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

Second External Review DraftEPA-452/P-10-004 February, 2010



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

Second 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 Dr. Stephen Graham, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, Research Triangle Park, North Carolina 27711 (email: graham.stephen@epa.gov ). Elements of this report have been provided to the U.S. Environmental Protection Agency (EPA) by Abt Associates, Inc. and TRI Environmental, In c. in partial fulfillment of Contract No. EP-D08-100, Work Assignments 0-08 and 1-15.February 2010 Draft -Do Not Quote or Cite i 

### Table of Contents

List of Figures........................................................................................................ iv

List of Tables ......................................................................................................... iv

1 INTRODUCTION................................................................................................. 1-1

1.1 BACKGROUND .................................................................................... 1-1
1.2 PREVIOUS REVIEWS AND ASSESSMENTS.................................... 1-3 1.3 CURRENT REVIEW, CASAC A DVICE AND PUBLIC COMMENT 1-6
1.4 REFERENCES ....................................................................................... 1-8

2 CONCEPTUAL OVERVIEW: ASSESSING AM BIENT CARBON
MONOXIDE EXPOSURE AND RISK .............................................................. 2-1

2.1 SOURCES OF CARBON MONOXIDE................................................ 2-1
2.2 EXPOSURE PATHWAYS AND IMPORTANT
MICROENVIRONMENTS................................................................... 2-2
2.3 EXPOSURE AND DOSE METRICS .................................................... 2-6
2.4 AT-RISK POPULATIONS .................................................................... 2-7
2.5 HEALTH ENDPOINTS ......................................................................... 2-9
2.5.1 Cardiovascular Disease- related Effects .............................................. 2-10
2.5.2 Other Effects ....................................................................................... 2-12
2.6 RISK CHARACTERIZATION APPROACH........................................ 2-13
2.7 KEY OBSERVATIONS......................................................................... 2-17
2.8 REFERENCES ....................................................................................... 2-19
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 General Patterns of CO Concentrations.............................................. 3-4 3.1.4 Policy-Relevant Background Concentrations ..................................... 3-11
3.1.5 Within-Monitor CO Concentration Trends......................................... 3-11
3.2 STUDY AREAS SELECTED FOR CURRENT ASSESSMENT......... 3-18
3.3 KEY OBSERVATIONS......................................................................... 3-18
3.4 REFERENCES ....................................................................................... 3-20February 2010 Draft -Do Not Quote or Cite ii 4 OVERVIEW OF APEX MODELING SYSTEM FOR ESTIMATING CO
EXPOSURE AND COHB DOSE LEVELS........................................................ 4-1

4.1 PURPOSE............................................................................................... 4-1
4.2 MODEL OVERVIEW............................................................................ 4-2
4.3 MODEL HISTORY AND EVOLUTION .............................................. 4-2
4.4 MODEL SIMULATION PROCESS...................................................... 4-3 4.4.1 Characterize Study Area ..................................................................... 4-6
4.4.2 Generate Simulated Individuals.......................................................... 4-6 4.4.3 Construct Activity Sequences ............................................................. 4-8
4.4.4 Calculate Microenvironme ntal Concentrations .................................. 4-13 4.4.5 Estimate Energy Expenditure and Ventilation Rates.......................... 4-30 4.4.6 Calculate Exposure ............................................................................. 4-32
4.4.7 Calculate Dose .................................................................................... 4-33 4.4.8 Model Output ...................................................................................... 4-34
4.6 KEY OBSERVATIONS......................................................................... 4-34
4.7 REFERENCES ....................................................................................... 4-35

5 APPLICATION OF APEX4.3 IN THIS ASSESSMENT.................................. 5-1

5.1 PURPOSE............................................................................................... 5-1
5.2 OVERVIEW ........................................................................................... 5-2
5.3 STUDY AREAS..................................................................................... 5-3
5.4 EXPOSURE PERIODS .......................................................................... 5-3
5.5 STUDY POPULATION......................................................................... 5-7 5.5.1 Total and Simulated Population.......................................................... 5-7 5.5.2 Selected at-Risk Subpopulation .......................................................... 5-7 5.5.3 Time-Location-Activity Patterns ........................................................ 5-10 5.5.4 Construction of Longitudinal Diaries ................................................. 5-10
5.6 EXPOSURE SCENARIOS..................................................................... 5-10 5.7 AMBIENT AIR QUALITY DATA ....................................................... 5-11 5.7.1 Unadjusted 1-Hour Ambient Concentrations...................................... 5-11 5.7.2 Method for Estimating of Missing 1- Hour Ambient Concentrations . 5-12 5.7.3 Adjusted 1-Hour Ambient Concentrations ......................................... 5-16
5.8 METEOROLOGICAL DATA ............................................................... 5-20 5.8.1 Method for Estimating of Missi ng 1-Hour Temperature Data ........... 5-21
5.9 MICROENVIRONMENTS MODELED ............................................... 5-22 5.9.1 The Micronenvironmental Model as Implemented by APEX4.3 ....... 5-22February 2010 Draft -Do Not Quote or Cite iii 5.9.2 Microenvironme ntal Mapping ............................................................ 5-25
5.9.3 Selection of Microenvir onmental Method Used................................. 5-26 5.9.4 Air Exchange Rates and Air Conditioning Prevalence....................... 5-26
5.10 KEY OBSERVATIONS......................................................................... 5-28
5.11 REFERENCES ....................................................................................... 5-30

6 SIMULATED EXPOSURE AND COHB DOSE RESULTS............................ 6-1

6.1 ESTIMATED EXPOSURES.................................................................. 6-2 6.1.1 Air quality as is ................................................................................... 6-2 6.1.2 Air quality adjusted to just m eet the current 8-hour standard............. 6-4 6.1.3 Air quality adjusted to just meet alternative air quality scenarios ...... 6-6
6.2 ESTIMATED COHB DOSE LEVELS .................................................. 6-10 6.2.1 Air quality as is ................................................................................... 6-10 6.2.2 Air quality adjusted to just m eet the current 8-hour standard............. 6-11 6.2.3 Air quality adjusted to just meet alternative air quality scenarios ...... 6-13
6.3 COMPARISON OF COHB ESTIMA TES OBTAINED FROM THE 2000
PNEM/CO AND DRAFT 2009 APEX /CO ASSESSMENTS ............... 6-14
6.4 EVALUATION OF ENDOGENOUS CO CONTRIBUTION TO COHB
LEVELS IN APEX SIMULATED INDIVIDUALS.............................. 6-17 6.4.1 Estimation of Endogenous CO C ontribution to Population COHb
Levels.................................................................................................. 6-17 6.4.2 Contribution of Endogenous CO Production and Ambient Exposures to
COHb Level ........................................................................................ 6-19
6.5 KEY OBSERVATIONS......................................................................... 6-23
6.6 REFERENCES ....................................................................................... 6-25

7 VARIABILITY ANALYSIS AND UNCERTAINTY

CHARACTERIZATION ..................................................................................... 7-1

7.1 ANALYSIS OF VARIABILITY............................................................ 7-1
7.2 CHARACTERIZATION OF UNCERTAINTY..................................... 7-3
7.3 KEY OBSERVATIONS......................................................................... 7-10
7.4 REFERENCES ....................................................................................... 7-11

8 SUMMARY OF KEY OBSERVATIONS .......................................................... 8-1February 2010 Draft -Do Not Quote or Cite iv 

### List of Figures

Figure 3-1 Spatial and Temporal Trends in th e 2nd Highest 1-hour (top) and 8-hour Average (bottom) CO Ambient Monitoring Concentr ations in Denver, Colorado(left) and Los Angeles, California (ri ght),Years 1993 – 2008 ......................................... 3-6 Figure 3-2 Diurnal Distribut ion of 1-hour CO Concentr ations in Denver (Monitor 080310002) by Day-type (weekdays-left; w eekends-right), Years 1995 (top) and 2006 (bottom). ................................................................................................ 3-9 Figure 3-3 Diurnal distributi on of 1-hour CO concentratio ns in Los Angeles (Monitor 060371301) by day-type (weekdays-left; w eekends-right), years 1997 (top) and 2006 (bottom). .................................................................................................. 3-10 Figure 3-4 Comparison of high concentration year (1997) with low concentration year (2006) ambient air quality in Los Angeles. The 0 through 100th percentiles of the quality distribution are plotted for each monitor-year...................................... 3-13 Figure 4-1 Conceptual model and simplified data flow for estimating population exposure and dose using APEX....................................................................................... 4-5 Figure 5-1 Ambient monitor locations, air dist ricts (black circles), meteorological zones (blue circles), and study area (red circ le) for the Denver exposure modeling domain.............................................................................................................. 5-4 Figure 5-2 Ambient monitor locations, air dist ricts (black circles), meteorological zones (blue and pink circles), and study area (red circle) for the Los Angeles exposure modeling domain.............................................................................................. 5-5 Figure 6-1 Histogram of the percent COHb ambient contribution estimated using Denver 1995 ambient concentrations adjusted to just meet the current standard. ........ 6-20 Figure 6-2 The contribution of endogenous CO production relative to an individual’s maximum end-of-hour COHb level usi ng 1995 Denver ambient concentrations adjusted to just meet the current standard. ....................................................... 6-21 Figure 6-3 Comparison of endogenous CO producti on relative to an individual’s maximum COHb ambient contribution using 1995 De nver ambient concentrations adjusted to just meet the current standard....................................................................... 6-22 Figure 6-4. Comparison of endogenous CO produc tion relative to an individual’s maximum COHb ambient contribution using 1995 De nver ambient concentrations adjusted to just meet the current standard....................................................................... 6-23



### List of Tables

Table 3-1. Within monitor temporal variabil ity in Denver using historical (1995-97) and recent (2005-07) air quality data - 2nd highest 8-hour average ....................... 3-15 Table 3-2. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (2005-07) air quality data - 2nd highest 8-hour average. ..................... 3-15 Table 3-3. Within monitor temporal variabil ity in Denver using historical (1995-97) and recent (2005-07) air quality data – 99th percentile 1-hour daily maximum..... 3-16 Table 3-4. Within monitor temporal variabil ity in Los Angeles using historical (1995-97) and recent (2005-07) air quality data – 99th percentile 1-hour dailyFebruary 2010 Draft -Do Not Quote or Cite v maximum.......................................................................................................... 3-16 Table 3-5. Within monitor temporal variab ility in Denver using historical (1995-97) and recent (2005-07) air quality data – 99th percentile 8-hour daily maximum..... 3-17 Table 3-6. Within monitor temporal variabil ity in Los Angeles using historical (1995-97) and recent (2005-07) air quality data – 99th percentile 8-hour daily maximum.......................................................................................................... 3-17 Table 4-1. Summary of activity pattern studie s comprising the recent version of CHAD 4-10 Table 4-2. Variables used by APEX4.3 in the mass balance model.................................. 4-15 Table 4-3. Variables used by APEX4.3 in the factors model ............................................ 4-16 Table 4-4. Estimated values of distribution pa rameters and variables in equation 4-11 as implemented in the application of pNEM/CO to Denver and Los Angeles (Johnson et al., 2000). ...................................................................................... 4-21 Table 4-5. Parameters of B ounded Lognormal Distributions De fined for Proximity Factors Used in Applications of APEX3.1 to Los Angeles (Johnson and Capel, 2003)................................................................................................................. 4-29 Table 5-1. Attributes of fixed-site mon itors selected for the Denver study area............... 5-3 Table 5-2. Attributes of fixed-site monitors selected for the Los Angeles study area....... 5-4 Table 5-3. National prevalence rates for coronary heart disease by age range.................. 5-8 Table 5-4 National prevalence rates for coronary heart disease by gender ...................... 5-8 Table 5-5 National prevalence rates for coronary heart disease stratified by age range and gender ............................................................................................................... 5-8 Table 5-6 National prevalence rates for corona ry heart disease, including diagnosed and undiagnosed cases, stratified by age and gender.............................................. 5-9 Table 5-7 Descriptive statistics for hourly carbon monoxide con centrations before and after estimation of missing values – Denver 1995.................................................... 5-13 Table 5-8 Descriptive statistics for hourly carbon monoxide con centrations before and after estimation of missing values – Denver 2006.................................................... 5-13 Table 5-9 Descriptive statistics for hourly carbon monoxide con centrations before and after estimation of missing values – Los Angeles 1997 ........................................... 5-14 Table 5-10 Descriptive statistics for hourly ca rbon monoxide concentrat ions before and after estimation of missing values – Los Angeles 2006 ........................................... 5-15 Table 5-11 Design values and adjustment factor s used to represent air quality just meeting the current and potential a lternative standards ................................................. 5-17 Table 5-12 Descriptive statistics for hourly ca rbon monoxide concentrat ions after adjusting to just meet the current 8-hour standa rd – Denver (adjusted 1995 data). ........ 5-19 Table 5-13 Descriptive statistics for hourly ca rbon monoxide concentrat ions after adjusting to just meet the current 8-hour standa rd – Los Angeles (adjusted 1997 data). 5-19 Table 5-14 Locations of meteorological stations selected for Denver ............................... 5-20 Table 5-15 Locations of meteorological stations selected for Los Angeles ....................... 5-21 Table 5-16 Parameters of Bounded Lognormal Di stributions Defined for Proximity Factors to be Used in the Proposed Application of APEX4.3 to Los Angeles and Denver .............................................................................................................. 5-24 Table 5-17 List of microenvironments modeled and calculation methods used ................ 5-26 Table 5-18 Lognormal distributions of indoor air exchange rate s used in Los Angeles .... 5-27 Table 5-19 Lognormal distributi ons of indoor air exchange rates used in Denver ............ 5-28February 2010 Draft -Do Not Quote or Cite vi Table 6-1. Estimated Number (and Percentage ) of Persons and Person-Days with a Daily Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Denver Study Area – “As Is” Air Quality.............................................................................................................. 6-3 Table 6-2. Estimated Number (and Percentage ) of Persons and Person-Days with a Daily Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area – “As Is” Air Quality........................................................................................................ 6-4 Table 6-3 Estimated Number (and Percentage) of Persons and Person-Days with a Daily Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality Just Meeting the Current 8-Hour Standard.............................................................. 6-5 Table 6-4 Estimated Number (and Percentage) of Persons and Person-Days with a Daily Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air Quality Just Meeting the Cu rrent 8-Hour Standard.......................................... 6-5 Table 6-5 Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour Exposure At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Denver Study Area – Air Qualit y Just Meeting Pote ntial Alternative Standards .......................................................................................................... 6-6 Table 6-6 Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour Exposure At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Los Angeles Area – Air Qual ity Just Meeting Potential Alternative Standards .......................................................................................................... 6-7 Table 6-7. Estimated Number (and Percentage ) of Persons with a Daily Maximum 8-Hour Exposure At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Denver Study Area – Air Qualit y Just Meeting Pote ntial Alternative Standards. ......................................................................................................... 6-9 Table 6-8. Estimated Number (and Percentage ) of Persons with a Daily Maximum 8-Hour Exposure At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Los Angeles Area – Air Qual ity Just Meeting Potential Alternative Standards .......................................................................................................... 6-9 Table 6-9. Estimated Number (and Percentage ) of Persons and Person-Days with a Daily Maximum End-of-hour COHb Level At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality As Is ..................................................................................................................... 6-11 Table 6-10. Estimated Number (and Percentage ) of Persons and Person-Days with a Daily Maximum End-of-hour COHb Level At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air Quality As Is..................................................................................................... 6-11 Table 6-11 Estimated Number (and Percentage ) of Persons and Person-Days with a Daily Maximum End-of-hour COHb Level At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality Just Meeting the Current 8-hour Standard............................................................... 6-12February 2010 Draft -Do Not Quote or Cite vii Table 6-12 Estimated Number (and Percentage ) of Persons and Person-Days with a Daily Maximum End-of-hour COHb Level At or Above the Specified Level – Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air Quality Just Meeting the Cu rrent 8-hour Standard .......................................... 6-12 Table 6-13 Estimated Number (and Percentage ) of Persons with a Daily Maximum End-ofhour COHb Level At or Above the Speci fied Level – Adults With Coronary Heart Disease (CHD) in the Denver St udy Area – Air Quality Just Meeting Potential Alternative Standards........................................................................ 6-13 Table 6-14 Estimated Number (and Percentage ) of Persons with a Daily Maximum End-ofhour COHb Level At or Above the Speci fied Level – Adults With Coronary Heart Disease (CHD) in the Los Angele s Study Area – Air Quality Just Meeting Potential Alternative Standards........................................................................ 6-14 Table 6-15 Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated to Experience a Daily Maximum End-of- hour COHb Level At or Above the Specified Percentage – Air Quality Ju st Meeting the Current Standard .......... 6-16 Table 6-16 Percentage of Los Angeles Adults with Coronary Heart Disease (CHD) Estimated to Experience a Daily Maximum End-of -hour COHb Level At or Above the Specified Percentage – Air Quality Ju st Meeting the Current Standard .......... 6-16 Table 6-17 Estimated Number (and Percentage ) of Persons with a Daily Maximum End-ofhour COHb Level At or Above the Speci fied Level – Adults With Coronary Heart Disease (CHD) in the Denver and Los Angeles Study Areas – Zero Ambient Exposures .......................................................................................... 6-18 Table 6-18 Descriptive Statistics for the Percent COHb Ambient Contribution Estimated Using Denver 1995 Ambient Concentrations Adjusted to Just Meet the Current Standard............................................................................................................ 6-19 Table 7-1 Summary of How Variability Was In corporated Into the Second Draft REA. 7-2 Table 7-2 Characterization of Key Uncertainti es in the Second Draft REA for Denver and Los Angeles Areas............................................................................................ 7-7February, 2010 Draft – Do Not Cite or Quote 1-1 

### 1 INTRODUCTION 1

This draft document describes the quanti tative human exposure assessment and risk 2 characterization being conducted to inform the U. S. Environmental Protection Agency’s (EPA’s) 3
current review of the National Ambient Air Quality Standards (NAAQS) for carbon monoxide 4 (CO). This draft report, Risk and Exposure Assessment to Su pport the Review of the Carbon 5 Monoxide Primary National Ambient Air Qualit y Standards: Second Exte rnal Review Draft , is 6 being provided to the Clean Air Scientific Advisory Committee (CASAC) CO Panel and the 7 public for review in advance of a public meeti ng of the CASAC CO panel planned for March 22- 8 23, 2010. Following that meeting, we will take CASAC and public comments into account in 9 preparing the final document. We plan to co mplete the final Risk and Exposure Assessment 10 report in May 2010. Given the significan t time constraints of this review, 1 results of the 11 assessment are provided in th is document without substant ial interpretation. Rather, 12 interpretative discussion of these results is provided in the draft Policy Assessment. 13

1.1 BACKGROUND 14
The EPA is presently conducting a review of the national ambient air quality standards 15
for CO. Sections 108 and 109 of the Clean Air Ac t (Act) govern the estab lishment and periodic 16 review of the NAAQS. These standards are established for certain pollutants that may 17 reasonably be anticipated to endanger public he alth and welfare, and whose presence in the 18
ambient air results from numerous or diverse mob ile or stationary sources. The NAAQS are to 19 be based on air quality criteria, which are to accurately reflect the latest scientific knowledge 20 useful in indicating the kind and extent of iden tifiable effects on public health or welfare that 21 may be expected from the presence of the pollutant in ambient air. Based on periodic reviews of 22 the air quality criteria and standards, the Admini strator is to make revi sions in the criteria and 23 standards, and promulgate any new standards, as may be appropriate . The Act also requires that 24 an independent scientific revi ew committee advise the Administ rator as part of this NAAQS 25 review process, a function performed by the CASAC. 26 The current NAAQS for CO includes two prim ary standards to provide protection for 27 exposures to carbon monoxide. In 1994, EPA reta ined the primary standards at 9 parts per 28 million (ppm), 8-hour average and 35 ppm, 1-hour average, neither to be exceeded more than 29 once per year (59 FR 38906). These standards we re based primarily on the clinical evidence 30 relating carboxyhemoglobin (COHb) levels to va rious adverse health endpoints and exposure 31 modeling relating CO exposures to COHb levels. With the 1994 decision, EPA also reaffirmed 32

1 As noted below, the schedule for this review is governed by the terms of a court order.February, 2010 Draft – Do Not Cite or Quote 1-2 an earlier decision that th e evidence did not support the need for a secondary standard for CO (59 1 FR 38906). 2 A subsequent review of the CO NAAQS was initiated in 1997, which led to the 3 completion of the 2000 Air Quality Criteria Do cument for Carbon Monoxide (US EPA, 2000) 4 and a draft exposure analysis methodology document (US EPA, 1999). EPA put on hold the 5 NAAQS review when Congress re quested that the National Res earch Council (NRC) review the 6 impact of meteorology and topography on ambien t CO concentrations in high altitude and 7 extreme cold regions of the U.S. In res ponse, the NRC convened the Committee on Carbon 8 Monoxide Episodes in Meteorological and Top ographical Problem Areas, which focused on 9 Fairbanks, Alaska as a case-st udy. A final report, “Managing Carbon Monoxide Pollution in 10 Meteorological and Topographical Problem Ar eas” (NRC, 2003), offered a wide range of 11 recommendations regarding management of CO air pollution, cold start emissions standards, 12 oxygenated fuels, and CO monitoring. Following completion of this NRC report, EPA did not 13 conduct rulemaking to complete the review. 14
EPA initiated the current review of th e NAAQS for CO on September 13, 2007, with a 15 call for information from the public (72 FR 52369) requesting the submission of recent scientific 16 information on specified topics. A works hop was held on January 28–29, 2008 (73 FR 2490) to 17 discuss policy-relevant scientific and technical information to in form EPA’s planning for the CO 18 NAAQS review. Following the workshop, EPA outlin ed the science-policy questions that would 19 frame this review, outlined the process and schedu le that the review would follow, and provided 20 more complete descriptions of the purpose, contents, and approach for developing the key 21 documents for the review in a draft Plan for Review of the National Ambient Air Quality 22 Standards for Carbon Monoxide (US EPA, 2008a). After CASAC and public input on the draft 23 plan, EPA made the final plan available in August 2008 (US EPA, 2008b). In January, 2010, 24 EPA completed the process of assessing the latest available policy-relevant scientific information 25
to inform the review of the CO standards. Th is assessment, the Integrated Science Assessment 26 for Carbon Monoxide (hereafter, “ISA”) (US EPA, 2010a), includes an evaluation of the 27
scientific evidence on th e health effects of CO, including in formation on exposure, physiological 28 mechanisms by which CO might adversely impact human health, an evaluation of the clinical 29 evidence for CO-related morbidity, and an evalua tion of the epidemiological evidence for CO30 related morbidity and mortality associations. 2 31 EPA’s Office of Air Quality Planning and St andards (OAQPS) has developed this second 32 draft Risk and Exposure Assessment (REA) desc ribing the quantitative assessment conducted by 33

2 The ISA also evaluates scientific evidence for th e effects of CO on pub lic 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.February, 2010 Draft – Do Not Cite or Quote 1-3 the Agency to support the review of the primary CO standards. This s econd draft document is a 1 concise presentation of the methods, key resu lts, observations, and related uncertainties 2 associated with the quantitative analyses perfor med. This REA builds upon the health effects 3 evidence presented in the final ISA, as well as CASAC advice (Brain and Samet, 2009; Brain 4 and Samet, 2010) and public comments on a scope and methods planning document for the REA 5 (hereafter, “Scope and Methods Plan”) (US EP A, 2009a) and on the first draft REA (US EPA, 6 2009b). The final REA will reflect considera tion of CASAC and public comments on this 7 second draft REA. The final REA will be comp leted by May 28, 2010, consistent with the court 8 order governing the schedule for completion of this review. The court order also specifies that 9 EPA sign for publication notices of proposed and final rulemaking concerning its review of the 10 CO NAAQS no later than October 2 8, 2010 and May 13, 2011, respectively. 11 The final ISA and final REA will inform the policy assessment and rulemaking steps that 12 will lead to final decisions on the CO NAAQS. The policy assessment will be described in a 13 Policy Assessment (hereafter, “PA”) document, which will include staff analysis of the scientific 14 basis for alternative policy options for consider ation by the Administrator prior to rulemaking. 15 The PA will integrate and interpret informati on from the ISA and the REA to frame policy 16 options for consideration by the Administrator. The PA is intended to help “bridge the gap” 17 between the Agency’s scientific and technical assessments, presented in the ISA and REA and 18 the judgments required of the Admi nistrator in determining whether it is appropriate to retain or 19
revise the standards. The PA is also intended to facilitate CASAC’s advice to the Administrator 20 on the adequacy of existing standards, and any ne w standards or revisions to existing standards 21 as may be appropriate. A draft PA is being prepared (USEPA, 2010b) fo r release for review by 22 CASAC, as well as for public comment, in conjunction with CASAC review and public 23
comment of this document. 24

1.2 PREVIOUS REVIEWS AND ASSESSMENTS 25
Reviews of the CO NAAQS completed in 1985 and 1994 included analyses of exposure 26 to ambient CO and associated internal dose, in terms of COHb levels, which were used to 27 characterize risks for populations of interest (50 FR 37484; 59 FR 38906). These prior risk 28 characterizations compared the numbers and pe rcent of the modeled population that exceeded 29 several potential health effect benchmarks, expr essed in terms of COHb levels. The COHb 30
levels of interest in these revi ews were drawn from the evidence of COHb levels associated with 31 exercise-induced aggravation of angina in c ontrolled human exposure studies involving short- 32February, 2010 Draft – Do Not Cite or Quote 1-4 term (shorter than 8 hours) exposures of patient s with diagnosed ischemic heart disease (IHD) 3 to 1 elevated CO concentrations (US EPA, 1979; US EPA, 1984; US EPA, 1991). 2 In the review completed in 1994, this char acterization was performed for the population 3 of interest in the city of Denver, Colorado (U S EPA, 1992; Johnson et al., 1992). That analysis 4
indicated that if the current 8-hour standard were just met, the proportion of the nonsmoking 5 population with cardi ovascular disease 4 experiencing exposures at or above 9 ppm for 8 hours 6 decreased by an order of magnitude or more as compared to the proportion under then-existing 7 ambient CO levels, down to less than 1 percent of the total pe rson-days in that population. 8 Likewise, just meeting the current 8-hour sta ndard reduced the propor tion of the nonsmoking 9 cardiovascular-disease population pe rson days at or above COHb le vels of concern by an order 10 of magnitude or more relative to then-existing ambient CO levels. More specifically, upon just 11 meeting the 8-hour standard, EPA estimated that less than 0.1% of the nonsmoking 12 cardiovascular-disease population would experien ce a COHb level of about 2.1%. A smaller 13 percentage of the at-risk populati on was estimated to exceed highe r COHb levels. The analysis 14 also considered additional expos ure scenarios that included cert ain indoor sources (e.g., passive 15 smoking, gas stove usage). While these indoor sour ces were shown to contribute to total CO 16 exposure, it was determined to be of limited ut ility in the risk assessment because these source 17 emissions would not be effectively mitigated by setting more stringent ambient air quality 18 standards (59 FR 38914). 19 In the review initiated in 1997, EPA consulte d with CASAC on a draft exposure analysis 20 methodology document, Estimation of Car bon Monoxide Exposures and Associated 21 Carboxyhemoglobin Levels in Denver Residents using pNEM /CO (Version 2.0) (Mauderly, 22 1999; Johnson, 1999). Although the EPA did not comp lete the review initiated in 1997, OAQPS 23 continued work on the CO exposure assessment to further develop the exposure assessment 24 modeling component of the Total Risk Integrat ed Methodology (TRIM) system. A subsequent 25 draft technical report (Johnson et al., 2000) was produced documenti ng the application of the CO 26

3 Ischemic heart disease is a category of cardiovascular disease associated with narr owed heart arteries; it is often also called coronary artery disease (CAD) and coronary heart disease (CHD). Individuals with CHD have myocardial ischemia, which occurs when the heart muscle receives insufficient oxygen delivered by the blood. Exercise-induced angina pectoris (chest pain) occurs in many of them. Among all patients with diagnosed CAD, the predominant type of ischemia, such as that indicated by ST segment depression, is asymptomatic (i.e., silent). Also, patients who experience angina typica lly have additional ischemic episodes that are asymptomatic (2000 AQCD, section 7.7.2.1).

4 In characterizing the population of interest with regard to demographics (age and sex), the assessment for the review completed in 1994 drew from estimates of the prevalence of ischemic hear t disease (IHD) provided by the National Health Interview Survey and corresponding estimates of undiagnosed ischemia developed by EPA. Estimates of undiagnosed IHD were based on two assumptions: (1) there are 3.5 million persons in U.S. with undiagnosed IHD (drawn from estimate by American Heart Association) and (2) persons with undiagnosed IHD are distributed within the population in the same manner as persons with diagnosed IHD (USEPA, 1992).February, 2010 Draft – Do Not Cite or Quote 1-5 exposure and dose modeling methodology for two study areas (Denver and Los Angeles). The 1 exposure and dose estimates were obtained by applying pNEM/CO version 2.1, a predecessor to 2 APEX, to adults with IHD re siding within each urban area. 5 This report was subjected to an 3 external peer review by three exposure modeling experts and convened by Science Applications 4 International Corporation (SAIC, 2001). 5 In the 2000 pNEM/CO assessment, the Denver st udy area was defined as all census tracts 6
located within 10 km of each of six fixed-site monitors within the Denver metropolitan area. Air 7 quality data for 1995 reported by these monitors were used to represent ex isting conditions in the 8 study area. Because the second highest non-ove rlapping 8-hour average CO concentration 9 equaled 9.5 ppm, the existing conditions in Denve r for 1995 were considered to approximate just 10 meeting the 8-hour average CO standard. 6 In a similar manner, the Los Angeles study area was 11
defined as all census tracts within 10 km of ten fixed-site mon itors in the Los Angeles area, 12 though air quality data for 1997 were adjusted down wards so that the concentrations associated 13 with the design monitor just met the 8-hour NAAQS . A total of 15 distinct microenvironments 14 were modeled using a mass balance model accoun ting for the infiltration of outdoor (ambient) 15 concentrations, air exchange rate s, as well as CO emissions from two indoor sources (residential 16 gas stoves and passive cigarette smoke). 17 In the 2000 pNEM/CO assessment, approximately 0.5% of the non-smoking IHD 18 population in both urban areas was estimated to experience a maximum end-of-hour COHb level 19 of about 2.0% upon just meeting the current 8-hour standard. 7 A smaller percentage of the at20 risk population was estimated to exceed higher COHb levels (e.g., <0.1% of persons were 21
estimated to have COHb levels at or above 3.0% in either location). Indoor CO sources were an 22 important contributor to COHb though these impacted a much la rger portion of the simulated 23 population at the higher COHb leve ls (i.e., those persons with >1 % COHb). For example, in 24 Denver with indoor sources included, nearly 20% of persons with IHD were estimated to have a 25
maximum end-of-hour COHb level of about 2.0%. In Los Angeles with indoor sources included, 26 the estimated percent of persons having a COHb level at or above 2.0% was lower (i.e., about 27 17%), though still a much greater pe rcentage than that estimated in the absence of indoor sources 28 (i.e., <1%). 29

5 This is consistent with the demographic group modeled in the 1992 assessment described above (Johnson et al., 1992; USEPA, 1994), and drew from updated information with regard to prevalence demographics (Johnson et al., 2000, p. section 2.5.2).

6 A rounding convention allows the second highest 8-hour average CO concentration (i.e., the design value (DV)) to be as high as 9.4 ppm for the 8-hour CO NAAQS of 9 ppm.

7 Note that the contemporaneous design value for Denver was 0.1 ppm above just meeting the current 8- hour standard (9.5 versus 9.4 ppm).February, 2010 Draft – Do Not Cite or Quote 1-6 1.3 CURRENT REVIEW, CASAC ADVI CE AND PUBLIC COMMENT 1
In preparing the Scope and Methods Plan fo r the REA, we consid ered the scientific 2 evidence presented in the first draft ISA (US EPA, 2009c) and the key science policy issues 3 raised in the IRP (US EPA, 2008b). EPA held a consultation with CASAC to solicit comments 4 on the Scope and Methods Plan during a May 2009 CASAC meeting at which CASAC also 5 provided comments on the first draft ISA (Brain, 2009). Public comments were also requested 6 (74 FR 15265). Those CASAC and public comments were considered in advance of the conduct 7 of the analyses and results presented in the first draft REA (US EPA, 2009b). 8
As a result of the notable limitations in available ambient monitoring and 9 microenvironmental concentration data, staff im plemented a much-simplified, screening-level 10 approach to assess population e xposure and dose for the first draft APEX/CO REA (US EPA, 11 2009b). Two urban study areas, Denver and Los A ngeles, were defined as all census tracts 12 within 20 km of a single fixed-site monitor, us ing the design value monitor (i.e., the monitor 13 recording the highest concentration in each area) for the specified year of the assessment. 14 Therefore, ambient monitoring data associated with the site measuring the highest CO 15 concentrations were applied to all people within the surrounding study area. This was the case 16
for scenarios that included as is air quality (year 2006 for either lo cation) and air quality adjusted 17 to just meeting the current standard (adj usted from 1995 for Denver and from 1997 for Los 18 Angeles). In the first draft REA, no adjustment was made for spatial variability in ambient 19
concentrations across each area and at most two microenvironments were included in the 20 exposure model simulations (in-vehicle and all others). The in-vehicle microenvironmental 21 factor was constrained to a point estimate of 2.0, that is, these microenvironmental 22 concentrations would always be twice that observed at the single ambient monitor. In the design 23 of the assessment, it was noted that this focu s on high concentration CO monitors and other 24 model simplifications would have a tendency to produce higher CO exposures in most simulated 25 persons than results generated from the 2000 pNEM /CO assessment. The results were consistent 26 with this statement. The estimated percent of the population at any COHb level was greater in 27
the first draft REA than that estim ated by Johnson et al. (2000). 28 On November 16-17, 2009, the CASAC CO panel met to discuss the first draft REA. 29
The final written comments and recommendati ons were provided to EPA in February 2010 30 (Brain and Samet, 2010). The design of the current second draft REA builds upon these 31 recommendations from CASAC, information pres ented in the final ISA (US EPA, 2010a), as 32 well as comments made by the public. 8 Specifically in this assessment, EPA has: 33

8 Public Comments on the first draft REA were submitted to the docket for this review and also presented in November, 2009 at the CASAC meeting.February, 2010 Draft – Do Not Cite or Quote 1-7  Expanded each of the modeling domains to include a greater number of 1 ambient monitors used as input to APEX; 2

 Increased the number of microenvironm ents modeled from two to eight; 3

 Improved the representation of variability in estimated 4 microenvironmental concentrations, including in-vehicles; 5

 Included an algorithm that adjusts for spatial heterogeneity in estimated 6 outdoor concentrations across each model domain; 7

 Implemented the mass-balance model for estimating concentrations in all 8 indoor microenvironments; 9

 Implemented the algorithm that al lows commuters to experience home10 tract and work-tract ambient concentrations; and 11

 Expanded the at-risk population to ad dress the undiagnosed persons with 12
CHD. 13 The purpose of this second draft REA is to seek CASAC review and public comment 14 regarding our characteriza tion of the results presented consid ering the improvements made to the 15 assessment design and inputs used, and CASAC’s advice on the role of this assessment in 16 informing the current review of the CO NAAQS. 17
18February, 2010 Draft – Do Not Cite or Quote 1-8 1.4 REFERENCES 1

Brain JD. (2009). Letter from Dr. Joseph Brain to Ad ministrator Lisa Jackson. Re: Consultation on EPA’s Carbon 2 Monoxide National Ambient Air Quality Standards: Sc ope and Methods Plan for Health Risk and Exposure 3 Assessment. CASAC-09-012. July 14, 2009. 4

Brain JD and Samet JM. (2009). Letter from Drs. J.D. Br ain and J.M. Samet to Administrator Lisa Jackson. Re: 5 Review of the EPA’s Integrated Science Assessment for Carbon Monoxide ( First External Review Draft). 6 EPA-CASAC-09-011. June 24, 2009. 7

Brain JD and Samet JM. (2010). Letter from Drs. J.D. Br ain and J.M. Samet to Administrator Lisa Jackson. Re: 8 Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO) Primary 9 National Ambient Air Quality Standards: First External Review Draft. EPA-CASAC-10-006. February 12, 10 2010. 11

Johnson T, Capel J, Paul R, Wijnberg L. (1992). Estimation of Carbon Monoxide Exposure and associated 12 Carboxyhemoglobin levels in Denver Residents Using a Probabalistic verion of NEM, prepared by 13 International Technology for U.S. EPA, Office of Air Quality Planning and Standards, Durham, NC, 14 Contract No. 68-D0-0062, 15

Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (1999). Estimation of Carbon Monoxide Exposures and 16 Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.0) prepared by 17 ICF Kaiser Consulting Group for U.S. EPA, Office of Air Quality Planning and Standards, under Contract 18 No. 68-D6-0064, WA Nos. 1- 19 and 2-24. Available at: http:// www.epa.gov/ttn/fera/human_related.html 19

Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 20 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 21 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 22 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 23 at: http://www.epa.gov/ttn/fera/human_related.html.. 24

Mauderly J. (1999). Letter from Dr. Joe Mauderly, Ch air, Clean Air Scientific Advisory Committee, to 25 Administrator Carole M. Browner. Re: Notification of a Consultation on the Estimation of Carbon 26 Monoxide Exposures and Associated Carboxyhemoglobin Levels in De nver Residents using pNEM/CO 27 (Ver. 2.0) . July 12, 1999. 28

National Research Council. (2003). Managing Carbon Monoxide Pollution in Meteorological and Topographical 29 Problem Areas. Washington, D.C. The National Academies Press. 30

SAIC. (2001). Memo to Harvey Richmmond, EPA, Technical Peer Review (including reviewers comments) of 31 “Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels for Residents of 32 Denver and Los Angeles Using pNEM/CO (version 2.1)”, Docket EPA-HQ-OAR-2008-0015. Available 33 at: http://www.epa.gov/ttn/fera/human_related.html 34

US EPA. (1979). Air Quality Criteria for Carbon Mon oxide. Research Triangle Park, NC: Office of Health and 35 Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-79-022. 36

US EPA. (1984). Review of the NAAQS for Carbon Monoxide: Reassessment of Scientific and Technical 37 Information. Office of Air Quality Planning and Standards, report no. EPA-450/584-904. Research 38 Triangle Park, NC. 39

US EPA. (1991). Air Quality Criteria for Carbon Monoxide. Research Triangle Park, NC: Office of Health and 40 Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-90/045F. 41February, 2010 Draft – Do Not Cite or Quote 1-9 US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of 1 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 2 EPA/452/R-92-004. 3

US EPA. (1999). Total Risk Integrated Methodology - TRIM.Expo Technical Support Document, External Review 4 Draft, November 1999. Office of Air Quality Pla nning and Standards, U.S. Environmental Protection 5 Agency, Research Triangle Park, NC, report no. EPA-453/D-99-001. Available at: 6 http://www.epa.gov/ttn /fera/trim_fate.html#1999historical. 7

US EPA. (2000). Air Quality Criteria for Carbon M onoxide. National Center for Environmental Assessment, 8 Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 9 report no. EPA/600/P-99/001F. June 2000. Available at: 10 http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=18163. 11

US EPA. (2008a). Draft Plan for Re view of the National Ambient Air Quality Standards for Carbon Monoxide. 12 U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452D-08-001. Also 13 known as Draft Integrated Review Plan . Available at: 14 http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_pd.html. 15

US EPA. (2008b). Plan for Review of the National Ambient Air Quality Standards for Carbon Monoxide. U.S. 16 Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452R-08-005. Also known 17

as Integrated Review Plan. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html. 18

US EPA. (2009a). Carbon Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health 19 Risk and Exposure Assessment U. S. Environmenta l Protection Agency, Research Triangle Park, NC, 20 report no. EPA-452/R-09-004. Available at: http ://www.epa.gov/ttn/naaqs/standa rds/co/s_co_cr_pd.html. 21

US EPA. (2009b). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary 22 National Ambient Air Quality Standards: First Extern al Review Draft. U.S. Environmental Protection 23 Agency, Research Triangle Park, NC, repo rt no. EPA-452/P-09-008. Available at: 24 http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_rea.html 25

US EPA. (2009c). Integrated Science Assessment for Carbon Monoxide – First External Review Draft. U.S. 26 Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/600/R-09/019. Available 27 at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html. 28

US EPA. (2010a). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency, 29 Washington, DC, EPA/600/R-09/019F. Available at: 30 http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_isa.html. 31

US EPA. (2010b). Policy Assessment to Support the Re view of the Carbon Monoxide Primary National Ambient 32 Air Quality Standards: External Review Draft. U. S. Environmental Protection Agency, Research Triangle 33 Park, NC, report no. EPA-452/P-10-005. Available at: 34 http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_rea.html. 35February, 2010 2-1 Draft – Do Not Cite or Quote 

### 2 CONCEPTUAL OVERVIEW: A SSESSING AMBIENT CARBON 1 

### MONOXIDE EXPOSURE AND RISK 2

In this chapter, we have summarized the conceptual model for assessing exposure to 3
ambient CO and associated health risk. Subsections focus on di fferent components of the model 4 including identification of the key emission sources to am bient concentrations (section 2.1), 5 exposure pathways and relevant microenvironm ents (section 2.2), identification of at-risk 6 populations (section 2.3), the dose metric (section 2.4), health endpoints (section 2.5), and the 7 risk characterization approach (s ection 2.6). Section 2.7 presents the key observations for this 8 chapter. 9

2.1 SOURCES OF CARBON MONOXIDE 10
Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 11 carbon-containing fuels and photoche mical reactions in the atmosphere. The amount of CO 12 emitted from these reactions, relative to the amount of carbon dioxide (CO 2 ) generated, is 13
sensitive to conditions in the combus tion zone. CO production relative to CO 2 generally 14 decreases with any increase in fuel oxygen (O 2 ) content, burn temperatur e, or mixing time in the 15 combustion zone (ISA, section 3.2). As a result , CO emissions from large fossil-fueled power 16 plants are typically very low because optimi zed fuel consumption conditions make boiler 17 combustion highly efficient. In contrast, inte rnal combustion engines commonly used to power 18 mobile sources have widely varying operating cond itions. Therefore, higher and more variable 19 CO emission levels result from the operation of these mobile sources (ISA, section 3.2). In 20 2002, CO emissions from on-road vehicles account ed for a substantial majority of total 21 emissions by individual source sectors in the U.S. (ISA, Figure 3-1). 1 As in previous NAAQS 22 reviews, mobile sources continue to be a significant emission source of CO to ambient air. 23 Sources of CO inside buildings include inf iltration of ambient air indoors, as well as, 24 where present, indoor (nonambient ) sources such as gas stoves and tobacco smoke (ISA, section 25 3.6.5.2). In addition to infiltration of ambient air, CO inside motor vehicles may also receive 26
contributions from nonambient sources in th e cabin, which can be substantial under air 27
ventilation modes that limit inflow from outside the vehicle (ISA, p. 3-89). However, the focus 28 of this REA, which is conducted to inform the current review of the CO NAAQS, is on sources 29 of ambient CO. We provide quantitative esti mates of population exposure and dose originating 30 from ambient CO in two urban areas (details on s ite selection are provided in chapter 3 below). 31

1 The 2002 National Emissions Inventory (NEI) was th e most recently available NEI meeting data quality objectives for the ISA. The NEI includes data from various sources such as industries and state, tribal, and local air agencies (ISA, p. 3-1).February, 2010 2-2 Draft – Do Not Cite or Quote The exposure modeling in this assessment does not quantitatively estimat e the contribution of 1 indoor sources to an individual’s total expos ure and dose. This assessment does however 2 qualitatively draw upon available information rega rding potential indoor s ource contributions to 3 estimated population exposure and dose (described in section 2.2 below). 4

2.2 EXPOSURE PATHWAYS AND IMPO RTANT MICROENVIRONMENTS 5 Human exposure to CO involves the contact (via inhalation) between a person and the 6 pollutant in the various locations (or microenvi ronments) in which people spend their time. 7 Studies of personal exposure have generally found that the largest portion of the day is generally 8 spent indoors and the largest percen tage of the time in which an individual is exposed to ambient 9 CO occurs indoors (ISA, sections 2.3 and 3.6). As a result, CO concentrations in indoor 10 microenvironments are an important determinant of an individual’s total CO exposure. Recent 11 population exposure studies conducted in Milan, Italy support this conclusion (Bruinen de Bruin 12 et al., 2004), indicating that ove r 80% of the population exposure to CO can occur in indoor 13 microenvironments (ISA, Table 3-13). Taking into account the infiltration of ambient CO 14 indoors, indoor CO concentrations are similarly an important determinant in an individual’s 15 exposure to ambient CO. 16 Microenvironments that may in fluence CO exposures typicall y include residential indoor 17 environments and other indoor locations, near -traffic outdoor microenvironments and other 18 outdoor locations, and inside ve hicles. Consideration of microenvironmental exposures 19 illustrates the variability in the relationship between pe rsonal exposure and ambient 20 concentrations. For example, one study summa rized the relationship between personal CO 21 exposure concentrations in five broadly define d microenvironments (i.e ., indoor residence, 22 indoor other, outdoor near road, ou tdoor other, and in-v ehicle) and ambient CO concentrations in 23 Baltimore, MD (ISA, section 3.6.5.2; Chang et al., 2000). On average, the indoor-to-ambient 24
and outdoor-to-ambient concentration ratios were about one, though most of the ratios observed 25
across this set of indoor and out door microenvironments were less than one. With the exception 26
of ratios for the in-vehicle mi croenvironments, which as a grou p were generally above one, few 27 ratios were above unity (ISA, p. 3-85, Figure 3-46). Given the expected stability of CO as it 28
infiltrates indoor microenvironments from outdoor air and the lack of significant removal 29
mechanisms of CO in outdoor microenvironmen ts, it is likely that the variability in 30 personal/microenvironmental-to-ambient and ou tdoor-to-ambient concen tration ratios is the 31 result of spatial and temporal variability in outdo or concentrations with respect to simultaneously 32
measured ambient concentrations at fixed-site monitors, and also reflects the impact of lag time 33 associated with attaining steady state relationships, as well as pot ential presence of non-ambient 34

sources. 35February, 2010 2-3 Draft – Do Not Cite or Quote Typically the highest CO exposure concentratio ns are experienced while inside vehicles. 1 Because motor vehicles continue to be importa nt contributors to ambient CO concentrations, 2 both the time spent in motor vehicles and the elev ated CO concentrations occurring on and near 3 heavily trafficked roads continue to be impor tant contributors to pe rsonal exposures. For 4 example, in the study summarized above on pers onal exposures occurrin g within particular 5 microenvironments (i.e., Chang et al., 2000) , most in-vehicle CO exposure-to-ambient 6 concentration ratios were greater than one, with the median being approximately 2.5. The 7 average ratio was approximately 2.5 in summ er, but a few somewhat higher in-vehicle 8 measurements in the winter period, contributed to a winter average of approximately 4 (ISA, 9 section 3.6.5.2, Figure 3-46; Chang et al., 2000 Figure 5). 2 Given this relationship, it should not 10 be surprising that while about 8% of a person’s ti me per day is spent in transit, 13-17% of their 11 total daily exposure occurs within an in-vehicle microenvironmen t (e.g., Bruinen de Bruin et al., 12 2004; Scotto di Marco et al., 2005). 13
A similar influence of mobile source-influ enced microenvironments was observed in the 14 CO population exposure studies cond ucted in Denver CO and Wash ington, DC during the winter 15 of 1982 and 1983 (Akland et al., 1985). 3 In both cities, when co mparing the distribution of 16
measured CO concentrations from the monitori ng network to measured personal exposures, two 17 common phenomena were observed. At the lowest percentiles of each distribution, ambient CO 18 concentrations were consistently greater than th e personal exposures. At the highest percentiles 19 of each distribution, ambient concentrations were consistently lower than the personal exposures 20 (US EPA, 2000). These studies determined that the highest average CO concentrations occurred 21 when subjects were in a mobile source-infl uenced microenvironment (e.g., inside parking 22 garages, in-vehicles). Commute time was also a factor; those who co mmuted 6 hours or more 23 per week had higher average exposures than those who commuted fewer hours per week. 24 Furthermore, mean CO concentrations within in -vehicle microenvironments (ranging from 7.0 to 25 9.8 ppm) were greater than co mmon outdoor locations (ranging from 1.4 to 3.2 ppm) (US EPA, 26 2000). In considering the results from the De nver and Washington personal exposure studies it 27 is important to recognize that CO emissions from motor vehicle sources have declined 28 dramatically since the early 1980’s when these studies were conducted. Consequently, both 29
ambient fixed-site CO concentrations and in-vehic le CO concentrations have also been reduced 30 significantly since that time period. 31

2 Information on the distance of the ambient monitors from highly trafficked roadways or potential for invehicle (nonambient) sour ces was not provided.

3 Both studies collected measurements and activity patte rn diaries from a random sample of the population, defined as including non-institutionalized, non-smoking resi dents, 18 to 70 years of age, who lived in each respective city’s metropolitan area (Akland et al., 1985).February, 2010 2-4 Draft – Do Not Cite or Quote Given their influence on ambient exposures, exposures to CO near roadways and in 1 vehicle microenvironments are of particular import ance in this assessment. Data from several 2 studies that have compared concen trations inside vehicles to c oncentrations immediately outside 3 vehicles, indicates that indoor/ outdoor concentration (I/O) ratio s on average range from just 4 above to just below unity (Chan et al., 1991; Rodes et al., 1998; B oulter and McCrae, 2005; 5 Sharp and Tight, 1997). These studies are support ed by a review by Flachs bart (1999) regarding 6 other studies published between 1982 and 1992 th at measured interior and exterior CO 7 concentrations simultaneously during motor vehicle trips and reported I/O ra tios just below unity 8 (Petersen and Allen, 1982; Koushi et al., 1992). Some studies repor ted no effect of ventilation 9 setting on I/O ratios, while others reported an effect. For exam ple, one study described in the 10 ISA indicated I/O ratios could exceed unity with the ventilation set to re-circulate vehicle air 11 (Abi Esber and El-Fadel, 2008). However, th e study authors attributed this finding to 12
unaccounted sources of CO that cau sed increases in CO concentra tions within the vehicle cabin 13 under those conditions (ISA, section 3.6.6.2; Abi Esber and El-Fadel, 2008). 14 In general, the above results suggest that th e I/O ratio tends toward unity when there are 15 no interior sources of CO, the au tomobile engine does not contribut e directly to its own interior 16 concentrations, and the measurement probes ar e properly installed on the vehicle. This 17
conclusion is consistent with th eoretical expectations for a non-re active pollutant. For example, 18
CO concentrations inside vehicles can be esti mated as a function of outside CO concentration, 19 air exchange rate, a penetrati on factor, and the emission rates of indoor sources (e.g., exhaust 20 leaks, smoking). If one assumes that (1) steady- state ventilation condition s exist, (2) the indoor 21 removal rate ( k ) is zero (i.e., no loss of CO as it moves from outside to inside the vehicle), and 22 (3) there are zero emissions from interior sources, then the CO concentration inside a vehicle can 23 be simplified to a function of outside CO concentra tions and the penetration rate (i.e., infiltration 24 is generally equivale nt to penetration). 4 Under these stated cond itions, the I/O ratio would 25 ultimately converge to unity. 26
There are a few studies that have measured both in-vehicle and fixed-site monitoring 27
concentrations. The data from these studi es can also inform the development of 28 microenvironmental factors used for estimating in -vehicle CO exposures. The ISA notes that 29
studies summarized in the 2000 CO AQCD found that in-vehicle CO concentrations were 30 generally two to five times higher than ambien t CO concentrations obtained at fixed-site 31
monitors within the cities studied. For ex ample, Shikiya et al. (1989) reported such 32 concentrations measured as part of a southern California study. When using the reported in33 vehicle CO measurements, one could estimate concentration ratios ranging from 1.8 to 2.7, a 34

4 See section 3.6.2 of the ISA.February, 2010 2-5 Draft – Do Not Cite or Quote range of ratios dependent on the time-of-year meas urements were collected. Note however that 1
there are several factors that could contribute to variability in reported or calculated 2 concentration ratios. For example, often times in these measurement studies, the averaging time 3 associated with the companion measurements differ, that is there may be a much shorter 4 sampling interval for the in-vehicle measuremen t when compared with that of the ambient 5 monitor. More specifically, Sh ikiya et al. (1989) measured in-v ehicle CO concentrations during 6 commutes lasting, on average, 33 minutes, while fixed site monitoring values averaged over 4- 7 hours. It is likely that the time- averaged concentrations are less th an that of the true fixed-site 8 concentrations that occurred during the 33 minute commute, perhaps resulting in an 9
overestimation of the concentration ratios when us ing this data. Furthermore, Shikiya et al. 10 (1989) reported seasonal differences for the in-v ehicle CO concentratio ns (winter averaged 10.1 11 ppm; summer averaged 6.5 ppm), but not for the fixed-site monitor (average for both seasons 12 was 3.7 ppm). Typically ambient concentrations are greater in winter (e.g., ISA Figure 3-22 for 13 Los Angeles). Therefore, when using the fixed- site seasonal average and in-vehicle seasonally 14 stratified measurements from Shikiya et al. (1989) to calculate the ratio s as was done above, the 15 winter value may be overestimated while the summe r value could be underestimated. In addition 16 to the factors mentioned above, this relationship can vary based on several other factors that may 17
influence the fixed-site monito r concentration, such as the n earby roadway traffic density, the 18 monitor siting characteristics (e.g., proximity to the roadway), and local scale meteorology (e.g., 19 downwind), with each described in greater detail in chapter 3. Ofthe few studies reporting in20 vehicle and companion fixed-site measurements , most do not measure all of the potentially 21 influential factors or provide the data stratified by such factors. Thus, a general range of two to 22 five may be adequate to represen t the total variability for this particular relationship, recognizing 23
that there are limitations in the available measurem ent data to define this relationship. 24 Although not the focus of this review, indoor sources such as gas stoves and 25 environmental tobacco smoke can, where present, also be important cont ributors to total CO 26 exposure and may be of concern for such at-risk populations as individuals with cardiovascular 27 disease, among others (see sect ion 2.3 below). For example, some assessments performed for 28 previous reviews have included modeling simulati ons both without and with indoor sources (gas 29 stoves and tobacco smoke) to provide context fo r the assessment of ambient CO exposure and 30 dose (e.g., US EPA, 1992; Johnson et al., 2000). The 2000 pNEM/CO simulations with indoor 31 sources indicated that the impact of such sources on the proportion of the population 32February, 2010 2-6 Draft – Do Not Cite or Quote experiencing higher exposures and COHb levels can be substantial, as summ arized in section 1.2 1
above. 5 2

2.3 EXPOSURE AND DOSE METRICS 3 Exposure concentration over a time period of interest (e.g., one hour or eight hours) is a 4 common exposure metric which refl ects the integration of exposures to pollutant concentrations 5
that occur in each microenvironment in which time is spent (see section 4.4.6 below). In the case 6 of CO, for which the common mechanism underlying biological response is binding to heme 7 proteins, COHb level in blood is well recognized as an important internal dose metric used in 8 evaluating CO exposure and the potential for heal th effects (ISA, p. 2-4, sections 4.1, 4.2, 5.1.1). 9 Accordingly, COHb levels are used in this assessment. 10 Carboxyhemoglobin occurs in the blood due to endogenous CO production from 11
biochemical reactions associated with normal breakdown of heme proteins, as well as in 12 response to inhaled (exoge nous) ambient and nonambient 6 CO exposures (ISA, section 4.5). 7 13 Levels of endogenous COHb in healthy individual s have been described to range down to 0.3% 14 and generally be less than 1% (ISA, pp. 4-9, 4- 23, 2-6). However, the production of endogenous 15 CO and levels of endogenous COHb vary with several physiological char acteristics (e.g., slower 16 COHb elimination with increasing age), as well as some disease states, which can lead to higher 17 endogenous levels in some indivi duals (ISA, section 4.5). Other factors affecting CO uptake and 18 elimination include physi cal activity and altit ude (ISA, section 4.4). 19 The amount of COHb formed in response to exogenous CO is dependent on the CO 20 concentration and duration of exposure, exercise (which increases the amount of air removed and 21 replaced per unit of time for gas exchange), the pulmonary diffusing capacity for CO, ambient 22 pressure, health status, and the specific metabo lism of the exposed individual. The formation of 23 COHb is a reversible process, but the high affini ty of CO for Hb, which affects the elimination 24 half-time for COHb, can lead to accumulation of COHb in some circumstances. Fortunately, 25 mechanisms exist in normal, healthy individual s to compensate for the reduction in tissue oxygen 26 caused by increasing levels of COHb . Cardiac output increases a nd blood vessels dilate to carry 27
more blood so that the tissue can extract ad equate amounts of oxygen from the blood (ISA, 28 chapter 4). As discussed in sections 2.4 and 2.5 below, however, there are several medical 29

5 As has been recognized in previous CO NAAQS review s, such sources cannot be effectively mitigated by setting more stringent ambient air quality standards (59 FR 38914), and are therefore not a focus of this assessment.

6 A significant source of nonambient CO long recognized as contributin g to elevated COHb levels is tobacco smoking (e.g., ISA, Figure 4-12).

7 The dosimetry and pharmacokinetics of CO are discussed in detail in chapter 4 of the ISA.February, 2010 2-7 Draft – Do Not Cite or Quote disorders that can make an individual more sus ceptible to the potential adverse effects of low 1 levels of CO, especially during exercise. 2 As described in section 4.4.7 below, blood levels of COHb have been estimated in this 3 REA using a nonlinear solution of the Coburn- Forster-Kane (CFK) model (Coburn et al., 1965), 4 which remains “the most extensively validated and applied model for COHb prediction (ISA, 5 section 4.2.3). 6

2.4 AT-RISK POPULATIONS 7 The term ‘susceptibility’ (and the term “at-risk”) has been used to recognize populations 8 that have a greater likelihood of experiencing effects related to ambient CO exposure (ISA, 9 section 5.7). This increased likelihood of res ponse to CO can potentially result from many 10 factors, including pre-existing me dical disorders or disease stat es, age, gender, lifestyle or 11 increased exposures (ISA, section 5.7). For exam ple, medical disorders that limit the flow of 12 oxygenated blood to the tissues have the potential to make an indivi dual more susceptible to the 13 potential adverse effects of low levels of CO, especially during exercise. Based on the available 14 evidence in the current review, coronary artery disease (CAD), also known as coronary heart 15 disease (CHD) is the “most important susceptibility characteristic for increased risk due to CO 16 exposure” (ISA, p. 2-11). While persons with a normal cardiovascular system can tolerate 17
substantial concentrations of CO if they vasodilate or increase cardiac output in response to the 18 hypoxia produced by CO, those that are unable to vasodilate in response to CO exposure may 19
show evidence of ischemia at low concentrations of COHb (ISA, p. 2-10). There is strong 20 evidence for this in controlled human exposure studies of exercising in dividuals with CAD, 21 which is supported by results from recent epidem iologic studies reporting associations between 22 short-term CO exposure and increased risk of emergency department visits and hospital 23 admissions for individuals affected with ischemic heart disease (IHD) 8 and related outcomes 24 (ISA, section 5.7). This combined evidence, briefly summarized in section 2.5.1 below and 25 described in more detail in the ISA, supports th e conclusion that individuals with CAD represent 26 the population most susceptible to increased risk of CO-induced health effects (ISA, sections 27 5.7.1.1 and 5.7.8). The 2007 estimate of the size of the U.S. population with coronary heart 28 disease, inclusive of those with angina pect oris (cardiac chest pa in) and those who have 29

8 Ischemic heart disease is a category of cardiovascul ar disease associated with narrowed heart arteries, which is often also called CAD (coronary artery disease) and CHD (coronary heart disease). Individuals with CHD have myocardial ischemia, which occurs when the heart mu scle receives insufficient oxygen delivered by the blood. Exercise-induced angina pectoris (chest pain) occurs in many of them. Among all patients with diagnosed CAD, the predominant type of ischemia, as identified by ST segment depression, is asymptomatic (i.e., silent). Also, patients who experience angina typically have additional ischemic episodes that are asymptomatic (2000 AQCD, section 7.7.2.1). In addition to such chronic conditions, CHD can include myocardial infarction (ISA, p. 5-24).February, 2010 2-8 Draft – Do Not Cite or Quote experienced a heart attack (ISA, Table 5-26) is 13.7 million people, some fraction of whom have 1 IHD (ISA, pp.5-117). Further, there are estimated to be several million additional people with 2 silent ischemia or undiagnosed IHD (AHA, 2003). In combination this represents a large 3 population that is more susceptible to ambien t CO exposure when compared to the general 4 population (ISA, section 5.7). 5 Other types of cardi ovascular disease 9 may also potentially contribute to increased 6 susceptibility to the adverse effects of low levels of CO, especially during exercise (ISA, section 7 5.7.1.1). For example, some evidence with regard to other types of cardiovascular disease such 8 as congestive heart failure, arrhythmia, and non-sp ecific cardiovascular di sease, although more 9 limited for peripheral vascular and cerebrovascular disease, indicates that “the continuous nature 10 of the progression of CAD and its close relations hip with other forms of cardiovascular disease 11 suggest that a larger pop ulation than just those individuals wi th a prior diagnosis of CAD may be 12 susceptible to health effects fr om CO exposure” (ISA, p. 5-117). 13 Other populations considered to be potentially at increased ri sk relative to the general 14 population due to gender differences, aging, or pr eexisting disease or b ecause of the use of 15 medications or alterations in their environment, include fetuses and youn g infants; the elderly, 16 especially those with compromised cardiovascu lar function; individuals with hematologic 17 diseases (e.g., anemia) that affect oxygen-carryin g capacity or transport in the blood; those with 18 chronic obstructive pulmonary disease; people usi ng medicinal or recreatio nal drugs with central 19 nervous system depressant propert ies; individuals exposed to ot her chemical substances that 20 increase endogenous formation of CO; individuals who have not adapted to high altitude and are 21 exposed to a combination of high altitude and CO ; and people that spend a substantial amount of 22 time on or near heavily traveled which may cont ribute to higher CO exposures (ISA, section 23 5.7). For example, although the effects of CO on maternal-fetal rela tionships are not well 24 understood, fetal circulation is like ly to have a higher COHb level than the maternal circulation 25 because of differences in uptake and elimination of CO from fetal Hb, which may contribute to 26 an enhanced sensitivity to CO exposure duri ng gestation (ISA, section 5.7.2.2). Additionally, 27 although there are no controlled human exposure or epidemiological studies examining potential 28 CO-induced effects in people suffering with anemia, it is reasonable to assume that the potential 29 combination of hypoxic effects of CO together w ith reduced oxygen availability and/or elevated 30

9 Cardiovascular disease comprises many types of medical disorders, in cluding heart disease, cerebrovascular disease (e.g., stroke), hypertension (high blood pressure), and peripheral vascular diseases. Heart disease, in turn, comprises several type s of disorders, including ischemic heart disease (i.e., coronary heart disease [CHD], CAD, myocardial infarction, and angina), congest ive heart failure, and disturbances of cardiac rhythm (dysrhythmias and arryhthmias) (2000 AQCD, p. 7-7).February, 2010 2-9 Draft – Do Not Cite or Quote baseline COHb levels in people suffering with anemia 10 may make this popul ation susceptible to 1 CO-induced effects (ISA, section 5.7.1.4). As thma and COPD are other oxygen-limiting 2 diseases which may be exacerbated by CO-related oxygen limitation. Other individuals that may 3 be potentially susceptible to CO are those th at may have increased endogenous production of CO 4 and potentially higher endogenous COHb leve ls such as diabetics, for which a few 5 epidemiological studies provide suggestive ev idence of increased risk for cardiovascular 6 emergency department visits and hospital admissi ons compared to non-diabetics in response to 7 short-term CO concentrations (ISA, section 5.7.1.3). 8 Based on the current evidence, most particular ly with regard to quantitative information 9 of COHb levels and association with specific he alth effects, the primary target population for 10 purposes of the quantitative asse ssment described in this document is adults with CHD (also 11
known as ischemic heart disease IHD or CAD), both diagnosed and undiagnosed. 11 Little 12 empirical evidence is available by which to specify health effects associated with CO exposures 13 in the other, potentially at-risk groups identified above. Such evidence ch aracterizing the nature 14 of specific health effects of CO in these populations is limited and does not include COHb levels 15
related to health effects in thes e groups. As a result, while we continue to recognize the potential 16 susceptibility of the larger cardiovascular diseas e population to health effects of CO, as has been 17 recognized in past reviews, as well as the poten tial susceptibility of se veral other populations 18 identified above (ISA, section 5.7), the at-risk population simu lated in this assessment is 19 individuals with CAD (diagnosed and undiagnosed and inclusive of individuals with angina 20 pectoris and heart attacks). We note, however, that the larger cardiovas cular disease population 21 and the potential susceptibility of other populations is further c onsidered with regard to the 22 review of the CO NAAQS in the draft Policy Assessment document (US EPA, 2010b). 23

2.5 HEALTH ENDPOINTS 24
Carbon monoxide elicits various health e ffects by binding to reduced iron in heme 25 proteins and altering the functioni ng of a number of heme protei ns (ISA, sections 4.6 and 5.1). 26 The level of CO bound to hemoglobin as carboxy hemoglobin (COHb) in the blood is the best 27 characterized dose metric for evaluating CO expos ure and the potential for associated health 28 effects, as described in section 2.3 above. 29

10 Individuals affected with anemias of different etiologies may have low hematocrit, reduced capacity of the blood to carry oxygen, or increased COHb levels, all of which would decrease the oxygen available for organs and tissues (ISA, pp. 118-119).

11 As described in section 1.2 above, this is the same population group that was the focus of the exposure/dose assessments conducted previously (e.g., US EPA, 1992; Johnson et al., 2000).February, 2010 2-10 Draft – Do Not Cite or Quote The best characterized health effect associ ated with CO levels of concern is hypoxia 1 (reduced oxygen availabili ty) induced by increased COHb leve ls in blood (ISA, section 5.1.2). 2
The formation of COHb reduces the oxygen carrying capacity of the blood and impairs the 3 release of oxygen from oxy-hemoglobin complexes to the tissues. Accordi ngly, CO is especially 4
harmful in individuals with im paired cardiovascular systems (a s discussed in section 2.4 above) 5
and the clearest evidence of causa l relationships with CO exists for cardiovascular effects. In 6 characterizing the combined evidence, the ISA conc luded that cardiovascul ar effects are likely 7 causally related to short-term exposures to CO at relevant concentrati ons, with “relevant CO 8 concentrations” defined in the ISA as “generally within one or two orders of magnitude of 9 ambient CO concentrations” (ISA, p. 2-5). The “most compelling evidence of CO-induced 10 effects on the cardiovascular system comes from a series of controlled human exposure studies 11 among individuals with coronary heart dise ase (CHD) (ISA, sections 5.2.4 and 5.2.6). 12 Other potential effects of CO which are le ss well characterized at relevant exposure 13
concentrations are those on the central nervous system, reproducti on and prenatal development, 14 and the respiratory system (ISA, section 2.5). Th ese additional health endpoints, for which the 15 limited available evidence is suggestive of cau sal relationships (ISA, sections 5.3, 5.4 and 5.5), 16
are also considered in this review and are disc ussed in detail in the ISA and summarized briefly 17 in section 2.5.2 below. Across the health endpoi nts identified here, howev er, the focus of the 18 quantitative analysis described in this document is on cardiovascula r disease-related effects that 19 have been observed in adults with CHD, most specifically decreased time to exercise-induced 20 angina and changes to the “ST” segment of an electrocardiogram that are indicative of 21 myocardial ischemia. This focus is based on th e strength of the evidence and availability of 22 quantitative information from human studies of c ontrolled CO exposures in which the resulting 23
COHb levels were associated with these eff ects (as discussed in sections 2.5.1 and 2.6 below). 24

2.5.1 Cardiovascular Disease-related Effects 25
The best characterized cardiovascular diseas e-related effects associated with CO are 26 markers of myocardial ischemia observed in studies of controlled CO exposures of CHD 27 patients 12 and effects on exercise duration and maxi mal aerobic capacity observed in controlled 28 exposure studies of healthy adults. 13 As noted in the ISA, th e decreases in exercise duration 29

12 Epidemiological studies have consistently shown associations between ambient CO measurements and emergency department visits and hospital admissions for IHD, which is coherent with the effects observed in controlled human exposure studies of CAD patients. Additional studies have shown associations between ambient CO and hospital admissions for congestive heart failure and cardiovascular disease as a whole (which includes IHD), although this evidence is not as cons istent among studies as the IHD evidence.

13 Human clinical studies of individuals without diagnosed heart disease that were conducted since the 2000 CO AQCD did not report an association between CO and ST-segment changes or arrhythmia (ISA, section 2.5.1).February, 2010 2-11 Draft – Do Not Cite or Quote among healthy adults (associated with COHb levels from 3 up to 20%) were relatively small and 1 only likely to be noticed by competing athlet es, although they are c onsidered to provide 2
coherence with the exercise-induced cardiovascula r effects of greater concern that have been 3 demonstrated in CHD patients. The controlle d human exposure studie s involving individuals 4 with preexisting CHD provide str ong evidence for an association between short-term exposure to 5 CO and measures of ischemia (US EPA, 2000, section 6.2.2; ISA, section 5.2.4). Multiple 6 controlled human exposure studies have shown that short-term exposure to CO and subsequent 7 elevation of COHb to levels of approximately 2-6% reduces time to onset of exercise-induced 8 myocardial ischemia in individua ls with preexisting CAD, with no evidence of a threshold at the 9 lowest levels tested (ISA, section 5.2.4). 10 The controlled exposure study of principal im portance is a large multi-laboratory study 11 designed to evaluate myocardial ischemia, as documented by reductions in time to change in the 12 ST-segment of an electrocardiogram 14 and in time to onset of angina, during a standard treadmill 13 test, at CO exposures targeted to result in mean subject COHb levels of 2% and 4%, as measured 14 by gas chromatographic technique 15 (ISA, section 5.2.4, from Allred et al., 1989a, 1989b, 1991). 15 In this study, subjects on three separate occasi ons underwent an initial graded exercise treadmill 16 test, followed by 50- to 70-min exposures under re sting conditions to average CO concentrations 17 of 0.7 ppm (room air concentration range 0-2 ppm), 117 ppm (range 42-202 ppm) and 253 ppm 18 (range 143-357 ppm). After the 50- to 70-min e xposures, subjects underwent a second graded 19 exercise treadmill test, and the percent change in time to onset of angina and time to ST endpoint 20
between the first and second exercise tests was de termined. Relative to clean-air exposure that 21 resulted in a mean COHb level of 0.6% (pos t-exercise), exposures to CO resulting in post22
exercise mean COHb concentrations of 2.0% and 3.9% 16 were shown to decrease the time 23 required to induce ST-segment changes by 5.1% (p=0.01) and 12.1% (p <0.001), respectively. 24 These changes were well correlated with the onset of exercise-induced angi na the time to which 25 was shortened by 4.2% (p=0.027) and 7.1% (p=0.002) , respectively, for the two CO exposures 26 (ISA, section 5.2.4; (All red et al., 1989a,1989b, 1991). 27

14 The S-T segment is a portion of the electrocardiogram, depression of which is an indication of insufficient oxygen supply to the heart muscle tissue

15 As stated in the ISA, the gas chromatographic t echnique for measuring COHb levels “is known to be more accurate than spectrophotometric measurements, particularly for sample s containing COHb concentrations < 5%” (ISA, p. 5-41). CO-oximetry is a spectrophotometric method commonly used to rapidly provide approximate concentrations of COHb during controlled exposures (ISA, p. 5-41). At the low concentrations of COHb (<5%) more relevant to exposures to ambient CO, co-oximeters ar e reported to overestimate CO Hb levels compared to GC measurements, while at higher concentrations, this method is reported to produce underestimates (ISA, p.4-18).

16 The corresponding co-oximeter measured post-exercise levels were 2.7% and 4.7%. The post-exposure, pre-exercise COHb levels for the two CO exposures were 2.4% and 4.7% by GC and 3.2% and 5.6% by co-oximetry (ISA, p. 5-41).February, 2010 2-12 Draft – Do Not Cite or Quote No human clinical studies have been specifi cally designed to eval uate the effect of 1 controlled exposures to CO resulting in study mean COHb levels lower than 2% (ISA, section 2 5.2.6). However, an important finding of th e multi-laboratory study was the dose-response 3 relationship observed between COHb and ischemia without evidence of a measurable threshold 4 effect (Allred et al., 1991; A llred et al., 1989b). As reporte d by the authors, the results 5 comparing “the effects of increasing COHb from baseline levels (0.6%) to 2 and 3.9% COHb 6 showed that each produced further changes in objective ECG measures of ischemia” implying 7
that “small increments in COHb could advers ely affect myocardial function and produce 8 ischemia” (Allred et al., 1991; Allred et al., 198 9b). For each 1% increase in COHb resulting 9 from the experimentally increased CO exposur e concentrations the dose-response analysis 10 performed by the authors indicated decreases of 1.9% in time to exer cise-induced angina and 11 3.9% in time to exercised-induced ST-segment change in persons with pre-existing CAD (ISA, 12 section 5.2.4; Allred et al., 1989a,1989b, 1991). 13 Other controlled human exposure studies (Adams et al. 1988, Anderson et al. 1973, 14 Kleinman et al. 1989, Kleinman et al., 1998) invo lving individuals with stable angina have 15
confirmed the Allred et al findi ngs at COHb concentrations between 3 and 6% (as measured by 16 CO-oximeter) (ISA, section 5.2.4). Among the ev idence is also a study of a small group of 17 patients with CAD which reported no change in time to onset of angina or maximal exercise time 18 following a 1 hour exposure targeted to result in 4% COHb. A subsequent study conducted by 19 the same laboratory reported a significant increa se in number of ventricular arrhythmias during 20 exercise relative to room air among individuals with CAD following a 1-hr CO exposure targeted 21 to yield 6% COHb, but not following a 1-hr exposure targeted to yield a COHb level of 4% (ISA, 22 p. 5-42; Sheps et al., 1990; Sh eps et al., 1987). Although there was no clear pattern across the 23 different studies with respect to the magnitude of the decreased time to onset of angina versus 24 dose level, differences in study protocols and anal ytical methods do not allow for an informative 25 pooled or quantitative metaanalysis of the dose- response relationship acro ss studies (ISA, section 26 5.2.4). 27 Although the subjects evaluate d in the controlled human exposure studies described 28 above are not necessarily representative of the most sensitive population, the level of disease in 29 these individuals ranged from moderate to severe, with the majority either having a history of 30 myocardial infarction or having ≥ 70% occlusion of one or more of the coronary arteries (ISA, p. 31 5-43). 32

2.5.2 Other Effects 33
Other health effects for which the evidence is suggestive of causal relationships with 34 short-term CO exposures include some effect s on the central nervous system, reproduction and 35February, 2010 2-13 Draft – Do Not Cite or Quote prenatal development, and the respiratory syst em (ISA, section 2.5). High CO exposures have 1 “long been known to adversely affect central nervous system (CNS ) function”, although a 2 relationship close to ambient leve ls is less clear (ISA, pp. 5-49). Further, the evidence indicates 3 that healthy adults may be protected against such effects at ambient levels through compensatory 4 responses such as increased cardiac output and cerebral blood flow, althou gh these compensatory 5 mechanisms may be impaired among suscep tible groups, including those with reduced 6 cardiovascular function (ISA, section 5.3.3). Limite d evidence indicates an association of CO 7 exposure during early pregnancy with pre-term births and birth defect s (ISA, p. 2-8). New 8 epidemiologic studies report pos itive associations for CO-i nduced lung-related outcomes, 9 although interpretation is affected by uncertainti es including with regard to the biological 10 mechanism that could explain CO-induced respiratory outcomes (ISA, section 5.5.5). 11

2.6 RISK CHARACTERIZATION APPROACH 12 In identifying an approach to characterize th e risk of cardiovascular effects of exposures 13 to ambient CO, we considered 1) approaches em ployed in previous assessments, 2) the currently 14 available evidence regarding associations be tween CO concentrati ons and cardiovascular 15 outcomes, and 3) advice from CASAC (Brain, 20 09; Brain and Samet, 2009; Brain and Samet, 16 2010). As summarized in section 1.2 above, the last CO NAAQS review included analyses of 17 exposure to ambient CO and associated internal do se, in terms of COHb levels, which were used 18
to characterize risks for th e population of interest (USE PA, 1992). The prior risk 19
characterization considered the percent of the modeled population that exceeded COHb levels of 20
interest which were drawn from the evidence of COHb levels associated with exercise-induced 21 aggravation of angina in cont rolled human exposure studies invol ving short-term (shorter than 8 22 hours) exposures of patients with diagnosed CAD 17 to elevated CO concentrations (US EPA, 23 1991). 24 In the current review, the controlled huma n exposure studies among individuals with 25 CAD continue to provide the clearest evidence of CO-induced effects on the cardiovascular 26 system as the most sensitive endpoint. In contrast to epidemiological studies, human exposure 27 studies also provide quantitativ e information linking CO exposures through COHb levels with 28 these effects. Among these studies, the multila boratory study of Allred et al (1989a, 1989b, 29 1991) continues to be the principal study inform ing our understanding of the effects of CO on 30 individuals with pre-existing CAD at the low end of the range of COHb levels studied (USEPA, 31 1991, 2000, 2010). The strength of the evidence mo re broadly continues to support the use of 32

17 Study subjects met certain criteria w ith respect to evidence of coronary artery disease, often also called CHD or IHD.February, 2010 2-14 Draft – Do Not Cite or Quote COHb level as the internal dos e metric for assessing exposure to ambient levels of CO and 1 characterizing associated potential for health ri sk. Thus, based on the strength of the evidence 2 and the availability of quantitative information from controlled human exposure studies, this 3 REA also focuses on estimates of the percent of the simulated population expected to experience 4
maximum end-of-hour COHb levels of intere st based on findings of those studies. 5 We also note that, in the current review, a number of epidemiological studies are now 6 available that investigate associ ations of cardiovascular morbidity with ambient measurements of 7 CO (ISA, sections 5.2.4 and 5.2.5). 18 These studies have observed associations between ambient 8
monitor CO concentrations and increases in emergency department visits and hospital 9 admissions for cardiovascular effects (ISA, sect ions 5.2.1.9). While these studies are coherent 10 with the controlled human exposure studies (IS A, section 5.2.6), we recognize a number of 11 uncertainties that complicate their use for our pur poses in a quantitative risk assessment (ISA, 12 pp. 2-14 to 2-17, section 5.2.3). 13

 “This [epidemiological] evidence for ischem ia-related outcomes is coherent with 14 effects observed in controlled human exposur e studies, although un certainty regarding 15 the extent of reduced O 2 delivery to tissues following exposure to ambient CO 16 concentrations contributes to the uncertainty in quantitative interpretation of effect 17 estimates.“ [ISA, p. 2-14] 18

 The correlation between concentrations of CO and other combustion-related pollutants 19 “complicates the quantitative interpretation of effect estimates in these studies to 20 apportion the relative extent to which CO at ambient concen trations is independently 21 associated with cardiovascular or other eff ects, and the extent to which CO acts as a 22 marker for the effects of a nother combustion-related polluta nt or mix of pollutants.” 23 [ISA, p. 2-16] 24

 Although the epidemiological evidence indicates that CO associations generally remain 25 robust in copollutant models, uncertainty a ssociated with the us e of these models 26 “complicates quantitative interpretation of the effect estimates reported in 27 epidemiologic studies” [ISA, p. 2-16] 28

 “Some of these uncertainties [identified in 2000 AQCD] remain and complicate the 29 quantitative interpretation of the epidemio logic findings, particularly regarding the 30 biological plausibility of health effects occurring at COHb le vels resulting from 31 exposures to ambient CO concentrations m easured at AQS monitors.” [ISA, p. 2-17] 32 Given these uncertainties in the quantitative interpretation of epidemiological studies for 33 CO, and the longstanding body of ev idence that links exposures to effects through an internal 34 dose metric, we have characterized health risk of ambient CO expos ures in this assessment using 35

18 One additional controlled exposure study in CHD patients is available since the last review. It involved higher COHb levels than the study by Allred et al (1989a, 1989b, 1991) and is not a focus here (2000 AQCD, section 6.2.2).February, 2010 2-15 Draft – Do Not Cite or Quote estimates of associated COHb levels and a benchmark level approach, with benchmarks 1
identified in consideration of the controlled human exposure literature. 19 This is supported by 2 the fact that COHb levels repor ted in controlled human exposure st udies are a bette r indicator of 3 personal exposure and dose than concentrations measured at fixed site ambient monitors. In 4 addition, controlled human exposure studies can examine the health effects of short-term 5 exposure to CO in the absence of co-pollutant s that can confound results in epidemiologic 6 analyses; thus, health effects obs erved in controlled human exposure studies can confidently be 7
attributed to a defined COHb dose level re sulting from short-term CO exposures. 8 In drawing from the results of the contro lled human exposure studies to inform the 9 characterization of potential CO risk in this as sessment, staff considered a number of factors, 10
listed below. 11

 Myocardial ischemic effects, as documented by reductions in times to exercise-induced 12 change in the ST-segment of an electroc ardiogram and to exercise-induced onset of 13 angina, were observed in response to CO exposures involving subjects with pre14 existing CAD. Staff gives primary focus he re to the multi-laboratory study in which 15 COHb was analyzed by the more accurate GC method. (Allred et al., 1989a,b, 1991). 16

 Relative to clean-air exposure that resulte d in a mean level of 0.6% COHb (post17 exercise), exposures to CO resulting in pos t-exercise mean COHb levels of 2.0% and 18 3.9% 20 were shown to decrease the time re quired to induce ST-segment changes by 19 5.1% (p=0.01) and 12.1% (p<0.001), respectivel y. These changes were well correlated 20 with the onset of exercise-induced angina , the time to which was shortened by 4.2% 21 (p=0.027) and 7.1% (p=0.002), respectively, for the two CO exposures (Allred et al., 22 1989a, 1989b, 1991). 23

 There is no evidence of a threshold for the measures assessed at the lowest levels 24 tested, with incremental additions of COHb fr om baseline mean levels of 0.6% to 2 and 25 3.9% COHb showing changes in the monitore d measures of ischemia (Allred et al., 26 1989b, 1991). The average of the regressions of the individual study subject data for 27 these measures at baseline COHb and the two COHb levels resulting from the two 28 controlled CO exposures was summarized by the authors as indicating decreases of 29 roughly 1.9% in time to exercise-inducedangina and 3.9% in time to exercised30 induced ST-segment change per 1% increase in COHb concentration in persons with 31 pre-existing CAD (ISA, section 5.2.4; Allred et al., 1989a,1989b, 1991). 32

19 While not used for the purposes of this quantitativ e assessment, EPA is considering all of the current health evidence, including the epidemiological studies, in the Policy Assessment, along with considerations based on the risk and exposure assessment findings.

20 Subjects were exposed to two levels of CO exposure , resulting in COHb levels in the range of 2.0 to 2.4% and 3.9 to 4.7%, respectively. The upper end of each range is the aver age COHb level obtained post-exposure and the lower end is the average COHb level obtained afte r the subsequent exercise te st (Allred et al., 1989a,b, 1991).February, 2010 2-16 Draft – Do Not Cite or Quote  Studies have not been designe d to evaluate similar effect s of exposures to increased 1 CO concentrations eliciting average COHb le vels below the 2% target level of Allred 2 et al (1989a, 1989b, 1991). In addition, these st udies do not address the fraction of the 3 population experiencing a specified health effect at various dose levels. These aspects 4 of the evidence contributed to EPA’s conclusi on that at this time there are insufficient 5 controlled human exposure data to support the development of quantitative dose6 response relationships which would be require d in order to conduct a quantitative risk 7 assessment for this health endpoint, rather than the benchmark level approach. 8 In drawing on this information, staff r ecognize the uncertainty associated with 9 interpretation of COHb levels estimated to result from CO e xposure concentrations in this 10 assessment that are much lower than the CO exposur e concentrations used in the clinical studies 11 to elicit increases in participant’s COHb levels to target levels for the study. 12
We have reviewed COHb estimat es developed in this assessme nt with attention to both 13
COHb in absolute terms and also based on consid eration of the contribution to COHb associated 14 with ambient CO exposures. With regard to COHb in absolute terms, staff identified benchmark 15 levels of 1.5%, 2.0%, 2.5% and 3% COHb ba sed on consideration of the evidence from 16 controlled human studies of CHD pa tients discussed above, and is inclusive of the range of levels 17 considered in the review completed in 1994 (USEPA, 1992). This range extends below the 18 lowest mean COHb level (e.g., 2.0% post-exerci se in Allred et al ., 1989b) resulting from 19 controlled exposure to increased CO concentration in the clinic al evidence. This extension 20 reflects comments from the CASAC CO panel on the draft Analysis Plan (Brain and Samet, 21 2009) and consideration of the un certainties regarding th e actual COHb levels experienced in the 22 controlled human exposure studies; that these st udies did not include i ndividuals with most 23 severe cardiovascular disease; the lack of studies evalua ting effects of controlled short-term CO 24 exposures resulting in COHb levels below study m ean 2.0-2.4% and the lack of evidence of an 25
effect threshold at these levels. We note that CASAC comments on the first draft REA 26
recommended the addition of a benchmark at 1% COHb and staff has presented results for this 27 COHb level in this draft REA. In considering th is advice, we recognize, however, that a level of 28 1% COHb overlaps with the upper part of the range of endogenous le vels in health individuals as 29 characterized in the ISA (ISA, p. 2-6) and with the upper part of the range of baseline COHb 30 levels in the study by Allred et al (1989b, Appendix B). As a result, while noting population 31 dose estimates in relation to this level, we have not placed weight on th is level as a potential 32
health effects benchmark in discussions of the results below and in the draft Policy Assessment 33 document. We additionally consider, however, th e observations of the multi-laboratory clinical 34 study with regard to response per 1% increase in COHb concentration resulting from short-term 35February, 2010 2-17 Draft – Do Not Cite or Quote controlled CO exposure exposures of persons with pre-existing CAD (ISA, section 5.2.4; Allred 1 et al., 1989a, 1989b, 1991). 21 2
The benchmark levels identified are used to in terpret COHb levels estimated to occur in 3 the modeled population in response to exposures to ambient CO in different air quality scenarios 4 in light of the evidence for cardiovascular effect s in individuals with CHD when exposed to CO. 5 That is, we have estimated the number of person s and percent of the si mulated at-risk population 6 expected to experience COHb levels below each of these potential health effect benchmark 7 levels as a result of ambient CO exposures as sociated with a set of air quality scenarios 8 employed to inform the current review of the CO NAAQS (see chapter 5 below). As noted in 9 chapter 1 above, given the significant time constraints of this review, results are provided in this 10 document without substantial interpretation. Rather , discussion of health risk and public health 11 implications of these results in the context of the NAAQS review is provided in the draft Policy 12
Assessment. 13

2.7 KEY OBSERVATIONS 14 Presented below are key observations regardi ng the current evidence for health effects 15 associated with exposures to ambient CO. 16

 Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 17 carbon-containing fuels and photochemical reac tions in the atmosphere, with on-road 18 mobile sources representi ng significant sources of CO to ambient air. 19

 Microenvironments influenced by on-road mob ile sources are important contributors to 20 ambient CO exposures, particularly in urban areas. 21

 The formation of COHb is a key step in the elicitation of various health effects by CO. 22 Further, COHb level is commonly used in exposure assessment and is considered the 23 best biomarker for CO health effects of concern. 24

 Individuals with CHD are th e population with greatest sus ceptibility to short-term 25 exposure to CO, and the population for whic h the current evidence indicates health 26 effects occurring at the lowest exposures. Th e evidence further indicates a potential for 27 other underlying cardiovascular conditions to co ntribute susceptibility to CO effects. 28 Other populations potentially at risk include individuals with diseases such as chronic 29

21 Relative to clean-air exposure that resulted in a mean COHb level of 0.6% (post-exercise), exposures to CO resulting in post-exercise mean COHb concentrations of 2.0% and 3.9% were shown to decrease the time required to induce ST-segment changes by 5.1% (p=0.01) and 12.1% (p<0.001), respectiv ely. These changes were well correlated with the onset of exerci se-induced angina the time to which was shortened by 4.2% (p=0.027) and 7.1% (p=0.002), respectively, for the two CO exposure s. A dose-response analysis in which the individual regressions of study subject responses at baseline COHb and at the two increas ed COHb levels were averaged was summarized as indicating decreases of roughly 1.9% in time to exercise-induced angina and 3.9% in time to exercised-induced ST-segment change per 1% increase in COHb concentra tion in persons with pre-existing CAD (ISA, section 5.2.4; Allred et al., 1989a,1989b, 1991).February, 2010 2-18 Draft – Do Not Cite or Quote obstructive pulmonary disease (COPD), anem ia, or diabetes, and individuals in 1 prenatal or elderly life stages. 2

 Cardiovascular effects are the health endpoint for which the evidence is strongest and 3 indicative of a likely causal relationship with CO exposur es. Other endpoints for 4 which the evidence is suggestive of such a relationship include effects on the central 5 nervous system, reproduction and prenatal de velopment, and the respiratory system. 6

 Risk is characterized in this REA through ev aluation of COHb estimated to result from 7 ambient CO exposure in individuals w ith CHD (including undiagnosed persons) 8 considering potential health effect benc hmarks for daily maximum COHb levels. 9 Results are reported in terms of percent of population expected to experience daily 10 maximum COHb levels at or above a series of levels that range as low as 1%. These 11 results are considered in th e Policy Assessment document in light of potential health 12 effects benchmarks ranging from 1.5%, wh ich is below the lowe st study mean COHb 13 level resulting from experimental CO e xposure in controlled human exposures of 14 subjects with CAD, up to 3.0%, a level associated with adverse effects in those studies. 15February, 2010 2-19 Draft – Do Not Cite or Quote 2.8 REFERENCES 1
Abi-Esber L and El-Fadel M. (2008). In-vehicle CO ingression: Validation through field measurements and mass 2 balance simulations. Sci Total Environ . 394:75-89. 3

Adams KF, Koch G, Chatterjee B, Goldstein GM, O'Ne il JJ, Bromberg PA, Sheps DS, McAllister S, Price CJ, 4 Bissette J. (1988). Acute elevation of blood carboxyhemoglobin to 6% impairs exercise performance and 5 aggravates symptoms in patients with ischemic heart disease. J Am Coll Cardiol . 12:900-909 6

Akland GG, Hartwell TD, Johnson TR, Whitmore RW. (1985). Measuring human exposure to carbon monoxide in 7 Washington, DC, and Denver, Colorado, during the winter of 1982-1983. Environ Sci Technol . 19:911- 8 918. 9

Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD , Pagano M, Selvester RH, Walden 10 SM, Warren J. (1989a). Short-term effects of carbon monoxide exposure on the exercise performance of 11 subjects with coronary artery disease. N Engl J Med . 321:1426-1432. 12

Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD, Hayes D, Pagano M, Selvester RH, 13 Walden SM, Warren J. (1989b). Ac ute effects of carbon monoxide exposu re on individuals with coronary 14 artery disease. Cambridge, MA: Health Ef fects Institute; resear ch report no. 25. 15

Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD , Pagano M, Selvester RH, Walden 16 SM, Warren J. (1991). Effects of car bon monoxide on myocardial ischemia. Environ Health Perspect 17 91:89-132. 18

AHA. (2003). Heart and Stroke Facts. American Heart Association, Dallas, TX. Available at: 19 http://www.americanheart.org/downloadab le/heart/1056719919740HSFacts2003text.pdf 20

Anderson EW, Andelman RJ, Strauch JM, Fortuin NJ, Knelson JH. (1973). Effect of low level carbon monoxide 21 exposure on onset and duration of angina pectoris. Annals of Internal Medicine . 79:46-50. 22

Boulter P and McCrae I. (2005). Carbon Monoxide Inside Vehicles: Implications for Road Tunnel Ventilation. In: 23 Annual Research Review 2005. TRL Academy. 24

Brain JD. (2009). Letter from Dr. Joseph Brain to Administrator Lisa Jackson. Re: Consultation on EPA’s Carbon 25 Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure 26 Assessment. CASAC-09- 012. July 14, 2009. 27

Brain JD and Samet JM. (2009). Letter to EPA Admini strator Lisa Jackson: Clean Air Scientific Advisory 28 Committee’s (CASAC) Peer Review of the Agency’s 1st Draft Carbon Monoxide Integrated Science 29 Assessment. EPA-CASAC-09-011. June 24, 2009. 30

Brain JD and Samet JM. (2010). Letter from Drs. J.D. Br ain and J.M. Samet to Administrator Lisa Jackson. Re: 31 Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO) 32 Primary National Ambient Air Quality Standards: First External Review Draft. CASAC-XX-XXX. 33 January XX, 2010. 34

Bruinen de BruinY, Hanninen O, Carter P, Maroni M, Kephalopoulos S, Scotto De Marco G, Jantunen M. (2004). 35 Personal carbon monoxide exposure levels: Contribution of local sources to exposures and 36
microenvironmental concentrations in Milan. J Expo Anal Environ Epidemiol . 14:312-322. 37

Chan C, Ozkaynak H, Spengler J, Sheldon L. (1991). Driver exposure to volatile organic compounds, CO, ozone, 38
and NO 2 under different driving conditions. Environ Sci Technol . 25(5) :964-972. 39February, 2010 2-20 Draft – Do Not Cite or Quote Chang LT, Koutrakis P, Catalano PJ, Suh HH. (2000). Hourly personal exposures to fine particles and gaseous 1 pollutants - Results from Baltimore, Maryland. J Air Waste Manage Assoc . 50:1223-1235. 2

Coburn R, Forster R, Kane P. (1965). Consideration of the physiological variables that determine the blood 3 carboxyhemoglobin concentrations in man. J Clin Invest . 44:1899-1910. 4

Davies DM and Smith DJ. (1980). Electrocardiographic changes in healthy men during continuous low-level 5 carbon monoxide exposure. Environ Res . 21:197-206. 6

Flachsbart P. (1999). Human exposure to carbon monoxide from mobile sources. Chemosphere – Global Change 7

Science . 1:301-329. 8

Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 9 Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.1), prepared by 10 ICF Kaiser Consulting Group for U.S. EPA, Office of Air Quality Planning and Standards, under Contract 11 No. 68-D6-0064, WA Nos. 1-19, 2-24, 2-30, and 3-3, Docket EPA-HQ-OAR-2008-0015-0010. 12

Kleinman MT, Davidson DM, Vandagri ff RB, Caiozzo VJ, Whittenberger JL. (1989). Effects of short-term 13 exposure to carbon monoxide in subj ects with coronary artery disease. Arch Environ Health . 44:361-369. 14

Kleinman MT, Leaf DA, Kelly E, Caiozzo V, Osann K, O'Niell T. (1998). Urban angina in the mountains: effects 15 of carbon monoxide and mild hypoxemia on subjects with chronic stable angina. Arch Environ.Health

. 16 53:388-397. 17

Koushi P, Al-Dhowalis K, Niazi S. (1992). Vehicle occupant exposure to carbon monoxide. J Air Waste Manag

. 18 42:1603-1608. 19

Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S, Frazier C. (1998). 20 Measuring Concentrations of Selected Air Pollutants Inside California Vehicles. Final Report. Prepared 21 by Research Triangle Institute under Contract No. 95 -339. California Air Resources Board. Sacramento, 22
California. 23

Scotto di Marco G, Kephalopoulos S, Ruuskanene J, Jantunene M. (2005). Personal carbon monoxide exposure in 24 Helsinki, Finland. Atmos Environ . 39Z:2697–2707. 25

Sharp D and Tight M. (1997). Vehicle occupant exposure to air pollution. In: Policy, Planning, and Sustainability: 26 Proceedings of Seminars C and D Held at PTRC Euro pean Transport Forum, Brunel University. Pages 27 481-492. 28

Sheps DS, Adams KF Jr, Bromberg PA, Goldstein GM, O'Neil JJ, Horstman D, Koch G. (1987). Lack of effect of 29 low levels of carboxyhemoglobin on cardiovascular fu nction in patients with ischemic heart disease. Arch 30 Environ Health . 42:108-116. 31

Sheps DS, Herbst MC, Hinderliter AL, Adams KF, Ekelun d LG, O'Neil JJ, Goldstein GM, Bromberg PA, Dalton 32 JL, Ballenger MN, Davis SM, Koch GG. (1990). Production of arrythmias by elevated 33 carboxyhemoglobin in patients with coronary artery disease. Ann Intern Med . 113:343-351. 34

Shikiya D, Liu C, Kahn M, Juarros J, Barcikowski W. ( 1989). In-Vehicle Air Toxics Characterization Study in the 35 South Coast Air Basin. Office of Planning and Rules, South Coast Air Quality Management District. May. 36

US EPA. (1991). Air Quality Criteria For Carbon Mono xide. Research Triangle Park, NC: Office of Health and 37 Environmental Assessment, Environmental Criteria an d Assessment Office; report no. EPA/600/8-90/045F. 38February, 2010 2-21 Draft – Do Not Cite or Quote US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of 1 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 2 EPA/452/R-92-004. 3

US EPA. (2000). Air Quality Criteria for Carbon M onoxide. National Center for Environmental Assessment, 4 Office of Research and Development, U.S. Environmental Protection Agen cy, Research Triangle Park, NC 5 27711; report no. EPA/600/P-99/001F. June 2000. Available: 6 http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=18163. 7

US EPA. (2006). 2002 National Emissions Inventory Data and Documentation. U.S. Environmental Protection 8 Agency, Office of Air and Radiation, Office of Ai r Quality Planning and Stan dards, Research Triangle 9 Park, NC. http://www.epa.gov/ttn/chief/net/2002inventory.html. 10

US EPA. (2010a). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency, 11 Washington, DC, EPA/600/R-09/019F. Available at: 12 http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_isa.html. 13

US EPA. (2010b). Policy Assessment for the Review of the Carbon Monoxide National Ambient Air Quality 14 Standards, External Review Draft. Office of Air Qu ality Planning and Standards Staff Paper, report no. 15 EPA-452/P-10-005. 16February 2010 Draft – Do Not Quote or Cite 3-1 

### 3 AIR QUALITY CONSIDERATIONS 1

Ambient air quality data can be used as an indicator of exposure or used in conjunction 2
with other information to estimat e exposure concentrations. How well the ambient air quality is 3 represented in a particular location is depe ndent on the ambient monitoring network design 4 relative to the spatial and temporal characterist ics of the pollutant as well understanding the 5 concentration contribution from important loca l source emissions. This chapter summarizes 6 findings about the current air quality conditions and their spatial distri bution, with particular 7 focus on aspects informative to the design a nd conduct of this asse ssment and including 8 descriptions of CO measurement methods, mon itor siting requirements, and monitor locations 9 (section 3.1). Section 3.2 then draws upon the information presented in sections 3.1, among 10 other data, to select ambient air quality/study loca tions most useful in me eting the objectives of 11 the REA. Finally, key observations of the chapter are pres ented in section 3.3. 12

3.1 AMBIENT CO MONITORING 13 In this section, a broad overview of the monitoring network is provided (section 3.1.1) 14 and is followed by a summary of analytical de tection issues (secti on 3.1.2). Ambient CO 15 concentrations and their spatial and temporal variability are characte rized in section 3.1.3. 16 Estimates of policy-relevant background (PRB) concentrations which are defined as those 17
ambient concentrations that would occur in the US in the absence of an thropogenic emissions in 18 continental North America are presented in s ection 3.1.4. And finally, section 3.1.5 presents an 19 analysis of the specific CO concentration trends observed in individual monitors. 20

3.1.1 Monitoring Network 21
Ambient CO concentrations are measured by monitoring networks that are operated by 22 state and local monitoring agencies in the US, and are funded in part by the EPA. The main 23 network providing ambient data for use in comp arison to the NAAQS is the State and Local Air 24 Monitoring Stations (SLAMS) network. The su bsections below provide specific information 25 regarding the methods used for obtaining ambien t CO measurements and the requirements that 26 apply to states in the design of the CO network. 27 Minimum monitoring requirements for CO were revoked in the 2006 revisions to ambient 28 monitoring requirements (see 71 FR 61236, October 17, 2006). This action was made to allow 29 for reductions in measurements of some pollutants (CO, SO 2 , NO 2 , and Pb) where measured 30 levels were well below the appl icable NAAQS and air quality probl ems were not expected. CO 31 monitoring activities have been maintained at some SLAMS and these measurements of CO are 32 required to continue until discontinuation is approved by the EPA Regional Administrator. 33February 2010 Draft – Do Not Quote or Cite 3-2 CO monitors are typically sited to reflect one of the following spatial scales. 1 1

 Microscale: Data represent concentrations within a 100 m radius of the monitor. For 2 CO, microscale monitors are sited 2 – 10 m from a roadway. Measurements are 3 intended to represent the near-roa d or street canyon environment. 4

 Middle scale: Data represent concentrations av eraged over areas defined by 100 – 500 5 m radii. Measurements are intended to represent several city blocks. 6

 Neighborhood scale: Data represent concentrations averaged over areas defined by 0.5 7 – 4.0 km radii. Measurements are intended to represent extended portions of a city. 8 In addition to monitoring required for de termining compliance with the NAAQS, the 9 EPA is currently in the process of implementing plans for a new network of multi-pollutant 10
stations called NCore that is intended to meet multiple monitoring objectives. A subset of the 11 SLAMS network, NCore stations are intended to address integrated air quality management 12 needs to support long-term trends analysis, m odel evaluation, health and ecosystem studies, as 13 well as the more traditional objectives of NAA QS compliance and Air Quality Index reporting. 2 14 The complete NCore network, required to be fully implemented by January 1, 2011, will consist 15 of approximately 63 urban and 20 rural stations and will include some existing SLAMS sites that 16
have been modified to include additional measur ements. Each state will contain at least one 17 NCore station, and 46 of the states plus Washingt on, D.C. will have at least one urban station. 18 CO will be measured using high sensitivity monitors, as will SO 2 , NO, and NO Y
. 3 The majority 19 of NCore stations will be sited to represent neighborhood, urban, and region al scales, consistent 20 with the NCore network design objective of representing exposu re expected across urban and 21
rural areas in locations that are not dominated by local sources. 22

3.1.2 Analytical Sensitivity 23 To promote uniform enforcement of the air quality standards set forth under the CAA, 24 EPA has established provisions in the Code of Federal Regulations (CFR ) under which analytical 25 methods can be designated as federal reference methods (FRMs) or federal equivalent methods 26 (FEMs). Measurements for determinations of NAAQS compliance must be made with FRMs or 27
FEMs. 4 Specifications for CO monitoring are designe d to help states utilize equipment that has 28

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.

2 (http://www.epa.gov/ttn/amtic/ncore/index.html).

3 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 temperat ure (http://www.epa.gov/ttn/amtic/ncore/index.html).

4 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 incl ude the gas filter correlationFebruary 2010 Draft – Do Not Quote or Cite 3-3 met performance criteria utilized in the FRM or FEM approval process; operational parameters 1 are documented in 40 CFR Part 53, Table B-1. Gi ven the levels of the CO NAAQS (35 ppm, 1- 2 hour; 9 ppm, 8-hour average), the require d 1.0 ppm lower detectable limit (LDL) 5 is well below 3 the NAAQS levels and is therefore sufficient for demonstration of compliance. However, with 4 ambient CO levels now routinely near or below 1 ppm, there is greater unc ertainty in a larger 5 portion of the distri bution of monitoring data because a larg e percentage of these measurements 6
are below the LDL of conventiona l monitors. For this reason, a new generation of ambient CO 7 monitors has been designed that provides measurements with improved sensitivity at or below 8 the typical ambient CO levels measured in most urban and all rural loca tions. Additionally, the 9 higher sensitivity CO measurements are needed to support additional objectives such as 10 validating the inputs to chemical transport models and assessing the role of transport between 11 urban and rural areas because policy relevant background CO concentratio ns on the order of 0.1 12 ppm are well below the LDL of conventional m onitors. Newer GFC instruments have been 13 designed for automatic zeroing to minimize drift (US EPA, 2000). 14 Currently, a total of 13 appr oved FRMs are in use in the SLAMS network, based on a 15 retrieval of data reported between 2005 and 2009. Among these methods, nine are “legacy” 16 monitors with a federal method detection limit (MDL) 6 given as 0.5 ppm according to records in 17 EPA’s Air Quality System (AQS). 7 As discussed in the ISA, many of the reported 18 concentrations in recent years are near or below these MDLs (ISA, section 3.5.1.2). Four of 19 these new methods are high sensitivity methods w ith a federal MDL of 0.02 ppm and there are a 20 growing body of ambient data from high sensitiv ity CO instruments is becoming available. 21 Among newer GFC high sensitivity instruments, manufacturer-declared LDLs range from 0.02 – 22 0.04 ppm, with 24-hour zero drif t varying between 0.5% within 1 ppm and 0.1 ppm, and 23 precision varying from 0.5% to 0.1 ppm. EPA performed MDL testing on several high 24 sensitivity CO monitors in 2005 and 2006 following the 40 CFR Part 136 procedures. Those 25

(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).

5 Defined in 40 CFR Part 53.23 as the minimum pollu tant concentration which pr oduces a signal of twice
the noise level.

6 Defined in 40 CFR Part 136 as the minimum concentration of a substance that can be measured and reported with 99% confidence that the analyte concentration is gr eater than zero and is determined from analysis of a sample in a given matrix containing the analyte.

7 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 (Michie et al., 1983).February 2010 Draft – Do Not Quote or Cite 3-4 tests demonstrated MDLs of approximately 0.017 – 0.018 ppm, slightly below the stated LDL of 1 0.02 – 0.04 ppm. 2 Based on a retrieval of data reported to AQS for the time period between 2005 and 2009, 3 a total of 36 high sensitiv ity CO monitors have reported data w ith the majority of these monitors 4 currently active. The majority of these active mo nitors are associated w ith the implementation of 5 the NCore network. The extent to which high sensitivity monitors become integrated into non6 NCore SLAMS stations, however, will depend on the availability of funding for states to replace 7 well-operating legacy CO monitors as well as th e possibility that monitoring requirements for 8 CO might either encourage or requir e such technological improvements. 9

3.1.3 General Patterns of CO Concentrations 10 As discussed in the ISA, the spatial and tem poral patterns of ambient CO concentrations 11 are heavily influenced by the patterns associat ed with mobile source emissions (ISA, section 12 3.2.1). Based on the 2002 National Emissions I nventory (NEI), on-road mobile sources 13 comprise about half of the total anthropogenic CO emissions, though in metropolitan areas of the 14 US the contribution is as high as 75% of all CO emissions due to greater motor vehicle density. 15 For example, emissions in Denver county origina ting from on-road mobile sources is about 71% 16 of total CO emissions (ISA, section 3.2). When considering all mobile sources (non-road and 17 on-road combined), the contribu tion to total CO emissions is roughly 80% nationwide and can be 18 higher in some metropolitan areas. Again usi ng Denver County as an example, all mobile 19
sources contribute about 98% of total CO emissions. Temporally, the national-scale 20 anthropogenic CO emissions have decreased 35% between 1990 and 2002. Nearly all the 21
national-level CO reductions sinc e 1990 are the result of emission reductions in on-road vehicles 22 (ISA, Figure 3-2). 23 Nearly 400 ambient monitoring stations re port continuous hourly averages of CO 24 concentrations across the US. Over the pe riod 2005-2007, 291 out of 376 monitors met a 75% 25 completeness requirement, spread among 243 countie s, cities, or municipalities (ISA, section 26 3.4.2.2). No violations of the NAAQS were repor ted at these monitoring sites during this time 27 period. For example, in 2007, none of the moni tors reported a second-highest 1-hour CO 28 concentration above 35 ppm, the level of th e current 1-hour NAAQS, while only two sites 29 reported 2 nd highest 1-hour CO concentrations between 15.1 and 35.0 ppm (ISA section 3.5.1.1). 30 Only five counties reported a 2 nd highest 8-hour CO concentra tion of 5.0 ppm or higher. 31
The current levels of ambient CO across the U.S. reflect the steady declines in ambient 32 concentrations that have occurred over the past several years. On average across the US the 33
decline has been on the order of 50% since the early 1990s (ISA, Figure 3-34). As an example, 34 Figures 3-1 illustrate the trends observed in Denver and Los Ange les ambient concentrations, for 35February 2010 Draft – Do Not Quote or Cite 3-5 several selected monitors within the urba n core of each area during 1993 through 2008. Note 1 that there is a significa nt decrease in the 2 nd highest 1-hour and 8-hour average CO 2
concentrations since the last review. 3 Ambient monitor siting characteristics can influence ambient CO concentrations. 4 Microscale and middle scale monitors are commonly used to measure significant source impacts, 5 while neighborhood and urban scale monitors ar e designated for population-oriented monitoring 6 (40 CFR Part 58 Appendix D). As CO concen trations primarily originate from vehicle 7 emissions, the microscale and middle scale data can be a useful indicator of near-road air quality. 8 Such data analyzed in the ISA were concluded to be consistent with hourly concentrations 9 reported in the literature for the near road envi ronment in the US (ISA, p. 3-57). Further, when 10 considering monitoring scale ac ross ambient monitors in the US, the median hourly CO 11
concentration measured at microscale monitors was about 25% higher than at middle scale 12 monitors and 67% higher than at neighborhood scal e monitors (ISA, Table 3-12). In general, 13 similar patterns were present in the 1-hour da ily max, 1-hour daily average, and 8-hour daily 14 max distributions (ISA, Table 3-12 ). These patterns are also c onsistent with findings presented 15 by other researchers regarding the relative decrease in concentr ation with increasing distance 16 from roadways, though the magnitude of the relati onship can vary. Two studies summarized in 17 the ISA (Zhu et al., 2002; Baldauf et al., 2008) i ndicate that near-road CO concentrations (i.e., 18
measured within 20 meters of an interstate hi ghway) can range from 2 – 10 times greater than 19
CO concentrations measured as far as 300 meters from a major road possibly influenced by wind 20
direction and on-road vehicle dens ity (ISA, Figures 3-29 and 3-30). 21 While recognizing that monitoring site attribut es are not available fo r all monitors in the 22
current network and that data for some at tributes may not refl ect current conditions, 8 the ISA 23 also evaluated the average annual daily traffic ( AADT) data available for each ambient monitor. 24 The ISA noted that only two microscale monitors and two middle scale m onitors in the existing 25
network are sited at roads with ≥ 100,000 AADT, although it is not uncommon for roadways 26
within Consolidated Statistical Areas (CSAs) to have severa l roads with AADT > 100,000. The 27 AADT ranged from 160,000-178,000 for the near-road monitors used in the aforementioned 28 study by Zhu et al. (2002) where CO concentrations were up to 10 times greater than monitors 29 sited at 300 m from a major road. 9 Existing microscale sites n ear roads having only moderate 30

8 Recorded AQS monitoring site attributes are not al ways 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 for 16 (ISA, Figure 3-22).

9 Local-scale meteorology may have also contributed to the heightened concentrations, given that the Zhu et al. (2000) study was designed to capture CO concentrations downwind of the roadway.Februaryr 20103-6Draft – Do Not Quote or Cite1Figure 3-1.Spatial and Temporal Trends in the 2ndHighest 1-hour (top) and 8-hour Average (bottom) CO Ambient2Monitoring Concentrations in Denver, Colorado (left) and Los Angeles, California (right), Years 1993 – 2008.30246810121416182022

2 nd Highest 1-hour Average CO (ppm)080310002080310013080310014080590002060370113060371002060371103060371201060371301060372005060374002060590001060591003060595001DenLos Angeles0246810121416 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Year

2 nd Highest 8-hour Average CO (ppm)080310002080310013080310014080590002 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Year060370113060371002060371103060371201060371301060372005060374002060590001060591003060595001February 2010 Draft – Do Not Quote or Cite 3-7 traffic count data (<100,000 AADT) may record c oncentrations that are not substantially 1 different from those obtained from neighbor hood scale measurements (ISA, section 3.5.1.3). 2 Within a specific urban area, however, cons ideration of only monitor scale or other 3 attributes reported in AQS, such as AADT estim ates, may be of limited use in efforts to 4 characterize the monitoring data as to its repres entation of local near-road CO concentrations. 5 For example, of the five monitors meeting a 75% completeness criteri on in the Denver CSA, 6 three were microscale and two were neighborhood scale (ISA, section 3.5.1.2). While one of the 7
microscale monitors sited within downtown De nver measured the highest hourly ambient CO 8 concentrations (ID 080310002), another microsca le monitor (ID 080130009) located outside the 9 urban core measured the lowest hourly ambient CO concentrations (ISA, Figure 3-19). Further, 10 the AADT estimate for a major road near the mi croscale monitor within the urban core (ID 11 080310002, AADT=17,200) was lower than that listed for the microscale monitor outside the 12 urban core (ID 080130009, AADT=20,000) (ISA, Table A- 2). And, a third microscale monitor 13
located 1.3 km from monitor ID 080310002, within the urban core, and measuring somewhat 14 lower CO concentrations (but not lower than the monitor outside the urban core) had only 500 15 AADT listed for the nearest major road. It is like ly that the higher CO concentrations measured 16
at the downtown monitor reflect influences of the denser ro adway network surrounding that 17 monitor in the downtown Denver area (ISA, Figure 3-17). 10 18 Thus, to better characterize the representati on of near-road CO concentrations for many 19 of the existing ambient monitors, additional anal yses, beyond consideration of AQS attributes 20 such as monitoring scale and traffic count, loca l meteorology, would likely need to be performed 21 (e.g., using GIS to determine mon itor distance from roads, the number and type of roads within 22 close proximity of the monitor, and obtaining current traffic count data for all roads). 23
Carbon monoxide also exhibits ho urly variability within a day, with two distinct temporal 24 patterns noted for weekdays and weekends (ISA, section 3.5.2.2). The diurnal variation is 25 inherently linked to the typical commute times-o f-day that occur within urban locations. In 26 general, in recent years observed mean and medi an concentrations for all hours of the day and 27
across all monitors within urban areas de monstrated limited variability, however 90 th and 95 th 28 percentile hourly concentrations generally exhibit early-morni ng and late afternoon peak CO 29 concentrations during weekdays (ISA, Figure 3-36) . The weekend diurnal variation in ambient 30
CO concentrations was much lower than that oc curring during weekdays as expected due to the 31
relative absence of commuter vehi cle traffic during the morning and evening hours of the day. 32 Most urban areas have relatively stable concentrations throughout weekend days at each of the 33

10 Staff also recognizes some uncertainty in how we ll the AQS AADT estimates reflect current conditions
at this monitor site.February 2010 Draft – Do Not Quote or Cite 3-8 selected percentiles, though a fe w locations (e.g., Phoenix, Los Ange les, Seattle) did have a more 1 pronounced late afternoon p eak (ISA, Figure 3-37). 2 Staff investigated local hourly variation at two separate CO monitors located in Denver 3 and Los Angeles to illustrate similar trends. The monitor in Denver is a microscale monitor 4 located in downtown Denver and expected to reflect concentrations resulting from dense 5 downtown traffic in that city; it is the monitor measuring the highe st ambient CO concentrations 6 in the Denver area. The monitor in Los Angele s is a middle scale monitor located in Lynwood; 7 it is also the monitor measuring the highest ambient CO concentr ations in the Los Angeles area 8 Figure 3-2 indicates that on average, peak ambien t CO concentrations th at occur during typical 9 commute times in Denver ranged from about 1 to 5 ppm during weekdays in 1995, while, 10 currently, ambient CO concentrations during mo rning and afternoon commutes range from about 11 1 to 2 ppm. Weekends tend to exhibit less va riability throughout the day. On average, CO 12 ambient concentrations generally ranged from 1 to 3 ppm throughout the day in 1995, while 13 current weekend concentrations are less than 1 pp m for most hours of the day. In Los Angeles, 14
both the concentration levels and variability are greater than when compared with similar years 15 and times of day in Denver (Figure 3-3). Peak ambient CO concentrations are more prominent 16 during morning commutes and generally ranged fr om 2 to 10 ppm in 1995, while currently (year 17 2006) most commuting times are associated with concentrations ranging from between 1 and 5 18 ppm. The weekend profile exhibits some va riation when considering either year, with 19
maximum concentration levels and variabil ity exhibited during the overnight hours. 20February 2010 Draft – Do Not Quote or Cite3-91Figure 3-2.Diurnal Distribution of 1-hour CO Concentrationsin Denver (Monitor 080310002) by Day-type (weekdays-left;2weekends-right), Years 1995 (top) and 2006 (bottom).The box encompasses concentrations from the 25th to 75th3percentiles or Interquartilerange (IQR), the line bisecting the box is the median, the solid dot within the box is the4mean, the whiskers represent 1.5 times theIQR, and concentrations outside the whiskers are indicated by open circles.5Note there are differences in the y-axis scale for the two time periods.6February 2010 Draft – Do Not Quote or Cite3-101Figure 3-3.Diurnal distribution of 1-hour CO concentrations inLos Angeles (Monitor 060371301) by day-type (weekdays2left; weekends-right), years 1997 (top) and 2006 (bottom).The box encompasses concentrations from the 25th to375th percentiles or IQR, the line bisecting the box is the median, the solid dotwithin the box is the mean, the whiskers4represent 1.5 times the IQR, andconcentrations outside the whiskers are indicated by open circles.Note there are5differences in the y-axis scale for the two time periods.6February 2010 Draft – Do Not Quote or Cite 3-11 3.1.4 Policy-Relevant Background Concentrations 1 EPA has generally conducted NAAQS risk assessm ents that focus on the risks associated 2 with ambient levels of a pollu tant that are in excess of po licy-relevant background (PRB). 3 Policy-relevant background levels are defined, for purposes of this document, as concentrations 4 of a pollutant that would occur in the US in the absence of anthropogeni c emissions in the US, 5 Canada, and Mexico. 6 Over the continental US (CONUS), th e 3-year (2005-2007) average CO PRB 7 concentration is estimated to range from 0.118 to 0.146 ppm (ISA, section 3.5.4). Outside the 8 CONUS, the 3-year average CO PRB in three Al askan sites is estimated to range from 0.127 to 9 0.135 ppm, and from 0.095 to 0.103 ppm in two Ha waiian monitoring locations. The estimated 10 PRB concentrations exhibit significant within -location seasonal vari ation, with minimum 11
concentrations observed in the summer and fall and maximum concentrati ons occurring in the 12 winter and spring. For example, PRB in two Calif ornia sites is estimated to range from about 13 0.085 to 0.170 ppm, and one site in Colora do, ranged from about 0.080 to 0.140 ppm (ISA, 14 Figure 3-43). 15
Given that ambient concentrati ons of interest in this REA are well above the estimated 16 PRB levels discussed above and, thus the co ntribution of PRB to overall ambient CO 17 concentrations is very small, EPA is characte rizing risks associated w ith ambient CO levels 18 without regard to estimated PRB levels. 19

3.1.5 Within-Monitor CO Concentration Trends 20 The previous section addressed general trends in ambient concentrations. Of particular 21
interest in this assessment is how concentratio ns have changed within a specific monitor over 22 time. This is an important consideration in determining how best to address alternative air 23 quality conditions. These alterna tive air quality conditions are us eful in evaluating how varying 24 distributions of air quality might affect different exposure scen arios. In other recent NAAQS 25
reviews for NO 2 (US EPA, 2008) and SO 2 (US EPA, 2009) it was determined the relationship 26 between high concentration and low concentratio n years of ambient monitoring data was mainly 27 proportional (Rizzo, 2008; 2009), that is all concentrations across the entire dist ribution at a 28 single monitor changed in equivale nt amounts over time. Staff needed the relationship to adjust 29 current air quality because, at the time of the NAAQS reviews, the current ambient NO 2 and SO 2 30 concentrations were far below that expect ed to just meet the current standards. 31 Knowledge of this relationship for ambient CO concentrations is also needed to develop 32 alternative air quality conditions fo r use in some of the exposure scenarios investigated in this 33
draft REA. Ambient CO concentr ation data were obtained for se veral monitors in Los Angeles 34 for two years: 1997 – representing a high con centration year and 2006 – representing a low 35February 2010 Draft – Do Not Quote or Cite 3-12 concentration year. First, all reported hourly CO concentrations were used to calculate the full 1 percentiles of the distribution (0-100 by 1 pct incr ements) for each year. Then the percentiles for 2 the high concentration year were plotted against that of the low concentration year for each 3 individual monitor (Figure 3-4). A simple lin ear regression was also plotted, along with the 4 regression slope, intercept, and fit statistic (R 2 ). As shown by the relationships, there is a very 5 strong linear relationship when comparing each year of data within each monitor, and the 6 regression intercepts for most of the monitors ar e small, indicating there is adequate support for 7 adjusting air quality by a proportional method. 8 Staff was also interested in estimating the within-monitor temporal variability for three 9 air quality metrics. The first air quality metr ic was the current design value, that is, the 2 nd 10 highest 8-hour average concentration in a year. The next two air quality metrics compared by 11
staff were the 99 th percentile 1-hour and 8-hour daily maximum CO concentrations. 12February 2010 Draft – Do Not Quote or Cite3-131Figure 3-4.Comparison of a high concentration year (1997) witha low concentration year (2006) in Los Angeles. The 02through 100thpercentiles of the hourly air quality distribution are plotted for each monitor-year.30600370113y = 0.4078x + 0.0783R2= 0.9903012340123456781997 (0-100) Percentile CO (PPM)

2006 (0-100) Percentile CO (PPM)0600371103y = 0.4114x - 0.0117R2= 0.9953012340 2 4 6 8 101997 (0-100) Percentile CO (PPM)

2006 (0-100) Percentile CO (PPM)0600371301y = 0.4232x + 0.0129R2= 0.996301234567890 5 10 15 20 251997 (0-100) Percentile CO (PPM)

2006 (0-100) Percentile CO (PPM)060591003y = 0.4556x - 0.0077R2= 0.9942012340123456781997 (0-100) Percentile CO (PPM)

2006 (0-100) Percentile CO (PPM)February 2010 Draft – Do Not Quote or Cite 3-14 Staff evaluated the within-monitor temporal variability using two comparisons: one using 1 historical versus current air quali ty data and the other comparing y ear-to-year variability of these 2 upper percentile concentrations wi thin the air quality distribu tion. Two three-year periods 3 (1995-1997 and 2005-2007) were chosen by staff to represent historical and recent air quality, 4 respectively. Staff limited the analysis to four monitors within the Denver CSA and ten monitors 5 within the Los Angeles CSA, with all monitor data meeting standard requirements for data 6 completeness. In addition to the temporal evaluation of the air qua lity metrics, a limited analysis 7 of the spatial variability across the two periods is also provided for the selected monitors in each 8

area. 9 Tables 3-1 and 3-2 provide results for the first air quality metric in Denver and Los 10 Angeles, respectively. As shown by the Tables, ther e is a wide range in the temporal variability 11
of the 2 nd highest 8-hour average CO concentrati on in both locations, however, the relative 12 variability, as indicated by the coefficient of variation (COV), 11 is generally less for the recent air 13 quality when compared with the historical air quality. For example, in Denver the COV ranges 14 from 4-27 percent (mean = 13%) for the historic al data, while the recent data temporal COV 15 ranges from 3-23 percent (mean = 10%) (Table 3-1) . In addition, the magnitude of the spatial 16 variability tends to vary from year-to-year as indicated by the COV, thoug h there are differences 17
in the historical versus recent ai r quality pattern by location. In Denver, there was generally less 18 spatial variability in the 2 nd highest 8-hour concentration when comparing the recent and 19 historical air quality data. There was no apparent trend in year-to-year sp atial variability for Los 20 Angeles as both air quality periods had a mean COV of about 31% (Table 3-2). 21 Similar temporal trends are observed with the 99 th percentile 1-hour daily maximum 22 concentrations when comparing historical vers us recent air quality (Tables 3-3 and 3-4 for 23 Denver and Los Angeles, respectively). The tem poral variability in the recent air quality was 24 also less than that of the prior air quality metric (i.e., the 2 nd highest 8-hour average), averaging 25
about 4% COV in Denver and 7% COV in Los A ngeles across that 3-year period. The year-to26 year spatial variabil ity for this metric is consistent with that stated above. In Denver, the COV 27 on average was less for the recent ai r quality when compared with the historical data. There was 28 little difference in the year-to-year spatial variability in Los Angeles when considering the two 29 air quality periods. Results for the 99 th percentile 8-hour daily ma ximum concentrations were 30
more similar to the results for the 2 nd highest 8-hour average concentration than the 99 th 31 percentile 1-hour daily maximum (Tables 3- 5 and 3-6, for Denver and Los Angeles, 32 respectively). 33

11 The COV is calculated here by dividing the standard deviation (std) by the mean, then multiplying by
100.February 2010 Draft – Do Not Quote or Cite3-15Table 3-1.Within monitor temporal variability inDenver using historical (1995-97) and recent (2005-07) air quality data -12ndhighest 8-hour average.2Historical Air Quality – 2ndhighest 8-hour average Recent Air Quality – 2ndhighest 8-hour averageMonitor1995 1995 1997 mean stdCOV 2005 2006 2007 mean stdCOV31-0002 9.5 7.3 5.5 7.4 2.0272.6 3.1 2.8 2.8 0.3931-0013 6.2 5.2 4.7 5.4 0.8142.4 2.5 2.4 0.1331-0014 5.9 5.7 6.2 5.9 0.242.1 3.0 2.5 0.62359-0002 4.6 4.3 4.9 4.6 0.371.8 2.0 1.9 0.16mean 6.6 5.6 5.3 5.82.2 2.6 2.8 2.4std 2.1 1.3 0.6 1.20.3 0.5 0.4COV32221220141916Table 3-2.Within monitor temporal variability in Los Angeles using historical (1995-97) andrecent (2005-07) air quality3data - 2ndhighest 8-hour average.4Historical Air Quality – 2ndhighest 8-hour average Recent Air Quality – 2ndhighest 8-hour averageMonitor1995 1996 1997 mean stdCOV 2005 2006 2007 mean stdCOV37-0113 9.4 8.5 4.1 7.3 2.8391.9 1.9 1.6 1.8 0.21037-1002 10.9 8.5 7.2 8.9 1.9213.2 3.4 2.7 3.1 0.41237-1103 7.9 7.5 5.9 7.1 1.1152.6 2.5 2.1 2.4 0.31237-1201 9.4 6.7 7.7 7.9 1.3173.4 3.4 2.7 3.2 0.41237-1301 11.7 14.3 15.0 13.6 1.7135.6 5.6 4.9 5.3 0.4837-2005 8.6 6.9 5.4 7.0 1.6232.8 2.7 2.2 2.6 0.31337-4002 6.3 6.2 6.4 6.3 0.122.9 3.3 2.5 2.9 0.41559-0001/7 7.3 6.1 5.4 6.3 1.0163.1 2.9 2.3 2.8 0.41559-1003 5.3 6.5 5.0 5.6 0.8143.1 2.5 2.5 2.7 0.31259-5001 6.4 6.3 5.7 6.1 0.462.9 2.9 2.5 2.8 0.28mean 8.3 7.7 6.8 7.63.1 3.1 2.6 3.0std 2.1 2.5 3.1 2.30.9 1.0 0.9 0.9COV2532453130323331February 2010 Draft – Do Not Quote or Cite3-16Table 3-3.Within monitor temporal variability inDenver using historical (1995-97) and recent (2005-07) air quality data –199thpercentile 1-hour daily maximum.2Historical Air Quality – 99thpercentile 1-hour daily maximum Recent Air Quality – 99thpercentile 1-hour daily maximumMonitor1995 1995 1997 mean stdCOV 2005 2006 2007 mean stdCOV31-0002 13.5 13.4 9.1 12.0 2.5213.8 4.5 4.4 4.2 0.4931-0013 11.1 9.0 8.6 9.6 1.3143.5 3.7 3.6 0.1331-0014 8.2 7.3 7.8 7.8 0.563.3 3.2 3.2 0.1359-0002 8.6 6.8 7.2 7.5 0.9123.4 3.4 3.4 0.00mean 10.4 9.1 8.2 9.23.5 3.7 4.4 3.6std 2.5 3.0 0.9 2.10.2 0.6 0.4COV2432102261612Table 3-4.Within monitor temporal variability in Los Angeles using historical (1995-97) andrecent (2005-07) air quality3data – 99thpercentile 1-hour daily maximum.4Historical Air Quality – 99thpercentile 1-hour daily maximum Recent Air Quality – 99thpercentile 1-hour daily maximumMonitor1995 1996 1997 mean stdCOV 2005 2006 2007 mean stdCOV37-0113 13.9 7.5 6.1 9.2 4.2452.6 2.7 2.1 2.5 0.31337-1002 11.6 9.7 8.2 9.9 1.7173.9 4.1 3.6 3.9 0.3737-1103 9.0 9.4 7.4 8.6 1.1133.1 2.9 2.6 2.9 0.3937-1201 10.6 8.4 8.4 9.1 1.3144.0 3.9 3.4 3.8 0.3837-1301 16.2 20.2 18.5 18.3 2.0117.1 7.4 6.8 7.1 0.3437-2005 10.3 8.8 6.2 8.4 2.0243.4 3.3 3.0 3.2 0.2637-4002 7.6 8.4 7.6 7.9 0.563.8 3.8 3.1 3.6 0.41259-0001/7 9.1 8.2 7.7 8.4 0.793.6 3.6 3.2 3.5 0.2659-1003 7.3 8.4 6.9 7.5 0.8113.6 3.2 3.2 3.3 0.2659-5001 10.7 11.6 10.3 10.9 0.765.2 5.4 5.2 5.3 0.12mean 10.6 10.1 8.7 9.84.0 4.0 3.6 3.9std 2.8 3.7 3.6 3.11.3 1.4 1.4 1.3COV2637423232353834February 2010 Draft – Do Not Quote or Cite3-17Table 3-5.Within monitor temporal variability inDenver using historical (1995-97) and recent (2005-07) air quality data –199thpercentile 8-hour daily maximum.2Historical Air Quality – 99thpercentile 8-hour daily maximum Recent Air Quality – 99thpercentile 8-hour daily maximumMonitor1995 1995 1997 mean stdCOV 2005 2006 2007 mean stdCOV31-0002 7.3 7.2 5.2 6.6 1.2182.4 2.8 2.7 2.6 0.2931-0013 5.4 5.2 4.7 5.1 0.472.2 2.1 2.2 0.0231-0014 5.7 5.5 5.8 5.7 0.122.1 2.8 2.4 0.52259-0002 4.1 3.8 4.8 4.2 0.5121.8 1.8 1.8 0.02mean 5.6 5.4 5.1 5.42.1 2.4 2.7 2.3std 1.3 1.4 0.5 1.00.3 0.5 0.4COV24261018122116Table 3-6.Within monitor temporal variability in Los Angeles using historical (1995-97) andrecent (2005-07) air quality3data – 99thpercentile 8-hour daily maximum.4Historical Air Quality – 99thpercentile 8-hour daily maximum Recent Air Quality – 99thpercentile 8-hour daily maximumMonitor1995 1996 1997 mean stdCOV 2005 2006 2007 mean stdCOV37-0113 8.6 5.2 3.7 5.8 2.5431.9 1.8 1.5 1.8 0.21237-1002 9.7 8.3 6.8 8.3 1.4173.0 3.2 2.6 3.0 0.31137-1103 7.5 7.0 5.6 6.7 1.0152.6 2.4 2.0 2.4 0.31337-1201 9.0 6.7 7.3 7.6 1.2153.2 3.1 2.6 2.9 0.31237-1301 11.2 13.9 13.1 12.7 1.4114.9 5.1 4.5 4.8 0.3737-2005 8.5 6.8 5.0 6.7 1.8262.8 2.6 2.1 2.5 0.31437-4002 5.9 6.2 5.9 6.0 0.232.9 2.7 2.4 2.7 0.2959-0001 6.5 5.7 5.1 5.8 0.7122.7 2.7 2.1 2.5 0.31459-1003 4.7 6.4 4.9 5.3 0.9173.0 2.2 2.4 2.6 0.41659-5001 6.3 5.9 5.3 5.8 0.592.6 2.7 2.5 2.6 0.15mean 7.8 7.2 6.3 7.13.0 2.9 2.5 2.8std 2.0 2.5 2.6 2.20.8 0.9 0.8 0.8COV2535423125313129February 2010 Draft – Do Not Quote or Cite 3-18 3.2 STUDY AREAS SELECTED FOR CURRENT CO REA 1
Staff identified several criteria to select the exposure assessmen t study areas drawing 2 from information discussed in the earlier sections of this Chapter and additional scientific 3
evidence in the ISA. We selected Denver a nd Los Angeles as areas to focus the current 4 assessment because (1) both cities have b een included in prior CO NAAQS exposure 5 assessments and thus serve as an important c onnection with past assessments, (2) they have 6 historically had among the highest CO ambient concentrations among urban areas in the U.S., 7 and (3) Denver is at high altitude and represents a scenario of interest due to the potentially 8 increased susceptibility of visito rs to high altitude from exposure to CO. In addition, of 10 urban 9 areas across the US having monitors meeting a 75% completeness criteria, the two locations 10
were ranked 1 st (Los Angeles) and 2 nd (Denver) regarding percent of elderly population within 5, 11 10, and 15 km of monitor locations, a nd ranked 1st (Los Angeles) and 5 th (Denver) regarding 12
number of 1-hour and 8-hour da ily maximum CO concentration measurements (ISA, section 13 3.5.1.1). 14

3.3 KEY OBSERVATIONS 15 Presented below are key observations resul ting from the air quality considerations. 16

 Mobile sources (i.e., gasoline powered vehi cles) are the primary contributor to CO 17 emissions, particularly in urban areas due to greater vehicle and roadway densities. 18

 Recent (2005-2007) ambient CO concentratio ns across the US are lower than those 19 reported in the previous CO NAAQS review and are also well below the current CO 20 NAAQS levels. Further, a large proportion of the reported concentrations are below 21 the conventional instrument lowe r detectable limit of 1 ppm. 22

 The currently available information for CO m onitors indicates that siting of microscale 23 and middle scale monitors in the current ne twork is primarily limited to roads where 24 traffic density described for them is moderate (<100,000 AADT), however, factors 25 other than reported AADT (e.g., orientation with regard to dense urban roadway 26 networks) can contribute to sites re porting higher CO concentrations. 27

 Ambient CO concentrations are highest at monitors sited closest to roadways (i.e., 28 microscale and middle scale monitors) and e xhibit a diurnal variation linked to the 29 typical commute times of day, with peaks generally observed duri ng early morning and 30 late afternoon during weekdays. 31

 Policy relevant background (PRB) concentratio ns across the US are generally less than 32 0.2 ppm, far below that of interest in this REA with regard to ambient CO exposures. 33

 Historical trends in ambient monitoring data indicate that at individual sites, ambient 34 concentrations have generally decreased in a proportional manner. This comparison 35 included air quality distributions with concentrations at or above the current standard 36 and those reflecting current ( as is ) conditions. 37February 2010 Draft – Do Not Quote or Cite 3-19  The temporal variability in selected uppe r percentile ambient concentrations (e.g., 99 th 1 percentile 1-hour daily maximum) at indivi dual monitors is relatively small across a 2 three year monitoring period, particular ly when considering recent air quality. 3February 2010 Draft – Do Not Quote or Cite 3-20 3.4 REFERENCES 1 2 Baldauf R, Thoma E, Hays M, Shores R, Kinsey J, Gullett B, Kimbrough S, Isakov V, Long T, Snow R, Khlystov 3 A, Weinstein J, Chen FL, Seila R, Olson D, Gilmour I, Cho SH, Watkins N, Rowley P, Bang J. (2008). 4 Traffic and meteorological impacts on near-road air quality: Su mmary of methods and trends from the 5 Raleigh near-road study. J Air Waste Manag Assoc . 58: 865-878. 6

Michie Jr RM, McElroy FF, Sokash JA, Thompson VL, Da yton DP, Sutcliffe CR. (1983). Performance Test 7 Results and Comparative for Designated Equivalence Methods for Carbon Monoxide. EPA-600/S4-83- 8 013. 9

Rizzo M. (2008). Investigation of how distributions of hourly nitrogen dioxide concentrations have changed over 10 time in six cities. Nitrogen Dioxide NAAQS Revi ew Docket (EPA–HQ–OAR–2006–0922 ). Available at 11 http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_rea.html

. 12

Rizzo M. (2009). Investigation of How Distributions of Hourly Sulfur Dioxide Concentrations Have Changed Over 13 Time in Six Cities. Sulfur Dioxide Review Docket. Docket ID No. EPA-HQ-OAR-2007-0352. Available 14 at: http://www.regulations.gov. 15

US EPA. (2000). Air Quality Criteria for Carbon M onoxide. EPA 600/P-99/001F. US Environmental Protection 16 Agency, Research Triangle Park, NC. 17

US EPA. (2009). Integrated Science Assessment for Carbon Monoxide –Final Draft. U.S. Environmental 18 Protection Agency, Research Triangle Park, NC, report no. EPA/600/R-09/019F. Available at: 19 http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_isa.html. 20

Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C. (2002). Study of ultrafine particles near a major highway with 21 heavy-duty diesel traffic. Atmos Environ . 36:4323-4335. 22

23February 2010 Draft – Do Not Quote or Cite 4-1 

### 4 OVERVIEW OF APEX MODELING SYSTEM FOR ESTIMATING 1 

### CO EXPOSURES AND COHB DOSE LEVELS 2

4.1 PURPOSE 3 This chapter presents an overview and descri ption of the overall approach to estimating 4 human exposure and dose for past and recent NAAQS reviews. Section 4.2 provides a brief 5 overview of EPA’s Air Pollutants Exposure model (A PEX), the model used in this assessment to 6 estimate population exposure and dose. This overview is followed by a short history that 7 explains the evolution of exposure and dose models used by OAQPS to conduct exposure and 8 dose assessments for CO and other NAAQS re views (section 4.3). Section 4.4 provides a 9 generalized description of the APEX simulation process, though ha ving detailed focus on a few 10 of the important approaches used for modeling CO exposure and dose. This includes expanded 11 discussion on the approach used to estimate microenvironmental concentrations (section 4.4.4) 12 and COHb dose levels (section 4.4.7). 13

4.2 MODEL OVERVIEW 14 The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program 15 designed to estimate human exposure to criteria and air toxic pollu tants at the local, urban, and 16 consolidated metropolitan levels. APEX, al so known as TRIM.Expo, is the human inhalation 17 exposure module of EPA’s Total Risk Integr ated Methodology (TRIM) model framework (US 18 EPA, 1999), a modeling system with multimedia capabilities for assessing human health and 19 ecological risks from hazardous and criteria air pollutants. 1 20 APEX estimates human exposure using a 21 stochastic, microenvironmental approach (see caption). 22 The model randomly selects data for a sample of 23 hypothetical individuals fr om an actual population 24
database and simulates each individual’s movements 25 through time and space (e.g., indoors at home, inside 26 vehicles) to estimate his or he r exposure to a pollutant. 27
APEX can account for travel to and from work locations 28 (i.e., commuting) and provide estimates of exposures at 29

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

. 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.February 2010 Draft – Do Not Quote or Cite 4-2 both home and work locations for indi viduals who work away from home. 1

4.3 MODEL HISTORY AND EVOLUTION 2
APEX was derived from the National Am bient Air Quality Standards (NAAQS) 3 Exposure Model (NEM) series of models. Th e NEM series was developed to estimate 4 population exposures to the criteria polluta nts (e.g., CO, ozone) . In 1988, OAQPS first 5 incorporated probabilistic elem ents into the NEM methodology and used activity pattern data 6 based on available human activity diary studies to create an early versi on of probabilistic NEM 7 for ozone (i.e., pNEM/O 3 ). In 1991, a probabilistic versi on of NEM was developed for CO 8 (pNEM/CO) that included a one-compartment mass-balance model to estimate CO 9
concentrations in indoor microenvironments. The application of this model to Denver, Colorado 10 is summarized in Johnson et al. (1992). Between 1999 and 2001, updated versions of pNEM/CO 11 (versions 2.0 and 2.1) were devel oped that rely on detailed activ ity diary data compiled in EPA’s 12 Consolidated Human Activities Database (CHAD) (McCurdy et al., 2000; US EPA, 2002) and 13 enhanced algorithms for simulating gas stove usag e, estimating alveolar ventilation rate (a 14 measure of human respiration), and modeling home-to-work commuti ng patterns. A draft report 15 by Johnson et al. (2000) describe s the application of Version 2.1 of pNEM/CO to Denver and 16 Los Angeles. 17 The first version of APEX was essentially identical to pNEM/CO (version 2.0) except 18
that it ran on a PC instead of a mainframe. The next ve rsion, APEX2, was substantially 19 different, particularly in the us e of a personal profile approach rather than a cohort simulation 20 approach. APEX3 introduced a number of new f eatures including automatic site selection from 21 national databases, a series of new output tabl es providing summary expos ure and dose statistics, 22 and a thoroughly reorganized method of describi ng microenvironments and their parameters. 23 Johnson and Capel (2003) describe a case study in which Version 3.1 of APEX was used to 24 estimate population exposure to CO in Los Angeles. 25 The current version of APEX (Version 4.3) (US EPA, 2008a; 2008b) was used to 26 estimate CO exposure and dose as described in ch apter 5 of this document. This version was 27 also recently used to estimate ozone (O 3 ) exposures in 12 urban areas for the O 3 NAAQS review 28 (US EPA, 2007), to estimate populati on exposures to nitrogen dioxide (NO 2 ) in Atlanta as part of 29
the NO 2 NAAQS review (US EPA, 2008c), a nd to estimate sulfur dioxide (SO 2 ) exposures for 30
asthmatics and asthmatic child ren in two study areas in Missouri as part of the SO 2 NAAQS 31 review (US EPA, 2009a). There have been severa l recent enhancements to APEX since the prior 32 1994 CO NAAQS review, including: 33February 2010 Draft – Do Not Quote or Cite 4-3  Algorithms for the assembly of multi-day (long itudinal) activity diaries that model intra1 individual variance, inter-ind ividual variance, an d day-to-day autocorrelation in diary 2 properties; 3

 Methods for adjusting diary-based energy expenditures for fatigue and excess post4 exercise oxygen (EPOC) consumption; 5

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

 The ability to use air quality data and model exposures over flexible time scales; 7

 New output files containing diary event-level, time-step level, and hourly-level exposure, 8 dose, and ventilation data, and hourly-level microenvironmental data; 9

 The ability to model the prevalence of disease states such as asthma or coronary heart 10 disease (CHD); 11

 New output exposure tables that report expos ure statistics for popul ation groups and life12 stages such as children and active people at varying ventilation rates; 13

 The inclusion of tract-level commu ting data from the 2000 census; and 14

 Expanded options for modeling microenvironments. 15

4.4 MODEL SIMULATION PROCESS 16 APEX4.3 is designed to simulate population exposure to crite ria and air toxic pollutants 17 at local, urban, and regional scales. The user sp ecifies the geographic area to be modeled and the 18 number of individuals to be simulated to repr esent this population. APEX4.3 then generates a 19 personal profile for each simulated person that sp ecifies various parameter values required by the 20 model. The model next uses diary-derived time/ac tivity data matched to each personal profile to 21 generate an exposure event se quence (also referred to as a time-location

- activity pattern or 22 composite diary ) for the modeled individual that spans a specified time period, such as a calendar 23 year. Each event in the sequen ce specifies a start time, exposur e duration, a geographic location, 24 a microenvironment inhabited, and an activity performed. Probabilistic algorithms are used to 25 estimate the pollutant concentration and ventila tion (respiration) rate associated with each 26 exposure event. The estimated pollutant con centrations account for the effects of ambient 27 (outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate, 28 and proximity to emission sources, each dependi ng on the microenvironment, available data, and 29 the estimation method selected by the user. The ventilation rate is derived from an energy 30 expenditure rate estimated for each individual wh en performing the specif ied activity. Because 31 the simulated individuals represent a random sample of the population of in terest, the distribution 32 of modeled individual exposures can then be extrapolated to the larger population of interest. 33 The model simulation generally includes up to seven steps as follows: 34February 2010 Draft – Do Not Quote or Cite 4-4  Characterize study area: APEX4.3 selects sectors (e.g., census tracts) within a study 1 area—and thus identifies the potentially exposed population — usually based on the 2 user-defined center and radius of the st udy area and availability of air quality and 3 weather input data for the area (section 4.4.1). 4

 Generate simulated individuals: APEX4.3 stochastically generates a sample of 5 simulated individuals based on the census data for the study area and human profile 6 distribution data (such as age-specific empl oyment probabilities or disease prevalence) 7 (section 4.4.2) 8

 Construct activity sequences: APEX4.3 constructs an exposure event sequence (time9 location-activity pattern) spanning the simu lation period for each of the simulated 10 persons based on the CHAD diaries (section 4.4.3). 11

 Calculate microenvironmental concentrations: APEX4.3 enables the user to define 12 microenvironments that people in a study area would visit (e.g., by grouping location 13 codes included in the activity pattern da tabase). The model then calculates time14 averaged concentrations (e.g., hourly) of each pollutant in each of the 15 microenvironments for each simulated person for the period of simulation based on the 16 user-provided ambient air qua lity data (section 4.4.4). 17

 Estimate energy expenditure and ventilation rates: APEX4.3 constructs a time18 series of energy expenditures for each i ndividual’s exposure profile based on the 19 sequence of activities performed. The sequence of ener gy expenditures are adjusted to 20 ensure that they are physiologi cally realistic and then used to estimate activity-specific 21 alveolar ventilation rates (section 4.4.5). 22

 Calculate exposure: APEX4.3 assigns a concentration to each exposure event based 23 on the microenvironment occupied during th e event and the person’s activity. These 24 values are time-averaged (e.g., hourly) to produce a sequence of exposures spanning 25 the specified exposure period (typically one ye ar). The hourly values may be further 26 aggregated to produce 8-hour, daily, mont hly, and annual average exposure values 27 (section 4.4.6). 28

 Calculate dose: APEX4.3 optionally calcu lates hourly, daily, monthly, and annual 29 average dose values for each of the simula ted individuals. For the application of 30 APEX to CO, a module within the model estimates the pe rcent COHb level in the 31 blood at the end of each hour based on the time-series of CO concentrations and 32 alveolar ventilation rates experienced by the simulated person (section 4.4.7). 33 The model simulation continues until exposures (and associated COHb dose levels) are 34 calculated for the user-specified number of simulated individuals. Figure 4-1 presents a 35 conceptual model and simplified data flow di agram illustrating the implementation of APEX4.3 36 to estimate CO exposure and dose. The follo wing sections provide ad ditional details on the 37 general procedures and algorithms used in each of the seven simulation steps listed above, 38 though more complete discussion can be found in US EPA (2008a, b). The specific input data 39 and microenvironmental factors used in applyi ng APEX4.3 to CO for the current assessment are 40
further described in section 5.1. 41February 2010 Draft – Do Not Quote or Cite4-5Air Exchange Rates(AER) & PrevalenceCHADTime-Location-ActivityPatternsUS CensusTract-Level Population& Commuting DataAmbient Air Quality1-Hour Ambient MonitorCO ConcentrationsMicroenvironmentalFactors/DistributionsExposure Algorithms(mass balance or factors)Dose Algorithm(CFK model)Outdoor CO ConcentrationAlgorithm(spatial & temporal adjustment)Persons & Person-DaysAt or AboveExposure LevelsPersons & Person-DaysAt or AboveBenchmark Dose LevelsUSDHHS/CDCDisease PrevalenceMeteorological DataTemperatureEnergy Expenditure &Ventilation Algorithm(alveolar respiration rate)Air Exchange Rates(AER) & PrevalenceCHADTime-Location-ActivityPatternsUS CensusTract-Level Population& Commuting DataAmbient Air Quality1-Hour Ambient MonitorCO ConcentrationsMicroenvironmentalFactors/DistributionsExposure Algorithms(mass balance or factors)Dose Algorithm(CFK model)Outdoor CO ConcentrationAlgorithm(spatial & temporal adjustment)Persons & Person-DaysAt or AboveExposure LevelsPersons & Person-DaysAt or AboveBenchmark Dose LevelsUSDHHS/CDCDisease PrevalenceMeteorological DataTemperatureEnergy Expenditure &Ventilation Algorithm(alveolar respiration rate)1Figure 4-1.Conceptual model and simplified data flow for estimating population exposure and dose using APEX4.3.23February 2010 Draft – Do Not Quote or Cite 4-6 4.4.1 Characterize Study Area 1 An initial study area in an APEX4.3 assessment consists of a set of basic geographic units 2 called sectors, typically defined by US census data reported at the census tract level. The user 3 may provide the geographic cente r (latitude/longitude) and radius of the study area and then 4
APEX4.3 calculates the distances to the center of the st udy area of all the sect ors included in the 5 sector location database, and fina lly selects the sectors within the radius of the study area. 6 APEX4.3 then maps the user-provided air quali ty and meteorological data for specified 7 monitoring districts to the select ed sectors. The sectors identified as having acceptable air 8 quality and meteorological data w ithin the radius of the study area are selected to comprise a 9 final study area for the APEX4.3 simulation analys is. This final study area determines the 10 population make-up of the simulated pers ons (profiles) to be modeled. 11

4.4.2 Generate Simulated Individuals 12 APEX4.3 stochastically generates a user-sp ecified number of simulated persons to 13 represent the population in the st udy area. Each simulated pers on is represented by a personal 14 profile. APEX4.3 generates the simulated person by probabilistically selecting values for a set of 15 profile variables. The profile variables include: 16

 Demographic variables that are generated based on US census data (e.g., age, gender, 17 home sector, work sector); 18

 Residential variables that are generated ba sed on sets of distribution data (e.g., air 19 conditioning prevalence); 20

 Physiological variables that are generated based on age- and gende r-specific distribution 21 data (e.g., blood volume, body mass, resting metabolic rate); and 22

 Daily varying variables that are generated based on distribu tion data that change daily 23 during the simulation period (e.g., daily work status). 24

APEX4.3 first selects and cal culates demographic, resi dential, and physiological 25 variables (except for daily values) for each of the user-specified number of simulated individuals. 26
APEX4.3 then follows each simulated individu al over time and calculates exposures (and 27 optionally doses) for the individual over the duration of the assessment period. The complete 28 listing of profile variables used by APEX4.3 and deta iled description can be found in section 5 of 29 US EPA (2008b). An overview of the data sour ces used and their implementation in APEX4.3 is 30 provided below. 31

4.4.2.1 Population Demographics 32 APEX4.3 takes population characteristics into account to develop accurate 33 representations of study area demographics. Specifically, populati on counts by area and 34February 2010 Draft – Do Not Quote or Cite 4-7 employment probability estimates are used to develop representative profiles of hypothetical 1
individuals for the simulation. 2
APEX4.3 is flexible in the reso lution of population data provided. As long as the data are 3 available, any resolution can be used (e.g., c ounty, census tract, census block). For this 4 application of the model, censu s tract level data were used. Tract-level population counts are 5 obtained from the 2000 Census of Population and Housing Summary File 1 (SF-1). This file 6 contains data compiled from the questions asked of all respondents and about every housing unit. 7 As part of the population demographics input s, it is important to integrate working 8 patterns into the assessm ent. In the 2000 US Census, estimates of employment were developed 9 by census information (US Census Bureau, 2007). The employment statistics are broken down 10 by gender and age group, so that each gender/ag e group combination is given an employment 11 probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are: 12 16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75. 13 Children under 16 years of age were assumed to be not employed. 14

4.4.2.2 Commuting Database 15 In addition to using estimates of employmen t by tract, APEX4.3 also incorporates home16 to-work commuting data. Commuting data we re derived from the 2000 Census and were 17 collected as part of the Cens us Transportation Planning Packag e (CTPP) (US DOT, 2007). The 18
data used contain counts of indi viduals commuting from home to work locations at a number of 19 geographic scales. These data were processed to calculate fractions for each tract-to-tract flow to 20 create the national commuting da ta distributed with APEX4.3. This database contains 21 commuting data for each of the 50 states and Washington, D.C. 22 Several assumptions were made in the de velopment of the database and with the 23 modeling of a person’s commute in this assessment as follows. 24

 Commutes within tracts and home workers: There is no differentiation between 25 people that work at home from those th at commute within their home tract. 26

 Commute distance cutoff: All persons in home-work flows up to 120 km are assumed 27 to be daily commuters and no persons in mo re widely separated flows would commute 28 daily. This means that the list of destinati ons for each home tract was restricted to only 29 those work tracts that are within 120 km of the home tract. This distance is based on 30 the presence of a near-constant relati onship between commute flows and distance 31 traveled up to 120 km. 32

 Eliminated Records: Tract-to-tract pairs representi ng workers who either worked 33 outside of the US (9,631 tract pairs with 107,595 workers) or worked in an unknown 34 location (120,830 tract pairs with 8,940,163 wo rkers) were eliminated from the 35 database. An additional 515 workers in th e commuting database whose data were 36February 2010 Draft – Do Not Quote or Cite 4-8 missing from the original files, possibly due to privacy conc erns or errors, were also 1 deleted. 2

 Commuting outside the study area: APEX4.3 allows for some flexibility in the 3 treatment of persons in the modeled populat ion who commute to destinations outside 4 the study area. Users can either retain these persons and include them as part of the 5 population exposed or have them eliminated from the model simulation. In the first 6 instance (i.e., “KeepLeavers = Yes”), APEX4.3 can assign in put concentrations based 7 on the available ambient concentration data within the model domain. For the second 8 option (i.e., “KeepLeavers = No”) , people who work inside th e study area but live 9 outside of it are not mode led, nor are people who live in the study area but work 10 outside of it. 11

4.4.2.3 Profile Functions File 12

A Profile Functions file contains settings us ed to generate results for variables related to 13
simulated individuals. While cer tain settings for individuals are generated automatically by 14 APEX4.3 based on other input files, including de mographic characterist ics, others can be 15 specified using this file. For example, the file may contain settings for determining whether the 16 profiled individual’s residence has an air conditioner, a gas stove, etc. 17

4.4.2.4 Physiology File 18
The APEX4.3 physiology.txt file contains age- and gende r-based information for several 19 physiological parameters used in human exposure modeling. This information includes various 20 equations, distributional shapes, and parameters for all age and gender cohorts from age 0 to 100 21 years for variables such as normalized maxi mal oxygen uptake, body mass, resting metabolic 22 rate (RMR), and blood hemoglobin content. Appendix A provides an evaluation of a few 23 important variables used by APEX 4.3 in this exposure and dose assessment as well as their 24 updated values or distributions (e.g., new ag e-gender body mass distributions derived from 1999- 25
2004 National Health and Nutrition Examination Su rvey data). Details regarding any other 26 physiology variable distributions and their parameters not discu ssed in this draft CO REA and 27 associated appendices can be found in US EPA (2008a, b). 28

4.4.3 Construct Activity Sequences 29 Different human activities, such as spending time outdoors, indoors, or driving, will be 30 associated with varying pollutant concentrations. Therefore, to accurately model individuals and 31 their exposure to pollutants, it is critical to understand people’s da ily activities and use such data 32 in the exposure model. EPA’s Consolidated Human Activity Database (CHAD) provides diary33 derived data indicating where people spend time a nd the activities they perform at each location 34 (US EPA, 2002). CHAD was designed to prov ide a basis for conducting multi-route, multi35 media exposure assessments (McCur dy et al., 2000). The data c ontained within CHAD originate 36February 2010 Draft – Do Not Quote or Cite 4-9 from multiple activity pattern surv eys with varied structures (T able 4-1), however the surveys 1 have commonality in containing daily diaries of human activities performed, locations visited, 2 and personal attributes (e.g., age and gender). 3 There are four CHAD-related input files us ed in APEX4.3. The first three can be 4 considered standard input files for most model simulations; the user typically does not modify 5 their contents. These include the human activity diaries file, the pers onal data file, and a 6 metabolic information file, each of which are disc ussed briefly below. The fourth CHAD-related 7 input file maps the five-d igit location codes used in the diary file to APEX4.3 8 microenvironments; this file is commonly modified by the user and is discussed in section 5.8 9 (i.e., specific microenvironments modeled in this CO assessment). And finally, section 4.4.3.4 10
discusses how these diaries are linked together to form a continuous time-location-activity 11 pattern for each individual acro ss the entire simulation period. 12February 2010 Draft – Do Not Quote or Cite 4-10 Table 4-1. Summary of activity pattern studies comprising the recent version of CHAD. 1

Study Name CHAD Prefix Study Years Number of Diary Days Reference Baltimore BAL 1997-1998 391 Williams et al. (2000) CARB: Adults CAA 1987-1988 1579 Wiley et al. (1991a) CARB: Adolescents CAY 1987-1988 183 Wiley et al. (1991a) CARB: Children CAC 1989-1990 1200 Wiley et al. (1991b) Cincinnati (EPRI) CIN 1985 2614 Johnson (1989) Denver (EPA) DEN 1982-1983 805 Johns on (1984); Akland et al. (1985) Los Angeles: Elementary LAE 1989 51 Spier et al. (1992)

Los Angeles: High School LAH 1990 43 Spier et al. (1992)

NHAPS A NHA 1992-1994 4723 Klepeis et al . (1996); Tsang and Klepeis (1996) NHAPS B NHW 1992-1994 4663 Klepeis et al . (1996); Tsang and Klepeis (1996)

PSID 1 (U Michigan I) UMC 1997 5616 University of Michigan (2010)

PSID 2 (U Michigan II) ISR 2002-2003 4782 University of Michigan (2010)

Valdez VAL 1990-1991 397 Goldstein et al. (1992) Washington, DC WAS 1982-1983 699 Hartwell et al. (1984); Akland et al. (1985) RTI Ozone Averting Behavior OAB 2002-2003 2907 Mansfield and Corey (2003); Mansfield et al. (2004; 2006) RTP Panel Study RTP 2000-2001 1003 Williams et al. (2003a,b) Seattle Study SEA 1999-2002 1693 Liu et al. (2003) Internal EPA Study 2006-2007 EPA 2006-2007 434 Isaacs et al. (2009)

EPA Longitudinal 1 EPA 1999,2002 736 Isaacs et al. (2009) EPA Longitudinal 2 EPA 2000 197 Isaacs et al. (2009) EPA Longitudinal 3 EPA 2008 62 Isaacs et al. (2009)
2

4.4.3.1 Personal Information file 3
Personal attribute data are c ontained in the CHAD questionnaire file that is distributed 4
with APEX4.3. This file also has information for each day individuals have diaries. The 5
different variables in this file are: 6

 The study, person, and diary day identifiers 7

 Day of week 8February 2010 Draft – Do Not Quote or Cite 4-11  Gender 1

 Employment status 2

 Age in years 3

 Maximum temperature in degrees Celsius for the diary day 4

 Mean temperature in degrees Celsius for the diary day 5

 Occupation code (if requested) 6

 Time, in minutes, during this diary day for wh ich no data are included in the database 7

4.4.3.2 Diary Events File 8 The human activity diary data ar e contained in the events file that is distributed with 9
APEX4.3. This file contains the locations visite d and the activities performed for the nearly 10 35,000 person-days of data with event intervals ranging from a minimum of one minute upwards 11 to a one hour maximum duration. A study individua ls’ diary can vary in length from one to 15 12 days (i.e., referring to the number of person-days). The diary even ts file contains the following 13
variables: 14

 The study, person, and diary day identifiers 15

 Start time of the event 16

 Number of minutes for the event 17

 Activity code (a record of what the individual was doing) 18

 Location code (a record of where the individual was) 19

4.4.3.3 Activity-Specific Metabolic File 20
The metabolic file contains the distributi onal forms and parameters for the activity21 specific metabolic equivalents (METs) used to quantitatively assign exertion levels to each 22 activity performed by simulated individuals (McC urdy, 2000). Some activities are specified as a 23 single point value (for in stance, sleep), while others, such as athletic endeavors or manual labor, 24 are represented by normal, lognormal, or similar statistical distributions . APEX4.3 samples from 25
these distributions and calculates values to simulate the variable nature of activity levels among 26 different people. The CHAD User’s guide provides details on the distributions used, parameters, 27 and sources for each activity (US EPA, 2002). 28

4.4.3.4 Longitudinal Diary Processing 29 APEX4.3 probabilistically creates a compos ite longitudinal diary for each of the 30 simulated persons by selecting a 24-hour diary record – or diary day – from an activity database 31 for each day of the simulation period. The EPA’ s CHAD data (US EPA, 2002) are supplied with 32 APEX4.3 for this purpose. A co mposite diary is a sequence of events that simulates the 33February 2010 Draft – Do Not Quote or Cite 4-12 movement of a modeled person through varying geographical lo cations and microenvironments 1 for the duration of the simulation period. Each diary event is define d by geographic location, 2 start time, duration, microenvironment vi sited, and an activity performed. 3 The activity database input to APEX4.3 contai ns the following information for each diary 4 day: age, gender, employment status, occupa tion, day-of-week (or day-type), and maximum 5 hourly average temperature. This information en ables APEX4.3 to select data from the activity 6
database that tend to match the characteristic s of the simulated pers on, the study area, and the 7 specified time period. APEX4.3 develops a composite diary for each of the simulated 8 individuals according to the following steps. 9

 Divide diary days in the CH AD database into user-def ined activity pools, based on 10 day-type and temperature. 11

 Assign an activity pool number to each day of the simulation period, based on the user12 provided daily maximum/average temperature data. 13

 Calculate a selection probability for each of th e diary days in each of the activity pools, 14 based on age/gender/employment similarity of a simulated person to a diary day. 15

 Probabilistically select a diary day from available diar y days in the activity pool 16 assigned to each day of the simulation period. 17

 Estimate a MET value for each activity performed while in a location, based on a 18 random sampling of the particular distribu tion of each specific activity. The METs 19 values are used to calculate an activity-sp ecific ventilation rate (see section 4.4.5) for 20 the simulated person. 21

 Map the CHAD locations in the selected diary to the user-defined modeled 22 microenvironments. 23

 Concatenate the selected diar y days into a sequential longi tudinal diary for a simulated 24 individual covering all days in the simulated period. 25 APEX4.3 provides an optional longitudinal di ary-assembly algorithm that enables the 26 user to create composite diaries that reflect th e tendency of individuals to repeat activities on a 27 day-to-day basis. The user specifies values for two statistical variables (i.e., D and A ) that relate 28 to a key daily variable, typically the time spent per day in a part icular microenvironment (e.g., in 29 a motor vehicle). The D statistic reflects the relative impor tance 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. APEX4.3 then constructs composite di aries 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 by Glen et al. (2008) and in section 6.3 of US EPA 34 (2008b). 35February 2010 Draft – Do Not Quote or Cite 4-13 4.4.4 Calculate Microenvironmental Concentrations 1 Probabilistic algorithms are used by APEX4.3 to estimate the pollutant concentration 2 associated with each exposure event. The es timated pollutant concentr ations account for the 3 effects of ambient (outdoor) pollu tant concentration, penetrati on factor, air exchange rate, 4 decay/deposition rate, and proximity to emission sources, depending on the microenvironment, 5 available data, and the estimation method selected by the user. 6
APEX4.3 calculates air concentrations in th e various microenvironments visited by the 7 simulated person by using the ambient air data for the relevant census tracts and the user8 specified method and parameters that are spec ific to each microenvironment. In typical 9 applications, APEX4.3 calculates hourly concentr ations in all the microenvironments at each 10
hour of the simulation for each of the simulate d individuals, based on the hourly ambient air 11 quality data specific to the geographic locatio ns visited by the indivi dual. APEX4.3 provides 12 two methods for calculating microenvironmental concentrations: the mass balance method and 13 the transfer factors method (described briefly below). The user is required to specify a 14
calculation method for each of the microenviron ments; there are no restrictions on the method 15 specified for each microenvironment (e.g., some microenvironments can use the mass balance 16
method while the others can use the transfer f actors method). Each of these approaches is 17 described in sections 4.4.4.1 and 4.4.4.2, respectively. 18 When using an exposure model to estimate population exposures to CO it is best to 19 estimate the outdoor (ambient) CO concentra tion in the immediate vicinity of each 20
microenvironment. This is because concentratio ns measured at a fixed-site monitor may not 21 adequately represent the spatial and temporal he terogeneity in concentrations expected with 22 distance from the ambient monitor location. There can be different ways to accomplish this. For 23 example, one can use an emission-based dispersi on model to estimate ambient concentrations at 24 a fine temporal (e.g., hourly) and spatial scale (e.g., census block-level or 500 meter grids). 25 Another method is to use a statistically-based approach that addresse s the variability in 26 concentrations in a similar manner as a dispersi on model, only that im portant physical factors 27
that influence concentration leve ls are represented by and/or possi bly combined with a series of 28 regression equation coefficients and are related to an ambient monitor CO concentration. 29 Ultimately, it is this estimated outdoor CO concen tration that is then used as input to the 30 algorithm (either the mass balance model or factors method) employed to estimate CO 31
microenvironmental concentrations. 32 Staff selected a statistically-based approach to adjust ambient monitor concentrations. 33 The approach was designed to reflect both the spa tial and temporal variabili ty expected to occur 34 outside microenvironments, while also appropria tely linking the estimated microenvironmental 35
concentrations to observed concentrations at a fixed-site ambient monitor. The approach was 36February 2010 Draft – Do Not Quote or Cite 4-14 developed using personal exposure, fixed-site monitor, and outdoor concentration measurement 1 data and first implemented in the pNEM/CO model for use in the most recent CO exposure 2 assessment (Johnson et al., 2000). This approach was proposed as a method to address spatial 3 and temporal variability in outdoor and microenvir onmental concentrations in the draft scope and 4 methods plan (US EPA, 2009b), though not fu lly described there as is done here. 5 The microenvironmental algorithm and data used by pNEM/CO to estimate variable 6 parameters is described in section 4.4.4.3. The pNEM/CO approach was then adapted and 7 implemented in APEX3.1, a model more similar in structure to the curre nt version of APEX 8 (version 4.3) than pNEM/CO. This approach is described in section 4.4.4.4. The details 9 regarding selection of microenvir onments and parameters used by APEX4.3 in this assessment is 10 provided in section 5.9. 11

4.4.4.1 Overview of the Mass Balance Model 12
The mass balance method models an enclosed microenvironment as a well-mixed volume 13 in which the air concentration is spatially uniform at any specific time. The concentration of an 14 air pollutant in such a microenvironment is estimated using the following four processes: 15

 Inflow of air into the microenvironment; 16

 Outflow of air from the microenvironment; 17

 Removal of a pollutant from the microenvi ronment due to deposition, filtration, and/or 18 chemical degradation; and 19

 Emissions from sources of a polluta nt inside the microenvironment. 20

Table 4-2 lists the parameters required by the mass balance method to calculate 21
concentrations in a microenvironment. The proximity factor (

f

proximity ) is used to account for 22
differences in ambient concentrations between the geographic locati on represented by the 23 ambient air quality data (e.g., a fixed-site monitor) and the geographic location of the 24 microenvironment (e.g., near a roadwa y). This factor could take a value either greater than or 25
less than 1. Emission source ( ES ) represents the emission rate for the emission source, and 26
concentration source ( CS ) is the mean air concentration resu lting from the source (these are not 27
used in the current assessment. The factor R removal is defined as the removal rate of a pollutant 28 from a microenvironment due to deposition, filtration, and chemical reaction. The air exchange 29

rate ( R air exchange ) is expressed in air changes per hour. 30February 2010 Draft – Do Not Quote or Cite 4-15 Table 4-2. Variables used by APEX4.3 in the mass balance model. 1

Variable Definition Units Value Range

f proximity Proximity factor unitless 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

2 The mass balance equation for a pollutant in a microenvironment is described by the 3 differential equation 4

source removal out in C C C C   

 

   dt (t) dC ME (4-1) 5

where: 6

d C ME (t) = Change in concentration in a microenvironment at time t (ppm), 7
 C in = Rate of change in microenvironmental concentration due to influx 8 of air (ppm/hour), 9
 C out = Rate of change in microenvironm ental concentration due to outflux 10 of air (ppm/hour), 11
 C removal = Rate of change in microenvironmental concentration due to 12 removal processes (ppm/hour), and 13
 C source = Rate of change in microenvironmental concentration due to an 14 emission source inside the microenvironment (ppm/hour). 15

Within the time period of an hour each of the rates of change,  C in ,  C out ,  C removal , and 16
 C source , is assumed to be constant. The change in microenvironmental concentration due to 17 influx of air is represente d by the following equation: 18

exchange air n penetratio proximity ambient in
in R x f x f x C dt t dC C    ) ( (4-2) 19

where: 20February 2010 Draft – Do Not Quote or Cite 4-16 C

ambient = Ambient hourly outdoor concentration (ppm) 1 f proximity = Proximity factor 2 f penetration = Penetration factor 3

R air exchange = Air exchange rate (1/hour) 4

The change in microenvironmental concentra tion due to outflux of air is described by: 5

) ( ) ( t C R dt t dC C ME exchange air out

out     (4-3) 6

The change in concentration due to deposition, filtration, and chemical degradation in a 7
microenvironment is simulated by a first-order equation: 8

(t) C R (t) )C R R (R dt (t) dC C ME remova ME chemical filtration deposition removal
removal        (4-4) 9

where: 10

R deposition = Removal rate of a pollutant from a microenvironment due to 11 deposition (1/hour) 12

R filtration = Removal rate of a pollutant from a microenvironment due to 13 filtration (1/hour) 14

R chemical = Removal rate of a pollutant from a microenvironment due to 15 chemical degradation (1/hour) 16

R removal = Removal rate of a pollutant from a microenvironment due to 17 overall removal (1/hour) 18

As discussed in Section 2.2, EPA has not m odeled indoor emissions of CO in the current 19 exposure assessment; consequently, the optional term  C source was uniformly set equal to 0.0 for 20 this study. 21

Combining Equation 4-1 with Equati ons 4-2, 4-3, and 4-4 yields 22

) ( ) ( d t (t) dC ME t C R t C R C ME removal ME exchange air in 

 

   (4-5) 23

24

The solution to this differential equation is 25February 2010 Draft – Do Not Quote or Cite 4-17 ) exp( ) ) 0 ( ( ) ( t R R C C R C t C combined combined in

ME combined in

ME  

  

 (4-6) 1

where: 2

C ME (0) = Concentration of a pollutant in a microenvironment at the 3 beginning of a hour (ppm) 4
C ME (t) = Concentration of a pollutant in a microenvironment at time t within 5 the time period of a hour (ppm) 6

R combined = R air exchange + R removal (1/hour) 7

Based on Equation 4-6, the following three hou rly concentrations in a microenvironment 8
are calculated: 9

combined in ME equil ME R C t C C 

    ) ( (4-7) 10

) ( exp ) ) 0 ( ( combined equil ME ME equil ME end hourly ME R C C C C

    (4-8) 11

combined combined equil ME ME equil ME hourlymean ME R R C C C
dt dt t C
C ) ( exp 1 ) ) 0 ( ( ) (

1

0 1

0  

    

## 


(4-9) 12

where: 13

equil ME C = Equilibrium concentration in a microenvironment (ppm) 14

C ME (0) = Concentration in a microenvir onment at the beginning of an hour 15 (ppm) 16

end hourly ME C = Concentration in a microenvironment at the end of an hour (ppm) 17

hourlymean ME C = Hourly mean concentration in a microenvironment (ppm) 18

At each hour time step of the simulation period, APEX4.3 uses Equations 4-7, 4-8, and 4- 19 9 to calculate the hourly equilibrium, hourly en ding, and hourly mean concentrations. APEX4.3 20 reports hourly mean concentration as hourly conc entration for a specific hour. The calculation 21 continues to the next hour by using end hourly ME C for the previous hour as C ME ( 0)

. 22February 2010 Draft – Do Not Quote or Cite 4-18 4.4.4.2 Overview of the Factors Model 1 The factors model approach is conceptually simpler than the mass balance method and 2 has fewer user-specified parameters. It estimat es the concentration in a microenvironment as a 3
linear function of ambient concen tration of that hour, regardless of the c oncentration in the 4 microenvironment during the pre ceding hour. Table 4-3 lists th e parameters required by the 5 factors model approach to calcu late concentrations in a micr oenvironment without emissions 6

sources. 7

Table 4-3. Variables used by APEX4.3 in the factors model. 8

Variable Definition Units Value Range
f proximity Proximity factor unitless f proximity > 0
f penetration Penetration factor unitless 0 ≤ f penetration ≤ 1 9 The factors model approach uses the following equation to calculate hourly mean 10
concentration in a microenvironment from the user-provided hourly air quality data: 11

n penetratio proximity ambient hourlymean ME 

### f f C C    (4-10) 12

where 13

hourlymean ME C = Hourly concentration in a microenvironment (ppm) 14

C ambient = Hourly concentration in ambient environment (ppm) 15 f proximity = Proximity factor (unitless) 16 f penetration = Penetration factor (unitless) 17
18 The proximity factor ( f

proximity ) is used to account for differences in ambient 19 concentrations between the geographic location repr esented by the ambient air quality data (e.g., 20 a fixed-site monitor) and the ge ographic location of the partic ular microenvironment. For 21 example, persons travelling inside motor vehicles may be located on a heavily-trafficked 22 roadway, whereby the ambient air outside the ve hicle would likely have elevated levels of 23 mobile source pollutants such as carbon monoxide relative to the ambi ent monitor. In this case, 24 a value greater than one for the proximity factor would be appropriate to represent the increase in 25
concentrations outside the vehi cle relative to the ambient monitor. Additionally, for some 26 pollutants the process of infiltra tion may remove a fraction of the pollutant from the air. The 27
fraction that is retained in the indoor/enclosed microenvironm ent is given by the penetration 28 factor ( f

penetration ) and is dependent on the particular po llutant’s physical and chemical removal 29

rates. 30February 2010 Draft – Do Not Quote or Cite 4-19 4.4.4.3 Description of the Original pNEM/CO Microenvironmental Model 1 Version 2.1 of pNEM/CO determined the hourly outdoor CO concentration applicable to 2 each microenvironment through a Monte Carlo process based on the following equation 3
4
CO out (c,m,d,h) = M(m) x L(c, m, d) x T(c,m,d,h) x [CO mon (d,h)] A (4-11) 5
6 where, 7
8
CO out (c,m,d,h) = outdoor CO concentration (ppm) for cohort c with respect to 9 microenvironment m in district d during hour h, 10 M(m) = multiplier (> 0) specific to microenvironment m, 11 L(c,m,d) = location multiplier (> 0) sp ecific to cohort c, microenvironment m, 12 and district d (held c onstant for all hours), 13 T(c,m,d,h) = time-of-day multiplier (> 0) specific to cohort c, microenvironment 14 m, district d, and hour h, 15
CO mon (d,h) = ambient monitor-derived CO concentration (ppm) for hour h in 16 district d, and 17 A = exponent (A > 0). 18
19 This equation was used to generate a year-long sequence of outdoor one-hour CO 20 concentrations for each combination of cohort ( c ), microenvironment ( m ), and district ( d ) by 21 Johnson et al. (2000). The exponent A was set equal to 0.621 and held constant for all 22 sequences. The value of M(m) varied only with microenvironmen t as indicated in Table 4-4 [and 23
is identical to Table 2-6 in Johnson et al. (2000)]. 24
A value of the location factor L(c, m, d) was specified for each individual sequence and 25 held constant for all hours in the sequence. The value was randomly selected from a lognormal 26 distribution with geometric mean (GM L ) equal to 1.0 and geometric standard deviation (GSD L ) 27 equal to 1.5232. The natural logarithms of th is distribution can be characterized by a normal 28 distribution with an arithmetic mean ( μ L ) equal to zero and an arithm etic standard deviation ( σ L ) 29 equal to 0.4208. 30 A value of the time-of-day factor T(c, m, d, h) was randomly selected for each hour 31 within a sequence from a lognormal di stribution with geometric mean (GM T ) equal to 1.0 and 32 geometric standard deviation (GSD T ) equal to 1.6289. The natural l ogarithms of this distribution 33 follow a normal distribution with an arithmetic mean ( μ T ) equal to zero and an arithmetic 34 standard deviation ( σ T ) equal to 0.4879. 35February 2010 Draft – Do Not Quote or Cite 4-20 The CO out (c, m, d, h) term is interpreted as the ou tdoor CO concentration in the 1 immediate vicinity of microenvironment m in district d during hour h

. CO mon (d, h) is the CO 2 concentration reported for hour h by a nearby fixed-site monitor selected to represent district d

. 3 The mass balance model in pNEM/CO included a penetration factor that was set equal to 4 1.0 for CO. Consequently, there is no change in CO concentration as ambient (outdoor) air 5 moves into a microenvironment, though the CO concentration within th e microenvironment will 6 be affected by air exchange rate an d the presence of indoor sources. 7February 2010 Draft – Do Not Quote or Cite 4-21 Table 4-4. Estimated values of distribution paramet ers and variables in equation 4-11 as 1 implemented in the application of pNEM/CO to Denver and Los Angeles 2 (Johnson et al., 2000). 3

Microenvironment a Parameter Estimates for Equation 4-11

Code General location Specific location Activity diary locations included in

microenvironment A σ L σ T M(m)
1 Indoors Residence Indoors - residence 0.621 0.4208 0.4879 1.034
2 Indoors Nonresidence A Service station Auto repair 0.621 0.4208 0.4879 2.970

3 Indoors Nonresidence B Other repair shop Shopping mall 0.621 0.4208 0.4879 1.213

4 Indoors Nonresidence C Restaurant 0.621 0.4208 0.4879 1.213
5 Indoors Nonresidence D Bar 0.621 0.4208 0.4879 1.213
6 Indoors Nonresidence E Other indoor location Auditorium 0.621 0.4208 0.4879 1.213

7 Indoors Nonresidence F Store Office Other public building 0.621 0.4208 0.4879 1.213

8 Indoors Nonresidence G Health care facility School Church Manufacturing facility 0.621 0.4208 0.4879 0.989

9 Indoors Residential garage Residential garage 0.621 0.4208 0.4879 1.034
10 Outdoors Near road Near road Bicycle Motorcycle 0.621 0.4208 0.4879 1.607

11 Outdoors Other locations Outdoor res. garage Construction site Residential grounds School grounds Sports arena Park or golf course Other outdoor 0.621 0.4208 0.4879 1.436

12 Vehicle Automobile Automobile 0.621 0.4208 0.4879 3.020

13 Vehicle Truck Truck 0.621 0.4208 0.4879 3.020
14 Vehicle Mass transit vehicles Bus Train/subway Other vehicle 0.621 0.4208 0.4879 3.020

15 Outdoor Public parking or fueling facility Indoor parking garage Outdoor parking garage Outdoor parking lot Outdoor service station 0.621 0.4208 0.4879 2.970

Notes:

a Aggregate microenvironments defined for statistical analysis of Denver PEM data: residence (1 and 9), service/parking (2 and 1 5), commercial (3 through 7), and vehicle (12 through 14).February 2010 Draft – Do Not Quote or Cite 4-22 1

4.4.4.3.1 Data Used To Estimate pNEM/CO Microenvironmental Model Parameters 2 During a residential monitoring study descri bed by Wilson, Colome, and Tian (1995), 3
researchers measured 10-minute CO concentratio ns outside 293 residences throughout California 4
in 1992. These residences were customers of Pacific Gas and El ectricity (PG&E) (129 5
residences in Northern Californi a), San Diego Gas and Electric Company (89 residences in the 6 San Diego area), and Southern California Gas Co rporation (75 residences in the Los Angeles 7 area). After excluding the PG&E data (not part of the Los Angeles study area) and homes for 8 which valid CO data were not available, analysts used the remaining subset of 156 residences, 70 9 from Los Angeles and 86 from San Diego, as the basis for estimating values of σ L , σ T , and A 10 applicable to the Los Angeles study area. 2 The data subset contained 44,726 valid 10-minute 11 averages measured outside of residences, of wh ich less than 1% were negative (smallest value = 12 -1.0 ppm), 14,817 (33 %) were equal to 0 ppm, and the remainder were positive (maximum = 13 68.7 ppm). The valid 10-minute values were th en averaged by clock hour to permit comparison 14 with hourly-average CO c oncentrations reported by n earby fixed-site monitors. 15 Analysts determined that the negative values in the data set were most likely caused by 16
the subtraction of an offset from all measured va lues to account for monitor drift. To adjust for 17 this offset and to prevent the occurrence of negati ve and zero values (which could not be used in 18 fitting equation 4-11), analysts added a constant offset of 0.5 ppm to each hourly-average value 19 measured outside a residence. In addition, seve nteen (0.2%) of the orig inal hourly averages less 20 than or equal to -0.5 ppm were discarded. Each of the resulting one-hour outdoor CO 21 concentrations was paired with the one-hour CO concentration measured simultaneously at the 22 nearest fixed-site monitor [based on data obtaine d from EPA’s Aerometric Information Retrieval 23 System (AIRS)]. This approach yielded a fi nal database containing 6,330 pairs of hourly 24 average concentrations, in which each pair was i ndexed by date, time, residence identifier, fixed25 site monitor identifier, and fixed- site monitor scal e (e.g., neighborhood). 26
The M(m) values of equation 4-11 were based on data provided by the Denver Personal 27 Monitoring Study (Akland et al, 1985; Johnson, 1984). During this study, each of approximately 28 450 subjects carried a personal exposure mon itor (PEM) for two 24-hour periods. Each PEM 29 measured CO concentration continuously. The PEM readings were averaged by exposure event 30
such that each event was asso ciated with a single microenvi ronment and a single clock hour 31 (e.g., 1 pm to 2 pm). Event durations ranged from one minute to one hour. The 32

2 Note these same coefficient values were also applied to estimate exposures in the Denver study area, as researchers were unable to identify a usable data set specific to Denver.February 2010 Draft – Do Not Quote or Cite 4-23 microenvironment assigned to each PEM reading wa s determined from entries made in a real1 time diary carried by the subject. 2
Researchers created a data base in wh ich each PEM value was matched to the 3 corresponding hourly-average CO c oncentration reported by the nearest fixed-site monitor. The 4 data were first processed by excluding cases with missing measurements, cases in which 5 measurements failed a quality control check, and cas es in which applicable diary data indicated 6 the potential presence of smokers or gas stoves. Each PEM CO concentration was then assigned 7 to a microenvironment, m , based on entries in the activity dairy. In some cases, data for two or 8 more similar microenvironments were aggregated to provide more stable estimates than those 9 based on the very limited amount of data availa ble for specific microenvironments (see Table 4-4 10 footnote). For consistency with the Wilson, Colo me, and Tian (1995) database, all cases with a 11 zero measurement from the personal exposure monito r were excluded, as were all cases in which 12
the fixed site monitor concentration was zero af ter rounding to the neares t integer ppm. Note 13
that the Denver fixed-site data were recorded to the nearest 0.1 ppm, whereas the Los Angeles 14 fixed-site data were only recorded to the nearest integer. 15

4.4.4.3.2 Development of the pNEM/CO Micro environmental Model Equation 16 Equation 4-11 was based on the results of da ta analyses that suggested that the 17 relationship between CO out (c, m, d, h) and CO mon (d, h) should account for the specific 18 microenvironment, the geographic location of th e microenvironment, and the time-of-day. 19 Analysts recognized that numerous statistical models could have been developed. In specifying 20 the model that was ultimately used (i.e., equation 4-11), analysts attempted to balance the need 21 for simplicity and parsimony with the need to repr esent the patterns in c oncentration variability 22
observed in the available data. Most of the model development was based on a comparison of 23 hourly averages of 10-minute CO concentrations measured outside resi dences in southern 24 California (Wilson, Colome, and Tian, 1995) with hourly average CO con centrations measured 25 at the nearest fixed-site monitor. For this case, m represented the residence microenvironment in 26
the district d . The district d was initially taken to be the en tire study region where measurements 27 were collected (i.e., San Diego and Los Angeles areas). 28 Analysts began by considering a simple linear regression model of the form 29
30
CO out (c,m,d,h) = a(m,d) + A x [CO mon (d,h)] + e(c,m,d,h) (4-12) 31
32
where the residual term e(c,m,d,h) was assumed to be independent and normally distributed with 33 a mean of zero. For simplicity a nd parsimony, the slope coefficient A was assumed to be the 34 same for all microenvironments ( m ) and districts ( d ). 35February 2010 Draft – Do Not Quote or Cite 4-24 Although the coefficient of determination (R 2 ) for this linear regression model was 1 moderate (0.53), 3 the model was found to be unacceptabl e because it does not properly reflect 2 the strong correlations that were observed between concentrations measured outside the same 3 location. Instead, this form of regression model assumes that the residuals associated with a 4 particular residential location are independent. In other words, this model does not properly 5 separate out the variation between locations fr om the variation within locations. Analysts 6
identified two other deficiencies in this model: (1) large negative values of the randomly-selected 7 e(c,m,d,h) term could produce impossible negative out door concentrations, a nd (2) the model did 8 not generate outdoor concentrations charac terized by lognormal distributions. Various 9 researchers (e.g., Ott, 1995) have demonstrated that ambient CO concentrations tend to be 10 characterized by lognormal distributions rather than normal distributions. 11 To better address these latter concerns, analys ts evaluated an altern ative model where the 12 natural logarithm of outdoor c oncentration was expressed as a linear function of the natural 13 logarithm of monitor concentration: 14
15 LN[CO out (c,m,d,h)] = a(m,d) + A x LN[CO mon (d,h)] + e(c,m,d,h), (4-13) 16
17 In this equation and those that follow, LN[ ] indicates the natural logarithm of the 18 quantity in brackets. To properly separate th e variability between and within locations, the 19 intercept term a(m,d) was also permitted to vary with the cohort location, c , leading to the final 20
selected model: 21
22 LN[CO out (c,m,d,h)] = a(c,m,d) + A x LN[CO mon (d,h)] + e(c,m,d,h). (4-14) 23
24 Exponentiating both sides of equation 4-14 yiel ds the equivalent formulation to that 25 presented above in equation 4-11: 26
27
CO out (c,m,d,h) = M(m) x L(c,m,d) x T(c,m,d,h) x [CO mon (d,h)] A , (4-15) 28
29
where 30 M(m) = exp{mean [a(c,m,d)]}, averaged over cohorts, 31
32

3 Note that the R 2 goodness-of-fit statistic is not an appropriate measure of model adequacy when the true, underlying errors are highly correlated.February 2010 Draft – Do Not Quote or Cite 4-25 L(c,m,d) = exp{a(c,m,d) - mean [a(c,m,d)]}, and 1
2 T(c,m,d,h) = exp[e(c,m,d,h)]. 3
4
Several alternative statistical models were considered by analysts during the development 5 of the selected model formulation. Early in the process, analysts evaluated a series of 6 autoregressive time series models, in which m odel predictions were influenced by the past 7 history of CO concentrations at the monito r and outdoors of the microenvironment. These 8 models were rejected for several reasons: (1) they were inherently complex, (2) they yielded a 9
wide variation in model coefficients which di d not always produce reasonable estimates when 10 applied to specific California residences, and (3 ) they required microenvironment-specific time 11
series data for coefficient estimation which were not readily available for non-residential 12
microenvironments. 13 Analysts also evaluated models similar to equation 4-11 in which the exponent A varied 14 with microenvironment. These models were rej ected due to the need for parsimony and the lack 15 of sufficient, suitable data for estima ting microenvironment-specific values of A . A simpler 16 model in which the exponent A is fixed at 1 was rejected becau se fits of equation 4-11 to the 17
California data indicated that A differed significantly from 1 (p<0.01). In addition, the 18 assumption that A = 1 produced unrealistically high predic tions for outdoor CO concentrations 19 when the model was applied to monitoring data obtained from the Denver Broadway site (ID 31- 20 0002). These high values were found to be a direct result of setting A = 1, which forced the 21 geometric standard deviation of the estimated ou tdoor concentrations to significantly exceed the 22 geometric standard deviation of the monitor values. 23 Analysts ultimately arrived at equation 4-11 (equivalent to Equation 4-15), which permits 24
the A exponent to differ from 1.0. The model was fitted using statistical software for a mixed 25 (random and fixed effects) model which employe d restricted maximum likelihood estimation. 26 The fit yielded estimates of σ L = 0.4208, σ T = 0.4879, and A = 0.621, the values subsequently 27 used in the pNEM/CO runs described by J ohnson et al. (2000). The fitted value of M(m)
, 28 representing residences in Lo s Angeles during 1992, was actually 0.9706. An alternative value 29 (1.034), based on the additional analyses described below, was applied to the indoor-residence 30 microenvironment in the pNEM/CO runs (see Table 4-4). 31 This model, considered a reasonable co mpromise between model simplicity and 32 performance, is completely sp ecified by four parameters [ M(m)
, σ L , σ T , and A ]. Note that σ L , σ T , 33 and A are defined to be independent of the microenvironment, whereas M(m) is 34 microenvironment-specific. At the time of the initial model development, researchers were 35 unable to find a single data source capable of providing estimates of all four parameters. 36February 2010 Draft – Do Not Quote or Cite 4-26 Consequently, values for σ L , σ T , and A were estimated by analyzi ng data obtained from the 1 California study conducted by Wilson, Colome, and Tian (1995), whereas the specific M(m) 2 values were based on data provided by the Denver Personal Monitori ng Study (Akland et al, 3 1985; Johnson, 1984). 4 Researchers conducted a series of sensitivity an alyses to evaluate the potential effects on 5 parameter estimates of variations in the regional location and scal e of the fixed-site monitor. 6 Equation 4-11 was fitted to a series of data subsets defined by region (Los Angeles or San 7 Diego) or by the scale of the fixed-site mon itor (based on the estimated maximum distance from 8 the monitor represented by the measured concen trations: micro, middle, neighborhood, or urban 9 scale). The fitted values of σ L , σ T , A, and M(m) were very similar across the different subsets, 10 supporting the assumption that these parameters can be assumed to be representative of 11 concentration patterns outside residences in othe r regions and for other time periods, and can be 12 chosen to be the same value for all monitoring scales. Due to a lack of additional suitable data, 13
the values of σ L , σ T , and A are also assumed to be applicable to concentrations outside all other 14 microenvironments, although M(m) varies with the particular microenvironment (see below). 15 In equation 4-11, the CO out (c, m, d, h) term represents the outdoor CO concentration 16 associated with a particular microenvironment m , even when the microenvironment is an indoor 17 location. Few of the outdoor personal expos ure measurement (PEM) values reported by the 18 Denver study could be reliably associated w ith particular indoor microenvironments. 19 Consequently, researchers employed a simplified procedure for estimating M(m) values which 20
assumed that the mean of the indoor PEM values associated with each indoor microenvironment 21 was approximately equal to the mean of the outdoor concentration fo r the microenvironment. 4 22 This assumption is consistent with the results of applying mass-balance modeling to non-reactive 23 pollutants in enclosed spaces where the only source of the pollutant is the out side air. In such 24 cases, the mean indoor concentr ation approximates the mean outdoor concentration, with the 25
instantaneous indoor concentra tion exhibiting a lower degree of variability than the 26 corresponding outdoor concentration. 27 When equation 4-11 is expressed in a logar ithmic form (i.e., as in equation 4-14) and 28 averaged over cohorts, one obtains the equation 29
30

4 Because the simplified approach was also less sensitiv e to the wide variation in averaging times exhibited by the PEM values (i.e., one minute to 60 minutes), analysts were able to use the majority of PEM values in the statistical analysis. Limiting the anal ysis to one-hour PEM values would have significantly reduced the pool of
usable data.February 2010 Draft – Do Not Quote or Cite 4-27 Mean{LN[CO out (c, m, d, h)} 1 = Mean[a(c, m, d)] + A x Mean{LN[CO mon (d, h)]} + Mean[e(c, m, d, h)] 2 = LN[M(m)] + A x Mean{LN[CO mon (d, h)]}. 3
4 Therefore, the value of M(m) equals 5
6 M(m) = exp{Mean LN[COout(c, m, d, h)]

− A x Mean LN[COmon(d, h)]} (4-16) 7
8
where A = 0.621 (as above). This equation was then used to obtain estimates of M(m) for each 9 particular microenvironment, or aggregate of microenvironments, as indicated in Table 4-4 using 10 the available Denver PEM study data (Akland et al, 1985; Johnson, 1984). The same value of 11 M(m) was applied to each specific microenvir onment within an aggregate. 12

4.4.4.4 The Micronenvironmental Model as Implemented by APEX3.1 13 As discussed in section 4-3, the pNEM/CO mo del effectively evolved into what is known 14 today as the APEX model. In APEX3.1, the portion of the outdoor con centration af fecting the 15
indoor concentration is de termined by the formula 16
17
CO out = Ambient * Proximity * Penetration (4-17) 18
19 Note that we can represent Proximity and Penetration as distributions in APEX3.1. 20 These distributions can be sampled hourly, daily , or yearly. Let us make the following 21
substitutions of the variables used to estimate the outdoor concentrations: 22
23 Ambient = [CO mon (d,h)] A (4-18) 24 Proximity = M(m) x L(c, m, d) (4-19) 25 Penetration = T(c,m,d,h). (4-20) 26
27 which gives 28
29
CO out = M(m) x L(c, m, d) x T(c,m,d,h) x [CO mon (d,h)] A (4-21) 30
31 and is identical to equation 4-11 above. 32
To obtain results from APEX3.1 that are co mparable to that generated by pNEM/CO, 33 Johnson and Capel (2003) preprocessed the hourly am bient monitor data assigned to the district 34 containing the microenvironment using the formula 35February 2010 Draft – Do Not Quote or Cite 4-28 1 Ambient = [CO mon (d,h)] 0.621 (4-22) 2
3
where CO mon (d,h) is expressed in ppm. For each profile, a value for the Proximity term was 4 selected for each microenvironment from a lognorma l distribution with geom etric mean equal to 5 M(m) and geometric standard deviation equal to 1.5232. The natural logarithms of this 6
distribution were characteriz ed by a normal distribution wi th an arithmetic mean ( μ L ) equal to 7 ln[M(m)] and an arithme tic standard deviation ( σ L ) equal to 0.4208. Consistent with the 8 pNEM/CO algorithm, Proximity values were not permitted to fall below the 5 th percentile of the 9 specified distributi on or above the 95 th percentile of the distribu tion. Table 4-5 lists the 10 parameter values applicable to each of the 15 microenvironmen ts defined by Johnson and Capel 11 (2003). 12
Penetration values were randomly selected for each hour from a lognormal distribution 13 with geometric mean (GM T ) equal to 1.0 and geometric standard deviation (GSD T ) equal to 14 1.6289. As indicated above, the natural logar ithms of this distri bution followed a normal 15 distribution with an arithmetic mean ( μ T ) equal to zero and an arithm etic standard deviation ( σ T ) 16 equal to 0.4879. In agreemen t with the pNEM/CO algorithm, Penetration values were not 17 permitted to fall below the 5 th percentile of the specified dist ribution (0.4482) or above the 95 th 18 percentile of the distribution (2.2313). 5 19

5 Note the Penetration factor was not used according to its intended purpose in the APEX model. As discussed in Volume I of the APEX3.1 User’s Guide (US EPA, 2003), the Penetration factor is typically used to account for removal of pollutants during the transfer of outdoor air to a micr oenvironment. The Penetration factor was used to represent the T(c,m,d, h) term in equation 4-11 because Penetration is the only APEX3.1 parameter available for this purpose, given that the Proximity factor is being used to represent the product of M(m) and L(c,m,d). The product of M(m), L(c,m,d), and T(c,m,d,h) could not be represented by a single term because L(c,m,d) and T(c,m,d,h) have different averaging times (day vs. hour).February 2010 Draft – Do Not Quote or Cite 4-29 Table 4-5. Parameters of Bounded Lognormal Di stributions Defined for Proximity 1 Factors Used in Applications of APEX3.1 to Los Angeles (Johnson and Capel, 2 2003). 3

Microenvironment Parameters of bounded lognormal distribution

Code General Location Specific location Activity diary locations included in

microenvironment GM GSD Minimum (5 th pct) Maximum (95 th pct)
1 Indoors Residence Indoors - residence 1.034 1.5232 0.5175 2.0661
2 Indoors Nonresidence A Service station Auto repair 2.970 1.5232 1.4864 5.9345

3 Indoors Nonresidence B Other repair shop Shopping mall 1.213 1.5232 0.6071 2.4237

4 Indoors Nonresidence C Re staurant 1.213 1.5232 0.6071 2.4237
5 Indoors Nonresidence D Bar 1.213 1.5232 0.6071 2.4237
6 Indoors Nonresidence E Other indoor location Auditorium 1.213 1.5232 0.6071 2.4237

7 Indoors Nonresidence F Store Office Other public building 1.213 1.5232 0.6071 2.4237

8 Indoors Nonresidence G Health care facility School Church Manufacturing facility 0.989 1.5232 0.4950 1.9762

9 Indoors Residential garage Residential garage 1.034 1.5232 0.5175 2.0661

10 Outdoors Near road Near road Bicycle Motorcycle 1.607 1.5232 0.8042 3.2110

11 Outdoors Other locations Outdoor res. garage Construction site Residential grounds School grounds Sports arena Park or golf course Other outdoor 1.436 1.5232 0.7187 2.8693

12 Vehicle Automobile Automobile 3.020 1.5232 1.5114 6.0344

13 Vehicle Truck Truck 3.020 1.5232 1.5114 6.0344
14 Vehicle Mass transit vehicles Bus Train/subway Other vehicle 3.020 1.5232 1.5114 6.0344

15 Outdoor Public parking or fueling facility Indoor parking garage Outdoor parking garage Outdoor parking lot Outdoor service station 2.970 1.5232 1.4864 5.9345February 2010 Draft – Do Not Quote or Cite 4-30 4.4.5 Estimate Energy Expenditure and Ventilation Rates 1
APEX4.3 includes a module that estimates C OHb levels in the blood as a function of 2 alveolar ventilation rate, the CO concentration of the respir ed air, endogenous CO production 3 rate, and various physiological variables such as blood volume and pulmonary CO diffusion rate. 4
Alveolar ventilation rate is estimated as a function of oxygen uptake ra te, which in turn is 5 estimated as a function of energy expenditure rate . This section provides a brief summary of the 6 algorithm used to estimate alveolar ventilation ra te. A detailed descri ption of the algorithm, 7
based on the nonlinear solution to the Coburn- Forster-Kane (CFK) equation (Coburn et al., 8 1965), together with the distributi ons and estimating equations used in determining the value of 9 each parameter in the algorithm, can be found in Appendix B. 10

4.4.5.1 Energy Expenditure 11 McCurdy (2000) has recommended that measures of human ventilati on (respiration) rate 12 be estimated as functions of energy expenditu re rate. The energy expended by an individual 13 during a particular activity can be expressed as 14

EE = (METS)  (RMR) (4-23) 15
where EE is the average energy expenditure rate (kcal min -1 ) during the activity and RMR is the 16 resting metabolic rate of the in dividual expressed in terms of number of energy units expended 17 per unit of time (kcal min -1 ). METS (i.e., metabolic equivalent of work) is a ratio specific to the 18 activity and is dimensionless. 19 The METS concept provides a means for estimating the alveolar ventilation rate 20 associated with each activity. For convenience, let EE( i,j,k ) indicate the energy expenditure rate 21
associated with the i th activity on day j for person k . Equation 4-23 can now be expressed as 22

EE( i,j,k ) = [METS( i,j,k )]  [RMR( k )] (4-24) 23

where RMR( k ) is the average value for resting metabolic rate specific to person k . Note that 24 METS( i,j,k ) is specific to a particul ar activity performed by person k

. 25

4.4.5.2 Oxygen Requirements for Energy Expenditure 26 Energy expenditure requires oxygen which is supplied by ventila tion (respiration). 27 ECF( k ) represents an energy conversion factor de fined as the volume of oxygen required to 28 produce one kilocalorie of energy in person k . The oxygen uptake rate (VO 2 ) associated with a 29 particular activity can be expressed as 30

VO 2 ( i,j,k ) = [ECF( k )]  [EE( i,j,k )] (4-25) 31February 2010 Draft – Do Not Quote or Cite 4-31 where VO 2 ( i,j,k ) has units of liters oxygen min -1 , ECF( k ) has units of liters oxygen kcal -1 , and 1 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 ) 2 by substituting Equation 4-24 into Equa tion 4-25 to produce the relationship 3

VO 2 ( i,j,k ) = [ECF( k )]  [METS( i,j,k )]  [RMR( k )] (4-26) 4

4.4.5.3 Excess Post-Exercise Oxygen Consumption 5 At the beginning of exercise, there is a lag between work expended and oxygen 6 consumption. During this work/ventilation mi smatch, an individual’s energy needs are met by 7 anaerobic processes. The magnitude of the mi smatch between expenditure and consumption is 8
termed the oxygen deficit . During heavy exercise, further oxy gen deficit (in addition to that 9 associated with the start of exercise) may be accumulated. At some point, oxygen deficit reaches 10 a maximum value, and performance and energy expe nditure deteriorate. After exercise ceases, 11 ventilation and oxygen consumption will remain elev ated above baseline levels. This increased 12 oxygen consumption was historically labeled the oxygen debt or recovery oxygen consumption

. 13 However, the term excess post-exercise oxygen consumption (EPOC) has been adopted here to 14 represent this phenomenon. APEX4.3 has an algo rithm for adjusting the MET values to account 15 for EPOC. This algorithm is described in detail in section 7.2 of US EPA (2008b). 16

4.4.5.4 Alveolar Ventilation Rate 17 Alveolar ventilation (V A ) represents the portion of the minute ventilation that is involved 18 in gaseous exchange with the blood. VO 2 is the oxygen uptake that occu rs during this exchange. 19
The absolute value of V A is known to be affected by total lung volume, lung dead space, and 20 respiration frequency – parameters that vary according to the person a nd/or exercise rate. 21 However, it is reasonable to assume that the ratio of V A to VO 2 is relatively constant regardless 22 of a person’s physiological characteristics or en ergy expenditure rate. Consistent with this 23 assumption, APEX4.3 converts each estimate of VO 2 ( i,j,k ) to an estimate of V A ( i,j,k ) by the 24 proportional relationship 25

V A ( i,j,k ) = (19.63)  [VO 2 ( i,j,k )] (4-27) 26

where both V A and VO 2 are expressed in units of liters min -1 . This relationship was obtained 27 from Joumard et al. (1981), who based it on resear ch by Galetti (1959). Equation 4-15 can also 28 be expressed by the equivalent equation 29

V A ( i,j,k ) = (19.63)  [METS( i,j,k )]  [ECF( k )]  [RMR( k )] (4-28) 30February 2010 Draft – Do Not Quote or Cite 4-32 If ECF and RMR are specified for an indivi dual, then Equation 4-28 requires only an 1 activity-specific estimate of METS to produce an estimate of the energy expenditure rate for a 2 given activity. APEX4.3 processes time-locati on-activity data obtaine d from the CHAD to 3 create a sequence of activity-specific METS va lues for each simulated individual. APEX4.3 4 estimates RMR as a function of body mass based on probabilistic equations specific to age and 5 gender using equations reported by Schofield (1 985). A value of ECF is selected for each 6
individual from a uniform dist ribution (minimum = 0.20, maxi mum = 0.21) based on data 7 provided by Esmail et al. (1995). Using Equati on 4-28 and these inputs, APEX4.3 calculates a 8 sequence of V A values for each simulated individual. Th ese values are provided to the algorithm 9 that estimates the percent COHb in the blood re sulting from the simulated exposure (see section 10 4.4.7 and Appendix B). 11

4.4.6 Calculate Exposure 12 APEX4.3 calculates exposure as a time series of exposure concentra tions that a simulated 13 individual experiences during th e simulation period. APEX4.3 determines the exposure using 14 hourly ambient air concentrations, calculated co ncentrations in each microenvironment based on 15 these ambient air concentrations, and the minut es spent in a sequence of microenvironments 16 visited according to the composite diary. The hourly exposure concentration at any clock hour 17 during the simulation period is determ ined using the following equation: 18



### T t C
C N

j j hourlymean j ME

i 

# 

 

###  1 ) ( ) ( (4-29) 19

where 20
C i = Hourly exposure concentration at clock hour i of the simulation period 21 (ppm) 22 N = Number of events (i.e., varied microenvironments visited/activities 23 performed) in clock hour i of the simulation period. 24

hourlymean j ME C ) ( = Hourly mean concentration in microenvironment j (ppm) 25

t (j) = Time spent in microenvironment j (minutes) 26
T = 60 minutes 27 From the hourly exposures, APEX4.3 calculate s time series of 8-hour and daily average 28 exposure concentrations that a simulated indi vidual would experience during the simulation 29 period. APEX4.3 then statistically summarizes and tabulates the number of persons and person30 days at or above selected hourly, 8-hour, and daily average exposure concentrations in a series of 31 output tables. 32February 2010 Draft – Do Not Quote or Cite 4-33 4.4.7 Calculate Dose 1 Using time-location-activity pattern data obt ained from several diary studies, APEX4.3 2 constructs a composite diary for each simula ted person in the specified population. The 3 composite diary consists of a sequence of events spanning the specified period of the exposure 4 assessment (typically one calendar year). Each event is defined by a start time, duration, a 5 geographic location, a microenvir onment, and an activity. Using the algorithms described above 6 in sections 4.4.4, 4.4.5, and 4.4.6, APEX4. 3 provides estimates of CO microenvironmental 7 concentrations and the persons’ al veolar ventilation rate for each event in the composite diary, 8
for each simulated individual. APEX4.3 then uses these data, together with estimates of various 9 physiological parameters specific to the simulate d individual, to estimate the percent COHb in 10 the blood at the end of each event. The percen t COHb calculation is base d on the solution to the 11 nonlinear Coburn-Forster-Kane (CFK ) equation (Coburn et al., 1965), as detailed in Appendix B. 12 Briefly, the CFK module in APEX4.3 describes th e rate of change in COHb blood levels as a 13 function of the following quantities: 14

 Inspired CO pressure; 15

 COHb level; 16

 Oxyhemoglobin (O 2 Hb) level; 17

 Hemoglobin (Hb) content of blood; 18

 Blood volume; 19

 Alveolar ventilation rate; 20

 Endogenous CO production rate; 21

 Mean pulmonary capillary oxygen pressure; 22

 Pulmonary diffusion rate of CO; 23

 Haldane coefficient (M); 24

 Barometric pressure; and 25

 Vapor pressure of water at body temperature (47 torr). 26

If all of the listed quantities except COHb level are constant over some time interval, the 27 CFK equation has a linear form over the interval and is readily integrated. The solution to the 28 linear form gives reasonably accurate results fo r lower levels of COHb (ISA section 4.2.1). 6 29 However, CO and oxygen can compete for binding w ith the available hemoglobin and, therefore, 30

6 US EPA (2009b).February 2010 Draft – Do Not Quote or Cite 4-34 are not independent of each othe r. If this dependency is taken into account, the resulting 1 differential equation is no longer linear. Pete rson and Stewart (1975) proposed a heuristic 2 approach to account for this dependency which a ssumed the linear form and then adjusted the 3
O 2 Hb level iteratively based on the assumption of a linear relationship between COHb and 4
O 2 Hb. This approach was used in the COHb mo dule of the original CO-NEM exposure model 5 (Biller and Richmond, 1982; Johnson and Paul, 1983). 6 Alternatively, it is possible to determine C OHb at any time by nume rical integration of 7 the nonlinear CFK equation if one assumes a pa rticular relationship between COHb and O 2 Hb. 8 Muller and Barton (1987) demons trated that assuming a linear relationship between COHb and 9
O 2 Hb leads to a form of the CFK equation equivale nt to the Michaelis-Menten kinetic model that 10 can be analytically integrated. However, the analytical solution in this case cannot be solved 11 explicitly for COHb. Muller and Barton (1987 ) demonstrated a binary search method for 12 determining the COHb value. 13 The COHb module used in pNEM/CO employe d a linear relationship between COHb and 14
O 2 Hb which was consistent with the basic assu mptions of the CFK model. The approach 15 differed from the linear forms used by other m odelers in that the Muller and Barton (1987) 16 solution was employed. However, instead of the simple binary search described in the Muller 17 and Barton paper, a combination of the binary search and Newton-Raphson root finding methods 18 was used to solve for COHb (Press et al., 1986). 19 As mentioned above, the current COHb m odule included in APEX4.3 is based on the 20 solution to the nonlinear CFK equation using the assumption adopted by Muller and Barton 21 (1987) which employs a linear relationship between O 2 Hb and COHb. The CFK equation does 22 not have an explicit solution, so an iterative solution or approxima tion is needed to calculate each 23 percent COHb value. APEX4.3 solves the CFK equation using a 4 th -order Taylor’s series with 24 subintervals. This method, first incorporat ed in APEX3 (Glen, 2002), is summarized in 25 Appendix B. The selected method (4 th -order Taylor series with subintervals) was chosen 26 because of its simplicity, fast execution speed , and ability to produce relatively accurate 27 estimates of percent COHb at both low and high levels of CO exposure. While there may be 28 other approaches available (e.g., Bruce and Br uce (2003) multi-compartment model), both the 29
nonlinear and linear CFK models remain the most widely accepted and validated approaches 30 used to estimate COHb levels (ISA, section 4.2.3) 31

4.4.8 Model Output 32 All of the output files written by APEX4.3 are ASCII text files; the complete list and 33 their descriptions can be found in Table 5-1 of the APEX4.3 Use r’s Guide (US EPA, 2008a). In 34 general, the simulation output files most relevant to results generated for the assessment include 35February 2010 Draft – Do Not Quote or Cite 4-35 tabulations of hourly exposure, ventilation, and energy expenditure. Detailed event-level 1 information can also be output. However, give n the potential size of the files that can be 2 generated for a large population and assessment duration, it is not common to generate event3 level files outside of research purposes. Sp ecific outputs generated for the purposes of the 4 current CO exposure and dose assessme nt are discussed in section 6.1. 5

4.5 KEY OBSERVATIONS 6 Presented below are key observations relate d to the modeling system used for the 7 population assessment of CO exposure and dose. 8

 APEX, an EPA human exposure and dose model, has a long history of use in estimating 9 exposure and dose for many of the cr iteria pollutants including CO, O 3 , SO 2 , and NO 2
. 10 Over time, staff have improved and deve loped new model algorithms, incorporated 11 newer available input data and parameter di stributions, as well as performed several 12 model evaluations, sensitivity analyses, and uncertainty characterizations for the above 13 pollutants. Based on this analysis, APEX wa s judged to be an appropriate model to use 14 for assessing CO exposure and dose. 15

16
17February 2010 Draft – Do Not Quote or Cite 4-36 4.7 REFERENCES 1 2 Akland GG, Hartwell TD, Johnson TR, Whitmore RW. (1985). Measuring human exposure to carbon monoxide in 3 Washington, D. C. and Denver, Colorado during the winter of 1982-83. Environ Sci Technol . 19:911-918. 4

Biller WF and Richmond HM. (1982). Sensitivity Anal ysis on Coburn Model Pred ictions of COHb Levels 5 Associated with Alternative CO Standards. Report to Strategies and Air Standards Division of the Office 6 of Air quality Planning and Standards, U.S. Enviro nmental Protection Agency, Research Triangle Park, 7 NC. November, 1982. 8

Bruce EN and Bruce MC. (2003). A multicompart ment model of carboxyhem oglobin and carboxymyoglobin 9 responses to inhalation of carbon monoxide. J Appl Physiol . 95:1235-1247. 10

Coburn RF, Forster RE, Kane RB. (1965). Considerations of the physiology and variables that determine blood 11 carboxyhemoglobin concentration in man. J Clin Invest . 44:1899-1910. 12

Esmail S, Bhambhani Y, Brintnell S. (1995). Gender differences in work performance on the Baltimore therapeutic 13 equipment work simulator. Amer J Occup Therapy . 49:405-411. 14

Galetti PM. (1959). Respiratory exchanges during muscular effort. Helv Physiol Acta . 17:34-61. 15

Glen G. (2002). Programmer’s Guide for the APEX3 Model. Prepared by ManTech Environmental Technology, 16 Inc., for the U.S. Environmental Protection Agen cy, Research Triangle Park, NC. April, 2002. 17

Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal diary assembly for 18 human exposure modeling. J Expos Sci Environ Epidem . 18: 299-311. 19

Goldstein BR, Tardiff G, Hoffnagle G, Kester R. (1992). Valdez Health Study: Summary Report. Alyeska Pipeline 20 Service Company, Anchorage AK. 21

Hartwell TD, Clayton CA, Ritchie RM, Whitmore RW, Zelon HS, Jones SM, Whitehurst DA. (1984). Study of 22 Carbon Monoxide Exposure of Residents of Washington, DC and Denver, Colorado. Research Triangle 23 Park, NC: U.S. Environmental Protection Agency, Office of Research and Development, Environmental 24 Monitoring Systems Laboratory. EPA-600/4-84-031. 25

Isaacs K, McCurdy T, Errickson A, Forbes S, Glen G, Graham S, McCurdy L, Nysewander M, Smith L, 26 Tulve N, Vallero D. (2009). St atistical properties of longitudinal time-activity data for use in EPA 27 exposure models. Poster presented at the American Time Use Research Conference; College Park MD, 28 June 26, 2009. 29

Joumard R, Chiron M, Vidon R, Maurin M, Rouzioux JM. (1981). Mathematical models of the uptake of carbon 30 monoxide on hemoglobin at low carbon monoxide levels. Environ Health Persp . 41:277-289. 31

Johnson T and Capel J. (2003). Total Risk Integrated Methodology. TRIM ExpoInhalation User’s Document. 32 Volume III: Air Pollutant Exposure Model Criteri a Air Pollutants Case Study (Carbon Monoxide 33 Exposures in Los Angeles). Report prepared by TR J Environmental, Inc., for the Office of Air Quality 34 Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. 35

Johnson T and Paul R. (1983). The NAAQS Exposure Model (NEM) Applied to Carbon Monoxide. EPA Report 36 No. 450/5-83-003. Prepared by PEDCo Environmental, Inc. for the Office of Air Quality Planning and 37 Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 38February 2010 Draft – Do Not Quote or Cite 4-37 Johnson T. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Research Triangle 1 Park, NC: U.S. Environmental Protection Agency, En vironmental Monitoring Sy stems Laboratory. EPA2 600/4-84-014. 3

Johnson T. (1989). Human Activity Patterns in Cincinnati, Ohio. Palo Alto, CA: Electric Power Research Institute. 4 EPRI EN-6204. 5

Johnson T, Capel J, Olaguer E, Wijnberg L. (1992). Estimation of Carbon Monoxide Exposures and Associated 6 Carboxyhemoglobin Levels in Denver Residents Using a Probabilistic Version of NEM. Report prepared 7 for the Office of Air Quality Planning and Standard s, U.S. Environmental Protection Agency, Research 8 Triangle Park, North Carolina. 9

Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 10 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 11 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 12 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 13
at: http://www.epa.gov/ttn/fera/human_related.html . June 2000. 14

Ott W. (1995). Environmental Statistics and Data Analysis. CRC Press, Boca Raton. 15

Klepeis NE, Tsang AM, Behar JV. (1996). Analysis of the National Human Activity Pattern Survey (NHAPS) 16 Respondents from a Standpoint of Exposure Assessment. Washington, DC: U.S. Environmental Protection 17 Agency, Office of Research and Development. EPA/600/R-96/074. 18

Liu L-JS, Box M, Kalman D, Kaufman J, Koenig J, Larson T, Lumley T, Sheppard L, Wallace L. (2003). Exposure 19 assessment of particulate matter for susceptible populations in Seattle. Environ Health Persp . 111: 909- 20 918. 21

Mansfield CA and Corey C. (2003). Analysis of Surv ey Data on Ozone Alert Days. Research Triangle Park: 22 Research Triangle Institute. (Task 4 Report) 23

Mansfield C, Johnson FR, Van Houtven G. (2006). The missing piece: averting beha vior for children’s ozone 24
exposures. Resource Energy Econ . 28:215-228. 25

Mansfield C, Van Houtven G, et al. (2004). Parental Av erting Behavior With Respect To Ozone Alerts. Research 26 Triangle Park NC: Research Triangle Institute. 27

McCurdy T. (2000). Conceptual basis for multi-route intake dose modeling using an energy expenditure approach. 28 J Expos Anal Environ Epidemiol . 10:1-12. 29

McCurdy T, Glen G, Smith L, and Lakkadi Y. (2000). The National Exposure Research Laboratory’s Consolidated 30 Human Activity Database. J Expos Anal and Environ Epidemiol . 10:566-578. 31

Muller KE and Barton CN. (1987). A nonlinear version of the Coburn, Forster and Kane model of blood 32 carboxyhemoglobin. Atmos Environ . 21:1963-1967. 33

Peterson JE and Stewart RD. (1975). Predicting th e carboxyhemoglobin levels re sulting from carbon monoxide 34
exposures. J Appl Physiol . 39(4): 633-638. 35

Press WH, Flannery BP, Teukolsky SA, Vettering WT. (19 86). Numerical Recipes. Cambridge University Press. 36

Schofield WN. (1985). Predicting basal metabolic rate, new standards, and review of previous work. Hum Nutr 37 Clin Nutr . 39C(S1): 5-41. 38February 2010 Draft – Do Not Quote or Cite 4-38 Spier CE, Little DE, Trim SC, Johnson TR, Linn WS, Hackney JD. (1992). Activity patterns in elementary and 1 high school students exposed to oxidant pollution. J Expo Anal Environ Epidemiol . 2:277-293. 2

Tsang AM and Klepeis NE. (1996). Descriptive Statistics Tables from a Detailed Analysis of the National Human 3 Activity Pattern Survey (NHAPS) Data. U.S. Environmental Protection Agency. EPA/600/R-96/148. 4

US Census Bureau. (2007). Employment Status: 2000- Supplemental Tables. Available at: 5 http://www.census.gov/population/www/cen2000/phc-t28.html

. 6

US DOT. (2007). Part 3-The Journey To Work files. Bureau of Transportation Sta tistics (BTS). Available at: 7 http://transtats.bts.gov/

. 8

US EPA. (1999). Total Risk Integrated Methodology. Available at: 9 http://www.epa.gov/ttnatw 01/urban/trim/trimpg.html

. 10

US EPA. (2002). EPA’s Consolidated Human Activities Database. Available at: http://www.epa.gov/chad/

. 11

US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas (July 2007). Office of Air 12 Quality Planning and Standards, Research Triang le Park, NC. EPA-452/R-07-010. Available at: 13 http://epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html. 14

US EPA. (2008a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 15 (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report No. EPA-452/B-08-001a. Office of 16 Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 17 Available at: http://www.epa.gov/ttn/f era/human_apex.html 18

US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 19 (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report No. EPA-452/B-08- 20 001b. Office of Air Quality Planning and Standard s, U.S. Environmental Protection Agency, Research 21 Triangle Park, NC. Available at: http://www.epa.gov/ttn/fera/human_apex.html 22

US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO 2 Primary National Ambient 23 Air Quality Standard. EPA-452/R-08-008a. November 2008. Available at: 24 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf. 25

US EPA. (2009a). Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air 26 Quality Standard. EPA-452/R-09-007. August 2009. Available at: 27 http://www.epa.gov/ttn/naaqs/standards/s o2/data/200908SO2REAFinalReport.pdf

. 28

US EPA. (2009b). Carbon Monoxide National Ambient Ai r Quality Standards: Scope and Methods Plan for Health 29 Risk and Exposure Assessment, EPA-452/R-09-004. Available at: 30 http://www.epa.gov/ttn/naaqs/st andards/co/s_co_cr_pd.html 31

32

University of Michigan. (2010). The Panel Study of Income Dynamics (PSID). Data and documentation Available 33 at: http://www.psidonline. isr.umich.edu/Data/. 34

Wiley JA, Robinson JP, Piazza T, Garrett K, Cirksena K, Cheng Y-T, Martin G. (19 91a). Activity Patterns of 35 California Residents: Final Report. California Air Resources Board, Sacramento, CA. ARB/R93/487. 36 Available from: NTIS, Springfield, VA., PB94-108719. 37

Wiley JA, Robinson JP, Cheng Y-T, Piazza T, Stork L, Pladsen K. (1991b). Study of Children's Activity Patterns: 38 Final Report. California Air Resources Board, Sacramento, CA. ARB-R-93/489. 39February 2010 Draft – Do Not Quote or Cite 4-39 Williams R, Suggs J, Creason J, Rode s C, Lawless P, Kwok R, Zweidinger R, Sheldon L. (2000). The 1998 1 Baltimore particulate matter epidemiology-exposure stud y: Part 2. Personal exposure associated with an 2 elderly population. J Expo Anal Environ Epidemiol . 10(6):533-543. 3

Williams R, Suggs J, Rea A, Leovic K, et al. (2003). The Research Triangle particulate panel study: PM mass 4 concentrations relationships. Atmos Environ . 37:5349-5363. 5

Williams R, Suggs J, Rea A, Sheldon L, et al. (2003). The Research Triangle particulate panel study: modeling 6 ambient source contributions to personal and residential PM mass concentrations. Atmos Environ

. 7 37:5365-5378. 8

Wilson AL, Colome SD, Tian Y. (1995). California Resi dential Indoor Air Quality Study. Volume III: Ancillary 9 and Exploratory Analysis. Integrated Environmental Services, Irvine, California. 10February 2010 Draft – Do Not Quote or Cite 5-1 

### 5 APPLICATION OF APEX4.3 IN THIS ASSESSMENT 1

5.1 PURPOSE 2 This chapter presents detailed information re garding the varied input data sources, the 3 APEX model settings, and input variable parameterizations used in estimating population 4 exposure and dose in the Denver and Los Angeles st udy areas. In particular , this chapter (and its 5 associated appendices) describes the: 6

 geographic study areas and time periods define d for the exposure and dose analyses, 7

 method and parameters used to construct a comp osite diary for each simulated individual, 8

 study area population, the modeled at-risk population and associat ed CHD prevalence 9 rates, 10

 exposure scenarios under evaluation, 11

 air quality and meteorological data used for each study area and exposure scenario, 12

 method used to estimate local outdoor a nd microenvironmental CO concentrations. 13
Note that the APEX model version used in this asse ssment was APEX4.3, but for 14 simplicity will be referred to as APEX in much of the discussion that follows. 15

5.2 OVERVIEW 16 As summarized above in section 1.3, the pr evious analysis of population exposure to 17 carbon monoxide (CO) employed the pNEM/CO m odel in Denver and Los Angeles study areas, 18 comprising the majority of census tracts within those metropolitan areas (Johnson et al., 2000). 19 In this earlier exposure assessmen t, air quality data were obtained from multiple fixed-site 20 monitors within the study areas, and the e xposure assessment accounted for the effects of 21 geographic location, a diverse set of microenvi ronments, commuting within the study area, and 22 selected indoor sources (e.g., pass ive smoking, gas stoves). In the specific application of APEX 23
described in this second draft CO REA, a similar exposure and dose modeling approach has been 24 developed by staff, though without inclusion of i ndoor source emissions. The detailed approach 25 presented here was designed to include the major comments and recommendations made by the 26 CASAC and public regarding the geographically constricted and simplified exposure modeling 27 approach used in the first draft CO REA (US EPA, 2009a). 28 The general description of AP EX, the standard databases us ed, modeling capabilities, as 29 well as the history of the pNEM /APEX series of exposure models, can be found in chapter 4. 30
This includes use of the national data files obtained from the US Census Bureau (i.e., the 2000 31 Census data) for the following types of information: 32February 2010 Draft – Do Not Quote or Cite 5-2  Population data and employment probabil ities by gender, age, and census tract; 1

 Locations of census tracts (latitude and longitude); and 2

 Commuting flows for combinations of home and work census tracts. 3

Other default input files provided within APEX include tables of age- and gender-specific 4 physiological parameters (e.g., body weight) and activ ity-specific metabolic equivalents (METs). 5 The contents of each of these default files and their use were summarized in chapter 4. They are 6 described in greater detail in the APEX User s Guide (US EPA, 2008a) and the APEX Technical 7 Support Document (US EPA, 2008b). 8

5.3 STUDY AREAS 9 As discussed in section 3.2, staff selected areas within Denver, Colorado, and Los 10 Angeles, California, for the cu rrent exposure and dose assessment. Briefly, considerations in 11 selection of these areas included: the prior anal ysis of these locations in CO NAAQS reviews, 12 the areas having historically elevated CO concentr ations, and the areas currently having some of 13 the most complete ambient monitoring data availabl e. The monitors selected for use in defining 14 the air quality in each urban area are listed in Tables 5-1 (Denver) and 5-2 (Los Angeles). 15 The actual study areas were defined as including all census tracts within 10 km of these 16
selected fixed-site monitors. These areas are illustrated in Figures 5-1 and 5-2, which indicate 17
the locations of the fixed-site monitors and the circular 10-km region surrounding each ambient 18 monitor. Each 10 km region defines the aforementioned air district that includes the geographic 19 area (i.e., the census tracts) represen ted by data from the associated CO monitor. Note that all air 20 districts have the same radius (10 km), a valu e specified by the “AirRadius” input parameter of 21
APEX. 22 In addition to defining the air districts, th e model user must specify a location for the 23 center of the study area and a value for “CityRadi us.” The circular area defined by the city 24
center location and the value of “CityRadius” must be large e nough to include all census tracts 25
included in the air districts. For Denver, staff used the locati on of monitor ID 31-0014 (Denver - 26 Julian) for the city center and set the “CityRadius” equal to 20 km (Figure 5-1). Staff used the 27 location of monitor ID 37-1103 (Los Angeles) for the center city of Los Angeles and set the 28 “CityRadius” equal to 65 km (Figure 5-2). 29

5.4 EXPOSURE PERIODS 30 EPA selected the following calendar year s as the study periods for each area: 31
Denver: 1995 and 2006 32 Los Angeles: 1997 and 2006 33February 2010 Draft – Do Not Quote or Cite 5-3 The year 2006 was selected for both cities because it was the most recent year of 1 monitoring data that met the 75% completeness requirement for the ambient monitors listed 2 above. Note, the CO levels reported for 2006 were well below the 8-hour NAAQS (see Tables 3 5-1 and 5-2) and are considered representative of the as is air quality in each study area for 4 purposes of this assessment. The year 1995 for Denver and the year 1997 for Los Angeles were 5 selected as periods for which the ambient monito ring concentrations were near or exceeding the 6 8-hour average CO NAAQS of 9 ppm. Staff judged that these historical monitoring data would 7 be most useful in representing air quality that ju st meets the current or alternative CO standards 8 and, following an appropriate concentration level adjustment, would represent a particular air 9 quality scenario (see sections 5.6 and 5.7.3). 10

Table 5-1. Attributes of fixed-site monitors selected for the Denver study area. 11

Monitor ID 031-0002 a 031-0013 a 031-0014 a 059-0002 a City Denver Denver Denver Arvada Local Name CAMP NJH-E Carriage - Latitude 39.751184 39.738578 39.800333 39.751761 Longitude -104.987625 -104.939925 -105.099973 -105.030681 Elevation (m) 1593 1620 1640 1621 Scale Microscale Neighborhood - Neighborhood

Objective Highest Concentration Population Exposure Unknown Population Exposure 1995 2 nd Highest 8-hour avg CO (ppm) 9.5 6.2 5.9 4.6 2006 2 nd Highest 8-hour avg CO (ppm) 3.1 2.5 3 2 Notes:

a Identified monitor was used in the 2000 pNEM/CO analysis (Johnson et al., 2000). 12February 2010 Draft – Do Not Quote or Cite5-4Table 5-2.Attributes of fixed-site monitors selected for the Los Angeles study area.1Monitor ID037-0113a037-1002a037-1103a037-1201 037-1301a037-2005a037-4002a059-0001/7a,b059-1003 059-5001aCityWest LA BurbankLosAngeles Reseda Lynwood PasadenaLongBeach AnaheimCostaMesa La HabraLocal Name- - - - - - - - - -Latitude34.05111 34.17605 34.06659 34.19925 33.9289934.1326 33.82376 33.83062 33.67464 33.92513Longitude-118.45636 -118.31712 -118.22688 -118.53276 -118.21071 -118.1272 -118.18921 -117.93845 -117.92568 -117.95264Elevation (m)91 168 87 226 27 250 6 45 0 82Scale- - - - Middle - - Neighborhood Middle -ObjectiveUnknown Unknown Unknown UnknownHighestConc.Unknown UnknownPopulationExposureUnknownPopulationExposure1997 2ndHighest 8-houravg CO (ppm)4.1 7.2 5.9 7.7 15 5.4 6.4 5.4 5 5.72006 2ndHighest 8-houravg CO (ppm)1.9 3.4 2.5 3.4 5.62.7 3.3 2.9 2.5 2.9Notes:aIdentified monitor was used in the 2000 pNEM/CO analysis (Johnson et al., 2000).bWhen considering the two monitoring periods (1997 and 2006), two separate ambient monitor IDs were noted (059-0001 and 059-0007) thougheffectively the locations of both monitors were the same.February 2010 Draft – Do Not Quote or Cite 5-5 1

Figure 5-1. Ambient monitor locations, air district s (black circles), meteorological zones 2 (blue circles), and study area (red circle) for the Denver exposure modeling 3 domain. 4February 2010 Draft – Do Not Quote or Cite 5-6 1
Figure 5-2. Ambient monitor locations, air district s (black circles), meteorological zones 2 (blue and pink circles), and study a rea (red circle) for the Los Angeles 3 exposure modeling domain. 4February 2010 Draft – Do Not Quote or Cite 5-7 5.5 STUDY POPULATION 1

5.5.1 Total and Simulated Population 2 The population estimates obtained from the 2000 US Census were used “as is” for each 3 study area and scenario modeled; there were no adjustments made for modeling the most recent 4 air quality data (2006) or for each of the altern ative exposure scenarios modeled (a hypothetical 5 year). The total population in this study was restricted to those ag ed 18 years or older. Based on 6
the census tracts included and removal of resident s commuting outside of the study area, the total 7 modeled population was 617,020 persons (or 81.1% of the total population). The corresponding 8 figure for Los Angeles was 5,017,551 persons (or 88.5% of the total pop ulation within the 9 modeled census tracts). To obtai n adequate representation of th e simulated population while also 10 keeping the model runs tractable, fifty-thousa nd exposure profiles (or simulated individuals) 11 were run by APEX for each study area and exposure scenario. 12

5.5.2 Selected at-Risk Subpopulation 13 The at-risk population simulated within each st udy area is comprised of adults ages 18 14 and older with CHD (diagnosed and undiagnosed). This focus on adults is consistent with the 15 previous CO exposure assessment (Johnson et . al, 2000) and the completed 1994 CO NAAQS 16 review (US EPA, 1992), as the incidence of CHD in younger individuals is extremely small 17 (CDC, 2009). In this assessment, the term CHD is used consistent with its use in the National 18 Health Interview Survey (NHIS) where it is inclus ive of coronary heart disease, angina pectoris 19 and heart attack (CDC, 2009). 20 For estimates of adults with diagnosed C HD, staff obtained CHD prevalence data from 21 the NHIS for 2007 (CDC, 2009). The estimated CHD prevalence for the population above 18 22 years of age is about 6% (ISA, section 5.7.2.1). 1 Staff assumed the natio nal prevalence rates for 23 CHD were appropriate to use in each of th e two study areas because there was a general 24 similarity in the reported regional rates. Staff desired the prevalence rates to be stratified by age 25
and gender, though the available da ta were stratified by age or gender. Table 5-3 provides 26 national prevalence data for CHD by age and Tabl e 5-4 provides CHD stratified by gender. The 27 gender-only data were used to estimate gender-spec ific adjustment factor s to apply to the age28 only data set. For males, the adjustment f actor = 0.080/0.061 = 1.31; for females, the adjustment 29 factor = 0.045/0.061 = 0.74. Table 5-5 provides the estimated national prevalence rates for CHD 30 by age range adjusted for gender us ing these adjustment factors. 31

1 Note that in the last CO NAAQS review complete d in 1994, the estimated number of individuals with CHD represented about 3% of the entire (all ages) US population (US EPA, 1992).February 2010 Draft – Do Not Quote or Cite 5-8 Table 5-3. National prevalence rates for coronary heart disease by age range. 1

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
Notes:

a Source: Coronary heart disease statistics in Table 2 of NHIS (CDC, 2009), which include coronary heart disease, angina pectoris and heart attack.

Table 5-4. National prevalence rates for coro nary heart disease by gender. 2

Prevalence rate (fraction) for coronary heart disease a Age range Total Males Females
18+ 0.061 0.080 0.045
Notes:

a Source: Coronary heart disease statistics in Table 2 of NHIS (CDC, 2009), which include coronary heart disease, angina pectoris and heart attack.

Table 5-5. National prevalence rates for coronary heart disease, stratified by age and 3 gender. 4

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
Notes:

a Values listed in Table 5-3 were multiplied by 1.31 for males and 0.74 for females using data from Table 5-4. 5 Staff has expanded the selected at-risk populat ion to also include undiagnosed cases of 6 coronary heart disease using a method similar to that developed by OAQPS for use in the 2000 7 exposure assessment (see Appendix F of Johnson et al., 2000). Briefly, in the prior assessment 8 the prevalence estimates of diagnosed IHD 2 were stratified by age and sex (Adams and Marano, 9

2 The NHIS prevalence rates used in the 2000 assessment used the term IHD, rather than CHD (Adams and Marano, 1995).February 2010 Draft – Do Not Quote or Cite 5-9 1995) and constituted approximately 8.0 million indi viduals in the civilian, non-institutionalized 1 population. 3 In addition, as many as three to four m illion persons were estimated to have silent 2 ischemia or undiagnosed IHD (American Heart Asso ciation, 1990). Staff used this information 3 to provide estimates of the undiagnosed IHD popul ation for use in the pNEM/CO model. Staff 4 assumed 3.5 million persons had undiagnosed IHD a nd assumed the prevalence to be distributed 5 by age and gender in the same manner as diagnosed IHD. These data yield an adjustment factor 6 of 0.438 (i.e., 3.5 million/8.0 million) to apply to the diagnosed prevalence for use in estimating 7 the undiagnosed prevalence. Consequently, this factor can be interpreted as the undiagnosed 8 cases may be 43.8% of the diagnosed prevalence. 9
Table 5-6 lists the resu lts of applying the 0.438 factor to the age and gender stratified 10 prevalence rates listed in Table 5- 5. This assumes that CHD and IHD are identical with respect 11 to the ratio of undiagnosed cases to diagnosed cases and this ratio has not changed since 1990. 12 The total prevalence listed for each gender (diagnosed and undiagnosed combined) was used by 13 APEX in estimating the selected at-risk population. 14 When using these prevalen ce rates in the AP EX model runs, there were 383,040 15 simulated persons (or 7.6% of the total simu lated population) with either diagnosed or 16 undiagnosed CHD in the Los Angeles study area , while in Denver there were 53,656 simulated 17 persons (or 8.7% of the total si mulated population) within the sa me selected at-risk population. 18

Table 5-6. National prevalence rates for coronary heart disease, including diagnosed and 19 undiagnosed cases, stratified by age and gender. 20

Prevalence rate (fraction) for coronary heart disease
Males Females Age range Diagnosed Undiagnosed a Total Diagnosed Undiagnosed a Total

18 to 44 0.012 0.005 0.017 0.007 0.003 0.010
45 to 64 0.088 0.039 0.127 0.050 0.022 0.072
65 to 74 0.244 0.107 0.351 0.138 0.060 0.198

75+ 0.310 0.136 0.446 0.175 0.077 0.252
Notes:

a Values listed in Table 5-5 (diagnosed CHD) we re multiplied by 0.438 to estimate the undiagnosed prevalence. Staff assumed CHD and IHD are identica l with respect to the ratio of undiagnosed cases (3.5 million) to diagnosed cases (8.0 million) and that this ratio has not changed since 1990 (see Appendix F of Johnson et al. (2000)). 21

3 These estimates did not include individuals in the military or individuals in nursing homes or other institutions.February 2010 Draft – Do Not Quote or Cite 5-10 5.5.3 Time-Location-Activity Patterns 1 APEX constructs a 365-day longitudinal diary for each simulated individual by selecting 2
24-hour diaries from those available in CHAD. In performing the exposure assessments 3 described in this report, all available diaries for persons a bove age 17 in the CHAD database 4
were used. 5

5.5.4 Construction of Longitudinal Diaries 6 As discussed in section 4.4.3.4, APEX provide s a longitudinal diar y assembly algorithm 7
that enables the user to create co mposite diaries that reflect the te ndency of individuals to repeat 8 day-to-day activities (Glen et al., 2008). The user specif ies values for two statistical variables ( D 9
and A ) that relate to a key daily variable, typically the time spent per day in a particular 10 microenvironment (e.g., in a motor vehicle). The D statistic reflects the relative importance of 11 intra- and inter-personal variance within the selected key daily variable. The A variable 12 quantifies the day-to-day autocorrelation in th e selected key daily va riable. APEX then 13 constructs composite diaries that exhibit the statistical propertie s defined by the specified values 14
of D and A

. 15 In this exposure assessment, we used the longitudinal diary algorithm to construct year16 long activity patterns for each simulated individual to reflect the day-to-day correlation of time 17 spent inside motor vehicles. Each diary day in the CHAD database was tagged with the number 18 of minutes spent in the vehicle microe nvironment. Parameter settings of D = 0.31 and A = 0.19 19 were specified to control the day-to-day repe tition of time spent in motor vehicles in the 20 constructed composite diar ies. These particular D and A values were obtained from Isaacs et al. 21 (2009) (see Appendix C). 22 In selecting particular diarie s to represent the simulated population, the CHAD data are 23 categorized or separated by APEX into data pools. The pools we re defined by three ranges for 24 the maximum temperature of the diary day (< 55.0 ◦ F, between 55.0 and 83.9 ◦ F, and ≥ 84.0 ◦ F) 25 and two day-types (i.e., weekend and weekday); thus, there were 3 x 2 = 6 diary pools. The 26 window for age was set at 15%. For example, diar ies can be selected for a simulated individual 27 of age 60 from CHAD individuals ranging from ages 51 though 69 (i.e., 60 +/- 15 percent). 28

5.6 EXPOSURE SCENARIOS 29 In this second draft CO REA, the exposure scenario refers to the air quality conditions 30
considered for each APEX simulation. Staff ev aluated five exposure scenarios for each study 31 area. The first exposure scenario used unadjus ted 2006 ambient air quality as input to APEX; 32 this is designated as the as is air quality exposure scenario. The purpose of this scenario is to 33 determine the number of persons that may expe rience COHb levels at or above selected 34February 2010 Draft – Do Not Quote or Cite 5-11 benchmarks when considering current air quality conditions. The next four exposure scenarios 1 used adjusted high concentration year ambien t data in each location (i.e., the 1995 monitoring 2
data in Denver and the 1997 mon itoring data in Los Angeles). Th e purpose of these scenarios is 3 to determine the number of persons that may e xperience COHb levels at or above selected 4
benchmark levels when consideri ng air quality conditions that just meet a selected level, form, 5 and averaging time of interest. This is not the sa me as considering exposures associated with the 6
as is air quality conditions. 7 The first of these adjusted air quality exposure scenarios considered ambient 8 concentrations adjusted to just meeting th e 8-hour average CO NAAQS of 9 ppm. This 9 particular form was selected wh en considering the two current standard forms (8-hour average 10 and 1-hour) because it is the controlling standard. 4 The second of these exposure scenarios using 11 the historical monitoring data also consider ed the current form of the current 8-hour CO 12 standard, but with the ambient concentrations in each study adjusted to meet an alternative 13 standard level of 5 ppm. The next two scen arios considered percen tile forms of potential 14 alternative standards, consistent with the alternative standards investigated for other criteria 15 pollutants (e.g., NO 2 (US EPA, 2008c); and SO 2 (US EPA, 2009b)). The first of these potential 16 percentile forms considered the 99 th percentile daily maximum 8-hour average CO 17 concentrations, while the second c onsidered the same form though with a 1-hour averaging time. 18 Details regarding the concentration adjustments asso ciated with each of the current and potential 19 alternative standards are pr ovided in section 5.7.3. 20

5.7 AMBIENT AIR QUALITY DATA 21

5.7.1 Unadjusted 1-Hour Ambient Concentrations 22 Ambient monitoring data serve as an impor tant input in estimating CO exposure and 23 dose. Descriptive statistics were generated fo r the hourly CO concentrations measured at the 24
identified ambient monitors in each location an d monitoring year (Tables 5-7 to 5-10). As 25 expected, CO concentrations are about a factor of two or greater when comparing the high 26 concentration year (1995 or 1997) to the more rece nt year (2006) of ambient monitoring data in 27 either location. In general, there is similarity in the concentration dist ribution for both locations 28 within a given year, with the following exceptions. There is one monitor in Los Angeles (ID 37- 29 1301) reporting exceptionally high concen trations at each of the per centiles of the distribution in 30 this location when compared with the other Los Angeles monitors for either year. In addition, 31

4 The controlling standard by definition would be the st andard that allows air quality to have either a 2 nd highest 8- hour average concentration of ≤ 9.4 ppm (i.e., the 8-hour standard is the controlling standard) or to have a 2 nd highest 1-hour concentration of ≤ 35.4 ppm (i.e., the 1-hour standard is the controlling standard).February 2010 Draft – Do Not Quote or Cite 5-12 there is a sharper rate of increase in the upper percentile c oncentrations (i.e., ≥ 95 th percentiles) in 1 Denver when compared with the Los Angeles ambi ent concentration distri bution, for either year. 2

5.7.2 Method for Estimating of Missing 1-Hour Ambient Concentrations 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 four 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 six 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 available 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 regression models were 16 available for a particular time-of-day) and the gap was less than nine hours, the missing 17 values were estimated by linear interpolati on between the valid values at the ends of 18 the gap. 19 4) All remaining missing values were substitute d with the 1-hour concentration from the 20 same day and hour as the nearest monitor. The hourly concentration used was 21 normalized to the respective monitors ’ monthly mean concentrations. 22

Tables 5-7 to 5-10 provide the descriptive stat istics for 1-hour CO concentrations in each 23 data set, before and after estimating missing va lues. The agreement between these statistics 24
indicates that the addition of the estimated mi ssing-value concentrations did not significantly 25
affect the overall distribution of the hourly CO concentrations in either year or location. 26February 2010Draft – Do Not Quote or Cite5-13Table 5-7.Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing1values – Denver 1995.2CO concentration (ppm)1-hour values(n)PercentileMonitorIDMissingvaluesfilled?Present MissingMean SD Min25th50th75th90th95th99th99.9thMaxNo 8697 63 1.50 1.20 0.0 0.8 1.2 1.8 2.7 3.4 6.1 13.1 24.531-0002Yes 8760 0 1.50 1.20 0.0 0.8 1.2 1.8 2.7 3.4 6.1 13.1 24.5No 8647 113 1.25 1.08 0.1 0.6 0.9 1.5 2.5 3.4 5.5 8.9 14.631-0013Yes 8760 0 1.25 1.08 0.1 0.6 0.9 1.5 2.5 3.4 5.5 8.8 14.6No 8701 59 1.09 1.05 0.0 0.5 0.7 1.3 2.3 3.2 5.3 7.7 10.431-0014Yes 8760 0 1.09 1.05 0.0 0.5 0.7 1.3 2.3 3.2 5.3 7.8 10.4No 8680 80 0.96 0.93 0.1 0.4 0.6 1.1 2.0 2.7 4.8 7.5 11.959-0002Yes 8760 0 0.96 0.93 0.1 0.4 0.6 1.1 2.0 2.7 4.8 7.5 11.9Table 5-8.Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing3values – Denver 2006.4CO concentration (ppm)1-hour values(n)PercentileMonitorIDMissingvaluesfilled?Present MissingMean SD Min25th50th75th90th95th99th99.9thMaxNo 8672 88 0.62 0.39 0.0 0.4 0.5 0.7 1.0 1.3 2.2 4.1 6.431-0002Yes 8760 0 0.62 0.39 0.0 0.4 0.5 0.7 1.0 1.3 2.1 4.1 6.4No 8635 125 0.49 0.36 0.0 0.3 0.4 0.6 0.9 1.2 1.8 3.4 4.431-0013Yes 8760 0 0.49 0.36 0.0 0.3 0.4 0.6 0.9 1.2 1.8 3.4 4.4No 8557 203 0.47 0.38 0.0 0.3 0.4 0.5 0.9 1.2 2.0 3.1 3.931-0014Yes 8760 0 0.47 0.38 0.0 0.3 0.4 0.5 0.9 1.2 2.0 3.1 3.9No 8603 57 0.40 0.37 0.0 0.2 0.3 0.5 0.8 1.1 1.9 2.8 3.659-0002Yes 8760 0 0.40 0.37 0.0 0.2 0.3 0.5 0.8 1.1 1.9 2.8 3.6February 2010Draft – Do Not Quote or Cite5-14Table 5-9.Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing1values – Los Angeles 1997.2CO concentration (ppm)1-hour values(n)PercentileMonitorIDMissingvaluesfilled?Present MissingMean SD Min25th50th75th90th95th99th99.9thMaxNo 8360 400 0.84 0.86 0.0 0.2 0.6 1.2 2.0 2.6 3.7 5.1 7.337-0113Yes 8760 0 0.84 0.85 0.0 0.2 0.6 1.2 2.0 2.6 3.6 5.1 7.3No 8025 735 1.75 1.27 0.0 0.9 1.4 2.2 3.5 4.5 6.1 7.8 8.837-1002Yes 8760 0 1.73 1.24 0.0 0.9 1.4 2.1 3.5 4.4 6.0 7.7 8.8No 8292 468 1.36 1.19 0.0 0.5 0.9 1.9 3.1 3.9 5.4 7.2 8.937-1103Yes 8760 0 1.36 1.17 0.0 0.5 1.0 1.9 3.0 3.8 5.4 7.1 8.9No 8245 515 1.15 1.25 0.0 0.4 0.7 1.5 2.8 3.8 6.0 8.4 11.737-1201Yes 8760 0 1.17 1.24 0.0 0.4 0.7 1.5 2.8 3.8 5.9 8.3 11.7No 8302 458 2.35 2.19 0.0 1.1 1.7 2.8 4.9 6.8 11.3 17.2 19.237-1301Yes 8760 0 2.34 2.17 0.0 1.1 1.7 2.8 4.9 6.7 11.2 17.2 19.2No 8250 510 1.11 0.84 0.0 0.6 0.9 1.4 2.1 2.8 4.2 6.1 8.137-2005Yes 8760 0 1.10 0.83 0.0 0.6 0.9 1.4 2.1 2.8 4.2 6.0 8.1No 8347 413 1.11 1.10 0.0 0.4 0.7 1.3 2.7 3.6 5.2 7.3 9.037-4002Yes 8760 0 1.11 1.11 0.0 0.4 0.7 1.4 2.7 3.6 5.2 7.2 9.0No 8354 406 1.11 0.91 0.0 0.6 0.8 1.4 2.3 2.9 4.6 6.9 8.459-0001/7Yes 8760 0 1.11 0.90 0.0 0.6 0.8 1.4 2.3 2.9 4.6 6.9 8.4No 8325 435 0.74 1.01 0.0 0.2 0.3 0.9 2.1 3.0 4.7 6.3 7.359-1003Yes 8760 0 0.74 1.00 0.0 0.2 0.3 0.9 2.1 3.0 4.6 6.2 7.3No 8230 530 1.36 1.21 0.0 0.6 1.0 1.7 2.8 3.7 6.2 9.9 11.959-5001Yes 8760 0 1.36 1.19 0.0 0.6 1.0 1.7 2.8 3.7 6.2 9.9 11.93February 2010 Draft – Do Not Quote or Cite5-15Table 5-10.Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing1values – Los Angeles 2006.2CO concentration (ppm)1-hour values(n)PercentileMonitorMissingvaluesfilled?Present MissingMean SD Min25th50th75th90th95th99th99.9thMaxNo 8365 395 0.42 0.37 0.0 0.2 0.3 0.6 0.9 1.2 1.7 2.5 2.937-0113Yes 8760 0 0.43 0.37 0.0 0.2 0.3 0.6 0.9 1.2 1.7 2.5 2.9No 8345 415 0.67 0.61 0.0 0.3 0.5 0.8 1.5 2.0 2.9 4.0 4.337-1002Yes 8760 0 0.67 0.61 0.0 0.3 0.5 0.8 1.5 2.0 2.9 3.9 4.3No 8265 495 0.55 0.50 0.0 0.2 0.4 0.7 1.3 1.6 2.3 2.9 3.537-1103Yes 8760 0 0.56 0.50 0.0 0.2 0.4 0.8 1.3 1.6 2.2 2.9 3.5No 8375 385 0.55 0.54 0.0 0.2 0.4 0.6 1.2 1.7 2.7 3.8 4.837-1201Yes 8760 0 0.56 0.53 0.0 0.2 0.4 0.7 1.2 1.7 2.7 3.7 4.8No 8275 485 1.00 0.89 0.0 0.5 0.7 1.1 2.0 2.9 4.7 6.9 8.437-1301Yes 8760 0 1.01 0.90 0.0 0.5 0.7 1.1 2.0 2.9 4.6 6.8 8.4No 8258 502 0.73 0.49 0.0 0.4 0.6 1.0 1.4 1.7 2.4 3.2 4.137-2005Yes 8760 0 0.73 0.49 0.0 0.4 0.6 1.0 1.3 1.7 2.4 3.1 4.1No 8216 544 0.74 0.55 0.0 0.4 0.6 0.9 1.5 1.9 2.7 3.7 4.237-4002Yes 8760 0 0.75 0.54 0.0 0.4 0.6 0.9 1.5 1.9 2.7 3.7 4.2No 8342 418 0.43 0.47 0.0 0.1 0.3 0.5 1.0 1.4 2.3 3.4 4.559-0001/7Yes 8760 0 0.43 0.47 0.0 0.1 0.3 0.5 1.0 1.4 2.3 3.4 4.5No 8358 402 0.33 0.45 0.0 0.1 0.1 0.4 0.9 1.4 2.1 3.1 3.559-1003Yes 8760 0 0.33 0.45 0.0 0.1 0.1 0.4 0.9 1.4 2.1 3.0 3.5No 8227 533 0.64 0.57 0.0 0.3 0.4 0.7 1.3 1.8 3.0 4.7 6.059-5001Yes 8760 0 0.64 0.56 0.0 0.3 0.4 0.7 1.3 1.8 2.9 4.6 6.0345February 2010 Draft – Do Not Quote or Cite 5-16 5.7.3 Adjusted 1-Hour Ambient Concentrations 1 In addition to modeling exposures based on recent as is air quality (i.e., ambient 2 monitoring data for year 2006), exposures and re sulting dose were estimated for air quality 3 conditions that just meet the current 8-hour CO NAAQS and the potential al ternative standards. 4 Because CO concentrations in recent years we re significantly lower than the current NAAQS, 5
staff first selected an earlier year for each city (1995 for Denve r and 1997 for Los Angeles) to 6 represent air quality conditions that were near th e current 8-hour CO standard. Consistent with 7 the data adjustment approach employed in th e previous draft CO exposure assessment (Johnson 8 et al., 2000), and approaches used in prio r REAs supporting other pollutant NAAQS reviews 9 (e.g., US EPA, 2008c; US EPA, 2009b), staff conc luded (1) that the po licy-relevant background 10 levels of CO were negligible in each area (sec tion 3.1.4), and (2) that the fixed-site monitoring 11 data could be adjusted to simulate just mee ting the current CO standards by use of a simple 12 proportional adjustment of all hourly values (section 3.1.5). Consequently, the following 13 adjustment equation was employed: 14
15
CO adj ( m , h ) = (NAAQS/DV) x CO( m , h ). (5-1) 16
17 CO( m , h ) is the 1-hour CO concentration at hour h for monitor m . It follows that CO adj ( m , h ) is 18 the adjusted CO concentration for hour h at monitor m through the use of the specific design 19 value (DV) for monitor m . Although the current 8-hour NAAQS for CO specifies a maximum 20 concentration of 9 ppm, which is not to be ex ceeded more than one time in a year, the NAAQS 21 term in Equation 5-1 is equivalent to 9.4 ppm due to the application of a standard data rounding 22
convention used in calculati ng design values (DVs) for CO. 5 23 The DVs for Denver (1995) and for Los Angeles (1997) were 9.5 ppm and 15 ppm, 24 respectively. The Denver DV is calculated as the second-highest 8-hour average CO 25 concentration reported by monitor ID 080310002 for 1995. The adjustment factor (or 26 NAAQS/DV) that was applied equa lly to all 8,760 hourly ambient CO concentrations at that 27 monitor is thus 9.4/9.5, or 0.989. In a similar mann er, the DV used in Los Angeles is the second28 highest 8-hour average CO concentration reported at monitor ID 060371301 for 1997, giving an 29 ambient concentration adjustment factor of 9.4/15, or 0.627 which was applied equally to all 30 8,760 hourly ambient CO concentrations from the Los Angeles monitor. 31

5 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 NAAQS, the design value is the highest annual second maximum non-overlapping 8- hour concentration during the most recent two years. The design value for the 1-hou r CO NAAQS is the highest annual second maximum 1-hour concentration during the most recent two years. The latest update (2007-2008) on the CO design values can be found at: http://www.epa.gov/airtrends/pdfs/dv_co_2006_2008.pdfFebruary 2010 Draft – Do Not Quote or Cite 5-17 Staff evaluated three additional air quality scenarios considering potential alternative 1 standard levels, averaging times, and forms. A ssuming a similar form and averaging time of the 2 current 8-hour standard (2 nd highest non-overlapping 8-hour av erage CO concentration), staff 3 selected a level of 5 ppm for the fi rst potential alternative standard. 6 As was done for other 4 recent NAAQS reviews (US EPA, 2008c; US EPA, 2009b), staff selected percentiles of the air 5 quality distribution and averaging times to identifyi ng potential levels associated with alternative 6 standards. The second potential alternative standard considered by staff also uses an 8-hour 7 average concentration, though having a 99 th percentile daily maximu m CO concentration of 5.0 8

ppm. 7 The final potential alternative sta ndard that staff evaluated was a 99 th percentile daily 9 maximum 1-hour CO concentration of 8.0 ppm. Table 5-11 summarizes the adjustment factors 10 that were developed from equati on 5-1 and used to adjust the hi gh concentration year air quality 11 data in each study area. 12

Table 5-11. Design values and adjustment factor s used to represent air quality just 13 meeting the current and potentia l alternative standards. 14

Standard
Study Area Averaging Time Form Level (ppm) Design Value a (ppm) Adjustment
Factor

9 0.989 b 2 nd highest 5 9.5 0.568 8-hour
99 th pct daily max 5.0 7.3 0.685 Denver

1-hour 99 th pct daily max 8.0 13.5 0.593
9 0.627 b 2 nd highest 5 15 0.360 8-hour
99 th pct daily max 5.0 13.1 0.380 Los Angeles

1-hour 99 th pct daily max 8.0 18.5 0.432
Notes:

a All design values were obtained from monitor ID monitor ID 080310002 in Denver (1995 data) and monitor ID 060371301 in Los Angeles (1997 data). b Adjustment factor for just meeting t he current 8-hour average CO standard. 15
16
17

6 Note that this would allow a 2 nd highest non-overlapping 8-hour concentration up to 5.4 ppm (hence the design value).

7 It was assumed that there are an infinite number of zeros, that is, the level is exactly 5.0 ppm. This rounding convention applies to the other potential alternative standard selected, the level is exactly 8.0.February 2010 Draft – Do Not Quote or Cite 5-18 Table 5-12 and 5-13 provides the descriptive statistics for the Denver and Los Angeles 1 ambient monitor 1-hour CO concentrations, respectively, after applying the appropriate 2 adjustment factor to simulate just meeting th e current standard. As expected, the adjusted 3 monitoring concentrations for De nver 1995 are very similar to the unadjusted data set given that 4 the adjustment factor used was close to unity. For example, the maximum concentration at the 5 design monitor was reduced from 24.5 ppm to 24.2 pp m. The change in CO concentrations was 6 much greater in Los Angeles compared with that of Denver as a result of differences in the 7 adjustment factor used in each study area. For example, the maximum CO concentration at the 8 design monitor in Los Angeles was reduced from 19.2 ppm to 12.0 ppm. Considering the 9 patterns described above in se ction 5.7.1 for the unadjusted air quality and given that the 10 concentration adjustment was proportional, additional remarks can be made regarding 11 differences in the air quality adjusted to ju st meet the current 8-hour CO NAAQS. When 12 comparing the adjusted concentrations in Denver a nd Los Angeles, there is still a sharper rate of 13
increase in CO concentra tions at and above the 95 th percentiles of the distribution, only now all 14 of the Denver monitors have greater CO con centrations at these upper percentiles when 15 compared with concentrations observed at a ll of the Los Angeles monitors (excluding 16 concentrations at the Los Angeles design monitor). 17 Given the proportional approach used to adju st ambient concentrations for each of the 18 other exposure scenarios (e.g., 99 th percentile daily maximum 1- hour concentration of 8.0); 19 similar patterns in concentrations were e xpected and are therefor e not summarized here. 20February 2010 Draft – Do Not Quote or Cite5-19Table 5-12.Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just meet the current 8-1hour standard – Denver (adjusted 1995 data).2Hourly-average CO concentration (ppm)MonitorID Mean SD 25.0 50.0 75.090.0 95.0 99.0 99.5 99.9 MaxDV(ppm)31-0002 1.5 1.2 0.8 1.2 1.8 2.7 3.4 6.0 7.6 13.0 24.2 9.431-0013 1.2 1.1 0.6 0.9 1.5 2.5 3.4 5.4 6.4 8.7 14.4 6.131-0014 1.1 1.0 0.5 0.7 1.3 2.3 3.2 5.3 6.4 7.7 10.3 5.859-0002 1.0 0.9 0.4 0.6 1.1 2.0 2.7 4.8 5.7 7.4 11.8 4.53Table 5-13.Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just meet the current 8-4hour standard – Los Angeles (adjusted 1997 data).5Hourly-average CO concentration (ppm)MonitorID Mean SD 25.0 50.0 75.090.0 95.0 99.0 99.5 99.9 MaxDV(ppm)37-0113 0.5 0.5 0.1 0.4 0.8 1.3 1.6 2.3 2.6 3.2 4.6 2.637-1002 1.1 0.8 0.6 0.9 1.3 2.2 2.8 3.8 4.1 4.8 5.5 4.537-1103 0.9 0.7 0.3 0.6 1.2 1.9 2.4 3.4 3.6 4.5 5.6 3.737-1201 0.7 0.8 0.3 0.4 0.9 1.8 2.4 3.7 4.3 5.2 7.3 4.837-1301 1.5 1.4 0.7 1.1 1.7 3.1 4.2 7.0 8.5 10.8 12.0 9.437-2005 0.7 0.5 0.4 0.6 0.9 1.3 1.8 2.6 2.9 3.8 5.1 3.437-4002 0.7 0.7 0.3 0.4 0.8 1.7 2.3 3.3 3.7 4.5 5.6 4.059-0001 0.7 0.6 0.4 0.5 0.9 1.4 1.8 2.9 3.4 4.3 5.3 3.459-1003 0.5 0.6 0.1 0.2 0.6 1.3 1.9 2.9 3.2 3.9 4.6 3.159-5001 0.9 0.7 0.4 0.6 1.1 1.8 2.3 3.8 4.5 6.1 7.5 3.6February 2010 Draft – Do Not Quote or Cite 5-20 5.8 METEOROLOGICAL DATA 1 A few algorithms within APEX require meteorol ogical data (primarily temperature) from 2
stations located within the study area. For example, in sel ecting a CHAD diary to simulate an 3 individual’s daily activities, a ra nge of daily maximum temperatures is used to categorize diaries 4 for sampling purposes so as to best match the temperature observed on the simulation day within 5 the study area (section 5.5.4). In addition, mean te mperatures are used by APEX to select from 6 an appropriate air exchange rate distribu tion to estimate indoor microenvironmental 7 concentrations (section 5.8). Fo r the analyses described in this report, hourly temperature data 8 were obtained from meteorological stations located at or near the fixed-site CO monitor specified 9 for each study area. 10 Tables 5-14 and 5-15 list the meteorological st ations staff selected for use in modeling 11 the Denver and Los Angeles study areas, respectivel y. Ideally, staff would have used the same 12 station (Long Beach: 37-4002) matched for bot h monitoring years (1997 and 2006) in Los 13 Angeles. Because this station did not repor t a complete year of data for 1997, we have 14 substituted data reported by the Long Beach Da ugherty Field station located approximately 3.6 15 km from the 37-4002 station. The same two stations (31-0002 and 59-0002) will be used for the 16 Denver study area for 1995 and Denver 2006, because there were adequate data for both years 17
for both sites. 18 To run APEX, a “ZoneRadius” is specified by the user as the maximum radius for the 19 region surrounding each meteorological station that wi ll be represented by the temperature data 20 provided by the station. In this a ssessment, staff set this at a valu e that includes all census tracts 21
within the air districts. A radius of 15.5 km met this requi rement for Denver (Figure 5-1), while 22 Los Angeles required a larger radi us of 70.5 km (Figure 5-2). 23

Table 5-14. Locations of meteorological st ations selected for Denver. 24

Monitoring Year Meteorological station Location coordinates 1995 2006

Monitor ID County Latitude Longitude 1-hour values

(n) Mean temp

( ◦ F) 1-hour values

(n) Mean temp

( ◦ F)
31-0002 Denver 39.751184 -104.987625 8742 53.3 8749 55.2
59-0002 Jefferson 39.800333 -105.099973 8702 49.7 8758 51.5 25February 2010 Draft – Do Not Quote or Cite 5-21 1

Table 5-15. Locations of meteorological stat ions selected for Los Angeles. 2

Monitoring Year Meteorological station Coordinates 1997 2006

Monitor ID County Latitude Longitude 1-hour values

(n) Mean temp

( ◦ F) 1-hour values

(n) Mean temp

( ◦ F) Daugherty Field Long Beach 33.81667 -118.15 8751 65.8 -- --

37-4002 Long Beach 33.82376 -118.18921 -- -- 8759 63.8

5.8.1 Method for Estimating of Miss ing 1-Hour Temperature Data 3 APEX also requires a complete (full) meteorolog ical data set to run properly. In 4 checking the meteorological data for completeness, staff noted all stations and years had at least 5 one missing hourly value for temperature (Tables 5- 14 and 5-15). To generate the full year of 6 temperature data set, we estimated the missing values for the selected meteorological (MET) 7 stations in Denver and Los Angeles as follows. 8 For the Denver study area, staff selected tw o MET stations for use in 1995 and 2006. All 9 missing values in year 2006 were filled using li near interpolation. For the missing values in 10 1995, staff used linear interpolation to fill in short gaps. Where there were long gaps in the data 11 (e.g., more than 16 continuous hours of missing va lues), linear interpolation was judged as 12 inappropriate because this method would likely no t produce reasonable estimates of the potential 13 variability in temperature (particularly the daily maximum) that might occur during this gap. In 14 these instances, staff applied an alternative a pproach whereas the average temperature of the 15 previous day and the latter day were averaged and then substituted for the corresponding hours. 16 For example, if the temperature data wa s missing from 1AM to 11pm on 2/8/1995, staff 17 averaged the hourly temperature of 2/7/1995 and 2/9/1995 for 1AM, 2AM ..., 11PM to fill the 18 missing hours (all eleven hours have an individual value). 8 19 For Los Angeles, staff evaluated the two sites noted here as site 1 (ID 037-4002) and site 20 2 (located at Daugherty Field). Both locations reported temperatur e in both years of interest; 21 however, the degree of completeness for each varie d. Given their close proximity to one another 22 (3.6 km), staff decided that a complete data set would be best generated by using a composite of 23 the two monitors, using the monitor with the gr eatest number of measurements as the primary 24

8 Calculating the average temperature using this method does not apply if 1) the long gap occurs on January 1 or December 31, or if 2) the temperatur e data in the previous day or the latter day are not available. In such cases, we used the non-missing values in the previous day or the latter day, whichever was available.February 2010 Draft – Do Not Quote or Cite 5-22 data set. Because site 1 had fewer missing values than site 2 for 2006, site 1 was selected as the 1 primary meteorological site to represent the Los Angeles area for that year. For the one missing 2 value on Site 1 in 2006, the corresponding temperatur e from Site 2 was used to fill the missing 3 value for 2006. For 1997, there were 2,263 missing va lues on Site 1 while only 9 missing values 4 on Site 2. As a result, Site 2 was selected by st aff as the primary meteor ological station for 1997. 5 Two of the nine missing values from Site 2 we re available from Site 1. Therefore, these 6 temperatures were directly subs tituted with values from the corresponding hours of the Site 1 7 data set. To fill the remaining seven missing va lues, we used linear interpolation by connecting 8 successive straight line segments and fitting a continuous curve to the data. 9 9 The temperature distributions before and af ter filling missing values were compared at 10 for each station in each year to assess the impact (if any) of the substitution method. Given the 11 limited number of missing values in the original data sets, there were negligible differences when 12 comparing mean, median, varian ce and percentile statistics. 13

5.9 MICROENVIRONMENTS MODELED 14 This section briefly discusse s the approach and specific factors used to estimate CO 15
microenvironmental concentrations in the curr ent assessment. As described in section 4.4.4.3, 16 the approach was originally developed for pNEM /CO and used the previous assessment (Johnson 17 et al., 2000). 18

5.9.1 The Micronenvironmental Model as Implemented by APEX4.3 19 Section 8.2.2 of US EPA (2008b) indicates that the mass balance model in APEX4.3 20 models the portion of outdoor air that enters the microenvironment as 21
22
CO out = f proximity x f penetration x CO ambient (5-2) 23
24 Since this is effectively equivalent to th e method used by APEX3.1 described in section 25 4.4.4.4, we used the same method here with resp ect to application of the proximity and 26 penetration factors in APEX4.3 to implement equa tion 4-11. First, to obt ain the appropriate CO 27 concentrations outside each microenvironment, ambient CO concentration were adjusted by an 28 exponential factor of 0.621 (see equation 4-22 ). Then for each profile, a value for f proximity term 29 would be sampled for each microenvironment fr om a lognormal distribution with geometric 30 mean (GM) equal to M(m) and geometric standard deviation (GSD) equal to 1.5232. A value for 31 f penetration for each hour would also be sampled from a lognormal distribution with geometric 32 mean (GM T ) equal to 1.0 and geometric standard deviation (GSD T ) equal to 1.6289. 33

9 This was done in SAS using a procedure “PROC EXPAND” with “JOIN” option.February 2010 Draft – Do Not Quote or Cite 5-23 Table 5-16 presents the algorithm parameters proposed for the eight microenvironments 1 currently defined for the app lication of APEX to Los Ange les and Denver. These eight 2
microenvironments were selected rather than the fifteen selected in earlier assessments (see REA 3 Table 4-4 and 4-5) based on the locations having the same proximity factors and air exchange 4 rates distributions, or when using a similar mi croenvironmental approach (see section 5.9.5). 5 Note that when this algorithm is impl emented within the APEX framework, the 6 application of Equation 4-11 produces a “comp ression” effect in which the ratio of CO out to 7
CO mon tends to become smaller (on average) as CO mon increases. This effect is consistent with 8 data reported by field studies such as Wilson, Colome, and Tian (1995) which have compared 9 outdoor concentrations with simultaneously measured fixed-site concentrations. 10February 2010 Draft – Do Not Quote or Cite 5-24 Table 5-16. Parameters of Bounded Lognormal Di stributions Defined for Proximity 1 Factors to be Used in the Proposed Application of APEX4.3 to Los 2 Angeles and Denver. 3

Microenvironment Parameters of bounded lognormal distribution for proximity factor

Code General location Specific location Activity diary locations included in microenvironment GM GSD Minimum (5 th pct) Maximum (95 th pct)
1 Indoors Residence Indoors - residence 1.034 1.5232 0.5175 2.0661
2 Indoors Service station and auto repair Service station Auto repair 2.970 1.5232 1.4864 5.9345

3 Indoors Other indoor locations A Other repair shop Shopping mall Other indoor location Auditorium Store Office Other public building Bars Restaurants 1.213 1.5232 0.6071 2.4237

4 Indoors Other indoor locations B Health care facility School Church Manufacturing facility 0.989 1.5232 0.4950 1.9762

5 Outdoors Near road locations Bus stop Bicycle Motorcycle Other near road 1.607 1.5232 0.8042 3.2110

6 Outdoor Public parking or fueling facility Indoor parking garage Outdoor parking garage Outdoor parking lot Outdoor service station 2.970 1.5232 1.4864 5.9345

7 Outdoors Other outdoor locations Outdoor res. garage Construction site Residential grounds School grounds Sports arena Park or golf course Other outdoor 1.436 1.5232 0.7187 2.8693

8 Vehicle Automobile and mass transit Automobile Truck Bus Train/subway Other vehicle 3.020 1.5232 1.5114 6.0344

4February 2010 Draft – Do Not Quote or Cite 5-25 5.9.2 Microenvironmental Mapping 1 In APEX, microenvironments repr esent the exposure locations for simulated individuals. 2 For exposures to be estimated accu rately, it is important to have realistic microenvironments that 3 match closely to the locations wher e actual people spend time on a daily basis. It is necessary to 4 map the CHAD location codes to one of the eight specific microenvironments selected for this 5 exposure assessment or to a supplemental category (e ither -1 or 0). As a reminder, these eight 6 microenvironments were selected based on having suitable data to use for proximity factors and 7 air exchange rates (when using a mass balance approach). The -1 code is assigned to events 8
where the location code is missi ng (X) or the location is classi fied as uncertain (U); the -1 code 9
instructs APEX to use the last known microenvironment for that pe rsons diary in determining the 10 exposure concentration. The 0 code is assigned to an ai rplane microenvironment (CHAD 11 location code: 31160) and instructs APEX to set th e exposure concentration equal to 0 ppm. See 12 Appendix D Figure D-1 that describes th e specific mapping of CHAD codes to 13
microenvironments. 14 The microenvironment mapping file also perm its the user to assign a home/work/other 15 (H/W/O) location to each CHAD location code. The home/work/other location determines the 16 source of the hourly-average monitoring data that will represent the ambi ent CO concentration 17 for the microenvironment: the home district monitor, the work district monitor, or other. 18 The initial APEX assignments of H/W/O to the CHAD location codes were used as a 19 starting point (see Appendix D Figure D-1) and modi fied using a few of th e options available in 20 APEX. First, staff overrode the H/W/O desi gnations listed in the microenvironment mapping 21 file for selected activities by compiling a list of CHAD activity codes that will always be 22 associated with the work district (regardless of th e CHAD location code). This list is inserted in 23 the “CustomWork” parameter found in the simulati on control file. The default list of work 24 activity codes, which were used in this a pplication, includes code s 10000 through 10300 (see 25 Appendix D Table D-1). As a result of using this option, APEX will assign the simulated person 26
to the work district whenever the activity code falls between 10000 and 10300. This assignment 27 will override the home/work/assignment a ssociated with the applicable CHAD location code

. 28 There will still be exposure events in which the simulated person is assigned to the 29
“other” location. In the default mode, APEX uses an average of all monitor values to determine 30
the ambient concentration for these events. Note that this averaging approach will tend to 31 smooth the data; that is, it will produce ambient CO concentrations that have slightly less 32 variance than a comparable set of ambient concen trations obtained from a single monitor. To 33 avoid this effect, staff chose to specify the opti on OtherDistricts = 1, so that only one monitor is 34 used to represent “other.” The monitor used in the model application is randomly selected from 35
the set of all monitors. 36February 2010 Draft – Do Not Quote or Cite 5-26 5.9.3 Selection of Microenvironmental Method Used 1 As discussed in chapter 4, the two methods available in APEX for calculating pollutant 2

levels within microenvironments are mass balan ce or a factors approach. Table 5-17 lists the 3

microenvironments used in this st udy and the calculation method used. 4

Table 5-17. List of microenvironments modele d and calculation methods used. 5

Microenvironment

Code Location Name Calculation
Method

1 Indoors Residence Mass balance
2 Indoors Service station and auto repair Mass balance

3 Indoors Other indoor locations A Mass balance

4 Indoors Other indoor locations B Mass balance

5 Outdoors Near road locations Factors
6 Outdoor Public parking or fueling facility Factors

7 Outdoors Other outdoor locations Factors

8 Vehicle Automobile and mass transit Factors
6

5.9.4 Air Exchange Rates and Air Conditioning Prevalence 7 For the microenvironments using the mass balance method (i.e., all indoor 8 microenvironments), air exchange rate (AER) and air conditioning prevalence data are needed to 9

estimate microenvironmental concentrations. Ai r exchange rate data used for the indoor 10

residential microenvironment were the same used in APEX for the most recent O 3 NAAQS 11 review (US EPA, 2007). As part of that earlie r review, AER data were reviewed, compiled and 12 evaluated from the extant literature to generate location-specific AER distributions 10 categorized 13 by influential factors, namely temperature a nd presence of air conditioning. In general, 14 lognormal distributions provided th e best fit, and are defined by a geometric mean (GM) and 15 standard deviation (GSD). To avoid unusua lly extreme simulated AER values, bounds of 0.1 16

and 10 were selected for mini mum and maximum AER, respec tively. Tables 5-18 and 5-19 17

10 There were AER measurement data specific to the Lo s Angeles study area; these were used by US EPA (2007) to develop AER distributions. Denver was not a location of interest in US EPA (2007); therefore there were no Denver-specific AER developed for this study area. Consistent with what was done in US EPA (2007) for cities not having location-specific AER data available, the composite AER distributions developed using data from cities outside California were applied in this study to Denver.February 2010 Draft – Do Not Quote or Cite 5-27 summarize the AER distributions used in m odeling indoor exposures, classified by A/C 1 prevalence and temperature categories. For all other indoor microenvironments, the AER 2 distributions used here (Tables 5-18 and 5-19) were based data pr ovided by an indoor air quality 3 study (Persily et al., 2005). These are the same AER distributions used for the APEX 4

assessments in the most recent O 3 NAAQS review (US EPA, 2007), NO 2 REA (EPA, 2008c) and 5

SO 2 REA (US EPA, 2009b). 6

Because the selection of an ai r exchange rate distribution is conditioned on the presence 7 or absence of an air-conditioner (A/C), the air conditioning status of the residential 8

microenvironments in each modeled area is si mulated randomly using the probability that a 9

residence has an air conditioner. A value of 55% was used to represent the A/C prevalence rate 10 in Los Angeles, based on data obtained from US EPA (2007). For Denver, residential A/C 11 prevalence was estimated to be 69% of home s, a value obtained from AHS (2005). Air 12 conditioning prevalence is noted as being distinct from usage rate, the latter being represented by 13 the air exchange rate distribution and is dependent on temperature. 14

Table 5-18. Lognormal distributions of indoor air exchange rates used in Los 15 Angeles. 16

Classification category Parameters of bounded lognormal distribution a
Microenvironment A/C present? Mean Temp (degrees F) GM GSD Minimum Maximum

≤ 50 0.589 1.894 0.1 10.0
50 – 67 0.589 1.894 0.1 10.0
68 – 76 1.100 2.365 0.1 10.0
77 – 85 0.813 2.415 0.1 10.0 Yes b

86+ 0.266 2.790 0.1 10.0
< 50 0.543 3.087 0.1 10.0
50 – 67 0.747 2.085 0.1 10.0
68 – 76 1.372 2.283 0.1 10.0
77 – 85 0.988 1.967 0.1 10.0 Indoors - residence

No

86+ 0.988 1.967 0.1 10.0
Indoors - other - - 1.109 3.015 0.1 c 10.0 c

Notes:

a Obtained from Table D-4 of US EPA (2007). b Estimated air conditioning prevalence rate for Los Angeles = 55 percent (see page 47 and Table A-3 of US EPA, 2007).

c Assumed here to be consistent with other approximated lower and upper bounds.February 2010 Draft – Do Not Quote or Cite 5-28 Table 5-19. Lognormal distributions of indoor ai r exchange rates used in Denver. 1

Classification category Parameters of bounded lognormal distribution a
Microenvironment A/C present? Mean Temp (degrees F) GM GSD Minimum c Maximum c

≤ 50 0.9185 1.8589 0.1 10.0

50 – 68 0.5636 1.9396 0.1 10.0

68 – 77 0.4676 2.2011 0.1 10.0

77 – 86 0.4235 2.0373 0.1 10.0 Yes b

86+ 0.5667 1.9447 0.1 10.0

≤ 50 0.9258 2.0836 0.1 10.0

50 – 68 0.7333 2.3299 0.1 10.0 Indoors - residence

No

68+ 1.3782 2.2757 0.1 10.0

Indoors - other - - 1.109 3.015 0.1 10.0
Notes:

a Obtained from Table D-4 of US EPA (2007) and derived from locations outside California. b Estimated air conditioning prevalence rate for Denver = 69% (see Table 1-4 in AHS, 2005).

c Assumed here to be consistent with other approximated lower and upper bounds. 2

5.10 KEY OBSERVATIONS 3 The following presents the key observations for this chapter: 4

 Two exposure model domains (Denver and Lo s Angeles study areas) were defined by 5 overlaying ambient monitor locations having 10 km rad ii with US census tract 6 population data. Monitors selected comprised the bulk of the urban core in each 7 location, where ambient m onitoring data exist. 8

 The selected at-risk population was simulate d by combining the tract-specific age and 9 gender population distribution and the CHD prevalence, also stratified by age and 10 gender. In using this approach, staff can re present the variability that exists in the 11 CHD population that resides in each census tract and within each study area. 12

 Staff expanded the selected at-risk populati on to include an estimate of persons with 13 undiagnosed CHD. 14

 Compared with the single-monitor approach used for the first draft CO REA, staff 15 expanded the number of ambient monitors used in this second draft CO REA to better 16 capture the spatial variability in ambient c oncentrations. In Denve r, a total of four 17 monitors were used, in Los Angeles, the total number of monitors was ten. 18

 Compared with the two microenvironments mo deled in the first draft CO REA, staff 19 has expanded the number modeled in each location to eight. This approach is designed 20 to better represent the expected variability in microenvironmental CO concentrations. 21February 2010 Draft – Do Not Quote or Cite 5-29  Compared with the approach used to estim ate microenvironmental concentrations in 1 the first draft CO REA (factors approach only), all indoor microenvironments were 2 modeled using a mass balance model in this second draft assessment. Use of the mass 3 balance model will better repr esent temporal variability in indoor CO concentrations 4 with respect to the outdoor CO concentration variability. In addition, distributions of 5 microenvironmental factors were used in this second draft CO REA for all 6 microenvironments rather than using point estimates (as wa s done for the first draft CO 7 REA). Using distributions of microenvir onmental factors will better represent both 8 spatial and temporal variability in estima ted microenvironmental CO concentrations. 9February 2010 Draft – Do Not Quote or Cite 5-30 5.11 REFERENCES 1

2 AHS. (2005). American Housing Survey for th e Denver Metropolitan Area: 2004. Available at: 3 http://www.census.gov/prod/2005pubs/h170-04-46.pdf 4

Adams PF and Marano MA. (1995). Current Estimates from the National Health Interview Survey, 1994. National 5 Center for Health Statistics. Vital Health Stat. 10(193). 6

American Heart Association. (1990). Heart and Stroke Facts. American Heart Association, Dallas, TX. p.13. 7

CDC. (2009). Summary Health Statistics for U.S. Adu lts: National Health Interview Survey, 2007. Series 10, 8 Number 240. U.S. Department of Health an d Human Services, Hyattsville, MD, May 2009. 9

Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new meth od of longitudinal diary assembly for 10 human exposure modeling . J Expos Sci Environ Epidem . 18:299-311. 11

Isaacs K, McCurdy T, Errickson A, Fo rbes S, Glen G, Graham S, McCurdy L, Nysewander, M, Smith L, Tulve N, 12 Vallero D. (2009). Statistical properties of longitudi nal time-activity data for use in EPA exposure models. 13 Poster presented at the American Time Use Resear ch Conference; College Park MD, June 26, 2009. 14

Johnson TR. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Report No. EPA15 600/54-84-014, U. S. Environmental Protection Agency, Research Triangle Park, North Carolina. 16

Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 17 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 18 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 19 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 20
at: http://www.epa.gov/ttn/fera/human_related.html . June 2000. 21

Persily A, Gorfain J, Brunner G. (2005). Ventilati on design and performance in U.S. Office buildings. ASHRAE 22 Journal . April 2005, 30-35. 23

US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of 24 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 25 EPA/452/R-92-004. 26

US EPA. (2003). Total Risk Integrated Methodolog y TRIM.ExpoInhalation User's Document. Volume I: Air 27 Pollutants Exposure Model (APEX, version 3). Available at: 28 http://www.epa.gov/ttn/fer a/data/apex322/apexusersguidevoli4-24-03.pdf

. 29

US EPA. (2007). Ozone Population Exposure Analysis fo r Selected Urban Areas. O ffice of Air Quality Planning 30 and Standards, Research Triangle Park, NC. Ju ly 2007. EPA-452/R-07-010. Available at: 31 http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_07_o3_exposure_tsd.pdf

. 32

US EPA. (2008a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 33 (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report No. EPA-452/B-08-001a. Office of 34 Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 35

US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 36 (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report No. EPA-452/B-08- 37 001b. Office of Air Quality Planning and Standard s, U.S. Environmental Protection Agency, Research 38 Triangle Park, NC. 39February 2010 Draft – Do Not Quote or Cite 5-31 US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO 2 Primary National Ambient 1 Air Quality Standard. EPA-452/R-08-008a. November 2008. Available at: 2 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf

. 3

US EPA. (2009a). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary 4 National Ambient Air Quality Standard. First External Review Draft. EPA-45 2/P-09-008. Available at: 5 http://www.epa.gov/ttn/naaqs/standards/ co/data/COREA1stDraftOct2009.pdf 6

US EPA. (2009b). Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air 7 Quality Standard. EPA-452/R-09-007. August 2009. Available at 8 http://www.epa.gov/ttn/naaqs/standards/s o2/data/200908SO2REAFinalReport.pdf. 9

Wilson AL, Colome SD, Tian Y. (1995). California Resi dential Indoor Air Quality Study. Volume III: Ancillary 10 and Exploratory Analysis. Integrated Environmental Services, Irvine, California. 11February 2010 Draft – Do Not Quote or Cite 6-1 

### 6 SIMULATED EXPOSURE AND COHB DOSE RESULTS 1

This chapter summarizes the CO exposure and dose results for the Denver and Los 2 Angeles study areas that were generated using EPA’s APEX model descri bed in chapters 4 and 3 5. Staff considered exposures associated with five air quality scenarios; air quality (1) as is , (2) 4 adjusted to just meet the current 8-hour standa rd of 9 ppm, (3) adjust ed to just meet a 2 nd highest 5 8-hour average concentration of 5.4 ppm, (4) adjusted to just meet a 99 th percentile daily 6 maximum 8-hour average of 5.0 ppm, a nd (5) adjusted to just meet a 99 th percentile daily 7 maximum 8-hour average of 8.0 ppm. 8 The chapter is divided into five main secti ons. The first section (6.1) summarizes the 9 estimated exposures associated with each of the fi ve air quality scenarios. The simulated at-risk 10 population includes individuals with diagnosed CHD as well as t hose persons with potentially 11 undiagnosed CHD. 1 For simplicity, they will be combined and referred to as the CHD 12 population in this chapter. The exposure metrics of interest in this REA and generated by APEX 13 include the number and percent of persons at or above staff-selected ex posure levels and the 14 corresponding number of person-days. 2 Two exposure averaging tim es were also selected: 1- 15 hour and 8-hour daily maximum exposures. Se ction 6.2 summarizes the estimated COHb dose 16 levels for persons in the simulated at-risk popu lation residing in each study area. The dose 17 metric of interest in this REA and genera ted by APEX includes the number and percent of 18 persons at or above staff selected COHb leve ls and the corresponding number of person-days. 19 Consistent with prior CO exposure assessments , the daily maximum end-of-hour COHb level 20 was recorded. In section 6.3, staff compares th e dose estimates in this second draft CO REA 21 with those estimated in the 2000 exposure assessme nt (Johnson et al., 2000). The fourth section 22 (6.4) presents an evaluation of endogenous CO production for the APEX simulated individuals. 23 This includes analysis of the COHb ambient cont ribution attributed to am bient CO in a select 24 group of simulated persons. Fina lly, key observations are presented in the final section (6.5). As 25 mentioned in Chapter 1, exposure and risk re sults are provided here without substantial 26 interpretation. Rather, interpretative discussion of these results is provided in the CO Policy 27
Assessment. 28

1 As described in section 5.5 above, in characterizing the population of interest with regard to demographics (age and gender), the assessment drew from estimates of the prevalence of coronary heart disease (CHD) provided by the National Health Interview Survey (w hich includes CHD, angina pectoris and heart attack) and corresponding estimates of undiagnosed ischemia developed by EPA.

2 Because the duration of the exposure assessment is one year, there are opportu nities for individuals to experience more than one day in the year above a sel ected exposure concentration, hence use of the term persondays

.February 2010 Draft – Do Not Quote or Cite 6-2 6.1 ESTIMATED EXPOSURES 1 The section summarizes the estimated exposures for the simulated individuals in a series 2 of tables, separated by the five air qual ity scenarios and study areas considered. 3

6.1.1 Air Quality “As Is” 4 As described in section 5.6, ambient monito ring data from each location for year 2006 5 were used to represent the as is air quality. Table 6-1 summari zes the distribution of the 1-hour 6 and 8-hour daily maximum CO exposures expe rienced by the CHD population in the Denver 7 Study area. About 80% of the simulated C HD population did not experience a 1-hour daily 8 maximum exposure above 9 ppm; 99.9% did not experience a 1-hour da ily maximum exposure 9 concentration above 20 ppm. Of the nearly 20 million person-days, over 99% were associated 10 with a 1-hour daily maximum expos ure below 6 ppm. Very few individuals were estimated to 11 experience an 8-hour daily maximum exposure above 8 ppm (0.4% of the CHD population). 12 Approximately 99% of simulated person-days were associated with 8-hour daily maximum 13 exposure concentrations of less than 3 ppm. These results ar e consistent with the ambient 14
concentration distribution used to represent this scenario, where upper percentile concentrations 15 range from about 2 to 6.4 ppm (see Table 5-8). No te also that the highest estimated 1-hour daily 16 maximum exposures are likely a function of mi croenvironmental concentrations (e.g., in17 vehicles or near-roads) that, in general, may be a factor of two to five times higher than ambient 18
CO concentrations. 19 In Los Angeles, there were a greater number of individua ls experiencing exposures at 20 each of the selected exposure levels (Table 6-2) when compared with Denver (Table 6-1), given 21 that the overall exposure mode ling domain extended over a larg er area with a higher total 22 population. The estimated percentage of persons exposed in Los Angeles is also greater when 23 compared with the corresponding exposure levels evaluated for the Denver study area. For 24 example, approximately 32% of the population was estimated to experience a 1-hour daily 25 maximum exposure of at least 9 ppm in Los Angele s (Table 6-2) while in Denver this same level 26 was experienced by approximately 20% of the CHD population (Table 6-1). This result is likely 27 driven by the differences noted in the as is air quality data, where in Los Angeles, the 2006 28 ambient concentrations were generally higher than those observed for Denver (section 5.7.1). 29 In addition, the maximum 1-hour daily maximu m exposure was estimated to be at or 30 above 30 ppm but less than 40 ppm in the Los Angeles study area, t hough limited to a small 31 fraction of the population (<0.1%). The corresponding maximum 1-hour daily maximum 32 exposure in the Denver study area was at or above 20 ppm but less than 25 ppm, and was 33 experienced by approximately 0.1% of the CHD p opulation. Therefore, th e overall range of the 34 exposure distribution was wider in Los Angeles when compared with that of Denver when 35February 2010 Draft – Do Not Quote or Cite 6-3 considering the as is air quality scenario. Si milar to Denver, over 98% of the person-days in Los 1 Angeles were associated with 1-hour daily maximum exposures below 6 ppm and very few 2 persons (0.8%) experienced 8-hour daily maxi mum exposures above 8 ppm. These exposure 3
results are also consistent with the distribution of ambient air quality used to represent this 4 scenario, where upper percentile concentrations extend from about 2 to 8.4 ppm (Table 5-10). 5

Table 6-1. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 6 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – 7 Adults With Coronary Heart Disease (CHD) in the Denver Study Area –“As 8 Is” Air Quality. 9

1-Hour 8-Hour Persons Person-days Persons Person-days Daily Maximum Exposure

(ppm) Number Percent Number Percent Number Percent Number Percent 0 53,656 100 19,580,000 100 53,656 100 19,580,000 100 3 53,397 100 2,188,000 11.2 31,036 58 189,500 1.0 6 32,517 60.6 170,400 0.9 1,715 3.2 2,851 <0.1 9 10,662 19.9 24,560 0.1 62 0.1 86 <0.1 12 3,048 5.7 4,677 <0.1 12 <0.1 12 <0.1 15 876 1.6 1,061 <0.1 0 0 0 0 20 62 0.1 62 <0.1 0 0 0 0 25 0 0 0 0 0 0 0 0 30 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 0 0 Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air quality scenario. 10February 2010 Draft – Do Not Quote or Cite 6-4 Table 6-2. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 1 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – 2 Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area – 3 Air Quality As Is. 4

1-Hour 8-Hour Persons Person-days Persons Person-days Daily Maximum Exposure

(ppm) Number Percent Number Percent Number Percent Number Percent 0 383,040 100 139,800,000 100 383,040 100 139,800,000 100 3 382,739 100 23,620,000 16.9 294,430 77 3,793,000 2.7 6 287,606 75.1 2,423,000 1.7 36,528 9.5 72,150 0.1 9 122,428 32.0 408,300 0.3 3,011 0.8 3,412 <0.1 12 42,850 11.2 83,990 0.1 301 0.1 301 <0.1 15 13,949 3.6 20,170 <0.1 0 0 0 0 20 2,208 0.6 2,509 <0.1 0 0 0 0 25 401 0.1 502 <0.1 0 0 0 0 30 100 <0.1 100 <0.1 0 0 0 0 40 0 0 0 0 0 0 0 0 Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air quality scenario. 5

6.1.2 Air quality adjusted to just me et the current 8-hour standard 6 As described in section 5.6, hi storical ambient monitoring da ta from each study area were 7 adjusted to represent air quality that just m eets the current 8-hour st andard. For both Denver 8 (year 1995) and Los Angeles (year 1997), air quality needed to be adjusted downwards to meet a 9
2 nd highest 8-hour average concentration of 9.4 ppm. Note that even with a downward 10 proportional adjustment, these ambient concentrations remain higher than as is ambient air 11 quality. Table 6-3 summarizes the CHD populati on exposure results generated for the Denver 12 study area when using these adjusted ambient CO concentrations as an input to APEX and using 13 the same modeling assumptions and parameter distri butions described in chapters 4 and 5. Over 14 half of the Denver CHD population was estimat ed to experience a 1-hour daily maximum 15 exposure at or above 12 pp m. This is nearly a factor of 10 greater than that estimated when 16 using the as is air quality (Table 6-1). The maxi mum 1-hour daily maximum exposure was 17 estimated to be at or above 40 ppm when consider ing air quality adjusted to just meet the current 18 standard, though only experienced by 0.2% of the CHD population. Thus, there is a wider range 19 in the exposure levels experienced by the C HD population. The number and percent of persons 20 experiencing 8-hour daily maximum exposures is al so greater for this scenario when compared 21 with corresponding levels using the as is air quality. Nearly 10% of the CHD population was 22 estimated to experience an 8-hour daily maximum exposure at or above 9 ppm (Table 6-3) when 23 considering air quality just meeting the current 8-hour sta ndard. Most of the CHD population 24February 2010 Draft – Do Not Quote or Cite 6-5 (99.6%) would not experience an 8-hour daily maxi mum concentration at that same level when 1 considering the as is air quality scenario (Table 6-1). 2 Similarly in Los Angeles, the number and pe rcent of persons expos ed above selected 3 exposure concentrations is greater when consider ing the air quality adjusted to just meet the 4 current standard than when using as is air quality. For example, nearly 50% of the CHD 5 population was estimated to experience a 1-hou r daily maximum exposure of 9 ppm when 6 considering air quality just meeting the current standard (Table 6-4), while only 32% were 7 estimated to experience a similar concentration using as is air quality (Table 6-2). The range of 8 the 1-hour daily maximum exposure distribution extends upward to 40 ppm, but less than 60 9 ppm for this scenario. This estimate of an upper level is consistent with the maximum in-vehicle 10 concentration of 46 ppm measured by Shikiya (1 989) during 112 southern California commutes 11 in wintertime. However, Rodes et al. (1998) reported maximum in-vehicle and on road CO 12 concentrations of only 7.6 and 9.0 ppm during Los Angeles commutes in 1997. Note though the 13 scripted commutes in this study were time-avera ged for two hours, the sample size was limited 14 (about 30 total samples), and conducted ove r a nine days in the fall. 15 When comparing the overall population distribution for Los Angeles to Denver for this 16 exposure scenario, there are a greater percentage of persons and person-days estimated for the 17 Denver CHD population at the same exposure le vel. For example, only 2.7% of the CHD 18 population was estimated to experi ence an 8-hour daily maximum exposure at or above 9 ppm in 19 Los Angeles (Table 6-4), while in Denver, the estimated percent of the CHD population exposed 20
at this level was over a factor of three greater (9.4%) (Table 6-3). This result is likely driven by 21 differences observed at the upper tails of the air quality distribution noted in section 5.7.3, even 22 though both study areas have ambien t concentrations adjusted to just meet the same 8-hour 23 average CO concentration of 9.4 ppm. 24February 2010 Draft – Do Not Quote or Cite 6-6 Table 6-3. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 1 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – 2 Adults With Coronary Heart Disease (CHD) in the Denver Study Area – Air 3 Quality Just Meeting the Current 8-Hour Standard. 4

1-Hour 8-Hour Persons Person-days Persons Person-days Daily Maximum Exposure

(ppm) Number Percent Number Percent Number Percent Number Percent 0 53,656 100 19,580,000 100 53,656 100 19,580,000 100 3 53,656 100 8,638,000 44.1 52,706 98 2,690,000 13.7 6 53,039 98.9 1,625,000 8.3 23,879 44.5 97,760 0.5 9 44,598 83.1 404,800 2.1 5,060 9.4 9,724 <0.1 12 28,469 53.1 127,300 0.7 1,037 1.9 1,382 <0.1 15 16,610 31.0 46,710 0.2 309 0.6 346 <0.1 20 6,022 11.2 10,290 0.1 37 0.1 37 <0.1 30 691 1.3 802 <0.1 0 0 0 0 40 86 0.2 86 <0.1 0 0 0 0 60 0 0 0 0 0 0 0 0 Ambient concentrations from 1995 were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm. 5

Table 6-4. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 6 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – 7 Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area – 8 Air Quality Just Meeting the Current 8-Hour Standard. 9

1-Hour 8-Hour Persons Person-days Persons Person-days Daily Maximum Exposure (ppm) Number Percent Number Percent Number Percent Number Percent 0 383,040 100 139,800,000 100 383,040 100 139,800,000 100 3 383,040 100 36,430,000 26.1 342,598 89 8,655,000 6.2 6 335,975 87.7 4,826,000 3.5 75,966 19.8 262,600 0.2 9 189,563 49.5 982,300 0.7 10,336 2.7 18,670 <0.1 12 83,693 21.8 257,200 0.2 1,505 0.4 2,308 <0.1 15 36,126 9.4 80,180 0.1 301 0.1 401 <0.1 20 8,731 2.3 14,450 <0.1 100 <0.1 100 <0.1 30 803 0.2 803 <0.1 0 0 0 0 40 0 0 0 0 0 0 0 0 60 0 0 0 0 0 0 0 0 Ambient concentrations from 1997 were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm.

6.1.3 Air quality adjusted to just meet alternative air quality scenarios 10 Three potential alternative air quality scenar ios were investigated to observe how the 11 selected averaging times, forms and target levels would affect the estimated exposure 12February 2010 Draft – Do Not Quote or Cite 6-7 concentrations (section 5.7.3). The data for the 1-hour and 8-hour daily maximum exposure 1 concentrations are presented here, only with a focus on the number and percent of persons 2 exposed at selected concentrations. Table 6- 5 summarizes the 1-hour exposure results for each 3
of the three alternative standa rds scenarios in the Denver stud y area, while the following table 4 presents the same information for the Los Ange les study area (Table 6-6) . Tables 6-7 and 6-8 5 contain the corresponding 8-hour ex posure distribution for the pot ential alternative standard 6 scenarios in each respective location. 7 In comparing the exposure results for each pot ential alternative scenario within each 8 study area and exposure averaging time, genera lly similar numbers of persons and their 9 respective percentages of the CHD population ar e observed at the same level. This was by 10 general design, that is, to invest igate differing forms of the potenti al alternative standards that 11 would generate potentially simila r exposure (and dose) results. Again, there is a wider range in 12 the 1-hour exposure levels experienced by the CHD population in Denver (Table 6-5) when 13 compared with that of Los Angeles (Table 6-6) when considering the same potential alternative 14
standard. This is also consiste nt patterns in the estimated di stribution of 8-hour daily maximum 15 exposures experienced by the CHD population, t hough the upper range of that 8-hour maximum 16 exposure is of course less than that of th e 1-hour daily maximum in each respective location 17 (Tables 6-7 and 6-8). There is some variabil ity in the percent of persons exposed when 18 considering a particular level, form , and study area. For example, the 2 nd highest 8-hour CO 19 concentration of 5.4 ppm best limited the numbe r and percent of expos ed persons in each 20 location when compared to results for the other potential alternative standard, though in Denver 21 there were still a few persons estimated to e xperience a 1-hour daily maximum at or above 30 22 ppm (Table 6-7). In Los Angeles, the upper level of the 1-hour daily maximum exposure 23 concentration experienced by the simulated C HD population was just at or above 20 ppm and 24 below 30 ppm. 25February 2010 Draft – Do Not Quote or Cite 6-8 Table 6-5. Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour 1 Exposure At or Above the Specified Level – Adults With Coronary Heart 2 Disease (CHD) in the Denver Study Area – Air Quality Just Meeting Potential 3 Alternative Standards. 4

2 nd highest 8-hour average of 5.4 ppm 99 th pct 8-hour Daily Max of 5.0 ppm 99 th pct 1-hour Daily Max of 8.0 ppm

Persons Persons Persons Daily Maximum

1-hour Exposure

(ppm) Number Percent Number Percent Number Percent 0 53,656 100 53,656 100 53,656 100 3 53,656 100 53,656 100 53,656 100 6 47,264 88.1 50,534 94.2 48,239 89.9 9 25,174 46.9 32,147 59.9 26,877 50.1 12 11,082 20.7 16,326 30.4 12,192 22.7 15 4,850 9.0 7,676 14.3 5,282 9.8 20 975 1.8 1,888 3.5 1,222 2.3 30 62 0.1 136 0.3 74 0.1 40 0 0 0 0 0 0 60 0 0 0 0 0 0 Ambient concentrations from 1995 were adjusted to just meet the level of the potential alternative standard indicated. 5

Table 6-6. Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour 6 Exposure At or Above the Specified Level – Adults With Coronary Heart 7 Disease (CHD) in the Los Angeles Area – Air Quality Just Meeting Potential 8 Alternative Standards. 9

2 nd highest 8-hour average of 5.4 ppm 99 th pct Daily 8-hour Max of 5.0 ppm 99 th pct Daily 1-hour Max of 8.0 ppm

Persons Persons Persons Daily Maximum

1-hour Exposure

(ppm) Number Percent Number Percent Number Percent 0 383,040 100 383,040 100 383,040 100 3 378,624 99.0 379,728 99.0 381,535 99.6 6 215,454 56.2 229,302 59.9 260,913 68.1 9 68,540 17.9 77,571 20.3 98,244 25.6 12 19,769 5.2 24,285 6.3 34,721 9.1 15 6,322 1.7 7,827 2.0 10,738 2.8 20 903 0.2 1,305 0.3 2,709 0.7 30 0 0 0 0 0 0 40 0 0 0 0 0 0 60 0 0 0 0 0 0 Ambient concentrations from 1997 were adjusted to just meet the level of the potential alternative standard indicated. 10
11February 2010 Draft – Do Not Quote or Cite 6-9 Table 6-7. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour 1 Exposure At or Above the Specified Level – Adults With Coronary Heart 2 Disease (CHD) in the Denver Study Area – Air Quality Just Meeting Potential 3 Alternative Standards. 4

2 nd highest 8-hour average of 5.4 ppm 99 th pct 8-hour Daily Max of 5.0 ppm 99 th pct 1-hour Daily Max of 8.0 ppm

Persons Persons Persons Daily Maximum

8-hour Exposure

(ppm) Number Percent Number Percent Number Percent 0 53,656 100 53,656 100 53,656 100 3 44,574 83.1 48,819 91.0 45,808 85.4 6 6,590 12.3 10,650 19.8 7,380 13.8 9 839 1.6 1,555 2.9 926 1.7 12 111 0.2 296 0.6 123 0.2 15 25 <0.1 49 <0.1 25 <0.1 20 0 0 0 0 0 0 30 0 0 0 0 0 0 40 0 0 0 0 0 0 60 0 0 0 0 0 0 Ambient concentrations from 1995 were adjusted to just meet the level of the potential alternative standard indicated. 5

Table 6-8. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour 6 Exposure At or Above the Specified Level – Adults With Coronary Heart 7 Disease (CHD) in the Los Angeles Area – Air Quality Just Meeting Potential 8 Alternative Standards 9

2 nd highest 8-hour average of 5.4 ppm 99 th pct Daily 8-hour Max of 5.0 ppm 99 th pct Daily 1-hour Max of 8.0 ppm

Persons Persons Persons Daily Maximum

8-hour Exposure

(ppm) Number Percent Number Percent Number Percent 0 383,040 100 383,040 100 383,040 100 3 214,149 55.9 230,807 60.3 264,425 69.0 6 17,060 4.4 20,672 5.4 28,801 7.5 9 903 0.2 1,204 0.3 2,007 0.5 12 301 <0.1 301 <0.1 301 <0.1 15 0 0 100 <0.1 100 <0.1 20 0 0 0 0 30 0 0 0 0 40 0 0 0 0 60 0 0 0 0 Ambient concentrations from 1997 were adjusted to just meet the level of the potential alternative standard indicated. 10February 2010 Draft – Do Not Quote or Cite 6-10 6.2 ESTIMATED COHB DOSE LEVELS 1 Consistent with section 6.2, th is section summarizes the estimated COHb levels for the 2 simulated CHD population in a series of tables, separated by the air quality scenarios and study 3
areas considered. In addition to reporting the number and percen tage of persons and person-days 4
associated with the dose metric of interest (daily maximum end-of- hour COHb level), staff 5 provides the person-days per person at or above the selected COHb levels. This dose metric can 6
be calculated in two manne rs. The first, termed on average , is the number of person-days at a 7 given level divided by the total number of CHD persons in each model simulation. Therefore, 8 this metric gives an estimate of, on average, th e number of days an in dividual in the entire CHD 9 population might experience a daily maximum end-of -hour COHb concentra tion at or above the 10 selected COHb level. The second, termed at level , is calculated by divi ding number of person11 days estimated for a given level by the number of persons estimated for the same COHb level. 12 Therefore, this 2 nd metric will provide an estimate of, for persons that experience a selected 13 COHb level, the average number of days in the year they may experience that selected level. 14 This second metric (at level) will always be la rger than the first (on average) because it only 15 includes the persons experiencing a selected COHb dose level. 3 16

6.2.1 Air Quality “As Is” 17 Table 6-9 provides the COHb levels (%) fo r the simulated COHb population in Denver, 18 when considering the as is air quality. No persons were esti mated to have a daily maximum end19
of-hour COHb level at or above 2.0%, while only a few (<0.1%) were estimated to have a COHb 20
dose level ≥ 1.8%. Over 99% of the CHD population ha d a daily maximum end-of-hour COHb 21 level below 1.5%. Most of the person-days were associated with daily maximum end-of-hour 22
COHb levels below 1.0%. It follows that the majority of the person-days per person on average 23
were also limited to COHb leve ls at or below 1.0%. When in dividuals did have an estimated 24 daily maximum end-of-hour COHb level at or above 1.5%, it occurred on multiple days (i.e., 25 between 8 and 14 (Table 6-9), depending on the level). 26 Similarly in Los Angeles, very few pers ons (98.5%) had an estimated daily maximum 27
end-of-hour COHb level at or above 1.5% when considering the as is air quality (Table 6-10). 28 There were, however, a few persons (0.1%) estim ated to have daily maximum end-of-hour 29 COHb levels at or above 2.0% in this study area . Of these 301 simulated individuals, all were 30 estimated to have only one person-day per person at that level. The majority of the person-days 31 and person-days per person on average were limited to COHb levels at or below 1.0%. 32

3 This averaging of person-days would underestimate the number of person-days any one simulated individual might experience above a given benchmark in a year. For example, 10 exceedances occurring in one individual would give the same average as 1 exceedance occurring in 10 individuals.February 2010 Draft – Do Not Quote or Cite 6-11 Table 6-9. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 1 Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 2 With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality 3 As Is. 4

Persons Person-days Person-days/person COHb Level

(%) Number Percent Number Percent On Average At Level 0.0 53,656 100 19,580,000 100 365 365 1.0 7,873 14.7 293,000 1.5 5.5 37.2 1.5 333 0.6 4,652 <0.1 <0.1 14.0 1.8 12 <0.1 99 <0.1 <0.1 8.2 2.0 0 0 0 0 0 0 2.5 0 0 0 0 0 0 3.0 0 0 0 0 0 0 4.0 0 0 0 0 0 0 Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is

air quality scenario. 5

Table 6-10. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 6 Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 7 With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air 8 Quality As Is. 9

Persons Person-days Person-days/person COHb Level

(%) Number Percent Number Percent On Average At Level 0.0 383,040 100 139,800,000 100 365 365 1.0 98,043 25.6 1,645,000 1.2 4.3 16.8 1.5 5,820 1.5 86,800 0.1 0.2 14.9 1.8 1,505 0.4 8,630 <0.1 <0.1 5.7 2.0 301 0.1 301 <0.1 <0.1 1.0 2.5 0 0 0 0 0 0 3.0 0 0 0 0 0 0 4.0 0 0 0 0 0 0 Unadjusted ambient concentrations from ten monitors in 2006 were used to represent the As Is

air quality scenario.

6.2.2 Air Quality Adjusted to Just Meet the Current 8-hour Standard 10 Consistent with the estimated exposure con centrations, dose levels experienced by the 11 CHD population in each study area were greater when considering simulated exposures 12 associated with air quality adjusted to just meet the current standard than when using as is air 13 quality. For example, in Denver, just over 3% of the CHD population was estimated to have a 14 daily maximum end-of-hour COHb level at or ab ove 2.0% (Table 6-11). There were no persons 15 in Denver estimated above this COHb level based on estimated ambient exposures associated 16
with as is air quality. A similar pattern is obser ved for the CHD population in Los Angeles 17February 2010 Draft – Do Not Quote or Cite 6-12 (Table 6-12), though a lower percentage of pe rsons (0.5%) was estimated to have daily 1
maximum end-of-hour COHb levels at or above 2.0% when compared results for Denver. In 2 both study areas, a few persons had daily ma ximum end-of-hour COHb levels extending 3 upwards to 3.0%. Most of the persons that did experience these higher COHb levels ( ≥ 2.0%) 4 however, experienced them for fewer than 2 days in a year (Table 6-12). 5

Table 6-11. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 6 Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 7 With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality 8 Just Meeting the Current 8-hour Standard. 9

Persons Person-days Person-days/person COHb Level

(%) Number Percent Number Percent On Average At Level 0.0 53,656 100 19,580,000 100 365 365 1.0 40,921 76.3 829,300 4.2 15.5 20.3 1.5 10,267 19.1 35,520 0.2 0.7 3.5 2.0 1,814 3.4 2,480 <0.1 <0.1 1.4 2.5 346 0.6 370 <0.1 <0.1 1.1 3.0 86 0.2 86 <0.1 <0.1 1.0 4.0 0 0 0 0 0 0 Ambient concentrations from 1995 were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm. 10

Table 6-12. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 11 Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 12 With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air 13 Quality Just Meeting the Current 8-hour Standard. 14

Persons Person-days Person-days/person COHb Level

(%) Number Percent Number Percent On Average At Level 0.0 383,040 100 139,800,000 100 365 365 1.0 155,243 40.5 2,472,000 1.8 6.4 15.9 1.5 17,561 4.6 120,100 0.1 0.3 6.8 2.0 2,007 0.5 3,111 <0.1 <0.1 1.6 2.5 301 0.1 401 <0.1 <0.1 1.3 3.0 100 <0.1 100 <0.1 <0.1 1.0 4.0 0 0 0 0 0 0 Ambient concentrations from 1997 were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm. 15February 2010 Draft – Do Not Quote or Cite 6-13 6.2.3 Air Quality Adjusted to Just Meet Alternative Air Quality Scenarios 1 Consistent with results described for the expos ure, the percentage of persons estimated to 2 experience maximum end-of-hour C OHb at selected levels are ge neral similar across the three 3 potential alternative standard scenarios. For example, in Denver most of the population (>99%) 4 were estimated to not experience a daily maximu m end-of-hour COHb level above 2.0% (Table 5 6-13). There are a few study area differences worthy of note. As expected, the corresponding 6 estimated percent of the CHD population in Denv er is greater than that estimated for Los 7 Angeles, even when considering the same potenti al alternative standard form and air quality 8 level (Table 6-14). For example, when considering a 99 th percentile daily maximum 8-hour 9 average CO concentration of 5.0 ppm, 0.8% of the CHD population had an estimated daily 10
maximum end-of-hour COHb level at or abov e 2.0%; in Los Angeles this dose level was 11 estimated for only 0.1% of the CHD population. 12

Table 6-13. Estimated Number (and Percentage) of Persons with a Daily Maximum End13 of-hour COHb Level At or Above the Sp ecified Level – Adults With Coronary 14 Heart Disease (CHD) in the Denver Study Area – Air Quality Just Meeting 15 Potential Alternative Standards. 16

2 nd highest 8-hour average of 5.4 ppm 99 th pct Daily Max 8- hour of 5.0 ppm 99 th pct Daily Max 1- hour of 8.0 ppm Persons Persons Persons COHb Level (%) Number Percent Number Percent Number Percent 0.0 53,656 100 53,656 100 53,656 100 1.0 19,560 36.5 26,692 49.7 21,040 39.2 1.5 2,061 3.8 3,826 7.1 2,271 4.2 1.8 444 0.8 1,012 1.9 568 1.1 2.0 197 0.4 407 0.8 234 0.4 2.5 62 0.1 86 0.2 62 0.1 3.0 0 0 12 <0.1 0 0 4.0 0 0 0 0 0 0 Ambient concentrations from 1995 were adjusted to just meet the level of the potential alternative standard indicated. 17February 2010 Draft – Do Not Quote or Cite 6-14 Table 6-14. Estimated Number (and Percentage) of Persons with a Daily Maximum End1 of-hour COHb Level At or Above the Sp ecified Level – Adults With Coronary 2 Heart Disease (CHD) in the Los Angeles Study Area – Air Quality Just 3 Meeting Potential Alternative Standards. 4

2 nd highest 8-hour average of 5.4 ppm 99 th pct Daily Max 8- hour of 5.0 ppm 99 th pct Daily Max 1-hour of 8.0 ppm Persons Persons Persons COHb Level (%) Number Percent Number Percent Number Percent 0.0 383,040 100 383,040 100 383,040 100.0 1.0 53,086 13.9 60,913 15.9 77,772 20.3 1.5 2,408 0.6 3,312 0.9 5,319 1.4 1.8 602 0.2 803 0.2 1,204 0.3 2.0 301 0.1 301 0.1 401 0.1 2.5 0 0 0 0 0 0 3.0 0 0 0 0 0 0 4.0 0 0 0 0 0 0 Ambient concentrations from 1997 were adjusted to just meet the level of the potential alternative standard indicated. 5

6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000 6 PNEM/CO AND DRAFT 2010 APEX/CO ASSESSMENTS 7 As described above in chapters 2 and 4, popul ation exposure and dose were estimated in 8 2000 using pNEM/CO, a predecessor to APEX, for adults with ischemic heart disease residing in 9 a defined study area within the same two urban areas (Johnson et al., 2000). As described in 10 section 1.2 above, IHD is also called CHD and w ith regard to characte rizing the population of 11 interest with regard to dem ographics (age and sex), the 2000 assessment, like the current 12 assessment, drew from estimates of the preval ence provided by the NHIS (which includes CHD 13 or IHD, angina pectoris and heart attack) a nd corresponding estimates of undiagnosed ischemia 14 developed by EPA. As part of this current (2010) second draft REA, staff has used APEX to 15 estimate CO exposures and resulting COHb leve ls using a largely similar approach, modeling 16 domains, years of ambi ent concentration data, 4 and defined at-risk population. There are some 17 differences that exist when comparing the specific methodologies: 18

 number of ambient monitors used (e.g., prev iously 6 in Denver versus 4 used here), 19

 location of ambient monitors used (e.g., only 7 of the same monitors used previously 20 were used here for Los Angeles), 21

 number of microenvironments modeled (p reviously 15 versus the 8 modeled here) 22

4 When considering the exposure scenario that uses air quality just meeting the current standard.February 2010 Draft – Do Not Quote or Cite 6-15  use of mass balance modeling (previously all 12 enclosed MEs used mass balance, here 1 only indoor MEs use mass balance) 2

 cohort approach (pNEM) versus individual approach (APEX) , and 3

 two indoor sources of CO included in the 2000 pNEM/CO assessment for residential 4 microenvironments: gas stoves and passive smoking. 5 Despite these differences and others not liste d, staff still did not expect to see greatly 6 different results when comparing the two asse ssments given the similarities in the likely 7 influential variables (i.e., ambient concentr ations, microenvironmenta l approach, CFK module 8 used, etc.). Table 6-15 presents estimates fo r the percentage of De nver adults with CHD 9 estimated to experience a daily maximum end-of-hour COHb level at or above the specified level 10 under the specified air quality conditions for 1995. Table 6-16 presents similar estimates for Los 11 Angeles using the adjusted 1997 ambient air quality to just meet the current 8-hour standard. 12 Each table provides two sets of estimates for the 2000 pNEM/CO asse ssment (indoor sources 13 “on” and “off”) and one set generated for th e current (2010) second draft APEX/CO REA. 14 As expected, the COHb levels estimated by the 2000 pNEM/CO a ssessment are higher 15 when internal sources are turned on, though in th e absence of indoor sources, the range of dose 16 estimates are generally similar in both study areas. However at selected COHb levels in Denver, 17 the current approach estimate d a higher percent of the CHD population than when compared 18 with the previous Johnson et al. (2000) assess ment. For example, approximately 3.4% of the 19 CHD population was estimated to have a daily ma ximum end-of-hour COHb level at or above 20 2.0% in this current assessment. The correspond ing value estimated in the Johnson et al. (2000) 21 assessment was approximately 0.5% of the IHD population. 22February 2010 Draft – Do Not Quote or Cite 6-16 Table 6-15. Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated to 1 Experience a Daily Maximum End-of-h our COHb Level At or Above the 2 Specified Percentage – Air Quality Just Meeting the Current Standard. 3

Percentage of CHD Adults at or Above COHb Level

Johnson et al. (2000) pNEM/CO a 2010 2 nd draft REA APEX/CO b COHb Level

(%) Internal sources on Internal sources off Internal sources off 6.0 0.2 0 0 5.0 0.6 0 0 4.0 1.6 0 0 3.0 5.5 < 0.1 0.2 2.5 10.4 0.2 0.6 2.0 19.9 0.5 3.4 1.5 37.6 6.7 19.1 1.0 83.2 65.0 76.3 0 100 100 100

a Used Denver 1995 CO ambient concentrations with no adjustment (2 nd highest 8-hour CO concentration was 9.5 ppm, close in value to the design value of 9.4 ppm). b Denver 1995 ambient CO concentrations adjusted to just meet the current 8-hour standard (9.4 ppm). 4 5

Table 6-16. Percentage of Los Angeles Adults wi th Coronary Heart Disease (CHD) 6 Estimated to Experience a Daily Maxi mum End-of-hour COHb Level At or 7 Above the Specified Percentage – Ai r Quality Just Meeting the Current 8 Standard. 9

Percentage of CHD Adults at or Above COHb Level

Johnson et al. (2000) pNEM/CO a 2010 2 nd draft REA APEX/CO b COHb Level

(%) Internal sources on Internal sources off Internal sources off 6.0 0.2 0 0 5.0 0.8 0 0 4.0 2.2 0 0 3.0 5.1 <0.1 <0.1 2.5 9.0 <0.1 0.1 2.0 16.8 0.5 0.5 1.5 32.3 5.2 4.6 1.0 79.0 58.1 40.5 0 100 100 100

a Los Angeles 1997 ambient CO concentrations adjusted to just meet the current 8- hour standard (9.4 ppm).February 2010 Draft – Do Not Quote or Cite 6-17 6.4 EVALUATION OF ENDOGENOUS CO CONTRIBUTION TO COHB 1 LEVELS IN APEX SIMULATED INDIVIDUALS 2
As summarized in section 4.4.7 and described fu lly in Appendix B, staff estimated COHb 3 levels in each simulated individual using the CFK dose module within APEX. Theoretically, in 4
the absence of ambient concentrations or ot her sources of CO, one can perform an APEX 5 simulation to estimate endogenous CO production a nd its effect on COHb levels. Staff has 6 performed such a simulation using APEX for both study locations. The results of these 7 simulations, conducted in a similar manner as the above exposure scenarios (i.e., a 50,000 person 8 simulation and the random assignment of the CHD population from this simulation) only in the 9 absence of environmental CO concentrations, are provided in section 6.3.1. A second set of 10 simulations was performed to better characterize the endogen ous CO production rate and its 11 impact on estimated COHb levels. This second se t of simulations consid ered both the estimated 12
COHb levels associated with and without ambient exposures for an identical, yet smaller, set of 13 individuals. The purpose of this focused anal ysis was to determine the contribution of the 14 endogenous CO and ambient exposure to total C OHb levels. Because the generation of these 15 data required hourly concentration output and multiple model runs, the sample size was restricted 16 to less than 100 simulated persons to maintain a manageable data file. In addition, this 17 subpopulation was a random sample from the enti re adult population resi ding within the Denver 18 study area. Details of this second set of simulations are provided in section 6.4.2. 19

6.4.1 Estimation of Endogenous CO Contri bution to Population COHb Levels 20 As mentioned above, these two additional simu lations were conducted in each the Denver 21 and Los Angeles study areas. Fifty thousand persons were simulated, as was done when 22 considering the five exposure scenarios. Th e only difference between these APEX simulations 23 and those for the five air quality scenarios was the ambient concentration input files used. In 24 these two model runs, all ambient CO concentra tions equaled zero. The same output described 25 above for the five air quality scenarios were gene rated, only in this instan ce, the distribution of 26 population exposures effectively equals zero. We evaluated smaller bins of the maximum end27
of-hour COHb level below 1.0% in these simulati ons (i.e., 0.25%) given that even when using as 28
is air quality, most persons had COHb levels at or below 1.0%. We report the daily maximum 29
end-of-hour COHb levels for the simulated C HD population and, given this, the results can be 30 considered as a single view of population-level endogenous CO production. 5 We caution against 31 assuming that there is a direct correlation be tween the endogenous CO production estimated here 32 and the total COHb levels estimated for each study area in the prior sections. That is, the daily 33
maximum end-of hour COHb level estimated to result from endogenous CO production 34

5 Theoretically the APEX model can estimate all hourly values as was done in section 6.4.2.February 2010 Draft – Do Not Quote or Cite 6-18 exclusively may not necessarily be correlated w ith the daily maximum end-of hour COHb level 1 for the sum of endogenous CO production and ambi ent CO exposures. Nevertheless, it still 2 provides some information rega rding the potential effect of the endogenous CO production on 3 estimated COHb levels in the simulated population. 4 Table 6-17 summarizes the daily maximum e nd-of-hour COHb levels estimated for the 5 CHD population in Denver and Los Angeles, wi th no ambient CO exposure contribution. In 6 general, the population distributions for the two areas are very sim ilar, with the majority of the 7 population (about 99%) having a daily maximum e nd-of-hour COHb level of less than 1.0%. 8
The mean and median COHb level would a pproximately fall between 0.25 and 0.50% in both 9 populations, with the percent population estimate d to experience maximum end-of-hour COHb 10 levels at or above 1.0% greater in the Denver study area (1.5%) than in the LA area (0.8%). 11
These estimated distributions do no t appear outside of what might be expected given some of the 12 available data reported in the extant literature. However, as a reminder, these output data are for 13 the maximum value that occurred in an entire ye ar simulation. The distribution for the average 14 end-of-hour COHb level associated with endoge nous CO production would surely be less than 15 that indicated here and likely more comparable to any available measurement data. Staff notes 16 that these distributions represen t a population sample and is unique in its own right; even if a 17 mean estimate could be constructed it may not be comparable to measurements performed on a 18 smaller (and possibly not random) po pulation of limited study subjects. 19

Table 6-17. Estimated Number (and Percentage) of Persons with a Daily Maximum End20 of-hour COHb Level At or Above the Sp ecified Level – Adults With Coronary 21 Heart Disease (CHD) in the Denver and Los Angeles Study Areas – Zero 22 Ambient Exposures. 23

Denver Los Angeles COHb Level

(%) Persons Percent Persons Percent 0.00 53,656 100 383,040 100 0.25 47,362 88.3 327,546 85.5 0.50 13,537 25.2 85,098 22.2 0.75 3,209 6.0 17,260 4.5 1.00 790 1.5 3,211 0.8 1.25 140 0.3 803 0.2 1.50 12 <0.1 301 <0.1 1.80 0 0 100 <0.1 2.00 0 0 0 0 24February 2010 Draft – Do Not Quote or Cite 6-19 6.4.2 Contribution of Endogenous CO Pro duction and Ambient Exposures to 1 COHb Level in Limited Simulations 2 Two APEX model simulations were performe d for this second evaluation: one using 3 1995 Denver ambient concentrations adjusted to ju st meet the current standard (9.4. ppm) and 4 the second using ambient concentrations equal to zero. Each of the new runs simulated 8,760 5 hours of exposure for each of 92 persons (n= 92 people x 8,760 hrs/person = 805,920 person6 hours per run). By design, the simulated pers ons in each of these two model runs line up 7 perfectly in terms of physiology and activities performed, enabli ng staff to compare the COHb 8 levels across the two runs hour by hour. We first calculated all 805,920 hour-by-hour ambient 9 contributions ( COHb ambient contribution ) in corresponding COHb levels (i.e, COHb ambient 10
contribution = % COHb with ambient exposure minus % COHb for zero exposure), effectively 11 giving the ambient contribution to estimated COHb levels. Tabl e 6-18 provides the descriptive 12
statistics for the COHb ambient contribution experienced by the select ed population. Below are 13
listed selected statistic s for COHb ambient contribution based on the entire hourly data set (N = 14 805,920). Figure 6-1 provides the same information onl y in graphical form; note that most of the 15
ambient contribution to COHb levels values fall between 0.1 and 0.4% COHb and can be well 16 represented by a lognormal distribution {GM 0.205, GSD 1.57}. The complete distribution of 17 the endogenous contribution to COHb levels is also provided in Table 6-18. Most of the 18 simulated hours for the population had an endoge nous end-of-hour COHb level contribution of 19 less than 0.5%, though for a limited number of hours, the endogenous contribution could be over 20 1.0% COHb. On average, this limited population wa s estimated to have just over half of their 21 hourly total COHb level attribut ed to endogenous COHb production. 6 22

Table 6-18. Descriptive statistics for the % COHb ambient contribution estimated using 23 Denver 1995 ambient concentrations adju sted to just meet the current 24 standard. 25

Statistic (N = 805,920) COHb Ambient Contribution

(% COHb) COHb Endogenous

(% COHb) COHb Total (% COHb) Arithmetic Mean 0.2264 0.255 0.4814

Arithmetic SD 0.109 0.1476 0.1757 Geometric Mean 0.2046 0.2206 0.4529 Geometric SD* 1.5682 1.7085 1.4151 Minimum 0.003 0.0329 0.0988 10 th percentile 0.1155 0.1106 0.2912 20 th percentile 0.1416 0.1373 0.3378

6 One should not go beyond comparing the means or 50 th percentiles of the ambient and endogenous contribution as the other percentiles of the distribution are likely not correlated.February 2010 Draft – Do Not Quote or Cite 6-20 Statistic (N = 805,920) COHb Ambient Contribution

(% COHb) COHb Endogenous

(% COHb) COHb Total (% COHb) 30 th percentile 0.1628 0.1631 0.3766 40 th percentile 0.1833 0.1882 0.413 50 th percentile 0.2046 0.2177 0.4504 60 th percentile 0.2284 0.2544 0.4915 70 th percentile 0.2573 0.299 0.5407 80 th percentile 0.2965 0.3526 0.6063 90 th percentile 0.3615 0.4404 0.7098 95 th percentile 0.4268 0.5343 0.8034 98 th percentile 0.5192 0.6405 0.9332 99 th percentile 0.5949 0.778 1.062 99.5 th percentile 0.6759 0.9006 1.188 99.8 th percentile 0.8006 1.07 1.319 99.9 th percentile 0.9086 1.2 1.394 Maximum 1.9158 1.541 2.323 Notes: * dimensionless 1

2

Figure 6-1. Histogram of the % COHb ambient co ntribution estimated using Denver 3 1995 ambient CO concentrations adjusted to just meet the current standard. 4

Two other metrics were calculated for each individual using the same hourly output for 5 the limited simulation run. First, staff cal culated the 1-hour COHb ambient contribution 6February 2010 Draft – Do Not Quote or Cite 6-21 associated with the maximum total 1-hour COHb (i.e., endogenous and ambient contribution) 1 and second, the 1-hour total COHb associated wi th the maximum COHb ambient contribution. 2 The purpose of these metrics was to determin e the relative contribu tion the endogenous CO 3 production and ambient exposure have on the maximum hourly COHb level. 4 Figure 6-2 illustrates the contribution of e ndogenous CO production relative to that of 5 each person’s maximum end-of-hour COHb level. As can be seen in the figure, there is not a 6 strong relationship between the tw o variables, that is, the end ogenous CO production does little 7 to influence a persons maximum end-of-hour COHb level, given this air quality scenario (i.e., 8 just meeting the current standard in Denver). Consistent with the population distribution 9 described above (section 6.4.1), ther e are very few individuals th at were estimated to have 10
maximum end-of-hour COHb levels at or above 1.0% in the abse nce of ambient CO exposure. 11 As described earlier, it was e xpected that there would not be a relationship between the 12 contribution of endogenous CO production to COHb level and the ambient exposure contribution 13 to COHb. This expectation is confirme d by the results presented in Figure 6-3. 14

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Maximum End-of Hour (Total) COHb Level (% COHb)Endogenous End-of Hour COHbContribution (% COHb)

15

Figure 6-2. The contribution of endogenous CO pr oduction relative to an individual’s 16 maximum end-of-hour COHb le vel using 1995 Denver ambient 17 concentrations adjusted to just meet the current standard. 18February 2010 Draft – Do Not Quote or Cite 6-22 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Maximum Ambient End-of-Hour COHb Contribution (% COHb)Endogenous End-of Hour COHbContribution (% COHb)

1

Figure 6-3. Comparison of endogenous CO produc tion relative to an individual’s 2 maximum COHb ambient contribu tion using 1995 Denver ambient 3 concentrations adjusted to just meet the current standard. 4February 2010 Draft – Do Not Quote or Cite 6-23 Alternatively, one can see the strong rela tionship between the maximum end-of-hour 1 COHb level and the contribution from ambient expos ure (Figure 6-4). For the majority of the 92 2 simulated persons, the maximum end-of-hour tota l COHb level (i.e., the combined contribution 3 from both endogenous CO production and ambient CO exposure) is largely driven by the 4 contribution from ambient CO exposure. 5

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Maximum Ambient End-of-Hour COHb Contribution (% COHb)Maximum End-of Hour (Total) COHb Level(% COHb)

6
Figure 6-4. Comparison of endogenous CO produc tion relative to an individual’s 7 maximum COHb ambient contribu tion using 1995 Denver ambient 8 concentrations adjusted to just meet the current standard. 9

6.5 KEY OBSERVATIONS 10 Presented below are key observations resu lting from the exposure and dose assessment 11
for ambient CO. 12

 Ambient CO exposures and resulting COHb le vels in the blood of exposed individuals 13 were estimated for populations in two study areas in the Los Angeles and Denver areas 14 under five air quality scenarios: as is air quality, air quality adjusted to simulate just 15 meeting the current 8-hour CO N AAQS, and air quality adjust ed to just meet three 16 potential alternative standards. 17

 More than 98% of the simulated at-risk popul ation in each study area was estimated to 18 experience a daily maximum end-of-hour COHb level below 1.5% over the course of a 19 year considering as is air quality in either study area, with more than 99.9% of the 20 selected at-risk population in both areas ha ving daily maximums COHb levels below 21 2%. 22

 The distribution of maximum end-of-hour COHb levels extended slightly higher for the 23 Los Angeles population when using as is air quality, while th e Denver population was 24February 2010 Draft – Do Not Quote or Cite 6-24 estimated to experience higher levels under c onditions of air quality adjusted to just 1 meet the current standard. 2

 More than 95% of the simulated at-risk population in the Los Angeles study area was 3 estimated to experience an annual daily maximum end-of-hour COHB level below 4 1.5%, and 99.5% with maximum COHb less than 2%, with air quality adjusted to just 5 meet the current standard. In contrast, 80.1% of the simulated at-r isk population in the 6 Denver study area was estimated to experi ence a daily maximum end-of-hour COHB 7 level below 1.5%, and greater than 95% w ith maximum COHb less than 2%, in air 8 quality conditions adjusted to just meet the current standard. 9

 Alternative standards that we considered in this document included two potential 10 alternative forms for the 8-hour standard and one potential alternative form for the1- 11 hour standard. Beyond the results for altern ative forms presented in this document, 12 results for alternative standard levels for any given combination of averaging time and 13 form are presented and discussed in the draft Policy Assessment. 14

 The three potential alternative standards considered in this document resulted in 15 generally similar percentages of individuals exposed at selected concentrations and 16 maximum end-of hour COHb levels. Each of these potential al ternative standards 17 generated fewer persons and a lower per cent of the CHD population at or above 18 selected COHb levels (e.g., <1% at a 2.0% COHb level in Denver) when compared 19 with corresponding COHb levels associated wi th air quality adjusted to just meet the 20 current 8-hour standard (e.g., 3.4% at a 2.0% COHb level in Denver). When 21 considering the potential alte rnative standards in Los A ngeles, only 0.1% of the CHD 22 population was estimated to experience a ma ximum end-of-hour COHb level at or 23 above 2.0 % COHb, compared to 0.5% at that same COHb level associated with air 24 quality adjusted to just meet the current standard. 25

 A few simulations with a small number of i ndividuals in which no external CO sources 26 were included provided limited informati on regarding the contribution of endogenous 27 COHb to the total COHb estimates produced. This quite limited information indicates 28 that most simulated individuals have endoge nous COHb levels below 1% and that the 29 upper end of total COHb (reflecting ambien t plus endogenous contributions) may arise 30 as a result of an individual receiving high external CO exposures rather than having 31 higher endogenous levels. 32

 Results generated in the current assessment for the air quality conditions just meeting 33 the current NAAQS were compared with es timates from the assessment conducted in 34 2000 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study 35 areas (section 6.3). While the two assessments employed similar approaches and 36 similar, although not identical, air quality data for this scenario, they used different 37 versions of the exposure model (APEX vs. pN EM). Results were quite similar for the 38 1.5% and 2% COHb level in Los Angeles st udy area and somewhat different in the 39 Denver study area. 40

41February 2010 Draft – Do Not Quote or Cite 6-25 6.6 REFERENCES 1
Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 2 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 3 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 4 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 5
at: http://www.epa.gov/ttn/fera/human_related.html .. June 2000. 6

Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S, Frazier C. (1998). 7 Measuring Concentrations of Selected Air Pollutants Inside California Vehicles. Final Report. Prepared 8 by Research Triangle Institute under Contract No. 95 -339. California Air Resources Board. Sacramento, 9 California. 10

Shikiya D, Liu C, Kahn M, Juarros J, Barcikowski W. ( 1989). In-Vehicle Air Toxics Characterization Study in the 11 South Coast Air Basin. Office of Planning and Rules, South Coast Air Quality Management District. May. 12

13February, 2010 Draft – Do Not Cite or Quote 7-1 

### 7 VARIABILITY ANALYSIS AND UNCERTAINTY 1 

### CHARACTERIZATION 2

An important issue associated with any popul ation exposure or risk assessment is the 3
characterization of variab ility and uncertainty. Variability refers to the inherent heterogeneity in 4 a population or variable of interest (e.g., residential air exchange ra tes). The degree of variability 5 cannot be reduced through further research, only better characterized with additional 6

measurement. Uncertainty refers to the lack of knowledge regarding the values of model input 7 variables (i.e., parameter uncertainty ), the physical systems or relationships used (i.e., use of 8 input variables to estimate exposure or risk or model uncertainty ), and in specifying the scenario 9 that is consistent with pu rpose of the assessment (i.e., scenario uncertainty ). Uncertainty is, 10 ideally, reduced to the maximum extent po ssible through improved measurement of key 11 parameters and iterative model refinement. The approaches used to assess variability and to 12 characterize uncertainty in this REA are discusse d in the following two sections. Each section 13 also contains a concise summary of the identi fied components contributing to uncertainty and 14 how each source may affect the estimated exposures. 15

7.1 ANALYSIS OF VARIABILITY 16 The purpose for addressing variability in this REA is to ensure that the estimates of 17 exposure and risk reflect the variability of am bient CO concentrations and associated CO 18 exposure and health risk acro ss the study locations and populati on. In this second draft CO 19 REA, there are several algorithms that account for variability of input data when generating the 20
number of estimated benchmark exceedances or hea lth risk outputs. For example, variability 21 may arise from differences in the population resi ding within census tracts (e.g., age distribution) 22 and the activities that may a ffect CO population exposure and dose (e.g., time spent inside 23 vehicles, moderate or greater exertion outdoors). A complete range of potential exposure levels 24 and associated risk estimates can be generate d when appropriately addressing variability in 25 exposure and risk assessments; note however that the range of values obtained would be within 26 the constraints of the input para meters, algorithms, or modeling system used, not necessarily the 27 complete range of the true exposure or risk values. 28 Where possible, staff identified and incorporat ed the observed variability in input data 29 sets and estimated parameters within the expos ure and dose assessment performed rather than 30 employing standard default assumptions and/or usi ng point estimates to describe model inputs. 31 The details regarding any variability distributions used in data i nputs are described in chapter 5. 32 To the extent possible given the data available for the assessment, staff accounted for variability 33 within the exposure and dose modeling. APEX ha s been designed to account for variability in 34 some of the input data, including the physiologi cal variables that are important inputs to 35February, 2010 Draft – Do Not Cite or Quote 7-2 determining ventilation rates and COHb dose levels . As a result, APEX addresses much of the 1 variability in factors that affect human exposure and dose. The variability accounted for in this 2 analysis is summarized in Table 7-1. 3

Table 7-1. Summary of How Variability Was Incorporated Into the Second Draft CO 4 REA. 5

Component Variability Source Comment

Population data Individuals are randomly sampled from US census tracts used in model domains, by age (single years) and gender (US Census Bureau, 2007).

Commuting data Individuals are probabilist ically assigned ambient concentrations originating from either their home or work tract based on US Census derived commuter data (US Census Bureau, 2007).

Activity patterns Data diaries are randomly selected from CHAD master (35,000 diaries) 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). The CHAD diaries capture real locations persons visit and activities performed, ranging from 1 minute to 1- hour in duration (US EPA, 2002).

Longitudinal profiles A sequence of diaries is linked together for eachindividual that preserves both the inter- and intrapersonal variability in human activities (Glen et al., 2008). 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 (CDC, 2009)

Measured ambient CO concentrations Temporal: 1-hour CO for an entire year predicted using ambient monitoring data. Spatial: Four monitors were used to represent ambient conditions in Denver; ten monitors used in Los Angeles; each monitor was assigned a 10 km zone of influence. Ambient Input

Meteorological data Spatial: Local surface NWS stations used. Temporal: 1-hour NWS temperature data for each year.

Microenvironments Eight total microenvironmen ts were represented, including those expected to be associated with high exposure concentrations (i.e., in-vehicle and near-road). This results in differential exposure estimates for each individual (and event) when spending time within each.

Proximity factors In the current APEX approac h, microenvironmental concentrations were estimated using proximity factors to adjust the outdoor CO concentrations. All proximity factors were represented by lognormal distributions whose values are randomly selected for every individual exposure event. Microenvironmental Approach

Mass balance model For the indoor microenvironments, using a mass balance model accounts for CO concentrations occurring during a previous hour (and of ambient origin) to calculate current indoor CO concentrations.February, 2010 Draft – Do Not Cite or Quote 7-3 Component Variability Source Comment

Air exchange rates Several lognormal distributions are sampled based on five daily mean temperature ranges, two regions, and location specific A/C prevalence rates.
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) (McCurdy, 2000; 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 Survey (NHANES), for the years 1999-2004 (Isaacs and Smith (2005) in Appendix A).

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 B for details).

Blood volume Values determined according to gender using equations based on work by Allen et al. (1956) (see Appendix B for details).
Hemoglobin content of the blood Values randomly selected from distributions developed by gender and age categories based on NHANES study (see Isaacs and Smith (2005) in Appendix A).
Pulmonary CO diffusion rate Values selected according to gender, height, and age based on equations adapted from Salorinne (1976) (see Appendix B 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 (data obtained from eight independent studies; see Appendix B for details).

7.2 CHARACTERIZATION OF UNCERTAINTY 1 While it may be possible to capture a range of exposure or risk values by accounting for 2 variability inherent to influential factors, the true exposure or risk fo r any given individual is 3 largely unknown. To characterize health risks, exposure and ri sk assessors commonly use an 4 iterative process of gathering da ta, developing models, and estima ting exposures and risks, given 5 the goals of the assessment, scale of the assessm ent performed, and limitations of the input data 6 available. However, signifi cant uncertainty often remains a nd emphasis is then placed on 7 characterizing the nature of that uncertainty and its impact on exposure and risk estimates. 8 Staff has used such an iterative process in characterizing the uncertainty associated with 9 the approach and data used in the 1 st draft REA. Following a review of that 1 st draft REA by 10 CASAC, a few sources of uncertainty were id entified as most important in improving the 11February, 2010 Draft – Do Not Cite or Quote 7-4 approach used to estimate exposure and dose for the second draft CO REA. This included 1) 1 expanding the number of monitors used to bette r address spatial variability in ambient CO 2 concentrations, 2) increasing th e number of microenvironments modeled from two to eight, 3) 3 using distributions of proximity factors to estim ate all microenvironmental concentrations rather 4 than simple point estimates, and 4) additional analysis of historical trends in ambient CO 5
concentrations at individual monitors. These additional analyses and a pproaches used are not 6 without their own uncertainties, and following th is iterative process, also need to be 7
characterized. 8 The characterization of uncertainty can in clude either qualitative or quantitative 9 evaluations, or a combination of both. The appro ach can also be tiered, th at is, the analysis can 10 begin with a simple qualitative uncertainty characterization then progress to a complex 11 probabilistic uncertainty analysis . This second level of analys is may be appropriate when a 12 lower tier analysis indicates ther e is a high degree of uncertainty for certain identified sources, 13 the sources of uncertainty are hi ghly influential variables in es timating the exposure and risk, and 14
sufficient information and other resources are available to conduct a qua ntitative uncertainty 15 assessment. This is not to suggest that quant itative uncertainty analyses should always be 16 performed in all exposure and risk assessments. The decision regarding the type of uncertainty 17 characterization performed is also informed by the intended scope and purpose of the 18 assessment, whether the selected analysis will provide additional information to the overall 19 decision regarding health protection, whether su fficient data are available to conduct a complex 20 quantitative analysis, and whether time and resources are available for higher tier 21 characterizations (US EPA, 2004; WHO, 2008). 22 The primary purpose of the uncertainty character ization approach selected in this second 23 draft CO REA is to identify and compare the relative impact that important sources of 24 uncertainty may have on the estimated potential h ealth effect endpoints. The approach used to 25 evaluate uncertainty was adapted from gui delines outlining how to conduct a qualitative 26 uncertainty characterization (WHO, 2008) and applied in the most recent NO 2 (US EPA, 2008) 27
and SO 2 NAAQS reviews (US EPA, 2009). While it may be considered ideal to follow a tiered 28 approach in the REA to quantitatively characteri ze all identified uncertainties, staff selected the 29 mainly qualitative approach give n the extremely limited data available to inform probabilistic 30 analyses. 31 The qualitative approach used in this REA va ries from that of WHO (2008) in that a 32 greater focus was placed on evaluati ng the direction and the magnitude 1 of the uncertainty; that 33 is, qualitatively rating how the source of uncertainty, in the presence of alternative information, 34

1 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2.February, 2010 Draft – Do Not Cite or Quote 7-5 may affect the estimated exposures and health risk results. In addition and consistent with the 1 WHO (2008) guidance, staff discuss the uncertainty in the knowledge-base (e.g., the accuracy of 2 the data used, acknowledgement of data gaps) and decisions made where possible (e.g., selection 3 of particular model forms), though qualitativ e ratings were assigned only to uncertainty 4 regarding the knowledge-base. 5 First, staff identified the key aspects of the assessment approach that may contribute to 6 uncertainty in the exposure and risk estimates and provide the rationale for their inclusion. Then, 7 staff characterized the magnitude and direction of the influence on the assessment results for 8 each of these identified sources of uncertainty. Consistent with the W HO (2008) guidance, staff 9 subjectively scaled the overall im pact of the uncertainty by consid ering the degree of severity of 10 the uncertainty as implied by the relationship between the source of th e uncertainty and the 11 exposure concentrations and COHb dose levels. 12 Where the magnitude of uncertainty was rated low , it was judged that large changes 13 within the source of uncertainty would have only a small effect on the exposure results. For 14 example, a statistical procedure was used to substitute missing ambient concentrations in each 15 ambient data set. Staff compared the air qual ity distributions and found negligible differences 16
between the substituted data set and the one with missing values (e.g., Tables 5-7 through 5-10). 17 There is still uncertainty in the approach used, since there are a variety of methods available to 18 use. However, staff judged that the quantitative comparison of the data se ts indicates that there 19 would likely be little influence on exposure es timates by the data substitution procedure. 20 A magnitude designation of medium implies that a change within the source of 21 uncertainty would likely have a moderate (or pro portional) effect on the results. For example, 22 the magnitude of uncertainty associated with usi ng the historical data to represent a hypothetical 23
future scenario was rated as low-medium . While we do not have in formation regarding how the 24
ambient CO concentration distri bution might look in the future , we do know however what the 25 distribution might look like based on historical trends and the prim ary emission sources. If these 26
trends in observed concentrations and emissions remain consistent in the future, then the 27 magnitude of the impact to estimated exposures in this assessment w ould be judged as likely low 28 or having negligible impact on the exposure and dose estimates. However, if there are new 29 emission sources, the magnitude of influence might be greater. When adjusting air quality in 30 each location to simulate the various exposure scenarios, staff observed mainly proportional 31 differences (e.g., a factor of two or three) in th e estimated exposure and dose levels. Assuming 32 that these types of ambient con centration adjustments could refl ect the addition of a new source 33 in each area carries its own uncertainties, however based on this information, staff also judged 34 the magnitude of influence in using the historic al air quality data to represent a hypothetical 35 future scenario as high as medium . A characterization of high implies that a small change in the 36February, 2010 Draft – Do Not Cite or Quote 7-6 source would have a large effect on results. This rating would be used where model was 1 extremely sensitive to the identified source of uncertainty. 2
Staff also included the direction of influen ce, indicating how the s ource of uncertainty 3 was judged to affect estimated exposures or risk estimates; either the estimated values were 4 likely over - or under-estimated . In the instance where the com ponent of uncertainty can affect 5 the assessment endpoint in either di rection, the influence was judged as both . Staff characterized 6
the direction of influence as unknown when there was no evidence available to judge the 7 directional nature of uncertainty associated with the particular source. Staff also subjectively 8 scaled the knowledge-base uncertainty associated with each identified source using a three level 9
scale: low indicated significant confidence in the data used and its applicability to the assessment 10 endpoints, medium implied that there were some limitations regarding consistency and 11 completeness of the data used or scientific evidence presented, and high indicated the extent of 12 the knowledge-base was extremely limited. 13 The output of the uncertainty characteri zation was a summary describing, for each 14 identified source of uncertainty, the magnitude of the impact and the direction of influence the 15 uncertainty may have on the exposure and risk characterization results. There are several 16 sources of uncertainty associated with this simplified approach fo r modeling CO population 17 exposure/dose and associated potential health ri sk, each summarized and discussed in Table 7-2. 18 As mentioned in section 1 above, given the significant time constraints of this review, 19 results of the assessment are provided in this document without substantial interpretation. 20 Rather, interpretative discussion of these results, including further consideration of public health 21 implications, is provided in the draft Policy Assessment. 22February, 2010 Draft – Do Not Cite or Quote7-7Table 7-2.Characterization of Key Uncertainties in the Second Draft CO REA for Denver and Los Angeles Areas.1Sources of UncertaintyInfluence ofUncertainty onExposure/DoseEstimatesCategory Element Direction MagnitudeKnowledgeBaseUncertaintyCommentsaDatabase 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. Note also that the uncertainty regarding lowconcentration data near the monitor detection limit is unlikely to influence the number of benchmark 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 andTemporalRepresentationBoth Low - Medium MediumINF: Use of several ambient monitors better represents spatial-temporal variability in ambient CO levels throughouteach study area when compared with the simplified approach used in the 1stdraft CO REA. Analysis of the monitoringconcentrations indicates there is spatial variability in monitoring concentrations across each area, but that it is relativelylimited, particularly for more recent ambient concentrations (low magnitude). In comparing results generated using thesimplified approach used in the 1stdraft CO REA however, selection of the particular monitor(s) used may have amedium magnitude of influence on estimated exposures.KB: Each ambient monitor has specific objectives and monitoring scale that may not appropriately capture the truespatial and temporal variability in CO concentrations. In the absence of 1) a monitoring network designed to measurespatial variability in CO concentrations, 2) performing air quality modeling to estimate fine scale spatial and temporalvariability in CO concentrations and, 3) analysis of additional monitoring data that can potentially indicate spatialconcentration gradients, staff judge the uncertainty in the knowledge-base as medium.Ambient COMonitorConcentrationsMissing 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 to SimulateJust Meeting theCurrent andPotential AlternativeStandardsHistorical Data Used Unknown Low - Medium MediumINF & 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 and potential alternative standards, the condition simulated ishypothetical. Based on observed trends in air quality over time and the results generated using adjusted ambientconcentrations, staff judges that, at most, the magnitude of influence would be a medium level. However, there isuncertainty in how influential factors such as emission levels per vehicle, vehicular traffic, and meteorology comparebetween an earlier period of time and the hypothetical scenario of just meeting the current standard some time in thefuture. It is possible that these historical patterns can serve as a reasonable basis for predicting future air qualityscenarios, though these patterns would not account for the influence of a new CO emission source(s). Therefore, staffjudges the knowledge base uncertainty as medium.February, 2010 Draft – Do Not Cite or Quote7-8Sources of UncertaintyInfluence ofUncertainty onExposure/DoseEstimatesCategory Element Direction MagnitudeKnowledgeBaseUncertaintyCommentsaProportionalApproach UsedBoth Low LowINF: The magnitude of the adjustment applied to historical ambient concentration data was wide ranging. For example,in Denver, to just meet the current standard 0.99 was the adjustment applied. In comparison, to just meet a 2ndhighest8-hour average CO concentration in Los Angeles, a greater adjustment was needed (0.36). However, in comparingrecent and historical ambient CO concentrations for several ambient monitors in Los Angles (Figure 3-4), a strongproportional relationship is present when comparing the recent and historic CO concentrations.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). In addition, little difference was observedhere when comparing exposure results using the1997 LA data adjusted downwards to level similar to that observedwith theas isair quality with exposure results using the 2006 air quality.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.APEX Inputs andAlgorithmsActivity 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 E-1 in Appendix E regarding thepatterns of typical commuting in CHAD versus the urban locations modeled in this assessment). CHAD is comprised ofdata from individuals that may or may not have had an identified health condition and are assumed to represent theactivities of persons with normal health status as well as those with certain health conditions that may not affect generalactivity patterns. A statistical analysis was performed on a subset of the CHAD data where persons were specificallyasked whether they had angina (see Appendix F of CO REA and Johnson et al., 2000). Activity patterns for personswith angina were compared to those individuals not having angina using various exertion level metrics and time spentoutdoors or inside-vehicles. The percentages of time spent outdoors or in a vehicle were generally not statisticallysignificantly different between angina and non-angina subjects While there were statistically significant differences inthe exertion rate between angina and non-angina subjects, it was likely a function of the large sample size for the nonangina subjects since actual differences were generally numerically small compared to the mean values. Thedifferences in activity and exertion between angina and non-angina subjects, although statistically significant, werejudged not large enough to severely impact the validity of APEX (or pNEM/CO) modelingresults that do not adjust foran angina/non-angina difference.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.February, 2010 Draft – Do Not Cite or Quote7-9Sources of UncertaintyInfluence ofUncertainty onExposure/DoseEstimatesCategory Element Direction MagnitudeKnowledgeBaseUncertaintyCommentsaLongitudinal ProfileAlgorithmBoth Low – Medium MediumINF: This assessment focused on persons having at least one exposure or dose above a selected level (lowmagnitude), however when considering multi-day exposures, the magnitude of potential influence is judged as medium.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 5 of US EPA, 2009).Note however, long-termdiary profiles (i.e., monthly, annual) do not exist for a population.Meteorological Data Both Low LowINF & KB: Data are from the National Weather Service, a well-known and quality-assured source. Daily maximumtemperatures are used when selecting appropriate diaries to simulate individuals. The bin ranges used are wide suchthat erroneous temperature data would likely have limited impact to exposure results. Daily mean temperatures areused when selecting air exchange rates. Given the overlap of the AER distributions and the wide temperature rangesused to categorize them, there is likely limited impact by erroneous temperature data.MicroenvironmentalAlgorithm and InputDataUnknown Medium MediumINF & KB: In this second draft CO REA, the number of microenvironments selected captures the likely locationspersons spend time and where CO exposures would occur. Using distributions of proximity factors derived frommeasurement data in Denver and applied to estimate microenvironmental concentrations is reasonable. However, howthese data derived from a Denver study reflect similar relationships in Los Angeles has greater uncertainty.Additionally, the Denver measurement data were collected in the 1980’s, therefore there is also uncertainty as to howthese data might reflect relationships observed for other years modeled in this assessment (i.e., 2006). However, formost of the distributions, in particular those used to estimate high exposure microenvironments, there are othercomparable measurement data and relationships available (albeit limited in number) to generally support thedistributions applied in this assessment.CommutingAlgorithmBoth Low LowINF & KB: In this second draft REA, the commuting algorithm within APEX was implemented. Use of this algorithmbetter represents individual exposures across each modeling domain. The data are derived from the US Census, awell-known and quality-assured source.CHD Prevalence Both Low MediumINF & 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. Staff used gender-specific ratios and applied them to all age groups uniformly even though there may beuncertainty in the accuracy of the prevalence estimates for specific age and gender groups. In addition, potentiallyundiagnosed individuals with CHD were included toexpand the total CHD population considered.This was based onseveral assumptions including using 1990 estimates of the population with undiagnosed IHD.February, 2010 Draft – Do Not Cite or Quote7-10Sources of UncertaintyInfluence ofUncertainty onExposure/DoseEstimatesCategory Element Direction MagnitudeKnowledgeBaseUncertaintyCommentsaPhysiologicalFactorsUnknown Low – Medium MediumINF & KB: Many of the parameters used to estimate the physiological attributesof the CHD population were developedfrom healthy individuals; there were no adjustments made to account for a particular health condition. While the ISAnotes some variability in some parameters in individuals with specific health conditions that might affect CO uptake andelimination, most conditionsmay not necessarily be associated with the simulatedat-risk population, i.e., CHDindividuals. In addition, some of the parameters used to estimate COHb levels are based on older publications (somedating back to the mid 20thcentury). It is possible that most of the relationships still remain appropriate in modeling thecurrent population however, in the absence of conducting a comprehensive review and comparing the historical data torecent measurements, staff judges the knowledge-base uncertainty as medium.Potential HealthEffect BenchmarkLevelsSimulated At-RiskPopulationUnknown 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 study mean COHb levels as low as 2.0-2.4%which were increased from a baseline mean of 0.6-0.7%.as a result of short (~1hour) experimentally controlledincreases in CO exposures (study mean of 117 ppm CO). No laboratory study has been specifically designed toevaluate the effect of experimentally increased exposure to CO resulting in an increase in COHb levels to a studymean below 2.0%. However, based on analysis of individual study subject responses at baseline and at the twoincreased COHb levels, study authors concluded that each increase in COHb produced further changes in the studyresponse metric, without evidence of a measurable threshold effect. There is no established no adverse effect leveland, thus there is greater uncertainty about the lowest benchmark level identified (i.e., 1.5%) and uncertainty aboutwhether individuals with themost severe CHD are adequately represented. Additionally the COHblevels estimated inthis assessment result from CO exposure concentrations much lower than the experimental exposure concentrationsused to increase study subject COHb levels to the study targets (e.g., 2.0%) and with which the responses wereassociated. Given that the evidence supporting the choice of benchmark levels is based on controlled humanexposure data, staff judged the influence of this uncertainty on the risk characterization as being low.Notes:aINF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rating.bThis entry focuses on the uncertainty associated with the benchmark levels in their application to estimated COHb levels for the simulated at-risk population (i.e., individualswith diagnosed/undiagnosed CHD, inclusiveof angina pectoris and heart attacks). With regard to otherpotentially susceptiblepopulations (as described in section 2.4 above), we additionally note the lack of studies that describe COHb levels and health effects thatmight be expected as a result of short-term elevations in CO exposure in those populations.February 2010 Draft – Do Not Quote or Cite 7-11 7.3 KEY OBSERVATIONS 1 Based on an overall qualitative judgment of the identified so urces of uncertainty in the assessment approach, selections made regardi ng input data, and algor ithms used, and their
characterization as to direc tion and magnitude of influen ce on exposures and doses, staff consider the exposure and dose estimates reasona ble for the simulated population the assessment is intended to represent (i.e., the CHD population residing within the urban core of each study area). This is because:

 Only two sources of uncertainty were associ ated with a potential directional influence: 2 data base quality (overestim ation) and missing data subst itution (underestimation), and 3 both were judged to have a low magnitude of influence on estimated exposures and 4 doses. 5

 Twelve of the identified sources of uncertain ty were judged by staff to have either 6 bidirectional influence (eight sources ) or unknown (four s ources) direction: 7

 One source of uncertainty (i.e., microe nvironmental algorithm and data inputs) 8 was judged as having a potentially medi um magnitude of influence on exposure 9 and dose estimates. 10

 Five of the remaining eleven sources (i .e., spatial and tem poral representation, 11 historical data used, activity pattern database, longitudina l profile algorithm, 12 physiological factors) were judged as having low to medium magnitude of 13 influence, the level of which varied based on whether an identified condition 14 existed. 15

 Six of the sources were judged to have a low magnitude of influence on estimated 16 exposures and doses (i.e., proportional approach used, population database, 17 meteorological data, commuting data, CHD prevalence, and benchmark levels for 18 the simulated at-risk pop ulation). 19 There was a wide-ranging level of uncertainty in the knowle dge-base for the identified

sources:

 Eight sources were judged by staff as having medium knowledge-base uncertainty 20 including: spatial and temporal representa tion, historical data used, activity pattern 21 database, longitudinal profile algorithm, microenvironmenta l algorithm and input data, 22 CHD prevalence, physiological factors, and th e benchmark levels for the simulated at23 risk population. 24

 The knowledge-base uncertainty was judged as low for four of the identified sources 25 having either unknown or bidirectional influe nce. This included the proportional 26 approach used, population database, me teorological data, commuting data. 27

 The knowledge-base uncertainty was also judg ed as low for the two sources identified 28 above as being associated with either under- or overestimating exposures. 29February 2010 Draft – Do Not Quote or Cite 7-12 The ratings of the knowledge-base uncertainty ca n indicate the need fo r additional data or 1 analyses to better characterize the uncertainty. When combined with the potential magnitude of 2 influence associated with each identified source, a prioritization can be given to the higher rated 3
influential sources. Based on the results of this uncertainty characterizati on, staff judges that six 4 sources (i.e., the spa tial and temporal repres entation, historical data used, activity pattern 5 database, longitudinal profile algorithm, microenvironmental algorithm and input data, and 6 physiological factors) remain as the most important uncertainties in this assessment. 7February 2010 Draft – Do Not Quote or Cite 7-13 7.4 REFERENCES 1

2 Allen TH, Peng MT, Chen KP, Huang TF, Chang C, Fang HS. (1956). Prediction of blood volume and adiposity in 3 man from body weight and cube of height. Metabolism . 5:328-345. 4

Brainard J and Burmaster D. (1992). Bivariate distributions for height and weight of men and women in the United 5 States. Risk Analysis . 12(2):267-275. 6

CDC. (2009). Summary Health Statistics for U.S. Adu lts: National Health Interview Survey, 2007. Series 10, 7 Number 240. U.S. Department of Health an d Human Services, Hyattsville, MD, May 2009. 8

Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new meth od of longitudinal diary assembly for 9 human exposure modeling . J Expos Sci Environ Epidem . 18:299-311. 10

Issacs K and Smith L. (2005). New Values for Physio logical Parameters for the Exposure Model Input File 11 Physiology.txt. Technical memorandum to Tom McCu rdy, NERL WA10. Decembe r 20, 2005. Provided 12 in Appendix A of the second draft CO REA. 13

Johnson T. (1998). Memo No. 5: Equations for Converting Weight to Height Proposed for the 1998 Version of 14 pNEM/CO. Memorandum Submitted to U.S. Environmental Protection Agency. TRJ Environmental, Inc., 15 713 Shadylawn Road, Chapel Hill, North Carolina 27514. 16

Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 17 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 18 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 19 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 20

at: http://www.epa.gov/ttn/fera/human_related.html .. June 2000. 21

McCurdy T. (2000). Conceptual basis for multi-route intake dose modeling using an energy expenditure approach. 22 J Expos Anal Environ Epidemiol . 10:1-12. 23

SAIC. 2001. Technical Peer Review of “Estima tion of Carbon Monoxide Exposures and Associated 24 Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO (version 2.1)” 25 Prepared by Science Applications International Corporation under EPA Contract No. 68-D-98-113. 26 Available at: http://www.epa.gov/ttn/fer a/human_related.html

. 27

Salorinne Y. (1976). Single-breath pulmonary diffusing capacity. Scand J Resp Diseases . Supplementum 96. 28

US Census Bureau. (2007). Employment Status: 2000- Supplemental Tables. Available at: 29 http://www.census.gov/population/www/cen2000/phc-t28.html

. 30

US EPA (1992). Review of the National Ambient Air Qu ality Standards for Carbon Monoxide: Assessment of 31 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 32 EPA/452/R-92-004. 33

US EPA. (2002). EPA’s Consolidated Human Activities Database. Available at: http://www.epa.gov/chad/

. 34

US EPA. (2004). An Examination of EPA Risk Assessmen t Principles and Practices. Staff Paper prepared by the 35

US EPA Risk Assessment Task Force. EPA/100/B-04/001. Available at: 36 http://www.epa.gov/OSA/pdfs/ratf-final.pdf

. 37February 2010 Draft – Do Not Quote or Cite 7-14 US EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO 2 Primary National Ambient Air 1 Quality Standard. EPA-452/R-08-008a. November 2008. Available at: 2 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf

. 3

US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air 4 Quality Standard. EPA-452/R-09-007. August 2009. Available 5 athttp://www.epa.gov/ttn/naaqs/standards /so2/data/200908SO2REAFinalReport.pdf. 6

WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on characterizing and 7 communicating uncertainty in exposure assessment. Available at: 8 http://www.who.in t/ipcs/methods/harmonization/areas/exposure/en/. 9February 2010 Draft – Do Not Quote or Cite 8-1 

### 8 SUMMARY OF KEY OBSERVATIONS 1

Presented together below are the key observations made in each of the chapters. 2
3
Introduction 4

 This draft document describes the quanti tative human exposure assessment and risk 5 characterization being conducted to info rm the U.S. Environmental Protection 6 Agency’s (EPA’s) current review of the National Ambient Air Quality Standards 7 (NAAQS) for carbon monoxide (CO). The pr evious review of the CO NAAQS was 8 concluded in 1994 with confirmation of the current standards. An assessment of 9 ambient CO exposure/dose was developed in an earlier phase of this review in the late 10 1990s. The design of this 2 nd draft REA builds upon recommendations from CASAC, 11 information presented in the final ISA, as well as comments made by the public. 12

13 Conceptual Overview: Assessing Ambi ent Carbon Monoxide Exposure and Risk 14

 Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 15 carbon-containing fuels and photochemical reac tions in the atmosphere, with on-road 16 mobile sources representi ng significant sources of CO to ambient air. 17

 Microenvironments influenced by on-road mob ile sources are important contributors to 18 ambient CO exposures, particularly in urban areas. 19

 The formation of COHb is a key step in the elicitation of various health effects by CO. 20 Further, COHb level is commonly used in exposure assessment and is considered the 21 best biomarker for CO health effects of concern. 22

 Individuals with CHD are th e population with greatest sus ceptibility to short-term 23 exposure to CO, and the population for whic h the current evidence indicates health 24 effects occurring at the lowest exposures. Th e evidence further indicates a potential for 25 other underlying cardiovascular conditions to co ntribute susceptibility to CO effects. 26 Other populations potentially at risk include individuals with diseases such as chronic 27 obstructive pulmonary disease (COPD), anem ia, or diabetes, and individuals in 28 prenatal or elderly life stages. 29

 Cardiovascular effects are the health endpoint for which the evidence is strongest and 30 indicative of a likely causal relationship with CO exposur es. Other endpoints for 31 which the evidence is suggestive of such a relationship include effects on the central 32 nervous system, reproduction and prenatal de velopment, and the respiratory system. 33

 Risk is characterized in this REA through ev aluation of COHb estimated to result from 34 ambient CO exposure in individuals w ith CHD (including undiagnosed persons) 35 considering potential health effect benc hmarks for daily maximum COHb levels. 36 Results are reported in terms of percent of population expected to experience daily 37 maximum COHb levels at or above a series of levels that range as low as 1%. These 38 results are considered in th e Policy Assessment document in light of potential health 39February 2010 Draft – Do Not Quote or Cite 8-2 effects benchmarks ranging from 1.5%, wh ich is below the lowe st study mean COHb 1 level resulting from experimental CO e xposure in controlled human exposures of 2 subjects with CAD, up to 3.0%, a level associated with adverse effects in those studies. 3

4 Air Quality Considerations 5

 Mobile sources (i.e., gasoline powered vehi cles) are the primary contributor to CO 6 emissions, particularly in urban areas due to greater vehicle and roadway densities. 7

 Recent (2005-2007) ambient CO concentratio ns across the US are lower than those 8 reported in the previous CO NAAQS review and are also well below the current CO 9 NAAQS levels. Further, a large proportion of the reported concentrations are below 10 the conventional instrument lowe r detectable limit of 1 ppm. 11

 The currently available information for CO m onitors indicates that siting of microscale 12 and middle scale monitors in the current ne twork is primarily limited to roads where 13 traffic density described for them is moderate (<100,000 AADT), however, factors 14 other than reported AADT (e.g., orientation with regard to dense urban roadway 15 networks) can contribute to sites re porting higher CO concentrations. 16

 Ambient CO concentrations are highest at monitors sited closest to roadways (i.e., 17 microscale and middle scale monitors) and e xhibit a diurnal variation linked to the 18 typical commute times of day, with peaks generally observed duri ng early morning and 19 late afternoon during weekdays. 20

 Policy relevant background (PRB) concentratio ns across the US are generally less than 21 0.2 ppm, far below that of interest in this REA with regard to ambient CO exposures. 22

 Historical trends in ambient monitoring data indicate that at individual sites, ambient 23 concentrations have generally decreased in a proportional manner. This comparison 24 included air quality distributions with concentrations at or above the current standard 25 and those reflecting current ( as is ) conditions. 26

 The temporal variability in selected uppe r percentile ambient concentrations (e.g., 99 th 27 percentile 1-hour daily maximum) at indivi dual monitors is relatively small across a 28 three year monitoring period, particular ly when considering recent air quality. 29

30 Overview Of Approach Used for Estima ting Co Exposure and COHb Dose Levels 31

 APEX, an EPA human exposure and dose m odel, has a long history of use in 32 estimating exposure and dose for many of th e criteria pollutants including CO, O 3 , 33 SO 2 , and NO 2 . Over time, staff have improved and developed new model algorithms, 34 incorporated newer available input data and parameter distributions, as well as 35 performed several model evaluations, se nsitivity analyses, and uncertainty 36 characterizations for the above pollutants. Based on this analysis, APEX was judged to 37 be an appropriate model to use for assessing CO exposure and dose. 38

39February 2010 Draft – Do Not Quote or Cite 8-3 Application of APEX4.3 in this Assessment 1

 Two exposure model domains (Denver and Lo s Angeles study areas) were defined by 2 overlaying ambient monitor locations having 10 km rad ii with US census tract 3 population data. Monitors selected compri sed the bulk of the urban core in each 4 location, where ambient m onitoring data exist. 5

 The selected at-risk population was simulate d by combining the tract-specific age and 6 gender population distribution and the CHD prevalence, also stratified by age and 7 gender. In using this approach, staff can re present the variability that exists in the 8 CHD population that resides in each census tract and within each study area. 9

 Staff expanded the selected at-risk populati on to include an estimate of persons with 10 undiagnosed CHD. 11

 Compared with the single-monitor approach used for the first draft CO REA, staff 12 expanded the number of ambient monitors used in this second draft CO REA to better 13 capture the spatial variability in ambient c oncentrations. In Denve r, a total of four 14 monitors were used, in Los Angeles, the total number of monitors was ten. 15

 Compared with the two microenvironments mo deled in the first draft CO REA, staff 16 has expanded the number modeled in each location to eight. This approach is designed 17 to better represent the expected variability in microenvironmental CO concentrations. 18

 Compared with the approach used to estim ate microenvironmental concentrations in 19 the first draft CO REA (factors approach only), all indoor microenvironments were 20 modeled using a mass balance model in this second draft assessment. Use of the mass 21 balance model will better repr esent temporal variability in indoor CO concentrations 22 with respect to the outdoor CO concentration variability. In addition, distributions of 23 microenvironmental factors were used in this second draft CO REA for all 24 microenvironments rather than using point estimates (as wa s done for the first draft CO 25 REA). Using distributions of microenvir onmental factors will better represent both 26 spatial and temporal variability in estima ted microenvironmental CO concentrations 27

28 Simulated Exposure and COHb Dose Results 29

 Ambient CO exposures and resulting COHb le vels in the blood of exposed individuals 30 were estimated for populations in two study areas in the Los Angeles and Denver areas 31 under five air quality scenarios: as is air quality, air quality adjusted to simulate just 32 meeting the current 8-hour CO NAAQS, and air quality adjusted to just meet three 33 potential alternative standards. 34

 More than 98% of the simulated at-risk popul ation in each study area was estimated to 35 experience a daily maximum end-of-hour COHb level below 1.5% over the course of a 36 year considering as is air quality in either study area, with more than 99.9% of the 37 selected at-risk population in both areas ha ving daily maximums COHb levels below 38 2%. 39

 The distribution of maximum end-of-hour COHb levels extended slightly higher for the 40 Los Angeles population when using as is air quality, while th e Denver population was 41February 2010 Draft – Do Not Quote or Cite 8-4 estimated to experience higher levels under c onditions of air quality adjusted to just 1 meet the current standard. 2

 More than 95% of the simulated at-risk population in the Los Angeles study area was 3 estimated to experience an annual daily maximum end-of-hour COHB level below 4 1.5%, and 99.5% with maximum COHb less than 2%, with air quality adjusted to just 5 meet the current standard. In contrast, 80.1% of the simulated at-r isk population in the 6 Denver study area was estimated to experi ence a daily maximum end-of-hour COHB 7 level below 1.5%, and greater than 95% w ith maximum COHb less than 2%, in air 8 quality conditions adjusted to just meet the current standard. 9

 Alternative standards that we considered in this document included two potential 10 alternatives for the 8-hour standard and one potential alte rnative 1-hour standard. The 11 results looking at al ternative levels for any given co mbination of averaging time and 12 form are presented and discussed in the draft Policy Assessment. 13

 For the three potential alternative standard scenarios for which results are presented in 14 this document provided generally similar per cent of individuals e xposed at selected 15 concentration levels and maximum end-of hour COHb levels. Each of these potential 16 alternative standards generated fewer pe rsons and a lower percent of the CHD 17 population at or above selected COHb leve ls (e.g., <1% at a 2.0% COHb level in 18 Denver) when compared with corresponding COHb levels and using air quality 19 adjusted to just meeting the current sta ndard (e.g., 3.4% at a 2.0% COHb level in 20 Denver). When considering the potential alternative standard scenarios in Los 21 Angeles, only 0.1% of the CHD population was estimated to experience a maximum 22 end-of-hour COHb level of 2.0 % COHb comp ared to 0.5% at that same COHb level 23 and using air quality adjusted to just meet the current standard. 24

 A few simulations with a small number of i ndividuals in which no external CO sources 25 were included provided limited informati on regarding the contribution of endogenous 26 COHb to the total COHb estimates produced. This quite limited information indicates 27 that most simulated individuals have endoge nous COHb levels below 1% and that the 28 upper end of total COHb (reflecting ambien t plus endogenous contributions) may arise 29 as a result of an individual receiving high external CO exposures rather than having 30 higher endogenous levels. 31

 Results generated in the current assessment for the air quality conditions just meeting 32 the current NAAQS were compared with es timates from the assessment conducted in 33 2000 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study 34 areas (section 6.3). While the two assessments employed similar approaches and 35 similar, although not identical, air quality data for this scenario, they used different 36 versions of the exposure model (APEX vs. pN EM). Results were quite similar for the 37 1.5% and 2% COHb level in Los Angeles st udy area and somewhat different in the 38 Denver study area. 39

40February 2010 Draft – Do Not Quote or Cite 8-5 Variability Analysis and Uncertainty Characterization 1

 Based on an overall qualitative judgment of the identified so urces of uncertainty in the 2 assessment approach, selections made rega rding input data, and algorithms used, and 3 their characterization as to direction and magnitude of influence on exposures and 4 doses, staff consider the exposure and dos e estimates reasonable for the simulated 5 population the assessment is intended to re present (i.e., the CHD population residing 6 within the urban core of each study area). This is because: 7

 Only two sources of uncertainty were associated with a potential directional 8 influence: data base quality (overestimation) and missing data substitution 9 (underestimation), and both were judged to have a low magnitude of influence 10 on estimated exposures and doses. 11

 Twelve of the identified sources of uncer tainty were judged by staff to have 12 either bidirectional influence (eight sources) or unknown (four sources) 13 direction: 14

- One source of uncertainty (i.e., microenvironmental algorithm and 15 data inputs) was judged as having a potentially medium magnitude 16 of influence on exposure and dose estimates. 17

- Five of the remaining eleven sources (i.e., spatial and temporal 18 representation, historical data used, activity pattern database, 19 longitudinal profile algorithm, physio logical factors) were judged 20 as having low to medium magnitude of influence, the level of 21 which varied based on whether an identified condition existed. 22

- Six of the sources were judged to have a low magnitude of 23 influence on estimated exposures and doses (i.e., proportional 24 approach used, population data base, meteorological data, 25 commuting data, CHD prevalence, and benchmark levels for the 26 simulated at-risk population). 27

 There was a wide-ranging level of uncertainty in the knowle dge-base for the identified 28

sources: 29

 Eight sources were judged by staff as having medium knowledge-base 30 uncertainty including: spatia l and temporal representati on, historical data used, 31 activity pattern database, longitudinal profile algorithm, microenvironmental 32 algorithm and input data, CHD prevalen ce, physiological factors, and the 33 benchmark levels for the simulated at-risk population. 34

 The knowledge-base uncertainty was judged as low for four of the identified 35 sources having either unknown or bidirec tional influence. This included the 36 proportional approach used, population database, meteorological data, 37 commuting data. 38

 The knowledge-base uncertainty was also judged as low for the two sources 39 identified above as being associated with either under- or overestimating 40
exposures. 41February 2010 Draft – Do Not Quote or Cite 8-6  The ratings of the knowledge-base uncertainty can indicate the need for additional data 1 or analyses to better characterize the uncerta inty. When combined with the potential 2 magnitude of influence associated with each identified source, a prioritization can be 3 given to the higher rated infl uential sources. Based on the results of this uncertainty 4 characterization, staff judges that six sources (i.e., the spatial and temporal 5 representation, historical data used, activ ity pattern database, longitudinal profile 6 algorithm, microenvironmental algorithm a nd input data, and phys iological factors) 7 remain as the most important un certainties in this assessment. 8
9February 2010 A-1 Draft – Do Not Cite or Quote 

### Appendix A

Technical Memorandum on Updates To APEX Physiology.Txt File (Isaacs And Smith, 2005)

The following contains a tech nical memo provided by Isaacs and Smith (2005) in its original format. Staff included page numbe rs and performed some minor formatting to text and table headers for th e purposes of inclusion into the draft CO REA appendices.February 2010 A-2 Draft – Do Not Cite or Quote 

### TECHNICAL MEMORANDUM

TO: Tom McCurdy, WA-COR, NERL WA 10 FROM: Kristin Isaacs and Luther Smit h, Alion Science and Technology DATE: December 20, 2005 SUBJECT: New Values for Physiological Parame ters for the Exposure Model Input File Physiology.txt.



### Table of Contents

List of Figures................................................................................................................ ..... 3 1. Introduction............................................................................................................... ..... 4 2. Evaluation of the Current Physiology File Data............................................................ 4 2.1 Normalized Maximal Oxygen Uptake (nvo2max)............................................... 4 2.2 Body Mass. .......................................................................................................... 5 2.3 Resting Metabolic Rate......................................................................................... 5 2.4 Hemoglobin Content and Blood Volume Factor. ................................................ 5 2.5 Summary of Findings............................................................................................ 5 3. Derivation of New Dist ributions for Body Mass........................................................... 6 3.1 The NHANES Body Mass Dataset...................................................................... 6 3.2 Calculation of the New Sampling We ights for the Combined NHANES Dataset. ....................................................................................................................... 6 3.3 Fitting the Body Mass Data. ................................................................................. 7 4. Derivation of New Distributio ns for Normalized Vo2max .......................................... 13 4.1 The Nvo2max Data ............................................................................................. 13 4.2 Determining the NVo2max Distributions........................................................... 17 5. Derivation of New Distri butions for Hemoglobin Content (Hemoglobin Density) ..... 25 6. Blood Volume as a Function of Height and Weight.................................................... 28 References..................................................................................................................... .... 29 Appendix A. SAS Code for Estimating the Body Mass Distributions ............................. 40 Appendix B. SAS Code for Estimating th e Normalized Vo2Max Distributions ............. 41 Appendix C. SAS Code for Estimatin g the Hemoglobin Content Data ........................... 42 Appendix D. The New Physiology.txt file....................................................................... 43 Appendix E. All Derived Physiological Parameters........................................................ 54February 2010 A-3 Draft – Do Not Cite or Quote 

### LIST OF FIGURES

Figure 1. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data. ................................... 9 Figure 2. Geometric Standard Deviations fo r the Best-fit Lognormal Distributions for Body Mass as a Function of Age, Deri ved from NHANES 1999-2004 Study Data........ 10 Figure 3. Minimums (1 st Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data. .................................................................................... 11 Figure 4. Maximums (99 th Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data. ........................................................................... 12 Figure 5. Individual Nvo2max Measurements for Males and Females, Derived from Literature Studies and Expe rimental Measurements. ....................................................... 14 Figure 6. Grouped Mean Nvo2max Measurements for Males and Females, Derived from Literature Studies............................................................................................................. . 15 Figure 7. Nvo2max Standard Deviations for Ma les and Females, Derived from Literature Studies........................................................................................................................ ....... 16 Figure 8. Combined Nvo2max Group Means for Males and Females ............................ 19 Figure 9. Combined Nvo2max Group Standard Deviations............................................. 20 Figure 10. Nvo2max Normal Distribution Fits : Raw Fit Means and Smoothed Fits. ..... 21 Figure 11. Nvo2max Normal Distribution Fi ts: Raw Fit Standard Deviations and Smoothed Fits. ................................................................................................................. . 22 Figure 12. Nvo2max Minimums. 1 st Percentile of the Best-f it Normal Distribution. .... 23 Figure 13. Nvo2max Maximums. 99 th Percentile of the Best-f it Normal Distribution. .. 24 Figure 14. Mean Values of Hemoglobi n Content as Derived from the 1999-2002 NHANES Dataset, with Comparison to Current Physiology.txt Values.......................... 26 Figure 15. Values of Hemoglobin Content Stan dard Deviation as Derived from the 1999- 2002 NHANES Dataset, with Comparison to Current Physiology.txt Values................. 27February 2010 A-4 Draft – Do Not Cite or Quote 

### 1. INTRODUCTION

The purpose of this memo is to presen t an updated version of the physiological parameters input file (Physiology.txt) for the APEX model. Portions of this file are also used as input for SHEDS-PM and SHEDS-AirToxics.

The physiology file contains age- and gender-b ased information for several physiological parameters used in human exposure modeling. This information in cludes distributional shapes and parameters for all age and gender cohorts from age 0 to 100 years for normalized maximal oxygen uptake (nvo2max ), body mass, resting metabolic rate (RMR), and blood hemoglobin content. In addition, a parameter called blood volume factor (BVF), which is a cohort-dependent parameter in the equation for blood volume as a function of body mass, is pres ent in the file as well.

New age- and gender-dependent distributions were developed based the best available physiological data from the literature. In this report, a summary of th e current state of the physiology file is presented, followed by the derivation of new physiological data for body mass, normalized vo2max, and hemoglobin cont ent. Portions of the SAS code used for analysis are included (Appendices A-C), as is the new Physiol ogy.txt file (Appendix D). The final appendix (Appe ndix E) contains tables of all the derived physiological parameters.



### 2. EVALUATION OF THE CU RRENT PHYSIOLO GY FILE DATA

The physiology.txt file was originally genera ted for the PNEM model by T. Johnson. It was last updated 6/11/1998, as documented in the report User’s Guide: Software for Estimating Ventilation (Respiration) Rates for Use in Dosimetry Models , (T. Johnson and J. Capel). In that report, the original referenc es for the data in the file were provided. An evaluation of the data in the file was include d in a previous memo to the WA-COR under this work assignment. A summary of those findings is repeated here.

2.1 Normalized Maximal Oxygen Uptake (nvo2max).

The nvo2max data were derived from a nu mber of sources. The data for males, especially, were pieced togeth er from a variety of studies (a total of 6), leading to discontinuities in the distribut ional parameters. However, in each age and gender cohort, the distributions parameters were derive d from a single published study. Additionally, much of the nvo2max data is quite old. The data for males at age 20 and at 28-69 came from a study from 1960 [1]. Data for males aged 0-8 and 16-19, and females 0-19 came from a figure in a textbook from 1977 [2], which in turn was based on limited earlier data. An additional issue with the 1977 data is (according to the report mentioned above) that values for certain ages (very young or elderly) were acquired by simple tangential extrapolation of the data in the figure.February 2010 A-5 Draft – Do Not Cite or Quote In addition, in some cases it was not clear how the parameters were derived from the referenced studies. For example, Heil et al. [3] was referenced as the source of the values for females aged 66-100. However, an examination of that study provided no clues as to how the values were actually determined. As far as can be determined, in no place did the authors break down the means and SDs of their data into gr oups separated by both gender and age simultaneously.

2.2 Body Mass.

The current body mass data were derived from an in-depth analysis [4, 5] of the second CDC National Health and Nutrition Exam ination Survey (NHANES II) body mass data [6]. The data were relatively comprehensive, and the methods used to generate the lognormal distributions were sound. However, the NHANES II data were compiled for the years 1976-1980, so an analys is of more recent data is necessary to accurately account for changes in human activity pattern s in adults and especially children.

2.3 Resting Metabolic Rate.

Not included for evaluation, per discussion with WA-COR.

2.4 Hemoglobin Content and Blood Volume Factor.

The original references for the hemoglobin content or blood volume factor values given in the current physiology.txt f ile could not be identified. Th erefore, their validity could not be evaluated and it was desirable that new statistics be calculated.

2.5 Summary of Findings

 In some cases, especially for nvo2max, th e data are unnecessar ily and confusingly disjointed across ages.

 It is also unclear how some of the nvo2max values were derived from the referenced studies.

 With the exception of the Schofield equati ons for the BM/RMR regression, parameter distributions at each age a nd gender cohort were derived from data from a single study.

 Many of the studies used are very old (ex. 1960, 1977).

 Some the data is of questionable validity (for example, the extr apolation of a textbook figure is used), although it may have been the best available at the time of the compilation of the file.

 The original source of the hemoglobin c ontent and blood volume factor data could not be identified.

 Given these conclusions, we recommended a full review and update of the current physiology.txt file data. Specifically, we recommended that where possible, newFebruary 2010 A-6 Draft – Do Not Cite or Quote distributions or equation s should be developed based on thorough, compiled data from appropriate studies.



### 3. DERIVATION OF NEW DI STRIBUTIONS FOR BODY MASS

3.1 The NHANES Body Mass Dataset.

New body mass distributions were generated from data from the National Health and Nutrition Examination Survey (NHANES). Th is survey is an ongoing study carried out by the National Center for Health Statistics of the Centers for Disease Control. EPA recognizes the utility of this dataset in characterizing th e American population for risk assessment and policy support purposes [7].

Older NHANES data (for the years 1976-1980) have been used previously to develop population estimates of body mass distributions [4,5]. The current Physiology.txt file body mass distributions are based on this work. However, the analysis presented here is based on the most recent NHANES data, for the years 1999-2004 [8].

Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES studies were downloaded from the NHANES we bsite. The files were downloaded as SAS xpt datasets. The downloaded files were as follows:

1999-2000 BMX.xpt Demo.xpt 2001-2002 BMX_b_r.xpt

Demo_b.xpt 2003-2004 BMX_c.xpt Demo_c.xpt

The Demographic datasets contained the age and gender values for each survey participant, while the Body Measurement da tasets contained the body weights for each subject. The combined dataset comprise d 31,126 individuals. This resulted in approximately 400-500 persons in each age 0-18 year cohort, and approximately 80-150 persons in each age 19-85 year cohort (the NHANES studies more heavily sampled children).

3.2 Calculation of the New Sampling Weig hts for the Combined NHANES Dataset.

In the analysis of the NHANES data, sampling we ights must be used to ensure that the data are weighted to appropriately repres ent the national population. Sampling weights for the combined NHANES body mass dataset were derived as recommended by the documentation provided with the most recent NHANES release [9]. Specifically, the sampling weight for each subject was calculated as:February 2010 A-7 Draft – Do Not Cite or Quote 2004 2003 3 1

  w w combined (1)

2002 1999 3 2

  w w combined (2)

where w combined is the sampling weight for the combined dataset, w 2003-2004 is the weight for the subjects in the most recent study, and w 1999-2002 is the weight for subjects in combined 4-year (1999-2000 and 2001-2002) NHANES dataset. (Both weights are provided with the appropriate NHANES re lease. The combined 1999-2002 weight, which is not a simply half of that for th e corresponding 2-year periods, was explicitly calculated for researcher use by CDC since th e two 2-year periods use different census data.)

By using the sampling weights, once can c onsider any 2-year NHA NES dataset or any combination of datasets as a nationally representative sample.

3.3 Fitting the Body Mass Data.

In the current physiology file, body mass is modeled as a two-parameter lognormal distribution. The NHANES body mass data were fit to several type s of distributions (including normal, beta, and three-parameter l ognormal distributions). It was determined that overall, the distribution that provided the best comb ination of good behavior over ages and good fit to the data was a two-parameter lognormal distribution.

The data were fit to the lognormal dist ributions using the SAS PROC UNIVARIATE procedure. The FREQ option of the procedure was used to apply the sampling weights. The SAS code used to generate the body ma ss distributions is pr ovided in Appendix A.

As the NHANES 1999-2003 studies only covered persons up to age 85, linear forecasts were made for ages 86-100, as base d on the data for ages 60 and greater.

3.4 Body Mass Results.

Geometric means and standard deviations (SD) for the best-fit lognor mal distributions for body mass are given in Figures 1 and 2. The means behaved fairly smoothly across ages. Note that for children age 0-18, the values of the new fits are similar, but slightly higher than those in the current P hysiology.txt file, which were derived from earlier NHANES studies. The new means also capture the tr end towards decreasing body weight in older persons that was previously negl ected in the Physiology.txt file.

The maximum and minimum values for the distri butions are presented in Figures 3 and 4. The minimums and maximums were calculated as the 1 st and 99 th percentile of the raw body mass data for the cohort. (Note th at these values differ from the 1 st and 99 thFebruary 2010 A-8 Draft – Do Not Cite or Quote percentiles of the fitted lognormals.) While the minimum value is consistent with the current Physiology.txt (which was based on earlier NHANES studies), the new cohort maximums are generally higher than before.

The behavior of several of the body mass parame ters (especially the SD) is fairly noisy, especially for adults. This is most likely due to the smaller number of samples for adults as compared to children. Therefore, it ma y desirable to use age- grouped data or running averages over years in these age ranges. Wh ile the attached prepar ed Physiology.txt file uses the “raw” parameters, smoothed results using 5-year running averages are provided in the attached data tables (Appendix E, plots not shown). These could be used at the direction of EPA; changing th e “official” release Physiology. txt file would be trivial.February 2010 A-9 Draft – Do Not Cite or Quote MALES: Body Mass Geometric Mean

0.000 10.000 20.000 30.000 40.000 50.000 60.000 70.000 80.000 90.000 100.000

0 20 40 60 80 100
Age (years)Mass (kg) New Values

Current Physiology File

FEMALES: Body Mass Geometric Mean

0.000 10.000 20.000 30.000 40.000 50.000 60.000 70.000 80.000 90.000

0 20 40 60 80 100
Age (years)Mass (kg)

New Values

Current Physiology File

Figure 1. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.February 2010 A-10 Draft – Do Not Cite or Quote MALES: Body Mass GSD

1.000 1.050 1.100 1.150 1.200 1.250 1.300 1.350 1.400

0 20 40 60 80 100
Age (years)Mass (kg) New Values

Current Physiology File

FEMALES: Body Mass GSD

1.000 1.050 1.100 1.150 1.200 1.250 1.300 1.350

0 20 40 60 80 100
Age (years)Mass (kg) New Values

Current Physiology File

Figure 2. Geometric Standard Deviations for the Best-fit Lognormal Distributions for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.February 2010 A-11 Draft – Do Not Cite or Quote FEMALES: Body Mass Minimum

0 10 20 30 40 50 60

0 20 40 60 80 100
Age (years)Mass (kg)

New Values Current Physiology File MALES: Body Mass Minimum

0 10 20 30 40 50 60 70

0 20 40 60 80 100
Age (years)Mass (kg)

New Values

Current Physiology File

Figure 3. Minimums (1 st Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.February 2010 A-12 Draft – Do Not Cite or Quote MALES: Body Mass Maximum

0 50 100 150 200 250

0 20 40 60 80 100
Age (years)Mass (kg) New Values

Current Physiology File

FEMALES: Body Mass Maximum

0 20 40 60 80 100 120 140 160 180 200

0 20 40 60 80 100
Age (years)Mass (kg) New Values

Current Physiology File

Figure 4. Maximums (99 th Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.February 2010 A-13 Draft – Do Not Cite or Quote 

### 4. DERIVATION OF NEW DI STRIBUTIONS FOR NORMALIZED VO2MAX

4.1 The Nvo2max Data

The NHANES studies do report data for vo2max in individuals. However, the NHANES vo2max values are estimated values, i.e. they are not measured directly. Such estimated values are not appropriate for use in this co ntext (as per discussion with the WA-COR). Therefore, nvo2max distributi onal shapes were determined from a large database of experimental and literature vo2max measur ements for different age/gender cohorts.

A PubMed-based literature search located a number of studies in which vo2max was directly measured. In add ition, a large number of scien tific papers (~350) reporting vo2max were also provided to Alion by the WA -COR. All the studies were evaluated for use by determining if: 1) any normalized vo2max data for individuals were reported or 2) any group means for narrow age-gender cohorts were reported. Studies in which the studied age group was very broa d or contained both males and females were discarded. Also discarded were any st udies in which vo2max was not normalized by body mass, or for which no age data were reported. Data fo r ill or highly-trained individuals were not used; however, studies in which subjects underwent mild or moderate exercise training were included. Two large databases, one of individual vo2max data and one of grouped means and SDs, were constructed from the valid studies.

The database of individual data comprise d age versus nvo2max data for 1949 men and 1558 women. The data were pulled from either tables or graphs in 20 published studies [11-30]. Additional raw experimental data were provided by the WA-COR [31]. In the case of the graphical data, the original sour ce was digitized and the data points were pulled from the digital figure using gra phics software. (This was accomplished by calibrating the pixels of the digitized image w ith the range of age and nvo2max values.) The individual nvo2max data for males and females are shown in Figure 5.

The grouped mean and SD data were derive d from 136 studies [32-167]. These data comprised approximately 550 means and SDs fo r different age/gender cohorts. Single age/gender cohort means and SD values for the Adams data [31] were also included in this dataset. Only data fo r subject groups having an age SD of less than approximately 2- 3 years were considered. The grouped mean values for men and women are shown in Figure 6, while the group SD va lues are shown in Figure 7.February 2010 A-14 Draft – Do Not Cite or Quote 

### MALES: Nvo2max

0 20 40 60 80 100

0 20 40 60 80 100
Age (years)nvo2max (ml/kg)nnn



### FEMALES: Nvo2max

0 10 20 30 40 50 60 70 80

0 20 40 60 80 100
age (years)nvo2max (ml/kg

Figure 5. Individual Nvo2max Measurements for Males and Females, Derived from Literature Studies and Experimental Measurements. N=1558 N=1949February 2010 A-15 Draft – Do Not Cite or Quote MALES: Study Means, Nvo2max

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0

0.0 20.0 40.0 60.0 80.0 Age (Years)nvo2max (ml/kg

FEMALES: Study Means, Nvo2max

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 Age (Years)nvo2max (ml/kg

Figure 6. Grouped Mean Nvo2max Measurements for Males and Females, Derived from Literature Studies.February 2010 A-16 Draft – Do Not Cite or Quote MALES: Study Standard Deviations: Nvo2max

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

0.0 20.0 40.0 60.0 80.0 Age (Years)nvo2max (ml/kg

FEMALES: Study Standard Deviations: Nvo2max

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 Age (Years)nvo2max (ml/kg

Figure 7. Nvo2max Standard Deviations for Males and Females, Derived from Literature Studies.February 2010 A-17 Draft – Do Not Cite or Quote 4.2 Determining the NVo2max Distributions

Both the grouped mean and the individual data sets were evaluated for use in deriving the nvo2max parameters.

The group means and SD were combined into single age/gender cohort values. The combined means were calculated as mean of the group means, weighted by the number of subjects. The group SD were calculate d by transforming each group SD to a group variance, calculating the mean variance (w eighted by the number of subjects in each study) and retransforming the variances to SD s. The combined group means and SDs are given in Figures 8 and 9.

The combined group means were fairly well-b ehaved across age and gender cohorts (see Figure 8), while the SD data (Figure 9) were noisier. These data may be appropriate for use in the Physiology. txt file; however, it was noted that the group mean data, while plentiful for children, were not very well represented in the adult (30+ years) age range (especially for women). This is mainly due to the fact that very few investigators use narrow age cohorts when studying adults, rather , it was far more common for broader age groups to be used. These data were not in cluded in the grouped mean analysis, as the mean nvo2max for a broad age group cannot be assumed valid for the cohort represented by the study age mean. Therefore, we opted to use the database of individual nvo2max measurements to develop new distributions for the Physiology.txt file.

The individual nvo2max data were fit to seve ral types of distributi ons (including normal, beta, and lognormal distributions). It was determined that the normal distribution fit the data best. The parameters (means and standa rd deviations) of the best-fit distributions were obtained using the SAS PR OC UNIVARIATE procedure. The SAS code used to fit the data is given in Appendix B.

Both raw and smoothed nvo2max fits were calculated. Calcul ating 5-year running averages did not smooth the data considerab ly. Therefore, the smoothed fits were determined by choosing a best-fit functional form for the nv02max data. The data were fit to functions as follows:

Mean (Age 0-20): Linear function Mean (Age 21-100): Parabolic function SD (Age 0-26): Linear function SD (Age 27-100): Parabolic function

Fitting the data in this manner also allowed for all age/gender cohorts to be represented. Since only cohorts having N>10 were fit to distributions, there were some cohorts for which no parameters were calculated. The ra w and smoothed fits for means are given in Figure 10; analogous data for SD is given in Figure 11. The raw nvo2max parameters were not as clean across ages as the body mass data (proba bly due to the much smaller sample size), and thus the smoothed fits were selected for use in the attached Physiology.txt file. As with body mass, the raw fits may be used at the direction of EPA.February 2010 A-18 Draft – Do Not Cite or Quote The results for the nvo2max means were in fact quite close to those in the current file. However, the values exhibited much more cons istent behavior across ages, and the values for elderly persons were lower than previously . The SD values were also in the same range as the current values, yet they no l onger demonstrate nonsen sical discontinuities
across ages.

The minimum and maximum nvo2max values were assumed to be the 1 st and 99 th percentile of the best-fit lognormal distribution. ( Note : this is different from the method used for estimating the body mass limits. In that case, the samples were large enough that the percentiles of the raw data were ap propriate for use as minimum and maximum. As the nvo2max data cohorts had much sm aller N than the NHANES studies, the raw percentiles were less appropriate.) The maximum and minimum values are shown in Figures 12 and 13.February 2010 A-19 Draft – Do Not Cite or Quote MALES: Nvo2max, Combined Group Means

0 10 20 30 40 50 60

0 20 40 60 80
Age (Years)nv02max (ml/kg

FEMALES: Nvo2max, Combined Group Means

0 10 20 30 40 50

0 10 20 30 40 50 60 70
Age (Years)nv02max (ml/kg

Figure 8. Combined Nvo2max Group Means for Males and FemalesFebruary 2010 A-20 Draft – Do Not Cite or Quote MALES: Nvo2max, Combined Group SD

0 2 4 6 8 10 12 14 16

0 20 40 60 80
Age (Years)nv02max (ml/kg

FEMALES: Nvo2max, Combined Group SD

0 2 4 6 8 10

0 10 20 30 40 50 60 70
Age (Years)nv02max (ml/kg

Figure 9. Combined Nvo2max Group Standard Deviations.February 2010 A-21 Draft – Do Not Cite or Quote MALES: MEAN Nvo2max

0 10 20 30 40 50 60 70

0 20 40 60 80 100 Age (Years)nvo2max (ml/kg

Raw Fit Values Smoothed Fit Values Current Physiology.txt

FEMALES: MEAN Nvo2max

0 10 20 30 40 50 60

0 20 40 60 80 100
Age (Years)nvo2max (ml/kg

Raw Fit Values Smoothed Fit Values Current Physiology.txt

Figure 10. Nvo2max Normal Distribution Fits: Raw Fit Means and Smoothed Fits.February 2010 A-22 Draft – Do Not Cite or Quote MALES: Nvo2max Standard Deviation

0 2 4 6 8 10 12 14

0 20 40 60 80 100
Age (Years)nvo2max (ml/kg Raw Values

Fit Values Current Physiology.txt

FEMALES: Nvo2max Standard Deviation

0 2 4 6 8 10 12

0 20 40 60 80 100
Age (Years)Nvo2max (ml/kg Raw Values

Fit Values Current Physiology.txt

Figure 11. Nvo2max Normal Dist ribution Fits: Raw Fit Standa rd Deviations and Smoothed Fits.February 2010 A-23 Draft – Do Not Cite or Quote 

### MALES: Nvo2max Minimum

0 10 20 30 40 50 60

0 20 40 60 80 100
Age (Years)nvo2max (ml/kg 

### New Values
Current Physiology.txt

FEMALES: Nvo2max Minimum

0 5 10 15 20 25 30 35 40 45

0 20 40 60 80 100 Age (Years)Nvo2max (ml/kg 

### New Values
Current Physiology.txt

Figure 12. Nvo2max Minimums. 1 st Percentile of the Best-f it Normal Distribution.February 2010 A-24 Draft – Do Not Cite or Quote 

### MALES: Nvo2max Maximum

0 10 20 30 40 50 60 70 80

0 20 40 60 80 100
Age (Years)nvo2max (ml/kg



### New Values
Current Physiology.txt

FEMALES: Nvo2max Maximum

0 10 20 30 40 50 60 70

0 20 40 60 80 100
Age (Years)Nvo2max (ml/kg



### New Values
Current Physiology.txt

Figure 13. Nvo2max Maximums. 99 th Percentile of the Best -fit Normal Distribution.February 2010 A-25 Draft – Do Not Cite or Quote 

### 5. DERIVATION OF NEW DIST RIBUTIONS FOR HEMOGLOBIN CONTENT (HEMOGLOBIN DENSITY)

The new hemoglobin content values were derived from the combined NHANES 1999- 2000 and 2001-2002 datasets. As of December 2005, hemoglobin data had not yet been released for the 2003-2004 study. The age data was provided in the Demographic datasets (Demo.xpt and Demo_b.xpt, previ ously downloaded for the body mass analysis) for the two survey periods, while hemogl obin content (in g/dL) was provided in the Laboratory #25 (Complete Blood Count) datasets (lab25.xpt and l25_b.xpt, which were downloaded for this analysis). The datase t comprised 20,321 indivi duals; appropriate sample weights were used for the combined 4-year (1999-2002) data set as provided with the NHANES 2001-2002 data release. Similarly to the body mass data, the hemoglobin content values were analyzed in SAS. Th e age and hemoglobin datasets were merged and fit to normal distributions using th e SAS PROC UNIVARIATE procedure. The FREQ option of the procedure was used to ap ply the sampling weights. The SAS code in provided in the Appendix C.

Hemoglobin content statistics were estimated for single-year age and gender cohorts for ages 1-19, as the behavior of the means we re smooth in this age range. For persons 20 and over, the data were groupe d in 5-year cohorts (20-24, 25-29, etc.) No blood count data were available for subject s under 1 year of age or greater than 90. The age 0 mean values were obtained by a linear regression of ages 1-20 (males) or 1 to 11 (females) back to age 0. These were the ages for whic h the hemoglobin content demonstrated an increase with age. The 91-95 and 96- 100 mean values were obtained by a linear regression of the 61-65 and older age groups. As the standard deviat ions did not appear to behave as smoothly with age as did th e mean values, the age 0 value was assumed equal to the age 1 value, and the age 91-95 and 96-100 value was assumed equal to the age 90-94 value.

The resulting means and standard deviations for the best-for normal distributions for hemoglobin content are gi ven in Figures 14 and 15

. The current hemoglobin content values are shown for comparison.

The main conclusions that can be made is that the current Phys iology.txt input file overestimates mean hemoglobin content in child ren and in older persons. The standard deviation values in the current physiology.txt file are fairly close to those found in this analysis. The new values are not very smoot h over ages; EPA may elect to continue to use the current values. It should be noted that the original refe rence for the current hemoglobin statistics is unknown.

Note: In the current implementation of APEX , the hemoglobin conten t statistics affect only the CO dose algorithm calculations.February 2010 A-26 Draft – Do Not Cite or Quote MALES: Mean Hemoglobin Content

10 11 12 13 14 15 16 17

0 20 40 60 80 100
Age (years)Hb Content (g/dL) New Values
Current Physiology.txt

FEMALES: Mean Hemoglobin Content

10 11 12 13 14 15 16 17

0 20 40 60 80 100
Age (years)Hb Content (g/dL)

Figure 14. Mean Values of Hemoglobin C ontent as Derived from the 1999-2002 NHANES Dataset, with Comparison to Current Physiology.txt ValuesFebruary 2010 A-27 Draft – Do Not Cite or Quote MALES: Hemoglobin Content Standard Deviation

0 0.5 1 1.5

2

0 20 40 60 80 100
Age (years)Hb Content (g/dL)

New Values

Current Physiology.txt

FEMALES: Hemoglobin Content Standard Deviation

0 0.5 1 1.5

2

0 20 40 60 80 100
Age (years)Hb Content (g/dL)

Figure 15. Values of Hemoglobin Content Stan dard Deviation as Derived from the 1999- 2002 NHANES Dataset, with Comparison to Current Physiology.txt ValuesFebruary 2010 A-28 Draft – Do Not Cite or Quote 

### 6. BLOOD VOLUME AS A FUNC TION OF HEIGHT AND WEIGHT

In APEX, blood volume is estimated as a func tion of height and weight by the following equation:

30 3

- + K*Height BVF*Weight V

blood 

where V blood is the blood volume (ml), Weight is in pounds, and height is in inches. BVF is the blood volume factor that is read in from the physiology file, and K is a genderdependent constant (0.00683 for males, 0.00678 fo r females). This is a modification of Allen’s equation [168] to in clude the age/gender dependent BVF and adjusted for the given units.

As previously mentioned, the data upon wh ich the BVF values in the physiology file were based could not be iden tified. The available documentation for pNEM documents a non-age-dependent use of these equations.

In addition, no appropriate data were found for deriving new estimates for the BVF variable as a function of age and gender fo r use with the Allen equations. It should be noted however, that these equations were m odified by Nadler [169]. These equations seem to be used somewhat more often than the originals in the literature.

In addition, other (more recent) equations exist for estimation of blood volume from height and weight specifi cally in children [170,171] or body surface area [172]. In particular, Linderkamp et al. [170] derive d prediction equations for blood volume as a function of a number of physiological parame ters for children in three different age groups. It is recommended that further analys is of this study and others be undertaken.

However, inclusion of new blood volume e quations in APEX would require changes beyond the current physiology file (i.e. other, more intensive, code changes would be needed). Thus, at the present time, no sp ecific improvements to the current BVF values in the physiology file can be made.February 2010 A-29 Draft – Do Not Cite or Quote 

### REFERENCES

1. Astrand, I. 1960. Aerobic work capac ity in men and women with special reference to age. Acta. Phys. Scand. 49: Suppl. 169. 2. Astrand, PO and Rodhal K. 1977. Textbook of Work Physiology. 2nd addition, McGraw-Hill, NY. 3. Heil DP, Freedson PS, Ahlquist LE, Pri ce J, and Rippe JM. 1995. Nonexercise regression models to estimat e peak oxygen consumption. Med. Sci. Sports Exer 27:599-606. 4. Brainard J and Burmaster D. 1992. Bivariat e distributions for height and weight of men and women in the United Stat es. Risk Analysis, 12(2):267-275. 5. Burmaster DE and Crouch E. 1997. Lognorma l distributions of body weight as a function of age for males and females in the United States, 1976-1980. Risk Analysis, 17(4): 499-505. 6. Centers for Disease Control and Preventi on (CDC). National Center for Health Statistics (NCHS). National Health a nd Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of H ealth and Human Services, Centers for Disease Control and Prevention, 1976-1980. <http://www.cdc.gov/nchs/nhanes.htm>. 7. U.S. Environmental Protection Agency (EPA). (2003) Handbook for use of data from the national health and nutrition examination surveys (NHANES). National Center for Environmental Assessment, Washington, DC: EPA/600/R-02/044. Available from: National Technical Information Service, Springfield, VA, and

< http://www.epa.gov/ncea >. 8. Centers for Disease Control and Preventi on (CDC). National Center for Health Statistics (NCHS). National Health a nd Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of H ealth and Human Services, Centers for Disease Control and Prevention, 1999-2004. <http://www.cdc.gov/nchs/nhanes.htm>. 9. Center for Disease Control. National Cent er For Health Statistics. The National Health and Nutriation Examination Survey (NHANES). Analytic and Reporting Guidelines. December 2005. Available online at www.cdc.gov. 10. Andersen KL, Ilmarinen J, Rutenfranz J, Ottmann W, Berndt I, Kylian H. Leisure time sport activities and maximal aerobic power during late adolescence. Eur J Appl Physiol 1984; 52: 431-6. 11. Armstrong N, Kirby BJ, McManus AM, Welsman JR. Aerobic fitness of prepubescent children. Ann Hum Biol. 1995 Sep-Oct;22(5):427-41. 12. Armstrong N, Welsman JR. Assessment and interpretation of aerobic fitness in children and adolescents.Exer c Sport Sci Rev. 1994;22:435-76. 13. Astrand I. Aerobic work capacity in me n and women with special reference to age. Acta Physiol Sca nd. 1960;49(Suppl 169):1-92. 14. Eliakim A, Scheett T, Allmendinger N, Br asel JA, Cooper DM. Training, muscle volume, and energy expenditure in nonobese American girls. J Appl Physiol. 2001 Jan;90(1):35-44.February 2010 A-30 Draft – Do Not Cite or Quote 15. Falgairette, G. 1989. Evolution de la puissance maximal aerobie de l’enfance a l’age adulte: influence de l’activite physi que et al. sportive. Revue Des Sciences et al. Techniques en Activites Phys iques et al. Sportives. 10:43-58. 16. Fleg JL, Lakatta EG. Role of muscle loss in the age-associated reduction in VO2 max. J Appl Physiol. 1988 Sep;65(3):1147-51. 17. Ghesquiere J, Reybrouck T, Faulkner JA, Cattaert A, Fagard R, Amery A. Anaerobic threshold for long-term exercise and maximal exercise performance. Ann Clin Res. 1982;14 Suppl 34:37-41. 18. Kamon E, Pandolf KB. Maximal aerobi c power during laddermill climbing, uphill running, and cycling. J Appl Physiol. 1972 Apr;32(4):467-73. 19. Katch VL, Park MW. Minute-by-minute oxyge n requirement and work efficiency for constant-load exercise of increasi ng duration. Res Q. 1975 Mar;46(1):38-47. 20. McMiken DF. Oxygen deficit and repayment in submaximal exercise. Eur J Appl Physiol Occup Physiol. 1976 Jun 21;35(2):127-36. 21. Nevill A, Rowland T, Goff D, Martel L, Ferrone L. Scaling or normalising maximum oxygen uptake to predict 1-mile run time in boys. Eur J Appl Physiol. 2004 Jul;92(3):285-8. Epub 2004 Apr 9. 22. Pivarnik JM, Fulton JE, Taylor WC, Sn ider SA. Aerobic capacity in black adolescent girls. Res Q Ex erc Sport. 1993 Jun;64(2):202-7. 23. Posner JD, Gorman KM, Klein HS, Cline CJ. Ventilatory threshold: measurement and variation with age. J Appl Physiol. 1987 Oct;63(4):1519-25. 24. Reybrouck T, Heigenhauser GF, Faulkne r JA. Limitations to maximum oxygen uptake in arms, leg, and combined arm-leg ergometry. J Appl Physiol. 1975 May;38(5):774-9. 25. Ribisl PM, Kachadorian WA. Maxima l oxygen intake prediction in young and middle-aged males. J Sports Med Phys Fitness. 1969 Mar;9(1):17-22. 26. Schiller BC, Casas YG, Desouza CA, Seals DR. Maximal aerobic capacity across age in healthy Hispanic and Cau casian women. J Appl Physiol. 2001 Sep;91(3):1048-54. 27. Seals DR, Hagberg JM, Hurley BF, Ehsa ni AA, Holloszy JO. Endurance training in older men and women. I. Cardiovascular responses to exercise. J Appl Physiol. 1984 Oct;57(4):1024-9. 28. Sheel A, Seddon N, Knight A, McKenzie DC, R Warburton DE. Physiological responses to indoor rock-climbing and their relationship to maximal cycle ergometry. Med Sci Sports Exerc. 2003 Jul;35(7):1225-31. 29. Talbot LA, Metter EJ, Fleg JL. Leis ure-time physical activities and their relationship to cardiorespiratory fitne ss in healthy men and women 18-95 years old. Med Sci Sports Exerc. 2000 Feb;32(2):417-25. 30. Tanaka H, Desouza CA, Jones PP, Stev enson ET, Davy KP, Seals DR. Greater rate of decline in maximal aerobic cap acity with age in physically active vs. sedentary healthy women. J Appl Physiol. 1997 Dec;83(6):1947-53. 31. Adams. Personal Communication – T. McCurdy EPA. 32. Ahmad F, Kavey RE, Kveselis DA, Ga um WE, Smith FC. Responses of nonobese white children to treadmill exerci se. J Pediatr. 2001 Aug;139(2):284-90. 33. Andersen KL, Hermansen L. Aerobic work capacity in middle-aged Norwegian men. J Appl Physiol. 1965 May;20(3):432-6.February 2010 A-31 Draft – Do Not Cite or Quote 34. Andersen KL, Ilmarinen J, Rutenfranz J, Ottmann W, Berndt I, Kylian H, Ruppel M. Leisure time sport activities a nd maximal aerobic power during late adolescence. Eur J Appl Physio l Occup Physiol. 1984;52(4):431-6. 35. Andersen KL, Seliger V, Rutenfranz J, Mocellin R. Physical performance capacity of children in Norway. Part I. Population parameters in a rural inland community with regard to maximal ae robic power. Eur J Appl Physiol Occup Physiol. 1974;33(3):177-95. 36. Armstrong N and Welsman J. Daily physic al activity estimated from continuous heart rate monitoring and laboratory indi ces of aerobic fitness in pre-adolescent children. (abs.) Res Q Exer Sport 64:A-24, 1993. 37. Armstrong N, Balding J, Gentle P, Kirby B. Estimation of coronary risk factors in British schoolchildren: a prelimin ary report. Br J Sports Med. 1990 Mar;24(1):61-6. 38. Armstrong N, Balding J, Gentle P, Kir by B. Patterns of physical activity among 11 to 16 year old British childre n. BMJ. 1990 Jul 28;301(6745):203-5. 39. Armstrong N, Bray S. Physical activity patterns defined by continuous heart rate monitoring. Arch Dis Child. 1991 Feb;66(2):245-7. 40. Armstrong N, Davies B, Heal M. The speci ficity of energy utilisation by trained and untrained adolescent boys. Br J Sports Med. 1983 Sep;17(3):193-9. 41. Armstrong N, Kirby BJ, McManus AM, Welsman JR. Aerobic fitness of prepubescent children. Ann Hum Biol. 1995 Sep-Oct;22(5):427-41. Erratum in: Ann Hum Biol 1996 Mar-Apr;23(2):188. 42. Armstrong N, Welsman JR, Kirby BJ. Submaximal exercise and maturation in 12-year-olds. J Sports Sc i. 1999 Feb;17(2):107-14. 43. Armstrong N, Welsman JR, Kirby BJ. P eak oxygen uptake and maturation in 12- yr olds. Med Sci Sports Exerc. 1998 Jan;30(1):165-9. 44. Armstrong N, Williams J, Balding J, Gentle P, Kirby B. The peak oxygen uptake of British children with reference to age, sex and sexual maturity. Eur J Appl Physiol Occup Physiol. 1991;62(5):369-75. 45. Atomi Y, Fukunaga T, Yamamoto Y, Hatta H. Lactate threshold and vo2max of trained and untrained bo ys relative to muscle mass and composition. In: Rutenfranz J, Mocellin R, Klimt F, eds. children and exercise XII. International series on sport sciences, Vol 16. Cham paign, IL: Human Kinetics, 1986:227-232. 46. Atomi Y, Iwaoka K, Hatta H, Miyashita M, Yamamoto Y. Daily physical activity levels in preadolescent boys related to VO2max and lactate threshold. Eur J Appl Physiol Occup Physiol. 1986;55(2):156-61. 47. Bailey, D.A., Ross, W.D., Mirwald, R.L. and Weese, C. Size dissociation of maximal aerobic power during growth in boys. Medicine in Sport 1978; 11: 140- 151. 48. Bale P. Pre- and post-adolescents' physiol ogical response to exer cise. Br J Sports Med. 1981 Dec;15(4):246-9. 49. Bar-Or O, Skinner JS, Bergsteinova V, Shearburn C, Royer D, Bell W, Haas J, Buskirk ER. Maximal aerobic capacity of 6--15 year-old gi rls and boys with subnormal intelligence quotients. Acta Paediatr Scand Suppl. 1971;217:108-13. 50. Becker DM, Vaccaro P. Anaerobic thre shold alterations caused by endurance training in young children. J Sports Me d Phys Fitness. 1983 Dec;23(4):445-9.February 2010 A-32 Draft – Do Not Cite or Quote 51. Binkhorst 1984 (Ref??) From T. McCu rdy spreadsheet, dated Nov 15, 2000. 52. Binkhorst RA, Saris WHM, Noordeloos AM, et al. Maximal oxygen consumption of children (6 to 18 years) predicted fr om maximal and submaximal values in treadmill and bicycle tests. In: Rutenfranz J, Mocellin R, Klimt F, eds. children and exercise XII. International series on sport sciences, Vol 16. Champaign, IL: Human Kinetics, 1986:227-232. 53. Boileau RA, Bonen A, Heyward VH, Ma ssey BH. Maximal aerobic capacity on the treadmill and bicycle ergometer of boys 11-14 years of age. J Sports Med Phys Fitness. 1977 Jun;17(2):153-62. 54. Bonen A, Heyward VH, Cureton KJ, Boileau RA, Massey BH. Prediction of maximal oxygen uptake in boys, ages 7-15 years. Med Sci Sports. 1979 Spring;11(1):24-9. 55. Brown, J.D., A.D. Mahon and D.M. Pla nk. Attainment of maximal exercise criteria in boys and men. Journal of S ports Medicine and Physical Fitness, 42: 135-140, 2002. 56. Buskirk E, Taylor HL. Maximal oxygen intake and its relation to body composition, with special reference to chronic physical activity and obesity. J Appl Physiol. 1957 Jul;11(1):72-8. 57. Chausow 1984 (Ref??) In T. McCurdy spreadsheet, dated Nov 15, 2000. 58. Cooper DM, Berry C, Lamarra N, Wasserman K. Kinetics of oxygen uptake and heart rate at onset of exer cise in children. J Appl P hysiol. 1985 Jul;59(1):211-7. 59. Cooper DM, Weiler-Ravell D, Whipp BJ, Wa sserman K. Aerobic parameters of exercise as a function of body size during gr owth in children.J Appl Physiol. 1984 Mar;56(3):628-34. 60. Cumming GR, Borysyk LM. Criteria for maximum oxygen uptake in men over 40 in a population survey. Med Sci Sports. 1972 Spring;4(1):18-22. 61. Cumming GR, Everatt D, Hastman L. Bruce treadmill test in children: normal values in a clinic population. Am J Cardiol. 1978 Jan;41(1):69-75. 62. Cumming GR, Goulding D, Baggley G. Fa ilure of school physical education to improve cardiorespiratory fitness. Ca n Med Assoc J. 1969 Jul 26;101(2):69-73. 63. Cunningham DA, Paterson DH, Blimkie CJ, Donner AP. Development of cardiorespiratory function in circumpubert al boys: a longitudinal study. J Appl Physiol. 1984 Feb;56(2):302-7. 64. Cunningham DA, Stapleton JJ, MacDonald IC, Paterson DH. Daily energy expenditure of young boys as related to maximal aerobic power. Can J Appl Sport Sci. 1981 Dec;6(4):207-11. 65. Cunningham DA, Telford P, Swart GT. The cardiopulmonary capacities of young hockey players: age 10. Med Sc i Sports. 1976 Spring;8(1):23-5. 66. Cunningham DA, van Waterschoot BM, Pa terson DH, Lefcoe M, Sangal SP. Reliability and reproducibility of maximal oxygen uptake measurement in children. Med Sci Sports . 1977 Summer;9(2):104-8. 67. Cureton KJ, Sloniger MA, Black DM, Mc Cormack WP, Rowe DA. Metabolic determinants of the age-related improveme nt in one-mile run/walk performance in youth. Med Sci Sports Exerc. 1997 Feb;29(2):259-67.February 2010 A-33 Draft – Do Not Cite or Quote 68. Cureton KJ, Sloniger MA, O'Bannon JP, Black DM, McCormack WP. A generalized equation for prediction of VO2peak from 1-mile run/walk performance. Med Sci Sports Exerc. 1995 Mar;27(3):445-51. 69. Davies CT. Limitations to the pred iction of maximum oxygen intake from cardiac frequency measurements. J Appl Physiol. 1968 May;24(5):700-6. 70. Davies CT. Metabolic cost of exercise and physical performance in children with some observations on external loading. Eur J Appl Physiol Occup Physiol. 1980;45(2-3):95-102. 71. Dehn MM, Bruce RA. Longitudinal variat ions in maximal oxygen intake with age and activity. J Appl P hysiol. 1972 Dec;33(6):805-7. 72. Docherty D, Wenger HA, Collis ML. The effects of resistance training on aerobic and anaerobic power of young boys.Med Sc i Sports Exerc. 1987 Aug;19(4):389- 92. 73. Eiberg S, Hasselstrom H, Gronfeldt V, Froberg K, Svensson J, Andersen LB. Maximum oxygen uptake and objectively m easured physical activity in Danish children 6-7 years of age: the Copenhagen school child intervention study. Br J Sports Med. 2005 Oc t;39(10):725-30. 74. Eisenman PA, Golding LA. Comparison of e ffects of training on Vo2max in girls and young women. Med Sci Spor ts. 1975 Summer;7(2):136-8. 75. Fawkner S, Armstrong N. Modeling th e vo2 kinetic response to moderate intensity exercise in children. Acta Kinesiol Universit Tartu 7:80-84, 2002. 76. Fox EL, Bartels RL, Billings CE, O'Brien R, Bason RM, Mathews DK. Frequency and duration of interval tr aining programs and changes in aerobic power. J Appl Physiol 1975; 38:481-84 77. Fredriksen PM, Ingjer F, Nystad W, Thaulow E. A comparison of VO2(peak) between patients with congenital heart dis ease and healthy subjects, all aged 8-17 years. Eur J Appl Physiol Occ up Physiol. 1999 Oct;80(5):409-16. 78. Gastin PB, Costill DL, Lawson DL, Krzeminski K, McConell GK. Accumulated oxygen deficit during supramaximal all-out and constant intensity exercise. Med Sci Sports Exerc. 1995 Feb;27(2):255-63. 79. George JD. Alternative approach to maximal exercise testing and VO2 max prediction in college students. Res Q Exerc Sport. 1996 Dec;67(4):452-7. 80. Gilliam TB, Sady S, Thorland WG, We ltman AL. Comparison of peak performance measures in children ages 6 to 8, 9 to 10, and 11 to 13 years. Res Q. 1977 Dec;48(4):695-702. 81. Gilliam, T. & Freedson, P. Effects of a twelve-week school physical fitness program on peak VO2, body composition, a nd blood lipids in 7 to 9 year old children. Int J of Sports Med 1980;1:73-78. 82. Gutin B, Barbeau P, Owens S, Lemmon CR, Bauman M, Allison J, Kang HS, Litaker MS. Effects of ex ercise intensity on cardiovascular fitness, total body composition, and visceral adiposity of obese adolescents. Am J Clin Nutr. 2002 May;75(5):818-26. 83. Hansen, H. S., K. Froberk, J. R. Nielsen, and N. Hyldebrandt. A new approach to assessing maximal aerobic power in ch ildren: the Odense School Child Study. Euro. J. Appl. Physiol. 58: 618-624, 1989.February 2010 A-34 Draft – Do Not Cite or Quote 84. Harms C, Cordain L, Stager J, Sockler J, and Harris M. Body fat mass affects postexercise metabolism in males of si milar lean body mass. Med Exer Nutr Health 4:33-39, 1995. 85. Hebestreit H, Bar-Or O, McKinty C, Ri ddell M, Zehr P. Climate-related corrections for improved estimation of energy expenditure from heart rate in children. J Appl Physiol. 1995 Jul;79(1):47-54. 86. Hebestreit H, Kriemler S, Hughson RL, Bar-Or O. Kinetics of oxygen uptake at the onset of exercise in boys and men. J Appl Physiol. 1998 Nov;85(5):1833-41. 87. Hebestreit H, Mimura K, Bar-Or O. R ecovery of muscle power after highintensity short-term exercise: compari ng boys and men. J Appl Physiol. 1993 Jun;74(6):2875-80. 88. Hermansen L, Andersen KL. Aerobic work capacity in young Norwegian men and women. J Appl Physiol. 1965 May;20(3):425-31. 89. Hossack KF, Bruce RA. Maximal cardiac function in sedentary normal men and women: comparison of age-related change s. J Appl Physiol. 1982 Oct;53(4):799- 804. 90. Humphrey LD, Falls HB. Circulatory and metabolic responses of young women at HR = 180 and HR = max. Res Q. 1975 Dec;46(4):498-504. 91. Hunt B. R, George J.D., Vehrs P.R., Fi sher A. G., Fellingham G. W. (2000), Validity of a submaximal 1-mile track jog test in predicting VO2max in fit teenagers. Pediatric exer cise science 12(1):80-90. 92. Ikai M, Kitagawa K. Maximum oxygen uptake of Japanese related to sex and age. Med Sci Sports. 1972 Fall;4(3):127-31. 93. Kaminsky L, and Whaley M. Effect of interval-type exercise on excess postexercise oxygen consumption in obese and normal-weight women. Med Exer Nutr Health 2:106-111, 1993. 94. Kaminsky LA, Wehrli KW, Mahon AD, Robbi ns GC, Powers DL, Whaley MH. Evaluation of a shallow water running test for the estimation of peak aerobic power. Med Sci Sports Ex erc. 1993 Nov;25(11):1287-92. 95. Kanaley JA, Boileau RA. The onset of th e anaerobic threshold at three stages of physical maturity. J Sports Med P hys Fitness. 1988 Dec;28(4):367-74. 96. Katch FI, McArdle WD, Pechar GS. Rela tionship of maximal leg force and leg composition to treadmill and bicycle er gometer maximum oxygen uptake. Med Sci Sports. 1974 Spring;6(1):38-43. 97. Kemper H and Vershuur R. Maximal aerobic power in 13 and 14 year old teenagers in relation to biologic age. Int J Sports Med. 2:97-100, 1981. 98. Koch G, Eriksson BO. Effect of phys ical training on pulmonary ventilation and gas exchange during submaximal and maxi mal work in boys aged 11 to 13 years. Scand J Clin Lab Invest . 1973 Jan;31(1):87-94. 99. Kohrt WM, Malley MT, Coggan AR, Spina RJ, Ogawa T, Ehsani AA, Bourey RE, Martin WH 3rd, Holloszy JO. Effect s of gender, age, and fitness level on response of VO2max to training in 60 -71 yr olds. J Appl Physiol. 1991 Nov;71(5):2004-11. 100. Krahenbuhl GS, Pangrazi RP, Burkett LN, Schneider MJ, Petersen G. Field estimation of VO2 max in children eight years of age. Med Sci Sports. 1977 Spring;9(1):37-40.February 2010 A-35 Draft – Do Not Cite or Quote 101. Krahenbuhl GS, Pangrazi RP, Petersen GW , Burkett LN, Schneider MJ. Field testing of cardiorespirator y fitness in primary school children. Med Sci Sports. 1978 Fall;10(3):208-13. 102. Krahenbuhl GS, Pangrazi RP. Charact eristics associated with running performance in young boys. Med Sci Sports Exerc. 1983;15(6):486-90. 103. Loftin M, Strikmiller P, Warren B, Myers L, Schroth L, Pittman J, Harsha D, and Sothern M. Comparison and relationshi p of Vo2peak and physical activity patterns in elementary and high school fe males. Ped Exer Sci. 10:153-163, 1998. 104. Macek M and Vavra J. The adjustment of oxygen uptake at the onset of exercise: a comparison between prepubertal boys and young adults. In J Sports Med 1:70- 72, 1980. 105. Macek M, Vavra J, Novosadova J. Prol onged exercise in prepubertal boys. I. Cardiovascular and metabolic adjustment . Eur J Appl Physiol Occup Physiol. 1976 Sep 23;35(4):291-8. 106. Macek M, Vavra J. Cardiopulmonary and metabolic changes during exercise in children 6-14 years old. J Appl Physiol. 1971 Feb;30(2):200-4. 107. Mahler DA, Mejia-Alfaro R, Ward J, Baird JC. Continuous measurement of breathlessness during exercise: validity, re liability, and responsiveness. J Appl Physiol. 2001 Jun;90(6):2188-96. 108. Mahon AD, Anderson CS, Hipp MJ, Hunt KA. Heart rate recovery from submaximal exercise in boys and girls. Med Sci Sports Exerc. 2003 Dec;35(12):2093-7. 109. Mahon AD, Gay JA, Stolen KQ. Differentia ted ratings of perceived exertion at ventilatory threshold in ch ildren and adults. Eur J Appl Physiol Occup Physiol. 1998 Jul;78(2):115-20. 110. Mahon AD, Vaccaro P. Ventilatory thre shold and VO2max changes in children following endurance training. Med Sci S ports Exerc. 1989 Aug;21(4):425-31. 111. Mahon, A.D., C.C. Cheatham, K.Q. Kelsey , and J.D. Brown. Plasma fibrinogen, physical activity and aerobic fitness in ch ildren. In: Children and Exercise XIX: 19th International Seminar on Paediatric Work Physiology, N. Armstrong, B. Kirby, and J. Wellsman, Editors. London: Chapman & Hall Ltd., 1997. rs. London: Chapman & Hall Ltd., 997 112. Maksud MG, Coutts KD, Hamilton LH. Oxygen uptake, ventilation, and heart rate. Study in Negro children during st renuous exercise. Arch Environ Health. 1971 Jul;23(1):23-8. 113. Mandigout S, Lecoq AM, Courteix D, Gue non P, Obert P. Effect of gender in response to an aerobic trai ning programme in prepubertal children. Acta Paediatr. 2001 Jan;90(1):9-15. 114. Massicotte DR, Macnab RB. Cardioresp iratory adaptations to training at specified intensities in children. Med Sci Sports. 1974 Winter;6(4):242-6. 115. Matsui H, Miyashita M, Miura M, Kobayashi K, Ho shikawa T. Maximum oxygen intake and its relationship to body weight of Japanese adolescents.Med Sci Sports. 1972 Spring;4(1):27-32. 116. McArdle WD and Magel JR. Physical work capacity and maximum oxygen uptake in treadmill and bicycle exerci se. Med Sci Sports 2: 118-126, 1971 McCardle et al. 1972February 2010 A-36 Draft – Do Not Cite or Quote 117. McDonough JR, Kusumi F, Bruce RA. Variations in maximal oxygen intake with physical activity in middle-aged men. Circulation. 1970 May;41(5):743-51. 118. McManus AM, Armstrong N, Williams CA. Effect of training on the aerobic power and anaerobic performance of pre pubertal girls. Acta Paediatr. 1997 May;86(5):456-9. 119. McMurray RG, Guion WK, Ainsworth BE, Harrell JS. Predicting aerobic power in children. A comparison of two met hods. J Sports Med Phys Fitness. 1998 Sep;38(3):227-33. 120. Mercier J, Varray A, Ramonatxo M, Mercier B, Prefaut C. Influence of anthropometric characteristics on changes in maximal exercise ventilation and breathing pattern during grow th in boys. Eur J Appl Physiol Occup Physiol. 1991;63(3-4):235-41. 121. Murray TD, Walker JL, Jackson AS, Morro w JR Jr, Eldridge JA, Rainey DL. Validation of a 20-minute steady-state j og as an estimate of peak oxygen uptake in adolescents. Res Q Exerc Sport. 1993 Mar;64(1):75-82. 122. Nakagawa A, Ishiko T. Assessment of aerobic capacity with special reference to sex and age of junior and senior highschool students in Japan. 123. Palgi Y, Gutin B, Young J, Alejandro D. Physiologic and anthropometric factors underlying endurance performance in children. Int J Sports Med. 1984 Apr;5(2):67-73. 124. Paterson DH, Cunningham DA, Bumstead LA . Recovery O2 and blood lactic acid: longitudinal analysis in boys aged 11 to 15 years. Eur J Appl Physiol Occup Physiol. 1986;55(1):93-9. 125. Paterson DH, Cunningham DA, Donner A. The effect of different treadmill speeds on the variability of VO2 max in children. Eur J Appl Physiol Occup Physiol. 1981;47(2):113-22. 126. Paterson DH, McLellan TM, Stella RS, Cunningham DA. Long itudinal study of ventilation threshold and ma ximal O2 uptake in athletic boys. J Appl Physiol. 1987 May;62(5):2051-7. 127. Petzl DH, Haber P, Schuster E, Popow C, Haschke F. Reliability of estimation of maximum performance capacity on the basis of submaximum ergometric stress tests in children 10-14 years old. Eu r J Pediatr. 1988 Feb;147(2):174-8. 128. Pivarnik JM, Bray MS, Hergenroeder AC , Hill RB, Wong WW. Ethnicity affects aerobic fitness in US adolescent gi rls. Med Sci Sports Exerc. 1995 Dec;27(12):1635-8. 129. Pivarnik JM, Dwyer MC, Lauderdale MA . The reliability of aerobic capacity (VO2max) testing in adolescent girls. Res Q Exerc Sport. 1996 Sep;67(3):345-8. 130. Pivarnik, J.M., W.C. Taylor, and S.S. Cummings. Longitudinal assessment of aerobic fitness in middle-school African Am erican girls. Pediatr Exerc Sci 10: 21- 27, 1998. 131. Reybrouck T, Ghesquiere J, Weymans M, Amery A. Ventilatory threshold measurement to evaluate maximal enduran ce performance. Int J Sports Med. 1986 Feb;7(1):26-9. 132. Rivera-Brown, A.M. and Frontera, W.R. Ac hievement of plateau and reliability of VO2max in active adolescents: Effects of testing ergometer. Ped. Exer. Sci., 10:164-175, 1998.February 2010 A-37 Draft – Do Not Cite or Quote 133. Rivera-Brown, A.M., Alvarez ,A., Rodrígue z-Santana, J., Benetti, P. Anaerobic power and achievement of VO2 plateau in pre-pubertal boys. Int. J. Sports Med. 22:111-115, 2001. 134. Rivera-Brown, AM, Rivera, MA, Frontera, WR (1992) Applicability of criteria for VO2max in active children Pediatr Exerc Sci 4,331-339. 135. Rogers DM, Olson BL, Wilmore JH. Scaling for the VO2-to-body size relationship among children and adults. J Appl Physiol. 1995 Sep;79(3):958-67. 136. Rogers, DM, Turley, KR, Kujawa, KI, Harper, KM, Wilmore, J. (1995) Allometric scaling factors for oxygen uptak e during exercise in children Pediatr Exerc Sci 7,12-25. 137. Rowland T, Kline G, Goff D, Martel L, and Ferrone L. Physiological determinants of maximal aerobic power in healthy 12-year-old boys. Ped Exer Sci 11:317-326, 1999. 138. Rowland TW, Green GM. Physiological responses to treadmill exercise in females: adult-child differences. Med Sci Sports Exerc. 1988 Oct;20(5):474-8. 139. Rowland TW, Martel L, Vanderburgh P, Manos T, Charkoudian N. The influence of short-term aerobic training on blood lipids in healthy 10-12 year old children. Int J Sports Me d. 1996 Oct;17(7):487-92. 140. Rowland, T, Miller K, Vanderburgh P, Goff D, Martel L, and Ferrone L. Cardiovascular fitness in premenarcheal girls and young women. Int J Sports Med 20: 1-5, 1999. 141. Rutenfranz J, Macek M, Lange Anders en K, Bell RD, Vavra J, Radvansky J, Klimmer F, Kylian H. The relations hip between changing body height and growth related changes in maximal ae robic power. Eur J Appl Physiol Occup Physiol. 1990;60(4):282-7. 142. Sady, S. Transient oxygen uptake and heart ra te responses at the onset of relative endurance exercise in prepubertal boys and adult men. Int. J. Sports Med. 2: 240- 244, 1981. 143. Sargent 1985 (Ref??) From T. McCu rdy spreadsheet, dated Nov 15, 2000. 144. Saris WH, Noordeloos AM, Ringnalda BEM, et al. Binkhorst RA, Kemper HCG, Saris WHM, editors. Children and Exer cise XI. Champaign: Human Kinetics Publisher; 1985; Reference values for aer obic power of healthy 4- to 18-year-old Dutch children: Prelimin ary results. p. 151-60. 145. Savage MP, Petratis MM, Thomson WH, Be rg K, Smith JL, Sady SP. Exercise training effects on serum lipids of pre pubescent boys and adult men. Med Sci Sports Exerc. 1986 Apr;18(2):197-204. 146. Shuleva K, Hunter G, Hester D, Dunawa y D. Exercise oxygen uptake in 2 to 6 year olds. Ped Exer Sci 2:130-139, 1990. 147. Sobolova V, Seliger V, Grussova D, Machovc ova J, Zelenka V. The influence of age and sports training in swimming on phys ical fitness. Acta Paediatr Scand Suppl. 1971;217:63-7. 148. Spurr, G.B. & Reina, J.C. (1990). Daily pattern of %VO2 max and heart rates in normal and undernourished school children. Med. Sci. Sports Exerc. 22:643-652. 149. Stewart KJ, Gutin B. Effects of physical training on cardiorespi ratory fitness in children. Res Q. 1976 Mar;47(1):110-20.February 2010 A-38 Draft – Do Not Cite or Quote 150. Stoedefalke K, Armstrong N, Kirby BJ, We lsman JR. Effect of training on peak oxygen uptake and blood lipids in 13 to 14-year-old girls. Acta Paediatr. 2000 Nov;89(11):1290-4. 151. Sundberg S, Elovainio R. Cardiorespir atory function in competitive endurance runners aged 12-16 years compared with ordinary boys. Acta Paediatr Scand. 1982 Nov;71(6):987-92. 152. Sunnegardh J, Bratteby LE. Maximal oxyge n uptake, anthropometry and physical activity in a randomly selected sample of 8 and 13 year old children in Sweden. Eur J Appl Physiol Occup Physiol. 1987;56(3):266-72. 153. Tolfrey K, Campbell IG, Batterham AM. Exer cise training induced alterations in prepubertal children's lipi d-lipoprotein profile. Med Sci Sports Exerc. 1998 Dec;30(12):1684-92. 154. W Bell (1997) Aerobic fitness and fat patt erning in adolescent boys. In: Children and Exercise (Eds N Armstrong, B Kir by, J Welsman) pp 63-68. London: E & F Spon. 155. W.D. McArdle et al. (19 72) Reliability and interrela tionships between maximal oxygen uptake, physical work capacity and st ep test scores in college women. Medicine and Science in Sports, Vol 4, p182-186. 156. Washington RL, van Gundy JC, Cohen C, Sondheimer HM, Wolfe RR. Normal aerobic and anaerobic exercise data for North American school-age children. J Pediatr. 1988 Feb;112(2):223-33. 157. Welsman JR, Armstrong N, Nevill AM, Wint er EM, Kirby BJ. Scaling peak VO2 for differences in body size. Med Sci Sports Exerc. 1996 Feb;28(2):259-65. 158. Welsman JR, Armstrong N, Withers S. Re sponses of young girls to two modes of aerobic training. Br J Sports Med. 1997 Jun;31(2):139-42. 159. Welsman JR, Armstrong N. Daily physical activity and blood lactate indices of aerobic fitness in children. Br J Sports Med. 1992 Dec;26(4):228-32. 160. Williams CA, Armstrong N, Powell J. Aer obic responses of prepubertal boys to two modes of training. Br J Sports Med. 2000 Jun;34(3):168-73. 161. Williford HN, Blessing DL, Duey WJ, Barksdale JM, Wang N, Olson MS, Teel S. Exercise training in black adolescents: changes in blood lipids and Vo2max. Ethn Dis. 1996 Autumn;6(3-4):279-85. 162. Wilmore JH, Sigerseth PO. Physical work capacity of young girls, 7-13 years of age. J Appl Physiol. 1967 May;22(5):923-8. 163. Yamaji K, Miyashita M, Shephard RJ. Relationship between heart rate and relative oxygen intake in male subjects aged 10 to 27 years. J Hum Ergol (Tokyo). 1978 Sep;7(1):29-39. 164. Yoshizawa S, Ishizaki T, and Honda H. Aerobic power and endurance running in young children. In: Rutenfranz J, Mo cellin R, Klimt F, eds. children and exercise XII. International series on sport sciences, Vol 16. Champaign, IL: Human Kinetics, 1986:227-232. 165. Yoshizawa S, Honda H, Nakamura N. Effects of an 18-month endurance run program on maximal aerobic power in 4- to 6- year old girls. Pediatr Exer Sci 9:33-43, 1997.February 2010 A-39 Draft – Do Not Cite or Quote 166. Yoshizawa S, Honda H, Urushibara M, Na kamura N. Effects of endurance run on circulorespiratory system in young children. J Hum Ergol (Tokyo). 1990 Jun;19(1):41-52. 167. Yoshizawa S, Ishizaki T, Honda H. Phys ical fitness of children aged 5 and 6 years. J Hum Ergol (Tokyo). 1977 Sep;6(1):41-51. 168. Allen TH, Peng MT, et al. Prediction of blood volume and adiposity in man from body weight and cube of height. Metabolism. 1956; 5: 328. 169. Nadler SB, Hidalgo JU, Bloch T. Pred iction of blood volume in normal human adults. Surgery. 1962;51:224-232. 170. Linderkamp O, Versmold HT, et al. Estim ation and prediction of blood volume in infants and children. Europ J Pediat. 1977; 125: 227-234. 171. Cropp GJA. Changes in blood and plasma volumes during growth. J Pediatrics. 1971; 78: 220-229. 172. Smetannikov Y, Hopkins D. Intraoperati ve bleeding: A mathematical model for minimizing hemoglobin loss. Transfusion. 1996; 36: 832-835.February 2010 A-40 Draft – Do Not Cite or Quote 

### Appendix A. SAS Code for Estimati ng the Body Mass Distributions

/* This program calculates lognormal distributions for BM from the NHANES 1999-2004 Data

K K Isaacs 10/2005 Alion Science and Technology

Distributions are derived from raw body mass and age data downloaded from the CDC site at http://www.cdc.gov/nchs/about/major/nhanes/

The data are stored in the downloaded datasets:

1999-2000 (SAS export files) BMX.xpt (NHANES Body Measurement Data, contains body wt in kg) Demo.xpt (NHAMES Demographic Data, contains age in years or months)

2001-2002 (SAS export files) BMX_b_r.xpt (NHANES Body Measurement Data, contains body wt in kg) Demo_b.xpt (NHAMES Demographic Data, contains age in years or months)

2003-2004 (SAS export files) BMX_c.xpt (NHANES Body Measurement Data, contains body wt in kg) Demo_c.xpt (NHAMES Demographic Data, contains age in years or months)

*/

* Merge the Body Measurement and Demographics datasets;

**Data** weight;
merge Demo Demo_b Demo_c Bmx Bmx_b_r Bmx_c; by SEQN;
mass=BMXWT; gen=RIAGENDR; ageyrs=RIDAGEYR; agemonths=RIDAGEEX; wt = ( **2** / **3** )*WTMEC4YR; if (SEQN> **21004** ) THEN wt=( **1** / **3** )*WTMEC2YR; if agemonths< **12** and agemonths> **0** THEN ageyrs= **0** ; keep SEQN mass gen ageyrs agemonths wt;

**run** ;

**proc sort**  data =weight; by gen ageyrs;

**run** ;

**Proc univariate**  data =weight; by gen ageyrs;

var mass; freq wt; histogram mass / lognormal ;

**run** ;February 2010 A-41 Draft – Do Not Cite or Quote 

### Appendix B. SAS Code for Estimating the Normalized Vo2max Distributions

/***********************************************************************/ /* This is a program to fit the V02Max (Adams and others) data to different distributional shapes.

Adams experimental data provided in Excel form by Stephen Graham and Tom McCurdy, EPA

Other data collected by Alion Science and Tech.

This work was performed for WA 10, APEX/SHEDS Physiology File Update

K. K. Isaacs October 2005

Alion Science and Technology /***********************************************************************/ ;

*load datasets;

**Data** alldata ; infile 'H:\kki-05-PHYSIOLOGY_10\NVO2MAX\vo2max.csv' DLM = "," END =eof; input age nvo2max gender; output alldata;

**proc sort**  data =alldata; by gender age;

**run** ;

**Proc univariate**  data =alldata; by gender age;

var nvo2max; histogram nvo2max / normal ; output out =outputdata1 N =samplesize mean =Mean std =StdDeviation ProbN =NormalFit;

**run** ;

**Proc export**  data=outputdata1 outfile= "H:\kki-05-PHYSIOLOGY_10\Alldata_vo2max.csv" replace;

**run** ;February 2010 A-42 Draft – Do Not Cite or Quote 

### Appendix C. SAS Code for Estimating the Hemoglobin Content Data

/* This program calculates best fit normal distributions for hemoglobin content from the NHANES 1999-2000 and 2001-2002 datasets.

Alion Science and Technology K K Isaacs 12/2005

Distributions are derived from hemoglobin content and age data downloaded from the CDC site at http://www.cdc.gov/nchs/about/major/nhanes/nhanes99-00.htm and http://www.cdc.gov/nchs/about/major/nhanes/nhanes01-02.htm

The data are stored in the downloaded datasets:

1999-2000 lab25.xpt (NHANES Lab dataset #25) Demo.xpt (NHANES Demographic Data, contains age in years or months)

2001-2002 l25_b.xpt (NHANES Lab dataset #25) Demo_b.xpt (NHANES Demographic Data, contains age in years or months) */

*Data are read into SAS by loading the xpt files.

* Merge the Laboratory and Demographics datasets;

**Data** Hb;
merge Demo Lab25 Demo_b L25_b; by SEQN; * Sample number; Hb=LBXHGB; * Hb content g/dL; gen=RIAGENDR; * Gender; ageyrs=RIDAGEYR; * Age in years; agemonths=RIDAGEEX; * Age in months; wt = WTMEC4YR; * 4-year sample weights; if agemonths< **12** and agemonths> **0** THEN ageyrs= **0** ; * Age 0; if ageyrs> **20** then ageyrs=(floor(ageyrs/ **5** )+ **1** )* **5** ; * Bin in 5-year incs; keep SEQN Hb gen ageyrs agemonths wt;

**run** ;

**proc sort**  data =Hb; by gen ageyrs;

**run** ;

**Proc univariate**  data =Hb; by gen ageyrs;

var Hb;
req wt; * Apply sample weights; histogram Hb / normal ; * Fit to Normal; output out =outputs N =samplesize mean =Mean std =StdDeviation ProbN =NormalFit;

**run** ;

**Proc export**  data=outputs outfile= "H:\kki-05-PHYSIOLOGY_10\Hemoglobin\HbFitswt.csv" replace;

**run** ;February 2010 A-43 Draft – Do Not Cite or Quote 

### Appendix D. The New Physiology.Txt File

Note: The values contained in the file confor m to the current APEX read formats. That is, the number of decimal places for each parame ter is dictated by the APEX code. It is likely that this will change in the future, at which point more significant digits could be added to the Physiology.txt file.

Males age 0-100, then females age 0-100 (last revised 12-20-05) NVO2max distribution Age Source Distr Mean SD Lower Upper Assumptions 0 NA Normal 48.3 1.7 44.3 52.2 1 NA Normal 48.6 2.0 43.8 53.3 2 NA Normal 48.9 2.4 43.4 54.4 3 NA Normal 49.2 2.7 43.0 55.4 4 NA Normal 49.5 3.0 42.5 56.5 5 NA Normal 49.8 3.3 42.1 57.6 6 NA Normal 50.1 3.7 41.6 58.6 7 NA Normal 50.4 4.0 41.2 59.7 8 NA Normal 50.8 4.3 40.8 60.8 9 NA Normal 51.1 4.6 40.3 61.8 10 NA Normal 51.4 5.0 39.9 62.9 11 NA Normal 51.7 5.3 39.4 64.0 12 NA Normal 52.0 5.6 39.0 65.0 13 NA Normal 52.3 5.9 38.6 66.1 14 NA Normal 52.6 6.2 38.1 67.2 15 NA Normal 53.0 6.6 37.7 68.2 16 NA Normal 53.3 6.9 37.3 69.3 17 NA Normal 53.6 7.2 36.8 70.4 18 NA Normal 53.9 7.5 36.4 71.4 19 NA Normal 54.2 7.9 35.9 72.5 20 NA Normal 54.5 8.2 35.5 73.6 21 NA Normal 54.2 8.5 34.5 74.0 22 NA Normal 53.4 8.8 32.9 74.0 23 NA Normal 52.6 9.2 31.4 73.9 24 NA Normal 51.8 9.5 29.8 73.9 25 NA Normal 51.1 9.8 28.3 73.9 26 NA Normal 50.3 10.7 25.5 75.2 27 NA Normal 49.6 10.5 25.2 74.0 28 NA Normal 48.8 10.3 24.9 72.8 29 NA Normal 48.1 10.1 24.6 71.6 30 NA Normal 47.4 9.9 24.3 70.4 31 NA Normal 46.7 9.7 24.0 69.3 32 NA Normal 46.0 9.6 23.8 68.2 33 NA Normal 45.3 9.4 23.5 67.1 34 NA Normal 44.6 9.2 23.2 66.0 35 NA Normal 44.0 9.0 23.0 65.0 36 NA Normal 43.3 8.9 22.7 64.0 37 NA Normal 42.7 8.7 22.4 62.9 38 NA Normal 42.1 8.6 22.2 61.9 39 NA Normal 41.4 7.3 25.5 54.1 40 NA Normal 40.8 5.5 28.4 50.0 41 NA Normal 40.2 5.5 28.4 50.0 42 NA Normal 39.7 5.5 28.4 50.0 43 NA Normal 39.1 5.5 28.4 50.0 44 NA Normal 38.5 5.5 28.4 50.0 45 NA Normal 38.0 5.5 28.4 50.0 46 NA Normal 37.4 5.5 28.4 50.0 47 NA Normal 36.9 5.5 28.4 50.0 48 NA Normal 36.4 5.5 28.4 50.0 49 NA Normal 35.9 5.5 28.4 50.0 50 NA Normal 35.4 4.9 23.5 42.7 51 NA Normal 34.9 4.9 23.5 42.7 52 NA Normal 34.5 4.9 23.5 42.7 53 NA Normal 34.0 4.9 23.5 42.7 54 NA Normal 33.6 4.9 23.5 42.7 55 NA Normal 33.1 4.9 23.5 42.7 56 NA Normal 32.7 4.9 23.5 42.7 57 NA Normal 32.3 4.9 23.5 42.7 58 NA Normal 31.9 4.9 23.5 42.7 59 NA Normal 31.5 4.9 23.5 42.7 60 NA Normal 31.1 5.3 21.0 41.8 61 NA Normal 30.7 5.3 21.0 41.8 62 NA Normal 30.4 5.3 21.0 41.8 63 NA Normal 30.0 5.3 21.0 41.8 64 NA Normal 29.7 5.3 21.0 41.8February 2010 A-44 Draft – Do Not Cite or Quote 65 NA Normal 29.4 5.3 21.0 41.8 66 NA Normal 29.1 5.3 21.0 41.8 67 NA Normal 28.8 5.3 21.0 41.8 68 NA Normal 28.5 5.3 21.0 41.8 69 NA Normal 28.2 5.3 21.0 41.8 70 NA Normal 27.9 5.7 16.1 38.3 71 NA Normal 27.7 5.7 16.1 38.3 72 NA Normal 27.4 5.7 16.1 38.3 73 NA Normal 27.2 5.7 16.1 38.3 74 NA Normal 27.0 5.7 16.1 38.3 75 NA Normal 26.7 5.7 16.1 38.3 76 NA Normal 26.5 5.7 16.1 38.3 77 NA Normal 26.4 5.7 16.1 38.3 78 NA Normal 26.2 5.7 16.1 38.3 79 NA Normal 26.0 5.7 16.1 38.3 80 NA Normal 25.8 5.7 16.1 38.3 81 NA Normal 25.7 5.7 16.1 38.3 82 NA Normal 25.6 5.7 16.1 38.3 83 NA Normal 25.4 5.7 16.1 38.3 84 NA Normal 25.3 5.7 16.1 38.3 85 NA Normal 25.2 5.7 16.1 38.3 86 NA Normal 25.1 5.7 16.1 38.3 87 NA Normal 25.1 5.7 16.1 38.3 88 NA Normal 25.0 5.7 16.1 38.3 89 NA Normal 24.9 5.7 16.1 38.3 90 NA Normal 24.9 5.7 16.1 38.3 91 NA Normal 24.9 5.7 16.1 38.3 92 NA Normal 24.8 5.7 16.1 38.3 93 NA Normal 24.8 5.7 16.1 38.3 94 NA Normal 24.8 5.7 16.1 38.3 95 NA Normal 24.8 5.7 16.1 38.3 96 NA Normal 24.9 5.7 16.1 38.3 97 NA Normal 24.9 5.7 16.1 38.3 98 NA Normal 25.0 5.7 16.1 38.3 99 NA Normal 25.0 5.7 16.1 38.3 100 NA Normal 25.1 5.7 16.1 38.3 0 NA Normal 35.9 5.9 22.2 49.6 1 NA Normal 36.2 6.0 22.3 50.2 2 NA Normal 36.5 6.1 22.4 50.7 3 NA Normal 36.9 6.2 22.5 51.3 4 NA Normal 37.2 6.3 22.6 51.8 5 NA Normal 37.5 6.4 22.7 52.4 6 NA Normal 37.9 6.5 22.8 52.9 7 NA Normal 38.2 6.6 22.9 53.5 8 NA Normal 38.5 6.7 23.0 54.0 9 NA Normal 38.9 6.8 23.1 54.6 10 NA Normal 39.2 6.9 23.3 55.1 11 NA Normal 39.5 7.0 23.4 55.7 12 NA Normal 39.9 7.0 23.5 56.2 13 NA Normal 40.2 7.1 23.6 56.8 14 NA Normal 40.5 7.2 23.7 57.3 15 NA Normal 40.9 7.3 23.8 57.9 16 NA Normal 41.2 7.4 23.9 58.5 17 NA Normal 41.5 7.5 24.0 59.0 18 NA Normal 41.8 7.6 24.1 59.6 19 NA Normal 42.2 7.7 24.2 60.1 20 NA Normal 42.5 7.8 24.4 60.7 21 NA Normal 42.1 7.9 23.7 60.5 22 NA Normal 41.5 8.0 22.9 60.1 23 NA Normal 40.8 8.1 22.0 59.6 24 NA Normal 40.2 8.2 21.1 59.2 25 NA Normal 39.6 8.3 20.3 58.8 26 NA Normal 39.0 8.4 19.5 58.4 27 NA Normal 38.4 8.4 18.9 57.8 28 NA Normal 37.8 8.1 18.8 56.7 29 NA Normal 37.2 7.9 18.7 55.6 30 NA Normal 36.6 7.7 18.6 54.6 31 NA Normal 36.0 7.6 18.5 53.6 32 NA Normal 35.5 7.4 18.4 52.6 33 NA Normal 34.9 7.2 18.2 51.7 34 NA Normal 34.4 7.0 18.1 50.7 35 NA Normal 33.9 6.8 18.0 49.8 36 NA Normal 33.4 6.7 17.8 48.9 37 NA Normal 32.9 6.5 17.7 48.0 38 NA Normal 32.4 6.4 17.6 47.2 39 NA Normal 31.9 6.2 17.4 46.4 40 NA Normal 31.4 6.1 17.3 45.6 41 NA Normal 31.0 6.0 17.1 44.8 42 NA Normal 30.5 5.8 17.0 44.0 43 NA Normal 30.1 5.7 16.8 43.3 44 NA Normal 29.6 5.6 16.6 42.6February 2010 A-45 Draft – Do Not Cite or Quote 45 NA Normal 29.2 5.5 16.5 41.9 46 NA Normal 28.8 5.4 16.3 41.2 47 NA Normal 28.4 5.3 16.1 40.6 48 NA Normal 28.0 5.2 16.0 40.0 49 NA Normal 27.6 5.1 15.8 39.4 50 NA Normal 27.2 5.0 15.6 38.8 51 NA Normal 26.8 4.9 15.4 38.3 52 NA Normal 26.5 4.8 15.2 37.7 53 NA Normal 26.1 4.8 15.1 37.2 54 NA Normal 25.8 4.7 14.9 36.7 55 NA Normal 25.5 4.7 14.7 36.3 56 NA Normal 25.2 4.6 14.5 35.9 57 NA Normal 24.9 4.6 14.3 35.4 58 NA Normal 24.6 4.5 14.1 35.1 59 NA Normal 24.3 4.5 13.9 34.7 60 NA Normal 24.0 4.5 13.6 34.3 61 NA Normal 23.7 4.4 13.4 34.0 62 NA Normal 23.5 4.4 13.2 33.7 63 NA Normal 23.2 4.4 13.0 33.4 64 NA Normal 23.0 4.4 12.8 33.2 65 NA Normal 22.7 4.4 12.5 33.0 66 NA Normal 22.5 4.4 12.3 32.7 67 NA Normal 22.3 4.4 12.1 32.5 68 NA Normal 22.1 4.4 11.9 32.3 69 NA Normal 21.9 4.4 11.7 32.1 70 NA Normal 21.7 4.4 11.5 32.0 71 NA Normal 21.6 4.4 11.4 31.8 72 NA Normal 21.4 4.4 11.2 31.6 73 NA Normal 21.3 4.4 11.1 31.5 74 NA Normal 21.1 4.4 10.9 31.3 75 NA Normal 21.0 4.4 10.8 31.2 76 NA Normal 20.9 4.4 10.7 31.1 77 NA Normal 20.8 4.4 10.6 31.0 78 NA Normal 20.7 4.4 10.4 30.9 79 NA Normal 20.6 4.4 10.4 30.8 80 NA Normal 20.5 4.4 10.3 30.7 81 NA Normal 20.4 4.4 10.2 30.6 82 NA Normal 20.3 4.4 10.1 30.6 83 NA Normal 20.3 4.4 10.1 30.5 84 NA Normal 20.3 4.4 10.0 30.5 85 NA Normal 20.2 4.4 10.0 30.4 86 NA Normal 20.2 4.4 10.0 30.4 87 NA Normal 20.2 4.4 10.0 30.4 88 NA Normal 20.2 4.4 10.0 30.4 89 NA Normal 20.2 4.4 10.0 30.4 90 NA Normal 20.2 4.4 10.0 30.4 91 NA Normal 20.2 4.4 10.0 30.4 92 NA Normal 20.3 4.4 10.1 30.5 93 NA Normal 20.3 4.4 10.1 30.5 94 NA Normal 20.4 4.4 10.2 30.6 95 NA Normal 20.4 4.4 10.2 30.6 96 NA Normal 20.5 4.4 10.3 30.7 97 NA Normal 20.6 4.4 10.4 30.8 98 NA Normal 20.7 4.4 10.5 30.9 99 NA Normal 20.8 4.4 10.6 31.0 100 NA Normal 20.9 4.4 10.7 31.1 Males age 0-100, then females age 0-100 (last revised 12-20-05) Body mass distribution, kg Age Source Distr GM GSD Lower Upper Assumptions 0 CDC LN 7.8 1.301 3.6 11.8 1 CDC LN 11.4 1.143 8.2 16.1 2 CDC LN 13.9 1.146 9.8 20.9 3 CDC LN 16.0 1.154 11.7 23.7 4 CDC LN 18.5 1.165 11.1 28.1 5 CDC LN 21.6 1.234 13.7 42.4 6 CDC LN 23.1 1.213 16.1 41.1 7 CDC LN 27.1 1.216 19.3 46.8 8 CDC LN 31.7 1.302 19.1 66.2 9 CDC LN 34.7 1.265 24.0 69.9 10 CDC LN 38.3 1.280 24.3 72.9 11 CDC LN 44.1 1.308 26.2 83.8 12 CDC LN 48.0 1.315 27.7 94.8 13 CDC LN 55.4 1.340 27.7 106.6 14 CDC LN 62.8 1.293 35.7 121.0 15 CDC LN 67.7 1.255 41.5 117.9 16 CDC LN 72.5 1.267 45.8 139.1 17 CDC LN 73.1 1.248 49.9 136.6 18 CDC LN 75.1 1.243 51.2 144.2 19 CDC LN 77.2 1.245 52.6 134.5 20 CDC LN 78.0 1.250 50.5 130.0 21 CDC LN 78.2 1.297 46.8 199.2February 2010 A-46 Draft – Do Not Cite or Quote 22 CDC LN 83.8 1.292 53.3 155.4 23 CDC LN 80.6 1.222 50.5 137.6 24 CDC LN 81.7 1.251 50.6 132.6 25 CDC LN 84.8 1.206 50.2 136.1 26 CDC LN 81.8 1.273 48.9 164.5 27 CDC LN 85.2 1.249 50.0 153.9 28 CDC LN 84.3 1.272 51.0 167.2 29 CDC LN 82.1 1.236 50.6 147.2 30 CDC LN 81.6 1.262 52.5 139.0 31 CDC LN 81.3 1.249 48.8 170.6 32 CDC LN 84.7 1.235 49.7 135.8 33 CDC LN 88.2 1.231 64.8 146.3 34 CDC LN 81.2 1.221 53.1 136.9 35 CDC LN 87.2 1.251 61.0 193.3 36 CDC LN 83.4 1.228 45.8 140.5 37 CDC LN 85.8 1.241 59.3 150.9 38 CDC LN 84.1 1.260 52.8 149.7 39 CDC LN 84.6 1.196 61.2 140.6 40 CDC LN 90.1 1.246 58.5 154.0 41 CDC LN 87.4 1.173 61.3 117.7 42 CDC LN 88.3 1.205 62.2 144.0 43 CDC LN 88.4 1.233 54.0 145.3 44 CDC LN 88.5 1.200 56.6 128.9 45 CDC LN 87.1 1.205 60.6 160.2 46 CDC LN 88.2 1.243 54.2 154.3 47 CDC LN 86.5 1.229 49.9 188.3 48 CDC LN 84.8 1.186 56.3 128.3 49 CDC LN 86.2 1.240 47.0 171.3 50 CDC LN 84.7 1.179 53.4 124.4 51 CDC LN 88.0 1.208 57.9 143.6 52 CDC LN 89.9 1.216 55.2 144.9 53 CDC LN 89.0 1.228 58.2 143.3 54 CDC LN 90.1 1.216 64.1 155.2 55 CDC LN 88.3 1.222 55.1 138.6 56 CDC LN 84.8 1.195 45.0 110.3 57 CDC LN 87.5 1.253 58.3 160.0 58 CDC LN 85.1 1.266 51.6 179.0 59 CDC LN 84.2 1.182 58.7 112.4 60 CDC LN 87.0 1.232 57.3 141.7 61 CDC LN 89.0 1.207 49.9 162.8 62 CDC LN 84.8 1.228 56.0 152.1 63 CDC LN 89.1 1.262 56.3 171.6 64 CDC LN 90.0 1.193 59.1 119.0 65 CDC LN 89.9 1.215 58.1 126.3 66 CDC LN 86.8 1.228 54.0 150.1 67 CDC LN 86.2 1.207 43.1 127.5 68 CDC LN 85.2 1.191 61.2 163.2 69 CDC LN 87.1 1.222 50.7 127.2 70 CDC LN 82.8 1.210 46.5 125.5 71 CDC LN 79.6 1.240 51.0 122.8 72 CDC LN 82.0 1.204 51.9 132.7 73 CDC LN 85.6 1.196 56.2 128.3 74 CDC LN 83.0 1.217 53.3 120.0 75 CDC LN 84.5 1.185 56.5 133.5 76 CDC LN 78.7 1.207 55.9 121.1 77 CDC LN 79.4 1.170 58.7 109.3 78 CDC LN 79.9 1.195 41.1 115.1 79 CDC LN 77.6 1.155 56.4 107.8 80 CDC LN 79.9 1.174 56.0 111.9 81 CDC LN 75.4 1.157 55.8 111.9 82 CDC LN 76.8 1.180 54.4 111.8 83 CDC LN 74.6 1.158 53.2 107.0 84 CDC LN 75.3 1.205 41.5 109.5 85 CDC LN 71.8 1.191 46.9 105.8 86 CDC LN 74.0 1.170 50.6 101.1 87 CDC LN 73.4 1.170 50.4 99.1 88 CDC LN 72.7 1.160 50.2 97.2 89 CDC LN 72.1 1.160 50.0 95.2 90 CDC LN 71.5 1.160 49.8 93.2 91 CDC LN 70.9 1.160 49.6 91.3 92 CDC LN 70.3 1.160 49.4 89.3 93 CDC LN 69.6 1.150 49.3 87.4 94 CDC LN 69.0 1.150 49.1 85.4 95 CDC LN 68.4 1.150 48.9 83.4 96 CDC LN 67.8 1.150 48.7 81.5 97 CDC LN 67.1 1.140 48.5 79.5 98 CDC LN 66.5 1.140 48.3 77.6 99 CDC LN 65.9 1.140 48.1 75.6 100 CDC LN 65.3 1.140 47.9 73.6 0 CDC LN 7.4 1.304 3.7 12.1 1 CDC LN 11.1 1.163 7.4 15.3February 2010 A-47 Draft – Do Not Cite or Quote 2 CDC LN 13.3 1.158 10.1 20.4 3 CDC LN 15.6 1.160 11.0 27.9 4 CDC LN 18.0 1.171 12.8 29.1 5 CDC LN 20.4 1.229 12.6 40.4 6 CDC LN 22.5 1.194 15.9 36.7 7 CDC LN 26.5 1.239 16.9 51.0 8 CDC LN 30.5 1.315 19.8 60.8 9 CDC LN 35.2 1.271 20.3 58.6 10 CDC LN 40.6 1.304 22.7 71.2 11 CDC LN 46.6 1.302 27.7 84.6 12 CDC LN 50.7 1.274 27.8 93.3 13 CDC LN 56.6 1.275 33.4 99.5 14 CDC LN 57.2 1.248 37.7 110.0 15 CDC LN 60.1 1.249 34.9 108.4 16 CDC LN 61.6 1.255 40.9 113.8 17 CDC LN 61.2 1.248 41.5 133.1 18 CDC LN 64.6 1.281 42.4 123.6 19 CDC LN 66.2 1.274 41.6 118.5 20 CDC LN 67.0 1.262 41.5 122.6 21 CDC LN 67.2 1.262 39.7 123.7 22 CDC LN 66.8 1.273 42.0 123.5 23 CDC LN 69.7 1.304 40.3 143.0 24 CDC LN 70.3 1.289 47.5 144.5 25 CDC LN 66.3 1.283 44.8 131.8 26 CDC LN 73.0 1.281 45.3 128.9 27 CDC LN 70.6 1.281 41.4 140.9 28 CDC LN 74.4 1.312 44.3 142.1 29 CDC LN 69.1 1.250 39.3 116.3 30 CDC LN 70.6 1.305 42.1 151.5 31 CDC LN 73.0 1.278 43.7 125.9 32 CDC LN 72.9 1.281 41.5 139.7 33 CDC LN 72.7 1.307 44.9 135.2 34 CDC LN 69.8 1.230 46.6 115.3 35 CDC LN 73.0 1.306 44.2 138.4 36 CDC LN 73.5 1.289 44.6 150.1 37 CDC LN 70.0 1.284 48.1 152.1 38 CDC LN 75.6 1.295 43.7 151.7 39 CDC LN 72.3 1.251 41.6 123.1 40 CDC LN 72.9 1.289 45.5 137.4 41 CDC LN 73.4 1.268 50.5 156.9 42 CDC LN 73.7 1.270 47.1 146.1 43 CDC LN 73.4 1.314 45.6 159.5 44 CDC LN 75.7 1.266 49.5 153.0 45 CDC LN 76.8 1.308 41.6 141.5 46 CDC LN 77.5 1.304 46.6 145.8 47 CDC LN 72.8 1.298 47.8 130.6 48 CDC LN 74.6 1.303 44.2 166.0 49 CDC LN 72.8 1.261 45.1 125.5 50 CDC LN 75.2 1.292 48.4 175.7 51 CDC LN 72.9 1.240 42.5 120.2 52 CDC LN 74.5 1.283 45.7 146.6 53 CDC LN 74.7 1.259 46.2 176.6 54 CDC LN 72.4 1.281 44.3 123.1 55 CDC LN 76.0 1.231 53.6 125.6 56 CDC LN 77.3 1.315 45.6 134.9 57 CDC LN 72.4 1.252 48.6 122.6 58 CDC LN 74.5 1.267 45.0 117.7 59 CDC LN 80.6 1.277 50.9 133.0 60 CDC LN 75.8 1.260 51.3 128.3 61 CDC LN 77.1 1.240 50.7 125.6 62 CDC LN 73.3 1.198 49.7 121.1 63 CDC LN 72.3 1.238 46.9 119.9 64 CDC LN 75.4 1.281 41.1 132.5 65 CDC LN 72.9 1.254 35.9 113.7 66 CDC LN 73.1 1.242 48.4 113.3 67 CDC LN 75.8 1.266 47.2 123.8 68 CDC LN 73.2 1.250 39.3 120.7 69 CDC LN 74.4 1.225 48.0 118.0 70 CDC LN 69.0 1.188 45.9 102.8 71 CDC LN 69.1 1.232 45.5 108.1 72 CDC LN 69.9 1.240 40.7 103.8 73 CDC LN 71.4 1.240 47.4 127.6 74 CDC LN 70.4 1.277 37.4 106.4 75 CDC LN 70.5 1.216 46.8 117.4 76 CDC LN 69.5 1.199 48.8 101.7 77 CDC LN 70.1 1.240 40.3 119.8 78 CDC LN 66.4 1.211 44.1 109.8 79 CDC LN 67.8 1.200 46.2 98.4 80 CDC LN 62.2 1.255 41.2 121.4 81 CDC LN 65.4 1.184 42.7 91.4 82 CDC LN 64.8 1.260 40.6 120.0February 2010 A-48 Draft – Do Not Cite or Quote 83 CDC LN 62.9 1.196 44.7 101.2 84 CDC LN 62.2 1.216 43.5 108.4 85 CDC LN 61.5 1.209 42.3 93.2 86 CDC LN 62.4 1.210 41.9 101.2 87 CDC LN 61.8 1.210 41.7 100.3 88 CDC LN 61.3 1.210 41.5 99.4 89 CDC LN 60.7 1.210 41.3 98.4 90 CDC LN 60.2 1.210 41.1 97.5 91 CDC LN 59.6 1.200 40.9 96.6 92 CDC LN 59.1 1.200 40.7 95.7 93 CDC LN 58.5 1.200 40.5 94.8 94 CDC LN 58.0 1.200 40.3 93.9 95 CDC LN 57.4 1.200 40.1 93.0 96 CDC LN 56.9 1.200 39.9 92.1 97 CDC LN 56.3 1.200 39.7 91.2 98 CDC LN 55.8 1.190 39.5 90.3 99 CDC LN 55.2 1.190 39.3 89.4 100 CDC LN 54.7 1.190 39.1 88.5 Males age 0-100 then females age 0-100 (last revised 6-11-98) Regression equation Estimate for RMR Age Source DV IV Slope Interc SE Units med. wgt 0 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.1 1 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.7 2 R47g BMR BM 0.244 -0.127 0.280 MJ/day 3.2 3 R47h BMR BM 0.095 2.110 0.280 MJ/day 3.6 4 R47h BMR BM 0.095 2.110 0.280 MJ/day 3.8 5 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.0 6 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.3 7 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.5 8 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.8 9 R47h BMR BM 0.095 2.110 0.280 MJ/day 5.0 10 R47i BMR BM 0.074 2.754 0.440 MJ/day 5.4 11 R47i BMR BM 0.074 2.754 0.440 MJ/day 5.7 12 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.0 13 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.3 14 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.9 15 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.2 16 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.7 17 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.6 18 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.3 19 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.4 20 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 21 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 22 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 23 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 24 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 25 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 26 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 27 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 28 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 29 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 30 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 31 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 32 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 33 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 34 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 35 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 36 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 37 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 38 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 39 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 40 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 41 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 42 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 43 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 44 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 45 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 46 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 47 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 48 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 49 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 50 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 51 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 52 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 53 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 54 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 55 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 56 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 57 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 58 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 59 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3February 2010 A-49 Draft – Do Not Cite or Quote 60 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 61 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 62 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 63 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 64 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 65 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 66 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 67 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 68 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 69 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 70 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 71 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 72 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 73 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 74 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 75 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 76 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 77 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 78 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 79 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 80 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 81 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 82 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 83 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 84 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 85 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 86 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 87 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 88 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 89 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 90 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 91 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 92 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 93 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 94 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 95 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 96 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 97 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 98 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 99 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 100 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 0 R47a BMR BM 0.244 -0.130 0.250 MJ/day 2.0 1 R47a BMR BM 0.244 -0.130 0.250 MJ/day 2.5 2 R47a BMR BM 0.244 -0.130 0.250 MJ/day 3.0 3 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.3 4 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.5 5 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.7 6 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.9 7 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.1 8 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.4 9 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.7 10 R47c BMR BM 0.056 2.898 0.470 MJ/day 4.9 11 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.2 12 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.5 13 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.7 14 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.9 15 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.0 16 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.1 17 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.2 18 R47d BMR BM 0.062 2.036 0.500 MJ/day 5.7 19 R47d BMR BM 0.062 2.036 0.500 MJ/day 5.8 20 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 21 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 22 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 23 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 24 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 25 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 26 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 27 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 28 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 29 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 30 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 31 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 32 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 33 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 34 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 35 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 36 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 37 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 38 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 39 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7February 2010 A-50 Draft – Do Not Cite or Quote 40 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 41 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 42 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 43 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 44 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 45 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 46 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 47 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 48 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 49 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 50 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 51 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 52 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 53 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 54 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 55 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 56 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 57 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 58 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 59 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 60 R47e BMR BM 0.038 2.755 0.450 MJ/day 5.2 61 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 62 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 63 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 64 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 65 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 66 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 67 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 68 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 69 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 70 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 71 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 72 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 73 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 74 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 75 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 76 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 77 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 78 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 79 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 80 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 81 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 82 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 83 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 84 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 85 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 86 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 87 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 88 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 89 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 90 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 91 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 92 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 93 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 94 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 95 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 96 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 97 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 98 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 99 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 100 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 Males age 0-100 then females age 0-100 (HG last revised 12-20-05) Blood Volume factor and Hemoglobin content Age BLDFAC HGMN HGSTD 0 17.0 11.9 1.0 1 17.0 12.2 1.0 2 17.0 12.4 0.8 3 17.0 12.7 0.8 4 17.0 12.8 0.8 5 17.0 13.0 0.9 6 17.0 13.2 0.9 7 17.0 13.5 0.8 8 17.0 13.4 0.8 9 17.0 13.6 1.0 10 17.0 13.6 0.9 11 17.0 13.7 0.7 12 17.0 14.0 1.0 13 17.0 14.3 1.0 14 17.0 14.7 1.0 15 17.0 15.1 1.0 16 17.0 15.4 1.0February 2010 A-51 Draft – Do Not Cite or Quote 17 17.0 15.5 1.0 18 17.0 15.7 1.0 19 20.4 15.8 0.8 20 20.4 15.8 0.9 21 20.4 15.7 0.9 22 20.4 15.7 0.9 23 20.4 15.7 0.9 24 20.4 15.7 0.9 25 20.4 15.7 0.9 26 20.4 15.7 1.0 27 20.4 15.7 1.0 28 20.4 15.7 1.0 29 20.4 15.7 1.0 30 20.4 15.7 1.0 31 20.4 15.6 1.0 32 20.4 15.6 1.0 33 20.4 15.6 1.0 34 20.4 15.6 1.0 35 20.4 15.6 1.0 36 20.4 15.4 1.0 37 20.4 15.4 1.0 38 20.4 15.4 1.0 39 20.4 15.4 1.0 40 20.4 15.4 1.0 41 20.4 15.4 1.1 42 20.4 15.4 1.1 43 20.4 15.4 1.1 44 20.4 15.4 1.1 45 20.4 15.4 1.1 46 20.4 15.4 1.1 47 20.4 15.4 1.1 48 20.4 15.4 1.1 49 20.4 15.4 1.1 50 20.4 15.4 1.1 51 20.4 15.3 1.1 52 20.4 15.3 1.1 53 20.4 15.3 1.1 54 20.4 15.3 1.1 55 20.4 15.3 1.1 56 20.4 15.3 1.0 57 20.4 15.3 1.0 58 20.4 15.3 1.0 59 20.4 15.3 1.0 60 20.4 15.3 1.0 61 20.4 15.1 1.2 62 20.4 15.1 1.2 63 20.4 15.1 1.2 64 20.4 15.1 1.2 65 20.4 15.1 1.2 66 20.4 15.0 1.2 67 20.4 15.0 1.2 68 20.4 15.0 1.2 69 20.4 15.0 1.2 70 20.4 15.0 1.2 71 20.4 14.7 1.4 72 20.4 14.7 1.4 73 20.4 14.7 1.4 74 20.4 14.7 1.4 75 20.4 14.7 1.4 76 20.4 14.5 1.5 77 20.4 14.5 1.5 78 20.4 14.5 1.5 79 20.4 14.5 1.5 80 20.4 14.5 1.5 81 20.4 14.5 1.4 82 20.4 14.5 1.4 83 20.4 14.5 1.4 84 20.4 14.5 1.4 85 20.4 14.5 1.4 86 20.4 14.0 1.8 87 20.4 14.0 1.8 88 20.4 14.0 1.8 89 20.4 14.0 1.8 90 20.4 14.0 1.8 91 20.4 13.8 1.8 92 20.4 13.8 1.8 93 20.4 13.8 1.8 94 20.4 13.8 1.8 95 20.4 13.8 1.8 96 20.4 13.5 1.8 97 20.4 13.5 1.8February 2010 A-52 Draft – Do Not Cite or Quote 98 20.4 13.5 1.8 99 20.4 13.5 1.8 100 20.4 13.5 1.8 0 17.0 12.2 0.7 1 17.0 12.3 0.7 2 17.0 12.6 0.8 3 17.0 12.5 1.0 4 17.0 12.8 0.8 5 17.0 12.9 1.0 6 17.0 13.0 0.8 7 17.0 13.1 0.8 8 17.0 13.3 0.8 9 17.0 13.4 0.8 10 17.0 13.6 1.0 11 17.0 13.5 0.9 12 17.0 13.6 0.9 13 17.0 13.5 1.0 14 17.0 13.6 1.0 15 17.0 13.5 0.9 16 17.0 13.5 1.1 17 17.0 13.5 1.1 18 17.0 13.5 1.2 19 14.6 13.4 1.1 20 14.6 13.5 1.1 21 14.6 13.5 1.2 22 14.6 13.5 1.2 23 14.6 13.5 1.2 24 14.6 13.5 1.2 25 14.6 13.5 1.2 26 14.6 13.3 1.1 27 14.6 13.3 1.1 28 14.6 13.3 1.1 29 14.6 13.3 1.1 30 14.6 13.3 1.1 31 14.6 13.3 1.1 32 14.6 13.3 1.1 33 14.6 13.3 1.1 34 14.6 13.3 1.1 35 14.6 13.3 1.1 36 14.6 13.5 1.2 37 14.6 13.5 1.2 38 14.6 13.5 1.2 39 14.6 13.5 1.2 40 14.6 13.5 1.2 41 14.6 13.5 1.3 42 14.6 13.5 1.3 43 14.6 13.5 1.3 44 14.6 13.5 1.3 45 14.6 13.5 1.3 46 14.6 13.6 1.2 47 14.6 13.6 1.2 48 14.6 13.6 1.2 49 14.6 13.6 1.2 50 14.6 13.6 1.2 51 14.6 13.7 1.1 52 14.6 13.7 1.1 53 14.6 13.7 1.1 54 14.6 13.7 1.1 55 14.6 13.7 1.1 56 14.6 13.8 1.2 57 14.6 13.8 1.2 58 14.6 13.8 1.2 59 14.6 13.8 1.2 60 14.6 13.8 1.2 61 14.6 13.8 1.1 62 14.6 13.8 1.1 63 14.6 13.8 1.1 64 14.6 13.8 1.1 65 14.6 13.8 1.1 66 14.6 13.8 1.1 67 14.6 13.8 1.1 68 14.6 13.8 1.1 69 14.6 13.8 1.1 70 14.6 13.8 1.1 71 14.6 13.8 1.1 72 14.6 13.8 1.1 73 14.6 13.8 1.1 74 14.6 13.8 1.1 75 14.6 13.8 1.1 76 14.6 13.8 1.3 77 14.6 13.8 1.3February 2010 A-53 Draft – Do Not Cite or Quote 78 14.6 13.8 1.3 79 14.6 13.8 1.3 80 14.6 13.8 1.3 81 14.6 13.6 1.2 82 14.6 13.6 1.2 83 14.6 13.6 1.2 84 14.6 13.6 1.2 85 14.6 13.6 1.2 86 14.6 13.4 1.6 87 14.6 13.4 1.6 88 14.6 13.4 1.6 89 14.6 13.4 1.6 90 14.6 13.4 1.6 91 14.6 13.2 1.6 92 14.6 13.2 1.6 93 14.6 13.2 1.6 94 14.6 13.2 1.6 95 14.6 13.2 1.6 96 14.6 13.0 1.6 97 14.6 13.0 1.6 98 14.6 13.0 1.6 99 14.6 13.0 1.6 100 14.6 13.0 1.6February 2010 A-54 Draft – Do Not Cite or Quote 

### Appendix E. All Derived Physiological Parameters

Table 1. Nv02max Values for Males: Raw and Smoothed Fits.

MALES MEAN MEAN SD SD MIN MAX

Age Raw Fit Values Smoothed Fit Values Raw Fit Values Smoothed Fit Values (1st Pctl) (99th Pctl)
0.00 48.25 1.71 44.26 52.24 1.00 48.56 2.04 43.82 53.30 2.00 48.88 2.36 43.39 54.37 3.00 49.19 2.68 42.95 55.43 4.00 49.50 3.01 42.51 56.50 5.00 49.82 3.33 42.07 57.56 6.00 50.13 3.65 41.63 58.63 7.00 51.37 50.44 2.86 3.98 41.19 59.70 8.00 53.46 50.76 2.86 4.30 40.76 60.76 9.00 51.10 51.07 6.26 4.62 40.32 61.83 10.00 51.28 51.39 5.87 4.95 39.88 62.89 11.00 50.13 51.70 6.04 5.27 39.44 63.96 12.00 50.70 52.01 7.13 5.59 39.00 65.02 13.00 52.74 52.33 5.13 5.92 38.56 66.09 14.00 52.93 52.64 4.72 6.24 38.13 67.16 15.00 53.18 52.95 5.57 6.56 37.69 68.22 16.00 49.46 53.27 6.06 6.89 37.25 69.29 17.00 49.77 53.58 6.93 7.21 36.81 70.35 18.00 51.98 53.90 7.48 7.53 36.37 71.42 19.00 59.88 54.21 9.65 7.86 35.93 72.48 20.00 56.80 54.52 9.31 8.18 35.50 73.55 21.00 54.60 54.23 8.17 8.50 34.45 74.01 22.00 54.61 53.42 8.40 8.83 32.89 73.95 23.00 53.76 52.63 9.60 9.15 31.35 73.91 24.00 57.23 51.84 10.44 9.47 29.81 73.88 25.00 50.90 51.07 10.63 9.80 28.29 73.86 26.00 50.06 50.31 9.66 10.69 25.45 75.17 27.00 46.38 49.56 8.95 10.49 25.16 73.96 28.00 48.32 48.82 10.47 10.29 24.88 72.77 29.00 51.02 48.10 12.31 10.10 24.60 71.59 30.00 45.59 47.38 9.91 9.92 24.32 70.44 31.00 45.86 46.67 10.14 9.73 24.04 69.31 32.00 46.90 45.98 11.03 9.55 23.76 68.20 33.00 42.08 45.30 9.08 9.38 23.49 67.10 34.00 44.48 44.63 8.95 9.20 23.22 66.03 35.00 38.63 43.97 10.10 9.03 22.95 64.98 36.00 42.63 43.32 7.11 8.87 22.69 63.95 37.00 40.41 42.68 8.81 8.71 22.42 62.94 38.00 39.70 42.05 6.22 8.55 22.16 61.94 39.00 40.62 41.44 8.01 8.40 21.90 60.97 40.00 39.02 40.83 8.28 8.25 21.64 60.02February 2010 A-55 Draft – Do Not Cite or Quote MALES MEAN MEAN SD SD MIN MAX

Age Raw Fit Values Smoothed Fit Values Raw Fit Values Smoothed Fit Values (1st Pctl) (99th Pctl)
41.00 39.72 40.24 9.96 8.10 21.39 59.09 42.00 35.58 39.66 9.85 7.96 21.14 58.18 43.00 39.98 39.09 6.46 7.82 20.89 57.28 44.00 38.65 38.53 7.60 7.69 20.64 56.41 45.00 40.15 37.98 6.59 7.56 20.40 55.56 46.00 40.67 37.44 7.89 7.43 20.16 54.73 47.00 41.51 36.92 9.68 7.31 19.91 53.92 48.00 38.92 36.40 10.52 7.19 19.68 53.12 49.00 34.65 35.90 7.68 7.07 19.44 52.35 50.00 33.85 35.41 6.49 6.96 19.21 51.60 51.00 32.52 34.92 4.51 6.86 18.98 50.87 52.00 36.31 34.45 7.08 6.75 18.75 50.16 53.00 36.23 34.00 7.31 6.65 18.52 49.47 54.00 33.91 33.55 5.29 6.56 18.30 48.79 55.00 33.40 33.11 5.08 6.46 18.08 48.14 56.00 31.68 32.69 6.52 6.37 17.86 47.51 57.00 32.47 32.27 6.33 6.29 17.64 46.90 58.00 33.24 31.87 6.32 6.21 17.43 46.31 59.00 33.05 31.48 6.45 6.13 17.22 45.74 60.00 29.02 31.10 3.59 6.06 17.01 45.19 61.00 31.68 30.73 6.95 5.99 16.80 44.66 62.00 29.72 30.37 5.09 5.92 16.60 44.14 63.00 30.90 30.02 8.06 5.86 16.40 43.65 64.00 30.65 29.69 5.32 5.80 16.20 43.18 65.00 29.86 29.36 6.90 5.75 16.00 42.73 66.00 28.60 29.05 5.51 5.70 15.80 42.30 67.00 29.47 28.75 5.25 5.65 15.61 41.89 68.00 28.95 28.46 5.63 5.61 15.42 41.50 69.00 31.13 28.18 6.43 5.57 15.23 41.13 70.00 27.12 27.91 3.44 5.53 15.05 40.78 71.00 27.65 5.50 14.86 40.45 72.00 28.56 27.41 5.71 5.47 14.68 40.13 73.00 27.62 27.17 5.03 5.45 14.50 39.84 74.00 27.84 26.95 6.27 5.43 14.33 39.57 75.00 26.74 5.41 14.15 39.32 76.00 25.05 26.54 6.68 5.40 13.98 39.09 77.00 23.74 26.35 4.99 5.39 13.81 38.88 78.00 26.17 5.38 13.65 38.69 79.00 26.00 5.38 13.48 38.52 80.00 25.84 5.39 13.32 38.37 81.00 23.68 25.70 5.88 5.39 13.17 38.22 82.00 25.57 5.39 13.04 38.09 83.00 25.44 5.39 12.92 37.97 84.00 25.33 5.39 12.81 37.86 85.00 25.23 5.39 12.70 37.76 86.00 25.14 5.39 12.62 37.67February 2010 A-56 Draft – Do Not Cite or Quote MALES MEAN MEAN SD SD MIN MAX

Age Raw Fit Values Smoothed Fit Values Raw Fit Values Smoothed Fit Values (1st Pctl) (99th Pctl)
87.00 25.06 5.39 12.54 37.59 88.00 25.00 5.39 12.47 37.52 89.00 24.94 5.39 12.42 37.47 90.00 24.90 5.39 12.37 37.42 91.00 24.86 5.39 12.34 37.39 92.00 24.84 5.39 12.32 37.37 93.00 24.83 5.39 12.31 37.36 94.00 24.83 5.39 12.31 37.36 95.00 24.84 5.39 12.32 37.37 96.00 24.87 5.39 12.34 37.39 97.00 24.90 5.39 12.37 37.43 98.00 24.95 5.39 12.42 37.47 99.00 25.00 5.39 12.48 37.53 100.00 25.07 5.39 12.54 37.60

Table 2. Nv02max Values for Females: Raw and Smoothed Fits

FEMALES MEAN MEAN SD SD MIN MAX

Age Raw Fit Values Smoothed Fit Values Raw Fit Values Smoothed Fit Values (1st Pctl) (99th Pctl)
0.00 35.88 5.90 22.15 49.61 1.00 36.21 6.00 22.26 50.17 2.00 36.54 6.09 22.37 50.72 3.00 36.87 6.19 22.48 51.27 4.00 37.20 6.28 22.59 51.82 5.00 37.54 6.38 22.70 52.37 6.00 37.87 6.47 22.81 52.93 7.00 38.20 6.57 22.92 53.48 8.00 38.53 6.66 23.03 54.03 9.00 30.56 38.86 9.90 6.76 23.14 54.58 10.00 45.53 39.19 6.27 6.85 23.25 55.13 11.00 43.88 39.52 5.26 6.95 23.36 55.69 12.00 43.03 39.85 6.88 7.04 23.47 56.24 13.00 42.00 40.18 7.48 7.14 23.58 56.79 14.00 37.57 40.51 6.79 7.23 23.69 57.34 15.00 39.57 40.85 5.43 7.33 23.80 57.89 16.00 35.51 41.18 5.36 7.42 23.91 58.45 17.00 38.22 41.51 8.86 7.52 24.02 59.00 18.00 45.67 41.84 8.53 7.61 24.13 59.55 19.00 43.87 42.17 7.83 7.71 24.24 60.10 20.00 42.52 42.50 7.69 7.80 24.35 60.65February 2010 A-57 Draft – Do Not Cite or Quote FEMALES MEAN MEAN SD SD MIN MAX

Age Raw Fit Values Smoothed Fit Values Raw Fit Values Smoothed Fit Values (1st Pctl) (99th Pctl)
21.00 43.45 42.10 8.51 7.90 23.73 60.48 22.00 43.22 41.45 7.59 7.99 22.86 60.05 23.00 43.87 40.81 10.13 8.09 21.99 59.63 24.00 41.14 40.18 8.22 8.18 21.14 59.22 25.00 38.20 39.56 7.09 8.28 20.30 58.82 26.00 38.98 38.95 11.12 8.37 19.47 58.43 27.00 34.94 38.35 8.02 8.35 18.93 57.76 28.00 38.08 37.75 9.80 8.14 18.82 56.69 29.00 35.13 37.17 6.30 7.94 18.71 55.64 30.00 35.79 36.60 9.10 7.74 18.59 54.61 31.00 35.22 36.04 7.89 7.55 18.47 53.60 32.00 36.06 35.48 6.93 7.37 18.35 52.62 33.00 34.95 34.94 9.51 7.19 18.23 51.66 34.00 38.13 34.41 7.08 7.01 18.10 50.72 35.00 32.63 33.88 4.88 6.84 17.97 49.80 36.00 33.59 33.37 6.17 6.68 17.83 48.91 37.00 31.11 32.87 5.13 6.52 17.70 48.04 38.00 33.12 32.37 3.76 6.37 17.55 47.19 39.00 28.80 31.89 5.14 6.22 17.41 46.37 40.00 29.06 31.42 5.74 6.08 17.26 45.57 41.00 29.54 30.95 8.00 5.95 17.11 44.79 42.00 30.90 30.50 6.82 5.82 16.96 44.03 43.00 27.60 30.05 4.32 5.70 16.80 43.30 44.00 29.33 29.62 4.17 5.58 16.64 42.59 45.00 28.53 29.19 4.90 5.47 16.48 41.90 46.00 29.41 28.78 6.00 5.36 16.31 41.24 47.00 30.49 28.37 7.15 5.26 16.14 40.60 48.00 27.92 27.97 6.05 5.16 15.97 39.98 49.00 26.48 27.59 5.36 5.07 15.79 39.38 50.00 29.80 27.21 5.13 4.99 15.61 38.81 51.00 27.49 26.84 3.66 4.91 15.43 38.26 52.00 28.95 26.49 5.83 4.83 15.24 37.73 53.00 23.77 26.14 3.56 4.77 15.06 37.23 54.00 25.34 25.80 4.61 4.70 14.86 36.74 55.00 26.05 25.48 4.29 4.65 14.67 36.29 56.00 26.30 25.16 4.91 4.60 14.47 35.85 57.00 26.06 24.85 4.07 4.55 14.27 35.44 58.00 24.55 4.51 14.06 35.05 59.00 24.27 4.48 13.85 34.68 60.00 23.67 23.99 4.81 4.45 13.64 34.33 61.00 24.70 23.72 4.65 4.43 13.43 34.01 62.00 21.63 23.46 4.99 4.41 13.21 33.71 63.00 26.64 23.21 7.38 4.40 12.99 33.44 64.00 23.84 22.97 3.77 4.39 12.76 33.18 65.00 20.26 22.74 3.83 4.39 12.53 32.95February 2010 A-58 Draft – Do Not Cite or Quote FEMALES MEAN MEAN SD SD MIN MAX

Age Raw Fit Values Smoothed Fit Values Raw Fit Values Smoothed Fit Values (1st Pctl) (99th Pctl)
66.00 20.38 22.52 4.39 12.31 32.73 67.00 20.49 22.31 4.39 12.10 32.52 68.00 22.05 22.11 3.90 4.39 11.90 32.32 69.00 21.92 21.92 4.56 4.39 11.71 32.13 70.00 20.38 21.74 4.15 4.39 11.53 31.95 71.00 25.30 21.57 4.39 11.36 31.78 72.00 21.21 21.41 4.39 11.20 31.62 73.00 20.46 21.26 4.59 4.39 11.05 31.47 74.00 20.63 21.12 4.39 10.91 31.33 75.00 20.60 20.99 3.80 4.39 10.78 31.20 76.00 20.91 20.87 4.39 10.66 31.08 77.00 22.27 20.76 4.39 10.55 30.97 78.00 19.93 20.65 4.39 10.44 30.86 79.00 22.80 20.56 4.39 10.35 30.77 80.00 23.19 20.48 4.39 10.27 30.69 81.00 19.29 20.41 4.39 10.20 30.62 82.00 13.44 20.34 4.39 10.13 30.55 83.00 28.03 20.29 4.39 10.08 30.50 84.00 17.00 20.25 4.39 10.04 30.46 85.00 18.69 20.21 4.39 10.00 30.42 86.00 18.18 20.19 4.39 9.98 30.40 87.00 20.18 4.39 9.97 30.39 88.00 27.15 20.17 4.39 9.96 30.38 89.00 20.18 4.39 9.97 30.39 90.00 18.18 20.20 4.39 9.98 30.41 91.00 20.22 4.39 10.01 30.43 92.00 20.26 4.39 10.05 30.47 93.00 20.30 4.39 10.09 30.51 94.00 20.36 4.39 10.15 30.57 95.00 20.42 4.39 10.21 30.63 96.00 20.50 4.39 10.28 30.71 97.00 20.58 4.39 10.37 30.79 98.00 20.67 4.39 10.46 30.88 99.00 20.78 4.39 10.57 30.99 100.00 20.89 4.39 10.68 31.10

Table 3. Body Mass Raw Fits.February 2010 A-59 Draft – Do Not Cite or Quote MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
0.00 7.767 1.301 3.6 11. 8 7.429 1.304 3.7 12.1 1.00 11.440 1.143 8.2 16. 1 11.119 1.163 7.4 15.3 2.00 13.932 1.146 9.8 20. 9 13.258 1.158 10.1 20.4 3.00 15.967 1.154 11.7 23.7 15.587 1.160 11 27.9 4.00 18.475 1.165 11.1 28. 1 18.005 1.171 12.8 29.1 5.00 21.618 1.234 13.7 42. 4 20.353 1.229 12.6 40.4 6.00 23.142 1.213 16.1 41. 1 22.454 1.194 15.9 36.7 7.00 27.072 1.216 19.3 46.8 26.483 1.239 16.9 51 8.00 31.651 1.302 19.1 66. 2 30.534 1.315 19.8 60.8 9.00 34.656 1.265 24 69.9 35.235 1.271 20.3 58.6 10.00 38.329 1.280 24.3 72. 9 40.550 1.304 22.7 71.2 11.00 44.149 1.308 26.2 83. 8 46.579 1.302 27.7 84.6 12.00 47.988 1.315 27.7 94. 8 50.673 1.274 27.8 93.3 13.00 55.364 1.340 27.7 106. 6 56.649 1.275 33.4 99.5 14.00 62.832 1.293 35.7 121 57.214 1.248 37.7 110 15.00 67.650 1.255 41.5 117. 9 60.091 1.249 34.9 108.4 16.00 72.460 1.267 45.8 139. 1 61.582 1.255 40.9 113.8 17.00 73.081 1.248 49.9 136. 6 61.229 1.248 41.5 133.1 18.00 75.060 1.243 51.2 144. 2 64.591 1.281 42.4 123.6 19.00 77.182 1.245 52.6 134. 5 66.156 1.274 41.6 118.5 20.00 77.952 1.250 50.5 130 66.981 1.262 41.5 122.6 21.00 78.239 1.297 46.8 199. 2 67.218 1.262 39.7 123.7 22.00 83.845 1.292 53.3 155.4 66.823 1.273 42 123.5 23.00 80.607 1.222 50.5 137.6 69.721 1.304 40.3 143 24.00 81.706 1.251 50.6 132. 6 70.284 1.289 47.5 144.5 25.00 84.818 1.206 50.2 136. 1 66.300 1.283 44.8 131.8 26.00 81.812 1.273 48.9 164. 5 72.973 1.281 45.3 128.9 27.00 85.166 1.249 50 153.9 70.604 1.281 41.4 140.9 28.00 84.321 1.272 51 167.2 74.363 1.312 44.3 142.1 29.00 82.144 1.236 50.6 147. 2 69.110 1.250 39.3 116.3 30.00 81.581 1.262 52.5 139 70.616 1.305 42.1 151.5 31.00 81.275 1.249 48.8 170. 6 73.039 1.278 43.7 125.9 32.00 84.715 1.235 49.7 135. 8 72.938 1.281 41.5 139.7 33.00 88.188 1.231 64.8 146. 3 72.710 1.307 44.9 135.2 34.00 81.163 1.221 53.1 136. 9 69.773 1.230 46.6 115.3 35.00 87.192 1.251 61 193.3 73.044 1.306 44.2 138.4 36.00 83.404 1.228 45.8 140. 5 73.547 1.289 44.6 150.1 37.00 85.759 1.241 59.3 150. 9 70.019 1.284 48.1 152.1 38.00 84.132 1.260 52.8 149. 7 75.587 1.295 43.7 151.7 39.00 84.611 1.196 61.2 140. 6 72.295 1.251 41.6 123.1 40.00 90.071 1.246 58.5 154 72.888 1.289 45.5 137.4 41.00 87.425 1.173 61.3 117. 7 73.363 1.268 50.5 156.9 42.00 88.290 1.205 62.2 144 73.697 1.270 47.1 146.1 43.00 88.423 1.233 54 145.3 73.438 1.314 45.6 159.5 44.00 88.528 1.200 56.6 128.9 75.742 1.266 49.5 153 45.00 87.102 1.205 60.6 160. 2 76.795 1.308 41.6 141.5February 2010 A-60 Draft – Do Not Cite or Quote MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
46.00 88.157 1.243 54.2 154. 3 77.544 1.304 46.6 145.8 47.00 86.547 1.229 49.9 188. 3 72.849 1.298 47.8 130.6 48.00 84.793 1.186 56.3 128.3 74.646 1.303 44.2 166 49.00 86.235 1.240 47 171.3 72.844 1.261 45.1 125.54 50.00 84.659 1.179 53.4 124. 4 75.217 1.292 48.4 175.7 51.00 87.975 1.208 57.9 143. 6 72.941 1.240 42.5 120.2 52.00 89.886 1.216 55.2 144. 9 74.472 1.283 45.7 146.6 53.00 89.012 1.228 58.2 143. 3 74.733 1.259 46.2 176.6 54.00 90.098 1.216 64.1 155. 2 72.413 1.281 44.3 123.1 55.00 88.268 1.222 55.1 138. 6 75.951 1.231 53.6 125.6 56.00 84.796 1.195 45 110.3 77.322 1.315 45.6 134.9 57.00 87.501 1.253 58.3 160 72.378 1.252 48.6 122.6 58.00 85.116 1.266 51.6 179 74.548 1.267 45 117.7 59.00 84.190 1.182 58.7 112.4 80.638 1.277 50.9 133 60.00 87.044 1.232 57.3 141. 7 75.777 1.260 51.3 128.3 61.00 89.007 1.207 49.9 162. 8 77.121 1.240 50.7 125.6 62.00 84.788 1.228 56.04 152. 1 73.347 1.198 49.7 121.1 63.00 89.137 1.262 56.3 171. 6 72.308 1.238 46.9 119.9 64.00 89.974 1.193 59.1 119 75.440 1.281 41.1 132.5 65.00 89.891 1.215 58.1 126. 3 72.910 1.254 35.9 113.7 66.00 86.814 1.228 54 150.1 73.101 1.242 48.4 113.3 67.00 86.207 1.207 43.1 127. 5 75.835 1.266 47.2 123.8 68.00 85.172 1.191 61.2 163. 2 73.207 1.250 39.3 120.7 69.00 87.116 1.222 50.7 127.2 74.368 1.225 48 118 70.00 82.775 1.210 46.5 125. 5 68.977 1.188 45.9 102.8 71.00 79.630 1.240 51 122.8 69.083 1.232 45.5 108.1 72.00 82.011 1.204 51.9 132. 7 69.898 1.240 40.7 103.8 73.00 85.590 1.196 56.2 128. 3 71.360 1.240 47.4 127.6 74.00 83.001 1.217 53.3 120 70.410 1.277 37.4 106.4 75.00 84.465 1.185 56.5 133. 5 70.526 1.216 46.8 117.4 76.00 78.733 1.207 55.9 121. 1 69.549 1.199 48.8 101.7 77.00 79.376 1.170 58.7 109. 3 70.128 1.240 40.3 119.8 78.00 79.909 1.195 41.1 115. 1 66.375 1.211 44.1 109.8 79.00 77.629 1.155 56.4 107. 8 67.780 1.200 46.2 98.4 80.00 79.866 1.174 56 111.9 62.214 1.255 41.2 121.4 81.00 75.405 1.157 55.8 111. 9 65.397 1.184 42.7 91.4 82.00 76.798 1.180 54.4 111.8 64.755 1.260 40.6 120 83.00 74.611 1.158 53.2 107 62.886 1.196 44.7 101.2 84.00 75.325 1.205 41.5 109. 5 62.215 1.216 43.5 108.4 85.00 71.776 1.191 46.9 105. 8 61.453 1.209 42.3 93.2 86.00 73.986494 1.17 50.57 101. 07 62.400356 1.21 41.85 101.16 87.00 73.364276 1.17 50.38 99. 113 61.847614 1.21 41.66 100.26 88.00 72.742058 1.16 50.19 97. 154 61.294872 1.21 41.47 99.351 89.00 72.11984 1.16 50 95.194 60.74213 1.21 41.27 98.445 90.00 71.497622 1.16 49.81 93. 235 60.189388 1.21 41.08 97.538 91.00 70.875404 1.16 49.62 91. 276 59.636646 1.2 40.88 96.632February 2010 A-61 Draft – Do Not Cite or Quote MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
92.00 70.253186 1.16 49.44 89. 317 59.083904 1.2 40.69 95.726 93.00 69.630968 1.15 49.25 87. 358 58.531162 1.2 40.49 94.82 94.00 69.00875 1.15 49.06 85. 399 57.97842 1.2 40.3 93.914 95.00 68.386532 1.15 48.87 83. 44 57.425678 1.2 40.1 93.008 96.00 67.764314 1.15 48.68 81. 481 56.872936 1.2 39.91 92.102 97.00 67.142096 1.14 48.49 79. 522 56.320194 1.2 39.71 91.195 98.00 66.519878 1.14 48.3 77. 563 55.767452 1.19 39.52 90.289 99.00 65.89766 1.14 48.11 75. 604 55.21471 1.19 39.32 89.383 100.00 65.275442 1.14 47.92 73. 645 54.661968 1.19 39.13 88.477 **Dark shading (age 86+) designates linear forecast.

Table 4. Body Mass Smoothed Fits (5-Year Running Averages).

MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
0.00 7.767209794 1.300901 3.6 11. 8 7.428916349 1.304229 3.7 12.1 1.00 11.44008024 1.143324 8.2 16. 1 11.11947416 1.162608 7.4 15.3 2.00 13.93227373 1.145566 9.8 20. 9 13.25797158 1.158434 10.1 20.4 3.00 15.96664726 1.153689 11.7 23.7 15.58684049 1.159883 11 27.9 4.00 18.47458493 1.164972 11.1 28. 1 18.00506307 1.17108 12.8 29.1 5.00 21.61756114 1.233822 13.7 42. 4 20.35285099 1.229237 12.6 40.4 6.00 23.14243627 1.213499 16.1 41. 1 22.45431948 1.194119 15.9 36.7 7.00 27.07246068 1.215834 19.3 46.8 26.48323788 1.23892 16.9 51 8.00 31.6505017 1.301873 19.1 66. 2 30.53391399 1.315137 19.8 60.8 9.00 34.65600448 1.265317 24 69. 9 35.23472141 1.271364 20.3 58.6 10.00 38.32939135 1.279707 24.3 72. 9 40.54996835 1.303997 22.7 71.2 11.00 44.14863459 1.30753 26.2 83. 8 46.57910267 1.302182 27.7 84.6 12.00 47.98795299 1.314848 27.7 94. 8 50.67329267 1.273946 27.8 93.3 13.00 55.36374737 1.33952 27.7 106. 6 56.64881107 1.275455 33.4 99.5 14.00 62.83159173 1.292533 35.7 121 57.21362103 1.24795 37.7 110 15.00 67.65031426 1.254999 41.5 117. 9 60.09135575 1.24897 34.9 108.4 16.00 72.45980541 1.267468 45.8 139. 1 61.58214656 1.255162 40.9 113.8 17.00 73.08089659 1.248405 49.9 136. 6 61.22931022 1.248057 41.5 133.1 18.00 75.06031573 1.243204 51.2 144. 2 64.59054256 1.281298 42.4 123.6 19.00 77.18236513 1.244928 52.6 134. 5 66.15556407 1.274083 41.6 118.5 20.00 77.95205826 1.250326 50.5 130 66.98146906 1.261822 41.5 122.6 21.00 78.45564692 1.265585 50.88 152. 66 66.35375002 1.270386 41.44 122.38 22.00 79.56489519 1.261251 50.74 151. 34 67.37976393 1.274844 41.02 126.26 23.00 80.46958232 1.262527 50.34 150. 96 68.20537834 1.277813 42.2 131.46 24.00 81.84267254 1.253588 50.28 152. 18 68.06901959 1.282127 42.86 133.3February 2010 A-62 Draft – Do Not Cite or Quote MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
25.00 82.55729313 1.248802 50.7 145. 24 69.21992781 1.285979 43.98 134.34 26.00 82.82151847 1.240222 50.04 144. 94 69.97607936 1.287735 43.86 137.82 27.00 83.56439112 1.250399 50.14 150. 86 70.90453453 1.289413 44.66 137.64 28.00 83.65195203 1.247428 50.14 153.78 70.66975978 1.28161 43.02 132 29.00 83.00459482 1.258753 50.6 154. 36 71.53295767 1.285847 42.48 135.94 30.00 82.89721864 1.253937 50.58 155. 58 71.54621552 1.285108 42.16 135.34 31.00 82.80701235 1.251132 50.52 151. 96 72.01313142 1.28495 42.18 135.1 32.00 83.58034187 1.242848 53.28 147. 78 71.6826276 1.283915 42.3 133.72 33.00 83.38418057 1.239735 53.78 145. 72 71.81523165 1.280002 43.76 133.52 34.00 84.50647805 1.237533 55.48 156. 58 72.30094254 1.280205 44.18 130.9 35.00 84.9321819 1.233184 54.88 150. 56 72.40264379 1.282492 44.36 135.74 36.00 85.14102649 1.234298 56.8 153. 58 71.81884258 1.283151 45.68 138.22 37.00 84.32994666 1.240177 54.4 154. 26 72.3941641 1.280709 45.44 141.52 38.00 85.01958212 1.235131 56.02 155 72.89859355 1.284821 44.44 143.08 39.00 85.59524544 1.233983 55.52 147. 14 72.86733489 1.281407 44.7 142.88 40.00 86.39949423 1.223065 58.62 142.58 72.830387 1.277165 45.88 144.24 41.00 86.90564401 1.215924 59.2 141. 2 73.56585153 1.274483 45.68 143.04 42.00 87.76379051 1.210495 59.44 140. 32 73.13604869 1.278433 46.06 144.6 43.00 88.54719729 1.211458 58.52 137. 98 73.82543503 1.281514 47.64 150.58 44.00 87.95342484 1.203416 58.94 139. 22 74.60684165 1.285327 46.86 151.4 45.00 88.09985934 1.217379 57.52 146. 54 75.44302619 1.292445 46.08 149.18 46.00 87.751282 1.222211 55.06 155.4 75.27348935 1.29795 46.22 146.08 47.00 87.02523405 1.212835 55.52 152 75.51517243 1.295658 45.94 147.38 48.00 86.56661258 1.220669 53.6 160. 48 74.93569966 1.29459 45.06 141.888 49.00 86.07815707 1.215489 52.16 153. 32 74.62001355 1.291492 46.42 148.728 50.00 86.04175058 1.208607 52.9 151. 18 73.69947055 1.278764 45.6 143.608 51.00 86.70964624 1.206031 53.96 142. 5 74.02400492 1.27574 45.18 146.808 52.00 87.55345712 1.2144 54.34 145.5 74.04127315 1.266893 45.58 148.928 53.00 88.32616726 1.209663 57.76 142. 28 73.95491798 1.270898 45.42 148.44 54.00 89.04784314 1.218268 58.1 145. 12 74.10188224 1.25873 46.46 138.42 55.00 88.4120991 1.215526 55.52 138. 46 74.97813364 1.273724 47.08 141.36 56.00 87.93495739 1.222906 56.14 141. 48 74.55937637 1.267547 47.66 136.56 57.00 87.15584772 1.230391 54.82 148. 62 74.52242942 1.269258 47.42 124.78 58.00 85.97418819 1.223617 53.74 140. 06 76.16748501 1.268509 48.74 126.76 59.00 85.72952642 1.22558 54.18 140. 68 76.13252691 1.274205 48.28 127.3 60.00 86.57173577 1.228074 55.16 151. 18 76.09221736 1.259138 49.3 125.44 61.00 86.0292098 1.222944 54.708 149. 6 76.28599922 1.248419 49.52 125.14 62.00 86.83331368 1.22222 55.648 148. 12 75.83796229 1.242552 49.9 125.58 63.00 87.99005122 1.224363 55.728 149. 44 74.79845832 1.243365 47.94 125.48 64.00 88.55927286 1.220869 55.888 146. 36 74.22522224 1.242246 44.86 122.56 65.00 88.12051692 1.225034 56.708 143. 82 73.42130739 1.242692 44.4 120.1 66.00 88.40439667 1.220898 54.12 138. 9 73.91902253 1.256261 43.9 120.64 67.00 87.61146369 1.206714 55.1 137. 22 74.09892054 1.258602 42.38 120.8 68.00 87.03986775 1.212565 53.42 138. 86 73.88448789 1.247351 43.76 117.9 69.00 85.61667034 1.211601 51.1 138. 7 73.09783811 1.234088 45.76 115.72 70.00 84.17987726 1.213949 50.5 133. 24 72.29417333 1.23216 45.18 114.68February 2010 A-63 Draft – Do Not Cite or Quote MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
71.00 83.34052655 1.213444 52.26 134. 28 71.10679374 1.227009 43.88 110.68 72.00 83.42413149 1.214423 51.26 127. 3 70.73734313 1.225132 45.5 112.06 73.00 82.60108145 1.213508 51.78 125. 86 69.94568967 1.235452 43.38 109.74 74.00 82.93914453 1.208433 53.78 127.46 70.25540111 1.241 43.56 112.66 75.00 82.75981666 1.201809 54.76 127. 12 70.34868617 1.23444 44.22 111.38 76.00 82.23282472 1.19492 56.12 122. 44 70.39465694 1.234265 44.14 114.58 77.00 81.09670348 1.194698 53.1 119. 8 69.39757689 1.228511 43.48 111.02 78.00 80.02242628 1.182282 53.72 117. 36 68.87151054 1.213229 45.24 109.42 79.00 79.10265965 1.180128 53.62 113. 04 67.20924709 1.221048 44.12 110.22 80.00 78.43698141 1.170309 53.6 111. 2 66.37891246 1.217987 42.9 108.16 81.00 77.92142176 1.17229 52.74 111. 7 65.30424541 1.222093 42.96 108.2 82.00 76.86173441 1.164819 55.16 110. 08 64.60647334 1.219074 43.08 106.48 83.00 76.40090269 1.174796 52.18 110. 42 63.49351577 1.22226 42.54 108.48 84.00 74.7828307 1.17822 50.36 109. 2 63.34131978 1.213219 42.76 102.84 85.00 74.4992137 1.180574 49.31362 107.03 43 62.74189138 1.218822 42.59095 104.7926 86.00 73.81250877 1.177977 48.50949 104.49 69 62.16041309 1.208932 42.80295 100.844 87.00 73.43871959 1.179377 47.90759 102.52 76 61.84223228 1.211505 42.15598 100.4742 88.00 72.79767378 1.170756 49.60794 99.666 46 61.5476723 1.209819 41.71005 98.48308 89.00 72.74205786 1.164535 50.19052 97.153 54 61.29487188 1.209189 41.46516 99.35077 90.00 72.11983988 1.162177 50.00172 95.194 46 60.74212987 1.207753 41.27036 98.44462 91.00 71.4976219 1.159818 49.81292 93.235 38 60.18938785 1.206317 41.07556 97.53846 92.00 70.87540393 1.157459 49.62412 91.276 31 59.63664584 1.20488 40.88075 96.63231 93.00 70.25318595 1.1551 49.43532 89.317 23 59.08390383 1.203444 40.68595 95.72615 94.00 69.63096798 1.152742 49.24652 87. 35815 58.53116181 1.202008 40.49115 94.82 95.00 69.00875 1.150383 49.05772 85.39908 57.9784198 1.200571 40.29634 93.91385 96.00 68.38653203 1.148024 48.86892 83. 44 57.42567778 1.199135 40.10154 93.00769 97.00 67.76431405 1.145665 48.68012 81.480 92 56.87293577 1.197699 39.90674 92.10154 98.00 67.14209607 1.143307 48.49132 79.521 85 56.32019375 1.196263 39.71193 91.19538 99.00 66.5198781 1.140948 48.30252 77.562 77 55.76745174 1.194826 39.51713 90.28923 100.00 66.20876911 1.139769 48.20812 76.583 23 55.49108073 1.194108 39.41973 89.83615February 2010 A-64 Draft – Do Not Cite or Quote Table 5. Hemoglobin Content.

MALES FEMALES Age MEAN STD MEAN STD
0 11.927 0.993545 12.209 0.729499905 1 12.20959 1.013091 12.27307 0.719158646 2 12.42075 0.823171 12.55018 0.843436666 3 12.69015 0.83159 12.4519 0.965868504 4 12.8006 0.80152 12.83442 0.773409545 5 12.95822 0.878515 12.87154 0.969254536 6 13.19574 0.893008 13.01866 0.828912341 7 13.46198 0.836639 13.09899 0.754370806 8 13.35161 0.833121 13.25291 0.826349227 9 13.59742 0.971019 13.36671 0.808377267 10 13.63062 0.906785 13.58919 1.034306588 11 13.66 0.726155 13.52681 0.90041802 12 13.9727 0.955869 13.6273 0.884271668 13 14.28293 1.036749 13.46986 0.97623121 14 14.70654 1.020254 13.58878 1.034527514 15 15.13583 1.04546 13.47154 0.856131982 16 15.36442 1.021623 13.50562 1.088863466 17 15.45945 0.979296 13.49842 1.117860417 18 15.7487 1.02514 13.46091 1.18250671 19 15.76812 0.831813 13.35445 1.090493585 20 15.79371 0.880956 13.5016 1.072791517 25 15.71703 0.91072 13.47168 1.170602542 30 15.70837 1.045808 13.2967 1.145254677 35 15.55635 0.959964 13.34583 1.134192006 40 15.43525 1.021741 13.4881 1.163867696 45 15.44038 1.105939 13.48617 1.348669176 50 15.41492 1.096952 13.61113 1.193756618 55 15.31983 1.123792 13.67737 1.106237392 60 15.27653 0.97796 13.83717 1.237714453 65 15.07274 1.192645 13.76529 1.093354796 70 14.96193 1.24457 13.81911 1.093565513 75 14.72786 1.418355 13.79013 1.056812752 80 14.51 1.476879 13.84426 1.30818261 85 14.52915 1.352814 13.57546 1.238910845 90 13.97647 1.757686 13.43767 1.552685662 95 13.801 1.757686 13.2085 1.552685662 100 13.534 1.757686 13.005 1.552685662February 2010 Draft – Do Not Cite or Quote B-1 

### Appendix B

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).

B.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 4 of the draft CO REA, APEX4.3 pr ovides estimates of CO concentr ation and alveolar ventilation rate for each event in the composite diary. APEX4.3 then uses these data, together with estimates of various physiological parameters specif ic to the simulated individual, to estimate the percent COHb in the blood (%COHb) as an av erage %COHb value over the duration of each exposure event and as an instantaneous %COHb level 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.February 2010 Draft – Do Not Cite or Quote B-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).

B.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. B-1)

where,

A O H B

Lco V P P

D B

 2 1

   (Eq. B-2)February 2010 Draft – Do Not Cite or Quote B-3 Table B-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)February 2010 Draft – Do Not Cite or Quote B-4 If the only quantity in equation (B-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. B-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. B-4)

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

COHb RHb CO   (Eq. B-5)

Hb O RHb O 2

2 

 (Eq. B-6)

Subtracting (B-6) from (B-5) yields (B-3). If(B-3) is in equilibrium, then (B-5) and (B6) are in equilibrium. If k represents the equilibrium constant for (B-6) then:

k RHb c P Hb O

O  ] [ ] [

2 2 (Eq. B-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 (B-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. B-8)

Eliminating [RHb] between (B-7 ) and (B-8) and solving for [O 2 Hb] yields:February 2010 Draft – Do Not Cite or Quote B-5 ]) [ ] ([ 1 ] [ 0 2

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

O O

   (Eq. B-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 (B-9) i nvolving the mean pulmonary capillary oxygen pressure and the equilibrium constant k . Substituting (B-9) into (B -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. B-10)

In working with the CFK model it is convenien t to express COHb as a percent of [RHb] 0
. Multiplying (B-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. B-11)

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

] %[ 100 ] %[ ] %[

1 COHb COHb C C dt COHb d

O

   (Eq. B-12)

where,

) ( ] [ 100

b I

b CO

O O BV P

V V
THb C CO    (Eq. B-13)

b O CO

MBV THb k P k C ] [ ) 1 ( 100

2 1 

 (Eq. B-14)

Given values for the atmospheric pressure and the physiological va riables in equations (B-12) through (B-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 (B-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. B-15)

The equation for %[COHb] ∞ is obtained by setting equati on (B-12) equal to zero and solving for %[COHb], which is now equal to %[COHb] ∞ :February 2010 Draft – Do Not Cite or Quote B-6 ) ( 100 ] %[

1 C C C COHb

O O
   (Eq. B-16)

Equation (B-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. B-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 (B-12) above, except that (B-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. B-18)

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

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

D = D 0 - A 1 (Eq. B-21)

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

The z variable is a re-scaled time variable that is dimensionless. It is used as theFebruary 2010 Draft – Do Not Cite or Quote B-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 (B-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. B-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. B-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. B-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. B-26)

Tests showed that the fourth-order Taylorseries expansion (B-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 (B-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.

B.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 B.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 individualFebruary 2010 Draft – Do Not Cite or Quote B-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 4.4.3 of the draf t CO REA. The sampling approach attempts to match the 24-hour activity patterns to the simulated individual a nd exposure period according to the demographic characteristics of the individua l and the season, day t ype (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 (B-12) through (B-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 B.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 (B-2) and (B-13) through (B-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.February 2010 Draft – Do Not Cite or Quote B-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 4.4.5 of the draft CO REA, the algorithms used to estimate V E and COHb require values for various physiological parameters such as body mass, blood volume, and pulmonary diffusion rate. Table B-2 provides a list and description of the principal parame ters; additional parameters are listed and described in Chapter 5 of US EPA (2008). An algorithm within APEX 4.3 probabilistically generates a value for each parameter on the list (collectively referred to as a physiological profile) for each simulated individual. Figure B-1 is a flow diagram showing the process by which each physiological profile is generated. Each of the generated physiological profiles is internally consistent, in that the functional relationships among the various parameters are maintained. For example, blood volume is determined 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, torr Haldane CoefficientFebruary 2010 Draft – Do Not Cite or Quote B-10 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 STPDFebruary 2010Draft – Do Not Cite or QuoteB-11Table B-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.February 2010Draft – Do Not Cite or QuoteB-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 the years 1999-2004 (seeIsaacs and Smith, 2005 in Appendix A)Units: grams of Hb per deciliter of bloodFebruary 2010Draft – Do Not Cite or QuoteB-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February 2010Draft – Do Not Cite or QuoteB-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 4.4.5 of draft CO REAand Chapter 5 of US EPA (2008).Energyconversion factor(ECF)VentilationrateGender See Section 4.4.5 of draft COREA and Chapter 5 of US EPA (2008).NVO2maxVentilationrateGenderAgeSee Section 4.4.5 of draft CO REA and Chapter 5 of US EPA (2008).VO2maxVentilationrateNVO2maxBody WeightSee Section 4.4.5 of draft CO REA and Chapter 5 of US EPA (2008).February 2010 Draft – Do Not Cite or Quote B-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 B-1. Flow Diagram for Phys iological Profile Generator. Input data is supplied at the start of the APEX4.3 computation.February 2010 Draft – Do Not Cite or Quote B-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. B-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. B-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 (B-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 andFebruary 2010 Draft – Do Not Cite or Quote B-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. B-29)

where HbAlt is the percent increase in Hb due to exposure to altitude a nd is given by (US EPA, 1978):

Altitude e HbAlt 0001249

. 0 76

. 2 
Hb in equation (B-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 B-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 B-2, [THb] 0 is calculated using equation (B-29). The weight percent MetHB, %MetHB, is ta ken 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.February 2010 Draft – Do Not Cite or Quote B-18 Determination of Height

The following equations were used to estimate height as a function of gender and weight. Equations B-30 and B-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. B-30)

females: height = 48.07 inches + (3.07) [ln(weight)] + (2.48 inch es)(z) (Eq. B-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. B-32)

Women: 97

. 5 115

. 0 556

. 0

 

   age height D

CO

L (Eq. B-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 B-32 and B-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 B-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 B-2 presen ts these distributions classified by class, gender , and menstrual phase.February 2010 Draft – Do Not Cite or Quote B-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 B-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. B-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. B-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.February 2010 Draft – Do Not Cite or Quote B-20 Alveolar Ventilation Rate

The main program supplies the COHb modul e with ventilation rate derived from the algorithm discussed in Section 4.4.5 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. B-36)

Table B-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 0.65February 2010 Draft – Do Not Cite or Quote B-21 B.5 References

Allen TH, Peng MT, Chen KP, Huang TF, Chan g C, Fang HS. (1956). Prediction of blood volume and adiposity in man from b ody weight and cube of height. Metabolism . 5:328- 345.

Biller WF and Richmond HM. (1982). Sensitiv ity 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 the gut on measurements of endogenous carbon monoxide production. Blood

. 45:67-69.

Brainard J and Burmaster D. ( 1992). Bivariate distributions 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 car bon 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). Interactio ns of carbon monoxide and hemoglobin at high altitude. Atmos Environ

. 17:723-728.

Delivoria-Papadoppulos M, Coburn RF, Forster RE. (1974). Cyclic varia tion 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.

Issacs K and Smith L. (2005). New Values for Physiological Parameters for the Exposure Model Input File Physiology.txt. Tec hnical memorandum to Tom McCurdy, NERL WA10. December 20, 2005. Provided in Appendix A of the 2 nd draft CO REA.

Johnson T and Paul RA. (1983). The NAAQS Model (NEM) Applied to Carbon Monoxide. EPA 450/5-83-003, U.S. Environmental Protec tion Agency, Research Triangle Park, NC.February 2010 Draft – Do Not Cite or Quote B-22 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 Quality 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). Variation in the rate of endogenous carbon monoxide production in normal human beings. J Lab Clin Med

. 79:85-95.

Marcus AH. (1980). Mathemati cal 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 ve rsion of the Coburn, Fo rster and Kane model of blood carboxyhemoglobin. Atmos Environ . 21:1963-1967.

Peterson JE and Stewart RD. (1970). Abso rption 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 levels resulting from carbon monoxide exposures. J Appl Physiol . 39:633-638.

Press WH, Flannery BP, Teukols ky 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). Oxygen and carbon monoxide equilibria of human adult hemoglobin at atmos pheric and elevated pressure. Blood . 33:57-65.February 2010 Draft – Do Not Cite or Quote B-23 Salorinne Y. (1976). Single-br eath pulmonary diffusing capacity. Scand J Resp Diseases

. Supplementum 96.

Tikuisis P, Madill HD, Gill BJ, Lewis 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 of 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). Endoge nous carbon monoxide pr oduction after bicycle exercise in healthy subjects and in patients with hereditary spherocytosis. Scan J Lab Invest

. 40:319-324.February 2010 Draft – Do Not Cite or Quote C-1 

### Appendix C

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 poster, include d at the end of this Appendix in its entirety, was originally presented at the American Time Use Research Conference , June 25-26, 2009, University of Maryland, College Park, MD.February 2010 Draft – Do Not Cite or Quote C-2 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 longitudinal activity patterns is necessary for appropriately reproducing the frequency and duration of polluta nt exposures in human exposure models. In EPA’s exposure models, longitudinal activity diarie s for simulated persons are constructed from the 1-day cross sectiona l activity diaries in the Consolidat ed 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 ventilati on 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.

INTRODUCTION Recently, new methods of assembling multi-day diaries in human exposure models from cross-sectional single-day diar ies have been proposed that are based on the variance and autocorrelation statistics of the simulated popul ation (Glen et al. 2008). 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. 2000) 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.February 2010 Draft – Do Not Cite or Quote C-3 BACKGROUND Exposure models require construction of huma n activity diaries that cover the entire simulation period of a model run. This period is of ten several months, a year, or even longer. In EPA’s models, human activity diaries are usua lly drawn 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 algorithm has been developed (Glen et al. 2007) based on the variance and autocorrelation pr operties 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- a nd between-person variances σ w 2 and σ b 2 by specifying the D statistic, defined to be:

D pertains to the population as a whole and is bounded by zero and one. A value of zero implies 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 and between-person variances through setting the D statistic, the new diary assembly method optionally 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:

The population autocorrelation A is the mean of the A values for all individuals. Autocorrelation could be of interest to the exposur e modeler if the concentration 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 tendency 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., 2008). 2

w 2 b 2 b 2 2 b σ σ σ

σ σ D   



#        

  

     N

1 j 2 j 1 j 1 N

1 j j y y / y y y y AFebruary 2010 Draft – Do Not Cite or Quote C-4 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 temperat ures and precipitation amounts were acquired for each day of the study.

Variance and Autocorrelation Statistics Variance and lag-one autocorr elation statistics were calculated for the studied individuals. Variance st atistics were estimated for both the ra w measured variables (i.e. 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 th e total variance (the sum of the between- and within-person variance) was calculated for the popul ation. This ratio, calculated us ing the raw variables, is the intraclass correlation coefficient (ICC), while the same ratio, calculated using the ranks, is D, the diversity statistic. The autocorrelation A was also calculated using bot h the raw variables 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 support decisions on optimal diary pools for exposure modeling. Time spent in each of the ex amined locations/activities were assessed as a function of day of the week (weekday versus weekend), day type (workday versus nonworkday), season, temperature, pr ecipitation, and gender. These analyses were undertaken to assess the utility of different diary pool defini tions. Optimal definitions of diary pools can adequately capture temporal pa tterns in activities while maximizing 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 ra nk test (for 2 groups) or the Kruskal-Wallis test (for more than 2 groups ). The Wilcoxon rank sum (two-sam ple) test was used to test differences between genders. RESULTS AND DISCUSSION Individual Variability Figure 1 shows 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 ]

.February 2010 Draft – Do Not Cite or Quote C-5 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.

Variance and Autocorrelation Statistics D, ICC, and A values for the population for tim e spent in different locations/activities are given in Table 1 . Values of the ICC are lower than D; while 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 day types ( Table 4 ) where possible.February 2010 Draft – Do Not Cite or Quote C-6 The D and ranked A values were compared to those calculated for children from the Southern California Chronic Ozone Exposure Study (SCCOES). The diversity (D) for this group of adults for outdoor time were higher than t hose calculated for the ch ildren (0.38 versus 0.19). The D values for travel time in the current st udy were also higher (0.18 in children versus 0.36 in this study). These differences reflect the increased heterogeneity in these va riables 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 day types were notable, even considering the small number subjects in this study. Ther e were gender differences observed in D; the mechanism of these differences is unclear, but is 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 day type. This is not unexpected, as it is reasonable that the behaviorof working adults is more consistent day-today on workdays. These trends should be confirme d by analysis of other longitudinal 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.February 2010 Draft – Do Not Cite or Quote C-7 Time Spent in Different Locations/Activities The time spent in different locations/activ ities 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 thus plots are not shown. The plotted data represent all days for all subjects. The medians are represented by the midline of the boxes, the firs t and third quartiles by the ends of the boxes, and the means by the stars. The whiskers extend to cover data that lies beyond the boxed but within the quartiles plus 1.5 times the interquart ile range. Points outside this range are plotted.

Results by day of the week and day type are presented in Figure 3 . Day type (workday versus non-workday) 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 th e larger, cross-sectiona l database of diaries from The National Human Activity Pattern Survey (NHAPS, data not sh own). That analysis indicated that a workday/non-workday was a bette r discriminator of time spent outside than a weekday/weekend split. As such, further compar isons are also presented for both workdays and non-workdays.February 2010 Draft – Do Not Cite or Quote C-8 Figure 3. Time spent in different locations/activi ties as a function of day of the week, and daytype (workday versus non-workdays).

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

Figure 4. Time spent in different locations/activi ties as a function of season and daytype.February 2010 Draft – Do Not Cite or Quote C-9 The effect of temperature category is shown in Figure 5 . The temperature category was defined as warmer = maximum temperature grea ter than or equal to 75 degrees, colder= maximum temperature less than 75 degrees. Temp erature category was better than or as good as season in discriminating behavior in time spent outdoors, even when day type 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 day type.

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 temperat ure, day type 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 wa s consistent with recent findings from crosssectional diary studies indicating that wor kdays/non-workdays 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 spent outdoors, especially when day type was considered. Such breakdowns by temperature and day type ma y eliminate the need for diary pools for different seasons, providing la rger pools for diary sampling on a given day. Further analysis with other time-activity data can confirm this trend.February 2010 Draft – Do Not Cite or Quote C-10 FUTURE WORK We plan to repeat this type of study period ically. 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 (E PA contract 68-D-00-206). It has been subjected to review by the EPA and approved for publication. Appr oval does not signify that the contents necessarily reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

REFERENCES Geyh AS, Xue J, Ozkaynak H, Spengler JD. (20 00). The Harvard Southern California Chronic Ozone Exposure study: assessing ozone exposure of grade school-age children in two southern California communities. Environ Health Perspect . 108:265–270.

Glen G, Smith L, Isaacs K, McCurdy T, Langs taff J. (2008). A new method of longitudinal diary assembly for human exposure modeling. J Expo Sci Environ Epidemiol . 18(3):299-311.

McCurdy T, Glen G, Smith L, Lakkadi L. (2000). The National Exposure Research Laboratory's Consolidated Human Activity Database. J Expo Anal Environ Epidemiol . 10:566- 78.February 2010Draft – Do Not Cite or QuoteC-11Isaacs et al. (2009) in original poster 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<0.002 P<0.04 NSP<0.02 P<0.006 NSNS NSNSP<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<0.002 P<0.04 NSP<0.02 P<0.006 NSNS NSNSP<0.003P<0.001 NSP<0.001P<0.001NSNSNSP<0.0001P<0.02NSNSP<0.001P<0.001P<0.005February 2010 Draft – Do Not Cite or Quote D-1 

### Appendix D

Microenvironmental Mapping

Figure D-1 presents how CHAD codes are mapped to the eight microenvironments used to model exposure in the draft CO REA. Table D-1 provides the CHAD activity codes used to id entify when a simulated individual was in a work air district.

Figure D-1. Microenvironmental Mapping Input F ile Showing Mapping of CHAD Location Codes to the Eight Microenvironments for Application of APEX4.3 to Carbon Monoxide.

! Mapping of CHAD location codes to nine APEX microenvironments defined ! by Option 4 of Memorandum dated 12/8/2009. CHAD Loc. Description APEX

--------- ---------------------------------------------- U Uncertain of correct code = -1 U X No data = -1 U 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 = 7 H 30210 Your residence, outdoor = 7 H 30211 ..., pool or spa = 7 H 30219 ..., other outdoor = 7 H 30220 Other residence, outdoor = 7 H 30221 ..., pool or spa = 7 H 30229 ..., other outdoor = 7 HFebruary 2010 Draft – Do Not Cite or Quote D-2 30300 Residential garage or carport = 1 H 30310 ..., indoor = 1 H 30320 ..., outdoor = 7 H 30330 Your garage or carport = 1 H 30331 ..., indoor = 1 H 30332 ..., outdoor = 7 H 30340 Other residential garage or carport = 1 H 30341 ..., indoor = 1 H 30342 ..., outdoor = 7 H 30400 Residence, none of the above = 1 H 31000 Travel, general = 8 O 31100 Motorized travel = 8 O 31110 Car = 8 O 31120 Truck = 8 O 31121 Truck (pickup or van) = 8 O 31122 Truck (not pickup or van) = 8 O 31130 Motorcycle or moped = 5 O 31140 Bus = 8 O 31150 Train or subway = 8 O 31160 Airplane = 0 O 31170 Boat = 7 O 31171 Boat, motorized = 7 O 31172 Boat, other = 7 O 31200 Non-motorized travel = 7 O 31210 Walk = 7 O 31220 Bicycle or inline skates/skateboard = 7 O 31230 In stroller or carried by adult = 7 O 31300 Waiting for travel = 7 O 31310 ..., bus or train stop = 5 O 31320 ..., indoors = 4 O 31900 Travel, other = 8 O 31910 ..., other vehicle = 8 O 32000 Non-residence indoor, general = 3 O 32100 Office building/ bank/ post office = 3 O 32200 Industrial/ factory/ warehouse = 4 O 32300 Grocery store/ convenience store = 3 H 32400 Shopping mall/ non-grocery store = 3 O 32500 Bar/ night club/ bowling alley = 3 O 32510 Bar or night club = 3 O 32520 Bowling alley = 3 O 32600 Repair shop = 3 O 32610 Auto repair shop/ gas station = 2 O 32620 Other repair shop = 3 O 32700 Indoor gym /health club = 3 O 32800 Childcare facility = 4 O 32810 ..., house = 1 O 32820 ..., commercial = 4 O 32900 Large public building = 3 O 32910 Auditorium/ arena/ concert hall = 3 O 32920 Library/ courtroom/ museum/ theater = 3 O 33100 Laundromat = 3 HFebruary 2010 Draft – Do Not Cite or Quote D-3 33200 Hospital/ medical care facility = 4 O 33300 Barber/ hair dresser/ beauty parlor = 3 H 33400 Indoors, moving among locations = 3 O 33500 School = 4 O 33600 Restaurant = 3 O 33700 Church = 4 H 33800 Hotel/ motel = 3 O 33900 Dry cleaners = 3 H 34100 Indoor parking garage = 6 O 34200 Laboratory = 3 O 34300 Indoor, none of the above = 3 O 35000 Non-residence outdoor, general = 7 O 35100 Sidewalk, street = 5 O 35110 Within 10 yards of street = 5 O 35200 Outdoor public parking lot /garage = 6 O 35210 ..., public garage = 6 O 35220 ..., parking lot = 6 O 35300 Service station/ gas station = 2 O 35400 Construction site = 7 O 35500 Amusement park = 7 O 35600 Playground = 7 H 35610 ..., school grounds = 7 O 35620 ..., public or park = 7 H 35700 Stadium or amphitheater = 7 O 35800 Park/ golf course = 7 O 35810 Park = 7 O 35820 Golf course = 7 O 35900 Pool/ river/ lake = 7 O 36100 Outdoor restaurant/ picnic = 7 O 36200 Farm = 7 O 36300 Outdoor, none of the above = 7 O

Table D-1. CHAD Work Related Activity Co des Used To identify Work Air Districts.

<10> Work and Other Income Producing Activities 10000: work and other income producing activities, general 10100: work, general 10110: work, general, for organizational activities 10111: work for professional/union organizations 10112: work for special interest identity organizations 10113: work for political party and civic participation 10114: work for volunteer/ helping organizations 10115: work of/ for religious groups 10116: work for fraternal organizations 10117: work for child/ youth/ family organizations 10118: work for other organizations 10120: work, income-related only 10130: work, secondary (income-related) 10200: unemployment 10300: breaks

### Appendix E

Analysis of CHAD Diaries fo r Time Spent in Vehicles.

The US Census Bureau (2009) provides an on-line facility fo r accessing the detailed census data included in their Summary File 3 (SF3 ). We obtained information on travel time to work for workers ages 16 years and over specif ic to Denver County, Colorado and Los Angeles, CA (US Census Bureau, 2009, Tabl e P31). Staff converted the counts listed in Table P31 for trips to work places other than home into the perc entages listed in Columns 2 and 3 of Table E-1. Although the P31 statistics apply to people 16 years or older, staff assume d that the statistics were generally applicable to people 18 years or older.
We next determined the number of 24- hour diaries in EPA’s Consolidated Human Activity Database (CHAD) (US EPA, 2002) that met the following criteria: the subject was ≥ 18 years of age and the diary repor ted at least one minute in a motor vehicle between 6 am and 9
am. The number of these diaries that had in-v ehicle times corresponding to the bins listed in Table E-1 are given in Column 4 and were convert ed to the percentages li sted in Column 5.

Table E-1. Comparison of Denver and LA co mmuting characteristics (US Census, 2009) to time spent in motor vehicles using CHAD Diaries (US EPA, 2002).

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

. US EPA. (2002). EPA’s Consolidated Human Activities Database. Available at: http://www.epa.gov/chad/

. 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 10.3 7.8 563 9.79 10 to 19 32.0 25.9 1,676 29.16 20 to 29 24.2 21.0 1,068 18.58 30 to 39 18.6 21.4 1,111 19.33
40 to 59 9.3 13.6 665 11.57
60 to 89 3.8 7.0 407 7.08

90+ 1.7 3.4 258 4.49 Total 100 100 5,748 100 Notes:

a Subjects are 18+ years of age. Diaries are those having ≥ one minute in motor vehicle time spent between 6 AM and 9 AM.February 2010 F-1 Draft – Do Not Cite or Quote 

### Appendix F

Differences in Human Activity Patterns Between Individuals With and Without Cardiovascular Disease

The following presents a memorandum by Cohen et al. (1999) that was included in the Johnson et al. (2000) CO exposure assessment (see Appendix J of that report). It is in its original form, with some minor editing performed by staff for inclusion into this second draft CO REA.February 2010 F-2 Draft – Do Not Cite or Quote 

## M E M O R A N D U M

TO: Harvey Richmond

FROM: Jonathan Cohen, Sergey Nikiforov, and Arlene Rosenbaum

DATE: January 15, 1999
SUBJECT: EPA 68-DO-0062 Work Assignment 2-24: Task 2: Evaluation of Differences in Human Activity Patterns Between Individuals With or Without Cardiovascular Disease

EVALUATION OF DIFFERENCES IN HUMAN ACTIVITY PATTERNS BETWEEN INDIVIDUALS WITH OR WITHOUT CARDIOVASCULAR DISEASE

SUMMARY

Activity pattern data from the National Human Activity Pattern Survey were used to compare activity patterns and exertion distributions between subjects with or without angina. The diary survey provided a 24-hour diary of activities. Exertion rates for each person in the survey were simulated 100 times. For each person, the body weight was simulated from a log-normal distribution specific to the age and gender. The resting metabolic rate was simulated using a regression against body weight, with coefficien ts depending on age and gender. Finally, the exertion rate was simulated for each activity and person by multiplying the simulated resting metabolic rate by a MET exertion ratio with a distribution specific to each type of activity. The current version of the probabilistic NAAQS Exposure Model for Carbon Monoxide (pNEM/CO), described in Johnson (1998), begins with the same set of physiological equations and statistical distributions for probabilistic simulation of exposure. The pNEM/CO model uses the much broader Consolidated Human Activity Data Base (CHAD) and simulates additional physiological variables, such as the ventilation rate. The description of the relevant probabilistic and physiological equations in this memorandum is largely based on Johnson (1998); see that memorandum for more detailed information.

Differences between angina and non-angina s ubjects were evaluated for several summary statistics: average and 95 th percentile of the maximum daily 8-hour exertion, percentage of time spent outdoors or in a vehicle, average percentage of time at light, moderate or heavy exertionFebruary 2010 F-3 Draft – Do Not Cite or Quote levels. Age and gender have very significant effects on these summary statistics of activity and exertion. Since angina patients tend to be much older and tend to include more females than the general population, it is very important to adjust for age and gender effects when comparing angina and non-angina groups. Otherwise, one cannot distinguish between the angina effect and the effects of age and gender. Statistical analys es comparing angina to non-angina subjects were performed, adjusting for age and gender either by stratification (comparing subjects in a given age/gender subgroup), or by fitting a general linear model (with separate terms for age, gender, and angina effects and their interactions). These analyses showed that, overall, angina subjects tended to have less extreme exertion levels. More specifically, the maximum 8-hour exertion energies tended to be lower, as did the percentages of time above moderate or high exertion rate thresholds. The percentages of time spent outdoors or in a vehicle were generally not statistically significantly different between a ngina and non-angina subjects.

The large sample of NHAPS subjects produced, in many cases, statistically significant differences in the exertion rate summaries between angina and non-angina subjects. However, those differences were generally numerically small compared to the mean values. Therefore we conclude that the differences in activity and exertion between angina and non-angina subjects, although statistically significant, are not large enough to severely impact the validity of pNEM/CO modeling results that do not adjust for an angina/non-angina difference.

METHODOLOGY

For these analyses we used the National Human Activity Pattern Survey (NHAPS) database, a telephone survey of human activity patterns conducted for the USEPA between October 1992 and September 1994 by the Survey Research Center at the University of Maryland. See Klepeis et al. (1996, 1998) and Tsang and Klepeis (1996) for more details about the NHAPS study and various statistical analyses of those data. The NHAPS data (Triplett, 1996) are included in CHAD. (Other CHAD studies did not include que stions about cardiovascular disease and so could not be used for these analyses comp aring angina and non-angina respondents.) A nationally representative sample of 9,386 respondents completed a detailed diary listing all their activities and locations over a 24-hour period (either from the previous day or a previous weekend day). A few respondents did not state th eir age and/or gender and their data was not used in our analysis. Our analysis used 9,149 of the surveys. Respondents were also asked demographic questions, including age and gender, and health questions, including whether or not they have been told by a doctor that they have angina: 243 respondents (2.6 percent) had angina. Respondents were asked about employment status (e.g. full-time, part-time, or unemployed) but not about their occupation. Other follow-up questions (not used in our analyses) related to the respondent’s exposure to either water or air pollution on the diary day. For each household, the respondent was randomly selected to be either the adult or child (under 18) with the next birthday; an adult provided proxy responses for a child.

The EPA report (Klepeis, Tsang and Behar, 1996), Section 3, shows that the sample is reasonably representative of the national population with respect to gender and age distributions. The NHAPS population slightly underrepresented males (46 % NHAPS compared to 49 % from the 1990 Census). The fraction of weekend (Saturday or Sunday) respondents was 33 %, close toFebruary 2010 F-4 Draft – Do Not Cite or Quote the desired ratio of 2:7, but Thursdays, Fridays and Saturdays were underrepresented. The Fall season was significantly underrepresented. The database includes weights to adjust for varying selection probabilities, due to differences in the numbers of adults or children in a selected household, the numbers of non-business phones in a household, the numbers of non-business telephones in each census region, and to the survey stratification between weekend or weekdays and between children and adults. Based on discussions with the EPA WAM, it was decided that the weights would not be used in these analyses; the raw, unweighted data would be treated as an approximately simple random sample. Note that the statistical weights: 1) were not used in the pNEM/CO exposure modeling effort, 2) could not be used to accurately estimate standard errors of weighted means, and 3) were close to 1 for most respondents.

In pNEM/CO, each activity is assigned a probability distribution of the exertion rate (kilocalories per minute). For this analysis, the 24-hour sequence of exertion rates was simulated 100 times for each person in the NHAPS sample; the sequence of activities is fixed but the simulated exertion rates vary. Following both CHAD and the exposure modeling methodology currently used in pNEM/CO, a constant simulated exertion rate is assumed throughout the time period of each listed activity in the 24-hour diary. If the individual repeats the same activity at a later time, with other activities intervening, the exertion rate is simulated again. SAS statistical software was used for the simulations and for the statistical analysis.

The assigned exertion rate distribution depends upon the type of activity, and the occupation, age, gender, and body weight of the respondent. The exertion rate (kilo-calories/minute = kcal/min), also referred to as average energy expenditure rate, EE, is defined as the product

EE = MET x RMR.

MET is the metabolic equivalent of work, a dimensionless ratio (i.e., exertion compared to the resting metabolic rate) specific to each activity, and, in some cases, to an age group. RMR is the resting metabolic rate (kcal/min), approximately equal to the basal metabolic rate. We used the same set of MET statistical distributions supplied by Tom McCurdy that are currently used in pNEM/CO (and CHAD). For the work activity “at main job,” the MET distribution depends on the occupation. Since occupation was not recorded in NHAPS, we followed the pNEM methodology and randomly selected the occupation based on census fractions of persons in each activity. The same occupation is assumed throughout a simulated person-day (in case the person repeats the work activity), but is randomly selected again for the next simulated person-day. Note that this procedure may bias the comparis on between angina and non-angina subjects, since the distribution of occupation is expected to differ between angina subjects and the general population.

A single RMR value was simulated to represent each person-day. Thus the same person would have 100 simulated RMRs, one for each of the 100 days simulated. This reflects the assumption that each person represents the activity pattern for a group of persons with the same age and gender. As in pNEM/CO, RMR was simulated from a normal distribution where the mean is of the form a + b (Body Mass), and the standard deviation is the constant

 . The values of a, b, and



are the values derived by Schofield (1985) for 12 age/gender combinations (this assumes basalFebruary 2010 F-5 Draft – Do Not Cite or Quote metabolic rate is equivalent to resting metabolic rate). In turn, the body mass was simulated using the log-normal distributions estimated by Brainard and Burmaster (1992) and Burmaster and Crouch (1994). The parameters of the log-normal distributions depend on age and gender.

The statistical analysis used the following summary statistics of the activity and simulated exertion patterns for each person in the NHAPS study. The selection of these summary statistics was based on recommendations from the EPA WAM:

 Average maximum 8-hour energy expenditure. For each 8-hour period in a simulated personday, starting every 10 minutes, integrate the simulated EE to give the energy expenditure in Mcal (millions of calories), i.e. sum the products of activity time and energy expenditure rate. For each simulated day, compute the maximum 8-hour energy expenditure, treating the simulated day in circular fashion so that the respondent is assumed to repeat exactly the same activity and exertion rate patterns on the day after the diary day. For example, the simulated activities for the period starting at 10 pm are assumed to follow the reported sequence of activities for the diary day from 10 pm to midnight and then the reported sequence from the beginning of the diary day until 6 am. To represent a typical value for the selected person, compute the average maximum 8-hour energy expenditure across the 100 simulations.

 95 th percentile maximum 8-hour energy expenditure. As in the last bullet, compute the maximum 8-hour energy expenditure for each simulated day. To represent an extreme value for the selected person, compute the fifth highest maximum 8-hour energy expenditure among the 100 simulations.

 Percentage time spent outdoors. This number is the same for all simulations, since the activity patterns are held constant.

 Percentage time spent in a vehicle. This number is the same for all simulations, since the activity patterns are held constant.

 Percentage time spent outdoors or in a vehicle. This number is the same for all simulations, since the activity patterns are held constant.

 Average percentage time with exertion rate above 2.39 kcal/min. For each simulated personday, the percentage of that day with an EE (rate) above the threshold level of 2.39 kcal/min was computed; then, this percentage was averaged over the 100 simulations for that person. The statistic estimates the percentage time spent at or above the threshold exertion rate level over a long period, assuming the daily activity pattern was the same every day. The threshold of 2.39 kcal/min, which equals 0.010 MJ/min, represents “light” exertion (see below).

 Average percentage time with exertion rate above 5.97 kcal/min. The threshold of 5.97 kcal/min, which equals 0.025 MJ/min, represents “moderate” exertion (see below).

_ Average percentage time with exertion rate above 9.55 kcal/min. The threshold of 9.55 kcal/min, which equals 0.040 MJ/min, represents “heavy” exertion (see below).February 2010 F-6 Draft – Do Not Cite or Quote The exertion rate thresholds used for these analysis were originally defined as 0.010, 0.025, and 0.040 mega-joules per minute, but were converted into the more commonly used calorie units (1 joule equals 0.2388 calories). For purposes of exposure assessment, exertion categories (i.e., light, moderate, or heavy exertion) are more usefully defined by the ventilation rate VE (liters air per minute) rather than the energy expenditure rate EE (kilo-calories per minute). For the EPA’s Ozone Criteria Document, the Environmental Criteria and Assessment Office categorized VE into ranges of 0-23, 24-43, 44-63, and 64+ liters of air per minute to define light, moderate, heavy, and very heavy exertion, respectively (based on a reference male adult with body weight 70 kg). To convert from EE to VE, EE is first multiplied by an energy conversion factor, ECF, to give the oxygen uptake rate VO2 (liters of oxygen per minute). ECF varies across the population, but is approximately 0.2 liters oxygen per kcal (Esmail, Bhambhani, and Brintnell, 1995). The “ventilatory equivalent rate” (VER) is the dimensionless ratio of VE (liters per minute) divided by VO2 (liters per minute) and has typical values from about 24 for light exertion to about 32 for peak exertion. Thus the selected energy expenditure rates are approximately equivalent to the following ventilation rates:

EE = 0.010 MJ/min = 2.39 kcal/min: VE = EE

 ECF

 VER = 2.39

 0.2

 24 = 11.5 liters/min = light exertion

EE = 0.025 MJ/min = 5.97 kcal/min: VE = EE

 ECF

 VER = 5.97

 0.2

 28 = 33.4 liters/min = moderate exertion

EE = 0.040 MJ/min = 9.55 kcal/min: VE = EE

 ECF

 VER = 9.55

 0.2

 32 = 61.1 liters/min = heavy exertion

The selected summary statistics were computed for each of the 243 angina subjects and 8,906 non-angina subjects in the NHAPS study. A statistical analysis compared the distributions of these summary statistics for persons with and without angina. For each summary statistic we compared the mean values between the angina and non-angina groups using standard t tests. Thesignificance level (p-value) for the difference in means was computed using the SmithSatterthwaite procedure, that tests for no difference in population means assuming that the two populations are normally distributed but may have different variances. P-values at or below 0.05 denote significant differences at the five percen t level of significance. By the central limit theorem, the p-values for the t test comparisons should be reasonably accurate for the largesamples used in the overall analyses, even if the normality assumption does not hold, but the pvalues will be less accurate for the analyses of specific gender and age subgroups. We also compared variances using a standard F test, that assumes normality of the two populations.

Since the normality assumption may not be a sufficiently good approximation, we also appliedtwo non-parametric tests that do not require specific parametric distributions. The nonparametric Wilcoxon test, also known as the Mann-Whitney-Wilcoxon test or the Rank Sum Test, was used to compare the central tendencies of the two distributions. This test assumes only that the populations have the same distributional shape, which may or may not be the normal distribution, but the distribution of values fo r angina population might be shifted by someFebruary 2010 F-7 Draft – Do Not Cite or Quote constant value, and thus might have a diffe rent median than the non-angina population. The Kolmogorov-Smirnov test was used to evaluate any possible differences between the two distributions, whether due to differences in means, medians, variances, or any other features of the distribution. This test uses the maximum absolute difference between the two cumulative distribution functions, assuming only that these distributions are continuous.

The mean, variance, median, and distribution function comparisons were made for all persons combined, separately for males and females, and then separately for four age groups within the male and female subgroups. Age groupings were chosen to include approximately 25 percent of angina subjects in each group. Separate comparisons for males and females are needed to distinguish whether any overall differences in exer tion or activity are explained by the fact that angina subjects are more likely to be female than in the general population. Since activity patterns and exertion rates differ between males and females, any overall difference between the angina and non-angina groups might be explaine d by the greater propensity for females to get angina, rather than the direct effect of angi na. Similarly, the subsetting by age group evaluates the effect of the different age distributions for angina subjects compared to the general population (angina subjects tend to be much older). This statistical analysis does not, and cannot, address questions as to whether the angina causes the change in exertion or activity patterns, or vice versa. We only examine whether or not the summary statistics of activity and exertion patterns are different for the two populations.

A general linear model approach was also used as an alternative method of adjusting for the effects of age and gender on the angina/non-angina comparison. We focused attention on a relatively simple statistical model with cubic terms in age (a simple linear function of age fitted poorly), gender, interactions between age and gender, and a single term for the effect of angina:

Summary Statistic = I(male){

 +

 (age) +

 (age) 2 +

 (age) 3 } + I(female){

 +

 (age) +

 (age) 2 +

 (age) 3 }

+

 I(angina) + error

where: I(male) = 1 for males, 0 for females; I(female) = 1 for females, 0 for males; I(angina) = 1 for persons having angina, 0 for persons not having angina. The errors are assumed to be normally distributed, statistically independent, and have mean zero and some constant variance.

This statistical model assumes that the expected value of the summary statistic is a cubic function of age, but is a different function for males and females. The selected model has the same coefficient for the cubic term for males and females, but different coefficients for the intercept, linear, and quadratic effects. The model also assumes that having angina changes the mean by a constant amount, which is the same factor for all age groups and both genders. A more sophisticated model might allow for interactions between angina and the age and gender variables, to allow for the possibility that the angina effect varies by gender and/or age. Note, however, that our statistical analysis clearly showed that age and gender were much more significant predictors of exertion patterns than the angina indicator, explaining most of the variability in the summary statistics.February 2010 F-8 Draft – Do Not Cite or Quote Project resources were insufficient for a detailed exploration of alternative statistical models. We tried using logarithmic transformations to improve the model fit, but could not reasonably use such models in view of the large number of cases where the observed summary statistic was zero (the logarithm is then undefined). The model fit for the selected model (without taking logarithms) varied with the summary statistic. R squared goodness-of-fit statistics were extremely low, less than 0.05, for the percentages of time spent outdoors and/or in a vehicle. For the summary statistics based on the maximum 8-hour exertion and the percentages of time above exertion rate thresholds, the R squared statistics ranged from a poor fit, 0.25, to a fairly good fit, 0.48. The cases of poor fitting models may be because the selected statistical models poorly represent the relationship between age, gender, and angina and the activity/exertion summary statistic and/or because the activity/exertion pattern varies substantially between people of the same age, gender, and angina status.

RESULTS

Age, Gender, and Angina Disease Distributions

Table 1 shows the number of subjects with or without angina by gender and by age group. The four age groups were chosen to have approximately the same numbers of angina subjects. The strong association between angina and age is illustrated by the fact that 52/243 = 21 % of angina subjects are under 55 but 6877/8906 = 77 % of non-angina subjects are under 55. Angina subjects tend to be significantly older than the general population. The association between angina and gender is weaker. 103/243 = 42.3 % of angina subjects are male, but 4116/8906 = 46.2 % of non-angina subjects are male.

Overall Comparisons of Activity and Exertion Summary Statistics between Angina and Non-Angina Subjects

Table 2 compares the means between the angina and non-angina subjects, without stratification by age or gender. The average and 95 th percentile of the maximum eight hour exertion has a statistically significantly lower mean for angina subjects. Furthermore, for each of the exertion levels 2.39, 5.97, and 9.55 kcal/min (0.010, 0.025, and 0.040 MJ/min), the mean percentage of time above each level was statistically significantly lower for the angina subjects. Non-angina subjects spend an average of 2.8 percent of their time doing activities requiring moderate or higher levels of exertion, defined by exertion rates above 5.97 kcal/min (0.025 MJ/min); angina subjects spend an average of 2.2 percent of their time doing such activities. All subjects spend over 75 percent of time in light or sedentary activities, with extertion rates below 2.39 kcal/min, including sleeping. All these exertion distribution comparisons show that angina subjects tend to do activities with less exertion than the general population. However, since the summary analyses in Table 2 do not take into account the marked differences between the age and gender distributions of angina and non-angina subjects, th e lower exertion rates could be associated with the tendency for angina subjects to be older (and female) rather than the disease itself. The average percentages of time spent outdoors are nearly identical, and are not statistically significantly different between angina and non-a ngina subjects, but angina subjects spend statistically significantly less time in vehicles (4.5 % rather than 5.5 %, on average).February 2010 F-9 Draft – Do Not Cite or Quote Table 2 compares the standard deviations using a F-test based on the variance ratio for angina vs. non-angina subjects. In most cases the F tests s how statistically significantly different variances (and, therefore, standard deviations).

Table 2 also uses the non-parametric Wilcoxon test to compare the central tendencies of the two distributions without the normality assumption required by the T test. Corresponding to the T test comparisons, the Wilcoxon test finds that the angina and non-angina distributions are significantly different in almost all cases; the angina subjects have a lower median value for each of the selected summary statistics. Exceptions are for the average maximum 8-hour exertion, justsignificant at the 7 % level, and the percentage of time spent outdoors, which has a nonsignificant p-value of 22 %.

Finally, Table 2 compares the distribution f unctions using the Kolmogorov-Smironov test. The distributions are statistically significantly different at the five and one percent levels in all cases except for the percentage of time spent outdoors, which shows no significant difference. For that variable, the T and Wilcoxon tests showed no statistically significant differences in central tendency although the F test showed a statistically significant difference in the population variances. If the population variances are differe nt, so are the two distribution functions. The discrepancy between the F and Kolmogorov-Smirnov tests is partly explained by the fact that theF test is very sensitive to the assumption of normal distributions, whereas the KolmogorovSmirnov test only requires the distributions to be continuous. (Both tests assume that the mean and variances are constant for each population, which is inconsistent with the variation of the means and variances with age and gender shown in the stratified analyses in Tables 3 and 4.) The discrepancy is also partly explained by the fact that the Kolmogorov-Smirnov test is less powerful (less likely to detect a difference) than the other tests, because it makes the fewest assumptions and considers the widest class of alternative hypotheses.

Stratified Comparisons of Activity and Exert ion Summary Statistics between Angina and Non-Angina Subjects

Tables 3 and 4 provide the same statistical comparisons as Table 2, stratified by gender and age group. The results show the mean values for the selected summary statistics are not consistently lower for each age and gender subgroup of angina subjects. For example, Table 2 showed that the angina subjects had a lower overall mean value of the average maximum 8-hour exertion than the non-angina subjects. Tables 3 and 4 show the mean is actually higher for angina subjects 0-54 of either gender and for males 75 or older. The mean average maximum 8-hour exertions are consistently higher for males of all age groups, with or without angina, compared to females. Similar patterns are found for the 95 th percentile of the maximum 8-hour exertion.

The comparisons of the percentages of time spent outdoors or in a vehicle also vary across age and gender subgroups. The largest, and most surprising, angina vs. non-angina difference is for the mean percentage of time spent outdoors by 0-54 year old males: angina subjects have a mean of 17 % compared to the mean of 9 % for non-angina subjects. However the angina subjects in the 55-64 and 65-74 age groups of either gender spend less time outdoors, on average, than non-February 2010 F-10 Draft – Do Not Cite or Quote angina subjects.

The Table 3 and 4 comparisons of the mean percentages of time above the light, moderate or high exertion levels show a variety of pattern s for different age groups, genders, and exertion levels.

Comparisons of Activity and Exertion Summary Statistics between Angina and NonAngina Subjects Adjusted for Age and Gender Differences

Table 5 gives the results of the fitted general linear model. As explained above, the fitted model assumes that for each gender, the average value of the summary statistic is a cubic function of age. Furthermore, having angina changes the expected value by a fixed amount, which is assumed to be the same value for every age and gender. This angina effect is the coefficient reported in the table, together with its standard error and p-value. P-values less than or equal to 0.05 indicate summary statistics where the angina effect was statistically significant at the 5 percent significance level. The angina coefficient can be thought of as the effect of angina after adjusting for age and gender. The effects of age and gender are not reported, but in all cases were extremely statistically significant compared to the angina effect.

Table 5 also reports the R squared goodness-of-fit statistic, which is the squared correlation between the observed and predicted values. R squared values vary from 0 (the worst possible fit) to 1 (a perfect fit), and are often interpreted as the fraction of the variability in the dependent variable (summary statistic) that is explained by the regression model.

The first two rows of Table 5 show that the angina effect on the average and 95 th percentile maximum 8-hour exertion is a statistically significant reduction (at the 6 and 1 % levels, respectively) for angina subjects compared to non- angina subjects. However, these reductions of 0.04 Mcal and 0.16 Mcal are small when compared to the overall mean values of 1.4 and 2.3 Mcal (non-angina subjects) reported in Table 2. The next three rows show that angina subjects tend to spend a little more time (0.7 percentage points) outdoors and a little less time (0.5 percentage points) in a vehicle compared to non-angina subjects; those differences are not statistically significant. The last four rows show that angina subjects tend to spend less time at moderate or high levels of exertion, after adjusting for age and gender, although the differences are at most 1 percentage point and are not sta tistically significant. For example, the unadjusted average percentage time above 2.39 kcal/min (0.010 MJ/min) was 23.5 % for non-angina subjects (Table 2), and the effect of angina is to reduce the expected percentage of time by 0.7. As shown in Tables 3 and 4, this is due to average reductions of up to 5 percentage points for ages 55 and older but increases of 6 (males) and 2 (females) percentage points for the 0-54 age
group.

R squared goodness-of-fit statistics were extremely low, 0.05 or less, for the percentages of time spent outdoors and/or in a vehicle. Thus the regression models for those percentages give very poor predictions. There are two possible reasons for this. First, the combination of age, gender, and angina status may be strongly associated with the percentages of time spent outdoors or in a vehicle but the assumed form of the regression model may poorly represent the functionalFebruary 2010 F-11 Draft – Do Not Cite or Quote relationship. Second, the combination of age, gender, and angina status may be poorly associated with the percentages of time spent outdoors or in a vehicle so that those activity percentages vary mainly with the effects of factors other than age, gender, and angina status. In either case, those regression models are not recommended for use in predicting the activity percentages.

For the summary statistics based on the maximum 8-hour exertion and the percentages of time above exertion rate thresholds, the R squared statistics ranged from a poor fit, 0.25, to a reasonably good fit, 0.48. As above, the cases of poor fitting models may be because the selected statistical models poorly represent the relationship between age, gender, and angina and the activity/exertion summary statistic and/or because the activity/exertion pattern varies substantially between people of the same age, gender, and angina status. Alternative general linear models, or the more sophisticated generalized linear models, could be developed to improve the predictive ability of the statistical models.February 2010 F-12 Draft – Do Not Cite or Quote REFERENCES

Brainard, J., and Burmaster, D. E. 1992. “Bivariate Distributions for Height and Weight of Men and Women in the United States

. ” Risk Analysis 12:2, pp. 267-275. Burmaster, D. E., and Crouch, E. A. C. 1994. “Lognormal Distributions of Body Weight as a Function of Age for Males and Females in the United States.” Risk Analysis 17:4, pp. 499- 508.
Esmail, Bhambhani, and Brintnell. 1995. “Gender Differences in Work Performance on the Baltimore Therapeutic Equipment Work Simulator.” Amer. J. Occup. Therapy 49, pp. 405- 411.
Johnson, T. R, 1998. Proposed Probabilistic Algorithm for Estimating Ventilation Rate in the 1998 Version of pNEM/CO . Memorandum submitted to EPA (September 25, 1998). Klepeis, N. E., Tsang, A. E., and Behar, J. V. 1996. Analysis of the National Human Activity Pattern Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment

. EPA/600/R-96/074. Klepeis, N. E., Nelson, W. C., Tsang, A. M., Robinson, J. P., Hern, S. C., Engelmann, W. H., and Behar, J. V. 1998. “The National Human Activity Pattern Survey (NHAPS): Data Collection Methodology and Selected Results.” Submitted to Journal of Exposure Analysis and Environmental Epidemiology

. Schofield, W. N. 1985. “Predicting Basal Metabo lic Rate, New Standards, and Review of Previous Work.” Hum Nutr Clin Nutr . 39C (Supp 1), pp. 5-41. Triplett, T. 1996. National Human Activity Pattern Survey, CD-ROM Version 2.0. University of Maryland Survey Research Center. Tsang, A. M. and Klepeis, N. E. 1996. Descriptive Statistics Tables from a Detailed Analysis of the National Human Activity Pattern Survey (NHAPS) Data . EPA/600/R-96/148.February 2010F-13Draft – Do Not Cite or QuoteTable 1. Distribution of subjects according to their age, gender and disease statusMales Females AllAge Group Angina (%)Non-angina(%)AllAngina (%)Non-angina(%)AllAngina (%)Non-angina(%)All0-54 35 (1.0) 3307 (98.9) 3342 17 (0.5) 3570 (95.5) 3587 52 (0.8) 6877 (99.2) 692955-64 28 (6.5) 400 (93.5) 428 28 (5.4 ) 491 (94.6) 519 56 (5.9) 891 (94.1) 94765-74 23 (7.9) 267 (92.1) 290 48 (9.6) 450 (91.4) 498 71 (9.0) 717 (91.0) 78875+ 17 (10.7) 142 (89.3) 159 47 (14.4) 279 (85.6) 326 64 (13.2) 421 (86.8) 485Total 103 (2.4) 4116 (97.6) 4219 140 (2.8) 4790 (97.2) 4930 243 (2.6) 8906 (97.4) 9149This table was modified by staff on 2-22-10 from the below originalversion due to issues related to the conversion from Word Perfect to MicrosoftWord.GenderAgegroupMalesAngina(%) Non-angina(%) AllFemalesAngina(%) Non-angina(%) AllAllAngina(%) Non-angina(%) All35 (1.0) 3307 (98.9)334217 (0.5) 3570 (95.5)358752 (0.8) 6877 (99.2)692928 (6.5) 400 (93.5)42828 (5.4 ) 491 (94.6)51956 (5.9) 891 (94.1)94723 (7.9) 267 (92.1)29048 (9.6) 450 (91.4)49871 (9.0) 717 (91.0)78817 (10.7) 142 (89.3)15947 (14.4) 279 (85.6)32664 (13.2) 421 (86.8)485103 (2.4) 4116 (97.6)4219140 (2.8) 4790 (97.2)4930243 (2.6) 8906 (97.4)9149February 2010F-14Draft – Do Not Cite or QuoteTable 2. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. AllVariableT Test Comparison ofMeansMean Mean P-valueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-valueAverage maximum 8hrexertion (Mcal)1.28 1.40 0.000.48 0.49 0.680.000.00Ninety fifth percentile ofmaximum 8hr exertion(Mcal)1.87 2.25 0.000.97 1.13 0.000.000.00Percentage of time spentoutdoors6.73 6.74 0.9912.87 11.63 0.020.220.23Percentage of time spent invehicle4.55 5.55 0.016.19 7.13 0.000.000.00Percentage of time spentoutdoors or in vehicle11.27 12.29 0.2714.33 13.45 0.150.000.00Average percentage of timewith exertion above 2.39kcal/min= 0.010MJ/min (light)19.98 23.53 0.0013.56 13.78 0.750.000.00Average percentage of timewith exertion above 5.97kcal/min= 0.025 MJ/ min (moderate)2.17 2.78 0.013.68 3.56 0.460.000.00Average percentage of timewith exertion above 9.55kcal/min0.213 0.406 0.000.554 0.761 0.000.000.00February 2010F-15Draft – Do Not Cite or Quote= 0.040 MJ/ min (heavy)Table 3. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. MalesVariableAgeGroupT Test Comparison ofMeansMean Mean PvalueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-valueAverage maximum8hr exertion (Mcal)0-5455-6465-7475+1.85 1.590.001.48 1.770.001.39 1.490.361.27 1.200.520.49 0.550.340.48 0.480.860.47 0.480.960.44 0.420.650.020.010.410.510.020.020.820.95Ninety fifthpercentile ofmaximum 8hrexertion (Mcal)0-5455-6465-7475+2.94 2.680.172.40 2.900.041.91 2.170.141.73 1.670.711.06 1.300.131.21 1.140.610.78 0.940.310.69 0.760.680.220.020.260.550.060.030.630.88Percentage of timespent outdoors0-5455-6465-7416.86 8.850.029.28 10.0219.16 13.750.0014.26 13.860.010.650.120.011.000.13February 2010F-16Draft – Do Not Cite or QuoteTable 3. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. MalesVariableAgeGroupT Test Comparison ofMeansMean Mean PvalueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-value75+0.796.43 10.400.138.23 7.090.720.7811.38 14.340.2012.63 10.050.160.580.66Percentage of timespent in vehicle0-5455-6465-7475+5.96 6.090.923.99 6.870.007.20 5.880.512.29 3.340.147.55 8.100.633.78 9.630.009.04 7.800.292.54 3.940.050.890.180.820.440.520.190.930.54Percentage of timespent outdoors orin vehicle0-5455-6465-7475+22.83 14.940.0213.27 16.890.2413.63 16.290.4510.51 10.430.9819.28 15.520.0515.30 16.180.7615.87 15.911.0012.86 10.390.190.020.170.190.690.020.220.290.41Average0-5434.76 27.7813.33 15.180.020.05February 2010F-17Draft – Do Not Cite or QuoteTable 3. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. MalesVariableAgeGroupT Test Comparison ofMeansMean Mean PvalueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-valuepercentage of timewith exertionabove 2.39kcal/min= 0.010MJ/min(light)55-6465-7475+0.0024.60 30.070.0621.92 23.320.6318.24 15.870.410.3414.51 12.030.1413.23 12.480.6411.13 10.370.630.060.670.390.140.840.74Averagepercentage of timewith exertionabove 5.97kcal/min= 0.025 MJ/ min(moderate)0-5455-6465-7475+6.63 4.460.053.43 5.440.012.27 3.270.082.02 1.620.536.37 4.310.003.55 4.410.172.47 3.460.062.42 2.410.920.020.010.200.430.010.010.400.59Averagepercentage of timewith exertionabove 9.55kcal/min= 0.040 MJ/ min(heavy)0-5455-6465-7475+0.662 0.7350.590.565 0.8460.190.155 0.3880.010.132 0.1570.792 0.9860.111.068 1.2220.400.361 0.7160.000.331 0.5120.550.050.060.550.150.170.040.96February 2010F-18Draft – Do Not Cite or QuoteTable 3. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. MalesVariableAgeGroupT Test Comparison ofMeansMean Mean PvalueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-value0.790.05Table 4. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. FemalesVariableAgeGroupT Test Comparison ofMeansMean Mean PvalueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-valueAverage maximum8hr exertion (Mcal)0-5455-6465-7475+1.30 1.270.691.21 1.270.331.05 1.100.290.96 0.980.630.31 0.38 0.340.32 0.33 1.000.30 0.31 0.940.33 0.30 0.340.720.560.310.440.730.220.440.66Ninety fifth0-541.98 2.010.82 0.910.990.86February 2010F-19Draft – Do Not Cite or QuoteTable 4. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. FemalesVariableAgeGroupT Test Comparison ofMeansMean Mean PvalueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-valuepercentile ofmaximum 8hrexertion (Mcal)55-6465-7475+0.861.79 1.920.411.42 1.510.261.27 1.310.590.690.80 0.770.680.53 0.570.570.56 0.520.430.330.310.430.280.620.47Percentage of timespent outdoors0-5455-6465-7475+3.64 5.110.424.31 4.590.882.84 4.140.143.79 2.270.407.29 9.580.209.53 8.220.235.51 7.340.0212.04 4.600.000.430.230.490.760.910.630.531.00Percentage of timespent in vehicle0-5455-6465-7475+4.54 5.350.554.21 5.600.355.26 4.150.232.82 2.940.865.48 5.980.727.53 7.230.715.94 6.180.774.37 4.340.910.400.060.150.720.350.120.190.96Percentage of time0-548.18 10.469.93 11.270.180.27February 2010F-20Draft – Do Not Cite or QuoteTable 4. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. FemalesVariableAgeGroupT Test Comparison ofMeansMean Mean PvalueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-valuespent outdoors orin vehicle55-6465-7475+0.368.52 10.180.488.10 8.290.886.61 5.210.450.5712.15 10.420.228.21 9.380.2612.36 6.250.000.040.990.770.050.860.97Averagepercentage of timewith exertionabove 2.39kcal/min= 0.010MJ/min(light)0-5455-6465-7475+23.50 21.400.4919.04 21.040.3313.97 15.460.2611.31 11.840.7312.28 12.150.8610.34 10.471.008.45 9.530.319.75 8.610.240.410.510.430.510.690.540.760.89Averagepercentage of timewith exertionabove 5.97kcal/min= 0.025 MJ/ min(moderate)0-5455-6465-7475+1.44 1.590.710.79 1.310.050.65 0.680.870.75 0.430.231.66 1.970.441.27 2.070.001.35 1.560.221.73 1.310.010.730.040.510.840.740.060.880.67Average0-540.101 0.1840.170 0.3240.630.80February 2010F-21Draft – Do Not Cite or QuoteTable 4. Statistical Tests for theAssociation between Angina and Various Variables Representing PhysicalExertion. FemalesVariableAgeGroupT Test Comparison ofMeansMean Mean PvalueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. PvalueAngina Non-anginaWilcoxonTestP-valueKolmogorov-SmirnovTestP-valuepercentage of timewith exertionabove 9.55kcal/min= 0.040 MJ/ min(heavy)55-6465-7475+0.060.095 0.0930.960.028 0.0300.890.025 0.0160.440.000.277 0.1980.010.076 0.1100.000.075 0.0770.900.670.850.281.000.990.83February 2010F-22Draft – Do Not Cite or QuoteTable 5. General Linear Models for the Association between Angina and Various Variables Representing PhysicalExertion.VariableAnginaCoefficient1StandardErrorP-valueR squaredAverage maximum 8hr exertion (Mcal)-0.04450.02370.06080.4819Ninety fifth percentile of maximum 8hr exertion(Mcal)-0.15530.05810.00750.4114Percentage of time spent outdoors+0.69750.76480.36180.0388Percentage of time spent in vehicle-0.48050.46790.30450.0325Percentage of time spent outdoors or in vehicle+0.21700.87770.80470.0520Average percentage of time with exertion above2.39 kcal/min = 0.010 MJ/min (light)-0.73590.69960.29290.4239Average percentage of time with exertion above5.97 kcal/min = 0.025 MJ/min (moderate)-0.17300.19100.36500.3570Average percentage of time with exertion above9.55 kcal/min = 0.040 MJ/min (heavy)-0.09330.04390.03340.24941. The angina coefficient is the expected difference (anginaminus non-angina) between the summary statistic for angina andnon-angina subjects of the same age and gender.United States Office of Air Quality Planning and Standards Publication No. EPA-452/P-10-004Environmental Protection Health and Environmental Impacts Division February, 2010Agency Research Triangle Park, NC