# Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary National Ambient Air Quality Standards:

First External Review DraftEPA-452/P-09-008 October 2009



## Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary National Ambient Air Quality Standards:

First External Review Draft

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Health and Environmental Impacts Division Research Triangle Park, North CarolinaDISCLAIMER

This document has been reviewed by the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency (EPA), and approved for publication. This draft document has been prepared by staff from the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or recommendations are those of the authors and do not necessarily reflect the views of the EPA Mention of trade names or commercial products is not intended to constitute endorsement or recommendation for use. This document is being provided to the Clean Ai r Scientific Advisory Committee for their review, and made available to the public for comment. Any questions or comments concerning this document should be addressed to Souad Benromdhane, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, Research Triangle Park, North Carolina 27711 (email: benromdhane.souad@epa.gov ). Elements of this report have been provided to the U.S. Environmental Protection Agency (EPA) by Abt Associates, Inc. in part ial fulfillment of Contract N o. EP-D-08-100, Work Assignment 0- 08.

ACKNOWLEDGEMENTS

In addition to EPA staff, personnel from Abt Asso ciates, Inc. contributed to the writing of this document. Specific chapters and appendice s where Abt Associates, Inc. made significant contributions include: chapter 5 (Approach to Exposure and Dose Assessment for the Current Review), chapter 6 (Exposure/ dose assessment), and appendices B (Mass Balance Model in APEX), and C (COHb Module for pNEM/CO).

### Table of Contents

List of Tables ...................................................................................................................... iv List of Figures..................................................................................................................... iv 1 INTRODUCTION.................................................................................................. 1-1 1.1 BACKGROUND ............................................................................................. 1-1 1.2 ASSESSMENTS FROM PREVIOUS REVIEWS .......................................... 1-3 1.3 CURRENT ASSESSMENT ............................................................................ 1-5 1.4 REFERENCES ................................................................................................ 1-6 2 OVERVIEW OF EXPOSURE AND DOSE ASSESSMENT CONCEPTUAL MODEL .................................................................................................................... 2-1 2.1 SOURCES OF CARBON MONOXIDE......................................................... 2-1 2.2 EXPOSURE PATHWAYS AND R ELEVANT MICROENVIRONMENTS ..... .......................................................................................................................... 2-1 2.3 AT-RISK-POPULATIONS ............................................................................. 2-2 2.4 EXPOSURE AND DOSE METRICS ............................................................. 2-4 2.5 RISK CHARACTERIZATION METRIC....................................................... 2-4 2.6 REFERENCES ................................................................................................ 2-6 3 AIR QUALITY CONSIDERATIONS ................................................................. 3-1 3.1 AMBIENT CO MONITORING...................................................................... 3-1 3.1.1 Monitoring Network ................................................................................ 3-1 3.1.2 Analytical Sensitivity............................................................................... 3-2 3.1.3 Patterns of CO Concentrations................................................................. 3-4 3.1.4 Policy-Relevant Background Concentrations........................................3-11 3.2 STUDY AREAS FOR CURRENT ASSESSMENTS................................... 3-12 3.3 KEY OBSERVATIONS................................................................................ 3-16 3.4 REFERENCES .............................................................................................. 3-17 4 APPROACH TO RISK CHARACT ERIZATION FOR THE CURRENT REVIEW................................................................................................................... 4-1 4.1 CARDIOVASSCULAR DISEAS E RELATED EFFECTS ............................ 4-1 4.2 HEALTH EFFECTS BENCHMARKS ........................................................... 4-2 4.3 KEY OBSERVATIONS.................................................................................. 4-4 4.4 REFERENCES ................................................................................................ 4-5

October 2009 i Draft – Do Not Quote or Cite5 APPROACH TO EXPOSURE AND DO SE ASSES SMENT FOR CURRENT REVIEW................................................................................................................... 5-1 5.1 MODEL OVERVIEW..................................................................................... 5-1 5.2 MODEL HISTORY AND EVOLUTION ....................................................... 5-2 5.3 MODEL SIMULATION PROCESS ............................................................... 5-3 5.3.1 Characterize the Study Area .................................................................... 5-5 5.3.2 Generate Simulated Individuals............................................................... 5-5 5.3.3 Construct Activity Sequences.................................................................. 5-5 5.3.4 Calculate Microenvironme ntal Concentrations .......................................5-7 5.3.5 Estimate Energy Expenditure and Ventilation Rates............................... 5-9 5.3.6 Calculate Exposure ................................................................................ 5-11 5.3.7 Calculate Dose ....................................................................................... 5-11 5.3.8 Model Output......................................................................................... 5-13 5.3.9 Model limitations................................................................................... 5-13 5.4 PERSONAL EXPOSURE AND TH E IN-VEHICLE MICROENVIRONMENT ........................................................................................................................ 5-15 5.4.1 Personal Exposure Monitoring Studies..................................................5-15 5.4.2 In-Vehicle Concentrations ..................................................................... 5-17 5.5 STRATEGY FOR CO EXPOSU RE/DOSE ASSESSMENT FOR THE CURRENT REVIEW .................................................................................... 5-23 5.5.1 Background for Current Assessment Strategy....................................... 5-23 5.5.2 Selected Approach for Current Review ................................................. 5-24 5.6 KEY OBSERVATIONS................................................................................ 5-30 5.7 REFERENCES .............................................................................................. 5-31 6 EXPOSURE/DOSE ASSESSMENT A ND RISK CHARACTERIZATION...... 6-1 6.1 APPLICATION OF APEX4.3 TO CARBON MONOXIDE.......................... 6-1 6.1.1 Study Areas and Exposure Periods..........................................................6-2 6.1.2 Exposure Scenarios.................................................................................. 6-6 6.1.3 Populations-at-Risk..................................................................................6-6 6.1.4 Air Quality and Mete orological Data ...................................................... 6-8 6.1.5 Microenvironments................................................................................ 6-13 6.1.6 Time/Activity Patterns........................................................................... 6-14 6.2 EXPOSURE AND DOSE ESTIMATES AND RISK CHARACTERIZATION. ........................................................................................................................ 6-14 6.2.1 Denver – Scenarios A and B..................................................................6-14 6.2.2 Los Angeles – Scenarios A and B.......................................................... 6-21

October 2009 ii Draft – Do Not Quote or Cite6.2.3 Comparison of Denver and Los Angeles Estimates for End-of-hour COHb Levels.....................................................................................................6-26 6.3 COMPARISON OF COHB ESTIMA TES OBTAINED FROM THE 2000 PNEM/CO AND 2009 APEX/CO ASSESSMENTS .................................... 6-26 6.3.1 Important Differences Between the 2000 pNEM/CO and 2009 APEX/CO Exposure Assessments........................................................................... 6-27 6.3.2 Comparison of Estimated COHb Levels in Adults with Coronary Heart Disease using the 2000 pNEM/CO and 2009 APEX/CO Assessments........ ................................................................................................................ 6-28 6.4 VARIABILITY ANALYSIS AND UN CERTAINTY CHARACTERIZATION ........................................................................................................................ 6-34 6.4.1 Analysis of Variability...........................................................................6-34 6.4.2 Characterization of Uncertainty.............................................................6-36 6.5 KEY OBSERVATIONS................................................................................ 6-42 6.6 REFERENCES .............................................................................................. 6-44



### List of Figures

Figure 3-1. Spatial and Temporal Trends in the 2nd Highest 1-hour (top) and 8-hour Average (bottom) CO Ambient Monitoring Concentrat ions in Denver, Colorado, Years 1993 – 2008 ........................................................................................................................... 3-5 Figure 3-2. Spatial and Temporal Trends in th e 2nd Highest 1-hour (top) and 8-hour Average (bottom) CO Ambient Monitoring Concentrat ions in Los Angeles, California, Years 1993 – 2008 ............................................................................................................... 3-6 Figure 3-3. Diurnal Distribution of 1-hour CO Concentrations in Denver (Monitor 08-031-0002) by Day-type (weekdays-left; weekends-righ t), Years 1995 (top) and 2006 (bottom) ...

............................................................................................................................... ....3-8 Figure 3-4. Diurnal distribution of 1-hour CO concentrations in Los Angeles (Monitor 06-037- 1301) by day-type (weekdays-left; week ends-right), years 1997 (top) and 2006 (bottom) ..................................................................................................................... 3- 9 Figure 5-1. Figure 5-1. ....... Locations of active am bient CO monitors meeting 75% completeness criterion in 2006 along with lo cations of inactive ambient CO monitors, within the metropolitan Denver (top) and metropolitan Los Angeles (bottom).......................5-27 Figure 5-2. Figure 5-2. . Distribution of 1-hour (t op) and 8-hour average daily maximum (bottom) CO concentrations at ambient CO monitors in Denver County, year 2006 ............ 5-27 Figure 5-3. Figure 5-3. . Distribution of 1-hour (t op) and 8-hour average daily maximum (bottom) CO concentrations at ambient CO monito rs in Los Angeles County, year 2006.... 5-29 Figure 6-1. Map of the Denver Study Area Defined as a Circle with Radius = 20 km Centered on Fixed-Site Monitor ID 080310002............................................................................6-4

October 2009 iii Draft – Do Not Quote or CiteFigure 6-2. Map of the Los Angeles Study Area Defined as a Circle with radius = 20 km Centered on Fixed-Site Monitor No. 060371301...................................................... 6-5 Figure 6-3. Percentage of Los Angeles and Denver Adults with Coronary Heart Disease (CHD) Estimated to Experience a Daily Maximu m End-of-hour COHb Level At or Above the Specified Percentage for Air Quality Adjusted to Just Meeting the Current Standard. Data taken from Tables 6-22 and 6-23...................................................6-33



### List of Tables

Table 3-1. Descriptive Statisti cs for CO Concentrations Meas ured at Selected Fixed-Site Monitors in the Denver Metropolita n Area for the Years 2005 – 2007 ..................3-13 Table 3-2. Descriptive Statistics for CO Concentrations Measured at Selected Fixed-Site Monitors in the Los Angeles Metropolitan Area for the Years 2005 – 2007..........3-14 Table 5-1. Parameters of the Factors Model............................................................................... 5-9 Table 5-2. Carbon Monoxide Conc entrations Inside and Immedi ately Outside Vehicles, and Indoor/Outdoor Vehicle Ratios ...............................................................................5-21 Table 5-3. Descriptive Statistics for CO Concentrations Measured Inside Vehicles and at FixedSite Monitors (from Shikiya et al., 1989)................................................................ 5-23 Table 6-1. National Prevalence Rates for Coronary Heart Disease by Age Range.................... 6-7 Table 6-2. National Prevalence Rates for Coronary Heart Disease by Gender..........................6-7 Table 6-3. National Prevalence Rates for Corona ry Heart Disease Used in APEX, Stratified by Age and Gender......................................................................................................... 6-8 Table 6-4. Site Characteristics of Fixed-site CO Monitors Sel ected to Represent the Denver and Los Angeles Study Areas .......................................................................................... 6-8 Table 6-5. Descriptive Statistics for 1-hour Carbon Monoxide Concentrations Reported by the Selected Denver and Los Angeles Monitors Before and After Estimation of Missing Values........................................................................................................................ 6 -9 Table 6-6. Descriptive Statistics for 1-hour Carbon Monoxide Concentrations Reported by the Denver and Los Angeles Monitors Before a nd After Adjustment to Simulate Just Meeting the Current 8-Hour CO NAAQS............................................................... 6-11 Table 6-7. Site Characteristics of Meteorological Monitoring Sta tions Selected to Represent the Denver and Los Angeles Study Areas..................................................................... 6-11 Table 6-8. Number of Person-da ys for Adults with Coronary Heart Disease (CHD) in the Denver Study Area Estimated to Experience a 1-hour Daily Maximum CO Exposure at or Above the Specified Concentration ................................................................6-17 Table 6-9. Number of Adults with Coronary Heart Diseas e (CHD) in the Denver Study Area Estimated to Experience a 1-hour Daily Maximum CO Exposure at or Above the Specified Concentration ..........................................................................................6-17 Table 6-10. Number of Person-da ys for Adults with Coronary Heart Disease (CHD) in the Denver Study Area Estimated to Expe rience an 8-hour Daily Maximum CO Exposure at or Above the Sp ecified Concentration ................................................ 6-18

October 2009 iv Draft – Do Not Quote or CiteOctober 2009 v Draft – Do Not Quote or Cite Table 6-11. Number of Adults with Coronary Heart Diseas e (CHD) in the Denver Study Area Estimated to Experience an 8-hour Daily Maximum CO Exposure at or Above the Specified Concentration ..........................................................................................6-18 Table 6-12. Number of Person-da ys for Adults with Coronary Heart Disease (CHD) in the Denver Study Area Estimated to Experien ce a Daily Maximum End-of-hour COHb Level at or Above the Specified Concentration ......................................................6-19 Table 6-13. Number of Adults with Coronary Heart Diseas e (CHD) in the Denver Study Area Estimated to Experience a Daily Maximu m End-of-hour COHb Level at or Above the Specified Concentration ....................................................................................6-19 Table 6-14. Estimated Average Number of Da ys with a Daily Maximum End-of-hour COHb Level At or Above the Specified Concen tration Per Adult W ith Coronary Heart Disease (CHD) in the Denver Study Area............................................................... 6-20 Table 6-15. Number of Person-Da ys for Adults with Coronary Heart Disease (CHD) in the Los Angeles Study Area Estimated to Experience a 1-hour Daily Maximum CO Exposure At or Above the Specified Concentration ...............................................................6-22 Table 6-16. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles Study Area Estimated to Experience a 1-hour Da ily Maximum CO Exposure At or Above the Specified Concentration ....................................................................................6-22 Table 6-17. Number Of Person-Da ys For Adults with Coronary Heart Disease (CHD) in the Los Angeles Study Area Estimated to Expe rience an 8-hour Daily Maximum CO Exposure At or Above the Sp ecified Concentration ............................................... 6-23 Table 6-18. Number of Adults with Coronary H eart Disease (CHD) in the Los Angeles Estimated to Experience an 8-hour Daily Maximum CO Exposure At or Above the Specified Concentration .......................................................................................................... 6-24 Table 6-19. Number of Person-Da ys For Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area Estimated to Experi ence a Daily Maximum End-of-hour COHb Level At or Above the Specified Concentration .....................................................6-24 Table 6-20. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles Study Area Estimated to Experience a Daily Ma ximum End-of-hour COHb Level At or Above the Specified Concentration.........................................................................6-25 Table 6-21. Estimated Average Number of Da ys with a Daily Maximum End-of-hour COHb Level At or Above the Specified Concen tration Per Adult with Coronary Heart Disease (CHD) in the Los Angeles Study Area ...................................................... 6-25 Table 6-22. Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated to Experience a Daily Maximum End-o-hour C OHb Level At or Above the Specified Percentage Under Specified Air Quality Conditions for 1995................................ 6-31 Table 6-23. Percentage of Los Angeles Adults w ith Coronary Heart Disease (CHD) Estimated to Experience a Daily Maximum End-of-hour COHb Level At or Above the Specified Percentage Under “Just Meet ing”Conditions for 1997 ...........................................6-32 Table 6-24. Summary of How Variability Was Inco rporated Into the Exposure Assessment .. 6-35 Table 6-25. Characterization of Key Uncertainties in the Draft Assessment for Denver and Los Angeles Areas.......................................................................................................... 6-38October, 2009 Draft – Do Not Cite or Quote 1-1 1 2

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### 1. INTRODUCTION

1.1 BACKGROUND

The U. S. Environm ental Protection Agency (EPA) is presently conducting a review of the national ambient air quality standards (NAAQS) for carbon monoxide (CO). Sections 108 and 109 of the Clean Air Act (Act) govern the es tablishment and periodic review of the NAAQS. These standards are established for pollutants th at may reasonably be anticipated to endanger public health and welfare, and whose presence in the ambient air results from numerous or diverse mobile or stationary sources. The NAAQS are to be based on air quality criteria, which are to accurately reflect the latest scientific knowledge useful in indicating the kind and extent of identifiable effects on public health or welfare th at may be expected from the presence of the pollutant in ambient air. The EPA Administrator is to promulga te and periodically review, at five-year intervals, “primary” (health-based) and “secondary” (welfare-based) NAAQS for such pollutants. Based on periodic reviews of the air quality criteria and standards, the Administrator is to make revisions in the criteria and standard s, and promulgate any new standards, as may be appropriate. The Act also requires that an inde pendent scientific revi ew committee advise the Administrator as part of this NAAQS review process, a function performed by the Clean Air Scientific Advisory Committee (CASAC). The current NAAQS for CO includes two prim ary standards to provide protection for exposures to carbon monoxide. In 1994, EPA reta ined the primary standards at 9 parts per million (ppm), 8-hour average and 35 ppm, 1-hour average, neither to be exceeded more than once per year (59 FR 38906). These standards we re based primarily on the clinical evidence relating carboxyhemoglobin (COHb) levels to va rious adverse health endpoints and exposure modeling relating CO exposures to COHb levels. The review completed in 1994 also reaffirmed an earlier decision that th e evidence did not support the need for a secondary standard for CO (59 FR 38906). A subsequent review of the CO NAAQS was initiated in 1997, which led to the completion of the 2000 Air Quality Criteria Do cument for Carbon Monoxide (US EPA, 2000) and a draft exposure analysis methodology document (US EPA, 1999). EPA put on hold the NAAQS review when Congress re quested that the National Res earch Council (NRC) review the impact of meteorology and topography on ambien t CO concentrations in high altitude andOctober, 2009 Draft – Do Not Cite or Quote 1-2 extreme cold regions of the U.S. In res ponse, the NRC convened the Committee on Carbon Monoxide Episodes in Meteorological and Top ographical Problem Areas, which focused on Fairbanks, Alaska as a case-st udy. A final report, “Managing Carbon Monoxide Pollution in Meteorological and Topographical Problem Ar eas” (NRC, 2003), offered a wide range of recommendations regarding management of CO air pollution, cold start emissions standards, oxygenated fuels, and CO monitoring. Following completion of this NRC report, EPA did not conduct rulemaking to complete the review. 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 EPA initiated the current review of th e NAAQS for CO on September 13, 2007, with a call for information from the public (72 FR 52369) requesting the submission of recent scientific information on specified topics. A works hop was held on January 28–29, 2008 (73 FR 2490) to discuss policy-relevant scientific and technical information to in form EPA’s planning for the CO NAAQS review. Following the workshop, EPA outlin ed the science-policy questions that would frame this review, outlined the process and schedu le that the review would follow, and provided more complete descriptions of the purpose, contents, and approach for developing the key documents that would be developed in the review in a draft Integrated Review Plan for the National Ambient Air Quality Standards for Ca rbon Monoxide (US EPA, 2008a). After CASAC and public input on the draft plan, EPA made th e final plan available in August 2008 (US EPA, 2008b). EPA is currently completing the proces s of assessing the latest available policyrelevant scientific information to inform the review of the CO standards. The latest draft of this assessment is contained in the second external re view draft of the Integrated Science Assessment for Carbon Monoxide (hereafter, “draft ISA” ) (US EPA, 2009c) which was released in September 2009 for review by the CASAC and for public comments. The draft ISA includes an evaluation of the scientific evidence on the he alth effects of CO, including information on exposure, physiological mechanisms by which CO might adversely impact human health, an evaluation of the clinical evidence for CO-related morbidity, and an evaluation of the epidemiological evidence for CO-related morbidity and mortality associations. 1 Building upon the health effects evidence pres ented in the draft ISA as well as CASAC advice (Brain and Samet, 2009) and public comments on a scope and methods planning document for the exposure/risk assessment (her eafter, “Scope and Methods Plan”) (US EPA,

1 The draft ISA also evaluates scientific evidence for the effe cts of CO on public welfare which EPA will consider in its review of the need for a secondary standard. EPA is not intending to do a quantitative risk assessment for the secondary standard review.October, 2009 Draft – Do Not Cite or Quote 1-3 2009a), EPA’s Office of Air Quality Planning and Standards (OAQPS) has developed this first draft Risk/Exposure Assessment describing the in itial quantitative assessments being conducted by the Agency to support the revi ew of the primary CO standards. This draft document is a concise presentation of the methods, key resu lts, observations, and related uncertainties associated with the quantitative analyses perf ormed. The final REA will draw upon the final ISA and will reflect consider ation of CASAC and public comments on this draft REA. 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 schedule for completion of this review is governed by a court order that specifies that EPA sign for publication notices of proposed and final rulemaking concerning its review of the CO NAAQS no later than October 28, 2010 and May 13, 2011, respectively. The order also sets dates for the following interim milestones: re lease of a first draft ISA by March 14, 2009 (completed), a first draft ri sk/exposure assessment by October 29, 2009, a final ISA by January 29, 2010, and a final risk/exposure assessment by May 28, 2010. The final ISA and final REA will inform the policy assessment and rulemaking steps that will lead to final decisions on the CO NAAQS. The policy assessment will be described in a Policy Assessment (hereafter, “PA”) document, which will include staff analysis of the scientific basis for alternative policy options for consid eration by senior EPA management prior to rulemaking. The PA will integrate and interpret information from the ISA and the REA to frame policy options for consideration by the Administrato r. The PA is intended to help “bridge the gap” between the Agency’s scientific and tech nical assessments, presented in the ISA and REA and the judgments required of the Administrator in determining whether it is appropriate to retain or revise the standards. Th e PA is also intended to f acilitate CASAC’s advice to the Administrator on the adequacy of existing standa rds, and any new standards or revisions to existing standards as may be appropriate. OAQPS cu rrently plans to release a draft PA in late February 2010 for review by CASAC, as well as for public comment, in conjunction with CASAC review and public comment of the second draft REA (US EPA, 2009c).

1.2 ASSESSMENTS FROM PREVIOUS REVIEWS

Reviews of the CO NAAQS com pleted in 1985 and 1994 included analysis of exposure to ambient CO and associated internal dose in terms of COHb levels which were used to characterize risks for at-risk populations ( 50 FR 37484; 59 FR 38906). These prior risk characterizations compared the numbers of at -risk individuals and pe rcent of the at-risk population exceeding several potential health eff ect benchmarks, expressed in terms of COHbOctober, 2009 Draft – Do Not Cite or Quote 1-4 levels. This characterization was based on COHb levels observe d in several controlled human exposure studies reporting aggravation of angina associated w ith short-term (< 8-hr) CO exposures and described in EPA’s Air Quality Criteria Document (AQCD) (US EPA, 1979; US EPA, 1984; US EPA, 1991). 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 In the review completed in 1994, this char acterization was performed for the at-risk population in the city of Denver, Colorado (US EPA, 1992). That analysis indicated that if the current 8-hr standard were just met, th e proportion of the nonsmoking population with cardiovascular disease expe riencing exposures at or above 9 ppm for 8 hrs decreased by an order of magnitude or more as compared to the propor tion under then-existing CO levels, down to less than 1 percent of the total person-days in that population. Likewise, meeting the current 8-hr standard reduced the proportion of the nons moking cardiovascular-disease population person days at or above COHb levels of concern by an order of magnitude or more relative to thenexisting CO levels. More specifically, upon meeti ng the 8-hr standard, EPA estimated that less than 0.1% of the nonsmoking cardiovascular-dis ease population would expe rience a COHb level of about 2.1%. A smaller percentage of the at -risk population was estimated to exceed higher COHb percentages. The analysis also took into account that certain indoor sources (e.g., passive smoking, gas stove usage) contributed to tota l CO exposure but could not be effectively mitigated by setting more stringent ambient air quality standards. In the subsequent review, initiated in 1997, EPA consulted with CASAC on a draft exposure analysis methodology document, Es timation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Le vels in Denver Residents using pNEM/CO (Version 2.0) (Johnson, 1999). Although the EPA did not co mplete the review initiated in 1997, OAQPS continued work on the CO exposure assessment to further develop the exposure assessment modeling component of the Total Risk Integrat ed Methodology (TRIM) system. A subsequent draft technical report (Johnson et al., 2000) was produced documenti ng the application of the CO exposure and dose modeling methodology (and version 2.1 of pNEM/CO) for two study areas (Denver and Los Angeles). This report was s ubjected to an external peer review by three exposure modeling experts convened by Science Applications International Corporation (SAIC, 2001).October, 2009 Draft – Do Not Cite or Quote 1-5 1.3 CURRENT 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 In preparing the Scope and Methods Plan for the current health risk/exposure assessment, we considered the scientific ev idence presented in the first draft ISA (US EPA, 2009b) and the key science policy issues raised in the IRP (US EPA, 2008b). EPA held a consultation with CASAC to solicit comments on the Scope a nd Methods Plan during a May 2009 CASAC

m eeting at which CASAC also provided comment s on the first draft of the ISA. Public comments were also requested (74 FR 15265). CASAC and public comments were considered in advance of the conduct of the analyses and resu lts presented in this draft REA. The design of the current risk assessment builds upon informati on presented in the second draft ISA (US EPA, 2009c) with particular attention to conclusions regarding the adequa cy of the air quality data for the purposes of exposure assessment. In this draft assessment we are relying on generally similar methodology and focusing on the same two urban areas (Denve r and Los Angeles) as that used in the assessment for the previous review. Although improvements have b een made to the exposure model since the time of the last review, we recognize significant data limitations in th e current review. In CASAC’s comments on the first draft ISA, the Committee stated that the “current ambient monitoring network is not well designed to characterize spatial and temporal variability in ambient concentrations” and that “it does not adequa tely support detailed assessments of human exposure” (Brain and Samet, 2009). As a result, th e draft assessment that we describe in this document has implemented a much-simplified, sc reening-level approach focused on a single monitor and an exposure situation of particular interest for ambient CO (as described in detail in chapters 5 and 6). Based on the concerns ra ised by CASAC regarding the adequacy of the current monitoring data for this purpose, staff decided not to perform a detailed analysis involving multiple monitors and comprehensive estimation of exposure concentrations in multiple microenvironments, as has been done in th e past. In presenting this draft, screeninglevel assessment, however, we reco gnize that the simplifications in this approach contribute to limitations and uncertainties in the interpretati on of the results. One purpose of this draft document is to seek CASAC views, and public comment, regarding our characterization of the results in light of uncertainties associated with the assessmen t design and inputs, and CASAC’s advice on the role of this assessment in info rming the current review of the CO NAAQS.October, 2009 Draft – Do Not Cite or Quote 1-6 1.4 REFERENCES 1 2 3 4
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. US EPA (2000). Air Quality Criteria for Carbon Monoxide . Nation al Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Pr otection Age ncy, Research Triangle Park, NC, report no. EPA/600/P-99/001F. June 2000. Available at: http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm ?deid=18163

. US EPA (2008a). Draft Integrated Review Plan for th e National Ambient Air Quality Standards for Carbon Monoxide. U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452D-08- 001. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html

. US EPA (2008b). Integrated Review Plan for the Nationa l Am bient Air Quality Standards for Carbon Monoxide. U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452R-08-005. Available at: http://www.epa.gov/ttn/naaqs/s tanda rds/co/s_co_cr_pd.html

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8 9 10 US EPA (2009a). Carbon Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure Assessment U. S. Environmenta l Pro tection Agency, Research Triangle Park, NC, report no. EPA-452/R-09-004. Available at: http://www.epa.gov/ttn/naaqs/st an dards/co/s_co_cr_pd.html

. US EPA (2009b). Integrated Science Assessment for Carbon Monoxide – First External Review Draft. U.S. Environmental Protection Agency, Research Triangle Pa rk, NC, report no. EPA/600/R-09/019. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html

. US EPA (2009c). Integrated Science Assessment for Carbon Monoxide – Second External Review Draft. U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/600/R-09/019B. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html

.October, 2009 2

- 1 Draft – Do Not Cite or Quote 

### 2. OVERVIEW OF EXPOSURE AND DOSE ASSESSMENT CONCEPTUAL MODEL 1 2

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25 26 27 In order to help inform the discussion of the CO assessment presented in chapters 5 and 6, staff has briefly summarized the conceptual model for the consideration of exposure to ambient CO and associated heal th risk, from key sources thro ugh the identification of at-risk population groups, dose metric, and the risk characterization approach.

2.1. SOURCES OF CARBON MONOXIDE 7

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels and photoche mical reactions in the atmosphere. The amount of CO emitted from these reactions, re lative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zone. CO production relative to CO 2 generally decreases with any increase in fuel oxygen (O 2 ) content, burn temperature, or mixing time in the combustion zone (draft ISA, section 3.2). As a result, CO emissions from larg e fossil-fueled power plants are typically very low because of the boilers highl y efficient combustion and optimized fuel consumption. In contrast, internal combustion engines used in many mobile sources have widely varying operating conditions. Therefore, higher and mo re varying CO formation results from the operation of these mobile sources (draft ISA, section 3.2). In 2002, CO emissions from on-road vehicles accounted for 63% of total emissions by indi vidual source sectors in the U.S. (draft ISA, Figure 3-1). 1 As with previous reviews, mobile sources continue to be a significant source sector for CO in ambient air. Sources of indoor CO include infiltration of ambient air indoors, as well as, where present, indoor (nonambient) sources such as gas stoves and environm ental tobacco smoke. (draft ISA, section 3.6.5.2).

2.2. EXPOSURE PATHWAYS AND RELE VANT MICROENVIRONMENTS Human exposure to CO involves the contact (via inhalation) between a person and the pollutant in the various locations (or microenvi ronments) in which people spend their time. Studies of personal exposure to ambient CO have s hown that the largest percentage of the time in

1 This was the most recent pub licly available data tracking CO emissions in the National Emissions Inventory (US EPA, 2006), which included data from various sources such as industries and state, tribal, and local air agencies (draft ISA, p. 3-2).October, 2009 2

- 2 Draft – Do Not Cite or Quote which an individual is exposed to ambient CO o ccurs indoors (draft ISA, section 2.3). As a result of people spending a si gnificant amount of their time i ndoors (whether at home, school, workplace or elsewhere), CO concentrations in indoor microenvironments are an important determinant of an individual’s CO exposures . Microenvironments that may influence CO exposures typically include resi dential indoor environments and other indoor locations, neartraffic outdoor environments and other outdoor locations, and inside vehicles. As is summarized further in section 5.4, the highest exposure conc entrations to ambient CO are experienced by individuals in transit on or near roadways (draft ISA, section 2.3) . Ambient concentrations near roadways are generally influenced by vehicle tr affic densities (draft ISA, section 3.5.2.2). As a consequence, near-road and in-vehicle exposur e to CO will be much higher during commuting times. Thus, exposure to CO near roadway and in vehicle microenvironments are of concern in this review and are a focus of this draft 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 Although not the focus of this review, indoor sources such as gas stoves and environmental tobacco smoke can, where present, also be important contributors to total exposure. For example, some assessments pe rformed for previous reviews have included modeling simulations both without and with in door sources (gas stov es and environmental tobacco smoke) to provide context for the assess ment of ambient CO exposure and dose (e.g., USEPA, 1994; Johnson et al., 2000). 2 As noted in section 5.5, th is draft assessment does not include a simulation with indoor sources on.

2.3. AT-RISK-POPULATIONS In considering populations for inclusion in th is exposure/risk assessment, we considered the evidence regarding those with increased susceptibility or vulnerability. The term ‘susceptibility’ has been used to characterize populations th at have a greater likelihood of experiencing effects related to ambient CO expos ure, and the term ‘vulnerability’ has been used to identify those periods during an individual’s life when th ey are more susceptible to environmental exposures (draft ISA, section 5.7). In reviewing and setting NAAQS, EPA is required to establish a primary standard that provides protection for population groups that may be at greater risk due to increased sus ceptibility and/or incr eased vulnerability.

2 As has been recognized in previous CO NAAQS reviews, such sources cannot be effectively mitigated by setting more stringent ambient air quality standards and are th erefore not a focus of this assessment of ambient CO exposure and dose.October, 2009 2

- 3 Draft – Do Not Cite or Quote The draft ISA states that the strongest ev idence regarding CO induced health effects relates to cardiovascular morbidity indicating that a causal relationship is likely to exist between relevant short-term CO exposures and cardiovascu lar morbidity, particularly in individuals with coronary artery disease (CAD), al so referred to as coronary heart disease (CHD) (draft ISA, section 5.8). This evidence comes from human e xposure studies of individuals with CAD, along with coherent results from recent epidemiologi c studies reporting asso ciations between shortterm CO exposure and increased risk of emergenc y department visits an d hospital admissions for individuals affected with ischemic heart diseas e (IHD) and related outcomes (draft ISA, section 5.7). Other subpopulations potentially at risk include individuals w ith diseases such as chronic obstructive pulmonary disease (COPD), anemia, or di abetes, and individuals in very early or late life stages, such as older adults or the deve loping young (draft ISA, section 2.6.1). There is limited evidence available from controlled human exposure, epidemiologi c, or toxicological studies characterizing the nature of specific health effects of CO in these 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 The ISA notes that 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 CHD (draft ISA, section 2.5.1). The draft ISA indicates that these st udies demonstrate consistent decr eases in the time to onset of exercise induced angina and ST-segment cha nges (as indicators of myocardial ischemia) following CO exposures resulting in COHb levels of 3-6%, with one multicenter study reporting similar effects at COHb levels as low as 2.0-2.4% . It also recognizes that no human clinical studies have evaluated the effect of controlled exposures to CO resulting in COHb levels lower than 2% (draft ISA, section 5.2.6). Furthermore, human clinical studies of individuals without diagnosed heart disease that were conducte d since the 2000 CO AQCD did not report an association between CO and ST-segment change s or arrhythmia (draft ISA, section 2.5.1) Therefore, the primary target population for the assessment described in this document will be adults with CHD (also known as ischemic h eart disease (IHD) or CAD). This is the same population group that was the focus of the expos ure/dose assessments conducted for previous CO NAAQS reviews. Coronary he art disease includes t hose who have angina pectoris (cardiac chest pain), as well as those who have experi enced a heart attack. Approximately 13.7 million people were diagnosed with CHD in 2007, which re present a large populati on that may be moreOctober, 2009 2

- 4 Draft – Do Not Cite or Quote susceptible to ambient CO exposure when compared to the general populati on (draft ISA, section 5.7). 1 2

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17 18 19 20 21 22 23 24 25 26 27 28 29 2.4. EXPOSURE AND DOSE METRICS

3 Upon inhalation, CO diffuses through the respir atory zone (alveoli) to the blood where it binds to a number of heme-containing mol ecules, mainly hemoglobin (Hb), forming carboxyhemoglobin (COHb). Inhaled ambient CO e licits various health effects through this binding and associated alteration of the functi on of a number of heme-containing molecules, mainly Hb (draft ISA, section 4.1). The dosim etry and pharmacokinetics of CO are discussed in detail in chapter 4 of the draf t ISA (US EPA, 2009). The best characterized health effect associated with CO levels of concern is hypoxia (reduced O 2 availability) induced by increased COHb levels in blood (draft ISA, section 5.1.2). Thus, the dose metric used to characterize health risks associated with exposure to ambient CO in this assessment is the level of COHb in the blood. The Coburn-Forster-Kan e (CFK) model (draft ISA, s ection 4.2.1) has been used to estimate dose (blood levels of COHb) for the ex posure/dose modeling in this assessment (see section 5.3.7 of this document).

2.5. RISK CHARACTERIZATION METRIC The category of health endpoints on which we focused in the risk and exposure assessment are those associated with coronary heart disease (see chapter 4). Similar to the approach used in prior CO NAAQS reviews, we have estimated CO exposures and resulting doses (i.e., COHb levels) for the defined at-ris k population (people with CHD) and characterized the risk for this population in ur ban study areas associated with CO levels representing recent air quality and air quality adjusted to simulate ju st meeting the current CO NAAQS. In previous reviews, the COHb estimates were compared to potential health benchmarks (see section 1.2 above). Although the draft ISA has described ep idemiologic findings from a group of studies, many of which were conducted since the 2000 CO AQCD, that observe associations between short term ambient CO exposures and increases in emergency department visits and hospital admissions for cardiovascular eff ects (draft ISA, section 5.2.1.9), a number of issues complicate the use of these studies in a quantitative risk assessment (draft ISA, section 5.2.3). In consideration of these issues and CASAC views on the Scope and Methods Plan (Brain andOctober, 2009 2

- 5 Draft – Do Not Cite or Quote Samet, 2009; US EPA, 2009), risk has been charac terized in this assessment using the potential health effect benchmark level approach. 1 2 3 4 5 6

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 3 More specifically, we have estimated the number of persons and percent of the at-ris k population (i.e., indi viduals with CHD) that exceed potential health effect benchmark levels, derived from the evaluation of the controlled human exposure studies mentioned above and specified in terms of COHb levels, associated with various CO air quality scenarios. The range of potential health effects benchm arks that we have used extends to lower levels than the range where cont rolled human exposure studies re ported CO-related health effects (i.e., 3-6% COHb with one multicenter study re porting effects at 2.0 to 2.4% COHb using gas chromatography (GC)) to take into considera tion both the uncertainty about the actual COHb levels experienced in the controlled human e xposure studies due to the use of different measurement methods and that these studies did not include individuals with more severe CHD who may respond at lower COHb levels relative to the subjects tested. In addition, there were no studies evaluating effects of CO below 2.0-2.4% COHb levels. Based on these considerations, staff has included 1.5-, 2.0-, 2.5-, and 3.0 % COHb as potential health effect benchmark levels in the current CO risk characterization. Two metrics in this CO exposure/dose assessm ent are considered: (1 ) estimates of the number of people, percentage of the at risk population, and total number of person days for CO at-risk populations exposed to CO at concentrations that exce ed selected benchmarks (1.5, 2.0, 2.5, and 3.0 % COHb) for various CO air quality scenarios; (2) estimates of the number of people and the total number of occurrences for wh ich potential health effect COHb benchmark levels are exceeded for the various CO air quality scenarios.

3 While not used for the purposes of this quantitative a ssessment, EPA will fully consider the health evidence, including the epidemiological studies, in the Policy Assess ment Document, along with considerations based on the risk and exposure assessment findings.October, 2009 2

- 6 Draft – Do Not Cite or Quote 2.6. REFERENCES 1
Brain JD and Samet JM (2009). Letter to EPA Administ rator Lisa Jackson: Clean Air Scientific Advisory Committee’s (CASAC) Peer Review of the Agency’s 1st Draft Carbon Monoxide Integrated Science Assessment. EPA-CASAC-09-011. June 24, 2009. 2 3 4
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25 26 27 CDC (2007). Summary Health Statistic s for U.S. Adults: National Health Interview Survey, 2007, series 10, number 240, May 2009.
Coburn R, Forster R, Kane P (1965). Consideration of the physiological variables that determine the blood carboxyhemoglobin concentrations in man. J. CLin. Invest. 44:1899-1910. Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.1), prepared by ICF Kaiser Consulting Group for U.S. EPA, Office of Air Qu ality Planning and Standards, under Contract No. 68-D6-0064, WA Nos. 1-19, 2-24, 2-30, and 3-3, Docket EPA-HQ-OAR-2008-0015-DRAFT-0009. US EPA. (1991). Air quality criteria for carbon monoxide . Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria an d Assessment Office; report no. EPA/600/8-90/045F.
US EPA (1992). Review of the National Ambient Air Qu ality Stan dards for Carbon Monoxide: Assessment of Scientific and Technical Information. Office of Air Qu ality Planning and Standards Staff Paper, report no. EPA/452/R-92-004. US EPA. (2006). 2002 National Emissions Inventory Data and Documentation. U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Ai r Quality Planning and Stan dards, Research Triangle Park, NC. http://www.epa.gov/ttn/chief/net/2002inventory.html

. US EPA (2000). Air Quality Criteria for Carbon Monoxide . Nation al Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Pr otection Age ncy, Research Triangle Park, NC, report no. EPA/600/P-99/001F. June 2000. Available at: http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm ?deid=18163

. US EPA. (2009). Draft Integrated Science Assessment for Carbon Monoxide – Health Criteria (Second External Review Draft). U.S. Environmenta l Protection Age ncy, Research Tria ngle Park, NC, Report no 600/R09/019B. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html

.October, 2009 3-1 Draft – Do Not Cite or Quote 

### 3. AIR QUALITY CONSIDERATIONS 1

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20 21 22 23 24 25 26 27 28 29 30 Ambient air quality data can be used as an indicator of exposure or used in conjunction with other information to estimat e exposure concentrations. How well the ambient air quality is represented in a particular location is depe ndent on the ambient monitoring network design relative to the spatial and temporal characterist ics of the pollutant as well understanding the concentration contribution from important loca l source emissions. This chapter summarizes findings about the current air quality conditions and their spatial distri bution, with particular focus on aspects informative to the design a nd conduct of this asse ssment and including descriptions of CO measurement methods, mon itor siting requirements, and monitor locations (section 3.1). 1 Section 3.2 then draws upon the inform ation presented in sections 3.1, among other data, to select ambient air quality/study loca tions most useful in me eting the objectives of the REA. Finally, key observations of the chapter are pres ented in section 3.3.

3.1 AMBIENT CO MONITORING

In this section, a broad overview of the m onitoring network is provided (section 3.1.1) and is followed by a summary of analytical de tection issues (secti on 3.1.2). Ambient CO concentrations and their spatial and temporal variability are characte rized in section 3.1.3. Lastly, estimates of policy-rele vant background (PRB) concentrati ons which are defined as those ambient concentrations that woul d occur in the U.S. in the abse nce of anthropogenic emissions in continental North America are presented in section 3.1.4 of this document.

3.1.1 Monitoring Network

Ambient CO concentrations are measured by monitoring networks that are operated by state and local monitoring agencies in the U.S., and are funded in part by the EPA. The main network providing ambient data for use in comp arison to the NAAQS is the State and Local Air Monitoring Stations (SLAMS) network. The su bsections below provide specific information regarding the methods used for obtaining ambien t CO measurements and the requirements that apply to states in the design of the CO network. Minimum monitoring requirements for CO were revoked in the 2006 revisions to ambient monitoring requirements (see 71 FR 61236, October 17, 2006). This action was made to allow for reductions in measurements of some pollutants (CO, SO 2 , NO 2 , and Pb) where measured levels were well below the appl icable NAAQS and air quality probl ems were not expected. CO

1 A complete description of spatial scales is listed in 40 CFR Part 58 Appendix D, section 1.2. Ambient monitoring of other NAAQS pollutants such as NO 2 and SO 2 follow the same general spatial scales.October, 2009 3-2 Draft – Do Not Cite or Quote monitoring activities have been maintained at some SLAMS and these measurements of CO are required to continue until discontinuation is approved by the EPA Regional Administrator. 1 2 3

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28 29 30 CO monitors are typically sited to reflect one of the following spatial scales 2 :

 Microscale : Data represents concentrations within a 100 m radius of the monitor. For CO, microscale monitors are sited 2 – 10 m from a roadway. Measurements are intended to represent the near-roa d or street canyon environment.

 Middle scale : Data represents concentrations averaged over areas defined by 100 – 500 m radii. Measurements are intended to represent several city blocks.

 Neighborhood scale : Data represents concentrations averaged over areas defined by 0.5 – 4.0 km radii. Measurements are intended to represen t extended portions of a city. In addition to monitoring required for de termining compliance with the NAAQS, the EPA is currently in the process of implementing plans for a new network of multi-pollutant stations called NCore that is intended to meet multiple monitoring objectives. A subset of the SLAMS network, NCore stations are intended to address integrated air quality management needs to support long-term trends analysis, m odel evaluation, health and ecosystem studies, as well as the more traditional objectives of NAA QS compliance and Air Quality Index reporting. 3 States were required to submit to EPA Annua l Monitoring Network Plans (AMNP) describing their candidate NCore stations by July 1, 2009. EPA is reviewing these plans and intends to provide station approvals later in 2009. The complete NCore network, required to be fully implemented by January 1, 2011, will consist of a pproximately 63 urban and 20 rural stations and will include some existing SLAMS sites that have been modified to include additional measurements. Each state will contain at leas t one NCore station, and 46 of the states plus Washington, D.C. will have at least one urban st ation. CO will be measured using trace-level monitors, as will SO 2 , NO, and NO y . 4 The majority of NCore stati ons will be sited to represent neighborhood, urban, and regional scal es, consistent with the NCor e network design objective of representing exposure expected across urban and rural areas in locations that are not dominated by local sources.

3.1.2 Analytical Sensitivity

To promote uniform enforcement of the air quality standards set forth under the CAA, EPA has established provisions in the Code of Federal Regulations (CFR ) under which analytical

3 ( http://www.epa.gov/ttn/amtic/ncore/index.html ). 4 NCore sites must measure, at a minimum, PM 2.5 particle mass using continuous and integrated/filter-based samplers, speciated PM 2.5 , PM 10-2.5 particle mass, speciated PM 10–2.5 , O 3 , SO 2 , CO, NO/NO Y , wind speed, wind direction, relative humidity, and ambient temperature ( http://www.epa.gov/ttn/amtic/ncore/index.html ).October, 2009 3-3 Draft – Do Not Cite or Quote methods can be designated as federal reference methods (FRMs) or federal equivalent methods (FEMs). Measurements for determinations of NAAQS compliance must be made with FRMs or FEMs. 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 5 Specifications for CO mon itoring are designed to help states utilize equipment that has met performance criteria utilized in the FRM or FEM approval process; operational parameters are documented in 40 CFR Part 53, Table B-1. Gi ven the levels of the CO NAAQS (35 ppm, 1- hour; 9 ppm, 8-hour), a 1.0 ppm lowe r detectable limit (LDL) is well below the NAAQS levels and is therefore sufficient for demonstration of compliance. However, with ambient CO levels now routinely near 1 ppm, there is greater uncerta inty in a larger portion of the distribution of monitoring data because a large percentage of these measurements are below the LDL of conventional monitors. For this reason, a new generation of ambient CO monitors has been designed that provides trace-level measurements with improved sensitivity at or below the typical ambient CO levels measured in most ur ban and all rural locatio ns. Additionally, tracelevel CO measurements are needed to support additional objectives such as validating the inputs to chemical transport models and assessing the ro le of transport between urban and rural areas because background CO concentrations on the order of 0.1 ppm are well below the LDL of conventional monitors. Newer GFC instruments have been designed for automatic zeroing to minimize drift (US EPA, 2000). Currently, a total of 13 appr oved FRMs are in use in the SLAMS network, based on a retrieval of data reported between 2005 and 2009. Among these methods, nine are “legacy” monitors with a federal method detection limit (MDL) listed as 0.5 ppm according to records in EPA’s Air Quality System (AQS). 6 As discussed in the draft ISA, many of the reported concentrations in recent years ar e near or below these MDLs (draft ISA, p. 3-43). Four of these methods are newer trace-level methods with a federal MDL of 0.02 ppm and a growing body ofambient data from trace-level CO instruments is becoming available. Among newer GFC tracelevel instruments, manufactur er-declared LDLs range from 0.02 – 0.04 ppm, with 24-hour zero drift varying between 0.5% with in 1 ppm and 0.1 ppm, and preci sion varying from 0.5% to 0.1 ppm. EPA performed MDL testing on severa l trace-level CO m onitors in 2005 and 2006 following the 40 CFR Part 136 procedures. Those tests demonstrated MDLs of approximately 0.017 – 0.018 ppm (17 – 18 ppb), slightly below the stated LDL of 0.02 – 0.04 ppm.

5 As of August 2009, twenty automated FRMs had been approved for CO measurement. All EPA FRMs for CO operate on the principle of non-dispersive infrared (NDIR) detection and can include the gas filter correlation (GFC) methodology. An extensive and comprehensive review of NDIR, GFC, and alternative, non-FRM techniques for CO detection was included in the 2000 CO AQCD (US EPA, 2000). 6 Among several of the older instruments (Federal Reference Method codes 008, 012, 018, 033, 041, 050, 051, and 054), performance testing has shown LDLs of 0.62 – 1.05 ppm, with 24-hour drift ranging from 0.044 – 0.25 ppm and precision ranging from 0.022 – 0.067 ppm at 20% of the upper range limit of the instrument (Mitchie et al., 1983).October, 2009 3-4 Draft – Do Not Cite or Quote Based on a retrieval of data reported betw een 2005 and 2009 to AQS, a total of 36 tracelevel CO monitors have reported data with the majority of these monitors currently active. The majority of these active monitors are associated with the implementation of the NCore network. The extent to which trace-level monitors beco me integrated into non-NCore SLAMS stations, however, will depend on the availability of fundi ng for states to replace well-operating legacy CO monitors as well as the possibility that monitoring requirements for CO might either encourage or require such technological improvements. 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 3.1.3 Patterns of CO Concentrations As discussed in the draft ISA, the spatia l and temporal patterns of ambient CO concentrations are heavily influenced by the patt erns associated with mobile source emissions (draft ISA, chapter 3). Based on the 2002 Nationa l Emissions Inventory (NEI), on-road mobile sources comprise about half of the total an thropogenic CO emissions, though in metropolitan areas of the U.S. the contribution can be as high a 75% of all CO emissions due to greater motor vehicle density. For example, emissions in Denver county originating from on-road mobile sources is about 71% of total CO emissions (d raft ISA, section 3.2). When considering all mobile sources (non-road and on-road combined), the contribution to total CO emissions can be over 80%. Again using Denver County as an exam ple, all mobile sources contribute to about 98% of total CO emissions. Temporally, the national-scale anthropogenic CO emissions have decreased 35% between 1990 and 2002. Nearly all the national-level CO reductions since 1990 are the result of emission reductions in on -road vehicles (draft ISA, Figure 3.-2). Nearly 400 ambient monitoring stations re port continuous hourly averages of CO concentrations across the U.S. Over the period 2005-2007, 291 out of 376 monitors met a 75% completeness requirement, spread among 243 countie s, cities, or municipalities (draft ISA, section 3.4.2.2). All CO concentr ations measured at these mon itoring sites are well below the current NAAQS. For example, in 2007, none of th e monitors reported a second-highest 1-hour CO concentration above 35 ppm, the level of the current 1-hour NAAQS, while only two sites reported a 2 nd highest 1-hour CO concentrations be tween 15.1 and 35.0 ppm. Only five counties reported 2 nd highest 8-hour CO concen tration 5.0 ppm or higher.October, 2009 3-5 Draft – Do Not Cite or Quote 0 2 4 6 8 10 12 14 16 18 201993199419951996199719981999200020012002200320042005200620072008

Year2ndHighest 1-hour Average CO (ppm) 080310002 080310013 080310014 080310019

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0 2 4 6 8 10 121993199419951996199719981999200020012002200320042005200620072008

Year2ndHighest 8-hour Average CO (ppm) 080310002 060370113 060371002 080310019

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4 5 Figure 3-1. Spatial and Temporal Trends in the 2 nd Highest 1-hour (top) and 8-hour 3 Average (bottom) CO Ambient Monitoring Concentrations in Denver, Colorado, Years 1993 – 2008.October, 2009 3-6 Draft – Do Not Cite or Quote 0 3 6 9 12 15 18 21 241993199419951996199719981999200020012002200320042005200620072008

Year2ndHighest 1-hour Average CO (ppm) 060370002 060370113 060371002 060371103 060371201 060371301 060371601 060371701 060372005 060374002 060375001

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0 3 6 9 12 15 181993199419951996199719981999200020012002200320042005200620072008

Year2ndHighest 8-hour Average CO (ppm) 060370002 060370113 060371002 060371103 060371201 060371301 060371601 060371701 060372005 060374002 060375001

2

4 5 Figure 3-2. Spatial and Temporal Trends in the 2nd Highest 1-hour (top) and 8-hour 3 Average (bottom) CO Ambient Monitori ng Concentrations in Los Angeles, California, Years 1993 – 2008.October, 2009 3-7 Draft – Do Not Cite or Quote The current levels of ambient CO across the U.S. reflect the steady declines in ambient concentrations that have occurred over the past several years. On average across the U.S. the decline has been on the order of 50% since the early 1990s (draft ISA, Figure 3-31). As an example, Figures 3-1 and 3-2 illustrate the trends observed in Denver and Los Angeles, respectively, for the period from 1993 through 2008. Both the 2 nd highest 1-hour and 8-hour concentrations are plotted for each year from a ll existing monitors in those metropolitan areas respectively. Note, these figures indicate both a significant decrease in the 2 nd highest 1-hour 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 and 8-hour average CO concentrations and a relativ e decrease in spatial variability in ambient CO concentrations since the last review. Carbon monoxide also exhibits ho urly variability within a day, with two distinct temporal patterns noted for weekdays and weekends (draft ISA, section 3.5.2.2). Th e diel variation is inherently linked to the typical commute times-of- day that occurs within urban locations. In general, in recent years observed mean and medi an concentrations for all hours of the day and across all monitors within urban areas demonstrated limited variability, however 90 th and 95 th percentile hourly concentrations generally exhibit early-morni ng and late afternoon peak CO concentrations during weekdays (draft ISA, Figur e 3-33). The weekend diel variation in ambient CO concentrations was much lower than that oc curring during weekdays as expected due to the relative absence of commuter vehi cle traffic during the morning and evening hours of the day. Most urban areas have relatively stable concentrations throughout weekend days at each of the selected percentiles, though a fe w locations (e.g., Phoenix, Los Ange les, Seattle) did have a more pronounced late afternoon peak (draft ISA, Figure 3-34). Staff investigated local hourly variation at two separate CO monitors located in Denver and Los Angeles to illustrate similar trends. Fi gure 3-3 indicates that on average, peak ambient CO concentrations that occur during typical commute times in Denver ranged from about 1 to 5 ppm during weekdays in 1995, while, currently, am bient CO concentrations during morning and afternoon commutes range from about 1 to 2 ppm. Weekends tend to exhibit less variability throughout the day. On average, CO ambient con centrations generally ranged from 1 to 3 ppm throughout the day in 1995, while cu rrent weekend concentrations are less than 1 ppm for most hours of the day. In Los Angeles, both the concen tration levels and variab ility are greater than when compared with similar years and times of day in Denver (Figure 3-4). Peak ambient CO concentrations are more prominent during morn ing commutes and generally ranged from 2 to 10 ppm in 1995, while currently (year 2006) mo st commuting times are associated with concentrations ranging from between 1 and 5 ppm. The weekend profile exhibits some variation when considering either year, with maximum co ncentration levels and variability exhibited during the overnight hours.October, 20093-8Draft – Do Not Cite or Quote12345Figure 3-3.Diurnal Distribution of 1-hour CO Concentrations inDenver (Monitor 08-031-0002) by Day-type (weekdays-left;weekends-right), Years 1995 (top) and 2006 (bottom).The box encompasses concentrations from the 25th to 75thpercentiles or Interquartilerange (IQR), the line bisecting the box is the median, the solid dot within the box is themean, the whiskers represent 1.5 times the IQR, and concentrationsoutside the whiskers are indicated by open circles.October, 20093-9Draft – Do Not Cite or Quote12345Figure 3-4. Diurnal distributionof 1-hour CO concentrations in Los Angeles (Monitor 06-037-1301) by day-type (weekdaysleft; weekends-right), years 1997 (top) and 2006 (bottom).The box encompasses concentrations from the 25thto 75thpercentiles or IQR, the line bisectingthe box is the median, the solid dot within the box is the mean, the whiskersrepresent 1.5 times the IQR, and concentrations outside the whiskersare indicated by open circles.October, 2009 Draft – Do Not Cite or Quote 3-10 Ambient monitor siting characteristics can also influence ambient CO concentration observations. Microscale and middle scale monito rs are commonly used to measure significant source impacts, while neighborhood and urban sc ale monitors are designated for populationoriented monitoring (40 CFR Part 58 Appendix D). As CO concen trations primarily originate from vehicle emissions, the microscale and middle s cale data can be a useful indicator of nearroad air quality. Such data analyzed in the draf t ISA were concluded to be consistent with hourly concentrations reported in the lite rature for the near road environment in the U.S. (draft ISA, p. 3-63). Further, when considering monitoring sc ale across ambient monitors in the U.S., the median hourly CO concentration measured at micr oscale monitors was about 25% higher than at middle scale monitors and 67% higher than at neighborhood scale monitors (draft ISA, Table 3- 12). In general, similar patterns were present in the 1-hour daily max, 1- hour daily average, and 8-hour daily max distributi ons (draft ISA, Table 3-12). These patterns are also consistent with findings presented by other resear chers regarding the relative decrease in concentration with increasing distance from roadways, though the ma gnitude of the relationship can vary. Two studies summarized in the draft ISA (Zhu et. al., 2002; Baldauf et. al., 2008) indicate that nearroad CO concentrations (i.e., measured within 20 m of an interstate highway) can range from 2 – 10 times greater than CO concentrations measured as far as 300 m from a major road (draft ISA, Figures 3-26 and 3-27). 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 While recognizing that monitoring site attribut es are not available fo r all monitors in the current network and that data for some at tributes may not refl ect current conditions, 7 the draft ISA also analyzed the information available for network monitors on average annual daily traffic (AADT). The ISA noted that only two microscale monitors and two middle scale monitors in the existing network are sited at roads with ≥ 100,000 AADT, although it is not uncommon for roadways within CSAs to have several roads with AADT > 100,000. The AADT ranged from 160,000-178,000 for the near-road monitors used in the aforementioned study by Zhu et al. (2002) where CO concentrations we re up to 10 times greater than m onitors sited at 300 m from a major road. Existing microscale sites near ro ads having only moderate traffic count data (<100,000AADT) may record concentrations that ar e not substantially different from those obtained from neighborhood scale measurem ents (draft ISA, section 3.5.1.3). Within a specific urban area however, consid eration of only monitor scale or other attributes reported in AQS, such as AADT estim ates may be of limited use in efforts to characterize the monitoring data as to its represen tation of local near-road CO concentrations. For example, of the five monitors meeti ng a 75% completeness criterion in the Denver

7 Note that recorded AQS monitoring site attributes are not always available for each monitor or may not always reflect potential source influences. For example, of 24 CO monitors in the Los Angeles CSA, AQS had no information regarding monitoring scale fo r 16 (draft ISA, Figure 3-20).October, 2009 Draft – Do Not Cite or Quote 3-11 Consolidated Statistical Area (CSA), three were microscale and two were neighborhood scale (draft ISA, section 3.5.1.2). While one of th e microscale monitors sited within downtown Denver measured the highest hourly ambien t CO concentrations (ID 080310002), another microscale monitor located outside the urban co re measured the lowest hourly ambient CO concentrations (draft ISA, Figure 3-18). Furthe r, the AADT estimate for a major road near the microscale monitor within the urban core (ID 080310002, AADT=17,200) was lower than that listed for the microscale monitor outside the urban core (ID 080130009, AADT=20,000) (draft ISA, Table A-2). And, a third microscale m onitor located 1.3 km from monitor ID 080310002, within the urban core, and measuring somewhat lo wer CO concentrations ( but not lower than the monitor outside the urban core) had only 500 AADT lis ted for the nearest majo r road. It is likely that the higher CO concentrations measured at the downtown monitor reflect influences of the denser roadway network surrounding that monito r in the downtown Denver area (Figure 3-17). 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 8 Thus, to better characterize the representati on of near-road CO concentrations for many of the existing ambient monitors, additional anal yses, beyond consideration of AQS attributes such as monitoring scale and tra ffic count, would likely need to be performed (e.g., using GIS to determine monitor distance from roads, the number and type of roads within close proximity of the monitor, and obtaining current traffic count data for all roads).

3.1.4 Policy-Relevant Background Concentrations

EPA has generally conducted NAAQS risk assessm ents that focus on the risks associated with ambient levels of a pollu tant that are in excess of po licy-relevant background (PRB). Policy-relevant background levels are defined, for purposes of this document, as concentrations of a pollutant that would occur in the U.S. in the absence of anthropogeni c emissions in the U.S., Canada, and Mexico. Over the continental U.S. (CONUS), the 3-year (2005- 2007) average CO PRB concentration is estimated to range from 11 8 to 146 ppb (draft ISA, section 3.5.4). Outside the CONUS, the 3-year average CO PRB in three Alaskan sites is estimated to range from 127 to 135 ppb, and from 95 to 103 ppb in two Hawaiian monitoring locations. The estimated PRB concentrations exhibit significant within-l ocation seasonal variation, with minimum concentrations observed in the summer and fall and maximum concentrati ons occurring in the winter and spring. For example, PRB in two Calif ornia sites is estimated to range from about 85 to 170 ppb, and one site in Colorado, ranged from about 80 to 140 ppb (Figure 3-40 of the draft ISA). Given that ambient concentrati ons of interest in this REA are well above the estimated PRB levels discussed above and, thus the co ntribution of PRB to overall ambient CO

8 Staff also recognizes some uncertainty in how well the AQS AADT estimates reflect current conditions at this monitor site.October, 2009 Draft – Do Not Cite or Quote 3-12 concentrations is very small, EPA is characte rizing risks associated w ith ambient CO levels without regard to estimated PRB 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 33 34 3.2 STUDY AREAS FOR CURRENT ASSESSMENT Staff identified several criteria to select the exposure assessmen t study areas drawing f rom information discussed in the earlier sections of this Chapter and additional scientific evidence in the draft ISA. We selected Denver and Los Angeles as areas to focus the current assessment because (1) both cities have b een included in prior CO NAAQS exposure assessments and thus serve as an important c onnection with past assessments, (2) they have historically had the highest CO ambient concentrations among ur ban areas in the U.S., and (3) Denver is at high altitude and represents an im portant risk scenario due to the increased susceptibility of individuals at hi gh altitude from exposure to CO. In addition, of 10 urban areas across the U.S. having monitors meeting a 75% co mpleteness criteria, the two locations were ranked 1 st (Los Angeles) and 2 nd (Denver) regarding pe rcent of elderly population within 5, 10, and 15 km of monitor locations, and ranked 1 st (Los Angeles) and 5 th (Denver) regarding number of 1- and 8-hour daily maximu m CO concentration measuremen ts (draft ISA, section 3.5.1.1). Maximum and 2 nd highest 1-hour and 8-hour average CO concentrations are provided in Table 3-1 for all monitors located within f our Denver-area counties (i.e., Adams, Boulder, Jefferson, and Denver) having at l east one year of ambient mo nitoring data for years 2005 through 2007. 9 Table 3-2 provides a similar concen tration summary for the Los Angeles monitors in four counties (i.e ., Los Angeles, Orange, Riversid e, and San Bernardino) that reported CO concentrations for at least one year between 2005 and 2007. Additional discussion regarding specific sites and monitoring data used in the exposure modeling is provided in sections 5.5 and 6.1.2. In order to investigate ambient CO concen trations in each study area, EPA initially considered the sites listed in Ta bles 3-1 and 3-2 that reported data for at least one year between 2005 and 2007 with 75% completeness. As shown in these tables, maximum concentrations as well as 2 nd highest 1-hour and 8-hour average CO concentrations generally are quite homogeneous and do not exhibit great variab ility. Focusing on Denver and Los Angeles Counties, however, the sites show higher concentr ations for the year 2006 in specific sites within each county. Considering the spatial scale and location of monitoring sites in these two areas, staff recognizes limitations related to the coverage provid ed by the available sites, particularly in the Denver area, to support development of a compre hensive population exposure assessment for these urban areas in this time period. This issue is discussed further in chapter 5.

9 There were no CO monitoring data reported for Arapahoe and Douglas Counties.October, 2009 Draft – Do Not Cite or Quote 3-13 1 2 3 4 Table 3-1. Descriptive Statistics for CO Con centrations Measured at Selected Fixed-Site Monitors in the Denver Metropolitan Area for the Years 2005 - 2007. CO concentration (ppm) 1-hour 8-hour average County Site ID (location) Year Number of 1-hour

values Maximum 2 nd

Highest Maximum 2 nd Highest 2005 8693 3.4 3.3 2.5 2.2 2006 8633 3.8 3.8 2.6 2.5 Adams 080013001 (Welby) 2007 8663 3.1 3 2.3 2.1 2005 8509 5 4.8 2.5 2.4 2006 8531 3.9 2.8 2.2 1.8 080130009 (440 Main St) 2007 8588 3.8 3.4 2.3 1.9 Boulder 080130010 (2150 28 th

St.) 2005 2978 3.6 3.2 2 1.9

2005 8680 4.6 4.3 2.9 2.5 2006 8672 6.4 4.6 3.4 3.1 080310002 (CAMP) 2007 8676 6 5.9 3.2 2.8 2005 8674 5.3 3.6 2.5 2.4 080310013 (NJE-E) 2006 8635 4.4 3.9 2.9 2.5 2005 8121 3.9 3.4 2.3 2.1 080310014 (Carriage) 2006 8557 3.9 3.5 3 3 2005 8640 5.6 4.2 2.4 2.3 2006 8569 9.3 5.7 3.1 2.6 Denver

080310019 (Firehouse #6) 2007 8412 4.2 4.1 2.5 2.4 2005 8461 4.1 3.6 2.1 2 Jefferson 080590002 2006 8603 3.6 3.5 2 2October, 2009 Draft – Do Not Cite or Quote 3-14 Table 3-2. Descriptive Statistics for CO Concen trations Measured at Selected Fixed-Site Monitors in the Los Angeles Metrop olitan Area for the Years 2005 - 2007. 1 2 3 CO concentration (ppm) 1-hour 8-hour average
County Site ID (location) Year Number of 1-hour

values Maximum 2 nd Highest Maximum 2 nd Highest 2005 8355 2.5 2.3 1.7 1.6 2006 8368 2.2 2.2 1.7 1.6 060370002

(Azusa) 2007 8344 2.6 2.4 1.8 1.7 2005 8350 3.4 3.1 2.1 2.0 2006 8365 2.9 2.8 2.0 1.9 060370113 (West Los Angeles) 2007 8267 2.7 2.5 2.0 1.6 2005 8279 4.4 4.0 3.4 3.2 2006 8345 4.3 4.2 3.4 3.4 060371002 (Burbank) 2007 8334 3.7 3.7 2.8 2.7 2005 8298 3.9 3.4 3.1 2.7 2006 8265 3.5 3.3 2.7 2.5 060371103 (Los Angeles) 2007 8148 3.2 3.1 2.2 2.1 2005 8018 5.1 4.9 3.5 3.4 2006 8375 4.8 4.7 3.5 3.4 060371201

(Reseda) 2007 7954 3.7 3.5 2.8 2.7 2005 8331 7.4 7.2 5.9 5.6 2006 8275 8.4 8.2 6.2 5.6 060371301 (Lynwood) 2007 8284 7.8 7.0 5.3 4.9 2005 2538 3.3 3.2 2.4 2.4 2006 4698 3.1 3.1 2.7 2.7 060371601 (Pico Rivera) 2007 8318 4.8 3.7 2.9 2.8 2005 8350 4.2 4.0 2.5 2.4 2006 8335 3.3 3.1 2.2 2.2 060371701 (Pomona) 2007 8293 3.3 3.1 2.0 2.0 2005 8274 4.3 4.3 2.8 2.8 2006 8258 4.1 3.7 2.8 2.7 060372005 (Pasadena) 2007 8338 3.3 3.2 2.3 2.2 2005 8340 4.2 4.0 3.5 3.1 2006 8216 4.2 4.0 3.4 3.3 060374002 (Long Beach) 2007 7769 3.3 3.2 2.6 2.4 2005 8364 2.8 2.7 2.1 2.1 2006 8356 2.8 2.7 2.3 2.1 060375005 (Los Angeles) 2007 8311 3.3 3.2 2.4 2.1 2005 8248 2.2 2.0 1.3 1.2 2006 8339 2.0 2.0 1.3 1.1 060376012 (Santa Clara) 2007 8339 1.9 1.9 1.2 1.2 2005 8265 2.9 2.5 1.5 1.5 2006 7710 3.2 2.8 1.6 1.6 Los Angeles

060379033 (Lancaster) 2007 8226 2.5 2.3 1.3 1.2 Orange 060590007 2005 8307 4.1 4 3.3 3.1October, 2009 Draft – Do Not Cite or Quote 3-15 CO concentration (ppm) 1-hour 8-hour average
County Site ID (location) Year Number of 1-hour

values Maximum 2 nd Highest Maximum 2 nd Highest 2006 8342 4.5 4.3 2.9 2.9 (Anaheim) 2007 7681 3.6 3.6 2.9 2.3 2005 8308 4.7 4.1 3.2 3.1 2006 8358 3.5 3.3 3.0 2.5 060591003 (Costa Mesa) 2007 8160 4.5 4.4 3.1 2.6 2005 8265 2.2 2.2 1.6 1.6 2006 8336 1.9 1.9 1.6 1.3 060592022 (Mission Viejo) 2007 8296 2.9 2.7 2.2 2.0 2005 8333 6.8 6.7 3.1 3.0 2006 8227 6.0 6.0 2.9 2.9 060595001 (La Habra) 2007 8211 6.3 6.3 2.9 2.7 2005 8190 4.0 3.7 2.4 2.2 2006 8385 3.8 3.8 2.4 2.1 060651003 (Riverside) 2007 8376 3.7 3.4 2.2 2.0 2005 8296 2.1 2.0 0.8 0.7 2006 8357 2.3 1.8 0.9 0.8 060655001 (Palm Springs) 2007 8351 1.5 1.5 0.8 0.7 2005 8216 3.4 3.3 2.5 2.3 2006 8348 2.7 2.7 2.3 2.1 060658001 (Rubidoux) 2007 8280 3.8 3.6 2.9 2.5 2005 8312 1.7 1.5 1.0 1.0 2006 8256 1.4 1.4 1.0 1.0 Riverside

060659001 (Lake Elsinore) 2007 8290 1.6 1.5 1.4 1.3 2005 8106 3.3 2.2 1.3 1.2 2006 7847 3.5 2.6 1.2 1.1 060710001 (Barstow) 2007 8217 1.4 1.3 0.7 0.6 2005 8289 2.5 2.1 1.6 1.4 2006 8225 2.2 2.2 1.6 1.5 060710306 (Victorville) 2007 8348 2.1 2.0 1.6 1.5 2005 8314 2.5 2.4 1.9 1.7 2006 8210 2.7 2.6 1.9 1.8 060711004

(Upland) 2007 8309 2.4 2.2 1.7 1.6 2005 8240 3.8 3.3 2.5 2.2 2006 8340 2.8 2.8 2.2 2.0 San Bernardino

060719004 (San Bernardino) 2007 8330 3.7 3.1 2.3 2.1 1October, 2009 Draft – Do Not Cite or Quote 3-16 3.3 KEY OBSERVATIONS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Presented below are key observations resul ting from the air quality considerations.

 Automobiles are the primary contributor to CO emissions, particularly in urban areas due to greater vehicle and roadway densities.

 Recent (2005-2007) ambient CO concentrations across the U.S. are lower than those reported in the previous CO NAAQS review and are also well below the current CO NAAQS levels. Further, a large proportion of the reported concentrations are below the conventional instrument detection limit of 1 ppm.

 Ambient CO concentrations are highest at monitors sited closest to roadways (i.e., microscale and middle scale monitors) and exhibit a diel variation linked to the typical commute times of day, with peak s generally observed during early morning and late afternoon during weekdays.

 The currently available information for CO monitors indicates that siting of microscale and middle scale monitors in the current network is primarily limited to roads where traffic density described for them is moderate (<100,000 AADT), however, factors other than reported AADT (e .g., orientation with regard to dense urban roadway networks) can contribute to sites reporting higher CO concentrations.

 Due to the limited number of existing ambient monitors and the monitor site characteristics, it is difficult to fully ch aracterize the current spatial and temporal variability in CO ambient concentrations ac ross the two urban areas that are the focus for this assessment, Denver and Los Angeles.October, 2009 Draft – Do Not Cite or Quote 3-17 3.4 REFERENCES 1 2 3 4 5
6 7
8 9 10
11 12 Baldauf R; Thoma E; Hays M; Shores R; Kinsey J; Gullett B; Kimbrough S; Isakov V; Long T; Snow R; Khlystov A; Weinstein J; Chen FL; Seila R; Ol son D; Gilmour I; Cho SH; Watkins N; Rowley P; Bang J. (2008). Traffic and meteorological im pacts on near-road air quality: Su mmary of methods and trends from the Raleigh near-road study. J Air Waste Manag Assoc, 58: 865-878. US EPA (2000). Air Quality Criteria for Carbon Mon o xide. EPA 600/P-99/001F. U.S. Environmental Protection Agency, Research Triangle Park, NC. US EPA (2009). Integrated Science Assessment for Carbon Monoxide –Second External Review Draft. U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA 600/R-09/019B. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html

.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

### 4. APPROACH TO RISK CH ARACTERIZATION FOR CURRENT REVIEW 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 This section describ es the health effect s evidence, dose metric of interest and approach to characterization of risk in support of the current review of the CO primary NAAQS. Similar to the approach used in prio r CO NAAQS reviews, the approach to risk characterization presented in th is section is based on the es timation of CO exposures and resulting doses (an internal biomarker) for a defined at-risk population within urban study areas associated with CO levels representing recent air quality and air quality adjusted to simulate just meeting the current CO NAAQS. Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are dependent upon concentrati on and duration of exposure, and that may involve multiple mechanisms including hypoxic stress and others such as free radical production and the initiation of cell signaling. However, binding of CO to reduced iron in heme proteins with subsequent alterati on of heme protein f unction is the common mechanism underlying the biological responses to CO (draft ISA, section 5.1). Similarly, based on the health effects evidence summarized in the draft ISA, th e best characterized dose metric for estimating exposure to CO associ ated with adverse hea lth effects is blood carboxyhemoglobin (COHb) levels. As described in the draft ISA, the most compelling evidence of a CO-induced effect on the cardiovasc ular system at COHb levels relevant to the current NAAQS comes from a series of controlled human exposure studies among individuals with coronary hear t disease (CHD) (draft ISA, section 5.2). Specifically for the current analysis, we characterize risk for the population of interest (CHD population) by using a potential health effects benchmark level approach, in combination with shortterm CO exposure and dose modeling, to estimate the number and percent of the population with CHD that would potentially exceed COHb levels of concern, upon just meeting various CO air quality scenarios. Section 4.1 presents a brief summary of the health effects evidence from controlled huma n exposure studies (draft ISA, section 5.2.4) and section 4.2 describes the rationale for the selection of poten tial health effects benchmarks and their use in the characterizati on of risk for adults with CHD. Section 4.3 presents key observations re levant to the approach fo r the risk characterization.

4.1. CARDIOVASCULAR DISEASE RELATED EFFECTS Controlled human studies provide strong evidence for an association between short-term exposure to CO and exacerbation of preexisting coronary heart disease. Several controlled human exposure studies discussed in the 2000 CO AQCD (section

October 2009 Draft – Do Not Cite or Quote 4-16.2.2, US EPA, 2000) showed that short-term exposure to CO and subsequent elevation of COHb levels enhance exercise -induced myocardial ischemia. 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 Among those studies the draft ISA places em phasis on the work of Allred et al., a large multi-laboratory study designed to evalua te myocardial ischemia, as documented by electrocardiogram ST-segment changes and ti me to onset of angina, during a standard treadmill test, at CO exposures targeted to result in COHb levels of 2% and 4%. As described in the draft ISA (draft ISA, s ection 5.2.4), other controlled human exposure studies (Adams et al. 1988, Anderson et al. 1973, Kleinman et al 1989, Kleinman et al., 1998) involving individuals with stable angina have also de monstrated the capacity of CO to decrease the time to onset of angina, as well as to reduce the duration of exercise at COHb concentrations between 3 and 6% (a s measured by CO-oximeter). A single study by Sheps et al. (1987) observed no change in ti me to onset of angina or maximal exercise time following a 1 h exposure to 100 ppm CO (targeted COHb of 4%) among a group of 30 patients with CHD. In a subsequent study conducted by the same laboratory, a significant increase in number of ventricula r arrhythmias during exercise was observed relative to room air among individuals with CHD followi ng a 1-hr exposure to 200 ppm CO (targeted COHb of 6%), but not followi ng a 1-hr exposure to 100 ppm CO (targeted COHb of 4%) (Sheps et al., 1990). The dr aft ISA notes that although the subjects evaluated in the studies described above are not necessarily represen tative of the most sensitive population, the level of disease in these individuals was relatively severe, with the majority either having a histor y of myocardial infarction or having ≥ 70% occlusion of one or more of th e coronary arteries. The draft ISA (draft ISA, section 5.2.4) st ates that no new human clinical studies involving controlled short-term CO exposur es among subjects with coronary artery disease have been published since the 2000 CO AQCD. However, a number of new studies have investigated the e ffects of CO in healthy adults. Adir et. al., (1999) showed that short-term exposure to CO at concen trations targeted to produce 4-6% COHb, followed by a treadmill test (at maximal exercise capacity), caused a decrease in the duration of exercise and in the metabolic equivalent units (indicative of the oxygen consumed by the body during exercise). The draft ISA notes that these results are in agreement with the findings of several studies cited in the 2000 CO AQCD which observed decreases in exercise duration and m aximal aerobic capacity among healthy adults at COHb levels ≥ 3% (draft ISA, section 5.2.4), which provides coherence with the observed effects of short-term exposure to CO on exercise-induced myocardial ischemia among patients with CHD.

October 2009 Draft – Do Not Cite or Quote 4-24.2. HEALTH EFFE CT BENCHMARKS 1 As in the review completed in 1994 and in the CO exposure/dose assessment completed in 2000 (section 6.3.2), a health effect benchmark level approach is used in the current analysis to estimate the number a nd percent of the population with CHD that would potentially exceed COHb levels of concern for speci fic CO air quality scenarios. Since the ISA has not identified new studies th at demonstrate CO eff ects at COHb levels lower than those described in the 2000 AQCD, we are relying in the same studies as we did on the review completed in 1994. As mentioned above, a number of studies, described in detail in the 2000 CO AQ CD (section 6.2.2, US EPA, 2000), showed statistically significant group mean responses, m easured in terms of reduced time to onset of exercise-induced angina, in the range of 3 to 6 %COHb (measured by CO-oximeter) in subjects with coronary heart disease. We note that the lowest COHb level at which reduced time to onset of angina was obser ved was in the range of 2.0 to 2.4% COHb (measured by gas chromatography), in a multi- center CO exposure study (Allred et al., 1989a,b, 1991; draft ISA, section 5.2.6). This range (2.0-2.4%) is re presentative of the two individual COHb level averages obtai ned post-exposure (2.4%) and post-exercise test (2.0%). However, there was no clear pattern across the different studies with respect to the magnitude of the decreased time to onset of angina versus dose level. In addition, these studies do not address the fraction of the population experiencing a specified health effect at various dose levels. Thus, base d on information in the draft ISA, staff concluded that at this time there is insufficient controlled human exposure data to support the development of quantitativ e dose-response relationships which would be required in order to conduct a quantita tive risk assessment for th is health endpoint. 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 Potential health effect benchmark values used in the risk characterization linked to the exposure/dose analyses were deri ved solely based on the controlled human exposure literature. This is primarily because CO concentrations reported in controlled human exposure studies represent actual person al exposures rather than concentrations measured at fixed site ambient monitors. In addition, controlled human exposure studiescan examine the health effects of short-term exposure to CO in the absence of copollutants that can confound results in epid emiologic analyses; thus, health effects observed in controlled human e xposure studies can confidently be attributed to a defined COHb dose level associated with am bient short-term CO exposures. In identifying the potential health e ffect benchmark levels for the risk characterization, staff consider ed a number of factors in drawing on the results of controlled human exposure studie s. As noted above, the lowest group mean COHb level

October 2009 Draft – Do Not Cite or Quote 4-31 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 at which reduced tim e to onset of angina wa s observed was in the range of 2.0 to 2.4% COHb (measured by gas chromatography(GC)) in a multi-center CO exposure study (Allred et al., 1989a,b, 1991; draft ISA, secti on 5.2.6 and 2.5.1). Similar effects have not been evaluated below this range. Staff identified potential health effect s benchmarks of 1.5%, 2.0%, 2.5% and 3% COHb levels based on the consideration of th e studies reporting adve rse effects at COHb levels as low as 2 to 2.4% (using GC) discu ssed above. These levels reflect comments from the CASAC CO panel on the draft Anal ysis Plan (Brain and Samet, 2009) and include the range of levels considered in the review completed in 1994 (US EPA, 1992). The potential health effects benchmarks ex tend lower than the range where controlled human exposure studies reported CO-related health effects to take into consideration both the uncertainty about the actual COHb leve ls experienced in the controlled human exposure studies due to the use of different measurement methods and that these studies did not include individuals with more seve re cardiovascular dis ease who may respond at lower COHb levels relative to the subjects tested.

4.3. KEY OBSERVATIONS Presented below are key observa tions relevant to the risk characterization approach in support of the current CO NAAQS review.

 An important at-risk population for shor t-term exposure to is the adult CHD population.

 The data from controlled human studi es do not support the development of a quantitative risk assessment due to lack of sufficient information to characterize the dose-response relationshi p within the rang e of interest. Instead, risk will be characterized in the current assessment using a potential health effect benchmark levels appr oach (as in previous assessments).

 Evaluation of health effects evidence reported in controlled human exposure studies of short-term CO exposure in the adult CHD population is the basis for the selection of potentia l health effects benchmarks of 1.5, 2.0, 2.5, and 3.0% COHb on the consideration of studies reporting adverse effects at COHb levels in the range of 2 to 2.4%.

October 2009 Draft – Do Not Cite or Quote 4-44.4. REFE RENCES 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
37 38 39
40 41 42 Adams, K.F.; Koch, G.; Chatterjee, B.; Goldstein, G.M.; O'Neil, J. J.; Bromberg, P.A.; Sheps, D.S.; McAllister, S.; Price, C.J.; Bisse tte, J. (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 Adir Y, Merdler A, Ben Haim S, Front A, Harduf R & Bitterman H ( 1999) Effects of Exposure to Low Concentrations of Carbon Monoxide on Exercise Performance and Myocardial Perfusion in Young Healthy Men. Occup Environm Med, 56: 535-538.
Allred, E.N.; Bleecker, E.R. ; Chaitman, B.R.; Dahms, T.E.; Gottlieb, S.O.; Hackney, J.D.; Pagano, M.; Selvester, R.H.; Walden, S.M.; Warren, J. (198 9a ) Short-term effects of carbon monoxide exposure on the exercise performan ce of s ubjects with coronary artery disease. N. Engl. J. Med. 321: 1426-1432.
Allred, E.N.; Bleecker, E.R. ; Chaitman, B.R.; Dahms, T.E.; Gottlieb, S.O.; Hackney, J.D.; Hayes, D.; Pagano, M.; Selvester, R.H.; Walden, S.M.; Warren, J. (1989b) Acute effects of carbon monoxide exposure on individuals with coronary artery di sease. Camb ridge, MA: Health Effects Institute; research report no. 25. Anderson EW, Andelman RJ, Strauch JM, Fortuin NJ and Knelson, JH (1973). Effect of low level carbon monoxide exposure on onset and duration of angina pectoris. Annals of Internal Medicine 79: 46- 50. Johnson T, Mihlan G, LaPointe J, Fletcher K, Cape l J. (2000). Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.1), prepared by ICF Kaiser Consulting Group for U. S. EPA, Office of Air Quality Planning andStandards, under Contract No. 68-D6-0064, WA Nos. 1-19, 2-24, 2-30, and 3-3, Docket EPA-HQOAR-2008-0015-DRAFT-0009. US EPA. (1991). Air Quality Criteri a fo r Carbon Monoxide. Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA/600/8-90/045F. US EPA (2000). Air Quality Criteria for Carbon Mono xide. National Center for Environmental Assessment, Office of Research and Developm ent , U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; report no. EPA/600/P-99/001F. June 2000. Available: http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm ?deid=18163

. Sheps, D.S.; Herbst, M.C.; Hinderlite r, A.L.; Adams, K.F.; Ekelund, L.G. ; O'Neil, J.J.; Goldstein, G.M.; Bromberg, P.A.; Dalton, J.L.; Ballenger, M.N.; D avis, S.M.; Koch, G.G. (1990) Production of arrythmias by elevated carboxyhemoglobin in patients with coronary artery disease. Ann. Intern. Med. 113: 343-351. Kleinman M T, Davidson DM, Vandag riff RB, Caiozzo VJ, Whittenberger JL .(1989). Effects of short-term exposure to carbon monoxide in subjects with coro n ary artery disease. Arch. Environ. 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. Arch. Environ.Health 53: 388-397.

October 2009 Draft – Do Not Cite or Quote 4-5October 2009 Draft – Do Not Cite or Quote 4-6 1 2 3 US EPA (2009). Integrated Science Assessment for Carbon Monoxide – Second External Review Draft.U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/600/R09/019B. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html

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### 5. APPROACH TO EXPOSURE AND DOSE ASSESSMENT FOR CURRENT REVIEW 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 This chapter presents an overview and description of the overall approach to estim ating human exposure and dose for past and recent assessments and presents a strategy for the current exposure and dose assessments for the CO NAAQS review. Section 5.1 provides a brief overview of the exposure model, followed by a shor t history that explains the evolution of the exposure and dose models used in NAAQS reviews in section 5.2. Section 5.3 provides a description of the exposure and dose models that have been used by OAQPS to conduct prior exposure and dose assessments for CO NAAQS and which serve as the basic modeling tools to be used for the current assessment. Section 5.4 briefly summarizes information about personal exposure and key microenvironments for CO. Section 5.5 discusses the current monitoring network in the two study areas selected for the current assessment and the limitations of the existing monitoring network for purposes of conducting an exposure and dose assessment in these areas. This final section also presents the strategy for the current exposure and dose analysis.

5.1 MODEL OVERVIEW

The Air Pollutants Exposure m odel (APEX) is a personal computer (PC)-based program designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and consolidated metropolitan levels. APEX, also known as TRIM.Expo, is the human inhalation exposure module of EPA’s Total Risk Integrated Methodology (TRIM) model framework (US EPA, 1999), a modeling system with multimedia capabilities for assessing human health and ecological risks from hazardous and criteria air pollutants. 1 APEX estimates human exposure to criteria and toxic air pollutants at the local, urban, or consolidated metropolitan area levels using a stochastic, “microenvironmental” approach. The model randomly selects data for a sample of hypothetical individuals from an actual population database and simulates each hypothetical individual’s movements through time and space (e.g., indoors at home, inside vehicles) to estimate A microenvironmentis a threedimensional space in which human contact with an environmental pollutant takes place and which can be treated as a well characterized, relatively homogeneous location with respect to pollutant concentrations for a specified time period.

1 Additional information on the TRIM modeling system, as well as downloads of the APEX Model, user guides (U.S. EPA 2008a, 2008b), and other supporting documentation, can be found at http://www.epa.gov/ttn/fera

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October, 2009 5-1 Draft – Do Not Cite or Quote1 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 his or her exposure to a pollutant. APEX can account for travel to and from work locations (i.e., commuting) and provide estimates of exposures at both home and work locations for individuals who work away from home.

5.2 MODEL HISTORY AND EVOLUTION

APEX was derived from the National Ambient Air Quality Standards (NAAQS) Exposure Model (NEM) series of models. The NEM series was developed to estimate population exposures to the criteria pollutants (e.g., CO, ozone). In 1988, OAQPS first incorporated probabilistic elements into the NEM methodology and used activity pattern data based on available human activity diary studies to create an early version of probabilistic NEM for ozone (i.e., pNEM/O 3 ). In 1991, a probabilistic version of NEM was developed for CO (pNEM/CO) that included a one-compartment mass-balance model to estimate CO concentrations in indoor microenvironments. The application of this model to Denver, Colorado is summarized in Johnson et al. (1992). Between 1999 and 2001, updated versions of pNEM/CO (versions 2.0 and 2.1) were developed that rely on detailed activity diary data compiled in EPA’s Consolidated Human Activities Database (CHAD) (McCurdy et al., 2000; US EPA, 2002) and enhanced algorithms for simulating gas stove usage, estimating alveolar ventilation rate (a measure of human respiration), and modeling home-to-work commuting patterns. A draft report by Johnson et al. (2000) describes the applica tion of Version 2.1 of pNEM/CO to Denver and Los Angeles. The first version of APEX was essentially identical to pNEM/CO (version 2.0) except that it ran on a PC instead of a mainframe. The next version, APEX2, was substantially different, particularly in the use of a personal profile approach rather than a cohort simulation approach. APEX3 introduced a number of new features including automatic site selection from national databases, a series of new output tables providing summary exposure and dose statistics, and a thoroughly reorganized method of describing microenvironments and their parameters. Johnson and Capel (2003) describe a case study in which Version 3.1 of APEX was used to estimate population exposure to CO in Denver and Los Angeles. The current version of APEX (Version 4.3) (US EPA, 2008a; 2008b) was used to estimate CO exposure and dose as described in chapter 6 of this document. This version was also recently used to estimate ozone (O3) exposures in 12 urban areas for the O3 NAAQS review (US EPA, 2007), in estimating population exposures to nitrogen dioxide (NO2) in Atlanta as part of the NO2 NAAQS review (EPA, 2008c), and in estimating sulfur dioxide (SO2) exposures for asthmatics and asthmatic children in two study areas in Missouri as part of the SO2 NAAQS review (US EPA, 2009a). There have been several recent enhancements to APEX since the prior 1994 CO NAAQS review, including:

October, 2009 5-2 Draft – Do Not Cite or Quote Algorithm s for the assembly of multi-day (longitudinal) activity diaries that model intra1 individual variance, inter-individual variance, and day-to-day autocorrelation in diary properties; 2 3

5

10 11 12 13 14 15 16 17 18 19

20 21  Methods for adjusting diary-based energy expenditures for fatigue and excess post4 exercise oxygen (EPOC) consumption;

 New equations for estimation of ventilation (i.e., breathing rate); 6

 The ability to model commuters leaving the study area; 7

 The ability to model air quality and exposure for flexible time scales; 8

 New output files containing diary event-level, time-step level, and hourly-level exposure, 9 dose, and ventilation data, and hourly-level microenvironmental data;

 The ability to model the prevalence of disease states such as asthma or coronary heart disease (CHD);

 New output exposure tables that report exposure statistics for population groups and lifestages such as children and active people under different ventilation levels;

 The inclusion of commuting data from the 2000 census; and

 Expanded options for modeling microenvironments. As discussed below in section 5.3, due to limitations in the CO ambient monitoring data, the current exposure/dose assessment does not take advantage of a number of the recent advances listed above.

5.3 MODEL SIMULATION PROCESS

APEX is designed to sim ulate population exposure to criteria and air toxic pollutants at local, urban, and regional scales. The user specifies the geographic area to be modeled and the 22 number of individuals to be simulated to represent this population. APEX then generates a 23 personal profile for each simulated person that specifies various parameter values required by the 24 model. The model next uses diary-derived time/activity data matched to each personal profile to 25 generate an exposure event sequence (also referred to as “activity pattern” or “composite diary”) 26 for the modeled individual that spans a specified time period, such as a calendar year. Each 27 event in the sequence specifies a start time, exposure duration, a geographic location, a 28 microenvironment, and an activity. Probabilistic algorithms are used to estimate the pollutant 29 concentration and ventilation (respiration) rate associated with each exposure event. The 30 estimated pollutant concentrations account for the effects of ambient (outdoor) pollutant 31 concentration, penetration factor, air exchange rate, decay/deposition rate, and proximity to 32 emission sources, depending on the microenvironment, available data, and the estimation method 33 selected by the user. The ventilation rate is derived from an energy expenditure rate estimated 34 for each individual and specified activity performed. Because the modeled individuals represent 35

October, 2009 5-3 Draft – Do Not Cite or Quotea random sample of the population of interest, th e distribution of modeled individual exposures 1 can then be extrapolated to the larger population of interest. 2 The model simulation generally includes up to seven steps. 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 1. Characterize the study area: APEX selects sectors (e.g., census tracts) within a study area—and thus identifies the potentially exposed population — usually based on the user-defined center and radius of the study area and availability of air quality and weather input data for the area (section 5.3.1). 2. Generate simulated individuals : APEX stochastically generates a sample of simulated individuals based on the census data for the study area and human profile distribution data (such as age-specific employment probabilities). (section 5.3.2) 3. Construct activity sequences: APEX constructs an exposure event sequence (activity pattern) spanning the simulation period for each of the simulated persons based on the CHAD activity pattern data (section 5.3.3). 4. Calculate microenvironmental concentrations: APEX enables the user to define microenvironments that people in a study area would visit (e.g., by grouping locationcodes included in the activity pattern database). The model then calculates timeaveraged concentrations (e.g., hourly) of each pollutant in each of the microenvironments for each simulated person for the period of simulation, based on the user-provided ambient air quality data (section 5.3.4). 5. Estimate energy expenditure and ventilation rates: APEX constructs a time-series of energy expenditures for each profile based on the activity event sequence. These expenditures are adjusted to ensure that they are physiologically realistic, and then used to estimate a number of ventilation metrics that are later used in estimating dose (section 5.3.5). 6. Calculate exposure : APEX assigns a concentration to each exposure event based on the microenvironment occupied during the event and the person’s activity. These values are time-averaged (e.g., hourly) to produce a sequence of exposures spanning the specified exposure period (typically one year). The hourly values may be further aggregated to produce 8-hour, daily, monthly, and annual average exposure values (section 5.3.6). 7. Calculate dose: APEX optionally calculates hourly, daily, monthly, and annual average dose values for each of the simulated individuals. For the application of APEX to CO, a module within the model estimates the percent COHb level in the blood at the end of each hour based on the time-series of CO concentrations and alveolar ventilation rates experienced by the simulated person (section 5.3.7). The model simulation continues until exposures (and associated COHb levels) are calculated for the user-specified number of si mulated individuals. Figure A-1 in Appendix A 37 presents a conceptual model and simplified data flow of APEX used in this assessment. The 38 following sections provide additional details on the general procedures and algorithm s used in 39

October, 2009 5-4 Draft – Do Not Cite or Quoteeach of the sim ulation steps listed above. The specific inputs and algorithms used in applying 1 APEX to CO for the current assessment are further described in section 6.1 2

5.3.1 Characterize the Study Area 3 4

14 15

19 20 21 22 23 24 25 26 27 28 29 30 31

32 33 An initial study area in an APEX analysis consists of a set of basic geographic units called sectors, typically defined as census tracts. The user m ay provide the geographic center 5 (latitude/longitude) and radius of the study area and then APEX calculates the distances to the 6 center of the study area of all the sectors included in the sector location database, and finally 7 selects the sectors within the radius of the study area. APEX then maps the user-provided air 8 quality and meteorological data for specified monitoring districts to the selected sectors. The 9 sectors identified as having acceptable air quality and meteorological data within the radius of 10 the study area are selected to comprise a final study area for the APEX simulation analysis. This 11 final study area determines the population make-up of the simulated persons (profiles) to be 12 modeled. 13

5.3.2 Generate Simulated Individuals APEX stochastically generates a user-specified num ber of simulated (hypothetical) persons to represent the population in the study area. Each simulated person is represented by a 16 “personal profile.” APEX generates the simulated person or profile by probabilistically selecting 17 values for a set of profile variables. The profile variables include: 18

 Demographic variables (e.g., age, gender, home sector, work sector) that are generated based on the census data;

 Residential variables (e.g., air conditioning prevalence) which are generated based on sets of distribution data;

 Physiological variables (e.g., blood volume, body mass, resting metabolic rate) that are generated based on age- and gender-specific distribution data; and

 Daily varying variables (e.g., daily work status) which are generated based on distribution data that change daily during the simulation period. APEX first selects and calculates demographic, residential, and physiological variables (except for daily values) for each of the specified number of simulated individuals. APEX then follows each simulated individual over time and calculates exposures (and optionally doses) for the individual over the specified time period. The profile variables are listed and described in detail in section 5 of US EPA (2008b).

5.3.3 Construct Activity Sequences

APEX probabilistically creates a com posite diary for each of the simulated persons by selecting a 24-hour diary record – or diary day – from an activity database for each day of the 34

October, 2009 5-5 Draft – Do Not Cite or Quotesim ulation period. (CHAD data are supplied with APEX for this purpose.) A composite diary is 1 a sequence of events that simulates the movement of a modeled person through geographical 2 locations and microenvironments during the simulation period. Each event is defined by 3 geographic location, start time, duration, microenvironment visited, and an activity performed. 4 The activity database input to APEX contains the following information for each diary day: age, gender, employment status, occupation, day-of-week (day-type), and maximum hourly average temperature. This information enables APEX to select data from the activity database that tend to match the characteristics of the simulated person, the study area, and the specified time period. APEX develops a composite diary for each of the simulated individuals according to the following steps. 5 6 7 8 9 10

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 1. Divide diary days in the CHAD database into user-defined activity pools, based on day-type and temperature. 2. Assign an activity pool number to each day of the simulation period, based on the userprovided daily maximum/average temperature data. 3. Calculate a selection probability for each of the diary days in each of the activity pools, based on age/gender/employment similarity of a simulated person to a diary day. 4. Probabilistically select a diary day from available diary days in the activity pool assigned to each day of the simulation period. 5. Estimate a metabolic value for each activity performed while in a CHAD location, based on the activity-specific metabolic distribution data. This value is used to calculate a ventilation rate for the simulated person performing the activity. 6. Map the CHAD locations in the selected diary to the user-defined modeled microenvironments. 7. Concatenate the selected diary days into a sequential longitudinal diary for a simulated individual covering all days in the simulated period. APEX provides an optional longitudinal diary assembly algorithm that enables the user tocreate composite diaries that reflect the tendency of individuals to repeat activities on a day-to27 day basis. The user specifies values for two statistical variables (i.e., D and A ) that relate to a 28 key daily variable, typically the time spent per day in a particular microenvironment (e.g., in a 29 motor vehicle). The D statistic reflects the relative importance of within person variance and 30 between person variance in the key variable. The A statistic quantifies the lag-one (day-to-day) 31 variable autocorrelation. APEX then constructs composite diaries that exhibit the statistical 32 properties defined by the specified values of D and A . The longitudinal diary assembly 33 algorithm is described in greater detail in section 6.3 of US EPA (2008b). 34

October, 2009 5-6 Draft – Do Not Cite or Quote5.3.4 Calculate Microenvironmental Concentrations 1 2

7 8 9 10 11 12 13 14 15 16 17 18 19 Probabilistic algorithm s are used to estimate the pollutant concentration associated with each exposure event. The estimated pollutant concentrations account for the effects of ambient 3 (outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate, 4 and proximity to emission sources, depending on the microenvironment, available data, and the 5 estimation method selected by the user. 6 APEX calculates air concentrations in the various microenvironments visited by thesimulated person by using the ambient air data for the relevant census tracts and the userspecified method and parameters that are specific to each microenvironment. In typical applications, APEX calculates hourly concentrations in all the microenvironments at each hour of the simulation for each of the simulated individuals, based on the hourly ambient air quality data specific to the geographic locations visite d by the individual. APEX provides two methods for calculating microenvironmental concentrations: the mass balance method and the transfer factors method (described briefly below). The user is required to specify a calculation method for each of the microenvironments; there are no restrictions on the method specified for each microenvironment (e.g., some microenvironments can use the transfer factors method while the others can use the mass balance method). As discussed in section 5.4, the current draft assessment employed a simplified approach relying on the factors model approach with particular focus on the in-vehicle microenvironment. Mass Balance Model 20 21 22 23 24

25 26 27 28 29 30 31 32 33 The mass balance method models an enclosed microenvironment as a well-mixed volume in which the air concentration is assumed to be spatially uniform at any specific time. The concentration of an air pollutant in such a microenvironment is estimated using the following four processes:

 Inflow of air into the microenvironment;

 Outflow of air from the microenvironment;

 Removal of a pollutant from the microenvironment due to deposition, filtration, and/or chemical degradation; and

 Emissions from sources of a pollutant inside the microenvironment (if indoor sources are modeled). The mass balance model feature of APEX (see Appendix B) has not been used in the APEX application for CO described in this draft REA.

Factors Model 34

October, 2009 5-7 Draft – Do Not Cite or Quote1 2 3 4 5 6 The factors m odel approach is conceptually simpler than the mass balance method and has fewer user-specified parameters. It estimates the concentration in a microenvironment as a linear function of ambient concentration of that hour, regardless of the concentration in the microenvironment during the preceding hour. Table 5-1 lists the parameters required by the factors model approach to calculate concentrations in a microenvironment without emissions

sources.

Table 5-1. Parameters of the Factors Model 7

Variable Definition Units Value Range

f

proximity Proximity factor N/A f proximity > 0

f

penetration Penetration factor N/A 0 ≤ f

penetration ≤ 1

8 9 10

11
12

13 14 15 16 17 18 19 20 21 22 23 24 25 26 The factors m odel approach uses the following equation to calculate hourly mean concentration in a microenvironment from the user-provided hourly air quality data: (5-1) npenetratio proximity ambient hourlymean ME 

### ffC C  where:
= Hourly concentration in a microenvironment (ppm) hourlymean ME C C

ambient = Hourly concentration in ambient environment (ppm) f

proximity = Proximity factor (unitless) f

penetration = Penetration factor (unitless)

The proximity factor ( f

proximity ) is used to account for differences in ambient concentrations between the geographic location represented by the ambient air quality data (e.g., a regional fixed-site monitor) and the geographic location of the particular microenvironment. For example, a residence might be located near a heavily-trafficked roadway, whereby the ambient air outside the house would likely have elevated levels of mobile source pollutants such as carbon monoxide. In this case, a value greater than one for the proximity factor would be appropriate to represent the increase in concentrations outside the home relative to the ambient monitor. Additionally, for some pollutants the process of infiltration may remove a fraction of the pollutant from the air. The fraction that is retained in the indoor air is given by the

October, 2009 5-8 Draft – Do Not Cite or Quotepenetration factor ( f

penetration ) and is dependent on the particular pollutant’s physical and chemical removal rates. Typically, the value of the penetration factor ranges from 0 to 1. 1 2

3 4 5 6 7 8 9 10 11 12 5.3.5 Estimate Energy Expenditure and Ventilation Rates APEX includes a m odule that estimates COHb levels in the blood as a function of alveolar ventilation rate, the CO concentration of the respired air, endogenous CO production rate, and various physiological variables such as blood volume and pulmonary CO diffusion rate. Alveolar ventilation rate is estimated as a function of oxygen uptake rate, which in turn is estimated as a function of energy expenditure rate. This section provides a brief summary of the algorithm used to estimate alveolar ventilation rate. A detailed description of the algorithm, together with the distributions and estimating equations used in determining the value of each parameter in the algorithm, can be found in Appendix C.

Energy Expenditure 13 14 15 16 McCurdy (2000) has recommended that measures of human ventilation (respiration) rate be estimated as functions of energy expenditure rate. The energy expended by an individual during a particular activity can be expressed as EE = (METS)  (RMR) (5-2) 17 18 19 20 21 22 23 24 in which EE is the average energy expenditure rate (kcal min -1 ) during the activity and RMR is the resting metabolic rate of the individual expressed in terms of number of energy units expended per unit of time (kcal min -1 ). METS (i.e., metabolic equivalent of work) is a ratio specific to the activity and is dimensionless. The METS concept provides a means for estimating the alveolar ventilation rate associated with each activity. For convenience, let EE( i,j,k ) indicate the energy expenditure rate associated with the i th activity on day j for person k . Equation 5-2 can now be expressed as EE( i,j,k ) = [METS( i,j,k )]  [RMR( k )] (5 3) 25 26 27 28 in which RMR( k ) is the average value for resting metabolic rate specific to person k

. Note that METS( i,j,k ) is specific to a particular activity performed by person k

. Oxygen Requirements for Energy Expenditure 29 30 31 32 33 Energy expenditure requires oxygen which is supplied by ventilation (respiration). ECF( k ) represents an energy conversion factor defined as the volume of oxygen required to produce one kilocalorie of energy in person k . The oxygen uptake rate (VO 2 ) associated with a particular activity can be expressed as VO 2 ( i,j,k ) = [ECF( k )]  [EE( i,j,k )], (5 4) 34

October, 2009 5-9 Draft – Do Not Cite or Quotein which VO 2 ( i,j,k ) has units of liters oxygen min -1 , ECF( k ) has units of liters oxygen kcal -1 , and EE( i,j,k ) has units of kcal min -1 . The value of VO 2 ( i,j,k ) can now be determined from MET( i,j,k ) by substituting Equation 5-3 into Equation 5-4 to produce the relationship 1 2 3 VO 2 ( i,j,k ) = [ECF( k )]  [METS( i,j,k )]  [RMR( k )]. (5-5) 4 5 Excess Post-Exercise Oxygen Consumption 6 7 8 9 10 11 12 13 14 15 16 17 18 At the beginning of exercise, there is a lag between work expended and oxygen consumption. During this work/ventilation mismatch, an individual’s energy needs are met by anaerobic processes. The magnitude of the mismatch between expenditure and consumption is termed the oxygen deficit. During heavy exercise, further oxygen deficit (in addition to that associated with the start of exercise) may be accumulated. At some point, oxygen deficit reaches a maximum value, and performance and energy expenditure deteriorate. After exercise ceases, ventilation and oxygen consumption will remain elevated above baseline levels. This increased oxygen consumption was historically labeled the “oxygen debt” or “recovery oxygen consumption.” However, the term “excess post-exercise oxygen consumption” (EPOC) has been adopted for this phenomenon. APEX has an algorithm for adjusting the MET values to account for EPOC. This algorithm is described in detail in section 7.2 of US EPA (2008b).

Alveolar Ventilation Rate 19 20 21 22 23 24 25 26 27 Alveolar ventilation (V A ) represents the portion of the minute ventilation that is involved in gaseous exchange with the blood. VO 2 is the oxygen uptake that occurs during this exchange. The absolute value of V A is known to be affected by total lung volume, lung dead space, and respiration frequency -- parameters which vary according to person and/or exercise rate. However, it is reasonable to assume that the ratio of V A to VO 2 is relatively constant regardless of a person’s physiological characteristics or energy expenditure rate. Consistent with this assumption, APEX converts each estimate of VO 2 ( i,j,k ) to an estimate of V A ( i,j,k ) by the proportional relationship V A ( i,j,k ) = (19.63)  [VO 2 ( i,j,k )] (5-6) 28 29 30 31 in which both V A and VO 2 are expressed in units of liters min -1 . This relationship was obtained from Joumard et al. (1981), who base d it on research by Galetti (1959). Equation 5-6 can also be expressed by the equivalent equation V A ( i,j,k ) = (19.63)  [METS( i,j,k )]  [ECF( k )]  [RMR( k )]. (5-7) 32 33 34 35 If ECF and RMR are specified for an indi vidual, then Equation 5-7 requires only an activity-specific estimate of METS to produce an estimate of the energy expenditure rate for a given activity. APEX processes time/activity data obtained from the CHAD to create a sequence

October, 2009 5-10 Draft – Do Not Cite or Quote1 2 3 4 5 6 7

8 9 10 11 12 13 14 of activity-specific METS values for each sim ulated individual. APEX estimates RMR as a function of body mass based on probabilistic equations specific to age and gender using equations reported by Schofield (1985). A value of ECF is selected for each individual from a uniform distribution (minimum = 0.20, maximum = 0.21) based on data provided by Esmail et al. (1995). Using Equation 5-7 and these inputs, APEX calculates a sequence of V A values for each simulated individual. These values are provided to the algorithm that estimates the percent COHb in the blood resulting from the simulated exposure (see section 5.3.7 and Appendix C).

5.3.6 Calculate Exposure

APEX calculates exposure as a tim e series of exposure concentrations that a simulated individual experiences during the simulation period. APEX determines the exposure using hourly ambient air concentrations, calculated concentrations in each microenvironment based on these ambient air concentrations, and the minutes spent in a sequence of microenvironments visited according to the composite diary. The hourly exposure concentration at any clock hour during the simulation period is determined using the following equation:



### T tC C N

j j hourlymean jME

i 

# 

 

###  1 )()( (5-8) 15

16 17 18 19 20 21
22 23 24 25 26 27

28 29 30 31 where: C i = Hourly exposure concentration at clock hour i of the simulation period (ppm) N = Number of events (i.e., varied microenvironments visited/activities performed) in clock hour i of the simulation period.
hourlymean jME C )( = Hourly mean concentration in microenvironment j (ppm)

t (j) = Time spent in microenvironment j (minutes) T = 60 minutes From the hourly exposures, APEX calculates time series of 8-hour and daily average exposure concentrations that a simulated i ndividual would experience during the simulation period. APEX then statistically summarizes and tabulates the hourly, 8-hour, and daily exposures in a series of output tables.

5.3.7 Calculate Dose

Using tim e/activity data obtained from several diary studies, APEX constructs a composite diary for each simulated person in the specified at-risk population. The composite diary consists of a sequence of events spanning the specified period of the exposure assessment

October, 2009 5-11 Draft – Do Not Cite or Quote1 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 (typically one calendar year). Each event is defined by a start tim e, duration, a geographic location, a microenvironment, and an activity. Using the algorithms described above in sections 5.3.5 and 5.3.6, APEX provides estimates of CO concentration and alveolar ventilation rate for each event in the composite diary, for each simulated individual. APEX then uses these data, together with estimates of various physiological parameters specific to the simulated individual, to estimate the percent COHb in the blood at the end of each event. The percent COHb calculation is based on the solution to the non-linear Coburn, Forster, Kane (CFK) equation, as detailed in Appendix C. Briefly, the CFK model describes the rate of change in COHb blood levels as a function of the following quantities:

 Inspired CO pressure;

 COHb level;

 Oxyhemoglobin (O2Hb) level;

 Hemoglobin (Hb) content of blood;

 Blood volume;

 Alveolar ventilation rate;

 Endogenous CO production rate;

 Mean pulmonary capillary oxygen pressure;

 Pulmonary diffusion rate of CO;

 Haldane coefficient (M);

 Barometric pressure; and

 Vapor pressure of water at body temperature (47 torr). If all of the listed quantities except COHb level are constant over some time interval, the CFK equation has a linear form over the interval and is readily integrated. The solution to the linear form gives reasonably accurate results for lower levels of COHb. However, CO and oxygen compete for the available hemoglobin and are, therefore, not independent of each other. If this dependency is taken into account, the resulting differential equation is no longer linear. Peterson and Stewart (1975) proposed a heuristic approach to account for this dependency which assumed the linear form and then adjusted the O 2 Hb level iteratively based on the assumption of a linear relationship between COHb and O 2 Hb. This approach was used in the COHb module of the original CO-NEM exposure model (Biller and Richmond, 1982, Johnson and Paul, 1983). Alternatively, it is possible to determine COHb at any time by numerical integration of the nonlinear CFK equation if one assumes a particular relationship between COHb and O2Hb.

October, 2009 5-12 Draft – Do Not Cite or QuoteMuller and Barton (1987) dem onstrated that assuming a linear relationship between COHb and O2Hb leads to a form of the CFK equation equivalent to the Michaelis-Menten kinetic model which is analytically integrable. However, the analytical solution in this case cannot be solved explicitly for COHb. Muller and Barton (1987) demonstrated a binary search method for determining the COHb value. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

22 23

30 31 The COHb module used in pNEM/CO employed a linear relationship between COHb and O 2 Hb which was consistent with the basic assumptions of the CFK model. The approach differed from the linear forms used by other modelers in that the Muller and Barton (1987) solution was employed. However, instead of the simple binary search described in the Muller and Barton paper, a combination of the binary search and Newton-Raphson root finding methods was used to solve for COHb (Press et al., 1986). As mentioned above, the current COHb module included in APEX is based on the solution to the non-linear CFK equation using the assumption adopted by Muller and Barton (1987) which employs a linear relationship between O 2 Hb and COHb. The CFK equation does not have an explicit solution, so an iterative solution or approximation is needed to calculate each percent COHb value. APEX4.3 solves the CFK equation using a fourth-order Taylor’s series with subintervals. This method, first incorporated in APEX3, is described in detail by Glen (2002) and summarized in Appendix C. The selected method (fourth order Taylor series with subintervals) was chosen because of its simplicity, fast execution speed, and ability to produce relatively accurate estimates of percent COHb at both low and high levels of CO exposure.
5.3.8 Model Output

All of the output files written by APEX are AS CII text files. Appendix D lists each of the output data f iles written for these simulations and provides descriptions of their content. 24 Additional output files that can be produced by APEX are listed in Table 5-1 of the APEX 25 User’s Guide (US EPA, 2008a). These include tabulations of hourly exposure, ventilation, and 26 energy expenditures. Detailed event-level information can also be output. Specific outputs 27 generated for the purposes of the current CO exposure and dose assessment are discussed in 28 section 6.1. 29

5.3.9 Model Limitations APEX attem pts to reasonably represent a sample of individuals who reside within a specific geographic area, and estimates the contact with the air pollutant given the inherent 32 variability in peoples’ locations and activities. This sample of individuals is a “virtual” sample, 33 created by the model according to the relative frequencies of various demographic variables and 34

October, 2009 5-13 Draft – Do Not Cite or Quotecensus data, with the goal of obtaining a representative sam ple (to the extent possible) of the 1 actual population of interest in the study area. The activity patterns of the sampled individuals 2 (e.g., the specification of indoor and other microenvironments visited and the time spent in each) 3 are assumed by the model to be comparable to individuals with similar demographic 4 characteristics, and are represented by actual time-location-activity patterns compiled in CHAD 5 (US EPA, 2002; McCurdy et al., 2000). The air pollutant exposure concentrations are estimated 6 by the model using a set of user-input ambient outdoor concentrations and information on the 7 physical factors that relate ambient pollutant to concentrations expected in various 8 microenvironments. Although this aspect of APEX is not fully employed in the current 9 simplified, screening-level assessment, the model structure would allow one to account for the 10 most significant factors contributing to inhalation exposure – the temporal and spatial 11 distribution of people and pollutant concentrations throughout the study area and among the 12 microenvironments, providing there is sufficient i nput data to characterize these distributions – 13 while also providing the flexibility to adjust some of these factors to meet the exposure 14 assessment objectives. This may include exposure scenarios where ambient air quality is 15 adjusted to simulate just meeting the current or alternative standards under consideration. 16 While APEX is designed to represent the most important personal attributes and physical factors that influence human exposure, all models have limitations and require the use of assumptions. Some of the general limitations of APEX are associated with the representativeness of the data distributions input to the model (e.g., human activity patterns) and assumptions made within various model algorithms including the following. 17 18 19 20 21

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  The population activity pattern data used in APEX (i.e., CHAD) are compiled from studies conducted in a variety of geographic areas and during time periods that differed as to season and calendar year, though a large portion of CHAD is from studies of national scope. Consequently, the data base may not have data diaries available that fully represent a particular study scenario. However, to better match the activity pattern data to the simulated population residing in a particular location, diary pools can be created by APEX that are for specific seasons and temperature ranges.

 Commuting pattern data were derived from the 2000 U.S. Census. The commuting data address only home-to-work travel. The population not employed outside the home is assumed to always remain in the residential census tract. Furthermore, although several of the APEX microenvironments account for time spent in travel, the travel activity is typically assumed to occur in a composite of the home and work tract. No provision is made for the possibility of passing through other tracts during travel.

 APEX creates seasonal or annual sequences of daily activities for a simulated individual by sampling human activity data from more than one subject. While there are input variables (e.g., time spent outdoors) used to simulate the correlation of day-to-day

October, 2009 5-14 Draft – Do Not Cite or Quote1 2

4 5 6

8 9 10 11

12 13

15 16

22 23 24 25 26 27 28 29 30 31  The model currently does not capture certain correlations among human activities that

3 can impact microenvironmental concentrations (for example, cigarette smoking leading to an individual opening a window, which in turn affects the rate that outdoor air enters the residence or vehicle).

 Certain aspects of the personal profiles (e.g., weight) are held constant, though in reality 7 they change as an individual ages. This is generally only an issue for simulations with long timeframes. These and other uncertainties in model inputs and algorithms, and how they may affect the estimated exposures and dose, are discussed in section 6.4.

5.4 PERSONAL EXPOSURE AND THE IN-VEHICLE MICROENVIRONMENT

This section sum marizes key findings from personal exposure studies with particular attention to microenvironments of importance to ambient CO exposures. 14

5.4.1 Personal Exposure Monitoring Studies This section sum marizes some of the findings from personal exposure studies, in particular, through identifying the important mi croenvironments in assessing CO exposure and 17 providing context for measured CO exposure levels relevant to this draft REA. Details regarding 18 personal exposure measurement studies of target populations are discussed in section 8.2 of the 19 1991 CO AQCD (US EPA, 1991), chapter 4 of the 2000 CO AQCD (US EPA, 2000), and 20 section 3.6 of the draft ISA. 21 As ambient CO concentrations have decreased dramatically over time, so have personal CO exposure levels. 2 However, while CO concentrations have declined over the past few decades, some general patterns in the relationship between ambient, microenvironmental, and exposure concentration still remain. First, as a result of the significant time people spend indoors - whether at home, at school, workplace, or other indoor location (secti on 3.6.2, draft ISA) - indoor CO concentrations are an important determinant of an individual’s CO exposures. Recent population exposure studies conducted in Milan, Italy support this conclusion (Bruinen de Bruin et al., 2004), indicating that over 80% of the population exposure to CO can occur in indoor microenvironments (draft ISA, Table 3-13). Ta king into account the infiltration of ambient CO

2 Many recently-conducted personal expos ure studies in the U.S. have not included CO as an analyte, possibly due to high detection limits of personal exposure m onitoring devices relative to ambient concentrations.

October, 2009 5-15 Draft – Do Not Cite or Quoteindoors, indoor CO concentrations are sim ilarly an important determinant in an individual’s exposure to ambient 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 Second, there is variability in the relationship between personal exposure and ambient concentrations, particularly when considering microenvironmental exposures. For example, the draft ISA summarized the relationship between pe rsonal CO exposures in five broadly defined microenvironments (i.e., indoor residence, indoor other, outdoor near road, outdoor other, and in-vehicle) and ambient CO concentrations in Baltimore, MD based on data provided in Chang et al. (2000). On average, the indoor-to-ambi ent and outdoor-to-ambient ratios were about one, though most of the ratios observed across this set of indoor and outdoor microenvironments were less than one. With the exception of those for the in-vehicle microenvironments, which as a group were generally above one, few ratios were above unity (draft ISA, p. 3-102, Figure 3-43). Given the expected stability of CO as it infiltrates indoor microenvironments from outdoor air and the lack of significant removal mechanisms of CO in outdoor microenvironments, it is likely that the variability in personal/microenvironmental-to-ambient and outdoor-to-ambient ratios is the result of spatial and temporal variability in outdoor concentrations with respect to simultaneously measured ambient concentrations at fixed-site monitors, and also reflects the impact of lag time associated with attaining steady state relationships, as well as potential presence of non-ambient sources. Third, because motor vehicles remain important contributors to ambient CO concentrations, both the time spent in motor vehicles and the presence of elevated on-road CO concentrations continue to be important contributors to personal exposures. For example, in the same study summarized by the draft ISA on personal exposures occurring within particular microenvironments (i.e., Chang et al., 2000), in-vehic le exposures were, on average, a factor of 3 to 4 greater than ambient concentrations (distance of ambient monitor to roadway not specified), with most in-vehicle exposure-to-ambient concentration ratios greater than one (median of approximately 2.5). Given this relationship, it shoul d not be surprising that while about 8% of aperson’s time per day is spent in transit, 13-17% of their total daily exposure occurs within an invehicle microenvironment (e.g., Bruinen de Bruin et al., 2004; Scotto di Marco et al., 2005). And finally, as for CO population exposure studies conducted in the U.S., two pertinent studies could be found: one conducted in Denve r CO and the other in Washington, DC during the winter of 1982 and 1983 (Akland et al., 1985). Both studies collected measurements andactivity pattern diaries from a random sample of the population, defined as including noninstitutionalized, non-smoking residents, 18 to 70 years of age, who lived in each respective city’s metropolitan area. In both cities, when comparing the distribution of measured CO concentrations from the monitoring network to measured personal exposures, two common

October, 2009 5-16 Draft – Do Not Cite or Quotephenom ena were observed. At the lowest percentiles of each distribution, ambient CO concentrations were consistently greater than the personal exposures. At the highest percentiles of each distribution, ambient concentrations were consistently lower than the personal exposures (US EPA, 2000). Again, ambient concentrations ma y be a reasonable indicator of exposure for a portion of the population, but given spatial and temporal variability in ambient concentrations and exposures associated with high concentration microenvironments, there will likely be a combination of exposures that are under- and over-estimated when considering ambient concentrations alone. As an example of the potential to underestimate exposure concentration when solely relying on ambient fixed-site concen trations as an indicator of exposure, over 10% of the daily maximum 8-hour personal exposures in Denver exceeded the NAAQS of 9 ppm, and about 4% did so in Washington (Akland et al., 1985) . This is in contrast to simultaneous CO measurements at ambient fixed-site monitors where CO concentrations exceeded 9 ppm about 3% of the time in Denver and never exceeded 9 ppm in Washington D.C. (Akland et al., 1985). 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 Consistent with the above discussion, the Denver and Washington studies determined that the highest average CO concentrations occurred when subjects were in a mobile source influenced microenvironment (e.g., inside parking garages, in-vehicles). Commute time was also a factor; those who commuted 6 hours or more per week had higher average exposures thanthose who commuted fewer hours per week. Furthermore, mean CO concentrations within invehicle microenvironments (ranging from 7.0 to 9.8 ppm) were greater than common outdoor locations (ranging from 1.4 to 3.2 ppm) (US EPA, 2000). In considering the results from the Denver and Washington personal exposure studies it is important to recognize that CO emissions from motor vehicle sources have declined dramatically since the early 1980’s when these studies were conducted. Consequently, both ambient fixed-site CO concentrations and in-vehicle CO concentrations have also been reduced significantly since that time period.

5.4.2 In-Vehicle CO Concentrations

Given the contribution of in-vehicle exposures to total CO exposure and our focus on exposure to am bient CO, consideration of the contribution of ambient CO to in-vehicle 27 concentrations is important to CO exposure assessment. Information useful to this consideration 28 includes the relationship between CO concentrations within vehicles to concentrations 29 simultaneously measured outside of vehicles and also at nearby fixed-site monitors. The utility 30 of such data that has been reported in the extant literature to the assessment conducted here can 31 be determined by broadly evaluating the funda mental study design and by considering potential 32 influential factors that might affect measured CO concentrations (e.g., fleet characteristics, 33 monitor siting). Accordingly, staff evaluated data reported in several U.S. and non-U.S. studies 34 that measured CO concentrations inside vehicl es, immediately outside vehicles, at roadside 35

October, 2009 5-17 Draft – Do Not Cite or Quotelocations, and at fixed-site m onitors. Particular attention is given to data available within the 1 published literature that may be most appropriate for the purposes of the current exposure 2 assessment. The research findings from a few of the more recent studies (i.e., since 1991) are 3 summarized below. In addition, discussion regarding these and supporting information from 4 studies conducted in the 1980’s follows. 5

5.4.2.1 Studies Comparing CO Concentrations Inside and Outside Motor Vehicles 6 7

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Table 3-1 sum marizes four relevant studies selected by staff that provided data comparing CO concentrations inside a motor ve hicle with concentrations immediately outside 8 the vehicle. Two of the studies reviewed were conducted in the U.S. (Chan et al., 1991; Rodes et 9 al., 1998). Given the low-reactivity of CO, it is exp ected that the ratio of the two concentrations 10 (inside-vehicle versus outside-vehicle or I/O) w ould be equivalent to one (in the absence of in11 vehicle sources). 12 Boulter and McCrae (2005) measured CO concentrations inside vehicles, immediately outside the vehicles, and under a range of vehicle ventilation conditions within two tunnels: one in Graz, Austria and the other in Liverpool, Engl and. On average the I/O ratio ranged from one to slightly above unity. Statistical analysis indicated that the air conditioning (AC), fan, and window operating conditions did not have a statistically significant affect on the I/O ratios. Chan et al. (1991) measured inside-vehicle concentrations of volatile organic compounds (VOCs) and three criteria air pollutants (ozone, CO, and NO 2 ) during the summer of 1988 in Raleigh, North Carolina. Two four-door sedans of different ages were used to evaluate in-vehicle concentrations of these compounds under different driving conditions. The study evaluated a variety of factors that could influence driver exposure, including varying traffic patterns, car models, vehicle ventilation conditions, and driving periods. The median I/O ratio for these two vehicles operated under a variety of conditions was 1.1 (Table 3.1). Chan et al. (1991) note that the slightly higher in-vehicle concentration may be a function of differences in interior and exterior sampling locations, and e ngine running loss emissions that contributed CO to the interior of the vehicle. During September and October 1997, Rodes et al. (1998) collected 2-hour pollutant concentration measurements inside two vehicles during scripted commutes in Sacramento and Los Angeles. Similar measurements were made simultaneously outside the vehicles, along the roadways, and at the nearest ambient monitoring stations. A variety of scenarios were studied based on variables such as roadway type, traffi c congestion, ventilation setting, and vehicle type. Two commutes, one in the morning and one in the afternoon, were typically conducted for each

October, 2009 5-18 Draft – Do Not Cite or QuoteOctober, 2009 5-19 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 scenario. 3 On average, all I/O ratios were less than one in both locations, though within the range of other researchers reporting this ratio. Sharp and Tight (1997) measured inside- and outside-vehicle CO concentrations using a single automobile and considering four different ventilation settings. The averaging time for CO measurements was one minute, thus the authors calculated I/O ratios based on both average data and peak data (Table 3-1). The I/O ratios were consistent with those of similar studies, with in-vehicle CO levels being slightly higher than those measured directly outside the vehicle. Peak concentration (inside and outside of the vehicle) comparisons did span a wider range of values that included I/O ratios both below and above unity. The choice of ventilation setting (i.e., window open or mechanical ventilation) had an e ffect on average and peak CO concentrations; in general, conditions associated with the lowest air exchange rates (i.e., windows closed and no mechanical ventilation) had the lowest I/O ratios in these studies. The findings reported in each of the above four studies are supported by a review by Flachsbart (1999) regarding other studies published between 1982 and 1992 that measured interior and exterior CO concentrations simultaneously during motor vehicle trips. The I/O ratio was similar for two studies: Petersen and Allen (1982) reported a ratio of 0.92 for a study in Los Angeles, California; and Koushi et al. (1992) reported a ratio of 0.84 for a study in Riyadh, Saudi Arabia. Both of these research studies reported no affect from altering ventilation conditions. In contrast, one study reported indicated I/O ratios could exceed unity with the ventilation set to recirculate vehicle air (Abi Esbe r and El-Fadel, 2008). It is possible that this was the result of a gradual build-up of CO concen trations within the vehicle cabin (ISA, section 3.6.6.2). In addition, Colwill and Hickman (1980) reported that internal CO levels were about 30 – 80% of exterior concentrations for a study conducted in London. However, the large difference in these I/O ratios when compared with those reported by most other researchers could be explained by the location of the exterior probe (i.e., at bumper height compared with probes commonly placed higher on the vehicle) (Flachsbart, 1999).

3 The study design also included several other driving scenarios: (1) a California school bus following a student route in Sacramento, (2) comparison of a sedan trav eling in a Los Angeles carpool lane versus one traveling in a congested right hand lane, and (3) a sedan encounter ing situations to maximize the in-vehicle pollutant concentration levels. These data are not included in this summary.Table 5-2.Carbon Monoxide Concentrations Inside and Immediately Outside Vehicles, and Indoor/Outdoor Vehicle Ratios.CO concentration (ppm)StudyInsideVehicleOutsideVehicleIndoor/Outdoor(I/O) RatioDrive ConditionsAveraging TimesBoulter andMcCrae(2005)6.19 ± 2.084.94 ± 1.80(mean ± std)7.13 ± 1.924.88 ± 1.86(mean ± std)0.99 ± 0.071.10 ± 0.07(mean ± std)Plabutsch Tunnel in Graz, AustriaKingsway Tunnel in Liverpool, EnglandStatistics are based on averageCO concentrations for tripsthrough tunnels.Chan et al.(1991)11.0(median)10.0(median)1.10(median)Two sedans were driven on three roadtypes (urban, interstate, rural) in Raleigh,NC during summer 1988Statistics are based on airsamples collected over one hourperiods.Rodes et al.(1998)Sacramento:1.4 – 3.5Los Angeles:3.5 – 5.4(mean range)Sacramento:2.2 – 4.2Los Angeles:4.4 – 5.6(mean range)Sacramento:0.73 – 0.90Los Angeles:0.88 – 0.96(mean range)Two vehicles (lead and following) weredriven on various road types in Sacramentoand Los Angeles during September andOctober 1997. Note, minimum quantitationlimit was 2 ppm for Draeger Model 190monitors used.Statistics are based on averagesof 120 one-minute valuesmeasured during two-hourmorning and afternoon commuteperiods.Sharp andTight (1997)1.19 to 1.43(mean range)0.65 to 1.38(max range)Nine test runs were conducted in Leeds,England on three road types under fourventilation conditions. Lowest valuesassociated with windows closed and nomechanical ventilation.Statistics are based on meanand maximum (max) COconcentrations recorded duringeach minute of each test run.October, 200920Draft – Do Not Cite or QuoteIn general, the above results suggest that the I/O ratio tends toward unity when there are no interior sources of CO, the autom obile engine does not contribute directly to its own interior concentrations, and the measurement probes are properly installed on the vehicle. 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 4 This conclusion is consistent with theoretical expectations for a non-reactive pollutant. For example, CO concentrations inside vehicles can be estim ated as a function of outside CO concentration, air exchange rate, a penetration factor, and the emission rates of indoor sources (e.g., exhaust leaks, smoking). If one assumes that (1) steady-state ventilation conditions exist, (2) the indoor removal rate ( k ) is zero (i.e., no loss of CO as it moves from outside to inside the vehicle), and (3) there are zero emissions from interior sources, then the CO concentration inside a vehicle can be simplified to a function of outside CO concentrations and the penetration rate (i.e., infiltration is generally equivalent to penetration). 5 Under these stated conditions, the I/O ratio would ultimately converge to unity.

5.4.2.2 Studies Comparing CO Concentrations Inside Motor Vehicles to Concentrations at Fixed-Site Monitors and Roadside Locations A report by Shikiya et al. (1989) describes an in-vehicle study conducted in the South Coast Air Basin of California during the sum mer of 1987 and winter of 1988. Participants were randomly-selected home-to-work commuters from a non-industrial business park. Measurements of hazardous air pollutants (HAPs), CO, and Pb were collected from within vehicles and contrasted with measurements at existing fixed-site monitoring stations. A total of 192 CO measurements were made each representing the average concentration of the round-trip commute. 6 On average, CO concentrations were 8.6 ppm, and even though the maximum observed CO concentration in a vehicle was as high as 46 ppm, only 3 percent of the in-vehicle commuting concentration measurements were greater than 20 ppm (Table 3-2). Shikiya et al. (1989) also investigated several potentially influential variables. Statistical differences in concentration were noted for season (p = 0.01), and age of vehicle (p = 0.05). Mean concentrations using several other classification variables did not differ significantly at the p = 0.05 level (e.g., ventilation status, vehicle speed, freeway density during commute).

4 Interior sources of CO to in-vehicle concentrations may include self-pollution such as that associated with defective exhaust systems or inadequate internal ventilation (draft ISA, p. 3-105). While automobile technology has advanced with improvements in these areas (e.g., Flachsbart et al., 1999), interior sources may contribute in some instances (e.g., older school buses, draft ISA, p. 3-105).

5 See section 3.6.2 of the draft ISA.

6 The average one-way commute time was 33 minutes. October 2009 Draft – Do Not Quote or Cite 5-211 2 3 4 5 6 7 8 9 10 11

13 Table 3-2 also presents descriptive statistics for averages of individual 4-hour integrated

sam ples collected during peak commuting hours at three fixed-site monitors that encompassed the general routes of the commuters: Long Beach (a coastal location), Los Angeles (a central location), and Pomona (an inland location). On average, CO concentrations were 3.7 ppm, though maximum CO concentrations were as high as 8.7 ppm. Other monitoring sites located in Los Angeles, Rubidoux, and Upland reported ambient 24-hour average CO concentrations of 2.2 ppm during the summer. This group of monitors were designated by Shikiya et al. (1987) to be less influenced by local roadway emissions compared with the three other fixed-site monitors. Using the mean for all in-vehicle CO concentrations during round-trip commutes (8.6 ppm) and dividing by the mean of the integrated fixed-site concentrations measured during peak commuting hours (3.7 ppm) gives an in-vehicle to ambient monitor ratio of about 2.4.

Table 5-3. Descriptive Statistics for CO Concentrat ions Measured Inside Vehicles and at 12 Fixed-Site Monitors (from Shikiya et al., 1989). CO concentration (ppm) a Measurement Grouping variable Category Number of samples Mean Std Max All -- 192 8.6 5.0 46.4 Summer 80 6.5 2.2 14.6 Season b Winter 112 10.1 5.8 46.4 1973 - 83 9.4 Average in-vehicle CO concentration during round-trip commute Vehicle year c 1984 - 88 7.8 Integrated fixed-site concentration during peak commuting hours (Long Beach, Los Angeles, and Pomona stations) All -- 19 3.7 2.1 8.6

Notes:

a Mean, std and max are the arithmet ic mean, arithmetic standard deviation, and maximum CO concentrations.

b Statistically significant at p = 0.01 level. c Summer: May – October, Winter: November – March.

c Statistically significant at p = 0.05 level. 14 15 16 17 18 19 20 Further, the draft ISA notes that studies sum marized in the 2000 CO AQCD found that in-vehicle CO concentrations were generally two to five times higher than ambient CO concentrations obtained at fixed-site monitors within the cities studied. However, several of these studies were conducted when CO vehicle emissions were much higher and/or under situations that are less relevant to the two urban areas of the U.S. included in the current exposure and dose assessment, discussed below in section 5.5. As described above, the findings

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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 37 reported by Shikiya et al. (1989) in a study conducted in Los Angles supports a ratio of about two. W e note, however, that based on several factors discussed above, such as the traffic characteristics of the roadway and the site characteristics of the fixed-site monitor, the relationship can vary (e.g., higher ratios would be obtained using more remotely sited monitors, and the size of relationship may vary with absolute magnitude of roadway concentrations).

5.5 STRATEGY FOR CO EXPOSURE/DOSE ASSESSMENT FOR THE CURRENT REVIEW
5.5.1 Background for Current Assessment Strategy

The draft Scope and Methods Plan for the current review of the prim ary CO NAAQS (US EPA, 2009b) described an approach based on the application of APEX to estimate human exposures to CO and the resulting dose and to characterize the potential health risks that are associated with recent ambient levels of CO and with ambient levels that just meet the existing standards in two urban study areas (Denver and Los Angeles). The characterization of health risks focused on development of estimates of COHb levels and the number of people and the total number of occurrences for which potential COHb benchmark levels are exceeded. In consideration of information on current locations of CO monitors discussed in chapter 3 of this document and in the draft ISA (sections 3.5-3.7), and CASAC comments on both the draft Scope and Methods Plan and the first draft ISA, however, staff notes significant limitations of the currently available CO monitors related to their use in detailed population exposure assessment.

 The number of CO monitors in Denver and Los Angeles counties has decreased since the previous review, from 9 to 3 or 4 monitors operating in Denver (depending on the year considered) and from 21 to 12 in Los Angeles.

 The current levels of ambient CO concentrations are much lower than in the last review, and a significant number of the measurements are near or below detection limits.

 Concentrations of ambient CO occurring in key microenvironments are not reflected by ambient monitors. As stated by the CASAC CO Panel, “Relying only on EPA’s fixed monitoring network CO measurements may underestimate CO exposures for specific vulnerable populations such as individuals resi ding near heavily trafficked roads and who commute to work on a daily basis” (Brain and Samet, 2009, p.2).

 As discussed chapter 3 above, the currently available CO monitors pose significant limitations in our ability to fully characterize the current spatial and temporal variability in CO ambient concentrations across the two urban areas of focus for this assessment, Denver and Los Angeles. These limitations affect our ability to derive detailed relationships about CO concentrations in ambient air across the study area from which detailed microenvironmental concentrations can be estimated. More broadly, the October 2009 Draft – Do Not Quote or Cite 5-23CASAC CO Panel expressed the view that “the current am bient monitoring network is not well designed to characterize spatial and temporal variability in ambient concentrations. Thus, it does not adequately support detailed assessments of human exposure or air quality modeling such as for photochemical oxidants” (Brain and Samet, 2009, p.11). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

22 23

29 30 31 32 33 34 35 36 In light of these limitations in the air quality data, including CASAC’s concerns about the adequacy of the monitoring network to perform detailed exposure analyses, and in light of the findings from the CO assessments completed for the 1994 review (USEPA, 1994) and subsequently (Johnson, et al., 2000), as well as the lack of new evidence in the draft ISA to support a quantitative risk characterization appro ach different from past assessments, staff decided not to perform a detailed analysis involving multiple monitors and comprehensive estimation of exposure concentrations in multiple microenvironments, as has been done in the past. Rather than develop such a detailed analysis using the available air quality data that has been recognized as limited by CASAC for such a purpose, we developed an alternative simplified, screening-level approach. We recognize that there are uncertainties associated with the revised approach and, thus, its utility is primarily as a screening assessment to provide some perspective on current ambient CO concentrations and associated CO exposure, dose and risk. One purpose of this draft document is to seek CASAC views on the extent to which this assessment design provides information useful to this current CO NAAQS review. The following section presents the approach used to develop the current CO exposures and COHb estimates for the two urban study areas presented in this document.

5.5.2 Selected Approach for Current Review

As discussed in section 5.5.1 above, despite the capabilities of the APEX m odel, staff felt that the lack of spatial and temporal variability in available ambient monitoring data precluded 24 the development of a credible broad-scale urban exposure assessment such as that conducted 25 recently for the O3 NAAQS (US EPA, 2007). Therefore, staff decided to perform a limited 26 exposure analysis using APEX and ambient data at a single monitor each in Denver and Los 27 Angeles counties. 28 In developing the approach, staff evaluated the monitoring data available in the selected locations (i.e., Denver County and Los Angeles County) for years 2005 through 2007 (see Table 3-1 for sites IDs, locations, and number of sample-hours). Staff noted that, following the examination of the existing and active monitors in Denver and Los Angeles counties and using a 75% completeness criterion, only two of the Denver County monitors had data for 2007. Three of the six Denver sites used in the exposure assessment conducted in 2000 (Johnson et al. 2000) have been removed from the monitoring network and are no longer reporting CO concentrations in Denver (Figure 5-1). Based on these observations, 2006 was chosen to be the most recent October 2009 Draft – Do Not Quote or Cite 5-24year to be analyzed. As a result of this choice, there were four m onitors in Denver County and twelve monitors in Los Angeles County (Figure 5- 1) with complete air quality data available. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 The monitor siting characteristics and breadth of spatial coverage are also important features to consider in representing the air quality in an area. Most of the monitors in Denver County are clustered within the central portion of the Denver County, with two monitors (IDs 080310002 and 080310019) within 1 mile of one another and generally having a similar concentration distribution when considering thei r 1-hour and 8-hour average CO concentrations (Figure 5-2). There is limited spatial variation among the twelve Los Angeles County monitors (Figure 5-3). Further, of the monitors available in Denver County to use in an exposure assessment, one monitor appears to best represent the highest population density in Denver County (draft ISA, Figure 3.4-3). This monito r (ID 08-031-0002) is located at 2105 Broadway. This particular monitor was included in the previous reviews and continues to report the some of the highest concentrations in the area (Figure 5-2 a nd Table 3-1). It is described as a micro-scale site, within 6 meters from a roadway having 17,200 vehicles/day traffic volume, 7 meters from a road with 10,000 vehicles/day, and 16 meters from a road with 1,000 vehicles/day. Based on the same criteria (i.e., to envelop a study area that captures the population centers and where ambient CO levels tend to be high), a single monitor in Los Angeles County (ID 060371301) was selected for use in the exposure assessment for the Los Angeles study area. This monitor is described as representing a middle scale, and it is near to an arterial road, but 350 m from a major freeway (the I-105) with a traffic count close to 35,000 vehicles/day. Staff note, however, that a study of ambient CO concentrations related to motor vehicle traffic in Los Angeles and Sacramento (Rodes et al., 1998) observed little difference in CO concentrations between arterial roads and freeways for Los Angeles (draft ISA, p. 3-65).

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2

4 5 Figure 5-1. Locations of active ambient CO monitors meeting 75% completeness 3 criterion in 2006 along with locations of inactive ambient CO monitors, within the metropolitan Denver (top) and metropolitan Los Angeles (bottom).
October 2009 Draft – Do Not Quote or Cite 5-261-hour CO (ppm)

0 2.5

5 7.5 10

080310002 080310013 080310014 080310019 Site ID1-hour CO (ppm)

0 2.5

5 7.5 10

080310002 080310013 080310014 080310019 Site ID 1

080310002 080310013 080310014 080310019 0 2 4 6 8

Site ID8-hour daily maximum CO (ppm)

080310002 080310013 080310014 080310019 0 2 4 6 8

Site ID8-hour daily maximum CO (ppm)

2

4 Figure 5-2. Distribution of 1-hour (top) and 8-ho ur average daily maximum (bottom) CO 3 concentrations at ambient CO monitors in Denver County, year 2006.

October 2009 Draft – Do Not Quote or Cite 5-272 4 6 8 10 060370002 = A 060370113 = B 060371002 = C 060371103 = D 060371201 = E 060371301 = F 060371701 = G 060372005 = H 060374002 = I 060375005 = J 060376012 = K 060379033 = L

060371002 060371201 060371701 060374002 060376012 01-hour CO (ppm)

A LKJIHGFED B C Site ID 2 4 6 8 10 060370002 = A 060370113 = B 060371002 = C 060371103 = D 060371201 = E 060371301 = F 060371701 = G 060372005 = H 060374002 = I 060375005 = J 060376012 = K 060379033 = L

060371002 060371201 060371701 060374002 060376012 01-hour CO (ppm)

A LKJIHGFED B C 2 4 6 8 10 060370002 = A 060370113 = B 060371002 = C 060371103 = D 060371201 = E 060371301 = F 060371701 = G 060372005 = H 060374002 = I 060375005 = J 060376012 = K 060379033 = L

060371002 060371201 060371701 060374002 060376012 01-hour CO (ppm)

A LKJIHGFED B C Site ID 1

060370002 060371002 060371201 060371701 060374002 060376012 0 2 4 6 8 060370002 = A 060370113 = B 060371002 = C 060371103 = D 060371201 = E 060371301 = F 060371701 = G 060372005 = H 060374002 = I 060375005 = J 060376012 = K 060379033 = L8-hour daily maximum CO (ppm)

A LKJIHGFED B C Site ID 060370002 060371002 060371201 060371701 060374002 060376012 0 2 4 6 8 060370002 = A 060370113 = B 060371002 = C 060371103 = D 060371201 = E 060371301 = F 060371701 = G 060372005 = H 060374002 = I 060375005 = J 060376012 = K 060379033 = L8-hour daily maximum CO (ppm)

A LKJIHGFED B C 060370002 060371002 060371201 060371701 060374002 060376012 0 2 4 6 8 060370002 = A 060370113 = B 060371002 = C 060371103 = D 060371201 = E 060371301 = F 060371701 = G 060372005 = H 060374002 = I 060375005 = J 060376012 = K 060379033 = L8-hour daily maximum CO (ppm)

A LKJIHGFED B C Site ID 2

4 Figure 5-3. Distribution of 1-hour (top) and 8-ho ur average daily maximum (bottom) CO 3 concentrations at ambient CO monitors in Los Angeles County, year 2006.

October 2009 Draft – Do Not Quote or Cite 5-28In each of the two study areas, two broadly defined air quality scenarios were investigated. In the first scenario (“Scenario A”), all m icroenvironmental concentrations are set equal to the ambient concentrations measured at the single fixed-site monitor selected to represent each exposure modeling domain. Staff used APEX to estimate the COHb levels in blood of the at-risk population (i.e., adults with CHD) by assuming that the population is exposed to the ambient concentrations measured at the selected near-road monitor for each studyarea throughout the entire simulation period. This general assumption likely results in overestimates of CO exposure and COHb levels for much of the population because CO peak hourly concentrations are typically somewhat lower indoors than outdoors due to consideration of air exchange (in the absence of indoor sources of CO). This scenario, however, may underestimate CO exposure for some small portion of the population that may live in close proximity to heavily trafficked roadways and spend appreciable time in transit on such roadways. These individuals, based on the analysis of air quality relationships and personal exposure measurements, would likely have periods of higher exposures than represented by Scenario A since CO concentrations in vehicles, and exposure concentrations for individuals in transit on roadways, are typically higher than the concentrations measured at a near-road monitor. The impact of such higher exposure periods on an individual’s COHb levels will vary depending on the magnitude and pattern of exposures in the prior and subsequent hours. 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 In the second scenario (“Scenario B”), we assume that the concentration outside a motor vehicle is twice the ambient concentration and that the concentration inside the vehicle is the same as the concentration immediately outside the vehicle; that is, the in-vehicle microenvironment is set equal to twice the ambient monitor concentrations. For Scenario B, all other microenvironmental concentrations are set equal to the ambient concentration based on the fixed-site monitor concentration, consistent with their treatment in Scenario A. The intent of this scenario is to determine the magnitude of the change in exposure and COHb levels when incorporating a rough estimate of the greater exposure concentrations occurring inside motor vehicles. Further details regarding the air quality scenarios and specific exposure modeling input data used for the assessment are given in chapter 6.

October 2009 Draft – Do Not Quote or Cite 5-295.6 KEY OBSERVATIONS 1 2

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 Presented below are key observations related to the approach for the population

assessm ent of CO exposure and dose. 3

 APEX, an EPA human exposure and dose model, has a long history of use in estimating 4 exposure and dose for many of the criteria pollutants including CO, O 3 , SO 2 , and NO 2 . Over time, staff have improved and developed new model algorithms, incorporated newer available input data and parameter distributions, as well as performed several model evaluations, sensitivity analyses, and uncertainty characterizations for the same pollutants. Based on this analysis, APEX was judged to be an appropriate model to use for assessing CO exposure and dose.

 Personal CO exposure studies indicate that in general, indoor exposures contribute the greatest portion of an individual’s total daily exposure, though variability in exposure concentrations may be driven largely by exposure in certain microenvironments, such as, with regard to ambient CO, inside motor vehicles or when outdoors near roadways. Accordingly, in estimating CO exposures and associated COHb levels an approach is needed to estimate the generally higher in-vehicle and in-transit exposure concentrations compared to the generally lower ambient concentrations concurrently reported by fixed site ambient monitors

.

 Given the limitations in the number of ambient monitors currently in operation, the limited spatial and temporal representation of ambient concentrations provided by the current monitoring network, and limited number of CO concentrations at or above the instrument detection limit, the simplified, screening-level approach used in this exposure assessment does not employ detailed microenvironmental concentration modeling and uses a single fixed-site monitor in each study area. The single monitoring site selected in each location typically reported a higher range of CO concentrations when compared with other monitors in each area, and thus, when used as an input to an exposure model, is generally considered likely to generate conservative (i.e., higher) estimates of exposure for the large majority of the population.

October 2009 Draft – Do Not Quote or Cite 5-305.7 REFERENCES 1 2 3
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38 Abi-Esber L; El-Fadel M. (2008). “In-vehicle CO ingression: Validation through field measurements and mass balance simulations.” Sci. Total Environ. 394: 75-89. Akland GG, Hartwell TD, Johnson TR, Whitmore RW. (1985). Measuring human exposure to carbon monoxide in Washington, DC, and Denver, Colorado, during the wi nter of 1982-1983. Environ Sci Technol. 19: 911- 918. Boulter, P., and McCrae, I. (2005). A C arbon Monoxide Inside Vehicles: Implications for Road Tunnel Ventilation. @ In: Annual Research Review 2005. TRL Academy. Brain JD and Samet JM (2009). Letter to EPA Administrator Lisa Jackson: Clean Air Scientific Advisory Committee’s (CASAC) Peer Review of the Agency’s 1st Draft Carbon Monoxide Integrated Science Assessment. EPA-CASAC-09-011. June 24, 2009. Biller WF and Richmond HM. (1982). Sensitivity Analysis on Coburn Model Predic tions of COHb Levels Associated with Alternative CO Standards. Report to Strategies and Air Standards Division of the Office of Air quality Planning and Standards, U.S. Envir o nmental Protection Agency, Research Triangle Park, NC. November, 1982. Biller WF, Feagans TB, Johnson TR, D uggan GR, Paul RA, McCurdy T, and Thom as HC. (1981). A General Model for Estimating Exposure Associated with Alternative NAAQS. Paper No. 81-18.4. Presented at the 74th Annual Meeting of the Air Pollution Control Association, Dallas, Texas. Chan, C., Ozkaynak, H., Spengler, J., and Sheldon, L. (1991). A Driver Exposure to Volatile Organic Com pounds, CO, Ozone, and NO2 Under Different Driving Conditions. @ Envi ron. Sci. Technol. Volume 25, No. 5, pp. 964 - 972. Chang LT; Koutrakis P; Catalano PJ; Suh HH. (2000). Hour l y personal exposures to fi ne particles and gaseous pollutants - Results from Baltimore, Maryland. J Air W aste Manage Assoc. 50: 1223-1235. Colwill, D., and Hickman, A. (1980). “Exposure of Driv ers to Carbon Monoxide.” J ournal of the Air Pollution Control Association. Volume 30, pp. 1316 – 1319. Esmail S, Bhambhani Y, Brintnell S. (1995). Ge nder Differences in W ork Performance on the Baltimore Therapeutic Equipment Work Simulator. Amer J Occup Therapy. 49: 405-411. Flachsbart, P. (1999). “Human Exposure to Carbon Monoxide from Mobile Sources.” Chemosphere – Global Change Science. Volume 1, pp. 301 – 329. Flachsbart, P., Mack, G., Howes, J., and Rodes, C. (1987). “Carbon Monoxide Exposures of Washington Commuters.” Journal of the Air Pollution Control Association. Volume 37, pp. 135 – 142. Galetti, P. M. (1959). Respiratory Exchanges During Mu scular Effort. Helv Physiol Acta. 17: 34-61. Glen, G. (2002). Programmer’s Guide for the APEX3 Model. Prepared by ManTech Environmental Technology, Inc., for the U.S. Environmental Protection Agency, Research Triangle Park, NC. April, 2002. Joumard R, Chiron M, Vidon R, Maurin M, and Rouzioux JM (1981). Mathematical Models of the Uptake of Carbon Monoxide on Hemoglobin at Low Carbon Monoxide Level s. Environ Health Persp. 41: 277 – 289.

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40 Johnson T. (2003). A Guide to Selected Algorithms, Di stributions, and Databases Used in Exposure Models Developed by the Office of Air Quality Planning and Sta n dards. Prepared by TRJ Environmental, Inc., for Office of Research and Development, U.S. Envir onm ental Protection Agency, Research Triangle Park, North Carolina. Johnson T and Capel J. (2003). Total Risk Integrated Methodology. TRIM ExpoInhalation User’s Document. Volume III: Air Pollutant Exposure Model Criteria Air Pollutants Case Study (Carbon Monoxide Exposures in Los Angeles). Report prepared by TRJ Environm ental, Inc., for the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Johnson T and Paul R. (1983). The NAAQS Exposure M odel (NEM) Applied to Carbon Monoxide. EPA Report No. 450/5-83-003. Prepared by PEDC o Environm ental, Inc. for the O ffice of Air Qua lity Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Johnson T, Capel J, Olaguer E, Wijnberg L. (1992). Est imation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver Residents Using a Probabilistic Version of NEM. Report prepared for the Office of Air Quality Planning and Standards, U.S. En vironmental Protection Agency, Research Triangle Park, North Carolina. Johnson T, Mihlan G, LaPointe J, et al. (2000). Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels in Residents of Denver and Los Angel es Using pNEM/CO (Version 2.1). Prepared by TRJ Environmental, Inc., for the O ffice o f Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Koushi, P., Al-Dhowalis, K., and Niazi, S. (1992). Vehi cl e Occupant Exposure to Carbon Monoxide. Journal of the Air and Waste Management Association. Volume 42, pp. 1603 – 1608. McCurdy T, Glen G, Smith L, and Lakkadi Y. (2000). The National Exposure Research Laboratory’s Consolidated Human Activity Database. J Exp Anals and Environ Epi. Volume 10, pp. 566-578. Muller, K. E., and C. N. Barton. (1987). A Nonlinear Versi on of the Coburn, Forster and Kane Model of Blood Carboxyhemoglobin. Atmos Environ. 21: 1963-1967. Peterson JE, and Stewart RD. (1975). Predicting the carboxyhem oglobin levels re sulting from carbon monoxide exposures. J. Appl. Physiol. 39(4): 633-638. Petersen, W., and Allen, R. (1982). Carbon Monoxide E xposures t o Los Angeles Area Commuters. Journal of the Air Pollution Control Association. Volume 32, pp. 826 – 833. Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vettering. (1986). Numerical Recipes. Cambridge University Press. Rodes, C., Sheldon, L., Whitaker, D., Clayton, A., Fitzgeral d, K., Flanagan, J., DiGenova, F., Hering, S., and Frazier, C. (1998). Measuring Concentrations of Selected Air Pollutants Inside California Vehicles. Final Report. Prepared by Research Triangle Institute under Contract No . 95-339. California Air Resources Board. Sacramento, California. Schofield WN. (1985). Predicting basal metabolic rate, ne w st andards, and review of previous work. Hum Nutr Clin Nutr. 39C(S1): 5-41. Scotto di Marco G, Kephalopoulos S, R uuskanene J, and Jantunene M. (2005). Personal carbon monoxide exposure in Helsinki, Finland. Atmos Environ. 39Z: 2697–2707.

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30 31 Sharp, D., and Tight, M. (1997). A Vehicle Occupant Exposure to Air Pollution. @ In: Policy, Planning, and Sustainability: Proceedings of Seminars C and D Held at PTRC European Transport Forum , Brunel University. Pages 481 - 492. Shikiya, D., Liu, C., Kahn, M., Juarros, J., and Barcikowski, W. (1989). In-Vehicle Air Toxics Characterization Study in the South Coast Air Basin. Office of Planning and Rules, South Coast Air Quality Management District. May. US EPA. (1991). Air Quality Criteria for Carbon Monoxide. Research Tria ngle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA/600/8-90/045F US EPA. (2000). Air Quality Criteria for Carbon M onoxide. National Center for Environm ental Assessment, Office of Research and Development, U.S. Environm ental Protection Agency, Re search Triangle Park, NC 27711; report no. EPA/600/P-99/001F. June 2000. Available: http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm ?deid=18163. US EPA. (2002). EPA’s Consolidated Human Activities Database. Available at: http://www.epa.gov/chadnet1/. US EPA. (2007). Ozone Population Exposure Analysis for Sel ected Urban Areas (July 2007). Office of Air Quality Planning and Sta ndards, Research Triangle Park, NC . EPA-452/R-07-010. Available at: http://epa.gov/ttn/naaqs/standa rds/ozone/s_o3_cr_td.htm l. US EPA. (2008a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report No. EPA-452/B-08-001a. Office of Air Quality Planning and Standards, U.S. Environmenta l Pro tection Agency, Research Triangle Park, NC. US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report No. EPA-452/B-08- 001b. Office of Air Quality Planni ng and Standards, U.S. Environm en tal Protection Agency, Research Triangle Park, NC. US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient Air Quality Standard. EPA-452/R-08-008a. Novem ber 2008. Available at: http://www.epa.gov/ttn/naaqs/standard s/nox/data/20081121_NO2_R EA_final.pdf. US EPA. (2009a). Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air Quality Standard. EPA-452/R- 09-007. August 2009. Available at: http://www.epa.gov/ttn /naaqs/standards/so2/data /200908SO2REAFinalReport.pdf. US EPA. (2009b). Carbon Monoxide National Am bient Air Quality Standa rds: Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-452/R-09-004. April 2009. Available at: http://www.epa.gov/ttn /naaqs/standards/co/data/ 2009_04_COScopeandM ethodsPlan.pdf

. 32
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36 US EPA (2009). Integrated Science Assessment for Carbon Monoxide –Second External Review Draft. U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA 600/R-09/019B. Available at: http://www.epa.gov/ttn/n aaqs/standards/co/s_co_cr_isa.htm l

..October, 2009 Draft – Do Not Cite or Quote 6-1 

### 6 EXPOSURE/DOSE ASSE SSMENT AND RISK 1 

### CHARACTERIZATION 2

This chapter describes the draft assessme nt of ambient CO exposure and dose and 3 associated characterization of risk and uncerta inty. Section 6.1 provides an overview of the 4 application of the APEX model in this assessment. In sectio n 6.2, we present the exposure and 5 dose estimates and describe the estimates in th e context of potential he alth benchmarks. The 6 current assessment and associated exposure and dose estimates are described in comparison to 7 estimates from an earlier CO assessment (Johnson et al., 2000) in sectio n 6.3. The final section 8 presents an analysis of how variability was c onsidered by staff in this assessment followed by a 9 qualitative characterization of uncertainty (section 6.4). 10

6.1 APPLICATION OF APEX4.3 TO CARBON MONOXIDE 11 The previous analysis of population expos ure to carbon monoxide (CO) employed the 12 pNEM/CO model (summarized in section 5.2 a bove) and focused on Denver and Los Angeles 13 study areas, comprising the majority of census tr acts within those metr opolitan areas (Johnson et 14 al., 2000). Air quality data were obtained from multiple fixed-site monitors within the study 15 areas, and the exposure assessment accounted for th e effects of geographic location, a diverse set 16 of microenvironments, commuting within the st udy area, and selected indoor sources (e.g., 17 environmental tobacco smoke, gas stoves). 18 In the specific application of APEX4.3 described in this re port, a different approach has 19 been taken. The simplified approach presented is intended to emphasize the contribution of CO 20 exposures occurring in vehicles to overall CO exposures. The contribution of in-vehicle 21 exposures is thought to be infl uential in producing the highest exposures to ambient CO and 22 resulting COHb levels (draft ISA, section 3.6.6.2). The ambient CO concentration throughout 23 the study areas and all microenvironmental concen trations are generally assumed to equal the 24 hourly-average concentrations measured by a single high CO concentration monitor in one of the 25 two scenarios (Scenario A). In a second scenario, in-veh icle concentrations are assumed to equal 26 twice the ambient monitor concentrations (Scenar io B). Therefore, the locations visited by 27 simulated persons in the study area are repr esented by at most two microenvironments ( in28 vehicle and all other ). The study areas and scenarios are br iefly described in sections 6.1.1 and 29 6.1.2 below. The study area population of interest was limited to adults with coronary heart 30 disease (CHD) that reside within a defined dist ance of a single downtown fixed-site monitor (as 31 discussed in sections 5.5.2 a nd 6.1.3). Given simplified assumptions in the approach, the 32 generated results would tend to represent population exposures experienced by persons 33 residing/travelling in high CO (of ambient orig in) concentration microenvironments. However, 34October, 2009 Draft – Do Not Cite or Quote 6-2 note that this assessment does not include the contribution of indoor sources to total CO 1 exposure, which in prior assessments have been shown to be important contributors to total 2 exposures (see US EPA, 1992; Johnson et al., 2000). 3 A general description of APEX 4.3 and its capabilities, as well as the history of the 4 pNEM/APEX series of exposure models can be f ound in chapter 5. This section focuses on the 5 development of the specific input files and pa rameters used in the current application of 6 APEX4.3 to CO in the Denver and Los Angeles st udy areas. In particular, this section (and 7 associated appendices) describes the 8

 Geographic areas and time periods de fined for the exposure analyses, 9

 Exposure scenarios under evaluation, 10

 Populations-at-risk and the associat ed prevalence rates for CHD, 11

 Air quality and meteorological data used for each study area, 12

 Microenvironments defined for each exposure scenario, and 13

 Methods used to construct a composite diary for each simulated individual. 14 In addition to the application-specific input data bases described in this section, we used a 15 number of default databases provided with AP EX4.3 as inputs to the model. These included 16 national data files obtained from the U.S. Cens us Bureau (i.e., the 2000 Census data) for the 17 following types of information (http://www. epa.gov/ttn/fera/apex_download.html#input): 18

 Population data by race, gender, age, and census tract; 19

 Employment probabilities by gender, age, and census tract; 20

 Locations of census tracts (latitude and longitude); and 21

 Commuting flows for combinations of home and work census tracts. 22 Another default input file provided tables of age- a nd gender-specific physiological 23 parameters (e.g., weight). The contents of these de fault files will not be desc ribed in this section; 24 they are described in detail in the APEX User s Guide (US EPA, 2008a) and the APEX Technical 25 Support Document (US EPA, 2008b). 26

6.1.1 Study Areas and Exposure Periods 27 As discussed in section 3.2, EPA selected areas within Denver, Colorado, and Los Angeles, California, for the exposure assessment. Briefly, considerations in selection of these areas include: the prior analysis of these lo cations in CO NAAQS reviews, the areas having historically elevated CO concentrations, and the areas currently having some of the most complete ambient monitoring data available. The actual study areas were defined as including all census tracts within 20 km of the following fixed-site monitors.October, 2009 Draft – Do Not Cite or Quote 6-3 Denver: Monitor No. 080310002; 2105 Broadway, Denver, CO (CAMP site). 1 Los Angeles: Monitor No. 060371301; 11220 Long Beach Blvd., Lynwood, CA. 2 The Denver study area includes most of the urbanized area inside the arc defined by 3 Highway 470 (Figure 6-1) within Denver County. The Los Angeles study area is centered at 4 Lynwood, CA and includes large portions of Lo s Angeles and Long Beach within Los Angles 5 County (Figure 6-2). 6 EPA selected the following calendar years as the study periods for each area. 7 Denver: 1995 and 2006 8 Los Angeles: 1997 and 2006 9 The year 2006 was selected for both cities becaus e it was the most recent year of monitoring data 10 that met the 75% completeness requirement for th e fixed-site monitors listed above. The CO 11 levels reported for 2006 were well below the 8-ho ur NAAQS and were consid ered representative 12 of current conditions in each study area. Th e year 1995 for Denver and the year 1997 for Los 13 Angeles were selected as periods for which th e monitoring data indicated higher CO conditions 14 near or exceeding, the 8-hour CO NAAQS (9 ppm). As discussed in section 6.1.4.3, staff 15 applied an adjustment to the monitoring data re ported for these years to simulate ambient CO 16 levels that would just meet the current 8-hour NAAQS. 17October, 2009 Draft – Do Not Cite or Quote 6-4 1 Figure 6-1. Map of the Denver Study Area Defined as a Circle with Radius = 20 km 2 Centered on Fixed-Site Monitor ID 080310002. 3

4October, 2009 Draft – Do Not Cite or Quote 6-5 1 Figure 6-2. Map of the Los Angeles Study Area Defi ned as a Circle with radius = 20 km 2 Centered on Fixed-Site Monitor No. 060371301. 3

4 5October, 2009 Draft – Do Not Cite or Quote 6-6 6.1.2 Exposure Scenarios 1 Two exposure scenarios were evaluated for each study area, designated as Scenario A 2 and Scenario B. The scenarios differed according to the modeling factors assumed for the two 3 broadly defined microenvironments modeled (i.e., in-vehicle and all others ). See section 6.1.5 4 for additional details regarding microenvironments. Both Scenario A and B were focused on the 5 adult CHD population living within each study area and are descri bed in more detail below. 6

6.1.2.1 Scenario A 7 In this scenario, the sequence of concentra tions in each microenvironment was set equal 8 to the ambient concentrations de rived from the fixed-site monitor selected to represent the study 9 area. Essentially, the microenvironment assignm ents in each diary entry for each simulated 10 individual did not affect exposure concentration levels (i.e., the individual was exposed to the 11 ambient concentrations as measured at the fi xed-site monitor for all hours in the exposure 12 period). 13 The time/activity database that was used as th e input for this scenario included all adult 14 diaries in CHAD and thus was not necessarily specific to the study area. Note however that the 15 APEX model samples from this pool of diaries to reflect the ac tual population distribution based 16 on the specific age and gender residing in each ce nsus tract. In addition, the sampling from the 17 broad diary pool is also gui ded by several temperature ra nges and applied to observed 18 temperatures for the specific geographic region. 19

6.1.2.2 Scenario B 20 Scenario B assumed that the concentrations ou tside a motor vehicle were greater than that 21 measured at the ambient fixed-site monitor and th at the concentrations in side the vehicle were 22 the same as the concentrations immediately outside the vehicle. The CO concentrations in all 23 other microenvironments were set equal to the am bient concentrations as measured at the fixed24 site monitor, consistent with their treatment in Scenario A. As in Scenario A, the input 25 time/activity database incl uded all diaries in CHAD. 26

6.1.3 Populations-at-Risk 27 Staff defined the population group at risk within each study area to include adults ages 18 28 or older with CHD. Coronary heart disease is caused by inade quate circulation of the blood to 29 the heart muscle, which is a result of the corona ry arteries being blocke d by cholesterol deposits 30 (ISA, section 5.2.1.9). The focus on adults with CHD is consistent with the previous (2000) 31 review of the CO NAAQS. The current and pr evious assessments focused on adults as the 32 incidence of CHD in younger indivi duals is extremely small. 33October, 2009 Draft – Do Not Cite or Quote 6-7 At the time of the review completed in 1994, the estimated number of individuals with 1 CHD represented about 3% of the entire (all ages) U.S. popul ation (US EPA, 1992). More 2 recently however, the National Health Intervie w Survey for 2007 reported a prevalence rate for 3 CHD of about 6 percent for the population above 18 years of age (CDC, 2009; draft ISA, section 4 5.7.2.1). The current exposure/dose assessment requi res estimates (by age and gender) of the 5 fraction of the population in the Denver and Los Angeles study areas who have CHD. Given the 6 general similarity in regional rates for CHD, st aff decided to apply nati onal prevalence rates for 7 CHD to each of the two study areas. Table 6- 1 provides national prevalence data for CHD by 8 age obtained from the National Health Inte rview Survey of 2007 (CDC, 2009). Table 6-2 9 provides national prevalence rates for CHD by ge nder obtained from the same source. These 10 data were used to estimate gender-specific adjustment factors of 1.31 = males/total = 0.080/0.061 11 for males and 0.74 = females/total = 0.045/0.061 fo r females. Table 6-3 provides estimated 12 national prevalence rates for CHD by age range adjusted for gender differences using these 13 values. 14

Table 6-1. National Prevalence Rates for Corona ry Heart Disease by Age Range. 15

Age range Prevalence rate (fraction) for coronary heart disease a 18 to 44 0.009 45 to 64 0.067 65 to 74 0.187 75+ 0.236

a Source: Coronary heart disease statistics in Table 2, “Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2007,” U.S. Department of Health and Human Serv ices, Center for Disease Control, Hyattsville, MD, May 2009.

Table 6-2. National Prevalence Rates for Coronary Heart Disease by Gender. 16 Prevalence rate (fraction) for coronary heart disease a Age range Total Males Females 18+ 0.061 0.080 0.045

a Source: Coronary heart disease statistics in Table 2, “Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2007,” U.S. Department of Health and Human Services, Center for Diseas e Control, Hyattsville, MD, May 2009. 17October, 2009 Draft – Do Not Cite or Quote 6-8 Table 6-3. National Prevalence Rates for Corona ry Heart Disease Used in APEX, 1 Stratified by Age and Gender. 2 Prevalence rate (fraction) for coronary heart disease a Age range Males Females

18 to 44 0.012 0.007 45 to 64 0.088 0.050 65 to 74 0.244 0.138 75+ 0.310 0.175

a Source: Values listed in Table 6-2 were multiplied by 1.31 (= 0.080/0.061) for males and 0.74 (= 0.045/0.061) for females. 3

6.1.4 Air Quality and Meteorological Data 4
6.1.4.1 Selection of Fixed-Site Monitors 5 Based on considerations described in sect ions 3.2 and 5.5.2, staff selected the downtown 6 “CAMP” monitor (ID 080310002) to represent ambient CO concentrations in the Denver, CO 7 study area and monitor 060371301 in Lynwood, CA to represent ambient CO concentrations in 8 the Los Angeles study area. Details regarding eac h monitor’s site characteristics are given in 9 (Table 6-4). Note that the am bient monitor in Denver is a micr oscale monitor sited within the 10 urban core and generally records the highest hourly CO concentrati ons within the county (Table 11 3-1). Similarly, the middle scale monitor in th e Los Angeles study also reported the highest CO 12 concentration levels in the Los Angeles study area (Table 3-2). 13October, 2009 Draft – Do Not Cite or Quote 6-9 Table 6-4. Site Characteristics of Fixed-site CO Monitors Selected to Represent the 1 Denver and Los Angeles Study Areas. 2 Site Characteristic Denver Los Angeles Site ID 080310002 060371301 Street address 2105 Broadway 11220 Long Beach Blvd. Town Denver, CO Lynwood, CA Local site name CAMP Latitude 39.751184 33.928990 Longitude -104.987625 -118.210710 Elevation (above sea level), meters 1593 27 Monitor start date January 1, 1971 January 1, 1973 Measurement scale Microscale Middle scale Sample collection frequency 1 hour 1 hour

Sample analysis method Non-dispersive infrared Non-dispersive infrared Monitor type SLAMS SLAMS

Reporting agency Colorado Department of Public health and Environment South Coast Air Quality Management District
6.1.4.2 Estimation of Missin g Air Quality Values 3 APEX requires that each site-y ear of monitoring data be comp lete (i.e., it is free of hourly 4 gaps in concentration levels). The missing va lues in each data set were estimated by the 5 sequential application of the following three methods. 6 1) If the data gap was less than six conti nuous missing values, the missing values were 7 estimated by linear interpolation using the valid values at the ends of the gap. 8 2) Where possible, data gaps of at least 6 hours were estimated as linear functions of 9 hourly values reported by other ambient CO monitors in the area. Linear regression 10 was used to develop a set of models that were specific to a time-of-day and at each 11 monitor. The model selected to estimate missing values for a particular time of day 12 was the model that maximized the variance explained (R 2 ) for that hour, subject to the 13 constraints that regression model R 2 was greater than 0.5 and the number of 14 measurements used in constructi ng the model was at least 50. 15 3) In cases where method 2 (above) could not be used (i.e., no models were available for a 16 particular time-of-day) and the gap was le ss than 9 hours, the missing values were 17 estimated by linear interpolation between the valid values at the ends of the gap. 18 Table 6-5 provides descriptive st atistics for 1-hour CO concentr ations in each data set, 19 before and after estimating missing values. The ag reement between these statistics indicates that 20 the addition of the estimated mi ssing-value concentra tions did not significantly affect the 21 distribution of the hourly CO data. 22October, 2009 Draft – Do Not Cite or Quote 6-10 Table 6-5. Descriptive Statistics for 1-hour CO Co ncentrations Reported by the Selected 1 Denver and Los Angeles Monitors Before and After Estimation of Missing 2 Values. 3

1-hour CO concentrations (ppm) Percentile

Area Year Missing value substitution Samples (n) Mean Std 50 90 95 99 99.9 2 nd highest Max No 8697 1.5 1.2 1.2 2. 7 3.4 6.1 13.1 16.4 24.5 1995 Yes 8760 1.5 1.2 1.2 2. 7 3.4 6.1 13.1 16.4 24.5 No 8672 0.6 0.4 0.5 1. 0 1.3 2.2 4.1 4.6 6.4 Denver a 2006 Yes 8760 0.6 0.4 0.5 1. 0 1.3 2.1 4.1 4.6 6.4 No 8302 2.4 2.2 1.7 4.9 6.8 11.2 17.2 18.8 19.2 1997 Yes 8760 2.3 2.2 1.7 4.9 6.7 11.2 17.2 18.8 19.2 No 8275 1.0 0. 9 0.7 2. 0 2.9 4.7 6.8 8.2 8.4 Los Angeles b 2006 Yes 8760 1.0 0.90 0.7 2. 0 2.9 4.6 6.8 8.2 8.4

a Site ID 080310002 b Site ID 060371301 6.1.4.3 Air Quality Adjustment to Si mulate Just Meeting NAAQS 4 In addition to modeling exposures based on re cent air quality, expos ures and resulting 5 dose were estimated for air quality conditions that just meet the current 8-hour CO NAAQS of 9 6 ppm. 1 Because CO concentrations in recent years were signifi cantly lower than the current 7 NAAQS, staff first selected an earlier year for each city (1995 for Denver and 1997 for Los 8 Angeles) to represent air quality conditions that were near the 8-hour CO standard. Consistent 9 with the data adjustment approach employed in the previous draft CO exposure assessment 10 (Johnson et al., 2000) and risk and exposure as sessments for other po llutants conducted in 11 support of other recent NAAQS reviews (e.g., US EPA, 2008c), as discussed in section 3.1.4 12 staff concluded (1) that the policy- relevant background levels of CO were negligible in each area 13 and (2) that the fixed-site monito ring data could be adjusted to simulate just meeting the current 14 CO standards by use of a simple proportional adjustment of all hour ly values. Consequently, the 15 following adjustment equation was employed: 16 17 CO adj ( m , h ) = (NAAQS/DV) x CO( m , h ). (6-1) 18 19 CO( m , h ) is the 1-hour CO concentration at hour h for monitor m . It follows that CO adj ( m , h ) is 20 the adjusted CO concentration for hour h at monitor m through the use of the specific design 21 value (DV) for monitor m . Although the current 8-hour NAAQS for CO specifies a maximum 22

1 The 8-hour CO NAAQS of 9 ppm was selected for purposes of simulating just meeting the CO NAAQS because it is the controlling standard from a control strategy development viewpoint.October, 2009 Draft – Do Not Cite or Quote 6-11 concentration of 9 ppm, which is not to be exceeded more than 1 time in a year, the NAAQS 1 term in Equation 6-1 is equivalent to 9.4 ppm due to the application of a standard data rounding 2 convention used in calculating design values 2 (DVs) for CO (Laxton, 1990). 3 The DVs for Denver for the year 1995 and for Los Angeles for 1997 were 9.5 ppm and 4 15 ppm, respectively. The Denver DV is cal culated as the sec ond-highest 8-hour CO 5 concentration reported by monitor ID 080310002 for 1995. The adjustment factor (or 6 NAAQS/DV) that was applied equa lly to all 8,760 hourly ambient CO concentrations at that 7 monitor is thus 9.4/9.5, or 0.99. In a similar mann er, the DV used in Los Angeles is the second8 highest 8-hour CO concentration reported at monitor 060371301 for 1997, giving an ambient 9 concentration adjustment factor of 9.4/15, or 0.63 which was a pplied equally to all 8,760 hourly 10 ambient CO concentrations from the Los Angeles monitor. 11 Table 6-6 lists descriptive statistics for the Denver and Los Angele s 1-hour data sets 12 before and after adjustment. As expected, the adjusted data set for Denver 1995 is very similar 13 to the unadjusted data set given that the adjustment factor used was close to unity. For example, 14 the maximum concentration was reduced from 24.5 ppm to 24.2 ppm. The change in CO 15 concentrations was greater as a result of adjus ting the Los Angeles ambient data. For example, 16 the maximum CO concentration was reduced fr om 19.2 ppm to 12.0 ppm. The adjusted data 17 sets, representing air quality simulated to just meet the current 8-hour CO NAAQS,for Denver 18 and Los Angeles, exhibit their greatest differe nces at the extreme upper percentiles of the 19 distribution (i.e., the 99.9 th percentile and above). 20
Table 6-6. Descriptive Statistics for 1-hour Carbon Monoxide Concentrations Reported 21 by the Denver and Los Angeles Monitors Before and After Adjustment to 22 Simulate Just Meeting the Current 8-Hour CO NAAQS. 23 1-hour CO concentrations (ppm) Percentile
Area Year Adjusted to just meeting NAAQS Mean Std 50 90 95 99 99.9 2 nd highest Max No 1.5 1.2 1.2 2.7 3.4 6.1 13.1 16.4 24.5 Denver a 1995 Yes 1.5 1.2 1.2 2.7 3.4 6.0 12.9 16.2 24.2 No 2.3 2.2 1.7 4.9 6.7 11.2 17.2 18.8 19.2 Los Angeles b 1997 Yes 1.5 1.4 1.1 3.1 4.2 7.0 10.8 11.8 12.0

a Site ID 080310002 b Site ID 060371301 24

2 A design value is a statistic that describes the air quality status of a given area or monitor relative to the level of the NAAQS. For the CO 8-hour standard, the design value is the second highest daily, non-overlapping, maximum 8- hour average concentration over a year. The design value for the 1-hour standard is the second highest daily maximum 1-hour average c oncentration over a year. The latest update (2007-2008) on the CO design values can be found at: http://www.epa.gov/airtrends/pdfs/dv_co_2006_2008.pdfOctober, 2009 Draft – Do Not Cite or Quote 6-12 6.1.4.4 Meteorological Stations 1 A few algorithms within APEX require meteorol ogical data (primarily temperature) from 2 stations located within the study area. For the analyses described in this report, hourly 3 temperature data were obtained from meteorological stations located at or near the fixed-site CO 4 monitor specified for each study area. Table 6-7 identifies the meteorologi cal stations used and 5 selected site characteristics. 6

Table 6-7. Site Characteristics of Meteorol ogical Monitoring Stations Selected to 7 Represent the Denver and Los Angeles Study Areas. 8
Site Characteristic Denver Los Angeles Site 1 Los Angeles Site 2 Site ID 080310002 060374002 Street address 2105 Broadway 3648 N. Long Beach Blvd. Daugherty Field Town Denver Long Beach, CA Long Beach, CA Latitude 39.751184 33.823760 33.81667 Longitude -104.987625 -118.189210 -118.15 Elevation (above sea level), meters 1593 6 9.4 Sample collection frequency 1 hour 1 hour 1 hour

Reporting agency Colorado Department of Public Health and Environment South Coast Air Quality Management District 9 The procedure used for generating a complete meteorological data set was as follows. 10

 Staff first checked on the availability of hourly temperature data for the specified years at 11 each CO fixed-site monitor specified for each study area. 12

 For Los Angeles, temperature data were not available for the specified CO monitoring 13 site (Site ID 060371301). Consequently, we ev aluated two alternative sites: Site 1 14 (located at CO monitoring site 060374002) and site 2 (located at Daugherty Field), which 15 are approximately 12 km and 15 km, respectively, from site 060371301. These sites 16 were separated from each other by a distance of only 3.6 km. Temperature data for the 17 two years considered for the exposure an alysis (i.e., 1997 and 2006) were reported by 18 both sites. Because Site 1 had fewer missi ng values for 2006, it was selected as the 19 primary meteorological site to represent the Los Angeles area for that year. Temperature 20 data from Site 2 were used to fill the single missing value in the Site 1 data set for year 21 2006. However, the 1997 data contained 2,263 missing values for Site 1 and only 9 22 missing values for Site 2. Consequentl y, Site 2 was selected as the primary 23 meteorological site to repres ent the Los Angeles area for 199 7. Two of the nine missing 24 values from Site 2 were available from S ite 1; staff replaced these two missing values 25 with corresponding values from Site 1. A linear interpolation, using the values at the end 26October, 2009 Draft – Do Not Cite or Quote 6-13 of existing gaps, was used to fill in the se ven remaining missing values in the 1997 data 1 set in Site 2. 3 2

 For Denver, temperature data were available for the CO ambient monitoring site used 3 (i.e., Site ID: 080310002) and both years consid ered in the exposure assessment (1995 4 and 2006). Linear interpolation was used to fill 18 of 41 mi ssing values considering the 5 1995 data and 11 of 11 missing values consider ing the 2006 data. In one instance, the 6 gap in hourly temperature data was 23 c ontinuous hours. Staff considered a linear 7 interpolation to be inappropriate in this situation because it would likely not produce 8 reasonable estimates of the variability in te mperature (particularly the daily maximum) 9 occurring during the 23-hour ga p. An alternative approach was used in which the 10 temperature data for corresponding hours in th e previous day were substituted for the 11 missing data. 12
6.1.5 Microenvironments 13 As mentioned earlier, two general microenvi ronments were defined for the exposure 14 analyses: in vehicle and all other . Each microenvironment was defined as an aggregation of the 15 location codes used in CHAD to specify where e ach exposure event occurred. Note that location 16 is interpreted here as referring to the microenvironmental charac teristics of a place (e.g., indoors 17 at school), rather than the pa rticular geographic location. Ap pendix E provides the mapping of 18 the CHAD location codes to the two AP EX modeled microenvironments. 19 The factors approach was used to esti mate a CO concentration in the two 20 microenvironments for each hour of the specified study period (Equation 5-1, section 5.3.4). The 21 penetration factor for all microenvironments was se t equal to 1 for both scen arios (see draft ISA, 22 section 3.6.5.1 for all indoor microenvironm ents and section 5.4.2.1 for the in-vehicle 23 microenvironment). The proximity factor wa s set equal to 1 for scenario A in both 24 microenvironments modeled (i.e., in vehicle and all other) and equal to 2 for the in-vehicle 25 microenvironment and 1 for the all other microenvir onment in scenario B. The values used in 26 representing in-vehicle concentrations for s cenario B were based on staff’s evaluation of 27 measurement studies that simultaneously measured CO concentrations within motor vehicles and 28 at nearby fixed-site monitors (see section 5.4.2.2). 29 Staff did not adjust ambient concentrations to estimate near-road microenvironmental CO 30 concentrations. This was because the ambient CO concentrations from the two monitors used in 31 this assessment had the highest hou rly CO concentrations recorded in each study area, and based 32 on the AQS noted monitoring scale, were alr eady designated to captu re near road CO 33 concentrations (i.e., microscale and middle scale) . While higher near-road CO concentrations 34 are possible, staff judged that these ambient data would al ready represent upper percentile 35

3 We used PROC EXPAND along with the JOIN option in SAS. The JOIN option fits a continuous curve to the data by connecting successive straight line segments.October, 2009 Draft – Do Not Cite or Quote 6-14 ambient CO concentrations experienced by mo st persons residing or spending time near 1 roadways in each study area. 2

6.1.6 Time/Activity Patterns 3 APEX constructs a 365-day longitudinal diary for each simulated individual by selecting 4 24-hour diaries from those available in CHAD. In performing the exposure assessments 5 described in this report, all available diaries for persons a bove age 17 in the CHAD database 6 were used regardless of particular commuting patterns. 7

6.1.6.1 Construction of Longitudinal Diaries 8 As discussed in section 5.3.3, APEX provides a longitudinal diary assembly algorithm 9 that enables the user to create co mposite diaries that reflect the te ndency of individuals to repeat 10 day-to-day activities. The user specifies values for two statistical variables ( D and A ) that relate 11 to a key daily variable, typically the time spent per day in a part icular microenvironment (e.g., in 12 a motor vehicle). The D statistic reflects the relative importance of intra- and inter-personal 13 variance within the selected key daily variable. The A variable quantifies the day-to-day 14 autocorrelation in the selected key daily variable . APEX then constructs composite diaries that 15 exhibit the statistical properties defined by the specified values of D and A

. 16 In this exposure assessment, we used the longitudinal diary algorithm to construct year17 long activity patterns for each simulated individual to reflect the day-to-day correlation of time 18 spent inside motor vehicles. Each diary day in the CHAD database was tagged with the number 19 of minutes spent in the vehicle microe nvironment. Parameter settings of D = 0.31 and A = 0.19 20 were specified to control the day-to-day repe tition of time spent in motor vehicles in the 21 constructed composite diar ies. These particular D and A values were obtained from Isaacs et al. 22 (2009) (see Appendix F). 23 In selecting particular diarie s to represent the simulated population, the CHAD data are 24 categorized or separated by APEX into data pools. In Scenario A and B, the pools were defined 25 by three ranges for the maximum te mperature of the diary day (< 55.0 ◦ F, between 55.0 and 83.9 26

◦ F, and ≥ 84.0 ◦ F) and two day-types (i.e., weekend and week day); thus, there were 3 x 2 = 6 27 diary pools. The window for age was set at 15%. For example, diaries can be selected for a 28 simulated individual of age 60 from CHAD individuals rangi ng from ages 51 though 69. 29

6.2 EXPOSURE AND DOSE ESTIMATES AND RISK CHARACTERIZATION 30
6.2.1 Denver – Scenarios A and B 31 Output files for APEX4.3 runs were generated for various co mbinations of calendar year 32 (1995 and 2006), exposure s cenario (A or B), and air quality condition ( as is or just meeting the 33October, 2009 Draft – Do Not Cite or Quote 6-15 8-hour NAAQS) in the Denver study area. These results are summarized in a series of tables that 1 follow (Tables 6-8 through Table 6-14). 2 Table 6-8 presents estimates for the number of person-days during the calendar year in 3 which members of the Denver population-at-ris k experienced a 1-hour daily maximum CO 4 exposure at or above each of the indicated CO concentrations . Results are presented for 5 Scenarios A and B for each of two air quality conditions: as is conditions represented by 2006 6 monitoring data and just meeting conditions as represented by 1995 monitoring data adjusted to 7 simulate just meeting the 8-hour NAAQS. The maximum possible value for person-days of 8 exposure is about 23.4 million person-days – th e product of the estimated population-at-risk 9 (about 64,000) and the number of days in the specified exposure period (365). 10 Using a format similar to Table 6-8, Table 6-9 presents estimates of the number of 11 persons in the population-at ri sk that experienced at leas t one 1-hour dail y maximum CO 12 exposure at or above each of th e indicated CO concentrations. In this table, the maximum 13 possible value is about 64,000 people – the estimat ed number of people in the population-at-risk. 14 Thus, each person can be counted no more than once in determining the value in Table 6-9. 15 Table 6-10 and Table 6-11 are comparable to Table 6-8 and Table 6-9, respectively 16 though they provide estimates for 8-hour daily maximum exposures rather than 1-hour daily 17 maximum exposures. Again, the maximum possible value for person-days of exposure in Table 18 6-10 is about 23.4 million person-days; the maximum possible value of persons exposed in Table 19 6-11 is about 64,000 people. 20 Table 6-12 and 6-13 are also analogous to th e prior tables, though they present estimates 21 of the number of person-days in which member s of the Denver population- at-risk experienced a 22 daily maximum end-of-hour COHb level at or ab ove each of the indicated levels. Again, the 23 maximum possible value for person-days is about 23.4 million for Table 6-12 and the maximum 24 number of persons experiencing a maximum COHb level in the y ear in the population-at-risk is 25 about 64,000 for Table 6-13. 26 Table 6-14 provides estimates for the mean number of days per person in which the 27 person experienced a daily maximum end-of-hour C OHb level at or above each of the indicated 28 levels. These values were calcu lated by dividing the values listed in Table 6-12 (tot al number of 29 person-days) by the comparable values in Tabl e 6-13 (number of people); hence the maximum 30 possible value is 365. 31 Table 6-9 through Table 6-15 exhi bit general patterns that are consistent with the input 32 data and parameter settings specified for the asso ciated model runs. The Scenario B values are 33 greater than the comparable Scenario A values because Scenario B specifically accounts for in34 vehicle microenvironmental CO concentrations , while Scenario A assumes in-vehicle CO 35 concentrations (and all other microenvironments) are equal to the ambient fixed-site monitor 36October, 2009 Draft – Do Not Cite or Quote 6-16 concentrations. However, the effect of better accounting for in-vehicle exposures (Scenario B) 1 to the overall estimated population exposures a nd doses is primarily limited to differences 2 observed in the upper percentiles of the distribution. For exam ple, about 4.8% of simulated 3 individuals are estimated to experience an end- of-hour COHb concentration at or above 1.0% 4 when considering Scenario A and the as is air quality (Table 6-13). When considering Scenario 5 B and as is air quality, about 7.0% of the population expe riences an end-of-hour COHb level at 6 or above 1.0%. Note however that when consider ing either scenario A or B, these data also 7 indicate that between 93 a nd 95% of the population experienced an end-of-hour COHb 8 concentration below 1.0%, suggesting that only an additional 2% of the population was affected 9 by the addition of the in-vehicle microenvironmen t. The effect of acc ounting for in-vehicle 10 concentrations (i.e, Scenario A results compared with Scenario B) was greater when considering 11 the air quality adjusted to just meeting the curr ent standard. For example, approximately 11% of 12 simulated individuals were estimated to experi ence at least one end-of-hour COHb above 2.5% 13 when considering Scenario B compared with ove r an order of magnitude fewer persons when 14 considering Scenario A. 15 The estimated number of person-days and person s considering air quality just meeting the 16 current standard is greater th an that estimated considering as is air quality at comparable target 17 concentrations. For example, the entire simulated population was estimated to experience at 18 least one end-of-hour COHb concentration at or above 1.5% when consideri ng the air quality just 19 meeting the current standard and Scenario A. This same COHb level was only experienced by 20 approximately 0.2% of the population when considering the as is air quality and Scenario A 21 (Table 6-13). This of course is because th e monitoring data used to represent ambient as is air 22 quality have significantly lower CO levels than the data used to represent just meeting the 8-hour 23 standard conditions in Denver (i.e., the adjusted 1995 air quality simulation). 24 As described in sections 2.6 and 4.2, our charact erization of health risk for CO in this 25 assessment focuses on several risk metrics involv ing comparison of estimated COHb levels in 26 the adult CHD population to poten tial health benchmarks (1.5-3.0% COHb). Assessment results 27 involving this comparison for the Denver study area are emphasized in bold type in Tables 6-12 28 through 6-14. Well below 1 percent of the at -risk population was estimated to reach COHb 29 levels at or above 1.5% under as is conditions in both scenarios. Under air quality conditions 30 just meeting the current standard, substantia lly greater percentages of the population were 31 estimated to reach COHb levels at or above al l of the potential health benchmark levels, with 32 100% of the at-risk population estimated to reach COHb levels ≥ 1.5% in Scenario B for these 33 conditions (Table 6-13). 34October, 2009 Draft – Do Not Cite or Quote 6-17 Table 6-8. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 1 Denver Study Area Estimated to Exp erience a 1-hour Daily Maximum CO 2 Exposure at or Above the Specified Concentration. 3 Number of person-days “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1995) b CO Concentration (ppm) Scenario A Scenario B Scenario A Scenario B

80 0 0 0 0 60 0 0 0 0 40 0 0 0 2.8E+03 30 0 0 0 1.5E+04 20 0 0 6.4E+04 1.0E+05 15 0 0 1.3E+05 2.4E+05 12 0 2.0E+03 3.2E+05 5.9E+05 9 0 1.1E+04 8.3E+05 1.1E+06 6 6.4E+04 1.4E+05 2.3E+06 3.2E+06 3 9.6E+05 1.4E+06 1.0E+07 1.3E+07 0 2.3E+07 2.3E+07 2.3E+07 2.3E+07

a “As Is” air quality dat a are for the year 2006. b Air quality data for the year 1995 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Denv er study area is estimated to be about 64,000. Table 6-9. Number of Adults with Coronary Heart Disease (CHD) in the Denver Study 4 Area Estimated to Experience a 1-hour Daily Maximum CO Exposure at or 5 Above the Specified Concentration. 6 Number of persons “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1995) b CO Concentration (ppm) Scenario A Scenario B Scenario A Scenario B

80 0 0 0 0 60 0 0 0 0 40 0 0 0 2,800 30 0 0 0 14,000 20 0 0 64,000 64,000 15 0 0 64,000 64,000 12 0 2,000 64,000 64,000 9 0 9,700 64,000 64,000 6 64,000 64,000 64,000 64,000 3 64,000 64,000 64,000 64,000 0 64,000 64,000 64,000 64,000

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1995 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Denv er study area is estimated to be about 64,000.October, 2009 Draft – Do Not Cite or Quote 6-18 Table 6-10. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 1 Denver Study Area Estimated to Experience an 8-hour Daily Maximum CO 2 Exposure at or Above the Specified Concentration. 3 Number of person-days “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1995) b CO Concentration (ppm) Scenario A Scenario B Scenario A Scenario B

80 0 0 0 0 60 0 0 0 0 40 0 0 0 0 30 0 0 0 0 20 0 0 0 3.4E+02 15 0 0 0 2.4E+03 12 0 0 0 2.0E+04 9 0 0 1.3E+05 1.5E+05 6 0 7.4E+01 4.5E+05 6.1E+05 3 1.3E+05 2.1E+05 4.1E+06 5.0E+06 0 2.3E+07 2.3E+07 2.3E+07 2.3E+07

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1995 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Denv er study area is estimated to be about 64,000. Table 6-11. Number of Adults with Coronary Heart Disease (CHD) in the Denver Study 4 Area Estimated to Experience an 8-ho ur Daily Maximum CO Exposure at or 5 Above the Specified Concentration. 6 Number of persons “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1995) b CO Concentration (ppm) Scenario A Scenario B Scenario A Scenario B

80 0 0 0 0 60 0 0 0 0 40 0 0 0 0 30 0 0 0 0 20 0 0 0 340 15 0 0 0 2,400 12 0 0 0 17,000 9 0 0 64,000 64,000 6 0 74 64,000 64,000 3 64,000 64,000 64,000 64,000 0 64,000 64,000 64,000 64,000

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1995 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Denv er study area is estimated to be about 64,000.October, 2009 Draft – Do Not Cite or Quote 6-19 Table 6-12. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 1 Denver Study Area Estimated to Exp erience a Daily Maximum End-of-hour 2 COHb Level at or Above the Specified Concentration. 3 Number of person-days “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1995) b COHb concentration (percent)

Scenario A Scenario B Scenario A Scenario B 8.0 0 0 0 0 6.0 0 0 0 0 5.0 0 0 0 0 4.0 0 0 0 2.5E+01 3.0 0 0 4.9E+01 1.8E+03 2.5 1.9E+03 1.9E+03 3.3E+03 1.1E+04 2.0 3.9E+03 3.9E+03 2.6E+04 5.8E+04 1.5 9.2E+03 9.3E+03 1.8E+05 2.4E+05 1.0 2.0E+05 2.1E+05 1.4E+06 1.7E+06 0 2.3E+07 2.3E+07 2.3E+07 2.3E+07

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1995 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Denv er study area is estimated to be about 64,000. Table 6-13. Number (and Percent) of Adults wi th Coronary Heart Di sease (CHD) in the 4 Denver Study Area Estimated to Exp erience a Daily Maximum End-of-hour 5 COHb Level at or Above the Specified Concentration. 6 Number of persons (percent c ) “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1995) b COHb concentration (percent) Scenario A Scenario B Scenario A Scenario B 8.0 0 0 0 0 6.0 0 0 0 0 5.0 0 0 0 0 4.0 0 0 0 25 (<0.1) 3.0 0 0 12 (<0.1) 1,700 (3) 2.5 12 (<0.1) 12 (<0.1) 250 (0.4) 7,100 (11) 2.0 12 (<0.1) 12 (<0.1) 16,000 (26) 36,000 (56) 1.5 160 (0.2) 160 (0.2) 64,000 (100) 64,000 (100) 1.0 3,100 (5) 4,500 (7) 64,000 (100) 64,000 (100) 0 64,000 (100) 64,000 (100) 64,000 (100) 64,000 (100)

a “As Is” air quality data are for the year 2006. b Air quality data for the year 1995 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS.

c Percent of adult CHD population. Note: Total adult population with CHD in the Denv er study area is estimated to be about 64,000.October, 2009 Draft – Do Not Cite or Quote 6-20 Table 6-14. Estimated Average Number of Days with a Daily Maximum End-of-hour 1 COHb Level At or Above the Specified Concentration Per Adult With 2 Coronary Heart Disease (CHD) in the Denver Study Area. 3 Average of person-days/person “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1995) b COHb concentration (percent) Scenario A Scenario B Scenario A Scenario B 8.0 0 0 0 0 6.0 0 0 0 0 5.0 0 0 0 0 4.0 0 0 0 0 3.0 0 0 <0.1 <0.1 2.5 <0.1 <0.1 <0.1 0.2 2.0 <0.1 <0.1 0.4 0.9 1.5 0.1 0.1 2.9 3.8 1.0 3.1 3.2 22 27 0 365 365 365 365

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1995 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Denv er study area is estimated to be about 64,000.
4October, 2009 Draft – Do Not Cite or Quote 6-21 6.2.2 Los Angeles – Scenarios A and B 1 Output files for APEX4.3 runs were generated for various co mbinations of calendar year 2 (1997 and 2006), exposure s cenario (A or B), and air quality condition ( as is or just meeting the 3 8-hour NAAQS) in the Los Angeles study area. Thes e results are summarized in a series of 4 tables that follow (Tables 6-15 through Table 6-21). 5 The same general patterns identified above using the Denver results were observed in the 6 Los Angles results when considering either m odeling scenario and air quality condition. For 7 example in Scenario A, approximately 3% of the simulated population ex perienced an end-of8 hour COHb level at or a bove 1.5% considering the as is air quality compared with about 67% of 9 the population having the same or greater COHb le vel when using air quality adjusted to just 10 meeting the current standard (Table 6-20). This is as expected gi ven the fixed-site input data for 11 2006 used to represent ambient as is conditions in Los Angeles ha ving lower CO concentrations 12 than the data used to represent just meeting the current standard (Table 6- 5 and 6-6). Similarly, a 13 greater number of persons and person-days are es timated when considering scenario B compared 14 with scenario A. For example, when considering the as is air quality, approximately 3% of the 15 simulated population experienced an end-of-hour COHb level at or above 1.5% considering 16 Scenario A compared with abou t 8% of the simulated population when separately accounting for 17 in-vehicle exposures (Scenario B). This is also as expected because, as in the case of Denver, 18 Scenario B assumes CO concentrations in the Los Angeles in-vehicle microenvironment were 19 twice the ambient concentrations, while Scenario A assumes the in-vehicle concentrations (and 20 all other microenvironments) are equal to the ambi ent concentrations measured at the fixed-site 21 monitor. 22 As described in sections 2.6 and 4.2, our charact erization of health risk for CO in this 23 assessment focuses on several risk metrics involvi ng comparison of COHb levels in adults with 24 CHD to potential health benchmarks (1.5-3.0% COHb). Assessment results involving this 25 comparison for the Los Angeles study area are empha sized in bold type in Tables 6-19 – 6-21. 26 Fewer than 1 percent of the at-r isk population was estimated to reach COHB levels at or above 27 2.0% under as is conditions when considering both scenario s, with 3% and 8% at or above 1.5% 28 COHb in Scenarios A and B, respectively. Under air quality conditions ju st meeting the current 29 standards, substantially greater percentages of the population we re estimated to reach COHb 30 levels at or above all of the potential health benchmark levels, with 67% and 80% of the at-risk 31 population estimated reach COHb levels ≥ 1.5% in Scenarios A and B, respectively, for these 32 conditions (Table 6-20). 33 34October, 2009 Draft – Do Not Cite or Quote 6-22 Table 6-15. Number of Person-Days for Adults with Coronary Heart Disease (CHD) in the 1 Los Angeles Study Area Estimated to Experience a 1-hour Daily Maximum 2 CO Exposure At or Above the Specified Concentration. 3 Number of person-days “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1997) b CO Concentration (ppm) Scenario A Scenario B Scenario A Scenario B

80 0 0 0 0 60 0 0 0 0 40 0 0 0 0 30 0 0 0 0 20 0 0 0 1.3E+04 15 0 9.2E+03 0 1.4E+05 12 0 6.3E+04 1.6E+05 6.1E+05 9 0 3.3E+05 2.4E+06 3.2E+06 6 1.9E+06 2.8E+06 7.9E+06 9.2E+06 3 1.5E+07 1.7E+07 2.4E+07 2.5E+07 0 5.7E+-7 5.7E+07 5.7E+07 5.7E+07

a “As Is” air quality dat a are for the year 2006. b Air quality data for the year 1997 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Los An geles study area is estimated to be about 160,000. Table 6-16. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles 4 Study Area Estimated to Experience a 1-hour Daily Maximum CO Exposure 5 At or Above the Specified Concentration. 6 Number of persons “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1997) b CO Concentration (ppm) Scenario A Scenario B Scenario A Scenario B

80 0 0 0 0 60 0 0 0 0 40 0 0 0 0 30 0 0 0 0 20 0 0 0 12,000 15 0 8,800 0 75,000 12 0 44,000 160,000 160,000 9 0 110,000 160,000 160,000 6 160,000 160,000 160,000 160,000 3 160,000 160,000 160,000 160,000 0 160,000 160,000 160,000 160,000

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1997 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Los Angeles study area is estimated to be 160,000.October, 2009 Draft – Do Not Cite or Quote 6-23 Table 6-17. Number Of Person-Days For Adults with Coronary Heart Disease (CHD) in 1 the Los Angeles Study Area Estimated to Experience an 8-hour Daily 2 Maximum CO Exposure At or Above the Specified Concentration. 3 Number of person-days “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1997) b CO Concentration (ppm) Scenario A Scenario B Scenario A Scenario B

80 0 0 0 0 60 0 0 0 0 40 0 0 0 0 30 0 0 0 0 20 0 0 0 0 15 0 0 0 6.8E+02 12 0 3.2E+01 0 8.5E+03 9 0 2.9E+03 3.1E+05 3.7E+05 6 1.6E+05 2.3E+05 2.2E+06 2.7E+06 3 6.3E+06 7.0E+06 1.6E+07 1.6E+07 0 5.7E+07 5.7E+07 5.7E+07 5.7E+07

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1997 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Los An geles study area is estimated to be about 160,000. 4October, 2009 Draft – Do Not Cite or Quote 6-24 1 Table 6-18. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles 2 Estimated to Experience an 8-hour Daily Maximum CO Exposure At or 3 Above the Specified Concentration. 4 Number of persons “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1997) b CO Concentration (ppm) Scenario A Scenario B Scenario A Scenario B

80 0 0 0 0 60 0 0 0 0 40 0 0 0 0 30 0 0 0 0 20 0 0 0 0 15 0 0 0 610 12 0 32 0 6,900 9 0 2,300 160,000 160,000 6 160,000 160,000 160,000 160,000 3 160,000 160,000 160,000 160,000 0 160,000 160,000 160,000 160,000

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1997 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Los An geles study area is estimated to be about 160,000. Table 6-19. Number of Person-Days For Adults With Coronary Heart Disease (CHD) in 5 the Los Angeles Study Area Estimated to Experience a Daily Maximum End6 of-hour COHb Level At or Above the Specified Concentration. 7 Number of person-days “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1997) b COHb concentration (percent) Scenario A Scenario B Scenario A Scenario B 8.0 0 0 0 0 6.0 0 0 0 0 5.0 0 0 0 0 4.0 0 0 0 0 3.0 0 0 0 0 2.5 0 0 6.5E+01 6.8E+02 2.0 1.6E+02 3.2E+02 6.1E+03 1.6E+04 1.5 2.8E+04 4.1E+04 3.2E+05 4.8E+05 1.0 1.6E+06 1.8E+06 5.1E+06 5.9E+0 0 5.7E+07 5.7E+07 5.7E+07 5.7E+07

a “As Is” air quality data is for the year 2006. b Air quality data for the year 1997 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS. Note: Total adult population with CHD in the Los An geles study area is estimated to be about 160,000.October, 2009 Draft – Do Not Cite or Quote 6-25 Table 6-20. Number (and Percent) of Adults wi th Coronary Heart Di sease (CHD) in the 1 Los Angeles Study Area Estimated to Experience a Daily Maximum End-of2 hour COHb Level At or Above the Specified Concentration. 3 Number of persons (percent) c “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1997) b COHb concentration (percent) Scenario A Scenario B Scenario A Scenario B 8.0 0 0 0 0 6.0 0 0 0 0 5.0 0 0 0 0 4.0 0 0 0 0 3.0 0 0 0 0 2.5 0 0 65 (<0.1) 680 (0.4) 2.0 130 (<0.1) 290 (0.2) 3,900 (2) 10,000 (7) 1.5 4,600 (3) 13,000 (8) 110,000 (67) 127,000 (80) 1.0 147,000 (93) 150,000 (97) 160,000 (100) 160,000 (100) 0 160,000 (100) 160,000 (100) 160,000 (100) 160,000 (100) a “ As Is” air quality data are for the year 2006. b Air quality data for the year 1997 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS.
c Percent of adult CHD population. Note: Total adult population with CHD in the Los An geles study area is estimated to be about 160,000. Table 6-21 Estimated Average Number of Days with a Daily Maximum End-of-hour 4 COHb Level At or Above the Specified Concentration Per Adult with 5 Coronary Heart Disease (CHD) in the Los Angeles Study Area. 6 Average of person-days/person “As Is” Air Quality (2006) a “Just Meeting” Air Quality (1997) b COHb concentration (percent) Scenario A Scenario B Scenario A Scenario B 8.0 0 0 0 0 6.0 0 0 0 0 5.0 0 0 0 0 4.0 0 0 0 0 3.0 0 0 0 0 2.5 0 0 0 0 2.0 <0.1 <0.1 <0.1 0.1 1.5 0.2 0.3 2.1 3.0 1.0 9.9 12 32 37 0 365 365 365 365

a “As Is” air quality dat a are for the year 2006. b Air quality data for the year 1997 adjusted downw ards to just meet 9 ppm, 8-hour NAAQS.October, 2009 6-26 Draft – Do Not Cite or Quote 6.2.3 Comparison of Denver and Los Angele s Estimates for End-of-hour COHb 1 Levels 2 We can best compare Denver and Los Angele s estimates using the results provided in 3 Tables 6-13 and 6-20. These tables provide es timates for the percentage of people in the 4 population-at-risk that are estimated to experien ce a daily maximum end-of-hour COHb level at 5 or above the specified value. For either scenar io, the percentage of people with COHb levels at 6 or above 1.5 percent is greater for Los A ngeles than for Denver when considering the as is air 7 quality. For example, in Los Angeles it was estimated that 3 and 8 percent of persons 8 experienced an end-of hour COHb level at or above this level for Scenarios A and B, 9 respectively, for the as is case. In Denver, virtually no pe rsons (0.2%) experienced an end-of10 hour COHb at or above 1.5% associated with the as is case. This pattern is consistent with the 11 fact that the monitoring data used to represent as is conditions in Los Angeles exhibited higher 12 CO levels than Denver (Table 6-5). 13 The pattern noted above is reversed when considering air quality that just meets the 14 current standard. For example, the estimated percent of persons having a COHb level at or 15 above 2 percent for Scenarios A and B are 26 pe rcent and 56 percent, respectively for Denver 16 (Table 6-13); the comparable estimates for Los Angeles are about 2 percent for Scenario A and 7 17 percent for Scenario B (Table 6-20). This pattern is also expecte d, since the air quality data used 18 to represent just meeting the current standard in Denver has higher CO levels at the upper 19 percentiles of the distri bution than the Los Angeles data (Table 6-6). 20

6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000 21 PNEM/CO AND DRAFT 2009 APEX/CO ASSESSMENTS 22 As part of the review of the CO N AAQS initiated in 1997, a draft CO exposure 23 assessment was prepared (Johnson, et al., 1999). S ubsequent to the discontinuation of that CO 24 NAAQS review, a revised document was comple ted (Johnson et al., 2000). The 2000 document 25 was subsequently subject to peer review by several exposure modeling experts (SAIC, 2001). 26 The 2000 CO population exposure assessment was c onducted for Denver using air quality data 27 for 1995 and for Los Angeles using air quality da ta for 1997. The exposure and dose estimates 28 were obtained by applying pNEM/CO, a predecessor to APEX, to adults with ischemic heart 29 disease residing in a defined study area within ea ch city (Johnson et al., 2000). As part of 30 current (2009) draft exposure assessment descri bed in section 6.1, staff has again used APEX to 31 estimate CO exposures and resulting COHb leve ls in a portion of Denver using 1995 air quality 32 data and in a portion of Los A ngeles using 1997 air quality data. In this case, the population-at33 risk was defined as adults with CHD which is approximately equivalent to the ischemic heart 34 disease definition used in the prior review. 35October, 2009 6-27 Draft – Do Not Cite or Quote In comparing the earlier pNEM/CO results wi th the exposure and dose estimates obtained 1 from the current draft APEX/CO assessment for the same cities and years, it is important to 2 understand the differences between the methodologi es employed in the two assessments. The 3 methods and results associated with the 2000 pN EM/CO analysis are desc ribed in detail in a 4 report by Johnson et al. (2000). The methods used in the curr ent (2009) draft APEX exposure 5 assessment are described above in section 6.1. Section 6.3.1 provides a brief discussion of the 6 important differences between the two assessmen ts that may account for some of the observed 7 differences in the exposure estimates. Section 6.3 .2 presents estimates of COHb levels in adults 8 with CHD obtained from the two assessments. 9

6.3.1 Important Differences Between th e 2000 pNEM/CO and 2009 draft APEX/CO 10 Exposure/Dose Assessments 11 In the 2000 pNEM/CO assessment, the Denve r study area was defined to include the 12 census tracts located within 10 km of each of si x fixed-site monitors in the Denver metropolitan 13 area. Air quality data for 1995 reported by these fixed-site monitors were used to represent 14 “existing conditions” in the st udy area. Because the second non-overlapping 8-hour maximum 15 CO concentration (design value) equaled 9.5 ppm, the existing conditions in Denver for 1995 16 were considered to approximate just meeting the 8-hour standard in which the DV equals 9.4 17 ppm. 18 In a similar manner, the Los Angeles study ar ea was defined to in clude all census tracts 19 within 10 km of ten fixed-site monitors in th e Los Angeles metropolitan area. Air quality data 20 for 1997 reported by these fixed-site monitors were used to represent “exis ting conditions” in the 21 study area. Because the 1997 CO levels in Los Angeles exceeded the 8-hour NAAQS, the 22 concentrations at each monitoring site were ad justed downwards so that the concentrations 23 associated with the DV site exactly met th e 8-hour NAAQS (i.e., the adjusted maximum CO 24 concentration at the DV s ite equaled 9.4 ppm). 25 Note that the air quality data used in th e pNEM/CO assessments for each city included 26 data from multiple sites (6 in Denver, 10 in Los Angeles) that represented areas of varying CO 27 levels. The monitoring data asso ciated with the DV (highest CO) site were only applied to those 28 people who resided in the circular area with in 10 km of that particular monitor. 4 Data from other 29 (lower CO) sites were applied to the people in the study area w ho resided within the 10 km 30 circular areas centered on those sites. 31 In the 2009 draft APEX/CO assessment, the Denver and Los Angeles study areas are 32 each defined to include all census tracts within 20 km of a single fixed-site monitor. This 33

4 In Denver, there was one instance where two m onitors in close proximity to each other were geographically combined to represent a single composite monitoring location.October, 2009 6-28 Draft – Do Not Cite or Quote monitor is the DV site for the specified year of the assessment (1995 for Denver, 1997 for Los 1 Angeles). Consequently, monitoring data associat ed with the DV (highest CO) site are applied 2 to all people within the surrounding study area. This focus on high concentration CO monitors 3 in the current assessment would tend to produce a greater percentage of persons exposed to 4 higher CO levels than would the approach used in the 2000 pNEM/CO assessment. 5 In the 2000 exposure assessment, pNEM/CO was set up to account for 15 distinct 6 microenvironments. Each of the 12 enclosed microenvironments (including 3 motor vehicle 7 microenvironments) was modeled using a sophisti cated mass balance model. This model used 8 probabilistic techniques to acc ount for outdoor (ambient) air qua lity, air exchange rate, and 9 indoor emissions. When applied to building micr oenvironments characterized by relatively low 10 air exchange rates, the ma ss balance model in pNEM/CO yields hourly-average CO 11 concentrations in the building that tend to have less varian ce than the corresponding hourly12 average ambient concentrations outside the building. Relative to the ambient concentrations 13 outside the building, the indoor conc entrations have lower peak values and the peaks are delayed 14 in time. This effect is not significant for in-v ehicle microenvironments that are characterized by 15 relatively high air exchange rates. In addition, two indoor sources of CO were evaluated in the 16 2000 pNEM/CO assessment for residential microenvi ronments: gas stoves and passive smoking. 17 The model was set up so that these sources could be turned on and off within the model. The 18 estimated number of people with COHb levels above 2.5 percent was noticeably higher when 19 pNEM/CO accounted for the specified indoor sources. 20 The 2009 draft APEX/CO assessment specifie s only two microenvironments (i.e., in 21 vehicle and all other). The CO concentrations in these microenvironments are modeled using a 22 simple proportionality factor (the proximity factor) applied to the corresponding ambient air 23 quality concentrations based on the fixed-s ite monitor values. For the in-vehicle 24 microenvironment, the proximity factor equals 1 for Scenario A and 2 for Scenario B. The 25 proximity factor for all other microenvironments equals 1 for both scenarios. And finally, no 26 provision has been made to account for the effects of indoor sources in this draft of the current 27 CO assessment given the simplif ied approach used. 28

6.3.2 Comparison of Estimated COHb Levels in Adults with Coronary Heart 29 Disease using the 2000 pNEM/CO a nd 2009 Draft APEX/CO Assessments 30 Table 6-22 presents estimates for the percen tage of Denver adults with CHD who would 31 experience a daily maximum end-of-hour COHb le vel at or above the specified level under the 32 specified air quality conditions for 1995. Table 6- 23 presents similar estimates for Los Angeles. 33 Each table provides two sets of estimates for the 2000 pNEM/CO asse ssment (indoor sources 34 “on” and “off”) and two sets for the current (2009) draft APEX/CO assessment (Scenarios A and 35October, 2009 6-29 Draft – Do Not Cite or Quote B). See section 6.3.1 above for a brief discu ssion of the modeling assumptions used in 1 developing each set of estimates. 2 As expected, the COHb levels estimated by the 2000 pNEM/CO a ssessment are higher 3 when internal sources are turned on. As stated above, it was also expected that estimated COHb 4 levels would be higher for Scenario B than fo r Scenario A in the current assessment, since 5 Scenario B uses a larger proximity factor for the in-vehicle microenvironment. 6 Because of the significant differences in modeling approaches employed by the two 7 assessments, it is difficult to make a direct comparison of the results for pNEM/CO 2000 with 8 the results for current APEX/CO draft assessm ent. As discussed in section 6.3.1, the two 9 assessments differ according to: 10

 Boundaries of the study area defined for each city, 11

 Monitors used to represent ambient CO levels, 12

 Defined microenvironments, 13

 Microenvironmental mode ling approach used, and 14

 Treatment of indoor sources. 15 With these caveats in mind, we observe that the estimated percentage of Denver adults 16 with CHD that experience end-of-hour COHb levels at or above 2 percen t is higher for the 2009 17 draft APEX/CO assessment (Scenario A or Scenar io B for the just meeting the current 8-hour 18 standard case) than for the 2000 pNEM/CO assessment ( as is case which was very close to just 19 meeting the 9 ppm CO NAAQS), regardless of whet her internal sources are turned on or off in 20 the pNEM assessment (Table 6-22). The corres ponding results for Los Angeles (Table 6-23) for 21 the just meeting the standard case show values for the 2009 draft APEX/CO assessment 22 (Scenario A or Scenario B) that are lowe r than the 2000 pNEM/CO assessment for the just 23 meeting the standard case when internal sources ar e turned on and higher when the sources are 24 turned off. Note that these patterns are not c onsistent across all COHb le vels. For example, the 25 estimate listed for either study area for COHb le vels at or above 4 pe rcent is higher for 2000 26 pNEM/CO (sources on) than for the correspond ing values for 2009 draft APEX/CO assessment 27 (Scenarios A and B). 28 In the current assessment, assuming the am bient concentrations contributing to all 29 microenvironments are equal to the concentrations reported at the fixed-site monitor (for 30 Scenario A) indicates that ther e would not be any spatial heter ogeneity in CO concentrations 31 across the study area, that is, the single monitor used in each study area is assumed to represent 32 all outdoor CO concentrations. However, there are other ambi ent monitors within the 20 km 33 study area having lower CO concentr ations and these were used in the previous 2000 assessment. 34 Therefore, the assumption of spatial homogeneity would tend to contribute to the greater CO 35October, 2009 6-30 Draft – Do Not Cite or Quote population exposures estimated in the current assessment compared to those estimated in the 1 previous 2000 assessment, when holding all other factors constant. Staff also assumed in the 2 current assessment that the penetration of CO into all microenvironments was equivalent to one. 3 This assumption would lead to a lack of attenuation of peak out door ambient CO concentrations 4 that is expected to occur in indoor micr oenvironments when not accounting for physical 5 processes described above. Ther efore, greater population exposu res would be estimated for the 6 current assessment when compared with the ex posures estimated in the prior 2000 assessment, 7 holding all other factors constant. The impact of these simplifying assumptions is best illustrated 8 in Figure 6-3 using the data provided in Tables 6-22 and 6-23 for the situation where no indoor 9 sources were modeled. Clearly, in the current assessment nearly all of the simulated population 10 reaches a higher estimated daily maximum COHb level (at least once per year) for both areas 11 when compared with the previous 2000 assessmen t. Once accounting for this higher population 12 distribution at COHb levels up to about 1% COHb, the ge neral shape of the population 13 distribution is very similar for both the locations when compared with results in the previous 14 assessment, particularly when including the co ntribution of the in-veh icle microenvironment 15 separately (Scenario B) in estimating exposures. 16October, 2009 6-31 Draft – Do Not Cite or Quote Table 6-22. Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated 1 to Experience a Daily Maximum End-o f-hour COHb Level At or Above the 2 Specified Percentage Under Specified Air Quality Conditions for 1995. 3 Percentage of adults with coronary heart disease a estimated to experience a daily maximum end-of-hour COHb level at or above the specified percentage 2000 pNEM/CO assessment for “existing” conditions b 2009 draft APEX/CO assessment for “just meeting” conditions c Internal sources off COHb concentration (percent) Internal sources on Internal sources off Scenario A Scenario B 6.0 0.2 0 0 0 5.0 0.6 0 0 0 4.0 1.6 0 0 <0.1 3.0 5.5 < 0.1 <0.1 2.7 2.5 10.4 0.2 0.4 11.2 2.0 19.9 0.5 25.6 56.5 1.5 37.6 6.7 99.7 99.8 1.0 83.2 65.0 100 100 0 100 100 100 100

a Characterized as “ischemic heart disease” in the 2000 pNEM/CO exposure assessment. b “Existing” conditions: Denver CO conditions during 1995 with no adjustment. Second non-overlapping 8-hour maximum CO concentration (design value) eq uals 9.5 ppm. These conditions approximate “just meeting” conditions for Denver (i.e., design value equals 9.4 ppm).

c “Just meeting” conditions: 1995 CO levels in Denver adjusted to simulate conditions when the second non-overlapping 8-hour maximum CO concentration at the design value site equals 9.4 ppm. 4 5October, 2009 6-32 Draft – Do Not Cite or Quote Table 6-23. Percentage of Los Angeles Adul ts with Coronary Heart Disease (CHD) a 1 Estimated to Experience a Daily Maxi mum End-of-hour COHb Level At or 2 Above the Specified Percentage Under “Just Meeting”Conditions b for 1997. 3 Percentage of adults with coronary heart disease a estimated to experience a daily maximum end-of-hour COHb level at or above the specified percentage 2000 pNEM/CO assessment 2009 draft APEX/CO assessment Internal sources off COHb concentration (percent) Internal sources on Internal sources off Scenario A Scenario B 6.0 0.2 0 0 0 5.0 0.8 0 0 0 4.0 2.2 0 0 0 3.0 5.1 <0.1 0 0 2.5 9.0 <0.1 <0.1 0.4 2.0 16.8 0.5 2.4 6.6 1.5 32.3 5.2 67.0 80.5 1.0 79.0 58.1 100 100 0 100 100 100 100

a Characterized as “ischemic heart disease” in the 2000 pNEM/CO exposure assessment. b “Just meeting” conditions: 1997 CO levels in Los Angeles adjusted to simulate conditions when the second non-overlapping 8-hour maximum CO concentration at the design value site equals 9.4 ppm.

4October, 2009 6-33 Draft – Do Not Cite or Quote Denver - Just Meeting the Current Standard

0 10 20 30 40 50 60 70 80 90 100

01234567 COHb level (%)Population at or Above SelectedLevel (%) 2000 pNEM/CO assessment Scenario A - Current Assessment Scenario B - Current Assessment

1 Los Angeles - Just Meeting the Current Standard

0 10 20 30 40 50 60 70 80 90 100

01234567 COHb level (%)Population at or Above SelectedLevel (%) 2000 pNEM/CO assessment Scenario A - Current Assessment Scenario B - Current Assessment

2 Figure 6-3. Percentage of Los Angeles and Denver A dults with Coronary Heart Disease 3 (CHD) Estimated to Experience a Daily Maximum End-of-hour COHb Level 4 At or Above the Specified Percentage for Air Quality Adjusted to Just 5 Meeting the Current Standard. Data taken from Tables 6-22 and 6-23. 6

7October, 2009 6-34 Draft – Do Not Cite or Quote 6.4 VARIABILITY ANALYSIS AND UN CERTAINTY CHARACTERIZATION 1 An important issue associated with any popul ation exposure or risk assessment is the 2 characterization of variab ility and uncertainty. Variability refers to the inherent heterogeneity in 3 a population or variable of intere st (e.g., residential air exchange rates) and cannot be reduced 4 through further research, only better charac terized with additional measurement. Uncertainty 5 refers to the lack of knowledge regarding the values of model input variables (i.e., parameter 6 uncertainty ), the physical systems or relationships used (i.e., use of input variables to estimate 7 exposure or risk or model uncertainty ), and in specifying the scenario that is consistent with 8 purpose of the assessment (i.e., scenario uncertainty ). Uncertainty is, ideally, reduced to the 9 maximum extent possible through improved measurem ent of key parameters and iterative model 10 refinement. The approaches used to assess vari ability and to characterize uncertainty in this 11 REA are discussed in the following two sections. Each section also contains a concise summary 12 of the identified components contributing to uncertainty and how each source may affect the 13 estimated exposures. 14

6.4.1 Analysis of Variability 15 The purpose for addressing variability in this REA is to ensure that the estimates of 16 exposure and risk reflect the variability of am bient CO concentrations and associated CO 17 exposure and health risk across the study locations and population. In this draft assessment, 18 there are several algorithms that account for variability of input data when generating the number 19 of estimated benchmark exceedances or health risk outputs. For example, variability may arise 20 from differences in the populat ion residing within census tracts (e.g., age distribution) and the 21 activities that may affect CO population exposure and dose (e.g., time spent inside vehicles, 22 moderate or greater exertion out doors). A complete range of pot ential exposure levels and 23 associated risk estimates can be generated when appropriately addressing variability in exposure 24 and risk assessments; note however that the range of values ob tained would be within the 25 constraints of the input parameters, algorithms, or modeling system used, not the complete range 26 of the true exposure or risk values. 27 Where possible, staff identified and incorporat ed the observed variability in input data 28 sets and estimated parameters within the expos ure and dose assessment performed rather than 29 employing standard default assumptions and/or usi ng point estimates to describe model inputs. 30 The details regarding any variability distributions used in data i nputs are described in section 6.1. 31 To the extent possible given the data available for the assessment, staff accounted for variability 32 within the exposure and dose modeling. APEX ha s been designed to account for variability in 33 some of the input data, including the physiologi cal variables that are important inputs to 34 determining ventilation rates and COHb dose levels . As a result, APEX addresses much of the 35October, 2009 Draft – Do Not Cite or Quote 6-35 Table 6-24. Summary of How Variability Was Incorporated Into the Exposure and Dose Assessment. Component Variability Source Comment

Population data Individuals are randomly sampled from U.S. census tracts used in model domains, by age (single years) and gender.

Activity patterns Data diaries are randomly selected from CHAD using six diary pools stratified by two day-types (weekday, weekend) and three temperature ranges (< 55.0 ◦ F, between 55.0 and 83.9 ◦ F, and ≥ 84.0 ◦ F). Simulated Individuals

Coronary heart disease (CHD) prevalence CHD prevalence is stratified by four age groups (18-44, 45-64, 65-74, and 75+) and both genders.
Measured ambient CO concentrations Temporal: 1-hour CO for an entire year predicted using ambient monitoring data. Ambient Input Meteorological data Spatial: Local surface NWS stations used. Temporal: 1-hour NWS temperature data for each year.

Resting metabolic rate Three age-group (18-29, 30-59, and 60+) by gender specific regression equations were used with body mass as the independent variable (Johnson et al., 2000). Metabolic equivalents by activity (METS) Values randomly sampled from distributions developed for specific activities (some age-specific) (US EPA, 2002). Oxygen uptake per unit of energy expended Values randomly sampled from a uniform distribution (Johnson et al., 2000).

Weight (body mass) Randomly selected from population-weighted lognormal distribution with geometric mean (GM) and geometric standard deviation (GSD) distribution specific to age and gender derived from data from the National Health and Nutrition Examination Su rvey (NHANES), for the years 1999-2004.

Height Values randomly sampled from distribution based on equations developed for each gender developed from analyses (Johnson, 1998) of height and weight data (Brainard and Burmaster, 1992) (see Appendix C for details)

Blood volume Values determined according to gender using equations based on work by Allen et al (1956) (see Appendix C for details). Hemoglobin content of the blood Values randomly selected from distributions developed by gender and age categories based on NHANES study (US DHHS, 1982) (see Appendix C for details). Pulmonary CO diffusion rate Values selected according to gender, height, and age based on equations adapted from Salorinne (1976) (see Appendix C for details). Physiological Factors Relevant to Ventilation Rate and Estimation of COHb Levels

Endogenous CO production rate Values randomly selected from lognormal distributions according to equations specific to age, gender, and menstrual phase (see Appendix C for details).October, 2009 Draft – Do Not Cite or Quote 6-36 variability in exposure and dose estimates given va riability in factors that affect human exposure 1 and dose. The variability accounted for in this analysis is summarized in Table 6-24. 2

6.4.2 Characterization of Uncertainty 3 While it may be possible to capture a range of exposure or risk values by accounting for 4 variability inherent to influential factors, the true exposure or risk fo r any given individual is 5 largely unknown. To characterize health risks, exposure and ri sk assessors commonly use an 6 iterative process of gathering da ta, developing models, and estima ting exposures and risks, given 7 the goals of the assessment, scale of the assessm ent performed, and the limitations of the input 8 data available. However, significant uncertain ty often remains and emphasis is then placed on 9 characterizing the nature of that uncertainty and its impact on exposure and risk estimates. 10 The characterization of uncertainty can in clude either qualitative or quantitative 11 evaluations, or perhaps a combination of both. Th e approach can also be tiered, that is, the 12 analysis can begin with a simple qualitative uncertainty characterization then progress to a 13 complex probabilistic uncertainty analysis. This may follow when a lower tier analysis indicates 14 there is a high degree of uncertainty for certain identified sources, the sour ces of uncertainty are 15 highly influential variables in estimating the ex posure and risk, and sufficient information and 16 other resources are available to conduct a quantitative uncertainty assessment. This is not to 17 suggest that quantitative uncertainty analyses sh ould always be performed in all exposure and 18 risk assessments. The decision re garding the type of uncertainty characterization performed is 19 also informed by the intended scope and purpose of the assessment, whether the selected analysis 20 will provide additional information to the overall decision regarding health protection, whether 21 sufficient data are available to conduct a complex quantitative analysis, and whether time and 22 resources are available for higher tier ch aracterizations (US EP A, 2004; WHO, 2008). 23 The primary purpose of the uncertainty character ization approach selected in this draft 24 REA is to identify and compare the relative imp act important sources of uncertainty may have on 25 the estimated potential health effect endpoints. The approach used to evaluate uncertainty was 26 adapted from guidelines outlining how to conduc t a qualitative uncertain ty characterization 27 (WHO, 2008) and applied in the most recent NO 2 (US EPA, 2008c) and SO 2 NAAQS reviews 28 (US EPA, 2009). While it may be considered ideal to follow a tiered approach in the REA to 29 quantitatively characterize all id entified uncertainties, staff selected the mainly qualitative 30 approach given the extremely limited data av ailable to inform probabilistic analyses. 31 The qualitative approach used in this REA va ries from that of WHO (2008) in that a 32 greater focus of the characteriz ation performed was placed on ev aluating the direction and the 33October, 2009 Draft – Do Not Cite or Quote 6-37 magnitude 5 of the uncertainty; that is, qualitatively rating how the source of uncertainty, in the 1 presence of alternative information, may affect th e estimated exposures and health risk results. 2 In addition and consistent with the WHO (2008) guidance, staff discuss the uncertainty in the 3 knowledge-base (e.g., the accuracy of the data used, acknowledgement of data gaps) and 4 decisions made where possible (e.g., selection of particular model forms), though qualitative 5 ratings were assigned only to uncer tainty regarding the knowledge-base. 6 First, staff identified the key aspects of the assessment approach that may contribute to 7 uncertainty in the exposure and risk estimates and provide the rationale for their inclusion. Then, 8 staff characterized the magnitude and direction of the influence on the assessment results for 9 each of these identified sources of uncertainty. Consistent with the W HO (2008) guidance, staff 10 subjectively scaled the overall im pact of the uncertainty by consid ering the degree of severity of 11 the uncertainty as implied by the relationship between the source of th e uncertainty and the 12 output of the air quality char acterization. Where the magnit ude of uncertainty was rated low , it 13 was judged that large changes within the source of uncertainty would have only a small effect on 14 the exposure results. A designation of medium implies that a change within the source of 15 uncertainty would likely have a moderate (o r proportional) effect on the results. A 16 characterization of high implies that a small change in the source would have a large effect on 17 results. Staff also included the direction of influence, indicating how the source of uncertainty 18 was judged to affect estimated exposures or risk estimates; either the estimated values were 19 likely over - or under-estimated . In the instance where the com ponent of uncertainty can affect 20 the assessment endpoint in either di rection, the influence was judged as both . Staff characterized 21 the direction of influence as unknown when there was no evidence available to judge the 22 directional nature of uncertainty associated with the particular source. Staff also subjectively 23 scaled the knowledge-base uncertainty associated with each identified source using a three level 24 scale: low indicated significant confidence in the data used and its applicability to the assessment 25 endpoints, medium implied that there were some limitations regarding consistency and 26 completeness of the data used or scientific evidence presented, and high indicated the 27 knowledge-base was extremely limited. 28 The output of the uncertainty characteri zation was a summary describing, for each 29 identified source of uncertainty, the magnitude of the impact and the direction of influence the 30 uncertainty may have on the exposure and risk characterization results. There are several 31 sources of uncertainty associated with this simplified approach fo r modeling CO population 32 exposure/dose and associated potential health risk, each summarized and discussed in Table 6- 33 25. 34

5 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2.October, 2009 Draft – Do Not Cite or Quote6-381Table 6-25. Characterization of KeyUncertainties in the Draft Assessment for Denver and Los Angeles Areas.2Sources of UncertaintyInfluence of Uncertaintyon Exposure/Dose orRisk EstimatesCategory ElementDirection MagnitudeKnowledgeBaseUncertainty CommentsaDatabase Quality Over Low LowINF: There may be a limited number of poor quality high concentration data within the analytical data sets, potentiallyinfluencing the number of benchmark dose level exceedances.KB: EPA’s Air Quality System data used inthe analyses are of high quality. There is no other source of monitoringdata as comprehensive. Data are being used in a manner consistent with one of the defined objectives of ambientmonitoring.Spatial andTemporalRepresentationOver Medium HighINF: Use of a single fixed-site ambient CO monitor likely does not adequately represent spatial temporal variability inambient CO levels throughout each study area. Given that typical in-vehicle to ambient concentration ratios range from2 to 4 and on-road sources tend to dominate CO emissions, it is likely that the spatial variability in ambientconcentrations across a region would be less than this value. Given that the single monitor selected for use in theexposure assessment had generally greater concentrations than other monitors within the broader metropolitan area, itis likely that exposures are overestimated for most simulated individuals.KB: In the absence of 1) a monitoring network designed to measure spatial variability in CO concentrations, 2)performing air quality modeling to estimate spatial and temporal variability in CO concentrations and, 3) analysis of anyexisting and representative monitoring data that can potentially indicate spatial concentration gradients, staff judge theuncertainty in the knowledge-base as high.Ambient COConcentrationsMissing DataSubstitutionUnder Low LowINF: Assuming there is an equal probability of missing low and high concentration hourly values, and that substituteddata are limited by the bounds of the algorithm (i.e., as defined by limits in the measurement data), there may be a fewmissing high concentration data that could lead to underestimation in exposure concentrations and doses. Thisassumes that the substitution of low-level concentration data with potentially higher concentrations (within the boundsof the algorithm) does not affect exposure results.KB: All available measurement data are quality assured. Very few data values were substituted with respect to thenumber of measured values available in each location.Adjustment of AirQuality toSimulate JustMeeting theHistorical Data Used Unknown Medium HighINF & KB: Even though the historical data represent a real air quality condition that may be similar to concentrationslevels expected to just meet the 8-hour current standard, the condition simulated is hypothetical. It is largely unknownhow influential factors such as emission levels per vehicle, vehicular traffic, and meteorology compare between anearlier period of time and the hypothetical condition of just meeting the current standard.October, 2009 Draft – Do Not Cite or Quote6-39Sources of UncertaintyInfluence of Uncertaintyon Exposure/Dose orRisk EstimatesCategory ElementDirection MagnitudeKnowledgeBaseUncertainty CommentsaCurrent 8-HourStandardProportionalApproach UsedBoth Low LowINF: The magnitude of the adjustment applied to historical ambient concentration data was minimal for Denver (i.e.,0.99 was the adjustment applied), though greater for Los Angeles (0.63). In comparing recent and historical ambientCO concentrations (Table 6-5), a linear relationship exists between the range of concentrations reported for the twotime periods in both locations. More importantly, a strong proportional relationship is present when comparing therecent and historic CO concentrations measured at the Los Angeles monitor.KB: A similar proportional approach was judged adequate in simulating air quality conditions just meeting the 8-hourCO NAAQS in prior assessments (US EPA,1992; Johnson et al., 2000). Ananalysis of the CO concentrationdistributions comparing 1995 and 1997 CO air quality data in the Denver and Los Angeles study areas, respectivelywith more recent CO air quality data (i.e., 2006) in these same two areas shows a roughly linear or proportional changethroughout the distribution.Population Database Both Low LowINF & KB: Population data are from the US Census Bureau, a reliable and quality assured source. Data used arespecifically for census tracts modeled in Denver and Los Angeles. Staff assumed any remaining uncertainties in thedatabase would have negligible influence on exposure and dose results.Activity PatternDatabaseUnknown Low-Medium MediumINF: Data are actual records of the time spent in specific locations while performing specific activities in particularlocations. While not specific to a particular area, the activity patterns of a population are generally well represented bythe mainly population-based and nationally-representative survey data (e.g., see Table G-1 in Appendix G regardingthe patterns of typical commuting in CHAD versus the urban locations modeled in this assessment).KB: Data are from a reliable and quality assured source (CHAD) and are from surveys of real persons. Features of anindividual’s activity pattern are well represented, adjustments are made to represent the population distribution in aspecific area (using age and gender), and temperature is used to link CHAD diaries with the simulated individualsresiding in a specific area. However, there are several assumptions made that contribute to uncertainty in its use. Forexample, activity patterns of persons surveyed over 30 years ago are assumed to represent a current persons activitypatterns.Longitudinal ProfileAlgorithmBoth Low – MediumMediumINF: The magnitude of potential influence would be mostly directed toward estimates of multi-day exposures, not thenumber or percent of persons having at least one exposure or dose above a selected level.KB: In developing the longitudinal method, the evaluation indicated that both theDandAstatistics are reasonablyreproduced for the population. In addition, the approach was compared to two other independent methods used forconstructing longitudinal activity patterns (see Appendix B, Attachment 5of US EPA , 2009). Note however, long-termdiary profiles (i.e., monthly, annual) do not exist for a population.APEX Inputs andAlgorithmsMeteorological Data Both Low LowINF & KB: Data are from the National Weather Service, a well-known and quality-assured source. Daily maximumtemperatures are only used when selecting appropriate diaries to simulate individuals.October, 2009 Draft – Do Not Cite or Quote6-40Sources of UncertaintyInfluence of Uncertaintyon Exposure/Dose orRisk EstimatesCategory ElementDirection MagnitudeKnowledgeBaseUncertainty CommentsaAlgorithm and InputData for In-VehicleCO ConcentrationsBoth Medium MediumINF: Given that on-road and in-vehicle CO concentrations are typically higher than ambient CO concentrations,Scenario A likely underestimates in-vehicle exposures. There is variability between in-vehicle and ambient COconcentrations that is not accounted for by using a single value to represent the relationship (i.e., a factor of two) suchas traffic density, local meteorology, driving conditions, and differences in vehicle age, technology, design and time ofoperation.KB: While most studies reviewed indicate that, on average, there may be a factor of two difference between theambient and in-vehicle CO concentrations, there are a limited number of studies that measured these concentrationsand even fewer were located in the U.S. It is largely unknown how this and other identified influential factors mightinfluence the true relationship between in-vehicle and ambient CO concentrations.Algorithm and InputData for All OtherMicroenvironmentalCO ConcentrationsOver Low-Medium MediumINF & KB: Even though CO is considered relatively inert, it is likely that the ambient contribution to exposures in allindoor microenvironments are overestimated. This is a result of not considering air exchange that would delay and limitinfiltration of outdoor CO concentrations, thereby reducingindoor microenvironmental peak concentrations due to COof ambient origin. It is also possible that the residential indoor or outdoor microenvironment concentrations ofsimulated individuals residing in close proximity to major roads may be underestimated. This is because the currentambient monitors are unlikely to reflect the higher CO concentrations expected to occur near all major roads (draft ISA,section 3.6.6.2). The simplified approach used an ambient monitor that may be representative of outdoor near-roadCO concentrations (i.e., microscale and middle scale monitors) experienced by only a portion of the population in eachstudy area. However, given the larger portion of time spentin locations other than in vehicles and outdoors near-roadsand the limited difference in exposures estimated when comparing scenario A to scenario B, it is likely that exposuresand doses have an overall tendency to be overestimated for most of the simulated population.CommutingAlgorithm Not UsedOver Medium LowINF & KB: In using the ambient monitor that has the greatest CO concentrations compared to other monitoring data inan area, it is assumed that these concentrations would represent conservative estimates of air quality in the area. Thiswould lead to overestimates in exposures, particularly when not considering commuting to other lower ambientconcentration locations.CHD Prevalence Both Low LowINF & KB: Data are from the Centers for Disease Control, a well-known and quality-assured source. Thoughprevalence data are not specific for each region, the national prevalence data were stratified by selected age-groupsand gender.Potential HealthEffectBenchmarkLevelsSusceptiblePopulationUnknown Low MediumINF & KB: Data from a well-conducted multi-center controlled human exposure study demonstrate cardiovasculareffects in subjects with moderate to severe coronary artery disease at COHb levels as low as 2.0-2.4%. No laboratorystudy has evaluated the effect of exposure to CO resulting in COHb levels below 2.0%. There is no established noadverse effect level and, thus there is greater uncertainty about the lowest benchmark level used (i.e., 1.5%) anduncertainty about whether individuals with the mostsevere CHD are adequately represented. Given that the evidencesupporting the choice of benchmark levels is based on controlled human exposure data, we judge the influence of thisuncertainty on the risk characterization as being low.aINF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rating.October, 2009 6-41 Draft – Do Not Cite or Quote Based on the qualitative judgments made by st aff for a range of sources of uncertainty and their characteri zation as to direction and magnitude of influence on exposures and doses, the exposure and dose estimates are possibly overestimat ed for a larger portio n of the population the assessment is intended to repr esent (i.e., those residing/trave lling in high CO concentration microenvironments). This is because:

 Of the four sources of uncertainty associ ated with potential overestimation (i.e., 1 spatial/temporal representation of monitori ng data, data base quality, and absence of 2 movement among different air quality dist ricts during commutes, algorithm and input 3 data for all other microenvironmental CO con centrations), three were estimated as having 4 medium magnitude of influence, while the remaining source (i.e., ambient monitoring 5 database quality) was ranked as having a low or a negligible magnitude of influence. 6

 The one source of uncertainty associated with potential underestimation (i.e., missing 7 data substitution) wa s judged to have a low magnit ude of influence on estimated 8 exposures and doses. 9

 Of the remaining identified sources of un certainty judged by staff to have either 10 bidirectional influence (six sources) or unknown (three sources) di rection, five sources 11 were judged to have a low magnitude of infl uence on estimated exposures and doses. 12 While there was a wide-ranging level of un certainty in the knowledge-base for the identified sources, there is relatively less uncer tainty in staff judgment s regarding the sources associated with potential overestimation of CO exposure and resulting COHb levels.

 A high degree of uncertainty in the knowle dge-base was assigned to two sources: the 13 spatial/temporal representation of monitoring da ta (direction of influe nce characterized as 14 over, with a medium rated magnitude) and the use of historical data in representing air 15 quality that just meet the current standard (direction of influe nce characterized as 16 unknown, with magnitude rated as medium). 17

 The knowledge-base uncertainty was low for thr ee of the five sources identified above as 18 being associated with either under- or ove restimating exposures (the rating for the 19 remaining two sources wa s medium and high). 20

 The knowledge-base uncertainty for sources w ith unknown or bidirectional influence was 21 low (five sources), medium (four sources), and high (one source). 22 23October, 2009 6-42 Draft – Do Not Cite or Quote 6.5 KEY OBSERVATIONS 1 Presented below are key observations resu lting from the exposure and dose assessment 2 for ambient CO. 3

 An important limitation in the assessment for th is review is the lack of detailed spatial 4 representation of the current ambient mon itoring data, which creates challenges for 5 estimating the spatial variab ility of CO concentrations across a study area. This 6 limitation contributed in part to the reas oning for the development of the simplified 7 approach used in this assessment. 8

o In this simplified approach, staff used a single monitor recording the highest CO 9 concentrations to represent the ambient air quality in each area. This was done to 10 accommodate the potentially greater CO e xposures expected to occur to persons 11 residing in areas with higher CO con centrations (i.e., those occurring on or 12 immediately near major roadways). 13

o Using a single monitor in each study area, however, still posed difficulties in 14 characterizing the full range of microenvi ronmental CO concentrations, such as 15 CO levels in vehicles or near major ro adways, concentrations outside residences, 16 as well as those occurring within indoor microenvironments for the simulated 17 population. This is a result of the limite d information across a broad geographic 18 area regarding the relationshi ps between specific ambien t monitor concentrations 19 and microenvironmental concentrations. 20

 One-hour and 8-hour average daily maximum exposures and the daily maximum end-of21 hour COHb blood levels were estimated usi ng a simplified exposure modeling approach 22 involving two scenarios in two study areas: urban areas of Denver and Los Angeles 23 counties. In Scenario A, CO concentrations in all microe nvironments were set equal to 24 the ambient monitor concentrations and in S cenario B, the CO concentration for the in25 vehicle microenvironment were increased over those of the ambient monitor and all other 26 microenvironments were set e qual to the ambient monitor co ncentrations. The two air 27 quality conditions invest igated by staff included as is air quality, and air quality for 28 higher CO levels, adjusted to simulate just meeting the current 8-hour CO NAAQS 29 (section 6.2). 30

o Fewer than 1% of the study population in each study area (< 0.2%) were 31 estimated to experience a daily maximum end-of-hour COHB level at or above 32 2.0% under as is air quality conditions in either scenario. 33

o Results for the two study areas differed appr eciably for air quality adjusted to just 34 meet the current standard. For these conditions, the estimates of percent of 35 population experiencing a daily maximum e nd-of-hour COHB level at or above 36 potential health benchmarks were substa ntially greater for the Denver study area 37 (e.g., differing by a factor of 8 or more for the 2% COHb benchmark). 38

 Results generated in the current assessment for the air quality conditions just meeting the 39 current NAAQS were compared with estimat es from the assessment conducted in 2000 40 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study areas 41October, 2009 6-43 Draft – Do Not Cite or Quote (section 6.3). While focused on similar ai r quality conditions, the two assessments 1 employed different versions of the exposure model (APEX vs pNEM) and there were 2 significant differences in the approach used in each assessment. For example, as 3 compared to the current assessment, the 2000 assessment employed more monitors to 4 represent ambient CO levels, differentia lly treated a much greater number of 5 microenvironments, and encompassed larger study areas. 6

o The estimated percent of persons with daily maximum end-of-hour COHb blood 7 levels when using air quality adjusted to just meet the current standard in both 8 Denver and Los Angeles was substantially gr eater in the current assessment when 9 compared to that estimated in the 2000 assessment (e.g., a difference of a factor of 10 10 or more at the 2% COHb benchmark). 11

 Based on an overall qualitative judgment of the identified so urces of uncertainty in the 12 assessment approach, selections made rega rding input data, and algorithms used, and 13 their characterization as to direction and magnitude of influence, the exposure and dose 14 estimates for much of the simulated population represented by either scenario in this 15 assessment are likely overestimated (section 6.4, Table 6-25). There may be a smaller 16 fraction of the simulated population (e.g., thos e residing in close proximity to major 17 roads, persons regularly commuting for extende d periods of time) where some periods of 18 exposure are underestimated due to the simp lified assumptions made in estimating in19 vehicle and near-road CO exposures, although li kely less so and for a yet smaller portion 20 of the population in scenario B. The impact of such potenti ally higher exposure periods 21 on the population COHb levels will vary depe nding on the overall pattern of exposures. 22

 Given the considerations described above re garding the characteriz ation of uncertainty 23 and the tendency of the assessment approach to overestimate exposure and dose, staff 24 finds the utility of this assessment for the purpose of considering the adequacy of the 25 current standards to be limited. 26October, 2009 6-44 Draft – Do Not Cite or Quote 6.6 REFERENCES 1 CDC. (2009). Summary Health Statistics for U.S. Adu lts: National Health Interview Survey, 2007. Series 10, 2 Number 240. U.S. Department of Health an d Human Services, Hyattsville, MD, May 2009. 3
Isaacs K., McCurdy, T Errickson A, Forbes S, Glen G, Graham S, McCurdy L, Nysewander, M, Smith L, 4 Tulve N, and Vallero D. (2009). Statistical proper ties of longitudinal time-activity data for use in EPA 5 exposure models. Poster presented at the American Time Use Research Conference; College Park MD, 6 June 26, 2009. 7
Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (1999 Draft). Estimation of Carbon Monoxide Exposures 8 and Associated Carboxyhemoglobin Levels in Denver Residents Using pNEM/CO (Version 2.0). Report 9 prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 68-D6-0064. U.S. 10 Environmental Protection Agency, Research Triangle Park, North Carolina. March 1999. 11
Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 12 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 13 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 14 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 15 at: http://www.epa.gov/ttn/fera/human_related.html .. June 2000. 16

SAIC. 2001. Technical Peer Review of “Estimati on of Carbon Monoxide Exposures and Associated 17 Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO (version 2.1)” 18 Prepared by Science Applications International Corporation under EPA Contract No. 68-D-98-113. 19 Available at: http://www.epa.gov/ttn/fer a/human_related.html

.. 20
US Census Bureau. (2009). American Fact Finder. Cens us Summary File 3 (SF3) – custom tables. Available at: 21 www.factfinder.census.gov

. 22
US EPA (1992). Review of the National Ambient Air Qu ality Standards for Carbon Monoxide: Assessment of 23 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 24 EPA/452/R-92-004. 25
US EPA. (2004). An Examination of EPA Risk Assessmen t Principles and Practices. Staff Paper prepared by the 26 US EPA Risk Assessment Task Force. EPA/100/B-04/001. Available at: 27 http://www.epa.gov/OSA/pdfs/ratf-final.pdf

. 28

US EPA. (2008a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 29 (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report No. EPA-452/B-08-001a. Office of 30 Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 31
US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 32 (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report No. EPA-452/B-08- 33 001b. Office of Air Quality Planning and Standard s, U.S. Environmental Protection Agency, Research 34 Triangle Park, NC. 35
US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO 2 Primary National Ambient 36 Air Quality Standard. EPA-452/R-08-008a. November 2008. Available at: 37 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf

. 38
US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air 39 Quality Standard. EPA-452/R-09-007. August 2009. Available 40 athttp://www.epa.gov/ttn/naaqs/standards /so2/data/200908SO2REAFinalReport.pdf. 41October, 2009 6-45 Draft – Do Not Cite or Quote WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on characterizing and 1 communicating uncertainty in exposure assessment. Available at: 2 http://www.who.in t/ipcs/methods/harmonization/areas/exposure/en/. 3

### Appendix A Conceptual Model and Simplified Data Flow of APEX.

October 2009 Draft – Do Not Cite or Quote A-1Air Exchange Rates &Building VolumesEmission Rates andUse Patterns forIndoor Sources(e.g., gas appliances,passive smoking)Ambient Fixed-SiteConcentrationsAir Quality SpecificationMass balance Modelor Factors Approachfor Indoor and in VehicleMicroenvironmentsExposure AlgorithmsSeasonal Considerations(Temperature)Human Activity andExertion PatternsPopulation &Commuting DataDistribution of People andOccurrences of ExposuresLinked with Breathing RateOutdoorMicroenvironmentConcentrationsDistribution of Peopleand Occurrences ofDose (COHb) LevelsDose AlgorithmAppendix A. Conceptual Model and Simplified Data Flow of APEX.September 2009Draft – Do Not Cite or QuoteA-2

### Appendix B
Mass Balance Model in APEX

The mass balance method models an enclosed microenvironment as a well-mixed volume in which the air concentration is spatially uniform at any specific time. The concentration of an air pollutant in such a microenvironment is estimated using the following four processes:

 Inflow of air into the microenvironment;

 Outflow of air from the microenvironment;

 Removal of a pollutant from the micr oenvironment due to deposition, filtration, and/or chemical de gradation; and

 Emissions from sources of a polluta nt inside the microenvironment. Table B-1 lists the parameters required by the mass balance method to calculate concentrations in a microenvironment. The proximity factor ( f

proximity ) is used to account for differences in ambient concentrations between the geographic locati on represented by the ambient air quality data (e.g., a regional fixed- site monitor) and the geographic location of the microenvironment (e.g., near a roadwa y). This factor could take a value either greater than or less than 1. Emission source (ES) represents the emission rate for the emission source, and concentration source (CS) is the mean air concentrati on resulting from the source. R removal is defined as the removal rate of a pollutant from a microenvironment due to deposition, filtration, and chemical reaction. The air exchange rate ( R air exchange ) is expressed in ai r changes per hour.

October 2009 Draft – Do Not Cite or Quote B-1 Table B-1. Parameters of the Mass Balance Mod el

Variable Definition Units Value Range

f proximity Proximity factor N/A f proximity > 0

CS Concentration source ppm CS ≥ 0

ES Emission source μg/hr ES ≥ 0

R removal Removal rate due to deposition, filtration, and chemical reaction 1/hr R removal ≥ 0

R air exchange Air exchange rate 1/hr R air exchange ≥ 0

V Volume of microenvironment m 3 V > 0

The mass balance equation for a pollutant in a microenvironment is described by the differential equation

source removal out in C CCC  dt (t)dC ME (B-1)

where:

d C ME (t) = Change in concentration in a microenvironment at time t (ppm),
) C in = Rate of change in microenvironmental concentration due to influx of air (ppm/hour),
) C out = Rate of change in microenvironm ental concentration due to outflux of air (ppm/hour),
) C removal = Rate of change in microenvironmental concentration due to removal processes (ppm/hour), and
) C source = Rate of change in microenvironmental concentration due to an emission source inside the microenvironment (ppm/hour). Within the time period of an hour each of the rates of change, ) C in , ) C out , ) C removal , and ) C source , is assumed to be constant.

October 2009 Draft – Do Not Cite or Quote B-2The change in m icroenvironmental concentration due to influx of air is represented by the following equation:

exchange airnpenetratio proximity ambient in in RxfxfxC dt tdC C  )( (B-2)

where:

C

ambient = Ambient hourly outdoor concentration (ppm) f

proximity = Proximity factor f penetration = Penetration factor R air exchange = Air exchange rate (1/hour) The change in microenvironmental concentra tion due to outflux of air is described by:

)( )( tC R dt tdC C ME exchange air out
out   (B-3)

The change in concentration due to deposition, filtration, and chemi cal degradation in a microenvironment is simulated by the first-order equation:



### (t)CR(t))CRR(R dt (t)dC C ME remova ME chemical filtration deposition removal removal 

###    (B-4)

where:

R deposition = Removal rate of a pollutant from a microenvironment due to deposition (1/hour) R

filtration = Removal rate of a pollutant from a microenvironment due to filtration (1/hour) R chemical = Removal rate of a pollutant from a microenvironment due to chemical degradation (1/hour) R removal = Removal rate of a pollutant from a microenvironment due to overall removal (1/hour)

As discussed in Section 6.2, EPA decided not to model indoor emi ssions of CO in the current exposure assessment; c onsequently, the optional term ) C source was uniformly set equal to 0.0 for this study. Combining Equation B-1 with Equati ons B-2, B-3, and B-4 yields

)( )( d t (t)dC ME tCRtC RC ME removal ME exchange airin   (B-5)

October 2009 Draft – Do Not Cite or Quote B-3The solution to this differential equation is

) exp())0(( )( tR R C C R C tC combined combined in ME combined in ME    

 (B-6)

where:

C

ME (0) = Concentration of a pollutant in a microenvironment at the beginning of a hour (ppm) C

ME (t) = Concentration of a pollutant in a microenvironment at time t within the time period of a hour (ppm)

R combined = R air exchange + R removal (1/hour)

Based on Equation B-6, the following three hou rly concentrations in a microenvironment are calculated:

combined in ME equil ME R C tCC   )( (B-7)

(B-8) )(exp))0(( combined equil ME ME equil ME endhourly ME R CCCC

 

combined combined equil ME ME equil ME hourlymean ME R R CCC dt dttC C )(exp1 ))0(( )(

1

0 1

0   

##   (B-9)

where: = Equilibrium concentration in a microenvironment (ppm) equil ME C C

ME (0) = Concentration in a microenvir onment at the beginning of an hour (ppm) = Concentration in a microenvironment at the end of an hour (ppm) endhourly ME C = Hourly mean concentration in a microenvironment (ppm)

hourlymean ME C

At each hou r time step of the simulation period, APEX uses Equations B-7, B-8, and B-9 to calculate the hourly equilibrium, hourly endi ng, and hourly mean concentrations. APEX reports hourly mean concentration as hourly conc entration for a specific hour. The calculation continues to the next hour by using for the previous hour as C ME ( 0)

. endhourly ME C

October 2009 Draft – Do Not Cite or Quote B-4October 2009 Draft – Do Not Cite or Quote C-1 

### Appendix C

COHb Module for APEX4.3
This appendix describes the probabilistic carboxyhemoglobin (COHb) module used in the current APEX4.3 model. The approach describe d here is based primarily on the COHb module originally described by Biller and Richmond in two reports (Johnson et al., 1992; Johnson et al., 2000) and used in EPA probabilistic NAAQS exposure model for CO (pNEM/CO), a predecessor of APEX4.3. This a ppendix also describes the princi pal changes made to the COHb module when it was incorporated into APEX4.3, including a change in the method used to solve the Coburn-Forster-Kane (CFK) e quation (Coburn et al., 1965). C.1 The Base Physiological Model for Computing COHb Levels Using time/activity data obtained from va rious diary studies, APEX constructs a composite diary for each simulated person in th e specified population at risk. The composite diary consists of a sequence of events spanni ng the specified period of the exposure assessment (typically one calendar year). Each event is defined by a start time, a duration, a geographic location, a microenvironment, and an activity. Using various algor ithms described in Section 5 of this report, APEX4.3 provides estimates of CO concentration and alveolar ventilation rate for each event in the composite diary. APEX4.3 then uses these data, togeth er with estimates of various physiological parameters specific to th e simulated individual, to estimate the percent COHb in the blood (%COHb) as an average %C OHb value over the duration of each exposure event and as an instantaneous %COHb le vel at the end of each event.

The %COHb calculation is based on the solution to the non- linear CFK equation, previously described in Appendix E of Johnson et al. (2000). The CFK m odel describes the rate of change of COHb blood levels as a function of the following quantities: 1. Inspired CO pressure 2. COHb level 3. Oxyhemoglobin (O 2 Hb) level 4. Hemoglobin (Hb) content of blood 5. Blood volume 6. Alveolar ventilation rate 7. Endogenous CO production rate 8. Mean pulmonary capillary oxygen pressure 9. Pulmonary diffusion rate of CO 10. Haldane coefficient (M) 11. Barometric pressure 12. Vapor pressure of water at body temperature (i.e., 47 torr). If all of the listed quantities except COHb level are constant over some time interval, the CFK equation has a linear form over the interval a nd is readily integrated. The solution to the linear form gives reasonably accurate results fo r lower levels of COHb. However, CO and oxygen compete for the available hemoglobin and are, therefore, not independe nt of each other.October 2009 Draft – Do Not Cite or Quote C-2 If this dependency is taken into account, the resulting di fferential equation is no longer linear. Peterson and Stewart (1975) proposed a heuristic a pproach to account for this dependency which assumed the linear form and then adjusted the O 2 Hb level iteratively based on the assumption of a linear relationship between COHb and O 2 Hb. This approach was used in the COHb module of the original CO-NEM exposure model (Bille r and Richmond, 1982, Johnson and Paul, 1983). Alternatively, it is possible to determine COHb at any time by nume rical integration of the nonlinear CFK equation (e.g., by use of the Runge -Kutta method) if one assumes a particular relationship between COHb and O 2 Hb. Muller and Barton (1987) de monstrated that assuming a linear relationship between COHb and O 2 Hb leads to a form of the CFK equation equivalent to the Michaelis-Menton kinetic model which can be analytically integrated. However, the analytical solution in this case cannot be solved explicitly for COHb. Muller and Barton (1987) demonstrated a binary search method for determining the COHb value. The COHb module used in pNEM/CO employe d a linear relationship between COHb and O 2 Hb which was consistent with the basic assu mptions of the CFK model. The approach differed from the linear forms used by other m odelers in that the Muller and Barton (1987) solution was employed. However, instead of the simple binary search described by Muller and Barton (1987), a combination of the binary search and Newton-Raphs on root-finding methods was used to solve for COHb (Press et al., 1986). Using the Muller and Barton (1987) solution increased computation time compared to the Pe terson and Stewart (1975) method but was shown to be faster than fourth-order R unge-Kutta numerical integration. APEX4.3 employs a different approach in wh ich the CFK equation is solved using a fourth-order Taylor’s series e xpansion with subintervals. This method, first incorporated in Version 3 of APEX, is described in Section C.2 of this appendix. A mo re detailed description can be found in the Programmer’s Guide for the APEX3 model (Glen, 2002).

C.2 The CFK Model for Estima tion of Carboxyhemoglobin Table C-1 defines the variables which appear in the equations of this section. Coburn, Forster, and Kane (1965) derived the following differential equation governing COHb levels in the blood upon exposure to CO.



### ] [ ] [ ] [

2 2 

### Hb O MBV COHb P BV P V V dt COHb d

b CO

b Ico

b co



###     (Eq. C-1)
where,

A O H B

Lco V P P D B

 2 1

   (Eq. C-2)October 2009 Draft – Do Not Cite or Quote C-3 Table C-1. Definitions of CFK Model Variables.

Variable Definition Units t Time from start of an exposure event minutes [COHb] Concentration of carboxy hemoglobin (COHb) in blood at time t ml CO per ml blood at STPD [O 2 Hb] Concentration of oxyhemoglobin (O 2 Hb) in blood at time t ml O 2 per ml blood at STPD [RHb] Concentration of reduced hemoglobin in blood equivalent ml CO per ml of blood at STPD [COHb] 0 [COHb] at t = 0 ml CO per ml blood at STPD [THb] 0 [RHb] + [COHb] + [O 2 Hb] %[COHb] [COHb] express ed as percent of [RHb] 0 % %[O 2 Hb] [O 2 Hb] expressed as percent of [RHb] 0 % %[COHb] 0 [COHb] at t = 0 % %[COHB] ∞ [COHb] at t = ∞ % P

I CO Pressure of inspired CO in air saturated with water vapor at body temperature torr

P C CO Mean pulmonary capillary CO pressure torr
P C O 2 Mean pulmonary capillary O 2 pressure torr 

### P B Barometric pressure torr 

### P H O 2 Vapor pressure of water at body temperature, or 47 torr



###  V A Alveolar ventilation rate ml/min STPD



###  V CO Endogenous CO production rate ml/min STPD 

### D L CO Pulmonary CO diffusion rate ml/min/torr, STPD

M Haldane coefficient k Equilibrium constant for reaction O 2 + RHb = O 2 Hb V b Blood volume ml Hb Total hemoglobin in blood g/100ml %MetHb Methemoglobin as weight percent of Hb % Notes: 1 Standard Temperature Pressure, and Dry (STPD)October 2009 Draft – Do Not Cite or Quote C-4 If the only quantity in equation (C-1) th at can vary with time is [COHb], the CFK equation is linear and can be readily integrated. However, since oxygen (O 2 ) and CO compete for the available Hb, [COHb] and [O 2 Hb] must be related. Increasing [COHb] will result in decreasing [O 2 Hb]. Thus the CFK equation is not lin ear and requires the relationship between the two quantities to be known if it is to be accurately inte grated over a wide range of COHb levels.

Various linear relationshi ps between [COHb] and [O 2 Hb] have been used (see Marcus, 1980; McCartney, 1990; Muller and Ba rton, 1987; and Tikuisis et al ., 1987). A relationship not previously used follows directly from the basi c assumptions of the CFK model. The CFK model employs the Haldane coefficient, which is the eq uilibrium constant associated with the following reaction representing th e replacement of O 2 in O 2 Hb by CO:

COHb O Hb O CO    2 2 (Eq. C-3)
The following equation, the Haldane relationshi p, applies approximately at equilibrium conditions.

M Hb O c P COHb

c P

CO O  ] [ ] [

2 2 (Eq. C-4)

The Haldane coefficient, M, is the chemical equilibrium constant for reaction (C-3). The above reaction can also be viewed as the diffe rence between two competing chemical reactions:

COHb RHb CO   (Eq. C-5)

Hb O RHb O 2

2 

 (Eq. C-6)

Subtracting (C-6) from (C-5) yields (C-3). If(C-3) is in equilibrium, then (C-5) and (C6) are in equilibrium. If k represents the equilibrium constant for (C-6) then:

k RHb c P Hb O

O  ] [ ] [

2 2 (Eq. C-7)
It is known that an individual breathing air free of CO for an extended period will have about 97% of their reactive Hb bound with oxygen (O 2 Hb) and the remainder (3%) as the reduced form (RHb). It is also known that at one atmosphere barometric pressure, the mean pulmonary capillary oxygen pressu re is approximately 100 torr. Substituting into (C-7) yields 0.32 as the approximate value of k at body temperature. From mass balance considerations:

O THb RHb COHb Hb O ] [ ] [ ] [ ] [ 2    (Eq. C-8)
Eliminating [RHb] between (C-7 ) and (C-8) and solving for [O 2 Hb] yields:October 2009 Draft – Do Not Cite or Quote C-5 ]) [ ] ([ 1 ] [ 0 2

2 2 COHb THb c P k c P k Hb O

O O

   (Eq. C-9)
This equation represents the aforementioned linear form of the CFK equation. It has the same form as a relationship given by McCart ney (1990), but replaces the constant in the McCartney equation by the term in (C-9) i nvolving the mean pulmonary capillary oxygen pressure and the equilibrium constant k . Substituting (C-9) into (C -1) yields a CFK equation free of [O 2 Hb] and fully consistent with Coburn, Fors ter, and Kane’s original derivation.

b O

O b I

b CO kMBV c P k COHb THb COHb BV P V V dt COHb d CO 2 1 ] [ ] [ ] [ ] [  

      (Eq. C-10)
In working with the CFK model it is convenien t to express COHb as a percent of [RHb] 0 . Multiplying (C-10) by 100 and dividing by [RHb] 0 yields the expression

b O CO

b I

b CO

O MBV RHb k P k COHb COHb BV P V V THb dt COHb d CO ] [ ) 1 ( 100 ] %[ 100 ] %[ ) ( ] [ 100 ] %[ 2  

     (Eq. C-11)

Equation (C-11) can be written in the form suggested by Muller and Barton (1987):

] %[ 100 ] %[ ] %[

1 COHb COHb C C dt COHb d

O

   (Eq.C-12)

where,

) ( ] [ 100

b I

b CO

O O BV P V V THb C CO    (Eq. C-13)

b O CO

MBV THb k P k C ] [ ) 1 ( 100
2 1 

 (Eq. C-14)

Given values for the atmospheric pressure and the physiological va riables in equations (C-12) through (C-14), the value of %[COHb] at time t can be found by numerical integration using such techniques as the f ourth-order Runge-Kutta method (Press et al., 1986). Muller and Barton (1987) demonstrated that an equation of the form of (C-12) is equivalent to a MichaelisMenton kinetics model which can be inte grated. The integration yields:

0 ] %[ ] %[ ]) %[ ] (%[ ln ) ] %[ 100 ( ] %[ ] %[ ) ( 1 

 

    



 
 O O O COHb COHb COHb COHb COHb COHb COHb t C C (Eq. C-15)

The equation for %[COHb] ∞ is obtained by setting equati on (C-12) equal to zero and solving for %[COHb], which is now equal to %[COHb] ∞ :October 2009 Draft – Do Not Cite or Quote C-6 ) ( 100 ] %[

1 C C C COHb

O O    (Eq. C-16)

Equation (C-15) cannot be solved explicitly for %[COHb]. Muller and Barton (1987) suggest the binary search method as one way to find the value of %[COHb]. Press et al. (1986) contend a combination of the bina ry search and Newton-Raphson meth ods is faster on average. Consequently, the pNEM/CO version of the COHb module used a combination of the binary search and Newton-Raphson root finding methods to solve for COHb (Press et al., 1986). Using the Muller and Barton (1987) solution increased the computation time when compared with the Peterson and Stewart (1975) method, however it was still shown to be faster than the fourthorder Runge-Kutta numerical integration. The current version of APEX (APEX4.3) empl oys an alternative approach in which the CFK equation is solved using a fourth-order Taylo r’s series expansion with subintervals. This method, first incorporated in Version 3 of AP EX, is described in detail in the Programmer’s Guide for the APEX3 Model by Glen (2002). This re ference also includes the results of various tests conducted on 10 candidate methods for solv ing the CFK equation. The selected method (fourth-order Taylor series with subintervals) was chosen becaus e of its simplicity, fast execution speed, and ability to produce relatively accurate estimates of %COHb at both low and high levels of CO exposure. Additional information concerning the %COHb calculation method and its theoretical basis can be found in Section 10.2 of US EPA (2008). In developing the fourth-order Taylor Seri es expansion approach, Glen (2002) began by defining N(t) as the %COHb level in the blood at time t , a quantity that is mathematically restricted to range between 0 and 100 (percent). N(t) satisfies the following differential equation: N`(t) = C 0 - C 1 N(t) / (100 - N(t) ) (Eq. C-17)

where C 0 and C 1 are constants (at least over the duration of one event) that depend on physical and physiological parameters and on the CO con centration in the air. Equation (C-17) is equivalent to (C-12) above, except that (C-12) uses the symbol %[COHb] instead of N(t). The task of expanding N(t) in a Taylor’s series becomes simpler if the following new variables are defined:

D 0 = 1- N(0) / 100 (Eq. C-18)

A 0 = C 0 / (C 0 + C 1 ) (Eq. C-19)

A 1 = C 1 / (C 0 + C 1 ) (Eq. C-20)

D = D 0 - A 1 (Eq. C-21) z = (C 0 + C 1 ) t / (100*D 0 *D 0 ) (Eq. C-22)

The z variable is a re-scaled time variable that is dimensionless. It is used as theOctober 2009 Draft – Do Not Cite or Quote C-7 independent variable for the Taylor’s series expa nsion. In equations expressed as functions of z rather than t , any primes will indicate the derivatives with respect to z

. Expressing (C-17) as a function of z yields the expression N`(z) = D 0 2 A 0 - D 0 2 A 1 N(z) / ( 100 - N(z) ) (Eq. C-23) The Taylor’s series about the origin (z = 0) for N(z) is given by N(z) = N(0) + N`(0) z + N``(0) z 2 / 2 + N```(0) z 3 / 6 + N iv (0) z 4 / 24 + ... (Eq. C-24) Through a series of algebraic substitutions, Glen (2002) shows that the Taylor series expansion of N(z) truncated to the fourth order can be represented by T4(z) = T3(z) - 100 A 1 D 0 D (A 1 2 - 8 D A 1 + 6 D 2 ) z 4 / 24 (Eq. C-25)

where T3(z) = N(0) +100 D 0 D z -100 A 1 D 0 D z 2 / 2 + 100 A 1 D 0 D (A 1 - 2D) z 3 / 6 (Eq. C-26) Tests showed that the fourth-order Taylorseries expansion (C-25) provided greater accuracy than the third-order expansion for z values close to one. Glen (2002) found that z values below one generally corre spond to N(0) values below fort y to fifty percent for one-hour exposure events.

The z value for a given event depends on the event duration, the initial %COHb level N(0), and on the physiological parameters, and can be directly evaluated at the start of each event. For events with a z value above some threshold, it is possible to improve the performance of (C-25) by dividing the event into smaller even ts (“subintervals”), each with a shorter duration and hence smaller z value. As the subinterval duration decreases, accuracy increases at the expense of program execution time. APEX4. 3 enables the user to select a limit on z which in turn determines the number of subintervals to be used in applying the fourth-order Taylor expansion. Glen (2002) r ecommends that the limit on z be set at 0.4 or 0.5. C.3 Application of the COHb Model in APEX4.3 Description of APEX4.3 for CO APEX4.3 follows the daily activities over an extended period of a fin ite set of simulated individuals residing within a gi ven geographic area. The period may be a single season or a calendar year. Each simulated individual is defined by a set of general demographic characteristics that includes age, gender, and bod y weight. The values of these factors are used to derive values for blood volume, menstrua l phase, endogenous CO production rate, and other factors required by the COHb module (see Section C.4). The exposure of each individual is represented by a continuous sequenc e of exposure events which span the time period of interest. Each exposure event represents a time interval of 60 minutes or le ss during which the individualOctober 2009 Draft – Do Not Cite or Quote C-8 resides in a single environment and engages in a single activity. To perm it calculation of hourly average exposures, exposure events are not perm itted to fall in more than one clock hour. Consequently, the passage from one exposure even t to the next is indicated by a change in microenvironment, activity, or cl ock hour. Algorithms within APEX 4.3 calculate an average CO concentration for each exposure event according to the time, district, and microenvironment specified for the event. As the exposure even ts for a simulated individual are contiguous, the model can combine these concentrations to outp ut distributions of one -hour and running eighthour exposures for each individual. The exposures calculated for the simulated individuals can then be population-weighted to produce e xposure distributions fo r population groups of particular interest (e.g., people w ith coronary heart disease). APEX4.3 constructs a year-long time/activit y pattern for each simulated individual by sampling 24-hour activity patterns from the Cons olidated Human Activity Data Base (CHAD), which is described in Section 5.3.3 of this repor t. The sampling approach attempts to match the 24-hour activity patterns to the simulated indi vidual and exposure period according to the demographic characteristics of the individual and the season, da y type (weekday/weekend), and maximum temperature of each day in the specified exposure period.

The COHb Module The COHb module in APEX4.3 currently employs the version of the CFK model represented by equations (C-12) through (C-14) to compute an average COHb value over the duration of each exposure event and an instantaneous COHb level at the end of each event. To perform these computations, the COHb module requires information on each of the quantities listed in the section describing th e CFK model. In addition, the COHb level at the beginning of the exposure event must be known. This latter qu antity is usually the C OHb level computed at the end of the previous contiguous exposure event. To obtain the in itial COHb at the start of the exposure period, the computation is started one day before the beginning of the period. The effect of the initial COHb value on the end value is negligible after about 15 hours. The program stores the calculated COHb values for each exposure event and outputs distributions of COHb levels by population group for averaging tim es ranging from one hour to one day. Assignment of CFK Model Input Data for an Exposure Event

Section C.4 describes the e quations and procedures used by the APEX4.3 COHb module to obtain the values of the input variables for eq uations (C-2) and (C-13) through (C-16). A brief overview is given here. The actual inspired CO level can change si gnificantly during an exposure event. The model supplies an average exposure concentration for the event, whic h is used as the CO input. The time constant for the change in COHb is suff iciently large that the use of concentrations based on averaging times up to one hour can be us ed in place of the instantaneous concentrations over the averaging time period with little loss of accuracy in es timating the COHb level at the end of the exposure event. Furthermore, applyi ng the average concentrations to a contiguous sequence of exposure events does not cau se an accumulation of error.October 2009 Draft – Do Not Cite or Quote C-9 The COHb model presently used in APEX4. 3 does not account for changing barometric pressure. It uses a constant barometric pressure which is a function of the average elevation of an area above sea level. The pressure at sea level is taken to be 760 torr. The remaining input variables to the CFK m odel are all physiological parameters. While the Haldane coefficient, the equilibrium consta nt k, and average pulmonary capillary oxygen pressure are treated as having the same consta nt values for all individuals, the remaining physiological input variables will vary among in dividuals. The next section describes the methods used to generate the various physiologi cal input variables for each combination of individual and calendar da y processed by APEX. C.4 Computation of Input Data for the COHb Module As discussed in the previous section and in Sections 5.3.5 and 5.3.7 of the main body of this report, the algorithms used to estimate V E and COHb require values for various physiological parameters such as body mass, bl ood volume, and pulmonary diffusion rate. Table C-2 provides a list and description of the principa l parameters; additional parameters are listed and described in Chapter 5 of US EPA (2008). An algorithm within AP EX4.3 probabilistically generates a value for each parameter on the lis t (collectively referred to as a physiological profile) for each simulated individual. Figure C-1 is a flow diagram showing the process by which each physiological profile is generated. Each of the gene rated physiological profiles is internally consistent, in that the functional relationships among the various parameters are maintained. For example, blood volume is determin ed as a function of weight and height, where height is estimated as a function of weight. Weight in turn is selected from a distribution specific to gender and age. For each simulated individual, APEX4.3 computes exposure for a contiguous sequence of exposure events spanning the total time period of the computation. This multi-day sequence of exposure events is determined by random sampling day-long event sequences from a set of pools of 24-hour activity patterns. An individual 24-hour pattern in one of these pools is referred to here as a unit exposure sequence (UES). Each pool consists of a collection of UESs that are specific to selected demographic characteristics of the individual (e.g., ag e and gender), season, day type (weekday/weekend), and maximum daily temperature. A UES is a contiguous set of exposure ev ents spanning 24 hours. Each event is characterized by start time, duration in minut es, home/work status, microenvironment, and activity. All exposure events are constraine d to occur entirely within a clock hour.

The CFK model within the COHb module is called for each exposure event. For each event it requires the following data.
Time duration of event, min Inspired CO partial pressure averaged over the event, torr Percent COHb at the start of the event Alveolar ventilation rate, ml/min STPD Average pulmonary capillary oxygen pressure, torrOctober 2009 Draft – Do Not Cite or Quote C-10 Haldane Coefficient Equilibrium constant for the reaction of O 2 Atmospheric pressure, torr Blood volume, ml Total potential reduced hemoglobin content of blood, ml CO/ml STPD Pulmonary CO diffusion rate, ml/min/torr STPD Endogenous CO production rate, ml/min STPDOctober 2009Draft – Do Not Cite or QuoteC-11Table C-2. Principal Parameters Included in the Physiological Profile for Adults forApplications of APEX4.3.ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValueAge COHbVentilationrateDemographic group Randomly selected from population-weighted distribution specific to demographicgroupGender COHbVentilationrateDemographic group Randomly selected from population-weighted distribution specific to demographicgroupBody Weight COHbVentilationrateGenderAgeRandomly selected from population-weighted lognormal distribution with geometricmean (GM) and geometric standard deviation(GSD) distribution specific to age andgender derived from data from the National Health and Nutrition ExaminationSurvey (NHANES), for the years1999-2004 (Isaacs and Smith, 2005)Height COHb WeightGenderEstimated using equations developed by Johnson (1998) using height and weightdata provided by Brainard and Burmaster (1992).Males: height = 34.43 inches + (6.67)[ln(weight)] + (2.38 inches)(z)Females: height = 48.07 inches + (3.07)[ln(weight)] + (2.48 inches)(z)The z term is randomly selected from a unit normal [N(0,1)] distribution. Units:height (inches), weight (lbs).Menstrual phase COHb GenderAgeIf gender = female, menstrual phase was randomly assigned in alternating 14-daycycles according to the following age-specific probabilities.Age < 12 or >50: 100% premenstrualAge 12 through 50: 50% premenstrual, 50% postmenstrual.October 2009Draft – Do Not Cite or QuoteC-12ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValueBlood volume COHb GenderWeightHeightBlood volume (Vb) was determined according to gender by the following equationswhich are based on work by Allen et al.(1956) which was modified to accept theunits used for height and weight.Males: Vb= (20.4)(weight) + (0.00683)(H3) - 30Females: Vb= (14.6)(weight) + (0.00678)(H3) - 30Units: blood volume (ml), weight (lbs), height (inches).Hemoglobincontent of theblood, HbCOHb GenderAgeRandomly selected from normal distribution with arithmetic mean (AM) andarithmetic standard deviation (ASD) determined by gender and agebasedobtained from data from the National Health and Nutrition Examination Survey(NHANES), for theyears 1999-2004. (Isaacs and Smith, 2005)Units: grams of Hbper deciliter of bloodOctober 2009Draft – Do Not Cite or QuoteC-13ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValuePulmonary COdiffusion rate,DLCOCOHb GenderHeightAgePulmonary CO diffusion rate (DL) was determined according to gender, height, andage according to the following equationsobtained from a paper by Salorinne (1976)and modified to conform to the units used in the COHb module.Males:DLCO= (0.361)(height) - (0.232)(age) + 16.3 ml/min/torrFemales:DLCO= (0.556)(height) - (0.115)(age) - 5.97 ml/min/torrUnits:DLCO(ml/min/torr), height (inches), age (years).Given the alveolar ventilation rate forthe exposure event the associated adjustedpulmonary diffusion rate is calculated as:7.5000845.0)()(ALLVBaseDAdjustedDCOCOOctober 2009Draft – Do Not Cite or QuoteC-14ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValueEndogenous COproduction rateCOHb GenderAgeMenstrual phaseEndogenous CO production rate was randomly selected from a lognormaldistribution with geometric mean (GM)and geometric standard deviation (GSD)determined according to the following equations specific to age, gender, andmenstrual phase.Males, 18+: GM = 0.473, GSD = 1.316Females, 18+, premenstrual: GM = 0.497, GSD = 1.459Females, 18+, postmenstrual: GM = 0.311, GSD = 1.459Units: GM (ml/hr), GSD (dimensionless).Restingmetabolic rate(RMR)VentilationrateGenderAgeBody WeightSee Section 5.3.5 of this reportand Chapter 5 of US EPA (2008).Energyconversion factor(ECF)VentilationrateGender See Section 5.3.5 of thisreport and Chapter 5 of US EPA (2008).NVO2maxVentilationrateGenderAgeSee Section 5.3.5 of this report and Chapter 5 of US EPA (2008).VO2maxVentilationrateNVO2maxBody WeightSee Section 5.3.5 of this report and Chapter 5 of US EPA (2008).October 2009 Draft – Do Not Cite or Quote C-15 Demographic Profile of

Simulated Individual

Age Gender Weight

Height

Menstrual Phase

Endogenous CO Production Rate

Blood Volume

Pulmonary CO Diffusion Rate

Maximum Normalized Oxygen Uptake Rate (NVO 2max )

Maximum Oxygen Uptake Rate (VO 2max ) Resting Metabolic Rate (RMR) Total Hemoglobin Content of the Blood

Energy Conversion Factor (ECF)

Figure C-1. Flow Diagram for Phys iological Profile Generator. Input data is supplied at the start of the APEX4.3 computation.October 2009 Draft – Do Not Cite or Quote C-16 Given these data as inputs, the module com putes the percent COHb at the end of the exposure event. This value is used by the m odule as the initial percent COHb for the next contiguous exposure event. The module also co mputes the average percent COHb value for each exposure event. The main program retains thes e values and uses them to calculate percent COHb values for averaging times ranging from one hour to one day. Some of the above data do not change duri ng an APEX4.3 computer run and, therefore, need to be supplied to the computer program only on ce at the start. Some of the data vary with the individual and therefore need to be supplied at the beginning of each activity day. Other data tend to change with the exposure event and ther efore need to be supplied for each new exposure event.

Barometric Pressure A constant barometric pressure is assumed for the study area based on the average height above sea level: ) 0000386

. 0 exp( 760 Altitude P

B 

   (Eq. C-27)

where altitude is the average height (in feet) of the study area above s ea level (US EPA, 1978). The altitude was set at 5,183 feet for Denver and 328 feet for Los Angeles. Average Pulmonary Capillary Oxygen Pressure The equation employed is based on an a pproximation used by Peterson and Stewart (1975) in which 49 torr is subtracted from the partial pressure of inspired oxygen. This leads to the following approximate relationship:

49 ) 47 ( 209

. 0
2    B O P c P (Eq. C-28)

where 0.209 is the mole fraction of O 2 in dry air and 47 is the va por pressure of water at body temperature. This expression was used in an in vestigation of the CFK equation by Tikuisis et al. (1987). Often times a value 100 torr is commonly used as Equation (C-28) generates this value for a barometric pressure equivalent to 760 torr.

Haldane Coefficient

The value of 218 was used for the Haldane co efficient. While measured values in the range 210 to 270 have been reported in the extent literature, most research ers use values within the range of 210 to 240. In the early 1980's, the Clean Air Scientific Advisory Committee (CASAC) expressed the opinion to EPA (Friedlande r, 1982) that the most careful work done in this area was that by Rodkey (1969), who determined a value of 218. This value was selected for use in the COHb module of th e earlier CO-NEM exposure mode l. Other researchers using values in the range 218 to 220 include Peters on and Stewart, 1970; Ma rcus, 1980; Collier andOctober 2009 Draft – Do Not Cite or Quote C-17 Goldsmith, 1983; and Muller and Barton, 1987. As the value 218 falls within the range currently used by researchers, EPA analysts have electe d to continue using this value in APEX4.3. Equilibrium Constant for the Reaction of O 2 and RHb This quantity was estimated in Section C.2 to have the value 0.32 based on the observation that %[RHb] is about 3% in individuals breathing air which is free of CO and a value of 100 torr for
2 CO P

. Total Reduced Hemoglobin in the Absence of O 2 and CO The quantity [THb] 0 is expressed as equivalent milliliters of O 2 or CO at STPD per milliliter of blood. Total Hb blood levels are cu stomarily expressed as grams per deciliter of blood. The total Hb level in the absence of COHb and O 2 Hb would consist principally of RHb which can react with O 2 or CO and MetHb which cannot. Tota l Hb blood levels also tend to be higher in people living at higher altitudes. To relate [THb] 0 to Hb, it is therefore necessary to correct for the MetHb present, ad just for the effect of altit ude, and convert to equivalent milliliters of CO at STPD. The later conversi on is based on the observa tion that a gram of reduced Hb can react with a maximum of 1.39 ml of O 2 or CO at STPD. The application of these three factors yields the equation:
       

   100 1 ) % 100 ( 39

. 1 ] [ HbAlt MetHb Hb RHb O (Eq. C-29)

where HbAlt is the percent increase in Hb due to exposure to altitude and is given by (EPA 1978):
Altitude e HbAlt 0001249

. 0 76

. 2  Hb in equation (C-29) is a sea level value. Hb level in a human population is normally distributed with the mean Hb and standard devi ation both dependent on gender and age class (see entry in Table C-2 for the distri butions of Hb by age and gender). Given the hemoglobin content of the blood based on the distributio ns listed in Table C-2, [THb] 0 is calculated using equation (29). The weight percent MetHB, %MetHB, is take n to be 0.5% of the weight of Hb (Muller and Barton, 1987).

Determination of Weight Body mass or weight (in kg) was determined by fitting lognormal distributions to data organized by age and gender from the National H ealth and Nutrition Examination Survey for the years 1999-2004 (Isaacs and Smith, 2005). Distribu tion parameters were estimated for singleyear age cohorts for both genders for ages 0-85. As the NHA NES 1999-2004 studies only covered persons up to age 85, linear forecasts for the parameters were made for ages 86-100, as based on the data for ages 60 and greater.October 2009 Draft – Do Not Cite or Quote C-18 Determination of Height The following equations were used to estimate height as a function of gender and weight. Equations C-30 and C-31 were derived by Johnson ( 1998) using height and weight data provided by Brainard and Burmaster (1992). males: height = 34.43 inches + (6. 67)[ln(weight)] + (2.38 in ches)(z) (Eq. C-30) females: height = 48.07 inches + (3.07) [ln(weight)] + (2.48 inch es)(z) (Eq. C-31)

where the z term was randomly selected from a unit normal [N(0,1)] distribution. Base Pulmonary Diffusion Rate of CO A base lung diffusivity of CO for the individual is calculated as follows:

Men: 3

. 16 232

. 0 361

. 0  

   age height D
CO

L (Eq. C-32)

Women: 97

. 5 115

. 0 556

. 0

 

   age height D
CO

L (Eq. C-33)

where height is in inches and age is in years. The regression equations were obtained from a paper by Salorinne (1976) and modified to conform to the units used in the COHb m odule. The Salorinne data were obtained for nonexercising individuals. Tikuisis et al. (1992), working with elev en male subjects at various exercise levels, showed significant increase in lu ng diffusivity of CO w ith increasing alveolar ventilation rate. Regression anal yses of data provided by Tikuis is for the indivi dual subjects in the study showed the relationship to be linear. Fr om this relationship and the heights and ages of the subjects in the Tikuisis et al. study, it was determined that the Salorinne equations for male subjects correspond to an alveolar ventilation rate of 6.69 l/min STPD. In the absence of other data it is assumed that this same value applie s to women. Thus, for each twenty-four hour period equations C-32 and C-33 are used to compute lung diffusion rates of CO for a base case alveolar ventilation rate of 6.69 1/min STPD. As will be seen, this value is adjusted to account for the actual ventilation rate experien ced by the simulated individual during each individual exposure event. Endogenous Rate of CO Production The endogenous CO production rates taken from a number of sources show the rate to be distributed lognormally in the population (see Table C-3 for data and sources ). The distribution is different for men and women. For a woman there is a further diffe rence depending on whether she is in her premenstrual or postmenstrual phase. Table C-2 presen ts these distributions classified by class, gender , and menstrual phase.October 2009 Draft – Do Not Cite or Quote C-19 For each male individual, APEX4.3 specifies a single value for endogenous CO production rate and uses it for all days of the year. For each female individual between 18 and 64 years of age, APEX4.3 specifies one value of endogenous CO production rate to represent premenstrual days and one value to represent postmenstrual days. Female individuals under 12 years and older than 50 are assumed to be pr emenstrual; consequently, APEX4.3 specifies a single value for endogenous CO producti on rate to be used for all days of the year. The specified values are randomly selected from the appropria te distributions presented in Table C-2. A random number, z, is sampled from the standard ized normal distribution, N(0,1) to make each selection. The appropriate endogenous CO production rate is then obtained from:

z CO D S geom mean geom V .)

. . ( )

. ( 01667

. 0     (Eq. C-34)

The constant term converts ml/hr to ml/min. A probabilistic algorithm within APEX4.3 assi gns a menstrual phase to each day of the year for female individuals aged 12 to 50 y ears. The algorithm randomly assigns a number between 1 and 28 to January 1. The number is increased by one for each successive day until number 28 is reached. The next day is numbere d 1 and the 28-day numbering cycle is repeated until each day of the year has been assigned a number between 1 and 28. Days numbered 1 through 14 are identified as post- menstrual days; days numbered 15 through 28 are identified as pre-menstrual days.

INPUT DATA SUPPLIED WITH EACH EXPOSURE EVENT Duration of Exposure Event The duration of the exposure event in minutes is supplied by the main program to the COHb module. Partial Pressure of Inspired Carbon Monoxide The main program supplies the inspired CO concentration averaged over the duration of the exposure expressed as ppm. This quant ity is converted to pressure via:

6 10 ) 47 ( ) (
 

   b I P CO P
CO (Eq. C-35)

Initial Percent COHb Level at Start of Exposure Event The program retains the per cent COHb computed at the en d of the previous exposure event and uses this value as the initial percent C OHb for the present event. The starting COHb at the beginning of an activity day is the final COHb level at the end of the preceding activity day. This latter procedure is used for the first ac tivity day of the overall computation since the program starts the day before the overall pe riod covered by the APEX4.3 computation.October 2009 Draft – Do Not Cite or Quote C-20 Alveolar Ventilation Rate The main program supplies the COHb modul e with ventilation rate derived from the algorithm discussed in Section 5.3.5 of this report. Adjusted Pulmonary Diffusion Rate of CO

Given the alveolar ventilation rate for th e exposure event the associated adjusted pulmonary diffusion rate can be calculated from:

7

. 5 000845

. 0 ) ( ) (

   A L L V Base D Adjusted D
CO CO  (Eq. C-36)

Table C-3. Literature Data Used to Deri ve Geometric Mean and Standard Deviation Lognormal Distribution of E ndogenous CO Production Rate.

Study Author Values for Endogenous CO Production Rate 0.81 0.57 0.33 0.7 0.58 0.38 0.51 0.55 Brouillard et al. (1975) 0.37 0.49 0.45 0.5 0.33 0.45 0.36 Burke et al. (1974) 0.43 0.58 0.52 0.59 0.8 0.72 0.54

0.35 0.4 0.39 0.43 0.35 0.51 0.42 0.57 Coburn et al. (1963) 0.45 0.45 0.26 0.6 0.45 0.39 0.4 0.57 0.54 0.72 0.99 0.48 0.53 0.43DelivoriaPapadopoules et al. (1974) 0.23 0.51 0.34 0.41 0.26 0.16 0.3 Luomanmaki and Coburn (1969) 0.38 0.42 0.41 0.54 0.38

0.4 0.81 0.26 0.65 0.51 0.62 0.44 0.72 0.37 0.23 0.33 0.42 0.44 0.29 0.48 Lynch and Moede (1972) 0.48 0.23 0.25 0.2 0.22 0.15 0.21 0.64 0.86 0.35 0.52 0.8 0.54 0.68 0.28 Merke et al. (1975) 0.4 0.47 0.23 0.24 0.55 0.32 0.43 0.35 Werner and Lindahl (1980) 0.54 0.76 0.48 0.31 0.7 0.36 0.65October 2009 Draft – Do Not Cite or Quote C-21 C.5 References Biller WF and Richmond HM. 1982. Sensitivity Analysis on Coburn Model Predictions of COHb Levels Associated with Alternative CO Standards. Report to Strategies and Air Standards Division of the Office of Air quality Planning and Standards, U.S. Environmental Protection Agency, Resear ch Triangle Park, NC, November, 1982. Brouillard RP, Contrad ME, Bensinger TA. 1975. Effect of blood in th e gut on measurements of endogenous carbon monoxide production. Blood

. 45:67-69.

Brainard J and Burmaster D. 1992. Bivariate di stributions for height and weight of men and women in the United States. Risk Analysis . 12(2):267-275. Burke 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. Coburn RF, Forster RE, Kane RB. 1965. Consid erations of the physiology and variables that determine blood carboxyhemoglobin concentration in man. J Clin Invest . 44:1899-1910. Coburn RF, Blakemore WS, Forster RE. 1963. Endogenous carbon monoxide production in
man. J Clin Invest . 42:1172-1178.

Collier C and Goldsmith JR. 1983. Interacti ons of carbon monoxide and hemoglobin at high altitude. Atmos Environ

. 17:723-728. Delivoria-Papadoppulos M, Coburn RF, Forster RE. 1974. Cyclic variation of rate of carbon monoxide production in normal women. J Appl Physiol . 36:49-71.

Friedlander SK. 1982. Letter from the Clean Air Scientific Advisory Committee to US EPA Administrator. August 31, 1982. Glen G. 2002. Programmer’s Guide for the APEX3 Model. Prepared by ManTech Environmental Technology, Inc., for the U.S. Environmental Protection Agency, Research Triangle Park, NC. April 30. Johnson T and Paul RA. 1983. The NAAQS Mo del (NEM) Applied to Carbon Monoxide. EPA 450/5-83-003, U.S. Environmental Protecti on Agency, Research Triangle Park, NC. Johnson T. 1998. Memo No. 5: Equations for Converting Weight to Height Proposed for the 1998 Version of pNEM/CO. Memorandum Subm itted to U.S. Environmental Protection Agency. TRJ Environmental, Inc., 713 Shadylawn Road, Chapel Hill, North Carolina 27514. Johnson T, Capel J, Olaguer E, Wijnberg L. 1992. Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver Residents Us ing a Probabilistic Version of NEM. Prepared by TRJ Environmental, Inc., for the Office of Air QualityOctober 2009 Draft – Do Not Cite or Quote C-22 Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Johnson T, Mihlan G, LaPointe J, et al. 2000. Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Le vels in Residents of Denver and Los Angeles Using pNEM/CO (Version 2.1). Prepared by TRJ E nvironmental, Inc., for the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Luomanmaki K and Coburn RF. 1969. Effect s of metabolism and distribution of carbon monoxide on blood and body stores. Am J Physiol . 217(2):354-362. Lynch SR and Moede AL. 1972. Variati on in the rate of endogenous carbon monoxide production in normal human beings. J Lab Clin Med

. 79:85-95. Marcus AH. 1980. Mathematical models for carboxyhemoglobin. Atmos Environ

. 14:841-844. McCartney ML. 1990. Sensitivity analysis applied to Coburn-Forster-Kane models of carboxhemoglobin rormation. Am Ind Hyg Assoc J . 51(3):169-177. Merke C, Cavallin-Stahl E, Lundh B. 1975. Carbon monoxide production and reticulocyte count in normal women: effect of contraceptive drugs and smoking. Acta Med Scan

. 198:155-160.

Muller KE. and Barton CN. 1987. A nonlinear vers ion of the Coburn, Forster and Kane model of blood carboxyhemoglobin. Atmos Environ . 21:1963-1967. Peterson JE and Stewart RD. 1970. Absorption and elimination of carbon monoxide by inactive young men. Arch Environ Health . 21:165-171.

Peterson JE and Stewart RD. 1975. Predicti ng the carboxhemoglobin leve ls resulting from carbon monoxide exposures. J Appl Physiol . 39:633-638. Press WH, Flannery BP, Teukol sky SA, Vettering WT. 1986. Numerical Recipes: The Art of Scientific Computing. 1 st edition. Cambridge University Press, Cambridge MA. Rodkey FL, O’Neal JD, Collison HA. 1969. O xygen and carbon monoxide equilibria of human adult hemoglobin at atmospheri c and elevated pressure. Blood . 33:57-65. Salorinne Y. 1976. Single-breat h pulmonary diffusing capacity. Scand J Resp Diseases

. Supplementum 96. Tikuisis P, Madill HD, Gill BJ, Le wis WF, Cox KM, Kane DM. 1987. A critical analysis of the use of the CFK equation in predicting COHb formation. Am Ind Hyg Assoc J

. 48(3):208-213. Tikuisis P, Kane DM, McLellan TM, Buick F, Fairburn SM. 1992. Rate of formation ofOctober 2009 Draft – Do Not Cite or Quote C-23 carboxyhemoglobin in exercising humans exposed to carbon monoxide. J Appl Physiol

. 72(4):1311-1319.

US EPA. 1978. Altitude as a Factor in Air Pollution. EPA-600/9-78-015. U.S. Environmental Protection Agency, Research Triangle Park, NC. US EPA. 2008. Total Risk Integrated Me thodology (TRIM) Air Pollutants Exposure Model Documentation (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report No. EPA-452/B-08-001b. Office of Air Quality Planning and Standards, U.S. Environmental Protecti on Agency, Research Triangle Park, NC. Werner B and Lindahl J. 1980. Endogenous carb on monoxide production af ter bicycle exercise in healthy subjects and in patient s with hereditary spherocytosis. Scan J Lab Invest

. 40:319-324.

### Appendix D Apex Output Files

October, 2009 Draft – Do Not Cite or Quote D-1October, 2009 Draft – Do Not Cite or Quote D-2 Appendix D. Apex Output Files.

Output File Type Description Log The Log file contains the record of the APEX model simulation as it progresses. If the simulation completes successfully, the log file indicates the input files and paramete r settings used for the simulation and reports on a number of different factors. If the simulation ends prematurely, the log file contains error messages describing the critical errors that caused the simulation to end. Profile Summary The Profile Summary file provides a summary of each profile modeled in the simulation. Each line lists the person’s age, gender and race, in addition to a number of other person al profile variable s that the model uses to simulate exposure.

Sites The Sites file lists the sectors, air distri cts, and zones in the study area, and identifies the mapping between them. Hourly The Hourly file provides an hour-by-hour time series of exposures, doses, and other variables for each modeled profile. Daily The Daily file provides a day-by-day time series of exposures, doses, and other variables for each modeled profile.

Events The Events file contains event-level information (including MET, exposure, ventilation, and dose) fo r individuals in the simulation. Settings in the Control file allow the user to write this information for all persons, every Nth person, or for a set of specified profile IDs.
Microenvironment Summary The Microenvironment Summary file provides a summary of the time and exposure by microenvironment for each profile modeled in the simulation.
Microenvironment Results The Microenvironment Results file provides an hour-by-hour time series of microenvironment concentrations and parameters for a pollutant for each modeled profile for each location (“Home”, “Work”, and “Other”). A Microenvironment Results file is generated for each pollutant. Output Tables The Output Tables file contains a series of tables summarizing the exposure (and dose, if calculated) results of the simulation for a pollutant. The percentiles and exposu re/dose cut-off points used in these tables are defined in the Control file. A Tables file is generated for each pollutant.

### Appendix E Mapping of CHAD Location Codes to Microenvironments Defined for Application of APEX4.3 to Carbon Monoxide.

October, 2009 Draft – Do Not Cite or Quote E-1Appendix E. Mapping Of Chad Location Codes To Microenvironments Defined For Application Of Apex4.3 To Carbon Monoxide.

! Mapping of CHAD activity locations to two APEX microenvironments: in-vehicle (2) and other (1) CHAD Loc. Description APEX

--------- ---------------------------------------------- U Uncertain of correct code = 1 H X No data = 1 H 30000 Residence, general = 1 H 30010 Your residence = 1 H 30020 Other residence = 1 H 30100 Residence, indoor = 1 H 30120 Your residence, indoor = 1 H 30121 ..., kitchen = 1 H 30122 ..., living room or family room = 1 H 30123 ..., dining room = 1 H 30124 ..., bathroom = 1 H 30125 ..., bedroom = 1 H 30126 ..., study or office = 1 H 30127 ..., basement = 1 H 30128 ..., utility or laundry room = 1 H 30129 ..., other indoor = 1 H 30130 Other residence, indoor = 1 H 30131 ..., kitchen = 1 H 30132 ..., living room or family room = 1 H 30133 ..., dining room = 1 H 30134 ..., bathroom = 1 H 30135 ..., bedroom = 1 H 30136 ..., study or office = 1 H 30137 ..., basement = 1 H 30138 ..., utility or laundry room = 1 H 30139 ..., other indoor = 1 H 30200 Residence, outdoor = 1 H 30210 Your residence, outdoor = 1 H 30211 ..., pool or spa = 1 H 30219 ..., other outdoor = 1 H 30220 Other residence, outdoor = 1 H 30221 ..., pool or spa = 1 H 30229 ..., other outdoor = 1 H 30300 Residential garage or carport = 1 H 30310 ..., indoor = 1 H 30320 ..., outdoor = 1 H 30330 Your garage or carport = 1 H 30331 ..., indoor = 1 H 30332 ..., outdoor = 1 H 30340 Other residential garage or carport = 1 H 30341 ..., indoor = 1 H 30342 ..., outdoor = 1 H 30400 Residence, none of the above = 1 H 31000 Travel, general = 1 H 31100 Motorized travel = 2 H 31110 Car = 2 H 31120 Truck = 2 H 31121 Truck (pickup or van) = 2 H 31122 Truck (not pickup or van) = 2 H 31130 Motorcycle or moped = 2 H 31140 Bus = 2 H 31150 Train or subway = 1 H 31160 Airplane = 1 H 31170 Boat = 1 H 31171 Boat, motorized = 1 H 31172 Boat, other = 1 H 31900 Travel, other = 2 H 31910 ..., other vehicle = 2 H 31200 Non-motorized travel = 1 H 31210 Walk = 1 H 31220 Bicycle or inline skates/skateboard = 1 H 31230 In stroller or carried by adult = 1 H 31300 Waiting for travel = 1 H

October, 2009 Draft – Do Not Cite or Quote E-231310 ..., bus or train stop = 1 H 31320 ..., indoors = 1 H 31900 Travel, other = 2 H 31910 ..., other vehicle = 2 H 32000 Non-residence indoor, general = 1 H 32100 Office building/ bank/ post office = 1 H 32200 Industrial/ factory/ warehouse = 1 H 32300 Grocery store/ convenience store = 1 H 32400 Shopping mall/ non-grocery store = 1 H 32500 Bar/ night club/ bowling alley = 1 H 32510 Bar or night club = 1 H 32520 Bowling alley = 1 H 32600 Repair shop = 1 H 32610 Auto repair shop/ gas station = 1 H 32620 Other repair shop = 1 H 32700 Indoor gym /health club = 1 H 32800 Childcare facility = 1 H 32810 ..., house = 1 H 32820 ..., commercial = 1 H 32900 Large public building = 1 H 32910 Auditorium/ arena/ concert hall = 1 H 32920 Library/ courtroom/ museum/ theater = 1 H 33100 Laundromat = 1 H 31200 Non-motorized travel = 1 H 31210 Walk = 1 H 31220 Bicycle or inline skates/skateboard = 1 H 31230 In stroller or carried by adult = 1 H 31300 Waiting for travel = 1 H 31310 ..., bus or train stop = 1 H 31320 ..., indoors = 1 H 31900 Travel, other = 2 H 31910 ..., other vehicle = 2 H 32000 Non-residence indoor, general = 1 H 32100 Office building/ bank/ post office = 1 H 32200 Industrial/ factory/ warehouse = 1 H 32300 Grocery store/ convenience store = 1 H 32400 Shopping mall/ non-grocery store = 1 H 32500 Bar/ night club/ bowling alley = 1 H 32510 Bar or night club = 1 H 32520 Bowling alley = 1 H 32600 Repair shop = 1 H 32610 Auto repair shop/ gas station = 1 H 32620 Other repair shop = 1 H 32700 Indoor gym /health club = 1 H 32800 Childcare facility = 1 H 32810 ..., house = 1 H 32820 ..., commercial = 1 H 32900 Large public building = 1 H 32910 Auditorium/ arena/ concert hall = 1 H 32920 Library/ courtroom/ museum/ theater = 1 H 33100 Laundromat = 1 H 33200 Hospital/ medical care facility = 1 H 33300 Barber/ hair dresser/ beauty parlor = 1 H 33400 Indoors, moving among locations = 1 H 33500 School = 1 H 33600 Restaurant = 1 H 33700 Church = 1 H 33800 Hotel/ motel = 1 H 33900 Dry cleaners = 1 H 34100 Indoor parking garage = 1 H 34200 Laboratory = 1 H 34300 Indoor, none of the above = 1 H 35000 Non-residence outdoor, general = 1 H 35100 Sidewalk, street = 1 H 35110 Within 10 yards of street = 1 H 35200 Outdoor public parking lot /garage = 1 H 35210 ..., public garage = 1 H 35220 ..., parking lot = 1 H 35300 Service station/ gas station = 1 H 35400 Construction site = 1 H 35500 Amusement park = 1 H

October, 2009 Draft – Do Not Cite or Quote E-3October, 2009 Draft – Do Not Cite or Quote E-4 35600 Playground = 1 H 35610 ..., school grounds = 1 H 35620 ..., public or park = 1 H 35700 Stadium or amphitheater = 1 H 35800 Park/ golf course = 1 H 35810 Park = 1 H 35820 Golf course = 1 H 35900 Pool/ river/ lake = 1 H 36100 Outdoor restaurant/ picnic = 1 H 36200 Farm = 1 H 36300 Outdoor, none of the above = 1 H

### Appendix F Isaacs et al. (2009) Reference Used in Developing D and A Statistics Input to APEX Model
The following presents a reformatted version of th e Isaacs et al. (2009) presentation to allow for easier reading. The presentati on is included at the end of the Appendix in its entirety.

Statistical Properties of Long itudinal Time-Activity Data for Use in EPA Exposure Models Kristin Isaacs 1 , Thomas McCurdy 2 , April Errickson 3 , Susan Forbes 3 , Graham Glen 1 , Stephen Graham 4 , Lisa McCurdy 5 , Melissa Nysewander 1 , Luther Smith 1 , Nicolle Tulve 2 , and Daniel Vallero 2

1 Alion Science and Technology, Re search Triangle Park, NC, 2 Human Exposure and Atmospheric Sciences Division, National Expos ure Research Laboratory, US Environmental Protection Agency, Research Triangle Park, NC, 3 School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 4 Office of Air Quality Planning and Standards, US Environmental Protecti on Agency, Research Triangle Park, NC, 5 Homemaker, Durham, NC.

ABSTRACT Realistic simulation of longitudi nal activity patterns is necessa ry for appropriately reproducing the frequency and duration of pollutant exposures in human exposure models. In EPA’s exposure models, longitudinal activity diaries fo r simulated persons are constructed from the 1- day cross sectional activity di aries in the Consolidated Human Activity Database (CHAD). Recently, new algorithms have b een developed to construct l ongitudinal diaries from CHAD diaries based on realistic variance and autocorrelat ion properties of diary ch aracteristics relevant to pollutant exposure. Characteri stics of particular in terest include time spent in particular microenvironments and time spent in activities that produce high ventila tion rates. However, few multi-day data are currently available for estimating accurate statistical properties for these quantities. Results from a recent time-activit y study of 10 adults and one newborn child are presented here. The participants recorded th eir personal location and activity for two-week periods in each of four seasons in 2006 and 2007. The data were recorded 24 hours a day, in increments as small as one minute. Additional r ecording periods for these same individuals are expected in the future. The diaries for all subj ects were assessed to calculate the between-person variance, the within-person variance, and the autoco rrelation for various lags in the time spent in outdoor, residence, indoor (non-residence), and ve hicle microenvironments, as well as for time spent performing high-METS activities. The effec tiveness of various day- type definitions (for example, weekend versus weekday, or workday versus non-workday) for grouping similar diary days is examined. Seasonal variation in activit y patterns is analyzed. These data have the potential to aid in the developm ent of improved input variance and autocorrelation statistics for longitudinal diary assembly algorith ms in EPA’s human exposure models.

October 2009 Draft – Do Not Cite or Quote F-1INTRODUCTION Recently, new m ethods of assembling multi-day di aries in human exposure models from crosssectional single-day diaries have been proposed that are based on the variance and autocorrelation statistics of the simulated popul ation (Glen et al. 2007). Appropriately modeling intra- and interindividu al variability using such algorithms may be essential in producing appropriate estimates of exposure. In addition, re producing realistic autoco rrelations in key diary properties may be required for the m odeling of episodic exposure patterns. Previously, longitudinal time activity-location data collected in child ren in the Southern California Chronic Ozone Exposure Study (Geyh et al. 1999) have been analyzed to obtain estimates of appropriate measures of variance and autocorrelation for use in the longitudinal algorithm. Data from a new st udy in adults are now presented.

BACKGROUND Exposure models require construction of human activity diaries that cove r the entire simulation period of a model run. This period is often seve ral months, a year, or even longer. In EPA’s models, human activity diaries are usually dr awn from EPA’s CHAD (Consolidated Human Activity Database; McCurdy et al., 2000; http ://www.epa.gov/chadnet1), which typically includes just one day (24 hours) of activities fr om each person. A “longitudinal” diary is one that covers the same person over a long period of time. While the SHEDS modeling period may be of user-specified duration, it is assumed in this section to be one year, to provide a concrete example. Recently, a new longitudinal diary assembly al gorithm has been developed (Glen et al. 2007) based on the variance and autocorrelation proper ties of the modeled simulation. The new method requires the user to: 1) select the diary property most relevant to exposure for the current application (such as outdoor time or time spent in vehicles) 2) specify the D statistic, which relates the within-person and betw een-person variances for this diary property; and 3) specify the 1-day lag autocorrelation in this diary property. The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxics models. The new method allows the modeler to apportion the total variance in the key diary property into the within- and between-person variances σ w 2 and σ b 2 by specifying the D statistic, defined to be

2
w 2 b 2 b 2 2 b σσ σ σ σ D   D pertains to the population as a whole and is bounded by zero and one. A value of zero im plies all persons have the same average behavior, wher eas a value of one implies the greatest possible difference in mean behavior that is consistent with the total variance. In addition to targeting the within-person a nd between-person variances through setting the D statistic, the new diary assembly method opti onally allows targeting of the day-to-day autocorrelation. This is a measure of the tend ency for similar diarie s to occur on consecutive days. The lag-one autocorrelation in a variable y is for a person defined as 

#        

  

  N

1j 2 j 1j 1N

1j j yy/yyyyA

October 2009 Draft – Do Not Cite or Quote F-2The population autocorrelation A is the m ean of th e A values for all individuals. Autocorrelation could be of interest to the e xposure modeler if the concentra tion time series were strongly episodic, for example. In the diary assembly, a positive autocorrelation indicates a tendency for diaries with x-scores near each other to be used on consecutive days, while a negative autocorrelation indicates a tende ncy for dissimilar x-scores to be used on consecutive days. Some preliminary values of A have been derived from the same data that were used to estimate D (Glen et al., 2007).

METHODS Activity Diary Study Activity-location data were colle cted from 10 adults. Nine of the adults were working professionals; one was a stay-at-home parent. Nine of the adults recorded their personal location and activity for two-week peri ods in each of four seasons in 2006 and 2007. Additional data were collected in one of the male subjects in 1999 , another male (the 10th adult) in 2002, and in one of the females in 2008 (collected during mate rnity leave). The data were recorded 24 hours a day, in increments as small as one minute. In this preliminary analysis, the time spent outdoors, indoors, in travel, and performing hard work each day were calculated from the diaries. “Hard work” was self-reported by each individual, as defined as activities requiring heavy breathing and/or sweating. Daily high temperatures and precipitation amounts were acquired for each day of the study.

Variance and Autocorrelation Statistics Variance and lag-one autocorrelation statistics were calculated for th e studied individuals. Variance statistics were estimated for both the ra w measured variables (ie. time in minutes) and the scaled ranks of the variable for each person on a given day. The ratio of the between-person variance to the total variance (the sum of the between- and within-person variance) was calculated for the population. This ratio, calculated using the raw variable s, is the intraclass correlation coefficient (ICC), while the same rati o, calculated using the ranks , is D, the diversity statistic. The autocorrelation A was also calcul ated using both the raw va riables and the scaled ranks of the variables on each day for each person in the study. Analysis of Time Spent in Locations/Activities The longitudinal data were assessed to suppor t decisions on optimal diary pools for exposure modeling. Time spent in each of the examined lo cations/activities were assessed as a function of day of the week (weekday versus weekend), da y type (workday vers us nonworkday), season, temperature, precipitation, and gende r. These analyses were undertaken to assess the utility of different diary pool definitions. Optimal defi nitions of diary pools can adequately capture temporal patterns in activities while maximi zing the number of activity diaries available for sampling on a given day for a simulated individual. Differences between groups were assessed with the Wilcoxon signed rank test (for 2 groups) or the Kruskal-Wallis test (for more than 2 groups ). The Wilcoxon rank sum (two-sample) test was used to test differences between genders.

October 2009 Draft – Do Not Cite or Quote F-3RESULTS AND DISCUSSION Individual Variability Figure 1 sh ows an example of the individual variability in time spent in different locations/activities for a single ma le subject; a 367-day period from this subject is depicted. Distributions of time for this subject are also shown. These figures demonstrate the large amount of intra-individual variability that can be seen in longitudinal activ ity studies. Distributions of time spent in locations/activities for the population is shown in Figure 4[ sic 2 ]

.

Figure 1. Time series and distributions of time spent in locations/activities for 367 days of data from a single male subject. Note high degree of interpersonal va riability in behavior.

Figure 2. Distributions of time spent in differe nt activities for all days for all subjects.

October 2009 Draft – Do Not Cite or Quote F-4Variance and Autocorre lation Statistics D, ICC, and A values for the population for time sp ent in different locations/activities are given in Table 1 . Values of the ICC are lower than D; wh ile A for the raw variables were higher than A for the scaled ranks. These trends were also consistent with observed tends in the Southern California data. Values were also calculated by gender ( Table 2 ), temperature categories ( Table 3 ), and daytypes ( Table 4 ) where possible. The D and ranked A values were compared to th ose calculated for children from the Southern California Chronic Ozone Exposure Study (SCCOES). The diversity (D) for th is group of adults for outdoor time were higher than those calcula ted for the children (0.38 versus 0.19). The D values for travel time in the current study were al so higher (0.18 in children versus 0.36 in this study). These differences reflect the increased he terogeneity in these variable in the studied adults versus the (relatively homogenous) studie d children. The A values calculated for outdoor time in this study were virtually identical to those estimated using data from SCCOES. In general, differences between D by temperatur e and daytypes were notable, even considering the small number subjects in this study. There were gender differences observed in D; the mechanism of these differences is unclear, but are likely influenced by the activity patterns of the female who was not a worker. There were observed differences in A by temperat ure, but especially by daytype. This is not unexpected, as it is reasonable that the behavior of working adults is more consistent day-to-day on workdays. These trends should be confirmed by analysis of other l ongitudinal data. Note however, that such differences in are only im portant when strongly episodic behavior or exposure is of interest. In general, the valu es of D are much more relevant to exposure.

October 2009 Draft – Do Not Cite or Quote F-5Time Spent in Differ ent Locations/Activities The time spent in different locations/activities for different day types, seasons, temperature categories are presented in Figures 3-6 . The effects of gender and precipitation were also studied. There were no significant differences for these categories, and th us plots are not shown. The plotted data represent all days for all subj ects. The medians are represented by the midline of the boxes, the first and third quartiles by the ends of the boxe s, and the means by the stars. The whiskers extend to cover data that lies beyond the boxedbut within the quartiles plus 1.5 times the interquartile range. Poin ts outside this range are plotted. Results by day of the week and day type are presented in Figure 3 . Day type (workday versus nonworkday) was at least as good as day of the week in categorizing time/activities. This trend is similar to that seen in a recent analysis of the la rger, cross-sectional databa se of diaries from The National Human Activity Pattern Survey (NHAPS, da ta not shown). That analysis indicated that a workday/nonworkday was a better discriminator of time spent out side than a weekday/weekend split. As such, further comparisons are also presented for both workdays and nonworkdays.

October 2009 Draft – Do Not Cite or Quote F-6Figure 3. Time spent in differen t locations/activi ties as a function of day of the week, and daytype (workday versus nonworkdays).

The effect of season on time spent in locations/activities is shown in Figure 4 . Seasonal effects were apparent for time spent outdoors on nonwor kdays, and for time spent doing hard work. Travel was also affected by seas on, likely due to the large number of work-related travel days in the fall for this particular group of workers.

Figure 4. Time spent in different locations/activi ties as a function of season and daytype.

October 2009 Draft – Do Not Cite or Quote F-7The effect of tem perature category is shown in Figure 5 . The temperature category was defined as warmer = maximum temperature greater than or equal to 75 de grees, colder= maximum temperature less than 75 degrees. Temperature category was better than or as good as season in discriminating behavior in time spent outdoors, even when daytype was considered.

Figure 5. Time spent in different locations/acti vities as a function of temperature category (colder: max temp< 75 degrees, warmer: max temp  75 degrees) and daytype.

CONCLUSIONS

• The diversity (D) and autocorr elation (A) for this group of adults for outdoor time were higher than those calculated for children in a previous study. Thus these data provide some justification for considering age when considering D and A input values for EPA’s exposure models.

• While the current data suggest possible effects of temper ature, daytype and gender on diversity (D) and autocorrelation (A), more data from this and other studies are needed to confirm these findings. Such results could ai d in the fine-tuning of the longitudinal diary algorithm.

• The analysis of the time spent in locations was consistent with recent findings from from cross-sectional diary studies indicating th at workdays/nonworkdays may be a better grouping for diary pools than weekdays/weekends.

• Temperature category was at least as good as season in discriminating behavior for this population for time sprnt outdoors, especially when daytype was considered. Such breakdowns by temperature and daytype may e liminate the need for diary pools different seasons, providing larger pools for diary samp ling on a given day. Further analysis with other time-activity data can confirm this trend.

October 2009 Draft – Do Not Cite or Quote F-8October 2009 Draft – Do Not Cite or Quote F-9 FUTURE WORK

• We plan to repeat this type of study peri odically. Data will be compared to/combined with analyses of other available long itudinal time/location/activity studies.

DISCLAIMER The information in this document has been funded wholly (or in part) by the U. S. Environmental Protection Agency (EPA contract 68-D-00-206). It has been subj ected to review by the EPA and approved for publication. Approval does not signif y that the contents ne cessarily reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

REFERENCES Geyh A.S., Xue J., Ozkaynak H., and Spengler J. D. (2000). The Harvard Southern California Chronic Ozone Exposure study: assessing ozone exposure of grad eschool-age children in two southern California communities. Environ Health Perspect . 108:265–270. Glen G., Smith L., Isaacs K., McCurdy T., and Langstaff J. (2008). A new method of longitudinal diary assembly for human exposure modeling. J Expo Sci Environ Epidemiol

. 18(3):299-311. Xue J., McCurdy T., Spengler J., and Ozkaynak H. (2004). Understanding variability in time spent in selected locations for 7-12-year old children. J Expo Sci Environ Epdemiol. 14:222-233

. McCurdy T., Glen G., Smith L., and Lakkadi L. (2000). The National Exposure Research Laboratory's Consolidated Human Activity Database. J Expo Anal Environ Epidemiol . 10:566- 78.Isaacs et al. (2009) in original format.BACKGROUNDExposure models require construction of human activity diaries that cover theentire simulation period of a model run. This period is often several months, ayear, or even longer. In EPA’s models, human activity diaries are usually drawnfrom EPA’s CHAD (Consolidated Human Activity Database; McCurdy et al.,2000; http://www.epa.gov/chadnet1), which typically includes just one day (24hours) of activities from each person. A “longitudinal” diary is one that covers thesame person over a long period of time. While the SHEDS modeling period maybe of user-specified duration, it is assumed in this section to be one year, toprovide a concrete example.Recently, a new longitudinal diary assembly algorithm has been developed (Glenet al. 2007) based on the variance and autocorrelation properties of the modeledsimulation. The new method requires the user to:1)select the diary property most relevant to exposure for the currentapplication (such as outdoor time or time spent in vehicles)2)specify the D statistic, which relates the within-person and betweenperson variances for this diary property; and3)specify the 1-day lag autocorrelation in this diary property.The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxicsmodels.The new method allows the modeler to apportion the total variance in the keydiary property into the within- and between-person variancesσw2 andσb2 byspecifying the D statistic, defined to beD pertains to the population as a whole and is bounded by zero and one. A valueof zero implies all persons have the same average behavior, whereas a value ofone implies the greatest possible difference in mean behavior that is consistentwith the total variance.In addition to targeting the within-person and between-person variances throughsetting the D statistic, the new diary assembly method optionally allows targetingof the day-to-day autocorrelation. This is a measure of the tendency for similardiaries to occur on consecutive days. The lag-one autocorrelation in a variable yis for a person defined asThe population autocorrelation A is the mean of the A values for all individuals.Autocorrelation could be of interest to the exposure modeler if the concentrationtime series were strongly episodic, for example. In the diary assembly, a positiveautocorrelation indicates a tendency for diaries with x-scores near each other tobe used on consecutive days, while a negative autocorrelation indicates atendency for dissimilar x-scores to be used on consecutive days. Somepreliminary values of A have been derived from the same data that were used toestimate D (Glen et al., 2007).INTRODUCTIONRecently, new methods of assembling multi-day diaries in human exposuremodels from cross-sectional single-day diaries have been proposed that arebased on the variance and autocorrelation statistics of the simulated population(Glen et al. 2007). Appropriately modeling intra- and interindividual variabilityusing such algorithms may be essential in producing appropriate estimates ofexposure. In addition, reproducing realistic autocorrelations in key diaryproperties may be required for the modeling of episodic exposure patterns.Previously, longitudinal time activity-location data collected in children in theSouthern California Chronic Ozone Exposure Study (Geyh et al. 1999) havebeen analyzed to obtain estimates of appropriate measures of variance andautocorrelation for use in the longitudinal algorithm. Data from a new study inadults are now presented.CONCLUSIONS• The diversity (D) and autocorrelation (A) for this group of adults for outdoor time werehigher than those calculated for children in a previous study. Thus these data providesome justification for considering age when considering D and A input values for EPA’sexposure models.•While the current data suggest possible effects of temperature, daytype and gender ondiversity (D) and autocorrelation (A), more data from this and other studies are needed toconfirm these findings. Such results could aid in the fine-tuning of the longitudinal diaryalgorithm.• The analysis of the time spent in locations was consistent with recent findings from fromcross-sectional diary studies indicating that workdays/nonworkdays may be a bettergrouping for diary pools than weekdays/weekends.•Temperature category was at least as good as season in discriminating behavior for thispopulation for time sprnt outdoors, especially when daytype was considered. Suchbreakdowns by temperature and daytype may eliminate the need for diary pools differentseasons, providing larger pools for diary sampling on a given day. Further analysis withother time-activity data can confirm this trend.FUTURE WORK• We plan to repeat this type of study periodically. Data will be compared to/combined with analyses ofother available longitudinal time/location/activity studies.Statistical Properties of Longitudinal Time-Activity Data for Use in EPA Exposure ModelsKristin Isaacs1, Thomas McCurdy2, April Errickson3, Susan Forbes3, Graham Glen1, Stephen Graham4, Lisa McCurdy5, Melissa Nysewander1, Luther Smith1, Nicolle Tulve2, and Daniel Vallero21Alion Science and Technology, Research Triangle Park, NC,2Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle Park, NC,3School of Information andLibrary Science, University of North Carolina at Chapel Hill, Chapel Hill, NC,4Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, NC,5Homemaker, Durham, NC.ABSTRACTRealistic simulation of longitudinal activity patterns is necessary for appropriately reproducing thefrequency and duration of pollutant exposures in human exposure models. In EPA’s exposure models,longitudinal activity diaries for simulated persons are constructed from the 1-day cross sectionalactivity diaries in the Consolidated Human Activity Database (CHAD). Recently, new algorithms havebeen developed to construct longitudinal diaries from CHAD diaries based on realistic variance andautocorrelation properties of diary characteristics relevant to pollutant exposure. Characteristics ofparticular interest include time spent in particular microenvironments and time spent in activities thatproduce high ventilation rates. However, few multi-day data are currently available for estimatingaccurate statistical properties for these quantities. Results from a recent time-activity study of 10 adultsand one newborn child are presented here. The participants recorded their personal location andactivity for two-week periods in each of four seasons in 2006 and 2007. The data were recorded 24hours a day, in increments as small as one minute. Additional recording periods for these sameindividuals are expected in the future. The diaries for all subjects were assessed to calculate thebetween-person variance, the within-person variance, and the autocorrelation for various lags in thetime spent in outdoor, residence, indoor (non-residence), and vehicle microenvironments, as well asfor time spent performing high-METS activities. The effectiveness of various day-type definitions (forexample, weekend versus weekday, or workday versus non-workday) for grouping similar diary daysis examined. Seasonal variation in activity patterns is analyzed. These data have the potential to aidin the development of improved input variance and autocorrelation statistics for longitudinal diaryassembly algorithms in EPA’s human exposure models.DISCLAIMERThe information in this document has been funded wholly (or in part) by the U. S. Environmental Protection Agency (EPAcontract 68-D-00-206). It has been subjected to review by the EPA and approved for publication. Approval does not signify thatthe contents necessarily reflect the views of the Agency, nor does mention of trade names or commercial products constituteendorsement or recommendation for use.METHODSActivity Diary StudyActivity-location data were collected from 10 adults. Nine of the adults wereworking professionals; one was a stay-at-home parent. Nine of the adultsrecorded their personal location and activity for two-week periods in each offour seasons in 2006 and 2007. Additional data were collected in one of themale subjects in 1999, another male (the 10thadult) in 2002, and in one of thefemales in 2008 (collected during maternity leave). The data were recorded 24hours a day, in increments as small as one minute. In this preliminary analysis,the time spent outdoors, indoors, in travel, and performing hard work each daywere calculated from the diaries. “Hard work” was self-reported by eachindividual, as defined as activities requiring heavy breathing and/or sweating.Daily high temperatures and precipitation amounts were acquired for each dayof the study.Variance and Autocorrelation StatisticsVariance and lag-one autocorrelation statistics were calculated for the studiedindividuals. Variance statistics were estimated for both the raw measuredvariables (ie. time in minutes) and the scaled ranks of the variable for eachperson on a given day. The ratio of the between-person variance to the totalvariance (the sum of the between- and within-person variance) was calculatedfor the population. This ratio, calculated using the raw variables, is theintraclass correlation coefficient (ICC), while the same ratio, calculated usingthe ranks, is D, the diversity statistic. The autocorrelation A was also calculatedusing both the raw variables and the scaled ranks of the variables on each dayfor each person in the study.Analysis of Time Spent in Locations/ActivitiesThe longitudinal data were assessed to support decisions on optimal diarypools for exposure modeling. Time spent in each of the examinedlocations/activities were assessed as a function of day of the week (weekdayversus weekend), day type (workday versus nonworkday), season,temperature, precipitation, and gender. These analyses were undertaken toassess the utility of different diary pool definitions. Optimal definitions of diarypools can adequately capture temporal patterns in activities while maximizingthe number of activity diaries available for sampling on a given day for asimulated individual.Differences between groups were assessed with the Wilcoxon signed rank test(for 2 groups) or the Kruskal-Wallis test (for more than 2 groups). The Wilcoxonrank sum (two-sample) test was used to test differences between genders.Time Spent in Different Locations/ActivitiesThe time spent in different locations/activities for different day types, seasons,temperature categories are presented inFigures 3-6. The effects of gender andprecipitation were also studied. There were no significant differences for thesecategories, and thus plots are not shown.The plotted data represent all days for all subjects. The medians are represented by themidline of the boxes, the first and third quartiles by the ends of the boxes, and the meansby the stars. The whiskers extend to cover data that lies beyond the boxedbut within thequartiles plus 1.5 times the interquartile range. Points outside this range are plotted.Results by day of the week and day type are presented inFigure 3. Day type (workdayversus nonworkday) was at least as good as day of the week in categorizingtime/activities. This trend is similar to that seen in a recent analysis of the larger, crosssectional database of diaries from The National Human Activity Pattern Survey (NHAPS,data not shown). That analysis indicated that a workday/nonworkday was a betterdiscriminator of time spent outside than a weekday/weekend split. As such, furthercomparisons are also presented for both workdays and nonworkdays.The effect of season on time spent in locations/activities is shown inFigure 4. Seasonaleffects were apparent for time spent outdoors on nonworkdays, and for time spent doinghard work. Travel was also affected by season, likely due to the large number of workrelated travel days in the fall for this particular group of workers.The effect of temperature category is shown inFigure 5. The temperature category wasdefined as warmer = maximum temperature greater than or equal to 75 degrees, colder=maximum temperature less than 75 degrees. Temperature category was better than oras good as season in discriminating behavior in time spent outdoors, even whendaytype was considered.REFERENCESGeyh A.S., Xue J., Ozkaynak H., and Spengler J.D. The Harvard Southern California Chronic Ozone Exposure study:assessing ozone exposure of gradeschool- age children in two southern California communities. Environ HealthPerspect 2000: 108: 265–270.Glen G., Smith L., Isaacs, K., and Langstaff, J. 2007. A new method of longitudinal diary assembly for human exposuremodeling.J Exposure Sci and Environmental Epi.Xue J., McCurdy T., Spengler J., and Ozkaynak, H. 2004. Understanding variability in time spent in selected locations for 7-12-year old children.J Exposure Sci and Environmental Epi.14:222-233.McCurdy, T., G. Glen, L. Smith, and Y. Lakkadi, 2000, The National Exposure Research Laboratory's Consolidated HumanActivity Database.J Expo Anal Environ Epidemiol10:566-78.2w2b2b22bσσσσσD   N1j2j1j1N1jjyy/yyyyAFigure 1. Time series and distributions of time spent in locations/activities for367 days of data from a single male subject. Note high degree ofinterpersonal variability in behavior.Figure 2. Distributions of time spent in different activities for all days forall subjects.Figure 3. Time spent in different locations/activities as a function of day of theweek, and daytype (workday versus nonworkdays).Figure 4. Time spent in different locations/activities as a function of season anddaytype.Figure 5. Time spent in different locations/activities as a function of temperaturecategory (colder: max temp< 75 degrees, warmer: max temp75 degrees) anddaytype.RESULTS AND DISCUSSIONIndividual VariabilityFigure 1shows an example of the individual variability in time spent in differentlocations/activities for a single male subject; a 367-day period from this subject is depicted.Distributions of time for this subject are also shown. These figures demonstrate the largeamount of intra-individual variability that can be seen in longitudinal activity studies.Distributions of time spent in locations/activities for the population is shown inFigure 4.Variance and Autocorrelation StatisticsD, ICC, and A values for the population for time spent in different locations/activities are giveninTable 1. Values of the ICC are lower than D; while A for the raw variables were higherthan A for the scaled ranks. These trends were also consistent with observed tends in theSouthern California data. Values were also calculated by gender (Table 2), temperaturecategories (Table 3), and daytypes (Table 4) where possible.The D and ranked A values were compared to those calculated for children from the SouthernCalifornia Chronic Ozone Exposure Study (SCCOES). The diversity (D) for this group ofadults for outdoor time were higher than those calculated for the children (0.38 versus 0.19).The D values for travel time in the current study were also higher (0.18 in children versus0.36 in this study). These differences reflect the increased heterogeneity in these variable inthe studied adults versus the (relatively homogenous) studied children. The A valuescalculated for outdoor time in this study were virtually identical to those estimated using datafrom SCCOES.In general, differences between D by temperature and daytypes were notable, evenconsidering the small number subjects in this study. There were gender differences observedin D; the mechanism of these differences is unclear, but are likely influenced by the activitypatterns of the female who was not a worker.There were observed differences in A by temperature, but especially by daytype. This is notunexpected, as it is reasonable that the behavior of working adults is more consistent day-today on workdays. These trends should be confirmed by analysis of other longitudinal data.Note however, that such differences in are only important when strongly episodic behavior orexposure is of interest. In general, the values of D are much more relevant to exposure.P<0.002P<0.04NSP<0.02 P<0.006NSNSNSNSP<0.003P<0.001 NSP<0.001P<0.001NSNSNSP<0.0001P<0.02NSNSP<0.001P<0.001P<0.005BACKGROUNDExposure models require construction of human activity diaries that cover theentire simulation period of a model run. This period is often several months, ayear, or even longer. In EPA’s models, human activity diaries are usually drawnfrom EPA’s CHAD (Consolidated Human Activity Database; McCurdy et al.,2000; http://www.epa.gov/chadnet1), which typically includes just one day (24hours) of activities from each person. A “longitudinal” diary is one that covers thesame person over a long period of time. While the SHEDS modeling period maybe of user-specified duration, it is assumed in this section to be one year, toprovide a concrete example.Recently, a new longitudinal diary assembly algorithm has been developed (Glenet al. 2007) based on the variance and autocorrelation properties of the modeledsimulation. The new method requires the user to:1)select the diary property most relevant to exposure for the currentapplication (such as outdoor time or time spent in vehicles)2)specify the D statistic, which relates the within-person and betweenperson variances for this diary property; and3)specify the 1-day lag autocorrelation in this diary property.The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxicsmodels.The new method allows the modeler to apportion the total variance in the keydiary property into the within- and between-person variancesσw2 andσb2 byspecifying the D statistic, defined to beD pertains to the population as a whole and is bounded by zero and one. A valueof zero implies all persons have the same average behavior, whereas a value ofone implies the greatest possible difference in mean behavior that is consistentwith the total variance.In addition to targeting the within-person and between-person variances throughsetting the D statistic, the new diary assembly method optionally allows targetingof the day-to-day autocorrelation. This is a measure of the tendency for similardiaries to occur on consecutive days. The lag-one autocorrelation in a variable yis for a person defined asThe population autocorrelation A is the mean of the A values for all individuals.Autocorrelation could be of interest to the exposure modeler if the concentrationtime series were strongly episodic, for example. In the diary assembly, a positiveautocorrelation indicates a tendency for diaries with x-scores near each other tobe used on consecutive days, while a negative autocorrelation indicates atendency for dissimilar x-scores to be used on consecutive days. Somepreliminary values of A have been derived from the same data that were used toestimate D (Glen et al., 2007).INTRODUCTIONRecently, new methods of assembling multi-day diaries in human exposuremodels from cross-sectional single-day diaries have been proposed that arebased on the variance and autocorrelation statistics of the simulated population(Glen et al. 2007). Appropriately modeling intra- and interindividual variabilityusing such algorithms may be essential in producing appropriate estimates ofexposure. In addition, reproducing realistic autocorrelations in key diaryproperties may be required for the modeling of episodic exposure patterns.Previously, longitudinal time activity-location data collected in children in theSouthern California Chronic Ozone Exposure Study (Geyh et al. 1999) havebeen analyzed to obtain estimates of appropriate measures of variance andautocorrelation for use in the longitudinal algorithm. Data from a new study inadults are now presented.CONCLUSIONS• The diversity (D) and autocorrelation (A) for this group of adults for outdoor time werehigher than those calculated for children in a previous study. Thus these data providesome justification for considering age when considering D and A input values for EPA’sexposure models.•While the current data suggest possible effects of temperature, daytype and gender ondiversity (D) and autocorrelation (A), more data from this and other studies are needed toconfirm these findings. Such results could aid in the fine-tuning of the longitudinal diaryalgorithm.• The analysis of the time spent in locations was consistent with recent findings from fromcross-sectional diary studies indicating that workdays/nonworkdays may be a bettergrouping for diary pools than weekdays/weekends.•Temperature category was at least as good as season in discriminating behavior for thispopulation for time sprnt outdoors, especially when daytype was considered. Suchbreakdowns by temperature and daytype may eliminate the need for diary pools differentseasons, providing larger pools for diary sampling on a given day. Further analysis withother time-activity data can confirm this trend.FUTURE WORK• We plan to repeat this type of study periodically. Data will be compared to/combined with analyses ofother available longitudinal time/location/activity studies.Statistical Properties of Longitudinal Time-Activity Data for Use in EPA Exposure ModelsKristin Isaacs1, Thomas McCurdy2, April Errickson3, Susan Forbes3, Graham Glen1, Stephen Graham4, Lisa McCurdy5, Melissa Nysewander1, Luther Smith1, Nicolle Tulve2, and Daniel Vallero21Alion Science and Technology, Research Triangle Park, NC,2Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle Park, NC,3School of Information andLibrary Science, University of North Carolina at Chapel Hill, Chapel Hill, NC,4Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, NC,5Homemaker, Durham, NC.ABSTRACTRealistic simulation of longitudinal activity patterns is necessary for appropriately reproducing thefrequency and duration of pollutant exposures in human exposure models. In EPA’s exposure models,longitudinal activity diaries for simulated persons are constructed from the 1-day cross sectionalactivity diaries in the Consolidated Human Activity Database (CHAD). Recently, new algorithms havebeen developed to construct longitudinal diaries from CHAD diaries based on realistic variance andautocorrelation properties of diary characteristics relevant to pollutant exposure. Characteristics ofparticular interest include time spent in particular microenvironments and time spent in activities thatproduce high ventilation rates. However, few multi-day data are currently available for estimatingaccurate statistical properties for these quantities. Results from a recent time-activity study of 10 adultsand one newborn child are presented here. The participants recorded their personal location andactivity for two-week periods in each of four seasons in 2006 and 2007. The data were recorded 24hours a day, in increments as small as one minute. Additional recording periods for these sameindividuals are expected in the future. The diaries for all subjects were assessed to calculate thebetween-person variance, the within-person variance, and the autocorrelation for various lags in thetime spent in outdoor, residence, indoor (non-residence), and vehicle microenvironments, as well asfor time spent performing high-METS activities. The effectiveness of various day-type definitions (forexample, weekend versus weekday, or workday versus non-workday) for grouping similar diary daysis examined. Seasonal variation in activity patterns is analyzed. These data have the potential to aidin the development of improved input variance and autocorrelation statistics for longitudinal diaryassembly algorithms in EPA’s human exposure models.DISCLAIMERThe information in this document has been funded wholly (or in part) by the U. S. Environmental Protection Agency (EPAcontract 68-D-00-206). It has been subjected to review by the EPA and approved for publication. Approval does not signify thatthe contents necessarily reflect the views of the Agency, nor does mention of trade names or commercial products constituteendorsement or recommendation for use.METHODSActivity Diary StudyActivity-location data were collected from 10 adults. Nine of the adults wereworking professionals; one was a stay-at-home parent. Nine of the adultsrecorded their personal location and activity for two-week periods in each offour seasons in 2006 and 2007. Additional data were collected in one of themale subjects in 1999, another male (the 10thadult) in 2002, and in one of thefemales in 2008 (collected during maternity leave). The data were recorded 24hours a day, in increments as small as one minute. In this preliminary analysis,the time spent outdoors, indoors, in travel, and performing hard work each daywere calculated from the diaries. “Hard work” was self-reported by eachindividual, as defined as activities requiring heavy breathing and/or sweating.Daily high temperatures and precipitation amounts were acquired for each dayof the study.Variance and Autocorrelation StatisticsVariance and lag-one autocorrelation statistics were calculated for the studiedindividuals. Variance statistics were estimated for both the raw measuredvariables (ie. time in minutes) and the scaled ranks of the variable for eachperson on a given day. The ratio of the between-person variance to the totalvariance (the sum of the between- and within-person variance) was calculatedfor the population. This ratio, calculated using the raw variables, is theintraclass correlation coefficient (ICC), while the same ratio, calculated usingthe ranks, is D, the diversity statistic. The autocorrelation A was also calculatedusing both the raw variables and the scaled ranks of the variables on each dayfor each person in the study.Analysis of Time Spent in Locations/ActivitiesThe longitudinal data were assessed to support decisions on optimal diarypools for exposure modeling. Time spent in each of the examinedlocations/activities were assessed as a function of day of the week (weekdayversus weekend), day type (workday versus nonworkday), season,temperature, precipitation, and gender. These analyses were undertaken toassess the utility of different diary pool definitions. Optimal definitions of diarypools can adequately capture temporal patterns in activities while maximizingthe number of activity diaries available for sampling on a given day for asimulated individual.Differences between groups were assessed with the Wilcoxon signed rank test(for 2 groups) or the Kruskal-Wallis test (for more than 2 groups). The Wilcoxonrank sum (two-sample) test was used to test differences between genders.Time Spent in Different Locations/ActivitiesThe time spent in different locations/activities for different day types, seasons,temperature categories are presented inFigures 3-6. The effects of gender andprecipitation were also studied. There were no significant differences for thesecategories, and thus plots are not shown.The plotted data represent all days for all subjects. The medians are represented by themidline of the boxes, the first and third quartiles by the ends of the boxes, and the meansby the stars. The whiskers extend to cover data that lies beyond the boxedbut within thequartiles plus 1.5 times the interquartile range. Points outside this range are plotted.Results by day of the week and day type are presented inFigure 3. Day type (workdayversus nonworkday) was at least as good as day of the week in categorizingtime/activities. This trend is similar to that seen in a recent analysis of the larger, crosssectional database of diaries from The National Human Activity Pattern Survey (NHAPS,data not shown). That analysis indicated that a workday/nonworkday was a betterdiscriminator of time spent outside than a weekday/weekend split. As such, furthercomparisons are also presented for both workdays and nonworkdays.The effect of season on time spent in locations/activities is shown inFigure 4. Seasonaleffects were apparent for time spent outdoors on nonworkdays, and for time spent doinghard work. Travel was also affected by season, likely due to the large number of workrelated travel days in the fall for this particular group of workers.The effect of temperature category is shown inFigure 5. The temperature category wasdefined as warmer = maximum temperature greater than or equal to 75 degrees, colder=maximum temperature less than 75 degrees. Temperature category was better than oras good as season in discriminating behavior in time spent outdoors, even whendaytype was considered.REFERENCESGeyh A.S., Xue J., Ozkaynak H., and Spengler J.D. The Harvard Southern California Chronic Ozone Exposure study:assessing ozone exposure of gradeschool- age children in two southern California communities. Environ HealthPerspect 2000: 108: 265–270.Glen G., Smith L., Isaacs, K., and Langstaff, J. 2007. A new method of longitudinal diary assembly for human exposuremodeling.J Exposure Sci and Environmental Epi.Xue J., McCurdy T., Spengler J., and Ozkaynak, H. 2004. Understanding variability in time spent in selected locations for 7-12-year old children.J Exposure Sci and Environmental Epi.14:222-233.McCurdy, T., G. Glen, L. Smith, and Y. Lakkadi, 2000, The National Exposure Research Laboratory's Consolidated HumanActivity Database.J Expo Anal Environ Epidemiol10:566-78.2w2b2b22bσσσσσD   N1j2j1j1N1jjyy/yyyyAFigure 1. Time series and distributions of time spent in locations/activities for367 days of data from a single male subject. Note high degree ofinterpersonal variability in behavior.Figure 2. Distributions of time spent in different activities for all days forall subjects.Figure 3. Time spent in different locations/activities as a function of day of theweek, and daytype (workday versus nonworkdays).Figure 4. Time spent in different locations/activities as a function of season anddaytype.Figure 5. Time spent in different locations/activities as a function of temperaturecategory (colder: max temp< 75 degrees, warmer: max temp75 degrees) anddaytype.RESULTS AND DISCUSSIONIndividual VariabilityFigure 1shows an example of the individual variability in time spent in differentlocations/activities for a single male subject; a 367-day period from this subject is depicted.Distributions of time for this subject are also shown. These figures demonstrate the largeamount of intra-individual variability that can be seen in longitudinal activity studies.Distributions of time spent in locations/activities for the population is shown inFigure 4.Variance and Autocorrelation StatisticsD, ICC, and A values for the population for time spent in different locations/activities are giveninTable 1. Values of the ICC are lower than D; while A for the raw variables were higherthan A for the scaled ranks. These trends were also consistent with observed tends in theSouthern California data. Values were also calculated by gender (Table 2), temperaturecategories (Table 3), and daytypes (Table 4) where possible.The D and ranked A values were compared to those calculated for children from the SouthernCalifornia Chronic Ozone Exposure Study (SCCOES). The diversity (D) for this group ofadults for outdoor time were higher than those calculated for the children (0.38 versus 0.19).The D values for travel time in the current study were also higher (0.18 in children versus0.36 in this study). These differences reflect the increased heterogeneity in these variable inthe studied adults versus the (relatively homogenous) studied children. The A valuescalculated for outdoor time in this study were virtually identical to those estimated using datafrom SCCOES.In general, differences between D by temperature and daytypes were notable, evenconsidering the small number subjects in this study. There were gender differences observedin D; the mechanism of these differences is unclear, but are likely influenced by the activitypatterns of the female who was not a worker.There were observed differences in A by temperature, but especially by daytype. This is notunexpected, as it is reasonable that the behavior of working adults is more consistent day-today on workdays. These trends should be confirmed by analysis of other longitudinal data.Note however, that such differences in are only important when strongly episodic behavior orexposure is of interest. In general, the values of D are much more relevant to exposure.P<0.002P<0.04NSP<0.02 P<0.006NSNSNSNSP<0.003P<0.001 NSP<0.001P<0.001NSNSNSP<0.0001P<0.02NSNSP<0.001P<0.001P<0.005October 2009Draft – Do Not Cite or QuoteF-10

### Appendix G Analysis of CHAD Diaries fo r Time Spent in Vehicles.
The U. S. Census Bureau (2009) provides an on-line facility for accessing the detailed census data included in their Summar y File 3 (SF3). Using this resource, we obtained information on travel time to work for “workers 16 years and over” (US Census Bureau, 2009, Table P31) specific to Denver County, Colorado and Los Angeles, CA. The counts in Table P31 for trips to work places other than home were converted into the percentages listed in Columns 2 and 3 of Ta ble G-1. Although the P31 statistics applied to people 16 years or older, we assumed that the statistics were ge nerally applicable to people 18 years or older. We next determined the number of 24 -hour diaries in CHAD that met the following criteria: the subject was 18+ years of age and the diary reported at least one minute in a motor vehicle between 6 am and 9 am. The number of th ese diaries that had in-vehicle times corresponding to the bins listed in Table G-1 are listed in Column 4 in and the values were converted to the pe rcentage values listed in Column 5.

Table G-1. Representation of Denver and LA Commuting Characteristics in CHAD Diaries.

a Subjects are 18+ years of age. Diaries include 1+ minute in motor vehicle between 6 am and 9 am. 24-hour diaries meeting inclusion criteria a Travel time

(minutes ) (1) Percent of commuters according

to SF3 census data for Denver County (2) Percent of commuters according to SF3 census data for Los

Angeles County (3) Number in CHAD (4) Percent in CHAD (5)

1 to 9 10.28 7.75 563 9.79 10 to 19 31.96 25.92 1,676 29.16 20 to 29 24.15 21.04 1,068 18.58 30 to 39 18.60 21.37 1,111 19.33 40 to 59 9.29 13.57 665 11.57 60 to 89 3.80 6.99 407 7.08 90+ 1.73 3.35 258 4.49 Total 100.00 100.00 5,748 100.00United StatesOffice of Air Quality Planning and StandardsPublication No. EPA-452/D-09-008Environmental ProtectionHealth and Environmental Impacts DivisionOctober 2009AgencyResearch Triangle Park, NC