did not meet the data completeness requirements for analysis; blue lines mark the boundaries of the 11 CSAs/CBSAs used in the data analysis for this assessment.

March 2009 3-14 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-7. Map of CO monitor lo cations with respect to population density in the Phoenix, AZ CBSA, total population.

March 2009 3-15 DRAFT – DO NOT CITE OR QUOTE

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

March 2009 3-16 DRAFT – DO NOT CITE OR QUOTE

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

March 2009 3-17 DRAFT – DO NOT CITE OR QUOTE

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

March 2009 3-18 DRAFT – DO NOT CITE OR QUOTE

# Table 3-1. Proximity to CO monito rs for the total population by city.

Region Total CSA/ CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km

N N % N % N % N % Anchorage, AK 352,225 5,391 1.53 131,608 37.36 212,834 60.43 239,842 68.09 Atlanta, GA 5,316,742 5,480 0.10 149,772 2.82 672,701 12.65 1,444,986 27.18 Boston, MA 7,502,707 95,732 1.28 1,180,054 15.73 2,432,846 32.43 3,418,353 45.56 Denver, CO 2,952,039 26,096 0.88 497,598 16.86 1,091,444 36.97 1,720,360 58.28 Houston, TX 5,503,320 2,9068 0.53 599,796 10.90 1,669,117 30.33 2,506,830 45.55 Los Angeles, CA 17,655,319 202,340 1.15 4,064,309 23.02 11,928,427 67.56 15,074,972 85.38 New York, NY 22,050,940 201,350 0.91 3,711,369 16.83 8,385,801 38.03 12,454,837 56.48 Phoenix, AZ 3,818,147 47,478 1.24 503,433 13.19 1,033,102 27.06 1,581,887 41.43 Pittsburgh, PA 2,515,383 29,136 1.16 369,965 14.71 895,252 35.59 1,359,596 54.05 Seattle, WA 3,962,434 4,814 0.12 94,649 2.39 279,976 7.07 699,490 17.65 St. Louis, MO 2,869,955 16,638 0.58 255,499 8.90 886,412 30.89 1,303,636 45.42



# Table 3-2. Proximity to CO monitors fo r adults aged 65 and older by city.

Region Total CSA/ CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km

N N % N % N % N % Anchorage, AK 17,742 361 2.03 8,986 50.65 12,038 67.85 12,990 73.22 Atlanta, GA 362,201 423 0.12 12,758 3.52 54,148 14.95 111,232 30.71 Boston, MA 945,790 8,272 0.87 131,198 13.87 297,392 31.44 430,502 45.52 Denver, CO 232,974 2,541 1.09 42,760 18.35 102,783 44.12 163,682 70.26 Houston, TX 377,586 1,703 0.45 42,312 11.21 130,567 34.58 182,049 48.21 Los Angeles, CA 1,626,663 17,974 1.10 380,079 23.37 1,069,188 65.73 1,355,461 83.33 New York, NY 2,710,675 29,534 1.09 427,601 15.77 940,121 34.68 1,429,215 52.73 Phoenix, AZ 388,150 2,877 0.74 35,839 9.23 77,244 19.90 125,300 32.28 Pittsburgh, PA 449,544 5,383 1.20 66,967 14.90 166,440 37.02 255,220 56.77 Seattle, WA 390,372 556 0.14 12,142 3.11 3,1036 7.95 69,858 17.90 St. Louis, MO 358,747 3,203 0.89 42,890 11.96 127,274 35.48 184,491 51.43



### March 2009 3-19 DRAFT – DO NOT CITE OR QUOTE

# 3.5. Environmental Concentrations

3.5.1. Spatial Variability

3.5.1.1. National Scale

The current NAAQS designates that the level of the NAAQS can only be exceeded once per year at a given location. Figures 3-11 and 3-12 show th e second-highest 1-h and second-highest 8-h countyaverage CO concentrations, respectively, over the U.S. along with estimates of the fraction of U.S. total population exposed to those concentrations. Although 93% of the U.S. counties are not represented in AQS reporting, based on their population densities and proximity to sources, those counties are not expected to have higher concentrations than the ones analyzed here in the absence of extreme events such as wildfires. Continuous hourly averages are reported from U.S. monitoring stations. 1-h and 8-h CO data were available for 243 counties and autonomous c ities 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 monito red location reported a second-highest 1-h CO concentration above 35 ppm, the level of the current 1-h NAAQS; see Fi gure 3-11. Moreover, only two monitored locations, one in Weber Co., UT and the other in Jefferson Co ., AL (including Birmingham, AL), reported secondhighest 1-h CO concentrations above 18 ppm, or a pproximately one-half the level of the 1-h standard. Figure 3-12 shows that no counties reported second-highest 8-h CO concentrations above 9 ppm, the level of the 8-h NAAQS. Only nine counties reported second -highest 8-h CO concentrations above 4.5 ppm: Jefferson Co., AL, Kay Co., OK, Imperial Co., CA, Weber Co., UT, Philadelphia Co., PA, Anchorage Municipality, AK, Los Angeles Co., CA, Jef ferson Co., OH, and San Diego Co., CA. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

March 2009 3-20 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-11. County-level map of second-highest 1-h avg CO concentr ations in the U.S. in 2007. The bar on the left shows the total U.S. population who live in counties with CO concentrations in the range indicated. No te that approximately 150 million people live in counties with no CO monitors.

March 2009 3-21 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-12. County-level map of second-highest 8-h avg CO concentr ations in the U.S. in 2007. The bar on the left shows the total U.S. population who live in counties with CO concentrations in the range indicated. No te that approximately 150 million people live in counties with no CO monitors.

March 2009 3-22 DRAFT – DO NOT CITE OR QUOTE

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

Percentiles

n Mean Min 1 5 10 25 50 75 90 95 99 Max NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS)

2005-2007 7,180,700 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.2 2.1 39.0 2005 2,391,962 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 1.0 1.3 2.3 22.3 2006 2,402,153 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.2 2.1 35.3 2007 2,386,585 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.1 1.9 39.0 Winter (December - February) 1,752,340 0.6 0.0 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.6 2.7 20.0 Spring (March - May) 1,826,167 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.0 1.7 35.3 Summer (June - August) 1,811,082 0.4 0.0 0.0 0.0 0.0 0.2 0.3 0.5 0.7 0.9 1.5 39.0 Fall (September - November) 1,791,111 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 1.0 1.3 2.2 24.1 NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES)

2005-2007 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.0 1.5 2005 285 0.5 0.0 0.0 0.1 0.2 0.4 0.5 0.6 0.8 0.9 1.3 1.6 2006 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.2 1.4 2007 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.1 1.5 Winter (December - February) 285 0.6 0.0 0.0 0.2 0.2 0.4 0.5 0.7 0.9 1.1 1.5 1.6 Spring (March - May) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.0 1.6 Summer (June - August) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1.1 1.5 Fall (September - November) 285 0.5 0.0 0.0 0.1 0.2 0.4 0.4 0.6 0.8 0.9 1.1 1.5 STATISTICS FOR INDIVIDUAL CSAS/CBSAS ( 2005-2007 ) (N = NUMBER OF OBSERVATIONS) Anchorage a 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 is not av ailable year-round, Anchorage is not inclu ded in the nationwide statistics shown in this table.



# 1 2 3 4 5 6 Table 3-3 contains the distribution of hourly CO measurements reported to AQS for 2005-2007. All monitoring locations meeting the 75% data completen ess criteria have been included in this table. Anchorage did not meet the data completeness criteria since its monitoring sites were required to report CO measurements during the first and fourth quarters of each year. Anchorage was included in the table, however, for an approximate comparison with the other CSAs and CBSAs reporting year-round measurements to AQS. (Anchorage was not, however, included in the national averages shown in the

March 2009 3-23 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 table.) The nationwide mean, median, and inter quartile range for 1-h measurements reported for 2005-2007 were 0.5, 0.4 and 0.4 ppm, respectively, and these statistics did not change by more than 0.1 ppm over the 3-year period. The largest recorded second-highest 1-h concentration, 26.3 ppm, for this period was reported in 2006 in Birmingham, AL (A QS site ID: 0107360004). The absolute highest 1-h concentration, 39 ppm, between 2005 and 2007, was re ported in Ogden, UT (AQS site ID: 490570006). Concentrations are generally highest in the winter (December-February) and fall (September-November) and decrease on average during the spring (March-May) and summer (June-August). Nationwide statistics pooled by site are listed in the center of Table 3-3 and illustrate the distribution of the site average CO concentrations re corded at the 285 monitoring sites for 2005-2007 (see Figure 3-6 for these sites). The site reporting the highest 3-year pooled 1-h avg CO concentration, 1.5 ppm, was located in San Juan, Puerto Rico (AQS site ID: 721270003). The eleven individual CSAs/CBSAs discussed earlier are included in the tabl e, none of which reported concentrations above the value of the 1-h NAAQS. Four of the eleven cities (B oston, Houston, Pittsbur gh 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 ex hibited a 1-h CO distribution shifted higher in concentration when compared to the U.S. average during fall or winter.

March 2009 3-24 DRAFT – DO NOT CITE OR QUOTE

# Table 3-4. Distributi on 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) Anchorage a 1,074 1.1 0.0 0.2 0.2 0.4 0.6 0.9 1.4 1.9 2.4 3.3 4.6 Atlanta 3,229 0.5 0.0 0.1 0.2 0.2 0.3 0.4 0.6 0.8 0.9 1.2 1.6 Boston 7,446 0.4 0.0 0.0 0.1 0.1 0.3 0.4 0.5 0.7 0.8 1.1 2.2 Denver 5,363 0.5 0.0 0.1 0.2 0.2 0.3 0.5 0.6 0.9 1.1 1.5 2.3 Houston 5,188 0.3 0.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.9 1.9 Los Angeles 25,803 0.5 0.0 0.0 0.1 0.1 0.2 0.4 0.6 1.0 1.2 1.7 3.8 New York 9,513 5.1 0.0 0.0 0.1 0.2 0.4 0.5 0.6 0.8 1.0 1.3 2.5 Phoenix 5,348 0.8 0.0 0.1 0.2 0.3 0.4 0.6 1.1 1.6 1.9 2.5 3.4 Pittsburgh 7,497 0.3 0.0 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.7 1.0 1.9 Seattle 1,079 0.8 0.1 0.2 0.3 0.4 0.5 0.7 0.9 1.2 1.4 1.8 2.4 St. Louis 3,216 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.8 1.0 1.9 Not in the 11 cities 230,161 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.8 1.1 1.6 7.0

*CO monitoring is only available for quarters 1 and 4; since m onitoring data is not available year-round, Anchorage is not incl uded in the nationwide statistics shown in this table.



# 1 2 3 4 5 Table 3-4 contains the distribution of 24-h avg CO concentrations derived from the 1-h concentrations reported to AQS and summarized in Table 3-3. The nationwide mean, median, and interquartile range for 24-h avg for 2005-2007 were 0.5, 0.4 and 0.3 ppm, respectively. These were similar to those for the 1-h values. The maximum 24-h avg concentration in these years, 7 ppm, was reported in Birmingham, AL (AQS site ID: 010736004).

March 2009 3-25 DRAFT – DO NOT CITE OR QUOTE

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

Percentiles

n Mean Min 1 5 10 25 50 75 90 95 99 Max NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS)

2005-2007 303,843 0.9 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.8 2.4 3.8 39.0 2005 101,184 1.0 0.0 0.0 0.2 0.3 0.5 0.8 1.3 2.0 2.6 4.1 22.3 2006 101,652 0.9 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.9 2.4 3.9 35.3 2007 101,007 0.8 0.0 0.0 0.1 0.2 0.4 0.7 1.1 1.7 2.1 3.4 39.0 Winter (December - February) 74,144 1.2 0.0 0.0 0.2 0.3 0.5 0.9 1.6 2.5 3.1 4.7 20.0 Spring (March - May) 77,317 0.8 0.0 0.0 0.1 0.3 0.4 0.7 1.0 1.6 2.0 3.0 35.3 Summer (June - August) 76,562 0.7 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.3 1.6 2.5 39.0 Fall (September - November) 75,820 1.0 0.0 0.0 0.2 0.3 0.5 0.8 1.3 2.0 2.5 3.8 24.1 NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES)

2005-2007 285 0.9 0.1 0.1 0.3 0.5 0.6 0.8 1.1 1.5 1.7 2.3 3.9 2005 285 1.0 0.1 0.1 0.4 0.5 0.7 0.9 1.2 1.6 2.0 2.5 3.7 2006 285 0.9 0.1 0.1 0.3 0.5 0.6 0.9 1.1 1.6 1.8 2.3 4.8 2007 285 0.8 0.1 0.1 0.3 0.4 0.6 0.8 1.0 1.4 1.6 2.0 3.1 Winter (December - February) 285 1.2 0.0 0.1 0.4 0.6 0.8 1.0 1.5 2.1 2.5 3.4 4.1 Spring (March - May) 285 0.8 0.1 0.1 0.3 0.4 0.6 0.8 1.0 1.3 1.5 2.1 4.0 Summer (June - August) 285 0.7 0.0 0.1 0.2 0.3 0.5 0.6 0.8 1.1 1.3 2.2 3.3 Fall (September - November) 285 1.0 0.1 0.1 0.3 0.5 0.7 0.9 1.2 1.7 2.0 2.4 4.1 STATISTICS FOR INDIVIDUAL CSAS/CBSAS ( 2005-2007 ) (N = NUMBER OF OBSERVATIONS) Anchorage a 1,074 2.6 0.0 0.3 0.6 0.8 1.3 2.2 3.5 5.0 6.1 7.6 13.1 Atlanta 3,229 0.8 0.0 0.2 0.3 0.3 0.4 0.7 1.1 1.4 1.7 2.2 10.8 Boston 7,446 0.7 0.0 0.1 0.2 0.3 0.4 0.6 0.9 1.2 1.6 2.6 10.0 Denver 5,363 1.2 0.1 0.2 0.4 0.5 0.7 1.0 1.5 2.2 2.7 3.9 9.3 Houston 5,188 0.7 0.0 0.0 0.1 0.2 0.4 0.6 0.8 1.3 1.7 2.6 4.6 Los Angeles 25,803 1.0 0.0 0.1 0.2 0.3 0.5 0.8 1.3 2.0 2.6 4.0 8.4 New York 9,513 0.9 0.0 0.1 0.2 0.4 0.6 0.8 1.1 1.5 1.8 2.5 5.8 Phoenix 5,348 1.9 0.0 0.3 0.5 0.6 0.9 1.6 2.5 3.5 4.1 5.3 7.8 Pittsburgh 7,497 0.6 0.0 0.0 0.0 0.1 0.2 0.5 0.8 1.1 1.4 2.0 6.7 Seattle 1,079 1.5 0.2 0.4 0.5 0.7 0.9 1.3 1.8 2.4 2.9 4.3 5.9 St. Louis 3,216 0.8 0.0 0.1 0.3 0.4 0.5 0.6 0.9 1.3 1.7 2.7 5.7 Not in the 11 cities 230,161 0.9 0.0 0.0 0.1 0.2 0.4 0.7 1.2 1.8 2.4 3.8 39.0

*CO monitoring is only available for quarters 1 and 4; since m onitoring data is not available year-round, Anchorage is not incl uded in the nationwide statistics shown in this table.



# 1 2 3 4 Table 3-5 contains the distribution of 1-h daily max CO concentrations derived from 1-h values reported to AQS for all monitors meeting the inclus ion criteria described earlier. The nationwide mean, median, and interquartile range for 1-h daily max con centrations reported for 2 005-2007 were 0.9, 0.7 and 0.8 ppm, respectively.

March 2009 3-26 DRAFT – DO NOT CITE OR QUOTE

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

Percentiles

n Mean Min 1 5 10 25 50 75 90 95 99 Max NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS)

2005-2007 303,843 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.3 1.7 2.6 10.9 2005 101,184 0.7 0.0 0.3 0.3 0.3 0.3 0.6 0.9 1.4 1.8 2.8 9.7 2006 101,652 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.3 1.7 2.6 9.8 2007 101,007 0.6 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.2 1.5 2.3 10.9 Winter (December - February) 74,144 0.9 0.0 0.3 0.3 0.3 0.4 0.7 1.1 1.7 2.1 3.2 9.8 Spring (March - May) 77,317 0.6 0.0 0.3 0.3 0.3 0.3 0.5 0.7 1.1 1.3 2.0 9.6 Summer (June - August) 76,562 0.5 0.0 0.3 0.3 0.3 0.3 0.4 0.6 0.9 1.1 1.7 10.9 Fall (September - November) 75,820 0.7 0.0 0.3 0.3 0.3 0.3 0.6 0.9 1.4 1.8 2.7 9.0 NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES)

2005-2007 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.7 2.1 2005 285 0.7 0.3 0.3 0.3 0.4 0.5 0.6 0.9 1.1 1.4 1.9 2.2 2006 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.8 1.1 1.2 1.8 2.4 2007 285 0.6 0.2 0.3 0.3 0.4 0.5 0.6 0.7 1.0 1.1 1.6 2.0 Winter (December - February) 285 0.9 0.2 0.3 0.4 0.4 0.6 0.8 1.1 1.4 1.7 2.4 2.6 Spring (March - May) 285 0.6 0.2 0.3 0.3 0.4 0.4 0.5 0.7 0.9 1.1 1.6 2.2 Summer (June - August) 285 0.5 0.2 0.3 0.3 0.3 0.4 0.5 0.6 0.8 0.9 1.5 2.0 Fall (September - November) 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.9 1.2 1.3 1.8 2.2 STATISTICS FOR INDIVIDUAL CSAS/CBSAS ( 2005-2007 ) (N = NUMBER OF OBSERVATIONS) Anchorage a 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 av ailable year-round, Anchorage is not inclu ded in the nationwide statistics shown in this table.



# 1 2 3 4 5 Table 3-6 contains the distribution of 8-h daily max concentrations derived from the 1-h CO concentrations reported to AQS. This was done by first calculating the average concentration for each successive 8-h period, thereby producing 24 8-h avg per day. The maximum of these values for a given monitor within a given day (midnight-to-midnight) w as used as the 8-h daily max statistic for that monitor and day. The nationwide mean, median, and interquartile range for 8-h daily max concentrations

March 2009 3-27 DRAFT – DO NOT CITE OR QUOTE

# 1 2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 reported for 2005-2007 were 0.7, 0.5, and 0.5 ppm, resp ectively. The highest 8-h daily max concentration, 10.9 ppm, was recorded at a monitor located five miles north of Newkirk, OK (AQS site ID: 400719010).



# 3.5.1.2. Urban Scale

This section covers urban variability in CO con centrations reported to AQS at the individual CSA/CBSA level. Phoenix, AZ and Pittsburgh, PA we re 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 incl uded in Appendix A. Maps of the Phoenix CBSA and Pittsburgh CSA shown in Figures 3-13 and 3-15, respectiv ely, illustrate the location of all CO monitors included in the analyses described above. Letters on th e maps identify the individual monitor locations and correspond with the letters provided in the accompan ying box plots (Figures 3-14 and 3-16) and pairwise comparison tables (Tables 3-7 and 3-8). The box plots for each monitor include the hourly CO concentration median and interquar tile 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 concentrati ons (P90) in ppm, the coefficient of divergence (COD) and the straight-line distance between monitor pairs (d) in km. The COD provides an indication of the variability across the monitoring sites within each CSA/CBSA and is defined as follows:

2

1 

# 1 

# ∑

= 

# ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ −

= p

i ikij ikij jk 

# XX XX

p COD

Equation 3-1



# 18 19 20 21 22 where Xij and Xik represent the observed hourly concentrations for time period i at sites j and k , and p is the number of paired hourly observations. A COD of 0 indicates there are no differences between concentrations at paired sit es (spatial homogeneity), while a COD approaching 1 indicates extreme spatial heterogeneity. Similar maps, box plots and comparis on tables for the nine remaining CSAs/CBSAs described earlier in this chapter along with Phoenix and Pittsburgh are included in Annex A.

March 2009 3-28 DRAFT – DO NOT CITE OR QUOTE

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

March 2009 3-29 DRAFT – DO NOT CITE OR QUOTEA B C D E Mean 0.93 0.84 0.58 0.76 0.79 Obs 25382 25589 25657 25414 25435 SD 0.95 0.88 0.64 0.72 0.64



# Figure 3-14. Box plots illustrating the seasonal distribu tion of 2005-2007 hourly CO concentrations in Phoenix, AZ. Note: 1 = wi nter, 2 = spring, 3 = summer, and 4 = fall on the x-axis. The box plots for each moni tor include the hourly CO concentration median and interquartile range with wh iskers extending from the 5th to the 95th percentile.

March 2009 3-30 DRAFT – DO NOT CITE OR QUOTE

# Table 3-7. Table of inter-sam pler comparison statistics, as de fined in the text, including Pearson r, P90 (ppm), COD and d (km) for each pair of hourly CO monitors reporting to AQS for 2005-2007 in Phoenix, AZ.

Phoenix A B C D E A 1.00 0.89 0.80 0.86 0.84 0.0 0.7 1.1 0.8 0.9 0.0 0.37 0.43 0.39 0.37 0.0 1.6 8.9 3.9 3.5 

### B 1.00 0.81 0.88 0.89 0.0 0.9 0.6 0.7 0.0 0.38 0.34 0.24 0.0 9.4 3.4 4.9 

### C 1.00 0.81 0.85 0.0 0.7 0.6 0.0 0.41 0.36 0.0 6.6 6.8 

### D 1.00 0.83 0.0 0.6 0.0 0.33 

### Legend 0.0 5.2 

### E r 1.00 P90 0.0 COD 0.0

d 0.0



### March 2009 3-31 DRAFT – DO NOT CITE OR QUOTE

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

March 2009 3-32 DRAFT – DO NOT CITE OR QUOTEA B C D E F G Mean 0.28 0.32 0.47 0.28 0.32 0.21 0.07 Obs 25655 25936 25818 25500 25745 25319 25785 SD 0.32 0.26 0.33 0.27 0.26 0.23 0.15



# Figure 3-16. Box plots illustrating the seasonal distribu tion of 2005-2007 hourly CO concentrations in Pittsburgh, PA. Note: 1 = winter, 2 = spring, 3 = summer, and 4 = fall on the x-axis. Median interquar tile range is shown with whiskers extending from the 5th to the 95th percentile.

March 2009 3-33 DRAFT – DO NOT CITE OR QUOTE

# Table 3-8. Table of inter-sam pler comparison statistics, as de fined in the text, including Pearson r, P90 (ppm), COD and d (km) for each pair of hourly CO monitors reporting to AQS for 2005-2007 in Pittsburgh, PA.

Pittsburgh A B C D E F G A 1.00 0.52 0.43 0.05 0.11 0.42 0.18 0.0 0.5 0.6 0.7 0.6 0.5 0.7 0.0 0.69 0.68 0.74 0.72 0.73 0.88 0.0 2.2 1.8 41.9 66.8 34.4 45.8 

### B 1.00 0.73 0.30 0.42 0.29 0.35 0.00 0.40 0.50 0.40 0.50 0.50 0.00 0.39 0.54 0.51 0.62 0.86 0.00 0.70 43.40 68.00 33.60 43.70 

### C 1.00 0.20 0.39 0.25 0.30 0.0 0.7 0.6 0.7 0.8 0.0 0.56 0.51 0.65 0.88 0.0 43.4 68.2 33.3 44.1 

### D 1.00 0.16 0.09 0.02 0.0 0.6 0.6 0.7 0.0 0.57 0.69 0.87 0.0 27.5 75.0 84.1 

### E 1.00 0.26 0.21 0.0 0.5 0.6 0.0 0.68 0.87 0.0 101.0 104.1 

### F 1.00 0.09 0.0 0.5 0.0 0.90 

### Legend 0.0 37.8 

### G r 1.00 P90 0.0 COD 0.0

d 0.0 

# 1 2 3 4 5 6 7 8 9 10 The Phoenix CBSA in Figure 3-13 incorporates an area of 37,786 km 2 

## with the CO monitors densely packed near the urban center. The maximum straight-line distance between CO monitors in the Phoenix CBSA is 9.4 km. By contrast, the Pittsburgh CS A in Figure 3-15 is less than half the size, having an area of 14,627 km 2 

## , but the monitors are farther apart with a maximum straight-line distance between them of 104.1 km. Of the eleven CSAs/CBSAs inv estigated, Phoenix has among the highest correlations across monitors, while Pittsburgh has among the lowest . This discrepancy is partly due to the close proximity of the monitors as noted here, but horizontal distance alone does not explain the range of correlations observed between monitors. For example, the three monitors located in the downtown urban core of Pittsburgh (sites A, B and C; AQS s ite IDs 420030010, 420030031 and 420030038; maximum

March 2009 3-34 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 distance = 2.2 km) exhibited considerably lower correlations (0.43 ≤ r ≤ 0.73) than any of the monitor pairs in Phoenix (0.80 ≤ r ≤ 0.89), located up to 9.4 km apart. The correlation structures for measurements at the monitors in each of the eleven CSAs/CBSAs included in this analysis reveal a wide range of response between monitors in each city and among the cities judged against each other. While this wide range is produced by the interactions of many physical and chemical elements, the location of each mon itor and the uniqueness of its immediate surroundings can often explain much of the agreement or lack th ereof. Compare, for example, Figures 3-17 and 3-18, the aerial views of monitors C (AQS site ID 040133002) and E (AQS site ID 040139997), respectively, in Phoenix. Monitor C has a correlation value with monitor E of 0.85 even though C is less than 1 km from the intersection of two major highways and E is surrounded by neighborhood residences. Because all other correlations of monitors in Phoenix are also >0.8 , the network of CO monitors there appears to have captured the behavior of emitted and transported CO with high fidelity. In contrast, monitors in Pittsburgh show a low degree of correlation, with only two values >0.5 and eight values ≤ 0.2. The topography of Pittsburgh is mountainous and therefore more complex than that of Phoenix, and this could account for some of the disparity across space. But in addition, several CO monitors in Pittsburgh are sited at distances far from the urban core, making them less like ly to correlate with those monitors in the highdensity urban core. Figure 3-19 depicts the three monito rs in the Pittsburgh urban core (monitor A: AQS site ID 420030010, monitor B: AQS site ID 420030031 , and monitor C: AQS site ID 420030038), which demonstrated the highest correlation values in this CSA for 2005-2007. Figure 3-20 shows the location of a more distant monitor, monitor F (AQS site ID 421250005), which does not correlate well with the three monitors located downtown, very likely because it is located at a great distance from any strong CO sources in the downtown urban core. This analysis de monstrates that agreement between monitors on an urban scale is a complex function of monitor siting, location to sources, geography, and micrometeorology. In addition to high correlations, Phoenix exhibits similar average concentration levels across monitors with 0.24 ≤ COD ≤ 0.43; see Table 3-7. The seasonal patterns shown in Figure 3-14 also demonstrate homogeneity across monitors for Phoeni x. By contrast, Pittsburgh had more variability between monitored values at the sites shown in Fi gure 3-16 with the range in COD shifted upward (0.39 ≤ COD ≤ 0.90); see Table 3-8. For the three sites located in the downtown urban core of Pittsburgh (sites A, B and C), the COD is as high as 0.69, indicati ng greater spatial variability in CO concentrations in Pittsburgh, even for these more proximal monitors. The information in Table 3-8 should be used with some caution since many of the reported concentrations for the years 2005-2007 are very near or even well below the monitors’ stated lowest detection limits. Because ambient concentrations are now in large part very near to the FRM detection limit of 1.0 ppm and the coarsely reported measurement resolution is 0.1 ppm, the comparison statistics shown in these tables might be biased to show a sp ecious heterogeneity in the box plots. These cautions

March 2009 3-35 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 are especially important for interpreting data from s ite G in Pittsburgh and selected other sites in other metropolitan regions where one or more monitors consis tently reported values below the stated detection limit. Such monitors include Houston site D, AQS site ID 482011035, and New York site I, AQS site ID 361030009, included in Annex A.



# Figure 3-17. Aerial vi ew of the location of CO monitor C. AQS ID 040133002 (marked by the yellow pin) in Phoenix, AZ, depicti ng its proximity to major roadways and neighborhood residences.

March 2009 3-36 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-18. Aerial view of the location of CO monitor E. AQS ID 040139997 (marked by the yellow pin) in Phoenix, AZ, depicting its proximity to ne ighborhood residences and secondary surface roads.

March 2009 3-37 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-19. Aerial view of the location of CO monitors A, B, and C. AQS IDs 420030010, 420030031, and 420030038 (marked by yellow pins ) in Pittsburgh, PA, depicting their proximity to major roadways and areas of high commercial density.

March 2009 3-38 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-20. Aerial view of th e location of CO moni tor F. AQS ID 421250005 (marked by the yellow pin) in the Pittsburgh, PA CSA, depicting its proximit y to secondary surface roads and water ways.



# 3.5.1.3. Neighborhood Scale

Roadway density is an important determinant of th e spatial distribution of CO concentrations in urban areas. Mobile sources are the largest single source of CO, and their abundance and density affect the magnitude of CO production. Urban topography around roadways also affects CO transport and dispersion (e.g., Mfula et al., 2005; Rigby and Toum i, 2008). CO concentration gradients have been observed in near-road environments (Baldauf et al., 2008; Pirjola et al., 2006; Zhu et al., 2002) such that CO concentrations are often higher on or near road ways compared with upwind sites, and can decrease with increasing distance from the road. For example, Zhu et al. (2002) found that on-road CO concentrations were approximately 10 times higher than at an upwind monitoring site, as shown in Figure 3-21. At 30 m downwind from the road, relative CO concentrations were decreased to 7 times above upwind levels, and were approximately 2.5 times the upwind levels at 100 m. Concentrations continued to 

# 1 2 3 4 5 6 7 8 9 10

March 2009 3-39 DRAFT – DO NOT CITE OR QUOTE

## decrease and were still somewhat higher than upwind levels at the final monitoring site 300 m away. Other traffic-related pollutants (BC, particle numbe r) followed a similar pattern, although with different downwind:upwind ratios. This indicates that near-road and on-road exposures are important in CO exposure assessment. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Field measurements, computational modeling, and wind tunnel experime nts have shown that roadway design and roadside features can affect CO and other pollutant concentrations near roadways. Field measurements reported by Baldauf et al. (2008) indicated that noise barriers could reduce near-road pollutant concentrations by as much as 50 percent, although this effect was highly dependent on meteorological conditons. This study also showed th at the presence of mature vegetation, where the noise barrier further reduced concentrati ons and flattened the concentration gradient away from the road. Urban dispersion and wind-field modeling by Bowker et al. (2007) also demonstrated the influence of noise barriers and vegetation on the concentr ations and spatial variability of inert pollutants emitted from traffic sources. Wind tunnel work reported by Baldauf et al. (In press) demonstrated the effects of noise barriers as well as roadway design characteristics, such as the presence of cut and elevated roadway segments. These results indicated that cut sections reduced concen trations and altered the gradient away from the road for inert gases emitted from traf fic sources such as CO. These results showed similar concentrations as Zhu et al. (2002) for roadway segments at-grade with no obstructions to air flow as well as elevated roadway segments with fill conditions. The geometry of urban street canyons has a profound effect on the distribution of CO concentrations on a micro-scale. A number of st udies have performed com putational and wind tunnel modeling of street canyons using nonreactive tracer s and demonstrated the potential variability in concentration within a canyon (e.g., Borrego et al., 2006; Chang and Meroney, 2003; Kastner-Klein and Plate, 1999; So et al., 2005; Xiaomin et al., 2006). B ecause CO is a pollutant with very low reactivity on urban and regional scales, results from these models are directly relevant to CO concentration distributions in street canyons. Influential parameters include canyon height to width ratio (H/W), source positioning, wind speed and direction, building sh ape, and upstream configuration of buildings. Figure 3-22 shows dimensionless concentrations obt ained from wind tunnel and computational fluid dynamics simulations of tracer gas transport and disper sion in an infinitely long street canyon with a line source centered at the bottom of the canyon (Xiaomin et al., 2006). When the canyon height was equal to the street width (typical of moderate density subur ban or urban fringe residential neighborhoods) and lower background wind speed existed, concentrations on the leeward (downwind) canyon wall were four times those of the windward (upwind) wall near ground level. When the canyon height was twice thestreet width (typical of higher-density cities) and background winds were somewhat higher, near groundlevel concentrations on the windward canyon wall were roughly three times higher than those measured at the leeward wall. These results suggest that the magnit ude of microscale CO concentrations may vary by factors of three or four times at different locations within a street canyon.

March 2009 3-40 DRAFT – DO NOT CITE OR QUOTESource: Zhu et al. (2002) 

# Figure 3-21. Relative concentrations of CO a nd copollutants at various distances from the 710 freeway in Los Angeles.



# 1 2 3 4 5 6 7 8 9 10 11 12 In a multi-site survey of curbside CO concentr ation in London, U.K., Croxford and Penn (1998) observed differences in concentration related to the side of the street on which the monitor was positioned relative to the wind direction. Bogo et al. (2001) meas ured CO concentrations in a street canyon with building height-street width ratio of 1 in Buenos Air es, Argentina using a continuous CO monitor. Similar to the Xiaomin et al. (2006) simulation results for a height-width ratio of 1, Bogo et al. (2001) observed slightly higher leeward concentrations than wi ndward concentrations within the canyon, where recirculating airflow inside the canyon causes pollutant s to collect in higher concentration on one side. For part of this study, they estimated aggregated emissions from several fixed sources, including thermal power stations and home heating, and from traffic. Mobile source emissions were estimated to be more than 800 times higher than fixed source emissions given assumptions regarding vehicle fleet. Moreover, they estimated that up to 30% of the measured CO concentrations came from within the local street canyon in which measurements were made.

March 2009 3-41 DRAFT – DO NOT CITE OR QUOTESource: Xiaomin et al. (2006) 

# Figure 3-22. Dimensionless tracer gas concentration as a function of elevation at windward and leeward locations and street canyon aspect ra tios (H/W). Z/H is the elevation of the measurement (Z) scaled by building height (H). (a) Dimensionless concentration on the windward and leeward sides of the canyon when H/W = 1 and wind speed = 3 m/s. (b) Dimensionless con centration on the windward a nd leeward sides of the canyon when H/W = 2 and wind speed = 5 m/s. Computati onal fluid dynamics modeling was performed, and measure ments were obtained in wind tunnel simulations.



# 1 2 3 4 5 6 7 8 9 Identification of neighborhood-scale variability is important for interpreting data from ambient CO monitors. Figure 3-23 shows a scatter plot of CO correlation as a function of distance (Pearson r) for monitors sited within 4 km of each other in four of the eleven CSAs and CBSAs. (The other seven CSAs or CBSAs were excluded from this analysis because th eir monitors were spaced farther than 4 km from each other.) The purpose of this plot is not to show a simple relationship between inter-sampler correlation and distance but to illustrate how this re lationship varies between c ities at the neighborhood scale. High correlation is seen for the monitors in th e Phoenix, AZ CBSA, while those in the Pittsburgh, PA, CSA have much lower inter-sample r correlations with steeper slope ( Δ r/ Δ d = -0.04 for Phoenix and Δ r/ Δ d = -0.27 for Pittsburgh). The inter-sampler corre lation observed for the Boston, MA CSA is also

March 2009 3-42 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 substantially lower than those observed for Phoenix. As discussed in section 3.5.1.2, these differences could be due to a variety of factors, including but not limited to natural and urban topography, different traffic conditions on roads located near monitors, a nd proximity of the monitors to the roadway.



# Figure 3-23. Inter-sampler co rrelations as a function of dist ance between CO monitors for samplers located within 4 km (neighborhood scale) for Boston, Denver, Phoenix, and Pittsburgh.

March 2009 3-43 DRAFT – DO NOT CITE OR QUOTE

# 3.5.2. Temporal Variability

3.5.2.1. Multi-year Trends

Figures 3-24 (top) shows ambient CO concentrations in ppm from 1980 to 2006 based on continuous measurements averaged over 8-h time seg ments. The 8-h NAAQS is indicative of exposures occurring over a sustained period of time, an outdoor worker’s exposure over the course of a day, for example. Figure 3-24 (bottom) depicts trends in th e annual second-highest 8-h CO concentrations for 144 sites in 102 counties nationwide having data either in the State and Local Air Monitoring Stations (SLAMS) network or from other special purpose monitors. 

# 1 2 3 4 5 6

Source: U.S. EPA (2008c) 

# Figure 3-24. (Top) Trends in ambient CO in the U.S., 1980-2006, reported as the annual second highest 8-h concentrations (ppm) for the mean, median, 10% and 90% values. (Bottom) Trends in ambient CO in th e U.S., 1980-2006, reported as the number of trend sites (y-axis) with annual second-high est 8-h concentrations above the level of the NAAQS (9 ppm).

March 2009 3-44 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 The 2006 annual second highest 8-h CO concentration averaged across 144 monitoring sites nationwide was 75% below that for 1980, and is the lowest concentration recorded during the past 27 years; see Figure 3-24 (top). Since 1992, more than 90% of all these sites have reported CO concentrations below the 8-h NAAQS of 9 ppm; see Figure 3-24. The mean annual second highest 8-h ambient CO concentration has been below 5 ppm sinc e 2004. The downward trend in CO concentrations in the 1990s parallels the downwar d trend observed in CO emissions, attributed largely to decreased mobile source emissions; see Figure 3-2. In addition, of the 144 sites used to determine this trend, from a total of 375 monitoring sites operating in 2006, th e number reporting second-highest CO concentrations above the level of the CO NAAQS declined to zer o over the same period; see Figure 3-24 (bottom). Consistent with the nationwide trends in emissions and concentrations, CO co ncentrations in all ten EPA Regions have steadily decreased since 1980, with reductions over this period ranging from 68% in Region 7 to 85% in Region 1; see Figure 3-25. This is also consistent with declining emissions seen in many regions of the U.S., shown in Figure 3-4. Re ductions in anthropogenic CO emissions in the U.S. and elsewhere in the extra-tropical northern hemis phere are a major cause for the observed decrease in hemispheric and global-average CO concentrations observed since 1991 (Bakwin et al., 1994), although ~30% of the decline between 1991 and 2001 was attributed to decreases in CO following the eruption of Mt. Pinatubo (Novelli et al., 2003).

March 2009 3-45 DRAFT – DO NOT CITE OR QUOTESource: U.S. EPA (2008c)



# Figure 3-25. Trends in ambient CO in the U.S., 1980-2005, reported as the annual second highest 8-h concentrations (ppm) for the EPA Re gions 1 through 10, along with a depiction of the geographic extent of those Regions



# 3.5.2.2. Hourly Variation

Weekday and weekend diel variation for the mean, median, 5th, 10th, 90th, and 95th percentiles of hourly CO concentration over 2005-2007 are shown in Figures 3-26 and 3-27, respectively among the eleven CSAs and CBSAs examined in this assessme nt. The weekday data showed that the Anchorage mean, median, 5th and 10th percentile CO concentr ation curves exhibit more pronounced morning and evening rush hour peak CO levels. Boston, Denver, H ouston, Los Angeles, Phoenix, Pittsburgh, and St. Louis all exhibited similar trends, although the magn itude of the concentrations shown was roughly twice as high for Anchorage as the other cities. The curv es 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 w ith slight elevations throughout the morning rush hour and a slight trough between 1:00 and 5:00 AM. Th e Seattle plot shows a daytime plateau beginning around 5:00 AM and lasting until roughly 10:00 PM, w ith higher concentrations during morning and afternoon rush hour. Differences in hourly variation among the eleven CSAs and CBSAs reflect city-to

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14

March 2009 3-46 DRAFT – DO NOT CITE OR QUOTE

## city variation in source characteristics and meteorology. For instance, the rush hour peaks in many cities likely correspond to increased mobile source emissi ons during those periods. Local meteorology and topography, which influence mixing heights, can also affect hourly variation in CO concentration. 

# 1 2 3 4 5 6 7 8 9 10 11 12 Figure 3-27 illustrates weekend diel trends for the eleven CSAs and CBSAs considered in this assessment. For Anchorage during the period 2005-20 07, 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 mo re pronounced. The shape of this plot is also characteristic of Atlanta, Boston, Denver, Houston, Los Angeles, Phoenix, Pittsburgh, Seattle, and St. Louis, although the Anchorage CO concentrations are nearly 100% higher than concentrations in the other cities. The weekend diel plot for New York shows th at the mean and median CO concentrations remain fairly constant throughout the day, with a slight reduction between 2:00 and 7:00 AM. The 90th and 95th percentile curves illustrate more diel variation.

March 2009 3-47 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-26. Diel plot generate d from weekday hourly CO data (ppm) for the eleven CSAs and CBSAs 2005-2007. Included are the number of monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles of CO concentration. No te that the y-axis of the Anchorage CBSA plot is scaled to 5. 2 ppm while the other plots are scaled to 2.8 ppm.

March 2009 3-48 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-27. Diel plot genera ted from weekend hourly CO data (ppm) for the eleven CSAs and CBSAs 2005-2007. Included are the number of monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles of CO concentration. No te that the y-axis of the Anchorage CBSA plot is scaled to 5. 2 ppm while the other plots are scaled to 2.8 ppm.

March 2009 3-49 DRAFT – DO NOT CITE OR QUOTE

# 3.5.3. Associations with Copollutants

Associations between hourly CO a nd other copollutants, including SO 2 

## , NO 2 

## , O 3 

## , PM 10 

## , and PM 2.5 

## are provided in box plots for all U.S. monitors in Figure 3-28. The figure also shows the correlation of the 24-h avg CO concentration with the daily max 1-h and daily max 8-h CO concentrations as well as the correlation between the daily max 1-h and daily max 8- h concentrations. AQS data were obtained from all available co-located monitors acro ss the U.S. after application of 75% completeness criteria. Pearson correlation coefficients (r) were calculated using 20 05-2007 data. Correlation plots analogous to Figure 3- 28 for select individual cities are provided in Annex A. 

# 1 2 3 4 5 6 7

Figure 3-28. Seasonal plots of nationwide correlations between hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations. Also shown are correlations between 24-h avg CO concen tration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (The numbers in this caption refer to those on the y-axis of each seasonal plot.) Red bars denote the median, and green stars denote the arithmetic mean.

March 2009 3-50 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 The nationwide mean and median of the correlation with CO were positive for NO 2 

## , PM 10 

## , and PM 2.5 

## ; near zero for SO 2 - 

## ; and negative for O 3 

## . These findings might reflect common combustion sources for CO, NO 2 

## , and PM 2.5 

## . In all cases, a wide range of correlation values were reported. There were significant positive correlations for all CO-CO (24-h avg, daily max 1-h, daily max 8-h) comparisons rendered. Among those copollutants with positive associations, NO 2 

## had the highest mean and median correlations, followed by PM 2.5 

## and PM 10 

## (correlations vary by season). The correlations of CO with SO 2 

## and PM 10 

## were not significantly different from zero for any season; SO 2 

## would not be expected to correlate well with CO because SO 2 

## emanates primarily from industrial sources. Correlations between CO and NO 2 

## were significant and positive for winter, spri ng, and fall. Correlations between CO and PM 2.5 

## were significant and positively correlated for wi nter and fall. Correlations between CO and O 3 

## were significant and negative for winter only, wh en CO emissions tend to be high and O 3 

## formation is low. The copollutant correlation plots for individual cities show n in Annex A illustrate highe r and more statistically significant correlations of CO with both PM 10 

## and PM 2.5 

## in several but not all of the select cities displayed. It is widely believed that sources of CO and PM 2.5 

## are highly correlated because they are both emitted directly during incomplete combustion and because secondary nitrate PM comes from NO X 

## , which is largely produced from mobile sources. The wide confidence inte rvals displayed in the nationwide plots reflect the large pool of data in add ition to the micrometeorological factors in each city. Additionally, CO monitors tend to be located apar t from other pollutant monitors based on the criteria from the CFR of siting at least one monitor near the hi ghest source of CO. Lack of co-location can affect the correlation of reported CO with PM 10 

## and PM 2.5 

## concentrations; this limitation also would inhibit the significance of correlations between CO and PM 10-2.5 

## . Several studies reported correlations between ambient CO and copollutants. Reported relationships were generally consistent with th e correlation data reported by the AQS. Sarnat et al. (2001) reported significant positive Spearman’s correlations of CO with NO 2 

## (r = 0.76) and PM 2.5 

## (r = 0.69) and significant negative correlations of CO with O 3 

## (r = -0.67) in Baltimore. Correlation of CO with SO 2 

## was insignificant (r = -0.12). The Sarnat et al. (2001) study focused on correlations of ambient and personal PM 2.5 

## with gaseous copollutants, so seasonal informati on is only available for the correlation between PM 2.5 

## and CO. High correlation of ambient CO with NO 2 

## is expected given that both are closely related to mobile source combustion emissions. Sarnat et al (2005) also reported significant year-round association between CO and PM 2.5 

## and significant associations between CO and SO 4 2- 

## aerosols. Tolbert et al. (2007) reported correlations between multiple pollutants in Atlanta and also showed the highest Spearman’s correlation for CO with NO 2 

## (r = 0.70). CO was also reported to have fairly high correlation with PM 2.5 

## elemental carbon (EC) (r = 0.66), PM 2.5 

## organic carbon (OC) (r = 0.59), and PM 2.5 

## total carbon (TC) (r = 0.63). Correlations were reported to be much lower for CO with O 3 

## (r = 0.27) and PM 2.5 

## SO 4 2- 

## (r = 0.14). Kim et al. (2006) measured CO, NO 2 

## , and PM 2.5 

## at ambient fixed sites in Toronto, Canada and

March 2009 3-51 DRAFT – DO NOT CITE OR QUOTE

# 1 2

3 4 5 6 7 8 9 10 found associations, averaged over monitoring stations, of CO with PM 2.5 

## (Spearman’s r = 0.38, nonsignificant) and of CO with NO 2 

## (r = 0.72, significant).



# 3.5.4. Policy-Relevant Background

Background concentrations of pollutants used for informing policy decisions about national standards in the U.S. are commonly referred to as po licy-relevant background (PRB) concentrations. PRB concentrations are those that are exclusive of anth ropogenic emissions in the U.S., Canada and Mexico (North America), and consist of world-wide bi ogenic emissions (including North America) and anthropogenic emissions elsewhere in the world. PRB concentrations of CO can best be determ ined from the extensive and long-running network of remote-site baseline CO measurements conduc ted by NOAA’s Earth System Research Laboratory (ESRL), Global Monitoring Division (GMD), as part of their Carbon Cycle Greenhouse Gases Group (CCGG) Cooperative Air Sampling Network (CASN) ( http://www.esrl.noaa.gov/gmd/ccgg/iadv ). CO data through December 2007 are available with extensiv e quality assurance and control information from a worldwide network of 72 nodes active in Dece mber 2008. ESRL GMD uses the highly sensitive gas chromatography-mercury liberation photometric detec tion technique with precision to 1 parts per billion (ppb) in 50 ppb (2 ppb in 200 ppb) and accuracy to 1.5 ppb in 500 ppb (2 ppb in 200 ppb) (Novelli et al., 2008). 

# 11 12 13 14 15 16

March 2009 3-52 DRAFT – DO NOT CITE OR QUOTE

# Figure 3-29. Map of the baseline monitor si tes used in this assessment to compute policyrelevant background concentrations.



# 1 2 3 4 5 6 7 8 9 10 11 12 This assessment used data from 2005 through 2007 at 12 remote sites in the U.S. to determine PRB (see the map in Figure 3-29): Cold Bay, AK; Barro w, AK; Shemya Island, AK; Cape Kumukahi, HI; Mauna Loa, HI; Trinidad Head, CA; Point Arena, CA; Wendover, UT; Niwot Ridge, CO; Park Falls, WI; Southern Great Plains, OK; and Key Biscayne, FL. Average concentrations for each month and for each year, 2005-2007 are shown for each site in Figure 3- 30. All sites demonstrate the well-known seasonality in background CO with minima in the summer and fall and maxima in the winter and spring. Summertime minima may be related to photochemical reac tion of CO with OH, as described in section 3.3. Analysis for North American PRB is made here by segregating the three Alaska sites (based on their high latitude) and the two Hawaii sites (based on their dist ance from the continent) and treating the remaining seven sites as representative of the CONUS backgrou nd. The 3-year avg CO PRB in Alaska ranged from 127 to 135 ppb with an average of 130 ppb; in Hawaii from 95.3 to 103.1 ppb with an average of 99.2 ppb; and over the CONUS from 118 to 146 ppb with an average of 132 ppb.

March 2009 3-53 DRAFT – DO NOT CITE OR QUOTE

# Figures 3-30. Monthly (circles) and annual (squar es) 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 H ead, CA; Point Arena, CA; Park Falls, WI; Southern Great Plains, OK; and Key Biscayne, FL.



# 3.6. Issues in Exposure Assessment

3.6.1. Summary of Findings from 2000 CO AQCD

The 2000 CO AQCD (U.S. EPA, 2000) describes the r esults of studies completed prior to 1999 on personal exposures and microenvir onmental concentrations of CO. Although these studies may no longer be representative of current exposur e levels due to declining ambien t CO concentrations, the personalmicroenvironmental-ambient relations hips are still instructive. Time spent commuting, particularly in cars, was a major contributor to personal CO exposur es. Many studies measured in-vehicle concentrations of CO and found elevated concentrations compared to fixed-site monitors. Roadside CO monitors were 

# 1 2 3 4 5 6

March 2009 3-54 DRAFT – DO NOT CITE OR QUOTE

## elevated compared to ambient levels, and equal to or lower than in-vehicle levels (e.g., Ott, 1994; Rodes et al., 1999). A small portion of the CO concentrations inside a vehicle cabin comes from the vehicle itself, while a substantial fraction comes from roadway traffic emissions entering the cabin via air exchange. Studies summarized in the 2000 CO AQCD f ound that in-vehicle CO concentrations were generally two to five times higher than ambient CO concentrations. High traffic volumes contributed to increased in-vehicle concentrations. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

21 22 23 24 Prior to the 2000 CO AQCD, it was well-known that CO levels in residences may be elevated above ambient due to non-ambient indoor sources, such as cooking, space heating, and smoking. Separation of indoor CO into ambient and non-am bient components is important for determining the effect of ambient CO concentrations, although this has not been done successfu lly in previous studies. Two large studies done in Denver, CO and Washington, DC in the early 1980s found that fixed-site monitor concentrations were higher than personal ex posures for those with low-level exposures, while fixed site monitor concentrations were lower than exposures for those with high-level exposures (Akland, 1985; Johnson, 1984). Non-ambient sources contributing to high total exposures likely obscured this relationship. In Denver, gas stove operation, passive smoking, and attached garages increased residential indoor exposure by 2.6, 1.6, and 0.4 ppm respectively compared to individuals without those sources present. Categorical analyses found significantly higher personal exposures on high ambient concentration days than on low ambient concentr ation days, suggesting that personal exposures are related to ambient levels. Non-am bient exposures tend to obscure the relationship between ambient CO concentrations and personal exposure.



# 3.6.2. General Exposure Concepts

A general framework for human exposure modeling was described in the 2008 Draft PM ISA, 2008 NO X 

## ISA, and 2008 SO X 

## ISA (U.S. EPA, 2008e, f). A brief conceptual model of human exposure is provided here with respect to CO for the readers’ c onvenience. An individual’s daily exposure to CO can be described based on a compartmentalization of th e person’s activities over a time period of interest: 

# ∫

= 

# dtCE j 

### Equation 3-2



# 25 26 27 where E = exposure over some duration, C j 

## = CO concentration at location j , and dt = time spent in location j. This basic equation can be broken down into a microenvironmental model that accounts for exposure to CO of ambient ( E

a 

## ) and non-ambient ( E

na 

## ) origin of the form:

March 2009 3-55 DRAFT – DO NOT CITE OR QUOTEnaa 

# EEE + = Equation 3-3



# 1 2 3 This assessment focuses on the ambient component of exposure because the non-ambient portion of exposure is subject to individual behavior. Daily E a 

## can be expressed in terms of the fraction of time spent outdoors and indoors being exposed to ambient CO concentration, C a 

## (Wilson et al., 2000): 

# ( ) ainfi oa 

# CFffE

i 

# ∑ += Equation 3-4



# 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 where f = fraction of time, subscript o = outdoor, subscript i = indoor, and F inf



## = infiltration factor. F inf quantifies the equilibrium fraction of the CO concentration outside the microenvironment that penetrates inside the microenvironment and remains in mixture. It is a function of the building air exchange characteristics and the properties of the gas. Assumi ng 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; F inf



## = Pa /( a + k ). Given that k Æ 0 for CO, F inf



## reduces to P . Equation 3-4 is subject to the constraint f o 

## + Σ f

i 

## = 1 to reflect the total exposure over a specified time period. The indoor term has a summation becau se indoor exposure can occur in various microenvironments throughout a time period of inter est. “Outdoor” exposure can occur in parks, yards, sidewalks, and on bicycles or motorcycles. “Indoor” refers to being inside any aspect of the built environment, e.g., home, office buildings, enclosed vehicles (automobiles, trains, buses), and/or recreational facilities (movies, restaurants, bars). The complex human activity patterns that dictate exposure across the population (all ages) are illustrated in Figure 3-31 (Klepeis et al., 2001). This figure illustrates the diversity of daily activities among the population as well as the proportion of time spent in each microenvironment. Although activities in Klepeis et al. (2001) are presented over a day, information from this figure can be extracted to deduce variation over a 1-h or 8-h period. As seen in equation 3-4, ambient exposure is a linear function of the ambient CO concentration. The ambient exposure factor, α , is the ratio between the personal e xposure to ambient CO and the ambient concentration of CO (or the ambient exposure factor); i.e., α = E a 

## / C a 

## . α is therefore the proportionality factor in equation 3-4, i.e., α = f o 

## + Σ f

i 

## ( 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 F inf 

## . If there are no significant local outdoor sources and sinks of CO, then C a 

## can be approximated by the concentration measured by an ambient monitor. If local sources and sinks exist and are significant, then the ambient component of outdoor air must be estimated using dispersion models, land use regression models, receptor models, fine scale chemistry-transport models or some combination of these techniques. i 

# F

inf



### March 2009 3-56 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 A variety of approaches can be used to estimate exposure to ambient CO. In some cases, individual personal exposures are measured w ith personal exposure monitors ( PEMs), where personal samples are taken to estimate population exposure. In other cases, ambient concentrations are used as an exposure indicator. The ambient concentration may be based on measurements made at a single ambient monitor or as the average of several ambient monitors. Very few recent exposure assessment studies utilized measurements of CO concentration and none of the recent exposure error studies used measurem ents of personal and am bient CO exposure data, although Sheppard et al. (2005) presented a concep tual model for nonreactive pollutants that could include CO. Many recent studies analyzing exposure e rror used PM data (e.g., Sheppard et al., 2005; Wilson and Brauer, 2006; Zeger et al., 2000). The lack of recent CO data contributes to uncertainty regarding personal-ambient relations hips. However, review of exposure error studies for PM, under the assumption that it is nonreactive, is instructive for discerning the relative influence of ambient and nonambient exposures if consideration is made for diff erences between CO and PM with respect to indoor source variability and infiltration. Wilson and Brauer (2006) showed significantly str onger associations between health effects and ambient exposure than between health effects and to tal personal exposure. The use of personal exposure in population exposure assessment studies could cause vari ous errors in the health effect estimate because the correlation between personal expos ure and ambient concentration may be different for each subject in a panel and may not be statistically significant (W ilson et al., 2007). The correlation between ambient concentration and personal exposure may be high for some subjects, in which case the exposure error caused by using ambient concentration instead of pers onal exposure may be small. In other subjects, the correlation may be low or negative (and not statistica lly significant). In this case, the exposure error will be high and may obscure relationshi ps between ambient exposure and health effects. Differences in correlation observed in these studies occurred in part because the ambient exposure factor, α , was different for each subject. The relationships between ambient concentration and the ambient component of personal exposure were statistically significant for all subjects. The analyses of Wilson and Brauer (2006) and Wilson et al. (2007) focused on PM, which is subject to indoor-outdoor differences in size distribution and chemical composition related to diff erential infiltration. However, this issue is still relevant for CO because non-ambient CO exposure is uncorrelated with am bient CO exposure and therefore could obscure health effects relationships observed in epidemiologic studies. Therefore, ambient concentration is better than total personal exposure as an indicator of ambient CO exposure.

March 2009 3-57 DRAFT – DO NOT CITE OR QUOTESource: Klepeis et al. (2001)) 

# Figure 3-31. Distribution of time that a sample population spe nds in various environments, from the National Human Activity Pattern Survey.



# 3.6.3. Monitoring Issues Associated with Exposure Assessment

3.6.3.1. Exposure Assessment using Community-Based Ambient Monitors

Instrumentation and associated monitoring errors are described in section 3.4; exposure error related to instrumental measurement error in ambient monitors is described here. Because there will likely be some random component to instrumental measure ment error, the correlation of the measured CO concentration with the true CO concentration will likely be less than 1. Sheppard et al. (2005) indicated that instrument error in the individual or daily averag e concentrations have “the effect of attenuating the estimate of α .” However, Zeger et al. (2000) stated that th e “instrument measurement error in the ambient levels...is close to the Berkson type” and in order for this error to cause substantial bias in later estimation of the health outcome, the error term (the differen ce between the true concentrations and the measured concentrations) must be strongly correlated with the measured concentrations. Zeger et al. (2000) suggested that, “Further investigations of this corre lation in cities with many monitors are warranted.” 

# 1 2 3 4 5 6 7 8 9 10

11 12 13 14 

# 3.6.3.2. Personal Exposure Monitors

Portable monitors for measuring personal CO e xposure include the Langan and Draeger monitors, both of which use electrochemical oxidation-reduc tion techniques (Langan, 1992). These monitors continuously log CO concentrations, making them suit able for use in personal monitoring studies. More detail on personal CO monitoring is provided in the 2000 CO AQCD (U.S. EPA, 2000).

March 2009 3-58 DRAFT – DO NOT CITE OR QUOTE

# Measurement Error in Pe rsonal Exposure Monitors

Personal electrochemical CO monitors are subject to interference and drift, and have a relatively high detection limit (approximately 1 ppm) relative to current ambient concentrations. Previous studies in the 1980’s and 1990’s, when ambient levels were highe r, were able to successfully deploy these monitors, but more recent exposure studies have avoided personal CO measurements due to the high percentage of non-detects. The lack of a suitable personal monitor for measuring low-level exposures (<1 ppm) has hampered field studies assessing personal exposure to ambient CO. Chang et al. (2001) evaluated the Langan CO monitor as part of an air quality sampling manifold. At high (0.4-3.0 ppm) CO concentrations, the instrument correlated well (R 2 

## = 0.93) with a reference CO monitor with the Langan underestimating the CO concentration by 41%. When am bient levels fell consistently below that level, coefficient of determination (R 2 

## ) between the Langan and re ference monitor fell to R 2 

## = 0.4 in summer and R 2 

## = 0.59 in winter with the arithmetic average co ncentration underestimated by 47% in summer and by 63% in winter. Chang et al. (2001) pointed out the need for frequent instrument zeroing to minimize instrument drift. Abi Esber et al. (2007a) evaluate d 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 depe ndent 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 w ith reference values when concentrations were low (differences = 20-71%). In general, it is difficult to separate the large instrumental measurement error seen at concentrations below instrument LOD from variation in non-ambient exposures. This large variation in personal measurements can result in high levels of classical measurement error (Sheppard et al., 2005). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

24 25 26 27 28 29 30 31 

# 3.6.4. Indoor/Outdoor Relationships and Infiltration

CO is a relatively inert gas, making the indoor decay rate negligible compared to typical air exchange rates (~1/h). In the absence of indoor so urces, this would lead to an indoor-outdoor concentration ratio (I/O) of approximately 1. For this reason, few studies have measured I/O for CO. Polidori et al. (2007) measured I/O of 0.94-1.21 fo r two retirement communities in the Los Angeles area. The authors suggested that similarity between I/O for CO and NO X 

## can be attributed to lack of indoor sources of either gas. Chaloulakou and Mavroidis (2002) reported I/O for CO measurements in the absence of indoor sources in a school building in Athe ns, Greece and found that I/O varies with season. During the summer, median I/O was reported to be 0.57 on weekdays, 0.91 on Saturdays, and 0.81 on

March 2009 3-59 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Sundays. In winter, median I/O was reported to be 0.82 during weekdays, 0.90 on Saturdays, and 0.74 on Sundays. The authors attributed the lower weekday I/O values to traffic-based peaks in outdoor CO concentrations that were not translated to indoors. In a related work, Chaloulakou et al. (2003) reported the median I/O over all days as 0.8 for the same sc hool and 0.9 for an Athens office building with no ETS (the presence of other sources was not clearly stated but assumed zero). However, observed indoor values are often greater than outdoor concentrations in the presence of indoor sources. A recent study in the U.K. reported I/O of 3.9-4.3 in homes with gas cookers (Dimitroulopoulou et al., 2006), which is consistent with previous studies. A multipollutant study conducted in 2000-2001 attempted to measure I/O for CO and calculated residential infiltration factors, but lo w CO concentrations resulted in a large number of non-detects (Williams et al., 2003).



# 3.6.5. Personal/Ambient Relationships

3.6.5.1. Panel and Population Exposure Studies

Although several multi-pollutant exposure studies have been conducted recently in the U.S., (e.g., Sarnat, 2006), most have not included CO in the suite of pollutants, possibly due to limitations in personal monitoring techniques. A few studies conducted in Europe and Canada me asured personal-ambient relationships for CO. The EXPOLIS study (Georgoulis et al., 2002) found that 48-h personal exposures were significantly correlated with ambient concentrations in each of five European cities (Athens, Basel, Helsinki, Milan, and Prague). Controlling for source terms, including ETS, traffic, and natural gas appliances, regression coefficients between persona l exposure and ambient concentration ranged from 0.28 in Milan to 1.99 in Helsinki. Regression coefficien ts greater than 1 may indicate location of a fixed site monitor away from local sources (e.g., roadwa ys), resulting in a lower ambient value than experienced in the urban core. As part of this stud y, personal CO exposure was measured for a panel of 50 office workers in Milan (Bruinen de Bruin et al., 2004). Average measured 1-h personal exposures were 7.3 ppm in comparison with 5.0 ppm for fixed site 1-h measurements. Average 8-h (3.3 ppm) and 24-h (2.1 ppm) CO concentrations were the same for pers onal and fixed site measurem ents. Percentage of time exposed, exposures, and percentage of exposure from the Bruinen de Bruin et al. (2004) study, in the absence of non-ambient CO from ETS and gas cooking, are shown in Table 3-9. The largest percentage of CO exposure was attributed to home indoor exposure in the absence of indoor sources, while the highest exposure levels were observed during tr ansit. Scotto di Marco et al. (2005) found similar results. Bruinen de Bruin et al. (2004) and Scotto di Marco et al . (2005) found that mobile source emissions were important contributors to personal exposure, as described in the following subsection.

March 2009 3-60 DRAFT – DO NOT CITE OR QUOTE

# Table 3-9. Percentage of time exposed to am bient CO (adjusted to reflect the absence of non-ambient CO from ETS and gas cooking) , average CO exposures, and percentage of exposure estimated for the population.

Percent of time exposed (%) Exposure (ppm) Percent of exposure (%)

INDOORS 89.6 81.1 Home 56.5 1.8 49.4 Work 29.1 1.9 26.8 Other 4.1 2.5 4.9 OUTDOORS 1.8 2.1 Home 0.2 2.3 0.2 Work 0.6 2.1 0.6 Other 1.0 2.6 1.2 IN-TRANSIT 8.5 16.8 Walking 3.0 3.0 4.4 Train/metro 0.7 3.0 1.0 Bus/tram 2.0 3.8 3.7 Motorbike 0.2 4.5 0.4 Car/taxi 2.6 5.7 7.2

Source: Bruinen de Br uin et al. (2004).



# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 EXPOLIS also looked at the special case of ch ildren’s exposure to CO because children generally do not produce CO in their daily activities and have no occupational exposures. Alm et al. (2000; 2001) reported higher personal exposures than ambient concentrations for children aged 3-6 years old in Helsinki. Their mean daily max 1-h exposure was 5.2 ppm, compared to 1.4 ppm measured at a fixed-site monitor. For daily max 8-h and 24-h avg concentr ations, 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 m onitor. 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 re gression model using questionnaire data found that parental smoking status, parental education, and pr esence of a gas stove explained 12% of the variability in the 8-h max exposures, indicating that other factors, such as time spent outdoors and proximity to roadways are likely to be important in determining personal exposure.Kim et al. (2006) reported mean CO concentrations of 1.4 ppm for a panel of 28 cardiaccompromised individuals in Toronto, Canada. Corr esponding fixed-site monito r mean concentrations ranged from 0.5-1.4 ppm, with an overall mean of 1.0 ppm. The observed higher personal exposures may have been due to both indoor sources and proximity to roadways when outdoors. Personal-ambient Spearman correlations ranged from -0.65 to 0.93, with a median of r = 0.31, indicating that while

March 2009 3-61 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 moderate correlations are observed overall, inter-individual differences based on time spent in different microenvironments have a strong in fluence on the observed correlation. Lai et al. (2004) measured relationships between personal CO exposure and microenvironmental (home indoor, home outdoor, and work indoor) concentrations in Oxford, U.K.. The highest personal exposures were associated with smoking, cooking, and transportation while low correlations were observed between personal and indoor residential c oncentrations, further indicating the importance of indoor sources and the need to separate ambient contributions to personal e xposure from total personal

exposure.



# 3.6.5.2. Commuting Time CO Exposure Studies

A number of studies focused on transit-time CO exposure, which can occur in a vehicle or while walking or cycling. Kaur (2005) found that transit time exposures in London, U.K. were significantly higher than measurements made at a fixed site bac kground monitor away from traffic (0.3 ± 0.1 ppm) for car riders (1.3 ± 0.2 ppm), taxi riders (1.1 ± 0.1 ppm), bicyclers (1.1 ± 0.2 ppm), walkers (0.9 ± 0.2 ppm), and bus riders (0.8 ± 0.1 ppm). Curbside measurements (1.5 ± 0.7 ppm) in this study were slightly higher than car riders’ exposures. Duci et al. (2003) found th at average in-transit concentrations were highest for cars (winter: 21.4 ± 4 ppm), followed by pedestrians (winter: 11.5 ± 2.6 ppm; summer: 10.1 ± 1.7 ppm), buses (winter: 10.4 ± 2.9 ppm; summer: 9.4 ± 3.6 ppm), trolley (winter: 9.6 ± 1.9 ppm; summer: 8.2 ± 3 ppm), and rail (winter: 4 ± 0.6 ppm; summer: 3.4 ± 0.7 ppm). Duci et al. (2003) did not provide fixed site CO concentrations but stated that in-transit exposures were higher in each case. Additional analyses from the EXPOLIS study indicated that on-road mobile source emissions were the most important source of CO exposure for non-ETS-exposed subjects (Bruinen de Bruin et al., 2004; Scotto di Marco et al., 2005). Scotto di Marco et al. (2005) found that, for a panel of 201 adult Helsinki residents (aged 25-55 years), subjects spent 8.1% of their time in transport, which accounted for 12.6% of their total exposure (range of means = 0.96 ppm on a train – 2.8 ppm in a car). Similarly, in a panel study of 50 office workers, Bruinen de Bruin et al. (2004) found that, in the absence of non-ambient sources, the subjects spent 8.5% of their time in transit, which accounted fo r 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 subject s living in low CO concentration suburban areas and commuting to work experienced higher levels th an urban residents with short commute times. Gómez-Perales et al. (2004; 2007) measured CO exposures on buses, mini-buses, and metro cars in Mexico City, Mexico to be 12 ppm, 15 ppm, and 7 ppm, respectively. These values are higher than CONUS measurements and those presented by Kaur et al. (2005), but the relative difference between the minibus and bus exposures in the Gómez-Perales et al. (2004; 2007) study are similar to those seen for the taxi-to-bus or car-to-bus comparisons in Kaur et al . (2005). Alm et al. (1999) reported in-vehicle CO

March 2009 3-62 DRAFT – DO NOT CITE OR QUOTE

## concentrations of 5.7 ppm in the morning and 3.1 ppm in the afternoon commute for Kuopio, Finland. Abi Esber et al. (2007b) report results from CO concen tration measurements taken within an automobile in Beirut, Lebanon during the morning commute period of 7:30 to 9:30 a.m. Weekday trip CO levels ranged from 10.8 ppm with the windows open and vents closed to 37.4 ppm when driving with windows and vents closed. Mean and standard deviation for am bient CO concentrations, obtained using a roadside monitor in Beirut during the periods Sept ember-December 2003, August-September 2004, and May-August 2005 were 1.4 ± 0.7, 1.6 ± 0.4, and 1.1 ± 0.7 ppm, respectively. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Abi Esber and El-Fadel (2008) compared the amount of CO produced by an automobile, driving the same route of Beirut described in Abi Esber et al. (Abi Esber et al., 2007b) above, by sampling CO directly from the engine of the vehicle and separatel y from the cabin of the car under three different ventilation conditions. For the case when one window w as half-open and vents were closed, engine CO concentrations averaged 12.6 ppm while in-vehicle concentrations averaged 17.7 ppm, which was a 40.5% increase. With windows closed and the air cond itioner operating on “recirculating air” mode, CO concentrations averaged 13 ppm from the engine and 30.2 ppm in the vehicle cabin, a 132% increase. With windows closed and the air conditioner on “fresh air” mode, engine CO concentrations averaged 18.3 ppm while in-vehicle concentrations were 20. 5 ppm, which was only a 12% increase. Figure 3-32 shows that the time series for the cabin and engine CO samples are very similar for the fresh air scenario, but for the recirculating air ventilation the concentration increases as a logarithmic-type function as CO builds up in the cabin of the vehicle.

Source: Abi Esber and El Fadel (2008) 

# Figure 3-32. Comparison of in-vehicle (solid line) and engine (dotted line) results for (left) driving with windows closed and air conditioner in r ecirculating air mode, and (right) driving with windows closed and air c onditioner in fresh air mode.

March 2009 3-63 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Riedeker et al. (2003) measured CO concentra tions inside patrol cars during shifts. Troopers recorded in a time-activity diary the ventilation settings of their cars and exit/entry from the vehicle, and the air conditioning was typically set to recirculation mode during the shifts. Riedeker et al. (2003) found that CO concentrations (mean, SD: 2.6 ± 1.1 ppm) were higher than ambient monitor concentrations (0.8 ± 0.3 ppm). They were also higher than roadside CO concentrations (1.1 ± 0.3 ppm), indicating that either the vehicle itself contributes to in-cabin CO, or on-road concentrations are higher than roadside concentrations, or both. Riedeker et al. (2003) noted that within-shift variability was higher than betweenshift variability, which underscores the variability in police officers’ activities during a given shift. No data was segregated by ventilation settings. Chang et al. (2000) measured CO concentrations during a scripted activity study in Baltimore, MD in 1998 and 1999. Mean 1-h CO concentrations were near the 1 ppm detection limit of the Langan CO monitor. Microenvironmental CO concentrations were significantly correlated with concentrations measured at a fixed-site ambient monitor for residential, other indoor, in-vehicle, and outdoor near-road microenvir onments during the winter. Significant correlations were observed only for residential microenvironments during the summer. The location of the ambient monitor near a roadway may have contributed to the lack of correlation with concentrations measured at outdoor locations away from roadways. Microenviro nmental concentrations inside vehicles were significantly higher than those for other microenvironments. Several factors may affect commuters’ exposures. Van Wijnen et al. (1995) observed that while bicycling, cyclists inhale 2.3 times as much air as ped estrians and drivers so that this group may, in fact, inhale the most CO. Likewise, in their review of roadway exposures to CO and PM, Kaur et al. (2007) listed a number of uncontrolled factors that may influe nce exposure. Vertical CO concentration gradients have been documented with concentrations decreasi ng with height; lower breathing zone height among children may make them more likely to be exposed to higher CO concentrations. With respect to transportation, Kaur et al. (2007) suggested that vehicle ventilation, speed, position in traffic, and start/stop activity influence in-vehicle exposures. Góm ez-Perales et al. (2007) also noted that meteorology can impact in-vehicle exposures, with evening increases in wind speed causing a 50% reduction in CO exposures among bus and minibus commuters. Alm et al. (1999) made a similar observation in a study of urban commuters’ exposure within a vehicle. Studies of vehicle self-pollution are instructive for considering potential for ambient CO infiltration in vehicles. Behrentz et al. (2004) used sulfur hexafluoride (SF 6 

## ) tracer gas emitted from school bus engines to determine the proportion of in-vehicle pollution related to self-pollution. Based on the SF 6 

## 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. SF 6 

## concentrations were higher when bus windows were closed.

March 2009 3-64 DRAFT – DO NOT CITE OR QUOTE

# 3.6.5.3. CO Exposure Assessment Variability and Error

In the context of determining the effects of am bient pollutants on human health, the association between the ambient component of personal exposures a nd ambient concentrations is more relevant than the association between total personal exposures (a mbient component + non-ambient component) and ambient concentrations. If there are no non-ambient s ources of a pollutant, the total personal exposure is equal to the ambient personal exposure. However, non-ambient sources could significantly affect personal exposures to CO. Unlike PM, CO has no chemical identif iers that can be used to apportion ambient and non-ambient sources of CO (other than radioactive isotop es, whose detection is rare). For the general U.S. population, exposure error analysis for epidemiologic studies indicates that fixed-site measured ambient CO concentration is generally a good indicator of ambient exposure to CO, as discussed in more detail below. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Figure 3-33 shows hourly vs. personal CO concentration data obtained by Chang et al. (2000) for a 1998-1999 multi-pollutant sampling campaign in Balti more, MD. Personal expos ures were obtained in five separate microenvironments in this study. A hi gh degree of scatter is evident in this figure, which suggests that these personal exposures are influenced by both ambient and non-ambient sources of CO. Figure 3-34 is a box plot of the personal-to-ambie nt CO concentration ratio for the same five microenvironments. Figure 3-34 shows that personal exposures in vehicles were on average 2.8 times higher than ambient during the summer and 4.1 times highe r than ambient in the winter. For the other four microenvironments tested, the average ratio was around 1. Wide variability is seen in these plots, particularly during the summer. Much of that variab ility could be due to the influence of non-ambient sources, which would then result in poor correlation between total personal exposure and ambient concentration. Zeger et al. (2000) stated that there are three error terms in the estimate of a person’s exposure: 1) error from using pooled population data in lieu of individual data, 2) error between total exposure and ambient concentration, and 3) error between the actual and measured ambient concentration. Zeger et al. (2000) also described that errors related to individua l variability and measurement (as stated above for the latter) are of the Berkson type and therefore not expected to bias estimates of exposure or health effects. Moreover, Sheppard et al. (2005) simulated am bient and non-ambient exposures to a non-reactive pollutant and observed that non-ambient CO exposur e has no effect on the association between ambient CO exposure and health outcomes for the cas e where ambient and non-ambient sources were independent.

March 2009 3-65 DRAFT – DO NOT CITE OR QUOTESource: Chang et al. (2000) 

# Figure 3-33. Hourly personal vs. ambient CO con centrations obtained in Baltimore, MD. Summer of 1998 in five settings: indoor residence, indoor other, outdoor near road, outdoor away from road, in vehicle.

Source: Adapted from Chang et al. (2000) 

# Figure 3-34. Box plots of the rati o of personal to ambient concentrations obtained in Baltimore, MD. Summer of 1998 and winter of 1999 in five settings: indoor residence, indoor other, outdoor near road, outdoor away from road, in vehicle. The dotted line shows the mean, and the solid line shows the median. S = summer; W = winter.

March 2009 3-66 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

31 32 For community time-series epidemiology, the co mmunity averaged concentration, not the concentration at each fixed monitoring site, is the con centration variable of concern (Zeger et al., 2000). The correlation between the concentration at a centr al community ambient monitor and the community averaged concentration depends on at least the following three factors. First, the distribution of monitors across space: CO emissions from traffic might show spatial heterogeneity near roadway sources but have a more homogeneous distribution farther downwind. It follows that use of a large number of samplers will dampen inter-sampler variability. Second, the rela tionship between the measurement at the ambient monitoring site and the community average concentration: if the site is selected to measure a “hot spot” or pollution from a nearby source, estimates of comm unity exposure could be skewed upwards. Third, terrain features or source location: different terrain features or source locations across sub-communities may differ in the temporal pattern of pollution. Intra- urban spatial heterogeneity was discussed in detail in Section 3.5.1.2. Community exposure may not be we ll-represented when monitors cover large areas with several sub-communities having different sources and to pographies. For example, with the exception of two closely located samplers, there is poor inte r-sampler correlation among the Pittsburgh, PA CO samplers. This variability reflects the wide topogr aphical differences throughout the Pittsburgh CSA as well as variation in traffic usage and distance to near-r oad sources at each point in contrast to the Phoenix CBSA, where inter-sampler correlation was high. Epidemiologic studies of long-term exposure rely on differences among communities in long-term average ambient concentrations. If exposure errors are different in the different communities, the differences in long-term ambient concentrations am ong communities may not represent the differences in long-term average exposures. For example, there may be community to community differences in measurement error, in the average ambient exposure factor ( α ) or the average non-ambient exposure. This could happen if exposure to newly formed pollutant s generated by vehicular traffic or pollutants from other localized sources differed among the spatial are as. Thus, in a regression of health effects against average concentration (as an indicator for average e xposure) there could be a different amount of error (either positive or negative) in the exposure indicator for each spatial area. This could add error and bias the slope up or down. Geostatistical tools enable the use of concentration fields that include spatial variations in concentration. However, it has not yet been possible to include individual or small-area variations in the personal exposure to ambient concentrations or va riations in personal exposures to indoor-generated pollutants in long-term studies of th e associations of pollutants with health effects.



# 3.6.6. Multi-pollutant Exposures

Since incomplete combustion is the primary sour ce of ambient CO, exposure to ambient CO is accompanied by exposure to other combustion-related pollutants, such as NO X 

## and PM. Thus, ambient

March 2009 3-67 DRAFT – DO NOT CITE OR QUOTE

## CO is often considered a surrogate for exposure to traffic-generated pollutants. However, the specific mix of CO with NO X 

## and PM depends on the source; for example, the mixture generated by gasoline engines differs from that produced by gas combustion. Correlations between ambient CO and ambient PM 2.5 

## , PM 10 

## , NO 2 

## , SO 2 

## , and O 3 

## from AQS data and the peer-reviewed lite rature were presented in section 3.5.3. Nationwide ambient CO was most highly correlated with ambient NO 2 

## followed by PM 2.5 

## and PM 10 

## . Correlations between CO and PM 2.5 

## were not always statistically significant on a national basis; correlations were insignificant for ambient CO with ambient SO 2 

## and ambient PM 10 

## , and ambient CO was negatively correlated with ambient O 3 

## . Correlations of ambient CO with PM 2.5 

## and PM 10 

## , were much higher and more statistically significant at some of the select cities, as shown in Annex A. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

31 32 Sarnat et al. (2001) analyzed the rela tionship between personal exposure to PM 2.5 

## and ambient CO concentrations and found significant associations duri ng winter of ambient CO with personal exposure to total PM 2.5 

## (slope = 3.99) and to ambient PM 2.5 

## (slope = 6.30). Ambient CO was not significantly associated with personal PM 2.5 

## exposure during summer. Sarnat et al. (2005) also reported significant association between ambient CO concentrations and personal PM 2.5 

## exposure for winter (three 12-day sessions in 2000, dates not specified) only and significant year-round associations between ambient CO concentrations and pe rsonal exposure to SO 4 2- 

## aerosols. Relationships between personal CO exposures and copollutants were reported less frequently in the literature, but results from these studies were consiste nt with the findings cited above. In a study of personal exposures to CO, PM 2.5 

## , and ultrafine PM in a street canyon, Kaur et al. (2005) found low Pearson’s correlation of persona l CO exposure with personal PM 2.5 

## exposure (r = 0.23). Personal CO exposure had much better correlation with personal ultrafine PM exposure (r = 0.68). Chang et al. (2000) reported correlations of persona l CO exposure with personal PM 2.5 

## , personal toluene, and personal benzene exposures in Baltimore, MD at five locations, labeled indoor residential, indoor nonresidential, outdoors near roadway, outdoors away from road, and in vehicle. Much variability was observed in the correlations for different locations and seasons (winter vs. summer). In general, the correlations tended to be stronger and more significant in the winter. Chang et al. (2000) pointed out that indoor air exchange rates tend to be lower in winter, which could cause th ese correlations to be more sensitive to non-ambient sources. Significant associations of CO with benzene and toluene were observed in-vehicle. Because CO exposures most often occur together with exposure to other combustion-related pollutants, interpretation of health studies using ambient CO data can be a challenge, as discussed further in Chapter 5.



# 3.6.7. Exposure Modeling

A number of modeling techniques to describe air pollutant exposures at the individual and population level have been published since 2002. Am bient CO exposure models often assume that

March 2009 3-68 DRAFT – DO NOT CITE OR QUOTE

## vehicular traffic is the sole source of emissions given that roughly 90% of urban CO is estimated to come from traffic (Gulliver and Briggs, 2005). Individual-level models can predict personal exposure based on a person’s path of travel and on conditions of the surrounding environment using traffic and meteorology data. One such example is the Space-Time Exposur e Modeling System (STEMS) (Gulliver and Briggs, 2005). Population-based methods, such as the Air Pollution Exposure (APEX) model ( 

# 1 2 3 4 5 www.epa.gov/ttn/fera/human_apex.html ), Stochastic Human Exposure and Dose Simulation (SHEDS) (Burke et al., 2001), and EXPOLIS models (Kruize et al., 2003), involve stochastic treatment of the model input factors. Another approach is to predic t location-based exposures us ing a deterministic model such as the CMAQ model, California Line Sour ce Dispersion Model (CALINE), CALPUFF (long-range plume transport model), or Operational Street Pollu tion Model (OSPM) for determination of street-level pollution coupled with infiltration m odels to represent indoor exposure to ambient levels (Appel et al., 2008; Gilliam et al.; Hering, 2007; Mensink a nd Cosemans, 2008; Wilson and Zawar-Reza, 2006). Stochastic and deterministic methods are often co mbined, as described below. Land use regression (LUR) models have also been developed to describe pollution levels as a f unction of geographic source behavior (Gilliland et al., 2005; Ryan and LeMasters, 2007; Veen et al., 1997). Similarly, other spatial interpolation techniques have been used to determine geographi c distributions of exposure (Marshall et al., 2008). Compartmental models, such as the Indoor Air M odel (INDAIR), can be used to assess exposure to infiltrated ambient air pollutants in a deterministic or probabilistic framewor k (Dimitroulopoulou et al., 2001; 2006). A detailed description of various m odeling techniques is provided in the 2008 NO X 

## ISA and 2008 SO X 

## ISA annexes (2008f, g), and an explanation of associated modeling errors is provided in the 2008 Draft PM ISA (U.S. EPA, 2008f). Applications of these models to assessment of CO exposure are described in the following paragraphs. 

# 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 The STEMS model maps exposures based on inputs for traffic levels, atmospheric dispersion, background concentrations, and geography. Gulliver and Briggs (2005) tested the STEMS model for CO and observed some correlation between mode led and measured CO concentrations (R 2 

## = 0.41), which was consistent with results for PM 10 

## and NO X 

## . Exposures were estimated from the predicted ambient CO concentration using a term similar to α that varied depending on whether the individual was walking or in a vehicle. Gulliver and Briggs (2005) noted that a lim itation 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 au thors only presented detailed results for a model of PM 10 

## 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 a nd meteorological conditions. Similar results can be expected for CO based on the tendency for variation of the CO concentration profile on the neighborhood and micro-scales (Jerrett et al., 2004). Flachsbart (Flachsbart) tested numerous meteorological, traffic, and background CO input variables in a regression appr oach to predicting CO exposure among individuals

March 2009 3-69 DRAFT – DO NOT CITE OR QUOTE

## while traveling in a vehicle. This work showed trav el time and average speed of on-road vehicles to be important determinants of CO exposure in a vehicle. Results from i ndividual models of this nature can be pooled to develop a distribution for examination of population effects or for comparison with population exposure models. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Bruinen de Bruin et al. (2004) utilized the EX POLIS model to predict CO population exposures in Milan, Italy. The simulation results showed that the U.S. 8-h NAAQS level was exceeded in one case out of 1,000. The model also showed that exposures exceed ed 20 ppm in one case out of 100,000. The results were not shown to be very sensitive to the number of microenvironments (e.g., outdoors, indoors, in vehicle) included in the model. The probabilistic NAAQS Exposure Model for CO (pNEM/CO) was used in the 1992 and 2000 CO AQCDs (U.S. EPA, 199 2, 2000) to estimate pe rsonal CO exposures among adult populations. Given reductions in ambient CO levels, details, and results from those efforts are provided here. The current version of this m odel, now known as APEX, was used for exposure assessment in the O 3 

## NAAQS review (U.S. EPA, 2006b). To examine indoor concentrations of ambient CO, Dimitroulopoulou et al. (2006) used the probabilistic formulation of the INDAIR model to examine indoor exposure to ambient CO, along with NO X 

## and PM for a given distribution of background CO le vels, meteorology, residential air exchange rate, and residential room dimensions. They found that 24-h avg CO concentration increased from 1.86 ppm outdoors to 1.90-1.93 ppm indoors in the absence of non-ambient sources, and that indoor 24-h avg CO concentration could increase to 1.93-2.00 ppm in th e presence of smoking and to 1.98-2.32 ppm in the presence of gas cooking. Similarity between the outdoor and non-source indoor concentrations was attributed to the lack of CO loss mechanisms. With maximum CO concentrations modeled at 3.3 ppm in the absence of sources, the probability density function of indoor exposure to ambient fell well below the 8-h NAAQS. In the Reducing Urban Pollution Exposure from Road Transport (RUPERT) study, Bell et al. (2004) presented methodology to use the probabilis tic form of INDAIR for development of personal exposure frequency distributions of CO, NO X 

## , and PM based on time spent in residential, transportation, school, office, and recreational environments with inputs from transportation source categories (Chen et al., 2008). Another set of approaches to improve exposure est imates in urban areas involves construction of a concentration surface over the geographic area. This does not estimate exposure directly because it does not account for activity patterns or concentrations in different microenvironments. It provides an improved estimate of the expected local outdoor concentration near residences, schools or workplaces, and roadways across the area. There are two main typ es of approaches: spatial interpolation of measured concentrations, and regression mode ls using land use, roadway characteristics, and other variables to predict concentrations at receptors in the domain. Rigorous first-principles models, such as dispersion models and chemical transport models, can also be used for this type of application, but are less suitable because they have intensive resource requirements and are typically applied over larger domains.

March 2009 3-70 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

18 19 20 21 22 23 24 25 26 Marshall et al. (2008) compared four spatial interpolation techniques for estimation of CO concentrations in Vancouver, BC. The investigators assigned a daily average CO concentration to each of the 51,560 postal code centroids using one of the following techniques: (1) the concentration from the nearest monitor within 10 km, (2) the average of a ll monitors within 10 km, (3) the inverse-distanceweighted (IDW) average of all monitors in the area, and (4) the IDW average of the three closest monitors within 50 km. Method 1 (the nearest-monitor appro ach) 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 averagi ng of extreme values inherent in IDW methods. The Pearson correlation coefficient between the two methods was 0.88. Methods 2 and 3 were considered suboptimal and were excluded from further analysis. In the case of method 2, a single downtown highconcentration monitor skewed the results in the vicin ity, 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 immediat ely adjacent to a monitoring site. LUR results were also reported in this study for NO and NO 2 

## , and indicated that LUR’s higher spatial precision reflects neighborhood-scale effects from nearby land use, but may not account for urban-scale variation. These results highlight the variation in local concentration estimates with choice of estimation technique.



# 3.7. Summary and Conclusions

3.7.1. Sources of CO

In the U.S., on-road mobile sources constituted more than half of total CO emissions in 2002, or ~63 MT of ~109 MT total. In metropolitan areas in th e U.S., for example, as much as 70-75% of all CO emissions can come from on-road vehicle exhaust (U.S. EPA, 2006a). The majority of these on-road CO emissions derive from gasoline-powered vehicles since the O2 content, pressure, and temperature required for diesel fuel ignition do not produce larg e 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 reducti ons. Nationally, biogenic emissions, excluding fires, were estimated to contribute ~5% of total CO emi ssions from all sources in 2002, and fires in 2002 added another 13%, or ~14.5 MT, to the national CO emissions total.

March 2009 3-71 DRAFT – DO NOT CITE OR QUOTE

# 3.7.2. Physics and Chemistry of Atmospheric CO

CO is produced by photooxidation of CH 4 

## and other VOCs in the atmosphere. Estimating the CO yield from oxidation of HCs larger than CH 4 

## 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 CO 2 

## and HO 2 

## . 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. 

# 1 2 3 4 5 6

7 8 9 10 11 12 13 14

15 16 17 18 19 20 21 22 23 24 25 26 27 28 

# 3.7.3. Ambient CO Measurements

As of March 2008, 19 automate d FRMs and no FEMs had been ap proved for CO. All EPA FRMs for CO operate on the principle of nondispersive infrared (NDIR) detection and can include gas filter correlation (GFC). The required lower detection limit for FRMs in the EPA network is 1.0 ppm. In 2007,there were 285 CO monitors meeting the 75% completeness requirements and reporting values yearround to the AQS in the 50 states, plus the District of Columbia, Puerto Rico, and the Virgin Islands. At least 70 monitors across the U.S. have been positioned at microscale within 10 m of a road to capture near-road concentrations. At larger scales, monitor dist ance from a road is inversely related to the road’s average daily traffic count to capture community averages.



# 3.7.4. Environmental CO Concentrations

CO concentration data for 1-h and 8-h interval s were available for 243 counties and autonomous cities or municipalities that maintained active CO monitoring stations meeting the 75% completeness criteria for the years 2005-2007. There were no violations of the 1-h or 8-h NAAQS in those years. The nationwide mean, median, and interquartile range for 1-h measurements reported between 2005-2007 were 0.5, 0.4, and 0.4 ppm, respectively, and these st atistics did not change by more than 0.1 ppm for each year of the 3-year period. The nationwide mean , median, and interquartile range for 8-h daily max concentrations reported between 2005- 2007 were 0.7, 0.5, and 0.5 ppm, respectively. The 2006 annual second highest 8-h CO concentration averaged acr oss 144 monitoring sites nationwide was 75% below that for 1980 and is the lowest concentration reco rded during the past 27 years. The mean annual second highest 8-h ambient CO concentration has been be low 5 ppm since 2004. The downward trend in CO concentrations in the 1990s parallels the downward tr end observed in CO emissions and can be attributed largely to decreased mobile source emissions. The correlation structures for measurements at the monitors in each of the 11 CSAs/CBSAs examined for this assessment reveal a wide range of response between monitors in each city and among

March 2009 3-72 DRAFT – DO NOT CITE OR QUOTE

## the cities judged against each other. While this wide range is produced by the interactions of many physical and chemical elements, the location of each monitor and the uniqueness of its immediate surroundings can often explain much of the agreement or lack thereof. CO concentrations can be elevated near roadways and decrease with increasing distan ce from the road. Likewise, micro- and neighborhoodscale variation related to urban topography or microenvironmental source distribution may have a significant effect on ambient CO concentrations with respect to street canyon concentrations. Anchorage, AK had concentrations roughly twice those of the other metropolitan areas. Most of the CSAs/CBSAs examined here had diel concentration curves with pronounced morning and evening rush hour peak CO levels, although diel CO concentrati ons had less variability for New York City, Atlanta, and Seattle than for the other eight cities. For most metropolitan ar eas examined here, concentrations were generally highest in the winter (December-February) and fa ll (September-November) and decreased, on average, during the spring (March-May) and summer (June-Augus t). Measurements near or below the required FRM lower detection limit of 1.0 ppm coupled w ith the coarsely reported measurement resolution (0.1 ppm) can artificially influence the comparison st atistics shown in the tables and result in apparent heterogeneity in the box plots (Figures 3-13 through Figures 3-16). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

29 30 31 32 For nationwide copollutant correlation data, am bient CO had the highest mean and median correlations with ambient NO 2 

## , followed by PM 2.5 

## (correlations vary by season). The SO 2 

## and PM 10 

## correlations did not appear to be statistically significantly different from zero for any season, and statistical significance was only observed in winter and fall for PM 2.5 

## . Correlations between CO and NO 2 

## were significant for all seasons but summer. Correlations between CO and O 3 

## were significant and negatively correlated for winter only. The copollutant correlation plots shown in Annex A illustrate higher and more statistically significant correlations between CO and PM in several but not all of the select cities displayed. This assessment has used data from 2005-2007 at 12 remote sites as part of the international CCGG CASN in the CONUS, Alaska, and Hawaii to determ ine PRB. All sites demonstrate the well-known seasonality in background CO with minima in the summe r and fall and maxima in the winter and spring. The 3-year avg CO PRB in Alaska was 130 ppb; in Hawaii it was 99 ppb; and over the CONUS it was 132 ppb.



# 3.7.5. Exposure Assessment and Implications for Epidemiology

Very few recent exposure assessment studies i nvolve CO concentration data. The studies of personal exposure to CO presented he re generally found that the largest fraction of an individual’s exposure to ambient CO occurs indoors but the highe st CO exposure levels occur in transit. Among commuters, exposures were highest for those traveling in automobiles in comparison with those traveling

March 2009 3-73 DRAFT – DO NOT CITE OR QUOTE

## in buses and motorbikes and with those cycling or walki ng. A portion of that exposure is thought to come from the vehicle in which the exposed person travels. Commuting time was an important predictor of exposure, such that subjects living in low-CO c oncentration suburban areas and commuting to work experienced higher CO levels than residents of hi gher-CO concentration urba n areas with short commute times. Additional analyses indicated that on-road mob ile emissions were the most important source of CO exposure for non-ETS-exposed subjects. Results have also indicated that time spent outdoors and proximity to roadways may have been important in determining persona l exposure in children. Uncontrolled factors that may influence exposure incl ude vertical CO concentration gradients; vehicle ventilation, speed, position in traffic, and start/stop activity; and meteorology. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 For the general U.S. population, exposure error analysis for epidemiologic studies indicates that fixed-site measured ambient CO con centration is generally a good indicator of ambient exposure to CO. Errors associated with exposure to non-ambient CO sources, for example smoking or gas cooking, are of the Berkson type and therefore not expected to bias estim ates of exposure or health effects. Simulations of ambient and non-ambient exposures to a non-reactive ge neric pollutant demonstrated that non-ambient exposure had no effect on health effects outcomes for the case where ambient and non-ambient sources were independent. Likewise for community time-se ries epidemiology, selected monitoring sites are thought to be representative of community averages. Measurement at a “hot spot” could skew community exposure estimates upward. Topographi cal or source variability could produce differences in the temporal pattern of pollution. Differences such as those prod uced by variation in local sources among communities in long-term exposure studies may also produce error in estimation of the ambient exposure factor. Such differences could add error, and therefore influence th e slope of health effects estimate regressions up or down. Several studies have examined multi-pollutant e xposure. Since incomplete combustion is the primary source of ambient CO, exposure to am bient CO is accompanied by exposure to other combustion-related pollutants, such as NO X 

## and PM. High correlations of ambient CO with NO 2 

## and PM 2.5 

## have been observed in the peer-reviewed litera ture and AQS data. Thus, ambient CO is often considered a surrogate for exposure to traffic-generated pollutants. Because CO exposures most often occur together with exposure to other combustion-rela ted pollutants, interpretation of health studies using ambient CO data can be a challenge, as discussed further in Chapter 5.

March 2009 3-74 DRAFT – DO NOT CITE OR QUOTE

# Chapter 4. Dosimetry and Pharmacokinetics of Carbon Monoxide

4.1. Introduction

Inhaled ambient CO elicits various health effect s 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 deoxyhem oglobin, resulting in COHb formation and consequent reduction in O 2 

## -carrying capacity of blood and impaired O 2 

## delivery to tissues. Research on the basics of CO pharmacokinetics date back to the 1890s, but si nce 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 messe nger. This chapter reviews the physiology and pharmacokinetics of CO. The chapter draws heavily from Chapter 5 of the previous AQCD (U.S. EPA, 2000). Relevant new data are included when available. New explanations of recent models of Hb binding are included, as well as discussion on tissue CO conc entrations using new methods of extraction. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 CO binds with a number of heme-containing mol ecules 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 th is event. The chapter discu sses 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 leve ls, meaning the smaller the animal, the faster the rate of absorption and elimination (Klimisch et al., 1975; Tyuma et al., 1981). However, the basic mechanisms of CO toxicity between experimental animals and humans ar e similar and are thus extrapolated from animals to humans in this chapter, keeping in mind a number of interspecies differences.

March 2009 4-1 DRAFT – DO NOT CITE OR QUOTE

# 4.2. Carboxyhemoglobin Formation

4.2.1. The Coburn-Forster-Kane and Other Models

Investigators have modeled the effect of CO binding to Hb in a number of ways. Empirical and mechanistic models are two distinct approaches that have been taken to model in vivo COHb formation after CO exposure. First, empirical models were used to predict COHb by regressing concentration and duration of exogenous CO exposure with COHb. These methods were reviewed in depth in the previous AQCD (U.S. EPA, 2000). These models are limited to estimating COHb in the exact conditions on which the models were based. These simple models include those by Peterson and Stewart (1970) and Ott and Mage (1978), as well as various others (Selvakumar et al., 1992; Singh et al., 1991). Using these simple models, it was shown that the presence of brief ambient CO concentration spikes averaged over hourly intervals may lead to underestimating the COHb concen tration by as much as 21% of the true value. To avoid this problem, it was suggested that ambient CO measurements be monito red and averaged over 10– 15 minute periods (Ott and Mage, 1978). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Secondly, mechanistic models use physical and physiological processes and an understanding of biological processes to predict COHb production. The most commonly used mechanistic method for predicting levels of blood COHb after CO inhalation is the Coburn-Forster-Kane equation or CFK model developed in 1965 (Coburn et al., 1965). This differ ential equation was developed to examine endogenous CO production, using the major physiological and physi cal 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; Stewar t et al., 1973b). The CFK model describes a fourelement, physical system containing an exogenous CO source, a transfer interface, an endogenous CO source, and a storage compartment. The linear CFK model assumes O 2 

## Hb concentration is constant and is as follows in Equation 4-1:



# [ ] [ ] [ ] ⎟ ⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜ ⎜

⎝ ⎛

− + + ⎟ ⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜ ⎜

⎝ ⎛

− + −=

• • •

A B

L I

A B

L 2 2 C 0 CO t b



# V 47P COD 1 COP

V 47P COD 1 1 MHbO OPCOHb V dt COHbd V

Equation 4-1



## where V b 

## is blood volume in milliliters (mL); [COHb] t 

## is the COHb concentration at time t in mL CO/mL blood, at standard temperat ure and pressure, dry (STPD); 

# 22

CO 

## V • 

## is the endogenous CO production rate in mL/min, STPD; [COHb] 0 

## is the COHb concentration at time zero in mL CO/mL blood, STPD; [O 2 

## Hb] is the O 2 

## Hb concentration in mL O 2 

## /mL blood, STPD; M is the Haldane coefficient representing the CO 

# 23 24 25

March 2009 4-2 DRAFT – DO NOT CITE OR QUOTE

## chemical affinity for Hb; 2 C 

# OP is the average partial pressure of O 2 

## in lung capillaries in mmHg; A 

# V • 

## is the alveolar ventilation in mL/min, STPD; D L 

## CO is the lung diffusing capacity of CO in mL/min/mmHg, STPD; P B 

## is the barometric pressure in mmHg; and P I 

## CO is the CO partial pressure in inhaled air in mmHg. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 This model assumes instant equilibration of COHb concentration between venous and arterial blood, gases in the lung, and COHb concentrations between blood and extravascular tissues, which is not physiologically representative. The nonlinear CFK eq uation incorporates the interdependence of COHb and O 2 

## Hb levels since they are derived from the same pool of blood Hb. The nonlinear equation is more physiologically accurate; however the linear CFK eq uation gives a good approximation to the nonlinear solution over a large range of values during CO uptake and during low levels of CO elimination (Smith, 1990). The linear equation prediction of COHb concentr ation at or below 6% will only differ ± 0.5% from the nonlinear equation prediction. Sensitivity analysis of the CFK equations has shown that alterations in each variable of the equation will affect the outcome variably at different times of exposure (McCartney, 1990). Figure 4-1 illustrates the temporal changes in fr actional sensitivities of the principal physiological determinants of CO uptake for the linear form of the CFK equation, where TH b 

## is the total blood concentration of Hb in g Hb/mL blood and F I 

## CO 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 de pendent. Thus, each variable influencing CO uptake and elimination will exert its maximal infl uence at different times of exposure.

March 2009 4-3 DRAFT – DO NOT CITE OR QUOTESource: modified from McCartney (1990) 

# Figure 4-1. Plot of fractiona l sensitivities of selected varia bles versus time of exposure.



# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 The mechanistic CFK model contains a number of assumptions under which the model is solely applicable, including 1) ventilation is a continuo us 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 O 2 

## or CO saturation versus pO 2 

## or pCO relation (McCartney, 1990). Estimations outside of these assumptions have been attempted but with less predictive agreement. For ex ample, transient exposures such as those that would simulate everyday conditions would violate the assumption of a single, well-mixed vascular compartment. COHb levels during exposure of subjects exposed to frequent but brief high CO exposures (667 to 7,500 ppm for 75 s to 5 min) were not accurately predicted by CFK modeling (Benignus et al., 1994; Tikuisis et al., 1987a; Tikuisis et al., 1987b). Cons istently, the predicted COHb value overpredicted venous COHb (0.8 to 6%) and underpredicted arterial COHb (1.5 to 6.1%) and this disparity increased after exercise. Individual differences between arte rial and venous COHb varied from 2.3 to 12.1% (Benignus et al., 1994). These inaccuracies between measured and predicted COHb values disappeared after simulated mixing of arterial a nd venous blood and thus are likely due to delays in mixing of arterial

March 2009 4-4 DRAFT – DO NOT CITE OR QUOTE

# 1 2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 and venous blood and differences in cardiac output and lung wash-in. A modified CFK was created to adjust for these issues and produce a more accurate COHb prediction (Smith et al., 1994).



# 4.2.2. Multicompartment Model

In addition to the limitations discussed above, the CFK model does not account for extravascular storage sites for CO, such as muscle Mb. CO will und ergo reversible muscle Mb binding, similar to Hb, as well as uptake into other extravascular tissues (V reman et al., 2006). A five compartment model has been proposed to predict CO uptake and distribu tion from acute inhalati on exposure and contains components for lung, arterial blood, venous blood, muscle tissue, and nonmuscle tissue (Bruce and Bruce, 2003; Bruce et al., 2008; 2006). This model structure is illustrated in Figure 4-2. This model includes the dynamics of CO storage in the lung and its dependence on ventilation and CO pressure of mixed venous blood, relaxes the assumption Hb is saturated by including the role of CO in altering the O 2 

## dissociation curve, includes a subcompartmentalized muscle tissu e compartment, accounts for dissolved CO in blood and tissue, and predicts COHb based on age and body dimensions. This mu lticompartment model is limited by its exclusion of cellular metabolism or Mb di ffusion, simplification of within tissue bed spatial variability, and assumes ventilation and P A 

## O 2 

## are constant. This model bette r predicts COHb levels when inspired CO levels change rapidly or when incomple te blood mixing has occurred, and better predicts the CO washout time course compared to the CFK equation. Bruce and Bruce (2003) compared the two models and found similar results for long te rm exposure settings (1,000 min), however, the multicompartment model predicted somewhat lower COHb levels compared to the CFK model over transient CO uptake conditions when using da ta taken from Peterson and Stewart (1970).

March 2009 4-5 DRAFT – DO NOT CITE OR QUOTESource: Modified from Bruce et al. (2008) 

# Figure 4-2. Overall structure of the multicompartment model of storage and transport of CO. Includes compartments for lung, arterial blood, venous blood, muscle tissue, and nonmuscle tissue. The muscle compartment is divided into two subcompartments for diffusion of gases within the tissue.



# 4.2.3. Mathematical Model Usage

Since measurements of COHb in the population are not readily available, mathematical models are used to predict the resulting COHb levels from vari ous CO exposure scenarios. Figure 4-3 illustrates the predictions of COHb after 1, 8, 12, or 24 h of CO exposure at a range of concentrations in a healthy, inactive, adult human. The Quantitative Circulatory Physiology (QCP) model, which integrates human physiology using over 4,000 variabl es and equations based on published biological in teractions, was used to predict these values (Abram et al., 2007; Benign us et al., 1987; 2006). This dynamic model uses the nonlinear CFK equation with modifications presented in Smith et al. (1994). The data in Figure 4-3 are presented as the change in percent COHb from e ndogenous due to CO exposure to NAAQS relevant levels from 0-35 ppm (A) and rele vant ambient levels from 0-6 pp m (B). Endogenous CO production varies as described in Section 4.5 but generally result s in less than 1% COHb, with a QCP modeled value of 0.39% at time zero. Figure 4-3 illustrates that 35 ppm CO for 1 h results in an increase of 0.56% COHb over endogenous levels and 9 ppm CO for 8 h results in a 0.83% increase over endogenous levels. Also, 

# 1 2 3 4 5 6 7 8 9 10 11 12

March 2009 4-6 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3

4 5 6 7 8 9 10 11 12 13 these graphs show that long term, low concentration CO exposure results in equi valent COHb levels to higher concentration, acute exposure. For exampl e, COHb resulting from 35 ppm for 1 h (0.56%) is approximately equivalent to 6 ppm for 8 h (0.55%) or 4 ppm for 24 h (0.57%).



# Figure 4-3. Predicted COHb increments over e ndogenous levels resulting from 1, 8, 12, and 24 h CO exposures in a modeled healthy human at rest. The QCP model used a dynamic nonlinear CFK with affinity constant M = 230. The data are presented as the change in percent COHb from endogenous levels due to CO exposure to 0-6 ppm (A) and 0-35 ppm (B). Note: Graph A is a subset of Graph B.



# 4.3. Absorption, Distribution, and Elimination

4.3.1. Pulmonary Absorption

Pulmonary uptake of CO accounts for all envi ronmental CO absorption and occurs at the respiratory bronchioles and alveolar ducts and sacs. CO and O 2 

## share various physico-chemical properties, thus allowing for the ex tension of the knowledge about O 2 

## kinetics to those of CO despite the differences in the reactivity of the gases. The exchange of CO betwee n 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 D L 

## CO, and CO uptake (V CO 

## ) representing the product of D L 

## CO and the mean alveolar pressure (P A 

## CO). The importance of dead space volume, gas mixing and homogeneity, and ventilation/perfusion matching were discussed in depth in the 2000 AQCD (U.S. EPA, 2000). 0 1 2 3 4 5 6

0 5 10 15 20 25 30 35 CO (ppm)COHb (percent over endogenous) 24 h 12 h 8 h 1 h

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0123456 CO (ppm)COHb (percent overendogenous) 24 h 12 h 8 h 1 h 

# A B

March 2009 4-7 DRAFT – DO NOT CITE OR QUOTE

# 4.3.1.1. Mass Transfer of Carbon Monoxide

Mass transfer refers to the molecular and convec tive transport of CO molecules within the body stores, driven by random molecule motion from high to low concentrations. CO enters through the airway opening (mouth and nose) and transfers in a gas phase to the alveoli. CO transport is due to convective flow, the mechanical action of the respiratory system, and diffusion in the acinar zone of the lung (Engel, 1983). Then, CO diffuses in a “liquid” phase across the air-blood interface, binding red blood cell (RBC) Hb. At environmental CO levels, CO uptake into RBC is limited by the reaction rate of binding of CO to O 2 

## Hb forming COHb. Pulmonary capillary RBC CO diffusion is rapidly achieved (Chakraborty et al., 2004; Gibson and Roughton, 1955; Reeves and Park, 1992; Roughton and Forster, 1957). The rate and level of COHb depends upon the pCO, pO 2 

## in the air, time of exposure, and the ventilation rate (Roughton and Forster, 1957). Most of the body CO is bound to Hb; however, 10-15% of the total body CO is located in extravascular tissues primarily bound to ot her heme proteins (Coburn, 1970a). Considerable concentrations of CO have been measured in spleen, lung, kidney, liver, muscle, and heart (Vreman et al., 2005; Vreman et al., 2006), whereas less CO is localized to fatty tissues, such as adipose and brain (Table 4-1). The transfer of CO occurs by a partitioning of CO between Hb and tissue. Less than 1% of the total body CO stores appear as dissolved in body fluids, due to the insolubility and sm all tissue partial pressure of CO (Coburn, 1970a). Transport pathways and body stores of CO are shown in Figure 4-4. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

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

Exposure Adipose Brain Muscle Heart Kidney Lung Spleen Blood % COHb Background 2 ± 1 3 ± 3 15 ± 9 31 ± 23 23 ± 18 57 ± 59 79 ± 75 165 ± 143 1.5 ± 1.2 Fire 5 ± 4 7 ± 5 24 ± 16 54 ± 33 27 ± 11 131 ± 127 95 ± 69 286 ± 127 3.8 ± 3.2 Fire + CO 18 ± 29 17 ± 14 168 ± 172 128 ± 63 721 ± 427 1097 ± 697 2290 ± 1409 3623 ± 1975 40.7 ± 28.8 CO asphyxiation 25 ± 27 72 ± 38 265 ± 157 527 ± 249 885 ± 271 2694 ± 1730 3455 ± 1347 5196 ± 2625 56.4 ± 28.9

Source: Vreman et al. (2006)



### March 2009 4-8 DRAFT – DO NOT CITE OR QUOTE

# Table 4-1b. CO concentration in pm ol/mg fresh weight – adult mouse.

Exposure Brain Muscle Heart Kidney Lung Spl een Liver Intestine Testes Blood % COHb Background 2 ± 0 10 ± 1 6 ± 1 7 ± 2 3 ± 1 6 ± 1 5 ± 1 4 ± 2 2 ± 1 45 ± 5 0.5 500 ppm CO 18 ± 4 14 ± 1 100 ± 18 120 ± 12 250 ± 2 229 ± 55 115 ± 31 9 ± 7 6 ± 3 2648 ± 400 28 30 μ M heme 2 ± 0 7 ± 1 14 ± 3 7 ± 2 8 ± 3 11 ± 1 8 ± 3 3 ± 1 2 ± 0 88 ± 10 0.9

Source: Vreman et al. (2005)

Source: Adapted from Coburn (1967) 

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



# 4.3.1.2. Lung Diffusion of Carbon Monoxide

Lung diffusion of CO is an entirely passive process of gas diffusion across the alveolo-capillary membrane, through the plasma, across the RBC membrane and into the RBC stroma, where CO binding to Hb rapidly occurs. Membrane and blood phase tr ansfer are governed by physico-chemical laws, including Fick’s first law of diffusion. The diffusing capacity of the lung for CO, represented as D L 

## CO, is a measurement of the partial pressure difference be tween inspired and expired CO. Due to the rapid binding of CO to Hb, a high pressure differential betw een air and blood exists when CO air levels are increased. Inhalation of CO-free air reverses the pressu re differential (higher CO pressure on the blood side than the alveolar side), and then CO is releas ed into the alveolar air. Since CO is also produced endogenously, CO release will also be affected by this production pressure. However, the air-blood 

# 1 2 3 4 5 6 7 8 9

March 2009 4-9 DRAFT – DO NOT CITE OR QUOTE

## gradient for CO is usually higher than the bloo d-air gradient; therefore, CO uptake will be a proportionately faster process than CO elimination. 

# 1 2 3 A number of factors have been found to affect D L 

## CO including Hb concentration, cardiac output ( •



## Q ), erythrocyte flow, COHb concentration, P A 

## CO 2 

## , body position, exercise, time of day, age, etc. (Forster, 1966; Hsia, 2002). D L 

## CO consistently decreases after intense bouts of exercise, likely due to the redistribution of blood volume to the periphery (Hanel et al., 1997; Manier et al., 1991). However, in going from rest to exercise D L 

## CO can increase linearly from: lung expansion leading to unfolding and distension of alveolar septa, opening and/or distension of capillaries as 

# 4 5 6 7

• 

## Q increases, increased capillary hematocrit, and more homogeneous distribution of capillary erythrocytes (Hsia, 2002). D L 

## CO 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). 

# 8 9 10 11

12 13 14 15 16

17 18 19 20 21 22 23 24 

# 4.3.2. Tissue Uptake

4.3.2.1. The Respiratory Tract

The upper respiratory tract contributes little to the overall COHb uptake. The lung has nearly constant exposure to CO; however, relatively little CO diffuses into the tissue except for at the alveolar region en route to the circulation. No detectable upt ake of CO was observed in the human nasal cavity or upper airway (Guyatt et al., 1981) or in the monke y oronasal cavity after high CO exposure (Schoenfisch et al., 1980).



# 4.3.2.2. The Blood

The blood is the largest reservoir for CO, where it re versibly binds to Hb. The chemical affinity of CO for adult human Hb is approximately 218 times greater than that of O 2 

## , meaning one part CO and 218 (210-250) parts O 2 

## would form equal parts of O 2 

## Hb and COHb (Engel et al., 1969; Rodkey et al., 1969; Roughton, 1970). This would happen when breathing air containing 21% O 2 

## and 960 ppm CO. This concept was presented by Haldane and Smith (1898) and later represented as the Haldane constant M (210-250) in the Haldane equation by Douglas, Haldane, and Haldane (1912). M is relatively unaffected by changes in physiological pH, CO 2 

## , temperature, or 2,3-diphosphoglycerate:

March 2009 4-10 DRAFT – DO NOT CITE OR QUOTE

# ) ( 2 2 

# pOpCOMHbOCOHb ÷ × = ÷ Equation 4-2



# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 The Hb association rate for CO is 10% slower than O 2 

## and occurs in a cooperative manner (Chakraborty et al., 2004; Sharma et al., 1976). Hb is composed of four globin chains each containing a heme group capable of binding CO or O 2 

## . The associative reaction rates become faster with successive heme binding, attributed to inter actions within the protein and to strains imposed on the heme and its ligands (Alcantara et al., 2007). More simply, the greater the number of heme sites bound to CO, the greater the affinity of free heme sites for O 2 

## , thus causing Hb to bind and retain O 2 

## 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 O 2 

## ( k CO 

## = 4 × 10 -4 

## k O2 

## ), which accounts for the high affinity values (Chakraborty et al., 2004). The half-time of dissociation reaction is about 11 seconds at 37 °C (Holland, 1970). In general, CO uptake to COHb e quilibrium is slower in humans and large animals, requiring 8-24 h, than in smaller species such as rats , which will equilibrate in 1-2 h (Penney et al., 1988). CO binding to Hb also has effects on the O 2 

## 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 O 2 

## binding (Roughton, 1970). This is refe rred to as the “Haldane effect” and causes tissues to have more trouble obtaining O 2 

## from the blood, even compared to the same extent of reduced Hb resulting from anemia. For example, Figure 4-5 (as explained in the 2000 AQCD) illustrates that an acute anemia patient (50% of Hb) at a venous pO 2 

## of 26 mmHg (v’ 1 

## ), 5 vol % of O 2 

## (50% saturation) was extracted from the blood. In contrast, a CO poisoned person with 50% COHb, the venous pO 2 

## will have to drop to 16 mmHg (v’ 2 

## ) to release the same 5 vol % O 2 

## . This more severe effect on O 2 

## pressure may lead to brain O 2 

## depletion and loss of consciousness if any higher demand of O 2 

## is needed (e.g., exercise).

March 2009 4-11 DRAFT – DO NOT CITE OR QUOTESource: U.S. EPA (1991) 

# Figure 4-5. O 2 

# Hb 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 O 2 

## diffusion from the muscle sarcoplasm to mitochondria, acting as an O 2 

## supply buffer to maintain adequate pO 2 

## for mitochondria when the O 2 

## supply changes, as in exercise. O 2 

## has a greater affinity for Mb than Hb, which allows small changes in tissue pO 2 

## to release large amounts of O 2 

## from O 2 

## Mb (Wittenberg et al., 1975). Small reductions in O 2 

## storage capacity of Mb, due to CO binding, may have a profound effect on the supply of O 2 

## to the tissue. 

# 1 2 3 4 5 6 7 Like Hb, Mb will undergo reversible CO binding, how ever the affinity constant is approximately eight-times lower than Hb (M = 20-40 versus 218, respectively) (Haab, 1990). The association rate

March 2009 4-12 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13

14 15 16 17 18 19 20 21 22 23 24 25 26

27 28 29 30 constant of CO and Mb is a pproximately 27 times lower than O 2 

## , however the dissociation rate constant is approximately 630 times lower than O 2 

## (Gibson et al., 1986) causing CO to be retained and possibly stored in the muscle. CO levels have been measured in human muscle and heart tissues with less than 2% COHb concentrations at background levels averag ing 15 and 31 picomole (pmol) CO/100 grams ww, respectively (Vreman et al., 2006). Under conditions of CO asphyxiation, tissue concentrations increased to 265 and 527 pmol CO/100 grams ww muscle and heart tissue, respectively; however, heart tissue concentrations varied widely between individuals (See Table 4-1a). The capacity for diffusion of CO into the muscle is represented by the coefficient D m 

## CO and is generally larger in males than in females, likely due to the differences in muscle mass and cap illary density (Bruce and Bruce, 2003). COMb concentrations in the heart and skeletal muscl e increase with work load, causing an increase in COMb/COHb that is not as greatly seen at rest (S okal et al., 1984). Subjects with 2% COHb, but not those with 20% COHb levels, showed a significant uptake of CO from the blood to the muscle with increasing work intensity of the quadricep s muscle (Richardson et al., 2002).



# 4.3.2.4. Other Tissues

CO binds with other hemoproteins, such as cytochrome P450, cytochrome c oxidase, catalase, and peroxidase, but the possibility of this binding influencing CO-O 2 

## kinetics has not been established. CO transfers between COHb and tissue, the extent of which varies between organs. Blood to tissue flux causes less CO to be expired following CO exposure than what is lost from the blood in terms of COHb (Roughton and Root, 1945). This value is estimated to be 0.3-0.4% min -1 

## or 0.24 mL/min (Bruce and Bruce, 2003; Prommer and Schmidt, 2007). The equilibra tion rate from blood to tissue is controversial. Newly modeled CO trafficking kinetics shows that CO continues to be taken up by the muscle and extravascular tissues well beyond the end of exposure because of a less than in stant equilibration (Bruce and Bruce, 2006). Tables 4-1a and 4-1b contain ti ssue CO concentrations from human and mouse under different CO exposure conditions. The distribution of CO between the different organs was shown to follow the same pattern versus percent of the blood CO concentration, irrespective of the level of blood CO (Vreman et al., 2006). These results are in conflic t with older papers sugg esting that negligible retention of CO occurs in the liver or brain (Sokal et al., 1984; Topping, 1975).



# 4.3.3. Pulmonary and Tissue Elimination

Blood COHb concentrations are generally consid ered to have a monotonically decreasing, secondorder (logarithmic or exponential) elimination rate from equilibrium. However, more recent reports have presented evidence for a biphasic wash out curve, especially after short-term CO exposure (Figure 4-6)(Bruce and Bruce, 2006; Shimazu et al., 2000; Wagner et al., 1975). This event is modeled by a twoMarch 2009 4-13 DRAFT – DO NOT CITE OR QUOTE

## compartment system where the initial rapid decrease is the washout rate from the blood, followed by a slower phase due to CO flux from the muscle and extr avascular compartments back to the blood. Tissue elimination rates have been reported as slower than those for blood (Landaw, 1973). The biphasic curve is more obvious after short-term CO exposure (less than 1 h), whereas long-term CO exposure (5 h or more) results in a virtually monoexponential elimination, which could account for the historical findings. However, this elimination curve also follows a biphasi c curve with a slightly higher rate of elimination initially (Shimazu et al., 2000). Differences in elimination kinetics could also be a result of the variation in CO exposure duration (Weaver et al., 2000). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 The elimination of COHb is affected by a number of factors, including duration of exposure, P a 

## O 2 

## ,minute ventilation, the time post-exposure for analysis due to extravascular stores, as well as interindividual variability (Bruce and Bruce, 2006; Landa w, 1973; Shimazu, 2001). The elimination rate does not seem to be dependent upon the CO exposure source (e.g., fire, n on-fire CO exposure) (Levasseur et al., 1996). In addition, in a series of poisoned patients, the COHb elimination half-life was not influenced by gender, age, smoke inhalation, history of loss of consciousness, concurrent tobacco smoking, degree of initial metabolic acidosis (base excess), or the in itial COHb level (Weaver et al., 2000). COHb elimination half-life falls as the fractional inspired O 2 

## concentration increases. While breathing air at sea level pressure, the expected half-life in adult males is approximately 285 minutes, but may be shorter in adult females. With inhalation of normobaric 40% O 2 

## , the half-life falls to 75 minutes and futher to 21 minutes when breathing 100% O 2 

## because of greater competition for Hb by O 2 

## (Landaw, 1973). Another study reports the half-life falls to 74 minutes (mean) after breathing 100% O 2 

## , although the range in this particular study was 26 to 148 minutes (Weaver et al., 2000). In addition, COHb half-life will fall further after normocapnic hyperoxic hyperpnea (i.e., hy perventilation while maintaining normal CO 2 

## pressure in high O 2 

## ) (Takeuchi et al., 2000).

March 2009 4-14 DRAFT – DO NOT CITE OR QUOTE

## 1 10 100

0 30 60 90 120 150 180 Time (min)COHb (%) 

### Short-term Long-term

Adapted from Shimazu et al. (2000) 

# Figure 4-6. Changes in blood COHb after s hort-term and long-term exposure to CO, representing the biphasic nature of CO el imination. Note: y- axis is log-scale.



# 4.4. Conditions Affecting Uptake and Elimination

4.4.1. Environment and Activity

Elevated CO exposure and COHb levels are depe ndent upon the changes in CO concentration in the local environment. Pedestrians are exposed to high levels of CO for short time periods from vehicle exhaust at busy intersections (see also Chapter 3. 6). Higher exposure can also result from riding in an automobile or stopping at busy intersections (Ott, 1994). Indoor exposure occurs from ETS and unvented combustion appliances, such as natural gas cooking st oves, attached garages, and gas fireplaces, which can accumulate to over 100 ppm (Dutton et al., 2001). Recreational exposure at levels exceeding 200 ppm and peaks of 1,600 ppm could occur in indoor ice ri nks using fossil fuel powered ice resurfacers and coliseums housing malfunctioning equipment or poor ve ntilation (Levesque et al., 2000; Pelham et al., 2002). Certain occupations provide instances and conditions for transient moderate-to-high CO levels, including fire fighters and machinery operators. Such transient exposures have the ability to increase COHb levels. For example, exposure for 5 minutes or less of a resting individual to 6,600 ppm CO will result in up to 20% COHb (Benignus et al., 1994). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Exercise is an important determinant of CO kineti cs and toxicity due to the extensive increase in gas exchange. O 2 

## consumption can increase more than 10 fold during exercise. Similarly, ventilation,

March 2009 4-15 DRAFT – DO NOT CITE OR QUOTE

## membrane and lung diffusing capacity, pulmonary capillary blood volume, and cardiac output increase proportional to work load. The majority of these cha nges facilitate CO uptake and transport, by increasing gas exchange efficiency. 

# 1 2 3 4 5 6 7 8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The COHb elimination rate decreases with phys ical activity (Joumard et al., 1981). Healthy subjects exposed to CO and achieving COHb levels of approximately 4-5% observed a significant detriment to exercise duration and maximal effort ca pability (measured by metabolic equivalent units) (Adir et al., 1999). It is possible that CO lowers the anaerobic threshold, allowing earlier fatigue of the skeletal muscles and decreased maximal effort capability.



# 4.4.2. Altitude

Increased altitude changes a number of factors that contribute to the uptake and elimination of CO. The relationship between altitude a nd CO exposure has been discussed in depth in the 2000 AQCD and other documents (U.S. EPA, 1978). In an effort to maintain proper O 2 

## transport and supply, physiological changes occur as compensatory mech anisms to combat the decreased barometric pressure and resulting altitude induced hyperbaric hypoxia (HH). HH, unlike CO hypoxia, causes humans to hyperventilate, which reduces arterial blood CO 2 

## (hypocapnia) and increases alveolar partial pressure of O 2 

## . Hypocapnia will lead to difficulty of O 2 

## dissociation and decreased blood flow, thus reducing tissue O 2 

## supply. HH increases blood pressure (BP) and cardiac output and lead s 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 elim ination. In hypoxic conditions both CO and O 2 

## bind reduced Hb through a competitive-parallel reaction (C hakraborty et al., 2004). Breathing CO (9 ppm) at rest at altitude produced higher COHb compared to sea level (McGrath et al., 1993), whereas high altitude exposure with exercise caused a decrease in COHb levels versus similar exposure at sea level (Horvath et al., 1988). This decrease could be a shift in CO st orage or suppression of COHb formation, or both. Altitude also increases the baselin e COHb levels by inducing endogenous CO production. Initial HH increased lung HO-1 protein and activity, whereas ch ronic HH induced endogen ous CO production in nonpulmonary sites (see Section 4.5) (Carraway et al., 2000). As the length of stay increases at high altitude, acclimatization occurs, inducing hyperventilation, polycythemia or increased red blood ce ll count, and increased tissue capillarity and Mb content in skeletal muscle, which could also favor increased CO uptake. Most of the early adaptive changes gradually revert to sea level values. However, differences in people raised at high altitude persist even after reacclimatization to sea level (Hsia, 2002).

March 2009 4-16 DRAFT – DO NOT CITE OR QUOTE

# 4.4.3. Physical Characteristics

Certain physical characteristics (e.g., age, sex, pre gnancy) can alter the vari ables that influence the uptake, distribution, and elimination of CO. Values of CO uptake and elimination change with age. Young children eliminate COHb more rapidly than adults after CO exposure (Joumard et al., 1981; Klasner et al., 1998). After infancy, the COHb half-life increases wi th age, nearly doubling between 2 and 70 years (Joumard et al., 1981). The rate of this increase in CO elimination is very rapid in the growing years (2 to 16 years of age), but slows beyond adolescence. Alveolar volume and D L 

## CO increase with increasing body length of infants and toddlers (Castillo et al., 2006), suggesting a further degree of lung development and faster CO uptake. After infancy, increasing age decreases D L 

## CO and increases V A 

## /Q mismatch, causing it to take longer to both load and eliminate CO from the blood (Neas and Schwartz, 1996). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 COHb concentrations are generally lower in female subjects than in male subjects (Horvath et al., 1988) and the COHb half-life may be longer in health y men than in women of the same age, which may be partially explained by differences in muscle mass or the slight correlation between COHb half-life and increased height (Joumard et al., 1981). The rate of decline of D L 

## CO with age is lower in middle-aged women than in men; however, it evens out toward s older age (Neas and Schwartz, 1996). Women also tended to be more resistant to a ltitude hypoxia (Horvath et al., 1988). Fetal CO pharmacokinetics do not follow the same kinetics as maternal CO exposure, making it difficult to estimate fetal COHb based on maternal levels. Human fetal Hb has a higher affinity for CO than adult Hb (Di Cera et al., 19 89). Maternal and fetal COHb concen trations have been modeled as a function of time using a modified CFK equation (Hill et al., 1977). At steady-state conditions, the fetal COHb is up to 10% higher than the maternal COHb levels, for example, exposure to 30 ppm CO results in a maternal COHb of 5% and a fetal COHb of 5. 5%. The fetal CO uptake lags behind the maternal for the first few hours but later may overtake the maternal values. Similarly, during washout, the fetal COHb levels are maintained for longer, with a half-life of around 7.5 hours versus the maternal half-life of around 4 hours. Ethnicity does alter physiological variables that determine CO uptake and kinetics. Lung volumes are 10-15% less in both Asian and African-American po pulations when compared to Caucasians. This causes a reduced alveolar surface area (20% less than estimated values) for gas exchange, leading to a 13% difference in diffusion capacity, D L 

## CO (Pesola et al., 2004; Pesola et al., 2006). Certain factors such as socioeconomic status (SES) were not controlled for in these studies. SES has been shown to affect pulmonary function, including decreasing D L 

## CO (Hegewald and Crapo, 2007).

March 2009 4-17 DRAFT – DO NOT CITE OR QUOTE

# 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 O 2 

## carrying capacity or content will result in a greater risk from COHb induced hypoxia and decreased tissue O 2 

## delivery. The severity of this effect depends upon the initial level of hypoxia. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

25 26 27 28 29 30 Anemias are a group of diseases that result in insufficient blood O 2 

## or hypoxia due to Hb deficiency through hemolysis, hemorrhage, or redu ced hematopoiesis. Anemia may result from pathologic conditions characterized by chronic inflammation su ch as malignant tumors or chronic infections (Cavallin-Stahl et al., 1976a, b). The bodies of pe ople with anemia compensate causing cardiac output to increase as both heart rate and stroke volume increas e. The endogenous producti on 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 prev alent anemias arises from a single-point mutation of Hb, causing sickle cell diseases. The Hb affinity for O 2 

## and O 2 

## carrying capacity is reduced causing a shift to the right in the O 2 

## 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 (V D 

## ) and ventilation-perfusion ratio (V/Q) inequality (Marthan et al., 1985), which could slow both CO uptake and elimination. Patients with pulmonary sarcoidosis may have a decrease in lung volumes, a loss of D L 

## CO, and gas exchange abnormalities during exercise, including decreased arterial oxygen pressure (P a 

## O 2 

## ) and increased alveolararterial oxygen pressure difference (Lamberto et al., 2004). Individuals with heart disease may be at a greater risk from CO exposure since they may already have compromised O 2 

## delivery. Time to onset of angina was reduced after exposure to 100 ppm carbon monoxide, compared to clean air (Kleinman et al., 1998). Hyperlipidemic patients may have decreased CO diffusion capacity, a loss of V/Q gradient, and a decrease in P a 

## O 2 

## (Enzi et al., 1976).



# 4.5. Endogenous CO Production and Metabolism

Humans breathing air containing no environmental sources of CO will still have a low measurable level of circulating COHb. This is due to e ndogenous CO production from heme protein catabolism among other sources. In the natural degradation of RBC Hb, the porphyrin ring of heme is broken at the methene bridge and catabolized in an O 2 

## dependent manner by HO complexed with NADPH-cytochrome P450 reductase and biliverdin reductase to biliverdin, Fe, and CO. Biliverdin is then further broken down into bilirubin, a powerful endogenous antioxidant . Two main HO isoforms exist, HO-1 and HO-2.

March 2009 4-18 DRAFT – DO NOT CITE OR QUOTE

## Expression of HO-1 is inducible, whereas HO-2 is constitutively expressed. The major site of heme catabolism, thus the major organ of CO production is the liver, followed by the spleen, brain, and erythropoietic system (Berk et al., 1976). These rates of CO formation may be due to higher levels of HO activity in these tissues. The whole body production rate of CO is approximately 16.4 μ mol/h (0.42 mL/h) and produces between 400-500 μ mol CO per day (Coburn et al., 1964; Coburn et al., 1966; Coburn, 1970b). The endogenous rate of CO formation has been shown to vary little between different tissues, ranging from 0.029 nmol/mg protein/h in chorionic villi of term human placenta to 0.28 nmol/mg protein/h in rat olfactory receptor neurons in culture and in rat liver perfusate (Marks et al., 2002), however these estimations are questionable since CO is quickly scavenged in the cytosol of living cells. CO is endogenously produced in the nose and paranasal sinus which may contribute to exhaled CO concentrations (Andersson et al., 2000). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 HO mediated metabolism functions as the rate-lim iting enzyme step in heme degradation and endogenous CO production (Wu and Wang, 2005). Thr ee isoforms of HO exist, but HO-1 is the only inducible form (Maines and Kappas, 1974; Maines et al., 1986; McCoubrey et al., 1997). Endogenous CO production can be increased by the up-regulation of HO-1 expression and activity by inducers such as oxidative stress, hypoxia, heavy metals, sodium arsenite , heme and heme derivatives, various cytokines, and also exogenous CO (Wu and Wang, 2005). High le vels of CO (2,500 ppm) have been shown to increase HO-1 activity in the brain of rats, as well as liberate intracellular heme to further stimulate endogenous CO production (Cronje et al., 2004). Not all endogenous CO production is derived from Hb breakdown. Other hemoproteins, such as Mb, cytochromes, peroxidases, and catalase, contri bute 20-25% to the total amount of endogenous CO (Berk et al., 1976). All of these sources result in a normal blood COHb concentration between 0.4 and 0.7% (Coburn et al., 1965). This b aseline level of endogenous production can be altered by drugs or a number of physiological conditions that alter RBC destru ction, other hemoprotein breakdown, or bilirubin production. Because of this sensitivity, ranges of endogenous COHb levels in the population are uncertain. Nicotinic acid (Lundh et al., 1975), allyl-containing compounds (acetamids and barbiturates) (Mercke et al., 1975b), diphenylhydantoin (Coburn, 1970b), progesterone (Delivoria-Papadopoulos et al., 1974), and contraceptives (Mercke et al., 1975a) w ill increase CO production. Compounds such as carbon disulfide and sulfur-containing chemicals (parathion and phenyltiourea) will increase CO by acting on P450 system moieties (Landaw et al., 1970). The P4 50 system may also cause large increases in CO produced from the metabolic degradation of dihalomethanes leading to very high (>10%) COHb levels (Manno et al., 1992), which can be further enhanced by prior exposure to HCs or ethanol (Pankow et al., 1991; Wirkner and Poelchen, 1996). HO can catalyze the release of CO from the auto-oxidation of phenols, photo-oxidation of organic compounds, and li pid peroxidation of cell membrane lipids (Rodgers et al., 1994).

March 2009 4-19 DRAFT – DO NOT CITE OR QUOTE

## Any disturbance in RBC hemostasis by acceleration of destruction of hemoproteins will lead to increased production of CO. Pathologic conditions such as anemias, hematomas, thalassemia, Gilbert’s syndrome with hemolysis, and other hematologi cal diseases and illness will accelerate CO production (Berk et al., 1974; Hampson, 2007; Meyer et al., 1998; Solanki et al., 1988). Patients with hemolytic anemia exhibit COHb levels 2- to 3-times higher than healthy individuals and CO production rates 2- to 8-times higher (Coburn et al., 1966). Endogenous CO pr oduction rate varied from 0.70 to 3.44 mL/h in anemic patients (Coburn et al., 1965). Women experi ence fluctuating COHb levels during pregnancy as well as through the menstrual cycle when endogenous CO production doubles in the progesterone phase (Delivoria-Papadopoulos et al., 1974; Mercke and Lundh, 1976). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27 28 29 30 31 32 Altitude has been shown to be positively associated with baseline COHb concentrations (McGrath, 1992; 1993). This increase in COHb with altitude i nduced hypoxia has also been associated with increases in the mRNA, protein, and activity of HO-1 in rats and cells leadi ng to enhanced endogenous CO production (Carraway et al., 2002; Lee et al., 1997). Whether other variables such as an accelerated metabolism or a greater pool of Hb, transient shifts in body stores, or a change in the elimination rate of CO play a role has not been explored. Endogenous CO is removed from the body mainly by expiration and oxidation. CO will diffuse across the alveolar-capillary membrane and then is exhaled. This event has been used as a noninvasive measurement of endogenous CO and CO body load (Ste venson et al., 1979). CO can also be oxidized to CO 2 

## by cytochrome c oxidase in the mitochondria (Fenn, 1970; Young and Caughey, 1986). However, the rates of CO metabolism are much slower than the ra tes of endogenous CO production, with the rate of consumption representing only 10% of the rate of CO production in dogs (L uomanmaki and Coburn, 1969).



# 4.6. Summary and Conclusions

CO elicits various health effects by binding with and altering the function of a number of hemecontaining molecules, mainly Hb. The formation of COHb reduces the O 2 

## -carrying capacity of blood and impairs the release of O 2 

## from O 2 

## Hb to the tissues. COHb formation has been modeled mainly by the CFK equation, but more recent models have incl uded Mb and extravascular storage compartments, as well as other dynamics of CO physiology. These models have indicated that CO has a biphasic elimination curve, due to initial washout from the bl ood followed by a slower flux from the tissues. The flow of CO between the blood and alveolar air or tissues is controlled by diffusion down the CO concentration gradient. The uptake of CO is governed not only by this CO pressure differential, but also by physiological factors, such as minute ventilation and lung diffusing capacity, that can, in turn, be affected by conditions such as exercise, age, and health. Susceptible populations, including health

March 2009 4-20 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 compromised individuals and developing fetuses, are at a greater risk from COHb induced health effects due to altered CO kinetics, compromised cardi opulmonary processes, and increased baseline hypoxia levels. Altitude may also significantly affect the kinetics of COHb formation. Compensatory mechanisms, such as increased cardiac output, co mbat the decrease in barometric pressure. Altitude also increases the endogenous production of CO through upregulation of HO-1. CO is c onsidered a second messenger and is endogenously produced from the catabolism of heme proteins by enzymes such as HO-1. Finally, CO is removed from the body by expiration and oxidation to CO 2 

## .

March 2009 4-21 DRAFT – DO NOT CITE OR QUOTE

# Chapter 5. Integrated Health Effects

5.1. Mode of Action of CO Toxicity

5.1.1. Introduction

The diverse effects of CO are dependent upon concentration and duration of exposure as well as on the cell types and tissues involved. Responses to CO are not necessarily due to a single process and may instead be mediated by a combination of ef fects including COHb-mediated hypoxic stress and mechanisms unrelated to tissue hypoxia including free radical production and the initiation of cell signaling. However, binding of CO to reduced iron in heme proteins with subsequent alteration of heme protein function is the common mechanism underlying the biological responses to CO. 

# 1 2 3 4 5 6

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 

# 5.1.2. Hypoxic Mechanisms

As discussed in the 2000 CO AQCD, the most well-known pathophysiological effect of CO is tissue hypoxia caused by binding of CO to Hb. Not only does the formation of COHb reduce the O 2 

## - carrying capacity of blood, but it also impairs the release of O 2 

## from O 2 

## Hb. Compensatory alterations in hemodynamics, such as vasodilation and increased cardiac output, protect from tissue hypoxia. At ambient CO concentrations, these compensatory changes are slight and likely tolerable in people with a healthy cardiovascular system. However, people with cardiovascular detriments may be unable to endure these small changes in hemodynamics which may lead to the presentation of health effects as described in Sections 5.2.1 and 5.2.2. The 2000 CO AQCD reported changes in vasodila tion due to CO levels between 500-2000 ppm (Kanten et al., 1983; MacMillan, 1975). Vasodilation can be dependent on or inde pendent of perturbations in O 2 

## supply (Koehler et al., 1982). For example, cerebral blood flow elevations that were independent of O 2 

## availability were blocked by the inhibition of nitric oxide synthase (NOS) indicating a role for the free radical species nitric oxide (NO • ) in mediating vasodilation (Meilin et al., 1996). Increased cardiac ouput was also di scussed in the 2000 CO AQCD as a compensatory response to CO-mediated tissue hypoxia. Findings of studies me asuring hemodynamic alterations following CO exposure were equivocal and someti mes contradictory (Penney, 1988). While most studies reported a positive correlation between COHb and cardiac output at COHb levels above 20%, one study demonstrated increased cardiac output in humans following acute exposure to 5% CO resulting in COHb levels around 9% (Ayres et al., 1973). There was no increase in cardiac output following acute exposure

March 2009 5-1 DRAFT – DO NOT CITE OR QUOTE

## to 0.1% CO in this latter study. Increased heart ra te and stroke volume (SV) were also observed in response to CO exposure (Stewart et al., 1973b); howe ver, some experiments found no change in SV in humans with 18-20% COHb (Vogel and Gleser, 1972) or 12.5% COHb (Klausen et al., 1968). The 2000 CO AQCD reported that BP was generally unchanged in human CO exposure studies, while a number of animal studies demonstrated CO-induced hypotension (Penney et al., 1988). No changes in forearm blood flow, BP, or heart rate were reported in humans experiencing approximately 8% COHb (Hausberg and Somers, 1997). However, high concentration animal exposures (3,000 to 10,000 ppm) showed diminished organ blood flow (Brown and Pian tadosi, 1992). In depth discussion of hemodynamic changes resulting from CO exposure in recent human clinical studies can be found in Section 5.2.2. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

18 19 20 21 22 23

24 25 26 27 28 29 30 Binding of CO to Mb, as discussed in the 2000 CO AQ CD and in Section 4.3. 2.3, can also impair the delivery of O 2 

## to tissues. Mb has a high affinity for CO ; however, physiological effects are seen only after high dose exposures to CO, resulting in COMb concentrations far above baseline levels. High energy phosphate production in cardiac myocytes was inhibited when COMb concentrations exceeded 40%, corresponding to an estimated COHb level between 20-40% (Wittenberg and Wittenberg, 1993). Conversely, rat hearts perfused with 6% CO (60,000 ppm) exhibited no change in high energy phosphate production, respiration rate, or contractile function (Chung et al., 2006; Glabe et al., 1998). These conflicting studies employed CO levels that ar e not relevant for ambient exposure to CO.



# 5.1.3. Non-Hypoxic Mechanisms

Non-hypoxic mechanisms underlying the biological effects of CO were discussed in the 2000 CO AQCD and are summarized below. Most of these mechan isms are related to CO’s ability to bind hemecontaining proteins other than Hb and Mb (Raub an d Benignus, 2002). Since then , additional experiments have confirmed and extended these findings. While the majority of the older studies utilized concentrations of CO far higher than ambient levels, many of the newer studies have employed more environmentally relevant levels of CO.



# 5.1.3.1. Non-Hypoxic Mechanisms Revi ewed in the 2000 CO AQCD

Inhibition of heme-containing proteins such as cytochrome c oxidase and cytochrome P450 oxidases may alter cellular function. CO interacts with the ferrous heme a 3 

## of the terminal enzyme of the electron transport chain, cytochrome c oxidase (Petersen, 1977). Cytochrome c oxidase inhibition not only interrupts cellular respiration and energy produc tion, but can also enhance reactive oxygen species (ROS) production. In vivo studies observed CO binding to cytochrome c oxidase under conditions where COHb concentrations are above 50% (Brown and Piantadosi, 1992). It is unlikely that this could arise under physiological conditions or under conditions relevant to ambient exposures.

March 2009 5-2 DRAFT – DO NOT CITE OR QUOTE

## Studies indicated that CO exposure produces a pro-oxidant cellular environment by a second mechanism. CO exposure at concentrations of 10-20 ppm and above caused platelets of laboratory rats, as well as cultured bovine pulmonary endothelial cells, to release NO • (Thom and Ischiropoulos, 1997). An increase in available NO · was also seen in the lung and brain of CO-exposed rats (Ischiropoulos et al., 1996; Thom et al., 1999b). NO • combines with superoxide to form the highly active oxidant species, peroxynitrite (Thom et al., 1997), which can lead to the activation and sequestration of leukocytes in brain vessels (Thom et al., 2001b) and aorta, oxidation of plasma lipoproteins (Thom et al., 1999a), and the formation of protein nitrotyrosine (Ischiropoulos et al., 1996; Thom et al., 1999a; Thom et al., 1999b). The mechanism leading to NO • release is likely the displacement of NO · from nitrosyl bound Mb (NO •

- Mb) or Hb (NO · -Hb) by CO. The rate of this event is slow; however it occurs at environmentally relevant concentrations of CO (Thom et al., 1997). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 CO exposure also increases the production of other pro-oxidant species, including hydrogen peroxide (H 2 

## O 2 

## ) and hydroxyl radical (OH  ̇ ). High level CO exposure (2,500 ppm) increases OH • in rat brain and this response is distinct from tissue hy poxia (Piantadosi et al., 1997). The mechanism for enhanced H 2 

## O 2 

## production is unclear. The release of H 2 

## O 2 

## in the lung of CO-exposed rats was dependent upon the production of NO • , as it was inhibited by the pretreatment with a NOS inhibitor (Thom et al., 1999b). It is possible that peroxynitrite formed after CO exposure inhibited electron transport at complexes I through III, or that cytochrome c oxidase interference led to mitochondrial dysfunction and ROS production. It has also been suggested that CO leads to vasorelaxation by three different mechanisms. First, CO may inhibit the P450-dependent synthesis of vasoco nstrictors (Wang, 1998). Vasodilation has been demonstrated via this P450-mediated mechanism with high concentrations of CO (approximately 90,000 ppm) (Coceani et al., 1988). In the case of cy tochrome P450 enzymes, tissue CO levels may have to be abnormally increased to elicit a physiological response since the in vitro Warburg binding coefficients, the ratio of CO to O 2 

## at which half the reactive sites ar e occupied by CO, for cytochrome P450s range from 0.1-12 (Pianta dosi, 2002). P450 inhibition may also reduce the hypoxia-induced expression of mitogens such as erythropoietin ( EPO), vascular endothelial growth factor (VEGF), endothelin-1 (ET-1), and platelet derived growth factor (PDGF) which may decrease smooth muscle proliferation in response to hypoxia (Wang, 1998) . CO may also interfere with the metabolism of barbiturates and other drugs; however, this is probably due to the hypoxic actions of CO rather than to P450 inhibition (Roth and Rubin, 1976a, b). Secondly, CO has been shown to play a physiological role in vasomotor control and in signal transduction by activation of soluble guanylate cy clase (sGC), causing a conversion of GTP to cyclic GMP (cGMP). CO interacts with sGC and forms an unsta ble complex with the heme core of the protein. The resulting porphyrin IX intermediate triggers cGMP production (Ndisang et al., 2004). CO causes vascular relaxation, independent of the endothelium, in human arterial rings (Achouh et al., 2008), rat tail

March 2009 5-3 DRAFT – DO NOT CITE OR QUOTE

## artery (Wang et al., 1997), and rat thoracic aorta (Lin and McGrath, 1988), but not cerebral vessels (Andresen et al., 2006; Brian et al., 1994). Ac tivation of sGC by CO has been linked to neurotransmission, vasodilation, bronchodilation, inhi bition of platelet aggregation, and inhibiton of smooth muscle proliferation (Brune and Ullrich, 1987; Cardell et al., 1998; Morita et al., 1997; Verma et al., 1993). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

28 29 30 CO-mediated vasorelaxation can also be caused by activation of voltage-activated potassium (K + 

## ) channels or calcium (Ca 2+ 

## )-activated K + 

## channels, which leads to membra ne hyperpolarization, voltagedependent Ca 2+ 

## channel closing, reduction of resting Ca 2+ 

## concentration, and then vascular tissue relaxation (Farrugia et al., 1993; Wang et al., 1997). Th is effect may be linked to sGC activity; however it has also been reported to occur independently (Dubuis et al., 2003; Naik and Walker, 2003). Developmental stage and tissue type will determine whether K + 

## channels or the sGC/cGMP pathway plays more of a role in vasorelaxation (Ndisang et al., 2004). Collectively, these older studies demonstrated that exposures to high concentrations of CO resulted in altered functions of heme proteins other than Hb and Mb. Decreased cellular respiration and energy production and increased pro-oxidant status following cytochrome c oxidase inhibition would likely predispose towards cellular injury and death. The increase in free NO • following its release from sequestered stores could also contribute to the pr o-oxidant status. Increased ROS and reactive nitrogen species are known to promote cell signaling events leading to inflammation and endothelial dysfunction. An inappropriate increase in vasorelaxation due to inhibition of vasoconstrictor production or to activation of vasodilatory pathways (sGC and i on channels) could potentially limit compensatory alterations in hemodynamics. Altern atively, CO-binding to sGC could result in decreased vasorelaxation by interfering with the binding of NO • to sGC. NO • can also activate sGC, and with a 30-fold greater affinity than CO is one-thousand fold more potent w ith respect to vasodilation and sGC activation (Stone and Marletta, 1994). CO could further contribute to endothelial dysfunction by this mechanism. Although the 2000 CO AQCD made no definitive links be tween these non-hypoxic mechanisms of CO and CO-mediated health effects, it did establish the poten tial for CO to interfere with basic cellular and molecular processes which could lead to dysfunction and/or disease.



# 5.1.3.2. Recent Studies of Non-Hypoxic Mechanisms

Since the 2000 CO AQCD, new studies have provided additional evidence for non-hypoxic mechanisms of CO which involve the binding of CO to reduced iron in heme proteins. These mechanisms, which may be inter-related, are described below and include:

March 2009 5-4 DRAFT – DO NOT CITE OR QUOTE

##  Alteration in NO • signaling 

# 1

2

3

4

5

6

11 12 13 14

15 16 17 18 19 20 21 22 23 24 25 26

27 28 29 30  Inhibition of cytochrome c oxidase

 Heme loss from protein

 Disruption of iron homeostasis

 Alteration in cellular redox status

This assessment evaluates these non- hypoxic mechanisms in terms of their potential to contribute to health effects associated with environmentally-relevant CO exposur es. As discussed above, CO at high 

# 7 concentrations may promote oxidative stress, cell injury and death, inflammation and endothelial 

# 8 dysfunction. Whether lower CO concentrations trigge r these same processes is of key interest since these 

# 9 can potentially contribute to adverse health effects. 

# 10 In addition, a large number of studies published since the 2000 CO AQCD has focused on the role of COs as an endogenous signaling molecule and the potential therapeutic effects of exogenous CO administered at high concentrations. This assessment addresses these topics only briefly, as they pertain to the evaluation of health effect s associated with environmentally-relevant CO exposures.



# Alteration in NO • Signaling

Work by Thorup et al. (1999) demonstrated altered NO • signaling in isolated rat renal resistance arteries. In one set of experiments, rapid release of NO • was observed in response to a supra-physiologic dose of CO (100 nM). These findings are similar to those of Thom and colleagues (Ischiropoulos et al., 1996; Thom et al., 1994; Thom and Ischiropoulos, 1997; Thom et al., 1997; Thom et al., 1999a; Thom et al., 1999b; Thom et al., 2000; Thom et al., 2006) who demonstrated NO • release, presumably from sequestered stores, in platelets, endothelial cells , aorta and lung in response to CO (see above). Furthermore in a second set of experiments, Thorup et al. (1999) demonstrated inhibition of NOS in isolated rat renal resistance arteries. Here rapid NOS-dependent release of NO • following carbachol stimulation was blocked by pretreatment with 100 nM NO • . Both sets of studies illustrate the potential of CO to alter processes dependent on endogenous NO • . This could be critical in the case of cGMPmediated vasodilation since, as discussed above, NO • activates sGC to a greater extent than CO. Thus in the presence of excess CO, NO • -dependent vasodilation may be significantly less. 

# Inhibition of Cytochrome c Oxidase

High concentrations of CO are known to inhibit cytochrome c oxidase, the terminal enzyme in the mitochondrial electron transport chain, resulting in inhi bition of cellular respira tion and the formation of superoxide from mitochondr ial substrates. Several recent studies demonstrated CO-mediated decreases in cytochrome c oxidase activity in model systems ranging from isolated mitochondria to whole animals. In

March 2009 5-5 DRAFT – DO NOT CITE OR QUOTE

## a study by Alonso et al. (2003), exposure of isolated mitochondria from human skeletal muscle to 50- 500 ppm CO for 5 min decreased cytochrome c oxidase activity. Similarly, exposure of cultured macrophages to 250 ppm CO for 1 h inhibited cytochrome c oxidase (Zuckerbraun et al., 2007). In this study, increased ROS were observed following exposur e to 250 ppm, as well as to CO concentrations as low as 50 ppm, for 1 h. Animal studies demonstrated that exposure of rats to 250 ppm CO for 90 min inhibited cytochrome c oxidase activity in myocardial fibers (Favory et al., 2006a) and exposure of mice to 1,000 ppm CO for 3 h decreased cytochrome c oxidase activity in heart mitochondria (Iheagwara et al., 2007). COHb levels were reported to be 61% in the latter study. 

# 1 2 3 4 5 6 7 8

9 10 11 12 13

14 15 16 17 18 19 20

21 22 23 24 25 26 27 28 29 30 

# Heme Content Loss from Proteins

In addition to decreasing the activity of cyctochr ome c oxidase, exposure of mice to 1,000 ppm CO for 3 h resulted in decreased protein levels and heme content of cytochrome c oxidase (Iheagwara et al., 2007). CO-mediated heme release was also seen in a study by Cronje et al. (2004), and was followed by increased endogenous CO production th rough 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 le vels of iron and ferritin in the bronchoalveolar lavage fluid (BALF), decreased lung non-heme iron and increased liver non-heme iron (Ghio et al., 2008). Furthermore in this same study, exposure of re spiratory epithelial cells to 10-100 ppm CO for 24 h caused a dose-dependent decrease in cellular non-heme iron and ferritin. Heme loss observed in other studies (Cronje et al., 2004; Iheagwara et al., 2007) might be expected to result in disruption of ironhomeostasis. Iron homeostasis is critical for the sequestration of free iron and the prevention of ironmediated redox cycling which can lead to ROS generation and lipid peroxidation. 

# Alteration in Cellular Redox Status

Recent studies demonstrated that exposure to low, moderate and high levels of CO increased cellular oxidant stress in cultured cells (Kim et al., 2008; Zuckerbraun et al., 2007). A dose-dependent increase in dichlorofluoroscein (DCF) fluorescence (a n indicator of ROS) occurred following 1-h exposure to 50-500 ppm CO in macrophages and following 1-h exposure to 250 ppm CO in hepatocytes. NOS inhibition had no effect on the increase in DCF fluorescence in CO-treated macrophages indicating that the effects were not due to an interaction of CO and NO • (Zuckerbraun et al., 2007). Mitochondria were identified as the source of the increased ROS si nce mitochondria-impaired cells (rho zero cells and treatment with antimycin A) did not respond to CO with an increase in DCF fluorescence. Furthermore, 1-h exposure to 250 ppm CO inhibited mitochondrial cytochrome c oxidase enzymatic activity in macrophages (Zuckerbraun et al., 2007). Recently, inhibition of cytochrome c oxidase was demonstrated

March 2009 5-6 DRAFT – DO NOT CITE OR QUOTE

## in HEK-293 cells transfected with HO-1 and in macr ophages with induced HO-1, and this effect was attributed to endogenously produced CO (D'Amico et al., 2006). However inhibition of cytochrome c oxidase at CO concentrations below 250 ppm has not been definitively demonstrated. In hepatocytes, exposure to 250 ppm CO for 1 h resulted in Akt phos phorylation and nuclear translocation of nuclear factor kappa B (NF- κ B), effects which were blocked by antioxidants (Kim et al., 2008). Significant increases in apoptosis were also observed in this model. Thus, CO appeared to uncouple mitochondrial respiration leading to ROS-induced programmed cell death. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

27

28 29 30 Further evidence for cellular redox stress is provi ded by studies in which glutathione stores were altered following CO exposure in vitro (Kim et al., 2008; Patel et al., 2003). In addition, mitochondrial redox stress was observed in livers of rats exposed to 50 ppm CO (Piantadosi et al., 2006). Furthermore, an adaptive increase in intracellula r antioxidant defenses (i.e., superoxide dismutase) was observed in endothelial cells exposed to 10 ppm CO for 40 min (T hom et al., 2000) and mitochondrial biogenesis was observed in hearts of mice exposed to 50 and 250 ppm CO for 1 h (Suliman et al., 2007). Several mechanisms could contribute to the ce llular redox stress elicited by CO exposure. As discussed above, inhibition of cytochrome c oxidase results in the formation of superoxide from mitochondrial substrates. However, inte ractions of CO with heme proteins can lead to the release of heme and free iron which could also lead to the generation of ROS. As mentioned above, increased ROS generation has been linked to cellular injury and d eath, 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 mitochondr ial pore transition observed in livers from rats exposed to CO (Piantadosi et al., 2006) and the cardiac mitochondr ial biogenesis observed in mice exposed to CO (Suliman et al., 2007) could be attributed sp ecifically to non-hypoxic mechanisms of CO. Recent studies also demonstrated non-hypoxic mech anisms of CO which do not directly involve heme protein interactions. These mechanisms are described below and include:

 Alteration in ion channel activity

 Modulation of protein kinase pathways 

# Alteration in Ion Channel Activity

Work by Dubuis et al. (2002) demonstrated increased current through Ca 2+ 

## -activated K + 

## channels in smooth muscle cells from pulmonary arteries of rats exposed to 530 ppm CO for 3 weeks. These findings provide further evidence for non cGMP-dep endent vasodilatory actions of CO.

March 2009 5-7 DRAFT – DO NOT CITE OR QUOTE

# Modulation of Protein Kinase Pathways

Endogenously produced CO is a gaseous second messenger molecule in the cell. Work from numerous laboratories has demonstrated the potential for CO to be used as a therapeutic gas with numerous possible clinical applications, since it can produce anti-inflammatory, anti-apoptotic, and antiproliferative effects (Ryter et al., 2006). These studies generally involved pretreatment with CO followed by exposure to another agent 18-24 h later. There is extensive literature on this topic as reviewed by Ryter et al. (2006) and others. A number of these processes are mediated through cGMP while others involve redox-sensitive kinase pathways, possibly secondary to CO-dependent generation of ROS. For example, 250 ppm CO inhibited growth of airway smooth mu scle cells by attenuating the activation of the extracellular signal-regulated kinase 1/2 (ERK 1/2) pathway, independent of sGC and other MAP kinases (Song et al., 2002). A second example is provided by the study of Kim et al. (2005) where 250 ppm CO inhibited PDGF- induced smooth muscle cell proliferation by upregulating p21 Waf1/Cip1 

## and caveolin-1, and down-regulating cyclin A expression. These events were dependent upon cG MP and the p38 MAPK pathway (Kim et al., 2005). Thirdly, rat endothelial cells exposed to 15 ppm CO escaped anoxiareoxygenation mediated apoptosis via modulation of the signaling pathways involving phosphoinositide 3-kinase (PI3K), Akt, p38 MAP kinase, Signal Transdu cers and Activators of Transcription (STAT-1) and STAT-3 (Zhang et al., 2005). In a fourth study, Akt was found to be responsible for the CO-induced activation of NF- κ B, protecting against hepatocyte cell death (Kim et al., 2008). While research focusing on therapeutic applications of CO generally involves high level, short-term exposure to CO (250- 1,000 ppm for up to 24 h), some studies found effects below 20 ppm (Zhang et al., 2005). Few if any studies on the therapeutic effects of CO have expl ored the dose-response relationship between CO and pathway activation/deactivation, so it remains unclear how these effects may be related to environmentally-relevant exposures. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27 28 29 30 31 

# Concentration-Response Relationships

In many cases the concentrations of exogenous CO required for these non-hypoxic mechanisms is much higher than what would be expected to result from exposures at ambient levels (Alonso et al., 2003; Favory et al., 2006b; Iheagwara et al., 2007; Thorup et al., 1999). However in some studies the effects are mimicked by upregulation of HO-1 which would result in increased local production of CO as well as of iron and biliverdin (D'Amico et al., 2006; Imai et al., 2001; Thorup et al., 1999). For example, HO-1 overexpression attenuated carbachol-mediated NO • release and NO • -mediated vasodilation, similar to the effects of exogenous CO in these same models (Imai et al., 2001; Thorup et al., 1999). In the study by D’Amico et al. (2006), overexpression of HO-1 in cells inhibited cellular respiration by 12% and decreased cytochrome c oxdase activity by 23%. It is not clear how comparable these conditions

March 2009 5-8 DRAFT – DO NOT CITE OR QUOTE

## involving increased intracellular concentrations of endogenous CO are to increased intracellular concentrations of CO resul ting from ambient CO exposures. 

# 1 2 3 4 5 6 7 8 9 10 11 12 There is a growing appreciation that non-hypoxic mechanisms may contribute to the effects associated with CO poisoning (Ischiropoulos et al., 1996; Thom et al., 1994; Weaver et al., 2007). On the other hand, recent studies suggest that exogenous CO at low concentrations may have beneficial antiinflammatory, anti-proliferative and cytoprotective effects under certain circumstances (Ryter et al., 2006). Since the focus of this assessme nt is on mechanisms which are relevant to ambient exposures it is important to understand which mechanisms may o ccur at “low” (50 ppm and less) and “moderate” (50-500 ppm CO) concentrations of CO. Hence, both recent animal studies and relevant older ones which add to the understanding of mechanisms in this range of CO concentrations are briefly summarized in Table 5-1. It should be noted that most of the above-mentioned non-hypoxic mechanisms are demonstrated at CO concentrations of 50 ppm and less.



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

Reference Model System CO Exposure Response Notes

IN VITRO

Alonso et al. (2003) Human muscle mitochondria 50, 100, 500 ppm 5 min Decreased cytochrome c oxidase activity

Thom and Ischiropoulos (1997) Rat platelets 10 ppm Increased free NO •

Thom et al. (1997) Bovine pulmonary artery endothelial cells 20 ppm 30-60 min Increased free NO • and peroxynitrite Reported to correspond to 7% COHb

Thom et al. (2000) Bovine pulmonary artery endothelial cells 10 ppm 40 min Increased MnSOD and protection against toxic effects of 100 ppm CO Adaptive responses

Song et al. (2002) Human aortic smooth muscle cells 50-500 ppm 24 h Inhibition of cellular proliferati on Blocked activation of ERK1/2 pathway, independent of sGC and other MAP kinases Kim et al. (2005) Ra t pulmonary artery smooth muscle cells 250 ppm 1 h Inhibited PDGF- induced smooth muscle cell proliferation Upregulated p21 Waf1/Cip1 and caveolin1, and down-regulated cyclin A expression. Kim et al. (2008) Rat hepatocytes 250 ppm 1 h 2x per day 250 ppm 1 h Blocked spontaneous apoptosis Increased mitochondrial ROS generation, increased mitochondrial glutathione oxidation, and decreased cellular ascorbic acid CO induced Akt phos phorylation via ROS production CO activated NF κ B

Zhang et al. (2005) Rat pulmonary artery endothelial cells 15 ppm 0.5-24 h Blocked anoxia-reoxygenation mediated apoptos is Modulation of PI3K/Akt/p38 MAP kinase and STAT-1 and STAT-3

Zuckerbraun et al. (2007) Mouse macrophages 50 and 250 ppm 1 h Increased ROS generation (dose dependent response for 50-500 ppm CO) Mitochondrial derived ROS and cytochrome c oxidase inhibition demonstrated for 250 ppm



### March 2009 5-9 DRAFT – DO NOT CITE OR QUOTE

### Reference Model System CO Exposure Response Notes Ghio et al. (2008) Human bronchial epithelial cells 10-100 ppm 24 h Dose-dependent decrease in cellular non-heme iron (effect at 10 ppm was signficant, effect at 50 ppm maximal) Dose-dependent decrease in cellular ferritin at 50- 100 ppm 50 ppm blocked iron uptake by cells 50 ppm increased iron release from cells Compare with in vivo experiment in same paper

IN VIVO Ghio et al. (2008) Rats 50 ppm 24 h Mild neutrophil accumulation in BALF Increased lavage MIP-2, protein, LDH Lavage iron and ferritin were increased by CO Lung non-heme iron was decreased by CO Liver non-heme iron was increased by CO Thom et al., (1999b) Rats 50 ppm 1 h

100 ppm 1 h Increased nitrotyrosine in aorta Leukocyte sequestration in aorta after 18 h Albumin efflux from skeletal muscle microvasculature 3 h after CO LDL oxidation Effects blocked by NOS inhibitor

Thom et al., (1999a) Rats 100 ppm 1 h 50 ppm 1 h Elevated free NO • during CO exposure (EPR) Elevated nitrotyrosine in lung homogenates Lung capillary leakage 18 h after exposure Inhibition of NOS abrogated CO effects

Sorhaug et al. (2006) Rats 200 ppm 72 weeks No changes in lung morphology No pulmonary hypertension No atherosclerotic lesions in systemic vessels Ventricular hypertrophy Loennechen et al. (1999) Rats 100 and 200 ppm 1-2 weeks Increased ET-1 mRNA in the heart ventricles, increased right and left ventricular weight 12 and 23% COHb

Favory et al. (2006b) Rats 250 ppm 90 min Complex IV inhibition in myocardial fibers Inhibition of vasodilatory response to acetylcholine and SNP, Increased coronar y perfusion pressure and contractility 11% COHb

Piantadosi et al. (2006) Rats 50 ppm CO or HH for 1, 3, or 7 days Liver mitochondrial oxidative and nitrosative stress, altered mitochondrial permeability pore transition sensitivity CO effects not mimicked by HH

Suliman et al. (2007) Mice 50 and 250 ppm 1 h Cardiac mitochondrial biogenesis Activation of GC involved. No role for NOS. Increased mitochondrial H 2 O 2 and activation of Akt proposed Wellenius et al. (2004) Rats Model of MI 35 ppm 1 h Decreased delayed ventricular beat frequency Altered arrhythmogenesis

Wellenius et al. (2006a) Rats Model of MI 35 ppm 1 h Decreased supraventricular ectopi c beats Altered arrhythmogenesis

Carraway et al. (2002) Rats Model of hypoxic pulmonary vascular remodeling Hypobaric hypoxia ± 50 ppm CO 3 weeks CO promoted remodeli ng and increased pulmonary vascular resistance

Gautier et al. (2007) Rats Model of right ventricle hypertrophy secondary to chronic hypoxia 3 weeks of HH with 50 ppm CO during last week Rats with pulmonary hypertension were more sensitive to CO which altered the right ventricular adaptive response to pulmonary hypertension leading to ischemic lesions

Melin et al. (2005) Rats Model of right ventricle hypertrophy secondary to chronic hypoxia 50 ppm 10 wk CO increased cardiac dilation and decreased left ventricular function



### March 2009 5-10 DRAFT – DO NOT CITE OR QUOTE

### Reference Model System CO Exposure Response Notes

Melin et al. (2002) Rats Model of right ventricle hypertrophy secondary to chronic hypoxia 50 ppm 10 wk CO increased right ventricular hypertrophy, decreased right ventricular diastolic function and increased left ventricular weights



# 5.1.3.3. Implications of Non-Hypoxic Mechamisms

A key issue in understanding the biological effect s of environmentally-relevant exposures to CO is whether the resulting partial pressures of CO in ce lls and tissues can initiate cell signaling or perturb signaling which normally is mediated by endogenously generated CO. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Several aspects need to be considered. One is that, for most cells and tissues, Hb acts as a buffer to limit the availability of free CO. At the same ti me, COHb delivers CO to cells and tissues. CO dissociation from Hb occurs as a function of CO diffusion down a concentration gradient. Hence, greater release of CO from COHb will occur under c onditions of low cell/tissue CO concentration. A second consideration is the role played by O 2 

## 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 O 2 

## is limited. Hence hypoxic conditions would be expected to enhance the effects of CO. This concept is demonstrated in the study by D’Amico et al. (2006). A third consideration is whether certain cell types serve as primary targets for the effects of CO. Besides the RBC, the first cells encountering CO whic h dissociates from Hb will be the endothelial cells lining blood vessels. White blood cells in the circulati on may also be first-line targets of Hb-dissociated CO. An exception to this situation is in the lungs where epithelial and inflammatory cells found in airways and alveoli are exposed to free CO prior to CO binding to Hb. These lung cells may also serve as unique targets for CO. Processes such as endothelia l dysfunction, inflammatory cell activation and respiratory epithelial injury may ensue as a result of preferential targeting of these cell types. Fourth, it should be considered that adaptation to chronic exogenous CO exposure might occur and that intermittent exogenous CO exposure might have unexpected effects. Since there is potential for exogenous CO to aff ect endogenous pools of CO, it is important to know the concentrations of CO in cells and tissu es before and after exogenous exposures. Table 5-2 summarizes findings from 4 recent studies relevant to this issue. It should be noted that exposure to 50 ppm CO resulted in a 3-5 fold incr eased in tissue CO concentration.

March 2009 5-11 DRAFT – DO NOT CITE OR QUOTE

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

Reference CO Exposure Tissue CO Concentrations COHb Notes Cronje, et al. (2004) Rat 2,500 ppm 45 min Blood: 27,500 (800) pmol/mg Heart: 800 (300) pmol/mg Muscle: 90 (80) pmol/mg Brain: 60 (40) pmol/mg Control levels in parentheses 66-72% CO concentration increased in the heart but not in brain or skeletal muscle after CO exposure A later report stated that these tissue CO values were too high due to a computational erro r (Piantadosi et al., 2006)

Vreman, et al. (2005) Mice 500 ppm 30 min Blood: 2648 ± 400 (45) pmol/mg Heart: 100 ± 18 (6) pmol/mg Muscle: 14 ± 1 (10) pmol/mg Brain: 18 ± 4 (2) pmol/mg Kidney: 120 ± 12 (7) pmol/mg Spleen: 229 ± 55 (6) pmol/mg Liver: 115 ± 31 (5) pmol/mg Lung: 250 ± 2 (3) pmol/mg Intestine: 9 ± (4) pmol/mg Testes: 6 ± 3 (2) pmol/mg Control levels in parentheses 28% CO concentration relative to 100% blood: Lung: 9.4% Spleen: 8.6% Kidney: 4.5% Liver: 4.3% Heart: 3.8% Brain: 0.7% Muscle: 0.5% Intestine: 0.3%, Testes: 0.2%

Piantadosi et al. (2006) Rats 50 ppm 1-7 days Liver: 30-40 pmol/mg Control liver 10 pmol/mg 4-5% Control 1% CO concentration plateaued after 1 day

Suliman et al. (2007) Mice 50, 250 and 1250 ppm 1 h Heart (left ventricle) 50 ppm: 50 pmol/mg 250 ppm: 95 pmol/mg 1250 ppm: 160 pmol/mg Control heart: 9 pmol/mg No mention of COHb% but exposures were similar to those in Cronje et al. (2004)



# 1 2 3 4 Furthermore, endogenous CO production is increased during inflammation, hypoxia, increased heme availability and other conditio ns where HO-1 or HO-2 activity is increased. A few studies reported increased COHb levels and/or cell and tissue concen trations of CO resulting from enhanced endogenous CO production. Table 5-3 summarizes these findings.



# Table 5-3. Tissue concentr ation of CO following in creased endogenous production.

Reference Exposure Tissue CO COHb Notes Carraway,et al. (2000) Rats Hypobaric Hypoxia for 21 days 1.5-2.8% Control 0.5% COHb highest after days 1 and 21 at 3-4 fold higher than controls

Piantadosi et al. (2006) Rats Hypobaric Hypoxia 1-7 days Liver: 5-12 pmol/mg Control liver 10 pmol/mg 1-1.25% Control 1% CO concentration plateaued after 1 day

Vreman et al. (2005) Mice 30 μ M 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%



### March 2009 5-12 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

18 19 20 21

22 23 24 25 26 It should be noted that increased cellular and tissue concentrations of biliverdin and iron accompany the increased endogenous production of CO by HO-1 and HO-2. Biliverdin and iron have known biological effects, with biliverdin exhibiting antioxidant properties and iron exhibiting pro-oxidant properties (Piantadosi, 2008), which could impact interpretation of results from studies in which HO-1 and HO-2 activities are increased. Hence the situations of increased endogenous CO production and that of exogenous CO exposure are not e quivalent. Nonetheless, additional measu rements of CO levels in cells and tissues following increased endogenous production and following inhalation of exogenous CO is essential for a better understanding of the relationship between the CO tissue dose and the response. In summary, CO is a ubiquitous cell signaling mo lecule and the physiological functions of HOderived CO are numerous. The endoge nous generation and release of CO from HO-1 and HO-2 is tightly controlled, as is any homeostatic process. Thus, exogenously-applied CO has the capacity to disrupt myriad heme-based signaling pathways due to its nonspecific nature. Only a limited amount of information is available regarding the impact of exogenous CO on tissue and cellular levels of CO. However recent animal studies demonstrated increased tissue CO levels and biological responses following exposure to 50 ppm CO. Whether or not envi ronmentally relevant exposures to CO can affect endogenous CO signaling pathways and lead to adverse health effects is an open question for which there are no definitive answers at this time.



# 5.2. Cardiovascular Effects

This section characterizes the evidence from epidemio logic, controlled human exposure and animal toxicological studies on the cardiovascular effects of CO. While epidemiologic studies evaluated the effects of ambient exposures, experimental studies em ployed higher than ambient concentrations of CO but not levels of exposure associated with poisoning.



# 5.2.1. Epidemiologic Studies

The 2000 CO AQCD concluded that epidemiologi c studies provided evidence that short-term variations in ambient CO concentrations were associ ated with daily hospital admissions for heart disease. The following section reviews the literature si nce the 2000 CO AQCD, including new studies on physiological cardiac endpoints and biomarkers and a dditional studies of daily hospital admissions for heart disease that support the ev idence in the 2000 CO AQCD.

March 2009 5-13 DRAFT – DO NOT CITE OR QUOTE

# 5.2.1.1. Epidemiologic Studies with Short-Term Exposure Heart Rate and Heart Rate Variability

Heart rate variability (HRV) refers to the beat-to- beat alterations in the heart and is generally determined by analyses of time and frequency domains measured by electrocardiograms (ECG). The time domains often analyzed are (a) normal-to-normal (NN or RR) time interval between each QRS complex, (b) standard deviation of the normal-to-normal in terval (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 anal yzed 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 (PNN 50 

## ), reflecting autonomic balance. D ecreased HRV is associated with a variety of adverse cardiac outcomes such as arrhythmia , myocardial infarction (MI), and heart failure (De Jong and Randall, 2005; Deedwania et al., 2005; Hu ikuri et al., 1999; Rajendra Acharya et al., 2006). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Two studies investigated the association between am bient air pollution, including CO, and HRV in Boston, MA and reported inconsistent results. The earlier of these studies recruited twenty-one 53-to 87-year old active residents and performed up to 12 ECG assessments on each subject over a period of 4 months (during summer 1997). Particles (PM 10 

## , PM 2.5 

## ) and several gaseous pollutants (O 3 

## , NO 2 

## , and SO 2 

## ) were monitored at fixed sites (up to 4.8 miles fro m the study site) while CO was monitored 0.25 miles from each participants’ residence. Lag periods for the preceding 1-h, 4-h, and 24-h before each subject’s HRV assessment were analyzed and results showed that only PM 2.5 

## and O 3 

## were associated with HRV parameters (Gold et al., 2000). A similar study by the same group of researchers two years later involved 28 older subjects (aged 61-89) who were living at or near an apartment complex located on the same street as the Harvard School of Public Health. The subjects were seen once a week for up to 12 weeks and HRV parameters (SDNN, r-MSSD, PNN 50 

## , LF/HF ratio) were measured for 30 minutes each session. Data for PM 2.5 

## , black carbon (BC), and CO were recorded at the Harvard School of Public Health (<1 km from the residence) while data for NO 2 

## , O 3 

## , and SO 2 

## were collected from government regulatory monitoring sites. There were moderate correlations between CO and PM 2.5 

## (r = 0.61) and NO 2 

## (r = 0.55), but not with SO 2 

## (r = 0.18) or O 3 

## (r = 0.21). Similarly PM 2.5 

## was associated with HRV, whereas in contrast to the previous study, CO was associated 1 

## 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 PNN 50 

## (% change: -46.25 [95% CI -103.95 to -9.38] per 0.5 ppm increase in 24-h avg CO concentration) (Schwartz et al., 2005).

1 The effect estimates from epidemiologic studies have been sta ndardized to a 1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO con centrations, and 0.5 ppm for 24-h avg CO c oncentrations throughout this section (tex t, tables, and figures).



### March 2009 5-14 DRAFT – DO NOT CITE OR QUOTE

## A later Boston, MA study examined HRV parameters (SDNN, LF, HF, LF/HF ratio) among 603 persons from the Normative Aging Study, a longitudinal study that originally recruited 2,280 men in the greater Boston area during 1963. The cohort members were examined (November 2000 to October 2003) and the ECG data were linked to air pollution data for PM 2.5 

## , particle number concentration, BC, O 3 

## , NO 2 

## , SO 2 

## , and CO. Lagged pollutant effects for a 4-h, 24-h , and 48-h moving avg were used. Since previous studies established variable CO results, the main pollutant effects were with PM 2.5 

## and O 3 

## while CO was not associated with HRV (Park et al., 2005b). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 A study in Mexico City selected 30 subjects from the outpatient clinic at the National Institute of Cardiology and followed them for ~10 hours (starting at 0900 hrs) (Riojas-Rodriguez et al., 2006). Each subject was connected to a Holter ECG monitor (e.g ., a portable ECG monitor) and also given personal PM 2.5 

## and CO monitors. The subjects went about their usual daily activities and the personal PM 2.5 

## and CO data were linked to various EC G parameters (heart rate [HR], R-R, LF, HF) at various lags. In copollutant models (PM 2.5 

## and CO) personal CO exposure for th e same 5-min period was significantly associated with a decrease in LF and very low energy frequency (VLF) parameters with coefficients equal to -0.024 (95% CI: -0.041 to -0.007) and -0.034 (95% CI: -0.061 to -0.007) respectively for a 1 ppm increase in 1-h CO concentration. In Mexico City, 34 residents from a nursing home underwent HRV analysis every other day for 3 months (Holguin et al., 2003). Exposure assessment for ambient PM 2.5 

## was based on data recorded at a monitor on the roof of the nursing home while exposures to ambient O 3 

## , NO 2 

## , SO 2 

## , 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 O 3 

## and PM 2.5 

## were positively associated with HRV. Wheeler et al. (2006) examined 18 individuals with COPD and 12 individuals with recent MI living in Atlanta, GA. Morning ECG readings were coll ected by a Holter system by a field technician in the subjects’ homes. Ambient air pollution exposures for PM 2.5 

## , O 3 

## , NO 2 

## , SO 2 

## 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 NO 2 

## and PM 2.5 

## , no quantitative results were reported for CO. After reviewing 2,000 patient charts, Dales (2004) recruited 36 subjects with CAD from the Toronto Western Hospital’s noninvasive cardiac diagnostic unit. HR and HRV (SDNN, N-N, HF, LF, HF/LH ratio) were assessed 1 day each week for up to 10 weeks by a Holter monitoring system. Personal air sampling for PM 2.5 

## and CO was carried out for the same 24-h period whereby subjects went about their usual daily activities for that period. Stratified results showed that among those not on beta-receptorblockers, personal CO exposure w as 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 PM 2.5 

## was not associated with HRV.

March 2009 5-15 DRAFT – DO NOT CITE OR QUOTE

## HR was examined among a sub-sample of the Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) study (n = 2,681) in Augsburg, Germany by Peters and colleagues (1999a). Total suspended particles (TSP), SO 2 

## , 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 pp m 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. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Liao et al (2004) investigated men and women aged 45-64 years from the Atherosclerosis Risk in Communities (ARIC) study (Washington County, MD; Forsyth County, NC; and selected suburbs of Minneapolis, MN). The sample sizes were 4,899, 5,4 31, 6,232, 4,390 and 6,784 for analyses involving PM 10 

## , O 3 

## , CO, NO 2 

## , and SO 2 

## respectively. County level exposure estimates for 24 h CO were calculated for 1, 2, and 3 days prior to clinical examination. A 0.5 ppm increase in 24-h CO concentration (at lag 1) was associated with an increase in HR (beats/minute) ( β = 0.357, p <0.05). CO was not significantly associated with changes in SDNN. The Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air (ULTRA) study was carried out in three European cities: Amsterdam, the Netherlands, Erfurt, Germany, and Helsinki, Finland, whereby a panel of subjects with CAD was fo llowed for 6 months with biweekly clinical visits, which included an ECG reading to assess HRV (Timonen et al., 2006). The time domain measures of HRV (SDNN and rMSSD) were analyzed along with fr equency domain measures, which included power spectrum densities for LF and HF. Exposures to ambient air pollution (PM 2.5 

## , PM 10 

## , NO 2 

## , CO) were derived from data recorded at fixed monitoring site ne tworks within each city. Correlation coefficients for NO 2 

## and CO ranged from 0.32 to 0.86 in the three cities. CO was moderately correlated with PM 10 

## in Helsinki (r = 0.40) and with PM 2.5 

## in Amsterdam (r = 0.58) and more highly correlelated with PM 10 

## in Erfurt (r = 0.77). Various lag periods were examined including lag 0 (24 h prior to the clinical visit) through a 0-2-day avg lag and a 0-4-day avg lag. In total there were 1,266 ECG recordings used in the final analyses. In the pooled analyses (e.g., across cities) a 0.5 ppm increase in 24-h CO concentration was associated with a decrease in LF/HF ratio at lag 1-day ( β -16.4 [95% CI: -29.9 to -0.3]), and a decrease in SDNN and HF at lag 2-day ( β -3.4 [95% CI: -6.1 to -0.4]; β = -17.6 [95% CI: -34.4 to -0.9], respectively). However, the same study reported no effect for CO on BP and HR (Ibald-Mulli et al., 2004). A small panel study in Kuopio, Finland, which was designed as the pilot study for the ULTRA study examined simultaneous ambulatory ECG and personally monitored CO readings among 6 male patients with CAD (Tarkiainen et al., 2003). The patient s were asked to follow their usual daily activities, but data were recorded only three times with 1-week intervals. The CO exposures were divided into low ( ≤ 2.7 ppm) and high (>2.7 ppm) and during the high CO exposure r-MSSD increased on average by 2.4 ms. However, there was no effect on RR or SDNN.

March 2009 5-16 DRAFT – DO NOT CITE OR QUOTE

## A study in Taiwan recruited 83 patients (age d 40-75) from the National Taiwan University Hospital, Taipei and conducted ambulatory ECG r eadings using a Holter system (Chan et al., 2005). Ambient air pollution exposures for PM 10 

## , NO 2 

## , SO 2 

## , and CO were derived fro m 12 fixed site monitoring stations across Taipei. Lag periods of 1 h to 8 h prior to the ECG reading were analyzed and only NO 2 

## was associated with HRV parameters (SDNN and LF). CO was not associated with HRV. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 The ST-segment of an ECG represents the period of slow repolarization of the ventricles and ST-segment depression can be associated with adverse cardiac outcomes. Gold et al. (2005) recruited a panel of 28 older adults living at or near an apartmen t complex located within 1 km of a monitoring site in Boston, MA. Each subject underwent weekly ECGs for 12 weeks in summer 1999 with the main outcome of interest being the ST-segment. Air pollution data in the form of PM 2.5 

## , black carbon (BC), and CO were collected from a central site within 0.5 km of the r esidences of the subjects and averaged over various lag periods (1-24 h, 12 h and 24 h moving average [ma] ) before the ECG. The final analyses included 24 subjects with 269 observations and results showed cons istent negative associations of ST-segment level with increased BC with the strongest association with the 5-h lag. CO during the same lag period also showed a negative association with ST-segment de pression, however only BC remained significant in multipollutant models. In summary, few studies have examined the effect of CO on HR and while two of the three studies reported a positive association, further research is warr anted to corroborate the current results. Similarly, while a larger number of studies have examined th e 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 studi es as to the HRV parameters that are positively associated with CO exposure. Table 5-4 shows a summary of the reviewed studies.



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

Study Location (Sample Size) Cardiac Endpoint Exposure Assessment Mean CO Level (ppm) Copollutants

Gold et al. (2000) Boston, MA (n = 21) HR, SDNN, r-MSSD Ambient Mean: 0.47(24 h) Range: 0.12-0.82 PM 10 , PM 2.5 , O 3 , NO 2 , SO 2 Schwartz et al. (2005) Boston, MA (n = 28) SDNN, r-MSSD, PNN, LF/HF Ambient 25th, 50th, 75th percentiles: 0.38, 0.45, 0.54 PM 2.5 , BC, NO 2 , O 3

Park et al. (2005b) Boston, MA (n = 4 97) SDNN, LF, HF, LF/HF Ambi ent Mean: 0.50 (24 h) Range: 0.13-1.8 PM 2.5 , BC, O 3 , NO 2 , SO 2

Riojas-Rodriguez et al. (2006) Mexico City, Mexico (n = 30) HF, LF, VLF, HR, R-R Personal Mean: 2.9 (11 h) Range: 0.1-18 PM 2.5

Holguin et al. (2003) Mexico City, Mexico (n = 34) HF,Double check LF, LF/HF Ambient Mean: 3.3(24 h) Range: 1.8-4.8 PM 2.5 , O 3 , NO 2 , SO 2



### March 2009 5-17 DRAFT – DO NOT CITE OR QUOTE

### Study Location (Sample Size) Cardiac Endpoint Exposure Assessment Mean CO Level (ppm) Copollutants

Wheeler et al. (2006) Atlanta, GA (n = 30) SDNN, r-MSSD, PNN, LF, HF, LF/HF Ambient Mean: 362 ppb (4h) 25th, 50th, 75th percentiles: 221.5, 304.3, 398.1 PM 2.5 , O 3 , NO 2 , SO 2

Dales et al. (2004) Toronto, Canada (n = 36) SDNN, HF, LF, LF/HF, N-N Personal Mean: 2.4* Range: 0.4-16.5 PM 2.5

Peters et al. (1999a) Augsburg, Germany (n = 2681) HR Ambient Mean: 3.6 Range: 1.5-7.1 TSP, SO 2

Liao et al. (2004) Mary land, North Carolina, Minnesota, (n = 4899-6784) HR, SDNN, LF, HF Ambient Mean: 0.65 (24 h) PM 10 , O 3 , NO 2 , SO 2

Timonen et al. (2006) Amsterdam, the Netherlands; Erfurt, Germany; Helsinki, Finland (n = 131) SDNN, HF, LF/HF Ambi ent Mean: 0.35-0.52 Range: 0.09-2.17 PM 2.5 , PM 10 , NO 2

Ibald-Mulli et al. (2004) Amsterdam, the Netherlands; Erfurt, Germany; Helsinki, Finland (n = 131) BP, HR Ambient Mean: 0.35-0.52 Range: 0.09-2.17 UFP, PM 10 , PM 2.5 , NO 2 , SO 2

Tarkiainen et al. (2003) Kuopio, Finland (n = 6) PNN, SDNN, r-MSSD Personal Mean: 4.6 Range: 0.5-27.4 None

Chan et al. (2005) Taipei, Taiwan (n = 83) SDNN, r-MSSD, LF Ambient Mean: 1.1 Range: 0.1-7.7 PM 10 , NO 2 , SO 2

Gold et al. (2005) Boston, MA (n = 28) ST-segment Ambient Median: 0.56 (12 h) Maximum: 1.04 PM 2.5 , BC

*95th percentile of 24-h levels



# Arrhythmia

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Cardiac arrhythmia refers to a broad group of co nditions where there is irregular electrical activity in the heart. The main types of arrhythmias are fibrillation, tachycardia, and bradycardia, all of which can be associated with the upper (atria) and lower (ventricle) chambers of the heart. Briefly, fibrillation refers to when a chamber of the heart quivers chaotically ra ther than pumps in an orderly fashion, tachycardia refers to a rapid heart beat (e.g., >100 beats/minute) while bradycardia refers to a slow heart beat (e.g., <60 beats/minute). A few air pollution panel st udies have examined th e occurrence of cardiac arrhythmias by analyzing data recorded by implan table cardioverter defibrillators (ICDs) among cardiac patients. The majority of these studies were conduc ted in North America with the main outcome investigated being tachycardia. Results of these st udies provide little evidence for an association between cardiac arrhythmia and ambient CO. For example, Dockery and colleagues (2005) an alyzed the relationship between ambient air pollution and the daily incidence of ventricular tach yarrhythmia among 203 patients with ICDs in Boston, MA. An hourly city average for the Boston metropolitan area was calculated for CO, O 3 

## , NO 2 

## , SO 2 

## , SO 4 2-



## , BC, and PM 2.5 

## . Although positive associations between ventri cular arrhythmic episode days were found for all mean pollutant levels on the same day and pr evious days, none of these associations approached statistical significance. However, when the analyses were stratified by patients who had a previous

March 2009 5-18 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 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 associat ed with incidence of ventricular arrhythmia (OR: 1.68 [95% CI: 1.18-2.41) among those who had a ve ntricular arrhythmia within the last 3 days. A similar study in eastern Massachusetts examined cardiac arrhythmia by analyzing defibrillator discharges precipitated by either ventricular tachycardia or fibrillation among 100 cardiac patients (Peters et al., 2000b). Exposure to ambient CO was estimated fo r the same day, 1-day, 2-day, 3-day, and a 5-day mean lag period. Co was mode rately correlated with PM 10 

## (r = 0.51) and PM 2.5 

## (r = 0.56) and more highly correlated with NO 2 

## (r = 0.71). When analyzing patients who had at least one defibrillator discharge (n = 33) there was no association with CO. Howeve r, 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 conduc ted in St Louis, MO (among 56 subjects) (Rich et al., 2006b), Atlanta, GA (among 518 subjects) (Metzger et al., 2007), Boston, MA (among 203 subjects) (Rich et al., 2005), and Vancouver, Canada (Rich et al., 2004; Vedal et al., 2004) (among 34 and 50 subjects respectively) did not find an associat ion between short term changes in ambient CO and occurrence of cardiac arrhythmia in patients with im plantable defibrillators. The study in Boston also examined atrial fibrillation episodes among the sam e group of subjects and also did not find an association with ambient CO (Rich et al., 2005). An alternative method used to assess the relations hip between cardiac arrhythmia and ambient air pollution is to analyze cardiac data recorded via ECG. Two studies have employed this method and reported inconsistent results. A study in Steubenville, OH, which is located in an industrial area, examined weekly ECG data among 32 non-smoking older adults for 24 weeks during summer and fall (Sarnat et al., 2006). Ambient exposures for up to 5 days prior to the health assessment (based on a 5-day moving average) were calculated for PM 2.5 

## , SO 4 2- 

## , elemental carbon (EC), O 3 

## , NO 2 

## , SO 2 

## , 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 ventricula r tachycardia recorded via monthly 24-h ECGs among 57 subjects over a 6 month period (Berger et al., 2006). Exposure estimates were calculated for ambient ultrafine particles, PM 2.5 

## , CO, NO, NO 2 

## , and SO 2 

## for various lag periods (0-23 h, 24-47 h, 48-71 h, 72-95 h, and 5-day avg) prior to the ECG. Results showed that a 0.5 ppm increase in ambient 24-h CO concentration (lag 0-4 days prior to ECG) was positively associated with the occurrence of supraventricular tachycardia (OR: 1.36 [95% CI: 1.08-1.74]). However, ambient CO was not associated with ventricular tachycardia. In summary, few studies have examined associati ons between CO and the occurrence of cardiac arrhythmias, and these studies provided little evidence of a CO effect on cardiac arrhythmias. While more

March 2009 5-19 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 studies analyzed data from ICDs, very few reported significant associations. This was similar for the mixed results from the two studies that analyzed ECG da ta to evaluate cardiac arrhythmias in association with CO exposures. Table 5.5 summarizes the reviewed studies.



# Table 5.5. Summary of studies investigating the effect of CO exposure on cardiac arrhythmias.

Study Location, Sample Size Cardiac Endpoint Exposure Assessment Mean CO Level (ppm) Copollutants

ARRHYTHMIAS (AMONG PATIENTS WITH ICDS) Dockery et al. (2005) Boston, MA (n = 203) Ventricular Tachycardia Ambient 25th, 50th, 75th, 95th, percentiles: 0.53, 0.80, 1.02, 1.37 (2-day) PM 2.5 , BC, O 3 , NO 2 , SO 2 , SO 4 2-

Peters et al. (2000b) Massachusetts, (n = 100) Ventricular fibrillation or tachycardia Ambient Mean: 0.58 (24 h) Max: 1.66 PM 2.5 , PM 10 , BC, O 3 , NO 2 , SO 2, SO 4 2-

Rich et al. (2006b) Boston, MA (n = 56) Ventricular arrhythmia Ambient 25th, 50th, 75th percentiles: 0.4, 0.5, 0.6 (24 h) PM 2.5 , EC, O 3 , NO 2 , SO 2

Metzger et al. (2007) Atlanta, GA (n = 518) Ventricular Tachycardia Ambient Mean: 1.7 (1 h) Range: 0.1-7.7 PM 10 , PM 2.5 , O 3 , NO 2 , SO 2

Rich et al. (2005) Boston, MA (n = 203) Atrial fibrillation Ambient 25th, 50th, 75th, 95th, percentiles: 0.53, 0.80, 1.02, 1.37 (2-day) PM 2.5 , BC, O 3 , NO 2 , SO 2

Rich et al. (2004) Vancouver, Canada (n = 34) ICD discharge due to arrhythmia Ambient Mean: 0.55 (24 h) IQR: 0.16 PM 2.5 , PM 10 , EC, O 3 , NO 2 , SO 2 , SO 4

Vedal et al. (2004) Vancouver, Canada (n = 50) ICD discharge due to arrhythmia Ambient Mean: 0.6 (24 h) Range: 0.3-1.6 PM 10 , O 3 , NO 2 , SO 2

ARRHYTHMIAS (VIA ECG) Sarnat et al. (2006) Steubenville, OH (n = 32) Atrial or ventricular tachycardia Ambient Mean: 0.2 (24 h) Range: 0.1, 1.5 PM 2.5 , O 3 , NO 2 , SO 2 , SO 4, EC

Berger et al. (2006) Erfurt, Germany (n = 57) Atrial or ventricular tachycardia Ambient Mean: 0.45 (24 h) Min, Med, Max 0.10, 0.38, 1.68 PM 10 , PM 2.5 , NO 2 , NO, SO 2, UF



# Cardiac Arrest

4 5 6 7 Cardiac arrest refers to the abrupt loss of heart function due to failure of the heart to contract effectively during systole, which can lead to sudden cardiac death if not treated immediately. Very few studies have investigated the association between ambi ent CO exposure and the risk of cardiac arrest and none reported a significant link between increased CO exposure and the occurrence of cardiac arrest.

March 2009 5-20 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17

18 19 20 21 22 23 24 25 26 27

28 29 Two similar studies were conducted in Seattle, WA, and both did not report an association between ambient CO and cardiac arrest. Both studies employed a case-crossover design and examined air pollution exposures for black smoke particles (BSP), PM 10 

## , SO 2 

## , and CO. The correlation coefficient for PM 10 

## and CO was 0.81. The first of these studies examined paramedic-attended out-of-hospital primary cardiac arrests among 362 cases (between 1998-1994) in Seattle a nd King County, WA whereby lags of 0-5 days were analyzed (Levy et al., 2001). The second of these studies examined out-of-hospital primary cardiac arrest for a ten year period (1985-1994) among subject s within a health organization database (the Group Health Cooperative of Puget Sound) whereby 0-day through 2-day lags were analyzed (Sullivan et al., 2003). 

# Myocardial Infarction

As previously stated, MI is commonly referred to as ‘heart attack’ and is another cardiac outcome that has received limited attention within the ar ea of air pollution research. Only one study has investigated the association between short-term chang es in ambient CO and the onset of MI. Peters and colleagues (2001) employed a case-crossover study desi gn to analyze short term exposures (0-5 h and 0- 5 days before the onset of MI) to particles (PM 10 

## , PM 2.5 

## , PM 10-2.5 

## , BC) and gases (CO, O 3 

## , NO 2 

## , SO 2 

## ) among 772 patients with MI in the greater Boston area. While all pollutants showed positive associations with the onset of MI, only PM 2.5 

## reached statistical significance with the main exposure period being 2 h before the onset.



# Blood Pressure

Only two studies have investigated whether shor t-term ambient CO influences BP. The earlier of these two studies examined BP among 2607 men and women aged 25-64 who participated in the Augsburg, Germany MONICA study (Ibald-Mulli et al., 2001). Exposures to ambient TSP, SO 2 

## 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 extract ed 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 PM 2.5 

## ) averaged over various periods up to 5 days before the visit (Zanobetti et al., 2004b). While results showed significant associations between increased BP and ambient PM 2.5 

## , SO 2 

## , O 3 

## , and BC, there was no significant effect for CO. 

# Blood Markers of Coagulation and Inflammation

Several studies have investigated the association between ambient CO and various blood markers related to coagulation and inflammation. The main endpoints analyzed have been plasma fibrinogen,

March 2009 5-21 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Factor VII, C-reactive protein (CRP), prothrombin, intercellular adhesion molecule (ICAM-1), and white blood cell count (WBC). Pekkanen et al. (2000) examined the association between daily concentrations of air pollution and concentrations of plasma fibrinogen measured among 4982 male and 2223 female office workers in Whitehall, London, U.K. between September 1991 and Ma y 1993. Plasma fibrinogen data were linked to ambient exposure to BS, PM 10 

## , O 3 

## , NO 2 

## , SO 2 

## , and CO, where the exposures were derived from data recorded at 5 fixed sites across London. There w as a high correlation between levels of CO and NO 2 

## (r = 0.81) and more moderate correlations with PM 10 

## (r = 0.57) and SO 2 

## (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 la gs was significantly associated with an increase in plasma fibrinogen. Results were similar for NO 2 

## while all other pollutants were not associated with an increase in plasma fibrinogen. Liao et al. (2005) examined associations betw een 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 PM 10 

## , NO 2 

## , SO 2 

## , O 3 

## and CO were calculated for days 1-3 prior to the blood sampling. A 0.5 ppm increment in 24-h CO concentration was significantly associated with 0.015 g/dL decrease in serum albumin among persons with a history of CVD. CO was not associated with other hemostatic or inflammatory factors. In Israel, Steinvil et al. (2008) examined WBC, fibrinogen, and CRP among 3,659 study subjects enrolled in the Tel-Aviv Sourasky Medical Center inflammation survey, in which subjects lived <11km from an ambient air pollution monitor. Air pollution data in the form of PM 10 

## , NO 2 

## , SO 2 

## , O 3 

## , and CO were derived from data recorded at fixed sites. The co rrelations coefficients were high between CO and NO 2 

## (r = 0.86) and PM 10 

## (r = 0.75). Exposures for lag days 1- 7 were analyzed and ambient CO had a significant negative effect on fibrinogen only among males. Significant associations were reported for lag 0 (e.g., same day) and lags 2-5 with the decrease in fibrinogen ranging from -5.5 mg/dL to -9.8 mg/dL per 0.5 ppm increase in 24-h CO concentration. A sim ilar negative effect for CO was observed on WBC among males only. The average CO exposure over the week prior to the sampling yielded the largest reduction in WBC (-263 cells/μL). In a German study, Rückerl and colleagues ( 2006) recruited 57 non-smoking male patients with CHD who were scheduled for 12 subsequent clinical visits where samples of blood were collected. The authors tested the primary hypothesis that CRP woul d increase in association with a rise in air pollution levels. CRP is an acute phase protein that increas es 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 (fibrinoge n, factor VII [FVII], prothrombin fragment 1+2) were also examined. Ambient ai r pollution in the form of PM 10 

## , ultrafine particles (UFP), EC, NO 2 

## , and CO was monitored at one central site and a 24-h avg immediately preceding the clinic visit (lag 0) and up

March 2009 5-22 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 to 5 days (lags 1-4) was calculated for each patient. For CRP, the odds of observing CRP concentrations above the 90th percentile were 2.41 (95% CI: 1.23-5.02) in association with a 0.5 ppm increase in 24-h CO concentration (lag 2). CO concentration during lags 1 and 2 was associated with observing ICAM-1 concentrations above the 90th percentile (OR: 2.41 [95% CI: 1.49-4.04]; OR: 3.17 [95% CI: 1.77-6.11], respectively). CO concentration during lags 0- 3 was associated with a decrease in FVII. A similar study by Ruckerl and colleagues (2007) was conducted among 1,003 MI survivors across 6 European cities (Athens, Greece; Augsburg, Germ any; Barcelona, Spain; Helsinki, Finland; Rome, Italy; Stockholm, Sweden). The study compared repe ated measurements of interleukin-6 (IL-6), CRP and fibrinogen with concurrent ambi ent levels of air pollution (p article number count [PNC], PM 10 

## , PM 2.5 

## , NO, NO 2 

## , O 3 

## , SO 2 

## , 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 assoc iated with any of the inflammatory endpoints. Baccarelli et al. (2007) recruited 1,218 healthy i ndividuals 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 ti me (PT) and activated partial thromboplastin time (APTT), which are measures of the quality of the coa gulation pathways, assuming that, if shortened these measures would reflect hypercoagulability. Air pollution data (PM 10 

## , O 3 

## , NO 2 

## , and CO) were obtained from 53 fixed stations across the Lombardia region, wh ich was divided into nine different study areas and a network average for each pollutant was calculated across the available monitors within each of the nine study areas. The analyses examined air pollution at the time of the blood sampling as well as averages for the 7 days prior and 30 days prior. Results showed that ambient CO at the time of blood sampling was associated with a decrease in PT (coefficie nt = -0.11 [95% CI: -0.18 to -0.05, p <0.001), indicating hypercoagulability. However, PM 10 

## and NO 2 

## at the time of blood sampling were also associated with a decrease in PT and results from multipollutant models were not reported. Acute phase reactants such as fibrinogen, and naturally occurring anticoagulants such as antithrombin, protein C and protein S were examined and none were associated with ambient air pollution. In summary, despite the small number of studies, there was some evidence of a significant link between CO exposure and blood markers of coagul ation and inflammation. Table 5.6 summarizes the reviewed studies.

March 2009 5-23 DRAFT – DO NOT CITE OR QUOTE

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

Study Location, Sample Size Cardiac Endpoint Exposure Assessment Mean CO Level (ppm) Copollutants

Pekkanen et al (2000) London, U.K. (n = 7205) Plasma fibrinogen Ambient Mean: 1.22 (24 h) 10th, 50th, 90th, Max: 0.61, 1.04, 2.0, 8.61 PM 10 , BS, O 3 , NO 2 , SO 2

Liao et al (2005) USA (n = 10.208) Fibrinogen, VII-C, WBC, albumin, vWF Ambient Mean: 1.4 (24 h) PM 10 , O 3 , NO 2 , SO 2

Steinvil et al (2008) Tel-Aviv, Israel (n = 3659) CRP, fibrinogen, WBC Ambient Mean: 0.8 25th, 50th, 75th percentiles: 0.7, 0.8, 1.0 PM 10 , O 3 , NO 2 , SO 2

Ruckerl et al (2006) Erfurt, Germany (n = 57) CRP, SAA, cell adhesions and coagulation Ambient Mean: 0.45 (24 h) Range: 0.10, 1.68 PM 10 , PM 2.5 , UFP, EC, NO 2

Rukerl et al (2007) Six European cities (n = 1003) IL-6, CRP, fibrinogen Ambient Mean (24 h): 0.29-1.48 PM 10 , PM 2.5 , O 3 , NO 2 , SO 2

Baccarelli et al (2007) Lombardia region, Italy (n = 1218) PT, APTT, fibrinogen, anticoagulants Ambient Mean: 1.14-3.11 Max: 5.52-11.43 PM 10 , O 3 , NO 2 , SO 2



# Hospital Admissions and Emergency Department Visits

1 2 3 4 5

6 7 8 9 10 11 12 13 14 15 16 Since the 2000 CO AQCD there have been a number of studies investigating the effect of ambient CO on hospital admissions and ED visits for cardiov ascular diseases. Some of these studies have focused solely on one specific CVD outcome, and these studies are discussed first. This is followed by a discussion of studies that investigated admissions fo r all CVD outcomes (e.g., non-specific) or a variety of specific CVD outcomes. 

# Ischemic Heart Disease, Myocardial Infarction, and Angina Pectoris Ischemic heart disease (IHD), also known as co ronary heart disease (CHD), is caused by inadequate circulation of the blood to the heart mu scle, which is a result of the heart arteries being blocked by cholesterol deposits. IHD can lead to sudden episodes such as MI (“heart attack”) or death, as well as chronic conditions such as angina pectoris (chest pain). 

# Ischemic Heart Disease. Very few studies have focused directly on hospitalizations for IHD. There is a lot of variation among these studies with rega rd to methods employed and results reported. It should be noted that within these studies IHD include d MI and angina pector is (ICD-9 codes 410-414; ICD-10 codes 120, 121-123, 124). Mann and colleagues (2002) investigated the modifying effect of secondary diagnosis of arrhythmia and congestive heart failure (CHF) on the relationship between hospital admissions for IHD (ICD-9: 410-414) and ambient air pollutants for the period of 1988 to 1995 in southern California. There were 54,863 visits analyzed and a 0.75 ppm increase in 8-h max CO

March 2009 5-24 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 concentration was associated with a 2.69% (95% CI: 1.21-4.19) increase in same-day IHD admissions among persons with a secondary diagnosis of CHF, a 2.23% (95% CI: 1.35-3. 13) increase among persons with a secondary diagnosis of arrhythmia, and a 1.21% (95% CI: 0.49-1.94) among persons without either secondary diagnosis. Of all pollutants examined (PM 10 

## , NO 2 

## , O 3 

## , CO), only NO 2 

## showed similar positive effects to CO and no multipollutant models were an alyzed. The correlation coefficients between CO and NO 2 

## ranged from 0.64 to 0.86 across the seven regions. This study indicated that people with IHD and accompanying CHF and /or arrhythmia are a sensitive group in relation to the effects of ambient air pollution. By using a time-series approach, ED visits for IHD (ICD-9: 410-414) in Montreal, Canada (1997-2002) were examined in relation to ambient CO concentrations (lags 0 and 1) (Szyszkowicz, 2007). A total of 4,979 visits were analyzed and results showed significant positive effects with a 0.5 ppm increase in 24-h CO concentration (lag 0) attributing to a 14.1% (95% CI: 5.8- 20.6) increase in daily ED visits among all patients. Stratified analyses showed that this effect was mostly among male patients (19.8% [95% CI: 9.2-31.6]). NO 2 

## was the only other pollutant examin ed, and it too showed significant positive associations with ED visits for IHD for same -day exposure; however, no multipollutant models were examined. Lee and colleagues (2003b) examined daily counts of hospital admissions for IHD in Seoul, Korea for the period from December 1997 to December 1999. Si ngle-day lags 0 to 5 were analyzed and the lag period with the strongest association for each pollutant was chosen. For CO, lag 5 showed the strongest effect with a 1 ppm increase in 1-h maximum (max) CO concentration associated with a daily increase in the number of hospital admissions for IHD; however, this was only among patients 64+ years of age (RR: 1.07 [95% CI: 1.01-1.13]). All other pollutants (PM 10 

## , O 3 

## , NO 2 

## ) except SO 2 

## showed similar significant effects and in a two-pollutant model with PM 10 

## the CO effect attenuated toward the null. Other studies have examined hospital admissions for IHD while investigating a broad group of CVD outcomes. A study was conducted in Atlanta, GA, where over 4 million ED visits from 31 hospitals for the period 1993 to 2000 were analyzed (Study of Particles and Health in Atlanta [SOPHIA]). Several articles have been published from this research with two examining cardiovascular admissions in relation to CO concentrations. The first of these (Metzger et al., 2004a) used a time-series design and analyzed a 3-day moving average over single-day lags 0-2 as the a priori lag structure. Although of borderline statistical significance, CO was positively associated with an increase in ED visits for IHD (RR 1.016 [95% CI: 0.999-1.034] per 1 ppm increase in 1-h max CO concentration). The second of these reports (Peel et al., 2007) examined the association of ambient air pollution levels and cardiovascular morbidity in visits with and without specific secondary conditions (e.g., comorbidity). Within a time-stratified case-crossover desi gn using the same lag structure already mentioned, the main results showed that a 1 ppm increase in 1-h max CO concentration was associated with an

March 2009 5-25 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 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 associ ations between ambient CO concentrations and increased hospital admissions for various CVD outcom es. The first of these studies (Barnett et al., 2006) analyzed data from 5 of the largest cities in Austra lia (Brisbane, Canberra, Melbourne, Perth, Sydney) and two New Zealand cities (Auckland, Christchurch ) for the period 1998-2001. A time-stratified casecrossover design was employed and the age groups of 15-64 years and ≥ 65 years were analyzed for the 0-1 lag period. The pooled estimates across all cities showed that a 0.75 ppm increase in 8-h max CO concentration was associated with a 1.9% (95% CI: 0.7-3.2) increase in admissions for IHD, but only among the elderly group ( ≥ 65 years). The second of the Australian studies (Jalaludin et al., 2006) examined ED visits for CVD outcomes in the elderly (65+ years) in Sydney for the period 1997 to 2001. Using a time-series approach, single-day lags of 0, 1, 2, 3 and an average over lags 0 a nd 1 were examined. A 0.75 ppm increase in 8-h max CO concentration (lag 0) was associated with incr eases in IHD emergency department visits of 3.1% (95% CI: 1.3-4.9). 

# Myocardial Infarction. Linn et al. (2000) examined the associ ation 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 incr ease in same-day 24-h avg CO concentration was associated with a 2.0% increase in MI hospital ad missions among people aged >30 years. When the analyses were stratified by season, no significant effects were observed (No quantitative seasonal effects reported). A time-series study in Denver, Colorado, investigated daily hospital admissions for various CVD outcomes among older adults (>65 years) across 11 hospitals (Koken et al., 2003). Data between July and August for the period 1993-1997 were analyzed. Single-day lags 0 to 4 were examined and CO showed no association with hospital admissions for MI (quantitative results were not reported). As part of the HEAPSS (Health Effects of Air Pollution among Susceptible Subpopulations) study, Lanki et al. (2006) investigated the association betw een traffic-related exposure to air pollutants and hospitalization for first acute myocardial infarction (AM I). 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 NO 2 

## ranged from 0.43 to 0.75 across the five cities, and for PM 10 

## the range was 0.21 to 0.56. A total of 26 ,854 hospitalizations were analyzed and pooled estimates from all 5 cities showed that there was a weak positive association with AMI hospitalizations and 24-h avg CO concentrations at lag 0 (RR: 1. 014 [95% CI: 1.000-1.029] per 0.5 ppm increase), but more so when only using data from the three cites (Helsinki, Rome, Stockholm) with hospital discharge registers (RR: 1.020 [95% CI: 1.003-1.035] per 0.5 ppm increase). When analyses were stratified by fatality and age, results showed that the CO effect was significantly associated with fatal AMI among the

March 2009 5-26 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 <75-year age group (RR: 1.080 [95% CI: 1.017-1.144), and with non-fatal AMI in the ≥ 75-year age group (RR: 1.044 [95% CI: 1.011-1.076). Further analyses within the HEAPSS cohort was conducted where the event of cardiac readmission among the first MI survivors (n = 22, 006) was linked to ambient air pollution (von Klot et al., 2005). The readmissions of interest were those with primary diagnosis of AMI, angina pectoris, dysrhythmia, and heart failure that occurred at least 29 days after the in dex event. Single-day lags 0 to 3 were examined and pooled estimates from all 5 cities showed that a 0.5 pp m increase in same-day (l ag 0) CO was associated with an increase in cardiac (e.g., any of the diagno ses) readmissions (RR: 1.041 [95% CI: 1.003-1.076]) and this persisted in two-pollutant models that included either PM 10 

## or O 3 

## . Correlation coefficients with CO ranged from 0.21 to 0.57 for PM 10 

## and 0.44 to 0.75 for NO 2 

## . A study in Rome, Italy, also found an association between ambient CO and hospitalizations for first episode MI among 6,531 subjects (January 1995-J une 1997) (D'Ippoliti et al., 2003). A case-crossover design with stratification of time into separate months was used to select referent days as the days falling on the same day of the week within the same month as the index day. CO concentration was positively associated for lag 2 (OR: 1.019 [95% CI: 1.001-1. 037]). The other pollutants analyzed were NO 2 

## 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, Au ckland, and Christchurch) for the period 1998-2001 also reported an association between CO and MI hos pitalization (Barnett et al., 2006). The pooled estimates across all cities showed that a 0.75 ppm increase in 8-h max CO concentration was associated with a 2.4% (95% CI: 0.6-4.1) increase in admi ssions for MI, but only among older adults ( ≥ 65 years). 

# Angina Pectoris. In the current literature, only one study was identified that focused solely on angina pectoris as an endpoint. Admissions data for angina pectoris were collected from 25 academic hospitals in Tehran, Iran, and linked to ambient ai r pollution for the period of 1996 to 2001 (Hosseinpoor et al., 2005). Using a time-series approach, single-day lags of 0 to 3 were analyzed and a 0.5 ppm increase in 24-h avg CO concentration at lag 1 was associated with increased hospital admissions for angina (OR: 1.005 [95% CI: 1.003-1.007). This result persisted in a multipollutant model that also included NO 2 

## , PM 10 

## , and O 3 

## with CO being the only significant pollutant (OR: 1.005 [95% CI: 1.001-1.008]).Figure 5-1 shows the effect estimates associated with daily admissions for various forms of IHD from selected studies. Table 5.7 shows a summary of the IHD hospital admission studies that examined CO exposures. In summary, the majority of studies reported significant increases in the daily number of admissions for IHD and MI in relation to CO exposures. In studies that stratified by age groups and/or sex, the effects were larger among the elderly an d males. Among the different lag periods being examined, the associations were more commonly observe d with same day CO (lag 0) or an average over the same day and previous day (lag 0-1).

March 2009 5-27 DRAFT – DO NOT CITE OR QUOTE

### Study Location Lag Mann et al. (2002) California, 0-3 CHF IHD Mann et al. (2002) Califor nia, 0-3 ARR Szyszkowicz (2007) Montreal, Canada 0 All Ages Szyszkowicz (2007) Montreal, Canada 0 All Ages, Males Szyszkowicz (2007) Montreal, Canada 0 All Ages, Females Szyszkowicz (2007) Montreal, Canada 0 >64 years Szyszkowicz (2007) Montreal, Canada 0 >64 years, Males Szyszkowicz (2007) Montreal, Canada 0 >64 years, Females Lee et al. (2003b) Seoul, Korea 5 All Ages Lee et al. (2003b) Seoul, Korea 5 >64 years Metzger et al. (2004a) Atlanta, GA 0-2 Peel et al. (2007) Atlanta, GA2002) 0-2 Barnett et al. (2006) Australia, New Zealand 0- 1 15-64 years Barnett et al. (2006) Australia, New Zealand 0- 1 >64 years Jalaludin et al. (2006) Sydney, Australia 0-1

Barnett et al. (2006) Australia, New Zealand 0-1 15-64 years MI Barnett et al. (2006) Australia, New Zealand 0-1 >64 years Linn et al. (2000) Los Angeles, CA 0 Lanki et al. (2006) Multi-city, Europe 0 >34 years, All cities Lanki et al. (2006) Multi-city, Europe 0 <75 years, Non-fatal Lanki et al. (2006) Multi-city, Europe 0 <75 years, Fatal Lanki et al. (2006) Multi-city, Europe 0 75+ years, Non-fatal Lanki et al. (2006) Multi-city, Europe 0 Fatal von Klot et al. (2005) Multi-city, Europe 0 D'Ippoliti et al. (2003) Rome, Italy 0-2 18+ years D'Ippoliti et al. (2003) Rome, Italy 0-2 18-64 years D'Ippoliti et al. (2003) Rome, Italy 0-2 65-74 years D'Ippoliti et al. (2003) Rome, Italy 0-2 75+ years

von Klot et al. (2005) Multi-city, Europe 0 Angina Hosseinpoor et al. (2005) Tehran, Iran 1



### Effect Estimate 

# Figure 5-1. Summary of effect estimates (95% confidence inter vals) associated with hospital admissions for various froms of IHD. Effect es timates have been standardized to a 1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO concentrations, and 0.5 ppm for 24-h avg CO concentrations.

March 2009 5-28 DRAFT – DO NOT CITE OR QUOTE

# Table 5-7. Summary of I HD hospital admission studies. 1



### Study Location Endpoints Examined Copollutants Lags Examined CO Levels (ppm)

STUDIES THAT FOCUSED SOLELY ON IHD, MI, OR ANGINA Mann et al. (2002) Southern California (1988-1995) IHD PM 10 , NO 2 , O 3 0,1,2, 2-4ma Mean: 2.07 (8h)

Szyszkowicz (2007) Montreal, Canada (1997-2002) IHD NO 2 0,1 Mean: 0.5 (24 h)

Lee et al. (2003b) Seoul, Korea (1997-1999) IHD PM 10 , NO 2 , SO 2 , O 3 0,1,2,3,4,5 Mean: 1.8

Lanki et al. (2006) 2 5 European cities (1992-2000) MI (first acute) PM 10 , NO 2 , O 3 , PNC 0,1,2,3 Highest city was Rome. 25th = 1.5 75th = 2.9 mg/m 3 von Klot et al. (2005) 2 5 European cities (1992-2001) MI, Angina, Cardiac* PM 10 , NO 2 , O 3 , PNC 0,1,2,3 Mean: highest city was Rome: 1.9 (24 h) D’Ippoliti et al. (2003) 2 Rome, Italy (1995-1997) MI TSP, NO 2 , SO 2 0,1,2,3,4, 0-2 Mean: 3.8 (24 h)

Hosseinpoor et al. (2005) 2 Tehran, Iran (1996-2001) Angina PM 10 , NO 2 , SO 2 , O 3 0,1,2,3 Mean: 9.4 (24 h)

STUDIES THAT EXAMINED IHD, MI, OR ANGINA AMONG OTHER CVDS Metzger et al. (2004a) Atlanta, GA (1993-2000) IHD, All CVD, CD, CHF, PVCD PM 10 , NO 2 , SO 2 , O 3 0-2ma Mean: 1.5 (1 h)

Peel et al. (2007) Atlanta, GA (1993-2000) IHD, All CVD, CD, CHF, PVCD PM 10 , NO 2 , SO 2 , O 3 0-2ma Mean: 1.5 (1 h)

Barnett et al. (2006) Australia and New Zealand (1998-2001) IHD, MI, All CVD, CA, Stroke PM 10 , NO 2 , O 3 Lag 0-1 Mean: (8 h) 0.5- 2.1

Jalaludin et al. (2006) Sydney, Australia (1997-2001) IHD, All CVD, Stroke, Cardiac PM 10 , NO 2 , SO 2 , O 3 0,1,2,3, 0-1 Mean: 0.82 (8 h)

Linn et al. (2000) Los Angeles, CA (1992-1995) MI, All CVD, CHF, CA, OS PM 10 , NO 2 , O 3 0 Mean: (24 h) Winter 1.7, Spring 1.0, Summer 1.2, Fall 2.1 Koken et al. (2003) Denver, CO (1993-1997) MI, CAth, PHD, CD, CHF PM 10 , NO 2 , SO 2 , O 3 0,1,2,3,4 Mean: 0.9 ppm (24 h)

1 Cardiac = AMI, angina, dysrhythmia, or HF; CA = Cardiac arrhythmia; CAth = Cardiac at herosclerosis; CD = cardiac dysrhthmias; C HF = Congestive heart failure; PHD = Pulmonary heart disease; OS = Occlusive stroke; PVCD = peripheral va scular and cerebrovascular disease, ma = moving average. 2 These studies presented CO concentrations in the units mg/m 3 . The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m 3 , which assumes standard atmosphere and tempterature.



### March 2009 5-29 DRAFT – DO NOT CITE OR QUOTE

# Stroke

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 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 th e outer membranes of the brain. The third type of stroke is a transient ischemic attack (TIA), or mini–stroke, which has the same early symptoms as a normal stroke but the symptoms disappear with in 24 hours, leaving no apparent deficits. A small number of air pollution studies have inv estigated hospital admissions for the three main forms of stroke with the majority reporting pos itive associations with ambient CO and lag periods between 0 and 3 days. A U.S. study across 9 cities investigated hospital admissions for ischemic and hemorrhagic stroke among Medicare beneficiaries aged 65+ years of age (155,503 ischemic and 19,314 hemorrhagic admissions from the ED) (Wellenius et al., 2005a). Si ngle-day lags 0 to 2 were examined and based on a pooled estimate, same-day CO (lag 0) was asso ciated with an increase in admissions of 1.98% (95% CI: 0.86-3.12) per 0.5 ppm increase in 24-h CO concentration) for ischemic stroke admissions but not hemorrhagic stroke admissions (-1.14%, 95% CI: -3.40 to 1.18). All other pollutants examined (PM 10 

## , NO 2 

## , SO 2 

## ) were associated with an increase in ischemic stroke admissions, but not hemorrhagic stroke admissions. Villeneuve and colleagues (2006a) studied ED visits for hemorrhagic strokes, acute ischemic strokes and transient ischemic attacks among individu als 65+ years of age at 5 hospitals within the Edmonton area in Canada between April 1992 and Ma rch 2002 (12,422 visits). Within a time-stratified case-crossover design the analyses were stratified by two seasonal groups (October-March and AprilSeptember) and CO only had an effect on ischemic st roke during April-September. A 0.5 ppm increase the CO concentration for a 3-day avg across lags 0-2 w as associated with a 32% increase in risk (OR: 1.32 [95% CI 1.09-1.60]). CO had no effect on any other str oke subtype. In two-pollutant models the CO effect on ischemic stroke persisted after controlling for PM 10 

## , PM 2.5 

## , SO 2 

## , and O 3 

## . When all seasons and all strokes were combined there was no statistically significant association between all the pollutants examined and increased admissions for stroke. In Kaohsiung City, Taiwan, CO averaged over lags 0-2 was associated with increased admissions for stroke across 63 hospitals (Tsai et al., 2003b). Fr om 1997 to 2000 a total of 23,179 admissions were analyzed and on warm days ( ≥ 20°C) the odds ratios for primary intracerebral hemorrhage and ischemic stroke were 1.39 (95% CI: 1.16-1.66) and 1.39 (95% CI: 1.25-1.53) respectively for a 0.5 ppm increase in 24-h CO concentration. For the same increase in CO on cool days (<20°C) the odds ratios were 1.33 (95% CI: 0.38-2.55) for intracerebral hemorrhage a nd 2.68 (95% CI: 1.59-4.49) for ischemic stroke.

March 2009 5-30 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 These results persisted in two-pollutant models that included PM 10 

## , SO 2 

## , and O 3 

## , but did not persist when controlling for NO 2 

## . Earlier research conducted in metropolitan Lo s Angeles examined hospital admissions for cardiopulmonary illnesses from 1992-1995 (Linn et al., 2000). Using a time-series approach, a 0.5 ppm increase in 24-h CO concentration (lag 0) was associated with a 2.18% (95% CI: 1.73-2.62) increase in occlusive (ischemic) stroke hospital admissions am ong people aged >30 years. When the analyses were stratified by season there was a 1.8% increase during winter, a 4.55% increase during summer, and a 1.6% increase during fall (results for spring were not reported). A study in Taipei, Taiwan analyzed 8,582 em ergency admissions for cerebrovascular diseases, hemorrhagic stroke, ischemic stroke, and all strok es during 1997 to 2002 (Chan et al., 2006). Single-day lags 0 to 3 were analyzed and a 0.75 ppm increase in 8-h max CO concentration (lag 2) was associated with an increase in cerebrovascular diseases (OR: 1.03 [95% CI: 1.01-1.05]) and all strokes (OR: 1.03 [95% CI: 1.01-1.05]). These results persisted in two- and three-pollutant models that included O 3 

## and PM 10 

## . There was no association with individual ischemic or hemorrhagic stroke. CO was moderately correlated with PM 10 

## (r = 0.47) and PM 2.5 

## (r = 0.44), and the correlation was higher with NO 2 

## (r = 0.77). The only time-series study that focused specifically on stroke hospital admissions that did not report a significant association with ambient CO was conducted in Dijon, France (Henrotin et al., 2007). Hospital admissions for different types of first-ever stroke (e.g., ischemic, hemorrhagic) among subjects over 40 years of age were analyzed for the period of 1994 to 2004. A bi-directional case-crossover study design was employed where single-day lags of 0 to 3 were examined and CO had no significant association across all lags. This was also the case when the analyses were stratified by gender and types of ischemic stroke (large arteries, lacunar, cardi oembolic, transient). Of all pollutants examined (PM 10 

## , NO X 

## , O 3 

## , SO 2 

## , CO) only O 3 

## showed a significant effect. Two Australian studies examined associations between ambient CO and hospital admissions for various CVDs. The first of these studies analyzed da ta from five of the largest cities in Australia (Brisbane, Canberra, Melbourne, Perth, Sydney) and two New Zealand cities (Auckland, Christchurch) for the period 1998-2001 (Barnett et al., 2006). A tim e-stratified case-crossover design was employed and the age groups of 15-64 years and ≥ 65 years were analyzed for the 0- 1 lag period (avera ge 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 vis its for CVDs in older adults (65+ years) in Sydney for the period from 1997 to 2001 (Jalaludin et al., 2006). Using a time-series approach, single-day lags of 0 to 3 and an average over lags 0 and 1 (e.g., lag 0-1) were examined and CO showed no effect on stroke ED visits. When the analyses were stratified by cool and warm periods a 0.75 ppm increase in 8-h max CO concentration during the cool period was associated with a 3.8% (95% CI: 0.76-6.94) increase in stroke ED visits.

March 2009 5-31 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 Figure 5-2 shows the effect estimates associated with daily admissions for stroke from selected studies. Table 5-8 shows a summary of the stroke hospital admission studies that examined CO exposures. In summary, there was some eviden ce that increased ambient CO concentrations were associated with an increase in the number of hospital admission s for stroke. The largest positive effects came from the Taiwan study in Kaohsiung (Tsai et al., 2003b) with slightly larger effects during the warmer period (>20°C). Similarly, in the Canadian study by V illeneuve and colleagues (2006a) there was a stronger effect during the warmer period (April-September).

March 2009 5-32 DRAFT – DO NOT CITE OR QUOTE

### Study Location Lag Wellenius et al. (200 5a) Multi-city, U.S. 0-2 65+ years Ischemic Stroke Villeneuve et al. (2006a) Edmonton, Canada 0-2 IS, 65+ years, all seasons Villeneuve et al. (2006a) Edmonton, Canada 0-2 IS, 65+ years, Apr-Sep Villeneuve et al. (2006a) Edmonton, Canada 0- 2 IS, 65+ years, Oct-Mar Villeneuve et al. (2006a) Edmonton, Canada 0-2 CIS, 65+ years, all seasons Villeneuve et al. (2006a) Edmonton, Canada 0-2 CIS, 65+ years, Apr-Sep Villeneuve et al. (2006a) Edmonton, Canada 0-2 CIS, 65+ years, Oct-Mar Tsai et al. (2003b) Kaohsiung, Taiwan 0-2 < 20°C Temp Tsai et al. (2003b) Kaohsiung, Taiwan 0-2 >20°C Temp Linn et al. (2000) Los Angeles, CA 0 Chan et al. (2006) Taipei, Taiwan 1 Henrotin et al. (2007) Dijon, France 3

Wellenius et al. (2005a) Multi-city, U.S. 0-2 65+ years Hemorrhagic Stroke Villeneuve et al. (2006a) Edmonton, Canada 0-2 65+ years, all seasons Villeneuve et al. (2006a) Edmonton, Canada 0-2 65+ years, Apr-Sep Villeneuve et al. (2006a) Edmonton, Canada 0-2 65+ years, Oct-Mar Tsai et al. (2003b) Kaohsiung, Taiwan 0-2 <20°C Temp Tsai et al. (2003b) Kaohsiung, Taiwan 0-2 >20°C Temp Chan et al. (2006) Taipei, Taiwan 1 Henrotin et al. (2007) Dijon, France 1

Chan et al. (2006) Taipei, Taiwan 2 Stroke (Non-Specific) Jalaludin et al. (2006) Sydney, Australia 0-1

IS= Ischemic Stroke, CIS=Cerebral Ischemic Stroke Effect Estimate



# Figure 5-2. Summary of effect estimates (95% confidence interval s) associated with ED visits and hospital admissions for stroke. Effect estimates have been standardized to a 1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO concentrations, and 0.5 ppm for 24-h avg CO concentrations.

March 2009 5-33 DRAFT – DO NOT CITE OR QUOTE

# Table 5-8. Summary of stro ke hospital admission studies. 1



### Study Location Type Of Stroke Examined Copollutants Lags Examined CO Levels (ppm)

STUDIES THAT FOCUSED SOLELY ON STROKE

Wellenius et al. (2005a) 9 cities, USA (1993-1999) Isch, Hem PM 10 , NO 2 , SO 2 0,1, 2 25th, 50th, 75th percentiles: 0.73, 1.02, 1.44

Villeneuve et al. (2006a) Edmonton, Canada (1992- 2002) Isch, Hem, TIA NO 2 , SO 2 , O 3 0,1, 0-2 Mean: 0.8 (24 h)

Tsai et al. (2003b) Kaohsiung, Taiwan (1997-2000) Isch, Hem PM 10 , NO 2 , SO 2 , O 3 0-2 Mean: 0.79 (24 h)

Chan et al. (2006) Taipei, Taiwan (1997-2002) All, Isch, Hem PM 10 , NO 2 , SO 2 , O 3 0,1,2,3 Mean: 1.7 (8h)

Henrotin et al. (2007) 2 Dijon, France (1994-2004) Isch, Hem PM 10 , NO X , SO 2 , O 3 0,1,2,3 Mean: 0.59 (24 h)

STUDIES THAT EXAMINED STROKE AMONG OTHER CVDS

Linn et al. (2000) Los Angeles, CA (1992-1995) Isch PM 10 , NO 2 , O 3 Lag 0 Mean: (24 h) Winter 1.7, Spring 1.0, Summer 1.2, Fall 2.1 Barnett et al. (2006) Australia and New Zealand (1998-2001) All PM 10 , NO 2 , O 3 Lag 0-1 Mean: (8h) 0.5-2.1 Jalaludin et al. (2006) Sydney, Australia (1997-2001) All PM 10 , NO 2 , SO 2 , O 3 0,1,2,3, 0-1 Mean: 0.82 (8h)

1 Isch = Ischemic; Hem = Hemorrhagic; TIA = transient ischemic attack 2 These studies presented CO concentrations in the units mg/m 3 . The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m 3 , which assumes standard atmosphere and tempterature.



# Congestive Heart Failure

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Heart failure (HF) is a condition in which the hear t is unable to adequately pump blood to the rest of the body. It does not refer to the cessation of the hear t, but more to the inability of the heart to operate at an optimal capacity. HF is often called congestiv e heart failure (CHF), wh ich refers to when the inadequate pumping leads to a buildup of flui d in the tissues. The underlying causes of CHF are hypertension, CAD, MI, and diabetes. Wellenius and colleagues (2005b) examined the rate of hospitalization for CHF among 55,019 Medicare recipients (aged ≥ 65 years) residing in Allegheny County, PA, during 1987-1999. A timestratified case-crossover design was employed and single -day lags of 0 to 3 were analyzed and a 1 ppm increase in 1-h max CO concentration on the sa me-day (lag 0) was associated with a 9.31% (95% CI: 6.77-11.92) increase in the rate of hospitaliza tion for CHF. This result persisted in two-pollutant models that included PM 10 

## , NO 2 

## , O 3 

## , and SO 2 

## . CO was moderately correlated with SO 2 

## (r = 0.54) and PM 10 

## (r = 0.57) and more highly correlated with NO 2 

## (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 (S ymons et al., 2006). The patients were interviewed after admission through the ED during their stays in overnight wards. The inte rview was designed to

March 2009 5-34 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 collect information about symptom onset, health conditions, and factors related to air pollution exposure. Various lag periods (single day and cumulative days 0 to 3) prior to the onset of symptoms were analyzed and although the focus of this study was exposure to PM 2.5 

## , of all the pollutants examined (PM 2.5 

## , CO, NO 2 

## , O 3 

## ) only 8-h max CO concentration at lag 2 was significantly associated with the onset of CHF symptoms (OR: 1.68 [95% CI: 1.28- 2.80]). Earlier research conducted in metropolitan Los Angeles, CA examined hospital admissions for cardiopulmonary illnesses 1992-1995 (2000). Using a time- series approach, a 0.5 ppm increase in sameday 24-h avg CO concentration was associated with a 1.25% increase in CHF hospital admissions among people aged >30 years. When the analyses were stratified by seasons only summer showed a significant increase (3.7%); however, the study did not report the results for the other seasons. A time-series study in Denver, Colorado, inves tigated daily admissions for various CVDs among older adults (>65 years) across 11 hospitals (Koken et al., 2003). Single-day lags 0 to 4 were examined and an increase of 0.5 ppm in 24-h avg CO concen tration 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 A tlanta, GA, where over 4 million ED visits from 31 hospitals for the period 1993 to 2000 were analyzed (Metzger et al., 2004a). A time-series design was used and a 3-day moving average over single-day lags 0-2 as the a priori lag structure was analyzed. Results showed that 1-h max CO concentration was not associated with an increase in ED visits for CHF (RR: 1.010 [95% CI: 0.988-1.032] per 1 ppm increase ). When the analyses ex amined the same CVDs among those with and without specific secondary conditions (e.g., co-morbidity) 1-h max CO concentration was associated with an increase in ED visits for CHF only among those with COPD (OR: 1.058 [95% CI: 1.003-1.115] per 1 ppm increase) (Peel et al., 2007). In Kaohsiung city, Taiwan, a study analyzed 13,475 admissions for CHF across 63 hosptials for the period 1996 through 2004 (Lee et al., 2007a). A 0.5 ppm increase in 24-h avg CO concentration averaged over lag days 0-2 was positively associated with CH F 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 o 

## C) (OR: 1.32 [95% CI: 1.15- 1.55]). These results persisted in two-pollutant models that included PM 10 

## , SO 2 

## , O 3 

## , and models with NO 2 

## only on warmer days, not with NO 2 

## on cooler days. Figure 5-3 shows the effect estimates for associa tions between CO and daily admissions for HF from selected studies. Table 5-9 summarizes the HF hospital admission studies that examined CO

exposures. In summary, many of the studies that examined associations between ambient CO concentrations and daily hospital admissions for CHF reported significant associations at lags of 0 to 3 days.

March 2009 5-35 DRAFT – DO NOT CITE OR QUOTE

### Study Location Lag

Wellenius et al. ( 2005b) Pittsburgh, PA 0

Symons et al. (2006) Baltimore, MD 0-3

Linn et al. (2000) Los Angeles, CA 0

Koken et al. (2003) Denver, CO 3

Metzger et al. (2004a) Atlanta, GA 0-2

Peel et al. (2007) Atlanta, GA 0-2

Lee et al. (2007a) Kaohsiung, Taiwan 0-2 <25 °C

Lee et al. (2007a) Kaohsiung, Taiwan 0-2 >25 °C



### Effect Estimate 

# Figure 5.3. Summary of effect estimates (95% confidence intervals) associated with hospital admissions for CHF. Effect estimates have been standardized to a 1 ppm increase in ambient CO for 1-h max CO con centrations, 0.75 ppm for 8-h max CO concentrations, and 0.5 ppm fo r 24-h avg CO concentrations.

March 2009 5-36 DRAFT – DO NOT CITE OR QUOTE

# Table 5-9. Summary of HF hospital admission studies.

Study Location Endpoints Examined Copollutants Lags Examined CO Levels (ppm)

STUDIES THAT FOCUSED SOLELY ON HF Wellenius et al. (2005b) Pi ttsburgh, PA (1987-1999) CHF PM 10 , NO 2 , SO 2 , O 3 0,1,2,3 Mean: 1.03 (24 h) Symons et al. (2006) Baltimore, MD (2002) CHF PM 2.5 , NO 2 , O 3 0,1,2,3 Mean: 0.4 (24 h) Lee et al. (2007a) Kaohsiung, Taiwan (1996-2004) CHF PM 10 , NO 2 , SO 2 , O 3 0-2 Mean: 0.76 (24 h)

STUDIES THAT EXAMINED HF AMONG OTHER CVDS

Linn et al. (2000) Los Angeles, CA ( 1992-1995) CHF, MI, All CVD, CA, OS PM 10 , NO 2 , O 3 0 Mean: (24 h) Winter 1.7; Spring 1.0 Summer 1.2; Fall 2.1 Koken et al. (2003) Denver, CO (1993-1997) CHF, MI, CAth, PHD, CD PM 10 , NO 2 , SO 2 , O 3 0,1,2,3 Mean: 0.9 (24 h) Metzger et al. (2004a) Atlanta, GA (1993-2000) CHF, IHD, All CVD, CD, PVCD PM 10 , NO 2 , SO 2 , O 3 0-2ma Mean 1.5 (1 h) Peel et al. (2007) Atlanta, GA ( 1993-2000) CHF, IHD, All CVD, CD, PVCD PM 10 , NO 2 , SO 2 , O 3 0-2ma Mean 1.5 (1 h)

*Cardiac = AMI, angina, dysrhythmia, or HF ; CA = Cardiac arrhythmia; CAth = Cardiac atherosclerosis; CD = cardiac dysrhthmias; CHF = Congestive heart failure; PHD = Pulmonary heart disease; OS = Occlusive stroke; PVCD = peripheral va scular and cerebrovascular disease, ma = moving average.



# Cardiovascular Diseases

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 The following section reviews studies that have inve stigated the effect of CO on ED visits and hospital admissions for all CVD outcomes (e.g., non-specific). Several of these studies also examined specific CVDs and were briefly discussed in previous sections. As discussed earlier, a study was conducted in Atla nta, GA where over 4 million ED visits from 31 hospitals for the period 1993 to 2000 were analyz ed (SOPHIA). Several articles have been published from this research with three exam ining cardiovascular admissions in relation to CO exposures. The first of these used a time-series design and analyzed a 3-day moving average over single-day lags 0-2 as the a priori lag structure (Metzger et al., 2004a). Resu lts showed that a 1 ppm increase in 1-h max CO concentration was associated w ith 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 NO 2 

## and PM 2.5 

## . The second of these publications examined the association of ambient air pollution levels and cardiovascular morbidity in visits with and withou t specific secondary conditions (Peel et al., 2007). Within a time-stratified case-crossover design, a 3-day moving average over single-day lags 0-2 was used as the a priori lag structure. Results from the case- crossover analyses on all cardiovascular and peripheral vascular and cerebrovascular disease were similar to th e time-series results presented earlier. Results from the various co-morbidity analyses are presented in Table 5-10. Similar to the results from the earlier publication, CO was mostly associated with peri pheral vascular and cerebrovascular disease (PVCD) among those with and without the co-morbidities, except among those with CHF. Overall, there is limited, if any, evidence of susceptibility to the effects of CO concentration for those with comorbid conditions.

March 2009 5-37 DRAFT – DO NOT CITE OR QUOTE

# Table 5-10. Association of ambient air pollution le vels and cardiovascular morb idity in visits with and without specific secondary conditions.

Co-morbidity IHD Dysryhthmias PVCD CHF

HYPERTENSION

- With 1.007 (0.978-1.037) 1.065 (1.015-1.118) 1.038 (1.004-1.074) 1.037 (0.997-1.079) - Without 1.022 (1.000-1.043) 1.008 (0.988-1. 029) 1.027 (1.002-1.054) 1.010 (0.985-1.037)

DIABETES

- With 0.985 (0.945-1.027) 1.058 (0.976-1. 146) 1.065 (1.012-1.121) 1.020 (0.975-1.067) - Without 1.023 (1.004-1.042) 1.014 (0.995-1. 034) 1.025 (1.003-1.048) 1.018 (0.993-1.044)

COPD

- With 0.996 (0.938-1.057) 0.972 (0.878-1.077) 1.113 (1.027-1.205) 1.058 (1.003-1.115) - Without 1.018 (1.000-1.036) 1.018 (0.999-1. 038) 1.026 (1.004-1.047) 1.011 (0.987-1.036)

CHF

- With 0.956 (0.907-1.007) 1.065 (0.968-1.173) 1.072 (0.981-1.172) - - Without 1.024 (1.006-1.042) 1.015 (0.996-1.034) 1.029 (1.008-1.051) - DYSRYHTHMIAS

- With 1.028 (0.985-1.072) - 1.072 (1.011-1.138) 1.004 (0.960-1.051) - Without 1.014 (0.995-1.033) - 1. 026 (1.004-1.048) 1.023 (0.998-1.049)

PVCD - peripheral vascular and cerebrovascular disease, I HD = ischemic heart disease, CHF = congestive heart failure. Source: Peel et al. (2007) 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 The third study utilizing the SOPHIA data exte nded the time period to in clude 1993 through 2004 (Tolbert et al., 2007) and focused on two larg e outcome groups: a respiratory diseases group and a cardiovascular diseases group. The co mbined cardiovascular case group included the following groups of primary ICD-9 diagnostic codes: IHD (410-414), cardiac dysrhythmias (427), CHF (428), and peripheral vascular and cerebrovascular disease (433-437, 440, 4 43-445, 451-453). Results showed that a 1 ppm increase in 1-h max CO concentration was associated with an increase in daily ED visits for all CVDs(RR: 1.016 [95% CI: 1.008-1.024]). CO was the strongest predictor of CVD effects in models with twopollutant combinations of NO 2 

## , CO and TC, as well as in a model including all three pollutants. Earlier research conducted in Los Angeles, CA, s howed that a 0.5 ppm increase in same-day 24-h avg CO concentration was associated with a 1. 6% increase in CVD hospital admissions among people aged >30 years (2000). When the analyses were stratified by season the significant CO effect was strongest during winter (1.9% increase) followed by summer (1.8%) and fall (1.4%) with no effect in spring. In contrast to other North American studies, a study in Spokane, WA, did not find an association between CO (lags of 1 to 3 days) and an increas e in the number of daily cardiac hospital admissions (quantitative results not reported) (Slaughter et al ., 2005). Similarly, a time-series study in Windsor, Ontario, did not find an association between ambi ent CO and daily hospital admissions for CVDs (defined as HF, IHD, or dysrhythmias) (Fung et al., 2005). A total of 11,632 cardiac admissions were

March 2009 5-38 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 analyzed for the period of 1995 to 2000. The lag period s 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 ag e group (lag 0) was -2.6 (95% CI: -6.2 to 3.3); and for the 65+ age group, 0.4 (95% CI: -1.9 to 2.7). Th e authors reported moderate to low correlations with NO 2 

## (r = 0.38), PM 10 

## (r = 0.21) and SO 2 

## (r = 0.16). Two case-crossover studies in Taiwan reported an association between ambient CO and hospital admissions for CVDs. In Taipei, a total of 74,509 CVD admissions from 47 hospitals for the period of 1997-2001 were analyzed (Chang et al., 2005). An in crease 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 PM 10 

## , SO 2 

## , or O 3 

## in two-pollutant models. An identical study in Kaohsiung analyzed 29,661 CVD admissions for the period 1997-2000 (Yang et al., 2004b). Results showed that a 0.5 ppm in crease in 24-h avg CO concentration was associated with an increase in CVD hospital admissions during both the warmer periods (OR: 1.50 [95% CI: 1.38-1.63) and cooler periods (OR: 1.89 [95% CI: 1.69-2.12]). Similarly, two Australian studies also reported associations between ambient CO concentrations and increased hospital admissions among older adults. The first of these studies analyzed data from five of the largest cities in Australia (Brisbane, Canbe rra, Melbourne, Perth, Sydney) and two New Zealand cities (Auckland, Christchurch) for the period 1998-2001 (Barnett et al., 2006). The combined estimates showed that an increase of 0.75 ppm in the averag e 8-h max CO concentration over the current and previous day (lag 0-1) was associated with a 1.8% (95% CI: 0.7-2.8) increase in all CVD admissions among those aged 65+ years. Among those aged 15- 64 years there was a smaller increase in CVD admissions (1.0% [95% CI: 0.2-1.7]). The second of th e Australian studies examined ED visits for CVDs in older adults (65+ years) in Sydney for the period 1997 to 2001 (Jalaludin et al., 2006). A 0.75 ppm increase in 8-h max CO concentration for single-day lags 0 and 1 was associated with increases in admissions of 2.5% (95% CI: 1.6-3. 5) and 1.4% (95% CI: 0.5-2.4) respectively. Based on an average over lags 0 and 1 (e.g., lag 0-1) there was an increase of 2.6% (95% CI: 1.5-3.6). There were positive increases of approximately 3% in CVD ED visits during the cool (May-October) period, but not the warm period (November-April). Very few studies investigating the association between CO and cardiovascular hospital admissions have been conducted in European cities. Ballester et al. (2001) analyzed emergency hospital admissions in Valencia, Spain for the period 1994 to 1996. Th e mean daily number of CVD admissions was 7 and when using a time-series approach there was no asso ciation 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 incr ease), and cerebrovascular diseases (RR: 0.985 [95% CI: 0.959-1.012] per 1 ppm increase). When th e analyses were stratified by hot and cold seasons,

March 2009 5-39 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 only CO concentrations during the hot season were associated with an increase in all cardiovascular admissions (RR: 1.033 [95% CI: 1.006-1.064] per 1 ppm increase), heart disease admissions (RR: 1.033 [95% CI: 1.000-1.067] per 1 ppm increase), and cerebrovascular admissions (RR: 1.074 [95% CI: 1.007-1.113] per 1 ppm increase). Ballester et al. (2006) extended this research to include data from 14 Spanish cities for the period of 1995 to 1999. An average exposure period over lags 0-1 was analyzed and for the combined estimates a 0.75 ppm increase in 8-h max CO concentration wa s associated with a 1.77% (95% CI: 0.56-2.99) increase in all cardiovascular emergency hospital admissions and a larger increase of 3.57% (95% CI: 1.12-6.08) for heart disease admissions. These results persisted in two-pollutant models that included NO 2 

## , O 3 

## and SO 2 

## . Table 5.11 summarizes the non-specific CVD hos pital admission studies that examined CO exposures. Figure 5-4 shows the effect estimates associ ated with daily admissions for non-specific CVD hospital admissions from selected studies. In summary, many of the studies that examined associations between ambient CO concentrations and ED visits and daily hospital ad missions for CVD reported significant associations at short (0-1 day) lags. Among studies that conducted stratified analyses, there were slightly stronger effects among older adults and possibly during warmer periods.

March 2009 5-40 DRAFT – DO NOT CITE OR QUOTEStudy Location Lag

Atlanta, GA 0-2 Tolbert et al. (2007)*

Linn et al. (2000) Los Angeles , CA 0 All year

Linn et al. (2000) Los Angel es, CA 0 Spring

Linn et al. (2000) Los Angeles, CA 0 Summer

Linn et al. (2000) Los Angeles, CA 0 Fall

Linn et al. (2000) Los Angel es, CA 0 Winter

Fung et al. (2005) Windsor, C anada 0-2 <65 years

Fung et al. (2005) Windsor, C anada 0-2 65+ years

Chang et al. (2005) Taipei, Taiwan 0-2 > 20°C Temp

Chang et al. (2005) Taipei, Ta iwan 0-2 < 20°C Temp

Yang et al. (2004a) Kaohsiung, Taiwan 0-2 < 20°C Temp

Yang et al. (2004a) Kaohsiung, Taiw an 0-2 > 20°C Temp

Barnett et al. (2006) Australia, Ne w Zealand 0-1 15-64 years

Barnett et al. (2006) Australia, Ne w Zealand 0-1 65+ years

Jalaludin et al. (2006) Sydney, Au stralia 0-1 65+ years

Ballester et al. (2006) Multi-city, Spain 0-1

Effect Estimate *Represents the results reported by Metzger et al. (2004b) and Peel et al. (2007) 

# Figure 5-4. Summary of effect estimates (95% confidence inter vals) associated with hospital admissions for CVD. Effect estimates have b een 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 fo r 24-h avg CO concentrations.

March 2009 5-41 DRAFT – DO NOT CITE OR QUOTE

# Table 5-11. Summary of non-specifi c CVD hospital admission studies.

Study Location CVD Codes Copollutants Lags Examined CO Levels (ppm) Metzger et al. (2004a) Atlanta, GA (1993-2000) All CVD PM 10 , NO 2 , SO 2 , O 3 0-2ma Mean: 1.5 (1 h)

Peel et al. (2007) Atlanta, GA (1993-2000) All CVD PM 10 , NO 2 , SO 2 , O 3 0-2ma Mean 1.5 (1 h)

Tolbert et al. (2007) Atlanta, GA (1993-2004) All CVD PM 10 , NO 2 , SO 2 , O 3 0-2ma Mean 1.6 (1 h)

Linn et al. (2000) Los Angeles, CA (1992-1995) All CVD PM 10 , NO 2 , O 3 0 Mean: (24 h) Winter 1.7; Spring 1.0; Summer 1.2; Fall 2.1 Slaughter et al. (2005) Spokane, WA (1995-2001) All CVD (ICD9: 390-459) PM 10 , PM 2.5 , CO 1,2,3 Mean: range across 5 monitors 0.42- 1.82 (24 h) Fung et al. (2005) Windsor, Canada (1995-2000) All CVD (HF, IHF, or Dysrhythmia) PM 10 , NO 2 , SO 2 , O 3 0, 0-1, 0-2 Mean: 1.3 (24 h)

Chang et al. (2005) Taipei, Taiwan (1997-2001) All CVD (ICD9: 410-429) PM 10 , NO 2 , SO 2 , O 3 0-2 Mean: 1.37 (24 h)

Yang et al. (2004b) Kaohsiung, Taiwan (1997-2000) All CVD (ICD9: 410-429) PM 10 , NO 2 , SO 2 , O 3 0-2 Mean: 0.79 (24 h)

Australia and New Zealand (1998-2001) All CVD (ICD9: 390-459) PM 10 , NO 2 , O 3 0-1 Mean: (8h) 0.5-2.1 Barnett et al. (2006)

Jalaludin et al. (2006) Sydney, Australia (1997-2001) All CVD (ICD9: 390-459) PM 10 , NO 2 , SO 2 , O 3 0,1,2,3, 0-1 Mean: 0.82 (8h)

Ballester et al. (2001) 1 Valencia, Spain (1994-1996) All CVD (ICD9: 390-459) BS, NO 2 , SO 2 , O 3 1,2,3,4,5 Mean: 0.54 (24 h)

Ballester et al. (2006) 1 Multi-city, Spain (1995-1999) All CVD (ICD9: 390-459) BS, PM 10 , TSP, NO 2 , SO 2 , O 3 0-1 Mean: range across 14 cities 0.12-0.24 (8h)

1 These studies presented CO concentrations in the units mg/m 3 . The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m 3 , which assumes standard atmosphere and tempterature.



# 1 2 3 4 5 6 Figure 5-5 summarizes the effects of CO concentrat ion 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 PM 10 

## , PM 2.5 

## , NO 2 

## , SO 2 

## , and O 3 

## . In all but one instance (Lee et al., 2007a) (<25°C adjusted for NO 2 

## ) when the single pollutant effect estimate was positive for CO, it remained positive after the addition of any of the copollutants investigated.

March 2009 5-42 DRAFT – DO NOT CITE OR QUOTE

### March 2009 5-43 DRAFT—DO NOT QUOTE OR CITE Study Outcome Lag Copollutant Wellenius et al. (2005b) CHF 0 CO alone PM 10 CO+PM 10 Chan et al. (2006) CD 2 CO alone CO+PM 10 Lee et al. (2007a) CHF 0 CO alone >25°C CO+ PM 10 >25°C D’Ippoliti et al. (2003) HA 0-2 CO alone CO+TSP Chang et al. (2005) CVD 0 CO alone ≥ 20°C CO+PM 10 ≥ 20°C Chang et al. (2005) CVD 0 CO alone <20°C CO+PM 10 20°C Lee et al. (2003b) IHD 0 CO alone 64+ years CO+PM 10 64+ years Ballester et al. (2006) CVD 0-1 CO alone CO+PM 10 von Klot et al. (2005) Cardiac 0 CO alone CO+PM 10 Lee et al. (2007a) CHF 0 CO alone <25°C CO+PM 10 <25°C Chan et al. (2006) CD 2 CO alone PM 2.5 CO+PM 2.5 Wellenius et al. (2005b) CHF 0 CO alone NO 2 CO+NO 2 Ballester et al. (2006) CVD 0-1 CO alone CO+NO 2 Chang et al. (2005) CVD 0 CO alone ≥ 20°C CO+NO 2 ≥ 20°C Chang et al. (2005) CVD 0 CO alone <20°C CO+NO 2 <20°C Lee et al. (2007a) CHF 0 CO alone >25°C CO+NO 2 >25°C Lee et al. (2007a) CHF 0 CO alone <25°C CO+NO 2 <25°C Wellenius et al. (2005b) CHF 0 CO alone SO 2 CO+SO 2 Ballester et al. (2006) CVD 0-1 CO alone CO+SO 2 Chang et al. (2005) CVD 0 CO alone ≥ 20°C CO+SO 2 ≥ 20°C Chang et al. (2005) CVD 0 CO alone <20°C CO+SO 2 <20°C Lee et al. (2007a) CHF 0 CO alone >25°C CO+SO 2 >25°C Lee et al. (2007a) CHF 0 CO alone <25°C CO+SO 2 <25°C Wellenius et al. (2005b) CHF 0 CO alone O 3 CO+O 3 Chan et al. (2006) CD 2 CO alone CO+O 3 Ballester et al. (2006) CVD 0-1 CO alone CO+O 3 Chang et al. (2005) CVD 0 CO alone ≥ 20°C CO+O 3 ≥ 20°C Chang et al. (2005) CVD 0 CO alone < 20°C CO+O 3 <20°C von Klot et al. (2005) Cardiac 0 CO alone CO+O 3 Lee et al. (2007a) CHF 0 CO alone >25°C CO+O 3 >25°C Lee et al. (2007a) CHF 0 CO alone <25°C CO+O 3 <25°C



### Effect Estimate 

# Figure 5-5. Effect estimates fr om studies of ED visits and hospital admissions for CVD outcomes other than stroke from single polluta nt (CO only, closed circles) and copollutant (CO plus PM, NO 2 

# , SO 2 

# and O 3 

# , open circles) models . Effect estimates are standardized to 1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO l evels, and 0.5 ppm for 24-h avg CO concentrations.

# 5.2.1.2. Epidemiologic Studies with Long-Term Exposure

Only two studies examined CVD outcomes in association with long-term exposure to CO. Rosenlund et al. (2006) investigated long-term exposure (30 years) to urban air pollution and the risk of 

# 2 MI in Sweden. The study included 2,246 cases and 3,206 controls aged between 45 to 70 years and 

# 3 residing in Stockholm County during 1992 to 1993. A detailed postal questionnaire was completed by 

# 4 4067 subjects and all addresses inhabited during more than 2 years since 1960 were geocoded. The 

# 5 exposures were then derived from dispersion calculati ons based on emissions data for each decade since 

# 6 1960. These calculations were estimates of a nnual mean levels of traffic-generated NO X 

## , NO 2 

## , CO, PM 10 

## , 

# 7 and PM 2.5 

## , with the addition of SO 2 

## from heating sources. The analyses were stratified by all cases, 

# 8 nonfatal cases, fatal cases, in-hospital death, and ou t-of-hospital death. Based on a 30 year avg exposure 

# 9 all pollutants were not associated with overall MI in cidence. However, increased CO was associated with 

# 10 out-of-hospital death from MI (OR: 1.81 [95% CI: 1.02-3.23] per 0.5 ppm increase in 30-year avg CO 

# 11 concentration). Similar results were reported for NO 2 

## . The correlation between the 30-year NO 2 

## and CO 

# 12 exposures was reasonably strong (r = 0.74) and multipo llutant models with both these pollutants included 

# 13 (NO 2 

## , CO) were not examined. No other pollutants we re significantly associated with all other MI 

# 14

outcomes. 

# 15

1

16 17 18 19 20 21 22 23

24 A small-area ecologic study analyzed mortality and hospital admissions for stroke across 1,030 census districts in Sheffield, U.K. (Maheswaran et al., 2005b). Stroke counts within each census district were linked to modeled air pollution data which was then grouped into quintiles of exposure. For stroke hospital admissions, when the analyses were adjusted for only sex and age demographics there was an exposure-response pattern exhibited across the quint iles of CO exposure with all levels reaching significance (RR: 1.37 [95% CI: 1.24-1.52] for the highest exposure group compared to the lowest group). However, this result did not persist when also adjusting for a deprivation index and smoking rates across the districts (RR: 1.11 [95% CI: 0.99-1.25]).



# 5.2.1.3. Summary of Epidemiologic Studies of Exposure to CO and Cardiovascular Effects

A substa ntial number of epidemiologic studies ha ve examined the potential association between exposure to CO and various physiological cardiac end points or biomarkers. Overall, despite some mixed 

# 25 results reported among panel and retr ospective cohort studies, there was evidence that exposure to CO has 

# 26 an effect on HR, various HRV parameters, and bl ood markers of coagulation and inflammation. 

# 27 Conversely, based on results from panel studies there was little evidence of a link between CO and 

# 28 cardiac arrhythmia, cardiac arrest, the occurrence of myocardial infarction, and increased BP. 

# 29

March 2009 5-44 DRAFT – DO NOT CITE OR QUOTE

## Studies of ED visits and hospital admissions provi de evidence that CO is associated with various forms of CVD with lag periods ranging from 0 to 3 days. There is little evidence that ambient CO is associated with an increase in hospital admissions fo r ischemic stroke. Studies of hospital admissions and ED visits for IHD and CHF provide the strongest ev idence of ambient CO being associated with adverse CVD outcomes. It is difficult to determine from th is group of studies the extent to which CO is independently associated with CVD outcomes or if CO is a marker for the effects of another trafficrelated pollutant or mix of pollutants. On-road vehicl e 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 m odels, are coherent with the effects demonstrated by controlled human exposur e and animal toxicological studies, and supports a direct effect of short-term CO exposure on CVD morb idity at ambient concentrations below the current NAAQS level. Such direct effects are plausible considering that long-term, low concentration CO exposure (See Section 4.2.3) could result in a COHB level approaching those used in controlled human exposure studies (See Section 5.2.2). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

16

29 30 31 32 

# 5.2.2. Controlled Human Exposure Studies

Contro lled human exposure studies provide valuable information related to the health effects of exposure to air pollutants following short term exposur es. These types of experiments are often referred to 

# 17 as human clinical studies and are conducted in a la boratory setting under carefully regulated exposure 

# 18 concentrations, environmental conditi ons, and subject activity levels. Human clinical studies are typically 

# 19 conducted using a randomized crossover study design with subjects exposed to both the pollutant(s) of 

# 20 interest and a clean air control. Results of cont rolled human exposure studies can be used to provide 

# 21 coherence with the evidence from epidemiologic st udies by expanding the understanding of potential 

# 22 mechanisms for the observed health outcomes. However, they may also provide information that can be 

# 23 used directly in quantitatively characterizing the expo sure concentration-health response relationships at 

# 24 ambient or near-ambient concentrations. Human clinical studies are limited by a number of factors 

# 25 including a small sample size and relatively s hort exposure time. In addition, although health

# 26 compromised individuals have been included in human clinical studies, all subjects participating must be 

# 27 relatively healthy and do not represent the most sensitive individuals in the population. 

# 28 Several human clinical studies cited in the 2000 CO AQCD observed changes in measures of cardiovascular function among individuals with CAD following short term exposures to CO. In a multilaboratory study of men with stable angina, Allred et al. (1989, 1991) evaluated the effect of CO exposure on exercise-induced angina and ST-segment changes indi cative of myocardial ischemia. Relative to clean

March 2009 5-45 DRAFT – DO NOT CITE OR QUOTE

## air exposure (COHb ≈ 0.6%), exposures to CO resulting in COHb concentrations of 2.4% and 4.7% were shown to decrease the time required to induce ST- segment changes by 5.1% (p = 0.02) and 12.1% (p <0.001), respectively. These cha nges were well correlated with the onset of exercise-induced angina. A number of other studies involving individuals with st able angina have also demonstrated a CO-induced decrease in time to onset of angina as well as reducti on in duration of exercise at COHb concentrations between 3 and 6% (Adams et al., 1988; Anderson et al., 1973) (Kleinman et al., 1989; Kleinman et al., 1998). However, Sheps et al. (1987) observed no change in time to onset of angina or maximal exercise time following a 1-h exposure to 100 ppm CO (targe ted 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 C OHb of 6%), but not following a 1-h exposure to 100 ppm CO (targeted COHb of 4%) (Sheps et al., 19 90). It should be noted that although the subjects evaluated in the studies described above are not necessarily representative of the most sensitive population, the level of disease in these individuals was relatively severe, with the majority either having a history of MI or having ≥ 70% occlusion of one or more of the coronary arteries. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 The 2000 CO AQCD presented very little eviden ce of CO-induced changes in cardiovascular function in healthy adults. Davies a nd Smith (1980) exposed healthy young adults continuously for 7 days to CO concentrations of 0, 15, or 50 ppm. In this study, a marked ST-segment depression was demonstrated in only 1 out of 16 subjects following exposure to 15 ppm CO (2.4% COHb) or 50 ppm CO (7.2% COHb). Since the publication of the 2000 CO AQCD, no new human clinical studies have been published involving controlled CO exposures among s ubjects with CAD. However, a number of new studies have evaluated changes in various measures of cardiovascular and systemic responses following controlled exposures to CO in healthy adults. Adir et al. (1999) exposed 15 young healthy adult males to room air or CO for approximately 4 min, using a CO exposure concentration which had been shown to produce the targeted COHb level of 4-6%. Followi ng each exposure, subjects performed an exercise treadmill test at their maximal capacity. Exposure to CO was not observed to cause arrhythmias, ST-segment changes, or changes in myocardial perfusion (thallium scintigraphy) during post-exposure exercise. However, CO was dem onstrated to decrease the post-exposure duration of exercise by approximately 10% (p = 0.0012). In addition, the authors reported significant CO-induced decreases in metabolic equivalent units (p <0.001), which is a relative measure of O 2 

## consumption. These results support the findings of several studies cited in the 2000 CO AQCD which observed decreases in exercise duration and maximal aerobic capacity am ong healthy adults at COHb levels ≥ 3% (Drinkwater et al., 1974; Ekblom and Huot, 1972; Horvath et al., 1975; Raven et al., 1974). While these decreases in exercise duration were relatively small and only likely to be noticed by competing athletes, the findings are nonetheless important in providing coherence with the observed effects of CO on exercise-induced myocardial ischemia among patients with CAD.

March 2009 5-46 DRAFT – DO NOT CITE OR QUOTE

## Kizakevich et al. (2000) evaluated the cardiov ascular effects of increasing CO concentration in healthy adults engaged in upper a nd 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 O 2 

## carrying capacity during CO exposure were demonstrated, with statistica lly 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 sm oking, Zevin et al. (2001) exposed 12 healthy male smokers for 7 consecutive days to clean air, CO, or cigarette smoke, with each subject serving as his own control. The COHb levels were similar between th e exposures to cigarette smoke and CO, with average concentrations of 6% and 5%, respectively. Cigarette smoke, but not CO, was observed to significantly increase plasma levels of CRP and plasma platelet factor 4 relative to the air control arm of the study. Neither cigarette smoke nor CO was shown to affect BP . Hanada et al. (2003) observed an increase in leg muscle sympathetic nerve activity (MSNA) following controlled exposures to CO (COHb ≈ 20%) under normoxic or hyperoxic conditions. Although an increase in the magnitude of sympathetic activation is typically associated with regional vasoconstriction, no CO-induced changes in femoral venous blood flow were observed in this study. These findings are in ag reement with those of Hausberg et al. (1997) who observed no change in forearm blood flow or BP in a study of 10 healthy men and women following a controlled exposure to CO (COHb ≈ 8%). Interestingly, one recent study did observe an increase in retinal blood flow, retinal vessel diameter, and choroidal bl ood flow following controlled exposures to CO at a concentration of 500 ppm (Resch et al., 2005). This prot ocol resulted in COHb concentrations of 5.6% and 9.4% following exposures of 30 and 60 min, respectively, with statistically significant increases in retinal and choroidal blood flow observed at both tim e points relative to synthetic air control. This CO-induced change in ocular hemodynamics may ha ve 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 aver age COHb level of 6% (Morse et al., 2008). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 In summary, controlled human exposures to CO am ong individuals with CAD have been shown to consistently increase markers of myocardial ischemia at COHb levels between 3 and 6%, with one study reporting similar effects following CO exposures resulti ng in COHb concentrations of 2.4%. No such effects have been observed in healthy adults following controlled exposures to CO. Although some studies have reported CO-induced hemodynamic chang es among healthy adults at COHb concentrations of as low as 5%, this effect has not been consistent across studies.

March 2009 5-47 DRAFT – DO NOT CITE OR QUOTE

# 5.2.3. Toxicological Studies

While novel toxicological research on environmenta l levels of CO was limited in the 2000 CO AQCD, several sections reported potential relationshi ps between CO exposure and cardiovascular effects. 

# 2 Conflicting experimental data relati ng to the role of CO in promoting atherosclerotic vessel disease was 

# 3 discussed. While some animal studies have linked chr onic CO exposure with atherosclerosis development 

# 4 resulting from increased fatty streaking and cellular lipid loading (Davies et al., 1976; Thomsen, 1974; 

# 5 Turner et al., 1979), other studies have failed to see this association (Penn et al., 1992; Stupfel and 

# 6 Bouley, 1970). Ventricular hypertrophy has also been shown after chronic CO exposure (Penney et al., 

# 7 1984; Penney et al., 1988). 

# 8 1

9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 The following sections describe recen t studies dealing with toxicity of relatively low levels of CO. There has been little new research with the overt purpo se of examining environmentally-relevant levels of CO. For the most part, studies were designed to mimi c exposures related to ciga rette smoke, either sidestream or mainstream, accidental CO poisoning, or for the purposes of therapeutic application. Thus, few studies examined levels of CO within the current 1 h (35 ppm) or 8 h (9 ppm) NAAQS levels, and fewer still examined concentration response curves to delineat e no effects levels. However, it is apparent that CO, at low to moderate levels (35-250 ppm), has pathophysiological effects on the cardiovascular system and on relatively ubiquitous cellular pathways. CO exposure at environmentally-re levant levels is unlikely to ove rwhelm normal physiology in a healthy cell; however, susceptibility may be rendered by disease or early development. A common theme appears to be the vulnerability of vascular cells, esp ecially the endothelium, which could be considered the first organ of contact once taken up into the ci rculation. While relatively little research has been conducted since the 2000 CO AQCD, several key stud ies conducted at near-environmental CO levels provide important clues to the potential public h ealth implications of ambient CO exposure.



# 5.2.3.1. Endothelial Dysfunction

While th e preferential binding to heme and effective displacement of O 2 

## by CO has been well established for over a century, new information from vari ous fields of study are beginning to elucidate 

# 24 non-hypoxic mechanisms that may lead to cardiovascular abnormalities associated with CO exposure. 

# 25 Research by Thom, Ischiropoulos, and colleagues (Ischiropoulos et al., 1996; Thom et al., 1994; Thom 

# 26 and Ischiropoulos, 1997; Thom et al., 1997; Thom et al., 1999a; Thom et al., 1999b; Thom et al., 2000; 

# 27 Thom et al., 2006) has focused on CO-mediated displacement of NO • from heme-binding sites. Some of 

# 28 this work demonstrates a specific pathway by whic h severe CO poisoning can lead to the release NO • 

# 29 from platelets with subsequent neutrophil activation and vascular injury (Ischiropoulos et al., 1996; Thom 

# 30 et al., 2006). The steps include (1) peroxynitrite generation from the reaction of NO • from platelets with 

# 31

March 2009 5-48 DRAFT – DO NOT CITE OR QUOTE

## neutrophil-derived superoxide followed by (2) stimul ation of intravascular neut rophil degranulation that 

# 1 can result in (3) myleoperoxidase deposition along th e vascular lining. Products from myeloperoxidase

# 2 mediated reactions can cause endothelial cell activation (Thom et al., 2006) and can lead to endothelial 

# 3 dysfunction. The concentrations used in these studi es are greatly in excess of the NAAQS levels, but 

# 4 certainly within the range of accidental or occupati onal exposures. Research by these same investigators 

# 5 at more environmentally-relevant CO levels was partially reviewed in the 2000 CO AQCD. The release of 

# 6 free NO • was noted in isolated rat platelets exposed to 10-20 ppm CO (Thom and Ischiropoulos, 1997). 

# 7 Increased nitrotyrosine content of the aorta was observed in rats exposed to 50 ppm CO for 1 h (1999a; 

# 8 Thom et al., 1999b). Furthermore in this same study, a 1-h exposure to 100 ppm CO led to albumin efflux 

# 9 from skeletal muscle microvasculature at 3 h and le ukocyte sequestration in the aorta at 18 h. LDL 

# 10 oxidation was also reported. These effects were depend ent on NOS but not on neutrophils or platelets. A 

# 11 second study demonstrated NO-dependent effects of 50- 100 ppm CO in lungs and is described in Section 

# 12 5.5.4 (Thom et al., 1999b). Studies in cultured endot helial cells were also conducted using buffer 

# 13 saturated with 10-100 ppm CO (Thom et al., 199 7). These experiments were designed to mimic 

# 14 conditions where blood COHb levels were between 3.8 and 28% resulting in exposure of endothelial cells 

# 15 to 11-110 nM CO. CO-stimulated release of NO • from endothelial cells along with peroxynitrite 

# 16 formation and delayed cell death was observed at CO concentrations of 22 nM and higher (Thom et al., 

# 17 1997). A more recent study demonstrated adaptive respon ses in endothelial cells exposed to this same 

# 18 range of CO concentrations (Thom et al., 2000). Specifically, 1-h exposure to 11 nM CO resulted in 

# 19 MnSOD and HO-1 induction and resistance to the ap optotic effects of 110 nM CO. These protective 

# 20 effects of CO were mediated by NO • , as demonstrated using an inhibitor of NOS and a scavenger of 

# 21 peroxynitrite. Collectively, these experiments demons trated altered oxidative stress, the initiation of 

# 22 inflammation, increased microvascular permeability and altered cell signaling in animals and isolated 

# 23 cells following exposure to 10-100 ppm CO. 

# 24 CO is an endogenous regulator of vasomotor tone through vasodilatory effects mediated by activation of soluble guanylate cyclase and activation of large conductance Ca 2+ 

## activated K + 

## channels. However, CO does not cause vasodilation in every vasc ular bed. For example, 5, 100, 500 and 2,500 ppm CO administered by inhalation to near-term fetal lambs did not induce pulmonary vasodilation and the HO inhibitor zinc protoporphyrin IX failed to affect baseline vascular tone (Grover et al., 2000). In some cases CO promotes vasoconstriction, which is thought to be mediated by inhibition of endothelial NOS (Johnson et al., 2003; Thorup et al., 1999) or decreased NO • bioavailability. An interesting series of studies has also suggested that endogenous CO deri ved from HO-1 which is induced in a variety of disease models (salt-sensitive forms of hypertension, meta bolic syndrome in obese rats) is responsible for skeletal muscle arterial endothelial dysfunction (Joh nson et al., 2003, 2004; Teran et al., 2005). Additional studies will be useful in determining whether envi ronmentally-relevant concen trations of CO have detrimental effect on pre-existing conditions such as hypertension, metabolic syndrome or pregnancy. 

# 25 26 27 28 29 30 31 32 33 34 35 36

March 2009 5-49 DRAFT – DO NOT CITE OR QUOTE

## Several recent animal studies examined the vascular effects of controlled exposures to complex combustion mixtures containing CO. Vascular dilatati on was decreased following exposure to diesel (4 h at 4 ppm) (Knuckles et al., 2008) and gasoline engine emissions (6 h/day x 1, 3, and 7 day at 80 ppm) (Lund et al., 2009). Furthermore, evidence of vascular ROS following gasoline emissions has been shown in certain animal models (6 h/day x 50 day at 8-80 ppm) (Lund et al., 2007). While none of these studies examined the potential role of CO, alone or in combin ation, it is clearly a common factor in the various combustion atmospheres, and future work will be n eeded to reveal its importance on vascular health. 

# 1 2 3 4 5 6 7

8

22 23 24 25 26 27 28 29 30 31 32 33 

# 5.2.3.2. Cardiac Remodeling Effects

Cardiomy opathy, 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 

# 9 ventricular chamber volume and a diminishing efficien cy of the heart. Such concentric hypertrophy 

# 10 typically occurs in response to chronic increases in load, as occurs with hypertension. Ischemia of the 

# 11 cardiac tissue can also lead to cardiac remodeling and myopathy. During and after an acute infarction or 

# 12 obstruction of major coronary vessels, downstream tissu es can suffer severe regional ischemia that leads 

# 13 to significant necrosis. Such regions will lose the ability to contract, and surrounding tissue will show 

# 14 deficits in contractility. Decreased contractility is ofte n a result of structural thinning of the ventricular 

# 15 wall, as well as metabolic impairments. Chronic isch emia, such as may result from CAD, may similarly 

# 16 impair cardiomyocyte function and cause decreased contractility and remodeling. However, ultimately 

# 17 cardiomyopathies are of a complex origin involving mismanagement of fluid balance, abnormal hormonal 

# 18 influences (epinephrine, angiotensin), and insuff icient perfusion/nutrition. Assessing the role of 

# 19 exogenous CO in altering pathways leading to cardiom yopathy is a relatively new endeavor and several 

# 20 new findings are of great interest. 

# 21 The heart is a known target for CO toxicity, potentially due to its high rate of O 2 

## consumption. Direct effects of CO on the healthy heart have only been observed at relatively high concentrations. For example, a recent study by Sorhaug et al. (2006) demo nstrated cardiac hypertrophy in rats exposed for 72 weeks to 200 ppm CO. COHb levels were reported to be 14.7%. Neither structural signs of hypertension in the pulmonary arteries or atherosclerotic lesions in the systemic arteries were observed. Cardiac hypertrophy was also demonstrated in rats exposed to 100-200 ppm CO for 1-2 weeks (Loennechen et al., 1999). This response was accompanied by an increase in endothelin-1 expression. COHb levels were reported to be 12-23% in this study. Direct effects of CO on the healthy heart have also been demonstrated following short-term exposures. In a study by Favory et al. (2006b), rats we re exposed to 90 min of 250 ppm CO, which led to peak COHb values of roughly 11%; recovery of 96 h was needed for COHb levels to return to baseline. The authors noted that within the first 24 hours of recovery, while COHb values decreased from 11% to

March 2009 5-50 DRAFT – DO NOT CITE OR QUOTE

## 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 vascula r bed to both acetylcholine and a NO • donor were reduced by CO exposure. The authors concluded that the discordant alterations in contractility (increased) and perfusion (decreased) may place the heart at risk of O 2 

## limitations following this exposure to CO. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Several studies examined the impact of lower leve ls (50 ppm) on pre-existing or concurrent cardiac pathologies. In one such study, CO exacerbated the effects of a hypoxia-based model of right ventricular remodeling and failure (Gautier et al., 2007). In cont rolled laboratory settings, chronic hypobaric hypoxia (HH) caused right ventricular hypertrophy as a result of pulmonary arterial vasoconstriction and increased pulmonary resistance. Using such a model (Wistar rats exposed for 3 weeks to hypoxia), CO (50 ppm during the last week of hypoxia, continuous) onl y increased COHb from 0.5% to 2.4% in the hypoxia model, yet had significant effects on blocking comp ensatory 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 necr osis was observed in the hypoxia + CO-exposed rats. The reduced coronary perfusion of the right ventricl e in hypoxia + CO-exposed rats may help explain the histopathological findings. The authors cited previous work demonstrating that exogenous CO can inhibit NOS (Thorup et al., 1999), which is essential for coronary dilation and angiogenesis. Thus, this study provided evidence that exogenous CO may interrupt or downregulate pathways that endogenous CO may activate. In two studies by Melin et al. (2002; 2005), Dark Agouti rats were exposed for 10 weeks to either HH, 50 ppm CO or HH plus 50 ppm CO. CO exposure am plified 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 ventricula r morphology and function which were not seen with either exposure alone. Changes in HRV were also reported. Results from both of these studies combined with results of Gautier and colleagues (2007) indicat ed that CO may interfere with normal homeostatic responses to hypoxia. This could occur by blocking HIF-1 α -responsive elements (vascular endothelial growth factor, erythropoietin) or other cell signaling pathways. In a similar study, Carraway et al. (2002) e xposed rats to HH (380 torr) with or without coexposure to CO (50 ppm). These exposures were conti nuous for up to 21 days and focused on pulmonary vascular remodeling. While the additio n 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 μ m) 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 ta ken together with results from the Gautier et al. (2007) and Melin studies (2002; 2005) suggested that the combined effect of low levels of CO with hypoxia is an enhanced right ventricl e workload and an exacerbated cardiomyopathy related

March 2009 5-51 DRAFT – DO NOT CITE OR QUOTE

## to pulmonary hypertension. The population at risk of primary pulmonary hypertension is low, but secondary pulmonary hypertension is a frequent complica tion of COPD and certain forms of heart failure. 

# 1 2

3

18 

# 5.2.3.3. Electrocardiographic Effects

In two r elated studies, Wellenius et al. (2004; 2006b) examined the effect of CO on a rat model of ischemia-related arrhythmia that was previously sh own to produce significant results with exposures to 

# 4 PM (Wellenius et al., 2002). ECG changes were obser ved during exposure to residual oil fly ash (ROFA) 

# 5 particles in a rat model of MI. Thus, using an anest hetized model of post-infarction myocardial sensitivity, 

# 6 Wellenius and colleagues tested the effects of 35 ppm CO (1-h exposure) on the induction of spontaneous 

# 7 arrhythmias in Sprague Dawley rats (Wellenius et al., 2004). CO exposure caused a statistically 

# 8 significant decrease (60.4%) in ventricular premature beat (VPB) frequency during the exposure period in 

# 9 rats with a high number of pre-exposure VPB. No interaction was observed with co-exposure to carbon 

# 10 concentrated particles, which independently re duced VPB frequency during the post-exposure period 

# 11 when administered alone. In a follow-up publication, results from the analysis of supraventricular ectopic 

# 12 beats (SVEB) were provided (Wellenius et al., 2006 b). A decrease in the number of SVEB was observed 

# 13 with CO (average concentration 37. 9 ppm) compared to filtered air. While the authors concluded that CO 

# 14 exposure did not increase risk of SVEB in this particular rodent model of coronary occlusion, the fact that 

# 15 cardiac electrophysiological dynamics are significantly altered by short-term exposure to low level CO 

# 16 may be of concern for other models of susceptibility. 

# 17



# 5.2.3.4. Summary of Cardiovascular Toxicology

Recent s tudies demonstrated that short-term exposure to 50-100 ppm CO resulted in aortic injury as measured by increased nitrotyrosine and the sequest ration of activated leukocytes in healthy rats. In 

# 19 addition, skeletal muscle microvascular permeability was increased. Short term-exposure to 35 ppm CO 

# 20 altered cardiac electrophysiology in a rat model of myocardial ischemia. Furthermore short-term exposure 

# 21 to 50 ppm CO exacerbated cardiac pathology and impaired function in animal models of hypertrophic 

# 22 cardiomyopathy and/or pulmonary hypertension. Ventri cular hypertrophy was observed in healthy rats in 

# 23 response to chronic exposures of 100-200 ppm CO. These studies provide a strong basis for the 

# 24 development of adverse health effects resulting from exposures to CO at environmentally-relevant 

# 25 concentrations. 

# 26

March 2009 5-52 DRAFT – DO NOT CITE OR QUOTE

# 5.2.4. Summary of Cardiovascular Effects

The mo st 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 

# 2 individuals with CAD (see Section 5.2). These st udies, described in the 1991 and 2000 CO AQCDs, 

# 3 demonstrate consistent decreases in the time to onset of exercise-induced angina and ST-segment changes 

# 4 following CO exposures resulting in COHb levels of 3-6%, with one multicenter study reporting similar 

# 5 effects at COHb levels as low as 2.4%. No human clinical studies have evaluated the effect of controlled 

# 6 exposures to CO resulting in COHb levels lower than 2.4%. Human clinical studies published since the 

# 7 2000 CO AQCD have reported no asso ciation between CO and ST-segment changes or arrhythmia; 

# 8 however, none of these studies included indi viduals with diagnoses of heart disease. 

# 9 1

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 While the exact physiological significance of the observed ST-segment changes among individuals with CAD is unclear, ST-segment depression is a known indicator of myocardial ischemia. It is also important to note that the individuals with CAD who participated in these controlled exposure studies were not representative of the most sensitive individuals in the population. In fact, the most sensitive individuals may respond to levels of COHb lower than 2.4%. Variability in activity patterns and severity of disease among individuals with CAD is likely to infl uence the critical level of COHb which leads to adverse cardiovascular effects. The degree of ambient CO exposure which leads to a ttainment of critical levels of COHb will also vary between individuals. First of all, endogenous CO production varies as described in Section 4.5, but generally results in less than 1% COHb. Secondly, nonambient exposures to CO, such as exposure to ETS, can increase COHb above baseline levels. Ambien t exposures will result in an additive increase in COHb. Using mathematical modeling to predict changes in COHb in h ealthy inactive adults (Quantitative Circulatory Physiology [QCP] model, Section 4.2.3), it can be estimated that exposure to 35 ppm CO for 1 h results in an increase of 0.6% COHb over baselin e and exposure to 9 ppm CO for 8 h results in an increase of 0.8% COHb over baseline. Furthermore, 24 h exposure to 3 ppm CO results in an increase of 0.4% COHb above baseline which can also be obtained following 1-h exposure to 30 CO ppm. Consequently, exposure to CO at concentrations relevant to the NAAQS has the potential to increase COHb to levels associated with adverse cardi ovascular health effects in some individuals. Findings of controlled human exposure studies are c oherent with findings of recent epidemiologic studies conducted since the 2000 CO AQCD, whic h observed associations between ambient CO concentration and ED visits and hospital admissi ons for IHD, CHF and all-cause cardiovascular disease. All but one of these epidemiologic studies were condu cted in locations where the entire distribution of CO concentrations were at or below the level of the current NAAQS, with mean 24-h avg concentrations ranging from 0.5 ppm (Montreal, Canada) to 9.4 ppm (Teh ran, Iran) (Table 5-7). A single study reported a negative association between CO concentration and ho spital admissions and ED visits for IHD among all

March 2009 5-53 DRAFT – DO NOT CITE OR QUOTE

## ages; all other associations were positive, with increases in hospital admissions and ED visits for IHD between 0.2% and 19.8% per standardized increas e in CO concentration (Figure 5-1). These recent studies build upon the conclusions of the 2000 CO AQCD that short-term variations in ambient CO concentrations are associated with da ily hospital admissions for heart disease. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

16 These health outcomes are consistent with a role for CO in limiting O 2 

## availability (i.e., hypoxic mechanisms) in individuals with CAD. However, rece nt toxicological studies suggested that CO may also act through non-hypoxic mechanisms by disrupting cellular signaling. Studies in healthy animals demonstrated oxidative injury and inflammation in response to 50-100 ppm CO while studies in disease models demonstrate effects on heart rhythm and ex acerbation of cardiomyopath y and vascular remodeling in response to 35-50 ppm CO. Furthermore, in utero exposure to 150 ppm CO alters postnatal elecrophysiological maturation in rat cardiomyocytes. Furt her investigations will be useful in determining the importance of non-hypoxic mechanisms followi ng environmentally-relevant CO exposures. Taken together, the evidence from epidemiologic, human clin ical and toxicological studies is sufficient to conclude that 

# a causal relationship is likely to exist between relevant short-term CO exposures and cardiovascular morbidity.



# 5.3. Central Nervous System Effects

5.3. 1. Controlled Human Exposure Studies

The be havioral effects of controlled human expos ures to CO have been examined by several laboratories, and these studies were summarized in the 2000 CO AQCD. Briefly, decreases in visual 

# 17 tracking as well as visual and auditory vigilance we re observed following exposures to CO resulting in 

# 18 COHb levels between 5% and 20% (Benignus et al ., 1987; Fodor and Winneke, 1972; Horvath et al., 

# 19 1971; Putz et al., 1979). One study reported similar behavioral effects (time discrimination) among a 

# 20 group of healthy volunteers with COHb levels <3 % (Beard and Wertheim, 1967); however, subsequent 

# 21 studies were unable to replicate these findings at su ch low exposure concentrations (Otto et al., 1979; 

# 22 Stewart et al., 1973a). These outcomes represent a pot entially important adverse effect of CO exposure 

# 23 resulting in COHb levels ≥ 5%, although it is important to note that these findings have not been 

# 24 consistent across studies. Similarly, some studies demonstrated decreases in reaction time as well as 

# 25 decrements in cognitive function and fine motor skills following controlled exposures to CO; however, 

# 26 these studies were not typically conducted using doub le-blind procedures, which may significantly affect 

# 27 the outcome of behavioral studies (Benignus, 1993). It should be noted that all behavioral studies of 

# 28

March 2009 5-54 DRAFT – DO NOT CITE OR QUOTE

## controlled CO exposure were conducted in normal, healthy adults. No new human clinical studies have 

# 1 evaluated CNS or behavioral effects of exposure to CO. 

# 2



# 5.3.2. Toxicological Studies

The ev idence for toxicological effects of CO expos ure in laboratory animal models comes from in utero or perinatal exposure to relatively low levels of CO (25 to 750 ppm). Affected endpoints from this 

# 4 early, developmental CO exposure include behavior , memory, learning, locomotor ability, peripheral 

# 5 nervous system myelination, auditory decremen ts, and neurotransmitter changes. These data are 

# 6 addressed in detail in the birth outcomes section of the ISA (Section 5.4.2). 

# 7 3

8

19 20 21 22 23 24 25 26 27 28 29 

# 5.3.3. Summary of Central Nervous System Effects

Exposu re 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, 

# 9 disorientation, and coma. However, the relationship between ambient levels of CO and neurological 

# 10 function is less clear and has not been evaluated in epidemiologic studies. Studies of controlled human 

# 11 exposures to CO discussed in the 2000 CO AQCD repor ted inconsistent neural and behavioral effects 

# 12 following exposures resulting in COHb levels of 5-20 %. No new human clinical studies have evaluated 

# 13 central nervous system or behavioral effects of exposure to CO. At ambient-level exposures, healthy 

# 14 adults may be protected against CO-induced neurol ogical impairment owing to compensatory responses 

# 15 including increased cardiac output and cerebral bl ood flow. However, these compensatory mechanisms 

# 16 are likely impaired among certain potentially susceptible groups, including individuals with reduced 

# 17 cardiovascular function. 

# 18 Toxicological studies that were not discussed in the 2000 CO AQCD employed rodent models to show that low level CO exposure during the in uter o or perinatal period can adversely affect adult outcomes including behavior, neuronal myelination, neurotransmitter leve ls or function, and the auditory system (discussed in Section 5.3). In utero CO e xposure, including both intermittent and continuous exposure, has been shown to impair multiple behavioral outcomes in offspring including active avoidance behavior (150 ppm CO), non-spatial memory (75 and 150 ppm CO), spatial learning (endogenous CO inhibition), homing behavior (150 ppm CO), locomo tor 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 transm ission in exposed male rodents.

March 2009 5-55 DRAFT – DO NOT CITE OR QUOTE

## Possible or demonstrated adverse outcomes from the CO-mediated aberrant neur otransmitter levels or transmission include respiratory dysfunction (200 ppm CO ), impaired sexual behavior (150 ppm CO), and an adverse response to hyperthermic insults resulti ng in neuronal damage (200 ppm). Finally, perinatal CO exposure has been shown to affect the developing auditory system of rodents, inducing permanent changes into adulthood. This is manifested with atro phy of cochlear cells innervating the inner hair cells (25 ppm CO), decreased immunostaining associated w ith 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 co rti (25 ppm CO), some of which is mechanistically proposed to be mediated by ROS (25 ppm CO). Functi onal tests of the auditory system of neonatally, low level CO-exposed rodents, using OAE testing (50 ppm ) and amplitude measurements of the 8th cranial nerve action potential (12, 25, 50, 100 ppm), revealed decrements in auditory function at PND22 and permanent changes into adulthood using action potenti al (AP) testing (50 ppm). Together, these animal studies demonstrated that in utero or perinatal e xposure to CO can adversely affect adult behavior, neuronal myelination, neurotransmissi on, and the auditory system in adult male rodents. Considering the combined evidence from controlled human exposure and toxicological studies, 

# the evidence is suggestive of a causal relationship between rel evant short- and long-term CO exposures and central nervous system effects. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

18

23 24 25 26 

# 5.4. Birth Outcomes and Developmental Effects

5.4. 1. Epidemiologic Studies

Althou gh 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 

# 19 effects of air pollution.Various dichotomized measures of birth weight, such as LBW, SGA, and IUGR, 

# 20 have been the most examined outcomes in air pollu tion research while preterm birth (PTB), congenital 

# 21 malformations, and infant mortality are less studied. 

# 22 In the 2000 CO AQCD only two studies were cited that examined the effect of ambient air pollution on adverse birth outcomes and both of these studies investigated LBW as an endpoint (Alderman et al., 1987; Ritz and Yu, 1999). At that ti me this area of research was in its infancy and since then there has been increasing interest.

March 2009 5-56 DRAFT – DO NOT CITE OR QUOTE

# 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 th e majority of U.S. studies conducted in southern 

# 2 California. The earliest of these studies examined exposures to ambient CO during the first month of 

# 3 pregnancy and the last 6 weeks prior to birth am ong a cohort of 97,158 births in southern California 

# 4 between 1989 and 1993 (Ritz et al., 2000). The exposur e assessment within this study was based on data 

# 5 from fixed site monitors that fell within a 2-mile radi us of the mother’s ZIP code area. The crude relative 

# 6 risks for PTB associated with a 1 ppm increase in 3-h avg CO concentration (6:00 to 9:00 a.m.) during the 

# 7 last 6 weeks prior to birth and the first month of pregnancy were 1.04 (95% CI: 1.03-1.5) and 1.01 

# 8 (95% CI: 1.00-1.03) respectively. However, when the authors controlled for other risk factors, only the 

# 9 effect associated with CO during the last 6 weeks prior to birth persisted (RR: 1.02 [95% CI: 1.01-1.03]). 

# 10 Furthermore, when the analyses included variables for either season or other pollutants the CO effect 

# 11 estimates generally were reduced. 

# 12

1

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Expanding on this research, Wilhelm and Ritz (2005) examined PTB among a cohort of 106,483 births in Los Angeles County, CA between 1994 and 2000. Based on data recorded at monitoring stations of varying proximities to the mother’s residence, the main exposure windows examined were the first trimester and the last 6 weeks prior to birth. Among women living within a 1-mile radius of a CO monitoring station, a 0.5 ppm increase in 24-h a vg 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 NO 2 

## and O 3 

## (RR: 1.05 [95% CI: 1.00-1.10]), but not with the inclusion of PM 10 

## into the regression model (RR: 0.99 [95% CI: 0.91-1.09]). The result from the single pollutant model for CO exposures averaged over the 6 weeks prio r 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 coll eagues (Ritz et al., 2007) were able to analyze detailed maternal information from a survey of 2, 543 of 6,374 women sampled from a cohort of 58,316 eligible births in 2003 in Los Angeles County. Based on data from the closest monitor to the mother’s ZIP code area, exposures to CO, NO 2 

## , O 3 

## , and PM 2.5 

## during the first trimester and last 6 weeks prior to delivery were examined and results from the ove rall cohort (n = 58,316) with limited maternal information were compared to the more detailed nested case-control cohort (n = 2,543). Within the overall cohort, CO during the first trimester was associat ed with an increased risk of 25% (OR: 1.25 [95% CI: 1.12-1.38]; highest exposure group >1.25 ppm vs. lowest ≤ 0.58 ppm). This result persisted within the nested case-control cohort (OR: 1.21 [ 95% CI: 0.88-1.65]) where factors such as passive smoking and alcohol use during pregnancy were includ ed in the model; however, the confidence intervals

March 2009 5-57 DRAFT – DO NOT CITE OR QUOTE

## were wider due to the smaller sample. Any possibl e association between CO and PTB was less evident during the last 6 weeks prior to birth. A strength of this study was that it also highlighted how there was little change in the air pollution effect estimates when controlling for more deta iled maternal information (e.g., smoking, alcohol use), as opposed to only controlli ng for more limited maternal information that is routinely collected on birth registry forms. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 In contrast to the Los Angeles studies, a case-cont rol study of PTB across California for the period 1999 through 2000 found a positive, though not statistically significant association, with 24-h CO concentration during the entire pregnancy (OR: 1.03 [95% CI: 0.98-1.09] per 0.5 ppm increase), the first month of gestation (OR: 1.05 [95% CI: 0.99-1.10] per 0.5 ppm increase), and the last 2 weeks of gestation (OR: 1.00 [95% CI: 0.96-1.04] per 0.5 ppm increase) (Huynh et al., 2006). Although there was an indication of an effect during early pregnancy, the small sample size (when compared to other studies) may have influenced the lack of statistical significance. Furthermore, exposures within this study were assigned based on a county-level average which may explain the lack of effect, given the poor level of exposure assessment. Studies outside of the U.S. have been conducted in Canada, Australia, and Korea with mixed results reported. In Vancouver, Canada, based on a city-wide average across available monitoring sites, 24-h avg CO concentration during the last month of pregnancy was associated with a 4% (OR: 1.04 [95% CI: 1.00-1.07]) increased risk of PTB per 0. 5 ppm increase while there was no association found during the first month of pregnancy (OR: 0.98 [95% CI: 0.94-1.00]) (Liu et al., 2003). This study investigated maternal exposures to ambient gaseous pollutants (CO, NO 2 

## , SO 2 

## , O 3 

## ) averaged over the first and last month of pregnancy among a cohort of 229,085 births between 1985 and 1998. In a cohort of 52,113 births in Incheon, Korea between 2001-2002, CO concentrations during the first trimester was associated with a 26% (RR: 1.26 [95% CI: 1.11-1.44]) increased risk of PTB for the highest quartile of exposure when compared to the lowest quartile (Leem et al., 2006). There was also a strong significant trend exhibited across the quartil es. A similar result was found for 24-h avg CO concentration during the last trimester although the effect was less pronounced (RR: 1.16 [95% CI: 1.01-1.24]). To assign the maternal exposures to CO, this study used a kriging technique, which is a statistical mapping technique that allows the pr ediction of an average concentration over a spatial region from data collected at specific points. The spatial average CO concentrations were then linked to each study subject’s residential address. Conversely, a study in Sydney, Australia, examined maternal exposure to ambient air pollution during the first and last month, and the first and last trimester of pregnancy among a cohort of 123,840 births between 1998-2000 and found no association between PTB and CO (Jalaludin et al., 2007). Maternal exposure estimates in this study were based on a city-wide average of available monitoring sites and also based on data from fixed sites within 5 km of the mother’s postcode area. The odds ratios for PTB associated with 8-h avg CO concentrations duri ng the first trimester and last three months of

March 2009 5-58 DRAFT – DO NOT CITE OR QUOTE

## 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, wh en all births were included in the analyses and the exposure was based on a city-wide average, these effe cts had become protective for the first trimester (OR: 0.82 [95% CI: 0.77-0.87]) and last three months of gestation (OR: 0.99 [95% CI: 0.92-1.07]). This suggests that exposures based on data from fixed sites cl oser to the mother’s address are more likely to detect an effect than a city-wide average. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Figure 5-6 shows the risk ratios for the risk of delivering a preterm infant from the reviewed studies. Table 5-12 provides a brief overview of the PTB studies. In summary there are mixed results across the studies. Although these studies are difficult to compare directly due to the different exposure assessment methods employed, there is some evidence that CO during early pregnancy (e.g., first month and trimester) is associated with an increased risk of PTB. The most consistency is exhibited within the studies conducted around Los Angeles, CA and su rrounding areas whereby all studies reported a significant association with CO exposure during ear ly pregnancy, and exposures were assigned from monitors within close proximity of the mother’s residential address (Ritz et al., 2000; Ritz et al., 2007; Wilhelm and Ritz, 2005). It should also be noted that the mixed results when analyzing different cohorts that resided within varying proximities to a mon itor may be attributable to analyzing different populations.

March 2009 5-59 DRAFT – DO NOT CITE OR QUOTE

### Location Exposure Period Exposure Area Study

Ritz et al (2000) California Month 1 <2 miles of monitor

Ritz et al (2000) California Last 6 weeks <2 miles of monitor

Los Angeles, CA First trimester ZIP code level Wilhelm & Ritz (2005)

Los Angeles, CA Last 6 weeks ZIP code level Wilhelm & Ritz (2005)

Huynh et al (2006) California First month County level

Huynh et al (2006) California Last two weeks County level

Huynh et al (2006) California Entire pregnancy County level

Vancouver, Canada First month City wide Liu et al. (2003)

Vancouver, Canada Last month City wide Liu et al. (2003)

Jalaludin et al. (2007) Sydney, Australia First month City wide

Jalaludin et al. (2007) Sydney, Australia First trimester City wide

Jalaludin et al. (2007) Sydney, Australia Last month City wide



### Effect Estimate 

# Figure 5-6. Summary of effect estimates (95% confidence intervals) for PTB associated with maternal exposure to ambient CO. Effect estimates have been standardized to a 1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO concentrations, and 0.5 ppm for 24-h avg CO concentrations.

March 2009 5-60 DRAFT – DO NOT CITE OR QUOTE

# Table 5-12. Brief summary of PTB studies.

Study Location (Sample Size) Mean CO (ppm) Exposure Assessment Exposure Window Ritz et al. (2000) California (n = 97,158) 2.7 (6-9 a.m.) <2 miles of monitor Month 1 Last 6 weeks Wilhelm and Ritz (2005) Los Angeles, CA (n = 106,483) 1.4 (24 h) Varying distances to monitor Month 1 Last 6 weeks Ritz et al. (2007) Los Angeles, CA (n = 58,316) 0.87 (24 h) Nearest monitor to ZIP code Entire pregnancy Trimester 1 Last 6 weeks Huynh et al. (2006) California (n = 42,692) 0.8 (24 h) County level Entire pregnancy Month 1 Last 2 weeks Liu et al. (2003) Vancouver, Canada (n = 229, 085) 1.0 (24 h) City wide average Month 1 Last month Leem et al. (2006) Incheon, Korea (n = 52,113) 0.9 (24 h) Residential addr ess within Dong-based on Kriging First trimester Last trimester Jalaludin et al. (2007) Sydney, Australia (n = 123,840) 0.9 (8 h) City wide average and <5 km from monitor First month First trimester Last trimester Last month



# 5.4.1.2. Birth Weight, Low Birth Weight, and Intr auterine Growth Restriction/Small for Gestational Age

With bir th weight routinely collected in vital sta tistics and a powerful predictor of infant mortality, it is the most studied outcome within air pollution- birth outcome research. Air pollution researchers have 

# 2 analyzed birth weight as a continuous variable, and/ or as a dichotomized variable in the forms of low 

# 3 birth weight (LBW) (<2,500g [5 lbs, 8 oz]) and SGA. 

# 4 1

5 6 7 8 9 10 11 12 13 14 It should be noted that the terms small for gesta tional age (SGA), which is defined as a birth weight <10th percentile for gestational age (and often sex), and intrauterine growth restriction (IUGR) are used interchangeably. However, this definition of SGA does have limitations. For example, using this definition of IUGR may overestimate the percentage of ‘growth-restricted’ neonates as it is unlikely that 10% of neonates have growth rest riction (Wollmann, 1998). On the other hand, when the 10th percentile is based on the distribution of live births at a population level the percenta ge of SGA among preterm births is most likely underestim ated (Hutcheon and Platt, 2008). Nevertheless, the terms SGA and IUGR are often used interchang eably and it therefore should be noted that SGA represents a statistical description of a small neonate, whereas the term IUGR is reserved for those with clinical evidence of abnormal growth. Meaning that all IUGR neonates will be SGA, but

March 2009 5-61 DRAFT – DO NOT CITE OR QUOTE

## not all SGA neonates will be IUGR (Wollmann, 1998) . In the following sections the terms SGA and IUGR are referred to as each cited study used the terms. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Over the past decade a number of studies examined various metrics of birth weight in relation to maternal exposure to CO with the majority conduc ted in the U.S. Given that most studies examined multiple birth weight metrics, in order to avoid overlap of the studies the following section focuses on each study only once and presents results for each metric within that study. Most of the U.S. studies have been conducted in southern California with inconsistent results reported with regard to gestational timing of the CO effects. The first of these studies was reviewed in the 2000 CO AQCD and is briefly summarized here. Ritz and Yu (1999) examined the effect of ambient CO during the last trimester on LBW among 125,573 births in Los Angeles between 1989 and 1993. When compared to neonates born to women in the low est CO exposure group (<2.2 ppm), neonates born to women in the highest exposure group (5.5 ppm - 95t h percentile) had a 22% (O R: 1.22 [95% CI: 1.03- 1.44]) increased risk of being born as LBW. Building upon this research, Wilhelm and Ritz (20 05) reported similar results when extending this study to include 136,134 births for the period of 1994–2000. Exposure to ambient CO during each trimester was based on data recorded at monitoring stations of varying proximities to the mother’s residence. For women residing within 1 mile of a station, there was 36% (OR: 1.36 [95% CI: 1.04-1.76]) increased risk of having a term LBW baby for women in the highest CO exposure category (1.84 ppm-75th percentile) for the third-trimester. Ther e was also an increased risk of term LBW (OR: 1.28 [95% CI: 1.12-1.47]) among women in the highest exposure group when the analyses included women within a 5 mile radius of a station. Howe ver, when the analyses included women within a 1-2 mile, or 2-4 mile radius of a station, the CO effect s failed to reach statistical significance and there was no evidence of an exposure-response pattern exhibited acro ss the varying distances to a station. Furthermore, none of the significant CO results persisted after cont rolling for other pollutants. Although standard errors were certainly increased after controlling for the othe r pollutants leading to non-significant results, some of the effect sizes were similar, providing some consistency. It is interesting to note, however, that maternal exposure to CO during trimesters one and two was not associated with LBW (results not reported). Further validation in association with exposure times was observed in an analysis using a susbset of participants in the Children’s Health Study. Sala m and colleagues (2005) found that CO only during the first trimester was associated with reduced fetal gr owth. Their research examined birth weight, LBW, and IUGR among a subset of participants in the Childre n’s Health Study (Peters et al., 1999b) who were born in California between 1975-1987 (n = 3901). The study examined term births with a gestational age between 37-44 weeks. Exposures in this study were based on CO data from up to the three nearest monitoring sites within 50 km of the centroid of the mother’s ZIP code. Exposures for the entire pregnancy and each trimester were analyzed and a 0. 5 ppm increase in 24-h CO concentration during the

March 2009 5-62 DRAFT – DO NOT CITE OR QUOTE

## first trimester was associated with a 7.8 g (95% CI: 15.1-0.4) decrease in birth weight, which also translated to a 6.7% (OR: 1.07 [95% CI: 1.00-1.13 ]) increased risk of IUGR; however, there was no significant association with LBW (O R: 1.00 [95% CI: 0.88-1.16]). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 In contrast to the previous studies, another Calif ornia study of 18,247 singleton births born at 40 weeks gestation during 2000 found no association be tween ambient 24-h CO concentration and reduced birth weight or SGA where the highest quartile of exposure was 0.98 ppm. Based on data from fixed sites within 5 miles of the mother’s residence, exposures to CO and PM 2.5 

## during the entire pregnancy and each trimester were analyzed. Although CO during th e 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 PM 2.5 

## . PM 2.5 

## was found to have a strong effect on birth weight within each trimester (Parker et al., 2005). Two similar studies were conducted in the Northeast of the U.S. with inconsistent results. A study of 89,557 singleton term births in Boston, MA, Hartford, CT, Philadelphia, PA, Pittsburgh, PA, and Washington, DC between 1994-1996 found that exposure to ambient CO during the third trimester was associated with an increased risk of LBW (OR: 1.14 [95% CI: 1.03-1.27] per 0.5 ppm increase) (Maisonet et al., 2001). When stratified by race this effect was only significant among African Americans for the first and third trimesters (First OR: 1.32 [95% CI : 1.22-1.43]; Third OR: 1.20 [95% CI: 1.09-1.32]). Exposures to PM 10 

## and SO 2 

## were examined and there was no strong evidence that these pollutants were associated with LBW. Exposures for this study were based on a city-wid e average of monitors within the mother’s city of residence. The second study examined 358,504 births at 32-44 weeks gestation between 1999-2002 in Connecticut and Massachu setts (Bell et al., 2007). 24-h CO exposures were estimated from fixed sites within each mother’s county of reside nce (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 (PM 10 

## , PM 2.5 

## , NO 2 

## , and SO 2 

## ) in two-pollutant models. However, this reduction in birth weight did not translate to an increased risk of LBW (OR: 1.05 [95% CI: 0.97-1.12]). When controlling for exposure during each trimester, the reduction in birth weight associated with a 0.5 ppm increase in 24-h CO concen tration during the first trimester ranged from 18.8 to 16.5 g while the reductions associated with third trimester exposure ranged between 23.3 and 27.2 g. It is interesting to note that, although the exposures were based on data averaged at the county level, CO was associated with a reduction in birth weight. Whereas, in a previously cited California study by Huynh and colleagues (Huynh et al., 2006) expo sures were also at the county level yet there was no association with PTB. This difference may be due to the counties be ing smaller in New England than in California, resulting in more precise exposure estimates. Two studies in Canada investigated the effects of ambient air pollution on fetal growth with exposures derived from a city-wide average across the available monitoring sites. The first of these studies was among a cohort of 229,085 singleton term bi rths (37-42 weeks gestation) in Vancouver, BC with monthly and trimester exposures to CO investigated in relation to LBW and IUGR (Liu et al., 2003).

March 2009 5-63 DRAFT – DO NOT CITE OR QUOTE

## For a 0.5 ppm increase in 24-h CO concentration dur ing 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 pollutant s. Conversely, maternal exposure to CO was not associated with LBW. The more recent of these 2 studies examined 386,202 singleton term births (37-42 weeks gestation) in Calgary, Edmonton and Montreal between 1986 and 2000 (Liu et al., 2007). The study examined monthly and trimester exposures to CO with IUGR being the only endpoint. A 0.5 ppm increase in 24-h CO concentration was associated with an incr eased 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 tr anslated 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 NO 2 

## and PM 2.5 

## . 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 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 avai lable monitoring sites. The first study examined 179,460 singleton term births during 1997 and found th at a 0.75 ppm increase in 8-h CO concentration averaged over the first trimester was associated with a 17.3 g (95% CI: 31.0-3.7) reduction in birth weight (Gouveia et al., 2004). The second of these studies ex amined 311,735 singleton births (37-41 weeks gestation) between 1998 and 2000 and reported a 6.0 g reduction in birth weight associated with a 0.5 ppm increase in 24-h CO concentration averaged over the first trimester (Medeiros and Gouveia, 2005). It is important to note that neither of these studies found an association between CO exposure and an increased risk of LBW. Therefore, despite CO dur ing the first trimester being associated with reduced birth weight, it was not associated with LBW. Similar to the two studies in Sao Paulo, Brazil, researchers in Seoul, South Korea conducted two studies using data from two consecutive time periods. Both of these studies based the exposure estimates on a city-wide average from all available fixed sites and as would be expected, the results pertaining to CO were similar for both studies. For example, Ha and colleagues (2001) examined maternal exposures to CO during the first and third trim esters among 276,763 singleton term births in Seoul between 1996 and 1997. Exposure to CO during the first trimester was asso ciated with a decrease in birth weight of 13.3 g, which also translated into an increased risk of LB W (RR: 1.10 [95% CI: 1.05-1.14] per 0.5 ppm increase in 24-h CO concentration). When Lee and colleagu es (2003a) extended this study to include singleton term births (37-44 weeks gestation) for the period of 1996 to 1998 with 24-h CO concentrations averaged over each month of pregnancy and tr imester, CO exposure during the first trimester was associated with an increased risk of LBW (OR: 1.04 [95% CI: 1.01-1. 07] per 0.5 ppm increase). No associations were

March 2009 5-64 DRAFT – DO NOT CITE OR QUOTE

## found in the third trimester for any of the pollutants. Monthly-specific exposures showed that the risk of LBW tended to increase with CO exposure between months 2-5 of pregnancy. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 In contrast to other studies reporting that early and late pregnancy are the critical periods for CO exposure, a Sydney, Australia study of 138,056 singleton births between 1998-2000 reported a reduction in birth weight of 21.7 g (95% CI: 38.2-5.1) and 17.2 g (95% CI: 33.4-0.9) associated with a 0.75 ppm increase in maternal exposure to 8-h CO averaged over the second and third trimesters respectively (Mannes et al., 2005). However, this result did not persist after controlling for other pollutants (PM 10 

## , NO 2 

## ) and was only significant when including births where the mother resided within 5 km of a monitor. Furthermore, this result did not translate to an incr eased risk of SGA, which was defined as a birth weight two standard deviations below the mean. The odds ratios for SGA for CO exposures during the first, second and third trimesters were 0.96 (95% CI: 0.91-1.03), 0.99 (95% CI: 0.92-1.07), and 1.01 (95% CI: 0.93-1.08) respectively. While the majority of studies restrict the analyses to term births as a method of controlling for gestational ag e, it is important to note that th e Sydney study used all births and controlled for gestational age in the birth weight analyses and SGA was derived from each gestational age

group. Of all studies reviewed, only two failed to find an association between maternal exposure to CO and adverse birth outcomes. In northern Nevada , Chen and colleagues (2002) examined CO, PM 10 

## , and O 3 

## exposures among a cohort of 39,338 term births ( 37-44 weeks gestation) be tween 1991 and 1999 and found no association between CO exposure during th e 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 we re based on data from all monitoring sites across Washoe County, Nevada. In a retrospective cohort study among 92,288 singleton term births (37-44 weeks gestation) in Taipei and Kaoshiung, Taiwan between 1995-1997, maternal exposures to CO, SO 2 

## , O 3 

## , NO 2 

## , and PM 10 

## in each trimester of pregnancy were examined and only SO 2 

## during the third trimester showed evidence of attributing to LBW. Exposure assessment was based on data from the monitor closest to the centroid of the mother’s residential district and the final anal yses only included mothers whose district centroid was within 3 km of a monitor. CO exposures were gr ouped into low (~1.1 ppm), medium (~1.2-15.0 ppm), and high (>15.0 ppm) and when compared to the lowest exposure group, the odds ratio for LBW in the highest exposure group was 0.90 (95% CI: 0.75-1.09) for the first trimester, 1.00 (95% CI: 0.82-1.22) for the second trimester, and 0.86 (95% CI: 0.71-1.03) for the third trimester (Lin et al., 2004a). Table 5-13 provides a brief overview of the birth weight studies. In summary, there is evidence of ambient CO during pregnancy having a negative ef fect on fetal growth. From the reviewed studies Figure 5-7 shows the change in birth weight (grams), Figure 5-8 shows the effect estimates for LBW, and Figure 5-9 shows the effect estimates for SGA. In ge neral the reported reductions in birth weight are

March 2009 5-65 DRAFT – DO NOT CITE OR QUOTE

## small (ranging ~10-20g). It is difficult to conclude whether CO is related to a small change in birth weight in all births across the population, or a ma rked effect in some subset of births. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Furthermore, there is a large degree of incons istency 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 th e birth cohorts analyzed. The main inconsistency among these findings is the gestational timing of the CO effect. Although the majority of studies reported significant effects during either the first or third trimester, other studies failed to find a significant effect during these periods. Several studies found an association with exposure during the entire pregnancy, providing evidence for a possible accumulative effect; however, these results are inconclusive and this may be the result of correlated exposure periods. Several studies examined various combinations of birth weight, LBW, and SGA/IUGR and inconsistent results are reported across these metrics. For example, several studies reported an association between maternal exposure to CO and decreased birt h weight yet the decrease in birth weight did not translate to an increased risk of LBW or SGA. Howeve r, it needs to be noted that a measureable change, even if only a small one, on a populatio n is different than an effect on a subset of susceptible births which may increase the risk of IUGR/LBW/SGA. The possibility exists that the small reductions in birth weight associated with maternal CO exposures are the result of residual confounding associat ed with other factors (e.g., other pollutants, temperature, and spatial/temporal variation in mate rnal factors) or other correlated pollutants. For example, in some studies the CO effect did not persis t after controlling for other pollutants (Mannes et al., 2005; Parker et al., 2005; Wilhelm and Ritz, 2005) while in some studies it did persist (Bell et al., 2007; Gouveia et al., 2004; Liu et al., 2003, 2007), and ot her studies did not report results from multipollutant models (Ha et al., 2001; Lee et al., 2003a; Maisone t et al., 2001; Medeiros and Gouveia, 2005). In addition, various methods have been employed to co ntrol 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 co mpared results from analyses stratified by race. The earlier of these studies found an association betw een CO and LBW among African Americans but not among whites and hispanics (Maisonet et al., 2001). In contrast, despite reporting an 11g reduction in birth weight among African-Americans and a 17 g redu ction among whites, the more recent of the two studies found no significant difference between these re ductions by race (Bell et al., 2007). Parker and colleagues (2005) also tested for interactions between race and found no significant association.

March 2009 5-66 DRAFT – DO NOT CITE OR QUOTE

### Location Exposure Period Exposure Area Study

Connecticut & Massachusetts Entire pregnancy County level Bell et al (2007)

California First trimester ZIP code level Salam et al. (2005)

California Second trimester ZIP code level Salam et al. (2005)

California Third trimester ZIP code level Salam et al. (2005)

California Entire pregnancy ZIP code level Salam et al. (2005)

Chen et al. (2002) Northern NV First trimester County level

Chen et al. (2002) Northern NV Second trimester County level

Chen et al. (2002) Northern NV Third trimester County level

Chen et al. (2002) Northern NV Entire pregnancy County level

Sydney, Australia First trimester City level Mannes et al. (2005)

Sydney, Australia Second trimester City level Mannes et al. (2005)

Sydney, Australia Third trimester City level Mannes et al. (2005)

Sydney, Australia Last month City level Mannes et al. (2005)

Gouveia et al. (2004) Sao Paulo, Br azil First trimester City level

Gouveia et al. (2004) Sao Paulo, Br azil Second trimester City level

Gouveia et al. (2004) Sao Paulo, Br azil Third trimester City level

Sao Paulo, Brazil First trimester City level Medeiros et al. (2005)

Sao Paulo, Brazil Second trimester City level Medeiros et al. (2005)

Sao Paulo, Brazil Third trimester City level Medeiros et al. (2005)



### Change in Birth weight (g) 

# Figure 5-7. Summary of change in birth weight (95% confidence intervals) associated with maternal exposure to ambient CO. Effect estimates have been standardized to a 1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO concentrations, and 0.5 ppm for 24-h avg CO concentrations.

March 2009 5-67 DRAFT – DO NOT CITE OR QUOTE

### Location Exposure Period Exposure Area Author

Connecticut & Massachusetts Entire pregnancy County level Bell et al (2007)

Northeastern USA First trimester City level Maisonet et al. (2001)

Northeastern USA Second trimester City level Maisonet et al. (2001)

Northeastern USA Third trimester City level Maisonet et al. (2001)

Wilhelm and Ritz (2005) Los Angeles County, CA Third trimester ZIP code level

California First trimester ZIP code level Salam et al. (2005)

California Second trimester ZIP code level Salam et al. (2005)

California Third trimester ZIP code level Salam et al. (2005)

California Entire pregnancy ZIP code level Salam et al. (2005)

Liu et al. (2003) Vancouver, C anada First month City level

Liu et al. (2003) Vancouver, Canada Second month City level

Seoul, Korea First trimester City level Ha et al (2001)

Seoul, Korea Third trimester City level Ha et al (2001)

Lee et al. (2003a) Seoul, Korea Fi rst trimester City level

Lee et al. (2003a) Seoul, Korea Se cond trimester City level

Lee et al. (2003a) Seoul, Korea Th ird trimester City level

Lee et al. (2003a) Seoul, Korea Entire pregnancy City level



### Effect Estimate



# Figure 5-8. Summary of effect estimates (95% confidence intervals) for LBW associated with maternal exposure to ambient CO. Effect estimates have been standardized to a 1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO concentrations, and 0.5 ppm for 24-h avg CO concentrations.

March 2009 5-68 DRAFT – DO NOT CITE OR QUOTE

### Location Exposure Period Exposure Area Study

Salam et al. (2005) California First trimester ZIP code level

Salam et al. (2005) California Second trimester ZIP code level

Salam et al. (2005) California Third trimester ZIP code level

Salam et al. (2005) California Entire pregnancy ZIP code level

Vancouver, Canada First month City level Liu et al. (2003)

Vancouver, Canada First trimester City level Liu et al. (2003)

Vancouver, Canada Second trimester City level Liu et al. (2003)

Vancouver, Canada Third trimester City level Liu et al. (2003)

Vancouver, Canada Last month City level Liu et al. (2003)

Liu et al. (2007) 3 Cities, Canada First trimester City level

Liu et al. (2007) 3 Cities, Canada Second trimester City level

Liu et al. (2007) 3 Cities, Canada Third trimester City level

Sydney, Australia First trimester City level Mannes et al. (2005)

Sydney, Australia Second trimester City level Mannes et al. (2005)

Sydney, Australia Third trimester City level Mannes et al. (2005)

Sydney, Australia Last month City level Mannes et al. (2005)



### Effect Estimate



# Figure 5-9. Summary of effect estimates (95% confidence intervals) for SGA associated with maternal exposure to ambient CO. Effect estimates have been standardized to a 1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO concentrations, and 0.5 ppm for 24-h avg CO concentrations.

March 2009 5-69 DRAFT – DO NOT CITE OR QUOTE

# Table 5-13. Brief summary of birth weight studies.

Outcomes Examined Location (Sample Size) Mean CO (ppm) Exposure Assessment Exposure Windows Study

UNITED STATES Ritz and Yu (1999) LBW Los Angeles, CA (n = 125, 573) 2.6 (6-9 a.m.) <2 miles of monitor Trimester 3

Wilhelm and Ritz (2005) LBW Los Angeles County, CA (n = 136, 134) 1.4 (24 h) Varying distances from monitor Trimesters 1, 2, 3

Salam et al. (2005) Birth weight LBW IUGR California (n = 3901) 1.8 (24 h) ZIP code level Entire pregnancy Trimesters 1, 2, 3

Parker et al. (2005) Birth weight SGA California (n = 18,247) 0.75 (8 h) <5 miles from monitor Entire pregnancy Trimesters 1, 2, 3

Maisonet et al. (2001) LBW Boston, MA; Hartford, CT; Philadelphia & Pittsburg, PA; Washington DC (n = 103,465) 1.1 (24 h) City wide average Trimesters 1, 2, 3

Bell et al. (2007) Birth weight LBW Connecticut and Massachusetts, (n = 358,504) 0.6 (24 h) County level average Entire pregnancy Trimesters 1, 3

Chen et al. (2002) Birth weight LBW Northern Nevada, (n = 36,305) 0.9 (8 h) County level Trimesters 1, 2, 3

CANADA Liu et al. (2003) LBW IUGR Vancouver, Canada (n = 229,085) 1.0 (24 h) City wide average Trimester 1

Liu et al. (2007) IUGR Cal gary, Edmonton, Montreal, Canada (n = 386,202) 1.1 (24 h) City wide average Trimesters 1, 2, 3

SOUTH AMERICA Gouveia et al. (2004) Birth weight LBW Sao Paulo, Brazil (n = 179,460) 3.7 (8 h) City wide average Trimesters 1, 2, 3

Medeiros et al. ( 2005) Birth weight LBW Sao Paulo, Brazil (n = 311,735) 3.0 (24 h) (Presented in graph) City wide average Trimesters 1, 2, 3

AUSTRALASIA Ha et al. (2001) Birth weight LBW Seoul, Korea (n = 276,763) 1.2 (24 h) City wide average Trimesters 1 and 3

Lee et al. (2003a) LBW Seoul, Korea (n = ?) 1.2 (24 h) City wide average Entire pregnancy Trimesters 1, 2, 3 Mannes et al. (2005) Birth weight SGA Sydney, Australia (n = 138,056) 0.8 (8 h) City wide average and <5 km from monitor Trimesters 1, 2, 3 Last 30 days Lin et al. (2004b) LBW Taipei, Kaoshiung, Taiwan (n = 92,288) Taipei 1.1, Kaoshiung 8.1 <3 km of monitor Entire pregnancy Trimesters 1, 2, 3



### March 2009 5-70 DRAFT – DO NOT CITE OR QUOTE

# 5.4.1.3. Congenital Anomalies

Despite the growing evidence of an association be tween ambient air pollution and various adverse birth outcomes, only three studies investigated the effect of temporal variations in ambient air pollution 

# 2 on congenital anomalies. Given the higher prevalence and associated mortality, heart defects have been 

# 3 the main focus of two of these three recent air pollution studies. The other study’s focus was cleft 

# 4 lip/palate. 

# 5 1

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 The first of these studies was conducted in southe rn California (Ritz et al., 2002). Exposure to ambient CO, NO 2 

## , O 3 

## and PM 10 

## during each of the first three months of pregnancy was examined among births during 1987-1993. Maternal exposure estimates we re based on data from the fixed site closest to the mother’s ZIP code area and when using a case-control design where cases were matched to 10 randomly selected controls, results showed that CO during the second month of pregnancy was associated with cardiac ventricular septal defects. The CO exposures were grouped by quartiles (25th = 1.14, 50 th 

## = 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 septa l defects across the 3 exposure groups were 1.62 (95% CI: 1.05-2.48), 2.09 (95% CI: 1.19-3.67), and 2.95 (95% CI: 1.44-6.05) respectively. In a multipollutant model a similar exposure-response pa ttern 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 O 3 

## during the second month of pregnancy, which was associated with aortic artery and valve defects. The second study was conducted in Texas (Gilboa et al., 2005), where exposure to ambient CO, NO 2 

## , SO 2 

## , O 3 

## and PM 10 

## during the 3rd to 8th week of gestation was examined among births between 1997-2000. Maternal exposure estimates were calculated by assigning the data from the closest monitor to the mother’s residential address. If data were missing on a particular day then data from the next closest site were used. The median distances from a monito r ranged from 8.6-14.2 km with maximum distances ranging from 35.5-54.5 km. The main results showed that CO was associated with multiple conotruncal defects and Tetralogy of Fallot. CO exposures were gr ouped into quartiles of much lower concentrations (25th = 0.4, 50th = 0.5, 75th = 0.7 ppm) than the California study and when compared to the lowest quartile, the odds ratios for conotruncal defect s across the 3 CO exposure groups were 1.38 (95% CI: 0.97-1.97), 1.17 (95% CI: 0.81-1.70), and 1.46 (1.03-2.08) respectively without a significant test for trend (p for trend = 0.0870). Whereas, a strong exposure-response pattern was exhibited across the quartiles of CO exposure for Tetralogy of Fallot (25th OR: 0.82 [95% CI: 0.52-1.62]; 50th OR: 1.27 [95% CI: 0.75-2.14]; 75th OR: 2.04 [95% CI: 1.26-3.29]; p for trend = 0.0017). The only significant associations found with other pollutants were between PM 10 

## and isolated atrial septal defects, and SO 2 

## and ventricular septal defects.

March 2009 5-71 DRAFT – DO NOT CITE OR QUOTE

## The last of these three studies was a case-control st udy that examined maternal exposure to various air pollutants during the first three months of pregnancy and the risk of delivering an infant with an oral cleft, namely cleft lip with or without palate (CL/P). Birth data from the Taiwanese birth registry from 2001 to 2003 was linked to air pollutant data that were spatially interpolated from all fixed monitoring sites across Taiwan. Based on data at the center of the townships or districts, exposure estimates for PM 10 

## , SO 2 

## , NO X 

## , O 3 

## , and CO were averaged over each of the first th ree months of pregnancy. Interestingly, of all the pollutants examined, only O 3 

## during the first two months of pregnancy was significantly associated with an increased risk of CL/P. In multipollutant models CO was not associated with CL/P (Hwang and Jaakkola, 2008). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 The main results from the southern California st udy showed that CO was associated with an increased risk of ventricular septal defects and this was exhibited by an exposure-response pattern across the quartiles of exposure, yet there was no indication that ambient CO concentration in Texas was associated with ventricular septal defects. Conversely, ambient CO concentration in Texas was associated with an increased risk of conotruncal defects, yet th ere was no indication that CO in southern California was associated with conotruncal de fects, and on the contrary, reported results of a protective effect. Interestingly, similar inconsis tencies were also found for PM 10 

## between these studies. For example, PM 10 

## in Texas was associated with an increased risk of atrial septal defects, yet there was no indication of such an effect in southern California where PM 10 

## concentrations were markedly higher. The authors of the Texas study (Gilboa et al., 2005) provide little discussion toward the inconsistent results with the southern California stud y. One suggestion is the different CO concentrations across the studies with the 75th quartile in southern California being 2.39 ppm while in Texas it was much lower at 0.7 ppm. However, this suggests that different defects are associated with different concentrations of CO, yet it still does not explain why particular associations we re reported in Texas and not southern California where concentrations were higher. Similarly, the authors of the Texas study (Gilboa et al., 2005) also suggested the inconsistenc y was due to different exposure periods. In Texas the exposures were averaged over the 3rd to 8th week while in southern California the exposures were averaged over the second month of pregnancy. Howe ver, there was no reason provided as to why this small difference in the examined exposure pe riod would explain the inconsistent results. Overall, there is little evidence that maternal exposur e to CO is associated with an increased risk of congenital anomalies, namely heart defects and clef t lip and palate. Further research is required to corroborate these findings.

March 2009 5-72 DRAFT – DO NOT CITE OR QUOTE

# 5.4.1.4. Neonatal and Post-Neonatal Mortality

A handfu l of studies examined the effect of am bient air pollution on ne onatal and post-neonatal mortality with the former the least studied. These st udies varied somewhat with regard to the outcomes 

# 2 and exposure periods examined, and study designs employed. 

# 3 1

4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 

# Neonatal

In Sao Paulo, Brazil, a time-series study examined daily counts of neonatal (up to 28 days after birth) deaths for the period of 1998-2000 in association with concurrent day exposure to SO 2 

## , CO, O 3 

## , PM 10 

## . Moving averages from 2 to 7 days were exam ined. 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 PM 10 

## (r = 0.71) and SO 2 

## (r = 0.55), only PM 10 

## and SO 2 

## were associated with an increase in the daily rate of neonatal deaths (Lin et al., 2004a).



# Post-Neonatal

Two studies in the U.S. examined the potential association between ambient CO and post-neonatal (from 28 days to 1 year after birth) mortality and inconsistent results were reported. These studies, however, varied somewhat in study design. The first of these studies employed a case-control d esign and examined all in fant deaths during the first year of life among infants born alive during 19 89-2000 within 16 km from a monitoring site within the South Coast Air Basin of California. Exposures for 2-week, 1-month, 2-month, and 6-month periods before death were linked to each individual death. Extensive analyses were conducted for all-cause infant deaths, respiratory causes of death, and sudden infant death syndrome (SIDS). Given the long time period of the data analyzed, in order to alleviate the confou nding trends in infant mort ality and CO levels this study was able to match by year (Ritz et al., 2006) . Ambient 1-h max CO concentrations averaged over the 2 months before death were associated with an 11% (OR: 1.11 [95% CI: 1.06-1.16]) increase in risk of all-cause post-neonatal death (per 1 ppm increase) and a 19% (OR: 1.19 [95% CI: 1.10-1.28]) increase in risk of SIDS. In the multipollutant models (including PM 10 

## , NO 2 

## , O 3 

## ) the positive CO mortality effect decreased by around 50% and failed to reach statistical significance. Based on exposure from 2 weeks before death, CO was associated with an increased risk of respiratory rela ted post-neonatal deaths occurring 28 days to 1 year after birth (OR: 1.14 [ 95% CI: 1.03-1.25] per 1 ppm increase) and 28 days to 3 months after birth (OR: 1.20 [95% CI: 1.02-1.40]), but no effect was observed for respiratory related deaths occurring 4-12 months after birth. These resu lts persisted in the multipollutant models and exposure-response patterns were exhibited across the exposures groupings of 1.02 to <2.08, and ≥ 2.08 ppm. To control for gestational age and birth weight the analyses were stratified by ‘term/normalweight infants’ and ‘preterm a nd/or LBW infants.’ When these two strata were analyzed, CO was

March 2009 5-73 DRAFT – DO NOT CITE OR QUOTE

## associated with an increased risk of all-cause death a nd 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). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 The second of these 2 studies examined 3,583,495 bi rths, including 6,639 post-neonatal deaths occurring in 96 counties throughout the U.S. (in counties with more than 250,000 residents) between 1989 and 2000 (Woodruff et al., 2008). Only exposure dur ing the first two months of life was examined and these were based on an average of CO concentrations recorded across all available monitors within the mother’s county of residence. In contrast to the other postnatal mortality study in California, CO averaged over the first two months of life w as not associated with all-cause death (OR: 1.01 [95% CI: 0.94-1.09] per 0.5 ppm increase in 24-h CO c oncentration), or with respiratory related deaths (OR: 1.08 [95% CI: 0.91-1.54] pe r 0.5 ppm increase in 24-h CO concentration), SIDS (OR 0.85 [95% CI: 0.70-1.04] per 0.5 ppm increase in 24-h CO concentration), or other causes of post-neonatal mortality (OR: 1.03 [95% CI: 0.96-1.09] per 0.5 ppm increase in 24-h CO concentration). These null findings may be due to higher error of the exposur e assessment at the county-level as opposed to using data from monitors within close proximity to the residence. The only other postnatal mortality studies have been conducted throughout Asia. Two identical studies in Taiwan failed to find an association between daily counts of post-neonatal deaths and ambient air pollutants, including CO. The data analyzed were from the cities of Taipei (Yang et al., 2006) and Kaohsiung (Tsai et al., 2006b) with ambient CO concen trations being 1.6 ppm and 0.8 ppm respectively. Both studies examined deaths for the period of 1994-2000 and employed a case-crossover design that compared air pollution levels 1 week be fore and after each infant’s death. Similarly, another study in South Korea examined post-neonatal mortality for the period of 1995- 1999 using a time-series design. Same-day CO was not associated with all-cause death (RR: 1.02 [95% CI: 0.97-1.06] per 0.5 ppm increase). However, same-day CO was associated with post-neonatal mortality when the analyses were restricted to resp iratory mortality (RR: 1.33 [95% CI: 1.01-1.76] per 0.5 ppm increase) (Ha et al., 2003). In general, the inconsistent exposure periods examined among th ese studies allows for limited direct comparison and interpretation. Nevertheless, there is limited evidence that CO is associated with an increased risk of infant mortality during the post-ne onatal period. The exposure periods examined varied from the same-day CO to lag periods up to a 6 mont h period prior to birth with one study alternatively exploring exposures averaged over the first two months of life. Furthermore, given that birth weight and gestational age are strong predictors of infant mortality, in all of the reviewed studies these factors have not been considered at either the design or analys is stage. Hence, the link between fetal exposures, neonatal exposures, and post-neona tal exposures, and the possible interaction that birth weight and gestational age may have on the results yielded from these examined exposure periods, needs further attention within this field of research.

March 2009 5-74 DRAFT – DO NOT CITE OR QUOTE

# 5.4.1.5. Summary of Epidemiologic Studies of Birth Outcomes and Developmental Effects

There is some evidence that CO during early pregnancy (e.g., first month and first trimester) is associated with an increased risk of PTB. Only two st udies examined the effects of CO on birth defects. 

# 2 Both studies found maternal exposure to CO to be associated with an increased risk of cardiac birth 

# 3 defects. This insult to the heart is coherent with th e CO effects on the heart characterized in Section 5.2. 

# 4 In general, there is limited evidence that CO is associ ated with an increased risk of infant mortality during 

# 5 the post-neonatal period. 

# 6 1

7 8 9 10 11 12 13 14 15 16 17

18 19 20 21 22 23 24 25 There is evidence of ambient CO during pregnanc y having a negative effect on fetal growth. In general, the reviewed studies (Figures 5.7 through 5.9) reported small reductions in birth weight (ranging ~10-20 g). Although the majority of studies reported significant effects during either the first or third trimester, other studies failed to find a significant effect during these periods. Several studies examined various combinations of birth weight, LBW, and SGA/ IUGR and inconsistent results are reported across these metrics. For example, six studies reported an association between maternal exposure to CO and decreased birth weight yet the decrease in birth weight did not translate to an increased risk of LBW or SGA. It should be noted that having a measurable, even if small, change in a population is different than having an effect on a subset of susceptible births, which may increase the risk of IUGR/LBW/SGA. It is difficult to conclude if CO is rela ted to a small change in birth weight in all births across the population, or a marked effect in some subset of births.



# 5.4.2. Toxicological Studies of Birth Outcomes and Developmental Effects

5.4.2. 1. Birth Outcomes

Decrease d Birth Weight

Multiple reports have been published associating lo w level CO exposure in laboratory animals and decrements in birth weight; some of these studies noted reduced growth rates persisting in the neonatal period. Prigge et al. (1977) saw significant decreases in near-term fetal body weight (GD21) after 21 days of continuous exposure of pregnant Wistar rats to CO (125, 250, or 500 ppm). Fechter and Annau (1977) exposed pregnant rats to 150 ppm CO continuous ly during gestation via inhalation and found 5% significantly decreased birth weights at PND1 in gest ationally exposed pups versus control animals with weight decrements persisting to weaning; lactati onal cross fostering did not ameliorate the reduced growth rates. Dams exposed to CO during gestation had COHb over gestation of 15% with control dams

March 2009 5-75 DRAFT – DO NOT CITE OR QUOTE

## having less than 1%. Decreased birth weight and pr e-weaning weight were seen in CO-exposed pups despite a lack of weight decrement in CO-expo sed dams versus air-exposed control dams. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

24 25 26 27 28 29 30 31 32 33 34 Penney et al. (1983) exposed pregnant rats to CO (157, 166, and 200 ppm) over GD6-GD19 and found significant decreases in near term fetal rat weight at GD20; gestation in rats is ~ 22 days. Carmines et al. (2008) exposed Sprague-Dawley rats to ~600 ppm CO via nose-only inhalation (levels similar to those seen in cigarette smoke) during GD6-GD19 of g estation for 2 h/day and found significant decreases in birth weight (0.5 g or 13%) of exposed pups ve rsus controls. Dam COHb was 30% immediately after exposure. The half life in rats at this exposure level is ~ 42 minutes. Maternal body weight was unchanged during gestation, but corrected terminal body weight (body weight minus uterine weight) was significantly elevated in CO-exposed dams at term. Th ere were no external malformations (teratogenicity) seen. Singh et al. (1992; 2006) found significant decreases in birth weight in gestationally CO-exposed mouse pups (65, 125, 250 or 500 ppm) in two studies. In the 2006 study, mice were exposed to CO for 6 h/day for the first 2 weeks of pregnancy, and in th e 1992 sutdy, dams were exposed to CO (GD0-GD18) longer with fetuses collected at GD18. Astrup et al. (1972) found significant decreases (11 and 20%, respectivelty) in birth weight of rabbits exposed to either 90 or 180 ppm CO continuously over the duration of gestation. Fechter and A nnau (1977) reported decreased birth weight, albeit not significant, of Long-Evans rats exposed in utero to 150 ppm CO continuously throughout gestation (dam COHb 15%), but by PND4 and through pre-weaning to PND21, expo sed pups weighed significantly less than controls. Tolcos et al. (2000a) found significant decreases in body weight and crown to rump length in guinea pig fetuses after being exposed to 200 ppm CO for 10h/day from GD23-GD25 until GD61-GD63, at which time the fetuses were collected (term ranges from GD 68 to GD72). However, in other studies, Tolcos found no significant differences in birth weight of gui nea pig pups after a similar exposure (GD23-GD25 to term). During pregnancy, fetal and maternal COHb levels were 13% and 8.5%, respectively. 

# Pregnancy Loss and Perinatal Death

Schwetz et al. (1979) exposed CF-1 mice or New Zealand rabbits to 250 ppm CO for either 7 h/day or 24 h/day over GD6-GD15 (mice) or GD6-GD18 (ra bbits), yielding 4 exposure paradigms. The fetuses were then collected by C-section at the termination of exposure, which was near term. Maternal COHb in the 7 h/day exposure groups was approximately 10- 15% COHb in rabbits and mice; COHb was not followed in the 24 h exposure groups. The mice exposed to 7 h/day CO had a significant increase in the number of resorbed pups (not seen in 24 h/day CO exposure). Fetal mouse weight was significantly greater than control in the 7 h exposures and si gnificantly less in the 24 h exposure groups with corresponding significant differences in crown to rump length in the two groups. Rabbits seemed to be less affected by CO exposure manifesting with no significant perinatal death or pregnancy loss. Astrup et al. (1972) studied the effect of CO on fetal development after continuous CO exposure (90 or 180 ppm CO) over the duration of gestation in rabbits. COHb was 16-18% and 8-9% in the 180

March 2009 5-76 DRAFT – DO NOT CITE OR QUOTE

## and 90 ppm exposure groups, respectively. In the immediate neonatal period, 24 h postpartum, 35% (180 ppm) and 9.9% (90 ppm) of CO-exposed animals died. In the postpartum period after the first 24 hours and extending out to PND21, 90 ppm CO-exposed pups experienced 25% mortality versus 13% in controls; there was no difference from control at th e 180 ppm CO exposure level. Gestation length was unchanged with CO exposure. 

# 1 2 3 4 5 6 7

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

25 26 27 28 29 30 Fechter and Annau (1977) exposed Long-Evans rats in utero to 150 ppm CO continuously through gestation (dam COHb 15%) and saw no effects of CO on litter mortality or pup number at PND1.



# Maternal Diet Maternal Protein Intake and Neonatal Mouse Mortality Pregnant CD-1 mice were exposed intermittently (6 h/day for first two weeks of pregnancy) to CO (0, 65, or 125 ppm) in combination with protein modified diets [27% (supplemental protein), 16% (control), 8% (low), or 4% (very low protein)] to ass ess the role of CO exposure coupled with low-protein diet on neonatal mortality at 1 week of age (Singh, 2006). Litter size was not affected by CO exposure. Pup weight was inversely related to CO exposure and directly related to dam diet protein content during pregnancy. Pup mortality at birth was directly related to CO exposure in certain protein groups (supplemental, and 4% protein) and inversely related to the dam’s dietary protein content. At 1 week of age, pup mortality was significantly increased by CO-exposure. Dietary protein restriction also induced pup mortality at 1 week of age; all pups in the 4% protein diet died by 1 week of age. CO exposure (65 ppm only) combined with a normal protein diet significantly increased pup mortality at 1 week. Animals receiving supplemental protein diets with CO exposure (65 and 125 ppm) had pups that had significant increases in mortality at 1 week versus control air pups. Control protein diet pups had significantly increased pup mortality at 1 week with CO exposure (65 ppm only) versus control air animals (0 ppm CO). Contrary to other findings, low protein diet (8%) combined with CO (125 ppm) led to a slight yet significant decrease in pup mortality at 1 week of age versus control (0 ppm CO). In summary, these data show that in utero CO exposure induced increased neonatal mouse deaths at 1 week in supplemental protein and normal protein diet exposure groups.



# Maternal Low Protein Diet a nd CO-Dependent Teratogenicity The role of diet as a contributor to teratogenicity of CO (0, 65, 125, or 250 ppm CO) in CD-1 mice given a various protein diets (27%, 16%, 8% or 4% protein) during pregnancy was explored by Singh et al. (1992). Timed pregnant CD-1 mice were exposed continuously to CO from GD8-GD18 of pregnancy at which point animals were sacrificed and fetuses co llected. Subsequent work by this group has shown that low protein diets plus CO exposure act in an additive fashion to increase placental COHb in mice (Singh et al., 1992; 2003). As expected, all levels of CO and the lowest protein diet (8 or 4% protein)

March 2009 5-77 DRAFT – DO NOT CITE OR QUOTE

## given to the dams during gestation resulted in signifi cantly decreased fetal weight of normal fetuses at GD18. CO exposure did not produce maternal toxi city except for a signifi cant 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 relate d to gross malformations including jaw changes. All concentrations of CO exposure significantly increased the percentage of litters with malformations. There was a CO dosedependent increase in percent of litters with malformati ons within each maternal dietary protein level. Skeletal malformations were present in offspring with the percent of litters affected inversely related to dietary protein levels. CO exposure concomitant w ith 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 concentra tion and inversely related to matern al dietary protein levels. CO and maternal dietary protein restriction had a synergistic effect on mouse offspring mortality and an additive effect on malformations. 

# 1 2 3 4 5 6 7 8 9 10 11 12

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

28 29 30 31 32 Zinc-Supplemented Protein-Deficient Maternal Diet and Mortality and Teratogenicity Further studies by Neggers and Singh (2006) explored how teratogenicity and fetal mortality were affected by zinc (Zn) modulation in CO-exposed preg nant dams (CD-1 mouse) given protein insufficient diets. Developmental toxicitiy of CO was atte nuated by protein supplementation, i.e., protein supplemented animals (27%) showed a significantly lower incidence of fetal mortality versus 8% and 16% protein groups. Further, dietary restriction of both protein and Zn with co-exposure to CO during gestation increased the incidence of pup mortality and malformations including gastroschisis. Earlier studies by Schwetz et al. (1979) found fetal skel etal alterations in lumbar ribs or spurs were significantly increased in the 7 h/day and 24 h/day gestationally CO-exposed fetal CF-1 mice collected near term after CO exposure (250 ppm), over GD6- GD15 (dam gestational COHb 10-15% for 7 h/day exposure, 24 h/day dam COHb not measured); these ch anges were not seen in similarly exposed fetal rabbits (1979). Astrup et al. (1972) studied the effect of CO e xposure on fetal rabbit development via continuous CO exposure (90 or 180 ppm with gestational dam COHb of 9% and 17%, respectively) over the duration of gestation. Three pups (n = 123) in the 180 ppm CO group had deformities in their extremities at birth, whereas no control and no 90 ppm CO-exposed anim als manifested with this malformation.



# Skeletal Abnormalities Earlier studies by Schwetz et al. (1979) found fetal skel etal alterations in lumbar ribs or spurs were significantly increased in the 7 h/day and 24 h/day gestationally CO-exposed fetal CF-1 mice collected near term after CO exposure (250 ppm), over GD6 -GD15 (dam gestational COHb 10-15% for 7h/day exposure, 24 h/day dam COHb not measured); these ch anges were not seen in similarly exposed fetal rabbits.

March 2009 5-78 DRAFT – DO NOT CITE OR QUOTE

## Astrup et al. (1972) studied the effect of CO e xposure on fetal rabbit development via continuous CO exposure (90 or 180 ppm with gestational dam C OHb of 9 and 17%, respectively) over the duration of gestation. Three pups (n = 123) in the 180 ppm CO group had deformities in their extremities at birth, whereas no control and no 90 ppm CO-exposed anim als manifested with this malformation. 

# 1 2 3 4

5 6 7 8 9 10 11 12 13

14 15 16 17 18 19 20 21 22 23 24

25 26 27 28 29 

# Endogenous CO Spontaneous Abortions Idiopathic spontaneous abortions are more freque nt in women with HO-1 polymorphisms in their genome (Denschlag et al., 2004). To evaluate the role of HO-1 in spontaneous abortion, a mouse model that spontaneously undergoes abortion (CBA/J x DBA/ 2J mice) (Zenclussen et al., 2006) was used with and without HO adenovirus treatment to see if pre gnancy outcome could be modulated by changing HO concentration. HO-1 is known to protect organs from rejection (Kotsch et al., 2006) and thus, HO activity may protect the developing fetus from rejection by the non-self maternal immune system. Pregnancy outcome was significantly better (abortion rate signi ficantly decreased) in mice overexpressing HO due to adenovirus transfer. Thus, in this model, it appears that upregulation of the HO/CO system is able to protect the developing fetus from spontaneous abortion. 

# Depressed Vascular Reactivity during Pregnancy and CO CO through the production of soluble guanylate cy clase is able to stimulate the relaxation of vascular smooth muscle (Villamor et al., 2000). Furthe r, the role of HO expression is important in the maintenance and outcome of pregna ncy and lactation. During pregnancy, there is increased blood volume without a concurrent increase in systemic BP; this is accomplished by a decrease in total peripheral vascular resistance, to which CO contributes (Zhao et al., 2008). In humans, genetic polymorphisms in HO-1 (microsatellite polymorphisms associated with a ltered HO-1 transcription) are linked to idiopathic recurrent miscarriages (Denschlag et al., 2004) and administering HO-inhibitors to pregnant rodents induced total litter loss (Alexandreanu and Lawson, 2 002). Various pathologies of pregnancy including IUGR and pre-eclampsia are associated with significant decreases in placental HO activity (Denschlag et al., 2004; McLaughlin et al., 2003). Thus, the HO/CO system appears to be crucial in maintaining

pregnancy. 

# CO and Vasular Relaxation during Pregnancy Isolated rat aortic rings and tail artery rings from pregnant dams (Spra gue-Dawley rats) can be relaxed by submersion in exogenous CO solutions (Longo et al., 1999). Further, the administration of the HO inhibitor SnMP induced increased maternal BP (systo lic, diastolic, and mean arterial pressure) during pregnancy in FVB mice (Zhao et al., 2008). Zhao also showed pregnancy induced increased total body CO excretion as measured in inhalation chambers, and that this increased CO production could be

March 2009 5-79 DRAFT – DO NOT CITE OR QUOTE

## significantly decreased by SnMP administration. Pre gnant dam abdominal aortas (AA) are significantly dilated with pregnancy and SnMP treatment leads to AA vasoconstriction to levels similar to nonpregnant mice. Zhao looked at blood flow chang es using Doppler technology and observed a significant increase in uterine artery blood flow velocity followi ng SnMP administration. Thus, it appears that the increased CO production during pregnancy may partia lly account for the decreased peripheral vascular resistance seen in pregnancy that prevents the incr eased blood volume of pregna ncy from affecting BP. 

# 1 2 3 4 5 6

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

30 31 32 Placenta Women living at high altitude (chronic hypoxia exposure), women with pre-eclampsia, or women who had pregnancies with fetal growth restricti ons (FGR) produced term placenta with significant decreases in HO-2 versus women living at lower altitude with uncomplicated pregnancies (Barber et al., 2001; Lyall et al., 2000). FGR and pre-eclampsia HO-2 changes were detected in endothelial cells; HO-1 immunostaining was very low in the placenta. Women living at high altitude have an increased risk of adverse pregnancy outcomes versus women living at lower altitudes (Zamudio et al., 1995). Oxygenation is important in early pregnancy and triggers trophob last invasion of the spiral arteries (Kingdom and Kaufmann, 1997). Isolated human placenta exposed to solutions containing CO demonstrated a concentration-dependent decrease in perfusion pressu re (Bainbridge et al., 2002) further demonstrating the role of CO in maintaining basal vasculature tone. The endogenous generation of CO in the placenta has been demonstrated in chorionic villi of term placenta (McLaughlin et al., 2001) with HO activity hi ghest in the placenta near term (McLean et al., 2000). CO can be generated from multiple endogenous sources; the source of CO in term human placental chorionic villi was found to be the catalysis of heme by HO and not endogenous lipid peroxidation (Ahmed et al., 2005). Term human placental cell types including syncytiotrophoblasts and cytotrophoblasts were grown in cell culture under b asal and hypoxic conditions to explore changes in HO expression (Newby et al., 2005). HO-1 was expressed at significantly lower amounts in syncytiotrophoblasts versus cytrotrophoblasts at normoxic conditions. HO-1 was unchanged in cytotrophoblasts under hypoxia, but HO-1 was significantly decreased in hypoxic syncytiotrophoblasts. HO-2 was unchanged in either cell type with hypoxia. These cell culture data can give insight into what cell types might be responsive to hypoxia through the HO/CO system in the human placenta. Nonetheless, these data should be interpreted remembering that isol ated cell culture models lack the interaction with the intact placenta and may lead to different physiological outcomes.



# Uterus at Parturition The addition of exogenous CO to isolated human and rat uterine tissue during pregnancy failed to induce relaxation of uterine tissue (Longo et al., 1999). Thus, exogenous CO failed to quiet the spontaneous contractility of rat or human myometrium (uterine smooth muscle). CO is not able to relax

March 2009 5-80 DRAFT – DO NOT CITE OR QUOTE

## all types of vascular smooth muscle (Brian et al., 199 4), and pregnancy appears to modulate the response of tissues to CO (Katoue et al., 2005). 

# 1 2 3 4 5 6

7 8 9 10 11 12 13 14 15 16 17 18 19

20 21 22 23 24 25 26 27 28 29 30 HO-1 induction and HO concentration have been s hown to be regulated by estrogen in the rat (Sprague Dawley) uterus (Cella et al., 2006) during pregnancy and in non-gravid rats. This agrees with work by Tschugguel et al. (2001) in which CO w as generated by primary endothelial cells from human umbilical veins and uterine arteries after exogenous 17- β estradiol administration.



# Ovaries

Ovarian Follicular Atresia. As a part of normal follicular maturation in the ovaries, the majority of follicles undergo artresia via apoptosis prior to ovulation. Harada et al. (2004) harvested porcine granulosa cells from ovaries and found that cells natu rally undergoing atresia or cell death more strongly expressed HO-1 than did successful follicles. Addition of the HO substrate hemin or the HO inhibitor Zn protoporphyrin IX (ZnPP IX) significantly induced or inhibited granulosa cell apoptosis, respectively. In this porcine model, HO was able to augment gra nulosa cell apoptosis allowing for proper follicular maturation. 

# Ovarian Steroidogenesis. HO-1 and HO-2 are localized in the ovaries in rats (Sprague Dawley) and treatment of rat ovaries in vitro with CrMP, an inhibitor of HO, or with hemin, a substrate for HO induced steroidogenic changes in the ovaries (Alexandreanu and Lawson, 2002). CrMP significantly decreased ovarian production of gonadotrophin-induc ed androstenedione a nd progesterone without affecting estradiol levels. Hemin treatment caused androstenedione and estradi ol production from rat ovaries in vitro. Thus, the HO/CO pathway may play an important role in rat ovarian steroidogenesis. 

# Anterior Pituitary and Heme Oxygenase HO-1 and HO-2 are expressed in rat (Sprague Dawley) anterior pituitary and the secretion of gonadotropins and prolactin is affected by HO inhi bitors and HO substrates (Alexandreanu and Lawson, 2003a). The estrogen-induced afternoon surge of LH was advanced forward in time by chronic administration of the HO inhibitor CrMP and this advance could be reversed by concomitant administration of hemin, a HO substrate. The serum FSH surge was unaffected by CrMP or hemin but in vitro treatment of GnRH-stimulated pituitaries with hemin led to a significant increase in FSH release. The estrogen-dependent afternoon prolactin surge was inhibited or delayed by CrMP and CrMP+hemin and CrMP significantly decreased prolactin release. In vitro studies using pituitary explants showed that LH release was significantly increased by CrMP admi nistration and unaffected by hemin. Modulation of the HO/CO system in the anterior pituitary of the fe male rat led to altered se cretion of gonadotropins and prolactin.

March 2009 5-81 DRAFT – DO NOT CITE OR QUOTE

# Lactation and Pregnancy and HO Modulation The role of the HO/CO system in estrous cyclicit y, pregnancy, and lactation were monitored using a Sprague Dawley rat model (Ale xandreanu and Lawson, 2002). Pregna ncy outcomes used dams that were injected daily (GD5-GD16) with hemin, a HO s ubstrate, or CrMP, a HO inhibitor that were followed until 2-days post-term at which point they were euth anized and uteri examined for implantation sites and fetuses. Another group of female rats were monito red for estrous cycle changes after receiving daily hemin (10 or 11 days of injections) or CrMP (11 or 14 days of injections). A third group of animals was monitored during lactation after receiving either CrMP or hemin exclusively during lactation (lactation days LD5-LD15); the lactational effect was monitored by weighing pups out to weaning (LD21). Hemin had no effect on any of the outcomes in this study, except a slight effect on litter weight gain during lactational exposure. CrMP decreased time in estr ous in a dose-dependent manner. CrMP exposure significantly increased uterine fetal resorptions and le d to no live births, possibly due to vasoconstriction and associated ischemia of the placental vascular bed. CrMP induced decreased litter weight gain during lactation, which the authors attributed to decreased maternal milk production or ejection problems as cross-fostered pups regained weight lost during nur sing on CrMP dams. The lactational effects seen in this model may be explained by changes in prolactin as previously reported (Alexandreanu and Lawson, 2002). These data indicate that the HO/CO pathway is important in estrous cyclicity, parturition, and lactation. Nonetheless, CrMP may also inhibit NO • production, a mechanism that is distinct from CO-dependent effects. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 

# Summary of Toxicological Studies on Birth Outcomes

Endogenous CO production by vari ous organs and the vascular system during pregnancy is important for maintaining pregnancy and for the pr oper development of the fetus and/or offspring postnatally. Exogenous CO addition has been shown to alter perinatal effects and birth outcomes in various toxicological studies. Endogenous CO producti on is required for maintenance of a pregnancy as has been shown by use of HO inhibitors; further, genetic polymorphisms in HO-1, which are associated with increased HO expression upon stress, are associated with idiopathic recurrent miscarriages in a study of a Caucasian population. A rodent study of sp ontaneous abortion demons trated better pregnancy outcome in animals in which HO-1 levels were increased by adenovirus transfer. Rodent studies have explored the role of exogenous CO exposure on bi rth outcomes. Exogenous CO exposure to rodent fetuses in utero significantly increased postnatal mortality. CO exposure in utero induced teratogenicity in offspring in a dose-depe ndent manner that was further exacerbated by dietary protein restriction or zinc depletion. Two studies noted skeletal abnormalities following in utero CO exposure (180-250 ppm). CO induces cGMP production by vascular smooth muscle beds, which can regulate vascular tone. Thus, endogenous CO pro duction is associated with maintena nce of decreased peripheral vascular

March 2009 5-82 DRAFT – DO NOT CITE OR QUOTE

## resistance during pregnancy that prevents the incr eased blood volume of pre gnancy from affecting BP. This has been demonstrated in the human placenta and cells from the placenta with exogenous CO addition further decreasing perfusion pressure. However, exogenous CO addition to human and rat uterine tissue during pregnancy was unable to relax spontaneous myometrial contraction. 

# 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Estrogens have been shown to upregulate HO-1 levels in the uterus in non-gravid and pregnant rat uteri; other studies have shown isolated cell culture sy stems administered 17-beta estradiol can generate CO. In the female reproductive tract, the endogenous HO/CO system is involved in proper follicular maturation, ovarian steroidogenesis, the secretion of gonadotropin and prolactin by the anterior pituitary, in maintenance of lactation, and estrous cyclicity in rodent studies.



# 5.4.2.2. Developmental Effects

CNS Deve lopmental Effects

Behavioral

Active Avoidance Behavior. To assess behavioral changes after in utero exposure, pregnant Wistar rats were exposed to CO (0, 75 or 150 ppm) conti nuously over GD0-GD20 (De Salvia et al., 1995). Male pups from exposed dams were evaluated for active a voidance behavior (mild shock avoidance) during acquisition and reacquisition. This work was designed to expand on the studies of Mactutus and Fechter (1985), who showed delayed acquisition (120 days of age) of an active avoidance task and disruption of retention at a later test date (360 days) after con tinuous in utero CO exposure (150 ppm CO, dam COHb concentrations of 15.6 ± 1.1%), and to determine if these behavioral changes were permanent. De Salvia et al. (1995) found there were no significant behavioral impairments in the low dose animals (75 ppm). Animals exposed to the higher dose of CO (150 ppm) in utero had significantly impaired acquisition (at 3 and 18 months of age) and reacquisition (at 18 mont hs of age) of conditioned avoidance behavior. The authors speculated that this CO-dependent behavior al change may be mediated through neurotransmitter signaling, specifically changes in dopamine in the neostriatum or nucleus accumbens. These studies demonstrate that low level CO exposure in utero can lead to permanent behavioral changes in male offspring. Mactutus and Fechter (1984) also found that acquisition in a two-way conditioned avoidance (flashing light warnings followed by mild footshock) test failed to improve with age of in utero CO-exposed (150 ppm, dam COHb 15%) Long-Evans rats (male and female offspring) in contrast to air-exposed controls who improved with age/maturati on, 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 ut ero CO exposure. Prenatal CO exposure induced learning and memory deficits in male and female offspring.

March 2009 5-83 DRAFT – DO NOT CITE OR QUOTE

# Habituation and Non-Spatial Working Memory . Investigators have used animal models to study how low dose CO exposure (75 or 150 ppm) duri ng gestation can affect behavioral outcomes (habituation, exploration of novel objects, and motor activity) in offspring as adults. Giustino et al. (1999) exposed primparious pregnant Wistar rats to CO (0, 75 or 150 ppm) by inhalation from GD0-GD20. Blood COHb concentrations (mean % ± SEM) on GD20 have been reported (0 ppm: 1.6 ± 0.1; CO 75 ppm: 7.36 ± 0.2; CO 150 ppm: 16.1 ± 0.9). At age 40 weeks, male offspring we re given two trials. In the first trial (T1), two similar objects were presented. In the second trial (T2), one object from the first trial was presented as well as one novel object. Exploration time was defined as time exploring objects during each trial. Global habituation was quantified as a comparison of the time spent exploring the two objects in T1 to time spent exploring objects in T2 . Discrimination between new and familiar objects was measured in T2 by contrasting the time spent explor ing the familiar object to time spent exploring the new object. This object recognition tests for the preferen ce that rats have for investigating novel objects in lieu of familiar objects and is a measurement of non- spatial working memory. The results of this study showed gestationally CO (75 and 150 ppm) exposed animals at 40 days of age have a significantly decreased T1 during exploration of novel objects. Gl obal habituation of control and 75 ppm CO rats showed exploratory times with T2 <T1. However, T1 and T2 were not significantly different in the 150 ppm CO group, showing a lack of habituation after the second exposure to a previously viewed object. Also, the decreased time spent with a familiar object by contro l rats was not seen in CO-exposed animals (75 or 150 ppm). Further, the authors specula ted that the mesolimbic dopaminergic system may be responsible for these changes, possibly involvin g the nucleus accumbens. The human literature shows a possible connection with these CO-dependent rode nt effects; infants whose mothers smoked during pregnancy manifest with habituati on defects. Nonentheless, CO is ju st one of many consitutients of cigarette smoke. The results from these animal toxicolo gy studies showed that in utero exposure to CO affects non-spatial working memory in young adult male rats. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Long-Term Potentiation and Spatial Learning. The above study agreed with earlier work showing that hippocampal long-term potentiation (LTP) (Zhuo et al., 1993) of brain sections was significantly affected by CO exposure with ZnPPIX, a HO inhibitor, blocking hippocampal LTP, but was in contrast to Bing et al. (1995) who showed enhanced spatial lear ning in the Morris water maze in rodents exposed to the HO inhibitor tin protoporphyrin (Sn-PP). Thus, distinct types of learning may be differentially regulated by CO exposure, and endogenous CO, as modulated with the above inhibitors, may manifest with different outcomes versus exogenous CO. 

# Homing Behavior. Development of homing behavior, orient ation by the rat toward its home cage, was significantly delayed in rats prenataly exposed continuously to 150 ppm CO (150 ppm) (Fechter and Annau, 1980). 

# Locomotor Effects. Fechter and Annau (1977) exposed Long-Evans rats in utero to 150 ppm CO continuously through gestation (dam COHb 15%) and saw significant effects of CO on pup locomotor

March 2009 5-84 DRAFT – DO NOT CITE OR QUOTE

## activity at PND1 and PND4 (both after subcutaneous L-DOPA administration to induce movement) and at PND14, but not at PND21. CO-exposed pups showed si gnificantly less activity than air-exposed controls through the pre-weaning window. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Fechter and Annau (1980) exposed rats to CO (150 ppm) continuously throughout gestation and found that exposed offspring manifested with poorer than normal performance on the negative geotaxis, a reflexive response that results in a directional moveme nt with or against gravity. In these studies, negative geotaxis was defined as performing a 180° turn to face th e top of an inclined plane. Continuous prenatal CO exposure (125 ppm, GD7-GD18) in CD-1 mice impa ired negative geotaxis at PND10 in a study by Singh (1986). The standardization and use of geotaxis as a vestibular, motor or postural metric in infant rodents has been debated in the litera ture (Kreider and Blumberg, 2005). Prenatal exposure to CO (125 ppm, GD7-GD18) significantly affected the righting reflex (the turning of an animal from its supine position to its feet) in exposed CD-1 mice on PND1. Also, the aerial righting score, or turning 180° and landing on the feet when dropped from the supine position at a height, was significantly decreased in pups exposed to CO in utero (65 and 125 ppm) (Singh, 1986) at PND14. These behavioral tests indicated neuromuscular, vesti bular or postural effects in the CO-exposed neonate. Earlier studies by Fechter and Annau (1977) identified an early window of sensitivity for CO-dependent motor activity defecits of PND1-PND4, with recove ry by PND21. Carratu’s (2000) PND40 and PND90 monitoring of motor activity of in utero CO-exposed animals may be too late to detect CO-dependent changes.



# Neuronal Sciatic Nerve Myelination. In utero exposure (GD0-GD20) to low levels of CO (0, 75 or 100 ppm) and its effect on sciatic nerve myelination in male of fspring was studied in Wistar rats (Carratù et al., 2000). The dam CO blood concentration expressed as %C OHb was determined for 0 ppm (GD10: 0.97 ± 0.02; GD20: 1.62 ± 0.1.), 75 ppm (GD10: 7.20 ± 0. 12; GD20: 7.43 ± 0.62), 150 ppm (GD10: 14.42 ± 0.52; GD20: 16.08 ± 0.88). There were significant in creases in %COHb in all CO-exposed animals. The myelin sheath thickness of the nerve fibers was signi ficantly decreased in CO-exposed animals (75 and 150 ppm). Axon diameter was not affected by CO expos ure. Even though CO affected myelination, it did not significantly affect motor activity of CO-exposed mice at 40 and 90 days. In conclusion, in utero exposure of male rats to CO induces decreased mye lination of the sciatic nerve without concomitant decreases in motor activity. 

# Brain and Peripheral Nervous System Myelination. The effect of in utero exposure (GD0-GD20) of Wistar rats to CO (0 or 150 ppm) on pup neuronal myelination was studied in the peripheral nervous system and the brain of male offspring at 90 days of age (Carratù et al., 2000). CO exposure during development induces hypoxia, and hypoxia can induc e sphingomyelin changes which could lead to impaired myelination and motor activity decrements . This study reported ma ternal COHb (mean % ±

March 2009 5-85 DRAFT – DO NOT CITE OR QUOTE

## SEM) as 1.9 ± 0.04 and 16.02 ± 0.98 in control and 150 ppm CO-exposed animals, respectively. Prenatal CO exposure had no effect on brain sphinganine (SA) or sphingosine (SO) levels in male offspring at 90 days of age. However, the sciatic nerve had signifi cant increases in SO after CO exposure, no changes in SA and a significant decrease in the SA/SO ratio at 90 days of age. Motor activity, which could be affected by changes in myelination, showed no diff erences between CO and control animals at 90 days of age. SO is an intermediate in sphingolipid turnove r and SA is an intermediate of de novo sphingolipid biosynthesis. These results demonstrate that sphing olipid homeostasis in the PNS but not CNS is interrupted in offspring exposed in utero to CO w ithout a manifestation of changes in motor activity. 

# 1 2 3 4 5 6 7 8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Neurotransmitter Changes Medullar Cholinergic and Catecholaminergic Ch anges after In Utero CO Exposure and SIDS. One theory related to Sudden Infant Death Syndrome (SIDS) relates to aberrant development of brain stem nuclei controlling respiratory, cardiovascular, a nd arousal activity. To address these changes in an animal model, Tolcos et al. (2000a) exposed pregnant guinea pigs to CO (0 or 200 ppm) for 10 hours per day over the last 60% of gestation leading to fetal a nd maternal COHb levels of 13% (versus control of 0.25%) and 8.5% (versus control of 1.6%), respectively. Guinea pigs are a good model because they display a similar time course of CNS development to humans with the majority of development occurring in utero. Pups and sows were collected near te rm and CO-exposed pups were found to have significant decrements in body, brain, and liver weights as well as decreased crown to rump length when compared to control pups. Medullar volume was also significan tly decreased in CO-exposed pups. Neurotransmitter systems were affected after CO exposure. Specifica lly, the catecholaminergic system of the brainstem displayed significant decreases in immunoreactivity for tyrosine hydroxy lase (TH), which is likely due to decreased cell number in specific medullar regions. This was consistent with earlier work showing aberrant respiratory responses (to asphyxia and CO 2 

## ) of offspring with prenatal CO exposure (McGregor et al., 1998). The cholinergic system is also affected by prenatal CO exposure with significant increases in choline acetyl-transferase (ChAT) immunoreactivity of the medulla and no changes in muscarinic acetylcholine receptor. This is in contrast to human infants with SIDS that show decreased brainstem muscarinic receptor binding (Kinney et al., 1995). ChAT changes in this study (Tolcos et al., 2000a) were from areas of the medulla associated with tongue i nnervation, which is crucial to swallowing and which may be impaired in SIDS infants. 

# Mesolimbic Dopaminergic Effects and Impaired Sexual Behavior. Exposure (GD0-GD20) of pregnant Wistar rats to CO (0, 75 or 150 ppm) and its effects on adult (5 and 10 months of age) male offspring sexual behavior and meso limbic dopaminergic function was a ccessed by Cagiano et al. (1998). Maternal COHb at GD10 was 1, 7, and 15% (0, 75 and 150 pm CO, respectively) and at GD20 was 1.5, 7, and 16% (0, 75, 150 ppm CO, respectively). Using th e aforementioned parameters, only animals at the 150 ppm CO exposure were significantly affected by CO exposure. At 5 months of age, CO-exposed male

March 2009 5-86 DRAFT – DO NOT CITE OR QUOTE

## offspring showed decrements in se xual 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 co pulatory activity, did not change CO-induced increases in mount to intromission latency or d ecreases in mount to intromission frequency. Basal extracellular dopamine concentration in the nucle us accumbens was unchanged after CO exposure. However, when stimulated with amphetamine admini stration, control rats had increased release of dopamine that was absent with CO-exposed rats. Th is demasculination of the CO-exposed offspring paralleled earlier studies of mice exposed gestationally to hypoxia (Hermans et al., 1993). When rats were followed at ten months of age, there were no significant changes in copulatory activity or neurochemical parameters of the CO-exposed male offspring showin g a recovery from earlier decrements. In summary, in utero exposure to CO induced impairment of c opulatory sexual behavior in male offspring with accompanying changes in the mesolimbic dopaminergic system at five months of age. By ten months of age, these changes were no longer de tectable in CO-exposed males. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Dopamine in the Neostriatum Is Affected by In Utero Plus Perinatal CO Exposure. Exposure of Long Evans rat dams and pups continuously to CO (75, 150, or 300 ppm with maternal COHb of 11, 19, and 27%, respectively) from conception to PND10 induced significant elevations in dopamine in the striatum at PND21 in CO-exposed offspring versus air exposed controls (Fechter et al., 1987). 

# Cerebellum Weight and Neurotransmitter Changes after In Utero and Perinatal CO Exposure. Long Evans dams and pups exposed to CO (75, 150, or 300 ppm with COHb 9%, 15% and 24%) over the duration of gestation and out to PND10, during the pe riod of cerebellular development in the rat which is equivalent to the in utero cerebellar development in the human, yielded a dose-dependent reduction in cerebellum wet weight (significant at 150 and 300 ppm) at PND21 with increases in norepinephrine (NE) concentration in the extrinsic noradrenergic system (a system that terminates in the cerebellum) in CO-exposed animals monitored from PND14 to PND42, whic h is in contrast to a lack of NE change seen in the cerebral cortex with in utero only CO exposure (Storm and Fechter, 1985a, b). With the same exposures as Storm and Fechter, CO-exposed (300 ppm only) pups at PND21 had significant decreases in cerebellar GABA content, decreased uptake of exogenous radio-labeled GABA, decreased fissures in the cerebellum and decreased cerebellum size (Storm and F echter, 1985b). Thus, in these studies, it appeared that the cerebellum and neurotransmitters of the cer ebellum were significantly affected by CO exposure during cerebellar development. 

# Neonatal Hyperthermia Effects on Neurotransmitters and Neuroglia. To explore the interaction of hyperthemia and hypoxia (CO-induced), two risk f actors for SIDS, pregnant guinea pigs were exposed to CO (0 or 200 ppm) for 10 h/day for the last 60% of gestation. At PND4 male pups were exposed to hyperthermia or ambient temperature as a control. Brai ns were then collected at 1 and 8 weeks of age. CO exposure had no effect on litter size or litter birth wei ght. In utero CO exposure sensitized some areas of the brain to future hyperthermic insults. Specifically, CO plus hyperthermia induced significant increases

March 2009 5-87 DRAFT – DO NOT CITE OR QUOTE

## in serotonin in multiple brain regions (NTS, DMV, and h ypoglossal nucleus) at 1 week of age; this change was no longer evident at 8 weeks of age. Hypertherm ia exposure alone induced decreased met-enkephalin neurotransmitter immunoreactivity at 1 week of age that was absent at 8 weeks and absent in CO plus hyperthermia exposed animals. Brain stem neurotra nsmitter (met-enkephalin, serotonin, TH, substance P) immunohistochemical differences were not apparent with CO treatment alone. At 8 weeks of age, CO plus hyperthermia exposure induced glial aggregations and gliosis surrounding infarct or necrotic areas in the brain and the medulla lesions stained positive for glial fibrillary acidic protein (GFAP). GFAP upregulation is classically seen with neuronal diseases or following neurodegeneration. Gross structural observations revealed no differences in the medulla or cerebellum following in utero CO exposure alone. Together, these data showed that CO exposure in ut ero sensitizes the brain to future hyperthermic insults leading to generation of necrotic lesions in th e brain and changes in neurotransmitter levels. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17 18 19 20 21 22 23

24 25 26 27 28 29 30 31 32 Glutamatergic Transmission Impairment by In Utero CO Exposure. Pregnant Wistar rats were exposed continuously to CO (75 ppm) during gesta tion (GD5-GD20) (Antonelli et al., 2006). Primary cell cultures obtained from the cerebral cortex of expo sed offspring (PND1) had decreased extracellular glutamate (basal and K + 

## -evoked) levels versus air exposed c ontrols, which may functionally impair cortical glutamatergic transmission in CO-exposed offspring, possibly affecting learning and memory. 

# Electrophysiological Changes Gestational exposure of pregnant Wistar rats to continuous CO (0, 75 or 150 ppm with dam COHb for 150 ppm CO of 15%) for the entirety of gestation yielded electrophysiological changes in the peripheral nervous system with reversible changes (present at PND40 and absent at PND270) in sodium channel inactivation kinetics and irreversible changes in the sodium equilibrium potential (Carratu et al., 1993) in CO-exposed pups (75 and 150 ppm) versus control pups. These voltage clamp studies showed that in utero CO exposure had both reversible and irreversible effe cts on sodium channels, which are essential for proper electrophysiological function of the PNS. 

# The Developing Auditory System The developing auditory system of rodents has r ecently been recognized to be a target of CO exposure. Low concentrations of CO exposure via inhalation (0, 12.5, 25, or 50 ppm) over PND8-PND22 during the period of extensive auditory developmen t and synaptogenesis was studied by Webber , who looked specifically at the inferior colliculus (IC), an auditory integrative section of the midbrain. Rodent pups were allowed to be maternally reared (MR) or were removed from their respective dams and nutritionally supported with gastronomy-reared nutriti on (AR), an artificial feeding system or received AR plus CO exposure (ARCO). AR reared pups were fe d a milk substitute comparable to natural rat milk via intragastric cannulation. AR allowed nursing pups to be exposed to CO without possible CO-dependent developmental changes being confo unded by materal CO co-exposure or lactational

March 2009 5-88 DRAFT – DO NOT CITE OR QUOTE

## exposures. Half of the pups were collected at PND27 and half were collected at PND75-PND77. Brains were sectioned and stained for c-Fos, a marker of neuronal activation in the nervous system. AR and MR exposed animals were found to have no significant differences in c-FOS staining. Further, c-Fos immunoreactivity in the central IC was significantly decreased in the ARCO-exposed animals at both PND27 and PND75-PND77 over all dose groups (12.5, 25, or 50 ppm CO); immnunostaining of other subregions of the IC were not affected by CO. Th ese studies showed exposure to low concentrations of CO during development can lead to permanent changes in the auditory system of mice that persist into adulthood. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Others have shown that mild CO exposure early in development impacts cochlear development (Lopez et al., 2003). Sprague Dawley rats were rear ed from PND6 to weaning (PND19-PND20) on milk substitutes using gastronomy-feeding (AR) or maternally reared (MR). These pups were exposed to low levels of CO (12 or 25 ppm, ARCO) via inhalation from PND6-PND27. At PND27, the animals were sacrificed and examinations of the cochlea were performed to determine the effect of mild CO exposure. In ARCO animals, there was no evidence of damage to the inner or outer hair cells. After CO exposure (25 ppm), there was atrophy or vacuolization of the ne rve cells that innervate the inner (not outer) hair cells. Also, fibers of the 8th cranial nerve at the leve l of the internal auditory canal of the ARCO animals exposed to 25 ppm CO had distorted myelination a nd vacuolization of the axoplasm. Energy production markers in the organ of corti and spiral gangli on neurons including cytochrome oxidase and NADH-TR were significantly decreased in 25 but not 12 ppm CO exposure groups versus control (AR and MR). Expression of the calcium-mediated myosin ATPase in th e organ of corti and spir al ganglion neurons was significantly decreased in the 25 ppm CO exposure group when compared to controls. Together, these findings show that specific areas of the cochlea were affected after low level developmental CO exposure. Two non-invasive measurements of auditory func tion, otoacoustic emissions testing (OAE) and auditory brainstem response (ABR) are routinely pe rformed on the majority of human newborns in hospitals throughout the U.S. to detect early hearing loss. OAE requires the insertion of a microphone and earphone into a sleeping newborn’s ear; when a sound is generated, an echo is recorded on the microphone in a functional ear and absent or reduced in an affected ear. The ABR measures brainwaves generated after exposure to a sound. Otherwise heal thy, term human infants born to smoking mothers have impaired cochlear development, albeit mild, with decreased amplitudes of transient evoked otoacoustic emissions versus newbor ns born to non-smokers (Korres et al., 2007); CO is one of many potential affective components of cigarette smoke. Stockard-Sullivan et al. (2003) used these tw o functional tests in Sprague-Dawley rat pups receiving ARCO (12, 25, or 50 ppm CO) to determin e how continuous (22 h/day) perinatal CO exposure (PND6-PND22) functionally affected hearing in the de veloping rat. OAEs were measured in two ARCO groups (25 and 50 ppm) at PND22. At 50 ppm, significan t reductions in OAE amplitude were detected at specific frequencies (7.13 and 8.01 kHz). ABR conducti on time was not affected in CO-exposed animals

March 2009 5-89 DRAFT – DO NOT CITE OR QUOTE

## (12, 25, 50, 100 ppm). Using another otoacoustic test revealed significant attenuation of the action potential of the 8th cranial nerve with ARCO exposure (12, 25, and 50 ppm CO) versus AR controls at PND22, but this is complicated by the finding that AR control animals had significant attenuation of the 8th cranial nerve AP versus MR control animals, im plying that artificial diet contributes to AP changes before CO was introduced. Nonetheless, the ARCO-dep endent attenuation of the 8th cranial nerve AP (versus AR control) was permanent, persisting un til adulthood in the 50 ppm CO exposure group (the only CO group monitored); OAE was not measured in adult animals. These functional tests reported that neonatal exposure to low concentrations of CO can induce auditory functional changes in rodents. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Multiple high dose CO studies have shown a role for ROS in CO-dependent ototoxicity. Studies using high dose CO (i.p. injection of pure CO [35 mL/kg] inducing 40% COHb) have shown CO-dependent otoxoxicity in adult guinea pigs as measured by loss of threshold of cochlear compound action potentials (CAP) could be attenuated using the free radical inhibitors PBN (a spin trap) or allopurinol (a superoxide scave nger), implicating ROS in high dose CO dependent cochlear damage (Fechter et al., 1997). Fechter et al. (2002) found that noise-induced hearing loss (NIHL) was potentiated by CO (500 ppm) coexposure. In the same manuscript, Fechter et al. (2002) showed potentiation of NIHL post-CO exposure (1,200 ppm for 30 minutes in adult rodents) with significant elevations seen in free radical production above control animals only in the CO+NIHL group; an interesting finding of this study was that the acute high dose CO exposure did not in duce significant increases in free radical generation with this high dose acute CO exposure alone. A po ssible mechanism for this high-dose CO cochlear damage is via glutamate release. Use of an NMDA inhibitor attenuated acute CO-dependent (1,200 ppm or i.p. injection of pure CO [35 mL/kg]) CAP threshold disruption at 15 minutes post CO-exposure. Glutamate is a known ROS generator (Liu and Fechter, 1995) . The glutamate receptor blockade did not afford long-term protection (1 h or 4 weeks post-CO exposure) from high doses of CO (1,200 ppm or i.p. injection of 35ml/kg pure CO) (Chen et al., 2001; Liu and Fechter, 1995) Cognizant of the ROS-dependent contribution to the auditory changes seen with high dose CO-exposure in animal toxicology models, Webber et al. investigated whether limiting iron availability through dietary iron restriction could confer protecti on against low dose perinatal CO-dependent auditory decrements. Briefly, rat pups were reared from P ND6 through the duration of lacation with gastronomyreared nutrition with adequate iron (AR) or AR with iron-deficient diets (ARID) or pups were reared with their respective dams (MR). Animals exposed to CO were denoted as ARCO and ARIDCO and received either 25 or 100 ppm CO from PND9 to PND24 with all animals collected on PND27. ARID, ARIDCO, and ARCO groups were compared to controls (MR and AR) to determine differences in the auditory system after low iron exposure and/or CO exposure. Coincidentally, ARIDCO mice became anemic but ARID mice were not. Cochlea were stained for tran sferrin, an iron-transport protein, and low iron exposure (ARID and ARIDCO) induced increased tran sferrin. Neurofilament loss from the spiral gangilian neurons and somas after ARCO treatment was rescued (no detectable neurofilament loss) with

March 2009 5-90 DRAFT – DO NOT CITE OR QUOTE

## low iron (ARIDCO); ARID treatment induced no cha nge in neurofilaments. CuZn superoxide dismutase (SOD1) was significantly increased with CO exposur e (ARCO) and rescued in ARIDCO animals; SOD1 was unchanged in low iron only animals (ARID). Lo w 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 exposur e (ARIDCO). These authors postulated that ROS generated via the interaction of pe roxide and iron (via the Fenton reac tion or Haber Weiss chemistry) can lead to an oxidative stress which leads to decreased c-fos expression in the IC and decreased numbers of neurofilaments; decreasing the available iron to the de veloping animal decreases the total pool available for ROS generation. Further, the attenuation of the elevated SOD levels with iron restriction post CO-exposure gives credence to this model. The chan ges in c-fos are not completely explained as ARID mice also show significant decreases in c-fos IC staining. 

# 1 2 3 4 5 6 7 8 9 10 11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Summary of Toxicological Studies on Developmental Centra l Nervous System Effects Toxicological studies employing rodent models ha ve shown that low level CO exposure during the in utero period can adversely affect adult out comes including behavior, neuronal myelination, neurotransmitter levels or function, and the auditory system. In utero CO exposure has been shown to impair active avoidance behavior in male offspring after in utero CO exposure. Other studies have shown that after in utero CO exposure, adult male offspring manifest with altered behavior including a lack of habituation and an absence of familiarity with previo usly viewed objects (non-spatial memory). In two separate studies, in utero CO exposure (75 and 150 ppm) was associated with significant PNS myelination decrements without associated changes in motor activity in adult animals. With in utero CO exposure peripheral myelination was decreased a nd the sphingolipid homeostasis (150 ppm CO) was disrupted; CNS myelination was not affected. Multiple studies demonstrated that in utero CO exposure affected glutamatergic (75 ppm), cholinergic (200 ppm), catecholaminergic (200 ppm), and dopaminergic (75 ppm) neurotransmitter levels or transmission in exposed male rodents. Possible or demonstrated adverse outcomes from the CO-mediated aberrant ne urotransmitter levels or transmission include respiratory dysfunction (150 ppm), impaired sexual behavior (150 ppm), and an adverse response to hyperthermic insults resulting in neuronal damage (200 ppm). Finally, in utero CO exposure has been shown to affect the developing auditory system of rodents, inducing permanent changes into adulthood. The IC showed decreased c-Fos staining indicatin g decreased neuronal activation post-perinatal CO exposure (12 and 25 ppm CO). The cochlea also showed adverse effects after in utero CO exposure with atrophy of nerve cells innervating the inner hair ce lls (25 ppm CO), impaired myelination of the 8th cranial nerve (25 ppm CO), and decreased energy pro duction in the organ of corti (25 ppm CO). Auditory functional testing using OAE (50 ppm CO) and attenuation of the 8th cranial nerve AP (12, 25, 50, 100 ppm CO) on rodents exposed perinataly to CO showed that CO-exposed nenonates had auditory decrements at PND22 (OAE and AP) and into adu lthood with AP decrements. Further studies limiting

March 2009 5-91 DRAFT – DO NOT CITE OR QUOTE

## iron consumption in the diet protected CO-exposed mi ce from cochlear damage at the spiral ganglion, possibly through a diminuation of ROS. Together, these animal studies demonstrate that in utero or perinatal exposure to CO can adversely affect adu lt behavior, neuronal myelin ation, neurotransmission, and the auditory system in rodents. 

# 1 2 3 4

5 6 7 8 9 10 11 12 13 14 15 16 17

18 19 20 21 22 23 24 25 26 27 28 29 30 

# Cardiovascular and Systemic Developmental Effects Myocardial Electrophysiological Maturation A rat model of in utero exposure was employed to study CO effects on the development of cardiac myocytes. Results demonstrated that in utero CO exposure (150 ppm) alters postnatal cellular electrophysiological maturation in the rat heart (Sartiani et al., 2004). Specifically, at 4 weeks of age, the action potential duration (APD) of isolated cardiac myocytes from CO-exposed animals failed to shorten or mature as did the APD of control animals. Further, the two ion conduction channels I to 

## (transient outward current, K + 

## -mediated) and I Ca,L 

## (L-type Ca 2+ 

## current), which largely c ontrol the rat APD, were significantly different from control animals after in utero CO exposure, at 4 weeks of age. These CO-dependent changes resolved by 8 weeks of age, reflecting a delayed maturation. The authors noted that human SIDS is increased in infants whose mothers are smokers and cigarette smoke is composed of approximately 4% CO. Further, these authors postulated that a CO-dependent delay in electrophysiological maturation of the cardiac myocyte (lack of APD shortening) could lead to arrhythmias and thus could be associated with SIDS deaths. However, no SIDS-like cardiac abberations were followed in intact Holter-m onitored rats in this study. 

# Heart Morphological changes after In Utero or Perinatal CO Exposure Multiple authors have reported cardiomegaly following in utero low level CO exposure. Prigge and Hochrainer (1977) reported increased fetal Wistar rat heart wet weight or cardiomegaly following continuous in utero CO (60, 125, 250, and 500 ppm) exposure with no decreases in near term fetal hematocrit or Hb levels seen at exposures below 250 ppm. Fechter et al (1980) found that prenatal exposure to CO affected cardiac development in exposed offspring. Long Evans rats that were exposed to CO continuously (150 ppm) during gestation manifested with significant elevations in wet heart weight at PND1 as well as heart weight in relation to body we ight; body weight was significantly decreased in CO-exposed pups. At no other age measured (PND4, PND14, or PND21) did the CO-exposed pups show increased heart weight (absolute or relative wet or dry weight) versus control. At PND14, control pups that had significantly increased body weight, also had significantly increased absolu te heart weights (dry and wet) versus CO-exposed offspring. Dry to wet wei ght ratios revealed that the increased heart weight of CO-exposed pups at birth was due to edema or water content. Penney et al. (1982) studied CO-dependent (500 ppm) cardiomegaly in neonates (continuous CO exposure for 32 days starting at

March 2009 5-92 DRAFT – DO NOT CITE OR QUOTE

## PND1). Other studies of adult male Charles River de rived rats exposed to CO for 6 weeks (at 400 or 500 ppm CO) as adults only develope d CO-dependent cardiomegaly during exposure that significantly regressed within a couple of months after termina tion of CO exposure (Styka and Penney, 1978). 

# 1 2 3

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

20 21 22 23 24 25 26 27 28 29 30 31 32 Systemic Immune Toxicology after In Utero CO Exposure In utero exposure (GD0-GD20) of male Wistar rats to relatively low CO (0, 75, or 150 ppm) concentrations induced reversible changes in macrop hage function (Giustino et al., 1993). The killing of Candida albicans (yeast) by splenic macrophages was signifi cantly decreased at PND15 in in utero CO-exposed male offspring (75 and 150 ppm) but re covered and maintained function by PND21 and PND60. Macrophage phagocytosis of Candida albicans was significantly reduced at PND15 and PND21 in CO-exposed males (150 ppm only) and recovery w as 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 le vels at PND60. In summary, CO exposure in utero leads to a reversible and dose dependent loss of f unction of splenic macrophages with decreased killing ability, decreased phagocytosis, and decreased ROS productio n during the macrophage respiratory burst. Further studies by the same laboratory showed that in utero exposure of male Wistar rats to CO exposure induced changes in the frequency of sple nic immunocompotent cells (Giustino et al., 1994). Specifically, there was a significant decrease in the num ber of leucocyte common antigen cells (LCA+) cells in PND21 male rats exposed during gestati on to 150 ppm CO; other cell subpopulations including macrophages, Major Histocompatibility (MHC) II cells, T and B lymphocytes did not show significant decreases in cell numbers with with CO exposure. 

# Summary of Toxicologica l Studies of Cardiovascular and Systemic Development In utero CO exposure is associated with various a dverse, albeit non-persistent, cardiac aberrations. Exposure to 150 ppm induced a delayed maturation of the cardiac action potential in CO-exposed offspring. Specifically, APD failed to shorten at 4 w eeks of age but shortened to control duration by 8 weeks of age. Also, ion channels in CO-exposed anim als, which are related to APD, were significantly different from controls at 4 weeks but matured to c ontrol levels by 8 weeks of age. In other studies, continuous in utero CO exposure (60, 125, 250, and 500 ppm) induced cardiomegaly at PND1 which was transient and regressed by PND4. Systemic immunoc ompromise was displayed in two studies focusing on splenic cells. In the first study, a reversible and dose dependent loss of function of splenic macrophages with decreased killing ability, decreased phagocytosis, and decreased ROS production during the macrophage respiratory burst was observed (150 ppm but not 75 ppm CO). In the second study, the distribution of splenic immunocompotent cells was skew ed because of a decrease in the number of LCA+ cells in PND21 male rats exposed during gestation to 150 ppm CO. In conclusion, in utero exposure to low doses of CO (60, 125, 150, 250, or 500 ppm) is able to induce transient changes in cardiac

March 2009 5-93 DRAFT – DO NOT CITE OR QUOTE

## morphology, cardiac action potentials, and systemic immunity that may make the immediate neonatal period a time for a CO-exposed animal more susceptible to other outside stressors. 

# 1 2

3

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 

# 5.4.3. Summary of Birth Outcomes and Developmental Effects

The mo st compelling evidence for a CO-induced eff ect on birth and developmental outcomes is for PTB and cardiac birth defects. These outcomes were not addressed in the 2000 CO AQCD, which 

# 4 included only two studies that examined the effect of ambient CO on LBW. Since then, a number of 

# 5 studies have been conducted looking at varied outco mes, including PTB, birth defects, fetal growth 

# 6 (including LBW), and infant mortality. 

# 7 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 reporte d consistent results whereby all studies reported a significant association with CO exposure during ear ly pregnancy, and exposures were assigned from monitors within close proximity of the mother’s r esidential address. Additional studies conducted outside of the U.S. provide supportive, though less cons istent, 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 associat ed with an increased risk of cardiac birth defects. This insult to the heart is coherent with results of human clinical studies demonstrating the heart as a target for CO effects (Section 5.2). Animal toxicol ogical studies provide additional evidence for such an insult to the heart, and reported transient cardiomegaly at birth after continuous in utero CO exposure (60, 125, 250 and 500 ppm CO), delayed myocardial el ectrophysiolgical maturation (150 ppm CO), or systemic splenic immunocompromise (75 or 150 ppm CO). Toxicological studies have also shown that exogenous continuous in utero CO exposure (250 ppm) induced teratogeni city in rodent offspring in a dose-dependent manner that was further exacerbated by dietary protein restriction (65 ppm CO) or zinc depletion (500 ppm CO). Toxicological studies of ex ogenous 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 durin g pregnancy having a negative effect on fetal growth in epidemiologic studies. In general, the re viewed studies, summarized in Figures 5-7 through 5-9, reported small reductions in birth weight (rangi ng ~5-20 g). Several studies examined various combinations of birth weight, LBW, and SGA/IUGR and inconsistent results are reported across these metrics. It should be noted that having a measurable, even if small, change in a population is different than having an effect on a subset of susceptible bi rths and increasing the risk of IUGR/LBW/SGA. It is

March 2009 5-94 DRAFT – DO NOT CITE OR QUOTE

## difficult to conclude if CO is rela ted to a small change in birth weight in all births across the population, or a marked effect in some subset of births. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

26 In general, there is limited epidemiologic evidence that CO is associated with an increased risk of infant mortality during the neonatal or post-neonatal periods. In support of this limited evidence, animal toxicological studies do provide some evidence that exogenous CO exposure to pups in utero significantly increased postnatal mortality (7 h/day and 24 h/day, 250 ppm CO; 24 h/day, 90 or 180 ppm CO) and prenatal mortality (7 h/day, 250 ppm CO). Evidence exists for additional developmental outcom es which have been examined in toxicological studies, but not epidemiologic or human clinical studies, including behavioral abnormalities, learning and memory deficits, locomotor effects, neurotransmitte r changes, and changes in the auditory system. Structural abberations of the cochlea involving neuronal activation (12.5, 25 and 50 ppm CO) and auditory related nerves (25 ppm CO) were seen in pup s after neonatal CO exposur e. Auditory functional testing using OAE (50 ppm CO) and 8th cranial nerve AP amplitude measurements (12, 25, 50, 100 ppm CO) on rodents exposed perinataly to CO showed that CO-exposed nenonates had auditory decrements at PND22 (OAE and AP) and permanent changes into adulthood with AP (50 ppm CO). Overall, there is limited, though positive, epidem iologic evidence for a CO-induced effect on PTB and birth defects, and weak evidence for a decrease in birth weight, other measures of fetal growth, and infant mortality. Animal toxicological studies provide support and coherence for these effects. Both hypoxic and non-hypoxic mechanisms that could lead to such effects have been proposed in the toxicological literature (Section 5.1), though a clear understanding of the mechanisms underlying reproductive and developmental effects is still lacking. Taking into consideration the positive evidence for some birth and developmental outcomes from epidemio logic studies and the resulting coherence for these associations in animal toxicological studies 

# , the evidence is suggestive of a causal relationship between long-term exposures to relevant CO con centrations and developmen tal effects and birth outcomes

.



# 5.5. Respiratory Effects

5.5. 1. Epidemiologic Studies with Short-Term Exposure

5.5.1. 1. Pulmonary Function, Respiratory Symptoms, and Medication Use

The 2000 CO AQCD briefly discussed the potential acute respiratory health effects associated with short-term exposure to CO. An evaluation of the ep idemiologic literature at the time did not find any 

# 27 evidence of an association between short-term exposur e to CO and lung function, respiratory symptoms, 

# 28

March 2009 5-95 DRAFT – DO NOT CITE OR QUOTE

## or respiratory disease. As a result, the 2000 CO AQCD did not conclude that a causal association exists 

# 1 between short-term exposure to CO and respiratory health effects. The following section evaluates the 

# 2 current literature that examines the potential associ ation between short-term exposure to CO and 

# 3 respiratory health effects. Table 5-14 lists the studies evaluated in this section along with the respiratory 

# 4 health outcomes examined and CO concentrations reported. 

# 5

Table 5-14. Range of CO concentrations repor ted in key respiratory morbidity studies that examined effects associated with short-term exposure to CO.

Location Years Health Outcome Metric Mean Concentration (ppm) Middle/Upper Percentile Concentrations (ppm) Author

Rabinovitch et al. (2004) Denver, CO Year 1: n = 41 Year 2: n = 63 Year 3: n = 43 11/1999-3/2000; 11/2000-3/2001; 11/2001-3/2002 Pulmonary function; Medication use 24-h avg 1.0 50th: 0.9 75th: 1.2 Maximum: 3.5

Silkoff et al. (2005) Denver, CO Year 1: n = 16 Year 2: n = 18 1999-2000 (winter); 2000-2001 (winter) Pulmonary function; Medication use 24-h avg 1999-2000: 1.2 2000-2001: 1.1 1999-2000 50th: 1.10 75th: 1.43 Maximum: 3.79 2000-2001 50th: 0.975 75th: 1.34 Maximum: 2.81 Fischer et al. (2002) 1 The Netherlands n = 68 March - April 2 Pulmonary function 24-h avg 0.80 Max: 1.34

Ranzi et al. (2004) 1 Emilia-Romagna Region, Italy n = 120 2/1999-5/1999 Pulmonary function; Respiratory symptoms; Medication use 24-h avg Urban: 1.34 Rural: 1.06 NR

Lagorio et al. (2006) 1 Rome, Italy (n = 29) 5/1999-6/1999; 11/1999-12/1999 Pulmonary Function 24-h avg Spring: 1.83 Winter: 10.7 Overall: 6.4 Overall Max: 25.1

Penttinen et al. (2001) 1 Helsinki, Finland n = 57 11/1996-4/1997 Pulmonary function 24-h avg NR 50th: 0.35 75th: 0.43 Maximum: 0.96 Kuopio, Finland n = 33 2/1994-4/1994 Pulmonary function 24-h avg 0.52 Maximum: 2.43 Timonen et al. (2002) 1

Chen et al. (1999) Taiwan n = 941 5/1995-1/1996 Pulmonary function 1-h max; 24-h avg NR 1-h max Maximum: 3.6 Delfino et al. (2003) Los Angeles, CA n = 22 11/1999-1/2000 Asthma symptoms 1-h max; 8-h max 1-h max: 7.7 8-h max: 5.0 1-h max 90th: 12.0 Maximum: 17 8-h max 90th: 7.9 Maximum: 10 Slaughter et al. (2003) Seattle, WA n = 133 12/1993-8/1995 Asthma symptoms; Medication use 24-h avg NR 50th: 1.47 75th: 1.87 Yu et al. (2000) Seattle, WA n = 133 11/1993-8/1995 Asthma symptoms 24-h avg 1.6 50th: 1.47 Maximum: 4.18 Schildcrout et al. (2006) 8 North American cities n = 990 11/1993-9/1995 Asthma symptoms; Medication use 24-h avg NR 50th: 0.63-1.49 75th: 0.77-1.90 90th: 0.95-2.40



### March 2009 5-96 DRAFT – DO NOT CITE OR QUOTE

### Author Location Years Health Outcome Metric Mean Concentration (ppm) Middle/Upper Percentile Concentrations (ppm)

von Klot et al. (2002) 1 Erfurt, Germany n = 53 10/1996-3/1997 Asthma symptoms; Medication use 24-h avg 0.78 50th: 0.70 75th: 1.04 Maximum: 2.60 Park et al. (2005a) Incheon, Korea n = 64 3/2002-6/2002 Asthma symptoms; Medication use 24-h avg Control days: 0.64 Dust days: 0.65 NR

Rodriguez et al. (2007) Perth, Australia n = 263 6/1996-7/1998 Symptoms associated with respiratory illness 8-h max 1.41 Maximum: 8.03

de Hartog et al. (2003) 1 Amsterdam, the Netherlands n = 37 Erfurt, Germany n = 47 Helsinki, Finland n = 47 1998-1999 (winter) Respiratory symptoms 24-h avg Amsterdam: 0.52 Erfurt: 0.35 Helsinki: 0.35 Maximum: Amsterdam: 1.39 Erfurt: 2.17 Helsinki: 0.87

1 These studies presented CO concentrations in the units mg/m 3 . The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m 3 , which assumes standard atmosphere and ambient tempterature. 2 These studies did not provide the year(s) in which air quality data was collected.



# Pulmonary Function

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Rabinovitch et al. (2004) examined the association between exposure to ambient air pollutants and asthma exacerbation in a panel of urban minority children , 6-12 years old, with moderate to severe asthma over three winters in Denver, CO. The investigators examined pulmonary function by measuring forced expiratory volume in 1 second (FEV 1 

## ) and peak expiratory flow (PEF) in the morning on school days, and also at night on weekends or other nonschool days . Using a 3-day moving average (lag 0-2) for all pollutants, Rabinovitch et al. (2004) did not find an association between CO and either lung function parameter during the morning or at night. Silkoff et al. (2005) also examined lung function in Denver during the winter months, but in a panel of former smokers that were at least 40 years old and had been diagnosed with COPD. In this study CO concentrations were similar to those reported in Rabinovitch et al. (2004). The authors examined the association between exposure to air pollutants and lung function (i.e., FEV 1 

## and PEF) in both the morning and the evening. Silkoff et al. (2005) found contradictory results when examining the effects of CO for each of the winter periods separately, 1999-2000 and 2000-2001. During the analysis of the first winter (i.e., 1999-2000) CO was not found to be associated w ith lung function decrements at any lag, but an increase in FEV 1 

## during the morning was observed at lag 1. For the second winter (i.e., 2000-2001) the authors found a significant negative association between CO exposure and FEV 1 

## in the evening at lag 2 (Figure 5-10). CO was not found to be associated with PEF at any lag during either winter period. Silkoff et al. (2005) postulated that the difference in results for the two study periods could be due to higher pollution concentrations along with somewhat lower temperatures and higher humidity in 2000-2001. However, mean CO levels remained relatively cons tant between the first and second winters, whereas, PM 10 

## , PM 2.5 

## , and NO 2 

## concentrations all increased. The decrements in FEV 1 

## observed in the second

March 2009 5-97 DRAFT – DO NOT CITE OR QUOTE

# 1 2 winter, therefore, may have been due to the slightly worse, although not significantly different, baseline lung function of the panel of subjects used during the second winter (Silkoff et al., 2005).

Source: Silkoff et al. (2005) 

# Figure 5-10. Estimates for FEV 1 

# change expressed per SD change of the individual pollutants PM 2.5 

# , PM 10 

# , CO, and NO 2 

# for the 1999-2000 (top) and 2000- 2001 (bottom) winters at lags 0, 1, and 2. The ast erisk indicates a significant association (p <0.05).

March 2009 5-98 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 In the recent literature, the majority of studies th at examined the association between short-term exposure to CO and lung function have been c onducted in Europe. The results from these studies contradict those reported in the U.S.-based studies previously discussed. Negative associations between short-term exposure to CO and lung function were observed primarily in individuals with underlying respiratory conditions; however, some evidence also ex ists for effects in children that live in urban environments. Penttinen et al. (2001) examined the association between CO and lung function in a panel consisting of 57 non-smoking adult asthmatics during th e winter and spring in Helsinki, Finland. The authors observed negative associations with PEF (L /min) for a 0.5 ppm increase in 24-h avg CO concentrations in the morning at lag 1 ( β = -0.54, SE = 0.084), and in the afternoon ( β = -1.52, SE = 0.29) and evening ( β = -1.81, SE = 0.27) for a 5-day average. In tw o-pollutant models with daily mean particle number concentration (PNC), CO effects on PEF in the morning were attenuated at lag 1, but remained negative. In addition, negative associations with PEF persisted in the afternoon and evening in a twopollutant model at lag 0. In this study, high co rrelations between UFP and other traffic generated pollutants (e.g., CO and NO X 

## ) make it difficult to attribute the observed respiratory effects to a specific pollutant. Lagorio et al. (2006) also conducted a study that examined the association between CO and lung function in adults. In this study, 3 panels of subject s that resided in Rome, Italy, were selected with underlying asthma, COPD, or IHD. The ages of the s ubjects varied depending on the panel, but overall the subjects ranged from 18-80 years old. In single-p ollutant models CO was found to be negatively associated with both FVC (forced vital capacity) and FEV 1 

## 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 FEV 1 

## in the IHD panel. Lagorio et al. (2006) did observe high correlations between CO and PM 2.5 

## , but not NO 2 

## . Unfortunately, copollutant models were not conducted in this anal ysis to identify whether the CO associations observed are confounded by other pollutants. Timonen et al. (2002) examined the effect of CO on bronchial responsiveness and pulmonary function (i.e., FVC, FEV 1 

## , MMEF, and AEFV) in a panel of children 7-12 years old with chronic respiratory symptoms during the winter in Kuopio, Finland. The authors found that CO was significantly associated with decrements in baseline lung function for FVC (mL) at lags 2 (-17.5 mL), 3 (-24.8 mL), and 4-day avg (-52.5 mL), and for FEV 1 

## (mL) at lag 3 (-20.9 mL) for a 0.5 ppm increase in 24-h avg CO concentration. However, CO was not associated with exercise induced changes in lung function. Overall, Timonen et al. (2002) found that high concen trations of combustion byproducts (i.e., BS, PM 10 

## , particle numbers, NO 2 

## , and CO) were associated with impairment in baseline lung function. These associations, along with the high correlation between pollutants, contri buted 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.

March 2009 5-99 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Chen et al. (1999) examined the effect of CO on lung function in a panel of 8-13 year old asthmatic children in Taiwan. The authors observed an association between short-term exposure to CO and decrements in FVC (mL) at a 2-day lag when us ing daytime average CO concentrations (from 0800 to 1800) in a single-pollutant model. In addition, th e authors found a high correlation between CO and NO 2 

## concentrations (r = 0.86-0.98), but di d not conduct multipollutant analyses to examine the effect of each pollutant. One additional study, Fischer et al. (2002), exam ined the association between CO and respiratory health, specifically lung function in a non-selected co hort study of 68 children ages 10-11 that live in an urban environment (i.e., Utrecht, the Netherlands). In this study, the authors examined whether eNO was a more sensitive measure of lung damage than the traditional pulmonary function measurements (i.e., FVC, FEV 1 

## , PEF, and MMEF). Fischer et al. (2002) fo und negative associations between CO and FEV 1 

## , PEF, and MMEF at both lags 1 and 2. Additonally, the authors found an association between CO and an increase in eNO at lag 1. However, the study did not present the correlations between pollutants or examine copollutant models. 

# Respiratory Symptoms in Asthmatic Individuals

Upon evaluating the literature that examined the association between short-term exposure to CO and respiratory symptoms in asthmatic individuals, consistent positive results were observed across studies. Studies consisting of children enrolled in the Childhood Asthma Management Program (CAMP) study found that CO was positively associated with asthma symptoms. Yu et al. (2000) found a 14% increase in asthma symptoms ([95% CI: 5-23] per 0.5 ppm increase in 24-h avg CO concentrations at lag 1) in a population of 5-13 year old children (n = 133) with asthma in Seattle, WA. These effects persisted when controlling for previous day’s asthma symptoms (12% [95% CI: 5-19] at lag 1). Using the same population of children, Slaughter et al. (2003) found a significant association between short-term exposure to CO at lag 1 and asthma severity both with and without controlling for the previous day’s asthma severity, (RR = 1.04 [95% CI: 1.01-1.08]) and (RR = 1.03 [95% CI: 1.00-1.05]), respectively. Schildcrout et al. (2006) examined the associati on between air pollutants and asthma symptoms in 990children ages 5-12 in 8 North American cities. The authors found a positive association between shortterm exposure to CO and asthma symptoms at lag 0 (OR = 1.04 [95% CI: 1.00-1.07] per 0.5 ppm increase in 24-h avg CO), but similar effects were also observed at lag 1, 2, and the 3-day moving sum. The CO effects observed persisted when NO 2 

## , PM 10 

## , and SO 2 

## where included in joint pollutant models. Two additional U.S. studies also found positive asso ciations between CO and asthma symptoms, Rabinovitch et al. (2004) and Delfino et al. (2003). Rabinovitch et al. (2004) found a positive association between 24-h avg CO concentrations for a 3-day movi ng average (lag 0-2) and asthma exacerbtions (OR = 1.02 [95% CI: 0.89-1.16] per 0.5 ppm increase in 24-h avg CO) in a population of urban poor children with moderate to severe asthma in Denver, CO. Delf ino et al. (2003) also observed positive associations

March 2009 5-100 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 between CO and asthma symptoms in a population of Hispanic children with asthma in a Los Angeles, CA, community. However, positive associations we re found only when using the previous day’s maximum 8-h avg CO concentration as the exposure metr ic. These results are in contrast to those studies reported above which found positive associations when using 24-h avg CO concentrations as the exposure metric. It should be noted that in comparison to Rabinovitch et al. (2004) and the other studies discussed above, the mean ambient concentrations for 1-h maximum and maximum 8-h avg reported by Delfino et al. (2003) were 7.7 ppm and 5.0 ppm, respectively, bot h of which are approximately 3.5 times higher than the corresponding 24-h avg concentrations reported in the other studies. In a panel study consisting of 53 adults with ast hma or asthma symptoms in Germany, von Klot et al. (2002) observed a marginal association between CO concentration and the prevalence of wheezing at lag 0 (OR = 1.03 [95% CI: 0.97-1.08] per 0.5 ppm increase in 24-h avg CO), and a positive association for a 5-day mean concentration (OR = 1.12 [95% CI : 1.05-1.21] per 0.5 ppm increase in 24-h avg CO). However, the authors found CO to be highly correlated with UFP, making it unclear if the effect observed was due to CO alone. Additionally, Park et al. (2005a) in a panel study of individuals 16-75 years old in Incheon, Korea with bronchial asthma did not find an association between CO and nighttime asthma symptoms or cough. Figure 5-11 summarizes the results from studies that provided usable quantitative results and examined the association between shor t-term exposure to CO and asthma or respiratory symptoms in asthmatic individuals. To further examine the effect of CO on asthma and asthma symptoms some studies also analyzed medication use in asthmatic individuals in response to an increase in air pollution levels. The majority of U.S.-based studies (i.e., Rabinovitch (2004), Slaughter et al. (2003), and Schildcrout et al. (2006)) focused on rescue inhaler use in ch ildren with ages ranging from 5-13 years old. Rabinovitch et al. (2004) found a weak association (OR = 1.08 [95% CI: 1.00-1. 17] per 0.5 ppm increase in 24-h avg CO) between rescue inhaler use in a population of 6-12 year ol d urban minority children with moderate to severe asthma in the winter in Denver, CO, which is evident by the large confidence intervals surrounding the estimate. Slaughter et al. (2003) in a population of 5-12 year old children with asthma in Seattle, WA found a positive and significant association with rescue inhaler use both with and without taking into consideration the previous day’s asthma severity, (RR: 1.04 [95% CI: 1.01-1.08] per 0.5 ppm increase in 24-h avg CO) and (RR: 1.03 [95% CI: 1.00-1.05] pe r 0.5 ppm increase in 24-h avg CO), respectively. Similar results were observed in a multi-city study co nducted by Schildcrout et al. (2006), which analyzed rescue inhaler use in 990 children ages 5-13 with asthma in eight North American cities. Schildcrout et al. (2006) found that short-term exposur e to CO was positively associated with rescue inhaler use at lags of 0, 2, and a 3-day moving sum, and that the association was fairly robust to an increase in other pollutants (i.e., NO 2 

## , PM 10 

## , and SO 2 

## ) when included in joint models with CO. However, both Slaughter et al. (2003) and Schildcrout et al. (2006) attributed the asso ciations observed to othe r combustion byproducts. Additional studies (Park et al., 2005a; Silkoff et al ., 2005; von Klot et al., 2002) conducted in Denver,

March 2009 5-101 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 CO; Erfurt, Germany; and Incheon, Korea, respectively, found results that are consistent with those previously reported, but in populations with combined ages ranging from 16-77. Figure 5-11 presents the risk estimates from studies that examined the asso ciation between short-term exposure to CO and medication use in asthmatic individuals.

Reference Location Population Age Lag Schildcrout et al. (2006) 8 U.S. cities Asthmatics (n = 990) 5-12 0-2 a Asthma Symptoms Children------- Respiratory Symptoms---------

Yu et al. (2000) Seattle, WA Mild-to-moderate asthmatics (n = 133) 5-13 1 Asthma Symptoms

Rabinovitch et al. (2004) Denver, CO Asthmatics (n = 147) 6-12 0-2 Asthma Exacerbation

Los Angeles, CA Hispanic children with asthma (n = 22) 10-16 1 Symptom Score >2 (1-h max) Delfino et al. (2003) Los Angeles, CA Hispanic children with asthma (n = 22) 10-16 1 Symptom Score >2 (max 8-h avg) Delfino et al. (2003)

(von Klot et al., 2002) Erfurt, Germany Asthma/asthma symptoms (n = 53) 37-77 0-5 Wheeze Adults------------------ Medication Use ----------------

Schildcrout et al. (2006) 8 U.S. cities Asthmatics (n = 990) 5-12 0-2 a Inhaler Use Children Slaughter et al. (2003) Seattle, WA Mild-to-moderate asthmatics n = 133) 5-13 1 Inhaler Use b Slaughter et al. (2003) Seattle, WA Mild-to-moderate asthmatics (n = 133) 5-13 1 Inhaler Use c

Rabinovitch et al. (2004) Denver, CO Asthmatics (n = 147) 6-12 0-2 Inhaler Use

(von Klot et al., 2002) Erfurt, Germany Asthma/asthma symptoms (n = 53) 37-77 0-5 Inhaler Use ( β 2-agonist) Adults (von Klot et al., 2002) Erfurt, Germany Asthma/asthma symptoms (n = 53) 37-77 0-5 Inhal er Use (Corti costeroid)



### Odds Ratios a Sum of 0-, 1-, and 2-day lags. b Estimate from model that did not contro l for previous day’s asthma severity. b Estimate from model that controlled for previous day’s asthma severity.



# Figure 5-11. Asthma symptoms, respiratory symptoms and me dication use in asthmatic individuals associated with short-term exposure to CO 1 

# . Effect estimates were standardized depending on the averaging ti me used in the study: 0.5 ppm for 24-h avg, 0.75 ppm for max 8-h avg, and 1.0 ppm for 1-h max.



# Respiratory Symptoms in Non-Asthmatic Individuals

In addition to examining the association between short-term exposure to CO and respiratory symptoms (e.g., cough, wheeze, shortness of breath, et c.) in asthmatic populations some studies examined these respiratory effects in individuals classified as non-asthmatics. Rodriguez et al. (2007) and de Hartog 

# 5 6 7

1 Effect estimates from Park et al. (2005a) were not included in this figure because the study did not provide the increment at which the effect estimates were calculated.



### March 2009 5-102 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13

14 15 16 17 18 19 20 21 22 23 24

25 et al. (2003) examined the effect of CO on respiratory symptoms in a panel of 263 children 0-5 years old at high risk for developing asthma in Perth, Australia, and a 3-city panel of individuals ≥ 50 years of age with CHD during the winter in Amsterdam, the Neth erlands, Erfurt, Germany, and Helsinki, Finland, respectively. Rodriguez et al., (2007) found CO concentrations to be positively associated with wheeze/rattle chest and runny/blocked nose at both a 5-day lag and a 0-5-day lag in Perth, Australia. It is unclear which pollutant is driving the effect observe d by Rodriguez et al. (2007) because multipollutant models were not examined and additional analyses were not conducted to further characterize the associations observed. In a panel of elderly individuals with CHF in three European locations, de Hartog et al. (2003) observed some marginal associations, specifically between CO concentration and the prevalence of the respiratory symptom shortness of breath, at lag 3 and 0-5-days. Although a marginal association was observed, the authors found that the associations between air pollution exposure and respiratory symptoms were stronger for PM 2.5 

## than for gaseous air pollutants. 

# Summary of Associations between Shor t-Term Exposure to CO and Pulmonary Function, Respiratory Symptoms, and Medication Use

A limited body of evidence is available that exam ined the effect of short-term exposure to CO on various respiratory health outcomes. Among asthmatic s, the studies reviewed generally find positive associations between short-term exposure to CO and resp iratory-related health effects (i.e., decrements in lung function/lung function growth, respiratory symp toms, and medication use). It is difficult to determine from this group of studies if CO is indepe ndently associated with respiratory outcomes or if CO is an indicator for other traffic-related polluta nts. On-road vehicle exhaust emissions are a nearly ubiquitous source of combustion pollutant mixtures that include CO and can be an important contributor to CO-related health effects in near-road locations . A lack of copollutant analyses among this group of studies complicates the efforts to disentangle the health effects attributed to CO from the larger trafficrelated pollutant mix. Additional uncertainty exists as to a biologically plausible mechanism which could explain the effect of CO on respiratory health.



# 5.5.1.2. Respiratory Hospital Admissions, ED Visits and Physician Visits

The 2000 CO AQCD (U.S. EPA, 2000) evaluated a lim ited amount of literature that examined the association between short-term exposure to CO and respiratory hospital admissions, ED visits, and 

# 26 physician visits in the U.S. (i.e., Seattle, WA, Reno, NV, and Anchorage, AK) and Europe (i.e., Barcelona, 

# 27 Spain). From these studies, the 2000 CO AQCD concl uded that positive associations were observed for 

# 28 short-term exposure to CO with several respiratory outcomes, including asthma and COPD. However, the 

# 29

March 2009 5-103 DRAFT – DO NOT CITE OR QUOTE

## lack of a biologically plausible mechanism for CO-indu ced respiratory morbidity at that time brought into 

# 1 question the validity of the results observed. 

# 2 Since the 2000 CO AQCD, the number of studies that examined the association between short-term exposure to CO and respiratory morbidity has increased; however, the total number of studies published is still considerably less than the number that examine th e health effects associated with exposure to other criteria air pollutants. This section focuses primarily on those studies conducted in the U.S. and Canada which examined the potential respiratory health effects associated with CO at concentrations at or similar to those found in the U.S. Unlike previous sections, which also evaluated studies conducted outside of the U.S., the expansive U.S.-based resp iratory hospital admission and ED vi sits literature provides adequate evidence to examine the association between short- term exposure to CO and respiratory HA and ED visits. Collectively, the studies conducted outside of the U.S. observed associations that are consistent with those observed in the U.S.-based studies evaluated below (see Annex C). 

# 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Overall, this section focuses on respiratory-related hospital admissions because the majority of the literature examines hospital admissions as opposed to ED visits or physician visits (Table 5-15 presents the studies evaluated in this section along with the range of CO concentrations measured in each study). It must be noted that when examining the association between short-term expos ure to CO and health outcomes that require medical attention, it is impor tant to distinguish between hospital admissions, ED visits, and physician visits for respiratory outcomes (more so than for cardiovascular outcomes). This is because it is likely that a small percentage of respir atory ED visits will be ad mitted to the hospital and, therefore, may represent potentially less serious, but mo re common outcomes. To adequately distinguish between the results presented in hospital admission, ED visit, and physician visit studies, each outcome is evaluated in individual sections. In addition, each sect ion presents results separately for respiratory health outcomes which includes all respiratory diagnoses (IC D-9: 460-519) or selected diseases (e.g., asthma, COPD, pneumonia and other respiratory infections) in or der to evaluate the potential effect of short-term exposure to CO on each outcome.



# Table 5-15. Range of CO concentrations repor ted in key respiratory hospital admission and ED visit studies that examine ef fects associated with short-term exposure to CO.

Location Type of Visit (ICD9) Metric Mean Concentration (ppm) Middle/Upper Percentile Concentrations (ppm) Author

Cakmak et al. (2006a) 10 Canadian cities Hospital Admissions: Respiratory disease (i.e., Acute bronchitis and bronchiolitis; Pneumonia; Chronic and unspecific bronchitis; Emphysema; Asthma; Bronchiectasis; Chronic airway obstruction) 24-h avg 0.8 Maximum: 6.5

Linn et al. (2000) Los Angeles, CA Hospital Admissions: Pulmonary; Asthma; COPD 24-h avg Winter: 1.7; Spring: 1.0; Summer: 1.2; Fall: 2.1 Maximum: Winter: 5.3; Spring: 2.2; Summer: 2.7; Fall: 4.3;



### March 2009 5-104 DRAFT – DO NOT CITE OR QUOTE

### Location Type of Visit (ICD9) Metric Mean Concentration (ppm) Middle/Upper Percentile Concentrations (ppm) Author

Slaughter et al. (2005) Spokane, WA ED Visits and Hospital Admissions: Respiratory; Asthma; COPD; Pneumonia; Acute Respiratory Infection 24-h avg Hamilton St.: 1.73 Backdoor Tavern: 1.29 Spokane Club: 1.41 Third and Washington: 1.82 Rockwood: 0.42 95th: 3.05

Burnett et al. (2001) Toronto, ON, Canada Hospital Admissions: Respiratory disease (i.e., Asthma; Acute bronchitis/bronchiolitis; Croup; Pneumonia) 1-h max 1.9 50th: 1.8; 75th: 2.3; 95th: 3.3; 99th: 4.0 Maximum: 6.0

Yang et al. (2003) Vancouver, BC, Canada Hospital Admissions: Respiratory diseases 24-h avg 0.98 50th: 0.82; 75th: 1.16 Maximum: 4.90 Lin et al. (2003) Toronto, ON, Canada Hospital Admissions: Asthma 24-h avg 1.18 50th: 1.10; 75th: 1.40 Maximum: 6.10 Lin et al. (2004c) Vancouver, BC, Canada Hospital Admissions: Asthma 24-h avg 0.96 50th: 0.80; 75th: 1.12 Maximum: 4.90 Cook County, IL; Los Angeles County, CA Hospital Admissions: COPD 24-h avg NR Cook: 50th: .99; 75th: 1.25 Maximum: 3.91 Los Angeles: 50th: 1.35; 75th: 2.16 Maximum: 5.96 Moolgavkar(2003a) (reanalysis of Moolgavkar 2000a)

Yang et al. (2005) Vancouver, BC, Canada Hospital Admissions: COPD 24-h avg 0.71 50th: 0.64 Maximum: 2.48 South Coast Air Basin, CA Hospital Admissions: Acute bronchiolitis 24-h avg Lag 1: Index: 1.730 Referrent: 1.750 Lag 4: Index: 1.760 Referrent: 1.790 Lag 1 : Index: 50th: 1.52; 75th: 2.26; 90th: 3.16 Maximum: 9.60 Referrent: 50th: 1.51; 75th: 2.29; 90th: 3.23 Maximum: 9.60 Lag 4: Index: 50th: 1.54; 75th: 2.31; 90th: 3.23 Maximum: 8.71 Referrent: 50th: 1.55; 75th: 2.35; 90th: 3.30 Maximum: 9.60 Karr et al. (2006)

South Coast Air Basin, CA Hospital Admissions: Acute bronchiolitis 24-h avg; Monthly avg 24-h avg: 1.720 Monthly: 1.770 24-h avg: 50th: 1.61; 75th: 2.08; 90th: 2.75 Maximum: 5.07 Monthly avg: 50th: 1.63; 75th: 2.13; 90th: 2.88 Maximum: 8.30 Karr et al. (2007)

Zanobetti and Schwartz (2006) Boston, MA Hospital Admissions: Pneumonia 24-h avg NR 50th: 0.48; 75th: 0.60; 95th: 0.88 Lin et al. (2005) Toronto, ON, Canada Hospital Admissions: Respiratory infections 24-h avg 1.16 50th: 1.05; 75th: 1.37 Maximum: 2.45



### March 2009 5-105 DRAFT – DO NOT CITE OR QUOTE

### Location Type of Visit (ICD9) Metric Mean Concentration (ppm) Middle/Upper Percentile Concentrations (ppm) Author

Peel et al. (2005) Atlanta, GA ED Visits: All respiratory; Asthma; COPD; URI; Pneumonia 1-h max 1.8 90th: 3.4

Tolbert et al. (2007) Atlanta, GA ED Visits: Respiratory diseases (i.e., Asthma; COPD; URI; Pneumonia; Bronchiolitis) 1-h max 1.6 50th: 1.3; 75th: 2.0; 90th: 3.0 Maximum: 7.7

Ito et al. (2007) New York, NY ED Visits: As thma 8-h max 1.31 50th: 1.23; 75th: 1.52; 95th: 2.11 Villeneuve et al. (2006b) Toronto, ON, Canada Physicians Visits: Allergic rhinitis 24-h avg 1.1 Maximum: 2.2



# Hospital Admissions Respiratory Disease All but two of the published hospital admission studies that examined the association between short-term exposure to CO and all respiratory di seases from North America were conducted in Canada, and only one study presented results from a combined analysis of multiple cities (Cakmak et al., 2006b). In a study of 10 of the largest Canadian cities, Ca kmak et al. (2006b) examined respiratory hospital admissions (ICD-9: 466, 480-486, 490-494, 496) in rela tion 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 cities 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 

## ). U.S.-based studies (Los Angeles and Spokane) reported similarly weak or null associations for respiratory disease hospital admissions (Linn et al., 20 00; Slaughter et al., 2005). In a study conducted in Toronto, Canada for the time period 1980-1994, Bu rnett et al. (2001) reported a relatively strong association between 1-h max CO and respiratory di sease hospital admissions in children less than two years of age, for the diagnoses of asthma (493), acu te bronchitis/bronchiolitis (466), croup (464.4), and pneumonia (480-486). The authors found a 9.7% (95% CI: 4.1-15.5) increase in hospital admissions for a 2-day avg (lag 0-1) per 1 ppm increase in 1-h max CO. In the two-pollutant model analysis, the estimates for both CO and O 3 

## remained elevated. Yang et al. (2003) reported similar results (OR = 1.04 [95% CI: 1.01-1.06] at lag 1 per 0.5 ppm increase in 24-h avg CO) for pediatric (<3 years of age) respiratory disease (ICD-9: 460-519) admissions in Vancouver for the time period 1986-1998. Yang et al. (2003) also reported elevated associations with 24-h avg CO and respiratory hospital admissions (ICD-9: codes 460-519) for ages 65 and over in Vancouver, Canada (OR = 1.02 [95% CI: 1.00-1.04]) at lag 1 for a 0.5 ppm increase in 24-h avg CO. The authors found th at the risk estimate remained elevated when O 3 

## was included in the model.

1 To determine the lag for the combined esti mate across all 10 cities, Cakmak et al. averaged the strongest associations from la gs 0-5 days from

each city.



### March 2009 5-106 DRAFT – DO NOT CITE OR QUOTE

# Asthma



# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

22 23 24 25 26 27 28 29 30 31 32 33 Some studies that examined the effect of short- term exposure to CO on asthma hospital admissions conducted all age and age-stratified analyses, specifically to examine effects in children. In two of these hospital admission studies conducted in Canada, evid ence was observed for increased pediatric (ages 6-12) asthma hospital admissions (ICD-9: 493) in boys, but not girls (Lin et al., 2003; 2004c); however, a biological explanation was not provided which could e xplain this difference. Lin et al. (2003) used a bidirectional case-crossover analysis in Toronto, Cana da for the years 1981–1993 reported an OR of 1.05 (95% CI: 1.00-1.11) per 0.5 ppm increase in 24-h avg CO for a 1-day lag for boys with similar results being reported when averaging CO concentrations up to 7 days prior to hospitalization. Risk estimates for girls did not provide evidence of an association using the same lag structure that was used in the boys’ analysis (OR = 1.00 [95% CI: 0.93-1.06]); lag 1). In a copollutant analysis, the estimates for boys were essentially unchanged when adjusting for all PM indices (Lin et al., 2003). It should be noted that this study used a bi-directional case-crossover analysis, which may be biased (Levy et al., 2001). Studies that examined the various referent selection strategies for the case-crossover study design have concluded that the preferred control selection strategy is the tim e-stratified framework (Levy et al., 2001). In an additional analysis conducted by Lin et al. (2004c ), the authors found less consistent evidence for a greater effect in boys versus girls in Vancouver during the years 1987-1998 using a time-series study design that stratified results by socioeconomic status (SES). In one additional study that examined asthma hospital admissions for all ages and genders combined, Slaughter et al. (2005) observed some evidence of an increase in asthma hospital admissions (ICD-9 493) in Spokane (1995-2000) for CO at lag 2 (RR = 1.03 [ 95% CI: 0.98-1.08]) for a 0.5 ppm increase in 24--h avg, but not for the other two lags examined (lag 1 and lag 3). 

# Chronic Obstructive Pulmonary Disease

A few of the studies examined the effect of shor t-term exposure to CO on COPD, or obstructive lung disease, and hospital admissions. Moolgavkar (20 03a) (a reanalysis of (Moolgavkar, 2000a) examined hospital admissions for COPD plus “a llied diseases” (ICD-9 490-496) in two U.S. counties (Cook County, IL and Los Angeles County, CA) for th e years 1987-1995 using Poisson generalized linear models (GLMs) or generalized additive models (G AM) with the more stringent convergence criteria. Overall, the results from both models were similar. Using the GAM models the study reported percent increases of 0.53-1.20% for all ages in Los Angel es County, and 0.17-1.41% for ages 65 and older in Cook County, for a 0.5 ppm increase in 24-h avg CO a nd lags ranging from 0 to 5 days. Yang et al. (2005) reported similar results for COPD hospital admissi ons (ICD-9 490-492, 494, 496) in Vancouver for ages 65 and older for the years 1994-1998 for a moving average of 0-6 day lags (RR = 1.14 [95% CI: 1.03- 1.23] per 0.5 ppm increase in 24-h avg CO). Howeve r, Slaughter et al. (2005) found no association between short-term exposure to CO and COPD hospital admissions (ICD-9 491, 492, 494, 496) in

March 2009 5-107 DRAFT – DO NOT CITE OR QUOTE

# 1 2

3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 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 reporte d for 2- and 3-day lags.



# Acute Bronchiolitis in Infants Two studies (Karr et al., 2006; 2007) from the South Coast Air Basin in California examined both short term (lag 0 or 1) and longe r term levels of CO in relation to acute bronchiolitis (ICD-9: 466) hospital admissions during the first year of life from 1995-2000. Karr et al. (2006) found no evidence of a short-term association between ambient CO concentra tions and hospital admissions for acute bronchiolitis at lag 1 day (OR= 0.99 [95%CI: 0.98- 1.01] per 0.5 ppm increase in 24-h avg CO). In addition, Karr et al. (2007), which examined longer term exposures (average in the month prior to hospitalization and lifetime average) in a matched case-control study, also did not provide any evidence of an association with CO. 

# Pneumonia and Other Respiratory Infections In addition to examining the effect of short-term exposure to CO on health outcomes that can limit the function of the respiratory system, some studies examined the effect of CO on individuals with pneumonia (ICD-9: 480-486) separately or in combin ation with other respiratory infections. Zanobetti and Schwartz (2006) examined pneumonia hospital admissions (ICD-9 480-487) in Boston, MA, for the years 1995-1999 for ages 65 and older using a time-stratified case-crossover analysis. The authors reported an increase in pneumonia hospital admissions at lag 0 of 5.4% (95% CI: 1.2-10.0) per 0.5 ppm increase in 24-h avg CO. While Zanobe tti and Schwartz (2006) did not report multipollutant results, they suggested that CO was most likely acting as a marker for traffic-related pollutants because CO was highly correlated with both BC (r = 0.80) and NO 2 

## (r = 0.67), and moderately correlated with PM 2.5 

## (r = 0.52). Instead of examining the effect of CO on pneum onia hospital admissions separately as was done by Zanobetti and Schwartz (2006), Lin et al. (2005) pr esented results for the overall effect of CO on respiratory infection hospital admissions (ICD-9: 464, 466, 480-487). In this analysis, Lin et al. (2005) examined the potential increase in resp iratory hospital admissions in child ren less than 15 years of age in Toronto, Canada for 199 8-2001 using a bi-directional case-cross over approach. The authors reported elevated estimates for boys (OR = 1.15 [95% CI: 1.02-1.29] per 0.5 ppm increase in 24-h avg CO for a 6- day ma) while the estimate for girls was weaker and with wider confidence intervals (OR = 1.06 [95%CI: 0.92-1.21]). Lin et al. (2005) did not provide an e xplanation as to why the estimates are stronger for boys than for girls. It should be noted that this study used a bi-directional case-crossover analysis, which may be biased (Levy et al., 2001). Studies that have examined the various referent selection strategies for the case-crossover study design have concluded that the preferred control selection strategy is the timestratified framework (Levy et al., 2001).

March 2009 5-108 DRAFT – DO NOT CITE OR QUOTE

### Reference Location Age Lag Endpoint

Burnett et al. (2001) To ronto, CAN <2 0-1 Yang et al. (2003) Vancouver, CAN <3 1 Linn et al. (2000) Los Angeles, CA 30+ 0 

### All Respiratory Yang et al. (2003) Vancouver, CAN 65+ 1 Cakmak et al. (2006b) 10 Canadian Cities All 2.8 Slaughter et al. (2005) Spokane, WA All 2

Lin et al. (2003) Toronto, CAN 6-12 1 Boys Lin et al. (2003) Toronto, CAN 6-12 1 Girls 

### Asthma Slaughter et al. (2005) Spokane, WA All 2

Slaughter et al. (2005) Spokane, WA 15+ 2 

### COPD Yang et al. (2003) Vancouver, CAN 65+ 0-6



### Pneumonia Zanobetti and Schwartz ( 2 006) Boston, MA 65+ 0

Karr et al. (2006) SQAB, CA <1 1 

### Acute Bronchiolitis

Lin et al. (2005) Toronto, CAN <15 0-5 Boys 

### Respiratory

Infection Lin et al. (2005) Toronto, CAN <15 0-5 Girls



### Effect Estimates



# Figure 5-12. Summary of associ ations between short-term expos ure to CO and respiratory hospital admissions. 1 , 2 

# Effect estimates were standardized depending on the averaging time used in the study: 0.5 ppm for 24-h avg, 0.75 ppm for max 8-h avg, and 1.0 ppm for 1-h max.



# Emergency Department Visits Respiratory Disease Peel et al. (2005) conducted a large single-city ED study in Atlanta, GA, which included data from 31 hospitals for the time period 1993–2000. In this st udy, results were reported for various respiratory

# 1 2

1 Risk estimates from Moolgavkar (2003a) were not included in this figure because the study presented a range of effect estimate s using different statistical models. The results from this study were more adequa tely highlighted in the evaluati on of the study in the COPD sec tion.

2 Risk estimates from Lin et al. (2004c) were not included in the figure because the results we re stratified by SES and therefor e could not be

readily compared to effect estimates from Lin et al..(2003).



### March 2009 5-109 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

29 30 31 32 related visits (ICD-9 460-466, 477, 480-486, 491-493, 496, 786.09). In an all ages analysis, the authors found a RR of 1.011 (95% CI: 1.004-1.019) for a 3-day avg (lag 0-2) per 1 ppm increase in 1-h max CO concentration for all respiratory diseases. Tolbert et al . (2007) expanded the time period used in the Peel et al. (2005) study to include ED visits through 2004, and reported similar results for respiratory ED visits (RR: 1.013 [95% CI: 1.007-1.018] per 1 ppm increase in 1-h max CO). The CO risk estimates from the Atlanta, GA, ED visits studies were attenuated when O 3 

## , NO 2 

## , or PM were added to the model (). In addition, Tolbert et al. (2007) repor ted high correlations between CO and NO 2 

## (r = 0.70) and EC (r = 0.66); and a moderate correlation with PM 2.5 

## (r = 0.51). One additional ED visits study that also examined respiratory disease (Slaughter et al., 2005) presen ted essentially null results at lag 1 and 2, but found similar results to Peel et al. (2005) and Tolbert et al. (2007) at lag 3 (RR = 1.02 [95% CI: 1.00-1.03] per 0.5 ppm increase in 24-h avg CO).



# Asthma The association between short-term exposure to CO and asthma ED visits (ICD-9 493, 786.09) was also examined in Atlanta, GA by Peel et al. ( 2005). In this study the authors reported results from distributed lag models including lags 0-13 in addition to a moving average of lags 0, 1, and 2 (lag 0-2) for specific respiratory outcomes (e.g., asthma). Effect estimates from the distributed lag models were stronger than those produced from models that used 3-day moving average CO concentrations (RR = 1.010 [95% CI: 0.999-1.022] for lags 0-2 compared to RR = 1.076 [95% CI: 1.047-1.105] for an unconstrained distributed lag of 0-13 for a 1 ppm increase in 1-h max CO). These results demonstrated the potential effect of CO exposures up to 13 days prio r to an asthma ED visit. Estimates were stronger for pediatric ED visits (ages 2-18 years) (RR = 1. 019 [95% CI: 1.004-1.035] per 1 ppm increase in 1-h max CO) for a 3-day avg (lag 0-2) compared to all ag es (Peel et al., 2005). Slaughter et al. (2005), which also examined ED visits for S pokane (1995-2001), reported an increase in asthma ED visits for all ages for CO at lag 3 (RR = 1.03 [95% CI: 1.00-1.05] per 0.5 ppm increase in 24-h avg CO), but not for the other two lags examined (lags 1 and 2). The results from Ito et al. (2007) also provide evidence of increased ED visits for asthma (ICD-9 493) for all ag es in New York City for 1999-2002, but quantitative results were not provided. In addition, Ito et al. ( 2007) stated that increased CO effect estimates were attenuated when NO 2 

## was included in the model, but effect est imates remained elevated in two-pollutant models with either PM 2.5 

## or O 3 

## . 

# Chronic Obstructive Pulmonary Disease

In the examination of the effect of short-term exposure to CO on COPD ED visits (ICD-9 491, 492, 496), Peel et al. (2005) reported elevated estimates for Atlanta, GA for 1993-2000 (RR = 1.03 [95%CI: 1.00-1.05] per 1 ppm increase in 1-h max CO for a moving average of lag 0-2) with similar results for the distributed lag model (RR = 1.03 [95% CI: 0.98-1.09). However, results from Slaughter et al. (2005) from

March 2009 5-110 DRAFT – DO NOT CITE OR QUOTE

# 1 2

3 4 5 6 7 8 9 10 11 12 13 14 15 Spokane were consistent with a null or slightly protective association at lag 1 (RR = 0.96 [95% CI: 0.92-1.00] per 0.5 ppm increase in 24-h avg CO at lag 1) with similar results for lags 2 and 3. 

# Pneumonia and Other Respiratory Infections Similar to the hospital admission analysis c onducted by Zanobetti and Schwartz (2006) discussed above, Peel et al. (2005) examined the effect of CO on pneumonia separately (ICD-9: 480-486), but also included an analysis of upper respiratory infection (ICD-9: 460-466, 477) ED visits for all ages in Atlanta, GA during the years 1993-2000. The authors reported a weak estimate for pneumonia for the three-day moving average (lag 0-2) (RR = 1.01 [95% CI: 0.996-1.021] per 1 ppm increase in 1-h max CO). However, when using an unconstrained distributed lag (days 0-13), Peel et al. (2005) observed evidence of an association (RR = 1.045 [95% CI: 1.01-1.08]). An examination of URI ED visits, the largest of the respiratory ED groups, found slightly increased risk estimates for both the three-day moving average (lag 0-2) (RR = 1.01 [95% CI: 1.00-1.02]) and the unconstrained distributed lag for days 0-13 (RR = 1.07 [ 95% CI: 1.05-1.09]) per 1 ppm increase in 1-h max CO. In copollutant models, CO risk estimates were largely attenuated when PM 10 

## , O 3 

## , or NO 2 

## were included in the model. Upon conducting an age-stratifed analysis, Peel et al. (2005) also found that infant (less than one year of age) and pediatric (ages 2-18) URI ED visit CO risk estimates were substantially stronger than all age risk estimates.

March 2009 5-111 DRAFT – DO NOT CITE OR QUOTE

### Reference Location Age Lag Endpoint Slaughter et al. (2005) Spokane, WA All 3 Peel et al. (2005) Atlanta, GA All 0-2 

### All Respiratory Tolbert et al. (2007) Atlanta, GA All 0-2

Peel et al. (2005) Atlanta, GA 2-18 0-2 Peel et al. (2005) Atlanta, GA All 0-2 Peel et al. (2005) Atlanta, GA All 0-13* 

### Asthma

Slaughter et al. (2005) Spokane, WA All 3

Slaughter et al. (2005) Spokane, WA 15+ 1 Peel et al. (2005) At lanta, GA All 0-2 

### COPD Peel et al. (2005) At lanta, GA All 0-13*

Peel et al. (2005) Atlanta, GA All 0-2 

### Pneumonia Peel et al. (2005) Atlanta, GA All 0-13*

Peel et al. (2005) At lanta, GA All 0-2 

### Respiratory

Infection Peel et al. (2005) At lanta, GA All 0-13*

* Unconstrained distributed lag 

### Effect Estimates 

# Figure 5-13. Summary of associat ions between short-term exposur e to CO and respiratory ED visits. Effect estimates were standardiz ed depending on the averaging time used in the study: 0.5 ppm for 24-h avg, 0.75 ppm fo r max 8-h avg, and 1.0 ppm for 1-h max.



# Physician Visits

Although hospital admissions and ED visits are the two most well studied measures of morbidity, a few studies also examined the effect of CO on unsch eduled physician visits. In a time-series study, Villeneuve et al. (2006b) examined the effect of CO on physician visits for allergic rhinitis in individuals 65 and older in Toronto, Canada. Although quantita tive results were only presented in figures, upon observation it was evident that estimates were consis tent with a null association for lags 0-6 (Villeneuve et al., 2006b). In an additional study, Sinclair et al. (2004) reported results for urgent care visits for asthma and respiratory infections in a health maintenance organization in Atlanta, GA; however, the study only reported statistically significant resu lts, of which none were for CO. 

# 1 2 3 4 5 6 7 8

March 2009 5-112 DRAFT – DO NOT CITE OR QUOTE

# Summary of Associations between Short-T erm Exposure to CO and Respiratory Hospital Admissions, ED Visits, and Physicians Visits

1 2 3 4 5 6 7 8 9 10 11 12 13

14

17 Relatively few studies evaluated the association be tween short-term exposure to ambient CO and hospital admissions and ED visits for various resp iratory outcomes compared to other criteria air pollutants (e.g., O 3 

## and PM). Although evidence for consistent positive associations (See Figures 5-12 and 5-13) has been found across these studies, various issues surrounding the association between short-term exposure to CO and respiratory-related health effect s have not been addressed due to: the lack of studies that examined potential confounders of the CO-respir atory hospital admission and ED visits relationship; and uncertainty as to a biologically plausible mechan ism which could explain the association between CO exposure and respiratory-related health effects. Some of the studies evaluated suggest that CO acts as an indicator of combustion (e.g., traffic), which is support ed by the moderate to high correlation between CO and other traffic-related pollutants such as NO 2 

## , PM 2.5 

## , EC, or BC and in addition complicates the results presented. Only two studies examined potential confou nding of CO risk estimates by other pollutants through copollutant models, and found that CO risk estimates were robust or attenuated but remained positive in two-pollutant models with O 3 

## , NO 2 

## , or PM indices.



# 5.5.2. Epidemiologic Studies with Long-Term Exposure

The 20 00 CO AQCD did not evaluate any studies that examined the effect of long-term exposure to CO on respiratory health. The following section discu sses those studies that analyze the effect of long

# 15 term exposure to CO on pulmonary function, ast hma/asthma symptoms, and allergic rhinitis. 

# 16



# 5.5.2.1. Pulmonary Function

Mortimer et al. (2008) examined the effect of prenatal and lifetime exposures to air pollutants on pulmonary function in 232 asthmatic children that r esided in the San Joaquin Valley of California. The 

# 18 strong temporal correlation between pollutants and po llutant metrics for different time periods in the 

# 19 study area contributed to the inability to draw conclusi ons about the effect of individual pollutant metrics 

# 20 on pulmonary function (Mortimer et al., 2008). In an a ttempt to remedy this problem the authors used a 

# 21 newly developed Deletion/Substitutio n/Addition (DSA) algorithm “to identify which pollutant metrics 

# 22 were most predictive of pulmonary function” (Mortimer et al., 2008). This methodology uses an 

# 23 exploratory process to identify the best predictive model for each outcome of interest. Using this 

# 24 approach, Mortimer et al. (2008) found that exposur e to CO early in life (ages 0-3) was negatively 

# 25 associated with FEV 1 

## /FVC and FEF 25-75 

## /FVC resulting in an effect size of -2.5% and -4.8%, respectively, 

# 26

March 2009 5-113 DRAFT – DO NOT CITE OR QUOTE

## per IQR increase in CO. 1 

## Additional negative associations were observed between exposure to CO during 

# 1 the first 6 years of life and FEF 25 

## (-6.7%), and prenatal, not trimester-specific exposure, and FEF 25-75 

## (% 

# 2 reduction not reported). Overall, Mortimer et al. (2008) found that the effects were limited to subgroups, 

# 3 including African Americans, individuals diagnosed with asthma before the age of 2 years, and 

# 4 individuals exposed to maternal smoki ng during pregnancy. It must be noted that research still needs to be 

# 5 conducted to validate the aforementioned results obtained using the DSA algorithm and the subsequent 

# 6 calculation of effect estimates using GEE because the current model could underestimate the uncertainty 

# 7 surrounding the associations reported (Mortimer et al., 2008). Although the authors did find associations 

# 8 between long-term exposure to CO and decrements in pulmonary function, they also observed high 

# 9 correlations between CO and NO 2 

## , which together are markers for pollutants generated by urban 

# 10 combustion sources (e.g., mobile sources) (Mortimer et al., 2008). 

# 11



# 5.5.2.2. Asthma and Asthma Symptoms

All of t he studies that examined the associati on between long-term exposure to CO and asthma and/or asthma symptoms presented consistent, positive results, which is similar to what was observed 

# 13 when evaluating the effects attributed to short-term exposure to CO. Two U.S.-based studies, Goss et al. 

# 14 (2004) and Meng et al. (2007), were evaluated which examined the effect of long-term exposure to CO 

# 15 on: (1) pulmonary exacerbations in a cohort of individu als with cystic fibrosis >6 years of age, and (2) 

# 16 poorly controlled asthma in a population of asthmatics ≥ 18 years old that resided close to cross-street 

# 17 intersections in Los Angeles County and San Diego County, respectively. Of these two studies only Goss 

# 18 et al. (2004) observed a positive association between a respiratory outcome (i.e., pulmonary 

# 19 exacerbations) and long-term exposure to CO. However, it is unclear if the effects observed are due to CO 

# 20 alone because the authors did not conduct co-pollutant analyses. 

# 21 12

22 23 24 25 26 27 28 29 30 31 When evaluating studies conducted in other countries, Hirsch et al. (1999), in a study conducted in Germany, and Guo et al. (1999), Wang et al. (1999), and Hwang et al. (2005), in studies conducted in Taiwan, all found positive associations between l ong-term exposure to CO and asthma or asthma symptoms in populations ranging from 6–16 years old. In these studies, the authors addressed the observed associations differently. Guo et al. (1999) and Hwang et al. (2005) both concluded that it is unlikely CO directly affects the respiratory system; Hirsch et al. (1999) attributed the increase in the prevalence of cough and bronchitis to exposure to traffic-related air pollutants (i.e., NO 2 

## , CO, and benzene); and Wang et al. (1999) did not interpret th e association between long-term exposure to CO and adolescent asthma. Only Hwang et al. (2005) conducted a co-pollutant analysis and found that the asthma effects observed were robust to the inclusion of PM 10 

## , SO 2 

## and O 3 

## in the model. This is of note because

1 The study did not present the IQR for CO; therefore, the effect estimates presented were not standardized using the approach m entioned

previously in this ISA.



### March 2009 5-114 DRAFT – DO NOT CITE OR QUOTE

## inclusion of the traffic-related pollutants NO X 

## or NO 2 

## in some of the aforementioned studies complicated the overall results because NO X 

## and NO 2 

## are highly correlated with CO, which makes it difficult to separate the effects attributed to each pollutant. 

# 1 2 3

4

11

17 

# 5.5.2.3. Allergic Rhinitis

Hwang etal. (2006) and Lee et al. (2003c) 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 

# 5 found an association between allergic rhinitis prevalen ce and CO, but they also observed an association 

# 6 with NO X 

## . As a result, although Hwang et al. (2006) and Lee et al. (2003c) observed an increase in the 

# 7 prevalence of allergic rhinitis in response to an in crease in long-term CO levels, they concluded that the 

# 8 combination of an association be ing observed for both CO and NO X 

## can be attributed to the complex 

# 9 mixture of traffic-related pollutants and not necessarily CO alone. 

# 10



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

To date, a limited number of studies have examin ed the potential association between long-term exposure to CO and respiratory morbidity. Althou gh studies have reported positive associations for 

# 12 various respiratory outcomes, the li mited evidence available, the new analytical methods employed, and 

# 13 the lack of studies that examined potential confounde rs of the CO-respiratory morbidity relationship, 

# 14 especially due to the high correlation between CO and ot her traffic-related pollutants, brings into question 

# 15 the validity of the associations observed. 

# 16



# 5.5.3. Controlled Human Exposure Studies

Human clinical studies provide very little and in consistent evidence of changes in pulmonary function following exposure to CO. In one older study, Chevalier et al. (1966) observed a significant 

# 18 decrease in total lung capacity following a short term exposure to 5,000 ppm resulting in a COHb level of 

# 19 4%. However, a similar study conducted at a higher CO concentration resulting in COHb levels of 

# 20 17-19% found no CO-induced changes in lung volume or mechanics (Fisher et al., 1969). The 2000 CO 

# 21 AQCD reported no evidence of CO-induced changes in exercise ventilation at COHb levels <15% during 

# 22 submaximal exercise (Koike et al., 1991). In two recent human clinical studies, exposure to CO (COHb ≈ 

# 23 10%) was not found to significantly affect resting pul monary ventilation compared with exposure to clean 

# 24 air under either hypoxic or hyperoxic exposure conditi ons (Ren et al., 2001; Vesely et al., 2004)`. The 

# 25 results of these studies demonstrate that the hypoxia- and CO 2 

## -induced increases in pulmonary ventilation 

# 26

March 2009 5-115 DRAFT – DO NOT CITE OR QUOTE

## are not affected by CO. One recent study evaluated th e potential anti-inflammatory effects of controlled 

# 1 exposures to CO in the airways of 19 individuals with COPD (Bathoorn et al., 2007). Subjects were 

# 2 exposed to both CO at concentrations of 100-125 ppm as well as room air for 2 h on each of four 

# 3 consecutive days. The authors reported a small decrea se in sputum eosinophils, as well as a slight increase 

# 4 in the provocative concentration of methacholine required to cause a 20% reduction in FEV 1 

## following 

# 5 exposure to CO. Although this study appears to de monstrate some evidence of an anti-inflammatory 

# 6 effect of CO among subjects with COPD, it must be noted that two of these patients experienced 

# 7 exacerbations of COPD during or following CO exposur e. A similar study found no evidence of systemic 

# 8 anti-inflammatory effects following exposure to higher CO concentrations (500 ppm for 1 h) in a group of 

# 9 healthy adults (Mayr et al., 2005). 

# 10



# 5.5.4. Toxicological Studies

As dis cussed in Section 5.2.3., the work of Thom, Ischiropoulos and colleagues (Ischiropoulos et al., 1996; Thom and Ischiropoulos, 1997; Thom et al., 1997; Thom et al., 1999a; Thom et al., 1999b) 

# 12 focused on CO-mediated displacement of NO • from heme-binding sites. Although the concentrations of 

# 13 CO used in many of their studies were far higher th an ambient levels, some of this research involved 

# 14 more environmentally-relevant CO le vels. In one study, 1-h exposure of rats to 50 ppm CO resulted in 

# 15 increased lung capillary leakage 18 h later (Thom et al., 1999a). Increased NO • was observed in the lungs 

# 16 by electron paramagnetic resonance during 1-h exposure to 100 ppm CO and was accompanied by 

# 17 increases in H 2 

## O 2 

## and nitrotyrosine. All of these effects were blocked by inhibition of NOS. These results, 

# 18 which were partially discussed in the 2000 CO AQC D, demonstrate the potential for exogenous CO to 

# 19 interact with NO • -mediated pathways and to lead to pathophysiological effects in the lung. 

# 20 11

21 22 23 24 25 26 27 28 29 30 31 32 Recent work by Ghio et al. (2008) showed a disruption of cellular iron homeostasis following exposure to a low level of CO (50 ppm x 24 h) in rats. In lungs of inhalation-exposed rats, non-heme iron was significantly reduced, while lavagable iron was in creased 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 re ported statistically significant effects at 10 ppm CO and an apparent maximal effect at 50 ppm CO (c oncentrations up to 500 ppm did not significantly enhance the iron loss beyond 50 ppm). Similar r esponses were observed for cellular ferritin. Both enhancement of iron removal and diminished iron upta ke were noted in CO-exposed cells. Furthermore, decreased oxidative stress, mediator release and prolifer ation were noted in respiratory cells. These effects were reversible with a recovery period in fresh air. In terestingly, the in vivo exposure to CO induced mild,

March 2009 5-116 DRAFT – DO NOT CITE OR QUOTE

## but significant neutrophilia in the lungs compared to air-exposed rats. This finding is contrary to the concept that CO acts as an anti-infl ammatory agent; however, with alterations in iron handling several potential pathways could be initiated to recruit infla mmatory cells into airways. The authors pointed out that while CO derived from HO activity may have an important role in iron regulation, the non-specific application of exogenous CO will have little capac ity to discriminate between excessive and/or inappropriate iron which catalyzes oxidative stress and iron which may be required for normal homeostasis. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

25 A chronic inhalation study by Sorhaug et al. (2006) demonstrated no alterations in lung morphology in Wistar rats exposed to 200 ppm CO fo r 72 weeks. COHb levels we re reported to be 14.7% and morphological changes were noted in th e heart as described in Section 5.2.3. A recent study by Carraway et al. (2002) involved continuous exposure of rats to HH (380 torr) with or without co-exposure to CO (50 ppm) for up to 21 days. The focus of this study was on remodeling of the pulmonary vasculature. While the addition of CO to HH did not alter the thickness or diameter of vessels in the lung, there was a significant increase in the number of small (<50 μ m) 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 th e combined CO + HH exposure, which the authors attribute to enhancement of muscular arterioles and β -actin. In summary, one older study (Thom et al., 1999a) and two new studies (Carraway et al., 2002; Ghio et al., 2008) demonstrated effects of 50-100 ppm CO on the lung. Responses included an increase in alveolar capillary permeability, disrupted iron home ostasis, mild pulmonary inflammation and an exacerbation of pulmonary vascular remodeling elicited by HH. These results should be considered in view of the potential for inhaled CO to interact directly with lung epithelial cells and resident macrophages. However, a chronic study involving 200 ppm CO demonstrated no changes in pulmonary morphology (Sorhaug et al., 2006).



# 5.5.5. Summary of Respiratory Health Effects

5.5.5. 1. Short-Term Exposure to CO

New epid emiologic studies, supported by the b ody of literature summarized in the 2000 CO AQCD, provide evidence of positive associations betw een short-term exposure to CO and respiratory

# 26 related outcomes including pulmonary function, r espiratory symptoms, medication use, hospital 

# 27 admissions, and ED visits. However, the interpreta tion of the results from epidemiologic studies is 

# 28 difficult due to the lack of extensive copollutant analyses along with the moderate to high correlation 

# 29 between CO and other combustion/traffic generated pollu tants. To date the majority of the literature has 

# 30 not extensively examined the association between CO and respiratory morbidity due to studies focusing 

# 31

March 2009 5-117 DRAFT – DO NOT CITE OR QUOTE

## primarily on effects associated with exposure to other criteria pollutants, namely PM and O 3 

## . This has 

# 1 contributed to the inability to disentangle the effect s attributed to CO from the larger complex air 

# 2 pollution mix (particularly motor vehicle emissions). In addition, uncertainty as to a biological 

# 3 mechanism to explain the respiratory-related effect s observed in the epidemiologic literature further 

# 4 complicates the interpretation of these results, especial ly considering the low ambient CO concentrations 

# 5 reported (24-h avg: 0.35-2.1 ppm). However, animal toxicological studies do provide some evidence that 

# 6 short-term exposure to CO (50-100 ppm) can cau se oxidative injury and inflammation and alter 

# 7 pulmonary vascular remodeling. Human clinical studies have not extensively examined the effect of 

# 8 short-term exposure on respiratory morbidity, specifi cally pulmonary function. The limited number of 

# 9 human clinical studies that have been conducted prio r to and since the 2000 CO AQCD provide very little 

# 10 evidence of any adverse effect of CO on the respirat ory system at COHb levels <10%. Although human 

# 11 clinical studies have not provided evidence to s upport CO-related respiratory health effects, the 

# 12 epidemiologic studies that examined the effects of short-term exposure to CO and lung-related outcomes 

# 13 show positive associations and animal toxicological studies demonstrate the potential for an underlying 

# 14 biological mechanism, which together provide 

# evidence that is suggestive of a causal relationship 

# 15 between short-term exposure to relevant CO concentrations and respiratory morbidity. 

# 16



# 5.5.5.2. Long-Term Exposure to CO

Currentl y, only a few studies have been conducted that examine the association between long-term exposure to CO and respiratory morbidity. Although some studies did observe associations between long

# 18 term exposure to CO and respiratory health outco mes key uncertainties still exist. These uncertainties 

# 19 include: the lack of replication and validati on studies to evaluate new methodologies (i.e., 

# 20 Deletion/Substitution/Addition (DSA) algorithm) that ha ve been used to examin e the association between 

# 21 long-term exposure to CO and respiratory health eff ects; whether the respiratory health effects observed 

# 22 in response to long-term exposure to CO can be e xplained by the proposed biological mechanisms; and 

# 23 the lack of co-pollutant analyses to disentangle the r espiratory effects associated with CO due to its high 

# 24 correlation with NO 2 

## and other combustion-related pollutants. Overall, 

# the evidence available is 

# 25 inadequate to conclude that a causal relations hip exists between long-te rm exposure to relevant 

# 26 CO concentrations and respiratory morbidity. 

# 27 17

March 2009 5-118 DRAFT – DO NOT CITE OR QUOTE

# 5.6. Mortality

5.6. 1. Epidemiologic Studies with Short-Term Exposure to CO

The re lationship between short-term exposure to CO and mortality has not been extensively examined over the years due to the majority of ep idemiologic studies focusing on mortality effects 

# 2 associated with exposure to PM and O 3 

## . As a result, a clear understanding of the association between 

# 3 short-term exposure to CO and mortality has yet to be developed. This section summarizes the main 

# 4 findings of the 2000 CO AQCD, and evaluates the newly available information on the relationship 

# 5 between short-term exposure to CO a nd daily mortality in an effort to disentangle the CO-mortality effect 

# 6 from those effects attributed to other criteria air pollutants. 

# 7 1

8

24 25 26 27 28 

# 5.6.1.1. Summary of Findings from 2000 CO AQCD

The 2000 CO AQCD examined the association between short-term exposure to CO and mortality through the analysis of twelve single-city time-serie s studies, and one multi-city study, which included 11 

# 9 Canadian cities. While the results presented by th ese studies did provide suggestive evidence that an 

# 10 association exists between CO and mortality the AQC D concluded that inadequate evidence existed to 

# 11 infer a causal association between mortality and short- term exposure to ambient concentrations of CO. 

# 12 The reasons for this conclusion, which are shared with those studies that examined the effect of short

# 13 term exposure to CO on morbidity, were due to: inte rnal inconsistencies and lack of coherence of the 

# 14 reported results within and across studies; the repr esentativeness of the average ambient CO levels of 

# 15 spatially heterogeneous ambient CO values derived from fixed monitori ng sites or of personal exposures 

# 16 that often include nonambient CO; and the lack of biological plausibility for any harmful effects 

# 17 occurring with the very small changes in COHb leve ls (from near 0 up to 1.0%) over typical baseline 

# 18 levels (about 0.5%) that would be expected with the low average ambient CO levels (<5.0 ppm, 1-h daily 

# 19 max) reported in the epidemiologic studies (U.S. EPA, 2000). Additionally, some epidemiologic studies 

# 20 have also suggested that CO is acting as an indicat or for other combustion-related pollutants, which has 

# 21 led to investigators questioning the CO-mortality relationship even when associations have been 

# 22 observed. 

# 23 To date the aforementioned issues have not been addressed primarily due to the majority of the recent time-series mortality studies focusing on the effects of only PM and O 3 

## . As such, CO has usually been considered one of the potential confounding copollutants in air pollution epidemiologic studies. As a result, the available CO information from these PM and O 3 

## studies most frequently consists of risk estimates from single- and multipollutant models. Given the limitation that most of these studies were not

March 2009 5-119 DRAFT – DO NOT CITE OR QUOTE

## conducted to examine CO, the goal of this review is to evaluate the CO-mortality association, and specifically the: magnitude of associations; evidence of confounding; and evidence of effect modification. 

# 1 2

3 

# 5.6.1.2. Multi-City Studies

The foll owing sections evaluate the recent literatu re that examined the association between shortterm exposure to CO and mortality, and in addition di scuss newly available information with regard to the 

# 4 issues specific to CO mentioned above. This eval uation focuses primarily on multi-city studies because 

# 5 they provide: a more representative sample of potential CO-related mortality effects; and especially useful 

# 6 information by analyzing data from multiple cities using a consistent method, a nd thus avoiding potential 

# 7 publication bias. 1 

## Table 5-16 lists the multi-city studies eval uated along with the mean CO concentrations 

# 8 reported in each study. 

# 9

Table 5-16. Range of CO concentrations reporte d in multi-city studies that examine mortality effects associated with s hort-term exposure to CO.

Location Years Averaging Time Mean Concentration (ppm) Range of Mean Concentrations Across Cities (ppm) Author

82 U.S. cities 1 (NMMAPS) 1987-1994 24-h avg 1.02 Baton Rouge = 0.43 Spokane = 2.19 Dominici et al. (2003b; 2005a) (reanalysis of Samet et al., 2000b) Burnett et al. (2004) 12 Canadian cities 1981-1999 24-h avg 1.02 Winnipeg = 0.58 Toronto = 1.31 Samoli et al. (2007) 2 19 European cities (APHEA2) 1990-1997 3 8-h max 2.12 Basel = 0.52 Athens = 5.3

1 The study actually consisted of 90 U.S. cities, but only 82 had CO data. 2 This study presented CO concentrations in the units mg/m 3 . The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m 3 , which assumes standard atmosphere and temperature. 3 The study period varied from city to city. These years represent the total years in which data was collected across all cities.



# National Morbidity, Mortality, and Air Pollution Study of 90 U.S. Cities

The time-series analysis of the largest 90 U.S. c ities (82 cities for CO) in the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) Dominici et al. (2003b; 2005a) (reanalysis of Samet et al., 2000b) is by far the largest multi-city study conducted to date to inves tigate the mortality effects of air pollution, but the study primarily focused on PM 10 

## . The range in 24-h avg CO concentrations in the largest 20 cities (by population size) was 0.66 ppm (Detroit, MI) to 2.04 ppm (New York City). The analysis in the original report used GAM with de fault convergence criteria. In response to the bias 

# 10 11 12 13 14 15

1 To compare studies in this section that used different averaging times, effects esti mates 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 repr esent the range of current mean ambient con centrations across the U.S.



### March 2009 5-120 DRAFT – DO NOT CITE OR QUOTE

## observed in the estimates generated using GAM models with default convergence criteria (Dominici et al., 2002), Dominici et al. (2003b; 2005a) (reanalysis of Samet et al., 2000b) used the data using GAM with stringent convergence criteria as well as GLM. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Focusing on the results obtained using GLM, PM 10 

## and O 3 

## (in summer) appeared to be more strongly associated with mortality than the other gaseous pollutants. The authors stated that the results did not indicate associations of CO, SO 2 

## , or NO 2 

## , with total (non-accidental) mortality. However, as with PM 10 

## , the gaseous pollutants CO, SO 2 

## , and NO 2 

## each showed the strongest association at a 1-day lag (for O 3 

## , a 0-day lag). Figure 5-14 presents the total mortality risk estimates for CO from Dominici et al. (2003b). The authors found a mortality risk estimate of 0.23% (95% PI: 0.09, 0.36) per 0.5 ppm increase in 24-h avg CO for a 1-day lag in a singl e-pollutant model. The inclusion of PM 10 

## or PM 10 

## and O 3 

## in the model did not reduce CO risk estimates. However, th e confidence bands were wider in the multipollutant models, but this could be attributed to: (1) PM 10 

## data in many of the cities being collected every 6th day, as opposed to daily data for g aseous pollutants; and (2) O 3 

## being collected in some cities only during warm months. The addition of NO 2 

## (along with PM 10 

## ) to the model resulted in a reduced CO risk estimate. Some caution is required when interpretin g this apparent reduction because a smaller number of cities (57 cities 1 

## ) were available for the CO multipollutant analysis with PM 10 

## and NO 2 

## compared to the single-pollutant CO analysis (82 cities). However, most of the cities that did not have NO 2 

## data (26 out of 32), and subsequently were not included in the multipo llutant analysis, were some of the least populated cities. Thus, the difference in the number of cities in the multi- and single-pollutant analyses is unlikely to be the underlying cause for the reduction in the CO risk estimate in the CO multipollutant analysis with PM 10 

## and NO 2 

## . In comparison to the PM 10 

## risk estimates, which were not reduced in multipollutant models, the CO risk estimates from multipollutant models indicate less consistent associations with mortality.

1 One city was excluded from the multipo llutant analysis because it contained NO 2 data, but did not contain CO data.



### March 2009 5-121 DRAFT – DO NOT CITE OR QUOTESource: Dominici et al. (2003b) 

# Figure 5-14. Posterior means and 95% posterior intervals of national average estimates for CO effects on total (non-accidental) mo rtality at lags 0, 1, and 2 within sets of the 90 U.S. cities with available pollutant data. Models A = CO alone; B = CO + PM 10 

# ; C = CO + PM 10 

# + O 3 

# ; D = CO + PM 10 

# + NO 2 

# ; E = CO + PM 10 

# + SO 2 

# . 

# Canadian Multi-City Studies

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Since the 2000 CO AQCD two Canadian multi-city studies have been published that examined the association between mortality and short-term expos ure to air pollutants: (1) an analysis of PM 10 

## , PM 2.5 

## , PM 10-2.5 

## , and gaseous pollutants in 8 cities from 1986 to 1996 (Burnett et al., 2000); and (2) an analysis of PM 10 

## , PM 2.5 

## , PM 10-2.5 

## , and gaseous pollutants in 12 cities from 1981 to 1999 (Burnett et al., 2004). The 2000 study utilized GAM with default convergence cr iteria, and upon reanalysis only examined PM indices (Burnett and Goldberg, 2003). Burnett et al. (2004) is the most extensive Cana dian multi-city study conducted to date, both in terms of the length of the study and the number of cities covered. Although the study focused on NO 2 

## because it was the best predictor of short-term mo rtality fluctuations among the pollutants examined (NO 2 

## , O 3 

## , SO 2 

## , CO, PM 2.5 

## , and PM 10-2.5 

## ), it did present single- and copollu tant results for all pollutants included in the analysis. The mean CO concentrations reported by Burnett et al. (2004) are similar to those reported in NMMAPS (see Table 5-14). Burnett et al. (2004) examined the effect of s hort-term exposure to CO on total (non-accidental) mortality. The authors found the strongest mortality association at lag 1-day for CO, SO 2 

## , PM 2.5 

## , PM 10-2.5 

## , PM 10 

## (arithmetic addition of PM 2.5 

## and PM 10-2.5 

## ), and CoH, whereas for NO 2 

## , it was the 3-day moving average (i.e., average of 0-, 1-, and 2-day lags), and for O 3 

## , it was the 2-day moving average. In this study, Burnett et al. (2004) used 24-h avg pollutant con centrations because these va lues showed stronger associations than the daily 1-h max values for all of the gaseous pollutants and CoH, but not for O 3 

## . In a single-pollutant model the CO risk estimate for to tal mortality was 0.33% (95% CI: 0.12-0.54) per

March 2009 5-122 DRAFT – DO NOT CITE OR QUOTE

## 0.5 ppm increase in 24-h avg CO with a 1-day lag. After adjusting for NO 2 

## , the CO risk estimate was reduced to 0.04% (95% CI: -0 .19 to 0.26), while the NO 2 

## risk estimate was only slightly affected (increased from 2.25% to 2.35%) when including CO in the model. In this analysis, a copollutant model including both CO and PM was not presented. The similarity between the results presented in this Canadian multi-city and NMMAPS is th at, in both analyses, CO risk estim ates appeared to be sensitive to the addition of NO 2 

## in the regression model. However, interpretation of these results requires some caution because: (1) NO 2 

## tends to have a more spatially uniform distribution within a city compared to CO; (2) CO and NO 2 

## share common sources (e.g., traffic); and (3) CO and NO 2 

## are often moderately to highly correlated. 

# 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 

# Air Pollution and Health: A European Approach

Most of the Air Pollution and Health: A Europ ean Approach (APHEA) analyses have focused on the mortality effects of PM (PM 10 

## and BS), SO 2 

## , NO 2 

## , and O 3 

## , but not CO. In addition, some of the analyses have not even considered CO as a pot ential confounder, such as the extended analysis (APHEA2) of PM (Katsouyanni et al., 2001), and NO 2. 

## One study, Gryparis et al. (Gryparis et al., 2004) did consider CO as a potential confounder in an analysis of O 3 

## , and found that the addition of CO increased O 3 

## mortality risk estimates both in the summer and winter although the number of cities included in the copollutant model were reduced from 21 to 19. However, the study did not present CO risk estimates. Unlike other APHEA studies (or the NMMAPS and Canadian multi-city studies), the Samoli et al. (2007) analysis focused specifically on CO. Samoli et al. (2007) investigated the effect of short-term exposure to CO on total (non-accidental) and cardiovascular mortality in 19 European cities participating in the APHEA2 project by using a twostage analysis to examine city-sp ecific effects and potential sources of heterogeneity in CO-mortality risk estimates. The mean levels of the max 8-h avg CO concentration in this study ranged from 0.52 ppm (Basel, Switzerland, and the Netherlands) to 5. 3 ppm (Athens, Greece). The max 8-h avg CO concentration for the APHEA2 study of 2.12 ppm is higher than the estimated max 8-h avg CO concentrations reported in U.S. (Dominici et al., 2003 b, 2005b) and Canadian (Burnett et al., 2004) cities of 1.53 ppm. 1 

## In APHEA cities, the correlation between CO and BS (r = 0.67-0.82) was higher than the correlation between CO and PM 10 

## (r = 0.16-0.70) or the correlation between CO and 1-h max NO 2 

## (r = 0.03-0.68). To examine the CO-mortality relationship, Samoli et al. (2007) conducted a time-series analysis of individual cities following the revised APHEA2 protocol. 2 

## The primary results presented by the authors are from a sensitivity analysis that used two alterna tive methods to select the extent of adjustment for

1 The max 8-h avg concentration for the Dominici et al. (2003b) and Burnett et al. (2004) studies was calculated using the conve rsion 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 penali zed splines as implemented in the statistical package R.



### March 2009 5-123 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 temporal confounding. These methods consisted of: (1) confining the extent of smoothing to 8 degrees of freedom per year (df/yr); and (2) selecting the appropriate extent of smoothing through minimization of the absolute value of the sum of partial auto-correla tion functions (PACF) of the residuals, which resulted in the analysis using on average 5 df/yr for total mortality and 4 df/yr for cardiovascular mortality. The authors also conducted copollutant analyses using PM 10 

## , BS, SO 2 

## , NO 2 

## , or O 3 

## (1 h). In the second stage model Samoli et al. (2007) examined heterogeneity in CO risk estimates between cities by regressing risk estimates from individual cities on potential effect modifi ers including: a) the air pollution level and mix in each city (i.e., mean leve ls of pollutants, ratio PM 10 

## /NO 2 

## ); b) the exposure (number of CO monitors, correlation between monitors’ measurements); c) variab les describing the health status of the population (e.g., crude mortality rate); d) the geographic area (northern, western, and central-eastern European cities); and e) the climatic conditions (mean temperature a nd relative humidity levels). Samoli et al. (2007) found that CO was associated with total and cardiovascular mortality. The primary results represent the combined random effect s estimate for a 0.75 ppm increase in max 8-h avg CO concentrations for the average of 0- and 1-day la g for total mortality (1.03% [95% CI: 0.55-1.53]) and for cardiovascular mortality (1.08% [95% CI: 0.25-1.90]). These results were obtained using PACF to choose the extent of adjustment for temporal tr ends. Although the results obtained using PACF are insightful, the use of 8 df/yr would have been more consistent with the NMMA PS 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/y r model are: 0.57% (95% CI: 0.23-0.91) for total mortality and 0.70% (95% CI: 0.31-1.09) for cardiovascu lar mortality. In the sensitivity analysis, Samoli et al. (2007) used 8 or 12 df/yr to adjust for temporal confounding. Both approaches led to similar risk estimates, but using PACF to choose the extent of smoothing generally resulted in larger CO risk estimates (by ~50 to 80%). During the examination of results obtained from th e copollutant models, the authors noted that there was indication of confounding of CO risk estimates by BS and NO 2, 

## but not PM 10 

## . These results are consistent with CO, BS, and NO 2 

## being part of the traffic pollution mixture and PM 10 

## likely including secondary aerosols that do not correlate well with traf fic-derived pollution. The risk estimates from the model using 8 df/yr that included NO 2 

## were: 0.26% (-0.09 to 0.61) for to tal mortality and 0.37% (-0.05 to 0.80) for cardiovascular mortality. Thus, the inclusion of NO 2 

## in the model nearly halved the CO risk estimates (whereas the NO 2 

## risk estimate was not sensitive to the inclusion of CO in the model). A similar magnitude of reduction in the CO risk estimates w as also observed when including BS in the model. Overall, the sensitivity of CO risk estimates to the inclusion of NO 2 

## in the model is consistent with the results presented in NMMAPS and the Canadian multi-city studies. In the second stage model, Samoli et al. (2007) found that geographic region was the most significant effect modifier, while the other effect modifiers (mentioned above) did not result in strong associations. Effects were primarily found in western and southern European cities, and were larger in

March 2009 5-124 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

22 23 24 25 26

27 28 29 30 31 32 cities where the standardized mort ality rate was lower. Earlier APHEA studies also reported a regional pattern of air pollution associations for BS and SO 2 

## , and found that western cities showed stronger associations than eastern cities. However, the heterogeneity in CO risk estimates by geographic region does not provide specific information to evaluate the CO-mortality association. An ancillary analysis conducted by Samoli et al . (2007) examined the possible presence of a CO threshold. The authors compared city-specific mode ls to the threshold model, which consisted of thresholds at 0.5 mg/m 3 

## (0.43 ppm) increments. Samoli et al. ( 2007) then computed the deviance between the two models and summed the deviances for a gi ven 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 ev idence (p-value >0.9) for a threshold. However, determining the presence of a threshold at the very low range of CO concentrations (i.e., at 0.43 ppm) in this data set is challenging, because, in seven of th e 19 European cities examin ed, the lowest 10% of the CO distribution was at or above 2 mg/m 3 

## (1.74 ppm). Thus, the interpretation of the suggestive indication of a threshold is limited. In summary, the APHEA2 analysis of CO in 19 cities found an association between CO and total and cardiovascular mortality in single-pollutant mode ls, but the associations were substantially reduced when NO 2 

## or BS was included in copollutant models. Th e evidence for potential confounding of CO risk estimates by NO 2 

## is consistent with the findings from th e NMMAPS and Canadian 12 cities studies. In addition, Samoli et al. (2007) found that geographi c region was a potential effect modifier, but such geographic heterogeneity is not specific to CO, b ased on previously conducted APHEA studies. Finally, examination of the CO concentration-response relati onship found weak evidence of a CO threshold. 

# Other European Multi-City Studies

An additional European multi-city study was conducted by Biggeri et al. (2005) in eight Italian cities. The authors examined the effect of short-te rm exposure to CO on mortality in single-pollutant models using a time-series approach. In this an alysis, because all of the pollutants showed positive associations with the mortality endpoints examined and the correlations among the pollutants were not presented, it is unclear if the observed as sociations are shared or confounded. 

# Summary of Multi-City Studies

In summary, the mortality risk estimates from si ngle-pollutant models are comparable for the NMMAPS and Canadian 12-city studies, 0.23 and 0.33 , respectively, with the estimate from the APHEA2 study being slightly larger (0.57%) (Figure 5-15). In both the NMMAPS and Canadian studies, a 1-day lag showed the strongest association; but the APHE A2 study used an a priori exposure window, the average of 0- and 1-day lags, which has been found to be the exposure window most strongly associated with mortality in PM analyses.

March 2009 5-125 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 The APHEA2 risk estimates presented in Figure 5-15 are from a model that used a fixed amount of smoothing to adjust for temporal confounding (8 df/y r), which is similar to that used in the NMMAPS study (7 df/yr). However, the APHEA2 sensitivity analysis suggested an approximate 50 to 80% difference in CO risk estimates between the models that used 8 or 12 df/yr, and the models that used minimization of the absolute value of the sum of PACF of the residuals as a criterion to choose the smoothing parameters. Thus, some model uncertainty li kely 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 NO 2 

## in a copollutant model (0.11, 0.03, and 0.26%, for the NMMAPS, Canadian 12-cities study, and APHEA2, respectively). Thus, these results suggest confounding by NO 2 

## . However, this interpretation is further complicated because as with CO, NO 2 

## itself may be an indicator of combustion sources, such as traffic. Because CO measurements tend to reflect more local impacts, due to the location of monitors, than NO 2 

## (which is a secondary pollutant and theref ore more spatially uniform) it is also possible that CO, the less precisely measured pollutant in terms of spatial distribution, may “lose” in the multipollutant model. Thus, it may not be accurate to interpret these results as evidence of “confounding by NO 2 

## .”

March 2009 5-126 DRAFT – DO NOT CITE OR QUOTEDominici et al. (2003a; 2003b) (re-analysis by 2000a) NMMAPS, lag 1

CO only (82 cities)

+ PM 10 (82 cities)

+ PM 10 and NO 2 (57 cities)

Burnett et al. (2004) 12 Canadian cities, lag 1

CO only

+ NO 2 a

Samoli et al. (2007) APHEA2 (19 cities), lag 0-1

CO only

+ PM 10

+ BS

+ NO 2

a NO 2 is the average of 0, 1, and 2 day lags.



### % Increase



# Figure 5-15. Summary of mortality risk estimates for short-term exposure to CO from multi-city studies. Estimates were standardized to 0.5 ppm and 0.75 ppm for studies that used 24-h avg CO and max 8-h avg CO exp osure metrics, respectively.



# 5.6.1.3. Meta-Analysis of All Criteria Pollutants

Stieb et al. (2002) reviewed the time-series mo rtality studies published between 1985 and 2000, and conducted a meta-analysis to estimate combined effects for PM 10 

## , CO, NO 2 

## , O 3 

## , and SO 2 

## . Because 

# 2 many of the studies reviewed in the 2000 analysis used GAM with default convergence criteria, 

# 3 Stieb et al. (2003) updated the estimates from the me ta-analysis by separating the GAM versus non-GAM 

# 4 studies. In this meta-analysis the authors also presented separate combined estimates for single- and 

# 5 multipollutant models. Overall, there were more GAM estimates than non-GAM estimates for all of the 

# 6 1

March 2009 5-127 DRAFT – DO NOT CITE OR QUOTE

## pollutants except SO 2 

## . For CO, 4 single-pollutant model risk estimates were identified, resulting in a 

# 1 combined estimate of 3.18% (95% CI: 0.76-5.66) per 0.5 ppm increase in 24-h avg CO, and only 

# 2 1 multipollutant model risk estimate (0.00% [95% CI: -1.71 to 1.74]) from the non-GAM studies. Thus, 

# 3 for CO, this study did not provide useful meta-estim ates because the number of studies that contributed to 

# 4 the combined estimates for CO was rather small. 

# 5



# 5.6.1.4. Single-City Studies

In addit ion to the multi-city studies discussed above, there have also been several single-city U.S.- and Canadian-based time-series mortality studies that ex amined CO. The single-city studies, similar to the 

# 7 multi-city studies, often focused on the PM-mortality association, but also provided additional 

# 8 information that is not available in the multi-city studi es. Because the sample size used in each single-city 

# 9 study is small, and subsequently results in wide confidence bands, a quantitative comparison of the results 

# 10 from single- and multi-city studies is difficult. In addition, some studies do not present CO results 

# 11 quantitatively adding to the inability to adequately comp are studies. Table 5-17 lists the single-city studies 

# 12 evaluated along with the mean CO conc entrations reported in each study. 

# 13

6

Table 5-17. Range of CO concentrations reported in single-city studies that examine mortality effects associated with s hort-term exposure to CO.

Location Years Averaging Time Mean Concentration (ppm) Upper Percentile Concentrations (ppm) Study

De Leon et al. (2003) New York, NY 1985-1994 24-h avg 2.45 95th: 4.04 Klemm et al. (2004) Atlanta, GA 1998-2000 1-h max 1.31 Max: 7.40 75th: 1.66 Vedal et al. (2003) Vancouver, BC , Canada 1994-1996 24-h avg 0.6 Max: 1.9 90th: 0.9 Villeneuve et al. (2003) Vancouver, BC , Canada 1986-1999 24-h avg 1.0 Max: 4.9 90th: 1.6 Goldberg et al. (2003) M ontreal, Quebec, Canada 1984-1993 24-h avg 0.8 Max: 5.1 75th: 1.0 Hoek et al. (2000; 2001; Hoek, 2003) The Netherlands 1986-1994 24-h avg Entire Country: 0.46 Four Major Cities: 0.59 Max. Entire Country: 2.6 Four Major Cities: 4.6

1 Study reported median CO concentrations.



# Single-City Studies Conducte d in the United States

14 15 De Leon et al. (2003) focused on the role of contributing respiratory diseases on the association between air pollution (i.e., PM 10 

## , O 3 

## , NO 2 

## , SO 2 

## , and CO) and primary non-respiratory mortality

March 2009 5-128 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 (circulatory and cancer) in New York City, NY dur ing the period 1985-1994. This study only presented risk estimates graphically for each of the pollutants analyzed, except PM 10 

## . In single-pollutant models, PM 10, 

## CO, SO 2 

## , and NO 2 

## all showed the same pattern of association with circulatory mortality for individuals ≥ 75, indicating a larger risk of death in in dividuals with contributing respiratory diseases compared to those without. In two-pollutant models, PM 10 

## and CO risk estimates were reduced, but each remained significantly positive. Klemm et al. (2004) analyzed 15 air pollutants for their associations with mortality in Atlanta, GA, for a two-year period starting in August 1998. These pollutants included PM 2.5 

## , PM 10-2.5 

## , UFP surface area and counts, aerosol acidity, EC, OC, SO 4 2- 

## , O 3 

## , CO, SO 2 

## , and NO 2 

## . 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 sens itive to the extent of smoothing. It should be noted that temporal smoothing using biweekly knots is a more aggressive modeling approach than the degrees of freedom approach used by most studies. In the single-pollutant models for all-cause mortality, the strongest association, which was also statistically significant, was found for PM 2.5 

## . CO, SO 4 2- 

## , and PM 10-2.5 

## also showed positive associations with all-cause morta lity, but they were not significant (CO: Quarterly knots and Monthly Knots β = 0.00002 [SE = 0.00001]; Biweekly knots β = 0.00001 [SE = 0.00002]). However, CO was significantly associated with circulatory mortality in older adults ( ≥ 65), and these associations remained when PM 2.5 

## was included in the model (results were presented graphically). 

# Single-City Studies Conducted in Canada

Vedal et al. (2003) examined the association between short-term exposure to “low levels” of air pollution (i.e., PM 10 

## , O 3 

## , NO 2 

## , SO 2 

## , and CO) and daily morality in Va ncouver, British Columbia, Canada for the years 1994-1996. In this analysis, all of the risk estimates were presented graphically; however, the results suggested that O 3 

## in the summer and NO 2 

## in the winter showed the strongest associations with mortality. Vedal et al. (2003) found that CO was pos itively, but not significantly associated with mortality. Additionally, the association between short-term exposure to NO 2 

## and mortality was found to be consistent with the results from the Canadian multi- city study conducted by Burnett et al. (2004). Villeneuve et al. (2003) also conducted an analys is using data from Vancouver, Canada, using a cohort of 550,000 individuals whose vital status wa s ascertained between 1986 and 1999. In this study, PM 2.5 

## , PM 10–2.5 

## , TSP, CoH, PM 10 

## , SO 4 2- 

## , O 3 

## , CO, SO 2 

## , and NO 2 

## were examined for their associations with all-cause, cardiovascular, and respiratory mortality . When examining the association between gaseous pollutants and all-cause mortality in this data set, NO 2 

## and SO 2 

## showed the strongest associations, while the association between CO and all-cause mortality were generally weaker than those for NO 2 

## and SO 2 

## . For cardiovascular mortality, SO 2 

## risk estimates were smaller than those for NO 2 

## or CO, while for respiratory mortality, SO 2 

## showed the strongest associations. However, the wider confidence bands of

March 2009 5-129 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10 11 12

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 these categories and the smaller daily counts make it difficult to assess CO associations with causespecific mortality outcomes. Goldberg et al. (2003) contrasted associations between air pollution and mortality in individuals with underlying CHF vs. mortality in individuals w ho were identified as having CHF one year prior to death based on information from the universal health insurance plan in Montreal, Quebec, Canada, during the period 1984-1993. In this study, Goldberg et al. (2003) examined associations between PM 2.5 

## , CoH, SO 4 2- 

## , O 3 

## , CO, SO 2 

## , and NO 2 

## , and mortality. The authors found no association between any of the air pollutants and mortality with underlying CHF. However, Goldberg et al. (2003) found positive associations between air pollution and mortality in in dividuals diagnosed with CHF one year prior to death. Of the air pollutants examined, CoH, NO 2 

## , and SO 2 

## were most consistently associated with mortality for ages 65 and older, while CO showed positive but weaker associations compared to these three pollutants. 

# Single-City Studies Conduc ted in Other Countries

Of the epidemiologic studies conducted in othe r countries that examine the association between short-term exposure to CO and mortality only tho se studies conducted in European countries that have CO levels comparable to the U.S. were evaluated. However, because Samoli et al. (2007) conducted a multi-city study of European cities that focused on short-term exposure to CO, there are only a few single-city studies that provide additional information, specifically those studies conducted in the Netherlands. The Netherland studies were evaluated because they provide risk estimates for multiple pollutants and cause-specific mortality, and consisted of relatively large sample sizes (i.e., the mortality time-series of the entire country was analyzed). Hoek et al. (2000) re-analyzed by (Hoek, 2003) examined associations between air pollution and all-cause, cardiovascular, COPD, and pneumonia deaths in the entire Netherlands, the four major cities combined, and the entire country minus the four ma jor cities for the period 1986 to 1994. The air pollutants analyzed included BS, PM 10 

## , O 3 

## , NO 2 

## , SO 2 

## , CO, SO 4 2- 

## and NO 3 - 

## . In the single-pollutant models, all of the pollutants were significantly associ ated with all-cause mortality at lag 1-day and 0- 6 days when using the entire Netherlands data set. In the two-pollutant model, CO risk estimates were reduced to null when PM 10 

## , BS, SO 4 2- 

## and NO 3 - 

## were included in the model while the risk estimates for these copollutants remained significantly positive. BS, CO, and NO 2 

## were highly correlated (r >0.85) in this data set, and the authors noted “all these polluta nts should be interpreted as indicators for motorized traffic emissions” (Hoek et al., 2000). The authors found that O 3 

## showed the most consistent and independent associations with mortality and that the risk estimates for all of the pollutants were substantially higher in the summer months than in the winter months. Pneumonia deaths showed the largest risk estimates for most pollutants including CO . The result from the Hoek et al. (2000) study is somewhat in contrast to the result from the Samoli et al . (2007) multi-city study in that, in the Hoek et al.

March 2009 5-130 DRAFT – DO NOT CITE OR QUOTE

# 1 2 3 4 5 6 7 8 9 10

11 12 13 14 15 16 17 18

19

25 26 27 28 29 30 31 (2000) analysis, CO was more sensitive to the addition of PM indices in copollutant models. This may be due to the high correlation between CO and PM indices in the Netherlands. Hoek et al. (2001) reanalysis by (Hoek, 2003) anal yzed the Netherlands data using more specific cardiovascular causes of death: MI and other IHD, ar rhythmia, heart failure, cerebrovascular mortality, and embolism/thrombosis. In this analysis, the authors analyzed O 3 

## , BS, PM 10 

## , CO, SO 2 

## , and NO 2 

## 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 cardiov ascular mortality outcome. While the results suggested larger impacts of air pollution on more specific cardiovascular causes, the authors did not provide evidence of an association that was specific to any particular pollutant which would aid in addressing the question of confounding. 

# Summary of Single-City Studies

Overall, it is difficult to identify a clear pattern of CO-mortality associations from the single-city studies evaluated because of the relatively small sample sizes, expected variation in the air pollution mix across cities, and differences in the analytical appr oaches used across studies. However, in all of these studies, CO showed, at least to some extent, pos itive associations with all-cause or circulatory/ cardiovascular deaths. In addition the one study that examined specific cardiovascular causes of death (Hoek et al., 2001)reanalysis by (Hoek, 2003) did not find an association that is specific to CO. Although the extent of sensitivity of CO-mortality associations varied across studies, it could be attributed to the likely variation in the correlation between CO and copollutants.



# 5.6.1.5. Summary of Mortality and S hort-Term Exposure to CO

Among th e gaseous pollutants examined in time-series mortality studies, CO is the least frequently studied criteria air pollutant. Because CO was mostly treated as a potential confounder in these studies, 

# 20 the information available regarding the nature of th e association between short-term exposure to CO and 

# 21 mortality is limited compared to the other pollutants. However, the recently available multi-city studies, 

# 22 which consist of larger sample sizes, and single-city studies generally confirmed the findings reported in 

# 23 the 2000 CO AQCD. 

# 24 The multi-city studies which were evaluated, reporte d comparable CO mortality risk estimates for total (non-accidental) mortality with the APHEA2 European multi-city study (Samoli et al., 2007) showing slightly higher estimates for cardiovascular mort ality in single-pollutant models. However, when examining potential confounding by copollutants these st udies consistently showed that CO mortality risk estimates were reduced when NO 2 

## was included in the model, but this observation may not be “confounding” in the usual sense in that NO 2 

## may also be an indicator of other pollutants or pollution sources (i.e., traffic).

March 2009 5-131 DRAFT – DO NOT CITE OR QUOTE

## Only one of the multi-city studies focused specifically on the CO-mortality association (Samoli et al., 2007), the APHEA2 study), and in the process ex amined: (1) model sensitivity; (2) the CO-mortality C-R relationship; and (3) potential effect modifier s of CO mortality risk estimates. The APHEA study performed a sensitivity analysis, which indicated an approximate 50 - 80% difference in CO risk estimates from a reasonable range of alternative models . In addition, the study examined the CO-mortality concentration-response relationship through a grid sear ch of varying threshold points, and found only weak evidence of a CO threshold at 0.5 mg/m 3 

## (0.43 ppm), but this result was complicated by the lowest 10% of the CO distribution for seven of the 19 cities examined being at or above 2 mg/m 3 

## (1.74 ppm). The examination of a variety of city-specific variab les to indentify potential effect modifiers of the CO-mortality relationship found that geographic region explained most of the heterogeneity in CO mortality risk estimates with the CO-mortality associ ations being stronger in western and southern European cities than eastern cities (Samoli et al., 2 007). A similar pattern has been reported for black smoke (BS) and SO 2 

## in previous APHEA studies, but the geographic variability observed does not provide specific information, which could be u sed to evaluate the CO-mortality association. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

28 The results from the single-city studies are genera lly consistent with the multi-city studies in that some evidence of a positive association was found for mortality upon short-term exposure to CO. However, the CO-mortality associations were often, but not always, attenuated when other copollutants were included in the regression models. In additio n, limited evidence was av ailable 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-c ity studies suggests that an association between short-term exposure to CO and mo rtality exists, but limited evidence is available to evaluate causespecific mortality outcomes associated with CO exposure; and it is unclear if CO is acting alone or as an indicator for other combustion-related pollutants. In addition, the results underscore the limitation of current analytical methods to disentangle the health ef fects associated with one pollutant in the complex air pollution mixture. Overall, the epidemiologic evidence 

# is suggestive of a causal relationship between short-term exposure to relevant CO concentrations and mortality.



# 5.6.2. Epidemiologic Studies with Long-Term Exposure to CO

The 20 00 CO AQCD did not evaluate the associa tion between long-term exposure to CO and mortality because there were no studies at the time that examined this relationship. Since then there have 

# 29 been several new studies that examin ed the association between long-term exposure to CO and mortality, 

# 30 but it should be noted that these studies primarily fo cused on PM, and CO was only considered in these 

# 31

March 2009 5-132 DRAFT – DO NOT CITE OR QUOTE

## studies as a potential confounder. Therefore, the info rmation available from these new long-term exposure 

# 1 studies is somewhat limited, especially in comparison to that for PM. 

# 2



# 5.6.2.1. U.S. Cohort Studies

American Cancer Society Cohort Studies

Pope et al. (1995) investigated associations be tween long-term exposure to PM and mortality outcomes in the ACS cohort. In this study, ambien t 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. PM 2.5 

## and SO 4 2- 

## were associated with total (non-accidental), cardiopulmonary, and lung cancer mortality, but not with mortality for all other causes (i.e., non-accidental minus cardi opulmonary and lung cancer). Gaseous pollutants were not analyzed in Pope et al. (1995). Jerrett et al. (2003) (using data from (Krewski et al., 2000) conducted an extensive sensitivity analysis of th e Pope et al. (1995). ACS data, augmented with additional gaseous pollutants data. Due to the smalle r number of CO monitors available compared to SO 4 2- 

## , the number of metropolitan statistical areas (MSAs) included in the CO analysis were reduced (from 151 with SO 4 2- 

## ) to 107. The mean annual CO concentra tions in these MSAs ranged from 0.19 to 3.95 ppm. CO was weakly negatively correlated with SO 4 2- 

## (r = -0.07). Among the gaseous pollutants examined (CO, NO 2 

## , O 3 

## , SO 2 

## ), only SO 2 

## showed positive associations with mortality, and in addition was the only copollutant that reduced SO 4 2- 

## risk estimates. For CO, the rela tive risk estimates for total (nonaccidental) mortality in single and copollutant models with SO 4 2- 

## was 0.99 (95% CI: 0.96-1.01) and 0.98 (95% CI: 0.96-1.01), respectively, per 0.5 ppm in crease in mean annual average CO concentrations. 

# 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Pope et al. (2002) conducted an extended analysis of the ACS cohort with double the follow-up time (to 1998) and triple the number of deaths compared to the original Pope et al. (1995) study. In addition to PM 2.5 

## , data for all of the gaseous pollutants were retrieved for the extended period and analyzed for their associations with mortality-specifi c outcomes. As in the 1995 analysis, the air pollution exposure estimates were based on the MSA-level averages. The authors found that PM 2.5 

## and SO 4 2- 

## were both associated with total, cardiopul monary, and lung cancer mortality. 1 

## In this study, the CO analysis used two different data sets. The first data set consisted of 1980 data a nd 113 MSAs; while the second data set used averages of the years 1982-1998 and 122 MSAs. The authors found, when using the 1980 data, that CO was not associated (risk estimates ~ 1) (See Figure 5-16) with all-cause, cardiopulmonary, lung cancer, or mortality for all other causes. However, the analysis of the 1982-1998 data found that CO was negatively (and significantly) associated with all- cause, cardio-pulmonary, and lung cancer mortality. It is unclear why significant negative associations we re observed when analyzing the 1982-1998 data, but

1 These results were presented graphically in Pope et al. (2002) and were estimated for Figure 5-16.



### March 2009 5-133 DRAFT – DO NOT CITE OR QUOTE

## evidence from other mortality studies that examined the association between long-term exposure to CO and mortality does not suggest that CO elicits a protective effect. 

# 1 2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

26 27 28 29 30 31 32 

# Women’s Health Initiative Cohort Study

Miller et al. (2007) studied 65,893 postmenopau sal women between the ages of 50 and 79 years without previous CVD in 36 U.S. metropolitan areas from 1994 to 1998. They examined the association between one or more fatal or nonfatal cardiovascular ev ents and air pollutant concentrations. Exposures to air pollution were estimated by assigning the annual mean levels of air pollutants measured at the nearest monitor to the location of residence of each subject on the basis of its five-digit ZIP code centroid, which allowed estimation of effects due to both within-c ity and between-city variation of air pollution. The investigators excluded monitors whose measurement objective focused on a single point source or those with “small measurement scale (0 to 100 meters).” T hus, presumably these criteria reduced some of the exposure measurement error associated with monito rs that are highly impacted by local sources. During the course of the study, a total of 1,816 women had one or more fatal or nonfatal cardiovascular events, including 261 cardiovascula r-related deaths. Hazard ratios for the initial cardiovascular event were estimated. The following results are for mode ls that only included subjects with non-missing exposure data for a ll pollutants (n = 28,402 subjects, resulting in 879 CVD events). In the single-pollutant models, PM 2.5 

## showed the strongest associations with CVD events among all pollutants (HR = 1.24 [95% CI: 1.04-1.48] per 10-μg/m 3 

## increase in annual average), followed by SO 2 

## (HR = 1.07 [95% CI: 0.95-1.20] per 5-ppb increase in the annual average). For CO the single-pollutant risk estimate was slightly (but not significantly) negative (HR = 0.96 [95%CI: 0.84-1.10]). In the multipollutant model, which included all pollutants (i.e., PM 2.5 

## , PM 10-2.5 

## , SO 2 

## , NO 2 

## , and O 3 

## ), the CO risk estimate was similar to the one presented in the singl e-pollutant model (HR = 0. 96 [95% CI: 0.82-1.14]). In addition, CO was not associated with CVD even ts in a single pollutant model (HR = 1.00 [95%CI: 0.90-1.10] per 0.5 ppm increase in mean annual averag e CO concentration) that used all available observations. This study did not examine the correlati ons among pollutants and, therefore, the extent of confounding could not be examined, but PM 2.5 

## was clearly the best predicto r of cardiovascular events. 

# The Washington University-EPRI Ve terans’ Cohort Mortality Studies

Lipfert et al. (2000a) conducted an analysis of a national cohort of ~70,000 male U.S. military veterans who were diagnosed as hypertensive in th e mid 1970s and were followed for approximately 21 years (up to 1996). Demographically, 35% of the coho rt consisted of African American men and 57% of the cohort was defined as current smokers; however, 81% of the cohort had been smokers at one time in their life. The study examined mortality effects in response to long-term exposure to multiple pollutants including, PM 2.5 

## , PM 10 

## , PM 10-2.5 

## , TSP, SO 4 2- 

## , CO, O 3 

## , NO 2 

## , SO 2 

## , and Pb. Lipfert et al. (2000a) estimated exposures by indentifying the county of residence at the time of entry to the study. Four exposure periods

March 2009 5-134 DRAFT – DO NOT CITE OR QUOTE

## (1960-1974, 1975-1981, 1982-1 988, and 1989-1996) were defined, and deaths during each of the three most recent exposure periods were considered. Th e 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 specif ication, exposure periods, and the inclusion of ecological and individual variables. Lipfert et al. (200 0a) reported that indications of concurrent mortality risks (i.e., associations between mortality and ai r quality for the same period) were found for NO 2 

## and peak O 3 

## . The estimated CO mortality risks were all negative, but not significant. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Lipfert et al. (2006b) examined associations between traffic density and mortality in the same Veterans’ Cohort, but in this analysis the follow-up period was extended to 2001. As in their 2000 study, four exposure periods were consid ered but more recent years were included in the 2006 analysis. The authors used the mean annual average of the 95th pe rcentile 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, NO 2 

## , O 3 

## . PM 2.5 

## , SO 4 2- 

## , non-SO 4 2- 

## PM 2.5 

## , and PM 10-2.5 

## ). In the multipollutant models, mortality risk estimates were not statistically signifi cant for any of the other pollutants, except O 3 

## . The natural log of the traffic density variable (VKTA = vehicle-km traveled per year) was not correlated with CO (r = -0.06), but moderately correlated with PM 2.5 

## (r = 0.50) in this data set. For the 1989-1996 data period, the estimated mortality relative risk was 1.02 (95% CI: 0.98-1.06) per 1 ppm increase in the mean annual 95th percentile of hourly CO concentration in a single -pollutant model. The two-pollutant model, which included the traffic density variable, resulted in a relative risk of 1.00 (95% CI: 0.96-1.04). Lipfert et al. (2006b) note that the low risk estimates for CO in this study were consistent with those observed in other long-term exposure studies, and may have been due to the localized nature of CO, which can lead to exposure errors when data from centralized monitors is used to represent an entire county. Interestingly, as Lipfert et al. (2006b) pointed out, the risk estimat es due to traffic density did not vary appreciably across these four periods even though regulated tailp ipe 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 environmen tal effects of vehicular traffic) along with spatial gradients in SES might contribute to the non-negative effects observed. Lipfert et al. (2006a) extended the analysis of the Veterans Cohort data to include the EPA’s Speciation Trends Network (STN) data, wh ich collected chemical components of PM 2.5 

## . The authors analyzed the STN data for the year 2002, and again used county-level averages. In addition, they analyzed PM 2.5 

## and gaseous pollutants data for 1999 through 2001. As in the other Lipfert et al. (2006b) study, traffic density was the most important predictor of mortality, but associations were also observed for EC, vanadium (V), nickel (Ni), and NO 3 - 

## . Ozone, NO 2 

## , and PM 10 

## also showed positive, but weaker associations. The authors found no association between the mean annual 95th percentile of hourly CO

March 2009 5-135 DRAFT – DO NOT CITE OR QUOTE

## values and mortality (RR = 0.995 [95% CI: 0.988-1.001] per 1 ppm increase in CO concentration) in a single-pollutant model. The study did not present multipollutant model results for CO. 

# 1 2

Endpoint Reference Cohort Years of Mortality

Data Y ear(s) of AQ Data

Pope et al. (2002) a ACS 1982-1998 1980

1982-1998 1982-1998 Pope et al. (2002) a ACS

Lipfert et al. (2006b) Veter ans 1976-2001 1976-2001 + ln(VKTA) d

Veterans 1976-2001 1976-2001 Lipfert et al. (2006b) 

### All-cause

Veterans 1997-2001 1999-2001 Lipfert et al. (2006a)

ACS 1982-1998 1982- c Jerrett et al. (2003)

Jerrett et al. (2003) ACS 1982-1998 1982- c + SO 4 2-

Pope et al. (2002) a ACS 1982-1998 1980 

### Cardio-pulmonary Pope et al. (2002) a ACS 1982-1998 1982-1998

Miller et al. (2007) b WHI 1994-1998 2000



### Cardiovascular

WHI 1994-1998 2000 + PM 2.5 , PM 10-2.5 , SO 2 , NO 2 , O 3 Miller et al. (2007) b

a The study does not present CO results quantitatively. This effect estimate and 95% confidence interval were estimated from Figure 5 in Pope et al. (2002). b Effect estimate is only for subjects with non-missing exposure data for all pollutants

c The study did not report the range of years of CO data used; however, it does specify that air quality data was obtained from pollution monitoring stations operating in 1982. d Natural log of vehicle-km traveled per year (VKTA). 

### Effect Estimates



# Figure 5-16. Summary of mortality risk estimates for long-term exposure to CO. Estimates were standardized to 0.5 ppm and 1.0 ppm for studies that used mean annual average CO and the mean annual 95th percentile of hourly CO values exposure metrics, respectively.

March 2009 5-136 DRAFT – DO NOT CITE OR QUOTE

# 5.6.2.2. U.S. Cross-Sectional Analysis

An ecolo gical cross-sectional analysis involves re gressing county- (or city) average health outcome values on county-average explanatory variables such as air pollution and census statistics. Unlike the 

# 2 cohort studies described above, to the extent that individual level confounders are not adjusted for, the 

# 3 cross-sectional study design is considered to be subject to ecologic confounding. However, all of the 

# 4 cohort studies described above are also semi-ecologi c in that the air polluti on exposure variables are 

# 5 ecologic (Kuenzli and Tager, 1997). In this sense, cro ss-sectional studies may be useful in evaluating the 

# 6 correlation among exposure variables. 

# 7 1

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Lipfert and Morris (2002) conducted ecological cross-sectional regressions for U.S. counties (except Alaska) during five periods: 1960-1969, 1 970-1974, 1979-1981, 1989-1991, and 1995-1997. They regressed age-specific (15-44, 45-64, 65-74, 76-84, and 85+) all-cause (excluding AIDS and trauma) mortality on air pollution, demography, climate, SES, lifestyle, and diet. The authors analyzed TSP, PM 10 

## , PM 2.5 

## , SO 4 2- 

## , SO 2 

## , CO, NO 2 

## , and O 3 

## . However, air pollution data was only available for limited periods of time depending on the pollutant: TSP up to 1991; PM 10 

## between 1995 and 1999; and PM 2.5 

## between 1979-1984 and 1999. In response to the varying number of counties with valid air pollution data by pollutant and time, Lipfert and Morris (2002) employed a staged regression approach. In the first stage, a national model was developed for each dependent variable, excluding air pollution variables. In the second stage, regressions were performed with the r esiduals on concurrent and previous periods’ air pollution variables to identify the pollutants of interest . Overall, there were too many results to summarize because of the large number of age groups, lagged exposure time windows, and mortality study periods examined in the study, but similar to the ACS cohort studies, PM 2.5 

## and SO 4 2- 

## were found to be consistently and positively associated with mort ality. Lipfert and Morris (2002) generally found the strongest associations in the earlier time periods, and when mortality and air quality were measured in different periods (e.g., mortality data 1995-1997 an d CO data 1970-1974). Also, consistent with the Lipfert et al. (2000a) and the Pope et al. (2002) co hort studies, CO was frequently negatively (and often significantly) associated with mortality in older ag e groups, especially when mortality was matched with CO levels in more recent time periods. The younger age group (15-44) often showed a positive association with CO, but considering the small number of deaths attributed to this age group (less than 1% of total deaths), the association was not inform ative. Overall, this st udy highlighted that the CO-mortality associations presented in purely ecologic study designs are generally consistent with those found in semi-individual cohort studies.

March 2009 5-137 DRAFT – DO NOT CITE OR QUOTE

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

The eval uation of new epidemiologic studies conducted since the 2000 CO AQCD (2000) that investigated the association between long-term exposur e to CO and mortality consistently found null or 

# 2 negative mortality risk estimates. No such studies were discussed in the 2000 CO AQCD. The re-analysis 

# 3 of the ACS data (Pope et al., 1995) by Jerrett et al. (2003) found no association between long-term 

# 4 exposure to CO and mortality. Similar results were obtained in an updated analysis conducted by Pope 

# 5 et al. (2002) when using earlier (1980) CO data, but negative associations were found when using more 

# 6 recent (1982-1998) data. The Women’s Health Initiativ e (WHI) Study also found no association between 

# 7 CO and CVD events (including mortality) using the data from recent years (1994-1998) (Miller et al., 

# 8 2007), while the series of Veterans Cohort studies foun d no association or a negative association between 

# 9 mean annual 95th percentile of hourly CO values and mortality (Lipfert et al., 2006a; Lipfert et al., 

# 10 2006b). One additional study was identified that used a cross-sectional study design, Lipfert and Morris 

# 11 (2002), which reported results for a study of U.S. coun ties that are generally consistent with the cohort 

# 12 studies: positive associations between long-term exposure to PM 2.5 

## and SO 4 2- 

## and mortality, and generally 

# 13 negative associations with CO. Overall, the consis tent null and negative associations observed across 

# 14 epidemiologic studies which included cohort pop ulations encompassing potentially susceptible 

# 15 subpopulations (i.e., post-menopausal women and hypertensive men) combined with the lack of evidence 

# 16 for respiratory and cardiovascular morbidity outcom es following long-term exposure to CO; and the 

# 17 absence of a proposed mechanism to explain the progr ession to mortality following long-term exposure to 

# 18 CO provide supportive evidence that is 

# suggestive of no causal relationship between long-term 

# 19 exposure to CO and mortality. 

# 20

1

21 

# 5.7. Public Health Impacts

This section addresses several issues relating to the broader public health impact from exposure to ambient CO through a discussion on: (1) the shape of the concentration-response (C-R) relationship for 

# 22 CO, which is based primarily on controlled human exposure studies; and (2) the identification of 

# 23 subpopulations which may experience increased ri sks from CO exposures, through either enhanced 

# 24 susceptibility (e.g., as a result of pre-existing disease, gene tic factors, age) and/or vulnerability associated 

# 25 with external factors or differential exposure (e.g., altitude, close proximity to sources, activity patterns). 

# 26

March 2009 5-138 DRAFT – DO NOT CITE OR QUOTE

# 5.7.1. Concentration-Response Relationship

Curre ntly, very limited information is available in the human clinical and epidemiologic literature regarding the CO C-R relationship and the potential presence of a CO threshold. Two human clinical 

# 2 studies described in the 2000 CO AQCD had evaluate d the C-R relationship between CO and onset of 

# 3 exercise-induced angina among individuals with CAD, but at the high end of CO concentrations (i.e., CO 

# 4 levels above the current NAAQS). Anderson et al. (1973) exposed 10 adult men with stable angina for 4 h 

# 5 to CO concentrations of 50 and 100 ppm, which r esulted in average COHb levels of 2.9% and 4.5%, 

# 6 respectively. Both exposures significantly decreased the time to onset of exercise-induced angina relative 

# 7 to room air control (1.6% COHb). However, th ere was no difference in response between the two 

# 8 exposure concentrations of CO. In a much larger study, 63 adults with stable angina were exposed for 1 h 

# 9 to two concentrations of CO (average exposure concentrations of 1 17 and 253 ppm) resulting in average 

# 10 pre-exercise COHb levels of 2.4% and 4.7% (Allr ed et al., 1989). Relative to control (average COHb 

# 11 0.7%), COHb levels of 2.4% and 4.7% were observed to decrease the tim e to onset of angina by 4.2% 

# 12 (p = 0.054) and 7.1% (p = 0.004), respectively. In addition, these investigators reported a statistically 

# 13 significant decrease in the time to exercise-induced ST-segment depression with increasing COHb levels. 

# 14 These findings provide some evidence of a significant C-R relationship at COHb concentrations between 

# 15 2.4 and 4.7%. However, the human clinical literature h as yet to evaluate the C-R relationship at lower CO 

# 16 concentrations or COHb levels. 

# 17

1

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 One study in the epidemiologic litera ture attempted to examine the C-R relationship at the low end of CO concentrations through a threshold analysis. Samoli et al. (2007) in their examination of the association between short-term exposure to CO and mort ality conducted an ancillary analysis to examine the potential presence of a CO threshold. In this analys is the authors compared city-specific models to the threshold model, which consiste d of thresholds at 0.5 mg/m 3 

## (0.43 ppm) increments. Samoli et al. (2007) then computed the deviance between the two mode ls 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 lin ear no-threshold model indicated weak evidence (p-value >0.9) for a threshold. However, determining the presence of a threshold at the very low range of CO concentrations (i.e., at 0.43 ppm) in this data set is challenging, because, in seven of the 19 European cities examined, the lowest 10% of the CO distribution was at or above 2 mg/m 3 

## (1.74 ppm). By only using the 12 cities in the analysis that had minimum CO concentrations approaching 0.5 mg/m 3 

## (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.

March 2009 5-139 DRAFT – DO NOT CITE OR QUOTE

# 5.7.2. Potentially Susceptible and Vulnerable Populations

Interi ndividual variation in human responses to ai r pollutants indicates that not all individuals exposed to pollutants respond similarly. That is, so me subpopulations are at increased risk to the 

# 2 detrimental effects of pollutant exposure. The NAAQS are intended to provide an adequate margin of 

# 3 safety for both general populations and susceptible and vulnerable subpopulatio ns, or those subgroups 

# 4 potentially at increased risk for ambient air pollution h ealth effects. For the purposes of this document, a 

# 5 susceptible subpopulation is defined as one with an in trinsic biological characteristic (e.g., disease) that 

# 6 might result in an adverse health effect at pollutant concentrations below those needed to elicit the same 

# 7 response in the general population, or result in a mo re adverse effect at the same concentration. A 

# 8 vulnerable subpopulation is one with a non-biological characteristic (e.g., differential exposure, living at 

# 9 altitude) that results in increased incidence of health effects from an ambient pollutant relative to the 

# 10 general population. The previous review of th e CO NAAQS identified certain groups within the 

# 11 population that may be more susceptible to the effects of CO exposure, including individuals (particularly 

# 12 older adults) with CAD and other vascular diseases, anemia patients, patients with obstructive lung 

# 13 disease, and infants. Other subgroups considered to be somewhat vulnerable in the previous review 

# 14 include individuals living at altitude and those usi ng certain medications that can increase endogenous CO 

# 15 production. Tables 5-18 and 5-19 prov ide an overview of the characteristics that contribute to susceptible 

# 16 and vulnerable subpopulations, respectiv ely, which have been observed in the examination of the NAAQS 

# 17 for all criteria pollutants. Those characteristics of susceptible/vulnerable subpopulations exposed 

# 18 specifically to CO, as mentioned in the literature that encompasses this ISA, are discussed below. 

# 19

1

Table 5-18. Characteristics of susceptible subpopulations.

Susceptibility Characteristics 1 Pre-existing disease: Cardiovascular diseas es, Anemia, Obesity, Diabetes, Respiratory diseases (e.g., asthma, obstructive lung disease) Age: Children, Older Adults (65+) Infants: Premature, Low Birth Weight Gender Pregnancy Birth Defects Race/Ethnicity Genetic Factors Nutritional status

1 Susceptible (i.e., intrinsic) refers to biological characteristics of an individual, which can include life stage, genetics, a nd pre-existing disease.



### March 2009 5-140 DRAFT – DO NOT CITE OR QUOTE

# Table 5-19. Characteristics of vulnerable subpopulations.

Vulnerability Characteristics 1

Altitude Level of Exercise Proximity to Roadways Medication Use Geographic Location Work Environment (e.g., outdoor workers) Air Conditioning Use SES Education Level Nutritional status

1 Vulnerable (i.e., extrinsic) refers to non-biological variables associated with an individual that can result in a health effe ct.



# 5.7.2.1. Susceptibility Characteristics

Cardiova scular Disease

Individuals with heart disease may be at a greater risk from CO exposure since they may already have compromised O 2 

## delivery. CO is notable among air pollutants because it is especially harmful in individuals with impaired cardiovascular systems. Persons with a normal cardiovascular system can tolerate substantial concentrations of CO, if they vasodilate in response to the hypoxemia produced by CO. In contrast, individuals unable to vasodilate in response to CO exposure may show evidence of ischemia at low concentrations of COHb. Many of the controlled human exposure studies have focused on individuals with IHD. 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 An estimated 81 million American adults (1 in 3) have one or more type of cardiovascular disease (CVD), with an estimated 47% of these being 60 or mo re years of age. CVD is the leading cause of death in the U.S. with nearly 2,400 deaths each day-an average of one death every 37 seconds (Rosamond et al., 2008). For the major diseases within the category of total CVD, about 73 million Americans have high BP, 16 million have CHD, 5 million have heart failure, 5 million have stroke, and the estimated prevalence of congenital cardiovascular defects is estimated to be between 650,000 to 1.3 million (Rosamond et al., 2008). In the U.S., IHD is the largest major killer, causing 1 in 5 deaths. Because the numbers of affected people are so high, even relativ ely small percent increases in cardiovascular mortality or morbidity in the population could have a large impact on public health. Each year in the U.S. approximately 780,000 people experience a new or recurrent stroke, with the majority of these being a first stroke (77%). On av erage this equates to someone in the U.S. having a stroke every 40 seconds with a death occurring every 3 to 4 minutes. When considered separately from

March 2009 5-141 DRAFT – DO NOT CITE OR QUOTE

## other CVDs, stroke ranks third among all causes of death, behind diseases of the heart and cancer (Rosamond et al., 2008). While epidemiologic evidence is weak regarding associations between ambient CO and stroke, diseased individuals could exhi bit increased sensitivity to CO exposure. 

# 1 2 3

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

21 22 23 24 25 26 27 28 29 30 31 32 

# Obstructive Lung Disease

Patients with obstructive lung disease, such as COPD, were identified as a susceptible subpopulation in the 2000 CO AQCD. COPD is a progressive disease resulting in decreased air flow to the lungs, and is especially prevalent among smokers. A majority of COPD patients have exercise limitations as demonstrated by a decrease in O 2 

## saturation during mild to moderate exercise. This makes individuals with hypoxia resulting from COPD particul arly sensitive to CO during submaximal exercise typical of normal daily activity. COPD patients w ho are smokers may have b aseline COHb levels of 4-8%, leaving little reserve for increases in COHb du e to ambient exposure. COPD is often accompanied by a number of changes in gas exchange, including increased V D 

## and V/Q inequality (Marthan et al., 1985), which could slow both CO uptake and eliminati on. Patients with pulmonary sarcoidosis may have

a decrease in lung volumes, a loss of D L 

## CO, and gas exchange abnormalities during exercise, including decreased P a 

## O 2 

## and increased alveolar-arterial oxygen pressure difference (Lamberto et al., 2004). A controlled human exposure study on 19 individuals w ith COPD (Bathoorn et al., 2007) found that two of the patients experienced COPD exacerbation during or following CO exposure at 100-125 ppm for 2 h, although a slight anti-inflammatory effect was al so observed. The few epidemiologic studies that evaluated the relationship between ambient CO and in creased hospital admissions or ED visits for COPD show weak positive associations. Epidemiologic resu lts were similar for asthmatics, who can also experience exercise-induced airflow limitation.



# Anemia

Health status can influence the toxicity involved with CO exposure by influencing the severity of hypoxia resulting from CO exposure. Any condition that would alter the blood O 2 

## carrying capacity or content will result in a greater risk from COHb induced hypoxia and decreased tissue O 2 

## delivery. The severity of this effect depends upon the initial level of hypoxia. Anemias are a group of diseases that result in insufficient blood O 2 

## 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 in fections (Cavallin-Stahl et al., 1976a, b). The bodies of people with anemia compensate causing cardiac output to increase as both heart rate and stroke volume increase. The endogenous production of CO, thus COHb, is increased in patients with hemolytic anemia due to increased heme catabolism, causing an increased baseline COHb concentration. One of the most prevalent anemias arises from a single-point mutation of Hb, causing sickle cell diseases. The Hb affinity for O 2 

## and O 2 

## carrying capacity is reduced causi ng a shift to the right in the O 2 

## dissociation curve. It is

March 2009 5-142 DRAFT – DO NOT CITE OR QUOTE

## well documented that African-American populations have a higher incidence of sickle cell anemia, which may be a risk factor for CO hypoxia. 

# 1 2 3 4 5 6 7 8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

24 25 26 27 28 29 30 31 32 Any disturbance in RBC hemostasis by acceleration of destruction of hemoproteins will lead to increased production of CO. Pathologic conditions such as anemias, hematomas, thalassemia, Gilbert’s syndrome with hemolysis, and other hematologi cal diseases and illness will accelerate CO production (Berk et al., 1974; Hampson, 2007; Meyer et al., 1998; Solanki et al., 1988). Patients with hemolytic anemia exhibit COHb levels 2- to 3-times higher than healthy individuals and CO production rates 2- to 8-times higher (Coburn et al., 1966). 

# Age and Gender

Age and gender alter the variables that influence the uptake, distribution, and elimination of CO. COHb levels decline more rapidly in young children th an adults after CO exposure (Joumard et al., 1981; Klasner et al., 1998). After infancy, the COHb half- life increases with age, practically doubling between 2 and 70 years (Joumard et al., 1981). The rate of this decrease in CO elimination is very rapid in the growing years (2 to 16 years of age), but slows beyond adolescence. Alveolar volume and D L 

## CO increased with increasing body length of infants and t oddlers (Castillo et al., 2006), suggesting a further degree of lung development and faster CO uptake. After infancy, increasing age decreases D L 

## CO and increases V A 

## /Q mismatch, causing it to take longer to both load and eliminate CO from the blood (Neas and Schwartz, 1996). COHb concentrations are generally lower in female subjects than in male subjects (Horvath et al., 1988) and the COHb half-life is longer in healthy me n than in women of the same age, which may be partially explained by differences in muscle mass or the slight correlation between COHb half-life and increased height (Joumard et al., 1981). The rate of decline of D L 

## CO with age is lower in middle-aged women than in men; however, it evens out toward s older age (Neas and Schwartz, 1996). Women also tended to be more resistant to a ltitude hypoxia (Horvath et al., 1988). 

# Newborns and Young Infants

Fetal CO pharmacokinetics do not follow the same kinetics as maternal CO exposure, making it difficult to estimate fetal COHb based on maternal levels. Human fetal Hb has a higher affinity for CO than adult Hb (Di Cera et al., 19 89). Maternal and fetal COHb concen trations have been modeled as a function of time using a modified CFK equation (Hill et al., 1977). At steady-state conditions, the fetal COHb is up to 10% higher than the maternal COHb levels, for example, exposure to 30 ppm CO results in a maternal COHb of 5% and a fetal COHb of 5. 5%. The fetal CO uptake lags behind the maternal for the first few hours but later may overtake the maternal values. Similarly, during washout, the fetal COHb levels are maintained for longer, with a half-life of around 7.5 hours versus the maternal half-life of around 4 hours. In addition, women experience fluc tuating COHb levels during pregnancy as well as

March 2009 5-143 DRAFT – DO NOT CITE OR QUOTE

## through the menstrual cycle when endogenous CO production doubles in the progesterone phase (Delivoria-Papadopoulos et al., 1974; Mercke and Lundh, 1976). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

31 32 Epidemiologic studies of birth outcomes have examin ed well-established clinical metrics of infant health. Preterm birth (PTB, <37 weeks gestation) a nd low birth weight (LBW, birth weight <2,500 g) have been established as strong predictors of infa nt mortality and morbidity (Barker et al., 2002; Berkowitz and Papiernik, 1993; Li et al., 2003; McInti re et al., 1999). In 2004, 36.5 percent of all infant deaths in the U.S. were preterm-related (MacDorman et al., 2007). Vital statistics for the year 2005 in the U.S. showed that the rate for PTB was 12.7%, whic h has risen 20% since 1990, and the rate for LBW was 8.2%, which has risen 17% since 1990 (Martin et al.). Congenital anomalies remain the lead ing cause of infant death in the U.S. (Martin et al.). In 2004, for every 1,000 live births in the U.S. the infant (up to 1 year of age) mortality rate was 6.8 deaths, while the neonatal (under 28 days of age) mortality rate was 4.5 deaths, an d the postneonatal (between 28 days and 1 year of age) mortality rate was 2.4 deaths (National Center for Health Statistics, 2007). From 1968 to 1995, the proportion of infant mortality attributab le to birth defects increased from 14.5% to 22.2 % (Centers for Disease Control and Prevention, 1998). Limited epidemiologic rese arch has been conducted on congenital anomalies because of their rare occurrence, which makes it difficult to detect small effects within defined exposure periods. For example, in 2005 the rates for spina bifida and anencephaly in the U.S. were 18.0 and 11.3 per 100,000 births respectivel y (Martin et al., 2007) and as a result of folic acid fortification the prevalence of these defects declin ed considerably from 1995 to 2002 (Williams et al., 2005). Survival rate among infants with spina bifida ha d also improved due to folic acid fortification (Bol et al., 2006). The rate of cleft lip/palate is higher at approximately 79.1 per 100,000 births (Martin et al., 2007). The rate of cardiovascular defects is much hi gher. Data from the Metropolitan Atlanta Congenital Defects Program (MACDP), which is one of the most comprehensive birth defect registries in the U.S., showed that the prevalence of congenital heart defects had increased betw een 1968 and 1997. During 1995-1997 the rate was 90.2 per 10,000 births (0.9%) and this had increased from 58.7 per 10,000 births since 1986-1972 (Botto et al., 2001). Cardiovascular def ects are the single largest contributor to infant mortality attributable to birth defects (Centers for Disease Control and Prevention, 1998). Between 1979 and 1997, 1 in 10 infant deaths (9.8%) was associated with a congenital heart defect, and 1 in 13 infant deaths (7.4%) was due to a congenital heart defect (Boneva et al., 2001).



# 5.7.2.2. Vulnerability Characteristics

Altitude

Increased altitude changes a number of factors that contribute to the uptake and elimination of CO. The relationship between altitude and CO exposure has been discussed in depth in the 2000 CO AQCD

March 2009 5-144 DRAFT – DO NOT CITE OR QUOTE

## and other documents (U.S. EPA, 1978). In an effort to maintain proper O 2 

## transport and supply, physiological changes occur as compensatory mechanis ms to combat the decreased barometric pressure and resulting altitude induced HH (HH). HH, unlike CO hypoxia, causes humans to hyperventilate, which reduces arterial blood CO 2 

## (hypocapnia) and increases alveolar partial pressure of O 2 

## . Hypocapnia will lead to difficulty of O 2 

## dissociation and decreased blood flow, thus reducing tissue O 2 

## supply. HH increases BP and cardiac output and leads to redistribu tion of blood from skin to organs and from blood to extravascular compartments. Generally these cha nges will favor increased CO uptake and COHb formation, as well as CO elimination. In hypoxic conditions both CO and O 2 

## bind reduced Hb through a competitive-parallel reaction (Chakrabor ty et al., 2004). Breathing CO (9 ppm) at rest at altitude produced higher COHb compared to sea level (McGrath et al., 1993), whereas high altitude exposure with exercise caused a decrease in COHb levels versus similar e xposure at sea level (Horvath et al., 1988). This decrease could be a shift in CO storage or suppr ession of COHb formation, or both. Altitude also increases the baseline COHb leve ls by inducing endogenous CO production. Early HH increased lung HO-1 protein and activity, whereas chronic HH i nduced endogenous CO production in nonpulmonary sites (see Section 4.5) (Carraway et al., 2000). 

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

27 28 29 30 31 32 33 34 As the length of stay increases at high altitude, acclimatization occurs, inducing hyperventilation, polycythemia or increased red blood ce ll count, and increased tissue capillarity and Mb content in skeletal muscle, which could also favor increased CO uptake. Mo st of the initial adaptive changes gradually revert to sea level values. However, differences in people raised at high altitude persist even after reacclimatization to sea level (Hsia, 2002). Altitude has been shown to be positively associated with baseline COHb concentrations (McGrath, 1992; McGrath et al., 1993). This increase in COHb with altitude induced hypoxia has also been associated with increases in the mRNA, protein, and activity of HO-1 in rats and cells leading to enhanced endogenous CO production (Ca rraway et al., 2002; Chin et al., 2007). Whether other variables (such as an accelerated metabolism or a greater pool of Hb , transient shifts in body stores, or a change in the elimination rate of CO) play a role has not been explored. 

# Activity Patterns

Exercise is an important determinant of CO kineti cs and toxicity due to the extensive increase in gas exchange. O 2 

## 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 cha nges facilitate CO uptake and transport, by increasing gas exchange efficiency. The COHb elimination rate decreases with phys ical activity (Joumard et al., 1981). Healthy subjects exposed to CO and achieving COHb levels of approximately 4-5% observed a significant detriment to exercise duration and maximal effort ca pability (measured by metabolic equivalent units)

March 2009 5-145 DRAFT – DO NOT CITE OR QUOTE

## (Adir et al., 1999). It is possible that CO lowers the anaerobic threshold, allowing earlier fatigue of the skeletal muscles and decre ased maximal effort capability. 

# 1 2

3 4 5 6 7 8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 

# Proximity to Roadways

Individuals that spend a substantial amount of tim e on or near heavily traveled roadways, such as commuters and those living or working near fre eways, are likely to experience elevated CO concentrations, as discussed in Chapter 3, and ther efore constitute a potentiall y vulnerable subpopulation due to differential exposure. Studies of commuters have shown that commuting time is an important predictor of CO exposure for those traveling by car, cycling, and walking, and that on-road CO concentrations are typically two to five times higher than concentrations measured at the roadside.



# Medication Use

Not all endogenous CO production is derived from Hb breakdown. Other hemoproteins, such as Mb, cytochromes, peroxidases, and catalase, contri bute 20-25% to the total amount of endogenous CO (Berk et al., 1976). All of these sources result in a blood COHb concentrati on between 0.4 and 0.7% (Coburn et al., 1965). This baseline level of endogenous pr oduction can be altered by drugs or a number of physiological conditions that alter RBC destru ction, other hemoprotein breakdown, or bilirubin production. Nicotinic acid (Lundh et al., 1975), allyl-c ontaining compounds (acetamids and barbiturates) (Mercke et al., 1975b), diphenylhydantoin (Coburn, 1970b), progesterone (Delivoria-Papadopoulos et al., 1974), and contraceptives (Mercke et al., 1975a) w ill increase CO production. Compounds such as carbon disulfide and sulfur-containing chemicals (parathion and phenyltiourea) will increase CO by acting on P450 system moieties (Landaw et al., 1970). The P4 50 system may also cause large increases in CO produced from the metabolic degradation of dihalomethanes leading to very high (>10%) COHb levels (Manno et al., 1992), which can be further enhanced by prior exposure to HCs or ethanol (Pankow et al., 1991; Wirkner and Poelchen, 1996). HO can catalyze the release of CO from the auto-oxidation of phenols, photo-oxidation of organic compounds, and li pid peroxidation of cell membrane lipids (Rodgers et al., 1994).

March 2009 5-146 DRAFT – DO NOT CITE OR QUOTE

# Annex A. Atmospheric Science

YukonKoyukuk



### Alaska State Emissions 2002

0 40 1,698 4,256 6,060 27,954 12,746 84,990 8,403,218 145,340

0 12,000,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons)



### Yukon-Koyukuk County Emissions 2002

0 0 52 0 0 8 398 3,192 8,095,363 2,494

0 12,000,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) 

# Figure A-1. CO emissions density map and dist ribution for the state ofAlaska and for YukonKoyukuk County in Alaska.

March 2009 A-1 DRAFT—DO NOT QUOTE OR CITE

## Weber

Utah

Grand

Utah State Emissions 2002

17 277 152

42,466 4,234 32,365 12,031 203,475 257,199 768,164

0 1,000,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Weber County Emissions 2002

3 24 0 2,079 56 497 1,046

12,491 916 46,841

0 150,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Utah County Emissions 2002

0 44 1 1,346 1 1,919 2,048 20,195 2,439 89,535

0 150,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Grand County Emissions 2002

0 2 1 32 0 298 44 1,771 123,963

13,242

0 150,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) 

# Figure A-2. CO emissions density map and distri bution for the state of Utah and for selected counties in Utah.

March 2009 A-2 DRAFT—DO NOT QUOTE OR CITE

### Suffolk Middlesex

Norfolk

Massachusetts State Emissions 2002

77 482 3,877 1,948 11,569 28,111

104,462 490,378 0 926,079

0 1,200,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Middlesex County Emissions 2002

7 77 323 287 492 7,063 492 101,247 0 214,556

0 260,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Norfolk County Emissions 2002

8 24 285 208 189 2,728 8,803 44,873 0 154,098

0 260,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Suffolk County Emissions 2002

0 110 1 143 225 1,689 2,326 63,447 0 64,900

0 260,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) 

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

March 2009 A-3 DRAFT—DO NOT QUOTE OR CITE

# Fulton Dekalb

Chatham

Liberty Glynn

Georgia State Emissions 2002

16 500

175,000 49,802 9,824 54,857 47,461 720,812 53,412 2,604,609

0 3,500,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Fulton County Emissions 2002

0 92 279 275 0 2,850 2,262 80,197 0 223,633

0 300,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Dekalb County Emissions 2002

7 5 254 104 0 1,907 2,010 68,502 0 142,648

0 300,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) 

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

March 2009 A-4 DRAFT—DO NOT QUOTE OR CITELiberty County Emissions 2002

0 4 55,427 385 0 234 75 4,832 0 19,711

0 300,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) 

## ` Chatham County Emissions 2002

0 15 73 1,849 189 2,388 414 25,904 0 67,821

0 300,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Glynn County Emissions 2002

0 4 1,129 6,619 377 3,056 134

15,467 0 27,024

0 3 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion

0 0 , 0 0 0 R esidential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) 

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

March 2009 A-5 DRAFT—DO NOT QUOTE OR CITE

### Los Angeles

San Diego Orange

California State Emissions 2002

379 3,548 22,308 34,908 28,471 98,990

287,944 1,191,454 733,553 3,436,059

0 4,500,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Los Angeles County Emissions 2002

27 674 143 6,960 9,814 10,795 7,220 226,349

62,576 824,676

0 1,000,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) San Diego County Emissions 2002

0 226 162 231 895 14,855 20,821

101,515 62,192 283,597

0 1,000,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Orange County Emissions 2002

8 184 107 134 655 2,871 1,660 105,638 978 220,477

0 1,000,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) 

# Figure A-5. CO emissions dens ity map and distribution for the state of California and for selected counties in California.

March 2009 A-6 DRAFT—DO NOT QUOTE OR CITE

## Jefferson

Alabama State Emissions 2002

228 539 135,164

106,529 11,212

76,631 60,654 375,495 277,465 1,275,969

0 1,600,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons) Jefferson County Emissions 2002

14 115 2,645 40,891 3,360 39,488 1,344 54,092 34 193,274

0 250,000 Solvent Use Miscellaneous Waste Disposal Industrial Processes Electricity Generation Fossil Fuel Combustion Residential Wood Combustion Non-Road Equipment Fires On-Road Vehicles

Emissions (Tons)



# Figure A-6. CO emissions de nsity map and distribution for the state of Alabama and for Jefferson County in Alabama.

March 2009 A-7 DRAFT—DO NOT QUOTE OR CITE

# Figure A-7. Map of CO monito r locations with respect to population density in the Anchorage CBSA, total population.

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

March 2009 A-8 DRAFT—DO NOT QUOTE OR CITE

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

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

March 2009 A-9 DRAFT—DO NOT QUOTE OR CITE

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

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

March 2009 A-10 DRAFT—DO NOT QUOTE OR CITE

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

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

March 2009 A-11 DRAFT—DO NOT QUOTE OR CITE

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

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

March 2009 A-12 DRAFT—DO NOT QUOTE OR CITE

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

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

March 2009 A-13 DRAFT—DO NOT QUOTE OR CITE

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

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

March 2009 A-14 DRAFT—DO NOT QUOTE OR CITE

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

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

March 2009 A-15 DRAFT—DO NOT QUOTE OR CITE

# Figure A-23. Map of CO monito r locations with respect to popul ation density in the St. Louis CSA, total population.

Figure A-24. Map of CO monito r locations with respect to popul ation density in the St. Louis CSA, ages 65 and older.

March 2009 A-16 DRAFT—DO NOT QUOTE OR CITE

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

Table A-1. Table of inter-sampler comparison st atistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Anchorage, AK.

Anchorage A B 1.00 0.73 

### A 0.0 1.1 0.00 0.32 0.0 9.0 Legend

r 1.00 

### B P90 0.0 COD 0.00

d 0.0



### March 2009 A-17 DRAFT—DO NOT QUOTE OR CITEA B Mean 1.04 1.10 Obs 12969 12703 SD 0.94 1.04



# Figure A-26. Box plots illustrating the seasonal di stribution of hourly CO concentrations in Anchorage, AK. Note: 1 = wint er, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-18 DRAFT—DO NOT QUOTE OR CITE

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

Table A-2. Table of inter-sampler comparison st atistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Atlanta, GA.

Atlanta A B C 1.00 0.60 0.12 

### A 0.0 0.5 0.7 0.00 0.27 0.37 0.0 22.5 74.7 1.00 0.10 

### B 0.0 0.7 0.00 0.38 Legend 0.0 61.7

r 1.00 

### C P90 0.0 COD 0.00

d 0.0



### March 2009 A-19 DRAFT—DO NOT QUOTE OR CITEA B C Mean 0.53 0.58 0.30 Obs 25531 25440 25712 SD 0.35 0.30 0.13



# Figure A-28. Box plots illustrating the seasonal di stribution of hourly CO concentrations in Atlanta, GA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-20 DRAFT—DO NOT QUOTE OR CITE

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

March 2009 A-21 DRAFT—DO NOT QUOTE OR CITE

# Table A-3. Table of inter-sampler comparison st atistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Boston, MA.

Boston A B C D E F G 1.00 0.35 0.50 0.49 0.41 0.50 0.40 

### A 0.0 0.4 0.4 0.5 0.4 0.6 0.4 0.00 0.58 0.48 0.42 0.41 0.44 0.40 0.0 37.2 39.7 57.9 41.3 18.3 89.1 1.00 0.52 0.34 0.27 0.35 0.34 

### B 0.0 0.4 0.6 0.5 0.7 0.4 0.00 0.56 0.59 0.58 0.60 0.55 0.0 2.5 58.0 78.2 55.1 60.2 1.00 0.37 0.26 0.38 0.36 

### C 0.0 0.5 0.5 0.6 0.4 0.00 0.45 0.45 0.46 0.47 0.0 58.9 80.7 57.5 58.7 1.00 0.40 0.46 0.34 

### D 0.0 0.4 0.5 0.5 0.00 0.28 0.25 0.39 0.0 85.8 61.5 58.9 1.00 0.49 0.29 

### E 0.0 0.5 0.4 0.00 0.30 0.37 0.0 26.1 128.6 1.00 0.43 

### F 0.0 0.6 0.00 0.39 

## Legend 0.0 102.6

r 1.00 

### G P90 0.0 COD 0.00

d 0.0



### March 2009 A-22 DRAFT—DO NOT QUOTE OR CITEA B C D E F G Mean 0.33 0.26 0.36 0.53 0.45 0.60 0.34 Obs 24362 24134 24260 24446 25197 25869 23707 SD 0.22 0.24 0.26 0.23 0.27 0.37 0.22



# Figure A-30. Box plots illustrating the seasonal di stribution of hourly CO concentrations in Boston, MA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-23 DRAFT—DO NOT QUOTE OR CITE

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

March 2009 A-24 DRAFT—DO NOT QUOTE OR CITE

# Table A-4. Table of inter-sampler comparison st atistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Denver, CO.

Denver A B C D E 1.00 0.53 0.59 0.64 0.52 

### A 0.0 0.6 0.6 0.5 0.6 0.00 0.43 0.29 0.37 0.34 0.0 38.5 10.1 10.9 68.2 1.00 0.46 0.49 0.54 

### B 0.0 0.7 0.7 0.6 0.00 0.44 0.47 0.43 0.0 46.9 47.0 44.6 1.00 0.76 0.45 

### C 0.0 0.5 0.7 0.00 0.34 0.36 0.0 1.3 78.3 1.00 0.46 

### D 0.0 0.7 0.00 0.42 

## Legend 0.0 79.0

r 1.00 

### E P90 0.0 COD 0.00

d 0.0



### March 2009 A-25 DRAFT—DO NOT QUOTE OR CITEA B C D E Mean 0.52 0.42 0.65 0.52 0.55 Obs 25920 25559 25959 25552 26048 SD 0.36 0.38 0.42 0.46 0.46



# Figure A-32. Box plots illustrating the seasonal di stribution of hourly CO concentrations in Denver, CO. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-26 DRAFT—DO NOT QUOTE OR CITE

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

March 2009 A-27 DRAFT—DO NOT QUOTE OR CITE

# Table A-5. Table of inter-sampler comparison st atistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Houston, TX.

Houston A B C D E 1.00 0.72 0.45 0.56 0.68 

### A 0.0 0.3 0.4 0.5 0.3 0.00 0.29 0.47 0.73 0.24 0.0 17.5 16.7 19.8 32.2 1.00 0.56 0.65 0.63 

### B 0.0 0.4 0.5 0.4 0.00 0.47 0.73 0.29 0.0 16.3 25.2 39.7 1.00 0.53 0.43 

### C 0.0 0.5 0.4 0.00 0.74 0.47 0.0 9.3 23.5 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

d 0.0



### March 2009 A-28 DRAFT—DO NOT QUOTE OR CITEA B C D E Mean 0.42 0.39 0.35 0.14 0.33 Obs 23997 25241 24922 25285 24480 SD 0.27 0.33 0.26 0.22 0.16



# Figure A-34. Box plots illustrating the seasonal di stribution of hourly CO concentrations in Houston, TX. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-29 DRAFT—DO NOT QUOTE OR CITE

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

March 2009 A-30 DRAFT—DO NOT QUOTE OR CITE

# Table A-6. Table of inter-sampler comparison st atistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in Los Angeles, CA.

Los Angeles A B C D E F G H I J K L M N O P Q R S T U V W X A 1.00 0.45 0.43 0.46 0.44 0.21 0.57 0.41 0.17 0.19 0.43 0.27 0.29 0.14 0.42 0.34 0.54 0.33 0.47 0.56 0.17 0.24 0.61 0.48 0.0 0.5 1.0 0.8 0.7 1.5 0.8 0.8 1.1 0.7 0.5 0.6 0.6 0.7 0.5 0.8 0.7 0.5 0.8 0.4 0.6 0.6 0.5 0.6 0.00 0.38 0.35 0.38 0.33 0.44 0.33 0.43 0.47 0.72 0.47 0.62 0.43 0.64 0.52 0.33 0.33 0.36 0.43 0.38 0.62 0.50 0.30 0.35 0 50.0 36.5 29.0 56.5 35.0 17.7 18.7 42.4 51.0 61.9 62.2 33.9 51.2 60.6 23.6 52.6 131.4 49.2 74.8 117.7 68.4 27.4 59.9 

### B 1.00 0.69 0.71 0.64 0.50 0.63 0.49 0.43 0.59 0.36 0.42 0.59 0.47 0.57 0.59 0.57 0.35 0.55 0.55 0.25 0.41 0.50 0.53 0.0 0.8 0.6 0.7 1.3 0.7 0.8 0.9 0.6 0.6 0.7 0. 5 0.7 0.6 0.6 0.7 0.6 0.7 0.5 0.7 0.6 0.6 0.6 0.00 0.38 0.38 0.34 0.50 0.41 0.49 0.48 0.69 0.50 0.60 0.39 0.59 0.50 0.37 0.42 0.38 0.46 0.39 0.62 0.50 0.40 0.41 0 18.9 21.2 17.8 26.4 65.0 31.6 35.2 11.4 37.4 74.9 53.7 64. 4 85.9 48.5 98.1 178.1 96.1 112.0 161.1 115.3 76.4 109.1 

### C 1.00 0.84 0.74 0.70 0.78 0.67 0.62 0.64 0.39 0.59 0.72 0.70 0.69 0.75 0.70 0.40 0.52 0.58 0.35 0.58 0.59 0.65 0.0 0.5 0.6 0.9 0.5 0.7 0.8 1.0 1.1 1.1 0.7 0.9 1.0 0.6 0.6 1.2 0.8 1.0 1.2 1.0 0.7 0.7 0.00 0.29 0.28 0.35 0.26 0.39 0.41 0.72 0.52 0.65 0.41 0.63 0.56 0.26 0.29 0.46 0.42 0.46 0.67 0.53 0.29 0.35 0 14.7 20.0 29.1 53.5 18.1 40.8 27.0 30.1 57.4 51.8 66.3 84.6 43.6 88.2 167.7 85.3 106.5 142.7 97.8 63.8 96.3 

### D 1.00 0.64 0.74 0.77 0.67 0.60 0.59 0.34 0.53 0.70 0.58 0.63 0.74 0.62 0.32 0.49 0.53 0.34 0.51 0.57 0.58 0.0 0.7 1.0 0.5 0.7 0.8 0.8 0.9 0.9 0.6 0.8 0.8 0.5 0.6 1.0 0.8 0.8 1.0 0.8 0.6 0.6 0.00 0.34 0.41 0.33 0.44 0.45 0.73 0.52 0.63 0.40 0.63 0.55 0.31 0.37 0.46 0.45 0.46 0.66 0.53 0.36 0.39 0 31.8 15.3 43.8 11.7 27.1 22.7 44.7 67.5 37.3 51.6 70.2 29.8 77.4 157.4 75.1 93.4 143.4 95.9 55.2 87.9 

### E 1.00 0.49 0.70 0.54 0.44 0.52 0.53 0.53 0.61 0.57 0.55 0.61 0.73 0.42 0.62 0.66 0.31 0.55 0.58 0.71 0.0 1.2 0.6 0.8 0.9 0.9 0.8 0.9 0.7 0.8 0.8 0.7 0.6 0.9 0.7 0.8 1.0 0.8 0.6 0.6 0.00 0.41 0.30 0.42 0.44 0.72 0.49 0.62 0.41 0.62 0.54 0.29 0.29 0.40 0.40 0.40 0.64 0.50 0.30 0.32 0 42.1 73.4 38.0 52.3 29.1 20.4 64.0 68.3 80.7 101.2 61.5 108.0 187.6 105.2 125.1 158.2 115.6 83.8 116.3 

### F 1.00 0.65 0.53 0.70 0.63 0.23 0.55 0.71 0. 67 0.57 0.78 0.54 0.28 0.35 0.39 0.33 0.51 0.39 0.44 0.0 1.0 1.0 1.0 1.5 1.6 1.6 1.1 1.3 1. 5 0.9 1.1 1.7 1.2 1.6 1.7 1.5 1.3 1.3 0.00 0.29 0.39 0.39 0.76 0.60 0.72 0.51 0.70 0.65 0.30 0.30 0.56 0.45 0.56 0.73 0.61 0.35 0.42 0 45.0 23.8 11.8 20.4 58.2 82.5 27.4 38.6 59.5 23.8 74.8 154.5 73.7 86.0 152.6 103.4 57.0 88.6 

### G 1.00 0.65 0.52 0.51 0.47 0.61 0.69 0.60 0.70 0.77 0.78 0.47 0.60 0.69 0.40 0.58 0.78 0.68 0.0 0.6 0.8 1.0 0.9 0.9 0.7 0.9 0.9 0.5 0.4 1.0 0.7 0.8 1.1 0.9 0.5 0.6 0.00 0.34 0.38 0.73 0.53 0.66 0.44 0.66 0.58 0.20 0.19 0.47 0.38 0.46 0.68 0.54 0.18 0.31 0 35.4 48.6 63.9 79.6 75.4 31.4 46.3 48.9 24.3 35.0 114.2 31.8 58.1 113.3 62.4 12.0 44.2 

### H 1.00 0.41 0.40 0.32 0.45 0.54 0. 50 0.55 0.58 0.53 0.39 0.39 0.47 0.31 0.46 0.53 0.52 0.0 0.9 1.0 1.0 1.0 0.8 0.9 0.9 0.7 0.7 1.0 0.8 0.9 1.1 0.9 0.7 0.7 0.00 0.46 0.75 0.58 0.70 0.52 0.68 0.62 0.37 0. 35 0.54 0.46 0.53 0.71 0.59 0.35 0.41 0 34.7 34.4 46.2 59.6 37.7 54.0 69.5 28.1 70.1 149.6 67.2 89.2 131.7 84.3 46.0 78.6 

### I 1.00 0.59 0.15 0.43 0.63 0.64 0.48 0.62 0.46 0.24 0.31 0.36 0.24 0.43 0.28 0.39 0.0 1.0 1.2 1.1 0.8 0.9 1.1 0.7 0.8 1.2 1.0 1.1 1.3 1.1 0.9 0.9 0.00 0.75 0.60 0.69 0.48 0.64 0.58 0.37 0.39 0.53 0.48 0.54 0.70 0.59 0.41 0.46 0 26.4 69.4 94.0 23.2 29.5 52.1 24.6 74.1 152.4 74.0 81.1 159.7 109.6 60.3 90.1 

### J 1.00 0.24 0.39 0.58 0.60 0.42 0.59 0.45 0.26 0.43 0.43 0.18 0.41 0.27 0.43 0.0 0.6 0.6 0.6 0.5 0.5 0.8 0.9 0.5 0.9 0.5 0.6 0.6 0.9 0.8 0.00 0.73 0.74 0.71 0.69 0.73 0.72 0.73 0.70 0.74 0.70 0.75 0.72 0.73 0.72 0 48.7 84.4 47.4 55.8 78.3 44.2 95.0 174.8 93.7 106.1 166.0 118.6 75.8 108.0 

### K 1.00 0.40 0.29 0.25 0.28 0.34 0.54 0.40 0.46 0.56 0.25 0.39 0.53 0.50 0.0 0.5 0.7 0.7 0.5 0.9 0.8 0.4 0.9 0.3 0.5 0.5 0.6 0.6 0.00 0.62 0.53 0.67 0.59 0.52 0.53 0.45 0.56 0.47 0.62 0.54 0.51 0.51 0 48.4 81.8 96.2 114.6 73.4 114.5 192.2 110.8 135.3 148.8 110.7 88.3 119.3 

### L 1.00 0.56 0.55 0.51 0.63 0.56 0.41 0.41 0.50 0.45 0.67 0.49 0.50 0.0 0.7 0.6 0.4 0.9 0.9 0.4 0.9 0.4 0.4 0.4 0.7 0.7 0.00 0.60 0.66 0.61 0.64 0.67 0.56 0.67 0.57 0.62 0.57 0.65 0.64 0 94.7 112.0 122.7 84.2 104.6 171.9 99.2 132.5 104.0 75.4 77.9 100.4 

### M 1.00 0.75 0.67 0.81 0.62 0.37 0.49 0.53 0.31 0.54 0.47 0.55 0.0 0.5 0.5 0.5 0.7 0.7 0.8 0.6 0.8 0.6 0.7 0.6 0.00 0.55 0.46 0.38 0.46 0.41 0.49 0.42 0.62 0.50 0.45 0.46 0 17.3 32.9 10.5 51.3 129.2 51.7 58.7 144.7 93.7 41.6 68.5



### March 2009 A-31 DRAFT—DO NOT QUOTE OR CITE

### N 1.00 0.62 0.72 0.56 0.34 0.40 0.46 0.28 0.55 0.36 0.50 0.0 0.5 0.7 0.8 0.6 0.9 0.5 0.7 0.6 0.8 0.7 0.00 0.56 0.62 0.66 0.59 0.67 0.59 0.69 0.62 0.65 0.64 0 23.7 27.9 57.1 129.6 59.3 55.1 158.5 107.5 54.9 76.9 

### O 1.00 0.67 0.61 0.44 0.43 0.51 0.33 0.45 0.57 0.51 0.0 0.8 0.8 0.3 0.9 0.3 0.4 0.4 0.7 0.6 0.00 0.54 0.58 0.47 0.59 0.50 0.63 0.56 0.56 0.56 0 41.5 43.3 107.9 47.5 32.4 152.3 102.5 52.6 64.6 

### P 1.00 0.66 0.40 0.50 0.55 0.34 0.58 0.54 0.55 0.0 0.6 1.0 0.8 0.8 1.0 0.8 0.6 0.7 0.00 0.24 0.42 0.38 0.43 0.66 0.51 0.25 0.33 0 51.0 130.6 50.2 63.7 137.1 86.4 35.8 65.7 

### Q 1.00 0.48 0.67 0.72 0.35 0.55 0.70 0.73 0.0 0.9 0.6 0.7 1.0 0.8 0.5 0.5 0.00 0.47 0.37 0.46 0.68 0.54 0.20 0.30 0 80.1 6.1 30.5 110.6 62.8 27.4 21.3 

### R 1.00 0.40 0.49 0.29 0.38 0.47 0.46 0.0 0.9 0.3 0.3 0.4 0.7 0.7 0.00 0.50 0.32 0.55 0.44 0.43 0.43 0 82.4 75.7 123.6 102.8 104.2 73.3 

### S 1.00 0.66 0.19 0.38 0.54 0.64 0.0 0.8 1.1 0.9 0.6 0.6 0.00 0.49 0.69 0.58 0.37 0.41 0 36.64 105.4 57.05 22.78 17.67 

### T 1.00 0.30 0.49 0.68 0.69 0.0 0.4 0.4 0.6 0.6 0.00 0.58 0.47 0.43 0.42 0 137.8 92.3 54.8 48.0 

### U 1.00 0.43 0.36 0.31 0.0 0.4 0.8 0.8 0.00 0.59 0.66 0.65 0 51.0 103.7 90.2 

### V 1.00 0.47 0.51 0.0 0.7 0.6 0.00 0.52 0.52 0 52.7 44.9 

### W 1.00 0.64 0.0 0.5 0.00 0.29 

## Legend 0 32.7 

### X r 1.00 P90 0.0 COD 0.00

d 0



### March 2009 A-32 DRAFT—DO NOT QUOTE OR CITEA B C D E F G H Mean 0.42 0.41 0.66 0.56 0.57 0.98 0.69 0.72 Obs 2,5001 24916 24892 24645 24281 24825 24912 24804 SD 0.27 0.36 0.59 0.50 0.54 0.89 0.45 0.48



# Figure A-36a. Box plots illustrati ng the seasonal distribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, = spri ng, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-33 DRAFT—DO NOT QUOTE OR CITEI J K L M N O P Mean 0.69 0.24 0.30 0.23 0.42 0.31 0.26 0.62 Obs 24259 24965 24860 24135 24264 24760 24831 24705 SD 0.56 0.37 0.25 0.29 0.46 0.47 0.25 0.55



# Figure A-36b. Box plots illustrating the seasonal dist ribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, = spri ng, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-34 DRAFT—DO NOT QUOTE OR CITEQ R S T U V W X Mean 0.67 0.25 0.60 0.29 0.17 0.30 0.59 0.53 Obs 24885 24938 24778 24792 24105 24796 24767 24844 SD 0.42 0.14 0.46 0.20 0.17 0.28 0.32 0.38



# Figure A-36c. Box plots illustrati ng the seasonal distribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-35 DRAFT—DO NOT QUOTE OR CITE

# Figure A-37. Map of CO monitor locations wi th AQS Site IDs for New York City, NY.

March 2009 A-36 DRAFT—DO NOT QUOTE OR CITE

# Table A-7. Table of inter-sampler comparison st atistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in New York City, NY.

New York A B C D E F G H I 1.00 0.58 0.65 0.55 0.40 0.56 0.56 0.41 0.30 A 0.0 0.4 0.7 0.4 0.4 0.4 0.4 0.5 0.8 0.00 0.22 0.28 0.25 0.25 0.24 0.23 0.28 0.75 0.0 7.0 15.9 45.8 70.6 43.7 10.5 17.8 76.5 B 1.00 0.64 0.55 0.35 0.54 0.54 0.59 0.49 0.0 0.8 0.4 0.5 0.4 0.4 0.4 0.7 0.00 0.29 0.23 0.26 0.23 0.23 0.23 0.74 0.0 16.8 45.4 71.5 38.1 15.0 24.5 82.9 1.00 0.54 0.32 0.48 0.52 0.43 0.31 C 0.0 0.9 0.9 0.9 0.7 0.9 1.3 0.00 0.35 0.34 0.34 0.24 0.35 0.81 0.0 29.9 55.0 35.7 8.9 20.5 85.5 D 1.00 0.50 0.57 0.41 0.46 0.33 0.0 0.4 0.4 0.4 0.4 0.7 0.00 0.24 0.23 0.28 0.27 0.72 0.0 27.5 36.7 37.5 45.1 107.8 1.00 0.47 0.33 0.33 0.32 E 0.0 0.4 0.4 0.4 0.6 0.00 0.23 0.25 0.27 0.73 0.0 61.9 61.0 65.0 120.3 F 1.00 0.41 0.34 0.31 0.0 0.4 0.4 0.7 0.00 0.26 0.27 0.72 0.0 43.6 55.8 119.7 1.00 0.46 0.29 G 0.0 0.4 0.7 0.00 0.26 0.77 0.0 12.3 76.8 H 1.00 0.43 0.0 0.6 0.00 0.73 

## Legend 0.0 65.1

r 1.00 I P90 0.0 COD 0.00

d 0.0



### March 2009 A-37 DRAFT—DO NOT QUOTE OR CITEA B C D E F G H I Mean 0.55 0.52 0.85 0.48 0.50 0.49 0.62 0.47 0.12 Obs 23113 25150 25646 25028 25727 25691 25547 25022 25749 SD 0.27 0.30 0.43 0.27 0.24 0.25 0.21 0.23 0.17



# Figure A-38. Box plots illustrati ng the seasonal distribution of hourly CO concentrations in New York City, NY. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-38 DRAFT—DO NOT QUOTE OR CITE

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

March 2009 A-39 DRAFT—DO NOT QUOTE OR CITEA Mean 0.75 Obs 25818 SD 0.49



# Figure A-40. Box plots illustrating the seasonal di stribution of hourly CO concentrations in Seattle, WA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-40 DRAFT—DO NOT QUOTE OR CITE

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

Table A-8. Table of inter-sampler comparison st atistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the text, for each pair of hourly CO monitors reporting to AQS in St. Louis, MO.

St. Louis A B C 1.00 0.19 0.60 A 0.0 0.5 0.3 0.00 0.40 0.24 0.0 21.2 9.5 B 1.00 0.19 0.0 0.5 0.00 0.42 Legend 0.0 19.8

r 1.00 C P90 0.0 COD 0.00

d 0.0



### March 2009 A-41 DRAFT—DO NOT QUOTE OR CITEA B C Mean 0.44 0.43 0.42 Obs 25325 25879 25938 SD 0.25 0.25 0.29



# Figure A-42. Box plots illustrati ng the seasonal distribution of hourly CO concentrations in St. Louis, MO. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis.

March 2009 A-42 DRAFT—DO NOT QUOTE OR CITE

# Figure A-43. Seasonal plots of correlations betw een hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations for Anchorage, AK. Also shown are correlations between 24-h average CO c oncentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Note that the data ar e not obtained for Anchorage during the summer, and so are not presented here.

March 2009 A-43 DRAFT—DO NOT QUOTE OR CITE

# Figure A-44. Seasonal plots of correlations betw een hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations for Atlanta, GA. Also shown are correlations between 24-h average CO concen tration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.)

March 2009 A-44 DRAFT—DO NOT QUOTE OR CITE

# Figure A-45. Seasonal plots of correlations betw een hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations for Boston, MA. Also shown are correlations between 24-h average CO concen tration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Red bars denote the median, and green stars denote the arithmetic mean.

March 2009 A-45 DRAFT—DO NOT QUOTE OR CITE

# Figure A-46. Seasonal plots of correlations betw een hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations for Denver, CO. Also shown are correlations between 24-h average CO concen tration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Red bars denote the median, and green stars denote the arithmetic mean.

March 2009 A-46 DRAFT—DO NOT QUOTE OR CITE

# Figure A-47. Seasonal plots of correlations betw een hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations for Los Angeles, CA. Also shown are correlations between 24-h average CO concentration with (6) daily max 1- h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the yaxis of each seasonal plot.)

March 2009 A-47 DRAFT—DO NOT QUOTE OR CITE

# Figure A-48. Seasonal plots of correlations betw een hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations for New York City, NY. Also shown are correlations between 24-h average CO concentration with (6) daily max 1- h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the yaxis of each seasonal plot.)

March 2009 A-48 DRAFT—DO NOT QUOTE OR CITE

# Figure A-49. Seasonal plots of correlations betw een hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations for Phoenix, AZ. Also shown are correlations between 24-h average CO c oncentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.)

March 2009 A-49 DRAFT—DO NOT QUOTE OR CITE

# Figure A-50. Seasonal plots of correlations betw een hourly CO concentration with hourly (1) SO 2 

# , (2) NO 2 

# , (3) O 3 

# , (4) PM 10 

# , and (5) PM 2.5 

# concentrations for Seattle, WA. Also shown are correlations between 24-h average CO concen tration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.)



# 1

March 2009 A-50 DRAFT—DO NOT QUOTE OR CITE

# Annex B. Dosimetry Studies

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

Reference Purpose Findings Alcantara et al. (2007) To use a quantum mechanics/molecular mechanics approach to understand the cooperativity of Hb ligand binding and differences in energy between T and R Hb functional states. The ligand binding energies between R and T states differ due to strain induced in the heme and its ligands and in protein contacts in the α and β chains.

Adir et al. (1999) To determine if lo w concentrations of CO would affect exercise performance and my ocardial perfusion in young healthy men. Men with COHb levels betw een 4-6% had decreased exercise performance measured by decreased mean duration of exercise (1.52 min) and maximal effort described by metabolic equivalent units (2.04). No changes were seen in lactate/pyruvate ratio, arrhythmias, or myocardial perfusion. Andersson et al. (2000) To investi gate if CO could be endogenously produced in the nose and paranasal sinuses. Both nose and paranasal sinuses co ntained HO-like immunoreactivity, mostly in the respiratory epithelium, indicating local CO production in the upper respiratory airways.

Bruce and Bruce (2003) To create a mathematical model to predict uptake and distribution of CO in both vascular and tissue compartments during constant or variable inhalation levels of CO. This model contains 5 compartment s: lung, arterial blood, venous blood, muscle tissue, and nonmuscle tissue. It was constructed to include tissue compartment flux and difference between venous and arterial COHb for short exposures which is not possible with the CFK model. Bruce and Bruce (2006) To use their mathematical multicompartment model along with experimental data to predict the factors that influence the washout rates of CO, along with predicting the rates of CO uptake, distribution in vascular and extravascular (muscle and non-muscle tissue) compartments, and washout over a range of exposure and conditions. Rates of CO washout follow a bi phasic 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 obtai ned, and individual variability.

Bruce et al. (2008) To develop a mathematical model able to integrate a large body of indirect experimental findings on the uptake and distribution of CO by accounting for arteriole to venule shunti ng via intra-tissue pathwaysand diffusion of blood gases into tissues from precapillary vessels like arterioles. The former model of Bruce and Bruc e (2006) was altered by adding a mass balance equation for O 2 so pO 2 is directly calculated in the compartments and the muscle compar tment is divided into two subcompartments of muscle and non-mu scle tissue. CO uptake from blood by muscle is much slower than O 2 , thus COHb% will fall rapidly while COMb% could remain high. Carraway et al. (2000) To test the hypothesis that HO-1 gene expression and protein are upregulated in t he 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 chr onic hypoxia. This HO-1 was localized to inflammatory cells and then to newly muscularized arterioles. To describe a new method for measurement of CO diffusing capacity (D L CO) and alveolar volume (V A ) in sleeping infants (6-22 mont hs old), using a single 4-sec breath-hold technique. V A30 and D L CO increased with increas ing body length and the method could be used as a measurement of lung development and growth. Castillo et al. (2006)

To present an analytical expre ssion for diffusing capacity of CO, NO, CO 2 , and O 2 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 so lubilities of the gas in RBC and boundary layer, hematocrit, and the slope of the dissociation curve. Results indicate the discoidal s hape of the RBC is optimal for O 2 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 P CO under normoxic and hyperoxic conditions, but diffusion rate limited under hypoxic and high P CO conditions. Chakraborty et al. (2004)

Cronje et al. (2004) To analyze CO uptake and elimination in the brain, muscle, heart, and blood of rats, with the intent of testing the Warburg hypothesis that CO partitioning is directly proportional to the CO/O 2 ratio. Results indicate that tissue and blood [CO] dissociate during CO inhalation, but [CO] does not follow blood [CO] or 1/pO 2 as in the Warburg theory during intake or elimination. Tissue [CO] increases later during the resolution peri od and varies significantly among animals and tissues. The deviation from the predicted values in the brain is likely due to the release of heme and increase in NADPH stimulating endogenous CO production by HO.



### March 2009 B-51 DRAFT—DO NOT QUOTE OR CITE

### Reference Purpose Findings Dutton et al. (2001) To monitor CO, NO 2 , 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, NO 2 , and PAH indoors when the fireplaces were used. CO concentrations could exceed 100 ppm. NO 2 concentrations average 0.36 ppm over 4 hours. PAH 4-h time average reached 35 ng/m 3

. Hampson (2007) To present a case study of a man with drug-induced hemolytic anemia and hepatic failure. The man had elevated endogenous CO production resulting in levels of COHb as high as 9.7%. Hsia (2002) To review the current concepts and practical relevance of the diffusing capacity/cardiac output interaction, in hopes of aiding in the interpretation of diffusing capacity, membrane diffusing capacity, and capillary blood volume. This review helped to understand the determinants of changes in diffusing capacity, including hematoc rit, erythrocyte di stribution, blood volume, lung volume, cardiac output, etc.

Lamberto et al. (2004) To evaluat e which component, alveolar membrane diffusing capacity (D m ) and pulmonary capillary blood volume (V c ), is responsible for decreased resting D L CO 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 D L CO, and gas exchange abnorma lities during exercise including decreased P a O 2 and increased alveolar-arterial oxygen pressure difference. D m accounted for the majority of the decrease in D L CO and was predictive for gas exchange abnormalities. Levesque et al. (2000) To describe the results of air quality monitoring in an indoor ice skating rink during Monster Truck and car demolition exhibitions. Maximum time-weighted average levels of CO were 100 ppm with several peaks exceeding 200 ppm (maximum: 1,600 ppm).

Marks et al. (2002) To review the analytical methods for measurement of endogenous formation of CO in a variety of tissues. A variety of methods have been used to measure endogenous CO. The rate of formation varies over a narrow range from 0.029 nmol/mg protein/h to 0.28 nmol/mg prot ein/h depending on tissue. Brain and liver regions tend to have the highes t rates of CO formation likely due to high levels of HO activity in these tissues. Merx et al. (2001) To investigate the ef fect 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 pressu re or coronary venous PO 2 . Partially O 2 -saturated Mb (87% O 2 Mb) exposed to 20% CO had significantly decreased LVDP (12%) and PvO 2 (30%) in wild-type but not myo -/- hearts. Pelham et al. (2002) To review the literature on exposure and effects of mainly CO and NO 2 in enclosed ice rinks. CO levels as high as 300 ppm were recorded after episodes of malfunctioning ice resurfacing equipment or inadequate ventilation. Pesola et al. (2004) To determine if healthy African Americans may be misdiagnosed has having resp iratory deficient due to comparison using Caucas ian-derived prediction equation estimates of D L CO. The lung volume of African Americ an individuals is 10-15% lower than Caucasians. The measured D L CO was consistently significantly lower in African Americans than what w ould be predicted, thus the authors suggest a race correction reduction of the Miller PEE for diffusion of 12%. Pesola et al. (2006) To determine if healthy Asians may be misdiagnosed has having respiratory deficient due to comparison using Caucasian-deriv ed prediction equation estimates of D L CO. The lung volume of Asian i ndividuals is 10-15% lower than Caucasians, thus a Chinese derived prediction for D L CO should be used.

Prommer and Schmidt (2007) To determine the error in total Hb mass measurements using the optimized CO-rebreathing method due to loss of CO to Mb Optimal blood mixing (when venous and arterial blood COHb% are equivalent) was determined to be after 6 min. A small volume of administered CO leaves the vascular space (0.32% per min). 2.3% increase in total Hb mass would be found if CO diffusion was not included. Richardson et al. (2002) To combine in vasive vascular measures of arterial and venous blood and muscle blood flow withnoninvasive magnetic spectroscopy of deoxymyoglobin and high energy phos phates 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% O 2 + CO. Maximum works rates and maximal oxygen uptake were reduced in H, CO norm , and CO hyper . CO and H caused elevated blood flow. Net muscle CO uptake from blood was less during 20% COHb trials than during normoxia and hypoxia (1-2%) trials. Shimazu et al. (2000) To study the effe cts of short-term (minutes) or longterm (several hours) CO exposure on COHb elimination and developing a mathematical model to simulate this event. COHb exhibited an initial rapid dec rease followed by a slower phase which is compatible with a 2-compartment model and biphasic elimination. Both exposures fit t he 2-compartment, single central outlet mathematical model. Shimazu (2001) To discuss the findi ngs of Weaver et al. (2000) on COHb t 1/2

. The authors discuss that CO elimi nation is biphasic and is heavily affected by duration of exposure which was not taken into account in the Weaver, et al. (2000) paper. Takeuchi et al. (2000) To exami ne the relationship between minute ventilation and rate of COHb reduction during breathing 100% O 2 and during normocapnic hyperoxic hyperpnea. Patients were exposed to 400 to 1,000 ppm CO, resulting in 10-12% COHb. The half-time of COHb reduction was 78 ± 24 min during 100% O 2 treatment and 31 ± 6 min during normocapnic hyperpnea with O 2 treatment. Vreman et al. (2005) To develop a se nsitive and reproducible method of CO quantification in rodent (mouse and rat) tissue pre- and post-exposure in hopes of understanding endogenous CO production. Tissues were sonicated mixed with sulfosalicylic acid for 30 min at 0°C and then liberated CO was anal yzed by gas chromatograph. Blood contained the highest CO concentration. Lowest concentrations were found in brain, testes, intestine, and lung (endogenously).



### March 2009 B-52 DRAFT—DO NOT QUOTE OR CITE

### Reference Purpose Findings Vreman et al. (2006) To test a method of CO quantification in frozen postmortem human tissues from 3 determined categories of fatalities: trauma with no suspected CO exposure (controls), fire-related, and CO asphyxiation. CO levels were analyzed in adipose, brain, muscle, heart, kidney, lung, spleen, and blood (ordered from approximate low to high tissue concentration). It was suggested that blood, muscle, brain, lung, and kidney are suitable for diagnosing death due to lethal CO exposure due to regression analysis against COHb values. Weaver et al. (2000) To determine in COHb half-life is influenced by CO poisoning vs. experimental CO exposure, loss of consciousness, concurrent tobacco smoking, or P a O 2

. COHb t 1/2 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 P a O 2 , however not influenced by age, gender, smoke inhalation, loss of consciousness, tobacco smoking, or method of O 2 treatment. Wu and Wang (2005) To review t he 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 si gnaling cascades. The review discusses possible roles for CO in the various organ systems. Also, it discusses the potential pharmacol ogical and therapeutic applications for CO.



### March 2009 B-53 DRAFT—DO NOT QUOTE OR CITE

# Annex C. Epidemiology Studies

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

Study Design Concentrations CO Effect Estimates (95% CI)

CHANGES IN HEART RATE AND HEART RATE VARIABILITY

Author: Chan et al. (2005) Period of Study: December 2001 – February 2002 Location: Taipei, Taiwan Health Outcome: Various measures of HRV via ambulatory ECG (Holter system) Study Design: Panel Statistical Analyses: Linear regression (mixed effects) Age Groups Analyzed: 40 – 75 years Sample Description: 83 patients from the National Taiwan University Hospital Averaging Time: 1-h ma Mean (SD) unit: 1.1 ppm Range (Min, Max): 0.1, 7.7 Copollutant: NR Increment: NR RR Estimate [Lower CI, Upper CI] Lags examined (-h ma): 1, 2, 3, 4, 5, 6, 7, 8 CO had no statisticall y significant effect on SDNN, rMSSD, LF, HF.

Author: Dales et al. (2004) Period of Study: NR Location: Toronto, Canada Health Outcome: Various measures of HRV via Holter system Study Design: Panel Statistical Analyses: Linear regression (mixed effects) Age Groups Analyzed: 51 – 88 years (mean 65 years) Sample Description: 36 subjects with pre-existing CAD Averaging Time: 24-h Mean (SD) unit: 2.40 ppm (95th percentile) Personal monitoring Range (Min, Max): 0.4, 16.5 Copollutant: correlation PM 2.5 : 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 S DNN among those not taking Beta-blockers. Slope = 0.0111 (0.002-0.020, p=0.02)

Author: Gold et al. (2000) Period of Study: June – September 1997 Location: Boston, MA Health Outcome (ICD9 or ICD10): Heart Rate and various measures of HRV via Holter system Study Design: Panel/Cohort Statistical Analyses: Linear regression (fixed effects/random effects) Age Groups Analyzed: 53 – 87 years Sample Description: 21 active Boston residents observed up to 12 times. Averaging Time: 24-h Mean (SD) unit: 0.47 ppm Range (Min, Max): 0.12, 0.82 Copollutant: NR Increment: 0.6 ppm % Change [Lower CI, Upper CI] Lags examined : 24-h No significant effect with CO (no results recorded)

Author: Gold et al. (2005) Period of Study: June – September 1999 Location: Boston, MA Health Outcome: ST- segment. Study Design: Panel Statistical Analyses: Linear regression (mixed models) Age Groups Analyzed: 61 – 88 years Sample Description: 24 Active Boston residents - each observed up to 12 times. Averaging Time: 1-h, 24-h Mean (SD) unit: NR Range (Min, Max): (ppm) (personal monitoring) 10th = 0.20 90th = 1.08 Copollutant: NR Increment: NR RR Estimate [Lower CI, Upper CI] Lags examined : 1-24 h Although CO was associated with ST-segment depression in single pollutant models, this result did not persist in multiple pollutant models.

Author: Holguin et al. (2003) Period of Study: February – April 2000 Location: Mexico City, Mexico Health Outcome: Various measures of HRV via ECG Study Design: Panel Statistical Analyses: GEE Age Groups Analyzed: 60-96 years (mean age 79 years) Sample Description: 34 patients who were permanent residents of a nursing home in the Northeast metropolitan area. Averaging Time: 24-h Mean (SD) unit: 3.3 ppm Range (Min, Max): 1.8, 4.8 Copollutant: NR Increment: 10 ppm Regression Coefficients [Lower CI, Upper CI] Lags examined : 0 Lag 0 : HF : 0.003 (-0.004 to 0.001) LF : 0.001 (-0.006 to 0.008) LF/HF : 0.001 (-0.005 to 0.002)



### March 2009 C-1 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Liao et al. (2004) Period of Study: 1996 - 1998 Location: Forsyth County, NC; Selected suburbs of Minneapolis, MN; Jackson, MI Health Outcome: Heart Rate & various rates of HRV. Study Design: Cohort Statistical Analyses: Linear regression Age Groups Analyzed: 45 – 64 years (mean 62 years) Sample Description: 6784 study subjects from the atherosclerosis risk in communities study Averaging Time: 24-h Mean (SD) unit: 0.65 ppm (0.44) Range (Min, Max): NR Copollutant: NR Increment: 0.44 ppm Regression coefficients Lags examined : 1 Lag 1 : HF (log transformed) : -0.033 LF (log transformed) : 0.006 SDNN : -0.274 Heart Rate (bpm) : 0.404* Confidence Intervals not recorded *p < 0.05 Author: Park et al. (2005b) Period of Study: 2000 - 2003 Location: Boston, MA Health Outcome: Various measures of HRV via ECG Study Design: Panel/Cohort Statistical Analyses: Linear regression Age Groups Analyzed: 21 – 81 years Sample Description: 497 men from the Normative aging study in Greater Boston Averaging Time: 24-h Mean (SD) unit: 0.50 ppm Range (Min, Max): 0.13, 1.8 Copollutant: NR Increment: 0.24 ppm % Change in HRV [Lower CI, Upper CI] Lags examined: 4-h ma, 24-h ma, 48-h ma Lag 4-h ma: SDNN (Log10): 2.0 (-2.9 to 7.3) HF (Log10): 8.8 (-4.6 to 24.1) LF(Log10) : 3.2 (-7.0 to 14.6) LF :HF(Log10) : -5.1 (-13.5 to 4.1) Lag 24-h ma: SDNN (Log10): -2.2 (-7.7 to 3.6) HF (Log10): -13.2 (-25.4 to 1.0) LF(Log10) : -0.6 (-11.9 to 12.1) LF :HF(Log10) : 14.5 (2.9-27.5) Lag 48-h ma: SDNN(Log10): -3.4 (-10.2 to 3.9) HF (Log10): -13.8 (-.28.9 to 4.4) LF (Log10): -2.4 (-16.2 to 13.6) LF :HF (Log10): 13.2 (-1.1 to 29.6) Author: Peters et al. (1999a) Period of Study: 1984 - 1985 Location: Augsburg, Germany Health Outcome: Heart Rate Study Design: Cohort Statistical Analyses: Linear regression (GEE) Age Groups Analyzed: 25 – 64 years Sample Description: 2681 men & women who participated in the MONICA study Averaging Time: 24-h Mean (SD) unit: During air pollution episode: 4.54 mg/m 3 Outside air pollution episode: 4.51 mg/m 3 Range (Min, Max): During air pollution episode: 2.39, 6.85 Outside air pollution episode: 0.91, 11.51 Respectively Copollutant: NR Increment: 6.6 mg/m 3 Mean Change in Heart Rate (beats/min) [Lower CI, Upper CI] Lags examined: 0, 5-day avg All Lag 0 : 0.97 (0.02-1.91) Lag 5-day avg : 0.70 (-0.09 to 1.48) Men Lag 0 : 0.95 (-0.37 to 2.27) Lag 5-day avg : 0.91 (-0.25 to 2.07) Women Lag 0 : 0.98 (-0.37 to 2.34) Lag 5-day avg : 0.52 (-0.55 to 1.59) Author: Riojas-Rodriguez et al. (2006) Period of Study: December 2001 – April 2002 Location: Mexico City, Mexico Health Outcome: Various measures of HRV via Holter system Study Design: Panel Statistical Analyses: Linear regression (mixed effects models) Age Groups Analyzed: 25 – 76 years (mean 55 years) Sample Description: 30 patients from the Outpatient clinic of the National Institute of Cardiology of Mexico Averaging Time: 24-h Mean (SD) unit: 2.9 ppm (personal monitor) Range (Min, Max): 0.1, 18.0 Copollutant: NR Increment: 1 ppm Regression Coefficients [Lower CI, Upper CI] Lags examined (per minutes) : 5, 10 Lag 5 minutes : HF : -0.006 (-0.023 to 0.010) LF : -0.024 (-0.041 to -0.007) VLF : -0.034 (-0.061 to -0.007) Notes: VLF = Very low frequency

Author: Schwartz et al. (2005) Period of Study: 1999 Location: Boston, MA Health Outcome: Measures of HRV via Holter system Study Design: Panel Statistical Analyses: Linear regression (hierarchical model) Age Groups Analyzed: 61 – 89 years Sample Description: 28 subjects living at or near an apartment complex located on the same street at the Harvard School of Public Health Averaging Time: 24-h Mean (SD) unit: NR Range (Min, Max): ppm 25th = 0.38; 75th = 0.54 Copollutant: correlation PM 2.5 : r = 0.61 NO 2 : r = 0.55 SO 2 : r = -0.18 O 3 : 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); rM SSD : -3.9 (-10.6 to 3.3); PNN50 : -3.5 (-13.7 to 8.0); LF :HF : 4.5 (-1.2 to 10.5) Lag 24-h: SDNN : -4.2 (-0.6 to -7.7); rMSSD : -10. 2 (-2.4 to -17.4); PNN50 : -14.8 (-3.0 to -25.2); LF :HF : 6.2 (-0.6 to 13.4)



### March 2009 C-2 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Tarkiainen et al. (2003) Period of Study: October 1997 – May 1998 Location: Kuopio, Finland Health Outcome: Various measures of HRV via Ambulatory ECG (Holter system) Study Design: Panel Statistical Analyses: ANOVA for repeated errors (GLM) Age Groups Analyzed: Age 55 – 68 years Sample Description: 6 male patients with angiographically verified CAD Averaging Time: 24-h Mean (SD) unit: 4.6 ppm (maximum of CO episode) (personal monitoring) Range (Min, Max): 0.5, 27.4 (maximum of CO episode) Copollutant: NR Increment: NR RR Estimate [Lower CI, Upper CI] Lags examined : 5 minute prio r to CO episode, 5 minute during CO episode CO had no statically significant effect on NN, SDNN or rMSSD. However, during high CO exposure (>2.7 ppm) CO was associated with an increase in rMSSD of 2.4ms (p=0.034).

Author: Timonen et al. (2006) Period of Study: 1998 - 1999 Location: 3 Cities in Europe: Amsterdam, Netherlands; Erfert, Germany; Helsinki, Finland Health Outcome: Stable CAD: Various measures of HRV via ambulatory ECG (Holter system) Study Design: Panel Statistical Analyses: Linear regression (mixed model) Age Groups Analyzed: Mean age across 3 cities; 64 – 71 years. Sample Description: 131 subjects with Stable CAD followed for 6 months with bi-weekly clinical visits. Averaging Time: 24-h Mean (SD) unit: Amsterdam: 0.6 mg/m 3 Erfert: 0.4 mg/m 3 Helsinki: 0.4 mg/m 3 Range (Min, Max): Amsterdam: 0.4, 1.6 Erfert: 0.1, 2.5 Helsinki: 0.1, 1.0 Copollutant: correlation Amsterdam: PM 2.5 : r = 0.58 NO 2 : r = 0.76 Erfert: PM 10 : r = 0.77 NO 2 : r = 0.86 Helsinki: PM 10 : r = 0.40 NO 2 : r = 0.32 Increment: 1 mg/m 3 Regression co-efficient [Lower CI, Upper CI] Lags examined (days): 0, 1, 2, 3, 5-day avg SDNN: Lag 0 : -1.21 (-4.44 to 2.03); Lag 1 : -1.71 (-6.05 to 2.63); Lag 2 : -5.69 (-10.7 to -0.72); Lag 3 : 0.66 (-3.83 to 5.15); 5-day avg: -3.60 (-9.88 to 2.68) HF: Lag 0 : 5.0 (-15.1 to 25.1); Lag 1 : -2.0 (-37.1 to 33.1); Lag 2 : -30.7 (-59.8 to -1.5); Lag 3 : -9.3 (-35.8 to -17.3); 5-day avg: -15.2 (-53.0 to 22.6) LF/HF: Lag 0 : -3.6 (-21.8 to 14.5); Lag 1 : -28.6 (-52.0 to -5.3); Lag 2 : -10.1 (-36.9 to 16.7); Lag 3 : 7.7 (-16.5 to 31.9); 5-day avg: -16.9 (-51.2 to 17.3)

Author: Wheeler et al. (2006) Period of Study: 1999 - 2000 Location: Atlanta, GA Health Outcome: Various measures of HRV via Holter system Study Design: Panel Statistical Analyses: Linear regression (mixed effects models) Age Groups Analyzed: Mean 65 years – IQR 55- 73 years. Sample Description: 18 subjects with COPD and 12 subjects with recent MI. Averaging Time: 1-h Mean (SD) unit: 362.0 ppb Range (Min, Max): 25th = 221.5; 75th = 398.1 Copollutant: correlation PM 2.5 : r = 0.43 Increment: NR RR Estimate [Lower CI, Upper CI] ; lag : Lags examined (h ma): 1, 4, 24 No CO results reported.

ONSET OF CARDIAC ARRHYTHMIA

Author: Berger et al. (2006) Period of Study: October 2000 – April 2001 Location: Erfurt, Germany Health Outcome: Runs of supraventricular and ventricular tachycardia recorded via 24-h ECG. Study Design: Panel Statistical Analyses: Poisson regression (GAM) Linear regression Age Groups Analyzed: 52 – 76 years (mean 76years) Sample Description: 57 men with CHD Averaging Time: 24-h Mean (SD) unit: 0.52 mg/m 3 Range (Min, Max): 0.11, 1.93 Copollutant: correlation NR Increment: All: 0.27 mg/m 3 5-day avg : 0.22 mg/m 3 RR Estimate [Lower CI, Upper CI] Lags examined (h): 0, 0-23, 24-47, 48-71, 72-95, 5-day avg Supraventricular extrasystoles: Lag 0 : 1.18 (1.00-1.38) Lag 0-23 : 1.16 (1.02-1.31); Lag 24-47 : 1.13 (1.00-1.28); Lag 48-71 : 1.18 (1.03-1.36); Lag 72-95 : 1.08 (0.98-1.20); 5- day avg: 1.18 (1.04-1.35) Mean % Change [Lower CI, Upper CI] Hourly Lags examined: 0, 0-23, 24-47, 48-71, 72-95, 5-day avg Ventricular extrasystoles: Lag 0 : 0.0 (-4.1 to 4.4); Lag 0-23 : 1.1 (-3.3 to 5.7); Lag 24-47 : 1.9 (-2.6 to 6.6); Lag 48-71 : 4.2 (-0.3 to 8.9); Lag 72-95 : 2.7 (-1.3 to 6.9); 5- day avg: 3.0 (-1.8 to 8.0)



### March 2009 C-3 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Dockery et al. (2005) Period of Study: 1995 - 2002 Location: Boston, MA Health Outcome: Tachyarrhythmias: Study Design: Panel Statistical Analyses: Logistic regression (GEE) Age Groups Analyzed: 19 – 90 years; mean age 64 years Sample Description: 203 cardiac patients with ICDs within 40km of air monitoring site at Harvard School of Public Health, Boston Averaging Time: 24-h Mean (SD) unit: NR Range (Min, Max): 25th = 0.53; 75th = 1.02 Copollutant: NR Increment: 0.48 ppm OR for Ventricular Arrhythmia [Lower CI, Upper CI] Lags examined (days): 0, 1, 2, 3 Lag 2day ma: 1.14 (0.95-1.29) Among those who had an Arrhythmia – within 3 days : 1.65 (1.17-2.33) later than 3 days : 1.04 (0.83-1.29)

Author: Metzger et al. (2007) Period of Study: 1993 - 2002 Location: Atlanta, GA Health Outcome: Cardiac Arrhythmia, ICD, Ventricular tachyarrhythmia Study Design: Panel Statistical Analyses: Logistic regression (GEE) Age Groups Analyzed: 15 – 88 years Sample Description: 518 patients with ICDs with at least one ventricular tachyarrhythmic event Averaging Time: 1-h Mean (SD) unit: 1.7 ppm Range (Min, Max): 0.1, 7.7 Copollutant: NR Increment: 1 ppm OR for Tachyarrhythmic event [Lower CI, Upper CI] Lags examined (days) : 0 Results for All events Lag 0 : 0.999 (0.970-1.028) Events resulting in cardiac pacing or defibrillation Lag 0 : 1.008 (0.964-1.054) Events resulting defibrillation Lag 0 : 1.012 (0.925-1.10.7)

Author: Peters et al. (2000b) Period of Study: 1995 - 1997 Location: Eastern Massachusetts Health Outcome: Defibrillated discharges for ventricular tachycardia or fibrillation Study Design: Panel Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: Mean age of 62 years Sample Description: 100 patients with ICDs Averaging Time: 24-h Mean (SD) unit: 0.58 ppm Range (Min, Max): 25th = 0.43; 75th = 0.66 Copollutant: correlation PM 10 : r = 0.51 PM 2.5 : r = 0.56 NO 2 : r = 0.71 SO 2 : r = 0.41 O 3 : r = -0.40 Increment: 0.65 ppm (Lags 0, 1, 2, 3) ; 0.42 ppm (Lag 5-day mean) OR for Defibrillated Discharge [Lower CI, Upper CI] Lags examined (days): 0, 1, 2 ,3, 5-day mean At least one discharge: Lag 0: 1.07 (0.62-1.86); Lag 1 : 1.06 (0.61-1.85); Lag 2: 1.05 (0.62-1.77); Lag 3 : 0.09 (0.65-1.83); Lag 5-day mean : 1.23 (0.71- 2.12) At least 10 discharges: Lag 0: 1.12 (0.54-2.32); Lag 1 : 1.13 (0.54-2.33); Lag 2: 1.62 (0.85-3.09); Lag 3 : 1.98 (1.05-3.72); Lag 5-day mean : 1.94 (1.01-.75) Author: Rich et al. (Rich et al.) Period of Study: February – December 2000 Location: Vancouver, Canada Health Outcome: Cardiac arrhythmia via patients ICD Study Design: Case-crossover Statistical Analyses: Conditional Logistic regression Age Groups Analyzed: 15 – 85 years Sample Description: 34 patients who experienced at least 1 ICD discharge ( 8201 person days) Averaging Time: 24-h Mean (SD) unit: 553.8 ppb Range (Min, Max): IQR: 162.7 Copollutant: correlation PM 10 : r = 0.40 SO 2 : r =0.75 NO 2 : r =0.68 O 3 : r = -0.56 Increment: NR RR Estimate [Lower CI, Upper CI] Lags examined (days): 0, 1, 2, 3 No significant effect (resul ts not reported in table).

Author: Rich et al. (2005) Period of Study: 1995 - 1999 Location: Boston, MA Health Outcome: Ventricular arrhythmias via ICD Study Design: Panel/Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: All Sample Description: 203 patients with implanted ICD at the New England Medical Center Averaging Time: 1-h & 24-h Mean (SD) unit: NR Range (percentiles): 1-h: 25th = 0.46 75th = 1.04 24-h: 25th = 0.52 75th = 1.03 Copollutant: NR Increment: 0.56 ppm; 0.54; 0.51; 0.49 respectively for results shown below OR Estimate [Lower CI, Upper CI] Ventricular Arrythmia Hours prior to event : 0-2 : 1.01 (0.87-1.18) 0-6 : 1.00 (0.85-1.17) 0-23 : 1.03 (0.84-1.25) 0-47 : 1.11 (0.88-1.40)



### March 2009 C-4 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Rich et al. (2006b) Period of Study: 2001 & 2002 Location: St. Louis, MO Health Outcome: Ventricular arrhythmia Study Design: Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: All Sample Description: 60 subjects with at least 1 ICD recorded arrhythmia who lived within 40 km of St. Louis – Midwest supersite. Averaging Time: 24-h Mean (SD) unit: NR Range (Min, Max): 25th = 0.4; 75th = 0.6 Copollutant: NR Increment: 0.2 ppm OR for Ventricular Arrhythmia [Lower CI, Upper CI] Lags examined : 0-23 h-ma 0-23h-ma : 0.99 (0.80-1.21)

Author: Rich et al. (2006a) Period of Study: 1995 - 1999 Location: Boston, MA Health Outcome: ICD Episode of Atrial fibrillation Study Design: Panel/case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: All Sample Description: 203 patients with ICDs at the New England Medical Center Averaging Time: 1-h & 24-h Mean (SD) unit: NR Range (Min, Max): 1-h: 25th = 0.46; 75th = 1.04 24-h: 25th = 0.52; 75th = 1.03 Copollutant: NR Increment: Lag (hrs) 0 : 0.58 ppm Lag (hrs) 0-23 : 0.51 ppm OR for Episode of Atrial Fibrillation [Lower CI, Upper CI] Lags (h) : 0, 0-23 Lag 0 : 0.87 (0.56-1.37) Lag 0-23 : 0.71 (0.39-1.28)

Author: Sarnat et al. (2006) Period of Study: 24 weeks during the Summer and Fall of 2000 Location: Steubenville, OH Health Outcome: Arrhythmia via ECG measurements Study Design: Panel Statistical Analyses: Logistic regression Age Groups Analyzed: 53-90 years (mean age 71) Sample Description: 32 non-smoking older adults Averaging Time: 24-h Mean (SD) unit: 0.02 ppm Range (Min, Max): -0.1, 1.5 Copollutant: correlation PM 2.5 : r = 0.45 SO 2 : r = 0.62 NO 2 : r = 0.66 O 3 : r = -0.37 Increment: 0.2 ppm RR Estimate [Lower CI, Upper CI] ; lag : Lags examined (days): 1, 2, 3, 4, 5, 5-day ma Lag 5-day ma : Supraventricular Ectopy SVE : 0.99 (0.76-1.29) Ventricular Ectopy VE : 1.05 (0.75-1.46)

Author: Vedal et al. (2004) Period of Study: 1997 - 2000 Location: Vancouver, Canada Health Outcome: Cardiac arrythmia via patients with ICD Study Design: Panel Statistical Analyses: Logistic regression (GEE) Age Groups Analyzed: Range from 12-77 (mean age 53) Sample Description: 50 patients who experienced 1 or more arrhythmia event days during the four years Averaging Time: 24-h Mean (SD) unit: 0.6 ppm Range (Min, Max): 0.3, 1.6 Copollutant: correlation PM 10 : r = 0.43 SO 2 : r = 0.62 NO 2 : r = 0.74 O 3 : r = -0.52 Increment: 0.2 ppm RR Estimate [Lower CI, Upper CI] Lags examined (days) : 0, 1, 2, 3 No significant effect for CO (results shown in plots)

CARDIAC ARREST

Author: Levy et al. (2001) Period of Study: 1988 - 1994 Location: Seattle, WA Health Outcome: Out of hospital primary cardiac arrest Study Design: Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: 25-75 years Sample Description: 362 cases Averaging Time: 24-h Mean (SD) unit: 1.79 ppm Range (Min, Max): 0.52, 5.92 Copollutant: correlation PM 10 : r = 0.81 SO 2 : r = 0.29 Increment: NR RR Estimate [Lower CI, Upper CI] Lags examined (days): 0, 1 Lag 1 : 0.99 (0.83-1.18)

Author: Sullivan et al. (2003) Period of Study: 1985 - 1994 Location: Washington State Health Outcome: Out of Hospital Cardiac Arrest. Study Design: Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: All Sample Description: 1,542 members of a large health maintenance organization Averaging Time: 24-h Mean (SD) unit: 1.92 ppm Range (Min, Max): 0.52, 7.21 Copollutant: NR Increment: 1.02 ppm OR Estimate [Lower CI, Upper CI] Lags examined (days) : 0, 1, 2 Lag 0 : 0.95 (0.85-1.05) Lag 1 : 0.97 (0.87-1.08) Lag 2 : 0.99 (0.89-1.11)



### March 2009 C-5 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

MYOCARDIAL INFARCTION

Author: Peters et al. (2001) Period of Study: 1995 - 1996 Location: Boston, MA Health Outcome: Onset of MI: Study Design: Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: All Sample Description: 772 participants Averaging Time: 24-h Mean (SD) unit: 1.09 Range (percentiles): ppm 5th = 0.49 95th = 1.78 Copollutant: NR Increment: 2 H – 1 ppm; 24 h – 0.6 ppm OR Estimate [Lower CI, Upper CI] Onset of MI: 2 h prior : 1.22 (0.89-1.67) 24 h prior : 0.98 (0.70-1.36)

Author: Rosenlund et al. (2006) Period of Study: 1992 - 1994 Location: Stockholm, Sweden Health Outcome: MI Study Design: Case-control Statistical Analyses: Logistic regression Age Groups Analyzed: 45 – 70 years Sample Description: 1,397 cases, 1,870 controls Averaging Time: Mean (SD) unit: 66.8 μg/m 3 (est. 30yr residential exposure) Range (percentiles): 5th = 13.9; 95th = 295.7 Copollutant: NR Increment: 300 μg/m 3 OR Estimate [Lower CI, Upper CI] ; lag : Estimated 30 yr avg. exposure All cases : 1.04 (0.89-1.21) Non-fatal cases : 0.98 (0.82-1.16) Fatal cases : 1.22 (0.98-1.52) In-hospital death : 1.16 (0.89-1.51) Out-of-hospital death : 1.36 (1.01-1.84)

CHANGES IN BLOOD PRESSURE

Author: Ibalde-Mulli et al. (2001) Period of Study: 1984 - 1985 Location: Augsburg, Germany Health Outcome: BP - SPB Study Design: Cohort Statistical Analyses: Gaussian regression for repeated measures Age Groups Analyzed: 25-64 years Sample Description: 2,607 men & women aged 25-64 years Averaging Time: 24-h Mean (SD) unit: 4.1 mg/m 3 Range (Min, Max): 1.7, 8.2 Copollutant: NR Increment: Lag 0 : 5.6 mg/m 3 5-day prior avg. Mean Change [Lower CI, Upper CI] SPB mmHg Lag 0 (days): All : 0.53 (-0.66 to 1.72); Men : 0.68 (-0.94 to 2.31); Women : 0.51 (-1.31 to 2.19) 5-day prior avg: All : 1.06 (-0.17 to 2.29); Men : 0.92 (-0.87 to 2.70); Women : 0.91 (-0.87 to 2.70) Author: Zanobetti et al. (2004a) Period of Study: 1999 - 2001 Location: Boston, MA Health Outcome: BP Study Design: Cohort/Panel Statistical Analyses: Random effects Age Groups Analyzed: 39 – 90 years Sample Description: 62 subjects with 631 total visits Averaging Time: 1-h & 120-h avg Mean (SD) unit: Same Hr: 0.81 ppm 120 Hr av: 0.66 ppm Range (Min, Max): Same h: 10th = 0.48; 90th = 1.22 120-h av: 10th = 0.48; 90th = 0.86 Copollutant: NR Increment: NR RR Estimate [Lower CI, Upper CI] CO had no significant effect on BP

CHANGES IN BLOOD MARKERS OF COAGULATION AND INFLAMMATION

Author: Baccarelli et al. (2007) Period of Study: 1995 - 2005 Location: Milan, Italy Health Outcome: Prothrombin time (PT) and Activated partial thromboplastin time (APTT) Study Design: Panel Statistical Analyses: GAMS Age Groups Analyzed: 11-84 years (mean 43years) Sample Description: 1,218 healthy individuals who were partners or friends of patients with thrombosis who attended the thrombosis center of the University of Milan. Averaging Time: 1-h Mean (SD) unit: NR Range (percentiles): Sept-Nov: 25th = 1.36; 75th = 3.52 Dec-Feb: 25th = 2.00; 75th = 4.31 Mar-May: 25th = 1.03; 75th = 2.14 Jun-Aug: 25th = 0.73; 75th = 1.58 Copollutant: NR Increment: NR Regression co-efficient [Lower CI, Upper CI] Lags examined (time of blood sampling – avg): 0, 7, 30 PT: Lag 0 : -0.11 (-0.18 to -0.05); Lag 7 : -0.07 (-0.14 to 0.01); Lag 30 : -0.05 (-0.13 to 0.02) APTT: Lag 0 : 0.03 (-0.04 to 0.10); Lag 7 : 0.04 (-0.04 to 0.11); Lag 30 : 0.06 (-0.01 to 0.14) Notes: CO had no effect on fibrinogen, functional antithrombin, functional pr otein C, protein C antigen, functional protein S, free prot ein S for all lag periods. Author: Liao et al. (2005) Period of Study: 1996 to 1998 Location: Forsyth County, NC; Selected suburbs of Minneapolis, MN; Jackson, MI Health Outcome: Various measures of hemostasis/ inflammation Study Design: Cohort Statistical Analyses: Linear regression Age Groups Analyzed: 45 – 64 years Sample Description: 10208 subjects from the Atherosclerosis Risk in Communities Study Averaging Time: 24-h Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Increment: 0.6 ppm Regression coefficients [SE] Lags examined (days): 1 Lag 1: Fibrinogen (mg/dL) : -0.16 (0.67) Factor VIII –C (%) : 0.45 (0.42) vWF % : -0.29 (0.50) WBC (x 10 3 /mm 3 ) : 0.003 (0.017) Albumin (g/dL) : -0.018 (0.003)** ** p < 0.01



### March 2009 C-6 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Pekkanen et al. (2000) Period of Study: 1991 - 1993 Location: London, England Health Outcome: Fibrinogen Study Design: Cohort Statistical Analyses: Logistic regression Age Groups Analyzed: 35 – 55 years Sample Description: 7,205 office workers Averaging Time: 8-h Mean (SD) unit: 1.4 mg/m 3 Range (Min, Max): Min = NR, Max = 9.9 Copollutant correlation: PM 10 : r = 0.57 NO 2 : r = 0.81 SO 2 : r = 0.61 O 3 : r = -0.45 Increment: 1.6 mg/m 3 % Change in Fibrinogen Concentration [p value] ; Lags examined : 0, 1, 2, 3 Lag 0 : 1.43 (<0.01); Lag 1 : 1.49 (<0.01); Lag 2 : 1.59 (<0.01); Lag 3 : 1.26 (<0.01) OR for having Fibrinogen above 3.19 g/l [p value] Lags examined : 0, 1, 2, 3 Lag 0 : 1.17 (0.05); Lag 1 : 1.09 (0.31); Lag 2 : 1.14 (0.11); Lag 3 : 1.22 (<0.01)

Author: Ruckerl et al. (2006) Period of Study: 2000 - 2001 Location: Erfert, Germany Health Outcome: Blood markers of inflammation and coagulation Study Design: Panel Statistical Analyses: Linear and logistic regression (fixed effects) Age Groups Analyzed: 51-76 years (mean age 66 years) Sample Description: 57 male patients with CHD Averaging Time: 24-h Mean (SD) unit: 0.52 mg/m 3 Range (Min, Max): 0.11, 1.93 Copollutant correlation: NO 2 : r = 0.82 Increment: 0.27 mg/m 3 OR Estimate for blood marker >90th percentile [Lower CI, Upper CI] Lags examined (h): 0-23, 24-47, 48-71, 5-day avg. CRP (C-reactive protein) 0-23 : 0.9 (0.7-1.2); 24-47 : 1.0 (0.7-1.5); 48-71 : 1.5 (1.1-2.1); 5-day avg 1.1 (0.8-1.6) ICAM-1 (Intercellular adhesion molecule 1) 0-23 : 0.8 (0.6-1.0); 24-47 : 1.5 (1.2-1.9); 48-71 : 1.7 (1.3-2.3); 5-day avg 1.2 (1.0-1.6) % of change from the mean of blood marker vWF (von Willebrand factor antigen) 0-23 : 4.4 (1.4- 7.5); 24-47 : 2.7 (-0.8 to 6.1); 48-71 : 2.0 (-1.7 to 5.8); 5-day avg : 4.9 (1.0-8.8) FVII (Factor VII) 0-23 : -1.4 (-3.8 to 1.1); 24-47 : -2.6 (-4.8 to 0.3); 48-71 : -2.8 (-5.1 to -0.4); 5-day avg : -3.0 (-5.5 to -0.4) Author: Ruckerl et al. (2007) Period of Study: May 2003-July 2004 Location: 6 cities across Europe: Athens, Greece; Augsburg, Germany; Barcelona, Spain; Helsinki, Finland; Rome, Italy; Stockholm, Sweden Health Outcome: Interleukin-6, C-reactive protein, Fibrinogen Study Design: Panel/Cohort Statistical Analyses: Linear regression (mixed effects) Age Groups Analyzed: 37-81 years Sample Description: 1,003 MI survivors who had at least 2 valid repeated blood samples Averaging Time: 24-h Mean (SD) unit: Athens: 1.48 mg/m 3 Augsburg: 0.58 mg/m 3 Barcelona: 0.59 mg/m 3 Helsinki: 0.31 mg/m 3 Rome: 1.40 mg/m 3 Stockholm: 0.29 mg/m 3 Range (Min, Max): NR Copollutant: NR Increment: 0.34 mg/m 3 % Change in mean [Lower CI, Upper CI] Lags examined: 0, 1, 2, 5-day avg (Pooled estimates) Interleukin-6 Lag 0: 0.57 (-0.63 to 1.79); Lag 1 : 0.44 (-0.79 to 1.68); Lag 2: -2.36 (-4.82 to 0.17); 5-day avg: -0.28 (-2.53 to 2.02) C-reactive protein Lag 0 : -0.01 (-1.72 to 1.73); Lag 1 : -1.51 (-3.30 to 0.32); Lag 2 : -2.35 (-6.84 to 2.36); 5-day avg : -0.85 (-.5.37 to 3.90) Fibrinogen Lag 0 : 0.24 (-0.54 to 0.92); Lag 1 : 0.32 (-0.35 to 1.00); Lag 2 : -0.44 (-1.11 to 0.23); 5-day avg : 0.12 (-0.81 to 1.05) Author: Steinvil et al. (2008) Period of Study: 2003 - 2006 Location: Tel Aviv, Israel Health Outcome: Various measures of inflammation sensitive biomarkers Study Design: Cohort Statistical Analyses: Linear regression Age Groups Analyzed: mean age 46 years Sample Description: 3659 subjects living within 11 km of monitoring site Averaging Time: 24-h Mean (SD) unit: 0.8 ppm Range (percentiles): 25th = 0.7; 75th = 1.0 Copollutant: correlation PM 10 : r = 0.75 NO 2 : r = 0.857 SO 2 : r = 0.671 O 3 : r = -0.656 Increment: 0.3 ppm Regression co-efficient [Lower CI, Upper CI] Lags examined (days): 0, 1, 2, 3, 4, 5, 6, 7, last week avg Fibrinogen – Men Lag 0 : -3.3 (-6.1 to -0.6); Lag 1 : -2.6 (-5.5 to 0.4); Lag 2 : -3.4 (-6.6 to -0.3); Lag 3 : -3.4 (-6.5 to -0.2); Lag 4 : -5.9 (-8.9 to -2.9); Lag 5 : -4.7 (-7.8 to -1.6); Lag 6 : -2.0 (-5.1 to 1.0); Lag 7 : -2.7 (-5.7 to 0.2); Last week avg: -7.7 (-12.1 to -3.3) Notes: No effect on fibrinogen among women. CO had no effect on CRP among men and no effect on CRP and WBC among women for all Lag times examined.



### March 2009 C-7 DRAFT—DO NOT QUOTE OR CITE

# Table C-2. Studies of CO expos ure and cardiovascular hospital admissions and ED visits.

Study Design Concentrations CO Effect Estimates (95% CI)

STROKE

Author: Chan et al. (2006) Period of Study: 1997 - 2002 Location: Taipei, Taiwan ED Visits Health Outcome (ICD9): Cerebrovascular disease (430-437); Stro kes (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 O 3 : r = 0.30 SO 2 :r = 0.63 NO 2 : r = 0.77 PM 2.5 : r = 0.44 PM 10 : 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 + O 3 : Lag 2, 1.03 (1.01-1.05) CO + PM 2.5 : Lag 2, 1.02 (1.00-1.04) CO + PM 10 : Lag 2, 1.03 (1.01-1.05) Author: Henrotin et al. (2007) Period of Study: 1994 - 2004 Location: Dijon, France Health Outcome (ICD9 or ICD10): Stroke (Ischemic & Hemorrhagic) Study Design: Bi-directional Casecrossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: ≥ 40 Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 683 μg/m 3 Range (Min, Max): 0, 4014 Copollutant: NR Increment: 10 μg/m 3 OR Estimate [Lower CI, Upper CI] Lags (days) examined: 0, 1, 2, 3. Ischemic: Lag 0 : 0.999 (0.997-1.001) Lag 1 : 0.998 (0.997-1.001) Lag 2 : 0.999 (0.998-1.001) Lag 3 : 1.000 (0.998-1.001) Hemorrhagic: Lag 0 : 1.000 (0.996-1.004) Lag 1 : 1.001 (0.997-1.005) Lag 2 : 0.999 (0.995-1.004) Lag 3 : 0.998 (0.994-1.002) Also not significant when stratified by sex. Author: Maheswaran et al. (2005b) Period of Study: 1994 - 1998 Location: Sheffield, UK Health Outcome (ICD9 or ICD10): Stroke deaths (ICD9: 430-438); Stroke Hospital admissions (ICD10: I60-I69) Study Design: Ecological Statistical Analyses: Poisson regression Age Groups Analyzed: ≥ 45 years Sample Description: 1,030 census districts Averaging Time: NR Mean (SD) unit: Quintiles Range (Min, Max): NR Copollutant: NR Increment: NR – Quintiles of exposure RR Estimate [Lower CI, Upper CI] Adjusted for sex, age, deprevation, smoking. Quintiles: 2nd : 1.04 (0.94-1.16) 3rd : 1.01 (0.91-1.13) 4th : 1.10 (0.99-1.23) 5th : 1.11 (0.99-1.25) Adjusted for sex, age: 2nd : 1.11 (1.01-1.22) 3rd : 1.15 (1.04-1.27) 4th : 1.29 (1.17-1.42) 5th : 1.37 (1.24-1.52) Author: Tsai et al. (2003b) Period of Study: 1997-2000 Location: Kaohsiung, Taiwan Study Design: Case-crossover Health Outcome (ICD9 or ICD10): Cerebrovascular diseases: ICD9: 430 to 438 (Subarachnoid hemorrhagic stroke 430, Primary intracerebral hemorrhage (PIH): 431-432, Ischemic stroke (IS): 433- 435). Statistical Analyses: NR Age Groups Analyzed: All Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 0.79 ppm Range (Min, Max): 0.24, 1.72 Copollutant: NR Increment: 0.8 ppm (IQR) RR Estimate [Lower CI, Upper CI] Lag (days): 0-2 >20oC PIH : OR 1.21 (1.09-1.34) IS : OR 1.21 (1.14-1.28) <20oC PIH : OR 1.18 (0.80-0.72) IS : OR 1.77 (1.31-2.39) Notes: 2-pollutant models: PIH results persisted when adjusting for SO 2 and O 3 IS results persisted when controlling for PM 10 , SO 2 and O 3



### March 2009 C-8 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Villeneuve et al. (2006a) Period of Study: 1992-2002 Location: Edmonton, Canada ED Visits (within 5 hospitals) Health Outcome (ICD9): Stroke (430- 438); Ischemic (434-436) Hemorrhagic (430-432); Transient Ischemic Attack (435) Study Design: Case-crossover Statistical Analyses: Conditional logistic regression Age Groups Analyzed: 65+ Sample Description: 12,422 visits Averaging Time: 24-h Mean (SD) unit: 0.8 ppm Range (percentiles): 25th = 0.5; 75th = 1.0 Copollutant correlation : O 3 : r = -0.54 PM 2.5 : r = 0.43 PM 10 : r = 0.30 Increment: 0.5 ppm OR Estimate [Lower CI, Upper CI] Lags (days) examined : 0, 1 & 0-2 Ischemic (April-Sept) Lag 0 : 1.16 (1.00, 1.33) Lag 1 : 1.17 (1.01, 1.36) Lag 0-2 : 1.32 (1.09, 1.60) Notes: - Not significant for all seasons or Oct-Mar. - Hemorrhagic : Not significant for all seasons or Oct-Mar, Apr-Sept. - Transient Ischemic Attack : Not significant for all seasons or Oct-Mar, Apr-Sept. Author: Wellenius et al. (2005a) Period of Study: NR Location: 9 U.S. cities: Chicago, Detroit, Pittsburgh, Cleveland, Birmingham, New Haven, Seattle, Minneapolis, Salt Lake City ED Visits Health Outcome: Stroke among Medicare beneficiaries: (Ischemic, hemorrhagic) Study Design: Time-series Statistical Analyses: Logistic regression Age Groups Analyzed: ≥ 65 years Sample Description: 155,503 visits Averaging Time: NR Mean (SD) unit: NR Range (percentiles): 25th = 0.73; 50th = 1.02; 75th = 1.44 (ppm) Copollutant: correlation PM 10 : r = 0.43 Increment: 0.71 ppm % Change [Lower CI, Upper CI] Lag : 0 Ischemic : 2.83 (1.23-4.46) Hemorrhagic : -1.61 (-4.79 to 1.68)

ISCHEMIC HEART DISEASE

Author: D’Ippoliti et al. (2003) Period of Study: 1995 - 1997 Location: Rome, Italy Hospital Admissions Health Outcome (ICD9): MI (410) Study Design: Case-crossover Statistical Analyses: Conditional logistic regression Age Groups Analyzed: 18+ Sample Description: 6,531 patients. Averaging Time: 24-h Mean (SD) unit: 4.4 mg/m 3 Range (percentiles): 25th = 2.8; 75th = 4.3 Copollutant: correlation TSP: r = 0.35 SO 2 : r = 0.56 NO 2 : r = 0.31 Increment: 1 mg/m 3 OR Estimate [Lower CI, Upper CI] ; lag : Lags examined (days): 0, 1, 2, 3, 4, 0-2 Acute MI Lag 0 : 1.021 (0.988-1.054) Lag 1 : 1.020 (0.988-1.054) Lag 2 : 1.033 (1.001-1.066) Lag 3 : 1.010 (0.982-1.040) Lag 4 : 1.025 (0.996-1.055) Lag 0-2 : 1.044 (1.000- .089 Author: Hosseinpoor et al. (2005) Period of Study: 1996 - 2001 Location: Tehran, Iran Health Outcome: Angina Pectoris (ICD9: 413; ICD10: I20) Study Design: Time-series Statistical Analyses: Poisson regression Age Groups Analyzed: All Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 10.8 mg/m 3 Range (Min, Max): 1.6, 57.8 Copollutant: NR Increment: 1 mg/m 3 RR Estimate [Lower CI, Upper CI] Lags examined (days): 0, 1, 2, 3 Lag 1 : 1.00957 (1.00600-1.01315)



### March 2009 C-9 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Lanki et al. (2006) Period of Study: 1994 - 2000 Location: 5 European cities: Augsburg, Germany Barcelona, Spain Helsinki, Finland Rome, Italy Stockholm, Sweden Health Outcome: First AMI (ICD9: 410; ICD10: I21, I22) Study Design: Time-series Statistical Analyses: Poisson regression (GAM) Age Groups Analyzed: 35+ years Sample Description: 26,854 Hospital Admissions Averaging Time: 24-h Mean (SD) unit: NR Unit: mg/m 3 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 PM 10 : r = 0.21 – 0.56 NO 2 : r = 0.43 – 0.75 O 3 : r = -.023 – 020 Increment: 0.2 mg/m 3 RR Estimate [Lower CI, Upper CI] ; lag : Lags examined : 0, 1, 2, 3 All 5 cities: Lag 0 : 1.005 (1.000-1.010) Lag 1 : 1.002 (0.996-1.007) Lag 2 : 1.002 (0.997-1.007) Lag 3 : 0.998 (0.992-1.003) 3 cities with Hospital Discharge Register(HDR): Lag 0 : 1.007 (1.001-1.012) Lag 1 : 1.002 (0.996-1.008) Lag 2 : 1.003 (0.998-1.009) Lag 3 : 1.004 (0.988-1.020) 3 cities with HDR – ≤ 75years Fatal: Lag 0 : 1.027 (1.006-1.048) Lag 1 : 1.021 (1.000-1.042) Lag 2 : 1.018 (0.997-1.039) Lag 3 : 1.015 (0.994-1.037) Non-Fatal: Lag 0 : 1.001 (0.995-1.008) Lag 1 : 1.000 (0.994-1.007) Lag 2 : 1.004 (0.998-1.011) Lag 3 : 0.999 (0.992-1.006) 3 cities with HDR – ≥ 75years Fatal: Lag 0 : 1.009 (0.992-1.006) Lag 1 : 1.001 (0.985-1.018) Lag 2 : 1.006 (0.990-1.023) Lag 3 : 1.000 (0.983-1.017) Non-Fatal: Lag 0 : 1.015 (1.004-1.086) Lag 1 : 1.006 (0.995-1.017) Lag 2 : 0.995 (0.983-1.006) Lag 3 : 0.998 (0.987-1.009) Author: Lee et al. (2003b) Period of Study: 1997 - 1999 Location: Seoul, Korea Study Design: Time-series Health Outcome (ICD9 or ICD10): Angina: ICD10: 120 AMI: ICD10: I21-I23 Other Acute IHDs: ICD10: I24 Statistical Analyses: Poisson regression, GAM Age Groups Analyzed: 64 + Sample Description: 822 days Averaging Time: Daily max Mean (SD) unit: 1.8 ppm Range (percentiles): 25th = 1.2 75th = 2.2 Copollutant: correlation PM 20 : 0.60 SO 2 : 0.81 NO 2 : 0.79 O 3 : -0.39 Increment: 1 ppm (IQR) RR Estimate [Lower CI, Upper CI] Lags examined (days) : 0, 1, 2, 3, 4, 5, 6 All year: Lag 5 : All ages : 0.94 (0.91 0.98) Lag 5 : 64+ age : 1.07 (1.01-1.13) Summer: Lag 5 : All ages : 1.19 (1.02-1.38) Lag 5 : 64+ age : 1.60 (1.27-2.03) 2-pollutant model: Lag 5 : 64+ age : CO + PM 10 : 1.04 (0.98-1.11)

Author: Maheswaran et al. (2005b) Period of Study: 1994 - 1998 Location: Sheffield, UK Emergency Hospital Admission Health Outcome (ICD9): CHD (410-414) Study Design: Ecological Statistical Analyses: Poisson regression Age Groups Analyzed: 45+ years Sample Description: 11,407 Emergency Hospital Admissions for CHD in patients 45+ years (within 1,030 census districts) Averaging Time: NR Mean (SD) unit: Quintiles Range (Min, Max): NR Copollutant: NR Increment: NA RR Estimate [Lower CI, Upper CI] Lowest quintile reference category Adjusted for sex, age, deprivation, smoking: 2nd : 0.97 (0.89-1.07) 3rd : 0.94 (0.86-1.04) 4th : 0.96 (0.97-1.06) 5th : 0.88 (0.79- 0.98) Adjusted for sex, age: 2nd : 1.09 (1.00-1.19) 3rd : 1.15 (1.05-1.26) 4th : 1.19 (1.09-1.30) 5th : 1.20 (1.09-1.32)



### March 2009 C-10 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Mann et al. (2002) Period of Study: 1988 - 1995 Location: Southern California Health Outcome (ICD9): IHD (IHD) (410- 414); Myocardial Infarction (MI) (410) Study Design: Time-series Statistical Analyses: Poisson regression, GAM Age Groups Analyzed: All Sample Description: 54,863 IHD admissions among Southern California Kaiser- Permanente members (within 20km of monitor) Averaging Time: 8-h Mean (SD) unit: 2.07 ppm Range (Min, Max): 0.30, 11.8 Copollutant: correlation Ranging across 7 regions: NO 2 : r = 0.64, 0.86 O 3 : r = -0.37, 0.28 PM 10 : r = 0.15, 0.40 Increment: 1 ppm % Change [Lower CI, Upper CI] Lags examined (days) : 0, 1, 2, 2 ma, 3 ma, 4 ma With arrythmia: Lag 0 : 2.99 (1.80-4.99) Lag 1 : 1.51 (0.37-2.66) Lag 2 : 1.26 (0.15-2.38) 2 ma : 2.66 (1.40-3.94) 3 ma : 2.59 (1.27-3.92) 4ma : 2.25 (0.90-3.63) With CHF: Lag 0 : 3.60 (1.620-5.63) Lag 1 : 3.34 (1.48-5.22) Lag 2 : 1.90 (0.11-3.72) 2 ma : 4.23 (2.13-6.37) 3 ma : 4.14 (1.96-6.37) 4 ma : 4.07 (1.81-6.38) Without secondary diagnosis: Lag 0 : 1.62 (0.65-2.59) Lag 1 : 1.45 (0.54-2.37) Lag 2 : 0.92 (0.04-1.82) 2 ma : 1.83 (0.80-2.86) 3 ma : 1.79 (0.72-2.87) 4 ma : 1.82 (0.71-2.94) Author: Szyszkowicz (2007) Period of Study: 1997 - 2003 Location: Montreal, Canada Study Design: Time-series Health Outcome (ICD9 or ICD10): ED Visits

. IHD: ICD9: 410-414 Statistical Analyses: Poisson regression (GLMM) Age Groups Analyzed: All Sample Description: 4979 ED Visits Averaging Time: 24-h Mean (SD) unit: 0.5 ppm Range (Min, Max): 0.1, 3.1 Copollutant: NR Increment: 0.2 ppm % Change [Lower CI, Upper CI] ; lag : Lags examined (days): 0, 1 All Patients: Lag 0 : 5.4 (2.3-8.5) Males: Lag 0 : 7.5 (3.6-11.6) Females: Lag 0 : 2.7 (-2.0 to 7.6) Ages ≥ 64 All Patients: Lag 0 : 4.9 (1.3-8.7) Males: Lag 0 : 7.5 (2.6-12.6) Females: Lag 0 : 2.4 (-3.0 to.0) Lag 1 not significant for all results Author: von Klot et al. (2005) Period of Study: 1992 - 2001 Location: 5 European cities: Augsburg, Germany Barcelona, Spain Helsinki, Finland Rome, Italy Stockholm, Sweden Health Outcome: Hospital Cardiac (Myocardial Infacrtion (MI), Angina, Dysrythmia, Heart Failure) Re-admissions Study Design: Prospective Cohort Statistical Analyses: Poisson regression Age Groups Analyzed: All Sample Description: 22006 survivors of first MI Averaging Time: 24-h Unit: mg/m 3 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 PM 10 : r = 0.21 – 0.57 NO 2 : r = 0.44 – 0.75 O 3 : r = -.027 – 0.47 Increment: 0.2 mg/m 3 (0.172 ppm) RR Estimate [Lower CI, Upper CI] Lags examined (days): 0, 1, 2, 3 Lag 0: MI :1.022 (0.998-.047) Angina : 1.009 (0.992-.02) Cardiac : 1.014 (1.001-.026)

HEART FAILURE

Author: Lee et al. (2007a) Period of Study: 1996 - 2004 Location: Kaohsiung City, Taiwan Hospital Admissions Health Outcome (ICD9): CHF (428) Study Design: Case-crossover Statistical Analyses: Conditional logistic regression Age Groups Analyzed: All Sample Description: 13475 Hospital Admissions (63 Hospitals) Averaging Time: 24-h Mean (SD) unit: 0.76 ppm Range (Min, Max): 0.14, 1.72 Copollutant: NR Increment: 0.31 ppm OR Estimate [Lower CI, Upper CI] Lag examined (days): 0-2 ≥ 25°C: 1.19 (1.09-1.31) <25°C: 1.39 (1.24-1.54) Adjusted for PM 10 : ≥ 25°C; 1.15 (1.04-1.27) <25°C; 1.21 (1.206-1.38) Adjusted for SO 2 : ≥ 25°C: 1.23 (1.11-1.36) <25°C: 1.39 (1.24-1.55) Adjusted for NO 2 : ≥ 25°C: 1.22 (1.08-1.39) <25°C: 0.94 (0.81-1.10) Adjusted for O 3 : ≥ 25°C: 1.17 (1.07-1.28) <25°C: 1.36 (1.22-1.51)



### March 2009 C-11 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Symons et al. (2006) Period of Study: 2002 (April – November) Location: Johns Hopkins Bayview Medical Center, Baltimore, MD Hospital Admissions Health Outcome: NR Study Design: Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: All Sample Description: 398 Hospital Admissions for CHF Averaging Time: 24-h Mean (SD) unit: 0.4 ppm Range (Min, Max): 0.1, 1.0 Copollutant: NR Increment: 0.2 ppm OR Estimate [Lower CI, Upper CI] Lags examined (days): 0, 1, 2, 3, cum 1, cum 2, cum 3 Lag 0 : 0.86 (0.67-1.11) Lag 1 : 0.90 (0.70-1.17) Lag 2 : 0.96 (0.73-1.26) Lag 3 : 0.88 (0.67-1.16) Cum. Lag1 : 0.82 (0.60-1.13) Cum. Lag2 : 0.80 (0.54-1.17) Cum. Lag3 : 0.27 (0.46-1.14) Author: Wellenius et al. (2005b) Period of Study: 1987 - 1999 Location: Pennsylvania, PA Hospital Admissions Health Outcome (ICD9): CHF (428, 428.1) Study Design: Case-crossover Statistical Analyses: Conditional logistic regression Age Groups Analyzed: 65+ Sample Description: 54019 Hospital Admissions among Medicare beneficiaries Averaging Time: 24-h Mean (SD) unit: 1.03 ppm Range (percentiles): 25th = 0.68; 75th = 1.23 Copollutant: correlation PM 10 : r = 0.57 NO 2 : r = 0.70 O 3 : r = -0.25 SO 2 : 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 PM 10 : 5.18 (3.49-6.89) Adjusted for NO 2 : 4.84 (3.06-6.66) Adjusted for O 3 : 4.35 (3.08-5.64) Adjusted for SO 2 : 4.51 (3.15-5.90)

CARDIOVASCULAR DISEASES – NON-SPECIFIC

Author: Ballester et al. (2001) Period of Study: 1994 - 1996 Location: Valencia, Spain ED Visits Health Outcome (ICD9: CVD (390-459); Heart Diseases (410-414, 427, 428); Cerebrovascular Disease (430 – 438) Study Design: Time-series Statistical Analyses: Poisson regression Age Groups Analyzed: All Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 6.2 mg/m 3 Range (Min, Max): 0.6, 17.8 Copollutant: correlation BS: r = 0.64 NO 2 : r = 0.03 SO 2 : r = 0.74 O 3 : r = -0.26 Increment: 1 mg/m 3 RR Estimate [Lower CI, Upper CI] ; lag: Lags examined (days): 0, 1, 2, 3, 4, 5 All cardiovascular: Lag 2 : 1.0077 (0.9912-1.0138) Heart Disease: Lag 1 : 1.0092 (0.9945-1.0242) Cerebrovascular Disease: Lag 1 : 0.9874 (0.9646-1.0107) Author: Ballester et al. (2006) Period of Study: 1995 - 1999 Location: 14 Cities in Spain Health Outcome (ICD9: All CVD (390-459);Hear t 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/m 3 Range (percentiles): 10th = 0.4-1.7; 90th = 2.0-3.9 Copollutant: NR Increment: 1 mg/m 3 % Change [Lower CI, Upper CI] Lags examined (days): 0-1 All CVD: Lag 0-1 : 2.06 (0.65-3.48) Heart Disease: Lag 0-1 : 4.15 (1.31-7.08)

Author: Barnett et al. (2006) Period of Study: 1998 – 2001 Location: Brisbane, Canberra, Melbourne, Perth, Sydney Australia Auckland & Christchurch, New Zealand Hospital Admissions with Cardiovascular Diseases Health Outcome (ICD9: Arrythmia (247); Cardiac Disease (390 – 429); Cardiac Failure (428); IHD (410-413); MI (410); Total CVD (390-459) Study Design: Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: 15-64 years & ≥ 65 years Sample Description: NR Averaging Time: 8-h Mean (SD) unit: ppm Brisbane: 1.7 Canberra: 0.9 Melbourne: 1.0 Perth: 1.0 Sydney: 0.8 Auckland: 2.1 Christchurch: 0.5 Range (Min, Max): ppm Brisbane: 0.0, 7.0 Canberra: 0.0, 5.8 Melbourne: 0.1, 8.0 Perth: 0.1, 4.0 Sydney: 0.0, 4.5 Auckland: 0.2, 7.9 Christchurch: 0.0, 5.4 Copollutant: NR Increment: 0.9 ppm % Change [Lower CI, Upper CI] Lags examined (days): 0-1 15-64 years Arrythmia: 2.5 (0.1-4.9) Cardiac: 1.7 (0.5-2.9) Cardiac Failure: 4.2 (0.6-7.8) IHD: 1.6 (-0.6 to 3.9) MI: 1.8 (-0.7 to 4.3) Total CVD: 1.2 (0.3-2.1) ≥ 65 years Arrythmia: 0.1 (-1.8 to 2.1) Cardiac: 2.8 (1.3-4.4) Cardiac Failure: 6.0 (3.5-8.5) IHD: 2.3 (0.9-3.8) MI: 2.9 (0.8-4.9) Total CVD: 2.2 (0.9-3.4)



### March 2009 C-12 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Chang et al. (2005) Period of Study: 1997 - 2001 Location: Taipei, Taiwan Health Outcome (ICD9: CVD Hospital Admissions (410 – 429) Study Design: Case-crossover Statistical Analyses: Conditional logist ic 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 PM 10 : ≥ 20°C: 1.171 (1.132-1.211) <20°C: 0.946 (0.892-1.003) Adjusted for SO 2 : ≥ 20°C: 1.232 (1.194-1.272) <20°C: 1.098 (1.034-1.165) Adjusted for NO 2 : ≥ 20°C: 1.048 (1.003-1.095 <20°C: 0.983 (0.914-1.058) Adjusted for O 3 : ≥ 20°C: 1.196 (1.161-1.232) <20°C: 1.092 (1.031-1.157) Author: Fung et al. (2005) Period of Study: 1995 - 2000 Location: Windsor, Ontario, Canada Hospital Admissions of Cardiovascular Diseases Health Outcome (ICD9): CHF (428); IHD (410 – 414); D ysrythmias (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 PM 10 : r = 0.21 NO 2 : r = 0.38 SO 2 : r = 0.16 O 3 : r = 0.10 Increment: 1.2 ppm % Change [Lower CI, Upper CI] Lags examined (days): 0, 0-1, 0-2 <65 years Lag 0 : -3.1 (-7.4 to 1.4) Lag 0-1 : -2.7 (-8.1 to 3.0) Lag 0-2 : -0.5 (-6.7 to 6.0) ≥ 65 years Lag 0 : 0.5 (-2.2 to 3.3) Lag 0-1 : 2.3 (-1.1 to 5.9) Lag 0-2 : 2.8 (-1.1 to 7.0) Author: Jalaludin et al. (2006) Period of Study: 1997 - 2001 Location: Sydney, Australia ED Visits Health Outcome (ICD9): All Cardiovascular (390-459); Cardiac Disease (390-429) ; IHD (410-413); Cerebrovascular or Stroke (430-438) Study Design: Time-series Statistical Analyses: GLM & GAM Age Groups Analyzed: 65+ years Sample Description: NR Averaging Time: 8-h Mean (SD) unit: 0.82 ppm Range (Min, Max): 0.02, 4.63 Copollutant: correlation PM 10 : r = 0.31 NO 2 : r = 0.71 SO 2 : r = 0.51 O 3 : r = 0.19 Increment: 0.69 ppm % Change [Lower CI, Upper CI] Lags examined (days): 0, 1, 2, 3, 0-1 All Cardiovascular: Lag 0 : 2.32 (1.45-3.19) Lag 1 : 1.33 (0.47-2.20) Lag 0-1 : 2.35 (1.39-3.32) Cardiac Disease: Lag 0 : 2.52 (1.50-3.54) Lag 1 : 1.85 (0.83-2.88) Lag 2 : 1.11 (0.0-2.15) Lag 0-1 : 2.85 (1.71-4.01) IHD: Lag 0 : 2.83 (1.22-4.48) Lag 1 : 1.58 (0.01-3.19) Lag 0-1 : 2.86 (1.07-4.68) Stroke: No results were significant for Stroke. All Cardiovascular Disease: Cool period: Lag 0 : 3.26 (2.00-4.53) Cardiac Disease: Cool period: Lag 0 : 3.43 (1.95-4.93) IHD: Cool period: Lag 0 : 3.64 (1.28-6.06) Warm period: Lag 0 : 2.29 (0.01-4.62) Stroke: Cool period: Lag 0 : 3.54 (0.78-6.37) Notes: Cool : May to October Warm : November to April Author: Koken et al. (2003) Period of Study: 1993 - 1997 Location: Denver, CO Hospital Admissions for Cardiovascular Disease Health Outcome (ICD9: MI (410-410.92); Coronary Atheroscler osis (414 – 414.05); Pulmonary Heart Disease (416 - 416.9); Cardiac Dysrythmia (427 – 427.9); CHF (428) Study Design: Time-series Statistical Analyses: GLM Age Groups Analyzed: >65 years Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 0.9 ppm Range (Min, Max): 0.3, 1.6 Copollutant: correlation PM 10 : r = 0.25 NO 2 : r = 0.73 SO 2 : r = 0.21 O 3 : 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 a ssociated with other Lag periods.



### March 2009 C-13 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Linn et al. (2000) Period of Study: 1992 - 1995 Location: Los Angeles, CA Health Outcome: Hospital Admissions for Cardiovascular, Cerebrovascular, Pulmonary. Study Design: Time-series Statistical Analyses: Ordinary least squares regression; Poisson regression Age Groups Analyzed: >30 years Sample Description: NR Averaging Time: 24-h Mean (SD) unit: Winter: 1.7; Spring: 1.0; Summer: 1.2; Fall: 2.1 Range (Min, Max): Winter: 0.5, 5.3; Spring: 0.4, 2.2; Summer: 0.3, 2.7; Fall: 0.2, 4.3 Copollutant: correlation Winter: PM 10 : r = 0.78; NO 2 : r = 0.89; O 3 : r = -0.43; Spring: PM 10 : r = 0.54; NO 2 : r = 0.92; O 3 : 0.29 Summer: PM 10 : r = 0.72; NO 2 : r = 0.94; O 3 : 0.03 Fall: PM 10 : r = 0.58; NO 2 : r = 0.84; O 3 : r = -0.36 Increment: 1 ppm Co-efficient [SE] Lags examined (lags) : 0, 1 Lag 0: Cardiovascular All: 0.032 (0.003)* (e.g. 3.2% increase) Winter: 0.038 (0.006)* Spring: 0.010 (0.015) Summer: 0.035 (0.014)* Fall: 0.027 (0.006)* Cerebrovascular All : 0.009 (0.007) Winter: -0.008 (0.014) Spring: 0.107 (0.033)* Summer: 0.030 (0.033) Fall: 0.008 (0.012) Myocardial Infarction All : 0.040 (0.009) * CHF All : 0.025 (0.009)* Cardiac Arrythmia All : 0.023 (0.009)* Stroke All : 0.044 (0.009)* Notes: * p < 0.05 Author: Metzger et al. (2004a) Period of Study: 1993 - 2000 Location: Atlanta, GA ED Visits (from 31 hospitals) Health Outcome (ICD9: Cardiovascular: IHD (410 – 414); Acute MI (410); Dysrythmia (427); Cardiac Arrest (427.5); CHF (428); Peripheral Vascular & Cereberovascular Dis ease (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 PM 10 : r = 0.47 NO 2 : r =0.68 SO 2 : r =0.26 O 3 : r =0.20 Increment: 1 ppm RR Estimate [Lower CI, Upper CI] Lags examined (days): 0-2ma All CVD : 1.017 (1.008-1.027) Dysrythmia : 1.012 (0.993-1.031) CHF : 1.010 (0.988-1.032) IHD : 1.016 (0.999-1.034) PVCD : 1.031 (1.010-1.052)

Author: Peel et al. (2007) Period of Study: 1993 - 2000 Location: Atlanta, GA ED Visits (from 31 hospitals) Health Outcome (ICD9: Cardiovascular: IHD (410 – 414); Dysr ythmia (427); CHF (428); PVCD (433 – 437, 440, 443, 444, 451-453) Study Design: Case-crossover Statistical Analyses: Conditional logistic regression Age Groups Analyzed: All Sample Description: 4,407,535 visits Averaging Time: 1-h Mean (SD) unit: 1.8 ppm Range (SD) : SD: 1.2 Copollutant: NR Increment: 1.2 ppm OR Estimate [Lower CI, Upper CI] Lags examined (days): 0-2ma IHD: Without Diabetes : 1.023 (1.004-1.420) Without CHF: 1.024 (1.006-1.042) Dysrythmias: With Hypertension : 1.065 (1.015-1.118) PVCD: With Hypertension : 1.038 (1.004-1.074) Without Hypertension: 1.027 (1.002-1.054) With Diabetes: 1.065 (1.012-1.121) Without Diabetes: 1.025 (1.003-1.048) With COPD: 1.113 (1.027-1.205) Without COPD: 1.026 (1.004-1.047) Without CHF : 1.029 (1.008-1.051) With Dysrythmias: 1.072 (1.011-1.138) Without Dysrythmias : 1.026 (1.004-1.048) CHF: With COPD : 1.058 (1.003-1.115) Author: Slaughter et al. (2005) Period of Study: 1995 - 2001 Location: Spokane, WA Health Outcome (ICD9: Cardiac Hospital Admissions: (390-459) Study Design: Time-series Statistical Analyses: Poisson regression (GLM & GAM) Age Groups Analyzed: All Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 0.42-1.82 Range (Min, Max): NR Copollutant correlation : PM 10 : r = 0.32 PM 2.5 : r = 0.62 Increment: NR RR Estimate [Lower CI, Upper CI] ; lag : Lags examined (days): 1, 2, 3 No significant association. Results not reported.



### March 2009 C-14 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Yang et al. (2004a) Period of Study: 1997 - 2000 Location: Kaohsiung City, Taiwan Health Outcome (ICD9: Cardiovascular diseases (410-429) Study Design: Case-crossover Statistical Analyses: Conditional logistic regression Age Groups Analyzed: All Sample Description: 29,661 Cardiovascular hospital admissions (63 hospitals) Averaging Time: 24-h Mean (SD) unit: 0.79 ppm Range (Min, Max): 0.24, 1.72 Copollutant: NR Increment: 0.28 ppm OR Estimate [Lower CI, Upper CI] Lag examined (days): 0-2 ≥ 25°C: 1.264 (1.205-1.326) <25°C: 1.448 (1.357-1.545) Adjusted for PM 10 : ≥ 25°C: 1.206 (1.146-1.270) <25°C: 1.314 (1.213-1.423) Adjusted for SO 2 : ≥ 25°C: 1.406 (1.327-1.489) <25°C: 1.3450 (1.352-1.555) Adjusted for NO 2 : ≥ 25°C: 1.246 (1.166-1.332) <25°C: 0.905 (0.819-0.999) Adjusted for O 3 : ≥ 25°C: 1.250 (1.191-1.311) <25°C: 1.447 (1.356-1.545)



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

Study Design Concentrations CO Effect Estimates (95% CI)

Author: Bell et al. (2007) Period of Study: 1999 - 2002 Location: Connecticut and Massachusetts Health Outcome: Birth weight and LBW Study Design: Retrospective cohort Statistical Analyses: Linear and logistic regression Age Groups Analyzed: NA Sample Description: 358,504 full term live singleton births (32-44 weeks) Averaging Time: 24-h Mean (SD) unit: 0.65 ppm (0.18) Range (Min, Max): NR Copollutant: NR Increment: Interquartile range – 0.30 ppm Regression co-efficient for birth weight (g) [Lower CI, Upper CI] Entire pregnancy: -16.2 (-19.7 to -12.6) Stratified by race. Black mother : -10.9 (-20.2 to -1.6) White mother : -17.5 (-21.3 to -13.7) OR for LBW [Lower CI, Upper CI] Entire pregnancy : 1.028 (0.983-1.074)

Author: Brauer et al. (2008) Period of Study: 1999 - 2004 Location: Vancouver, Canada Health Outcome: LBW, PTB and SGA Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 70249 live singleton births Averaging Time: Land use regression model Mean (SD) unit: 633 μg/m 3 Range (Min, Max): 124, 1409 Copollutant: correlation: PM 10 : r = 0.73 NO 2 : r = 0.75 SO 2 : r = 0.82 O 3 : r = -0.39 Increment: 100 μg/m 3 OR for SGA [Lower CI, Upper CI] ; Entire pregnancy : 1.06 (1.03-1.08) OR for term LBW [Lower CI, Upper CI] ; Entire pregnancy : 1.02 (0.96-1.09) OR PTB [Lower CI, Upper CI] ; Entire pregnancy : 1.16 (1.01-1.33)

Author: Chen et al. (2002) Period of Study: 1991 - 1999 Location: Northern Nevada Health Outcome: Birth weight & LBW Study Design: Retrospective cohort Statistical Analyses: Linear and logistic regression Age Groups Analyzed: NA Sample Description: 39,338 full term live singleton births (37-44 weeks) Averaging Time: 8-h Mean (SD) unit: 0.98 ppm Range (Min, Max): 0.25, 4.87 Copollutant: NR Increment: NR Regression co-efficient for birth weight (g) [SE] Trimesters: First : -1.02 (6.68) Second : -0.07 (6.58) Third : -3.95 (6.76) Entire pregnancy : -8.28 (14.9) Notes: CO not associated with LBW

Author: Conceicao et al. (2001) Period of Study: 1994 - 1997 Location: Sao Paulo, Brazil Health Outcome: Child mortality, under 5 years of age Study Design: Time-series Statistical Analyses: Poisson regression (GAM) Age Groups Analyzed: NA Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 4.4 ppm (2.2) Range (Min, Max): NR Copollutant: NR Increment: NR Regression co-efficient for Child mortality – under 5 years of age [SE] ; Lags examined : 0, 1, 2, 3 Lag 2 : 0.0306 (0.0076) (p < 0.01) Lag chosen for best fitting model



### March 2009 C-15 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Gilboa et al. (2005) Period of Study: 1997 - 2000 Location: Texas Health Outcome: Birth defects (heart defects & orofacial clefts) Study Design: Case-control Statistical Analyses: Conditional Logistic regression Age Groups Analyzed: NA Sample Description: NR Averaging Time: NR Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Increment: Exposure categories (ppm):<0.4; 0.4 – 0.5; 0.5 – 0.7; >0.7 OR for Birth Defects [Lower CI, Upper CI] ; Exposure period : weeks 3 – 8 of pregnancy Conotruncal defects: 1.00; 1.38 (0.97-1.97); 1.17 (0 .81-1.70); 1.46 (1.03-2.08) Tetralogy of Fallot: 1.00; 0.92 (0.52-1.62); 1.27 (0 .75-2.14); 2.04 (1.26-3.29) Notes: CO was not associated with the following defects: Aortic artery & valve, atrial septal, pulmonary artery & valve, ventricular septal, endocardial cushion & mitral valve , cl eft lip, cleft palate, aortic valve stenosis, coarctation of the aorta, ostium secundum. Author: Gouveia et al. (2004) Period of Study: 1997 Location: Sao Paulo, Brazil Health Outcome: Birth weight & LBW Study Design: Retrospective cohort Statistical Analyses: Linear and logistic regression Age Groups Analyzed: NA Sample Description: 179,460 live singleton term births (>37 weeks) Averaging Time: 8-h Mean (SD) unit: 3.7 ppm Range (Min, Max): 1.1, 11.4 Copollutant: NR Increment: 1 ppm Regression co-efficient for birth weight (g) [Lower CI, Upper CI] Trimesters: First : -23.1 (-41.3 to -4.9); Second : 3.2 (-18.2 to 24.5); Third : 1.9 (-18.2 to 22.0) OR for LBW ) [Lower CI, Upper CI] 4th quartile exposure (compared to lowest quartile): First : 1.02 (0.82-1.27); Second : 1.07 (0.88-1.30); Third : 0.93 (0.76-1.12)

Author: Ha et al. (2001) Period of Study: 1996 - 1997 Location: Seoul, South Korea Health Outcome: LBW Study Design: Retrospective cohort Statistical Analyses: Logistic regression (GAM) Age Groups Analyzed: NA Sample Description: 276 763 full term live singleton births (>37 weeks) Averaging Time: 24-h Mean (SD) unit: NR Range (Min, Max): Percentiles: 25th: 0.99 ppm 75th: 1.41 ppm Copollutant correlation: TSP: r = 0.73 NO 2 : r = 0.75 SO 2 : r = 0.82 O 3 : r = -0.39 Increment: 0.42 ppm RR for LBW [Lower CI, Upper CI] Trimesters: First : 1.08 (1.04, 1.12) Third : 0.91 (0.87, 0.96)

Author: Ha et al. (2003) Period of Study: 1995 - 1999 Location: Seoul, South Korea Health Outcome: Post-neonatal mortality (1 month – 1 yr) (also looked at older age groups) Study Design: Time-series Statistical Analyses: Poisson regression (GAM) Age Groups Analyzed: NA Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 1.2 ppm Range (Min, Max): 0.39, 3.38 Copollutant correlation: PM 10 : r = 0.63 NO 2 : r = 0.72 SO 2 : r = 0.75 O 3 : r = -0.46 Increment: 0.57 ppm RR for Post–neonatal mortality (1 month – 1 yr) [Lower CI, Upper CI] Lags examined : 0 Total mortality: Lag 0 : 1.020 (0.976-1.067) Respiratory mortality: Lag 0 : 1.388 (1.009-1.911)

Author: Huynh et al. (2006) Period of Study: 1999 - 2000 Location: California Health Outcome: PTB (24-36 weeks gestation) Study Design: Case-control Statistical Analyses: Conditional Logistic regression Age Groups Analyzed: Cases = 24-36 weeks gestation; Controls = 39 – 44 weeks Sample Description: 10,673 PTBs (cases); 32,119 term births (controls) Averaging Time: NR Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Increment: 1 ppm Exposure level – Quartiles of exposure for first month and last two weeks of gestation (mg/m 3 ) First : <0.61; Second : 0.61 – 0.82; Third : 0.82 – 1.07; Fourth : >1.07 Quartiles for entire pregnancy and last two weeks of pregnancy were similar. OR for PTB [Lower CI, Upper CI] First month of gestation: Per 1 ppm increase : 1.10 (0.99-1.20) Second quartile : 0.94 (0.88-1.01) Third quartile : 1.04 (0.97-1.11) Fourth quartile : 1.05 (0.96-1.14) Last two weeks of gestation: Per 1 ppm increase : 1.00 (0.93-1.09) Second quartile : 1.03 (0.97-1.10) Third quartile : 1.04 (0.97-1.12) Fourth quartile : 0.99 (0.91-1.08) Entire pregnancy: Per 1 ppm increase : 1.06 (0.95-1.18) Second quartile : 0.97 (0.91-1.04) Third quartile : 0.99 (0.92-1.05) Fourth quartile : 1.02 (0.94-1.09) Lowest quartile used as reference group



### March 2009 C-16 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Hwang and Jaakkola (2008) Period of Study: 2001-2003 Location: Taiwan Health Outcome: Oral clefts (with or without palate) Study Design: Case-control Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 6,530 cases from 721,289 newborns Averaging Time: 8-h Mean (SD) unit: 0.69 (0.4) Range (Min, Max): 0.25, 2.7 Copollutant correlation: PM 10 : r = -0.19 NO X : r = 0.82 SO 2 : r = 0.24 O 3 : r = -0.19 Increment: 100 ppb RR for oral cleft [Lower CI, Upper CI] Month 1 : 1.00 (0.96-1.04) Month 2 : 1.00 (0.96-1.03) Month 3 : 1.00 (0.96-1.03)

Author: Jalaludin et al. (2007) Period of Study: 1998 - 2000 Location: Sydney, Australia Health Outcome: PTB Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 123,840 full term live singleton births (<42 weeks) Averaging Time: 8-h Mean (SD) unit: 0.9 ppm (0.68) Range (Min, Max): NR Copollutant correlation: PM 10 : r = 0.28 NO 2 : r = 0.60 SO 2 : r = 0.24 O 3 : r = -0.21 Increment: 1 ppm RR for PTB [Lower CI, Upper CI] First month: All of Sydney : 0.89 (0.84-0.95) Within 5km of site : 1.03 (0.68-1.54) First trimester: All of Sydney : 0.77 (0.71-0.83) Within 5km of site : 1.24 (0.81-1.91) 1 month prior to birth: All of Sydney : 0.96 (0.88-1.04) Within 5km of site : 1.00 (0.86-1.15) 3 months prior to birth: All of Sydney : 0.99 (0.90-1.09) Within 5km of site : 1.11 (0.94-1.31) Author: Lee et al. (2003a) Period of Study: 1996 - 1998 Location: Seoul, South Korea Health Outcome: LBW Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 388,105 full term live singleton births (37-44 weeks) Averaging Time: 24-h Mean (SD) unit: 1.2 ppm Range (Min, Max): 0.4, 3.4 Copollutant correlation: PM 10 : r = 0.47 NO 2 : r = 0.77 SO 2 : r = 0.79 Increment: 0.5 ppm OR for LBW [Lower CI, Upper CI] First : 1.04 (1.01-1.07) Second : 1.03 (1.00-1.06) Third : 0.96 (0.93-0.99) Entire pregnancy : 1.05 (1.01-1.09)

Author: Leem et al. (2006) Period of Study: 2001 - 2002 Location: Incheon, Korea Health Outcome: PTB Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 52,113 live singleton births Averaging Time: Kriging was used to estimate exposure Mean (SD) unit: NR Range (Min, Max): NR Copollutant correlation: PM 10 : r = 0.27 NO 2 : r = 0.63 SO 2 : r = 0.31 Increment: Exposure level – Quartiles of exposure for first trimester (mg/m 3 ) First : 0.47-0.63; Second : 0.6 -0.77; Third : 0.78-0.90; Fourth : 0.91-1.27 - exposure groups for third trimester was similar OR for PTB [Lower CI, Upper CI] First Trimester: Second quartile : 0.92 (0.81-1.05) Third quartile : 1.14 (1.01-1.29) Fourth quartile : 1.26 (1.11-1.44) Third Trimester: Second quartile : 1.07 (0.95-1.21) Third quartile : 1.07 (0.94-1.22) Fourth quartile : 1.16 (1.01-1.34) Lowest quartile used as reference group. Author: Lin et al. (2004a) Period of Study: 1998 - 2000 Location: Sao Paulo, Brazil Health Outcome: Neonatal death (within first 28 days of life) Study Design: Time-series Statistical Analyses: Poisson regression (GAM) Age Groups Analyzed: NA Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 2.83 ppm Range (Min, Max): 0.54, 10.25 Copollutant correlation: PM 10 : r = 0.71 NO 2 : r = 0.67 SO 2 : r = 0.55 O 3 : r = 0.03 Increment: NR Regression coefficent for neonatal death [SE] Lags examined : 0 Lag 0 : 0.0061 (0.0110)



### March 2009 C-17 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Lin et al. (2004b) Period of Study: 1995 - 1997 Location: Taipei & Kaoshiung, Taiwan Health Outcome: LBW Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 92288 full term live singleton births (>37 weeks) within 3km of monitoring site. Averaging Time: 24-h Mean (SD) unit: Taipei (avg over 5 sites) 0.84 – 1.31 Kaohsiung (avg over 5 sites) 5.56 – 10.05 Range (Min, Max): NR Copollutant: NR Increment: Exposure groups M = Median exposure 1.1-14.2 ppm H = High exposure >14.2 ppm OR for LBW [Lower CI, Upper CI] Trimesters: First : M 1.01 (0.89, 1.16), H 0.90 (0.75, 1.09) Second : M 1.02 (0.90, 1.16), H 1.00 (0.82, 1.22) Third : M 0.88 (0.77, 1.00), H 0.86 (0.71, 1.03) Entire pregnancy : M 0.89 (0.77, 1.01), H 0.77 (0.63, 0.94) Notes: Cut off for exposures groups for second and third trimester were similar to those presented above. Author: Liu et al. (2003) Period of Study: 1985 - 1998 Location: Vancouver, BC, Canada Health Outcome: PTB, IUGR, LBW Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 229,085 live singleton births Averaging Time: 24-h Mean (SD) unit: 1.0 ppm Range (Min, Max): 25th: 0.7; 75th: 1.2 Copollutant: NR Increment: 1.0 ppm OR for LBW [Lower CI, Upper CI] Month of pregnancy: First month: 1.01 (0.93-1.09) Last month: 0.96 (0.88-1.04) OR for PTB [Lower CI, Upper CI] First month : 0.95 (0.89-1.01) Last month : 1.08 (1.01-1.15) OR for IUGR [Lower CI- Upper CI] First month : 1.06 (1.01-1.10) Last month : 0.98 (0.94-1.03) Trimester 1 : 1.05 (1.00-1.10) Trimester 2 : 0.97 (0.92-1.01) Trimester 3 : 0.97 (0.93-1.02) Author: Liu et al. (2007) Period of Study: 1995 - 2000 Location: Calgary, Edmonton, and Montreal Canada Health Outcome: IUGR Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 386,202 live singleton births Averaging Time: 24-h Mean (SD) unit: 1.1 ppm Range (Min, Max): 25th: 0.6; 75th: 1.3 Copollutant correlation: PM 2.5 : r = 0.31 NO 2 : r = 0.71 SO 2 : r = 0.21 O 3 : r = -0.42 Increment: 1 ppm RR for LBW [Lower CI, Upper CI] Notes: CO was associated with an increased risk of IUGR of approximately 16% and 23% in the first and nine month of pregnancy. (All results presented in Figures)

Author: Maisonet et al. (2001) Period of Study: 1994 - 1996 Location: Northeastern USA Health Outcome: Live birth weight Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 89,557 live singleton term births (37-44 weeks) Averaging Time: 24-h Mean (SD) unit: NR Range (Min, Max): Percentiles: 25th: 0.93 ppm; 75th: 1.23 ppm Copollutant: NR Increment: 1 ppm OR for LBW [Lower CI, Upper CI] Trimester: First : 1.08 (0.91-1.28); Second : 1.14 (0.83-1.58); Third : 1.31 (1.06-1.62) Stratified results among African-Americans: First : 1.43 (1.18-1.74); Second : 1.27 (0.87-1.86); Third : 1.75 (1.50-2.04) Notes: CO had no effect on whites or Hispanics



### March 2009 C-18 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Mannes et al. (2005) Period of Study: 1998 - 2000 Location: Sydney, Australia Health Outcome: Birth weight and SGA Study Design: Retrospective cohort Statistical Analyses: Linear and logistic regression Age Groups Analyzed: NA Sample Description: 138,056 full term all singleton births (including stillbirths) (at least 20 weeks gestation) Averaging Time: 8-h Mean (SD) unit: 0.8 ppm Range (Min, Max): 0.0, 4.6 Copollutant: correlation PM 10 : r = 0.26 NO 2 : r = 0.57 O 3 : r = -0.20 Increment: 1 ppm Regression coefficients for birth weight (g) [Lower CI, Upper CI] All births: First trimester : 1.86 (-8.31 to 12.03) Second trimester : -10.72 (-23.09 to 1.65) Third trimester : -6.63 (-18.57 to 5.31) One month prior to birth : -15.28 (-25.59 to -4.97) Births within 5 km of monitor: First trimester : -8.56 (-28.60 to 10.68) Second trimester : -28.87 (-50.98 to -6.76) Third trimester : -22.88 (-44.58 to -1.18) One month prior to birth : -10.41 (-30.03 to 9.21) OR for SGA [Lower CI, Upper CI] All births: First trimester : 0.95 (0.88-1.04) Second trimester : 0.99 (0.90-1.10) Third trimester : 1.01 (0.91-1.11) One month prior to birth : 1.06 (0.98-1.16) Births within 5km of monitor: First trimester : 0.99 (0.86-1.14) Second trimester : 1.06 (0.90-1.25) Third trimester : 1.05 (0.90-1.23) One month prior to birth : 1.10 (0.96-1.27) Author: Medeiros et al. (2005) Period of Study: 1998 - 2000 Location: Sao Paulo, Brazil Health Outcome: Birth weight and LBW Study Design: Retrospective cohort Statistical Analyses: Linear and logistic regression Age Groups Analyzed: NA Sample Description: 311,735 full term live singleton births (37-41 weeks) Averaging Time: 24-h Mean (SD) unit: Daily mean shown in Figure (see paper) Range (Min, Max): NR Copollutant: NR Increment: 1 ppm Regression co-efficient for birth weight (g) [Lower CI, Upper CI] Trimesters: First : -11.9 (-15.5 to -8.2); Second : 4.9 (0.5-9.3); Third : 12.1 (7.6-16.6) OR for LBW [Lower CI, Upper CI] 4th quartile exposure (compared to lowest quartile) First : 0.98 (0.91-1.06); Second : 0.97 (0.90-1.05); Third : 1.03 (0.96-1.11)

Author: Parker et al. (2005) Period of Study: 2000 Location: California Health Outcome: Birth weight & SGA Study Design: Retrospective cohort Statistical Analyses: Linear and logistic regression Age Groups Analyzed: NA Sample Description: 18,247 full term live singleton births (40 weeks) within 5 miles of a monitor Averaging Time: 24-h Mean (SD) unit: 0.78 ppm Range (Min, Max): NR Copollutant: NR Increment: Quartiles of exposure for first trimester First : <0.57; Second : 0.57-0.76 ; Third : 0.76- 0.93; Fourth : >0.93 - exposure groups for other trimesters were similar Regression co-efficient for birth weight (g) [Lower CI, Upper CI] Trimesters: 4th quartile exposure (compared to lowest quartile) First : -7.3 (-29.7 to 15.0); Second : 14.2 (-8.9 to 37.3); Third : -8.4 (-32.2 to 15.3); Entire pregnancy: -20.5 (-40.1 to -0.8) OR for SGA [Lower CI, Upper CI] 4th quartile exposure (compared to lowest quartile) First: 0.91 (0.76-1.09); Second: 0.80 (0.66-0.97); Third: 0.90 (0.75-1.10); Entire pregnancy: 0.95 (0.81-1.12) Author: Ritz et al. (2000) Period of Study: 1989 - 1993 Location: Southern California Health Outcome: PTB Study Design: Retrospective Cohort Statistical Analyses: Logistic regression Age Groups Analyzed: Eligible study subjects were singletons born at 26-44 weeks gestation Sample Description: 97,518 neonates born in Southern California Averaging Time: 6-9 a.m. Mean (SD) unit: 2.70 ppm Range (Min, Max): 0.36, 9.12 Copollutant correlation: PM 10 : r = 0.37 NO 2 : r = 0.60 O 3 : r = -0.44 Increment: 3 ppm RR for PTB [Lower CI, Upper CI] Adjusted for various risk factors and season of birth and conception 6 weeks prior to birth : 1.04 (0.99-1.10) 1st month of pregnancy : 1.04 (0.99-1.09) Adjusted for various risk factors 6 weeks prior to birth : 1.06 (1.02-1.10) 1st month of pregnancy : 1.01 (0.97-1.04)



### March 2009 C-19 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Ritz et al. (2002) Period of Study: 1987 - 1993 Location: Southern California Health Outcome: Birth defects (heart defects & orofacial clefts) Study Design: Case-control Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: NR Averaging Time: NR Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Increment: Exposure categories: ppm <1.14; 1.14-1.57; 1.57- 2.39; >2.39 OR for Birth defects [Lower CI, Upper CI]: Period of exposure – Second month of pregnancy. Aortic artery & valve defects: 1.00 (ref group); 1.10 (0.73- 1.66); 1.25 (0.74-2.13); 0.93 (0.47-1.85) Pulmonary artery & valve anomalies: 1.00 (ref group); 1.09 (0.69- 1.73); 0.92 (0.50-1.70); 1.00 (0.46-2.17) Ventricular septal defects: 1.00 (ref group); 1.62 (1.05- 2.48); 2.09 (1.19-3.67); 2.95 (1.44-6.05) Conotruncal defects: 1.00 (ref group); 0.79 (0.47- 1.32); 0.73 (0.36-1.47); 0.95 (0.38-2.38) Notes: Results also presented for more specific defects, however CO showed no association (see paper Tabl e 3.). CO not associated with orofacial clefts) Author: Ritz et al. (2006) Period of Study: 1989 - 2000 Location: Southern California Health Outcome: Post-neonatal mortality (28 days to 1 yr); All causes; SIDS Study Design: Case-control Statistical Analyses: Conditional Logistic regression Sample Description: Mothers residing within 16 km of monitoring site Averaging Time: 24-h Mean (SD) unit: 1.63 ppm Range (Min, Max): 0.38, 3.44 Copollutant: correlation PM 10 : r = 0.33 NO 2 : r = 0.72 O 3 : r = -0.57 Increment: 1 ppm OR for Post-neonatal death [Lower CI, Upper CI] Exposure period : 2 weeks prior to death, 1 month prior to death, 2 months prior to death, 6 months prior to death All causes: 2 weeks prior to death : 1.14 (1.03-1.25) 2 months prior to death : 1.11 (1.06-1.16) SIDS: 2 months prior to death : 1.19 (1.10-1.28) Term/normal weight births 2 months prior to death: All causes: 1.12 (1.05-1.19) SIDS : 1.17 (1.07-1.29) Respiratory : 1.14 (0.95-1.36) Preterm &/or LBW births 2 months prior to death: All causes: 1.12 (1.01-1.25) SIDS : 1.46 (1.09-1.94) Respiratory : 1.03 (0.83-1.27) Notes: These results did not persist in multipollutant models (CO, NO 2 , PM 10 , O 3 ) Author: Ritz et al. (2007) Period of Study: January – December 2003 Location: Los Angeles, CA Health Outcome: PTB Study Design: Nested case-control Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: A survey of 2,543 of 6,374 women sampled from a cohort of 58,316 eligible births in Los Angeles county. Averaging Time: 24-h Mean (SD) unit: NR Copollutant correlation: TSP: r = 0.73 NO 2 : r = 0.75 SO 2 : r = 0.82 O 3 : r = -0.39 Increment: Exposure categories (ppm): Less than 0.58: 0.59-0.91; 0.92-1.25; >1.25 RR for LBW [Lower CI, Upper CI] First trimester: 1.00 (Ref group); 1.17 (1.08-1.26); 1.15 (1.05-1.26); 1.25 (1.12-1.38) 6 weeks prior to birth 1.00 (Ref group); 1.00 (0.93-1.08); 1.08 (0.98-1.20); 1.03 (0.93-1.14) Entire pregnancy: 1.00 (Ref group); 0.76 (0.70-0.82); 0.84 (0.77-0.91); 1.03 (0.91-1.17)

Author: Salam et al. (2005) Period of Study: 1975 - 1987 Location: California Health Outcome: Birth weight, LBW , IUGR Study Design: Retrospective cohort Statistical Analyses: Linear and logistic regression Age Groups Analyzed: NA Sample Description: 3,901 infants from the California Children’s Health Study Averaging Time: 24-h Mean (SD) unit: 1.8 ppm (0.9) (Entire pregnancy) Range: NR Copollutant: correlation PM 10 : r = 0.41 NO 2 : r = 0.69 O 3 : r = -0.27 Increment: Entire pregnancy 1.2 ppm Trimesters: First : 1.4 ppm; Second : 1.4 ppm; Third : 1.3 ppm Regression co-efficient for birth weight (g) [Lower CI, Upper CI] Trimesters: First: -21.7 (-42.3 to -1.1); Second: 11.3 (-9.7 to 32.3); Third : 11.8 (-8.4 to 32.1); Entire pregnancy: 2.2 (-20.1 to 24.4) OR for LBW [Lower CI, Upper CI] Trimesters: First: 1.0 (0.7-1.5); Second: 0.9 (0.6-1.3); Third: 0.7 (0.5-1.1); Entire pregnancy: 0.8 (0.6-1.3) OR for IUGR [Lower CI, Upper CI] Trimesters: First: 1.2 (1.0-1.4); Second: 1.0 (0.9-1.1); Third: 1.0 (0.8-1.1); Entire pregnancy: 1.0 (0.9-1.2)



### March 2009 C-20 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Tsai et al. (2006b) Period of Study: 1994 - 2000 Location: Kaoshiung, Taiwan Health Outcome: Postneonatal death (27 days-1 yr old) Study Design: Case- crossover Statistical Analyses: Poisson regression Age Groups Analyzed: NA Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 8.27 ppm x10 Range (Min, Max): 2.26, 17.7 Copollutant: NR Increment: Interquartile range : 0.31 ppm OR for Post-neonatal mortality [Lower CI, Upper CI ] Lag examined : 0-2 Lag 0-2: 1.051 (0.304-3.630)

Author: Wilhelm et al. (2005) Period of Study: 1994 - 2000 Location: Los Angeles, CA Health Outcome: Term LBW and PTB Study Design: Retrospective cohort Statistical Analyses: Logistic regression Age Groups Analyzed: NA Sample Description: 518,254 births within 4 miles of a monitoring station. Varied according to analyses. Averaging Time: 24-h Mean (SD) unit: Trimester 1: 1.42 ppm Results for third trimester and 6 weeks prior to birth were similar to first trimester Range (Min, Max): 0.26, 2.82 Copollutant correlation: First Trimester: PM 10 : r = 0.12 PM 2.5 : r = 0.57 NO 2 : r = 0.81 SO 2 : r = -0.31 Increment: 1 ppm RR for PTB [Lower CI, Upper CI] First trimester: Less than 1 mile: 1.06 (1.00-1.12) 1-2 miles: 1.06 (1.03-1.10) 2-4 miles: 1.08 (1.06-1.09) ZIP code level: 1.04 (1.01-1.07) 6 weeks prior to birth: Less than 1 mile: 1.04 (0.98-1.09) 1-2 miles: .04 (1.01-1.08) 2-4 miles: 1.01 (0.99-1.02) ZIP code level: 1.03 (1.00-1.06) Notes: All results above did not persist in multipollutant model (CO, NO 2 , O 3 , PM 10 ) OR for term LBW [Lower CI, Upper CI] Third trimester: Less than 1 mile: 1.10 (0.98-1.23) 1-2 miles: 1.05 (0.99-1.13) 2-4 miles: 1.06 (1.02-1.10) ZIP code level: 1.12 (1.05-1.19) Notes: All results above did not persist in multipollutant model (CO, NO 2 , O 3 , PM 10 ) See paper for results based on exposure category groupings. Author: Woodruff et al. (2008) Period of Study: 1999 - 2002 Location: U.S. counties with >250,000 residents Health Outcome: Post-neonatal deathsAll causes; respiratory; SIDS; illdefined + SIDS; other causes. Study Design: Retrospective cohort Statistical Analyses: Logistic regression (GEE) Age Groups Analyzed: NA Sample Description: NR Averaging Time: 24-h Mean (SD) unit: All causes: 0.70 ppm Range (Min, Max): Percentiles: 25th: 0.48; 75th: 0.87 Copollutant correlation: PM 10 : r = 0.18 SO 2 : r = 0.27 O 3 : r = -0.46 Increment: 0.39 ppm OR for Post-neonatal mortality [Lower CI, Upper CI] Avg exposure over the first 2 months of life: All causes: 1.01 (0.95-1.07) Respiratory: 1.14 (0.93-1.40) SIDS: 0.88 (0.76-1.03) Ill-defined + SIDS: 0.93 (0.84-1.02) Other causes: 1.02 (0.97-1.07)

Author: Yang et al. (2004b) Period of Study: 1994 - 2000 Location: Taipei, Taiwan Health Outcome: Post-neonatal mortality (27 days-1 yr old) Study Design: Case-crossover Statistical Analyses: Poisson regression Age Groups Analyzed: NA Sample Description: NR Averaging Time: 24-h Mean (SD) unit: 15.8 ppm x10 Range (Min, Max): 3.20, 48.4 Copollutant: NR Increment: Interquartile range: 0.56 ppm OR for Post-neonatal mortality [Lower CI, Upper CI] Lag examined : 0-2 Lag 0-2: 1.038 (0.663-1.624)



### March 2009 C-21 DRAFT—DO NOT QUOTE OR CITE

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

Study Design Concentrations CO Effect Estimates (95% CI)

Author: Chen et al. (1999) Period of Study: 5/1995 – 1/1996 Location: 3 Taiwan communities Health Outcome: Lung function (FVC, FEV 1 , FEV 1 /FVC, FEF 25-75 %, PEF) Study Design: Cross-sectional survey Statistical Analyses: Multivariate linear model Population: 941 children (Boys: 453; Girls: 488) Age Groups Analyzed: 8-13 Pollutant: CO Averaging Time: 1-h maximum; 24-h avg Mean (SD) unit: NR Range (Min, Max): 1-h maximum: (0.4, 3.6) Copollutant correlation: NO 2 : r = 0.86 – 0.98 Note: To represent the schoolchildren’s exposure the daytime avg and peak concentrations were measured from 0800 to 1800. Increment: NR β Coefficient (SE); lag: FVC (mL) 24-h avg -66.6 (40.73); 1 -147.71 (64.48); 2 2.2 (48.13); 7 1-h maximum -33.25 (20.74); 1 -16.48 (19.67); 2 -5.18 (16.48); 7 FEV 1 (mL) 24-h avg 20.55 (38.24); 1 -82.42 (60.95); 2 48.23 (45.58); 7 1-h maximum 1.2 (19.48); 1 -1.44 (18.57); 2 20.96 (15.67); 7 Author: Chen et al. (2000) Period of Study: 8/1996 – 6/1998 Location: Washoe County, NV Health Outcome: School absenteeism Study Design: Time-series Statistical Analyses: Maximum likelihood Population: 1st to 6th grade children: 27,793 Age Groups Analyzed: 1st to 6th grade children Pollutant: CO Averaging Time: 1-h maximum Mean (SD) unit: 2.73 (1.154) ppm Range (Min, Max): (0.65, 2.73) Copollutant correlation: PM 10 : r = 0.721 O 3 : r = -0.204 Increment: 1.0 ppm % Increase (Lower CI, Upper CI); lag: 3.79% (1.04-6.55); 0

Author: de Hartog et al. (2003) Period of Study: 1998 – 1999 Location: Amsterdam, Netherlands; Erfurt, Germany; Helsinki, Finland Health Outcome: Respiratory symptoms (shortness of breath, being awakened by breathing problems, phlegm, wheezing, tripping heart) Study Design: Time-series Statistical Analyses: Logistic regression Population: Non-smoking indivi duals with CHD: Amsterdam: 37 Erfurt: 47 Helsinki: 47 Age Groups Analyzed: ≥ 50 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Amsterdam: 0.6 mg/m 3 Erfurt: 0.4 mg/m 3 Helsinki: 0.4 mg/m 3 Range (Min, Max): Amsterdam: (0.4, 1.6) Erfurt: (0.1, 2.5) Helsinki: (0.1, 1.0) Copollutant: PM 2.5 ; NO 2 Increment: 0.25 mg/m 3 Odds Ratio (Lower CI, Upper CI); lag: Incidence of symptoms Shortness of breath 1 (0.92-1.1); 0 0.96 (0.88-1.05); 1 1 (0.92-1.09); 2 1.07 (0.98-1.16); 3 1.03 (0.9-1.18); 0-4 Being awakened by breathing problems 1.02 (0.92-1.14); 1 1.03 (0.93-1.15); 2 1.11 (1-1.22); 3 1.16 (0.98-1.37); 0-4 Phlegm 1.05 (0.93-1.19); 0 1.02 (0.91-1.14); 1 1.08 (0.96-1.22); 2 1.09 (0.97-1.22); 3 1.13 (0.94-1.35); 0-4 Prevalence of symptoms Shortness of breath 1 (0.94-1.06); 0 0.99 (0.94-1.05); 1 0.99 (0.93-1.05); 2 1.01 (0.95-1.07); 3 0.98 (0.9-1.07); 0-4 Being awakened by breathing problems 1.01 (0.93-1.1); 1 0.99 (0.91-1.08); 2 1.1 (1.02-1.19); 3 1.13 (1-1.29); 0-4



### March 2009 C-22 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Delfino et al. (2003) Period of Study: 11/1999 – 1/2000 Location: Los Angeles, CA Health Outcome: Asthma symptoms (Cough, wheeze, sputum production, shortness of breath, chest tightness) (symptom scores >1, symptoms scores >2); Lung function (PEF) Study Design: Panel study Statistical Analyses: Asthma symptoms: GEE Lung function: Generalized linear mixed model Population: 22 asthmatic Hispanic children Age Groups Analyzed: 10-15 Pollutant: CO Averaging Time: 1-h maximum; 8-h maximum Mean (SD) unit: 1-h maximum: 7.7 (3.1) ppb 8-h maximum: 5.0 (2.0) ppb Range (Min, Max): 1-h maximum: (2, 17) 8-h maximum: (1, 10) Copollutant correlation: NO 2 : r = 0.65; O 3 : 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; PM 10 : r = 0.50; EC: r = 0.60; OC: r = 0.55; SO 2 : r = 0.69 Increment: 5.0 ppb & 3.0 ppb Odds Ratio (Lower CI, Upper CI); lag: 1-maximum Increment: 5.0 ppb Symptom scores >1 0.95 (0.52-1.75); 0 1.11 (0.75-1.65); 1 Symptom scores >2 0.48 (0.07-3.53); 0 .28 (0.53-3.12); 1 8-h maximum Increment: 3.0 ppb Symptom scores >1 0.95 (0.55-1.62); 0 1.2 (0.77-1.86); 1 Symptom scores >2 0.53 (0.10-2.92); 0 1.43 (0.41-5.00); 1

Author: Estrella et al. (2005) Period of Study: 1/2000 – 4/2000 Location: Quito, Ecuador Health Outcome: Acute respiratory infection Study Design: Prospective study Statistical Analyses: Logistic regression; Poisson Population: 960 children Age Groups Analyzed: 6-11 Pollutant: CO Averaging Time: NR Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Increment: NR Odds Ratio (Lower CI, Upper CI); lag: Acute respiratory infection ARI in children COHb >2.5% vs. COHb <2.5%: Adjusted Logistic Regression Model 3.25 (1.65-6.38) ARI in children COHb >2.5% vs. COHb <2.5%: Crude Logistic Regression Model 2.06 (1.30-3.20) Log-Linear Model (Each Percent Increase in COHb above 2.5%) 1.15 (1.03-1.28) Author: Fischer et al. (2002) Period of Study: NR Location: Utrecht, Netherlands Health Outcome: Lung function (FVC, FEV 1 , PEF, MMEF) Study Design: Panel study Statistical Analyses: Restricted maximum likelihood linear model Population: 68 children Age Groups Analyzed: 10-11 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 921 μg/m 3 Range (Min, Max): (319, 1540) Copollutant: PM 10 ; BS; NO 2 ; NO Increment: 100 μg/m 3 mL (SE); lag: FVC: 0.5 (0.4); 1; 0.1 (0.2); 2 FEV 1 : -0.4 (0.5); 1; -0.2 (0.2); 2 m/s (SE); lag: PEF: -1.1 (2.8); 1; -0.6 (1.1); 2 MMEF: -0.5 (1.4); 1; -0.3 (0.6); 2



### March 2009 C-23 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Lagorio et al. (2006) Period of Study: 5/1999 – 6/1999; 11/1999 – 12/1999 Location: Rome, Italy Health Outcome: Lung function (FVC, FEV 1 ) Study Design: Time-series panel study Statistical Analyses: Generalized estimating equations (GEE) Population: COPD panel: 11 Asthma panel: 11 IHD panel: 7 Age Groups Analyzed: COPD panel: 50-80 Asthma panel: 18-64 IHD panel: 40-64 Notes: Asthma panel was restricted to never smokers, while COPD and IHD panels include former smokers if smoking cessation occurred at least 1 year prior to enrollment. Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Overall: 7.4 (6.2) mg/m 3 Spring: 2.1 (0.3) mg/m 3 Winter: 12.3 (4.9) mg/m 3 Range (Min, Max): Overall: (1.6, 28.9) Copollutant correlation: PM 2.5 : r = 0.67 PM 10-2.5 : r = -0.09 PM 10 : r = 0.55 NO 2 : r = 0.05 O 3 : r = -0.87 SO 2 : r = 0.65 Increment: 1 mg/m 3 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 FEV 1 (% 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 FEV 1 (% 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 FEV 1 (% of predicted ) 0.204 (0.120); 0 0.114 (0.142); 0-1 0.159 (0.194); 0-2 Author: Park et al. (2002) Period of Study: 3/1996 – 12/1999 Location: Seoul, Korea Health Outcome: School absenteeism Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Population: ~ 1,264 children (671 Boys, 593 girls) Age Groups Analyzed: 1st through 6th grade students Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.11 (0.40) ppm Range (Min, Max): (0.39, 2.97) Copollutant correlation: PM 10 : r = 0.56; NO 2 : r = 0.70; SO 2 : r = 0.67; O 3 : r = -0.46 Increment: 0.52 ppm Relative Risk (Lower CI, Upper CI); lag: Total Absences : 0.95 (0.94-0.97); 0 Non-Illness Related Absences: 0.99 (0.96-1.02); 0 Illness-Related Absences: 0.96 (0.94-0.98); 0

Author: Park et al. (2005a) Period of Study: 3/2002 – 6/2002 Location: Incheon, Korea Health Outcome: Lung function (PEF variability (>20%), Mean PEF); Respiratory symptoms (night respiratory symptoms, cough, inhaler use) Study Design: Panel study Statistical Analyses: GEE; Poisson GAM Population: 64 bronchial asthmatics Age Groups Analyzed: 16-75 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Control days: 0.6368 (0.1522) ppm Dust days: 0.6462 (0.0945) ppm Range (Min, Max): NR Copollutant: NR Increment: NR Relative Risk (Lower CI, Upper CI); lag: PEF variability (>20%): 1.43 (0.54-3.75) Night respiratory symptoms : 0.98 (0.51-1.86) β Coefficient (SE); lag: PEF variability (>20%): 0.9737 (0.3187) Mean PEF (L/min) : -10.103 (2.7146) Night respiratory symptoms : -0.018 (0.3654) Cough: 0.0855 (0.1826) Inhaler Use : 0.0796 (0.1733)



### March 2009 C-24 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Penttinen et al. (2001) Period of Study: 11/1996 – 4/1997 Location: Helsinki, Finland Health Outcome: Lung function (PEF) Study Design: Panel study Statistical Analyses: First order autoregressive linear model Population: 57 non-smoking adult asthmatics Age Groups Analyzed: NR Pollutant: CO Averaging Time: 24-h avg Median unit: 0.4 mg/m 3 Range (Min, Max): (0.1, 1.1) mg/m 3 Copollutant correlation: PM 10 : r = -0.03 PM 10-2.5 : r = -0.30 PM 2.5 : 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 NO 2 : r = 0.44 Increment: 0.2 mg/m 3 β Coefficient (SE); lag: PEF Deviations (L/min) Morning 0.27 (0.38); 0 -1.08 (0.36); 1 0.23 (0.38); 2 -1.11 (1.19); 5-day avg Afternoon -0.4 (0.43); 0 -0.13 (0.41); 1 -0.71 (0.41); 2 -3.03 (1.06); 5-day avg Evening -0.7 (0.45); 0; -0.31 (0.44); 1 0.3 (0.44); 2 -3.62 (1.19); 5-day avg Co-pollutant models with PNC Morning: -0.67 (0.64); 1 Afternoon: -0.46 (0.69); 0 Evening: -0.46 (0.73); 0 Author: Rabinovitch et al. (2004) Period of Study: 11/1999 – 3/2000; 11/2000 – 3/2001; 11/2001 – 3/2002 Location: Denver, CO Health Outcome: Lung function (FEV 1 ); asthma exacerbation; bronchodilator use Study Design: Panel study Statistical Analyses: Pulmonary function: Mixed effects model; Asthma exacerbation and medication use: GLM Population: Urban poor asthmatic children: 1999-2000: 41 2000-2001: 63 2001-2002: 43 Age Groups Analyzed: 6-12 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.0 (0.4) ppm Range (Min, Max): (0.3, 3.5) Copollutant: PM 2.5 ; PM 10 ; NO 2 ; SO 2 ; O 3 Increment: 0.4 ppm β Coefficient (SE); lag: FEV 1 AM: -0.001 (0.008); 3-day ma PM : 0.015 (0.01); 3-day ma Odds Ratio (Lower CI, Upper CI); lag: Asthma exacerbation : 1.012 (0.913-1.123); 3-day ma Bronchodilator use : 1.065 (1.001-1.133); 3-day ma

Author: Ranzi et al. (2004) Period of Study: 2/1999 – 5/1999 Location: Emilia-Romagna, Italy Health Outcome: Lung function; respiratory symptoms, medication use Study Design: Panel study Statistical Analyses: GLM Population: 120 “asthma-like” school children Age Groups Analyzed: 6-11 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Urban area: 1.54 mg Rural area: 1.22 mg Range (Min, Max): NR Copollutant: NO 2 ; TSP; PM 2.5 The study did not present quantitative results for CO.

Author: Rodriguez et al. (2007) Period of Study: 1996 – 2003 Location: Perth, Australia Health Outcome: Respiratory symptoms (body temperature, cough, wheeze/rattle chest, runny/blocked nose) Study Design: Panel study Statistical Analyses: Logistic regression, GEE Population: 263 children at high risk of developing asthma Age Groups Analyzed: 0-5 Pollutant: CO Averaging Time: 8-h avg Mean (SD) unit: 1.408 ppm Range (Min, Max): (0.012, 8.031) Copollutant: NR Increment: NR Odds Ratio (Lower CI, Upper CI); lag: Body Temperature 1.024 (0.911-1.151); 0 1.056 (0.943-1.184); 5 0.991 (0.962-1.021); 0-5 Cough 1.001 (0.996-1.005); 0 1.064 (0.941-1.02); 5 1.028 (0.996-1.061); 0-5 Wheeze/Rattle Chest 1.089 (0.968-1.226); 0 1.136 (1.016-1.26); 5 1.035 (1.005-1.066); 0-5 Runny/Blocked Nose 1.094 (0.824-1.453); 0 1.38 (1.028-1.853); 5 1.101 (1.025-1.183); 0-5



### March 2009 C-25 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Schildcrout et al. (2006) Period of Study: 11/1993 – 9/1995 Location: 8 North American cities: Albuquerque, NM Baltimore, MD Boston, MA Denver, CO San Diego, CA Seattle, WA St. Louis, MO Toronto, ON, Canada Health Outcome: Asthma symptoms; rescue inhaler use Study Design: Panel study Statistical Analyses: Asthma symptoms: Logistic regression; Rescue Inhaler Use: Poisson regression Population: 990 asthmatic children Age Groups Analyzed: 5-12 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NO 2 ; O 3 ; PM 10 ; SO 2 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 NO 2 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 PM 10 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 SO 2 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 NO 2 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 PM 10 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 SO 2 1.04 (0.96-1.12); 0 1.04 (0.97-1.1); 1 1.08 (1.03-1.13); 2 1.04 (1-1.08); 0-2 Author: Silkoff et al. (2005) Period of Study: 11/11/1999 – 3/31/2000; 11/1/2000 – 3/16/2001 Location: Denver, CO Health Outcome: Lung function (FEV 1 , PEF); recorded symptoms; rescue medication use Study Design: Panel study Statistical Analyses: Rescue medication use and total symptom score: GEE; Lung function: Mixed effects model Population: 1st winter: 16 with a history of more than 10 pack years of tobacco use, airflow limitation with FEV 1 of less than 70% of predicted value, and FEV 1 / FVC ratio of less than 60% 2nd winter: 18 with a history of more than 10 pack years of tobacco use, airflow limitation with FEV 1 of less than 70% of predicted value, and FEV 1 / FVC ratio of less than 60% Age Groups Analyzed: ≥ 40 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1999-2000: 1.2 (0.555) ppm 2000-2001: 1.1 (0.5) ppm Range (Min, Max): 1999-2000: (0.340, 3.790) 2000-2001: (0.360, 2.810) Copollutant: NR The study did not present quantitative results for CO.



### March 2009 C-26 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: Slaughter et al. (2003) Period of Study: 12/1994 – 8/1995 Location: Seattle, WA Health Outcome: Asthma severity; medication use Study Design: Panel study Statistical Analyses: Asthma severity: Ordinal logistic regression; Medica tion use: Poisson Population: 133 mild-to-moderate asthmatic children Age Groups Analyzed: 5-13 Pollutant: CO Averaging Time: 24-h avg Median unit: 1.47 ppm IQR (25th, 75th): (0.23, 1.87) Copollutant: NR Increment: Increased asthma attack severity: 0.67 ppm Increased rescue inhaler use: 1.0 ppm Odds Ratio (Lower CI, Upper CI); lag: Increased asthma attack severity: Without transition: 1.21; 1 With transition: 1.17; 1 Increased rescue inhaler use: Without transition: 1.09 (1.03-1.16); 1 With transition: 1.06 (1.01-1.1); 1 Author: Steerenberg et al. (2001) Period of Study: NR Location: Bilthoven and Utrecht, the Netherlands Health Outcome: Lung function (PEF); exhaled nitric oxide; inflammatory nasal markers Study Design: Panel study Statistical Analyses: Restricted maximum likelihood linear model Population: 126 children Age Groups Analyzed: 8-13 Notes: The study was only conducted for a two month period: February and March. Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Utrecht: 0.8 mg/m 3 Bilthoven: 0.5 mg/m 3 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/m 3 β Coefficient (SE); lag: Exercise induced responsiveness Author: Timonen et al. (2002) Period of Study: 2/1994 – 4/1994 Location: Kuopio, Finland Health Outcome: Exercise induced bronchial responsiveness; Lung function (FVC, FEV 1 , MMEF, AEFV) Study Design: Panel study Statistical Analyses: Linear regression Population: 33 children with chronic respiratory symptoms Age Groups Analyzed: 7-12 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 0.6 mg/m 3 Range (Min, Max): (0.1, 2.8) Copollutant correlation: PM 10 : r = 0.52 BS: r = 0.80 PNC 0.01-0.03 : r = 0.81 PNC 0.03-0.1 : r = 0.87 PNC 0.1-0.3 : r = 0.71 PNC 0.3-1.0 : r = 0.60 PNC 1.0-3.2 : r = 0.84 PNC 3.2-10 : r = 0.79 NO 2 : r = 0.85 Δ FEV 1 (%) -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 Δ MMEF (%) 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 Δ

AEFV (%) 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 FEV 1 (mL) 19.2 (13.2); 0 -9.04 (5.45); 1 -9.15 (5.21); 2 -11.7 (5.77); 3 -17.5 (12.5); 0-3 MMEF (mL/s) 22.2 (36.9); 0 -23 (15.2); 1 -4.63 (14.7); 2 -30.9 (16); 3 -24.9 (34.8); 0-3 AEFV (L2/s) -0.093 (0.088); 0 -0.068 (0.036); 1 -0.06 (0.035); 2 -0.05 (0.039); 3 -0.076 (0.083); 0-3



### March 2009 C-27 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations CO Effect Estimates (95% CI)

Author: von Klot et al. (2002) Period of Study: 9/1996 – 3/1997 Location: Erfurt, Germany Health Outcome: Asthma symptoms; medication use Study Design: Panel study Statistical Analyses: Logistic regression Population: 53 adults with asthma or asthma symptoms Age Groups Analyzed: 37-77 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 0.9 mg/m 3 Range (Min, Max): (0.3, 3.0) Copollutant correlation: NC 0.01-0.1 : r = 0.66 NC 0.1-0.5 : r = 0.79 NC 0.5-2.5 : r = 0.46 MC 0.1-0.5 : r = 0.66 MC 0.01-2.5 : r = 0.65 PM 2.5 -10: r = 0.42 PM 10 : r = 0.69 NO 2 : r = 0.82 SO 2 : r = 0.32 Increment: 0 and 5-day avg lag: 0.6 mg/m 3 14-day avg lag: 0.54 mg/m 3 Odds Ratio (Lower CI, Upper CI); lag: Prevalence: Inhaled β 2-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 β 2-agonist use CO+MC0.01-2.5 : 1 (0.91-1.11); 0-4 CO+NC00.01-0.1: 1.01 (0.91-1.11); 0-4 Inhaled corticosteroid use CO+MC0.01-2.5 : 0.89 (0.81-0.98); 0-13 CO+NC: 0.01-0. 1: 0.81 (0.72-0.91); 0-13 Wheezing CO+MC0.01-2.5: 1.15 (1.04-1.27); 0-4 CO+NC0.01-0.1 : 1.09 (0.98-1.22); 0-4 Author: Yu et al. (2000) Period of Study: 11/1993 – 8/1995 Location: Seattle, Washington Health Outcome: Asthma symptoms (Wheezing, coughing, chest tightness, shortness of breath) Study Design: Panel study Statistical Analyses: Repeated measures l ogistic regression models (GEE) Population: 133 mild-to-moderate asthmatics Age Groups Analyzed: 5-13 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.6 ppm Range (Min, Max): (0.65, 4.18) Copollutant correlation: PM 1.0 : r = 0.82 PM 10 : r = 0.86 SO 2 : 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-ter m CO exposure and respiratory hos pital admissions and ED visits.

Study Design Concentrations Effect Estimates (95% CI)

Author: Anderson et al. (2001) Period of Study: 10/1994-12/1996 Location: West Midlands; U.K. Hospital Admission Health Outcome (ICD9): Respiratory Diseases Asthma (493) COPD (490-492, 494-496) Study Design : Time-series Statistical Analyses: Regression with quasi-likelihood approach and GAM Age Groups Analyzed: All ages 0-14 15-64 ≥ 65 Pollutant : CO Averaging Time: Maximum 8-h avg Mean (SD) unit: 0.8 (0.7) ppm Range (Min, Max): (0.2, 10) Copollutant: correlation PM 10 : r = 0.55; PM 2.5 : r = 0.54; PM 2.5-10 : r = 0.10; BS: r = 0.77; SO 4 2- : r = 0.17; NO 2 : r = 0.73; O 3 : r = -0.29; SO 2 : r = 0.49 Increment: 1.0 ppm % Increase (Lower CI, Upper CI); lag: Respiratory Diseases Age Group All ages : 0.3% (-1.10 to 1.70); 0-1 0-14 : 1.50% (-0.60 to 3.60); 0-1 15-64 : -0.70% (-3.60 to 2.30); 0-1 ≥ 65 : 0.00% (-2.10 to 2.10); 0-1 Asthma Age Group 0-14 : 3.90% (-0.50 to 8.50); 0-1 15-64 : -4.90% (-10.60 to 1.10); 0-1 COPD Age Group ≥ 65 : 1.00% (-2.50 to 4.60); 0-1



### March 2009 C-28 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Andersen et al. (2007) Period of Study: 1/1999-12/2004 Location: Copenhagen, Denmark Hospital Admission Health Outcome (ICD10): Respiratory diseases: Chronic bronchitis (J41-42), Emphysema (J43), COPD (J44), Asthma (J45), Status asthmaticus (J46), Pediatric asthma (J45), Pediatric asthmaticus (J46) Study Design : Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: 5-18; ≥ 65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 0.3 (0.1) ppm IQR (25th, 75th): (0.22, 0.34) Copollutant; correlation: PM 10 : 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, PM 10 : 1.001 (0.961-1.042); 0-4 Asthma Age Group: 5-18 CO: 1.104 (1.018-1.198); 0-5 CO, PM 10 : 1.023 (0.911-1.149); 0-5

Author: Atkinson et al. (1999) Period of Study: 1/1992-12/1994 Location: London, U.K. ED Visits Health Outcome (ICD9): Respiratory complaints: wheezing, inhaler request, chest infection, chronic obstructive lung disease (COLD), difficulty breathing, cough, other respiratory complaints. e.g., croup, pleurisy, noisy breathing; Asthma (493) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: All ages 0-14 15-64 ≥ 65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 0.8 (0.4) ppm Range (Min, Max): (0.2, 5.6) Copollutant; correlation: NO 2 O 3 SO 2 PM 10 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, NO 2 : 2.05% (-2.25, 6.54); 0 CO, O 3 : 4.48% (0, 9.16); 0 CO, SO 2 : 2.34% (-1.94, 6.81); 0 CO, PM 10 : 2.93% (-1.53, 7.58); 0 CO, BS: 4.19% (-0.04, 8.60); 0 Author: Bedeschi et al. (2007) Period of Study: 1/2001-3/2002 Location: Reggio Emilia, Italy ED Visits Health Outcome (ICD9): Asthma (493); Asthma-like disorders, i.e., asthma, bronchiolitis, dyspnea/shortness of breath; Other respiratory disorders (i.e., upper and lower respiratory illness including sinusitis, bronchitis, and pneumonia) Study Design : Time-series Statistical Analyses: Poisson GAM, penalized splines Age Groups Analyzed: <15 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.4 (0.7) mg/m 3 Range (Min, Max): (0.4, 4.6) Copollutant; correlation: PM 10 : r = 0.61 TSP: r = 0.61 SO 2 : r = 0.71 NO 2 : r = 0.77 The study did not provide quantitative results for CO.



### March 2009 C-29 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Bell et al. (2008) Period of Study: 1/1995-12/2002 Location: Taipei, Taiwan Hospital Admissions Health Outcome (ICD9): Pneumonia (486); Asthma (493) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: All ages Pollutant : CO Averaging Time: 24-h avg Mean (SE) unit: 0.9 ppm Range (Min, Max): (0.3, 3.6) Copollutant: NR Increment: 0.5 ppm % Increase (Lower CI, Upper CI); lag Asthma (avg correlation between monitor pairs = 0.75 (13 monitors)) 3.29% (-0.74 to 7.49); 0 .49% (-4.25 to 3.41); 1 -0.84% (-4.43 to 2.88); 2 0.48% (-4.02 to 3.18); 3 0.74% (-4.62 to 6.4); 0-3 Pneumonia (avg correlation between monitor pairs = 0.75 (13 monitors)) 1.91% (-1.97 to 5.95); 0 0.03% (-3.65 to 3.85); 1 0.36% (-3.2 to 4.04); 2 -1.29% (-4.77 to 2.32); 3 0.21% (-5.03 to 5.73); 0-3 Asthma (avg correlation between monitor pairs = 0.88 (5 monitors)) 1.68% (-1.68 to 5.15); 0 -1.19% (-4.29 to 2.01); 1 -0.83% (-3.83 to 2.26); 2 -0.35% (-3.32 to 2.71); 3 -0.31% (-4.9 to 4.5); 0-3 Pneumonia (avg correlation between monitor pairs = 0.88 (5 monitors)) 1.24% (-2.02 to 4.6); 0 -0.01% (-3.06 to 3.13); 1 0.57% (-2.4 to 3.62); 2 -0.85% (-3.78 to 2.16); 3 0.31% (-4.23 to 5.06); 0-3 Asthma (monitors with ≥ 0.75 between monitor correlations (11 monitors), avg correlation between monitor pairs = 0.81) 2.87% (-0.91 to 6.79); 0 -0.71% (-4.2 to 2.91); 1 -0.73% (-4.08 to 2.73); 2 -0.41% (-3.72 to 3.01); 3 0.51% (-4.6 to 5.89); 0-3 Pneumonia (monitors with ≥ 0.75 between monitor correlations (11 monitors) to avg correlation between monitor pairs = 0.81) 0.98% (-1.68 to 5.76); 0 -0.12% (-3.54 to 3.42); 1 0.37% (-2.95 to 3.8); 2 -1.08% (-4.34 to 2.3); 3 0.3% (-4.71 to 5.57); 0-3 Author: Bellini et al. (2007) Period of Study: 1996-2002 Location: 15 Italian cities Hospital Admissions Health Outcome: Respiratory Conditions Study Design : Time-series; Meta-analysis Statistical Analyses: 1. GLM for city-specific estimates 2. Bayesian random-effects for meta analysis Age Groups Analyzed: All ages Pollutant: CO Averaging Time: NR Mean (SD) unit: NR Range (Min, Max): NR Copollutant: correlation NR Increment: 1 mg/m 3 % Increase (Lower CI, Upper CI); Lag Respiratory conditions All ages: Season: Winter: 0.58%; 0-1 Summer: 3.47%; 0-1 All Season: 1.25%; 0-3 Note: Estimates from Biggeri et al. (2004)

Author: Braga et al. (2001) Period of Study: 1/1993-11/1997 Location: Sao Paulo, Brazil Hospital Admissions Health Outcome (ICD9): Respiratory (460-519) Study Design : Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: ≤ 2 3-5 6-13 14-19 0-19 Pollutant: CO Averaging Time: Maximum 8-h avg Mean (SD) unit: 4.8 (2.3) ppm Range (Min, Max): (0.6, 19.1) Copollutant: correlationCopollutant: correlation PM 10 : r = 0.60 O 3 : r = -0.07 SO 2 : r = 0.47 Increment: 3 ppm % Increase (Lower CI, Upper CI); lag: Respiratory Age Group: ≤ 2 : 5.00% (3.30-6.80); 0-6 3-5 : 4.90% (1.40-8.50); 0-6 6-13 : 1.00% (-2.50 to4.60); 0-6 14-19 : 11.30% (5.90-16.80); 0-6 0-19: 4.90% (3.50-6.40); 0-6



### March 2009 C-30 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Burnett et al. (1999) Period of Study: 1/1980-12/1994 Location: Toronto, ON, Canada Hospital Admissions Health Outcome (ICD9): Asthma (493); COPD (490-492, 496); Respiratory infection (464, 466, 480-487, 494) Study Design : Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All ages Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.18 ppm IQR (25th, 75th): (0.9, 1.4) Copollutant: correlation PM 2.5 : r = 0.49 PM 10-2.5 : r = 0.20 PM 10 : r = 0.43 NO 2 : r = 0.55 SO 2 : r = 0.37 O 3 : r = -0.23 Increment: 1.18 ppm % Increase (t-value); lag: Asthma : 5.35% (3.92); 0 COPD : 2.93% (1.48); 0 Respiratory Infection : 5.00% (4.25); 0 Asthma : Multi-pollutant model CO, SO 2 , O 3 : 5.15% CO, PM 2.5 , SO 2 , O 3 : 4.63% CO, PM 10-2.5 , SO 2 , O 3 : 5.25% CO, PM 10 , SO 2 , O 3 : 4.80% CO, PM 10-2.5 , O 3 : 4.00% COPD : Multi-pollutant model CO, SO 2 , O 3 : 3.02% CO, PM 2.5 , SO 2 , O 3 : 2.46% CO, PM 10-2.5 , SO 2 , O 3 : 3.00% CO, PM 10 , SO 2 , O 3 : 2.75% CO, PM 10-2.5 , O 3 : 3.00% Author: Burnett et al. (2001) Period of Study: 1/1980-12/1994 Location: Toronto, ON, Canada Hospital Admissions Health Outcome (ICD9): Asthma (493); Acute bronchitis/ bronchiolitis (466); Croup (464.4) ; Pneum onia (480-486) Study Design : Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: <2 Pollutant : CO Averaging Time: 1-h avg Mean (SD) unit: 1.9 ppm IQR (25th, 75th): (1.3, 2.3) Copollutant: correlation O 3 : r = 0.24 Increment: 1.9 ppm % Increase (Lower CI, Upper CI); lag Respiratory problems CO: 19.20%; 0-1 CO, O 3 : 14.30%; 0-1

Author: Cakmak et al. (2006b) Period of Study: 4/1993-3/2000 Location: 10 Canadian cities Hospital Admissions Health Outcome (ICD9): Actue bronchitis/bronchiolitis (466); Pneumonia (480-486); Chronic/ unspecified bronchitis (490, 491); Emphysema (492); Asthma (493); Bronchiectasis (494); Chronic airway obstruction (496) Study Design : Time-series Statistical Analyses: 1. Poisson 2. Restricted Maximum Likelihood Method Age Groups Analyzed: All ages Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 0.8 ppm Range (Min, Max): (0.0, 6.5) Copollutant: correlation SO 2 NO 2 O 3 Increment: 0.8 ppm % Increase (Lower CI, Upper CI); lag: Respiratory disease CO: 0.60% (0.20, 1); 2.8 CO, SO 2 , NO 2 , O 3 : -0.20% (-0.70- 0.30); 2.8

Author: Cheng et al. (2007) Period of Study: 1996-2004 Location: Kaohsiung, Taiwan Hospital Admissions Health Outcome (ICD9): Pneumonia (480-486) Study Design : Bi-directional case-crossover Statistical Analyses: Conditional logist ic 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 PM 10 SO 2 NO 2 O 3 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, PM 10 , ≥ 25 oC: 1.15 (1.11-1.2); 0-2 CO, PM 10 , <25 oC: 1.28 (1.21-1.35); 0-2 CO, SO 2 , ≥ 25 oC: 1.22 (1.17-1.27); 0-2 CO, SO 2 , <25 oC: 1.49 (1.42-1.56); 0-2 CO, NO 2 , ≥ 25 oC: 1.2 (1.15-1.27); 0-2 CO, NO 2 , <25 oC: 1.01 (0.95-1.08); 0-2 CO, O 3 , ≥ 25 oC: 1.16 (1.12-1.2); 0-2 CO, O 3 , <25 oC: 1.44 (1.38-1.5); 0-2



### March 2009 C-31 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Cho et al. (2000) Period of Study: 1/1996-12/1996 Location: 3 South Korea cities: Hospital Admissions Health Outcome (ICD9): Bronchial asthma; COPD; Bronchitis Study Design : Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All Ages Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: Daejeon: 1.424 (0.611) ppm Ulsan: 0.950 (0.211) ppm Suwon: 1.270 (0.549) ppm Range (Min, Max): Daejeon: (.364, 3.504) Ulsan: (.380, 1.675) Suwon: (.250, 3.616) Copollutant: correlation Daejeon SO 2 : r = 0.280; NO 2 : r = 0.041; TSP: r = 0.193; O 3 : r = -0.101; O 3 Max: r = -0.069 Ulsan SO 2 : r = 0.108; NO 2 : r = 0.446; TSP: r = 0.286; O 3 : r = -0.195; O 3 Max: r = -0.107 Suwon SO 2 : r = 0.556; NO 2 : r = 0.291; TSP: r = 0.496; O 3 : r = -0.371; O 3 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, SO 2 , NO 2 , O 3 : 1.21 (1.02-1.44) Ulsan CO: 3.55 (1.65-7.63) TSP, SO 2 , NO 2 , O 3 : 2.51 (1.06-5.93) Suwon CO: 1.24 (0.97-1.59) TSP, SO 2 , NO 2 , O 3 : 1.19 (0.88-1.61) Estimates obtained using actual measured integrated environmental pollution indicator values Daejeon CO: 1.34 (1.14-1.58) Ulsan CO: 1.27 (0.94-1.71) Suwon CO: 3.55 (1.27-9.93)

Author: Farhat et al. (2005) Period of Study: 8/1996-8/1997 Location: Sao Paulo, Brazil Hospital Visits & ED Visits Health Outcome (ICD9): Pneumonia/bronchopneumonia (480-486); Asthma (493); Bronchiolitis (466) Study Design : Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All ages Pollutant : CO Averaging Time: Maximum 8-h avg Mean (SD) unit: 3.8 (1.6) ppm Range (Min, Max): (1.1, 11.4) Copollutant: correlation PM 10 : r = 0.72; SO 2 : r = 0.49; NO 2 : r = 0.59; O 3 : r = -0.8 Increment: 1.8 ppm % Increase (Lower CI, Upper CI); lag: Lower Respiratory Tract Disease ED Visits CO, PM 10 : -0.10% (-5.60 to 5.30); 0-2 CO, NO 2 : -1.20% (-6.70 to 4.20); 0-2 CO, SO 2 : 3.70% (-1.00 to 8.40); 0-2 CO, O 3 : 4.80% (0.50-9.10); 0-2 CO, PM 10 , NO 2 , SO 2 , O 3 :- 0.64% (-6.90 to 5.60); 0-2 Pneumonia/ Bronchopneumonia Hospital Admissions CO, PM 10 : 4.40% (-7.90 to 16.70); 0-2 CO, NO 2 : 4.40% (-88.70 to 17.50); 0-2 CO, SO 2 : 7.80% (-2.50 to 18.20); 0-2 CO, O 3 : 9.60% (-0.50 to 19.70); 0-2 CO, PM 10 to NO 2 , SO 2 , O 3 :5.10% (-9.60 to 19.70); 0-2 Asthma/ Bronchiolitis Hospital Admissions CO, PM 10 : 6.10% (-14.90 to 27.10); 0-2 CO, NO 2 : 2.40% (-16.90 to 21.70); 0-2 CO, SO 2 : 10.60% (-6.60 to 27.80); 0-2 CO, O 3 : 12.40% (-3.60 to 28.40); 0-2 CO, PM 10 to NO 2 , SO 2 , O 3 : 8.80% (-15.60 to 33.30); 0-2 Author: Fung et al. (2006) Period of Study: 6/1995-3/1999 Location: Vancouver, Canada Hospital Admissions Health Outcome (ICD9): Respiratory Illness Study Design : 1. Dewanji and Moolgavkar 2. Time-series 3. Bi-directional case-crossover Statistical Analyses: 1. Dewanji and Moolgavkar 2. Poisson 3. Conditional logi stic regression Age Groups Analyzed: ≥ 65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 0.69 0.25) ppm Range (Min, Max): (0.28, 2.03) Copollutant: correlation CoH: r = 0.85; O 3 : r = -0.53; NO 2 : r = 0.74; SO 2 : r = 0.61; PM 10 : r = 0.46; PM 2.5 : r = 0.23; PM 10-2.5 : r = 0.51 Increment: 0.24 ppm Relative Risk (Lower CI, Upper CI); lag Dewanji and Moolgavkar 1.008 (0.997-1.02); 0 1.012 (0.999-1.025); 0-2 1.010 (0.995-1.025); 0-4 1.009 (0.991-1.026); 0-6 Time-series 1.012 (1.000-1.023); 0 1.017 (1.003-1.032); 0-2 1.017 (1.001-1.035); 0-4 1.016 (0.996-1.036); 0-6 Bi-directional case-crossover 1.010 (0.006-1.023); 0 1.012 (0.996-1.027); 0-2 1.012 (0.995-1.03); 0-4 1.010 (0.991 1.031; 0-6



### March 2009 C-32 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Fusco et al. (2001) Period of Study: 1/1995 10/1997 Location: Rome, Italy Hospital Admissions Health Outcome (ICD9): Respiratory conditions (460- 519, excluding 470-478); Acute respiratory infections plus pneumonia (460-466, 480-486); COPD (490-492, 494-496) Asthma (493) Study Design : Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages 0-14 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 3.6 (1.2) mg/m 3 IQR (25th, 75th): (2.8, 4.3) Copollutant: correlation All Year SO 2 : r = 0.56 NO 2 : r = 0.31 O 3 : r = -0.57 Cold Season SO 2 : r = 0.37 NO 2 : r = 0.41 O 3 : r = -0.44 Warm Season SO 2 : r = 0.44 NO 2 : r = 0.59 O 3 : r = -0.38 Increment: 1.5 mg/m 3 % 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, NO 2 : 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, NO 2 : 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, NO 2 : 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 , NO 2 : 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, NO 2 : 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, NO 2 : 6.90 (0.80-13.40); 1 Asthma 6.30 (-0.50 to 13.50); 0 8.20 (1.10-15.70); 1; -0.70 (-7.30 to 6.30); 2 3.50 (-3.20 to 10.60); 3; 4.80 (-1.90 to 12.00); 4 CO, NO 2 : 3.30 (-4.20 to 11.30); 1 Author: Gouveia and Fletcher (2000a) Period of Study: 11/1992-9/1994 Location: Sao Paulo, Brazil Hospital Admissions Health Outcome (ICD9): All respiratory diseases Pneumonia (480-486); Asthma (493); Bronchitis (466, 490, 491) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: <1 <5 Pollutant : CO Averaging Time: Maximum 8-h avg Mean (SD) unit: 5.8 (2.4) ppm Range (Min, Max): (1.3, 22.8) Copollutant: correlation PM 10 : r = 0.63 SO 2 : r = 0.65 NO 2 : r = 0.35 Increment: 6.9 ppm Relative Risk (Lower CI, Upper CI); lag: All respiratory diseases Age Group : <5 : 1.017 (0.971-1.065); 0 Pneumonia Age Group : <5 : 1.015 (0.961-1.071); 0; <1 : 1.035 (0.975-1.099); 2 Asthma Age Group : <5 : 1.081 (0.98-1.192); 0



### March 2009 C-33 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Hajat et al. (1999) Period of Study: 1/1992-12/1994 Location: London, U.K. General Practitioner Visits Health Outcome (ICD9): Asthma (493); Lower Respiratory Diseases (464, 466, 476, 480-483, 485-487, 490- 492, 494-496, 500, 501, 503- 505, 510-515, 518, 519, 786) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: All ages 0-14 15-64 ≥ 65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: All year: 0.8 (0.4) ppm Warm Season (April-September): 0.7 (0.3) ppm Cool Season (October-March): 1.0 (0.5) ppm Range (10th, 90th): All Year: (0.5, 1.3) Warm Season: (0.4, 1.0) Cool Season: (0.5, 1.6) Copollutant: correlation All Year NO 2 : r = 0.72; SO 2 : r = 0.51; BS: r = 0.85; O 3 : r = -0.40; PM 10 : r = 0.56 Warm Season NO 2 : r = 0.70; SO 2 : r = 0.32; BS: r = 0.65; O 3 : r = -0.12; PM 10 : r = 0.58 Cool Season NO 2 : r = 0.84; SO 2 : r = 0.58; BS: r = 0.87 Increment: 0.8 & 0.7 ppm % Increase (Lower CI, Upper CI); Lag All Year : Asthma – Single Day Lags Increment: 0.8 ppm Age Group 0-14 : 4.10% (-0.10 to 8.40); 2 15-64 : 0.90% (-2.10 to 4.10); 0 ≥ 65: 7.50% (0.50-14.90); 2 All ages: 1.60% (-1.20 to 4.60); 2 Asthma – Cumulative exposure Increment: 0.7 ppm Age Group 0-14: 6.90% (1.30-12.90); 0-3 15-64: 1.00% (-3.20 to 5.40); 0-2 ≥ 65: 8.20% (0.40-16.60); 0-2 All ages: 1.80% (-1.50 to 5.20); 0-2 Lower Respiratory Diseases – Single Day Lags Increment: 0.8 ppm Age Group 0-14: 4.40 (1.70-7.10); 2 15-64: 1.10 (-0.70 to 3.00); 2 ≥ 65: -2.60 (-4.80 to -0.30); 3 All ages: 2.00 (0.50-3.40); 2 Lower Respiratory Diseases – Cumulative exposure Increment: 0.7 ppm for 0-2 and 0-3; 0.8 for 0-1 Age Group 0-14 : 3.00% (-1.00 to 7.20); 0-3 15-64: -0.70% (-2.90 to 1.50); 0-1 ≥ 65: -1.60% (-5.10 to 2.00); 0-3 All ages: 1.80% (0.10-3.60); 0-2 Warm or Cold Seasons : Asthma

, Increment: 0.8 ppm Age Group & Season 0-14 & Warm Season: 11.40% (3.30-20.00); 2 0-14 & Cold Season: 2.90% (-3.20 to 9.40); 2 15-64 & Warm Season: 4.80% (-0.60 to 10.60); 0 15-64 & Cold Season: -0.30% (-4.80 to 4.50); 0 ≥ 65 & Warm Season: 15.60% (3.10-29.60); 2 ≥ 65 & Cold Season: 4.20% (-6.00 to 15.60); 2 Lower Respiratory Diseases

, Increment: 0.8 ppm Age Group & Season 0-14 & Warm Season: 2.70% (-2.90 to 8.60); 2 0-14 & Cold Season: 6.20% (2.30-10.20); 2 15-64 & Warm Season: 6.20% (2.30-10.20); 2 15-64 & Cold Season: 2.40% (-1.20 to 6.10); 2 ≥ 65 & Warm Season: 1.00% (-1.60 to 3.80); 2 ≥ 65 & Cold Season: -2.20% (-6.50 to 2.40); 3 Author: Hajat et al. (2002) Period of Study: 1/1992-12/1994 Location: London, U.K. General Practitioner Visits Health Outcome (ICD9): Upper Respiratory Diseases (URD) Study Design : Time-series Statistical Analyses: Poisson, GAM, LOESS Age Groups Analyzed: 0-14 15-64 ≥ 65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: All year: 0.8 (0.4) ppm Warm Season (April-September): 0.7 (0.3) ppm Cool Season (October-March): 1.0 (0.5) ppm Range (10th, 90th): All Year : (0.5, 1.3) Warm Season: (0.4, 1.0) Cool Season: (0.5, 1.6) Copollutant: NR Increment: 0.6 ppm, 0.8 ppm, & 1.1 ppm % Increase (Lower CI, Upper CI); lag: Warm Season

, Increment: 0.6 ppm Age Group 0-14: 2.90% (-0.60 to 6.40); 1 14-64: 7.90% (4.80-11.10); 1 ≥ 65: 4.90% (-1.80 to 12.10); 3 Cold Season

, Increment: 1.1 ppm Age Group 0-14: -2.50% (-4.90 to 0.10); 1 14-64: 0.60% (-1.60 to 2.90); 1 ≥ 65: 5.60% (0.90-10.60); 3 All Year

, Increment: 0.8 ppm Age Group 0-14: -2.20% (-4.00 to -0.30); 1 14-64: 2.70% (0.10-5.50); 1 ≥ 65: 5.80% (2.40 to 9.30); 3



### March 2009 C-34 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Hapcioglu et al. (2006) Period of Study: 1/1997-12/2001 Location: Istanbul, Turkey Hospital Admission s Health Outcome (ICD9): COPD (490-492, 494-496) Study Design : Cross-sectional Statistical Analyses: Pearson Correlation Coefficient Age Groups Analyzed: All ages Pollutant : CO Averaging Time: Monthly Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Correlation Coefficient: Between CO exposure and COPD: 0.57 Between CO exposure and COPD when controlling for temperature: 0.25

Author: Hinwood et al. (2006) Period of Study: 1/1992-12/1998 Location: Perth, Australia Hospital Admissions Health Outcome (ICD9): COPD (490.00-496.99 excluding asthma) Pneumonia/influenza (480.00- 489.99); Asthma (493) Study Design : Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: All ages Pollutant : CO Averaging Time: Maximum 8-h avg Mean (SD) unit: All Year: 2.3 (1.3) ppm; November-April: 2.2 (1.3) ppm; May-October: 2.4 (1.2) ppm Range (10th, 90th): All Year: (0.9, 4.2) November-April: (0.8, 4.2) May-October: (1.1, 4.2) Copollutant: correlation All Year : NO 2 : r = 0.57 O 3 : r = 0.00 November-April : NO 2 : r = 0.55 O 3 : r = 0.00 May-October : NO 2 : r = 0.57 O 3 : r = 0.16 Increment: 2.3 ppm Odds Ratio (Lower CI, Upper CI); Lag Pneumonia 0.99999 (0.9737-1.0268); 0 1.00650 (0.9806-1.0331); 1 1.00351 (0.9779-1.0298); 2 1.00424 (0.9790-1.0301); 3 1.00581 (0.9752-1.0374); 0-1 1.01005 (0.9755-1.0458); 0-2 1.00805 (0.9701-1.0474); 0-3 COPD 0.99915 (0.9693-1.0297); 0 1.00205 (0.9727-1.0323); 1 0.98630 (0.9577-1.0158); 2 0.98970 (0.9619-1.0182); 3 0.99960 (0.9647-1.0357); 0-1 0.99260 (0.9538-1.0329); 0-2 0.99160 (0.9493-1.0357); 0-3

Author: Hwang and Chan (2002) Period of Study: 1998 Location: 50 communities in Taiwan Clinic Visits Health Outcome (ICD9): Lower respiratory tract infections (466, 480-486) Study Design : Time-series Statistical Analyses: 1. General linear regression 2. Bayesian hierarchical modeling Age Groups Analyzed: All Ages 0-14 15-64 ≥ 65 Pollutant : CO Averaging Time: Maximum 8-h avg Mean (SD) unit: 1.00 (0.30) ppm Range (Min, Max): (0.51, 1.71) Copollutant: NR Increment: 0.1 ppm % Increase (Lower CI, Upper CI); Lag Age Group: All Ages 0.80% (0.60-1.00); 0 0.10% (-0.10 to 0.30); 1 0.10% (-0.10 to 0.30); 2 Age Group: 0-14 0.70% (0.50-1.00); 0 0.10% (-0.20 to 0.30); 1 0.20% (-0.10 to 0.40); 2 Age Group: 15-64 0.90% (0.60-1.10); 0 0.20% (0.00-0.50); 1 0.20% (-0.10 to 0.40); 2 Age Group: ≥ 65 1.10% (0.80-1.50); 0 0.60% (0.30-1.00); 1 0.40% (0.10-0.80); 2 Author: Ito et al. (2007) Period of Study: 1999-2002 Location: New York City, NY ED Visits Health Outcome (ICD9): Asthma (493) Study Design : Time-series Statistical Analyses: Poisson GLM Age Groups Analyzed: All ages Pollutant : CO Averaging Time: Maximum 8-h avg Mean (SD) unit: All Season: 1.31 (0.43) ppm Warm Months (April-September): 1.22 (0.32) ppm Cold Months (October-March): 1.41 (0.5) ppm Range (5th, 95th): All season: (0.77, 2.11) Warm Months (April-September): (0.75, 1.82) Cold Months (October-March): (0.78, 2.33) Copollutant: NR Increment: 1.3 ppm Relative Risk (Lower CI, Upper CI); Lag Warm months : 1.15 (1.07-1.25); 0-1



### March 2009 C-35 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Karr et al. (2007) Period of Study: 1995-2000 Location: South Coast Air Basin, CA Hospital Admissions Health Outcome (ICD9): Acute bronchiolitis (466.1) Study Design : Matched casecontrol Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: Infants: 3 weeks -1 yr Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: Chronic: 1,770 ppb Subchronic: 1,720 ppb Range (Min, Max): Chronic: (120, 8300) Subchronic: (130, 5070) Copollutant: NR Increment: 910 ppb, 960 ppb Odds Ratio (Lower CI, Upper CI); lag: Increment: 910 ppb Subchronic broncholitis : 1 (0.97-1.03) Increment: 960 ppb Chronic broncholitis : 1 (0.97-1.03)

Author: Karr et al. (2006) Period of Study: 1995-2000 Location: South Coast Air Basin, CA Hospital Admissions Health Outcome (ICD9): Acute bronchiolitis (466.1) Study Design : Case-Crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: Infants: 3 weeks – 1 year Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1-day lag: Index*: 1,730 ppb Referent*: 1,750 ppb 4-day lag: Index*: 1,760 ppb Referent*: 1,790 ppb Range (Min, Max): Lag 1: Index*: (4, 9600) Referent*: (4, 9600) Lag 4: Index* (4, 8710) Referent* (4, 9600) Copollutant: NR * Index days: days lagged in reference to date of hospitalization of a case. Referent days: are for each case and includes all days that are the same day of week and in the same month as the index day for that case for CO. Increment: 1361, 1400 ppb Odds Ratio (Lower CI, Upper CI); Lag Increment: 1361 ppb Age Group : Overall: 0.99 (0.96-1.02); 1 25-29 weeks : 0.86 (0.68-1.1); 1 29 1/7 – 34 weeks : 1 (0.86-1.15); 1 34 1/7 – 37 weeks : 0.95 (0.87-1.04); 1 37 1/7 – 44 weeks: 1 (0.97-1.03); 1 Increment: 1400 ppb Age Group: Overall: 0.97 (0.94-1); 4 25-29 weeks : 0.93 (0.72-1.2); 4 29 1/7 – 34 weeks: 0.89 (0.77-1.03); 4 34 1/7 – 37 weeks: 0.98 (0.90-1.08); 4 37 1/7 – 44 weeks: 0.97 (0.94-1); 4

Author: Kim et al. (2007) Period of Study: 2002 Location: Seoul, Korea Hospital Admissions Health Outcome (ICD10): Asthma (J45 and J46) Study Design : Bi-directional case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: All Ages Pollutant : CO Averaging Time: Maximum 8-h avg Mean (SD) unit: Daily Concentration: 8.6 (4.6) ppm Relevant Concentration: 2.8 (2.8) ppm Range (Min, Max): Daily Concentration: (0.8, 44.0) Relevant Concentration: (0.0, 30.4) Copollutant: NR Relative Risk (Lower CI, Upper CI); lag: Individual Level SEP Quintile 1: 1.06 (1.02-1.09); 1-3 ma Quintile 2: 1.05 (1.02-1.09); 1-3 ma Quintile 3: 1.05 (1.01-1.08); 1-3 ma Quintile 4: 1.07 (1.03-1.11); 1-3 ma Quintile 5: 1.05 (1.00-1.09); 1-3 ma Regional Level SEP Quintile 1: 0.99 (0.92-1.07); 1-3 ma Quintile 2: 1.06 (1.02-1.11); 1-3 ma Quintile 3: 1.04 (1.02-1.07); 1-3 ma Quintile 4: 1.10 (1.06-1.15); 1-3 ma Quintile 5: 1.06 (1.03-1.09); 1-3 ma Overall : 1.06 (1.04-1.07); 1-3 ma Relative Effect Modification for SES Individual Level SEP Quintile 1: 1 Quintile 2: 1 (0.95.1.04); 1-3 ma Quintile 3: 0.99 (0.94-1.03); 1-3 ma Quintile 4: 1.02 (0.97-1.06); 1-3 ma Quintile 5: 0.99 (0.94-1.04); 1-3 ma Regional Level SEP Quintile 1: 1 Quintile 2: 1.05 (0.97-1.14); 1-3 ma Quintile 3: 1.03 (0.96-1.11); 1-3 ma Quintile 4: 1.08 (1-1.16); 1-3 ma Quintile 5: 1.05 (0.97-1.13); 1-3 ma



### March 2009 C-36 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Kontos et al. (1999) Period of Study: 1/1987-12/1992 Location: Piraeus, Greece Hospital Admissions Health Outcome (ICD9): Respiratory conditions (laryngitis, bronchiolitis, tonsillitis, acute rhinopharyngitis, otitis, bronchopneumonia, pneumonia, asthma) Study Design : Time-series Statistical Analyses: Stochastic dynamical system approach Age Groups Analyzed: 0-14 Pollutant : CO Averaging Time: 24-h avg Mean Range (SD) unit: 1987: 4.2 mg/m 3 1992: 3.6 mg/m 3 Range (Min, Max): NR Copollutant: correlation 1987-1989 Smoke: r = 0.2979; SO 2 : r = 0.2166; NO 2 : r = 0.1913 1990-1992 Smoke: r = 0.5383; SO 2 : r = 0.43283; NO 2 : 0.5223 This study did not present quantitative results for CO.

Author: Lee et al. (2002) Period of Study: 12/1997-12/1999 Location: Seoul, Korea Hospital Admissions Health Outcome (ICD10): Asthma (J45, J46) Study Design : Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: <15 Pollutant : CO Averaging Time: 1-h maximum Mean Range (SD) unit: 1.8 (0.7) ppm IQR (25th, 75th): (1.2, 2.2) Copollutant: correlation PM 10 : r = 0.598 SO 2 : r = 0.812 NO 2 : r = 0.785 O 3 : r = -0.388 Increment: 1.0 ppm Relative Risk (Lower CI, Upper CI); lag: RR for asthma and exposure to various pollutants for children under 15 years old Pollutant : CO: 1.16 (1.10-1.22); 2-3 avg CO, PM 10 : 1.13 (1.07-1.20); 2-3 avg CO, SO 2 : 1.17 (1.08-1.27); 2-3 avg CO, NO 2 : 1.04 (0.95-1.14); 2-3 avg CO, O 3 : 1.16 (1.11-1.22); 2-3 avg CO, O 3 , PM 10 : 1.148 (1.084-1.217); 2-3 avg CO, O 3 , PM 10 , SO 2 : 1.168 (1.075-1.269); 2-3 avg CO, O 3 , PM 10 , SO 2 , NO 2 : 1.098 (0.994-1.214); 2-3 avg Author: Lee et al. (2006) Period of Study: 1/2002-12/2002 Location: Seoul, Korea Hospital Admissions Health Outcome (ICD10): Asthma (J45-46) Study Design : Time-series Statistical Analyses: GAM with stringent parameters Age Groups Analyzed: <15 Pollutant : CO Averaging Time: Maximum 2-h avg Mean (SD) unit: High SES: 6.08 (2.10) ppb Moderate SES: 6. 35 (2.44) ppb Low SES: 6.67 (2.59) ppb Range (Min, Max): NR Copollutant: correlation NO 2 : r = 0.55 SO 2 : r = 0.72 PM 10 : r = 0.28 O 3 : r = -0.36 Increment: 3.01 ppb, 0.26 ppb, 4.52 ppb, 3.68 ppb Relative Risk (Lower CI, Upper CI); lag: Increment: 3.01 ppb Overall: 1.07 (0.96-1.20); 0 Increment: 0.26 ppb High SES: 1.06 (0.96-1.17); 0 Increment: 4.52 ppb Moderate SES: 0.96 (0.84-1.10); 0 Increment: 3.68 ppb Low SES: 1.02 (0.85-1.24); 0

Author: Lee et al. (2007b) Period of Study: 1996-2003 Location: Kaohsiung, Taiwan Hospital Admissions Health Outcome (ICD9): COPD (490-492, 494, 496) Study Design : Bi-directional case-crossover Statistical Analyses: Conditional logist ic 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: PM 10 SO 2 NO 2 O 3 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. PM 10 <25°C : 1.257 (1.152-1.371); 0-2 ≥ 25°C : 1.149 (1.079-1.224); 0-2 CO, SO 2 <25°C : 1.396 (1.295-1.504); 0-2 ≥ 25°C : 1.241 (1.161-1.326); 0-2 CO, NO 2 <25°C : 0.973 (0.877-1.080); 0-2 ≥ 25°C : 1.196 (1.104-1.297); 0-2 CO, O 3 <25°C : 1.378 (1.286-1.477); 0-2 ≥ 25°C : 1.170 (1.105-1.239); 0-2



### March 2009 C-37 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Lin et al. (1999) Period of Study: 5/1991-4/1993 Location: Sao Paulo, Brazil ED Visits Health Outcome (ICD9): Respiratory illness (lower respiratory illness, upper respiratory illness, wheezing) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: <13 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 5 ppm Range (Min, Max): (1, 12) Copollutant: correlation PM 10 : r = 0.50 NO 2 : r = 0.35 SO 2 : r = 0.56 O 3 : 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, PM 10 , O 3 , SO 2 , NO 2 : 0.945 (0.808-1.105); 0-5 Lower Respiratory Illness CO: 1.203 (0.867-1.669); 0-5 CO, PM 10 , O 3 , SO 2 , NO 2 : 0.971 (0.641-1.472); 0-5 Upper Respiratory Illness CO: 1.237 (1.072-1.428); 0-5 CO, PM 10 , O 3 , SO 2 , NO 2 : 0.944 (0.785-1.135); 0-5 Wheezing CO: 0.813 (0.606-1.091); 0-5 CO, PM 10 , NO 2 , SO 2 , O 3 : 0.74 (0.505-1.085); 0-5 Author: Lin et al. (2003) Period of Study: 1/1981-12/1993 Location: Toronto, ON, Canada Hospital Admissions Health Outcome (ICD9): Asthma (493) Study Design : Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: 6-12 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.18 (0.50) ppm Range (Min, Max): (0, 6.10) Copollutant: correlation SO 2 : r = 0.37 NO 2 : r = 0.55 O 3 : r = -0.16 PM 2.5 : r = 0.45 PM 10-2.5 : r = 0.17 PM 10 : r = 0.38 Increment: 0.5 ppm Odds Ratio (Lower CI, Upper CI); lag: Boys : Adjusting for Daily Weather Variables 1.05 (1-1.11); 1 / 1.07 (1.01-1.14); 2 1.08 (1.01-1.16); 3 / 1.08 (1-1.17); 4 1.07 (0.99-1.16); 5 / 1.07 (0.98-1.17); 6 1.07 (0.98-1.17); 7 Adjusting for PM and Daily Weather Variables 1.05 (0.99-1.11); 1 / 1.08 (1.01-1.16); 2 1.09 (1.01-1.18); 3 / 1.10 (1.02-1.20); 4 1.09 (1.00-1.18); 5 / 1.09 (0.99-1.19); 6 1.09 (0.99-1.20); 7 Girls : Adjusting for Daily Weather Variables 1.00 (0.93-1.06); 1 / 1.01 (0.94-1.10); 2 1.00 (0.91-1.09); 3 / 0.98 (0.89-1.09); 4 1.01 (0.91-1.13); 5 / 1.03 (0.92-1.16); 6 1.04 (0.93-1.17); 7 Adjusting for PM and Daily Weather Variables 1.00 (0.93-1.07); 1 / 1.01 (0.92-1.10); 2 0.99 (0.90-1.09); 3 / 0.97 (0.87-1.08); 4 0.99 (0.89-1.11); 5 / 1.02 (0.90-1.15); 6 1.05 (0.93-1.20); 7 Author: Lin et al. (2004c) Period of Study: 1/1987-12/1998 Location: Vancouver, BC Canada Hospital Admissions Health Outcome (ICD9): Asthma (493) Study Design : Time-series Statistical Analyses: GAM, LOESS Age Groups Analyzed: 6-12 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 0.96 (0.52) ppm Range (Min, Max): (0.23, 4.90) Copollutant: correlation SO 2 : r = 0.67 NO 2 : r = 0.73 O 3 : r = -0.35 Increment: 0.5 ppm Relative Risk (Lower CI, Upper CI); lag: Boys High SES : 1.06 (0.98-1.14); 1 / 1.06 (0.97-1.15); 2 1.07 (0.97-1.17); 3 / 1.03 (0.93-1.14); 4 1.01 (0.91-1.12); 5 / 1.01 (0.91-1.13); 6 1.06 (0.94-1.18); 7 Low SES: 1.06 (0.99-1.14); 1 / 1.03 (0.95-1.12); 2 1.01 (0.93-1.11); 3 / 0.99 (0.90-1.09); 4 0.96 (0.87-1.06); 5 / 0.98 (0.88-1.08); 6 0.98 (0.88-1.09); 7 Girls High SES: 1.05 (0.94-1.16); 1 / 1.02 (0.90-1.15); 2 0.97 (0.85-1.11); 3 / 0.95 (0.83-1.10); 4 0.93 (0.80-1.08); 5 / 0.95 (0.82-1.11); 6 1.01 (0.87-1.19); 7 Low SES: 1.01 (0.92-1.11); 1 / 0.98 (0.89-1.10); 2 0.99 (0.88-1.11); 3 / 1.05 (0.93-1.19); 4 1.07 (0.94-1.21); 5 / 1.07 (0.94-1.23); 6 1.04 (0.91-1.20); 7



### March 2009 C-38 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Lin et al. (2005) Period of Study: 1998-2001 Location: Toronto, Canada Hospital Admissions Health Outcome (ICD9): Respiratory Infections (464, 466, and 480-487) Study Design : Bi-directional case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: <15 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.16 (0.38) ppm Range (Min, Max): (0.38, 2.45) Copollutant: correlation PM 2.5 : r = 0.10 PM 10-2.5 : r = 0.06 PM 10 : r = 0.10 SO 2 : r = 0.12 NO 2 : r = 0.20 O 3 : r = -0.11 Increment: 0.44 ppm Odds Ratio (Lower CI, Upper CI); Lag Boys No adjustment: 1.11 (1.01-1.22); 0-3 / 1.10 (1.00-1.22); 0-5 Adjustment for weather variables: 1.13 (1.03-1.24); 0-3 / 1.13 (1.02-1.25); 0-5 Adjustment for weather variables and PM : 1.08 (0.98-1.20); 0-3 / 1.08 (0.97-1.20); 0-5 Girls No adjustment : 0.99 (0.89-1.10); 0-3 / 1.00 (0.89-1.13); 0-5 Adjustment for weather variables : 1.02 (0.92-1.14); 0-3: / 1.05 (0.93-1.18); 0-5 Adjustment for weather variables and PM : 1.01 (0.90-1.13); 0-3 / 1.02 (0.90-1.15); 0-5 Total No adjustment: 1.06 (0.98-1.14); 0-3 / 1.06 (0.98-1.15); 0-5 Adjustment for weather variables : 1.09 (1.01-1.17); 0-3 / 1.10 (1.01-1.19); 0-5 Adjustment for weather variables and PM: 1.05 (0.97-1.14); 0-3 / 1.06 (0.97-1.15); 0-5 Author: Linn et al. (2000) Period of Study: 1992-1995 Location: Los Angeles, CA Hospital Admissions Health Outcome (ICD9): APR-DRG Codes: Pulmonary (75-101); COPD (88) ICD9 Codes: Asthma (493) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: 0-29, ≥ 30 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: Winter 1.7 (0.8) ppm Spring 1.0 (0.3) ppm Summer 1.2 (0.4) ppm Fall 2.1 (0.8) ppm Range (Min, Max): Winter: (0.5, 5.3) Spring: (0.4, 2.2) Summer: (0.3,2.7) Fall: (0.6, 4.3) Copollutant: correlation Winter NO 2 : r = 0.89; PM 10 : r = 0.78; O 3 : r = -0.43 Spring NO 2 : r = 0.92; PM 10 : r = 0.54; O 3 : r = 0.29 Summer NO 2 : r = 0.94; PM 10 : r = 0.72; O 3 : r = 0.03 Fall NO 2 : r = 0.84; PM 10 : r = 0.58; O 3 : r = -0.36 Increment: 1.0 ppm β (SE); lag: Pulmonary Age Group: ≥ 30 All Year: 0.007 Winter: 0.016 Spring: 0.014 Summer: 0.020 Fall: 0.020 Asthma Age Group 0-29 All Year: 0.036 Asthma Age Group: ≥ 30; All Year: 0.028 Winter: 0.045 Fall: 0.039 COPD Age Group: ≥ 30 All Year: 0.019 Winter: 0.035 Fall: 0.029



### March 2009 C-39 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Luginaah et al. (2005) Period of Study: 4/1995-12/2000 Location: Windsor, ON, Canada Hospital Admissions Health Outcome (ICD9): Respiratory illness (460-519) Study Design : Time-series and Case-crossover Statistical Analyses: 1. Time-series: Poisson 2. Case-crossover: conditional logistic regression Age Groups Analyzed: All ages 0-14 15-64 ≥ 65 Pollutant : CO Averaging Time: 1-h maximum Mean (SD) unit: 1.3 (1.0) ppm Range (Min, Max): (0, 11.82) Copollutant: correlation NO 2 : r = 0.38 SO 2 : r = 0.16 O 3 : r = 0.10 CoH: r = 0.31 PM 10 : r = 0.21 Increment: 1.17 ppm Relative Risk (Lower CI, Upper CI); Lag Females and Case-crossover study design Age Group : All ages : 1.037 (0.968-1.111); 1 1.063 (0.976-1.158); 2 1.087 (0.982-1.203); 3 Age Group: 0-14: 1.147 (1.006-1.307); 1 1.186 (1.020-1.379); 2 1.221 (1.022-1.459); 3 Age Group: 15-64 : 1.005 (0.884-1.141); 1 1.007 (0.859-1.181); 2 1.032 (0.858-1.240); 3 Age Group: ≥ 65: 1.014 (0.922-1.116); 1 1.024 (0.907-1.156); 2 1.035 (0.893-1.200); 3 Males and Case-crossover study design Age Group: All Ages: 0.950 (0.884-1.020); 1 0.945 (0.862-1.036); 2 0.965 (0.866-1.075); 3 Age Group: 0-14: 1.003 (0.904-1.113); 1 0.997 (0.871-1.141); 2 0.970 (0.824-1.141); 3 Age Group: 15-64 : 1.036 (0.870-1.233); 1 1.033 (0.821-1.299); 2 0.991 (0.760-1.293); 3 Age Group: ≥ 65 : 0.867 (0.775-0.970); 1 0.865 (0.752-0.994); 2 0.946 (0.807-1.109); 3 Female and Time-series study design Age Group: All Ages: 1.049 (0.993-1.108); 1 1.032 (0.993-1.188); 2 1.051 (0.993-1.112); 3 Age Group: 0-14: 1.077 (0.979-1.184); 1 1.068 (1.001-1.139); 2 1.100 (0.997-1.213); 3 Age Group: 15-64 : 1.072 (0.962-1.195); 1 1.025 ( 0.944-1.112); 2 1.081 (0.963-1.213); 3 Age Group: ≥ 65: 1.029 (0.957-1.118); 1 1.030 (0.928-1.144); 2 1.013 (0.899-1.142); 3 Male and Time-series study design Age Group: All Ages: 0.989 (0.932-1.049); 1 0.986 (0.946-1.029); 2 0.987 (0.929-1.048); 3 Age Group: 0-14: 1.034 (0.949-1.126); 1 0.996 (0.933-1.062); 2 0.968 (0.881-1.064); 3 Age Group: 15-64: 0.994 (0.854-1.157); 1 0.988 (0.884-1.104); 2 0.951 (0.806-1.121); 3 Age Group: ≥ 65 : 0.901 (0.817-0.994); 1 0.904 (0.803-1.019); 2 0.963 (0.845-1.098); 3



### March 2009 C-40 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Martins et al. (2002) Period of Study: 5/1996-9/1998 Location: Sao Paulo, Brazil ED Visits Health Outcome (ICD10): Chronic Lower Respiratory Disease (CLRD: J40-47) for chronic bronchitis, emphysema, other COPD, asthma, and bronchiectasia Study Design : Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: >64 Pollutant : CO Averaging Time: Maximum 8-h avg Mean (SD) unit: 3.7 (1.7) ppm Range (Min, Max): (1.0, 12.6) Copollutant: correlation NO 2 : r = 0.62; SO 2 : r = 0.51; PM 10 : r = 0.73; O 3 : r = 0.07 Increment: 1.63 ppm β (SE); lag: Chronic Lower Respiratory Diseases Age Group >64: 0.0489 (0.0274); 2

Author: Masjedi et al. (2003) Period of Study: 9/1997-2/1998 Location: Tehran, Iran ED Visits Health Outcome (ICD9): Total acute respiratory conditions; Asthma (493); COPD (490-492, 494, 496) Study Design : Time-series Statistical Analyses: Multiple stepwise regression Age Groups Analyzed: Adults Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 8.85 ppm Range (Min, Max): (2.15, 23.8) Copollutant: NR Increment: NR β (p-value); lag; Asthma: -0.779 (0.12) COPD: 0.012 (0.71) Acute Respiratory conditions: -0.086 (0.400) Correlation coefficients: Mean 3-day CO levels and asthma: -0.300 (0.149) Mean weekly CO level and asthma: -0.14 (0.2) Mean 10-day CO levels and asthma: -0.05 (0.43) Author: McGowan et al. (2002) Period of Study: 6/1988- 12/1998 Location: Christchurch, New Zealand Hospital Admissions Health Outcome (ICD9): Pneumonia (480-487); Acute respiratory infe ctions (460-466); Chronic lung Diseases (491- 492, 494-496); Asthma (493) Study Design : Time-series Statistical Analyses: Generalized Additive Model Age Groups Analyzed: <15; >64 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.16 (1.51) mg/m 3 Range (Min, Max): (0, 15.7) Copollutant: NR This study did not provide quantitative results for CO.

Author: Migliaretti et al. (2007) Period of Study: 1/1997 - 12/1999 Location: Turin, Italy Hospital Admissions Health Outcome (ICD9): Respiratory Illness (chronic bronchitis, emphysema, and other COPD) (490-496) Study Design : Case-control Statistical Analyses: Multiple logistic regression Age Groups Analyzed: ≥ 15 15-64 >64 Pollutant : CO Averaging Time: 8-h median Median (SD) unit: 3.36 (1.57) mg/m 3 Range (Min, Max): NR Copollutant: correlation TSP Increment: 1 mg/m 3 Odds Ratio (Lower CI, Upper CI); lag: CO Age Group ≥ 15: 1.053 (1.030-1.070) 15-64: 1.040 (0.987-1.085) >64: 1.054 (1.027-1.083) CO , TSP Age Group ≥ 15: 1.058 (1.024-1.096) 15-64: 1.062 (0.993-1.135) >64: 1.054 (1.011-1.099)



### March 2009 C-41 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Moolgavkar (2000a) Period of Study: 1987 – 1995 Location: 3 U.S. counties: Los Angeles County,CA Cook County, IL Maricopa County, AZ Hospital Admissions Health Outcome (ICD9): COPD plus asthma (490-496) Study Design : Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All Ages 0-19 20-64 ≥ 65 Pollutant : CO Averaging Time: 24-h median Median unit: Cook: 993 ppb LA: 1347 ppb Maricopa: 1240 ppb Range (Min, Max): Cook: (224, 3912) LA:(237,5955) Maricopa: (269, 4777) Copollutant: correlation Cook County : NO 2 : r = 0.63; SO 2 : r = 0.35; O 3 : r = -0.28 LA County: NO 2 : r = 0.80; SO 2 : r = 0.78; O 3 : r = -0.52 Maricopa County : NO 2 : r = 0.66; SO 2 : r = 0.53; O 3 : 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, PM 10 : 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, PM 2.5 : 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, PM 10 : 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, PM 10-2.5 : 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 , PM 10 : 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, PM 2.5 : 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, PM 10-2.5 : 2.80 (2.2); 0; / 2.50 (2.0); 1; / 0.60 (0.5); 2; 3.90 (3.2); 3; / 3.40 (2.8); 4; / 4.00 (3.4); 5 Author: Moolgavkar (2003b) Period of Study: 1987 – 1995 Location: 2 U.S. counties: Los Angeles County, CA, and Cook County, IL Hospital Admissions Health Outcome (ICD9): COPD plus asthma (490-496) Study Design : Time-series Statistical Analyses: Poisson GAM, Poisson GLM with natural splines Age Groups Analyzed: All Ages; ≥ 65 Pollutant : CO Averaging Time: 24-h median Median unit: Cook: 993 ppb LA: 1347 ppb Maricopa: 1240 ppb Range (Min, Max): Cook: (224, 3912) LA: (237,5955) Copollutant: correlation Cook County : NO 2 : r = 0.63; SO 2 : r = 0.35; O 3 : r = -0.28 Los Angeles County : NO 2 : r = 0.80; SO 2 : r = 0.78; O 3 : r = -0.52 Increment: 1 ppm % Increase (t-statistic); lag: COPD - Los Angeles County CO - GAM-30 (10 -8 ): 5.48 (17.67); 0; / 5.67 (18.22); 1; / 5.90 (19.01); 2; 5.28 (16.94); 3; / 4.59 (14.50); 4; / 4.10 (12.80); 5 GAM-100 (10 -8 ): 2.37 (8.67); 0; / 2.41 (8.73); 1; / 2.41 (8.76); 2; 1.81 (6.58); 3; / 1.38 (4.94); 4; / 1.07 (3.82); 5 NS-100: 2.28 (5.65); 0; / 2.29 (5.50); 1; / 2.32 (5.33); 2; 1.74 (4.10); 3; / 1.30 (3.16); 4; / 1.00 (2.46); 5 COPD - Cook County CO - GAM-100 (10 -8 ): 2.11 (1.62); 0; / 2.85 (2.16); 1; / 1.14 (0.86); 2; 1.05 (0.79); 3; / 0.43 (0.33); 4; / 0.34 (0.26); 5



### March 2009 C-42 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Neidell et al. (2004) Period of Study: 1992-1998 Location: California Hospital Admissions Health Outcome (ICD9): Asthma (493) Study Design : Time-series Statistical Analyses: Linear Regression Age Groups Analyzed: 0-1 1-3 3-6 6-12 12-18 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.777 (1.037) ppm Range (Min, Max): NR Copollutant: correlation O 3 PM 10 NO 2 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 O 3 , PM 10 , and NO 2 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 O 3 , PM 10 , NO 2 and Avoidance Behavior Age Group 0-1: -0.010 (0.010); 1-3: 0.027 (0.011); 3-6: 0.051 (0.011); 6-12: 0.025 (0.011); 12-18: 0.021 (0.009) Author: Norris et al. (1999) Period of Study: 9/1995- 12/1996 Location: Seattle, WA ED Visits Health Outcome (ICD9): Asthma (493) Study Design : Time-series Statistical Analyses: Semiparametric Poisson GAM Age Groups Analyzed: <18 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.6 (0.5) ppm Range (Min, Max): (0.6, 4.1) Copollutant: correlation PM 10 : r = 0.74 NO 2 (1-h max): r = 0.47 NO 2 (24-h avg.): r = 0.66 SO 2 (1-h max): r = 0.15 SO 2 (24-h avg.): r = 0.32 Increment: 0.6 ppm Relative Risk (Lower CI, Upper CI); Lag High Utilization: 1. 04 (0.93-1.16); 1 Low Utilization: 1. 15 (1.05-1.28); 1 All: 1.10 (1.02-1.19); 1

Author: Peel et al. (2005) Period of Study: 1/1993- 8/2000 Location: Atlanta, GA ED Visits Health Outcome (ICD9): Asthma (493, 786.09); COPD (491, 492, 496); URI (460-466, 477); Pneumonia (480-486) Study Design : Time-series Statistical Analyses: 1. Poisson GEE or asthma, URI, all respiratory 2. Poisson GLM for pneumonia and COPD Age Groups Analyzed: Primary Analysis: All Ages Secondary Analysis: 2-18 Pollutant : CO Averaging Time: 1-h maximum Mean (SD) unit: 1.8 (1.2) ppm Range (10th, 90th): (0.5, 3.4) Copollutant: NR Increment: 1.0 ppm Relative Risk (Lower CI, Upper CI); Lag Health Condition All respiratory illnesses: 1.011 (1.004-1.019); 0-2 URI: 1.012 (1.003-1.021); 0-2 / 1.066 (1.045-1.087); 0-13 Asthma : 1.010 (0.999-1.022); 0-2 1.076 (1.047-1.105); 0-13 Pneumonia: 1.009 (0.996-1.021); 0-2 1.045 (1.011-1.080); 0-13 COPD: 1.026 (1.004-1.048); 0-2 1.032 (0.975-1.092); 0-13 RR for asthma and exposure to CO for children age 2-18: 1.019 (1.004-1.035); 0-2 RR for all respiratory illnesses and CO exposure for all ages AQS (1/1/93- 8/31/00): 1.011 (1.004-1.019); 0-2 AQS (8/1/98- 8/31/00): 1.010 (1.000-1.021); 0-2 ARIES (8/1/98- 8/31/00): 1.018 (1.003-1.033); 0-2 Author: Sheppard et al. (1999) Period of Study: 1987-1994 Location: Seattle, WA Hospital Admissions Health Outcome (ICD9): Asthma (493) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: <65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1831 ppb IQR (25th, 75th): (1277, 2201) Copollutant: correlation PM 10 : r = 0.83; PM 2.5 : r = 0.78; PM 10-2.5 : r = 0.56; O 3 : r = -0.18; SO 2 : r = 0.24 Increment: 924 ppb % Increase (Lower CI, Upper CI); Lag CO: 6% (3, 9); 3 CO, PM 2.5 : 5% (1, 8); 3



### March 2009 C-43 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Slaughter et al. (2005) Period of Study: 1/1995 – 6/2001 Location: Spokane, WA Hospital Admissions & ED Visits Health Outcome (ICD9): Respiratory causes (460-519) Asthma (493); COPD (491, 492, 494, 496) Acute respiratory tract infections not including colds and sinusitis (464-466, 490) Study Design : Time-series Statistical Analyses: Poisson GLM, Natural Splines Age Groups Analyzed: All ages, Adults Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (5th, 95th): (1.25, 3.05) Copollutant: correlation PM 1 : r = 0.63 PM 2.5 : r = 0.62 PM 10 : r = 0.32 PM 10-2.5 : r = 0.32 Increment: 1.0 ppm Relative Risk (Lower CI, Upper CI); lag: ED Visits All Respiratory Illnesses Age Group: All Ages: 0.99 (0.96-1.02); 1 / 1.01 (0.98-1.04); 2 1.03 (1.00-1.06); 3 Asthma Age Group: All Ages: 1.00 (0.95-1.06); 1 / 1.01 (0.96-1.07); 2 1.06 (1.00-1.11); 3 COPD Age Group: Adults: 0.92 (0.85-1.00); 1 / 0.99 (0.91-1.08); 2 1.01 (0.93-1.10); 3 Hospital Admissions : All Respiratory Illnesses Age Group: All Ages: 0.99 (0.95-1.02); 1 / 1.00 (0.96-1.04); 2 0.99 (0.96-1.03); 3 Asthma Age Group: All Ages: 1.02 (0.92-1.13); 1 / 1.06 (0.96-1.17); 2 1.00 (0.91-1.11); 3 COPD Age Group: Adults: 0.94 (0.86-1.03); 1 / 1.04 (0.95-1.13); 2 0.97 (0.88-1.06); 3 Author: Steib et al. (2007) Period of Study: 7/1992- 3/1996 Location: Saint John, Canada ED Visits Health Outcome (ICD9): Asthma; COPD; Respiratory infections; All respiratory illnesses Study Design : Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All ages Pollutant : CO Averaging Time: 24-h avg 1-h maximum Mean (SD) unit: All year: 0.5 (0.3) ppm May-September: 0.6 (0.3) ppm All year: 1.6 (1.1) ppm, May-September: 1.7 (0.9) ppm Range (Min, Max): NR Copollutant: correlation H 2 S: r = -0.10; NO 2 : r = 0.68; O 3 : r = -0.05; SO 2 : r = 0.31; TRS: r = 0.07; PM 10 : r = 0.28; PM 2.5 : r = 0.27; H + : r = 0.23; SO 4 2- : r = 0.27; CoH: r = 0.55 Increment: 0.5 & 1.7 ppm % Increase (Lower CI, Upper CI); lag: All Respiratory Illnesses Increment: 0.5 ppm All Year: -3.40; 7 Increment: 1.7 ppm May- September: -5.70

Author: Sun et al. (2006) Period of Study: 1/2004- 12/2004 Location: Taiwan ED Visits Health Outcome (ICD9): Asthma (493) Study Design : Cross-sectional Statistical Analyses: Pearson correlation analysis Age Groups Analyzed: <16; 16-55 Pollutant : CO Averaging Time: Monthly Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Increment: NR Correlation Coefficient: Asthma Age Group: <16 : 0.653 16-55: 0.425



### March 2009 C-44 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Tenias et al. (2002) Period of Study: 1/1994- 12/1995 Location: Valencia, Spain ED Visits Health Outcome (ICD9): COPD (491, 492, 494, 496) Study Design : Time-series Statistical Analyses: 1. Poisson autoregressive 2. Sensitivity: GAM, LOESS Age Groups Analyzed: >14 Pollutant : CO Averaging Time: 24-h avg 1-h maximum Mean (SD) unit: 24-h avg All year: 3.1 mg/m 3 Warm Months: 2.5 mg/m 3 Cold Months: 3.7 mg/m 3 1-h avg All year: 6.7 mg/m 3 Warm Months: 5.4 mg/m 3 Cold Months: 8.0 mg/m 3 Range (Min, Max): 24-h avg: (0.9, 7.1) 1-h maximum: (1.6, 17.2) Copollutant: correlation SO 2 : r = 0.734; NO 2 : r = 0.180; O 3 : r = -0.517 Increment: 1 mg/m 3 Relative Risk (Lower CI, Upper CI); Lag 24-h avg All Year: 1.074 (0.998- 1156); 1 Cold Months: 1.070 (0.991-1.156); 1 Warm Months: 1.129 (0.960-1.329); 1 1-h maximum All Year: 1.039 (1.014-1.066); 1 Cold Months: 1.037 (1.010-1.064); 1 Warm Months: 1.058 (0.994-1.127); 1 All Year: sinusoidal terms: 1.039 (1.010-1.066); 1 All Year: humidity and temperature variables : 1.040 (1.014-1.067); 1 All Year: GAM, LOESS : 1.042 (1.019-1.066); 1

Author: Thompson et al. (2001) Period of Study: 1/1993- 12/1995 Location: Belfast, Northern Ireland ED Visits Health Outcome (ICD9): Asthma (493) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: Children Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: Warm Season: 0.57 (0.41) ppm Cold Season: 0.74 (0.73) ppm IQR (25th, 75th): Warm Season: (0.3, 0.7) Cold Season: (0.4, 0.8) Copollutant: correlation SO 2 (log): r = 0.64; PM 10 (log): r = 0.57; O 3 : r = -0.52; NOx (log): r = 0.74; NO (log): r = 0.71; NO 2 : r = 0.69 Increment: NR Relative Risk (Lower CI, Upper CI); lag: Temperature included in the model: 1.04 (1.00-1.09); 0 / 1.07 (1.02-1.12); 0-1 1.06 (1.00-1.12); 0-2 / 1.07 (1.00-1.14); 0-3 Warm Season: 1.06 (0.98-1.16); NR Cold Season: 1.07 (1.01-1.14); NR Adjusted for benzene level : 0.92 (0.83-.02); 0-1 avg. Note : The increment the study uses to calculate effect estimates is a doubling in CO levels, but The study did not provide this value.

Author: Tolbert et al. (2007) Period of Study: 1/1993- 12/2004 Location: Atlanta, GA ED Visits Health Outcome (ICD9): Respiratory Disease: Asthma (493, 786.07, 786.09); COPD (491, 492, 496); URI (460-465, 460.0, 477); Pneumonia (480- 496); Bronchiolitis (466.1, 466.11, 466.19)) Study Design : Time-series Statistical Analyses: Poisson GLM Age Groups Analyzed: All ages Pollutant : CO Averaging Time: 1-h maximum Mean (SD) unit: 1.6 ppm Range (Min, Max): (0.1, 7.7) Copollutant: correlation PM 10 : r = 0.51; O 3 : r = 0.27; NO 2 : r = 0.70; SO 2 : r = 0.28; Coarse PM: r = 0.38; PM 2.5 : r = 0.47; SO 4 : r = 0.14;EC: r = 0.66; OC: r = 0.59; TC: r = 0.63; OHC: r = 0.29 Increment: 1.22 ppm Relative Risk (Lower CI, Upper CI); lag: Respiratory Diseases : 1.016 (1.009-1.022); 3 Note: The study only provides results of the multi-pollutant models in figures, not quantitatively.

Author: Trapasso and Keith (1999) Period of Study: 1/1994- 12/1994 Location: Bowling Green, KY Hospital Admissions Health Outcome (ICD9): Asthma (493) Study Design : Time-series Statistical Analyses: Spearman Rank Correlation Coefficient Age Groups Analyzed: All ages Pollutant : CO Averaging Time: NR Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Increment: NR Correlation Coefficient (lag) CO Mean: r = 0.19;0 CO Mean: r = 0.27; 1 CO Mean: r = 0.21; 2 CO Max: r = 0.26; 0 CO Max: r = 0.36; 1 CO Max: r = 0.24; 2



### March 2009 C-45 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Tsai et al. (2006a) Period of Study: 1996 - 2003 Location: Kaohsiung, Taiwan Hospital Admissions Health Outcome (ICD9): Asthma (493) Study Design : Case-crossover Statistical Analyses: Conditional logist ic 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: PM 10 SO 2 NO 2 O 3 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, PM 10 <25°C : 1.251 (1.125-1.393); 0-2 ≥ 25°C : 1.178 (1.088-1.274); 0-2 CO, SO 2 <25°C : 1.207 (1.076-1.354); 0-2 ≥ 25°C : 1.290 (1.188-1.400); 0-2 CO, NO 2 <25°C : 0.916 (0.807-1.039); 0-2 ≥ 25°C : 1.249 (1.127-1.384); 0-2 CO, O 3 <25°C : 1.396 (1.282-1.520); 0-2 ≥ 25°C : 1.195 (1.113-1.284); 0-2 Author: Vigotti et al. (2007) Period of Study: 1/2000- 12/2000 Location: Pisa, Italy ED Visits Health Outcome (ICD9): Respiratory Disease: Asthma (493); Dry cough (468); Acute bronchitis (466) Study Design : Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: <10; >65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.5 (0.7) ug/m 3 Range (Min, Max): (0.3, 3.5) Copollutant: correlation NO 2 : r = 0.62 PM 10 : r = 0.70 Increment: 1mg/m 3 % Increase (Lower CI, Upper CI); Lag Age Group <10 : 18.60% (-6.90 to 51.10); 1 >65 : 26.50% (3.40-54.80); 4

Author: Villeneuve et al. (2006b) Period of Study: 1995-2000 Location: Toronto, ON, Canada Physician Visits Health Outcome (ICD9): Allergic rhinitis (177) Study Design : Time-series Statistical Analyses: Poisson GLM Age Groups Analyzed: >65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 1.1 (0.4) ppm Range (Min, Max): (0.0, 2.2) Copollutant: PM 2.5 PM 10 PM 10-2.5 SO 2 NO 2 O 3 Increment: 0.4 ppm Odds Ratio (Lower CI, Upper CI); Lag The study did not present quantitative results for CO.

Author: Xirasagar et al. (2006) Period of Study: 1998- 2001 Location: Taiwan Hospital Admissions Health Outcome (ICD9): Asthma (493) Study Design : Cross-sectional Statistical Analyses: Spearman Rank Correlations Age Groups Analyzed: 0-14; <2; 2-5; >5 Pollutant : CO Averaging Time: Monthly Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NR Increment: NR Correlation Coefficient (Lag) Age Group: <2 : r = -0.208 2-5 : r = -0.281 >5 : r = -0.134

Author: Yang et al. (2007) Period of Study: 1996 - 2003 Location: Taipei, Taiwan Hospital Admissions Health Outcome (ICD9): Asthma (493) Study Design : Case-crossover Statistical Analyses: Conditional logist ic 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: PM 10 SO 2 NO 2 O 3 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, PM 10 <25°C : 1.050 (0.983-1.122); 0-2 ≥ 25°C : 1.332 (1.216-1.459); 0-2 CO, SO 2 <25°C : 1.131 (1.059-1.207); 0-2 ≥ 25°C : 1.278 (1.174-1.392); 0-2 CO, NO 2 <25°C : 0.915 (0.839-0.997); 0-2 ≥ 25°C : 1.177 (1.049-1.320); 0-2 CO, O 3 <25°C : 1.169 (1.102-1.240); 0-2 ≥ 25°C : 1.275 (1.177-1.382); 0-2



### March 2009 C-46 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Yang et al. (2007) Period of Study: 1996-2003 Location: Taipei, Taiwan Hospital Admissions Health Outcome (ICD9): COPD: (490-492, 494, 496) Study Design : Case-crossover Statistical Analyses: Conditional logist ic 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: PM 10 SO 2 NO 2 O 3 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, PM 10 <20°C: 0.925 (0.863-0.992); 0-2 ≥ 20°C: 1.177 (1.123-1.235); 0-2 CO, SO 2 <20°C: 0.895 (0.832-0.962); 0-2 ≥ 20°C: 1.274 (1.219-1.331); 0-2 CO, NO 2 <20°C: 1.000 (0.910-1.099); 0-2 ≥ 20°C: 1.061 (0.998-1.129); 0-2 CO, O 3 <20°C: 0.935 (0.875-0.999); 0-2 ≥ 20°C: 1.234 (1.185-1.285); 0-2 Author: Yang et al. (2005) Period of Study: 1/1994- 12/1998 Location: Vancouver, Canada Hospital Admissions Health Outcome (ICD9): COPD (490-492, 494, 496) Study Design : Time-series Statistical Analyses: Poisson Age Groups Analyzed: ≥ 65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: .71 (0.28) ppm Range (Min, Max): (0.30, 2.48) Copollutant: correlation O 3 : r = -0.56 NO 2 : r = 0.73 SO 2 : r = 0.67 PM 10 : 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, O 3 : 1.11 (1.04-1.18); 0-6 CO, NO 2 : 1.04 (0.95-1.14); 0-6 CO, SO 2 : 1.11 (1.01-1.22); 0-6 CO, PM 10 : 1.02 (0.93-1.12); 0-6 CO, PM 10 , O 3 , NO 2 , SO 2 : 1.08 (0.96-1.22); 0-6 CO, O 3 , NO 2 , SO 2 : 1.10 (0.98-1.23); 0-6 Author: Yang et al. (2003) Period of Study: 1/1986- 12/1998 Location: Vancouver, BC, Canada Hospital Admissions Health Outcome (ICD9): Respiratory diseases (460-519) Study Design : Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: <3; ≥ 65 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 0.98 (0.54) ppm IQR (25th, 75th): (0.62, 1.16) Copollutant: correlation O 3 : r = -0.52 CoH NO 2 SO 2 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, O 3 : 1.04 (1.01-1.07); 1 CO, O 3 , CoH, NO 2 , SO 2 : 1.02 (0.96-1.08); 1 Age Group: ≥ 65 CO alone: 1.02 (1.00-1.04); 1 CO, O 3 : 1.02 (1.00-1.04); 1 CO, O 3 , CoH, NO 2 , SO 2 : 0.96 (0.93-1.00); 1 Author: Yang et al. (2004b) Period of Study: 6/1/1995 – 3/31/1999 Location: Vancouver, Canada Hospital Admissions Health Outcome (ICD9): Respiratory diseases (460-519); Pneumonia (480-486); Asthma (493) Study Design : Case-control Statistical Analyses: Pearson’s correlation coefficient Age Groups Analyzed: <3 Pollutant : CO Averaging Time: 24-h avg Mean (SD) unit: 0.70 (0.30) ppm IQR (25th, 75th): (0.50, 0.80) Copollutant: correlation PM 10 : r = 0.46; PM 2.5 : r = 0.24; PM 10-2.5 : r = 0.33; O 3 : r = -0.53; NO 2 : r = 0.74; SO 2 : r = 0.61 This study did not present quantitative results for CO.

Author: Zanobetti and Schwartz (2006) Period of Study: 1995-1999 Location: Boston, MA Hospital Admissions Health Outcome (ICD9): Pneumonia (480-487) Study Design : Case-crossover Statistical Analyses: Conditional logist ic 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 PM 2.5 : r = 0.52; BC: r = 0.82; NO 2 : r = 0.67; O 3 : r = -0.30 Increment: 0.475 ppm % Increase (Lower CI, Upper CI); lag: 5.45 (1.10, 9.51); 0 5.12 (0.83, 9.16); 0-1



### March 2009 C-47 DRAFT—DO NOT QUOTE OR CITE

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

Study Design Concentrations Effect Estimates (95% CI)

Author: Goss et al. (2004) Period of Study: 1999 – 2000 Location: U.S. Health Outcome: Lung function (FEV 1 , Cystic fibrosis pulmonary exacerbation) Study Design: Cohort Statistical Analyses: Logistic regression Population: 11,484 cystic fibrosis patients Age Groups Analyzed: >6 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: 0.692 (0.295) ppm IQR (25th, 75th): (0.48, 0.83) Copollutant: NR Increment: 1.0 ppm Odds Ratio (Lower CI, Upper CI); lag: Two or More Pulmonary Exacerbations During 2000 1.02 (0.85-1.22)

Author: Guo et al. (1999) Period of Study: 10/1995 – 5/1996 Location: Taiwan Health Outcome: Asthma Study Design: Cohort Statistical Analyses: Logistic regression Population: 331,686 non-smoking children Age Groups Analyzed: Middle-school children (mean age: 13.8) Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: 853 (277) ppb Range (Min, Max): (381, 1610) Copollutant: NR Increment: 326 ppb % Increase (Lower CI, Upper CI); lag: Boys Physician-diagnosed asthma: 1.17% (0.63-1.72) Questionnaire-diagnosed asthma: 1.10% (0.45-1.75) Girls Physician-diagnosed asthma: 0.84% (0.45-1.22) Questionnaire-diagnosed asthma: 1% (0.44-1.56) Author: Hirsch et al. (1999) Period of Study: Population: 9/1995-6/1996 Air: 4/1994 – 4/1995 Location: Dresden, Germany Health Outcome: Asthma symptoms in the past 12 months (wheeze, morning cough); Doctor's diagnosis (asthma, bronchitis); Lung func tion (bronchial hyperresponsiveness (BHR), FEV 1 <85% pred., FEF 25-75 % <70% pred.) Study Design: Cross-sectional Statistical Analyses: Multiple logistic regression Population: 5-7: 2,796; 9-11: 2,625 Age Groups Analyzed: 5-7 and 9-11 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: 0.69 mg/m 3 Range (Min, Max): (0.32, 1.54) Copollutant: NR Increment: 0.2 μg/m 3 Prevalence Odds Ratio (Lower CI, Upper CI); lag: Symptoms in the past 12 months: Wheeze Home Exposure Age Groups: 5-7; 9-11 : 1.05 (0.93-1.18) Home/School Exposure Age Groups: 9-11 : 1.02 (0.85-1.22) Morning Cough Home Exposure Age Groups: 5-7; 9-11 : 1.12 (1.01-1.23) Age Group: 9-11 : 1.13 (0.98-1.3) Doctor’s diagnosis: Asthma Home Exposure Age Groups: 5-7; 9-11: 1.07 (0.94-1.21) Age Groups: 9-11 : 1.16 (0.97-1.38) Doctor’s diagnosis: Bronchitis Age Groups: 5-7; 9-11 : 1.19 (1.11-1.27) Age Group: 9-11 : 1.24 (1.12-1.38) Lung function: BHR Age Groups: 5-7; 9-11: 0.79 (0.63-0.99) Age Group: 9-11: 0.77 (0.6-0.99) Lung function: FEV 1 <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: FEV 25-75 % <70% pred

. Age Groups: 5-7; 9-11: 1.15 (0.94-1.39) Age Group: 9-11: 1.07 (0.86-1.34) Symptoms in the past 12 months: Wheeze Age Groups: 5-7; 9-11 Atopic children: 1 (0.81-1.24) Nonatopic children: 1.05 (0.83-1.31) Morning cough Age Groups: 5-7; 9-11 Atopic children: 1.03 (0.82-1.29) Nonatopic children: 1.22 (1.05-1.41) Doctor’s diagnosis: Asthma Atopic children: 1.05 (0.83-1.32) Nonatopic children: 1.29 (1.05-1.59) Doctor’s diagnosis: Bronchitis Age Groups: 5-7; 9-11 Atopic children: 1 (0.86-1.16) Nonatopic children: 1.21 (1.1-1.33) Notes: Atopic Children were defined as those children with specific IgE to aeroallergens >0.7 kU-L-1; Nonatopic



### March 2009 C-48 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI) Children were defined as those children with specific IgE to aeroallergens ≤ 0.7 kU-L-1. Author: Hwang et al. (2006) Period of Study: 2001 Location: Taiwan Health Outcome: Allergic rhinitis Study Design: Cross-sectional Statistical Analyses: Two-stage hierar chical model (logistic and linear regression) Population: 32,143 Taiwanese school children Age Groups Analyzed: 6-15 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: 664 (153) ppb Range (Min, Max): (416, 964) Copollutant: correlation NOx: r = 0.88 O 3 : r = -0.37 PM 10 : r = 0.27 SO 2 : 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, SO 2 : 1.04 (1.02-1.06) CO, PM 10 : 1.05 (1.03-1.07) CO, O 3 : 1.07 (1.05-1.09) Male: 1.06 (1.03-1.08); Female: 1.05 (1.02-1.08) Parental atopy: Yes: 1.05 (1.02-1.08) Parental atopy: No: 1.06 (1.03-1.08) Parental Education: <6: 1 (0.91-1.09) Parental Education: 6-8: 1.07 (1.02-.12) Parental Education: 9-11: 1.05 (1.02-1.08) Parental Education: ≥ 12: 1.06 (1.03-1.09) ETS: Yes: 1.06 (1.03-1.08); ETS: No: 1.05 (1.02-1.08) Visible Mold: Yes: 1.07 (1.03-1.11) Visible Mold: No: 1.05 (1.03-1.07) Author: Hwang et al. (2005) Period of Study: 2001 Location: Taiwan Health Outcome: Asthma Study Design: Cross-sectional Statistical Analyses: Two-stage hierar chical model (logistic and linear regression) Population: 32,672 Taiwanese school children Age Groups Analyzed: 6-15 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: 664 (153) ppb Range (Min, Max): (416, 964) Copollutant: correlation NOx: r = 0.88 O 3 : r = -0.37 PM 10 : r = 0.27 SO 2 : r = 0.40 Increment: 100 ppb Adjusted Odds Ratio (Lower CI, Upper CI); lag: Physician-diagnosed asthma: 1.045 (1.017-1.074) CO, SO 2 : 1.066 (1.034-1.099) CO, PM 10 : 1.079 (1.047-1.112) CO, O 3 : 1.063 (1.1-1.474) CO, SO 2 , O 3 : 1.111 (1.074-1.15) CO, PM 10 , O 3 : 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 dur ing pregnancy: No: 1 Cockroaches noted monthly: Yes: 1.15 (1.03-1.29) Cockroaches noted monthly: No: 1 Water damage: Yes: 0.96 (0.81-1.12) Water damage: No: 1 Author: Lee et al. (2003c) Period of Study: 10/1995-5/1996 Location: Taiwan Health Outcome: Allergic rhinitis Study Design: Cohort Statistical Analyses: Multiple logistic regression Population: 331,686 non-smoking children Age Groups Analyzed: 12-14 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: 853 (277) ppb Range (Min, Max): (381, 1610) Copollutant: NR The study did not present quantitative results for CO.

Author: Meng et al. (2007) Period of Study: 11/2000 – 9/2001 Location: Los Angeles County and San Diego County, California Health Outcome: Asthma Study Design: Cohort Statistical Analyses: Logistic regression Population: 1,609 physician-diagnosed asthmatics Age Groups Analyzed: ≥ 18 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: correlation Traffic: r = -0.04; O 3 : r = -0.55; PM 10 : r = 0.42; PM 2.5 : r = 0.52; NO 2 : r = 0.55 The study did not present quantitative results for CO.



### March 2009 C-49 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Mortimer et al. (2008) Period of Study: 1989 – 2000 Location: San Joaquin Valley, CA Health Outcome: Lung function (FVC, FEV 1 , PEF, FEF 25-75 , FEV 1 /FVC, FEF 25-75 /FVC, FEF 25 , FEF 75 ) Study Design: Cohort Statistical Analyses: 1. DSA algorithm 2. GEE Population: 232 asthmatic children Age Groups Analyzed: 6-11 Pollutant: CO Averaging Time: 8-h maximum monthly mean Mean (SD) unit: NR Range (Min, Max): NR Copollutant; correlation: Lifetime NO 2 (24-h avg): r = 0.68 O 3 (8-h maximum): r = -0.40 PM 10 (24-h avg): r = 0.05 Prenatal CO (8-h maximum): r = 0.52 NO 2 (24-h avg): r = 0.37 O 3 (8-h maximum): r = -0.16 PM 10 (24-h avg): r = -0.05 Increment: NR Effect Size per IQR Increase in Pollutant (SE): FEF 25-75 : 24-h avg CO exposure during 1st trimester 0.90% (0.0113) FEV 1 /FVC Daily maximum CO exposure during ages 0 to 3 -2.50% (0.0016) FEF 25-75 /FVC 24-h avg CO exposure during ages 0 to 6 and diagnosed with asthma <2 years old -4.80% (0.0446) FEF 25 24-h avg CO exposure during ages 0 to 6 and diagnosed with asthma <2 years old plus 24-h avg PM 10 exposure during 2 nd trimester and mother smoked when pregnant -6.70% (0.015) Coefficient (SE): FVC 24-h avg CO exposure during 2 nd trimester -0.0878 (0.0415) FEF 25-75 Lifetime 24-h avg CO exposure -0.94454 (0.3975) FEF 25-75 /FVC -0.1090 (0.0303) FEV 1 /FVC Prenatal 8-h maximum CO exposure: 0.1711 (0.0653) Lifetime 1-h maximum CO exposure: -0.3242 (0.0919) 24-h avg CO exposure dur ing ages 0-3 and diagnosed with asthma <2 years old: -0.1814 (0.0599) FEF 25 24-h avg CO exposure dur ing ages 0-6 and diagnosed with asthma <2 years old: -1.0460 (0.1953) FEF 75 Lifetime 8-h maximum CO exposure: -0.4214 (0.1423) Author: Singh et al. (2003) Period of Study: NR Location: Jaipur, India Health Outcome: Lung function Study Design: Panel study Statistical Analyses: Parametric statistical methods Population: Campus panel: 142 Commuter panel: 158 Age Groups Analyzed: ~20 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: Roadside: 3,175 μg/m 3 Campus: 2,150 μg/m 3 Range (Min, Max): NR Copollutant: NR The study did not present quantitative results for CO.

Author: Wang et al. (1999) Period of Study: 10/1995-6/1996 Location: Kaohsiung and Pintong, Taiwan Health Outcome: Asthma Study Design: Cross-sectional Statistical Analyses: Multiple logistic regression Population: 165,173 high school students Age Groups Analyzed: 11-16 Pollutant: CO Averaging Time: Annual median Median (SD) unit: 0.80 ppm Range (Min, Max): NR Copollutant: NR Increment: NR Adjusted Odds Ratio (Lower CI, Upper CI); lag: CO Concentrations: <0.80 ppm: 1.0 CO Concentrations ≥ 0.80 ppm: 1.23 (1.19-1.28) Multivariate analysis with vari ables for exercise, smoking, alcohol, incense use, ETS: 1.15 (1.1-1.2)



### March 2009 C-50 DRAFT—DO NOT QUOTE OR CITE

# Table C-7. Studies of short-ter m CO exposure and mortality.

Study Design Concentrations Effect Estimates (95% CI)

Author: Anderson et al. (2001) Period of Study: 10/1994 – 12/1996 Location: West Midlands, United Kingdom Health Outcome (ICD9):Mortality: All-cause (nonaccidental) (<800); Cardiovascular (390-459); Respiratory (460-519) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: Maximum 8-h moving avg Mean (SD) unit: 0.8 (0.7) ppm Range (Min, Max): (0.2, 10.0) Copollutant correlation: PM 10 : r = 0.55; PM 2.5 : r = 0.54; PM 10-2.5 : r = 0.10; BS: r = 0.77; SO 4 2- : r = 0.17; NO 2 : r = 0.73; O 3 : r = -0.29; SO 2 : r = 0.49 Increment: 1.0 ppm % Increase (Lower CI, Upper CI); lag: All-cause 0.8% (-0.6 to 2.2); 0-1 Cardiovascular 2.5% (0.4-4.6); 0-1 Respiratory 1.2% (-2.1 to 4.6); 0-1

Author: Bellini et al. (2007) Period of Study: 1996 – 2002 Location: 15 Italian cities Health Outcome (ICD9):Mortality: All-cause (nonaccidental) (<800); Cardiovascular (390-459); Respiratory (460-519) Study Design: Meta-analysis Statistical Analyses: Poisson GLM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: SO 2 NO 2 O 3 PM 10 Increment: 1 mg/m 3 % Increase (Lower CI, Upper CI); lag: All-cause 1.19% (0.61-1.72); 0-1 Respiratory 0.66% (-1.46 to 2.88); 0-1 Cardiovascular 0.93% (-0.10 to 1.77); 0-1

Author: Biggeri et al. (2005) Period of Study: 1990-1999 Location: 8 Italian Cities (Turin, Milan, Verona, Bologna, Ravenna, Florence, Rome, and Palermo) Health Outcome (ICD9):Mortality: All-cause (nonaccidental) (<800); Cardiovascular (390-459);Respiratory (460-519); Cardiorespiratory Study Design: Meta-analysis Statistical Analyses: Poisson GLM, cubic splines Age Groups Analyzed: All ages Pollutant: CO Averaging Time: Maximum 8-h moving average Mean (SD) unit: Turin, 1991-1994: 5.8 mg/m 3 Turin, 1995-1998: 4.0 mg/m 3 Milan, 1990-1994: 5.9 mg/m 3 Milan, 1995-1997: 4.0 mg/m 3 Verona, 1995-1999: 2.5 mg/m 3 Ravenna, 1991-1995: 1.8 mg/m 3 Bologna, 1996-1998: 2.4 mg/m 3 Florence, 1996-1998: 2.7 mg/m 3 Rome, 1992-1994: 6.5 mg/m 3 Rome, 1995-1997: 5.4 mg/m 3 Palermo, 1997- 1999: 2.1 mg/m 3 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/m 3 % Increase (Lower CI, Upper CI); lag: Non-accidental Fixed: 0.93 (0.50-1.36); 0-1 Random: 0.93 (0.50-1.36); 0-1 Cardiovascular Fixed: 1.29 (0.62-1.96); 0-1 Random: 1.29 (0.62-1.96); 0-1 Respiratory Fixed: 2.44 (0.74-4.17); 0-1 Random: 2.47 (0.14-4.85); 0-1

Author: Botter et al. (2002) Period of Study: 1991-1993 Location: São Paulo, Brazil Health Outcome (ICD9): Mortality Study Design: Longitudinal study Statistical Analyses: State space model Age Groups Analyzed: ≥ 65 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: TSP; NO 2 ; O 3 ; SO 2 Increment: NR β (SE): Model 1: 0.0053 (0.0036) Model 2: 0.0046 (0.0028) Model 3: 0.0040 (0.0028) Model 4: 0.0032 (0.0028)



### March 2009 C-51 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Bremner et al. (1999) Period of Study: 1/1992–12/1994 Location: London, U.K. Health Outcome (ICD9):Mortality: All-cause (nonaccidental) (<800); Cardiovascular (390-459); Respiratory (460-519) Study Design: Time-series Statistical Analyses: Poisson, cubic splines Age Groups Analyzed: All ages 0-64 ≥ 65 65-74 ≥ 75 Pollutant: CO Averaging Time : 24-h avg Mean (SD) unit : 0.8 (0.4) ppm Range (Min, Max): (0.2, 5.6) Copollutant : NO 2 ; O 3 ; SO 2 ; PM 10 ; 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, SO 2 : 1.90% (0.18-3.64); 3 CO, PM 10 : 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, NO 2 : 2.55% (0.40-4.75); 1 CO, O 3 : 3.98% (0.85-7.21); 1 CO, PM 10 : 0.62% (-0.59 to 1.85); 1 CO, BS: 1.29% (-1.53 to 4.19); 1 Author: Burnett et al. (2000) Period of Study: 1986-1996 Location: 8 Canadian cities Health Outcome (ICD9):Mortality: All-cause (nonaccidental) (<800) Study Design: Time-series Statistical Analyses: 1. Single-pollutant models: Poisson GAM, LOESS 2. Multi-pollutant models: Principal com ponent 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 O 3 : r = -0.05 SO 2 : r = 0.42 PM 2.5 : r = 0.44 PM 10-2.5 : r = 0.29 PM 10 : r = 0.45 Increment: 0.9 ppm % Increase (t-value); lag: Temporally filtered daily non-accidental mortality (days in which PM 10 data available) CO: 0.4 (0.4); 0; 2.0 (2.3); 1 CO, PM 2.5 : -0.7 (-0.7); 0; 1.1 (1.1); 1 CO, PM 10-2.5 : 0.1 (0.2); 0; 1.8 (2.1); 1 CO, PM 10 : -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, PM 2.5 , PM 10-2.5 , O 3 , NO 2 , SO 2 : 0.7 (1.9) Model 2: CO, SO 4 , Ni, Fe, Zn, O 3 , NO 2 : 0.7 (1.7) Author: Bu r nett et al. (2004) Period of Study: 1981 – 1999 Location: 12 Canadian cities Health Outcome (ICD9):Mortality: All-cause (nonaccidental) (<800) Study Design: Time-series Statistical Analyses: 1. Poisson, natural splines 2. Random effects regression model Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.02 ppm Range (Min, Max): NR Copollutant: NO 2 ; O 3 ; SO 2 ; PM 2.5 ; PM 10-2.5 Increment: 1.02 ppm % Increase (t-value); lag: 0.68% (3.12); 1 CO, NO 2 : 0.07% (0.30); 1



### March 2009 C-52 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Cakmak et al. (2007) Period of Study: 1/1997 – 12/2003 Location: Chile – 7 cities Health Outcome (ICD9):Mortality: All-cause (nonaccidental) (<800); Cardiovascular diseases (390- 459); Respiratory diseases (460- 519) Study Design: Time-series Statistical Analyses: Poisson; Random effects regression model Age Groups Analyzed: All ages ≤ 64 65 – 74 75 – 84 ≥ 85 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.29 ppm Range (Min, Max): NR Copollutant correlation: O 3 : r = -0.55 to -0.01 SO 2 : r = 0.31 to 0.67 PM 10 : r = 0.49 to 0.82 Note: Correlations are between pollutants for seven monitoring stations. Increment: 1.29 ppm % Increase (t-value); lag: Non-accidental: 5.88% (6.42); 1; 9.39% (6.89); 0-5 CO+PM 10 +O 3 +SO 2 : 6.13% (4.34); 1 Age Group: ≤ 64 4.10% (2.52); 1; / 4.76% (2.19); 0-5 Age Group: 65-74 6.24% (3.17); 1; / 8.12% (3.88); 0-5 Age Group: 75-84 8.64% (4.82); 1; / 13.12% (5.12); 0-5 Age Group: ≥ 85 8.58% (4.45); 1; / 13.20% (4.82); 0-5 April-September 7.09% (4.02); 1; / 9.65% (4.50); 0-5 October-March 5.45% (1.14); 1; / 7.80% (1.89); 0-5 Cardiac 7.79% (4.56); 1; / 11.22% (4.8); 0-5 Respiratory 12.93% (5.78); 1; / 21.31% (6.34); 0-5



### March 2009 C-53 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Chock et al. (2000) Period of Study: 1989 – 1991 Location: Pittsburgh, PA Health Outcome (ICD9): Mortality: Respiratory (480-486, 490-496, 507); Cardiovascular (390-448); Infl uenza (487) Study Design: Time-series Statistical Analyses: Poisson GAM; Cubic B-spline basis functions Age Groups Analyzed: All ages <75 >75 Pollutant: CO Averaging Time: 1-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: PM 10 ; PM 2.5 ; O 3 ; SO 2 ; NO 2 Increment: NR β (SE); lag: Age Group: <75 CO alone: 0.0080 (1.56); 0 PM 10 , CO: 0.0030 (0.48); 0 PM 10 , NO 2 , CO: 0.0079 (1.14); 0 PM 10 , O 3 , SO 2 , NO 2 , 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, PM 10 , O 3 , SO 2 , NO 2 -0.01103 (-1.48); -3; / -0.00097 (-0.13); -2; 0.00514 (0.67); -1; / 0.00853 (1.15); 0; -0.00404 (-0.52); 1; / -0.00296 (-0.39); 2; -0.00346 (-0.46); 3 Season CO Winter: 0.00539 (0.78); 0 Spring: 0.01655 (1.90); 0 Summer: 0.00155 (0.14); 0 Fall: 0.00797 (1.14); 0 CO, PM 10 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, PM 10 , O 3 , SO 2 , NO 2 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, PM 10 : -0.0104 (-1.67); 0 CO, PM 10 , NO 2 : -0.0128 (-1.80); 0 CO, PM 10 , O 3 , SO 2 , NO 2 : -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, PM 10 , O 3 , SO 2 , NO 2 -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, PM 10 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, PM 10 , O 3 , SO 2 , NO 2 Winter: -0.03370 (-2.41); 0 Spring: -0.00652 (-0.39); 0 Summer: 0.01258 (0.61); 0 Fall: -0.01250 (-1.07); 0



### March 2009 C-54 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Cifuentes et al. (2000) Period of Study: 1988 – 1996 Location: Santiago, Chile Health Outcome (ICD9):Mortality: All causes (nonaccidental) (<800) Study Design: Time-series Statistical Analyses: Poisson GAM, GAM with filtered variables & GLM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 1-h avg Mean (SD) unit: 2.5 ppb Range (5th, 95th): (0.6, 6.2) Copollutant correlation: PM 2.5 : r = 0.80 PM 10-2.5 : r = 0.47 SO 2 : r = 0.62 NO 2 : r = 0.65 O 3 : r = -0.01 Increment: All year: 2.5 ppm Winter: 3.6 ppm Summer: 1.3 ppm Relative Risk (t-ratio); Lag All Year CO: 1.041 (7.2); 0-1 CO, PM 2.5 : 1.025 (3.5); 0-1 CO, PM 10-2.5 : 1.035 (4.9); 0-1 CO, SO 2 : 1.038 (6.0); 0-1 CO, NO 2 : 1.026 (3.9); 0-1 CO, O 3 : 1.036 (4.8); 0-1 Winter CO: 1.052 (5.9); 0-1 CO, PM 2.5 : 1.025 (2.1); 0-1 CO, PM 10-2.5 : 1.049 (4.3); 0-1 CO, SO 2 : 1.049 (5.0); 0-1 CO, NO 2 : 1.027 (2.6); 0-1 CO, O 3 : 1.051 (4.4); 0-1 Summer CO: 1.053 (6.0); 0-1 CO, PM 2.5 : 1.053 (5.3); 0-1 CO, PM 10-2.5 : 1.053 (5.3); 0-1 CO, SO 2 : 1.050 (5.2); 0-1 CO, NO 2 : 1.047 (5.2); 0-1 CO, O 3 : 1.042 (3.6); 0-1 All Year GAM model CO: 1.041 (7.2); 0-1 CO, PM 2.5 , PM 10-2.5 , SO 2 , NO 2 , O 3 : 1.032 (4.6); 0-1 GAM Filtered Variables CO: 1.030 (4.3); 0-1 CO, PM 2.5 , PM 10-2.5 , SO 2 , NO 2 , O 3 : 1.022 (2.4); 0-1 GLM CO: 1.023 (2.4); 0-1 CO, PM 2.5 , PM 10-2.5 , SO 2 , NO 2 , O 3 : 1.013 (1.1); 0-1 Author: Conceicao et al. (2001) Period of Study: 1994 – 1997 Location: Sao Paulo, Brazil Health Outcome (ICD9): Mortality: Respiratory diseases (460-519) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: <5 Pollutant: CO Averaging Time: Maximum 8-h moving avg Mean (SD) unit: Total: 4.4 (2.2) ppm 1994: 5.1 (2.4) ppm 1995: 5.1 (2.4) ppm 1996: 3.9 (2.0) ppm 1997: 3.7 (1.6) ppm Range (Min, Max): NR Copollutant: PM 10 ; SO 2 ; O 3 Increment: NR β (SE); lag: CO: 0.0306 (0.0076); 2 CO, SO 2 , PM 10 , O 3 : 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(tem perature)+humidity: 0.0309 (0.0076); 2 Model 4: 3+nonrespiratory counts: 0.0306 (0.0076); 2 Model 5: 4+autoregre ssive parameters: 0.0292 (0.0118); 2 Author: De Leon et al. (2003) Period of Study: 1/1985 – 12/1994 Location: New York, NY Health Outcome (ICD9): Mortality: Circulatory (390-459); Cancer (140-239) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages <75 >75 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 2.45 ppm IQR (25th, 75th): (1.80, 2.97) Copollutant: PM 10 ; O 3 ; SO 2 ; NO 2 The study did not present quantitative results for CO.



### March 2009 C-55 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Dominici et al. (2003b) Period of Study: 1987 – 1994 Location: 90 U.S. cities (NMMAPS) Health Outcome (ICD9):Mortality: All-cause (nonaccidental); Cardiovascular; Respiratory Study Design: Time-series Statistical Analyses: 1. GAM with S-PLUS default convergence criteria 2. GAM with more stringent convergence criteria 3. Poisson GLM with natural cubic splines Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: O 3 ; NO 2 ; SO 2 ; CO Increment: 1 ppm % Increase (Lower CI, Upper CI); Lag CO 0.08% (-0.18 to 0.34); 0 0.46% (0.18-0.73); 1 0.16% (-0.12 to 0.45); 2

Author: Fairley et al. (1999) Period of Study: 1989 – 1996 Location: Santa Clara, CA Health Outcome (ICD9): Mortality: Respiratory; Cardiovascular Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg; Maximum 8-h avg Median (SD) unit: 24-h avg: 1.4 (1.0) ppm Maximum 8-h avg: 2.1 (1.6) ppm Range (Min, Max): 24-h avg: (0.0, 7.6) Maximum 8-h avg: (0.2, 2.5) Copollutant: correlation PM 10 : r = 0.609; PM 2.5 : r = 0.435; PM 10-2.5 : r = 0.326; COH: r = 0.736; NO 3 : r = 0.270; SO 4 : r = 0.146; O 3 : 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, NO 3 : 1.03; CO, NO 3 , O 3 , COH: 1.00 1989-1996 CO: 1.02; 0; CO: 1.04; 1; CO, PM 2.5 : 0.98; CO, NO 3 : 1.01; CO, NO 2 , O 3 , NO 3 : 1.06 Respiratory mortality: CO: 1.08; 1 Cardiovascular mortality: CO: 1.04; 1 Author: Fischer et al. (2003) Period of Study: 1986 – 1994 Location: The Netherlands Health Outcome (ICD9): Mortality: Non-accidental (<800); Pneumonia (480-486); COPD (490-496); Cardiovascular (390-448) Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: <45 45-64 65-74 ≥ 75 Pollutant: CO Averaging Time: 24-h avg Median (SD) unit: 406 μg/m 3 Range (Min, Max): (174, 2620) Copollutant: PM 10 ; BS; O 3 ; NO 2 ; SO 2 Increment: 1,200 μg/m 3 Relative Risk (Lower CI, Upper CI); lag: Cardiovascular Age Group: <45: 0.965 (0.750-1.240); 0-6 45-64: 1.029 (0.941-1.125); 0-6 65-74: 1.038 (0.972-1.108); 0-6 ≥ 75: 1.024 (0.984-1.065); 0-6 COPD Age Group: <45: 1.710 (0.852-3.435); 0-6 45-64: 1.181 (0.850-1.640); 0-6 65-74: 1.377 (1.147-1.654); 0-6 ≥ 75: 1.072 (0.963-1.193); 0-6 Pneumonia Age Group: <45: 0.927 (0.463-1.856); 0-6 45-64: 2.691 (1.509-4.800); 0-6 65-74: 1.118 (0.743-1.683); 0-6 ≥ 75: 1.230 (1.090-1.389); 0-6 Author: Forastiere et al. (2005) Period of Study: 1998 – 2000 Location: Rome, Italy Health Outcome (ICD9): Mortality: IHD (410-414) Study Design: Time-stratified case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: >35 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 2.4 (1.0) mg/m 3 IQR (25th, 75th): (1.7, 2.9) Copollutant correlation: PNC: r = 0.89; PM 10 : r = 0.34; NO 2 : r = 0.54; SO 2 : r = 0.52; O 3 : r = 0.01 Increment: 1.2 mg/m 3 % Increase (Lower CI, Upper CI); lag: 6.5% (1.0-12.3); 0 4.7% (-0.9 to 10.7); 1 2.6% (-3.0 to 8.5); 2 -0.1% (-5.5 to 5.5); 3 7.0% (0.8-13.7); 0-1



### March 2009 C-56 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Forastiere et al. (2007) Period of Study: 1998 – 2001 Location: Rome, Italy Health Outcome (ICD9): Mortality: Malignant Neoplasms (140-208); Diabetes Mellitus (250); Hypertensive (401-405); Previous AMI (410, 412); IHD (410-414); Conducti on 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 logist ic regression Age Groups Analyzed: >35 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR IQR (25th, 75th): NR Copollutant: PM 10 ; PM 2.5 ; NO X ; Benzene This study did not present quantitative results for CO.

Author: Goldberg et al. (2001) Period of Study: 1984 – 1993 Location: Montreal, Quebec, Canada Health Outcome (ICD9): Mortality: Upper respiratory diseases (472-478); Acute Upper respiratory dis eases (460-465); Acute Lower Respiratory (466, 480-487, 512, 513, 518, 519) Study Design: Time-series Statistical Analyses: Poisson GAM; LOESS Age Groups Analyzed: <65; ≥ 65 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 0.8 (0.5) ppm Range (Min, Max): (0.1, 5.1) Copollutant: TSP; PM 10 ; PM 2.5 ; Sulfates; COH; SO 2 ; NO 2 ; NO; O 3 The study did not present quantitative results for CO

.

Author: Goldberg et al. (2003) Period of Study: 1984 – 1993 Location: Montreal, Quebec, Canada Health Outcome (ICD9): Mortality: CHF (428) Study Design: Time-series Statistical Analyses: Poisson GLM, natural splines Age Groups Analyzed: ≥ 65 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 0.8 (0.5) ppm Range (Min, Max): (0.1, 5.1) Copollutant: PM 2.5 ; Sulfate; SO 2 ; NO 2 ; O 3 Increment: 0.50 ppm % Increase (Lower CI, Upper CI); lag: Daily mortality from CHF -0.99% (-6.31 to 4.63); 0 0.12% (-5.29 to 5.84); 1 -1.38% (-8.81 to 6.66); 0-2 Daily mortality among persons classified as having CHF before death 2.10% (-0.24 to 4.49); 0 2.28% (-0.09 to 4.72); 1 2.86% (-0.46 to 6.29); 0-2 Author: Goldberg et al. (2006) Period of Study: 1984 – 1993 Location: Montreal, Quebec, Canada Health Outcome (ICD9): Mortality: Diabetes (250) Study Design: Time-series Statistical Analyses: Poisson, natural splines Age Groups Analyzed: ≥ 65 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 0.8 (0.5) ppm Range (Min, Max): (0.1, 5.1) Copollutant: PM 2.5 ; Sulfate; SO 2 ; NO 2 ; O 3 Increment: 0.50 ppm % Increase (Lower CI, Upper CI); lag: Daily mortality from diabetes 2.64% (-2.56 to 8.12); 0 6.54% (1.31-12.03); 1 8.08% (1.03-15.62); 0-2 Daily mortality among persons classified as having diabetes before death 1.15% (-1.69 to 4.07) ; 0 1.30% (-1.58 to 4.27) ; 1 2.63% (-1.42 to 6.85) ; 0-2 Author: Gouveia et al. (2000b) Period of Study: 1991 – 1993 Location: Sao Paulo, Brazil Health Outcome (ICD9): Mortality: Respiratory; Cardiovascular; All other causes Study Design: Time-series Statistical Analyses: Poisson Age Groups Analyzed: All ages >65 <5 Pollutant: CO Averaging Time: Maximum 8-h moving avg Mean (SD) unit: 5.8 (2.1) ppm Range (Min, Max): (1.3, 16.2) Copollutant: PM 10 ; SO 2 ; NO 2 ; O 3 Increment: 5.1 ppm Relative Risk (Lower CI, Upper CI); lag: Age Group: All ages : All-causes 1.012 (0.994-1.031); 0 Age Group: >65 All-causes: 1.020 (0.996-1.046); 0 Respiratory: 0.981 (0.927-1.037); 2 CVD: 1.041 (1.007-1.076); 0 Age Group: <5 Respiratory: 1.086 (0.950-1.238); 0 Pneumonia: 1.141 (0.962-1.321); 2



### March 2009 C-57 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Gwynn et al. (2000) Period of Study: 5/1988 – 10/1990 Location: Buffalo, NY Health Outcome (ICD9): Mortality: Respiratory (466, 480- 486); Circulator y (401-405, 410- 414, 415-417); All non-accidental causes (<800) Study Design: Time-series Statistical Analyses: Poisson GLM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant correlation: H+: r =0.15; SO 4 2- : r = 0.24; O 3 : r = -0.23; SO 2 : r = 0.11; NO 2 : r = 0.65 Increment: NR β (SE); lag: Respiratory mortality: 0.032466 (0.053802); 0 Circulatory mortality: 0.039216 (0.026544); 3 Total mortality: 0.040214 (0.015205); 3

Author: Hoek et al. (2001) Period of Study: 1986-1994 Location: The Netherlands Health Outcome (ICD9): Mortality: Heart Failure (428); Arrhythmia (427); Cerebrovascular (430-436); Thrombocytic (433, 434, 444, 452, 453); Cardiovascular (390- 448) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: O 3 ; BS; PM 10 ; SO 2 ; NO 2 Increment: 120 μg/m 3 Relative Risk (Lower CI, Upper CI); Lag Total CVD mortality: 1.026 (0.993-1.060); 0-6 MI and other IHD mortality: 1.050 (1.004-1.099); 0-6 Arrhythmia: 1.062 (0.937-1.203); 0-6 Heart failure mortality: 1.109 (1.012-1.216); 0-6 Cerebrovascular mortality: 1.066 (1.029-1.104); 0-6 Embolism, thrombosis: 1.065 (0.926-1.224); 0-6 Author: Hoek et al. (2000) Period of Study: 1986 – 1994 Location: The Netherlands Health Outcome (ICD9): Mortality: Pneumonia (480-486); COPD (490-496); Cardiovascular diseases (CVD) (390-448) Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Netherlands: 457 μg/m 3 Four Major Cities: 589 μg/m 3 Range (Min, Max): Netherlands: (174, 2620) Four Major Cities: (202, 4621) Copollutant correlation: PM 10 : r = 0.64; BS: r = 0.89; O 3 : r = -0.48; NO 2 : r = 0.89; SO 2 : r = 0.65; SO 4 2- : r = 0.55; NO 3 - : r = 0.58 Increment: Single-day lag (1): 1,500 μg/m 3 Weekly avg (0-6): 1200 μg/m 3 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 , PM 10 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, SO 4 2- Total mortality: 0.990 (0.951-1.030); 0-6 CVD: 0.999 (0.939-1.063); 0-6 Author: Honda et al. (2003) Period of Study: 1976-1990 Location: Tokyo, Japan Health Outcome (ICD9): Mortality: Total (non-accidental) (<800) Study Design: Time-series Statistical Analyses: Poisson Age Groups Analyzed: ≥ 65 Pollutant: CO Averaging Time: 24-h avg Median (SD) unit: 1.6 ppm Range (Min, Max): (0, 6.8) Copollutant correlation: NO: r = 0.403; NO 2 : r = 0.415; Oxidant: r = 0.396; SO 2 : r = 0.675 Increment: NR Rate Ratio (Lower CI, Upper CI); lag: CO concentration <1.1 ppm: 1.00 (reference category) 1.1-1.6 ppm: 1.017 (1.009, 1.026) 1.6-2.2 ppm: 1.031 (1.020, 1.041) >2.2 ppm: 1.051 (1.039, 1.063)

Author: Hong et al. (2002b) Period of Study: 1/1991-12/1997 Location: Seoul, Korea Health Outcome (ICD9): Mortality: Hemorrhagic and ischemic stroke (431-434) Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.44 (0.70) ppm Range (Min, Max): (0.430, 5.14) Copollutant: TSP; SO 2 ; NO 2 ; O 3 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, NO 2 : 1.06 (1.00, 1.11); 1 CO, SO 2 : 1.05 (1.01, 1.10); 1 CO, O 3 : 1.09 (1.05, 1.13); 1



### March 2009 C-58 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Hong et al. (1999a) Period of Study: 1/1995 - 12/1995 Location: Inchon, Korea Health Outcome (ICD9): Mortality: Cardiovascular (400- 440); Respiratory (460-519); Non- accidental causes (<800) Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.7 (0.8) ppm Range (Min, Max): (0.3, 5.1) Copollutant: SO 2 ; NO 2 ; O 3 Increment: 1 ppm Relative Risk (Lower CI, Upper CI); lag: Total mortality: 0.993 (0.950, 1.037); 0-4 Cardiovascular mortality: 0.965 (0.892, 1.044); 0-4

Author: Hong et al. (2002a) Period of Study: 1/1995 - 12/1998 Location: Seoul, Korea Health Outcome (ICD9): Mortality: Stroke (160-169) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.2 (0.5) ppm Range (Min, Max): (0.4, 3.4) Copollutant: correlation PM 10 : r = 0.22; NO 2 : r = 0.64; SO 2 : r = 0.90; O 3 : r = -0.35 Increment: 0.3 ppm % Increase (Lower CI, Upper CI); lag: CO: 2.2% (0.4, 4.1); 2 CO (stratified by PM 10 concentration): <median concentration of PM 10 : 1.1; 2 ≥ median concentration of PM 10 : 3.6; 2

Author: Hong et al. (1999b) Period of Study: 1/1995 - 8/1996 Location: Inchon, South Korea Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Respiratory; Cardiovascular Study Design: Time-series Statistical Analyses: Poisson GAM; LOESS Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 15.2 (7.1) ppb Range (Min, Max): (2.9, 51.2) Copollutant: PM 10 ; NO 2 ; SO 2 ; O 3 Increment: 100 ppb β (SE); lag: Total Mortality CO 0.0019 (0.0015); 1 0.0024 (0.0041); 0-4 CO, PM 10 , NO 2 , SO 2 , O 3 -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, PM 10 , NO 2 , SO 2 , O 3 -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, PM 10 , NO 2 , SO 2 , O 3 0.0121 (0.0079); 1 -0.0034 (0.0183); 0-4 Author: Keatinge et al. (2001) Period of Study: 1976-1995 Location: London, England Health Outcome (ICD9): Mortality: Non-accidental causes (<800) Study Design: Time-series Statistical Analyses: Single and multiple delay regression Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: SO 2 ; PM 10 The study did not present quantitative results for CO.

Author: Kettunen et al. (2007) Period of Study: 1998-2004 Location: Helsinki, Finland Health Outcome (ICD10): Mortality: Stroke (I60-I61, I63-I64) Study Design: Time-series Statistical Analyses:Poisson GAM, penalized thinplate splines Age Groups Analyzed: ≥ 65 Pollutant: CO Averaging Time: Maximum 8-h moving avg Median (SD) unit: Cold Season: 0.5 mg/m 3 Warm Season: 0.4 mg/m 3 Range (Min, Max): Cold Season: (0.1, 2.4) Warm Season: (0.1, 1.1) Copollutant: correlation Cold Season : PM 2.5 : r = 0.32; UFP: r = 0.47 Warm Season : PM 2.5 : r = 0.24; UFP: r = 0.39 Increment: 0.2 mg/m 3 % Increase (Lower CI, Upper CI); lag: Cold Season 0.47 (-3.29 to 4.39); 0; / -0.63 (-4.39 to 3.28); 1; -2.69 (-6.46 to 1.24); 2; / -0.19 (-3.93 to 3.69); 3 Warm Season 3.95 (-3.78 to 12.30); 0; / 8.33 (0.63 to 16.63); 1; 6.97 (-0.59 to 15.11); 2; / 7.54 (-0.05 to 15.71); 3



### March 2009 C-59 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Klemm et al. (2004) Period of Study: 8/1998-7/2000 Location: Fulton County and DeKalb County, GA (ARIES) Health Outcome (ICD9): Mortality: Non-accidental (<800); Cardiovascular (390-459); Respiratory (460-519); Cancer (140-239) Study Design: Time-series Statistical Analyses: Poisson GLM, natural cubic splines Age Groups Analyzed: <65; ≥ 65 Pollutant: CO Averaging Time: 1-h maximum Median (SD) unit: 1,310 (939.13) ppb Range (Min, Max): (303.58, 7400) Copollutant: PM 2.5 ; PM 10-2.5 ; O 3 ; NO 2 ; SO 2 ; Acid; EC; OC; SO 4 ; Oxygenated HCs; NMHCs; NO 3 Increment: NR β (SE); lag: Quarterly Knots: 0.00002 (0.00001); 0-1 Monthly Knots: 0.00002 (0.00001); 0-1 Biweekly Knots: 0.00001 (0.00002); 0-1

Author: Kwon et al. (2001) Period of Study: 1994-1998 Location: Seoul, Korea Health Outcome (ICD9): Mortality: CHF (428); Cardiovascular (390-459) Study Design: 1. Time-series 2. Bi-directional case-crossover Statistical Analyses: 1. Poisson GLM, LOESS 2. Conditional logi stic regression Age Groups Analyzed: <55 55-64 65-74 75-84 ≥ 85 Pollutant: CO Averaging Time: 1-h avg Mean (SD) unit: 12.4 ppb Range (Min, Max): (4.1, 38.0) Copollutant correlation: PM 10 : r = 0.713; NO 2 : r = 0.744; SO 2 : r = 0.843; O 3 : r = -0.367 Increment: 0.59 ppm Odds Ratio (Lower CI, Upper CI); lag: From GAM approach CHF patients: 1.054 (0.991-1.121); 0; 0 General Population: 1.022 (1.017- 1.029); 0 From case-crossover design CHF patients: 1.033 (0.946-1.127); 0 General Population: 1.007 (0.997- .016); 0 Modifiers and CHF patients (case-crossover design) Gender Male: 1.025 (0.890-1.180); 0 Female: 1.035 (0.925-1.157); 0 Age Group: <75: 0.948 (0.890-1.180); 0 ≥ 75: 1.116 (0.989-1.258); 0 Time from admission to death 4 or less weeks: 1.088 (0.907-1.306); 0 >4 weeks: 1.017 (0.920-1.124); 0 Total mortality: 1.033 (0.946-1.127); 0 Cardiovascular mortality: 1.033 (0.920-1.160); 0 Cardiac death: 1.052 (0.919-1.204); 0Two-pollutant model in CHF patients (casecrossover design ) CO alone: 1.054 (0.991-1.121); 0 CO, PM 10 : 1.096 (0.981-1.224); 0 CO, NO 2 : 1.022 (0.932-1.122); 0 CO, SO 2 : 1.014 (0.909-1.131); 0 CO, O 3 : 1.056 (0.992-1.124); 0 Author: Lee et al. (2007c) Period of Study: 1/2000-12/2004 Location: Seoul, Korea Health Outcome (ICD10): Mortality: Non-accidental (A00- R99) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: Maximum 8-h moving avg Mean (SD) unit: w/ Asian dust days: 0.92 (0.42) ppm w/o Asian dust days:0.92 (0.41) ppm Asian dust days only: 1.00 (0.47) ppm Range (Min, Max): NR Copollutant: PM 10 ; NO 2 ; SO 2 ; O 3 Increment: 0.54 ppm % Increase (Lower CI, Upper CI); lag: Model with Asian Dust Days: 3.3% (2.5-4.1); 1 Model without Asian dust days: 3.3% (2.5-4.2); 1



### March 2009 C-60 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Lipfert et al. (2000b) Period of Study: 5/1992 - 9/1995 Location: Philadelphia, PA, three nearby suburban Pennsylvania counties, and three nearby New Jersey counties Health Outcome (ICD9): Mortality: Respiratory (460-519); Cardiac (390-448); Cancer; Other causes (<800) Study Design: Time-series Statistical Analyses: Stepwise regression Age Groups Analyzed: <65 ≥ 65 Pollutant: CO Averaging Time: 24-h avg; 1-h maximum Mean (SD) unit: Camden: 24-h avg: 0.75 (0.40) ppm Philadelphia: 24-h avg: 0.63 (0.40) ppm 1-h maximum: 1.44 (1.04) Range (Min, Max): Camden: (0.10, 3.8) Philadelphia: 24-h avg: (0.10, 3.3) 1-h maximum: (0.0, 7.8) Copollutant: NO; NO 2 ; O 3 ; SO 2 ; SO 4 2- ; PM 10 ; PM 2.5 Increment: NR Attributable Risk; lag: Peak CO All-cause Philadelphia: 0.0054; 0-1 4 Pennsylvania Counties: 0.0081; 0-1 Pennsylvania + NJ: 0.0085; 0-1 CO All seven counties in Pennsylvania and New Jersey All ages Respirator y: -0.0067; Cardiac: 0.0131; Other: 0.0078 All-cause : <65: 0.0148; 0-1; ≥ 65: 0.0054; 0-1 Joint model with CO Philadelphia: 0.0059; 0-1 4 Pennsylvania Counties: 0.0089; 0-1 Pennsylvania + NJ: 0.0096; 0-1 Cardiac: 0.0135; 0-1; Other causes: 0.0084 <65: 0.0154; 0-1; ≥ 65: 0.0060; 0-1 Author: Lippmann et al. (2000) Period of Study: 1985-1990 1992-1994 Location: Detroit, MI and Windsor, ON Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Circulatory (390-459); Respiratory (460-519) Study Design: Time-series Statistical Analyses: Poisson GLM Age Groups Analyzed: ≥ 65 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1985-1990: 0.9 ppm 1992-1994: 0.72 ppm Range (5th, 95th): 1985-1990: (.46, 1.61) 1992-1994: (0.36, 1.2) Copollutant correlation: 1985-1990 PM 10 : r = 0.35; TSP: r = 0.28; TSP-PM 10 : r = 0.02; TSP-SO 4 2- : r = 0.18; O 3 : r = -0.22; SO 2 : r = 0.36; NO 2 : r = 0.58 1992-1994 PM 10 : r = 0.38; PM 2.5 : r = 0.38; PM 10-2.5 : r = 0.24; H+: r = 0.16; SO 4 2- :r = 0.32; O 3 : r = 0.16; SO 2 : r = 0.42; NO 2 : r = 0.68 Increment: 1985-1990: 11.5 ppm; 1992-1994: 8.4 ppm Relative Risk (Lower CI, Upper CI); lag: 1985-1990 Total Mortality: 0.9842 (0.9667-1.002); 0 1.0103 (0.9926-1.0284); 1 1.0075 (0.9898-1.0254); 2 1.0145 (0.9967-1.0326); 3 0.9968 (0.9789-1.0151); 0-1 1.0105 (0.9925-1.0288); 1-2 1.0134 (0.9954-1.0317); 2-3 1.0003 (0.9823-1.0187); 0-2 1.0152 (0.9971-1.0336); 1-3 1.0053 (0.9873-1.0236); 0-3 Circulatory Mortality: 0.9818 (0.9574-1.0068); 0 0.9991 (0.9745-1.0243); 1 0.9980 (0.9735-1.0232); 2 1.0088 (0.9841-1.0341); 3 0.9888 (0.9640-1.0144); 0-1 0.9981 (0.9732-1.0237); 1-2 1.0042 (0.9792-1.0298); 2-3 0.9900 (0.9650-1.0157); 0-2 1.0029 (0.9777-1.0287); 1-3 0.9944 (0.9692-1.0202); 0-3 Respiratory Mortality; 0.9644 (0.9042-1.0287); 0 1.0142 (0.9518-1.0808); 1 1.0483 (0.9845-1.1164); 2 1.0468 (0.9828-1.1149); 3 0.9868 (0.9248-1.053); 0-1 1.0372 (0.9730-1.1056); 1-2 1.0554 (0.9904-1.1246); 2-3 1.0088 (0.9457-1.0762); 0-2 1.0466 (0.9817-1.1158); 1-3 1.0205 (0.9569-1.0884); 0-3 Total minus respiratory and circulatory mortality: 0.9939 (0.9668-1.0217); 0 1.0278 (1.0001-1.0562); 1 1.0178 (0.9902-1.0461); 2 1.0227 (0.9948-1.0514); 3 1.0127 (0.9860-1.0412); 0-1 1.0269 (0.9989-1.0556); 1-2 1.0249 (0.9968-1.0538); 2-3 1.0172 (0.9893-1.0458); 0-2 1.0322 (1.0041-1.0612); 1-3 1.0229 (0.9950-1.0516); 0-3 1992-1994 Total Mortality 0.9933 (0.9636-1.024); 0



### March 2009 C-61 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI) 1.0162 (0.9860-1.0473); 1 1.0116 (0.9816-1.0426); 2 0.9947 (0.9648-1.0254); 3 1.0056 (0.9756-1.0366); 0-1 1.0165 (0.9864-1.0476); 1-2 1.0038 (0.9739-1.0476); 2-3 1.0098 (0.9796-1.0409); 0-2 1.0104 (0.9862-1.0414); 1-3 1.0064 (0.9755-1.0382); 0-3 Circulatory Mortality 1.0076 (0.9640-1.0531); 0 1.0307 (0.9865-1.0768); 1 1.0142 (0.9705-1.0598); 2 0.9523 (0.9102-0.9964); 3 1.0229 (0.9788-1.0688); 0-1 1.0267 (0.9827-1.0727); 1-2 0.9802 (0.9375-1.0248); 2-3 1.0243 (0.9801-1.0726); 0-2 0.9987 (0.9553-1.0441); 1-3 1.0019 (0.9573-1.0487); 0-3 Respiratory Mortality 0.9894 (0.8912-1.0984); 0 0.9474 (0.8521-1.0533); 1 0.9652 (0.8682-1.0732); 2 0.9931 (0.8934-1.1040); 3 0.9626 (0.8668-1.0691); 0-1 0.9485 (0.8535-1.0541); 1-2 0.9752 (0.8775-1.0838); 2-3 0.9555 (0.8802-1.0615); 0-2 0.9567 (0.8607-1.0635); 1-3 0.9584 (0.9604-1.0675); 0-3 Total minus respiratory and circulatory mortality: 0.9769 (0.9332-1.0227); 0 1.0135 (0.9682-1.0609); 1 1.0195 (0.9747-1.0664); 2 1.0429 (0.9974-1.0905); 3 0.9940 (0.9494-1.0406); 0-1 1.0197 (0.9746-1.0670); 1-2 1.0371 (0.9918-1.0845); 2-3 1.0045 (0.9596-1.0515); 0-2 1.0353 (0.9896-1.0831); 1-3 1.0215 (0.9749-1.0702); 0-3 Author: Maheswaran et al. (2005a) Period of Study: 1994-1998 Location: Sheffield, United Kingdom Health Outcome (ICD9): Mortality: CHD (410-414) Study Design: Ecological Statistical Analyses: Poisson Age Groups Analyzed: ≥ 45 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: NO X ; PM 10 Notes: Quintiles represent the following mean CO concentrations and category limits: 5: 482 μg/m 3 ( ≥ 455) 4: 443 μg/m 3 ( ≥ 433 to <455) 3: 426 μg/m 3 ( ≥ 419 to <433) 2: 405 μg/m 3 ( ≥ 387 to <419) 1: 360 μg/m 3 (<387) Increment: NR Rate Ratios (Lower CI, Upper CI): CO Adjusted for sex and age Quintile: 5 (highest): 1.24 (1.14, 1.36) 4: 1.30 (1.19, 1.41) 3: 1.15 (1.05, 1.25) 2: 1.08 (0.99, 1.17) 1: (lowest): 1.00 CO Adjusted for sex, age, deprivation, and smoking Quintile: 5 (highest): 1.05 (0.95, 1.16); 4: 1.16 (1.06, 1.28); 3: 1.04 (0.95, 1.14); 2: 1.03 (0.94, 1.13); 1 (lowest): 1.00 CO Adjusted for sex, age, deprivation, and smoking (spatially smoothed using a 1 km radius) Quintile: 5 (highest): 1.07 (0.96, 1.18); 4: 1.13 (1.03, 1.24); 3: 1.04 (0.95, 1.14); 2: 1.01 (0.92, 1.10); 1 (lowest): 1.00



### March 2009 C-62 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Maheswaran et al. (2005b) Period of Study: 1994-1998 Location: Sheffield, United Kingdom Health Outcome (ICD9): Mortality: Stroke deaths (430-438) Study Design: Ecological Statistical Analyses: Poisson Age Groups Analyzed: ≥ 45 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Quintile: 5: 482 μg/m 3 ; 4: 443 μg/m 3 ; 3: 426 μg/m 3 ; 2: 405 μg/m 3 ; 1: 360 μg/m 3 Range (Min, Max): NR Copollutant correlation : PM 10 : r = 0.88; NO X : r = 0.87 Notes: Quintiles represent the following mean CO concentrations and category limits: 5: 482 μg/m 3 ( ≥ 455) 4: 443 μg/m 3 ( ≥ 433 to <455) 3: 426 μg/m 3 ( ≥ 419 to <433) 2: 405 μg/m 3 ( ≥ 387 to <419) 1: 360 μg/m 3 (<387) Increment: NR Rate Ratios (Lower CI, Upper CI); lag: RR for mortality and CO modeled outdoor air pollution Adjusted for sex and age Quintile: 5 (highest): 1.35 (1.19, 1.53); 4: 1.40 (1.24, 1.58); 3: 1.08 (0.95, 1.23); 2: 1.10 (0.97, 1.24); 1 (lowest): 1.00 Adjusted for sex, age, deprivation, and smoking Quintile: 5 (highest): 1.26 (1.10, 1.46); 4: 1.32 (1.15, 1.50); 3: 1.07 (0.93, 1.22); 2: 1.12 (0.99, 1.28); 1 (lowest): 1.00 Not spatially smoothed CO outdoor air pollution Quintile: 5 (highest): 1.26 (1.10, 1.46); 4: 1.32 (1.15, 1.50); 3: 1.07 (0.93, 1.22); 2: 1.12 (0.99, 1.28); 1 (lowest): 1.00 Spatially smoothed using a 1-km radius Quintile: 5 (highest): 1.16 (1.01, 1.34); 4: 1.22 (1.07, 1.39); 3: 0.95 (0.83, 1.09); 2: 0.97 (0.85, 1.11); 1 (lowest): 1.00 Author: Mar et al. (2000) Period of Study: 1995-1997 Location: Phoenix, AZ Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Cardiovascular (390-449) Study Design: Time-series Statistical Analyses: Poisson Age Groups Analyzed: >65 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.5 (0.8) ppm Range (Min, Max): 1995: (0.5, 4.0) ppm 1996: (0.3, 4.0) ppm 1997: (0.3, 3.7) ppm Copollutant correlation: PM 2.5 : r = 0.85; PM 10 : r = 0.53; PM 10-2.5 : r = 0.34; NO 2 : r = 0.87; O 3 : r =-0.40; SO 2 : r =0.53 Increment: 1.19 ppm Relative Risk (Lower CI, Upper CI); lag: Total Mortality (CO exposure): 1.06 (1.02, 1.09); 0; 1.05 (1.01, 1.09); 1 Cardiovascular Mortality (CO exposure): 1.05 (1.00, 1.11); 0; 1.10 (1.04, 1.15); 1; 1.07 (1.02, 1.12); 2; 1.07 (1.02, 1.12); 3; 1.08 (1.03, 1.13); 4

Author: Moolgavkar et al. (2000b) Period of Study: 1987-1995 Location: Cook County, IL Los Angeles County, CA Maricopa County, AZ Health Outcome (ICD9): Mortality: Circulatory (390-448); Cardiovascular (390-429); Cerebrovascular (430-448); COPD (490-496); Asthma (493) Study Design: Time-series Statistical Analyses: Poisson GAM, spline smoother Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Median unit: Cook county: 993 ppb Los Angeles: 1347 ppb Maricopa: 1240 ppb Range (Min, Max): Cook county: (224, 3912) Los Angeles: (237, 5955) Maricopa: (269, 4777) Copollutant correlation : PM 10 : Cook: r = 0.30; LA: r = 0.45; Maricopa: r = 0.20 NO 2 : Cook: r = 0.63; LA: r = 0.80; Maricopa: r = 0.66 SO 2 : Cook: r = 0.35; LA: r = 0.78; Maricopa: r = 0.53 O 3 : 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, PM 10 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 PM 2.5 0.43 (-1.35, 2.20); 0; / 2.88 (1.16, 4.60); 1; 4.65 (2.93, 6.37); 2; / 5.93 (4.20, 7.65); 3; 3.88 (2.13, 5.63); 4; / 5.85 (4.12, 7.58); 5 Maricopa County CO 0.81 (-0.79, 2.39); 0; / 2.20 (0.61, 3.79); 1;



### March 2009 C-63 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI) Cook: r = -0.28; LA: r = -0.52; Maricopa: r = -0.61 3.05 (1.49, 4.61); 2; / 3.78 (2.27, 5.28); 3; 3.73 (2.27, 5.19); 4; / 2.25 (0.76, 3.72); 5 COPD Mortality Cook County CO -2.65 (-7.05, 1.75); 0; / 2.80 (-1.60, 7.19); 1; 0.98 (-3.34, 5.31); 2; / 2.20 (-2.12, 6.53); 3; 1.31 (-3.06, 5.68); 4; / 1.59 (-2.78, 5.97); 5 Los Angeles County CO 3.78 (2.31, 5.25); 0; / 5.23 (3.78, 6.69); 1; 5.71 (4.26, 7.17); 2; / 5.42 (3.95, 6.89); 3; 4.01 (2.51, 5.50); 4; / 3.82 (2.31, 5.33); 5 Maricopa County CO 1.29 (-2.19, 4.76); 0; / 4.63 (1.17, 8.09); 1; 0.07 (-3.36, 3.50); 2; / 3.00 (-0.30, 6.30); 3; 6.21 (3.02, 9.40); 4; / 3.27 (0.04, 6.50); 5 Cerebrovascular Disease Mortality Cook County -0.41 (-3.30, 2.47); 0; / 3.13 (0.23, 6.02); 1; 2.12 (-0.73, 4.97); 2; / 1.00 (-1.85, 3.86); 3; 2.50 (-0.36, 5.37); 4; / 1.88 (-1.00, 4.76); 5 Los Angeles County 3.31 (2.32, 4.31); 0; / 3.88 (2.89, 4.87); 1; 3.23 (2.25, 4.22); 2; / 2.65 (1.66, 3.65); 3; 2.11 (1.11, 3.12); 4; / 2.04 (1.02, 3.06); 5 Maricopa County 0.26 (-2.65, 3.16); 0; / 3.50 (0.60, 6.41); 1; 3.52 (0.66, 6.38); 2; / 4.61 (1.85, 7.37); 3; 4.78 (2.10, 7.46); 4; / 5.15 (2.45, 7.84); 5 Notes: Total Mortality effect estimates were not presented quantitatively. Author: Moolgavkar et al. (2003b) Period of Study: 1987-1995 Location: Cook County, Illinois & Los Angeles County, California Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Circulatory (390-448) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All Ages Pollutant: CO Averaging Time: 24-h avg Median unit: Cook County: 993 ppb LA County: 1347 ppb Range (Min, Max): Cook County: (224, 3912) ppb LA County: (237, 5955) ppb Copollutant correlation: Cook County: NO 2 : r = 0.63; O 3 : r = -0.22; SO 2 : r = 0.35; PM 10 : r = 0.30 LA County: NO 2 : r = 0.80; O 3 : r = -0.52; SO 2 : r = 0.78; PM 10 : r = 0.45; PM 2.5 : 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, PM 10 : -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, PM 10 : 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, PM 2.5 : -0.1% (-1.5); 0; / 1.5% (2.5); 1; / 2.4% (3.8); 2; 0.3% (0.5); 3; / 1.6% (2.8); 4; / 1.5% (2.6); 5 Total Mortality (Season-specific) Cook County Spring (CO): 0.8% (0.9); 0; / 2.4% (2.9); 1; / 0% (0); 2; 1.2% (1.5); 3; / 0.8% (1.0); 4; / -0.1% (-0.2); 5 Summer (CO): 1.2% (1.0); 0; / 3.6% (3.0); 1; / 4.2% (3.6); 2; -0.3% (-0.2); 3; / -1.1% (-1.0); 4; / -0.7% (-0.6); 5 Fall (CO): 1.2% (1.5); 0; / 2.1% (2.7); 1; / 0% (0); 2; 0% (0); 3; / -0.5% (-0.6); 4; / -0.7% (-0.9); 5 Winter (CO): -0.7% (-1.0); 0; / 1.8% (2.3); 1; / -0.2% (-0.3); 2; 0.5% (0.6); 3; / 1.2% (1.5); 4; / 1.0% (1.3); 5 Los Angeles County Total Mortality (Season-specific ) Spring (CO): 3.6% (6.3); 0; / 3.5% (6.2); 1; / 1.9% (3.4); 2; 0.6% (1.0); 3; / -0.5% (-0.8); 4; / -0.7% (-1.2); 5



### March 2009 C-64 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI) Summer (CO): 3.0% (3.0); 0; / 4.7% (4.6); 1; / 5.2% (5.1); 2; 4.1% (3.8); 3; / 1.9% (1.8); 4; / 1.4% (1.3); 5 Fall (CO): 1.8% (4.6); 0; / 2.0% (5.1); 1; / 1.0% (2.6); 2; 0.6% (1.5); 3; / 0.4% (1.2); 4; / 0.2% (0.6); 5 Winter (CO): 0% (0); 0; / 0.8% (2.5); 1; / 0.9% (3.1); 2; 1.0% (3.4); 3; / 0.5% (1.7); 4; / 0.5% (1.6); 5 CVD Mortality Cook County CO: -1.1% (-1.5); 0; / 1.8% (2.5); 1; / 1.5% (2.2); 2; 1.6% (2.4); 3; / 1.4% (2.1); 4; / 0.7% (1.0); 5 CO, PM 10 : -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, PM 10 : -0.8% (-1.2); 0; / 1.9% (3.0); 1; / 2.7% (4.3); 2; 1.3% (2.2); 3; / 0.5% (0.9); 4; / 2.8% (4.7); 5 CO, PM2.5: -2.2% (-2.7); 0; / 1.5% (1.8); 1; / 1.9% (2.0); 2; 1.9% (2.2); 3; / 2.1% (2.6); 4; / 3.7%(4.5); 5 CVD Mortality (Season Specifid) Cook County Spring (CO): 0.7% (0.5); 0; / 1.4% (1.1); 1; / 0.3% (0.3); 2; 1.1% (0.9); 3; / 0.4% (3.1); 4; / 0.1% (0.6); 5 Summer (CO): -2.6% (-1.4); 0; / 2.5% (1.4); 1; / 6.5% (3.7); 2; 0.9% (0.5); 3; / -1.9% (-1.1); 4; / -1.0% (-0.6); 5 Fall (CO): 0% (0); 0; / 2.9% (2.5); / 1; 0% (0); 2; 0% (0); 3; / -0.8% (-0.7); / 4; 0% (0); 5 Winter (CO): -2.5% (-2.2); 0; / 0.7% (0.6); 1; / 0% (0); 2; 1.3% (1.1); 3; / 0.8% (0.7); 4; / 0.4% (0.4); 5 Los Angeles County CVD Mortality (Season-specific ) Spring (CO): 3.0% (3.7); 0; / 3.3% (4.1); 1; / 2.3% (2.9); 2; 0.7% (0.9); 3; / -1.2% (-1.6); 4; / 0% (0); 5 Summer (CO): 4.0% (2.8); 0; / 5.2% (3.5); 1; / 6.3% (4.3); 2; 5.0% (3.3); 3; / 3.1% (2.0); 4; / 3.6% (2.3); 5 Fall (CO): 2.3% (4.2); 0; / 2.1% (3.7); 1; / 1.1% (1.9); 2; 1.2% (2.2); 3; / 1.5% (2.9); 4; / 1.0% (1.8); 5 Winter (CO): 0.3% (0.8); / 0; 0.7% (1.7); 1; / 0.8% (2.0); 2; 1.4% (3.4); 3; / 1.0% (2.3); 4; / 1.1% (2.5); 5 Author: Ostro et al. (1999) Period of Study: 1989-1992 Location: Coachella Valley, California Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Respiratory (460-519); Cardiovascular (393-440) Study Design: Time-series Statistical Analyses: Poisson GAM; LOESS Age Groups Analyzed: >50 Pollutant: CO Averaging Time: 1-h maximum Mean (SD) unit: 1.35 ppm Range (Min, Max): (0, 6.0) Copollutant correlation: PM 10 : r = -0.18; O 3 : r = -0.47; NO 2 : r = 0.65 Increment: NR β (SE); lag: CO: 0.0371 (0.0157); 2 CO, PM 10 : 0.0300 (0.0194); 2



### March 2009 C-65 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Penttinen et al. (2004) Period of Study: 1988-1996 Location: Helsinki, Finland Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Respiratory (460-519); Cardiovascular (393-440) Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All ages 15-64 years 65-74 years ≥ 75 Pollutant: CO Averaging Time: Maximum 8-h avg Median unit: 1.2 mg/m 3 Range (Min, Max): (0, 12.4) Copollutant correlation: O 3 : r = -0.46; NO 2 : r = 0.59; SO 2 : r = 0.55; PM 10 : r = 0.45; TSP: r = 0.26; TSP Blackness: r = 0.26 Increment: 1 mg/m 3 % Increase (Lower CI, Upper CI); lag: Total Mortality -1.50% (-2.78, -0.22); 0 0.15% (-1.09, 1.39); 1 -1.00% (-2.80, 0.81); 0-3 Cardiovascular Mortality -2.48% (-4.30, -0.66); 0 -0.84% (-2.61, 0.93); 1 -1.87% (-4.43, 0.69); 0- Respiratory Morality -0.48% (-4.84, 3.87); 0 -0.14% (-4.43, 4.15); 1 -1.49% (-7.73, 4.74); 0-3 Author: Peters et al. (2000a) Period of Study: 1982-1994 Location: Northern Bavaria (Rural Germany) and the Coal Basin of the Czech Republic Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Cardiovascular (390- 459); Respiratory (460-519); Cancer (140-239) Study Design: Time-series Statistical Analyses: (1) Poisson Regression Models by logistic r egression 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/m 3 Northeast Bavaria: 0.88 (0.69) mg/m 3 Range (Min, Max): Coal Basin: (-0.1, 2.88) Northeast Bavaria: (0.1, 6.2) Copollutant correlation: SO 2 : r = 0.37; TSP: r = 0.37; NO 2 : r = 0.32; O 3 : r = -0.57; PM 10 : r = 0.44; PM 2.5 : r = 0.42 Increment: 1 mg/m 3 Relative Risk (Lower CI, Upper CI); lag: Coal Basin of the Czech Republic Total Mortality: 1.016 (0.998, 1.035); 0; / 1.016 (0.998, 1.034); 1; 1.013 (0.996, 1.030); 2; / 1.012 (0.995, 1.028); 3 Northeast Bavaria Total Mortality: 1.014 (0.994, 1.034); 0; / 1.023 (1.005, 1.041); 1; 1.013 (0.995, 1.031); 2; / 1.003 (0.985, 1.021); 3 Cardiovascular Disease Mortality: 1.018 (0.994, 1.044); 0; / 1.012 (0.987, 1.038); 1; 1.016 (0.991, 1.041); 2; / 1.004 (0.980, 1.029); 3

Author: Rainham et al. (2003) Period of Study: 1980-1996 Location: Toronto, ON, Canada Health Outcome (ICD9): Mortality: Cardiac (390-459); Respiratory (480-519); Total (non-accidental) (<800) Study Design: Time-series Statistical Analyses: Poisson GAM, natural cubic splines Age Groups Analyzed: <65 ≥ 65 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.0 (0.4) ppm Range (Min, Max): (0.0, 4.0) Copollutant: O 3 ; NO 2 ; SO 2 The study did not present quantitative results for CO

.

Author: Roemer et al. (2001) Period of Study: 1/1987 - 11/1998 Location: Amsterdam Health Outcome (ICD9): Mortality: Total (non-accidental) (<800) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Air pollution background: 836 μg/m 3 Air pollution traffic: 1805 μg/m 3 Range (10th, 90th): Air pollution background: (448, 1315) μg/m 3 Air pollution traffic: (727, 3192) μg/m 3 Copollutant: BS; PM 10 ; SO 2 ; NO 2 ; NO; O 3 Increment: Lag 1 and 2: 100 μg/m 3 Lag 0-6: 50 μg/m 3 Relative Risk (Lower CI, Upper CI); lag: Total Population using Background sites 1.002 (1.000-1.004); 1; 1.001 (0.999-1.003); 2; 1.001 (1.000-1.003); 0-6 Traffic Population using Background Sites 1.003 (0.997-1.008); 1; 1.008 (1.003-1.013); 2; 1.003 (0.999-1.007); 0-6 Total population using Traffic Sites 1.000 (1.000-1.001); 1; 1.000 (0.999-1.001); 2; 1.000 (1.000-1.001); 0-6



### March 2009 C-66 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Samet et al. (2000b) Period of Study: 1987-1994 Location: 20 U.S. Cities: Los Angeles, CA; New York, NY; Chicago, IL; Dallas, TX; Houston, TX; San Diego, CA; Anaheim, CA; Phoenix, AZ; Detroit, MI; Miami, FL; Philadelphia, PA; Minneapolis, MN; Seattle, WA; San Jose, CA; Cleveland, OH; San Bernardino, CA; Pittsburgh, PA; Oakland, CA; Atlanta, GA; San Antonio, TX Health Outcome (ICD9): Mortality: Cardiovascular (390- 459); Respiratory (460-519); Other (non-accidental) (<800) Study Design: Time-series Statistical Analyses: Two-stage log li near 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: PM 10 : r = 0.45; O 3 : r = -0.19; NO 2 : r = 0.64; SO 2 : r = 0.41 This study did not provide quantitative results for CO.

Author: Samoli et al. (2007) Period of Study: 1990-1997 Location: 19 European Cities (APHEA2) Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Cardiovascular (390-459) Study Design: Time-series Statistical Analyses: Poisson and two-stage hierarchical model Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean Range (unit - mg/m3): Athens: 6.1; Barcelona: 0.9; Basel: 0.6; Birmingham: 1.0; Budapest: 5.1; Geneva: 1.5; Helsinki: 1.2; Ljubljana: 1.6; London: 1.4; Lyon: 3.8; Milano: 5.4; Netherlands: 0.6; Prague: 0.9; Rome: 4.1; Stockholm: 0.8; Teplice: 0.7; Torino: 5.5; Valencia: 4.1; Zurich: 1.2 Range (10th, 90th): Athens: (3.5, 9.2) Barcelona: (0.4, 1.7) Basel: (0.4, 1.1) Birmingham: (0.5, 1.6) Budapest: (3.3, 7.4) Geneva: (0.8, 2.6) Helsinki: (0.7, 1.9) Ljubljana: (0.6, 3.0) London: (0.7, 2.2) Lyon: (2.0, 6.0) Milano: (2.9, 8.7) Netherlands: (0.4, 1.2) Prague: (0.5, 1.5) Rome: (2.5, 5.9) Stockholm: (0.5, 1.2) Increment: 1 mg/m 3 % Increase (Lower CI, Upper CI); lag: Non-accidental mortality 8 Degrees of Freedom per year Fixed Effects: CO: 0.59% (0.41-0.78); 0-1 CO, BS: 0.35% (-0.03 to 0.72); 0-1 CO, PM 10 : 0.48% (0.24-0.72); 0-1 CO, SO 2 : 0.44% (0.21-0.67); 0-1 CO, O 3 : 0.66% (0.46-0.86); 0-1 CO, NO 2 : 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, PM 10 : 0.58% (0.12-1.04); 0-1 CO, SO 2 : 0.46% (0.07-0.85); 0-1 CO, O 3 : 0.76% (0.45-1.06); 0-1 CO, NO 2 : 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, PM 10 : 0.78% (0.55-1.00); 0-1 CO, SO 2 : 0.68% (0.47-0.90); 0-1 CO, O 3 : 1.12% (0.93-1.31); 0-1 CO, NO 2 : 0.72% (0.50-0.95); 0-1



### March 2009 C-67 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI) Teplice: (0.3, 1.2) Torino: (2.8, 9.1) Valencia: (2.4, 5.9) Zurich: (0.7, 2.0) Copollutant correlation: PM 10 : 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, PM 10 : 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, PM 10 : 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, PM 10 : 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, PM 10 : 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, PM 10 : 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 PM 10 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 PM 10 Levels: 25th Percentile: 0.74% (0.46-1.02); 0-1 75th Percentile: 1.07% (0.87-1.27); 0-1 Standardized Mortality Rate: 25th Percentile: 1.29% (1.06-1.52); 0-1 75th Percentile: 0.77% (0.56-0.98); 0-1 Western cities: 1.15% (0.90-1.40); 0-1 Southern cities: 1.08% (0.79-1.38); 0-1 Eastern cities: 0.27% (-0.20 to 0.74); 0-1 Cardiovascular Mortality 8 Degrees of Freedom per Year Mean O 3 : 25th Percentile: 1.04% (0.67-1.41); 0-1 75th Percentile: 0.82% (0.55-1.10); 0-1 Standardized Mortality Rate: 25th Percentile: 1.06% (0.71-1.42); 0-1 75th Percentile: 0.61% (0.30-0.93); 0-1 Population >75 years of age (%): 25th Percentile: 0.58% (0.25-0.92); 0-1 75th Percentile: 0.94% (0.64-1.24); 0-1



### March 2009 C-68 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI) Western cities: 1.06% (0.67-1.46); 0-1 Southern cities: 0.70% (0.26-1.14); 0-1 Eastern cities: 0.21% (-0.48 to 0.90); 0-1 PACF (Partial Autocorrelation Function) Mean O 3 : 25th Percentile: 1.32% (0.96-1.68); 0-1 75th Percentile: 1.09% (0.83-1.14); 0-1 Standardized Mortality Rate: 25th Percentile: 1.40% (1.06-1.75); 0-1 75th Percentile: 0.85% (0.55-1.14); 0-1 Population >75 years of age (%): 25th Percentile: 0.74% (0.41-1.06); 0-1 75th Percentile: 1.25% (0.96-1.54); 0-1 Western cities: 1.38% (1.00-1.76); 0-1 Southern cities: 0.90% (0.47-1.33); 0-1 Eastern cities: 0.48% (-0.14 to 1.11); 0-1 Author: Schwartz et al. (1999) Period of Study: 1989-1995 Location: Spokane, WA Health Outcome (ICD9): Mortality: Total (non-accidental) (<800) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 1-h avg Mean (SD) unit: Dust Storm Days : 09/08/1990: 6.37 ppm 09/12/1990: 3.40 ppm 10/04/1990: 3.15 ppm 11/09/1990: 2.45 ppm 11/23/1990: 2.50 ppm 09/13/1991: 4.60 ppm 10/16/1991: 2.10 ppm 10/21/1991: 2.20 ppm 09/04/1992: 3.43 ppm 09/12/1992: 1.80 ppm 09/13/1992: 1.65 ppm 09/25/1992: 2.95 ppm 09/26/1992: 4.30 ppm 10/08/1992: 3.85 ppm 09/11/1993: 1.88 ppm 11/3/1993: 5.33 ppm 07/24/1994: 2.10 ppm 08/30/1996: 2.85 ppm Range (Min, Max): NR Copollutant: PM 10 The study did not present quantitative results for CO

.

Author: Sharovsky et al. (2004) Period of Study: 1996-1998 Location: Sao Paulo, Brazil Health Outcome (ICD10): Mortality: Myocardial Infarction (I.21) Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: 35-109 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 3.7 (1.6) ppm Range (Min, Max): (1.0, 11.8) Copollutant: correlation SO 2 : r = 0.73; PM 10 : r = 0.51 Increment: NR β x 100 (SE); lag: CO: 1.42 (1.01) CO, SO 2 , PM 10 : 0.97 (1.27) Notes: The study did not present the lag used for CO.

Author: Slaughter et al. (2005) Period of Study: 1/1995 - 6/2001 Location: Spokane, WA Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Respiratory (460-519); Asthma (493); COPD (491, 492, 494, 496); Pneumonia (480-487); Acute Upper Respirator y Tract Infections (464-466, 490); Cardiac Outcomes (390-459) Study Design: Time-series Statistical Analyses: Log-linear Poisson GLM, natural splines for calendar time Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: Areas in Spokane Hamilton St: 1.73 (0.46) ppm Backdoor Tavern: 1.29 (0.23) ppm Spokane Club: 1.41 (0.32) ppm Third and Washington: 1.82 (0.33) ppm Rockwood: 0.42 (0.15) ppm Range (Min, Max): NR Copollutant correlation : PM1: r = 0.63; PM 2.5 : r = 0.62; PM 10 : r = 0.32; PM 10-2.5 : r = 0.32 The study did not present quantitative results for CO.



### March 2009 C-69 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Stieb et al. (2003) Period of Study: 1985-2000 Location: All locations Health Outcome (ICD9): Mortality: Non-accidental Study Design: Meta-analysis Statistical Analyses: NR Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: NR IQR (25th, 75th): NR Copollutant: NR Increment: 1.1 ppm % Excess Mortality (Lower CI, Upper CI); lag: Non-GAM: Single-pollutant model (4 studies): 4.7% (1.1-8.4) Multi-pollutant model (1 study): 0.0% (-3.8 to 3.8) GAM: Single-pollutant model (18 studies): 1.6% (1.1-2.1) Multi-pollutant model (11 studies): 0.7% (-0.1 to 1.5) Author: Stölzel et al. (2006) Period of Study: 9/1995-8/2001 Location: Erfurt, Germany Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Cardio-respiratory (390-459, 460- 519, 785, 786) Study Design: Time-series Statistical Analyses: Poisson GAM Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 0.47 (0.39) mg/m 3 IQR (25th, 75th): (0.23, 0.57) Copollutant correlation: MC 0.1-0.5 : r = 0.58; MC 0.01-2.5 : r = 0.57; PM 10 : r = 0.50; NO: r = 0.70; NO 2 : r = 0.71 Increment: 0.34 mg/m 3 Relative Risk (Lower CI, Upper CI); lag: Total (non-accidental) 1.000 (0.977-1.023); 0; 1.002 (0.980-1.024); 1; 1.013 (0.991-1.035); 2; 1.007 (0.986-1.029); 3; 1.012 (0.990-1.034); 4; 0.995 (0.974-1.017); 5

Author: Sunyer et al. (2001) Period of Study: 1990-1995 Location: Barcelona, Spain Health Outcome (ICD9): Mortality: COPD (491, 492, 494, 496) Study Design: Bi-directional case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: >35 Pollutant: CO Averaging Time: 8-h avg Mean (SD) unit: NR Range (Min, Max): NR Copollutant: PM 10 ; NO 2 ; O 3 Increment: 4.5 μg/m 3 Odds Ratio (Lower CI, Upper CI); lag: CO: 1.052 (0.990-1.117); 0-2 CO, PM 10 : 1.017 (0.947-1.091); 0-2

Author: Sunyer et al. (2002) Period of Study: 1985-1995 Location: Barcelona, Spain Health Outcome (ICD9): Mortality: Respiratory mortality Study Design: Case-crossover Statistical Analyses: Conditional logist ic regression Age Groups Analyzed: >14 Study population: Asthmatic individuals: 5,610 Pollutant: CO Averaging Time: 24-h avg Median (SD) unit: 7.7 μg/m 3 Range (Min, Max): (0.6, 66.0) Copollutant: PM 10 ; BS; NO 2 ; O 3 ; SO 2 Increment: 7.2 μg/m 3 Odds Ratio (Lower CI, Upper CI); lag: Asthmatic individuals with 1 ED visit 1.127 (0.895-1.418); 0-2 Asthmatic individuals with >1 ED visit 1.125 (0.773-1.638); 0-2 Asthma/COPD individuals with >1 ED visit 0.815 (0.614-1.082); 0-2 Author: Tsai et al. (2003a) Period of Study: 1994-2000 Location: Kaohsiung, Taiwan Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Respiratory (460-519); Circulatory (390-459) Study Design: Bidirectional case-crossover Statistical Analyses: Conditional logist ic 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: PM 10 ; SO 2 ; NO 2 ; O 3 Increment: 0.313 ppm Odds Ratio (Lower CI, Upper CI); lag: Total (non-accidental): 1.003 (0.968-1.039); 0-2 Respiratory: 1.011 (0.883-1.159); 0-2 Circulatory: 0.986 (0.914-1.063); 0-2

Author: Tsai et al. (2006b) Period of Study: 1994-2000 Location: Kaohsiung, Taiwan Health Outcome (ICD9): Mortality: Total (non-accidental) (<800) Study Design: Case-crossover Statistical Analyses: Conditional logist ic 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: PM 10 ; SO 2 ; O 3 ; NO 2 Increment: 0.31 ppm Odds Ratio (Lower CI, Upper CI); lag: Postneonatal Mortality 1.051 (0.304-3.630); 0-2

Author: Vedal et al. (2003) Period of Study: 1/1994-12/1996 Location: Vancouver, BC, Canada Health Outcome (ICD9): Mortality: Total (non-accidental) (<800); Respiratory (460-519); Cardiovascular (390-459) Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: All ages Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 0.6 (0.2) ppm Range (Min, Max): (0.3, 1.9) Copollutant correlation: Summer : PM 10 :r = 0.71; O 3 : r = 0.12; NO 2 : r = 0.81; SO 2 : r = 0.67 Winter : PM 10 : r = 0.76; O 3 : r = -0.65; NO 2 : r = 0.78; SO 2 : r = 0.83 The study did not present quantitative results for CO.



### March 2009 C-70 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Villeneuve et al. (2003) Period of Study: 1986-1999 Location: Vancouver, BC, Canada Health Outcome (ICD9): Mortality: Non-accidental (<800); Cardiovascular (401-440); Respiratory (460-519); Cancer (140-239) Study Design: Time-series Statistical Analyses: Poisson, natural splines Age Groups Analyzed: ≥ 65 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1.0 ppm Range (Min, Max): (0.2, 4.9) Copollutant: PM 2.5 ; PM 10 ; PM 10-2.5 ; TSP; SO 4 ; CO; COH; O 3 ; NO 2 ; SO 2 Increment: 1.1 ppb % Increase (Lower CI, Upper CI); lag: Non-accidental 0.5% (-1.9 to 2.9); 0-2; / -0.3% (-2.2 to 1.7); 0; 0.6% (-1.3 to 2.6); 1; / 0.5% (-1.4 to 2.5); 2 Cardiovascular 2.3% (-1.6 to 6.3); 0-2; / 1.6% (-1.5 to 4.7); 0; 1.2% (-2.0 to 4.5); 1; / 1.5% (-1.5 to 4.4); 2 Respiratory -1.0% (-7.3 to 5.8); 0-2; / 1.3% (-4.4 to 7.3); 0; -0.1% (-5.3 to 5.4); 1; - / 2.8% (-7.8 to 2.6); 2 Cancer -2.8% (-7.6 to 2.4); 0-2; / -3.0% (-6.9 to 1.1); 0; -1.6% (-5.6 to 2.4); 1; / -0.5% (-4.7 to 3.8); 2 Author: Wichmann et al. (2000) Period of Study: 9/1995 - 12/1998 Location: Erfurt, Germany Health Outcome (ICD9): Mortality: Non-accidental (<800); Cardiovascular (401-440); Respiratory (460-519) Study Design: Time-series Statistical Analyses: Poisson GAM, LOESS Age Groups Analyzed: <70 70-79 ≥ 80 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 0.6 (0.5) mg/m 3 Range (Min, Max): (0.10, 2.50) Copollutant correlation: PM 2.5 : r = 0.62; PM 10 : r = 0.58; TSP: r = 0.57; SO 2 : r = 0.59; NO 2 : r = 0.71 Increment: 0.5 ppm Relative Risk (Lower CI, Upper CI); lag: Single-Day Lag CO: 1.055 (1.003-1.110); 4 Polynomial Distributed Lag Multi-pollutant model: 1.076 (1.017-1.138); 4 Total Mortality CO: 1.012 (0.977-1.049); 0 Log-transformed: 1.016 (0.962-1.073); 0 1.004 (0.969-1.040); 1 Log-transformed: 1.027 (0.973-1.083); 1 1.020 (0.984-1.057); 2 Log-transformed: 1.024 (0.970-1.081); 2 1.019 (0.984-1.055); 3 Log-transformed: 1.037 (0.984-1.093); 3 1.029 (0.995-1.063); 4 Log-transformed: 1.055 (1.003-1.110); 4 0.997 (0.965-1.031); 5 Log-transformed: 1.014 (0.966-1.065); 5 Total Mortality (Season-specific): Log-transformed Winter: 1.002 (0.922-1.088); 4 Spring: 1.019 (0.942-1.102); 4 Summer: 1.085 (1.018-1.156); 4 Fall: 1.111 (1.039-1.188); 4 Winter-specific: Log-transformed 10/95-3/96: 1.046 (0.949-1.153); 4 10/96-3/97: 1.091 (0.998-1.193); 4 10/97-3/98: 1.028 (0.966-1.095); 4 One-pollutant Model: Log-transformed CO: 1.055 (1.003-1.110); 4 Author: Yang et al. (2004a) Period of Study: 1994-1998 Location: Taipei, Taiwan Health Outcome (ICD9): Mortality: Non-accidental (<800); Circulatory (390-459); Respiratory (460-519) Study Design: Bi-directional case-crossover Statistical Analyses: Conditional logist ic 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: PM 10 ; SO 2 ; NO 2 ; O 3 Increment: 0.52 ppm Odds Ratio (Lower CI, Upper CI); lag: Non-accidental: 1.005 (0.980-1.031); 0-2 Respiratory: 1.014 (0.925-1.110); 0-2 Circulatory: 0.996 (0.948-1.046); 0-2



### March 2009 C-71 DRAFT—DO NOT QUOTE OR CITE

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

Study Design Concentrations Effect Estimates (95% CI)

Author: Lipfert et al. (2000b) Period of Study: 1975-1996 Location: 32 Veterans Hospitals, USA Mortality Health Outcome (ICD9): Non-accidental Study Design: Cohort Study Population: ~90,000 hypertensive male U.S. veterans Statistical Analyses: Staged regression Age Groups Analyzed: NR Pollutant: CO Averaging Time: 95th Percentile Annual avg Mean (SD) unit: 1960-1974: 10.82 (5.15) ppm 1975-1981: 7.64 (2.94) ppm 1982-1988: 3.42 (0.95) ppm 1989-1996: 2.36 (0.67) ppm Range (Min, Max): 1960-1974: (0.94, 35.30) 1975-1981: (0.43, 22.38) 1982-1988: (0.30, 15.20) 1989-1996: (0.30, 7.10) Copollutants; correlation: 1960-1974 : O 3 : r = 0.004; NO 2 : r = 0.690; SO 4 2- : r = 0.469 1975-1981 : O 3 : r = 0.109; NO 2 : r = 0.249; SO 4 2- : r = -0.155; IP SO 4 2- : r = 0.356; PM 2.5 : r = 0.634; PM 10-2.5 : r = 0.498; PM 15 : r = 0.626 1982-1988 O 3 : r = 0.158; NO 2 : r = 0.413; SO 4 2- : r = -0.518; IP SO 4 2- : r = 0.075; PM 2.5 : r = 0.296; PM 10-2.5 : r = 0.135 PM 15 : r = 0.284 1989-1996 O 3 : r = 0.397; NO 2 : r = 0.492; SO 4 2- : r = -0.551 Increment: NR Coefficient: Baseline Model Exposure Period: up to 1975 Single Period: -0.000 Deaths, 1976-81: 0.0043 Deaths, 1982-88: -0.0002 Deaths after 1988: -0.0041 Exposure Period: 1975-81 Single Period: -0.013 Deaths, 1976-81: -0.0170 Deaths, 1982-88: -0.0217 Deaths after 1988: -0.0240 Exposure Period: 1982-88 Single Period: -0.028 Deaths, 1976-81: -0.0294 Deaths, 1982-88: -0.0484 Deaths after 1988: -0.0424 Exposure Period: 1989-96 Single Period: -0.046 Deaths, 1976-81: -0.0590 Deaths, 1982-88: -0.0581 Deaths after 1988: -0.0536 Final Model w/ Ecological Variables Exposure Period: up to 1975 Single Period: -0.001 Deaths, 1976-81: 0.0013 Deaths, 1982-88: -0.0022 Deaths after 1988: -0.0061 Exposure Period: 1975-81 Single Period: -0.008 Deaths, 1976-81: -0.0128 Deaths, 1982-88: -0.0186 Deaths after 1988: -0.0203 Exposure Period: 1982-88 Single Period: -0.009 Deaths, 1976-81: -0.0007 Deaths, 1982-88: -0.0246 Deaths after 1988: -0.0216 Exposure Period: 1989-96 Single Period: -0.009 Deaths, 1976-81: -0.0106 Deaths, 1982-88: -0.0136 Deaths after 1988: -0.0078 Notes: Mortality risks based on mean concentrations of pollutants less estimated background weighted by the number of subjects in each county, but The study did not present this value for each pollutant. Author: Lipfert and Morris (2002) Period of Study: 1960-1997 Location: U.S. counties Mortality Health Outcome (ICD9): Non-accidental Study Design: Ecological/ cr oss-sectional Statistical Analyses: Staged regression Age Groups Analyzed: 15-44 45-64 65-74 75-84 ≥ 85 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: 1960-1969: 13.81 (8.47) ppm 1970-1974:9.64 (5.63) ppm 1979-1981:5.90 (3.54) ppm 1989-1991:2.69 (1.22) ppm 1995-1997:1.72 (0.76) ppm Range (Min, Max): NR Copollutant: TSP SO 4 2- SO 2 NO 2 O 3 Increment: NR Attributable risk (SE): Attributable Risks of mortality (1960-4) Peak CO 1960-1964, All locations Ages 15-44: 0.1299 (0.0341) Ages 45-64: 0.0340 (0.0280) Ages 65-74: -0.0058 (0.0220) Ages 75-84: 0.0121 (0.0188) Ages ≥ 85: 0.0374 (0.0225) Log Mean: 0.0365 (0.0149) Attributable Risks of mortality (1970-4) Peak CO 1970-1974, All locations Ages 15-44: 0.0553 (0.0240) Ages 45-64: 0.0181 (0.0148) Ages 65-74: -0.0146 (0.0134) Ages 75-84: -0.0128 (0.0098) Ages ≥ 85: -0.0151 (0.0093) Log Mean: 0.0038 (0.0086)



### March 2009 C-72 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI) Attributable Risks of mortality (1979-81) Peak CO 1979-1981, All locations Ages 15-44: 0.0054 (0.0174) Ages 45-64: -0.0060 (0.0141) Ages 65-74: -0.0251 (0.0105) Ages 75-84: -0.0331 (0.0086) Ages ≥ 85: -0.0123 (0.0079) Log Mean: -0.0183 (0.0077) Peak CO 1970-1974, All locations Ages 15-44: 0.0218 (0.0200) Ages 45-64: 0.0327 (0.0161) Ages 65-74: -0.0136 (0.0119) Ages 75-84: -0.0250 (0.0105) Ages ≥ 85: -0.0202 (0.0085) Log Mean: -0.0048 (0.0077) Peak CO 1960-1969, All locations Ages 15-44: 0.0506 (0.0478) Ages 45-64: 0.0704 (0.0337) Ages 65-74: 0.0100 (0.0211) Ages 75-84: -0.0124 (0.0143) Ages ≥ 85: 0.0187 (0.0135) Log Mean: 0.0084 (0.0149) Peak CO 1979-1981, CO 1970-1974 Ages 15-44: 0.0244 (0.0209) Ages 45-64: 0.0016 (0.0181) Ages 65-74: -0.0183 (0.0128) Ages 75-84: -0.0382 (0.0108) Ages ≥ 85: -0.0201 (0.0089) Log Mean: -0.0165 (0.0089) Peak CO 1979-1981, CO 1960-1969 Ages 15-44: 0.0748 (0.0679) Ages 45-64: 0.0844 (0.0496) Ages 65-74: 0.0144 (0.0259) Ages 75-84: -0.0158 (0.0168) Ages ≥ 85: -0.0073 (0.0170) Log Mean: 0.0109 (0.0218) Peak CO 1979-1981, CO 1960-1969 Ages 15-44: 0.1191 (0.0709) Ages 45-64: 0.1163 (0.0491) Ages 65-74: 0.0177 (0.0310) Ages 75-84: -0.0120 (0.0212) Ages ≥ 85: -0.0040 (0.0202) Log Mean: 0.0211 (0.0231) Attributable Risks of mortality (1989-91) Peak CO 1989-1991, All locations Ages 15-44: 0.0404 (0.0322) Ages 45-64: -0.0262 (0.0162) Ages 65-74: -0.0397 (0.0115) Ages 75-84: -0.0464 (0.0097) Ages ≥ 85: -0.0209 (0.0073) Log Mean: -0.0178 (0.0098) Peak CO 1979-1981, All locations Ages 15-44: 0.0522 (0.0227) Ages 45-64: -0.0047 (0.0121) Ages 65-74: -0.0165 (0.0078) Ages 75-84: -0.0268 (0.0068) Ages ≥ 85: -0.0027 (0.0055) Log Mean: -0.0020 (0.0065) Peak CO 1970-1974, All locations Ages 15-44: 0.0685 (0.0274) Ages 45-64: 0.0022 (0.0148) Ages 65-74: -0.0051 (0.0091) Ages 75-84: -0.0158 (0.0079) Ages ≥ 85: -0.0069 (0.0060) Log Mean: 0.0038 (0.0077) Peak CO 1960-1969, All locations Ages 15-44: 0.0578 (0.0713) Ages 45-64: 0.0583 (0.0347) Ages 65-74: 0.0007 (0.0174) Ages 75-84: -0.0245 (0.0130) Ages ≥ 85: -0.0138 (0.0113) Log Mean: 0.0041 (0.0176) Attributable Risks of mortality (1995-97) Peak CO 1995-1997, All locations



### March 2009 C-73 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI) Ages 15-44: 0.0344 (0.0256) Ages 45-64: -0.0203 (0.0198) Ages 65-74: -0.0346 (0.0146) Ages 75-84: -0.0378 (0.0161) Ages ≥ 85: -0.0283 (0.0119) Log Mean: -0.0188 (0.0103) Peak CO 1989-1991, All locations Ages 15-44: 0.0289 (0.0248) Ages 45-64: -0.0192 (0.0192) Ages 65-74: -0.0466 (0.0140) Ages 75-84: -0.0497 (0.0147) Ages ≥ 85: -0.0301 (0.0108) Log Mean: -0.0240 (0.0096) Peak CO 1979-1981, All locations Ages 15-44: 0.0336 (0.0176) Ages 45-64: -0.0037 (0.0135) Ages 65-74: -0.0298 (0.0096) Ages 75-84: -0.0301 (0.0105) Ages ≥ 85: -0.0087 (0.0078) Log Mean: -0.0094 (0.0071) Peak CO 1970-1974, All locations Ages 15-44: 0.0464 (0.0202) Ages 45-64: 0.0202 (0.0155) Ages 65-74: -0.0032 (0.0112) Ages 75-84: -0.0157 (0.0122) Ages ≥ 85: -0.0142 (0.0084) Log Mean: 0.0007 (0.0077) Peak CO 1960-1969, All locations Ages 15-44: 0.0679 (0.0441) Ages 45-64: 0.0772 (0.0405) Ages 65-74: 0.0059 (0.0173) Ages 75-84: -0.0085 (0.0213) Ages ≥ 85: -0.0158 (0.0162) Log Mean: 0.0162 (0.0149) Author: Lipfert et al. (2006b) Period of Study: 1976-2001 Location: 32 Veterans Hospitals, USA Mortality Health Outcome (ICD9): Non-accidental Study Design: Cohort Study Population: ~70,000 hypertensive male U.S. veterans Statistical Analyses: Cox proportional- hazards model Age Groups Analyzed: NR Pollutant: CO Averaging Time: 95th Percentile Annual avg Mean (SD) unit: 1976-1981: 7.6 (2.9) ppm 1982-1988: 3.4 (9.5) ppm 1989-1996: 2.4 (0.67) ppm 1997-2001: 1.6 (5.6) ppm Range (Min, Max): NR Copollutants correlation: ln(VKTA): r = -0.06 Avg NO 2 : r = 0.43 Peak O 3 : r = 0.08 Peak SO 2 : r = -0.05 PM 2.5 : r = 0.08 SO 4 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, NO 2 : 1.012 (0.923-1.110) CO, lnVKTA, NO 2 +O 3 : 1.023 (0.939-1.115)



### March 2009 C-74 DRAFT—DO NOT QUOTE OR CITE

### Study Design Concentrations Effect Estimates (95% CI)

Author: Lipfert et al. (2006a) Period of Study: 1997-2002 Location: 32 Veterans Hospitals, USA Mortality Health Outcome (ICD9): Non-accidental Study Design: Cohort Study Population: ~18,000 hypertensive male U.S. veterans Statistical Analyses: Cox proportional- hazards model Age Groups Analyzed: NR Pollutant: CO Averaging Time: 95th Percentile Annual avg Mean (SD) unit: 1999-2001: 1.63 (0.84) ppm 1999-2001 (STN sites only): 1.73 (0.77) Range (Min, Max): 1999-2001: (0.40, 6.7) 1999-2001 (STN sites only): (0.47, 4.2) Copollutants correlation: ln(traffic density): r = -0.199 PM 2.5 : 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; SO 4 2- : r = -0.123 NO 3 -: r = -0.088 ∑ PM 2.5 comp.: r = 0.133 NO 2 : r = 0.418 Peak O 3 : r = 0.172 Peak SO 2 : r = 0.405 Increment: NR β coefficient (SE) ; t-statistic: -0.00000536 (0.0000324); -0.165

Author: Jerrett et al. (2003) Period of Study: 1982-1989 Location: 107 U.S. cities Mortality Health Outcome (ICD9): Cardiovascular; CHD; Cerebrovascular disease Study Design: Cohort Study Population: 65, 893 postmenopausal women without previous cardiovascular disease Statistical Analyses: Cox proportional- hazards model Age Groups Analyzed: ≥ 30 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: 1.56 ppm Range (Min, Max): (0.19, 3.95) Copollutants correlation: Sulfates: r = -0.07 NO 2 O 3 SO 2 Increment: 1 ppm Relative risk (Lower CI, Upper CI): CO: 0.98 (0.92-1.03) CO, Sulfates: 0.97 (0.92-1.03)

Author: Miller et al. (2007) Period of Study: 1994-1998 Location: 36 U.S. cities Mortality Health Outcome (ICD9): Cardiovascular; CHD; Cerebrovascular disease Study Design: Cohort Study Population: 65, 893 postmenopausal women without previous cardiovascular disease Statistical Analyses: Cox proportional- hazards model Age Groups Analyzed: 50-79 Pollutant: CO Averaging Time: Annual avg Mean (SD) unit: NR Range (Min, Max): NR Copollutants: PM 2.5 PM 10-2.5 SO 2 NO 2 O 3 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, PM 2.5 , PM 10-2.5 , SO 2 , NO 2 , O 3 : 0.93 (0.67, 1.30)

Author: Pope et al. (2002) Period of Study: 1980-1998 Location: All 50 States, Washington DC, and Puerto Rico (ACS-CPS-II) Mortality Health Outcome (ICD9): Total (non-accidental) (<800); Lung Cancer (162); Ca rdiopulmonary (401- 440, 460-519) Study Design: Prospective cohort Statistical Analyses: Cox proportional hazards model Age Groups Analyzed: ≥ 30 Pollutant: CO Averaging Time: 24-h avg Mean (SD) unit: 1980: 1.7 (0.7) ppm 1982-1998: 1.1 (0.4) ppm Range (Min, Max): NR Copollutant: PM 2.5 ; PM 10 ; TSP; SO 2 ; NO 2 ; O 3 The study presents results for CO graphically.



### March 2009 C-75 DRAFT—DO NOT QUOTE OR CITE

# Annex D. Controlled Human Exposure Studies

Table D- 1. Controlled human exposure studies.

Study Subjects Exposure Findings

Adir et al. (1999) 15 healthy nonsmoking males; 22-34 years old Subjects exposed to both CO and room air for 3 min 45 sec. Actual inhaled CO concentrati on not provided. Subjects exposed to CO concentration required to produce a venous COHb of 4-6%. Exposures were separated by 1 month with the order of exposur e randomly assigned. Exposure to CO resulted in a decrease in post-exposure exercise duration (Bruce prot ocol) relative to clean air exposure in 13 out of 15 subjects (p=0.0012). Statistically significant decreases in met abolic equivalent units (METs) were also reported following CO exposure (p = 0.0001). No CO-induced changes in heart rate (HR), BP, ECG parameters, or my ocardial perfusion were observed. Bathoorn et al. (2007) 19 former smokers with COPD; 18 males/1 female; 66-70 years old Exposures to both CO and room air conducted for 2 h on each of four consecutive days in a randomized crossover design. 9 subjects were exposed to 100 ppm and 10 subjects were exposed to 125 ppm. CO and room air exposures were separated by at least 1 week. Following the fourth day of ex posure, CO inhalation reduced sputum eosinophils relative to room air and also increased the provocative concentration of methacholine required to cause a 20% reduction in FEV 1 . Neither of these effects were shown to reach statistical significance. No changes insputum neutrophils, white blood cell counts or serum Creactive protein (CRP) were observed. Although this studyappears to demonstrate some evidence of an antiinflammatory effect of CO among subjects with COPD, it must be noted that two of these patients experienced exacerbations of COPD during or following CO exposure, with one patient requiring hospi talization two months after exposure (initial symptoms fi rst experienced 1-week postexposure). Hanada et al. (2003) 20 healthy males; 26 ± 1 years old 15 subjects exposed for 20 min (10 min rest, 5 min handgrip exercise, 2 min post-exercise ischemia, 3 min recovery) under the following four conditions : (1) normoxia (inspiratory O2 fraction 21.4%), (2) hypoxia (inspiratory O2 fraction 10.3%), (3) CO + normoxia, (4) CO + hyperoxia (inspiratory O2 fraction 95.9%). Inhaled CO administered to achieve ~20% COHb in venous blood. Each of the four conditions were separated by 20 min of rest. 5 subjects served as controls (four consecutive 20 min periods of normoxia). Blood oxygenation, BP, HR and respiratory rate were measured during exposure. Muscle sympathetic nerve activity (MSNA) and leg hemodynamics were evaluated in two subsets of the study group (n = 8 and 7, respectively). Arterial oxygen saturation (pulse oximetry) was significantly lower, and resting HR and ventilation significantly higher during the period of hypoxia co mpared to the other periods; none of these measures were affected by exposure to CO. MSNA was shown to increase during hypoxia and CO exposure relative to normoxia. Neither hypoxia nor CO was found to affect leg blood flow or vasoconstriction.16 healthy nonsmoking males; 18-29 years old Subjects exposed on 4 separate days to increasing COconcentrations during either upper-body exercise (handcrank) or lower-body exercise (treadmill). Targeted COHb levels were initially attained using short term (4-6 min) exposures to CO at concentrations between 1,000 and 3,000 ppm. Chamber exposures were then conducted at CO concentrations required to maintain COHb levels of <2% (room air), 5% (27 ppm), 10 % (55 ppm), 15% (83 ppm), and 20% (100 ppm). At all levels of upper- and lower-body exercise, exposures to CO resulted in increases in HR, cardiac output, and cardiac contractility relative to clean air exposures. Increases in HR reached statistical significance at COHb concentrations ≥ 5%, and increases in both cardiac output and cardiac contractility reached statisti cal significance at COHb concentrations ≥ 10%. CO exposure during exercise was not observed to cause ventricular arrhythmias or affect ECG wave shape (no evidence of ST-segment depression) at COHb concentrations ≤ 20%. Kizakevich et al. (2000)

Mayr et al. (2005) 13 healthy nonsmoking males; 18-38 years old Subjects exposed to both 500 ppm CO and clean air for 1 h, with exposures separated by a 6-week period. Immediately following exposure, subjects were administered an intravenous bolus dose (2 ng/kg) of lipopolysaccharide (LPS). The average COHb concentration was 7% following the 1 h CO exposure. Infusion of LPS significantly increased plasma concentrations of TNF- α , CRP, IL-6, and IL-8, with no difference in the inflammatory response between clean air and CO exposures. Morse et al. (2008) 12 healthy nonsmoking males; 25 ± 2.9 years old Exposures conducted on two separate occasions to both room air (6 min) and CO. Subjects were exposed to 3,000 ppm CO until COHb reached 6% (3-8 min exposures). Leg strength and muscle fatigue we re evaluated immediately following exposure. CO expos ure did not affect muscle strength (maximal voluntary is ometric contraction), but did cause a statistically significant increase in muscle fatigue (p <0.05)



### March 2009 D-1 DRAFT—DO NOT QUOTE OR CITE

### Study Subjects Exposure Findings

Ren et al. (2001) 12 healthy subjects; 11 nonsmokers; 9 males / 3 females; 20-32 years old Each subject underwent four different 8 h experimental protocols: (1) isocapnic hypox ia (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 bl ood withdrawal pr otocol and did not complete the study.

Resch et al. (2005) 15 healthy nonsmoking males; 27 ± 4 years old Subjects exposed to 500 ppm CO and synthetic air for 1 h at rest in a randomized crossover study design. Exposures were separated by a period of at least 1 week. COHb levels averaged 5.6% after 30 min and 9.4% after 60 min of exposure. Statistically si gnificant increases in retinal blood flow, retinal vessel diam eter, and choroidal blood flow were observed with CO exposure relative to synthetic air at both time points. Exposure to CO did not affect oxygen saturation of arterial blood. Vesely et al. (2004). 10 healthy nonsmoking males; 22-52 years old Each subject was exposed to CO for 30-45 min to achieve a COHb level of 10%. Prior to and following exposure, subjects performed hypoxic and hyperoxic rebreathing tests. Four subjects were exposed to hypoxic conditions first, while six subjects were exposed to hyperoxic conditions first, both prior to and following CO exposure. Ventilation rate was observed to significantly increase during hypoxic rebreathing relative to hyperoxic rebreathing. However, exposure to CO had no effect on ventilation under either hypoxic or hyperox ic conditions. The authors concluded that exposure to low levels of CO does not significantly affect chemoreflex sensitivity of the CO 2 - induced stimulation of ventilation. Zevin et al. (2001) 12 healthy male smokers; 27-47 years old Exposures were conducted over 21 consecutive days under three different protoc ols, with each protocol lasting 7 days. In one protocol, subjects smoked 20 cigarettes per day, one every 45 min. In the other tw o protocols, ev ery 45 min (20 times per day) subjects br eathed either air or CO (~1200 ppm) from a 1 liter bag once per min for 10 min at a time. Subjects completed all three protocols, with six subjects exposed sequentially to CO, smoking, then air, and the other six exposed sequentially to air, smoking, then CO. COHb levels were similar during smoking and exposure to CO, with average concentrations of 6% and 5%, respectively. Blood was drawn on day 4 of each exposure and analyzed for CRP, plasma pl atelet 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 incr ease HR, while no difference in BP was observed between any of the three exposures.



### March 2009 D-2 DRAFT—DO NOT QUOTE OR CITE

# Annex E. Toxicological Studies

Table E- 1. Human and animal studies.

Species / Model Exposure Duration CO Concentration Findings Reference

Acevedo and Ahmed (1998) Human HO-1 and HO-2 (mRNA and protein) were upregulated in pregnant myometrium when compared to non-pregnant myometrium. The HO activator hemin inhibited spontaneous and oxytocin-induced contract ility of the myometrium. Progesterone induced HO-1 and HO-2 mRNA expression. Human arteries Until equilibrium Approximately 30 μ M CO induced endothelium- and NO • -independent relaxation of precontracted human ITA and RA graft by partially stimulating cGMP production. The mechanism and extent of relaxation depended upon the tissue. Achouh et al. (2008)

Ahmed et al. (2000) Human Placental HO-1 was significantly higher at term. HO-1 significantly attenuated TNF α -dependent cellular damage in placental explants. HO-1 was significantly attenuated in preeclampsia pregnancies versus non-pre-eclamptic pregnancies. Placental arteries exposed to the HO activator hemin demonstrated reduced vascular tens ion (i.e., placental blood vessel relaxation). Ahmed et al. (2005) Human The source of CO in term human placental chorionic villi was found to be the catalysis of heme by HO and not endogenous lipid peroxidation. Alexander et al. (2007) Rat (Sprague Dawley) Adult female . Modulation of the HO/CO system in the anterior pituitary of the female rat led to altered secretion of gonadotropins and prolactin.

Alexandreanu et al. (2002) Rat (Sprague Dawley) The role of the HO/CO system in estrous cyclicity, pregnancy and lactation was evaluated using HO inhibitors and stubstrates. The HO inhibitor CrMP decreased time in estrous. Administering HO-inhibitors to pregnant rodents induced total litter loss. CrMP induced decreased litter weight gai n during lactation, which the authors attribute to maternal milk production or ejection problems as cross-fo stered pups regained we ight lost during nursing on CrMP dams. Alexandreanu and Lawson (2003a) Rat (Sprague Dawley) Adult female Modulation of the HO/CO system in the anterior pituitary of the female rat led to altered secretion of gonadotropins and prolactin.

Alexandreanu and Lawson (2003b) Rat (Sprague Dawley) Adult female ovary HO-1 and HO-2 were localized in the ovaries in rats and treatment of rat ovaries in vitro with CrMP, an inhibitor of HO, or with hemin, a substrate for HO induced steroidogenic changes in the ovaries. Alonso et al. (2003) Human muscle tissue mitochondria 5 min 50-500 ppm CO significantly reduced muscle mitochondrial cytochrome c oxidase activity by 20%, 42%, and 55% after treatment with 50, 100, and 500 ppm CO respectively but did not change the activity of three other electron transport proteins. Andresen et al. (2006) Rat (Long Evans) Male Mouse (C57BL/6J) Male Cerebral vessels 1-100 μM CO did not dilate rat or mouse cerebral arteries until 100 μM, which is not a physiological concentration. Also, the HO inhibitors constricted vessels in a nonspecific manner.

Antonelli et al. (2006) Rat (Wistar) GD5-GD20 75 ppm Pups exposed to CO in utero had significant impairment of cortical neuronal glut amanergic transmission at PND1 in both neurons at rest and in neurons st imulated with depolarization. Appleton and Marks (2002) Human placenta Endogenous CO production by HO in the human placenta was regulated by O 2 availability. Placental HO activity was directly dependent on O 2 availability; this does not vary between preeclamptic and normotensive placentas.



### March 2009 E-1 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Ashfaq et al. (2003) Human plancent a Placentas were collected from smokers and nonsmokers who gave birth to male infants. Premature aging and a statistically significant increase in apoptotic cells were seen in placentas from smokers vs nonsmokers. Astrup (1972) Rabbit (strain not identified) Continuous CO exposure over gestation 90 or 180 ppm Skeletal abnormalities: Three pups (from n = 123) in the 180 ppm CO group had deformities in their extremities at birth, whereas no control and no 90 ppm CO-exposed animals manifested with this malformation. Bainbridge et al. (2002) Human placenta Isolated human placent a exposed to solutions containing CO demonstrated a concentration-dependent decrease in perfusion pressure further demonstrating the role of CO in maintaining basal vasculature tone. Bainbridge et al. (2006) Human placenta 6 h Starting concentrations of CO: 3.9 μM CO in cell culture media (control) and CO-exposed groups: 116 μM, 145 μM, 181 μM. After 3 hours, the CO in the culture media was 3.7 μM (control), and CO-exposed cells 10.2, 12, and 15.9 μM. C-section placentas were collected from healthy term pregnancies.Villous explants of placentas were cultured under hypoxia followed by reoxygenation (H/R). H/R + CO-exposed placental tissue had decreased apoptosis and decreased PARP (a protein marker of apoptosis) versus control H/R exposed cells. Secondary necrosis of the placental tissue post H/R was inhibited by CO treatment.

Bainbridge and Smith (2005) Human placenta The role of HO in the placenta and during pregnancy are reviewed in this article. The conflicting data on the activity, localization, and expression of HO in the placentas of preeclamptic women are presented. Bamberger et al. (2001) Human placenta Expression and tissue localization of soluble guanylyl cyclase in human placenta using antibody localization were characterized. These tools can be used in future studies to elucidate the NO • /CO/cGMP pathway. Barber et al. (1999) Human HO and NOS di d not maintain human uterine quiescence during pregnancy. Barber et al. (2001) Human plac enta Women who had pregnancies with fetal growth restrictions (FGR) produced term placenta with significant decreases in HO2 versus healthy pregnancies. Baum et al. (2000) Human End-tidal CO measurements in women with pregnancy-induced hypertension and pre-eclampsia were significantly lower than in normotensive pregnant women. Bergeron et al. (1998) Rat Brain To address the developmental changes of HO staining in the brain, immunohistochemical st aining for HO-1 was performed on the developing rat brain at PND7, PND14, and PND21. HO-1 staining was most intense at PND7 and by PND21 reached its adult pattern of staining localizing to the hippocampus, thalamic and hypothalamic nuclei, with virtually no staining of endothelium, white matter and cortex. HO-2 is the dominant HO isoform in the brain. Bing et al. (1995) Rodent

. Spatial lear ning in the Morris water maze was enhanced in rodents exposed to the HO inhibi tor tin protoporphyrin (Sn-PP). Burmester et al. (2000) Human and Mouse Nb had a high oxygen affinity similar to Mb, thus may increase the availabilty of O 2 to brain tissue.

Bye et al. (2008) Rat (Wistar) Female 100 h/wk for 18 mo 200 ppm CO-exposed (11-14.7% COHb) rats experienced a 24% decrease in aerobic capacity evidenced by VO 2 max deficits. Left ventricular cardiomyocytes were longer and wider, had increased expression of gr owth-related proteins, and had impaired contraction-relaxation cycles. CO increased cGMP and impaired cardiomyocyte Ca 2+ handling. No change in BP was observed. Cagiano et al. (1998) Rat GD0-GD20 75 or 150 ppm At 5 months of age, CO-exposed male offspring showed decrements in sexual behavior including an increase in mount to intromission latency, a decreas e in mount to intromission frequency, and a decrease in ejaculation frequency. Basal extracellular dopamine concentration in the nucleus accumbens was unchanged after CO-exposure. However, when stimulated with amphetamine administration, control rats had increased release of dopamine that is absent with CO-exposed rats.



### March 2009 E-2 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Carmines and Rajendran (2008) Rat (Sprague Dawley) GD6-GD19 of gestation for 2 h/day 600 ppm Significant decreases in bi rth weight were reported after CO exposure. Maternal body weight was unchanged during gestation, but corrected terminal body weight (body weight minus uterine weight) was signifi cantly elevated in CO-exposed dams at term. Carratu et al. (1995) Rat (Wistar) 150 ppm Sphingoipid homeostasis was disrupted in male offspring of prenatally exposed rats, without a disruption in motor function. Carratu et al. (2000) Rat (Wistar) GD0-GD20 150 ppm Maternal COHb (mean % ± SEM) was 1.9 ±0.04 and 16.02 ± 0.98 in control and 150 ppm CO-exposed animals, respectively. Prenatal CO exposure had no effe ct on brain sphinganine (SA) or sphingosine (SO) levels in male offspring at 90 days of age. However, the sciatic nerve had signi ficant increases in SO after CO exposure, no changes in SA at 90 days of age. Motor activity, which could be affected by changes in myelination, showed no differences between CO and control animals at 90 days of age. Carratu et al. (2000) Rat (Wistar) GD0-GD20 75 or 100 ppm The myelin sheath thickness of t he nerve fibers was significantly decreased in CO-exposed animals (75 and 150 ppm). Axon diameter was not affected by CO exposure. Even though CO affected myelination, it did not significantly affect motor activity of CO-exposed rats at 40 and 90 days. Carraway et al. (2002) Rat model of hypoxic pulmonary vascular remodeling Strain of rat not stated 3 weeks Hypobaric hypoxia ± 50 ppm CO promoted remodeling and increased pulmonary vascular resistance in response to HH. The number of small muscular vessels was increased compared with HH alone. Changes in cell proliferation, apoptosis, actin and HO-1 gene and protein expression correlated with structural changes. COHb levels were <0.5% in controls, 1.5-2.8% in the HH treatment group and 3.5-3.9% in the HH + CO treatment group. Cella et al. (2006) Rat (Sprague Dawley) HO-1 production and HO concentration were shown to be regulated by estrogen in the rat uterus. Chung et al. (2006) Rat (Sprague Dawley) Male 3-6% CO inactivation of Mb does not induce any change in the respiration rate, contractile function, or high-energy phosphate levels in perfused rat hearts. Cronje et al. (2004) Rat (Sprague Dawley) Male 240-325 g 45 min 2,500 ppm Results indicate t hat tissue and blood [CO] (66-72% COHb) dissociate during CO i nhalation, but tissue [CO] does not follow blood [CO] or 1/pO 2 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 stimulatingendogenous CO production by HO. Immediately following exposure, tissue CO concentrations were found to be: Blood: 27,500 (800) pmol/mg Heart: 800 (300) pmol/mg Muscle: 90 (80) pmol/mg Brain: 60 (40) pmol/mg These values are estimates taken from a graph, with control levels in parentheses A later report stated that these tissue CO values were too high due to a computational error (Piantadosi et al., 2006) Cudmore et al. ( 2007) Human placenta Human (HUVEC) Mouse (HO-1 deficient mouse on 129/SV x C57BL/6 background) Pig (Porcine aortic endothelial cells) HUVEC cells, porcine aortic endothelial cells, HO-1 null mice and placental villous explants ( normotensive and pre-eclamptic pregnancies) were used in this study. The HO-1/CO system inhibited sFlt-1 and sEng releas e, two factors upregulated in pre-eclampsia.

D'Amico et al. (2006) Human embryonic kidney (HEK293) cells 0-30 min 20 μM Exogenous CO inhibit ed respiration in HEK293 cells under ambient O 2 concentration (21%). Inhibition was enhanced under hypoxic conditions. Increased endogenous CO resulting from HO-1 overexpression inhibi ted respiration by 12% and cytochrome c oxidase activity by 23%. This effect was enhanced under hypoxic conditions. Dani et al. (2007) Human (neonatal blood) CO was lower at birth and 48-72 hours postpartum in infants born by elective C-section and higher in vaginally born infants.



### March 2009 E-3 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

De Salvia et al. (1995) Rat (Wistar) GD0-GD20 75 or 150 ppm Animals exposed to the higher dose of CO (150 ppm) in utero had significantly impaired acqui sition (at 3 and 18 months) and reacquisition (at 18 months) of conditioned avoidance behavior. Denschlag et al. (2004) Human Genetic polymorphisms in human HO-1 are linked to idiopathic recurrent miscarriages. Dewilde et al. (2001) Nb exists as a reversibly hexacoordinated Hb type with a HisFe 2+ -His binding scheme. Dissociati on of the internal ligand by O 2 or CO is the rate limiting step. Dubuis et al. (2002) Rat (Wistar) Adult female 250 g 3 weeks 530 ppm Intrapulmonary resistanc e artery smooth muscle cells wereisolated from control and exposed rats. Electrophysiological recordings provided evidence of increased Ca 2+ -activated K + current consequent to chronic CO exposure. The authors speculated that this could in part explain the vasodilatory effect of CO in the pulmonary circulation. Dubuis et al. (2005) Rat (Wistar) Male 21 days 50 ppm CO attenuated PAHT by activating BK Ca channels in PA myocytes and reduced hemodynamic changes of PAHT.

Dubuis et al. (2003) Rat (Wistar) Male 21 days 50 ppm CO induced relaxation of pulmonary artery ri ngs in normoxic, hypoxic, and hypoxic-CO rats and it was not endothelium dependent. Chronic hypoxia decreased acute CO sensitivity, while CO-hypoxia increased it. K+ channel blocker reduced this effect while sGC blocker did not. Favory et al. (2006b) Rat 250-300 g Strain not stated 90 min 250 ppm CO inhibited myocardi al permeabilized fiber respiration (complex IV), increased cor onary 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 acetylc holine 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 my ocardial contractility. The authors speculated that CO may have a detrimental effect on heart O 2 supply-to-utilization whic h could potentially lead to myocardial hypoxia because of the increased O 2 demand resulting from increased contract ility, the inhibited mitochondrial respiration and the reduced coronary blood-flow reserve resulting from the decreased vasodilatory capacity. COHb was found to be 11% immediately after exposure. COHb levels gradually returned to baseline (1.5%) over the next 96 h. Fechter and Annau (1977) Rat (Long Evans) Continuous CO exposure throughout pregnancy 150 ppm CO The authors found a 5% significantly decreased birth weights at PND1 in gestationally CO-exposed pups versus control animals with weight decrements persisting to weaning; lactational cross fostering did not ameliorate the CO-dependent reduced growth rates. Dams exposed to CO during gestation had COHb over gestation of 15% with control dams having less than 1%. Decreased birth weight and pre-weaning weight were seen in CO-exposed pups despite a lack of weight decrement in CO-exposed dams versus air-exposed control dams. Fechter et al. (1980) Rat (Long Evans) Continous CO exposure throughout pregnancy 150 ppm CO-exposed animals had cardiomegaly at birth (wet heart weight) that dissipated by PND4.

Fechter and Annau (1980) Rat Continous CO exposure throughout pregnancy 150 ppm CO-exposed animals had dec reased birth weight, impaired righting reflexes, impaired negative geotaxis, and delayed homing behavior.

Fechter et al. (1987) Rat (Long Evans) Continuous CO exposure throughout pregnancy or from GD0 to PND10 75, 150, or 300 ppm The neostriatum of PND21 rat brains were collected and showed disrupted development fo llowing CO exposure (GD0- PND10 group, 300 ppm CO). Dopamine levels were also significantly elevated in CO-exposed animals (GD0-PND10, 150 and 300 ppm CO).



### March 2009 E-4 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Garofolo et al. (2002) Human infants Rat Rat: PND2-PND5 Human infants who die from SIDS show decreased brainstem muscarinic receptor binding vers us infants dying from other causes. ß-adrenergic m odulation of muscarinic receptors in developing heart was observed. Rodent ß-adrenergic agonists at PND2-PND5 induced muscarinic receptor decrement in adenylyl cyclase. Rat (Wistar) Adult male Model of right ventricular hypertrophy secondary to chronic hypoxia 3 weeks of HH ± CO in final week Or 1 week of CO 50 ppm CO altered the right vent ricular adaptive response to pulmonary hypertension which occurs secondar ily 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 tr eated with HH alone. CO alone had no effect on these measures. Hypobaric hypoxia had noeffect on left ventricular function while CO+ HH led to an increased left ventricular shorteni ng fraction (LVSF). CO alone led to a decrease in LVSF and the mitral E-to-A ratio, indicative of a LV filling impairment. Hypobaric hypoxia decreased the relative RV perfusion and increased the relative LV perfusion. These effects were prevented with concomitant exposure to CO although exposure to CO alone had no effects on myocardial perfusion. Morphologi c and histologic analysi s demonstrated RV hypertrophy in both the HH group and the CO+HH group and fibrotic lesions in the CO+HH group. The authors concluded that the 1-week exposure to 50 ppm CO had a deleterious effect on RV myocardial perfusion adaptat ion to chronic hypoxia and pressure overload. Although the reduced RV pressure overload was beneficial it was counterbalanc ed by impaired RV perfusion and redistribution of perfu sion toward the LV. Gautier et al. (2007)

Gaworski et al. (2004) Rat (Sprague Dawley) 2 h/day, 7 days/week by nose-only inhalation Males: 4 weeks prior to and during mating; and Females: 2 weeks prior to mating, during mating, and through weaning to PND21 Cigarette smoke: 150, 300, or 600 mg/m 3 Total Particulate Matter (TPM) Maternal exposure to high conc entrations 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 decr ease in lung non-heme iron and an increase in liver non-heme iron. Ghio et al. (2008)

Human bronchial epithelial cells (BEAS-2B) 2-24 h 10-100 ppm CO exposure for 24 h led to a dose-dependent decrease in cellular non-heme iron, with the effect at 10 ppm statistically significant and the effect at 50 ppm maximal. This effect wasreversible 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 ex pression 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 mimi cked by with the iron-depleting agent deferoxamine. The authors concluded that CO exposure altered lung iron homeostasis possi bly by initially causing heme release from proteins. Giustino et al. (1999) Rat (Wistar) Male and Pregnant female From GD0-GD20 of pregnancy 75 or 150 ppm This study showed that CO (75 and 150 ppm) exposed male animals at 40 days of age had a significantly decreased time of exploration of novel objects . The 150 ppm CO group showed a lack of habituation after the second exposure to a previously viewed object. Blood COHb concentrations (mean % ± SEM) on GD20 were reported (0 ppm: 1.6 ± 0.1; CO 75 ppm: 7.36 ± 0.2; CO 150 ppm: 16.1 ± 0.9).



### March 2009 E-5 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Giustino et al. (1993) Rat (Wistar) GD0-20 75, or 150 ppm CO exposure in ut ero 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 re spiratory burst. Glabe et al. (1998) Rat (Sprague Dawley) Male, Myocardium P CO = 0 - 107 Torr Increased P CO and increased COMb saturation did not alter high energy phosphate signals (ATP, phosphocreatine, P i ). MV O2 began to decline at 87.6% COMb and is likely not due to cytochrome c oxidase inhibition. Grover et al. (2000) Fetal lamb (mixed breed) 10 min 500 ppm Fetal methoxyhemoglobin (COHb%) ranged from 3.8 ± 0.2 to 8.1 ± 2.0 at 0 and 500 ppm CO, respectively. Inhaled 0- 500 ppm CO administered to near-term fetal lambs did not induce pulmonary vasodilation (m ain pulmonary artery, left pulmonary artery, aorta and left at rium) and the HO inhibitor zinc protoporphyrin IX failed to affect baseline vascular tone. Hara et al. (2002) Rat (Sprague Dawley) Male 40 min 1,000-3,000 ppm CO exposure increa sed extracellular dopamine levels and decreased its major metabolites in a Na + -dependent pathway. CO withdrawal and reoxygenation caused levels to return to control or overshoot which may suggest an increase in oxidative metabolism of CO, mediated by MAO-A. Harada et al. (2004) Pig (Granulosa cells) In this porcine model, HO was able to augment granulosa cell apoptosis allowing for proper follicular maturation. Hendler and Baum (2004) Human End-tidal breath CO measurements in pregnant women with contractions (term and pre-term) were lower than those measurements in non-contracting women. Hofmann and Brittain (1998) Human Partitioning of O 2 and CO in the human embryonic Hb is discussed. Iheagwara et al. (2007) Mouse (C57Bl6) Male 3 h 1,000 ppm CO significantly reduced cytochrome c oxidase activity and V max but not K m in myocardial mitochondr ia. Cytochrome c oxidase protein levels and heme content were significantly decreased. The average COHb level was 61% but no tissue hypoxia was observed in the heart. Imai et al. (2001) HO-1 transgenic micewhich specifically overexpress HO-1 in smooth muscle Transgenic mice had a significant increase in arterial pressure and impaired nitrovasodilatory aortic responses. The mice had enhanced NO • production and impaired sGC activity. The authors speculated that the effect of HO-1 overexpression was to suppress vasodilatory responses to NO • in vascular smooth muscle. Rat (Wistar) Male 200-290 g 60 min 40-60 min 1,000-3,000 ppm 1,000 ppm CO poisoning resulted in free NO • in brains as measured by electron paramagnetic resonance spectroscopy and in a 10-fold increase in nitrotyrosine as measured by immunohistochemical staining. These responses were bl ocked by pretreatment with a NOS inhibitor but not by neutrophil depletion. Brain nitrotyrosine formation wa s blocked by platelet depletion following 40 min but not 60 min exposure to 1,000 ppm CO. Following CO poisoning, myeloperox idase activity, a measure of leukocyte sequestration, was in creased in brain microvessels. This response was blocked by NOS inhibition but not by platelet depletion. Similar effects were noted for xanthine oxidase activation. The authors concluded that perivascular reactions mediated by peroxynitrite are key to CO poisoning effects in brain. Ischiropoulos et al. (1996)

Johnson and Johnson (2003) Rat (Sprague Dawley) Male 250-300 g 0-100 μM CO produced a concentration dependent, endotheliumdependent vasoconstriction in isol ated gracilis muscle arterioles, evident at 1 μM CO. Pre-treatment with a NOS substrate prevented this response while pret reatment with a NOS inhibitor converted this response to a vasodilation. The authors concluded that exogenous CO was acting through NOS inhibition.



### March 2009 E-6 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Johnson et al. (2003) Rat(Dahl/Rapp saltresistant and saltsensitive model) Male High-salt diet increased CO Hb, 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 lo w-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. Acetylchol ine-induced vasodilation was diminished in the high-salt diet group compared with the low-salt diet group. This effect was not seen using t he HO inhibitor. The high-salt diet did not alter endothelium-independent vasodilation. The authors concluded that HO-deriv ed CO caused dysfunction of the NO • system in salt-sensitive rats treated with a high-salt diet. Rat (Sprague Dawley) Male Deoxycorticosterone acetate (DOCA)-salt hypertension model WKY rats Spontaneously hypertensive rats (SHR) Salt-sensitive DOCA rats, but not SHR, had elevated aortic HO1 expression and blood COHb levels. Both had elevated mean arterial BP compared with c ontrols. Acetylcholine-mediated vasodilation of isolated gracilis muscle arterioles was attenuated in DOCA rats but not SHR. Pret reatment with a HO inhibitor restored the response in DOCA rats. The authors concluded that HO-1-derived CO contributes to endothelial dysfunction in DOCA but not SHR. Johnson et al. (2004)

Johnson et al. (2006) Rat (Zucker) Lean and obese Male 100 μM CO The obese rats had incr eased CO expiration and mean arterial pressure which was decreased by pretreatment with a HO inhibitor. No difference was obs erved in HO-1 protein betweenlean and obese rats. Acetylcholine- and flow-mediated vasodilation of isolated gracilis muscle arterioles was attenuated in obese but not lean rats. Pretreat ment with a HO inhibitor restored the response in obese rats. Exogenous CO prevented the restoration of flow-induced dila tion by the HO inhibitor. The authors concluded that HO-der ived CO contributes to endothelial dysfunction in this model of metabolic syndrome. Katoue et al. (2005) Rat (Wistar) HO activity in the aorta is significantly increased during pregnancy but aortic AVP-dependent vasoconstriction appears to be HO/CO independent. Katoue et al. (2006) Rat (Wistar) Pregnancy-induced modulation of calcium mobilization and down-regulation of Rho-kinase expression contributed to attenuated vasopressin-induced c ontraction of the rat aorta. Khan et al. (2006) Nb overexpressing BDF × CD1 mice Cerebral and myocardial infarc ts were decreased in neuroglobin overexpressing mice, decr easing ischemic injury. Kim et al. (2005) Primary rat pulmonary artery smooth muscle cells Inbred LEW rat (Sprague Dawley) 200-250 g 24 h or pretreatment for 1-2 h followed by 24 h posttreatment 250 ppm Exposure of cells in culture to 250 ppm CO for 24-h inhibited serum-stimulated cell proliferation, incr eased expression of p21 Waf1/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 theantiproliferative 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 p21 Waf1/Cip1 , cyclin A and caveolin-1. Experiments in fibroblasts deficient in caveolin-1 confirmed the role of caveolin-1 in the anti-proliferative effects of CO. In a model of neointimal injuries induced by balloon injuries in intact animals, exposure to CO inhibited neointimal formation and increased caveolin-1 expression in the intima and media.



### March 2009 E-7 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Kim et al. (2008) Primary rat hepatocytes Primary mouse hepatocytes Respiration-deficient human Hep3B cells 10-60 min 250 ppm Exposure of cells in culture to 250 CO for 1 h twice a day prevented spontaneous hepatocyte death over 6 days in culture. CO also decreased caspase-3 activity. Cell death was determined to be partly due to apoptosis. CO also increased ROS as measured by dichlorofluorescein fluorescence in rat hepatocytes, mouse hepatocytes and Hep3B cells but not in respiration-deficient Hep3B ce lls indicating that ROS were mitochondrial in origin. An in crease in mitochondrial oxidized glutathione was noted in rat hepatocytes treated with CO for 30 min. Increased Akt phosphoryl ation occurred following 10-30 min CO and was diminished by tr eatment with antioxidants. CO was found to activate NF κ B through a PI3K and oxidantdependent pathway. CO mediated spontaneous cell death was found to be dependent on ROS and Akt phosphorylation. The authors concluded that CO prevents hepatocyte apoptosis through redox mechanisms l eading to cytoprotection. Kinobe et al. (2006) Sheep (gravid and non-gravid sheep and their near term fetuses) There were no significant differences in hypoxic adult and hypoxic fetal sheep when compared to their normoxic controls.

Knuckles et al. (2008) Mous e 4 h Diesel emissions: 350 μ g/m 3 Diesel exhaust enhanced vasoconstriction in veins but not arteries. It was suggested that this is through the uncoupling of eNOS. Kreiser et al. (2004) Human End tidal CO c oncentrations were lower in pregnant women with gestational hypertension and pre- eclampsia than normotensive women. Lash et al. (2003) Human Term placental chroionic villi fromhealthy or preeclamptic placentas Infarcted areas of placenta had decreased HO expression (in pre-eclamptic placenta only).

Li et al. (2008) Mouse (ICR [CD-1]) Pregnant The effect of maternal LPS exposure on fetal liver HO was measured. HO-1 was upregulated in fetal livers post-LPS exposure and this HO-1 upregulat ion was attenuated with the spin trap agent PBN, pointing to a ROS dependent HO-1 upregualtion post maternal LPS treatment. Rat (Sprague Dawley) Female 220-240g 1 week 1 week 100 ppm and 1 week 200 pm 100 ppm 100-200 ppm Endothelin-1 expression increased by 53% and 54% in the left and right ventricle respectively during the 2 week exposure and by 43% and 12% in the left and right ventricle respectively during the 1 week exposure. Right ventricular to body weight ratio was increased by 18% and 16% in the 2 week and 1 week exposure groups respectively. COHb levels were 23% and 12% in the 2 week and 1 week exposure groups respectively. Loennechen et al. (1999)

Longo et al. (1999) Rat (Sprague Dawley) Human The addition of exogenous CO to isolated human and rat uterine tissue failed to induce relaxation of uterine tissue. Isolated rat aortic rings and tail artery rings from pregnant dams can be relaxed by submersion in exogenous CO solutions. Rat (Sprague-Dawley) Pregnant rats exposed to CO GD5-GD20 (Group A) or GD5-GD20 plus PND5-PND20 (Group B); Group C (control air exposure). 10 - 18 h/day 25 ppm CO exposure induced damage to the spiral ganglia neurons and inner hair cells with oxidativ e stress seen in cochlear blood vessels. At PND20 groups A and B show vacuolization of afferent terminals at the base of the cochlea. At PND3, group A shows decreased synapsin-1 staining of the efferent nerve terminals. At PND20, groups A and B show decreased neurofilament-IR (staining) in type I spiral ganglia neurons and afferent nerve fibers. At PND12 and PND20, group B shows increased HO-1 and SOD-1-IR in blood vessels of the stria vasularis; group A is similar to controls. From PND3-PND20, there is increased iNOS and increased nitrot yrosine-IR in blood vessels of the cochlea. Lopez et al. (2008)

Lopez et al. (2003) Rat (Sprague-Dawley) PND6 to weaning (PND19-PND20) 12 or 25 ppm In the cochlea, atrophy or vacuolization of the nerve cells that innervate the inner (not outer) hai r cells was seen. Fibers of the 8th cranial nerve (int ernal auditory canal of the ARCO animals, 25 ppm) had distorted myelination and vacuolization of the axoplasm. In the organ of corti and spiral ganglion neurons, cytochrome c oxidase and NADH-TR were significantly decreased in 25 ppm exposure group versus control. Expression of the calcium-mediated myosin ATPase in the organ of corti and spiral gangli on neurons was significantly decreased in the 25 ppm CO exposure group versus controls.



### March 2009 E-8 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Mouse (ApoE -/- ) Male High fat diet 6 h/day, 7days/week, 7 weeks 8, 40, or 60 μ g/m 3 PM whole gasoline exhaust; or filtered exhaust with gases matching the 60 μ g/m 3 concentration. CO concentrations were 9, 50, and 80 ppm corresponding to the 8, 40, and 60 μ g/m 3 PM whole exhaust exposures Both whole and filtered exhaust increased aortic mRNA expression of matrix metallopr oteinase-3 (MMP-3), MMP-7, and MMP-9, tissue inhibitor of metalloproteinases-2, endothelin-1 and HO-1 at 60 μ g/m 3 . Aortas also showed increased immunostaining for MMP-9 and nitrotyrosine in 60 μ g/m 3 PM whole exhaust and PM-filtered exhaust exposed groups. Aortic TBARS, a measure of lipid perox idation, was also increased in all treatment groups. Lund et al. (2007)

Mouse (ApoE -/- ) Male High fat diet 6 h/day, 1 or 7 days Gasoline engine exhaust containing 60 μ g/m 3 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 ET A receptor. Treatment with BQ-123 also reduced aortic MMP-2/9 activity in aortas following gasoline ex haust exposure. The authors concluded that ET A receptor pathway is a key mediator of gasoline engine exhaust effects in the vasculature. Lund et al. (2009)

Lyall and Myatt (2002) Human Women with pre-eclampsia, produced term placenta with significant decreases in HO-2 versus women with healthy pregnancies. Lyall et al. (2000) Human (placentas from 8-19 weeks pregnancy and term placentas) The use of a HO inhibitor, ZnPP, increased placental perfusion pressure. HO-1 and HO-2 were expressed in the placenta and placental bed and vary in expression over the course of pregnancy. HO may thus be involved in trophoblast invasion, placental function and perfusion pressure. Mactutus and Fechter (1984) Rat (Long Evans) Continous exposure to CO over gestation 150 ppm Acquisition as measured in a two-way conditioned avoidance (flashing light warnings followed by mild footshock) test failed to improve with age of in utero CO-exposed (150 ppm, dam COHb 15%) rats (male and female offspring) in contrast to air-exposed controls who improved with age/maturation, indicating a failure in the associative process of learning. The authors also found impairments in reacquisition perform ance, an index of retention, in PND31 rats that had received continuous in utero CO exposure. Prenatal CO exposur e induced learning and memory deficits in male and female offspring. McGregor and Westcott (1998) Guinea pig GD23-GD25 until term (approximately 68 days) 10 h/day 200 ppm Aberrant respiratory responses (to asphyxia and CO 2 ) of offspring with prenatal CO ex posure. The authors hypothesized this may be related to changes in the brainstem. COHb in maternal (8.53 ± 0.6% versus 0.25 ± 0.1%) and fetal blood (13.0 ± 0.4% versus 1.6 ± 0.1%) from CO-treated versus controls. McLaughlin et al. (2001) Human Various pathologies ofpregnancy including IUGR and preeclampsia are associated with significant decreases in placental HO activity. The endogenous generation of CO in the placenta has been demonstrated in chroionic villi of term placenta. McLaughlin et al. (2000) Human placenta Placental regional localization of HO was explored. The chorionic plate, chor ionic villi, basal pl ate and choorio-decidua had significantly higher HO ac tivity than the amnion. McLaughlin et al. (2003) Human placenta HO expression in va rious regions of term placentas was explored. Microsomal HO-2 prot ein 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 memb ranes from pre-eclamptic pregnancies versus normotensive pregnancies. McLean et al. (2000) Human placenta HO activi ty was highest in the placenta near term.



### March 2009 E-9 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Rat (Dark Agouti) Male Model of right ventricle hypertrophy secondary to chronic hypoxia (HH 10 weeks) 10 weeks 50 ppm alone or concomitant with HH Hb and hematocrit levels were increased above controls in HH rats, CO rats and HH+CO rats, with the increase due to the combined treatment significantly higher than the increase due to HH. COHb levels were 1.1% in controls, 1.3% in HH rats, 4.7% in CO rats and 9.1% in HH plus CO rats. HH treatment significantly increased right vent ricular (RV) heart weight above controls while CO treatment had no effect on any postmortem heart weights. Combined treatm ent with HH+CO resulted in a significant increase in left ventri cular plus septum (LV+S) weightand 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 ventri cular end diastolic pressure (LVESP), left ventricular maxi mal first derived pressure (+dP/ dt LV ), 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 ma ximal and minimal first derived pressure (+dP/ dt RV, , - dP/ dt RV ) and right ventricular work (RVW). CO significantly enhanced the effects of HH on RVEDP and significantly diminished the effects of HH on dP/ dt RV and RVW. The authors concluded that CO intensified the HH-induce RV hypertrophy, increased LV weight and induced severe hematological responses that could hamper adaptation. Melin et al. (2002)

Melin et al. (2005) Rat (Dark Agouti) Male and female Model of right ventricle hypertrophy secondary to chronic hypoxia (HH, 10 weeks) Half of the animals were exercise- trained to induce LV hypertrophy 10 weeks 50 ppm alone or concomitant with HH In untrained animals, combined treatment with HH+CO led to increased LV+S and RV weights compared with HH treatment alone. HH+CO led to several changes in measured echocardiographic par ameters including increased anterior and posterior wall thickness in diastole (AWTd, PWTd) and to increased fraction of shortening. These effects were not seen with HH alone. In addition RVEDP was enhanced in HH+CO compared with HH alone. HRV components were altered by HH+CO but not by CO alone.

Mereu et al (2000) Rat (Wistar) GD0-GD20 continuous CO exposure 150 ppm In utero exposure to CO disrupted hippocampal LTP with concomitant HO-2 and nNOS reductions. The authors surmised that these changes may be related to the memory deficits seen in animals exposed to CO in utero. Middendorff et al. (2000) Human Adult males aged 65- 75. Testicular tissue from orchiectomy Zn protoporphryin (ZnPP) and Hb both significantly reduced seminiferous tubular cGMP generation, suggesting a role for CO in human testicular tissue.

Naik and Walker (2003) Rat (Sprague-Dawley) Male 210 μL of CO/100 mL of physiological saline solution Endogenous CO mediated vasorelaxation involved cGMPindependent activation of vascular smooth muscle largeconductance Ca 2+ -activated K + channels. However exogenous CO vasodilation was cGMP dependent. Ndisang et al. (2004) Review of CO and hypertension. CO is a vasorelaxant due to activation of the big conductance calcium-activated potassium channels and soluble guanylate cyclase/cGMP pathway. Developmental stage and tissue type will determine which of these pathways plays more of a role in vasorelaxation. Neggers and Singh (2006) Mouse (CD-1) GD7-GD18 500 ppm Developmental toxiciti y of CO was attenuated by protein supplementation, i.e., protei n supplemented animals (27%) showed a significantly lower inci dence of fetal mortality versus 8% and 16% protein groups. Further , dietary restriction of both protein and zinc with CO-ex posure to CO during gestation increased the incidence of pup mortality and malformations including gastroschisis. Zinc supplementation to protein deficient diet in CO-exposed mice decreased fetal mortality and malformation.



### March 2009 E-10 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Newby et al. (2005) Human (placental cells in culture) Term human placental cells were grown in cell culture under basal and hypoxic conditions to explore changes in HO expression. HO-1 was unchang ed in cytotrophoblasts under hypoxia, but HO-1 was significantly decreased in hypoxic syncytiotrophoblasts. HO-2 was unchanged in either cell type with hypoxia. These cell culture data can give insight into what cell types might be responsive to hypoxia through the HO/CO system in the human placenta. Odrcich et al. (1998) Guinea pig Immunohistochem ical localization of HO in guinea pig placentae showed that HO-1 staining was highest near term (PND62) and lesser at term or earlier in pregnancy. HO-1 was localized in the advential layer of fetal blood vessels. Ozawa et al. (2002) Rat (Wistar) Adult male The role of HO-1 in spermatogenesis was explored. CdCl 2 induced testicular HO-1 and r educed HO-2 protein in rats. Pretreatment with ZnPPIX attenuated CdCl 2 -dependent apoptosis. Leydig cells use HO -1 derived CO to tirgger apoptosis of pre-meiotic germ cells and modulate spermatogenesis under CdCl 2 dependent oxidative stress. Rat (Sprague Dawley) Male 262 ± 30 g Isolated hearts 30 min Buffer saturated with 0.01 and 0.05% CO The ventricular glutathione content, both reduced and oxidized, decreased by 76% and 84% 90 min post-exposure to 0.01% and 0.05% CO, respectively. Treatment with antioxidants partially blocked the decreas es in glutathione. Increased creatine kinase activity was obs erved in heart perfusate during and after treatment. Patel et al. (2003)

Penney et al. (1983) Rat (strain not reported) GD17 - GD22 157, 166 or 200 ppm In utero CO exposure induced decreased fetal body weight, decreased placental weight, increas ed wet heart weight at birth, and altered cardiac enzymes at birth. Piantadosi (2002) Reviews the biochemical activities of CO, including various heme protein binding. The review stresses the importance of the CO/O 2 ratio in determining the ph ysiological effects of CO. Piantadosi (2008) Reviews the ph ysiologic responses to exogenous and endogenous CO and biochemical effects including the binding to heme proteins, the gener ation of reactive O 2 species and activation related signaling pathways. Piantadosi et al. (2006) Rat (Sprague Dawley) Adult male 1, 3, or 7 days 50 ppm or HH COHb produced CO Hb 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-1 α and mitochondrial antio xidant 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 nuc leotides to protect mitochondria from MPT. This effect was restored in the presence of a strong reductant. The authors conclude that CO causes mitochondrial pore stress independently of its hypoxic effects Prigge and Hochrainer (1977) Rat (Wistar, SPF) GD0-GD20 60, 100, 250, 500 ppm Fetuses were co llected by C-section after 21-days exposure. Significant increases in fetal heart weight were seen in fetusesexposed to CO in all dose groups. Fetal body weight was significantly decreased (NOAEL 125 ppm CO). Raub and Benignus (2002) Reviews the physiology of CO and the effects on the nervous system. It is estimated that COHb would have to rise to 15-20% before a 10% reduction in any behavioral or visual measurement could be observed. Richardson et al. (2002) Human Male 20% COHb 20% COHb did not influence O 2 Mb binding indicated by unaltered deoxy-myoblobin signal. Resting skeletal muscle metabolic rate was unaffected by 20% COHb. VO 2 max was decreased. No decrement in intracellular PO 2 was found. 20% COHb altered exercising bi oenergetics, pH, PCr, and ATP levels. Ryter et al. (2006) Reviews the basic science of exogenous and endogenous CO including HO-1 regulation. It also reviews some therapeutic applications for CO.



### March 2009 E-11 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Sartiani et al. (2004) Rat (Wistar) In utero inhalation exposure 150 ppm At 4 weeks of age, the action potential duration APD of isolated cardiac myocytes from CO-exposed animals failed to shorten or mature as did the APD of control animals. Further, the two ion conduction channels I to (transient outward current, K + -mediated) and I Ca ,L (L-type Ca 2+ current), which largely control the rat APD, were significantly different from control animals after CO exposure at 4 weeks of age. All of these CO-dependent changes were no longer different from controls at 8 weeks of age, showing a delayed maturation. Mouse (CF-1) Rabbit (New Zealand) 7 or 24-h/day GD6-GD15 (Mice) GD6-GD18 (Rabbits) 250 ppm In mice there was a significant increase in number of skeletal abnormalities in CO-exposed mice. Decreased birth weight in mice exposed to 24 h/day CO versus control. Increased birth weight in mice exposed to 7 h/day CO versus controls. No similar effects were seen in rabbits. Schwetz et al. (1979)

Singh et al. (1992) Mouse (CD-1) GD8-GD18 65, 125, or 250 ppm CO exposure conc omitant with a low protein diet exacerbated the percent of skeletal malformations in offspring. The percent of dead, resorbed, or grossly malf ormed fetuses was directly related to CO concentration and inversely related to maternal dietary protein levels. CO and maternal dietary protein restriction have a synergistic ef fect on offspring survival and an additive effect on malformations. Singh (2006) Mouse (CD-1) 6 h/day during the first 2 weeks of pregnancy 65 or 125 ppm Modulating dam protein intake during in utero CO exposure altered pup mortality.

Sitdikova et al. ( 2007) Frog neuro-muscular junctions 20 min 96 μM CO induced acetylcholine release, without effects on the presynaptic action potential or functional properties of post-synaptic receptors in frog neuro-muscular preparations. Song et al. (2002) Human Primary human aortic smooth muscle cells 0-48 h 10-250 ppm CO inhibi ted 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 p21 Cip1 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 ex erted through the inhibition of ERK MAPK activation since 15 min exposure to 250 ppm CO blocked serum-mediated ERK phosphorylation. Sorhaug et al. (2006) Rat (Wistar) Female 169 ± 4.5 g 20 h/day, x 5 days/week, x 72 weeks 200 ppm COHb was 14.7% in CO-exposed animals and 0.3% in controls. Total Hb was also increased in following CO exposure. CO caused no changes in lung morphology or pulmonary hypertenstion. No atherosclerotic lesions were found in aorta or femoral artery. Weight increases of 20% and 14% were observed in the right ventricle and left ventricle plus septum, respectively, indicative of v entricular hypertrophy following chronic CO exposure. Stockard-Sullivan et al. (2003) Rat (Sprague-Dawley) 22 h/day, PND6- PND22 12, 25, 50, or 100 ppm Using functional OAE testing and ABR showed that with perinatal CO exposure (50 and 100 ppm CO), there were significant decrements in OAE in CO-exposed animals. ABR showed no functional deficits with CO exposure. Using another otoacoustic test revealed significant attenuation of the AP of the 8th cranial nerve with CO ex posure (12, 25, and 50 ppm CO) versus controls at PND22.



### March 2009 E-12 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Suliman et al. (2007) Mouse (C57BL/6 wild-type and eNOS deficient) Male Rat (embryonic cardiomyocytes H9c2 cells) 1 h 50-1,250 ppm Or HH Or 100 mM dichloromethane 1-h exposure of mice to 1,250 ppm CO increased cardiac mitochondrial content of all 5 respiratory complexes 24 h later. The volume density of interfibrillar mitochondria was increased by 30% after 24 h demonstrating that CO caused cardiac mitochondrial biogenesis. The CO concentration in heart increased from 9 pmol/mg to 50-150 pmol/mg in mice exposed to 50-1,250 ppm CO for 1 h. These levels declined to baseline by 6 h. Peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1 α ) expression was increased 6 h following exposure to 50-1,250 ppm CO. Expression of DNA polymerase and mitochondrial transcr iption factor A (TFAM) was increased 6 and 24 h after exposure, while mitochondrial DNA was increased 2-3 fold 24 h after exposure. CO activated gene expression of these proteins in volved in cardiac mitochondrial biogenesis beginning at 2 h post exposure for PGC-1 α , 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-m ediated 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 mitochondria l DNA. Inhibition of GC or PI3K/Akt, but not p38, blocked the responses to CO. A role for mitochondrial H 2 O 2 in Akt regulation was demonstrated. Mitochondrial H 2 O 2 and the PI3K/Akt pathway were important mediators of TFAM expression. The authors concluded that CO exposure increased mitochondrial ROS which promoted mitochondrial biogenesis in the heart. Sun et al. (2001) Mouse Neuronal cultures prepared from the cerebral hemispheres of 16-day Charles River CD1 mouse embryos Nb expression was increased by neuronal hypoxia in vitro and focal cerebral ischemia in vivo . Inhibiting Nb reduced neuronal survival after hypoxia whereas Nb overexpression enhanced neuronal survival.

Tattoli et al. (1999) Rat (Wistar) Male and Pregnant female PND1-PND10 75 and 150 ppm Cognitive function was assessed in rats after postnatal CO exposure at 3 and 18 months of age. Postnatal CO exposure did not affect the acquisition and reacquisition of an active avoidance task. This is different from previous findings by the same laboratory indicating that in utero exposure to CO (75 and 150 ppm) induced long-lasting learning and memory deficits. Human Myometrium tissue obtained from gravid [pre-term (25-34 weeks gestation), term not in labor or term in labor] and non-gravid women cGMP was monitored in various myometrial tissues. cGMP was significantly higher than t hat from nonpregnant tissue and decreased at term, especially in tissue from laboring women. Telfer et al. (2001)

Teran et al. (2005) Rat (Dahl/Rapp salt-sensitive rats) Male 100 μM A high salt diet for 1-4 weeks resulted in increased aortic HO-1 protein expression, an increase in mean arterial pressure and time-dependent inhibition of flow - and acetylcholine-mediated vasodilation in isolated gracilis muscle arterioles. A smaller degree of inhibition of acetylcholine-mediated vasodilation was observed with a low salt diet for 1-4 weeks. Pretreatment with a HO inhibitor restored these responses but this effect was reversed in the presence of exogenous CO. Mean arterial pressure was decreased in intact animals fed a high salt diet for 4 weeks and then treated with a HO inhibitor. The authors concluded that the HO-derived CO contributed to the development of hypertension and the impairment of endothelium-dependent vasodilator responses in this model.



### March 2009 E-13 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

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 B 2 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 pl atelet-rich plasma was incubated with CO, PMN adherence was inhibited. The authors concluded that PMN B 2 integrin activity was inhibited by CO-dependent release of NO • from the platelets into the blood. Thom et al. (1994)

Thom and Ischiropoulos (1997) Rat (Wistar) Male 200-290 g Platelet-rich plasma from rats was used as the source of platelets Bovine pulmonary artery endothelial cells 1 h 30 min or 2 h 1 h 20-1,000 ppm 10-20 ppm 10-100 ppm Platelets isolated from rats ex posed 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 l ed to a diminished response to 100 ppm CO. There appears to be a discrepancy in the results depending on how NO • was measured (electrode versus Greiss reaction). Endothelial cells released NO • in response to 20-100 ppm CO. NOS inhibition blocked the response to 100 ppm CO. CO was found not to affect arginine transport or NOS activity in endothelial cells. Exposure to 40-100 ppm CO resulted in the release of short-lived oxidants. This response was blocked by NOS inhibition. Lysates from cells exposed to 50 and 100 ppm CO had increased nitrotyrosine content. This response was blocked by NOS inhibition. Cellu lar reduced sulfhydryls were not decreased by 100 ppm CO. Dihy drorhodamine 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 51 chromium. Cytotoxicity was evident 4 h fo llowing a 2-h incubation with 100 ppm CO, but not immediately after exposure. This response was not blocked by NOS inhibiti on, 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 • fromplatelets and endothelial cells in vitro. Platelets from rats that inhaled 20 ppm CO also released NO • in vitro. The authors suggested that CO-mediated NO • release from platelets and endothelial cells resulted from disrupted intracellular scavenging for NO • . They also suggested that peroxynitrite may have been generated in response to CO.



### March 2009 E-14 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Thom et al. (1997) Bovine pulmonary artery endothelial cells 30 min-4 h 10-100 ppm (11-110 nM) 1-h exposure to 111-110 nM CO led to a dose-dependent increase in NO • release, as measured by nitrite+nitrate. Significance was achieved at 22 nM (corresponding to an interstitial partial pressure of 20 ppm and a blood COHb level of 7%). NOS inhibition blocked the respons e to 110 nM CO. A dosedependent increase in cellular nitr otyrosine was also observed following a 2-h exposure to CO, wi th significance achieved at 55 nM CO. NOS inhibition blacked the response to 110 nM. CO exposure failed to decrease the concentration of reducedsulfhydryls, but did result in the extracellular release of a shortlived oxidant species which was blocked by NOS inhibition. Dihydrorhodamine oxidation, a measure of peroxynitrite formation, occurred in response to 110 nM CO, an effect which blocked by NOS inhibition. Cytoto xicity of CO was evaluated by the release of 51 chromium. 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 protecti ve effects under conditions of continuous CO exposure of 4 h. Exposure to 110 nM CO for 2 h led to the loss of membrane int egrity, 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 O 2 consumption, production of intracellular H 2 O 2 or cellular redox activity. Exposure to 110 nM did not alter argi nine transport or NOS activity. NO • release from cells which had been pre-treated with a NOS inhibitor and then ex posed briefly to 5% CO was measured using a NO-selective electrode suggesting that CO competed with intracellular binding sites of NO •

. The authors concluded that endothelial cells release NO • and NO • -derived oxidants in response to CO. A delayed cell death occurred following exposures to 22 nM and higher concentrations of CO. Thom, et al. (1999b) Rat (Wistar) Male 200-290 g Some rats fed a high cholesterol diet 1 h 50-1,000 ppm Nitrotyrosine immunoreactivity was found in aortic intima in rats exposed to CO for 1 h but not in controls. Nitrotyrosine content was quantitated and found to be increased in a dose-dependent manner following 1-h exposure to 50-1,000 ppm CO. The effect was significant at 50 ppm but the COHb content measured immediately after exposure was not different than controls. Platelet and neutrophil depletion did not alter nitrotyrosine content following CO exposure. Leukocyte adherence to the aorta occurred 18 h, but not immedi ately, 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 ex posure to 100 ppm CO but was not seen 18 h later. This effect was blocked by NOS inhibition. Rats fed a high cholesterol diet and exposed to 100 ppm CO for 1 h had increased aortic nitrotyrosine content which was not different than that in CO-exposed rats fed the standard diet. However, rats on the high cholesterol diet had a 6-fold increase in LDL oxidation immediately after 1-h exposure to 100 ppm CO. This effect was not blocked by NOS inhibition. The authors concluded that CO can alter vascular status by several mechanisms linked to NO • -derived oxidants.



### March 2009 E-15 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Thom et al. (1999a) Rat (Wistar) Male 200-290 g 1 h 50-1,000 ppm Leakage of albumin into lung parenchyma occurred 18 h, but not at earlier time points, after rats were exposed to 100 ppm CO for 1 h. This response was also observed using 50 and 1,000 ppm but not 20 ppm CO. Leakage resolved by 48 h. Furthermore no leakage occurred when rats which were exposed to 100 ppm CO were pretreated with a NOS inhibitor. COHb levels were 0.9% in controls and 4.8%, 10.6% and 53.7% following 1-h exposure to 50, 100 and 1,000 ppm CO, respectively. Elevated free NO • , determined by EPR, was observed in lungs of rats exposed to 100 ppm CO for 1 h. This effect was blocked when rats were pretreated with a NOS inhibitor. Lung H 2 O 2 was elevated by exposure to 100 ppm CO for 1 h and this effect was blocked when rats were pretreated with a NOS inhibitor. Elevat ed 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 inhibito r. No leukocyte sequestration was observed in lungs18 h following exposure to 100 ppm CO. CO-induced lung leak was not affected by neutrophil depletion. The authors concluded that CO causes lung vascular injury which is dependent on NO •

. Thom et al. (2000) Bovine pulmonary artery endothelial cells 40 min-2 h 11-110 nM (10-100 ppm) Increased uptake of ethidium homodimer-1, a measure of decreased membrane integrity and cell death, was observed in endothelial cells 18 h after exposure to 110 nM for 60-120 min. Exposures of 20-40 nM were ineffe ctive in this regard. Ethidium uptake was also increased by 2-h exposure to 88 nM CO. Preincubation for 2 h with an i nhibitor of eNOS, an antioxidant, and an inhibitor of peroxynitri te reactions blocked the CO-mediated cell death. Morphological changes in cells were observed 2 h following a 2-h exposur e to 110 nM CO. Cell death induced by 110 nM CO was also bl ocked by inhibition of protein synthesis and inhibition of ca spase-1 but of caspase-3. Caspase-1 activity was increas ed following 2-h exposure to 110 nM CO; this effect was blo cked by inhibiting eNOS. Preexposure of cells to 11 nM CO for 40 min followed by a 3-h incubation period resulted in an increased level of MnSOD and protection against cell death 18 h following a 2-h exposure to 110 nM CO. The authors concluded that exposure to 11 nM CO led to an adaptive response which protected cells from injury and apoptosis resulting from NO • -derived oxidants. Thom et al. (2001a) Rat Until lost consciousness 1,000-3,000 ppm Neutrophils sequestration wa s observed in the brain vessels of rats exposed to high dose CO. CO also led to increased nitrotyrosine formation in the brain vessels. These events were blocked by pretreatment with a peroxynitrite scavenger or a PAF receptor antagonist. Thom et al. (2006) Human Rat (Wistar), male Mouse (C57B6J, MPO-deficient) Blood samples and brain tissue 1 h Humans: acute CO poisoning Rats and mice: 1,000-3,000 ppm In humans, COHb was 20-30.5%. Increased cell surface expression of CD18 and PAC1 was observed in neutrophils from people with CO pois oning. Increased surface-bound myeloperoxidase (MPO, indicati ve 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 block ed these responses. Brains from poisoned rats had significant elevations in MPO which could reflect either an increase number of neutrophils or an increase in neutrophil degranulation. Perivascular MPO and nitrotyrosine were CO-localized in brain. CO poisoning also resulted in altered brain myelin basic protein. Similar changes were observed in blood of CO-poisoned mice. MPO deficiency blocked the CO -mediated alteration in brain myelin basic protein. The authors concluded that exposure to CO triggers intravascular interactions between platelets and neutrophils that lead to neutrophil degranulation in experimental animals and people with CO poisoning.



### March 2009 E-16 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Thorup et al. (1999) Rat (Sprague Dawley) Males 200-250 g 0.01-10 μ M Perfusion of Isolated rat r enal resistance arteries with CO-containing buffer (0.001-10 μ M) led to the biphasic release of NO • , peaking at 100 nM and declining to undetectable responses at 10 μ M. 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 concentrati ons and was inhibited by pretreatment with a NOS inhibi tor. Perfusion with 100 nM CO blocked carbachol-dependent NO • release from vessels. Rats were treated with a HO-1 inducer and renal resistance arteries were isolated 12 h later. Carbachol-induced NO • release was smaller in the HO-1 induced rats compared with controls suggesting that endogenous CO has a similar effect as 100 nM exogenous CO. This effect was reversed in the presence of excess arginine. Vasodilation was measured in blood- perfused afferent arterioles perfused with CO in solution. A biphasic vasodilatory response was observed as well as a blunted muscarinic vasorelaxation. CO (0.1-10 μ M) suppressed the release of NO • from purified recombinant eNOS in solution. The authors concluded that low levels of CO may release NO • and elicit vasorelaxation and modulate basal vascular tone while higher levels of CO may inhibit eNOS and NO • generation. Tolcos et al. (2000b) Guinea pig 10 h/day over the last 60% of gestation 200 ppm Fetal and maternal COHb were 13% and 8.5% respectively. Neurotransmitter systems were affected after CO exposure. The catecholaminergic system of the brainstem displayed significant decreases in immunoreactivity fo r 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 signi ficant increases in ChAT immunoreactivity of the medulla and no changes in muscarinic acetylcholine receptor. Tolcos et al. (2000a) Guinea pig 10 h/day for the last 60% of gestation 200 ppm Brains were collected at 1 and 8 weeks of age. These data showed that CO exposure in uter o sensitized the brain to hyperthermia at PND4 leading to generat ion of necrotic lesions in the brain and changes in neurotransmitter levels. Tschugguel et al. (2001) Human HUVEC CO was generated by primary endothelial cells from humanumbilical veins and uterine arteries after exogenous 17-ß estradiol administration. Vallone et al. (2004) Mouse protei n The authors present the X-ray structure of CO-bound ferrous murine Nb. When CO binds, the heme group slides deeper into the protein crevice. Vreman et al. (2000) Human umbilical cord (artery and vein) Rat Aorta, vena cavae, liver and heart HO activity was quantified in human umbilical cord and in the rat vasculature (aorta and vena cav ae). Human umbilical artery and vein HO activity were equal. The rat aorta and vena cavae produced equal amounts of HO activi ty (wet weight/g tissue) but generated 3x greater HO than the heart and 0.2x of the liver. HO activity in rat vasculature was 3x that of the human cord tissues. Use of the HO inhibitor CrMP effectively blocked HO activity in the rat liver and heart but was less effective at blocking HO activity in the human umbilical cord or the rat vasulature (only 50% effective). The activity of HO in the umbilical vessels may provide a role for CO in control of vasculature tone during pregnancy.



### March 2009 E-17 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Vreman et al. (2005) Mouse (BALB/c) 30 min 500 ppm OR Heme arginate 30 μ mol/kg body weight i.v. Following CO exposure, COHb levels were 28%. Tissue concentrations of CO were as fo llows with control levels in parenthesis. Blood: 2648 ± 400 (45) pmol/mg Heart: 100 ±18 (6) pmol/mg Muscle: 14 ± 1 (10) pmol/mg Brain: 18 ± 4 (2) pmol/mg Kidney: 120 ± 12 (7) pmol/mg Spleen: 229 ± 55 (6) pmol/mg Liver: 115 ± 31 (5) pmol/mg Lung: 250 ± 2 (3) pmol/mg Intestine: 9 ± 7 (4) pmol/mg Testes: 6 ± 3 (2) pmol/mg CO concentration relative to 100% blood: Lung: 9.4%, Spleen: 8.6% Kidney: 4.5%, Liver: 4.3%, Heart: 3.8%, Brain: 0.7%, Muscle: 0.5%, Intestine: 0.3%, Testes: 0.2% Injection of heme arginate resulted in a 3-fold increase in COexcretion reaching a maximaum at 60 min. Animals were sacrificed at 90 min. COHb levels were 0.9%. Tissue concentrations of CO were as follows with control levels in parenthesis. Blood: 88 ±10 (45) pmol/mg Heart: 14 ± 3 (6) pmol/mg Muscle: 7 ± 1 (10) pmol/mg Brain: 2 ± 0 (2) pmol/mg Kidney: 7 ± 2 (7) pmol/mg Spleen: 11 ± 1 (6) pmol/mg Liver: 8 ± 3 (5) pmol/mg Lung: 8 ± 3 (3) pmol/mg Intestine: 3 ± 1 (4) pmol/mg Testes: 2 ± 0 (2) pmol/mg CO concentration relative to 100% blood: Heart: 16% Spleen: 13% Lung: 9% Liver: 9% Kidney: 8% Muscle: 8% Intestine: 3% Brain: 2% Testes: 2% Weaver et al.(2007) Human Acute CO poisoning Mean COHb in humans with acute CO poisoning was 35%. Hyperbaric O 2 reduces cognitive sequelae in a randomized clinical trial of CO-poisoned patients. Risk factors for cognitive sequelae without hyperbaric O 2 included older age and longer CO exposures. Patients with loss of consciousness or high initial COHb levels should also be treated with hyperbaric O 2. Webber et al. (2003) Rat Strain not stated PND8-PND22 12.5, 25, or 50 ppm Immunostating of c-Fos, a marker of neuronal activation in the nervous system was followed. C-Fos immunoreactivity in the central IC was significantly decreased in the CO-exposed animals at both PND27 and PND75-PND77 over all dose groups of CO; immnunostaining of other subregions of the IC were not affected by CO. These studies show exposure to CO during development can lead to permanent changes in the auditory system of rats that persist into adulthood. Webber et al. (2005) Rat Strain not stated PND9-PND24 25 or 100 ppm Neurofilament lo ss from the spiral gangilian neurons and somas after ARCO treatment was rescued (no detectable neurofilament loss) with low iron+CO (ARIDCO); ARID (Iow iron) treatment induced no change in neurofilaments. CuZn superoxide dismutase (SOD1) was signifi cantly 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-fospositive cell numbers of the central IC, but c-fos levels were unchanged after low iron diet concomitant with CO exposure (ARIDCO).



### March 2009 E-18 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Wellenius et al. (2004) Rat (Sprague Dawley) 250 g Diazepam-sedated Model of acute MI induced by thermocoagulation 1 h, 12-18 h after surgery 35 ppm CO exposure decreased vent ricular premature beat frequency by 60.4% during the exposure per iod compared to controls. 1-h exposure to CAPs (318 μ g/m 3 ) decreased ventricular premature beat frequency in specific subgroups. Neither CAPs nor CO had an effect on heart rate. There were no significant interactions between their effects when rats were exposed to both CO and CAPs.

Rat (Sprague Dawley) 250 g Diazepam sedated Model of acute MI induced by thermocoagulation 1 h, 12-18 h after surgery 35 ppm Exposure to CO failed to in crease the probability of observing supraventricular ect opic beats (SVEB). Expos ure to CAPs (646 μ g/m 3 ) 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 signific ant decrease in number of SVEB during the exposure period was observed wi th either CO or CAPs exposure compared with controls. Wellenius et al. (2006b)

Yoshiki et al. (2001) Human HO localiz ation in human endometrium and its changes in expression over the menstral cycle were explored in this study. HO-1 was constitutively expressed throughout the menstral cycle and HO-2 was greater in the secretory than the proliferative phase of the menstral cycle. HO-1 was localized to the epithelial cells and macrophages. HO-2 was found in endothelial cells and smooth muscle cells of endometrial blood vessels. Zamudio et al. (1995) Human Women living at high altitude had an increased risk of adverse pregnancy outcomes versus women living at lower altitudes. Zenclussen et al. (2006) Mouse (CBA/J x DBA/2J) To evaluate the role of HO-1 in spontaneous abortion, a mouse model that spontaneously undergoes abortion (CBA/J x DBA/2J mice) was used with and without HO adenovirus treatment to see if pregnancy outcome could be modulated by changing HO concentration. Pregnancy outcome was significantly better (abortion rate significantly decr eased) in mice overexpressing HO due to adenovirus transfer. Zhang et al. (2005) Rat (pulmonary artery endothelial cells) 8-28 h 15 ppm Exposure to 15 ppm CO during anoxia resulted in decreased phosphorylation of STAT1 and increased phosphorylation of STAT3 at 8-24 h. Similar responses were observed when 24 h anoxia was followed by a period of reoxygenation (0.5-4 h). DNA binding of STAT1 was decreased while that of STAT3 was enhanced by CO treatment during anoxia/reoxygenation. Exposure to 15 ppm during 8- 24 h anoxia or 24 h anoxia followed by 0.5-4 h reoxygenation resulted in increased phosphorylation of Akt and p38 MAPK. Inhibitor studies demonstrated that activation of the PI3K pathway by CO was upstream of p38 MAPK activation during anoxia/reoxygenation. Similarly, the PI3K and p38 MAPK pathways were found to be upstream of STAT modulation. The anti-apoptotic effects of 15 ppm CO during anoxia-reo xygenation 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 modul ation of PI3K/Akt/p38 MAPK and STAT1 and STAT3. Zhang et al. (2007) Mouse A single dose of LPS administered to pregnant mice induced upregulation of HO-1 but not HO-2 in the mouse placenta 12-48 h post-LPS treatment. Pre-treatment of mice with the spin trap agent PBN or the TNF α inhibitor pentoxif ylline prevented the LPS-dependent HO-1 upregulation. Thus ROS may mediate the LPS-dependent upregulation of HO-1. Zhao et al. (2008) Mouse (FVB) With pregnancy, there was an increased blood volume without a concurrent increase in systemic BP; this was accomplished by a decrease in total vascular resistance, to which CO contributed as determined by usi ng HO inhibitors. Zhuo et al. (1993) Rodent Hippocampal LTP of brain sections is significantly affected by CO exposure with ZnPP IX, a HO inhibitor, blocking hippocampal LTP.



### March 2009 E-19 DRAFT—DO NOT QUOTE OR CITE

### Species / Model Exposure Duration CO Concentration Findings Reference

Zuckerbraun et al. (2007) Macrophages RAW 264.7 THP-1 cells, wild-typeand respirationdeficient 10 min - 24 h 50 – 500 ppm Exposure of RA W macrophages to 250 ppm CO for 10-60 min increased ROS generation, measured as dichlorofluorescein(DCF) fluorescence. ROS generation at 1 h was dosedependent with significant effects observed at 50, 250 and 500 ppm CO. This response was not blocked with a NOS inhibitor. A 1-h exposure to 250 ppm resulted in decreased intracellular glutathione levels. CO treatment was found to block TNF α production and to enhance p38 MAPK phosphorylation in LPS-stimulated cells. These ef fects were diminished by pretreatment with antioxidant s. The source of CO-derived oxidants was determined to be mi tochondrial since respirationdeficient THP-1 macrophages, unlike wild-type cells, failed to generate ROS in response to 250 ppm CO. Furthermore, treatment of RAW cells with the mitochondrial complex III inhibitor antimycin C, blocked ROS generation in response to 250 ppm CO. Exposure of RAW cells to 250 ppm CO for 1 h inhibited cytochrome c oxidase activity by 50%. Exposure to 250 ppm CO for 6 h had no effect on cellular ATP levels or mitochondrial membrane potential. Antimycin C treatment was found to reverse the effects of CO on LPS-mediated responses (TNF α and p38 MAPK), suggesting t hat mitochondrial-derived ROS mediated the effects of CO. The authors concluded that CO increased the generation of mitochondrial-derived ROS.



### March 2009 E-20 DRAFT—DO NOT QUOTE OR CITE

# References

Abi Esber AL; El-Fadel M; Shihadeh A. (2007a). Comp arison of trip average in -vehicle and exterior CO determinations by continuous and grab sampling using an electrochemical sensing method. Atmos Environ 41: 6087-6094. Abi Esber LA; El-Fadel M; Nuwayhid I; Saliba N. (2007b) . The effect of different ventilation modes on in-vehicle carbon monoxide exposure. Atmos Environ 41: 3644-3657. Abi Esber LA; El-Fadel M. (2008). In-vehicle CO ingr ession: Validation through fi eld measurements and mass balance simulations. The Science of the total environment 394: 75-89. Abram SR; Hodnett BL; Summers RL; Coleman TG; Hester RL. (2007). Quantitative Circulatory Physiology: an integrative mathematical model of human physiology for medical education. Advances in Physiology Education 31: 202. Acevedo CH; Ahmed A. (1998). Hemeoxygenase-1 inhibits human myometrial contractility via carbon monoxide and is upregulated by progesterone during pregnancy. J Clin Invest 101: 949-955. Achouh PE; Simonet S; Fabiani JN; Verbeuren TJ. (2008). Carbon monoxide induces relaxation of human internal thoracic and radial arterial grafts. Interactive CardioVascular and Thoracic Surgery 7: 959-962. Adams KF; Koch G; Chatterjee B; Goldstein GM; O'Neil JJ; Bromberg PA; Sheps DS. (1988). Acute elevation of blood carboxyhemoglobin to 6% impairs exercise performance and aggravates symptoms in patients with ischemic heart disease. J Am Coll Cardiol 12: 900-909. Adams SM. (2003). Establishing causality between environm ental stressors and effects on aquatic ecosystems. Hum Ecol Risk Assess 9: 17-35. Adir Y; Merdler A; Ben Haim S; Front A; Harduf R; Bitterm an H. (1999). Effects of exposure to low concentrations of carbon monoxide on exercise performance and myocardial perfusion in young healthy men. Occupational and Environmental Medicine 56: 535-538. Ahmed A; Rahman M; Zhang X; Acevedo CH; Nijjar S; Rushton I; Bussolati B; St John J. (2000). Induction of placental heme oxygenase-1 is protective against TN Falpha-induced cytotoxic ity and promotes vessel relaxation. Mol Med 6: 391-409. Ahmed H; McLaughlin BE; Soong J; Marks GS; Brien JF; Nakatsu K. (2005). The source of endogenous carbon monoxide formation in human placental chorionic villi. Cell Mol Biol (Noisy-l e-grand) 51: 447-451. Akland GG, Hartwell, T.D., Johnson, T.R., Whitmore, R.W. (1985). Measuring human exposure to carbon monoxide in Washington, D.C. and Denver, Colorado during the winter of 1982-1983. Environmental Science and Technology 19: 911-918. Alcantara RE; Xu C; Spiro TG; Guallar V. (2007). A quantu m-chemical picture of hemoglobin affinity. Proceedings of the National Academy of Sciences 104: 18451-18455. Alderman BW; Baron AE; Savitz DA. (1987). Maternal exposure to neighborhood carbon monoxide and risk of low infant birth weight. Public Health Rep 102: 410. Alexander PG; Chau L; Tuan RS. (2007). Role of nitric oxide in chick embryonic organogenesis and dysmorphogenesis. Birth Defects Res A Clin Mol Teratol 79: 581-594. Alexandreanu IC; Lawson DM. (2002). Effects of chronic administration of a heme oxygenase substrate or inhibitor on progression of the estrous cycle, pregnancy and lactation of Sprague-Dawley rats. Life Sci 72: 153-162. Alexandreanu IC; Lawson DM. (2003a). Heme oxygenase in the rat anterior pituitary: immunohistochemical localization and possible role in gonadotropin and prolactin secretion. Exp Biol Med (Maywood) 228: 64- 69. Alexandreanu IC; Lawson DM. (2003b). Heme oxygenase in the rat ovary: immunohistochemical localization and possible role in steroidogenesis. Exp Biol Med (Maywood) 228: 59-63. Allred EN; Bleecker ER; Chaitman BR; Dahms TE; Gottlie b 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. Allred EN; Bleecker ER; Chaitman BR; Dahms TE; Gottlie b 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. Alm S; Jantunen MJ; Vartiainen M. (1999). Urban comm uter exposure to particle matter and carbon monoxide inside an automobile. J Expo Anal Environ Epidemiol 9: 237-244.

March 2009 R-1 DRAFT—DO NOT QUOTE OR CITE

## Alm S; Mukala K; Jantunen MJ. (2000). Personal carbon monoxide exposures of preschool children in Helsinki, Finland: levels and determinants. Atmos Environ 34: 277-285. Alm S; Mukala K; Tiittanen P; Jantunen MJ. (2001). Pers onal carbon monoxide exposures of preschool children in Helsinki, Finland - comparison to ambient air concentrations. Atmos Environ 35: 6259-6266. Alonso JR; Cardellach F; Lopez S; Casademont J; Miro O. (2003). Carbon monoxide specifically inhibits cytochrome c oxidase of human m itochondrial respiratory chain. Pharmacol Toxicol 93: 142-146. Altshuller AP. (1991). Estimating product yields of carbon-containing products from the atmospheric photooxidation of ambient air alkenes. Journal of Atmospheric Chemistry 13: 131-154. American Thoracic Society. (1985). Guidelines as to what constitutes an adverse respiratory health effect, with special reference to epidemiologic studies of air pollution. Am Rev Respir Dis 131: 666-668. American Thoracic Society. (2000). What constitutes an advers e health effect of air pollution? . American Journal of Respiratory and Critical Care Medicine 161: 665-673. Andersen ZJ; Wahlin P; Raaschou-Nielsen O; Scheike T; Loft S. (2007). Ambient particle source apportionment and daily hospital admissions among children and elderly in Copenhagen. J Expo Sci Environ Epidemiol 17: 625-636. 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. Anderson HR; Bremner SA; Atkinson RW; Harrison RM; Wa lters S. (2001). Particulat e matter and daily mortality and hospital admissions in the west midlands conurbation of the United Kingdom: associations with fine and coarse particles, black smoke and sulphate. Occupational and Environmental Medicine 58: 504-510. Andersson JA; Uddman R; Cardell LO. (2000). Carbon monoxide is endogenously produced in the human nose and paranasal sinuses. J Allergy Clin Immunol 105: 269-273. Andreae MO. (1991). Biomass burning: Its history, use, and distribution and its impact on environmental quality and global climate. Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications (JS Levine, Ed.). In Ecological studies, Voll. 84. Analysis and synthesis. The MIT Press, Cambridge. Andresen JJ; Shafi NI; Durante W; Bryan RM, Jr. (2006). Effects of carbon monoxide and heme oxygenase inhibitors in cerebral vessels of rats and mice. Am J Physiol Heart Circ Physiol 291: H223-230. Antonelli T; Tomasini MC; Tattoli M; Cassano T; Fine tti S; Mazzoni E; Trabace L; Carratu MR; Cuomo V; Tanganelli S; Ferraro L. (2006). Prenatal exposure to the cannabinoid receptor agonist WIN 55,212-2 and carbon monoxide reduces extracellula r glutamate levels in primary rat cerebral cortex cell cultures. Neurochem Int 49: 568-576. Appel KW; Bhave PV; Gilliland AB; Sarwar G; Roselle SJ. (2008). Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacti ng model performance; Part II—particulate matter. Atmos Environ 42: 6057-6066. Appleton SD; Marks GS; Nakatsu K; Brien JF; Smith GN; Graham CH. (2002). Heme oxygenase activity in placenta: direct dependence on oxygen availability. Am J Physiol Heart Circ Ph ysiol 282: H2055-2059. Ashfaq M; Janjua MZ; Nawaz M. (2003). Effects of ma ternal smoking on placental morphology. J Ayub Med Coll Abbottabad 15: 12-15. Astrup P; Olsen HM; Trolle D; Kjeldsen K. (1972). Effect of moderate carbon-monoxide exposure on fetal development. Lancet 2: 1220-1222. Atkinson RW; Anderson HR; Strachan DP; Bland JM; Bremner SA; Ponce de Leon A. (1999). Short-term associations between outdoor air po llution and visits to accident and emergency departments in London for respiratory complaints. Eur Respir J 13: 257-265. Ayres SM; Giannelli S, Jr.; Mueller H; Criscitiello A. (1973). Myocardial and systemic vascular responses to low concentration of carboxyhemoglobin. Ann Clin Lab Sci 3: 440-447. Baccarelli A; Zanobetti A; Martinelli I; Grillo P; Hou L; Giacomini S; Bonzini M; Lanzani G; Mannucci PM; Bertazzi PA; Schwartz J. (2007). E ffects of exposure to air pollution on blood coagulation. Journal of Thrombosis and Haemostasis 5: 252-260. Bainbridge SA; Farley AE; McLaughlin BE; Graham CH ; Marks GS; Nakatsu K; Brien JF; Smith GN. (2002). Carbon monoxide decreases perfusion pressure in isolated human placenta. Placenta 23: 563-569. Bainbridge SA; Smith GN. (2005). HO in pregnancy. Free Radic Biol Med 38: 979-988. Bainbridge SA; Belkacemi L; Dickinson M; Graham CH; Smith GN. (2006). Carb on monoxide inhibits hypoxia/reoxygenation-induced apoptosis and secondary necrosis in syncytiotrophoblast. Am J Pathol 169: 774-783.

March 2009 R-2 DRAFT—DO NOT QUOTE OR CITE

## Bakwin PS; Tans PP; Novelli PC. (1994). Carbon monoxide budget in the Northern Hemisphere. Geophys Res Lett 21.Baldauf R; Thoma E; Khlystov A; Isakov V; Bowker G; Long T; Snow R. (2008). Impacts of noise barriers on nearroad air quality. Atmos Environ 42: 7502-7507. Baldauf RW, N. Watkins, D. Heist, C. Bailey, P. Ro wley, R. Shores. 2009. Near-Road Air Quality Monitoring: Factors Affecting Network Design and Interpretation of Data, J. of Air Quality, Atmosphere, & Health (in press). (In press). Ne ar-road air quality monitoring: Factors aff ecting network Design and Interpretation of data. J of Air Quality, Atmosphere, & Health. Ballester F; Tenias JM; Perez-Hoyos S. (2001). Air pollution and emergency hospital admissions for cardiovascular diseases in Valencia, Spain. J Epidemiol Community Health 55: 57-65. Ballester F; Rodriguez P; Iniguez C; Saez M; Daponte A; Galan I; Taracido M; Arribas F; Bellido J; Cirarda FB; Canada A; Guillen JJ; Guillen-Grima F; Lopez E; Pe rez-Hoyos S; Lertxundi A; Toro S. (2006). Air pollution and cardiovascular admissions association in Spain: results within the EMECAS project. J Epidemiol Community Health 60: 328-336. Bamberger AM; Koglin M; Kempfert J; Loning T; Scholz H; Behrends S. (2001). Expr ession and tissue localization of soluble guanylyl cyclase in the human placenta us ing novel antibodies directed against the alpha(2) subunit. J Clin Endocrinol Metab 86: 909-912. Barber A; Robson SC; Lyall F. (1999). Hemoxygenase an d nitric oxide synthase do not maintain human uterine quiescence during pregnancy. Am J Pathol 155: 831-840. Barber A; Robson SC; Myatt L; Bulmer JN; Lyall F. (2001). Heme oxygenase expression in human placenta and placental bed: reduced expression of placenta en dothelial HO-2 in preeclampsia and fetal growth restriction. FASEB J 15: 1158-1168. Barker DJ; Eriksson JG; Forsen T; Osmond C. (2002). Fetal origins of adult disease: strength of effects and biological basis. Int J Epidemiol 31: 1235-1239. Barnett AG; Williams GM; Schwartz J; Best TL; Neller AH; Petroeschevsky AL; Simpson RW. (2006). The effects of air pollution on hospitalizations for cardiovascular disease in elderly people in Australian and New Zealand cities. Environ Health Perspect 114: 1018-1023. Bathoorn E; Slebos DJ; Po stma DS; Koeter GH; van Oosterhout AJ; va n 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. Baum M; Schiff E; Kreiser D; Dennery PA; Stevenson DK; Rosenthal T; Seidman DS. (2000). End-tidal carbon monoxide measurements in women with pregnancy-in duced hypertension and preeclampsia. Am J Obstet Gynecol 183: 900-903. Beard RR; Wertheim GA. (1967). Behavioral impairment associated with small doses of carbon monoxide. In American Journal of Public Health (Vol. 57, pp. 2012). Bedeschi E; Campari C; Candela S; Collini G; Caranci N; Frasca G; Galassi C; Fran cesca G; Vigotti MA. (2007). Urban air pollution and respiratory emergency visits at pediatric unit, Reggio Emilia, Italy. J Toxicol Environ Health A 70: 261-265. Behrentz E; Fitz DR; Pankratz DV; Sabin LD; Colome SD; Fruin SA; Winer AM. (2004). Measuring self-pollution in school buses using a tracer gas technique. Atmos Environ 38: 3735-3746. Bell MC; Namdeo AK; Chen H; Ashmore MR; Terry AC; Dimitroulopoulou S. (2004). Reducing Urban Pollution Exposure from Road Transport (RUPERT). In Urban Transport (pp. 829-838). Southampton, UK: WIT Press. Bell ML; Ebisu K; Belanger K. (2007). Ambient air pollution and low birth weight in Connecticut and Massachusetts. Environ Health Perspect 115: 1118-1124. Bell ML; Levy JK; Lin Z. (2008). The effect of sandstorms and air pollution on cause-specific hospital admissions in Taipei, Taiwan. Occupational and Environmental Medicine 65: 104-111. Bellini P; Baccini M; Biggeri A; Terracini B. (2007). The meta-analysis of the Italian studies on short-term effects of air pollution (MISA): old and new issues on the interpretation of the statistical evidences. Environmetrics 18: 219-229. Benignus VA; Muller KE; Barton CN; Pr ah JD. (1987). Effect of low level carbon monoxide on compensatory tracking and event monitoring. Neurotoxicol Teratol 9: 227-234. Benignus VA. (1993). Importance of experimenter-blind procedure in neurotoxicology. Neurotoxicol Teratol 15: 45- 49. Benignus VA; Hazucha MJ; Smith MV; Bromberg PA. (1994). Prediction of carboxyhemoglobin formation due to transient exposure to carbon monoxide. J Appl Physiol 76: 1739-1745.

March 2009 R-3 DRAFT—DO NOT QUOTE OR CITE

## Benignus VA; Coleman T; Eklund CR; Kenyon EM. (2006). A general physiological and toxicokinetic (GPAT) model for simulating complex toluene exposure scenar ios in humans. Toxicol Mech Methods 16: 27-36. Bergamaschi P; Hein R; Heimann M; Cr utzen PJ. (2000). Inverse modeling of the global CO cycle 1. Inversion of CO mixing ratios. Journal of Geophysical Research 105: 1909-1927. Berger A; Zareba W; Schneider A; Ruckerl R; Ibald-Mu lli A; Cyrys J; Wichmann HE; Peters A. (2006). Runs of ventricular and supraventricular tachycardia triggered by air pollution in patients with coronary heart disease. J Occup Environ Med 48: 1149-1158. Bergeron M; Ferriero DM; Sharp FR. (1998). Developmental expression of heme oxygenase-1 (HSP32) in rat brain: an immunocytochemical study. Brain Res Dev Brain Res 105: 181-194. Berk PD; Rodkey FL; Blaschke TF; Collison HA; Waggoner JG. (1974). Comparison of plasma bilirubin turnover and carbon monoxide production in man. J Lab Clin Med 83: 29-37. Berk PD; Blaschke TF; Scharschmidt BF; Waggoner JG; Be rlin NI. (1976). A new approach to quantitation of the various sources of bilrubin in man. J Lab Clin Med 87: 767-780. Berkowitz GS; Papiernik E. (1993). Epidemiology of Preterm Birth. Epidemiol Rev 15: 414-443. Biggeri A; Baccini M; Bellini P; Terracini B. (2005). Meta-ana lysis of the Italian studies of short-term effects of air pollution (MISA), 1990-1999. Int J Occup Environ Health 11: 107-122. Bing O; Grundemar L; Ny L; Moller C; Heilig M. (1995). Modulation of carbon monoxide production and enhanced spatial learning by tin protoporphyrin. Neuroreport 6: 1369-1372. Bogo H; Gómez DR; Reich SL; Negri RM; San Román E. ( 2001). Traffic pollution in a downtown site of Buenos Aires City. Atmos Environ 35: 1717-1727. Bol KA; Collins JS; Kirby RS. (2006). Survival of infants w ith neural tube defects in the presence of folic acid fortification. Pediatrics 117: 803-813. Boneva RS; Botto LD; Moore CA; Yang QH; Correa A; Eric kson JD. (2001). Mortality associated with congenital heart defects in the United States - Trends and racial disparities, 1979-1997. Circulation 103: 2376-2381. Borrego C; Tchepel O; Costa AM; Martins H; Ferreira J; Miranda AI. (2006). Traffic-related particulate air pollution exposure in urban areas. Atmos Environ 40: 7205-7214. Botter DA; Jorgensen B; Peres AA. (2002). A longitudinal st udy of mortality and air pollution for Sao Paulo, Brazil. J Expo Anal Environ Epidemiol 12: 335-343. Botto LD; Correa A; Erickson JD. (2001 ). Racial and temporal variations in the prevalence of heart defects. Pediatrics 107: E32. Bowker GE; Baldauf R; Isakov V; Khlystov A; Petersen W. (2007). The effects of ro adside structures on the transport and dispersion of ultrafine particles from highways. Atmos Environ 37. Braga AL; Saldiva PH; Pereira LA; Menezes JJ; Conceica o GM; Lin CA; Zanobetti A; Schwartz J; Dockery DW. (2001). Health effects of air pollution exposure on ch ildren and adolescents in Sao Paulo, Brazil. Pediatr Pulmonol 31: 106-113. Brauer M; Lencar C; Tamburic L; Koehoorn M; Demers P; Karr C. (2008). A cohort study of traffic-related air pollution impacts on birth outcomes. Environ Health Perspect 116: 680-686. Bremner SA; Anderson HR; Atkinson RW; McMichael AJ; Strachan DP; Bland JM; Bower JS. (1999). Short-term associations between outdoor air pollution and mortality in London 1992-4. Occupational and Environmental Medicine 56: 237-244. Brian JE, Jr.; Heistad DD; Faraci FM. (1994). Effect of ca rbon monoxide on rabbit cerebral arteries. Stroke 25: 639- 643; discussion 643-634. Brown SD; Piantadosi CA. (1992). Recovery of energy metabolism in rat brain after carbon monoxide hypoxia. J Clin Invest 89: 666-672. Bruce EN; Bruce MC. (2003). A multicompartment model of carboxyhemoglobin and carboxymyoglobin responses to inhalation of carbon monoxide. J Appl Physiol 95: 1235-1247. Bruce EN; Bruce MC; Erupaka K. (2008). Prediction of the rate of uptake of carbon monoxide from blood by extravascular tissues. Respir Physiol Neurobiol 161: 142-159. Bruce MC; Bruce EN. (2006). Analysis of factors that influence rates of carbon monoxide uptake, distribution, and washout from blood and extravascular tissues using a multicompartment model. J Appl Physiol 100: 1171- 1180. Bruinen de Bruin Y; Hanninen O; Carrer P; Maroni M; Kephalopoulos S; Scotto di Marco G; Jantunen M. (2004). Simulation of working population exposures to carbon monoxide using EXPOLIS-Milan microenvironment concentration and time-activity data. J Expo Anal Environ Epidemiol 14: 154-163. Brune B; Ullrich V. (1987). Inhibition of platelet aggregation by carbon monoxide is mediated by activation of guanylate cyclase. Mol Pharmacol 32: 497-504.

March 2009 R-4 DRAFT—DO NOT QUOTE OR CITE

## Buck S; Dodds W; Fisher J; Hart D; Parker A; Stevenson J; Watson V; Welc h E. (2000). Nutrient criteria technicalguidance manual, rivers and streams: US Environmental Protection Agency, Office of Water. EPA-822-B00-002, Burke JM; Zufall MJ; Ozkaynak H. (2001). A population ex posure model for particulate matter: case study results for PM (2.5) in Philadelphia, PA. J Expo Anal Environ Epidemiol 11: 470-489. Burmester T; Weich B; Reinhardt S; Hankeln T. (2000). A vertebrate globin expressed in the brain. Nature 407: 520-523. Burnett RT; Smith-Doiron M; Stieb D; Cakmak S; Brook JR. (1999). Effects of particulate and gaseous air pollution on cardiorespiratory hospitalizations. Archives of Environmental Health 54: 130-139. Burnett RT; Brook J; Dann T; Delocla C; Philips O; Cakmak S; Vincent R; Goldberg MS; Krewski D. (2000). Association between particulate-and gas-phase compon ents of urban air pollution and daily mortality in eight Canadian cities. Inhal Toxicol 12: 15-39. Burnett RT; Smith-Doiron M; Stieb D; Raizenne ME; Brook JR; Dales RE; Leech JA; Cakmak S; Krewski D. (2001). Association between ozone and hospitalization for acute respiratory diseases in children less than 2 years of age. American Journal of Epidemiology 153: 444-452. Burnett RT; Goldberg MS. (2003). Size-fractionated partic ulate mass and daily mortality in eight Canadian cities. Revised Analyses of Time-Series Studies of Air Pollution and Health Special Report Boston: Health Effects Institute: 85–90. Burnett RT; Stieb D; Brook JR; Cakmak S; Dales R; Raizenne M; Vincent R; Dann T. (2004). Associations between short-term changes in nitrogen dioxide and mortality in Canadian cities. Archives of Environmental Health 59: 228-236. Bye A; Sorhaug S; Ceci M; Hoydal MA ; Stolen T; Heinrich G; Tjonna AE; Najjar SM; Nilsen OG; Catalucci D; Grimaldi S; Contu R; Steinshamn S; Condorelli G; Sm ith GL; Ellingsen O; Waldum H; Wisloff U. (2008). Carbon monoxide levels experienced by heavy smoke rs impair aerobi c capacity and cardiac contractility and induce pathological hypertrophy. Inhalation Toxicology 20: 635-646. Cagiano R; Ancona D; Cassano T; Tattoli M; Trabace L; Cuomo V. (1998). Effects of prenatal exposure to low concentrations of carbon monoxide on sexual behaviour and mesolimbic dopaminergic function in rat offspring. Br J Pharmacol 125: 909-915. Cakmak S; Dales RE; Judek S. (2006a). Respiratory Health Effects of Air Pollution Gases: Modification by Education and Income. Archives of Environmental Health 61: 5-10. Cakmak S; Dales RE; Judek S. (2006b). Do gender, education, and income modify the effect of air pollution gases on cardiac disease? J Occup Environ Med 48: 89-94. Cakmak S; Dales RE; Vidal CB. (2007). Air pollution and mortality in Chile: susceptibility among the elderly. Environ Health Perspect 115: 524-527. Cardell LO; Ueki IF; Stjarne P; Agusti C; Takeyama K; Linden A; Nadel JA. (1998). Bronchodilatation in vivo by carbon monoxide, a cyclic GMP related messenger. Br J Pharmacol 124: 1065-1068. Carmines EL; Rajendran N. (2008). Evidence for carbon monoxide as the major factor contributing to lower fetal weights in rats exposed to cigarette smoke. Toxicol Sci 102: 383-391. Carratu MR; Renna G; Giustino A; De Salvia MA; Cuomo V. (1993). Changes in peripheral nervous system activity produced in rats by prenatal exposure to carbon monoxide. Arch Toxicol 67: 297-301. Carratu MR; Cagiano R; De Salvia MA; Trabace L; Cuom o V. (1995). Developmenta l neurotoxicity of carbon monoxide. Arch Toxicol Suppl 17: 295-301. Carratu MR; Cagiano R; Tattoli M; Trabace L; Borracci P; Cuomo V. (2000). Prenatal exposure model simulating CO inhalation in human cigarette smokers: sphingomyelin alterations in the rat sciatic nerve. Toxicol Lett 117: 101-106. Carratù MR; Cagiano R; Desantis S; Labate M; Tattoli M; Trabace L; Cuomo V. (2000). Prenatal exposure to low levels of carbon monoxide alters sciatic nerve mye lination in rat offspring. Life Sci 67: 1759-1772. Carraway MS; Ghio AJ; Carter JD; Piantadosi CA. (2000). Expression of heme oxygenase-1 in the lung in chronic hypoxia. American journal of physiology Lung cellular and molecular physiology 278: L806. Carraway MS; Ghio AJ; Suliman HB; Carter JD; Whorton AR; Piantadosi CA. (2002). Carbon monoxide promotes hypoxic pulmonary vascular remodeling. American J ournal of Physiology- Lung Cellular and Molecular Physiology 282: 693-702. Carter WPL. (1998). Updated maximum incremental reactivity scale for regulat ory applications. Preliminary Report to California Air Resource s BoardContract 95308.

March 2009 R-5 DRAFT—DO NOT QUOTE OR CITE

## Castillo A; Llapur CJ; Martinez T; Kisling J; Williams-N komo T; Coates C; Tepper RS. (2006). Measurement of single breath-hold carbon monoxide diffusing capacity in healthy infants and toddlers. Pediatr Pulmonol 41: 544-550. Cavallin-Stahl E; Mercke C; Lundh B. (1976a). Erythr opoiesis and Carbon Monoxide Production in Hodgkin's Disease. Br J Haematol 32: 167-176. Cavallin-Stahl E; Mercke C; Lundh B. (1976b). Carbon monoxide production in patients with breast carcinoma. Br J Haematol 32: 177-182. CDC. (2004). The health consequences of smoking: a report of the Surgeon General. U.S. Department of Health and Human Services, National Center for Chronic Dis ease Prevention and Health Promotion, Office on Smoking and Health. Available from: http://www.cdc.gov/tobacco/data_sta tistics/sgr/sgr_2004/chapters.htm , Atlanta, GA. Available from: http://www.cdc.gov/tobacco/sgr/sgr_2004/index.htm

. Cella M; Farina MG; Keller Sarmiento MI; Chianelli M; Rosenstein RE; Franchi AM. (2006). Heme oxygenasecarbon monoxide (HO-CO) system in rat uterus: effect of sexual steroids and prostaglandins. J Steroid Biochem Mol Biol 99: 59-66. Centers for Disease Control and Prevention. (1998). Trends in infant mortality attributable to birth defects--United States, 1980-1995. MMWR Morb Mortal Wkly Rep 47: 773-778. Chakraborty S; Balakotaiah V; Bidani A. (2004). Diffusi ng capacity reexamined: relative roles of diffusion and chemical reaction in red cell uptake of O2, CO , CO2, and NO. J Appl Physiol 97: 2284-2302. Chaloulakou A; Mavroidis I. (2002). Comparison of indoor and outdoor concentrations of CO at a public school. Evaluation of an indoor air quality model. Atmos Environ 36: 1769-1781. Chaloulakou A; Mavroidis I; Duci A. (2003). Indoor and outdoor carbon monoxide concentration relationships at different microenvironments in the Athens area. Chemosphere 52: 1007. Chan CC; Chuang KJ; Su TC; Lin LY. (2005). Association between nitrogen dioxide and heart rate variability in a susceptible population. Eur J Cardiovasc Prev Rehabil 12: 580-586. Chan CC; Chuang KJ; Chien LC; Chen WJ; Chang WT. (2006). Urban air pollution and emergency admissions for cerebrovascular diseases in Taipei, Taiwan. Eur Heart J 27: 1238-1244. Chang CC; Tsai SS; Ho SC; Yang CY. (2005). Air pollution and hospital admissions for cardiovascular disease in Taipei, Taiwan. Environ Res 98: 114-119. Chang CH; Meroney RN. (2003). Concentration and flow distributions in urban street canyons: wind tunnel and computational data. Journal of Wind Engineering & Industrial Aerodynamics 91: 1141-1154. Chang LT; Koutrakis P; Catalano PJ; Suh HH. (2000). Hourly personal exposures to fine particles and gaseous pollutants - Results from Baltimore, Maryland. J Air Waste Manage Assoc 50: 1223-1235. Chang LT; Suh HH; Wolfson JM; Misra K; Allen GA; Catalano PJ; Koutrakis P. (2001). Laboratory and field evaluation of measurement methods for one-hour exposures to O-3, PM2.5, and CO. J Air Waste Manage Assoc 51: 1414-1422. Chen GD; Kong J; Reinhard K; Fechter LD. (2001). NM DA receptor blockage protect s against permanent noiseinduced hearing loss but not its potentiation by carbon monoxide. Hearing Res 154: 108-115. Chen H; Namdeo A; Bell M. (2008). Classification of road traffic and roadside pollution concentrations for assessment of personal exposure. Environmental Modelling & Software 23: 282-287. Chen L; Jennison BL; Yang W; Omaye ST. (2000). Elementary school absenteeism and air pollution. Inhalation Toxicology 12: 997-1016. Chen L; Yang W; Jennison BL; Goodrich A; Omaye ST. (2002). Air pollution and birth weight in northern Nevada, 1991-1999. Inhalation Toxicology 14: 141-157. Chen PC; Lai YM; Chan CC; Hwang JS; Yang CY; Wang JD. (1999). Short-term effect of ozone on the pulmonary function of children in primary school. Environ Health Perspect 107: 921-925. Cheng MF; Tsai SS; Wu TN; Chen PS; Yang CY. (2007). Air pollution and hospital admissions for pneumonia in a tropical city: Kaohsiung, Taiwan. J Toxicol Environ Health A 70: 2021-2026. Chevalier RB; Krumholz RA; Ross JC. (1966). Reaction of nonsmokers to carbon monoxide inhalation. Cardiopulmonary responses at rest and during exercise . Journal of the American Medical Association 198: 1061-1064. Chin BY; Jiang G; Wegiel B; Wang HJ; MacDonald T; Zh ang XC; Gallo D; Cszimadia E; Bach FH; Lee PJ. (2007). Hypoxia-inducible factor 1alpha stabilization by carbon monoxide results in cytoprotective preconditioning. Proceedings of the National Academy of Sciences 104: 5109. Cho B; Choi J; Yum YT. (2000). Air pollution and hospital admissions for respiratory disease in certain areas of Korea. J Occup Health 42: 185-191.

March 2009 R-6 DRAFT—DO NOT QUOTE OR CITE

## Chock DP; Winkler SL; Chen C. (2000). A study of the association between daily mortality and ambient air pollutant concentrations in Pittsburgh, Pennsylvania. J Air Waste Manage Assoc 50: 1481-1500. Chung Y; Huang SJ; Glabe A; Jue T. (2006). Implication of CO inactivation on myoglobin function. American Journal of Physiology- Lung Cellular and Molecular Physiology 290: C1616-1624. Cifuentes LA; Vega J; Kopfer K; Lave LB. (2000). Effect of the fine fraction of particulate matter versus the coarse mass and other pollutants on daily mortality in Santiago, Chile. J Air Waste Manage Assoc 50: 1287-1298. Clewell HJ; Crump KS. (2005). Quantitative Estimates of Ri sk for Noncancer Endpoints. An International Journal 25: 285-289. Coburn RF; Williams WJ; Forster RE. (1964). Effect of Er ythrocyte Destruction on Carbon Monoxide Production in Man. J Clin Invest 43: 1098-1103. Coburn RF; Forster RE; Kane PB. (1965). Considerations of the physiological variables that determine the blood carboxyhemoglobin concentration in man. J Clin Invest 44: 1899-1910. Coburn RF; Williams WJ; Kahn SB. (1966). Endogenous ca rbon monoxide production in patients with hemolytic anemia. J Clin Invest 45: 460-468. Coburn RF. (1967). Endogenous carbon monoxide production and body CO stores. Acta Med Scand Suppl 472: 269-282. Coburn RF. (1970a). The carbon monoxide body stores. Ann N Y Acad Sci 174: 11-22. Coburn RF. (1970b). Enhancement by phenobarbital and diphenylhydantoin of carbon monoxide production in normal man. N Engl J Med 283: 512-515. Coceani F; Breen CA; Lees JG; Falck JR; Olley PM. (198 8). Further evidence implicating a cytochrome P-450- mediated reaction in the contractile tension of the lamb ductus arteriosus. Circul Res 62: 471-477. Collier TK. (2003). Forensic Ecotoxicology: Establishing Causality between Contaminants and Biological Effects in Field Studies. Hum Ecol Risk Assess 9: 259 - 266. Conceicao GM; Miraglia SG; Kishi HS; Saldiva PH; SingerJM. (2001). Air pollution and child mortality: a timeseries study in Sao Paulo, Brazil. Environ Health Perspect 109 Suppl 3: 347-350. Conny JM. (1998). The isotopic characterization of carbon monoxide in the troposphere. Atmos Environ 32: 2669- 2683. Conrad R; Seiler W. (1985). Influence of temperature, moisture, and organic carbon on the flux of H 2 and CO between soil and atmosphere: Field studies in subtropical regions. Journal of Geophysical Research 90: 5699-5709. Cronje FJ; Carraway MS; Freiberger JJ; Suliman HB; Piantadosi CA. (2004). Carbon monoxide actuates O(2)- limited heme degradation in the rat brain. Free Radic Biol Med 37: 1802-1812. Croxford B; Penn A. (1998). Siting considerations for urban pollution monitors. Atmos Environ 32: 1049-1057. Crump KS; Hoel DG; Langley CH; Peto R. (1976). Fundam ental carcinogenic processes and their implications for low dose risk assessment. Cancer Res 36: 2973-2979. Cudmore M; Ahmad S; Al-Ani B; Fujisawa T; Coxall H; Chudasama K; Devey LR; Wigmore SJ; Abbas A; Hewett PW; Ahmed A. (2007). Negative regulation of soluble Flt-1 and soluble endoglin release by heme oxygenase-1. Circulation 115: 1789-1797. D'Amico G; Lam F; Hagen T; Moncada S. (2006). Inhibition of cellular respiration by endogenously produced carbon monoxide. J Cell Sci 119: 2291-2298. D'Ippoliti D; Forastiere F; Ancona C; Agabiti N; Fusco D; Michelozzi P; Perucci CA. (2003). Air pollution and myocardial infarction in Rome: a case-crossover analysis. Epidemiology 14: 528-535. Dales R. (2004). Ambient carbon monoxide may influence heart rate variability in subjects with coronary artery disease. J Occup Environ Med 46: 1217-1221. Dani C; Giannini L; Bertini G; Pratesi S; Corsini I; Longini M; Buonocore G; Masini E; Rubaltelli FF. (2007). Changes of nitric oxide, carbon monoxide and oxidative stress in term infants at birth. Free Radic Res 41: 1358-1363. Davies DM; Smith DJ. (1980). Electrocardiographic changes in healthy men during continuous low-level carbon monoxide exposure. Environ Res 21: 197-206. Davies RF; Topping DL; Turner DM. (1976). The effect of intermittent carbon monoxide exposure on experimental atherosclerosis in the rabbit. Atherosclerosis 24: 527-536. de Hartog JJ; Hoek G; Peters A; Timonen KL; Ibald-Mulli A; Brunekreef B; Heinrich J; Tiittanen P; van Wijnen JH; Kreyling W; Kulmala M; Pekkanen J. (2003). Effects of fine and ultraf ine particles on cardiorespiratory symptoms in elderly subjects with coronary heart disease: the ULTRA study. American Journal of Epidemiology 157: 613-623.

March 2009 R-7 DRAFT—DO NOT QUOTE OR CITE

## De Jong MJ; Randall DC. (2005). Heart rate variability anal ysis in the assessment of autonomic function in heart failure. J Cardiovasc Nurs 20: 186-195; quiz 196-187. De Leon SF; Thurston GD; Ito K. ( 2003). Contribution of respiratory di sease to nonrespiratory mortality associations with air pollution. American Journal of Respiratory and Critical Care Medicine 167: 1117- 1123. De Salvia MA; Cagiano R; Carratu MR; Di Giovanni V; Trabace L; Cuomo V. (1995). Irreversible impairment of active avoidance behavior in rats prenatally exposed to mild concentrations of carbon monoxide. Psychopharmacology (Berl) 122: 66-71. Deedwania PC; Stein PK; Koren A; Mukherjee R; Pitt B. (2 005). Decreased heart rate variability is an independent predictor of sudden cardiac death in post-ML heart fa ilure: Results of the EPHESUS arrhythmia and heart rate variability analysis. In Pharmaco therapy (pp. 3029): McGraw-Hill. Delfino RJ; Gong H, Jr.; Linn WS; Pellizzari ED; Hu Y. (2003). Asthma symptoms in Hispanic children and daily ambient exposures to toxic and criteria air pollutants. Environ Health Perspect 111: 647-656. Delivoria-Papadopoulos M; Coburn RF; Forster RE. (1974). Cyclic variation of rate of carbon monoxide production in normal women. J Appl Physiol 36: 49-51. Denschlag D; Marculescu R; Unfried G; Hefler LA; Ex ner M; Hashemi A; Riener EK; Keck C; Tempfer CB; Wagner O. (2004). The size of a microsatellite polymor phism of the haem oxygenase 1 gene is associated with idiopathic recurrent miscarriage. Mol Human Reprod 10: 211-214. Dewilde S; Kiger L; Burmester T; Hankeln T; Baudin-Creuza V; Aerts T; Marden MC; Caubergs R; Moens L. (2001). Biochemical characterization and ligand binding properties of neuroglobin, a novel member of the globin family. J Biol Chem 276: 38949-38955. Di Cera E; Doyle ML; Morgan MS; De Cristofaro R; Lan dolfi R; Bizzi B; Castagnola M; Gill SJ. (1989). Carbon monoxide and oxygen binding to human hemoglobin F0. Biochemistry 28: 2631-2638. Dimitroulopoulou C; Ashmore MR; Byrne MA; Kinnersley RP . (2001). Modelling of indoor exposure to nitrogen dioxide in the UK. Atmos Environ 35: 269-279. Dimitroulopoulou C; Ashmore MR; Hill MTR; Byrne MA; Ki nnersley R. (2006). INDAIR: A probabilistic model of indoor air pollution in UK homes. Atmos Environ 40: 6362-6379. Dockery DW; Luttmann-Gibson H; Rich DQ; Link MS; Mittleman MA; Gold DR; Koutrakis P; Schwartz JD; Verrier RL. (2005). Association of air pollution with increased incidence of vent ricular tachyarrhythmias recorded by implanted cardioverter defibrillato rs. Environ Health Perspect 113: 670-674. Dominici F; McDermott A; Zeger SL; Samet JM. (2002). On the use of generalized additive models in time-series studies of air pollution and health. American Journal of Epidemiology 156: 193-203. Dominici F; Sheppard L; Cl yde M. (2003a). Health Effects of Air Pollution: A Statistical Review. International Statistical Review 71: 243-276. Dominici F; McDermott A; Daniels M; Zeger SL; Samet JM. (2003b). Mortality among residents of 90 cities. Revised Analyses of Time-Series Studies of Air Po llution and Health Special Report Boston, MA: Health Effects Institute: 9–24. Dominici F; Zanobetti A; Zeger SL; Schwartz J; Same t JM. (2005a). The National Morbidity, Mortality, and Air Pollution Study Part IV: Hierarchical Bivariate Time-Series Models-A Combined Analysis of PM~ 1~ 0 Effects on Hospitalization and Mortality. Health Effects Institute, Dominici F; McDermott A; Daniels M; Zeger SL; Samet JM. (2005b). Revised analyses of the National Morbidity, Mortality, and Air Pollution Study: mort ality among residents of 90 cities. J Toxicol Environ Health A 68: 1071-1092. Douglas CG; Haldane JS; Haldane JBS. (1912). The laws of combination of haemoglobin with carbon monoxide and oxygen. The Journal of Physiology 44: 275. Drinkwater BL; Raven PB; Horvath SM ; Gliner JA; Ruhling RO; Bolduan NW; Taguchi S. (1974). Air pollution, exercise, and heat stress. Archives of Environmental Health 28: 177-181. Dubuis E; Gautier M; Melin A; Rebocho M; Girardin C; Bonnet P; Vandier C. (2002). Chronic carbon monoxide enhanced IbTx-sensitive currents in rat resistance pulmonary artery smooth muscle cells. American Journal of Physiology- Lung Cellular and Molecular Physiology 283: L120-129. Dubuis E; Gautier M; Melin A; Rebocho M; Girardin C; Bonnet P; Vandier C. (2003). Chronic carbon monoxide exposure of hypoxic rats increases in vitro sensitivity of pulmonary artery smooth muscle. Can J Physiol Pharmacol 81: 711-719. Dubuis E; Potier M; Wang R; Vandier C. (2005). Continuous inhalation of carbon monoxide attenuates hypoxic pulmonary hypertension development presumably through activation of BKCa channels. Cardiovasc Res 65: 751.

March 2009 R-8 DRAFT—DO NOT QUOTE OR CITE

## Duci A; Chaloulakou A; Spyrellis N. (2003). Exposure to carbon monoxide in the Athens urban area during commuting. Sci Total Environ 309: 47-58. Dutton SJ; Hannigan MP; Miller SL. (2001). Indoor pollutant levels from the use of unvented natural gas fireplaces in Boulder, Colorado. J Air Waste Manage Assoc 51: 1654-1661. Ekblom B; Huot R. (1972). Response to submaximal and maximal exercise at different levels of carboxyhemoglobin. Acta Physiol Scand 86: 474-482. Engel LA. (1983). Gas mixing within the acinus of the lung. J Appl Physiol 54: 609-618. Engel RR; Rodkey FL; O'Neal JD; Collison HA. (1969). Relative affinity of human fetal hemoglobin for carbon monoxide and oxygen. Blood 33: 37-45. Enzi G; Bevilacqua M; Crepaldi G. (1976). Disturbances in pulmonary gaseous exchange in primary hyperlipoproteinemias. Bull Eur Physiopathol Respir 12: 433-442. Ernst A; Zibrak JD. (1998). Carbon Monoxide Poisoning. New Engl J Med 339: 1603-1608. Estrella B; Estrella R; Oviedo J; Narvaez X; Reyes MT; Gutierrez M; Naumova EN. (2005). Acute respiratory diseases and carboxyhemoglobin status in school children of Quito, Ecuador. Environ Health Perspect 113: 607-611. Fairley D. (1999). Daily mortality and air pollution in Sa nta Clara County, California: 1989-1996. Environ Health Perspect 107: 637-641. Farhat SC; Paulo RL; Shimoda TM; Conceicao GM; Lin CA ; Braga AL; Warth MP; Saldiva PH. (2005). Effect of air pollution on pediatric respiratory emergency room visits and hospital admissions. Braz J Med Biol Res 38: 227-235. Farrugia G; Irons WA; Rae JL; Sarr MG; Szurszewski JH . (1993). Activation of whole cell currents in isolated human jejunal circular smooth muscle cells by carbon monoxide. Am J Physiol 264: G1184-1189. Favory R; Lancel S; Marechal X; Tissi er S; Neviere R. (2006a). Cardiovascul ar protective role for activated protein C during endotoxemia in rats. Intensive Care Med 32: 899-905. Favory R; Lancel S; Tissier S; Math ieu D; Decoster B; Neviere R. (2006b). Myocardial dysfunction and potential cardiac hypoxia in rats induced by carbon monoxide inhalation. American Journal of Respiratory and Critical Care Medicine 174: 320-325. Fechter LD; Annau Z. (1977). Toxicity of mild prenatal carbon monoxide exposure. Science 197: 680-682. Fechter LD; Thakur M; Miller B; Annau Z; Srivastava U. (1980). Effects of pr enatal carbon monoxide exposure on cardiac development. Toxicol Appl Pharmacol 56: 370-375. Fechter LD; Annau Z. (1980). Prenatal carbon monoxide exposure alters behavioral development. Neurobehav Toxicol 2: 7-11. Fechter LD; Karpa MD; Proctor B; Lee AG; Storm JE. ( 1987). Disruption of neostriatal development in rats following perinatal exposure to mild, but chronic carbon monoxide. Neurotoxicol Teratol 9: 277-281. Fechter LD; Liu Y; Pearce TA. (1997). Cochlear protection from carbon monoxide exposure by free radical blockers in the guinea pig. Toxicol Ap pl Pharmacol 142: 47-55. Fechter LD; Chen G; Rao D. ( 2002). Chemical asphyxiants and noise. Noise Health 4: 49. Fenn WO. (1970). The burning of CO in tissues. Ann N Y Acad Sci 174: 64-71. Finlayson-Pitts BJ; Pitts Jr JN. (2000). Chemistry of th e Upper and Lower Atmosphere Theory, Experiments, and Applications. San Diego: Academic Press. Fischer P; Hoek G; Brunekreef B; Verhoeff A; van Wijnen J. (2003). Air pollution and mortality in the Netherlands: are the elderly more at risk? European Respiratory Journal 21: 34S-38S. Fischer PH; Steerenberg PA; Snelder JD; van Loveren H; van Amsterdam JG. (2002). Association between exhaled nitric oxide, ambient air pollution and respiratory hea lth in school children. Int Arch Occup Environ Health 75: 348-353. Fisher AB; Hyde RW; Baue AE; Reif JS; Kelly DF. (1969). Effect of carbon monoxide on function and structure of the lung. J Appl Physiol 26: 4-12. Fishman J; Bowman KW; Burrows JP; Richter A; Chan ce KV; Edwards DP; Martin RV; Morris GA; Pierce RB; Ziemke JR. (2008). Remote Sensing of Tropospheric Pollution from Space. Bulletin of the American Meteorological Society 89: 805-821. Flachsbart PG. Models of exposure to carbon monoxide inside a vehicle on a Honolulu highway. Journal of Exposure Analysis and Environmental Epidemiology 9: 245. Fodor GG; Winneke G. (1972). Effect of low CO concentrations on resistance to monotony and on psychomotor capacity. Staub Reinhaltung Der Luft 32: 46-54.

March 2009 R-9 DRAFT—DO NOT QUOTE OR CITE

## Forastiere F; Stafoggia M; Picciotto S; Bellander T; D'Ippo liti D; Lanki T; von Klot S; Nyberg F; Paatero P; Peters A; Pekkanen J; Sunyer J; Perucci CA. (2005). A case-crossover analysis of out-of-hospital coronary deaths and air pollution in Rome, Italy. American Journal of Respiratory and Critical Care Medicine 172: 1549- 1555. Forastiere F; Stafoggia M; Tasco C; Picciotto S; Agabiti N; Cesaroni G; Perucci CA. (2007). Socioeconomic status, particulate air pollution, and daily mortality: differen tial exposure or differential susceptibility. Am J Ind Med 50: 208-216. Forster RE. (1966). Diffusion of gases in the lungs. Physiologist 9: 110-122. Fox GA. (1991). Practical causal infe rence for ecoepidemiologists. J Toxicol Environ Health 33: 359-373. 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. Fung KY; Khan S; Krewski D; Chen Y. (2006). Association between air pollution and multiple respiratory hospitalizations among the elderly in Vancouver, Canada. Inhalation Toxicology 18: 1005-1011. Fusco D; Forastiere F; Michelozzi P; Spadea T; Ostro B; Arca M; Perucci CA. (2001). Air pollution and hospital admissions for respiratory conditions in Rome, Italy. Eur Respir J 17: 1143-1150. Garofolo MC; Seidler FJ; Auman JT; Slotkin TA. (2002). ß-Adrenergic modulation of muscarinic cholinergic receptor expression and function in developing heart. American Jo urnal of Physiology- Regulatory, Integrative and Comparative Physiology 282: 1356-1363. Gautier M; Antier D; Bonnet P; Net JLL; Hanton G; Eder V. (2007). Continuous inhalation of carbon monoxide induces right ventricle ischemia and dysfunction in rats with hypoxic pulmonary hypertension. Am J Physiol Heart Circ Physiol 293: H1046. Gaworski CL; Carmines EL; Faqi AS; Rajendran N. (2004). In utero and lactation exposure of rats to 1R4F reference cigarette mainstream smoke: effect on prenatal and postnatal development. Toxicol Sci 79: 157- 169. Gee GC; Payne-Sturges DC. (2004). Environmental health disparities: a framework integrating psychosocial and environmental concepts. Environ Health Perspect 112: 1645-1653. Georgoulis LB; Hanninen O; Samoli E; Katsouyanni K; Kunzli N; Polanska L; Bruinen de Bruin Y; Alm S; Jantunen M. (2002). Personal carbon monoxide exposu re in five European cities and its determinants. Atmos Environ 36: 963-974. Gerritsen J; Carlson RE; Dycus DL; Faulkner C; Gibson GR. (1998). Lake and reservoir bioassessment and biocriteria: Technical guidance document. EPA 841-B-98-007. US Environmental Protection Agency, Office of Water, Washington, DC. http://www.epa.gov/owow/monitoring/tech/lakes.html , Ghio AJ; Stonehuerner JG; Dailey LA; Richards JH; Ma dden MD; Deng Z; Nguyen NB; Callaghan KD; Yang F; Piantadosi CA. (2008). Carbon Monoxide Reversibly Alters Iron Homeostasis and Respiratory Epithelial Cell Function. Am J Respir Cell Mol Biol. Gibson QH; Roughton FJ. (1955). The kinetics of dissociation of the first oxygen molecule from fully saturated oxyhaemoglobin in sheep blood solutions. Proc R Soc Lond B Biol Sci 143: 310-334. Gibson QH; Olson JS; McKinnie RE; Rohlfs RJ. (1986). A kinetic description of ligand binding to sperm whale myoglobin. J Biol Chem 261: 10228-10239. Gilboa SM; Mendola P; Olshan AF; Langlois PH; Savitz DA; Loomis D; Herring AH; Fixler DE. (2005). Relation between ambient air quality and selected birth defects, seven county study, Texas, 1997-2000. American Journal of Epidemiology 162: 238-252. Gilliam RC; Huber AH; Raman S; Neogi D. (2005). Metro politan-Scale Transport and Dispersion of Materiel Released from the New York World Trade Center Following September 11, 2001—Part II: The CALPUFF Plume Model. Pure and Applied Geophysics 162: 2005-2028. Gilliland F; Avol PK; Kinney PL; Jerrett M; Dvonch T; Lurma nn F; Buckley T; Breysse P; Keeler G; de Villiers T; McConnell R. (2005). Air Pollution Exposure Assessment for Epidemiologic Studies of Pregnant Women and Children: Lessons Learned from the Centers for Children’s Environmental Health and Disease Prevention Research. Environ Health Perspect 113: 1447-1454. Giustino A; Cagiano R; Carratu MR; De Salvia MA; Pa naro MA; Jirillo E; Cuomo V. (1993). Immunological changes produced in rats by prenat al exposure to carbon monoxide. Pharmacol Toxicol 73: 274-278. Giustino A; Carratu MR; Siro BG; De Salvia MA; Pellegrin o NM; Steardo L; Jirillo E; Cuomo V. (1994). Changes in the frequency of splenic immunocompetent cells in rats exposed to carbon monoxide during gestation. Immunopharmacol Immunotoxicol 16: 281-292.

March 2009 R-10 DRAFT—DO NOT QUOTE OR CITE

## Giustino A; Cagiano R; Carratu MR; Cassano T; Tattoli M; Cuomo V. (1999). Prenatal exposure to low concentrations of carbon monoxide alters habituatio n and non-spatial working memory in rat offspring. Brain Res 844: 201-205. Glabe A; Chung Y; Xu D; Jue T. (1998). Carbon monoxide inhibition of regulatory pathways in myocardium. Am J Physiol 274: H2143-2151. Gold DR; Litonjua A; Schwartz J; Lovett E; Larson A; Near ing B; Allen G; Verrier M; Cherry R; Verrier R. (2000). Ambient pollution and heart rate variability. Circulation 101: 1267-1273. Gold DR; Litonjua AA; Zanobetti A; Coull BA; Schwartz J; MacCallum G; Verrier RL; Nearing BD; Canner MJ; Suh H; Stone PH. (2005). Air pollution and ST-segment depression in elderly subjects. Environ Health Perspect 113: 883-887. Goldberg MS; Burnett RT; Bailar JC, 3rd; Tamblyn R; Ernst P; Flegel K; Brook J; Bonvalot Y; Singh R; Valois MF; Vincent R. (2001). Identification of persons with cardiorespiratory conditions who are at risk of dying from the acute effects of ambient air particles. Environ Health Perspect 109 Suppl 4: 487-494. Goldberg MS; Burnett RT; Valois MF; Flegel K; Bailar JC, 3rd; Brook J; Vincent R; Radon K. (2003). Associations between ambient air pollution and daily mortality am ong persons with congestiv e heart failure. Environ Res 91: 8-20. Goldberg MS; Burnett RT; Yale JF; Valois MF; Brook JR . (2006). Associations betw een ambient air pollution and daily mortality among persons with diab etes and cardiovascular disease. En vironmental Research 100: 255. Goldstein AH; Galbally IE. (2007). Known and unexplor ed organic constituents in the earth’s atmosphere. Environmental Science and Technology 41: 1514–1521. Gomez-Perales JE; Colvile RN; Nieuwenhuijsen MJ; Fe rnández-Bremauntz A; Gutiérrez-Avedoy VJ; PáramoFigueroa VH; Blanco-Jiménez S; Bueno-López E; Mandujano F; Bernabe-Cabanillas R. (2004). Commuters’exposure to PM2. 5, CO, and benzene in public transport in the metropolitan area of Mexico City. Atmos Environ 38: 1219-1229. Gomez-Perales JE; Colvile RN; Fernández-Bremauntz AA ; Gutiérrez-Avedoy V; Pára mo-Figueroa VH; BlancoJiménez S; Bueno-López E; Bernabé-Cabanillas R; Mandujano F; Hidalgo-Navarro M. (2007). Bus, minibus, metro inter-comparison of commuters’ exposure to air pollution in Mexico City. Atmos Environ 41: 890-901. Goss CH; Newsom SA; Schildcrout JS; Sheppard L; Ka ufman JD. (2004). Effect of ambient air pollution on pulmonary exacerbations and lung function in cystic fibrosis. American Journal of Respiratory and Critical Care Medicine 169: 816-821. Gouveia N; Fletcher T. (2000a). Resp iratory diseases in children and outdoor air pollution in Sao Paulo, Brazil: a time series analysis. Occupational and Environmental Medicine 57: 477-483. Gouveia N; Fletcher T. (200 0b). Time series analysis of air pollution and mortality: effects by cause, age and socioeconomic status. J Epidemiol Community Health 54: 750-755. Gouveia N; Bremner SA; Novaes HM. (2004). Association between ambient air pollution and birth weight in Sao Paulo, Brazil. J Epidemiol Community Health 58: 11-17. Grosjean D; Fung K. (1984). Hydrocarbons and carbonyl in Los Angeles air. J Air Pollut Control Assoc 34. Grover TR; Rairigh RL; Zenge JP; Abman SH; Kinsella JP. (2000). Inhaled carbon monoxide does not cause pulmonary vasodilation in the late-gestation fetal lamb. American Journal of Physiology- Lung Cellular and Molecular Physiology 278: L779-784. Gryparis A; Forsberg B; Katsouyanni K; Analitis A; Touloumi G; Schwartz J; Samoli E; Medina S; Anderson HR; Niciu EM. (2004). Acute Effects of Ozone on Mortality from the" Air Pollution and Health: A European Approach" Project. American Journal of Respirat ory and Critical Care Medicine 170: 1080. Gulliver J; Briggs DJ. (2005). Time-s pace modeling of journey-time exposure to traffic-related air pollution using GIS. Environ Res 97: 10-25. Guo YL; Lin YC; Sung FC; Huang SL; Ko YC; Lai JS; Su HJ; Shaw CK; Lin RS; Dockery DW. (1999). Climate, traffic-related air pollutants, and asthma prevalence in middle-school children in taiwan. Environ Health Perspect 107: 1001-1006. Guyatt AR; Holmes MA; Cumming G. (1981). Can carbon monoxide be absorbed from the upper respiratory tract in man? Eur J Respir Dis 62: 383-390. Gwynn RC; Burnett RT; Thurston GD. (2000). A time-series an alysis of acidic particulate matter and daily mortality and morbidity in the Buffalo, New York, region. Environ Health Perspect 108: 125-133. Ha EH; Hong YC; Lee BE; Woo BH; Schwartz J; Christiani DC. (2001). Is air pollution a risk factor for low birth weight in Seoul? Epidemiology 12: 643-648.

March 2009 R-11 DRAFT—DO NOT QUOTE OR CITE

## Ha EH; Lee JT; Kim H; Hong YC; Lee BE; Park HS; Christiani DC. (2003). Infant susceptibility of mortality to air pollution in Seoul, South Korea. Pediatrics 111: 284-290. Haab P. (1990). The effect of carbon monoxide on respiration. Experientia 46: 1202-1206. Hajat S; Haines A; Goubet SA; Atkinson RW; Anderson HR. (1999). Association of air pollution with daily GP consultations for asthma and other lower respiratory conditions in London. Thorax 54: 597-605. Hajat S; Anderson HR; Atkinson RW; Haines A. (2 002). Effects of air pollution on general practitioner consultations for upper respiratory diseases in London. Occupational and Environmental Medicine 59: 294- 299. Haldane J. (1898). Some improved methods of gas analysis. The Journal of Physiology 22: 465. Hampson NB. (2007). Carboxyhemoglobin elevation due to hemolytic anemia. J Emerg Med 33: 17-19. Hanada A; Sander M; Gonzalez-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. Hanel B; Teunissen I; Rabol A; Warberg J; Secher NH. (1997). Restricted postexercise pulmonary diffusion capacity and central blood volume depletion (Vol. 83, pp. 11-17): Am Physiological Soc. Hapcioglu B; Issever H; Kocyigit E; Disci R; Vatansever S; Ozdilli K. (2006). The effect of air pollution and meteorological parameters on chronic obstructive pulmonary disease at an Istanbul Hospital. Indoor and Built Environment 15: 147-153. Hara S; Mukai T; Kurosaki K; Kuriiwa F; Endo T. (2002). Modification of the striatal dopaminergic neuron system by carbon monoxide exposure in free-moving rats, as determined by in vivo brain microdialysis. Arch Toxicol 76: 596-605. Harada T; Koi H; Kubota T; Aso T. (2004). Haem oxygenase augments porcine granulosa cell apoptosis in vitro. J Endocrinol 181: 191-205. Harley RA; McKeen SA; Pearson J; Rodgers MO; Lonneman WA. (2001). Analysis of motor vehicle emissions during the Nashville/Middle Tennessee Ozone Study. Jo urnal of Geophysical Research 106: 3559–3567. Harley RA; Marr LC; Lehner JK; Giddings SN. (2005). Changes in motor vehicle emissions on diurnal to decadal time scales and effects on atmospheric composition. Environmental Science and Technology 39: 5356- 5362. Hausberg M; Somers VK. (1997). Neural circulatory responses to carbon monoxide in healthy humans. Hypertension 29: 1114-1118. Hegewald MJ; Crapo RO. (2007). Socioeconomic status and lung function. Chest 132: 1608-1614. Hendler I; Baum M; Kreiser D; Schiff E; Druzin M; Stevenson DK; Seidman DS. (2004). End-tidal breath carbon monoxide measurements are lower in pregnant women with uterine contractions. J Perinatol 24: 275-278. Henrotin JB; Besancenot JP; Bejot Y; Giroud M. (2007). Short-term effects of ozone air pollution on ischaemic stroke occurrence: a case- crossover analysis from a 10-year population-based study in Dijon, France. Occupational and Environmental Medicine 64: 439-445. Hering SV. (2007). Using Regional Data and Building Leak age to Assess Indoor Concentrations of Particles of Outdoor Origin. Aerosol Science and Technology 41: 639-654. Hermans RH; McGivern RF; Chen W; Longo LD. (1993). A ltered adult sexual behavior in the male rat following chronic prenatal hypoxia. Neurotoxicol Teratol 15: 353-363. Hill AB. (1965). The Environment and Disease: Associa tion or Causation? Proc R Soc Med 58: 295-300. Hill EP; Hill JR; Power GG; Longo LD. (1977). Carbon monoxide exchanges between the human fetus and mother: a mathematical model. Am J Physiol 232: H311-323. Hinwood AL; De Klerk N; Rodriguez C; Jacoby P; Runnion T; Rye P; Landau L; Murray F; Feldwick M; Spickett JCEH. (2006). The relationship between changes in daily air pollution and hospitalizations in Perth, Australia 1992-1998: a case-crossover study. Int J Environ Health Res 16: 27-46. Hirsch T; Weiland SK; von Mutius E; Safeca AF; Grafe H; Csaplovics E; Duhme H; Keil U; Leupold W. (1999). Inner city air pollution and respiratory health and atopy in children. Eur Respir J 14: 669-677. Hoek G; Brunekreef B; Verhoeff A; van Wijnen J; Fisc her P. (2000). Daily mortality and air pollution in The Netherlands. J Air Waste Manag Assoc 50: 1380-1389. Hoek G; Brunekreef B; Fischer P; van Wijnen J. (2001). The association between air pollution and heart failure, arrhythmia, embolism, thrombosis, and other cardiovascular causes of death in a time series study. Epidemiology 12: 355-357. Hoek G. (2003). Daily mortality and air pollution in The Neth erlands. In Revised analyses of time-series studies of air pollution and health. Health Effects Institute, Boston, MA. Hoel DG. (1980). Incorporation of background in dose-response models. Fed Proc 39: 73-75.

March 2009 R-12 DRAFT—DO NOT QUOTE OR CITE

## Hofmann OM; Brittain T. (1998). Partitioning of oxygen and carbon monoxide in the three human embryonic hemoglobins. Hemoglobin 22: 313-319. Holguin F; Tellez-Rojo MM; Hernandez M; Cortez M; Chow JC; Watson JG; Mannino D; Romieu I. (2003). Air pollution and heart rate variability among the elderly in Mexico City. Epidemiology 14: 521-527. Holland RA. (1970). Reaction rates of carbon monoxide and hemoglobin. Ann N Y Acad Sci 174: 154-171. Hong YC; Leem JH; Ha EH. (1999a). Air pollution and daily mortality in Inchon, Korea. J Korean Med Sci 14: 239- 244. Hong YC; Leem JH; Ha EH; Christiani DC. (1999b). PM(1 0) exposure, gaseous pollutants, and daily mortality in Inchon, South Korea. Environ Health Perspect 107: 873-878. Hong YC; Lee JT; Kim H; Ha EH; Schwartz J; Christiani DC. (2002a). Effects of air pollutants on acute stroke mortality. Environ Health Perspect 110: 187-191. Hong YC; Lee JT; Kim H; Kwon HJ. (2002b). Air pollution: a new risk factor in ischemic stroke mortality. Stroke 33: 2165-2169. Horvath SM; Dahms TE; O'Hanlon JF. (1971). Carbon monoxide and human vigilance. A deleterious effect of present urban concentrations. Archives of Environmental Health 23: 343-347. Horvath SM; Raven PB; Dahms TE; Gray DJ. (1975). Maximal aerobic capacity at different levels of carboxyhemoglobin. J Appl Physiol 38: 300-303. Horvath SM; Bedi JF; Wagner JA; Agnew J. (1988). Maximal aerobic capacity at several ambient concentrations of CO at several altitudes. J Appl Physiol 65: 2696-2708. Hosseinpoor AR; Forouzanfar MH; Yunesian M; Asghari F; Naieni KH; Farhood D. (2005). Air pollution and hospitalization due to angina pectoris in Tehran, Iran: a time-series study. Environ Res 99: 126-131. Hsia CC. (2002). Recruitment of lung diffusing capacity: update of concept and application. Chest 122: 1774-1783. Huikuri HV; Jokinen V; Syvanne M; Nieminen MS; Airaksinen KE; Ikaheimo MJ; Koistinen JM; Kauma H; Kesaniemi AY; Majahalme S; Niemela KO; Frick MH. (1999). Heart rate variability and progression of coronary atherosclerosis. Atertio Thromb Vasc Biol 19: 1979-1985. Hutcheon JA; Platt RW. (2008). The missing data problem in birth weight percentiles and thresholds for "Small-forGestational-Age". American Journal of Epidemiology 167: 786-792. Huynh M; Woodruff TJ; Parker JD; Schoendorf KC. (2006). Relationships between air pollution and preterm birth in California. Paediatr Perinat Epidemiol 20: 454-461. Hwang B; Jaakkola JK. (2008). Ozone and Other Air Pollutants and the Risk of Oral Clefts Environ Health Perspect 116: 1411–1415 Hwang BF; Lee YL; Lin YC; Jaakkola JJ; Guo YL. (2005). Tr affic related air pollution as a determinant of asthma among Taiwanese school children. Thorax 60: 467-473. Hwang BF; Jaakkola JJ; Lee YL; Lin YC; Guo YL. (2006). Relation between air pollution and allergic rhinitis in Taiwanese schoolchildren. Respir Res 7: 23. Hwang JS; Chan CC. (2002). Effects of air pollution on da ily clinic visits for lowe r respiratory tract illness. American Journal of Epidemiology 155: 1-10. IARC. (2006). IARC monographs on the evaluation of carc inogenic risks to humans: Preamble. . International Agency for Research on Cancer (IARC). Lyon, France. http://monographs.iarc.fr/ENG/ Preamble/CurrentPreamble.pdf Ibald-Mulli A; Stieber J; Wichmann HE; Koenig W; Peters A. (2001). Effects of air pollution on blood pressure: a population-based approach. Am J Public Health 91: 571-577. Ibald-Mulli A; Timonen KL; Peters A; Heinrich J; Wolke G; Lanki T; Buzorius G; Kreyling WG; de Hartog J; Hoek G; ten Brink HM; Pekkanen J. (2004). Effects of particulate air pollution on blood pressure and heart rate in subjects with cardiovascular dis ease: A multicenter approach. Environ Health Perspect 112: 369-377. Iheagwara KN; Thom SR; Deutschman CS ; Levy RJ. (2007). Myocardial cytoch rome oxidase activity is decreased following carbon monoxide exposure. Biochim Biophys Acta 1772: 1112-1116. Imai T; Morita T; Shindo T; Nagai R; Yazaki Y; Kuriha ra H; Suematsu M; Katayama S. (2001). Vascular smooth muscle cell-directed overexpression of heme oxygenas e-1 elevates blood pressure through attenuation of nitric oxide-induced vasodilation in mice. Circul Res 89: 55-62. Inman RE; Ingersoll RB; Levy EA. (1971). Soil: A Natural Sink for Carbon Monoxide. Science 172: 1229-1231. IOM (2008). Improving the Presumptive Disability Decision-Making Process for Veterans. In Samet James M, Bodurow CC (Eds.), IPCC. (2001). Climate Change 2001: The Scientific Basis. Cambridge Univ. Press, Cambridge, UK. IPCC. (2007). Climate Change 2007: The Physical Scien ce Basis. Cambridge Univ. Press, Cambridge, UK.

March 2009 R-13 DRAFT—DO NOT QUOTE OR CITE

## Ischiropoulos H; Beers MF; Ohnishi ST; Fisher D; Garner SE; Thom SR. (1996). Nitric oxide production and perivascular nitration in brain after carbon monoxide poisoning in the rat. J Clin Invest 97: 2260-2267. Ito K; Thurston GD; Silverman RA. (2007). Characterization of PM2.5, gaseous pollutants, and meteorological interactions in the context of time-series health ef fects models. J Expo Sci Environ Epidemiol 17 Suppl 2: S45-60. Jalaludin B; Morgan G; Lincoln D; Sheppeard V; Simpson R; Corbett S. (2006). Associations between ambient air pollution and daily emergency department attendances for cardiovascular disease in the elderly (65+ years), Sydney, Australia. J Expo Sci Environ Epidemiol 16: 225-237. Jalaludin B; Mannes T; Morgan G; Lincoln D; Sheppeard V; Corbett S. (2007). Impact of ambient air pollution on gestational age is modified by season in Sydney, Australia. Environ Health 6: 16. Jeffries HE. (1995). Photochemical air pollution. In Singh HB (Ed.), Composition, chemistry, and climate of the atmosphere (pp. 308-348). New York, NY: Van Nostrand Reinhold. Jerrett M; Burnett RT; Willis A; Krewski D; Goldberg MS; De Luca P; Finkelstein N. (2003). Spatial analysis of the air pollution-mortality relationship in the context of ecologic confounders. J Toxicol Environ Health A 66: 1735-1777. Jerrett M; Arain A; Kanaroglou P; Beckerman B; Potoglou D; Sahsuvaroglu T; Morrison J; Giovis C. (2004). A review and evaluation of intraurban air pollution exposure models. Journal of Exposure Science and Environmental Epidemiology 15: 185-204. Johnson FK; Johnson RA. (2003). Carbon monoxide promotes endothelium-dependent constriction of isolated gracilis muscle arterioles. American Journal of Physiology- Regulatory, Integrative and Comparative Physiology 285: 536-541. Johnson FK; Durante W; Peyton KJ; Johnson RA. (2003). Heme oxygenase inhibitor restores arteriolar nitric oxide function in Dahl rats. Hypertension 41: 149-155. Johnson FK; Durante W; Peyton KJ; Johnson RA. (2004). Heme oxygenase-mediated endothelial dysfunction in DOCA-salt, but not in spontaneously hypertensive, rat arterioles. Am J Physiol Heart Circ Physiol 286: 1681-1687. Johnson FK; Johnson RA; Durante W; Jackson KE; Stevenson BK; Peyton KJ. (2006). Metabolic syndrome increases endogenous carbon monoxide production to promote hypertension and endothelial dysfunction in obese Zucker rats. American Journal of Physiology- Regulatory, Integrative an d Comparative Physiology 290: 601-608. Johnson T. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Research Triangle Park, NC: US Environmental Protection Agency, Envi ronmental Monitoring Syst ems Laboratory; report no. EPA-600/4-84-014, Joumard R; Chiron M; Vidon R; Maurin M; Rouzioux JM. (1981). Mathematical models of the uptake of carbon monoxide on hemoglobin at low carbon monoxide levels. Environ Health Perspect 41: 277-289. Kanakidou M; Crutzen PJ. (1999). The photochemical so urce of carbon monoxide: Impor tance, uncertainties and feedbacks. Chemosphere-Global Change Science 1: 91-109. Kanten WE; Penney DG; Francisco K; Thill JE. (1983). He modynamic responses to acute carboxyhemoglobinemia in the rat. Am J Physiol 244: H320-327. Karr C; Lumley T; Shepherd K; Davis R; Larson T; Ritz B; Kaufman J. (2006). A case-crossover study of wintertime ambient air pollution and infant bronchiolitis. Environ Health Perspect 114: 277-281. Karr C; Lumley T; Schreuder A; Davis R; Larson T; Ritz B; Kaufman J. (2007). Effects of subchronic and chronic exposure to ambient air pollutants on infant bronchiolitis. American Journal of Epidemiology 165: 553- 560. Kastner-Klein P; Plate EJ. (1999). Wind-tunnel study of concentration fields in street canyons. Atmos Environ 33: 3973-3979. Katoue MG; Khan I; Oriowo MA. (2005). Increased expression and activity of heme oxygenase-2 in pregnant rat aorta is not involved in attenuated vasopressin- induced contraction. Naunyn Schmiedebergs Arch Pharmacol 372: 220-227. Katoue MG; Khan I; Oriowo MA. (2006). Pregnancy- induced modulation of calcium mobilization and downregulation of Rho-kinase expression contribute to attenuated vasopressin-induced contraction of the rat aorta. Vascul Pharmacol 44: 170-176. Katsouyanni K; Touloumi G; Samoli E; Gryparis A; Le Te rtre A; Monopolis Y; Rossi G; Zmirou D; Ballester F; Boumghar A. (2001). Confounding and Effect Modification in the Short-Term Effects of Ambient Particles on Total Mortality: Results from 29 European Cities within the APHEA2 Project. Epidemiology 12: 521.

March 2009 R-14 DRAFT—DO NOT QUOTE OR CITE

## Kaur S; Nieuwenhuijsen M; Colvile R. (2005). Personal exposure of street canyon intersection users to PM2.5, uhrafine particle counts and carbon monoxide in Central. London, UK. Atmos Environ 39: 3629-3641. Kaur S; Nieuwenhuijsen MJ; Colvile RN. (2007). Fine particulate matter and carbon monoxide exposure concentrations in urban street transport microenvironments. Atmos Environ 41: 4781-4810. Keatinge WR; Donaldson GC. (2001). Mortality related to cold and air pollution in London after allowance for effects of associated weather pa tterns. Environ Res 86: 209-216. Kettunen J; Lanki T; Tiittanen P; Aalto PP; Koskentalo T; Kulmala M; Salomaa V; Pekkanen J. (2007). Associations of fine and ultrafine particulate air po llution with stroke mortality in an area of low air pollution levels. Stroke 38: 918-922. Khalil MAK; Rasmussen RA. (1990). The global cycle of carbon monoxide: trends and mass balance. Chemosphere 20: 227-242. Khan AA; Wang Y; Sun Y; Mao XO; Xie L; Miles E; Gr aboski J; Chen S; Ellerby LM; Jin K; Greenberg DA. (2006). Neuroglobin-overexpressing transgenic mice ar e resistant to cerebral and myocardial ischemia. Proceedings of the National Academy of Sciences 103: 17944-17948. Kim D; Sass-Kortsak A; Purdham JT; Dales RE; Brook JR. (2006). Associations between personal exposures and fixed-site ambient measurements of fine particulate matter, nitrogen dioxide, and carbon monoxide in Toronto, Canada. Journal of Exposure Science and Environmental Epidemiology 16: 172-183. Kim HP; Wang X; Nakao A; Kim SI; Murase N; Choi ME; Ryter SW; Choi AM. (2005). Caveolin-1 expression by means of p38beta mitogen-activated protein kinase mediates the antiproliferative effect of carbon monoxide. Proceedings of the National Ac ademy of Sciences 102: 11319-11324. Kim HS; Loughran PA; Rao J; Billiar TR; Zuckerbraun BS . (2008). Carbon monoxide activates NF-kappaB via ROS generation and Akt pathways to protect against cell death of hepatocytes. American Journal of Physiology Gastrointestinal and Liver Physiology 295: G146-G152. Kim SY; O'Neill MS; Lee JT; Cho Y; Kim J; Kim H. (2007) . Air pollution, socioeconomic position, and emergency hospital visits for asthma in Seoul, Korea. Int Arch Occup Environ Health 80: 701-710. King GM. (1999). Characteristics and significance of atmospheric carbon monoxide consumption by soils. Chemosphere-Global Change Science 1: 53-63. Kingdom JCP; Kaufmann P. (1997). Oxyg en and placental villous development: Origins of fetal hypoxia. Placenta 18: 613-621. Kinney HC; Filiano JJ; Sleeper LA; Mandell F; Valdes -Dapena M; White WF. (199 5). Decreased muscarinic receptor binding in the arcuate nu cleus in sudden infant death s yndrome. Science 269: 1446-1450. Kinobe RT; Vlahakis JZ; Soong JM; Szarek WA; Brien JF ; Longo LD; Nakatsu K. (2006). Heme oxygenase activity in fetal and adult sheep is not altered by acclimatiza tion to high altitude hypoxia. Can J Physiol Pharmacol 84: 893-901. Kizakevich PN; McCartney ML; Hazucha MJ; Sleet LH; Jo chem 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. Klasner AE; Smith SR; Thompson MW; Scalzo AJ. (1998). Carbon monoxide mass exposure in a pediatric population. Acad Emerg Med 5: 992-996. Klausen K; Rasmussen B; Gjellerod H; Madsen H; Petersen E. (1968). Circulation, metabolism and ventilation during prolonged exposure to carbon monoxide and to high altitude. Scand J Clin Lab Invest Suppl 103: 26-38. Kleinman MT; Davidson DM; Vandagriff RB; Caiozzo VJ; Whittenberger JL. (1989). Effects of short-term exposure to carbon monoxide in subjects with coronary artery disease. Archives of Environmental Health 44: 361-369. Kleinman MT; Leaf DA; Kelly E; Caiozzo V; Osann K; O'Niell T. (1998). Urban angina in the mountains: effects of carbon monoxide and mild hypoxemia on subjects with chronic stable angina. Archives of Environmental Health 53: 388-397. Klemm RJ; Lipfert FW; Wyzga RE; Gust C. (2004). Daily mo rtality and air pollution in Atlanta: two years of data from ARIES. Inhalation Toxicology 16 Suppl 1: 131-141. Klepeis NE; Nelson WC; Ott WR; Robinson JP; Tsang AM; Switzer P; Behar JV; Hern SC; Engelmann WH. (2001). The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol 11: 231-252. Klimisch HJ; Chevalier HJ; Harke HP; Dontenwill W. (1975). Uptake of carbon monoxide in blood of miniture pigs and other mammals. Toxicology 3: 301-310.

March 2009 R-15 DRAFT—DO NOT QUOTE OR CITE

## Knuckles TL; Lund AK; Lucas SN; Campen MJ. (2008). Diesel exhaust exposure enhances venoconstriction via uncoupling of eNOS. Toxicol Appl Pharmacol 230: 346-351. Koehler RC; Jones MD; Traystman RJ. (1982). Cerebral circulatory response to carbon monoxide and hypoxic hypoxia in the lamb. American Journal of Physiology- Heart and Circulatory Physiology 243: 27-32. Koike A; Wasserman K; Armon Y; Weiler-Ravell D. (1991). The work-rate-dependent effect of carbon monoxide on ventilatory control during exercise. Respir Physiol 85: 169-183. Koken PJ; 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. Kontos AS; Fassois SD; Deli MF. (1999). Short-term effects of air pollution on childhood respiratory illness in Piraeus, Greece, 1987-1992: nonpar ametric stochastic dynamic analysis. Environ Res 81: 275-296. Korres S; Riga M; Balatsouras D; Papadakis C; Kanellos P; Ferekidis E. (2007). Influence of smoking on developing cochlea. Does smoking during pregnancy affect the amplitudes of transient evoked otoacoustic emissions in newborns? Int J Pediatr Otorhinolaryngol 71: 781. Kotsch K; Francuski M; Pascher A; Klemz R; Seifert M; Mittler J; Schumacher G; Buelow R; Volk HD; Tullius SG. (2006). Improved Long-Term Graft Survival after HO-1 Induction in Brain-Dead Donors. Am J Transplantation 6: 477. Kreider JC; Blumberg MS. (2005). Geotaxis and beyond: commentary on Motz and Alberts (2005). Neurotoxicol Teratol 27: 529-533. Kreiser D; Baum M; Seidman DS; Fanaroff A; Shah D; Hendler I; Stevenson DK; Schiff E; Druzin ML. (2004). End tidal carbon monoxide levels are lower in women with gestational hypertension and pre-eclampsia. J Perinatol 24: 213-217. Krewski D; Burnett RT; Goldberg MS; Hoover K; Siemiatyck i J; Jerret M. (2000). Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality. . Health Effects, Institute, Boston, MA. Kruize H; Hanninen O; Breugelmans O; Lebret E; Jantunen M. (2003). Description and demonstration of the EXPOLIS simulation model: two examples of modeling population exposure to particulate matter. J ExpoAnalEnvironEpidemiol 13: 87-99. Kuenzli N; Tager IB. (1997). The Semi-individual Study in Air Pollution Epidemiology: A Valid Design as Compared to Ecologic Studies. Environ Health Perspect 105: 1078-1083. Kwon HJ; Cho SH; Nyberg F; Pershagen G. (2001). Effects of ambient air pollution on daily mortality in a cohort of patients with congestive heart failure. Epidemiology 12: 413-419. Lagorio S; Forastiere F; Pistelli R; Iavarone I; Michelozzi P; Fano V; Marconi A; Ziemacki G; Ostro BD. (2006). Air pollution and lung function among susceptible adult subjects: a panel study. Environ Health 5: 11. Lai HK; Kendall M; Ferrier H; Lindup I; Alm S; Hanninen O; Jantunen M; Mathys P; Colvile R; Ashmore MR; Cullinan P; Nieuwenhuijsen MJ. (2004). Personal exposures and microenvironment concentrations of PM2.5, VOC, NO2 and CO in Oxford, UK. Atmos Environ 38: 6399-6410. Lamberto C; Nunes H; Le Toumelin P; Duperron F; Vale yre D; Clerici C. (2004). Membrane and capillary blood components of diffusion capacity of the lung for carbon monoxide in pulmonary sarcoidosis: relation to exercise gas exchange. Ch est 125: 2061-2068. Landaw SA; Callahan EW, Jr.; Schmid R. (1970). Catabo lism of heme in vivo: comparison of the simultaneous production of bilirubin and carbon monoxide. J Clin Invest 49: 914-925. Landaw SA. (1973). The effects of cigarette smoking on total body burden and excretion rates of carbon monoxide. J Occup Med 15: 231-235. Langan L. (1992). Portability in measuing exposure to carbon monoxide. Journal of Exposure Analysis and Environmental Epidemiology Suppl 1: 223-239. Lanki T; Pekkanen J; Aalto P; Elosua R; Berglind N; D'I ppoliti D; Kulmala M; Nyberg F; Peters A; Picciotto S; Salomaa V; Sunyer J; Tiittanen P; von Klot S; Forastiere F. (2006). Associations of traffic related air pollutants with hospitalisation for first acute myocardi al infarction: the HEAPSS study. Occupational and Environmental Medicine 63: 844-851. Lash GE; McLaughlin BE; MacDonald-Goodfellow SK; Sm ith GN; Brien JF; Marks GS; Nakatsu K; Graham CH. (2003). Relationship between tissue damage and heme oxygenase expression in chorionic villi of term human placenta. Am J Physiol Heart Circ Physiol 284: H160-167. Lee BE; Ha EH; Park HS; Kim YJ; Hong YC; Kim H; Lee JT. (2003a). Exposure to air pollution during different gestational phases contributes to risks of low birth weight. Hum Reprod 18: 638-643.

March 2009 R-16 DRAFT—DO NOT QUOTE OR CITE

## Lee IM; Tsai SS; Ho CK; Chiu HF; Yang C. (2007a). Air pollution and hospital admissions for congestive heart failure in a tropical city: Kaohsiung, Taiwan. Inhalation Toxicology 19: 899-904. Lee IM; Tsai SS; Chang CC; Ho CK; Yang CY. (200 7b). Air pollution and hospital admissions for chronic obstructive pulmonary disease in a tropical city: Kaohsiung, Taiwan. Inhalation Toxicology 19: 393-398. Lee JT; Kim H; Song H; Hong YC; Cho YS; Shin SY; Hyun YJ; Kim YS. (2002). Air pollution and asthma among children in Seoul, Korea. Epidemiology 13: 481-484. Lee JT; Kim H; Cho YS; Hong YC; Ha EH; Park H. ( 2003b). Air pollution and hospital admissions for ischemic heart diseases among individuals 64+ years of age residing in Seoul, Korea. Archives of Environmental Health 58: 617-623. Lee JT; Son JY; Kim H; Kim SY. (2006). Effect of air pollution on asthma-related hospital admissions for children by socioeconomic status a ssociated with area of residence. Arch Environ Occup Health 61: 123-130. Lee JT; Son JY; Cho YS. (2007c). A comparison of morta lity related to urban air particles between periods with Asian dust days and without Asian dust days in Seoul, Korea, 2000-2004. Environmental Research 105: 409-413. Lee PJ; Jiang BH; Chin BY; Iyer NV; Alam J; Semenza GL ; Choi AM. (1997). Hypoxia-inducible factor-1 mediates transcriptional activation of the heme oxygenase-1 gene in response to hypoxia. J Biol Chem 272: 5375- 5381. Lee YL; Shaw CK; Su HJ; Lai JS; Ko YC; Huang SL; Su ng FC; Guo YL. (2003c). Clim ate, traffic-related air pollutants and allergic rhinitis prevalence in middle-school children in Taiwan. Eur Respir J 21: 964-970. Leem JH; Kaplan BM; Shim YK; Pohl HR; Gotway CA; Bullard SM; Rogers JF; Smith MM; Tylenda CA. (2006). Exposures to air pollutants during pregnancy and preterm delivery. Environ Health Perspect 114: 905-910. Levasseur L; Galliot-Guilley M; Richter F; Scherrmann JM ; Baud FJ. (1996). Effects of mode of inhalation of carbon monoxide and of normobaric oxygen administration on carbon monoxide elimination from the blood. Hum Exp Toxicol 15: 898-903. Levesque B; Allaire S; Prud'homme H; Dupuis K; Belle mare D. (2000). Air quality monitoring during indoor Monster Truck and car demolition shows. J Expo Anal Environ Epidemiol 10: 58-65. Levy D; Sheppard L; Checkoway H; Kaufman J; Lumley T; Koenig J; Siscovick D. (2001). A case-crossover analysis of particulate matter air pollution and out-of-hospital primary cardiac arrest. Epidemiology 12: 193-199. Li CI; Daling JR; Emanuel I. (2003). Birthweight and risk of overall and cause-specific childhood mortality. Paediatr Perinat Epidemiol 17: 164-170. Li XY; Zhang C; Wang SF; Ji YL; Wang H; Zhao L; Xu DX. (2008). Maternally ad ministered lipopolysaccharide (LPS) upregulates the expression of heme oxygenase-1 in fetal liver: the role of reactive oxygen species. Toxicol Lett 176: 169-177. Liao D; Duan Y; Whitsel EA; Zheng ZJ; Heiss G; Chinchilli VM; Lin HM. (2004). Association of higher levels of ambient criteria pollutants with impaired cardiac autonomic control: a population-based study. American Journal of Epidemiology 159: 768-777. Liao D; Heiss G; Chinchilli VM; Duan Y; Folsom AR; Lin HM; Salomaa V. (2005). Association of criteria pollutants with plasma hemostatic/inflammatory markers: a population-based study. J Expo Anal Environ Epidemiol 15: 319-328. Lin CA; Martins MA; Farhat SC; Pope CA, 3rd; Con ceicao GM; Anastacio VM; Hatanaka M; Andrade WC; Hamaue WR; Bohm GM; Saldiva PH. (1999). Air pollutio n and respiratory illness of children in Sao Paulo, Brazil. Paediatr Perinat Epidemiol 13: 475-488. Lin CA; Pereira LA; Nishioka DC; Conceicao GM; Braga AL; Saldiva PH. (2004a). Air pollution and neonatal deaths in Sao Paulo, Brazil. Braz J Med Biol Res 37: 765-770. Lin CM; Li CY; Yang GY; Mao IF. (2004b). Association between maternal exposure to elevated ambient sulfur dioxide during pregnancy and term low birth weight. Environmental Research 96: 41-50. Lin H; McGrath JJ. (1988). Vasodilating effects of carbon monoxide. Drug Chem Toxicol 11: 371-385. Lin M; Chen Y; Burnett RT; Villeneuve PJ; Krewski D. (2003). E ffect of short-term exposure to gaseous pollution on asthma hospitalisation in children: a bi-directi onal case-crossover analysis . J Epidemiol Community Health 57: 50-55. Lin M; Chen Y; Villeneuve PJ; Burnett RT; Lemyre L; He rtzman C; McGrail KM; Krewski D. (2004c). Gaseous air pollutants and asthma hospitalization of children with low household income in Vancouver, British Columbia, Canada. American Journal of Epidemiology 159: 294-303. Lin M; Stieb DM; Chen Y. (2005). Coarse particulate ma tter and hospitalization for respiratory infections in children younger than 15 years in Toronto: a case-crossover analysis. Pediatrics 116: e235-240.

March 2009 R-17 DRAFT—DO NOT QUOTE OR CITE

## 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. Lipfert F; Baty J; Miller J; Wyzga R. (2006a). PM 2.5 Co nstituents and Related Air Quality Variables As Predictors of Survival in a Cohort of US Military Ve terans. Inhalation Toxicology 18: 645-657. Lipfert FW; Perry HM; Miller JP; Baty JD; Wyzga RE; Ca rmody SE. (2000a). The Washington University-EPRI Veterans' Cohort Mortality Study. Preliminary Results. Inhalation Toxicology 12: 41-73. Lipfert FW; Morris SC; Wyzga RE. (2000b). Daily mo rtality in the Philadelphia metropolitan area and sizeclassified particulate matter. J Air Waste Manage Assoc 50: 1501-1513. Lipfert FW; Morris SC. (2002). Temporal and spatial re lations between age specific mortality and ambient air quality in the United States: regression results fo r counties, 1960-97. Occupational and Environmental Medicine 59: 156-174. Lipfert FW; Wyzga RE; Baty JD; Miller JP. (2006b). Traffic density as a surrogate measure of environmental exposures in studies of air pollution health effects: Long-term mortality in a cohort of US veterans. Atmos Environ 40: 154-169. Lippmann M; Ito K; Nadas A; Burnett RT. (2000). Association of particulate matter components with daily mortality and morbidity in urban populations. Res Rep Health Eff Inst: 5-72, discussion 73-82. Liu S; Krewski D; Shi Y; Chen Y; Burnett RT. (2003). Association between gaseous ambient air pollutants and adverse pregnancy outcomes in Vancouver, Canada. Environ Health Perspect 111: 1773-1778. Liu S; Krewski D; Shi Y; Chen Y; Burnett RT. (2007) . Association between matern al exposure to ambient air pollutants during pregnancy and fetal growth restric tion. Journal of Exposure Science and Environmental Epidemiology 17: 426-432. Liu Y; Fechter LD. (1995). MK-801 protects against carbon monoxide-induced hearing loss. Toxicol Appl Pharmacol 132: 196-202. Lobert JM; Scharffe DH; Hao WM; Kuhlbusch TA; Seuwen R; Warneck P; Crutzen PJ. (1991). Experimental evaluation of biomass burning emissions: Nitrogen and carbon containing compounds. . In Levine J (Ed.), Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications (pp. 289-304). Cambridge, Massachusetts: The MIT Press. Loennechen JP; Beisvag V; Arbo I; Waldum HL; Sandvik AK; Knardahl S; Ellingsen O. (1999). Chronic carbon monoxide exposure in vivo induces myocardial endothelin-1 expression and hypertrophy in rat. Pharmacol Toxicol 85: 192-197. Longo M; Jain V; Vedernikov YP; Saade GR; Goodrum L; Facch inetti F; Garfield RE. (1999). Effect of nitric oxide and carbon monoxide on uterine contractility during human and rat pregnancy. Am J Obstet Gynecol 181: 981-988. Lopez I; Acuna D; Webber DS; Korsak RA; Edmond J. (2003). Mild carbon monoxide exposure diminishes selectively the integrity of the cochlea of the developing rat. J Neurosci Res 74: 666-675. Lopez IA; Acuna D; Beltran-Parrazal L; Espinosa-Jeffrey A; Edmond J. (2008). Oxidative stress and the deleterious consequences to the rat cochlea after prenatal chro nic mild exposure to carbon monoxide in air. Neuroscience 151: 854-867. Luginaah IN; Fung KY; Gorey KM; Webster G; Wills C. (2005). Association of ambient air pollution with respiratory hospitalization in a government-designated "area of concern": the case of Windsor, Ontario. Environ Health Perspect 113: 290-296. Lund AK; Knuckles TL; Obot Akata C; Shohet R; McDonald JD; Gigliotti A; Seagrave JC; Campen MJ. (2007). Gasoline Exhaust Emissions Induce Vascular Remodeling Pathways Involved in Atherosclerosis. Toxicol Sci 95: 485. Lund AK; Lucero J; Lucas S; Madden MC; McDonald JD; Seagrave JC; Knuckles TL; Campen MJ. (2009). Vehicular Emissions Induce Vascular MMP-9 Expressi on and Activity Associated With Endothelin-1- Mediated Pathways. Atertio Thromb Vasc Biol. Lundh B; Cavallin-Stahl E; Mercke C. (1975). Nicotinic acid and the endogenous pr oduction of carbon monoxide. Acta Med Scand 197: 173-176. Luomanmaki K; Coburn RF. (1969). Effects of metabolism and distribution of carbon monoxide on blood and body stores. Am J Physiol 217: 354-363. Lyall F; Barber A; Myatt L; Bulmer JN; Robson SC. (2000). Hemeoxygenase expression in human placenta and placental bed implies a role in regu lation of trophob last invasion and placenta l function. FASEB J 14: 208- 219. Lyall F; Myatt L. (2002). The role of the placenta in pre-eclamps ia--a workshop report. Pl acenta 23 Suppl A: S142- 145.

March 2009 R-18 DRAFT—DO NOT QUOTE OR CITE

## MacDorman MF; Callaghan WM; Mathews TJ; Hoyert DL; Koch anek KD. (2007). Trends in preterm-related infant mortality by race and ethnicity, United States, 1999-2004. Int J Health Serv 37: 635-641. MacKay C; Pandow M; Wolfgang R. (1963). On the chemistry of natural radiocarbon. J geophys Res 68: 3929– 3931. MacMillan V. (1975). Regional cerebral blood flow of the ra t in acute carbon monoxide intoxication. Can J Physiol Pharmacol 53: 644-650. Mactutus CF; Fechter LD. (1984). Prenat al exposure to carbon monoxide: l earning and memory deficits. Science 223: 409-411. Mactutus CF; Fechter LD. (1985). Moderate prenatal carbon monoxide exposure produces persistent, and apparently permanent, memory deficits in rats. Teratology 31: 1. Maheswaran R; Haining RP; Brindley P; Law J; Pearson T; Fryers PR; Wise S; Campbell MJ. (2005a). Outdoor air pollution, mortality, and hospital admissions from co ronary heart disease in Sheffield, UK: a small-area level ecological study. Eur Heart J 26: 2543-2549. Maheswaran R; Haining RP; Brindley P; Law J; Pearson T; Fryers PR; Wise S; Campbell MJ. (2005b). Outdoor air pollution and stroke in Sheffield, United Kingdom: a small-area level geographical study. Stroke 36: 239- 243. Maines MD; Kappas A. (1974). Cobalt induction of hepatic heme oxygenase; with evidence that cytochrome P-450 is not essential for this enzyme activity. Proceedings of the National Academy of Sciences 71: 4293-4297. Maines MD; Trakshel GM; Kutty RK. ( 1986). Characterization of two constitutive forms of rat liver microsomal heme oxygenase. Only one molecular species of the enzyme is inducible. J Biol Chem 261: 411-419. Maisonet M; Bush TJ; Correa A; Jaakkola JJ. (2001). Relation between ambient air pollution and low birth weight in the Northeastern United States. Environ Health Perspect 109 Suppl 3: 351-356. Manier G; Moinard J; Techoueyres P; Varene N; Guenard H. (1991). Pulmonary diffusion limitation after prolonged strenuous exercise. Respir Physiol 83: 143-153. 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 di sease in persons with congestive heart failure or arrhythmia. Environ Health Perspect 110: 1247-1252. Mannes T; Jalaludin B; Morgan G; Lincoln D; Sheppeard V; Corbett S. (2005). Impact of ambient air pollution on birth weight in Sydney, Australia. Occupational and Environmental Medicine 62: 524-530. Manno M; Rugge M; Cocheo V. (1992). Double fatal in halation of dichloromethane. Hum Exp Toxicol 11: 540- 545. Mar TF; Norris GA; Koenig JQ; Larson TV. (2000). Associations between air pollution and mortality in Phoenix, 1995-1997. Environ Health Perspect 108: 347-353. Marks GS; Vreman HJ; McLaughlin BE; Brien JF; Nakatsu K. (2002). Measurement of endogenous carbon monoxide formation in biological systems. Antioxid Redox Signal 4: 271-277. Marshall JD; Nethery E; Brauer M. ( 2008). Within-urban variability in ambient air pollution: comparison of estimation methods. Atmos Environ 42: 1359-1369. Marthan R; Castaing Y; Manier G; Guenard H. (1985). Gas exchange alterations in patients with chronic obstructive lung disease. Chest 87: 470-475. Martin JA; Hamilton BE; Sutton PD; Ventura SJ; Menacker F; Kirmeyer S; Munson ML. (2007). Births: final data for 2005. In National vital statistics reports. National Ce nter for Health Statistics, Hyattsville, MD. 48-11. Martins LC; Latorre Mdo R; Saldiva PH; Braga AL. (2002). Air pollution and emergency room visits due to chronic lower respiratory diseases in the elderly: an ecolog ical time-series study in Sao Paulo, Brazil. J Occup Environ Med 44: 622-627. Masjedi MR; Jamaati HR; Dokouhaki P; Ahmadzadeh Z; Taheri SA; Bigdeli M; Izadi S; Rostamian A; Aagin K; Ghavam SM. (2003). The effects of air pollution on acute respiratory conditions. Respirology 8: 213-230. Mayr FB; Spiel A; Leitner J; Marsik C; Germann P; Ullr ich R; Wagner O; Jilma B. (2005). Effects of carbon monoxide inhalation during experimental endotoxemia in humans. American Journal of Respiratory and Critical Care Medicine 171: 354-360. McCartney ML. (1990). Sensitivity analysis applied to Coburn-Forster-Kane models of carboxyhemoglobin formation. Am Ind Hyg Assoc J 51: 169-177. McClean PA; Duguid NJ; Griffin PM; Newth CJ; Zamel N. (1981). Changes in exhaled pulmonary diffusing capacity at rest and exercise in individuals with impa ired positional diffusion. Bull Eur Physiopathol Respir 17: 179-186. McClellan RO. (1991). CASAC Closure on the 1991 AQCD for Carbon Monoxide: US EPA-SAB-CASAC-LTR92-015, Washington, DC.

March 2009 R-19 DRAFT—DO NOT QUOTE OR CITE

## McClellan RO. (1992). CASAC Closure on the OAQPS St aff Paper for Carbon Monoxide: US EPA-SAB-CASACLTR-92-016, Washington, DC. McCoubrey WK, Jr.; Huang TJ; Maines MD. (1997). Isola tion and characterization of a cDNA from the rat brain that encodes hemoprotein heme oxygenase-3. Eur J Biochem 247: 725-732. McGowan JA; Hider RN; Chacko E; Town GI. (2002). Particulate air pollution and hospital admissions in Christchurch, New Zealand. Aust N Z J Public Health 26: 23-29. McGrath JJ. (1992). Effects of altitude on endogenous carboxyhemoglobin levels. J Toxicol Environ Health 35: 127-133. McGrath JJ; Schreck RM; Lee PS. (1993). Carboxyhemoglobi n levels in humans: effects of altitude Inhalation Toxicology 5: 241-249. McGregor HP; Westcott K; Walker DW. (1998). The effect of prenatal exposure to carb on monoxide on breathing and growth of the newborn guinea pig. Pediatr Res 43: 126-131. McIntire DD; Bloom SL; Casey BM; Leveno KJ. (1999). Birt h weight in relation to morbidity and mortality among newborn infants. N Engl J Med 340: 1234-1238. McLaughlin BE; Hutchinson JM; Graham CH; Smith GN; Marks GS; Nakatsu K; Brien JF. (2000). Heme oxygenase activity in term human placenta. Placenta 21: 870-873. McLaughlin BE; Lash GE; Graham CH; Smith GN; Vreman HJ; Stevenson DK; Marks GS; Nakatsu K; Brien JF. (2001). Endogenous carbon monoxide formation by chorionic villi of term human placenta. Placenta 22: 886-888. McLaughlin BE; Lash GE; Smith GN; Marks GS; Nakats u K; Graham CH; Brien JF. (2003). Heme oxygenase expression in selected regions of term human placenta. Exp Biol Med (May wood) 228: 564-567. McLean M; Bowman M; Clifton V; Smith R; Grossman AB. (2000). Expression of the heme oxygenase-carbon monoxide signalling system in hum an placenta. J Clin Endocr inol Metab 85: 2345-2349. Medeiros A; Gouveia N. (2005). Relationship between low birthweight and air pollution in the city of Sao Paulo, Brazil. Rev Saude Publica 39: 965-972. Meilin S; Rogatsky GG; Thom SR; Zarchin N; Guggenhei mer-Furman E; Mayevsky A. (1996). Effects of carbon monoxide on the brain may be mediated by nitric oxide. J Appl Physiol 81: 1078-1083. Melin A; Obert P; Rebocho M; Bonnet P. (2002). Cardiac morphology and function following long-term exposure to carbon monoxide at high altitude in rats. J Toxicol Environ Hea lth A 65: 1981-1998. Melin A; Bonnet P; Eder V; Antier D; Obert P; Fauchier L. (2005). Direct implication of carbon monoxide in the development of heart failure in rats with cardiac hype rtrophy subjected to air pollution. Cardiovasc Toxicol 5: 311-320. Meng YY; Wilhelm M; Rull RP; English P; Ritz B. (2007). Traffic and outdoor air pollution levels near residences and poorly controlled asthma in adults. Ann Allergy Asthma Immunol 98: 455-463. Mensink C; Cosemans G. (2008). From traffic flow simulations to pollutant concentrations in street canyons and backyards. Environmental Modelling & Software 23: 288-295. Mercke C; Cavallin-Stahl E; Lundh B. (1975a). Carbon monoxide production and reticulocyte count in normal women. Effect of contraceptive drugs and smoking. Acta Med Scand 198: 155-160. Mercke C; Cavallin-Stahl E; Lundh B. (1975b). Heme cat abolism during short-term treatment with phenobarbital, diazepam and oxazepam. Acta Med Scand 198: 149-154. Mercke C; Lundh B. (1976). Erythrocyte filterability and heme catabolism during the menstrual cycle. Ann Intern Med 85: 322-324. Mereu G; Cammalleri M; Fa M; Francesconi W; Saba P; Tattoli M; Trabace L; Vacca ri A; Cuomo V. (2000). Prenatal exposure to a low concentration of carbon monoxide disrupts hippocampal long-term potentiation in rat offspring. J Pharmacol Exp Ther 294: 728-734. Merx MW; Flogel U; Stumpe T; Godecke A; Decking UK; Schrader J. (2001). Myoglobin facilitates oxygen diffusion. FASEB J 15: 1077-1079. Metzger KB; Tolbert PE; Klein M; Peel JL; Flanders WD; Todd K; Mulholland JA; Ryan PB; Frumkin H. (2004a). Ambient air pollution and cardiovascular emergency department visits. Epidemiology 15: 46-56. Metzger KB; Tolbert PE; Klein M; Peel JL; Flanders WD; Mulholland JA. (2004b). Ambient air pollution and arrhythmic events in patients with implanted cardiov erter defibrillators, 1993-2002. Epidemiology 15: S22- S23. Metzger KB; Klein M; Flanders WD; Peel JL; Mulholla nd JA; Langberg JJ; Tolbert PE. (2007). Ambient air pollution and cardiac arrhythmias in patients with implantable defibrillators. Epidemiology 18: 585-592.

March 2009 R-20 DRAFT—DO NOT QUOTE OR CITE

## Meyer J; Prien T; Van Aken H; Bone HG; Waurick R; Theilmeier G; Booke M. (1998). Arterio-venous carboxyhemoglobin difference suggests carbon monoxide production by human lungs. Biochem Biophys Res Commun 244: 230-232. Mfula AM; Kukadia V; Griffiths RF; Hall DJ. (2005). Wind tunnel modelling of urban building exposure to outdoor pollution. Atmos Environ 39: 2737-2745. Middendorff R; Kumm M; Davidoff MS; Holstein AF; Muller D. (2000). Generation of cyclic guanosine monophosphate by heme oxygenases in the human testis--a regulatory role for carbon monoxide in Sertoli cells? Biol Reprod 63: 651-657. Migliaretti G; Dalmasso P; Gregori D. (2007). Air pollution effects on the respiratory health of the resident adult population in Turin, Italy. Int J Environ Health Res 17: 369-379. Miller KA; Siscovick DS; Sheppard L; Shepherd K; Su llivan JH; Anderson GL; Kaufman JD. (2007). Long-Term Exposure to Air Pollution and Incidence of Cardiovascular Events in Women. New Engl J Med 356: 447. Mobley D; Deslauriers M; Rojas-Brac hos L. (2005). Improving Emission Inventories for Effective Air-Quality Management across North America—a NARSTO Assessment. NARSTO, Pasco WA. NARSTO-05-001. http://www.narsto.org/section.src?SID=8#5

. Moolgavkar SH. (2000a). Air pollution and hospital admissions for chronic obstructive pulmonary disease in three metropolitan areas in the United States. Inhalation Toxicology 12 Suppl 4: 75-90. Moolgavkar SH. (2000b). Air pollution and daily mortality in three U.S. counties. Envi ron Health Perspect 108: 777-784. Moolgavkar SH. (2003a). Air pollution and daily deaths and hospital admissions in Los Angeles and Cook Counties in Health Effects Institute Special Report: Revised An alyses of Time-Series Studies of Air Pollution and Health. Moolgavkar SH. (2003b). Air pollution and daily mortality in two U.S. counties: season-specific analyses and exposure-response relationships. Inhalation Toxicology 15: 877-907. Morita T; Mitsialis SA; Koike H; Liu Y; Kourembanas S. (1997). Carbon monoxide controls the proliferation of hypoxic vascular smooth muscle cells. J Biol Chem 272: 32804-32809. Morse CI; Pritchard LJ; Wust RC; Jones DA; Degens H. (2008). Carbon monoxide inhalation reduces skeletal muscle fatigue resistance. Acta Physiol (Oxf) 192: 397-401. Mortimer K; Neugebauer R; Lurmann F; Alcorn S; Balmes J; Tager I. (2008). Air pollution and pulmonary function in asthmatic children: Effects of prenatal and lifetime exposures. Epidemiology 19: 550-557. Naik JS; Walker BR. (2003). Heme oxygenase-mediated vasodilation involves vascular smooth muscle cell hyperpolarization. Am J Physiol Heart Circ Physiol 285: H220-228. NAPAP. (1991). The experience and legacy of NAPAP. Report of the Oversight Review Board. : National Acid Precipitation Program., Washington, DC. National Center for Health Statistics. (2007). Health, Unite d States, 2007: With chartbook on trends in health of Americans. Center for Diseas e Control, Hyattsville, MD, Ndisang JF; Tabien HE; Wang R. (2004). Carbon monoxide and hypertension. J Hypertens 22: 1057-1074. Neas LM; Schwartz J. (1996). The determ inants of pulmonary diffusing capacity in a national sample of U.S. adults. American Journal of Respiratory and Critical Care Medicine 153: 656-664. Neggers YH; Singh J. (2006). Zinc supplementation to protein-deficient diet in CO-exposed mice decreased fetal mortality and malformation. Biol Trace Elem Res 114: 269-279. Neidell MJ. (2004). Air pollution, health, and socio-economic status: the effect of outdoor air quality on childhood asthma. J Health Econ 23: 1209-1236. Newby D; Cousins F; Myatt L; Lyall F. (2005). Heme oxygenase expression in cultured human trophoblast cells during in vitro differentiation: effect s of hypoxia. Placenta 26: 201-209. Norris G; YoungPong SN; Koenig JQ; Larson TV; Sheppard L; Stout JW. (1999). An association between fine particles and asthma emergency department visits fo r children in Seattle. Environ Health Perspect 107: 489-493. Novelli PC; Masarie KA; Lang PM; Hall BD; Myers RC; Elkins JW. (2003). Reanalysis of tropospheric CO trends: Effects of the 1997–1998 wildfires. Journal of Geophysical Research-Atmospheres 108: 4464. Novelli PC; Masarie KA; Atmospheric Carbon Monoxide Dr y Air Mole Fractions from the NOAA ESRL Carbon Cycle Cooperative Global Air Sampling Network -V--; ftp://ftp.cmdl.noaa.gov/ccg/co/flask/event/ . (2008). Atmospheric Carbon Monoxide Dry Air Mole Fractions from the NOAA ESRL Carbon Cycle Cooperative Global Air Sampling Network, 1988-2007. . NRC. (1999). Ozone-Forming Potential of Reformulated Gasoline. National Research Council, National Academies Press, Washington, D.C.

March 2009 R-21 DRAFT—DO NOT QUOTE OR CITE

## NRC. (2003). Managing carbon monoxide pollution in meteorological and topographical problem areas. Washington, D.C.: National Academies Press. Odrcich MJ; Graham CH; Kimura KA; McLaughlin BE; Ma rks GS; Nakatsu K; Brien JF. (1998). Heme oxygenase and nitric oxide synthase in the placenta of the guinea-pig during ge station. Placenta 19: 509-516. Ostro BD; Hurley S; Lipsett MJ. (1999). Air pollution and daily mortality in the Coachella Valley, California: a study of PM10 dominated by coarse particles. Environmental Research 81: 231-238. Ott W, Switzer, P., Willits, N. (1994). Carbon monoxide ex posures inside an automobile traveling on an urban arterial highway. J Air Waste Manage Assoc 44: 1010-1018. Ott WR; Mage DT. (1978). Interpreting urban carbon monoxide concentrations by means of a computerized blood COHb model. J Air Pollut Control Assoc 28: 911-916. Otto DA; Benigus VA; Prah JD. (1979). Carbon monoxide and human time discrimination: failure to replicate Beard-Wertheim experiments. Aviation Space and Environmental Medicine 50: 40-43. Ozawa N; Goda N; Makino N; Yamaguchi T; Yoshimura Y; Suematsu M. (2002). Leydig cell-derived heme oxygenase-1 regulates apoptosis of premeiotic germ cells in response to stress. J Clin Invest 109: 457-467. Pankow D; Matschiner F; Weigmann HJ. (1991). Influence of aromatic hydrocarbons on the metabolism of dichloromethane to carbon monoxide in rats. Toxicology 68: 89-100. Park H; Lee B; Ha EH; Lee JT; Kim H; Hong YC. (2002) . Association of air pollution with school absenteeism due to illness. Arch Pediatr Adolesc Med 156: 1235-1239. Park JW; Lim YH; Kyung SY; An CH; Lee SP; Jeong SH; Ju YS. (2005a). Effects of ambient particulate matter on peak expiratory flow rates and respiratory symptoms of asthmatics during Asian dust periods in Korea. Respirology 10: 470-476. Park SK; O'Neill MS; Vokonas PS; Sparrow D; Schwartz J. (2005b). Effects of air pollution on heart rate variability: the VA normative aging study. Environ Health Perspect 113: 304-309. Parker JD; Woodruff TJ; Basu R; Schoendorf KC. (2005). Air pollution and birth weight among term infants in California. Pediatrics 115: 121-128. Parrish DD; Trainer M; Hereid D; Williams EJ; Olszyna KJ; Harley RA; Meagher JF; Fehsenfeld FC. (2002). Decadal change in carbon monoxide to nitrogen oxide ra tio in US vehicular emissions. Journal of Geophysical Research 107: 4140. Parrish DD. (2006). Critical evaluation of US on-road vehicle emission inventories. Atmos Environ 40: 2288-2300. Patel AP; John Moody A; Handy RD; Robert Sneyd J. (2003). Carbon monoxide exposure in rat heart: glutathione depletion is prevented by antioxidants. Biochem Biophys Res Commun 302: 392-396. Peel JL; Tolbert PE; Klein M; Metzger KB; Flanders WD; Todd K; Mulholland JA; Ryan PB; Frumkin H. (2005). Ambient air pollution and respiratory emergency department visits. Epidemiology 16: 164-174. Peel JL; Metzger KB; Klein M; Flanders WD; Mulho lland JA; Tolbert PE. (2007). Ambient air pollution and cardiovascular emergency department visits in potentially sensitive groups. American Journal of Epidemiology 165: 625-633. Pekkanen J; Brunner EJ; Anderson HR; Tiittanen P; Atkinson RW. (2000). Daily concentrations of air pollution and plasma fibrinogen in London. Occupational and Environmental Medicine 57: 818-822. Pelham TW; Holt LE; Moss MA. (2002). Exposure to carbon monoxide and nitrogen dioxide in enclosed ice arenas. Occupational and Environmental Medicine 59: 224-233. Penn A; Currie J; Snyder C. (1992). In halation of carbon monoxide does no t accelerate arteriosclerosis in cockerels. Eur J Pharmacol 228: 155-164. Penney DG; Baylerian MS; Thill JE; Fanning CM; Yedava lly S. (1982). Postnatal carbon monoxide exposure: immediate and lasting effects in the rat. American Journal of Physiology- Heart and Circulatory Physiology 243: 328-339. Penney DG; Baylerian MS; Thill JE; Yedavally S; Fanning CM . (1983). Cardiac response of the fetal rat to carbon monoxide exposure. Am J Physiol 244: H289-297. Penney DG; Barthel BG; Skoney JA. (1984). Cardiac compliance and dimensions in carbon monoxide-induced cardiomegaly. Cardiovasc Res 18: 270-276. Penney DG. (1988). Hemodynamic response to carbon monoxide. Environ Health Perspect 77: 121-130. Penney DG; Davidson SB; Gargulinski RB; Caldwell-Ayre TM. (1988). Heart and lung hypertrophy, changes in blood volume, hematocrit and plasma renin activity in rats chronically exposed to increasing carbon monoxide concentrations. J Appl Toxicol 8: 171-178. Penney DG. (2007). Carbon Monoxide Poisoning. Boca Raton, FL CRC Press. Penttinen P; Timonen KL; Tiittanen P; Mirme A; Ruuskanen J; Pekkanen J. (2001). Ultrafine particles in urban air and respiratory health among adult asthmatics. Eur Respir J 17: 428-435.

March 2009 R-22 DRAFT—DO NOT QUOTE OR CITE

## Penttinen P; Tiittanen P; Pekkanen J. (2004). Mortality and air pollution in metropolitan Helsinki, 1988--1996. Scand J Work Environ Health 30 Suppl 2: 19-27. Pesola GR; Sunmonu Y; Huggins G; Ford JG. (2004). Measured diffusion capacity versus prediction equation estimates in blacks without lung disease. Respiration 71: 484-492. Pesola GR; Huggins G; Sherpa TY. (2006). Abnormal pred icted diffusion capacities in healthy Asians: an inequality with a solution. Respiration 73: 799-807. Peters A; Perz S; Doring A; Stieber J; Koenig W; Wi chmann HE. (1999a). Increases in heart rate during an air pollution episode. American Journal of Epidemiology 150: 1094-1098. Peters A; Skorkovsky J; Kotesovec F; Brynda J; Spix C; Wichmann HE; Heinrich J. (2000a). Associations between mortality and air pollution in central Europe. Environ Health Perspect 108: 283-287. Peters A; Liu E; Verrier RL; Schwartz J; Gold DR; M ittleman M; Baliff J; Oh JA; Allen G; Monahan K; Dockery DW. (2000b). Air pollution and incidence of cardiac arrhythmia. Epidemiology 11: 11-17. Peters A; Dockery DW; Muller JE; Mittleman MA. (2001). In creased particulate air pollution and the triggering of myocardial infarction. Circulation 103: 2810-2815. Peters JM; Avol E; Navidi W; London SJ; Gauderman WJ; Lurmann F; Linn WS; Margolis H; Rappaport E; Gong H; Thomas DC. (1999b). A study of twelve Southern California communities with differing levels and types of air pollution. I. Prevalen ce of respiratory morbidity. American Journal of Respiratory and Critical Care Medicine 159: 760-767. Petersen LC. (1977). The effect of inhibitors on the oxygen kinetics of cytochrome c oxidase. Biochim Biophys Acta 460: 299-307. Peterson JE; Stewart RD. (1970). Absorption and elimination of carbon monoxide by inactive young men. Archives of Environmental Health 21: 165-171. Peterson JE; Stewart RD. (1975). Predictin g the carboxyhemoglobin levels resulting from carbon monoxide exposures. J Appl Physiol 39: 633-638. Pfister G; Hess PG; Emmons LK; Lamarque JF; Wiedinmy er C; Edwards DP; Pétron G; Gille JC; Sachse GW. (2005). Quantifying CO emissions from the 2004 Alaskan wildfires using MOPITT CO data. Geophys Res Lett 32: 1-5. Piantadosi CA; Zhang J; Demchenko IT. (1997). Production of hydroxyl radical in the hippocampus after CO hypoxia or hypoxic hypoxia in the rat. Free Radic Biol Med 22: 725-732. Piantadosi CA. (2002). Biological chemistry of carbon monoxide. Antioxid Redox Signal 4: 259-270. Piantadosi CA; Carraway MS; Suliman HB. (2006). Carbon monoxide, oxidative stress, and mitochondrial permeability pore transition. Free Radic Biol Med 40: 1332-1339. Piantadosi CA. (2008). Carbon monoxide, reactive oxygen signaling, and oxidative stress. Free Radic Biol Med 45: 562-569. Pirjola L; Paasonen P; Pfeiffer D; Husse in T; Hämeri K; Koskentalo T; Vi rtanen A; Rönkkö T; Keskinen J; Pakkanen TA. (2006). Dispersion of particles and trace gases nearby a city highway: mobile laboratory measurements in Finland. Atmos Environ 40: 867-879. Polidori A; Arhami M; Sioutas C; Delfino RJ; Allen R. (2007). Indoor/outdoor relationships, trends, and carbonaceous content of fine particulate matter in re tirement homes of the Los Angeles basin. J Air Waste Manage Assoc 57: 366-379. Pollack A; Lindhjem C; Sto eckenius T; Tran C; Mansell G; Jimenez M; Wilson G; Coulter-Burke S. (2004). Evaluation of the US EPA MOBILE6 Highway Vehicle Emissions Factor Model. ENVIRON International, Novato, CA. CRC Project E-64. Pope CA, 3rd; Thun MJ; Namboodiri MM; Dockery DW; Ev ans JS; Speizer FE; Heath CW , Jr. (1995). Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. American Journal of Respiratory and Critical Care Medicine 151: 669-674. Pope CA, 3rd; Burnett RT; Thun MJ; Calle EE; Krew ski D; Ito K; Thurston GD. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Journal of the American Medical Associa tion 287: 1132-1141. Prigge E; Hochrainer D. (1977). Effects of carbon monoxide inhalation on erythropoiesis and cardiac hypertrophy in fetal rats. Toxicol Appl Pharmacol 42: 225-228.Prommer N; Schmidt W. (2007). Loss of CO from the intravascular bed and its impact on the optimised COrebreathing method. Eur J Appl Physiol 100: 383-391. Putz VR; Johnson BL; Setzer JV. (1979). A comparative study of the effects of car bon monoxide and methylene chloride on human performance. J Environ Pathol Toxicol 2: 97-112.

March 2009 R-23 DRAFT—DO NOT QUOTE OR CITE

## Rabinovitch N; Zhang LN; Murphy JR; Vedal S; Dutton SJ ; Gelfand EW. (2004). Effect s of wintertime ambient air pollutants on asthma exacerbations in urban minority ch ildren with moderate to severe disease. J Allergy Clin Immunol 114: 1131-1137. Rainham DG; Smoyer-Tomic KE. (2003). The role of air pollution in the relationship between a heat stress index and human mortality in Toronto. Environmental Research 93: 9-19. Rajendra Acharya U; Paul Joseph K; Kannathal N; Lim CM; Suri JS. (2006). Heart rate variability: a review. Med Biol Eng Comput 44: 1031-1051. Ranzi A; Gambini M; Spattini A; Galassi C; Sesti D; Bede schi M; Messori A; Baroni A; Cavagni G; Lauriola P. (2004). Air pollution and respiratory status in asthmatic children: hints for a locally based preventive strategy. AIRE study. Eur J Epidemiol 19: 567-576. Raub JA; Mathieu-Nolf M; Hampson NB; Thom SR. (2000). Carbon monoxide poisoning—a public health perspective. Toxicology 145: 1-14. Raub JA; Benignus VA. (2002). Carbon monoxide and the nervous system. Neurosci Biobehav Rev 26: 925-940. Raven PB; Drinkwater BL; Ruhling RO; Bolduan N; Taguch i S; Gliner J; Horvath SM. (1974). Effect of carbon monoxide and peroxyacetyl nitrate on man's maximal aerobic cap acity. J Appl Physiol 36: 288-293. Reeves RB; Park HK. (1992). CO uptake kinetics of red ce lls and CO diffusing capacity. Respir Physiol 88: 1-21. 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. 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. Rich DQ; Schwartz J; Mittleman MA; Link M; Luttmann-Gibson H; Catalano PJ; Speizer FE; Dockery DW. (2005). Association of short-term ambient air pollution conc entrations and ventricular arrhythmias. American Journal of Epidemiology 161: 1123-1132. Rich DQ; Mittleman MA; Link MS; Schwartz J; Luttmann- Gibson H; Catalano PJ; Speizer FE; Gold DR; Dockery DW. (2006a). Increased risk of paroxysmal atrial fi brillation episodes associated with acute increases in ambient air pollution. Environ Health Perspect 114: 120-123. Rich DQ; Kim MH; Turner JR; Mittle man MA; Schwartz J; Catalano PJ; Do ckery DW. (2006b). Association of ventricular arrhythmias detected by implantable cardioverter defibrillator and ambient air pollutants in the St Louis, Missouri metropolitan area. Occupational and Environmental Medicine 63: 591-596. Rich KE; Petkau J; Vedal S; Brauer M. (2004). A case-crossove r analysis of particulate air pollution and cardiac arrhythmia in patients with implantable cardioverte r defibrillators. Inhalation Toxicology 16: 363-372. Richardson RS; Noyszewski EA; Saltin B; Gonzalez-Alonso J. (2002). Effect of mild carboxy-hemoglobin on exercising skeletal musc le: intravascular and intracellular eviden ce. American Journal of PhysiologyRegulatory, Integrative and Comparative Physiology 283: R1131-1139. Riediker M; Williams R; Devlin R; Griggs T; Bromberg P. (2003). Exposure to Particulate Matter, Volatile Organic Compounds, and Other Air Pollutants Inside Patrol Cars. Environmental Science and Technology 37: 2084- 2093. Rigby M; Toumi R. (2008). London air pollution climatology: Indirect evidence for urban boundary layer height and wind speed enhancement. Atmos Environ 42: 4932-4947. Riojas-Rodriguez H; Escamilla-Cejudo JA; Gonzalez-Hermosillo JA; Tellez-Rojo MM; Vallejo M; Santos-Burgoa C; Rojas-Bracho L. (2006). Personal PM2.5 and CO exposures and heart rate variability in subjects with known ischemic heart disease in Mexico City. Journal of Exposure Science and Environmental Epidemiology 16: 131-137. Ritz B; Yu F. (1999). The effect of ambient carbon monoxide on low birth weight among children born in southern California between 1989 and 1993. Environ Health Perspect 107: 17-25. Ritz B; Yu F; Chapa G; Fruin S. (2000). Effect of air pollution on preterm birth among children born in Southern California between 1989 and 1993. Epidemiology 11: 502-511. Ritz B; Yu F; Fruin S; Chapa G; Shaw GM; Harris JA. (2002). Ambient air pollution and risk of birth defects in Southern California. American Journal of Epidemiology 155: 17-25. Ritz B; Wilhelm M; Zhao Y. (2006). Air pollution and infant death in southern California, 1989-2000. Pediatrics 118: 493-502. Ritz B; Wilhelm M; Hoggatt KJ; Ghosh JK. (2007). Ambient air pollution and preterm birth in the environment and pregnancy outcomes study at the University of California, Los Angeles. American Journal of Epidemiology 166: 1045-1052.

March 2009 R-24 DRAFT—DO NOT QUOTE OR CITE

## Rodes C; Sheldon L; Whitaker D; Clayton A; Fitzgerald K. (1999). Measuring concentrations of selected air pollutants inside California vehicles. Final report. Si erra Research Institute, Sacramento, CA. PB--99- 161028/XAB. Rodgers PA; Vreman HJ; Denn ery PA; Stevenson DK. (1994). Sources of carbon monoxide (CO) in biological systems and applications of CO detection technologies. Semin Perinatol 18: 2-10. Rodkey FL; O'Neal JD; Collison HA. (1969). Oxygen and carbon monoxide equilibria of human adult hemoglobin at atmospheric and elevated pressure. Blood 33: 57-65. Rodriguez C; Tonkin R; Heyworth J; Kusel M; De Klerk N; Sly PD; Franklin P; Runnion T; Blockley A; Landau L; Hinwood AL. (2007). The relationship between outdoor air quality and respiratory symptoms in young children. Int J Environ Health Res 17: 351-360. Roemer WH; van Wijnen JH. (2001). Daily mortality and air pollution along busy streets in Amsterdam, 1987-1998. Epidemiology 12: 649-653. Rosamond W; Flegal K; Furie K; Go A; Greenlund K; Haase N; Hailpern SM; Ho M; Howard V; Kissela B; Kittner S; Lloyd-Jones D; McDermott M; Meigs J; Moy C; Nichol G; O'Donnell C; Roger V; Sorlie P; Steinberger J; Thom T; Wilson M; Hong Y. (2008). Heart disease and stroke statistics - 2008 update - A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 117: E25-E146. Rosenlund M; Berglind N; Pershagen G; Hallqvist J; Jonson T; Bellander T. (2006). Long-term exposure to urban air pollution and myocardial infarction. Epidemiology 17: 383-390. Roth RA; Rubin RJ. (1976a). Comparison of the effect of carbon monoxide and of hypoxic hypoxia. I. In vivo metabolism, distribution and action of hexobarbital. J Pharmacol Exp Ther 199: 53-60. Roth RA; Rubin RJ. (1976b). Comparison of the effect of carbon monoxide and of hypoxic hypoxia. II. Hexobarbital metabolism in the isolated, perfused rat liver. J Pharmacol Exp Ther 199: 61-66. Rothman KJ; Greenland S. (1998). Modern epidemiology (2 ed.). Philadelphia, PA: Li ppincott-Raven Publishers. Roughton FJ; Root WS. (1945). The fate of CO in the body during recovery from mild carbon monoxide poisoning in man. Am J Physiol 145: 239-252. Roughton FJ; Forster RE. (1957). Relative importance of diff usion and chemical reaction rates in determining rate of exchange of gases in the human lung, with special reference to true diffusing capacity of pulmonary membrane and volume of blood in the lung capillaries. J Appl Physiol 11: 290-302. Roughton FJ. (1970). The equilibrium of carbon monoxide with human hemoglobin in whole blood. Ann N Y Acad Sci 174: 177-188. Ruckerl R; Ibald-Mulli A; Koenig W; Schneider A; Woelke G; Cyrys J; Heinrich J; Marder V; Frampton M; Wichmann HE; Peters A. (2006). Air pollution and mark ers of inflammation and coagulation in patients with coronary heart disease. Ameri can Journal of Respiratory and Critical Care Medicine 173: 432-441. Ruckerl R; Greven S; Ljungman P; Aalto P; Antoniades C; Bellander T; Berglind N; Chrysohoou C; Forastiere F; Jacquemin B; von Klot S; Koenig W; Kuchenhoff H; Lanki T; Pekkanen J; Perucci CA; Schneider A; Sunyer J; Peters A. (2007). Air pollution and inflammation (interleukin-6,C-reactive protein, fibrinogen) in myocardial infarction survivors. Environ Health Perspect 115: 1072-1080. Ryan PH; LeMasters GK. (2007). A Review of Land-use Regression Models for Char acterizing Intraurban Air Pollution Exposure. Inhalation Toxicology 19: 127-133. Ryter SW; Alam J; Choi AM. (2006). Heme oxygenase-1/carbon monoxide: from basic science to therapeutic applications. Physiol Rev 86: 583-650. Salam MT; Millstein J; Li YF; Lurmann FW; Margolis HG; Gilliland FD. (2005). Birt h outcomes and prenatal exposure to ozone, carbon monoxide, and particulat e matter: results from the Children's Health Study. Environ Health Perspect 113: 1638-1644. Samet JM; Dominici F; Curriero FC; Coursac I; Zeger SL. (2 000a). Fine particulate air pollution and mortality in 20 U.S. cities, 1987-1994. N Engl J Med 343: 1742-1749. Samet JM; Dominici F; Zeger SL; Schwartz J; Docker y DW. (2000b). The National Morbidity, Mortality, and Air Pollution Study. Part I: Methods and methodologic issues. Res Rep Health Eff Inst: 5-14; discussion 75-84. Samoli E; Touloumi G; Schwartz J; Anderson HR; Schind ler 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. Sarnat JA; Schwartz J; Catalano PJ; Suh HH. (2001). Gaseous pollutants in particulate matter epidemiology: confounders or surrogates? Environ Health Perspect 109: 1053-1061.

March 2009 R-25 DRAFT—DO NOT QUOTE OR CITE

## Sarnat JA; Brown KW; Schwartz J; Coull BA; Koutrakis P. (2005). Ambient gas concentrations and personal particulate matter exposures: implications for studying the health effects of particles. Epidemiology 16: 385-395. Sarnat SE. (2006). Factors affecting the association betw een ambient concentrations and personal exposures to particles and gases. Environ Health Perspect 114: 649-654. Sarnat SE; Suh HH; Coull BA; Schwar tz J; Stone PH; Gold DR. (2006). Ambient particulate air pollution and cardiac arrhythmia in a panel of older adults in Steubenville, Ohio. Occupational and Environmental Medicine 63: 700-706. Sartiani L; Cerbai E; Lonardo G; DePaoli P; Tattoli M; Cagiano R; Carratu MR; Cuomo V; Mugelli A. (2004). Prenatal exposure to carbon monoxide affects postnatal cellular electr ophysiological maturation of the rat heart: a potential substrate for arrhythmogenesis in infancy. Circulation 109: 419-423. Schildcrout JS; Sheppard L; Lumley T; Slaughter JC; Koenig JQ; Shapiro GG. (2006). Ambient air pollution and asthma exacerbations in chil dren: an eight-city analysis. American Journal of Epidemiology 164: 505-517. Schoenfisch WH; Hoop KA; Struelens BS. (1980). Carbon monoxide absorption through the oral and nasal mucosae of cynomolgus monkeys. Archives of Environmental Health 35: 152-154. Schwartz J; Norris G; Larson T; Sheppard L; Claiborne C; Koenig J. (1999). Episodes of high coarse particle concentrations are not associated with increased mortality. Environ Health Perspect 107: 339-342. Schwartz J; Litonjua A; Suh H; Verrier M; Zanobetti A; Syring M; Nearing B; Verrier R; Stone P; MacCallum G; Speizer FE; Gold DR. (2005). Traffic related pollution and heart rate variability in a panel of elderly subjects. Thorax 60: 455-461. Schwetz BA; Smith FA; Leong BK; Staple s RE. (1979). Teratogenic potential of inhaled carbon monoxide in mice and rabbits. Teratology 19: 385-392. Scotto di Marco G; Kephalopoulos S; Ruuskanen J; Jantunen M. (2005). Personal carbon monoxide exposure in Helsinki, Finland. Atmos Environ 39: 2697-2707. Seila RL; Lonneman WA; Meeks SA. (1989). Determination of C2 to C12 ambient air hydrocarbons in 39 US cities from 1984 through 1986. PB-89-214142/XAB, Computer Sciences Corp., Research Triangle Park, NC (USA), Selvakumar S; Sharan M; Singh MP. (1992). Mathematical model for the exchange of gases in the lungs with special reference to carbon monoxide. Med Biol Eng Comput 30: 525-532. Sharma VS; Schmidt MR; Ranney HM. (1976). Dissociation of CO from carboxyhemoglobin. J Biol Chem 251: 4267-4272. Sharovsky R; Cesar LA; Ramires JA. (2004 ). Temperature, air pollution, and mortality from myocardial infarction in Sao Paulo, Brazil. Braz J Med Biol Res 37: 1651-1657. Sheppard L; Levy D; Norris G; Larson TV; Koenig JQ. (1999). Effects of ambient air pollution on nonelderly asthma hospital admissions in Seattle, Washington, 1987-1994. Epidemiology 10: 23-30. Sheppard L; Slaughter JC; Schildcrout J; Liu LJ; Lumley T. (2005). Exposure and measurement contributions to estimates of acute air pollution effects. Journal of E xposure Analysis and Environmental Epidemiology 15: 366. Sheps DS; Adams KF, Jr.; Bromberg PA; Goldstein GM; O'Neil JJ; Horstman D; Koch G. ( 1987). Lack of effect of low levels of carboxyhemoglobin on cardiovascular fu nction in patients with ischemic heart disease. Archives of Environmental Health 42: 108-116. Sheps DS; Herbst MC; Hinderliter AL; Adams KF; Ekelund LG; O'Neil JJ; Goldstein GM; Bromberg PA; Dalton JL; Ballenger MN; et al. (1990). Production of arrhythmias by elevated carboxyhemoglobin in patients with coronary artery disease. Ann Intern Med 113: 343-351. Shimazu T; Ikeuchi H; Sugimoto H; Goodwin CW; Mason AD, Jr.; Pruitt BA, Jr. (2000). Half-life of blood carboxyhemoglobin after short-term and long-term exposure to carbon monoxide. J Trauma 49: 126-131. Shimazu T. (2001). Half-life of blood carboxyhemoglobin. Chest 119: 661-663. Silkoff PE; Zhang L; Dutton S; Langmack EL; Vedal S; Mur phy J; Make B. (2005). Winter air pollution and disease parameters in advanced chronic obstructive pulmonary disease panels residing in Denver, Colorado. J Allergy Clin Immunol 115: 337-344. Sinclair AH; Tolsma D. (2004). Associations and lags between air pollution and acute respiratory visits in an ambulatory care setting: 25-month results from the aerosol research and inhalation epidemiological study. J Air Waste Manage Assoc 54: 1212-1218. Singh J. (1986). Early behavioral alterations in mice following prenatal carbon monoxide exposure. Neurotoxicology 7: 475-481.

March 2009 R-26 DRAFT—DO NOT QUOTE OR CITE

## Singh J; Smith CB; Moore-Cheatum L. (1992). Additivity of protein defici ency and carbon monoxide on placental carboxyhemoglobin in mice. Am J Obstet Gynecol 167: 843-846. Singh J. (2006). Interaction of matern al protein and carbon monoxide on pup mortality in mice: implications for global infant mortality. Birth Defect s Res B: Dev Reprod Toxicol 77. Singh MP; Sharan M; Selvakumar S. (1991). A mathematical model for the computation of carboxyhaemoglobin in human blood as a function of exposure time. Philos Trans R Soc Lond B Biol Sci 334: 135-147. Singh V; Khandelwal R; Gupta AB. (2003). Effect of air po llution on peak expiratory flow rate variability. J Asthma 40: 81-86. Sitdikova GF; Islamov RR; Mukhamedyarov MA; Permyakova VV; Zefirov AL; Palotas A. (2007). Modulation of neurotransmitter release by carbon monoxide at the frog neuro-muscular junction. Curr Drug Metab 8: 177- 184. Slaughter JC; Lumley T; Sheppard L; Koenig JQ; Shapir o GG. (2003). Effects of ambient air pollution on symptom severity and medication use in children with asthma. Ann Allergy Asthma Immunol 91: 346-353. Slaughter JC; Kim E; Sheppard L; Sullivan JH; Larson TV; Claiborn C. (2005). Association between particulate matter and emergency room visits, hospital admissions and mortality in Spokane, Washington. J Expo Anal Environ Epidemiol 15: 153-159. Smith MV. (1990). Comparing solutions to the linear and nonlinear CFK equations for predicting COHb formation. Math Biosci 99: 251-263. Smith MV; Hazucha MJ; Benignus VA; Bromberg PA. (1994). Effect of regional circulation patterns on observed HbCO levels. J Appl Physiol 77: 1659-1665. So ESP; Chan ATY; Wong AYT. (2005). Large-eddy simulations of wind flow and pollutant dispersion in a street canyon. Atmos Environ 39: 3573-3582. Sokal JA; Majka J; Palus J. (1984). The content of carbon monoxide in the tissues of rats intoxicated with carbon monoxide in various conditions of acute exposure. Arch Toxicol 56: 106-108. Solanki DL; McCurdy PR; Cuttitta FF; Schechter GP. (1988) . Hemolysis in sickle cell disease as measured by endogenous carbon monoxide production. A preliminary report. Am J Clin Pathol 89: 221-225. Song R; Mahidhara RS; Liu F; Ning W; Otterbein LE; Choi AM. (2002). Carbon monoxide inhibits human airway smooth muscle cell proliferation via mitogen-activated protein kinase pathway. Am J Respir Cell Mol Biol 27: 603-610. Sorhaug S; Steinshamn S; Nilsen OG; Waldum HL. (2006). Chronic inhalation of carbon monoxide: effects on the respiratory and cardiovascular syst em at doses corresponding to to bacco smoking. Toxicology 228: 280- 290. Steerenberg PA; Nierkens S; Fischer PH; van Loveren H; Opperhuizen A; Vos JG; van Amsterdam JG. (2001). Traffic-related air pollution affects peak expiratory flow, exhaled nitric oxide, and inflammatory nasal markers. Archives of Environmental Health 56: 167-174. Steinvil A; Kordova-Biezuner L; Shapira I; Berliner S; Rogowski O. (2008). Short-term exposure to air-pollution and inflammation-sensitive biomarkers. En vironmental Research 106: 51-61. Stevenson DK; Ostrander CE; Johnson JD. (1979). Effect of erythrocyte destruction on the pulmonary excretion rate of carbon monoxide in adult male Wistar rats. J Lab Clin Med 94: 649-654. Stewart RD; Newton PE; Hosko MJ; Peters on JE. (1973a). Effect of carbon monoxide on time perception. Archives of Environmental Health 27: 155-160. Stewart RD; Peterson JE; Fisher TN; Hosko MJ; Bare tta ED; Dodd HC; Herrmann AA. (1973b). Experimental human exposure to high concentrations of carbon monoxide. Archives of Environmental Health 26: 1-7. Stieb DM; Judek S; Burnett RT. (2002). Meta-analysis of time-series studies of air pollution and mortality: effects of gases and particles and the influence of cause of death, age, and season. J Air Waste Manage Assoc 52: 470-484. Stieb DM; Judek S; Burnett RT. (2003). Meta-analysis of time-series studies of air pollution and mortality: update in relation to the use of generalized additive models. J Air Waste Manage Assoc 53: 258-261. Stieb DM; Evans GJ; Sabaliauskas K; Chen LI; Campbell ME ; Wheeler AJ; Brook JR; Guay M. (2007). A scripted activity study of the impact of protective advice on personal exposure to ultra-fine and fine particulate matter and volatile organic compounds. Journal of Exposure Science and Environmental Epidemiology 18: 495-502. Stockard-Sullivan JE; Korsak RA; Webber DS; Edmond J. (2003). Mild carbon monoxide exposure and auditory function in the developing rat. J Neurosci Res 74: 644-654.

March 2009 R-27 DRAFT—DO NOT QUOTE OR CITE

## Stölzel M; Breitner S; Cyrys J; Pitz M; Wolke G; Krey ling W; Heinrich J; Wichmann HE; Peters A. (2006). Daily mortality and particulate matter in different size cla sses in Erfurt, Germany. Journal of Exposure Science and Environmental Epidemiology 17: 458-467. Stone JR; Marletta MA. (1994). Soluble guanylate cyclase from bovine lung: activation with nitric oxide and carbon monoxide and spectral characterization of the ferrous and ferric states. Biochemistry 33: 5636-5640. Storm JE; Fechter LD. (1985a). Alteration in the postnata l ontogeny of cerebellar norepinephrine content following chronic prenatal carbon monoxide. J Neurochem 45: 965-969. Storm JE; Fechter LD. (1985b). Prenatal carbon monoxide exposure differentially affects postnatal weight and monoamine concentration of rat brain regions. Toxicol Appl Pharmacol 81: 139-146. Stupfel M; Bouley G. (1970). Physiological and biochemical effects on rats and mice exposed to small concentrations of carbon monoxide for long periods. Ann N Y Acad Sci 174: 342-368. Styka PE; Penney DG. (1978). Regression of carbon monoxide-induced cardiomegaly. American Journal of Physiology- Heart and Circulatory Physiology 235: 516-522. Suliman HB; Carraway MS; Tatro LG; Piantadosi CA. (2007). A new activating role for CO in cardiac mitochondrial biogenesis. J Cell Sci 120: 299. Sullivan J; Ishikawa N; Sheppard L; Siscovick D; Checkoway H; Kaufman J. (2003). Exposure to ambient fine particulate matter and primary cardiac arrest among persons with and without clinically recognized heart disease. American Journal of Epidemiology 157: 501-509. Sun HL; Chou MC; Lue KH. (2006). The relationship of air pollution to ED visits for asthma differ between children and adults. Am J Emerg Med 24: 709-713. Sun Y; Jin K; Mao XO; Zhu Y; Greenberg DA. (2001). Neuroglobin is up-regulated by and protects neurons from hypoxic-ischemic injury. Proceed ings of the National Academy of Sciences 98: 15306-15311. Sunyer J; Basagana X. (2001). Particles, and not gases, are associated with the risk of death in patients with chronic obstructive pulmonary disease. Int J Epidemiol 30: 1138-1140. Sunyer J; Basagana X; Belmonte J; Anto JM. (2002). Effect of nitrogen dioxide and ozone on the risk of dying in patients with severe asthma. Thorax 57: 687-693. 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. American Journal of Epidemiology 164: 421-433. 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. Takeuchi A; Vesely A; Rucker J; Sommer LZ; Tesler J; Lavine E; Slutsky AS; Maleck WH; Volgyesi G; Fedorko L; Iscoe S; Fisher JA. (2000). A simple "new " method to accelerate cl earance of carbon monoxide. American Journal of Respiratory and Critical Care Medicine 161: 1816-1819. Tarkiainen TH; Timonen KL; Vanninen EJ; Alm S; Hartikainen JE; Pekkanen J. (2003). Effect of acute carbon monoxide exposure on heart rate variability in patients with coronary artery disease. Clin Physiol Funct Imaging 23: 98-102. Tattoli M; Carratu MR; Cassano T; Cortese I; Di Giovanni V; Elia G; Renna G; Cagiano R. (1999). Effects of early postnatal exposure to low concentrations of carbon m onoxide on cognitive functions in rats. Pharmacol Res 40: 271-274. Telfer JF; Itoh H; Thomson AJ; Norman JE; Nakao K; Campa JS; Poston L; Tribe RM; Magness RR. (2001). Activity and expression of soluble and particulate guanylate cyclases in myometrium from nonpregnant and pregnant women: down-regulation of soluble guanylate cyclase at term. J Clin Endocrinol Metab 86: 5934- 5943. Tenias JM; Ballester F; Perez-Hoyos S; Rivera ML. (2 002). Air pollution and hospital emergency room admissions for chronic obstructive pulmonary disease in Valencia, Spain. Archives of Enviro nmental Health 57: 41-47. Teran FJ; Johnson RA; Stevenson BK; Peyton KJ; Jackson KE; Appleton SD; Durante W; Johnson FK. (2005). Heme oxygenase-derived carbon monoxide promotes arteriolar endothelial dysfunction and contributes to salt-induced hypertension in Dahl salt-sensitive ra ts. American Journal of Physiology- Regulatory, Integrative and Comparative Physiology 288: 615-622. Thom SR; Ohnishi ST; Ischiropoulos H. (1994). Nitric oxide released by platelets inhibits neutrophil B2 integrin function following acute carbon monoxide poisoning. Toxicol Appl Pharmacol 128: 105-110. Thom SR; Ischiropoulos H. (1997). Mechanism of oxidativ e stress from low levels of carbon monoxide. Res Rep Health Eff Inst: 1-19; discussion 21-17. Thom SR; Xu YA; Ischiropoulos H. (1997). Vascular endoth elial cells generate peroxynitrite in response to carbon monoxide exposure. Chem Res Toxicol 10: 1023-1031.

March 2009 R-28 DRAFT—DO NOT QUOTE OR CITE

## Thom SR; Ohnishi ST; Fisher D; Xu YA; Ischiropoulos H. (1999a). Pulmonary vascular stress from carbon monoxide. Toxicol Appl Pharmacol 154: 12-19. Thom SR; Fisher D; Xu YA; Garner S; Ischiropoulos H. (1999b). Role of nitric oxide-derived oxidants in vascular injury from carbon monoxide in the rat. Am J Physiol 276: H984-992. Thom SR; Fisher D; Xu YA; Notarfrancesco K; Ischir opoulos H. (2000). Adaptive responses and apoptosis in endothelial cells exposed to carbon monoxide. Procee dings of the National Academy of Sciences 97: 1305- 1310. Thom SR; Fisher D; Manevich Y. (2001a). Roles for platelet-activating factor and? NO-derived oxidants causing neutrophil adherence after CO poisoning. American Journal of Physiology- Heart and Circulatory Physiology 281: 923-930. Thom SR; Fisher D; Manevich Y. (2001b). Roles for platelet-activating factor and *NO-derived oxidants causing neutrophil adherence after CO poisoning. Am J Physiol Heart Circ Physiol 281: H923-930. Thom SR; Bhopale VM; Han ST; Clark JM; Hardy KR. (200 6). Intravascular neutrophil activation due to carbon monoxide poisoning. American Journal of Respirator y and Critical Care Medicine 174: 1239-1248. Thompson AJ; Shields MD; Patterson CC. (2001). Acute as thma exacerbations and air pollutants in children living in Belfast, Northern Ireland. Archives of Environmental Health 56: 234-241. Thompson AM; Cicerone RJ. (1986). Possible perturbations to atmospheric CO, CH4, and OH. Journal of Geophysical Research 91: 10853-10864. Thomsen HK. (1974). Carbon monoxide-indu ced atherosclerosis in primates. An electron-microscopic study on the coronary arteries of Macaca trus monk eys. Atherosclerosis 20: 233-240. Thorup C; Jones CL; Gross SS; Moore LC; Goligorsky MS. (1999). Carbon monoxide induces vasodilation and nitric oxide release but suppresses endothelial NOS. American Journal of Physiology- Renal Physiology 277: 882-889. Tikuisis P; Buick F; Kane DM. (1987a). Percent carboxyhemoglobin in resting humans exposed repeatedly to 1,500 and 7,500 ppm CO. J Appl Physiol 63: 820-827. Tikuisis P; Madill HD; Gill BJ; Lewis WF; Cox KM; Kane DM . (1987b). A critical analysis of the use of the CFK equation in predicting COHb formation. Am Ind Hyg Assoc J 48: 208-213. Timonen KL; Pekkanen J; Tiittanen P; Salonen RO. (2002) . Effects of air pollution on changes in lung function induced by exercise in children with chronic respiratory symptoms. Occupational and Environmental Medicine 59: 129-134. Timonen KL; Vanninen E; de Hartog J; Ibald-Mulli A; Brunekreef B; Gold DR; Heinrich J; Hoek G; Lanki T; Peters A. (2006). Effects of ultrafine and fine pa rticulate and gaseous air po llution on cardiac autonomic control in subjects with coronary artery disease: The ULTRA study. Journal of Exposure Science and Environmental Epidemiology 16: 332-341. 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. Journal of Exposure Science and Environmental Epidemiology 17 Suppl 2: S29-35. Tolcos M; Mallard C; McGregor H; Walker D; Rees S. (2000a). Exposure to prenatal carbon monoxide and postnatal hyperthermia: short and long-term effects on neurochemicals and neuroglia in the developing brain. Exp Neurol 162: 235-246. Tolcos M; McGregor H; Walker D; Rees S. (2000b). Ch ronic prenatal exposure to car bon monoxide results in a reduction in tyrosine hydroxylase-immunoreactiv ity and an increase in choline acetyltransferaseimmunoreactivity in the fetal medulla: implications fo r Sudden Infant Death Syndrome. J Neuropathol Exp Neurol 59: 218-228. Topping DL. (1975). Acute effects of carbon monoxide on the metabolism of perfused rat liver. Biochem J 152: 425-427. Trapasso LM; ¦Keith L. (1999). Relationships between se lected meteorological/pollution parameters and hospital admissions for asthma in South Central Kentucky. J Ky Acad Sci 60: 73-77. Tsai SS; Huang CH; Goggins WB; Wu TN; Yang CY. (2003a). Relationship between air pollution and daily mortality in a tropical city: Kaohsiung, Taiwan. J Toxicol Environ Health A 66: 1341-1349. Tsai SS; Goggins WB; Chiu HF; Yang CY. (2003b). Evid ence for an association between air pollution and daily stroke admissions in Kaohsiung, Taiwan. Stroke 34: 2612-2616. Tsai SS; Cheng MH; Chiu HF; Wu TN; Yang CY. (2006a). Air pollution and hospital admissions for asthma in a tropical city: Kaohsiung, Taiwan. Inhalation Toxicology 18: 549-554. Tsai SS; Chen CC; Hsieh HJ; Chang CC; Yang CY. (2006b). Air pollution and postneonatal mortality in a tropical city: Kaohsiung, Taiwan. Inhalation Toxicology 18: 185-189.

March 2009 R-29 DRAFT—DO NOT QUOTE OR CITE

## Tschugguel W; Stonek F; Zhegu Z; Dietrich W; Schneeb erger C; Stimpfl T; Waldhoer T; Vycudilik W; Huber JC. (2001). Estrogen Increases Endothelial Carbon Mo noxide, Heme Oxygenase 2, and Carbon MonoxideDerived cGMP by a Receptor-Mediated System (Vol. 86, pp. 3833-3839): Endocrine Soc. Turner DM; Lee PN; Roe FJ; Gough KJ. (1979). Atherogenesis in the White Carneau pigeon. Further studies of the role of carbon monoxide and dietary cholesterol. Atherosclerosis 34: 407-417. Tyuma I; Ueda Y; Imaizumi K; Kosaka H. (1981). Prediction of the carbonmonoxyhemoglobin levels during and after carbon monoxide exposures in various animal species. Jpn J Physiol 31: 131-143. U.S. Code. (1970). Clean Air Act, §108, air quality criteria and control techniques. Retrieved. from. U.S. EPA. (1978). Altitude as a factor in air pollution. Office of Research and Development, Environmental Criteria and Assessment Office, Research Tr iangle Park, NC. EPA 600/9-78-015. U.S. EPA. (1979a). Air quality crite ria for carbon monoxide. Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, U.S. Environmental Protec tion Agency, Research Triangle Park, NC. EPA-600/8-79-022. U.S. EPA. (1979b). Assessment of Adverse Health Effects from Carbon Monoxide and Implications for Possible Modifications of the Standard. Office of Air Qua lity Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. U.S. EPA. (1984a). Review of the NAAQS for Carbon Monoxide: Reassessment of Scientific and Technical Information. Office of Air Quality Planning and St andards, U.S. Environmental Protection Agency, Research Triangle Park, NC. U.S. EPA. (1984b). Revised evaluation of health effects associated with carbon monoxide exposure: an addendum to the 1979 EPA air quality criteria document for carbon monoxide. Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA600/8-83-033F. U.S. EPA. (1991). Air quality criteria for carbon monoxide. EPA/600/8-90/045F, Office of Health and Environmental Assessment., Environmental Criteria an d Assessment Office. Research Triangle Park, NC, Available from: NTIS, Springfield, VA, 1991. PB93-167492, U.S. EPA. (1992). Review of the national ambient air quality standards for carbon monoxide: 1992 reassessment of scientific and technical informati on. OAQPS staff paper. Research Triangle Park, NC : Office of Air Quality Planning and Standards; report no. EPA-452/R-92-004. U.S. EPA. (2000). Air quality criteria for carbon mono xide. National Center for Environmental Assessment, Research Triangle Park, N.C. EPA/600/P-99/001F. http://www.epa.gov/ncea/pdfs/coaqcd.pdf

. U.S. EPA. (2004). Air Quality Criteria for Particulate Ma tter. U.S. Environmental Protection Agency, Washington, D.C. EPA/600/P-99/002aF-bF. U.S. EPA. (2005). Guidelines for Carcinogen Risk Assessment. U.S. Environmental Protection Agency, Washington, D.C. EPA/630/P-03/001F. U.S. EPA. (2006a). 2002 National Emissions Inventory Data and Documentation. U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Air Quality Planning and Standa rds, Research Triangle Park, NC. http://www.epa.gov/ttn/chief/net/2002inventory.html

. U.S. EPA. (2006b). Air Quality Crite ria for Ozone and Related Photochemi cal Oxidants. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Research Triangle Park, NC. EPA 600/R-05/004aF. U.S. EPA. (2007). Documentation For The Final 2002 Mobile National Emissions Inventory - Version 3 U.S. EPA. (2008a). Inventory of U.S. Greenhouse Gas Emissions and Sinks, 1990-2006. U.S. Environmental Protection Agency, Office of Atmospheric Programs, Washington, D.C. EPA 430-R-08-005. U.S. EPA. (2008b). Plan for Review of the National Am bient Air Quality Standards for Carbon Monoxide. U.S. Environmental Protection Agency, National Center for Environmental Assessment, Office of Research and Development, Research Triangle Park, NC. U.S. EPA. (2008c). U.S. EPA's 2008 Report on the Environment (Final Report). U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington, DC. EPA/600/R-07/045F. U.S. EPA. (2008d). Integrated Review Plan for the National Ambient Air Quality Standards for Particulate Matter. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Re search Triangle Park, NC. U.S. EPA. (2008e). Integrated Scien ce Assessment for Oxides of Nitrogen – Health Criteria. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/071.

March 2009 R-30 DRAFT—DO NOT QUOTE OR CITE

## U.S. EPA. (2008f). Integrated Scie nce Assessment for Particulate Matter, First External Review Draft. U.S. Environmental Protection Agency , Office of Research and Development, National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. U.S. EPA. (2008g). Integrated Scie nce Assessment for Sulfur Oxides–Hea lth Criteria. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/047F-FA. Vallone B; Nienhaus K; Matthes A; Brunori M; Nienhaus GU. (2004). The structure of carbonmonoxy neuroglobin reveals a heme-sliding mechanism for control of ligand affinity. Proceed ings of the National Academy of Sciences 101: 17351-17356. van der Werf GR; Randerson JT; Giglio L; Collatz GJ ; Kasibhatla PS; Arellano Jr AF. (2006). Interannual variability in global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry and Physics 6: 3423–3441. van Wijnen JH; Verhoeff AP; Jans HW; van Bruggen M. (1995). The exposure of cyclists, car drivers and pedestrians to traffic-related air pollutants. Int Arch Occup Environ Health 67: 187-193. Vedal S; Brauer M; White R; Petkau J. (2003). Air pollu tion and daily mortality in a city with low levels of pollution. Environ Health Perspect 111: 45-52. Vedal S; Rich K; Brauer M; White R; Petkau J. (200 4). Air pollution and cardiac arrhythmias in patients with implantable cardioverter defibrillators. Inhalation Toxicology 16: 353-362. Veen AVD; Briggs DJ; Collins S; Elliott P; Fischer P; Kingham S; Lebret E; Pryl K; Van Reeuwijk H; Smallbone K. (1997). Mapping urban air pollution using GIS: A regression-based approach. International Journal of Geographical Information Science 11: 699-718. Verma A; Hirsch DJ; Glatt CE; Ronnett GV; Snyder SH. (1993). Carbon monoxide: a putative neural messenger. Science 259: 381-384. Vesely AE; Somogyi RB; Sasano H; Sasano N; Fisher JA; Duffin J. (2004). The effects of carbon monoxide on respiratory chemoreflexes in humans. Environmental Research 94: 227-233. Vigotti MA; Chiaverini F; Biagiola P; Rossi G. (2007). Urban air pollution and emergency visits for respiratory complaints in Pisa, Italy. J Toxi col Environ Health A 70: 266-269. Villamor E; PÉRez-VizcaÍNo F; Cogolludo AL; Conde- Oviedo J; ZaragozÁ-ArnÁEz F; LÓPez-LÓPez JG; Tamargo J. (2000). Relaxant Effects of Carbon Monoxide Compared with Nitric Oxide in Pulmonary and Systemic Vessels of Newborn Piglets. Pediatr Res 48: 546. Villeneuve PJ; Burnett RT; Shi Y; Krewski D; Goldberg MS ; Hertzman C; Chen Y; Brook J. (2003). A time-series study of air pollution, socioeconomic status, and mortality in Vancouver, Canada. J Expo Anal Environ Epidemiol 13: 427-435. Villeneuve PJ; Chen L; Stieb D; Rowe BH. (2006a). A ssociations between outdoor air pollution and emergency department visits for stroke in Edmonton, Canada. Eur J Epidemiol 21: 689-700. Villeneuve PJ; Doiron MS; Stieb D; Dales R; Burnett RT; Dugandzic R. (2006b). Is outdoor air pollution associated with physician visits for allergic rhinitis among the elderly in Toronto, Canada? Allergy 61: 750-758. Vogel JA; Gleser MA. (1972). Effect of carbon monoxide on oxygen transport during exercise. J Appl Physiol 32: 234-239. von Klot S; Wolke G; Tuch T; Heinrich J; Dockery DW; Schwartz J; Kreyling WG; Wichmann HE; Peters A. (2002). Increased asthma medication us e in association with ambient fine and ultrafine particles. European Respiratory Journal 20: 691-702. von Klot S; Peters A; Aalto P; Bellander T; Berglind N; D’Ippoliti D; Elosua R; Hörmann A; Kulmala M; Lanki T; Löwel H; Pekkanen J; Picciotto S; Su nyer J; Forastiere F. (2005). Ambient air pollution is associated with increased risk of hospital cardiac readmissions of myocar dial infarction survivors in five European cities. Circulation 112: 3073-3079. Vreman HJ; Wong RJ; Kim EC; Nabseth DC; Marks GS; Stevenson DK. (2000). Haem oxygenase activity in human umbilical cord and rat vascular tissues. Placenta 21: 337-344. Vreman HJ; Wong RJ; Kadotani T; Stevenson DK. (2005). Determination of carbon monoxide (CO) in rodent tissue: effect of heme administration and environmental CO exposure. Anal Biochem 341: 280-289. Vreman HJ; Wong RJ; Stevenson DK; Smialek JE; Fowl er DR; Li L; Vigorito RD; Zielke HR. (2006). Concentration of carbon monoxide (CO) in postmortem human tissues: effect of environmental CO exposure. J Forensic Sci 51: 1182-1190. Wagner JA; Horvath SM; Dahms TE. (1975). Carbon monoxide elimination. Respir Physiol 23: 41-47. Wang R; Wang Z; Wu L. (1997). Carbon monoxide-induced vasorelaxation and the underlying mechanisms. Br J Pharmacol 121: 927-934.

March 2009 R-31 DRAFT—DO NOT QUOTE OR CITE

## Wang R. (1998). Resurgence of carbon monoxide: an endogenous gaseous vasorelaxing factor. Can J Physiol Pharmacol 76: 1-15. Wang TN; Ko YC; Chao YY; Huang CC; Lin RS. (1999). Association between indoor and outdoor air pollution and adolescent asthma from 1995 to 1996 in Taiw an. Environmental Research 81: 239-247. Weaver LK; Howe S; Hopkins R; Chan KJ. (2000). Carboxyhemoglobin half-life in carbon monoxide-poisoned patients treated with 100% oxygen at atmospheric pressure. Chest 117: 801-808. Weaver LK; Valentine KJ; Hopkins RO . (2007). Carbon Monoxide Poisoning: Risk Factors for Cognitive Sequelae and the Role of Hyperbaric Oxygen. American Journal of Respiratory and Critical Care Medicine: 200701. Webber DS; Korsak RA; Sininger LK; Sampogna SL; Edmond J. (2003). Mild carbon monoxide exposure impairs the developing auditory system of the rat. J Neurosci Res 74: 655-665. Webber DS; Lopez I; Korsak RA; Hirota S; Acuna D; Edmond J. (2005). Limiting iron availability confers neuroprotection from chronic mild carbon monoxide exposure in the developing auditory system of the rat. J Neurosci Res 80: 620-633. Weinstock B; Niki H. (1972). Carbon monoxide balance in nature (Vol. 176, pp. 290-292): The American Association for the Adva ncement of Science. Wellenius GA; Saldiva PHN; Batalha JRF; Krishna Mu rthy GG; Coull BA; Verrier RL; Godleski JJ. (2002). Electrocardiographic Changes during Exposure to Residual Oil Fly Ash (ROFA) Particles in a Rat Model of Myocardial Infarction (Vol. 66, pp. 327-335): Soc Toxicology. Wellenius GA; Batalha JRF; Diaz EA; Lawrence J; Coull BA ; Katz T; Verrier RL; Godleski JJ. (2004). Cardiac effects of carbon monoxide and ambient particles in a rat model of myocardial infarction. Toxicol Sci 80: 367-376. Wellenius GA; Schwartz J; Mittleman MA. (2005a). Ai r pollution and hospital admissions for ischemic and hemorrhagic stroke among medicare beneficiaries. Stroke 36: 2549-2553. Wellenius GA; Bateson TF; Mittleman MA ; Schwartz J. (2005b). Particulate air pollution and the rate of hospitalization for congestive heart failure among medi care beneficiaries in P ittsburgh, Pennsylvania. American Journal of Epidemiology 161: 1030-1036. Wellenius GA; Coull BA; Batalha JRF; Diaz EA; Lawrence J; Godleski JJ. (2006a). Effects of ambient particles and carbon monoxide on supraventricular arrhythmias in a rat model of myocardial infarction. Inhalation Toxicology 18: 1077-1082. Wellenius GA; Schwartz J; Mittleman MA . (2006b). Particulate air pollution and hospital admissions for congestive heart failure in seven United States cities. Am J Cardiol 97: 404-408. Wheeler A; Zanobetti A; Gold DR; Schwartz J; Stone P; Suh HH. (2006). The relationship between ambient air pollution and heart rate variability differs for individuals with heart and pulmonary disease. Environ Health Perspect 114: 560-566. Wichmann HE; Spix C; Tuch T; Wolke G; Peters A; Hein rich J; Kreyling WG; Heyder J. (2000). Daily mortality and fine and ultrafine particles in Erfurt, Germany part I: role of particle number and particle mass. Health Effects Institute, Boston, MA. 1041-5505 (Print). http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=119 18089

. Wilhelm M; Ritz B. (2005). Local vari ations in CO and particulate air pollution and adverse birth outcomes in Los Angeles County, California, USA. Environ Health Perspect 113: 1212-1221. Williams LJ; Rasmussen SA; Flores A; Kirby RS; Edmonds LD . (2005). Decline in the prevalence of spina bifida and anencephaly by race/ethnicity: 1995 -2002. Pediatrics 116: 580-586. Williams R; Vette A; Croghan C; Sheldon L; Rodes C; Thor nburg J; Ejire A; Herbst M; Sanders W; Suggs J; Rea A; Leovic K. (2003). The Research Triangle Park particulate matter panel study: PM mass concentration relationships. Atmos Environ 37: 5349-5363. Wilson JG; Zawar-Reza P. (2006). In traurban-scale dispersion modelling of particulate matter concentrations: Applications for exposure estimates in cohort studies. Atmos Environ 40: 1053-1063. Wilson WE; Mage DT; Grant LD. (2000). Estimating sepa rately personal exposure to ambient and nonambient particulate matter for epidemiology and risk a ssessment: why and how. Journal of the Air & Waste Management Association (1995) 50: 1167. Wilson WE; Brauer M. (2006) . Estimation of ambient and non-ambient co mponents of particulate matter exposure from a personal monitoring panel study. Journal of Exposure Science and Environmental Epidemiology 16: 264-274.

March 2009 R-32 DRAFT—DO NOT QUOTE OR CITE

## Wilson WE; Mar TF; Koenig JQ. (2007). Influence of exposure error and effect modification by socioeconomic status on the association of acute cardiovascular mort ality with particulate matter in Phoenix. Journal of exposure science & environmental epidemiology 17: S11. Wirkner K; Poelchen W. (1996). Influence of long-term ethanol treatment on rat liver aniline and p-nitrophenol hydroxylation. Alcohol 13: 69-74. Wittenberg BA; Wittenberg JB; Caldwell PR. (1975). Role of myoglobin in the oxygen supply to red skeletal muscle. J Biol Chem 250: 9038-9043. Wittenberg BA; Wittenberg JB. (1993). Effects of carbon m onoxide on isolated heart muscle cells. Health Effects Institute, Boston, Massachusetts. Report No. 62. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=815 5298

. Wollmann HA. (1998). Intrauterine growth restriction: Definition and etiology. Hormone Res 49: 1-6. Woodruff TJ; Darrow LA; Parker JD. (2008). Air pollution and postneonatal infant mortality in the United States, 1999-2002. Environ Health Perspect 116: 110-115. Wu L; Wang R. (2005). Carbon monoxide: endogenous production, physiological functions, and pharmacological applications. Pharmacol Rev 57: 585-630. Xiaomin X; Zhen H; Jiasong W. (2006). The impact of urban street layout on local atmospheric environment. Build Environ 41: 1352-1363. Xirasagar S; Lin HC; Liu TC. (2006). Seasonality in pediatric asthma admissions: the role of climate and environmental factors. Eur J Pediatr 165: 747-752. Yang CY; Chang CC; Chuang HY; Tsai SS; Wu TN; Ho CK. (2004a). Relationship between air pollution and daily mortality in a subtropical city: Taipei, Taiwan. Environ Int 30: 519-523. Yang CY; Chen YS; Yang CH; Ho SC. (2004b). Relations hip between ambient air pollution and hospital admissions for cardiovascular diseases in kaohsiung, taiwan. J Toxicol Environ Health A 67: 483-493. Yang CY; Hsieh HJ; Tsai SS; Wu TN; Chiu HF. (20 06). Correlation between air pollution and postneonatal mortality in a subtropical city: Taipei, Taiwan. J Toxicol Environ Health A 69: 2033-2040. Yang CY; Chen CC; Chen CY; Kuo HW. (2007). Air pollution and hospital admissions for asthma in a subtropical city: Taipei, Taiwan. J Toxicol Environ Health A 70: 111-117. Yang CY; Chen CJ. (2007). Air pollution and hospital admissions for chronic obstructive pulmonary disease in a subtropical city: Taipei, Taiwan. J Toxicol Environ Health A 70: 1214-1219. 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. Inhalation Toxicology 15: 1297-1308. Yang Q; Chen Y; Krewski D; Burnett RT; Shi Y; McGrail KM. (2005). Effect of short-term exposure to low levels of gaseous pollutants on chronic obstructive pulmonary disease hospitalizations. Environmental Research 99: 99-105. Yoshiki N; Kubota T; Aso T. (2001). Identification of heme oxygenase in human endometrium. J Clin Endocrinol Metab 86: 5033-5038. Young LJ; Caughey WS. (1986). Oxygenation of carbon monoxide by bovine heart cytochrome c oxidase. Biochemistry 25: 152-161. Yu O; Sheppard L; Lumley T; Koenig JQ; Shapiro GG. (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. Zamudio S; Palmer SK; Regensteiner JG; Grindlay Moor e L. (1995). High altitude and hypertension during pregnancy. Am J Hum Biol 7: 183-193. Zanobetti A; Schwartz J; Ridker PM. (2004a). Air pollutio n and markers of cardiovascular risk. Epidemiology 15: S22. Zanobetti A; Canner MJ; Stone PH; Schwartz J; Sher D; Eagan-Bengston E; Gates KA; Hartley LH; Suh H; Gold DR. (2004b). Ambient pollution and blood pressure in cardiac rehabilitation patients. Circulation 110: 2184-2189. Zanobetti A; Schwartz J. (2006). Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health 60: 890-895. 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. Zenclussen ML; Anegon I; Bertoja AZ; Chauveau C; V ogt K; Gerlof K; Sollwedel A; Volk HD; Ritter T; Zenclussen AC. (2006). Over-expression of heme oxygenase-1 by adenoviral gene transfer improves pregnancy outcome in a murine model of abortion. J Reprod Immunol 69: 35-52.

March 2009 R-33 DRAFT—DO NOT QUOTE OR CITE

## Zevin S; Saunders S; Gourlay SG; Jacob P; Benowitz NL. (2001). Cardiovascular effects of carbon monoxide and cigarette smoking. J Am Coll Cardiol 38: 1633-1638. Zhang C; Li XY; Zhao L; Wang H; Xu DX. (2007). Lipopolysacchar ide (LPS) up-regulates the expression of haem oxygenase-1 in mouse placenta. Placenta 28: 951-957. Zhang X; Shan P; Alam J; Fu XY; Lee PJ. (2005). Carbon monoxide differentially modulates STAT1 and STAT3 and inhibits apoptosis via a phosphatidylinositol 3-kinase/Akt and p38 kinase-dependent STAT3 pathway during anoxia-reoxygenation injury. J Biol Chem 280: 8714-8721. Zhao H; Wong RJ; Doyle TC; Nayak N; Vreman HJ; Contag CH; Stevenson DK. (2008). Regulation of maternal and fetal hemodynamics by heme oxygenase in mice. Biol Reprod 78: 744-751.Zhu Y; Hinds WC; Kim S; Shen S; Sioutas C. (2002). Study of ultrafine particles near a major highway with heavyduty diesel traffic. Atmos Environ 36: 4323-4335.Zhuo M; Small SA; Kandel ER; Hawkins RD. (1993). Nitric oxide and carbon monoxide produce activitydependent long-term synaptic enhancement in hippocampus. Science 260: 1946-1950. Zuckerbraun BS; Chin BY; Bilban M; d'Avila JC; Rao J; Billiar TR; Otterbein LE. (2007). Carbon monoxide signals via inhibition of cytochrome c oxidase an d generation of mitochondria l reactive oxyg en species. FASEB J 21: 1099-1106.

March 2009 R-34 DRAFT—DO NOT QUOTE OR CITE