# Quantitative Risk and Exposure Assessment for Carbon MonoxideEPA-452/R-10-006 May 2010



## Quantitative Risk and Exposure Assessment for Carbon Monoxide

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 document has been prepared by staff from the Office of Ai r Quality Planning and Standards, U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or recommendations are those of the authors and do not necessarily reflec t the views of the EPA Mention of trade names or commercial products is not intended to const itute endorsement or recommendation for use. Any questions or comments concerning this do cument 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 TRJ Environmental, In c. in partial fulfillment of Contract No. EP-D08-100, Work Assignments 0-08 and 1-15.i 

### Table of Contents

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

....... v

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

...... vi

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

1.1 BACKGROUND................................................................................................... 1-1
1.2 PREVIOUS REVIEWS AND ASSESSMENTS .................................................. 1-3
1.3 CURRENT REVIEW, CASAC ADVICE AND PUBLIC COMMENT.............. 1-6
1.4 REFERENCES...................................................................................................... 1-9

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 ..................................................................................... 2-11
2.5.1 Cardiovascular Diseas e-related Effects ................................................. 2-12
2.5.2 Other Effects .......................................................................................... 2-14
2.6 RISK CHARACTERI ZATION APPROACH.................................................... 2-15
2.7 KEY OBSERVATIONS..................................................................................... 2-19
2.8 REFERENCES ................................................................................................... 2-21 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 FO R CURRENT ASSESSMENT..................... 3-20
3.3 KEY OBSERVATIONS..................................................................................... 3-20
3.4 REFERENCES ................................................................................................... 3-22ii 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.......................................................................................... 4-1
4.3 MODEL HISTORY AN D 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............................. 4-31 4.4.6 Calculate Exposure ................................................................................ 4-33
4.4.7 Calculate Dose ....................................................................................... 4-34 4.4.8 Model Output......................................................................................... 4-37 4.5 Key Observations................................................................................................ 4-37
4.6 REFERENCES ................................................................................................... 4-38

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

5.1 PURPOSE............................................................................................................. 5-1
5.2 OVERVIEW ......................................................................................................... 5-1
5.3 STUDY AREAS................................................................................................... 5-2
5.4 EXPOSURE PERIODS ........................................................................................ 5-3
5.5 STUDY POPULATION....................................................................................... 5-7 5.5.1 Simulated at-Risk Subpopulations........................................................... 5-7 5.5.2 Time-Location-Activity Patterns ........................................................... 5-12 5.5.3 Construction of Longitudinal Diaries .................................................... 5-12
5.6 EXPOSURE SCENARIOS................................................................................. 5-13 5.7 AMBIENT AIR QUALITY DATA ................................................................... 5-16 5.7.1 Unadjusted 1-Hour Ambient Concentrations......................................... 5-16 5.7.2 Method for Estimating Missing 1-H our Ambient Concentrations ........ 5-17 5.7.3 Adjusted 1-Hour Ambient Concentrations ............................................ 5-21
5.8 METEOROLOGICAL DATA ........................................................................... 5-25 5.8.1 Method for Estimating Missing 1-Hour Temperature Data................... 5-26iii 5.9 MICROENVIRONMENTS MODELED ........................................................... 5-27 5.9.1 The Micronenvironmental Model as Implemented by APEX4.3 .......... 5-27
5.9.2 Microenvironme ntal Mapping ............................................................... 5-30
5.9.3 Selection of Microenvir onmental Method Used.................................... 5-31 5.9.4 Air Exchange Rates and Air Conditioning Prevalence.......................... 5-31
5.10 ADDITIONAL EXPOSURE AND DOSE OUTPUT GENERATED USING REDUCED APEX SIMULATIONS....................................................... 5-33 5.10.1 Estimate of Microenvir onmnet Contribution to At-Risk Population Exposure Levels..................................................................................... 5-34
5.10.2 Estimate of Microenviron ment-to-Ambient Concentration Ratios........ 5-34 5.10.3 Estimate of Ambient Exposure Contribution to Total COHb Level...... 5-35 5.10.4 Comparison of the Expos ure and Dose Results Genererated Using 50,000 Persons Versus 5, 000 Persons Simulation................................ 5-36
5.11 KEY OBSERVATIONS....................................................................................... 5-38
5.12 REFERENCES ..................................................................................................... 5-40

6 SIMULATED EXPOSURE AND COHB 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................ 6-11 6.1.3 Air quality adjusted to just meet alternative air quality scenarios......... 6-19
6.2 ESTIMATED COHB DOSE LEVELS .............................................................. 6-22 6.2.1 Air quality “As Is” ................................................................................. 6-22 6.2.2 Air quality adjusted to just m eet the current 8-hour standard................ 6-27 6.2.3 Air quality adjusted to just meet alternative air quality scenarios......... 6-31
6.3 COMPARISON OF COHB ESTIMA TES OBTAINED FROM THE 2000 PNEM/CO AND 2010 APEX/CO ASSESSMENTS ........................................ 6-35
6.4 KEY OBSERVATIONS..................................................................................... 6-39
6.5 REFERENCES ................................................................................................... 6-42

7 VARIABILITY ANALYSIS AND UNCERTAINTY
CHARACTERIZATION .............................................................................................. 7-1
7.1 ANALYSIS OF VARIABILITY.......................................................................... 7-1
7.2 CHARACTERIZATION OF UNCERTAINTY .................................................. 7-3
7.2.1 Considerations in Character izing Sources and Uncertainty....................... 7-9 7.2.2 Sensitivity Analysis ................................................................................. 7-22
7.3 KEY OBSERVATIONS..................................................................................... 7-32
7.4 REFERENCES ................................................................................................... 7-34

8 SUMMARY OF KEY OBSERVATIONS ........................................................... 8-1iv 

### APPENDICES

A. Technical Memorandum on Updates to APEX Physiology.Txt File.............................. A-1
B. COHb Module for APEX4.3............................................................................................B-1
C. Isaacs et al. (2009) Reference Used in Developing D and A Statistics Input to APEX
Model ..................................................................................................................C-1
D. Microenvironment al Mapping ........................................................................................ D-1
E. Analysis of CHAD Diaries for Time Spent in Vehicles..................................................E-1
F. Differences in Human Activity Patterns Between Individuals With and Without
Cardiovascular Disease..............................................................................................F-1v 

### List of Figures

Figure 3-1 Spatial and temporal trends in th e 2nd highest 1-hour (t op) and 8-hour average (bottom) CO ambient monitoring concentr ations in Denver, Colorado(left) and Los Angeles, California (right), years 1993 – 2008 .................................................. 3-6 Figure 3-2 Diurnal distribution of 1- hour CO concentrations in Denver (monitor 080310002) by day-type (wee kdays-left; weekends-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) vers us a low concentration year (2006) at four ambient monitors in Denver............................................................. 3-13 Figure 3-5 Comparison of a high concentration year (1997) versus a low concentration year (2006) at four ambient monitors in Los Angeles..................................................... 3-14

Figure 4-1 Conceptual model and simplified da ta flow for estimating population exposure and dose using APEX4.3........................................................................................... 4-5

Figure 5-1 Ambient monitor locations, air distri cts (black circles), meteorological zones (blue circles), and study area (red circle) for the Denver exposure modeling domain....................................................................................................... 5-5 Figure 5-2 Ambient monitor locations, air distri cts (black circles), meteorological zones (blue and pink circles), and study area (r ed circle) for the Los Angeles exposure modeling domain....................................................................................................... 5-6 Figure 5-3 Relationship between microenvironmen t-to-ambient concentration ratios using estimated indoor-residential concentrati ons (left panel) and inside-vehicle concentrations (righ t panel) in Denver – as is air quality........................................ 5-35

Figure 6-1 Estimated microenviron mental contributions to time sp ent at or above selected exposure concentrations using the Denver CHD population – as is air quality. ....... 6-5 Figure 6-2 Estimated microenviron mental contributions to time sp ent at or above selected exposure concentrations usi ng the Los Angeles CHD population

– as is air quality....................................................................................................... 6-9 Figure 6-3 Estimated microenviron mental contributions to time spent at or above selected exposure concentrations using the Denve r CHD population – air quality just meeting the current 8-hour standard ........................................................................ 6-13 Figure 6-4 Estimated microenviron mental contributions to time spent at or above selected exposure concentrations using the Los Angeles CHD population– air quality just meeting the current 8-hour standard ........................................................................ 6-17 Figure 6-5 Estimated percent of the CHD popul ation in Denver (top) and Los Angeles (bottom) experiencing repeated CO Hb levels – as is air quality ............................. 6-26 Figure 6-6 Estimated percent of the CHD popul ation in Denver (top) and Los Angeles (bottom) experiencing repeated COHb levels – air quality just meeting the current 8-hour standard............................................................................................ 6-30vi Figure 7-1. Comparison of commute and trav el times for persons residing in Denver and Los Angeles counties to thos e persons surveyed in CHAD. ............................ 7-17



### 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 .............................. 3-17 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. ..................... 3-17 Table 3-3 Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) air quality data – 99 th percentile 1-hour daily maximum............ 3-18 Table 3-4 Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (2005-07) air quality data – 99th percentile 1-hour daily maximum..... 3-18 Table 3-5 Within monitor temporal variabil ity in Denver using historical (1995-97) and recent (2005-07) air quality data – 99 th percentile 8-hour daily maximum............ 3-19 Table 3-6 Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (2005-07) air quality data – 99th percentile 8-hour daily maximum............ 3-19

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......................................... 4-16 Table 4-3 Variables used by APEX4.3 in the factors model ................................................... 4-19 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). ............................................................................................. 4-22 Table 4-5. Parameters of bounded lognormal distri butions defined for proximity factors used in applications of APEX3.1 to Lo s Angeles (Johnson and Capel, 2003)................ 4-30

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 diagno sed coronary heart disease by age range ......... 5-8 Table 5-4 National prevalence ra tes for diagnosed coronary heart disease by gender.............. 5-9 Table 5-5 Estimated national prevalence rates fo r coronary heart disease stratified by age range and gender ....................................................................................................... 5-9 Table 5-6 Estimated national prevalence rates for coronary heart disease, including diagnosed and undiagnosed cases, st ratified by age and gender............................. 5-10 Table 5-7 National prevalence rate s for all types of diagnosed h eart disease by age range. .. 5-11 Table 5-8 National prevalence rate s for all types of diagnosed heart disease by gender........ 5-11 Table 5-9 Estimated national prevalence rates fo r all types of diagnosed heart disease, stratified by age and gender.................................................................................... 5-11 Table 5-10 Estimated national pr evalence rates for all types of diagnosed heart disease plus undiagnosed coronary heart disease, stratified by age and gender........................ 5-12 Table 5-11 Array of alternativ e standard forms and levels informed by modeled exposure scenarios in Denver................................................................................................ 5-15vii Table 5-12 Array of alternativ e standard forms and levels informed by modeled exposure scenarios in Los Angeles ....................................................................................... 5-16 Table 5-13 Descriptive statistics for hourly carbon monoxide concentr ations before and after estimation of missing values – Denver 1995 .................................................. 5-18 Table 5-14 Descriptive statistics for hourly carbon monoxide concentr ations before and after estimation of missing values – Denver 2006 .................................................. 5-18 Table 5-15 Descriptive statistics for hourly carbon monoxide concentr ations before and after estimation of missing values – Los Angeles 1997.......................................... 5-19 Table 5-16 Descriptive statistics for hourly carbon monoxide concentr ations before and after estimation of missing values – Los Angeles 2006.......................................... 5-20 Table 5-17 Design values and adjustment factors used to represent air quality just meeting the current and potential alternative standards ........................................................ 5-22 Table 5-18 Descriptive statistics for hourly ca rbon monoxide concentrat ions after adjusting to just meet the current 8-hour sta ndard – Denver (adjusted 1995 data). ............... 5-24 Table 5-19 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). ....... 5-24 Table 5-20 Locations of meteorological stations selected for Denver ...................................... 5-25 Table 5-21 Locations of meteorological stations selected for Los Angeles .............................. 5-26 Table 5-22 Parameters of bounded lognormal distributions defined for proximity factors used in the application of APEX4.3 to Los Angeles and Denver.................................... 5-29 Table 5-23 List of microenvironments modeled and calculation methods used ....................... 5-31 Table 5-24 Lognormal distributi ons of indoor air exchange rates used in Denver ................... 5-32 Table 5-25 Lognormal distributions of indoor air exchange rate s used in Los Angeles ........... 5-33 Table 5-26 Comparison of exposure summary output generated when simulating 50,000 persons versus that of simulating 5,000 persons – as is air quality......................... 5-37 Table 5-27 Comparison of dose summary out put generated when simulating 50,000 persons versus that of simulating 5,000 persons – just meeting the current standard.......... 5-38

Table 6-1. Estimated daily maximum 1-hour or 8-hour exposure fo r simulated at-risk populations in the Denver study area – as is air quality............................................ 6-3 Table 6-2 Estimated distribution of microenviro nment-to-ambient concentration ratios using the Denver CHD population – as is air quality ......................................................... 6-6 Table 6-3 Estimated distribution of microenvi ronmental concentrations using the Denver CHD population – as is air quality........................................................................... .6-6 Table 6-4 Estimated daily maximum 1-hour or 8-hour exposure for simulated at-risk populations in the Los Angeles study area – as is air quality ................................... 6-8 Table 6-5 Estimated distribution of microenviro nment-to-ambient concentration ratios using the Los Angeles CHD population – as is air quality............................................... 6-10 Table 6-6 Estimated distribution of microe nvironmental concentrations using the Los Angeles CHD population – as is air quality..................................................... 6-10 Table 6-7 Estimated daily maximum 1-hour or 8-hour exposure for simulated at-risk populations in the Denver study area – air qua lity just meeting the current 8-hour standard. ................................................................................................................ 6-12 Table 6-8 Estimated distribution of microenvi ronment-to-ambient concentration ratios using the Denver CHD population – air qu ality just meeting the current 8-hour standard ................................................................................................................ 6-14viii Table 6-9 Estimated distribution of microenvi ronmental concentrations using the Denver CHD population – air quality just me eting the current 8-hour standard................. 6-14 Table 6-10 Estimated daily maximum 1-hour or 8-hour exposure fo r simulated at-risk populations in the Los Angeles study area – air quality just meeting the current 8-hour standard........................................................................................................ 6-16 Table 6-11 Estimated distributi on of microenvironment-to-ambient concentration ratios using the Los Angeles CHD population – air qua lity just meeting the current 8-hour standard ................................................................................................................ 6-18 Table 6-12 Estimated distributi on of microenvironmental concentr ations using the Los Angeles CHD population – air quality just mee ting the current 8-hour standard............ ....6-18 Table 6-13 Estimated daily maximum 1-hour and 8-hour exposures for simulated at-risk populations in the Denver study area – alte rnative air quality scenarios ................ 6-20 Table 6-14 Estimated daily maximum 1-hour and 8-hour exposures for simulated at-risk populations in the Los Angeles study area – alternative air quality scenarios........ 6-21 Table 6-15 Portion of the si mulated at-risk populations in the Denver study area estimated to experience a daily maximum end-of-hour COHb at or above specified levels –as is air quality....................................................................................................... 6-23 Table 6-16 Portion of the si mulated at-risk populations in the Los Angeles study area Estimated to experience a daily maxi mum end-of-hour COHb at or above specified levels – as is air quality............................................................................ 6-23 Table 6-17 Percentages of the simulated C HD populations in the Denver and Los Angeles study areas estimated to experience a daily maximum end-of-hour COHb contribution from ambient exposure alone at or above specified levels

– as is air quality ..................................................................................................... 6-27 Table 6-18 Portion of the si mulated at-risk populations in the Denver study area estimated to experience a daily maximum end-of-hour COHb at or above specified levels – air quality just meeting the current 8-hour standard............................................ 6-29 Table 6-19 Portion of the si mulated at-risk populations in the Los Angeles study area Estimated to experience a daily maxi mum end-of-hour COHb at or above specified levels – air quality just meeting the current 8-hour standard................... 6-29 Table 6-20 Percentage of si mulated CHD populations in th e Denver and Los Angeles study areas estimated to experience daily ma ximum end-of-hour COHb contribution from ambient exposure alone at or above specified levels – air quality just meeting the current 8-hour standard........................................................................ 6-31 Table 6-21 Portion of the si mulated at-risk populations in the Denver study area estimated to experience a daily maximum end-of-hour COHb at or above specified levels – air quality just meeting poten tial alternative standards ........................................ 6-33 Table 6-22 Portion of the simu lated at-risk populations in the Los Angeles study area estimated to experience a daily maximum end-of- hour COHb at or above specified levels – air quality just meeting poten tial alternative standards ........................................ 6-34 Table 6-23 Percentage of si mulated CHD populations in th e Denver and Los Angeles study areas estimated to experience daily ma ximum end-of-hour COHb contribution from ambient exposure alone at or above specifi ed levels – air quality just meeting a 99 th percentile daily maximum 8-hour average concentr ation of 5.0 ppm

............... 6-35ix Table 6-24 Percentage of Denver adults with coronary heart disease (CHD) estimated to experience a daily maximum end-of-hour COHb level – air quality just meeting the current 8-hour standard ........................................................................................... 6-38 Table 6-25 Percentage of Los Angeles adults wi th coronary heart disease (CHD) estimated to experience a daily maximum end-of-hour COHb level – air quality just meeting the current 8-hour standard ........................................................................................... 6-38

Table 7-1. Summary of how variability was incorporated into the assessment. ........................7-2 Table 7-2. Characterization of key un certainties in the assessment. ..........................................7-8 Table 7-3. Frequency of CO concentrations reported as zero in Denver and Los Angeles ambient monitoring data.........................................................................................7-11 Table 7-4. Percentage of simulated at-risk CHD population in Los Angeles with highest daily maximum end-of-hour COHb levels at or above indicated COHb level considering potential alte rnative standards. ............................................................7-15 Table 7-5. Comparison of highest estimated daily maximum end-of-hour COHb levels for Denver HD population for two model simulatio ns - all monitor concentrations versus the design monitor concentr ations – as is air quality. ..................................7-24 Table 7-6. Comparison of highest estimated da ily maximum end-of-hour COHb levels for Los Angeles HD population for two m odel simulations - all monitor concentrations versus the de sign monitor concentrations – as is air quality...........7-24 Table 7-7. Comparison of highest estimated daily maximum end-of-hour COHb levels for Denver HD population for two model simulatio ns - all monitor concentrations versus the design monitor concentrations – air quality just meeting the current 8-hour standard........................................................................................................ 7-25 Table 7-8. Comparison of highest estimated da ily maximum end-of-hour COHb levels for Los Angeles HD population for two model si mulations - all mon itor concentrations versus the design monitor concentrations – air quality just meeting the current 8-hour standard........................................................................................................ 7-25 Table 7-9. Estimated alternative prevalence rate s for CHD, stratified by age and gender....... 7-26 Table 7-10. Comparison of the portion of the simulated CHD population in the Denver study area estimated to experience a daily ma ximum end-of-hour COHb at or above specified levels using base and altern ative undiagnosed CHD prevalence rates

– as is air quality. .................................................................................................... 7-27 Table 7-11. Comparison of the portion of the simulated CHD population in the Denver study area estimated to experience a daily ma ximum end-of-hour COHb at or above specified levels using base and altern ative undiagnosed CHD prevalence rates – air quality just meeting the current standard. .......................................................7-28 Table 7-12. Hemoglobin levels below which anemia is present in a population (from WHO et al., 2001). .................................................................................................. 7-29 Table 7-13. Descriptive statisti cs of blood hemoglobin content measured in various groups (from NHANES 1999-2008)...................................................................................7-30 Table 7-14. Comparison of the portion of the simulated CHD population in the Denver study area estimated to experience a daily ma ximum end-of-hour COHb at or above specified levels when sampling from th e base and anemic hemoglobin content distributions – air quality just m eeting the current standard. ..................................7-311-1 

### 1 INTRODUCTION

This document, Quantitative Risk and Exposure Assessment for Carbon Monoxide, describes the quantitative human exposure assess ment and risk characterization being conducted to inform the U.S. Environmental Protection Ag ency’s (EPA’s) current review of the National Ambient Air Quality Standards (NAAQS) for carbon monoxide (CO). Given the significant time constraints of this review, 1 results of the analyses are prov ided in this document without substantial interpretation. Rather, interpretative discussion of these resu lts is provided in the Policy Assessment document for the review (US EPA, 2010a).

1.1 BACKGROUND

The EPA is presently conducting a review of the national ambient air quality standards
for CO. Sections 108 and 109 of the Clean Air Ac t (Act) govern the estab lishment and periodic review of the NAAQS. These standards are established for certain pollutants that may reasonably be anticipated to endanger public he alth and welfare, and whose presence in the
ambient air results from numerous or diverse mob ile or stationary sources. The NAAQS are to be based on air quality criteria, which are to accurately reflect the latest scientific knowledge useful in indicating the kind and extent of iden tifiable effects on public health or welfare that may be expected from the presence of the pollutant in ambient air. Based on periodic reviews of the air quality criteria and standards, the Admini strator is to make revi sions in the criteria and standards, and promulgate any new standards, as may be appropriate . The Act also requires that an independent scientific revi ew committee advise the Administ rator as part of this NAAQS review process, a function performed by th e Clean Air Scientific Advisory Committee (CASAC). The current NAAQS for CO includes two prim ary standards to provide protection for exposures to carbon monoxide. In 1994, EPA reta ined the primary standards at 9 parts per million (ppm), 8-hour average and 35 ppm, 1-hour average, neither to be exceeded more than once per year (59 FR 38906). These standards we re based primarily on the clinical evidence relating carboxyhemoglobin (COHb) levels to va rious adverse health endpoints and exposure modeling relating CO exposures to COHb levels. With the 1994 decision, EPA also reaffirmed
an earlier decision that th e evidence did not support the need for a secondary standard for CO (59 FR 38906). A subsequent review of the CO NAAQS was initiated in 1997, which led to the completion of the 2000 Air Quality Criteria Document for Carbon Monoxide (US EPA, 2000;

1 As noted below, the schedule for this review is governed by the terms of a court order.1-2 henceforth referred to as the 2000 AQCD) and a draft exposure analysis methodology document (US EPA, 1999). EPA put on hold the NAAQS re view when Congress requested that the National Research Council (NRC) review the impact of meteorology and topography on ambient CO concentrations in high altitude and extreme cold regions of the U.S. In response, the NRC convened the Committee on Carbon Monoxide Epis odes in Meteorological and Topographical Problem Areas, which focused on Fairbanks, Alaska as a case-study. A final report, “Managing Carbon Monoxide Pollution in Meteorological and Topographical Problem Areas” (NRC, 2003), offered a wide range of recommendations regardi ng management of CO ai r pollution, cold start emissions standards, oxygenated fuels, and CO monitoring. Following completion of this NRC report, EPA did not conduct rule making to complete the review.
EPA initiated the current review of th e NAAQS for CO on September 13, 2007, with a call for information from the public (72 FR 52369) requesting the submission of recent scientific information on specified topics. A works hop was held on January 28–29, 2008 (73 FR 2490) to discuss policy-relevant scientific and technical information to in form EPA’s planning for the CO NAAQS review. Following the workshop, EPA outlin ed the science-policy questions that would frame this review, outlined the process and schedu le that the review would follow, and provided more complete descriptions of the purpose, contents, and approach for developing the key documents for the review in a draft Plan for Review of the National Ambient Air Quality Standards for Carbon Monoxide (US EPA, 2008a). After CASAC and public input on the draft plan, EPA made the final plan available in August 2008 (US EPA, 2008b). In January, 2010, EPA completed the process of assessing the latest available policy-relevant scientific information
to inform the review of the CO standards. Th is assessment, the Integrated Science Assessment for Carbon Monoxide (hereafter, “ISA”) (US EPA, 2010b), includes an evaluation of the
scientific evidence on th e health effects of CO, including in formation on exposure, physiological mechanisms by which CO might adversely impact human health, an evaluation of the clinical evidence for CO-related morbidity, and an evalua tion of the epidemiological evidence for COrelated morbidity and mortality associations. 2 EPA’s Office of Air Quality Planning and St andards (OAQPS) has developed this Risk and Exposure Assessment (REA) describing th e quantitative assessment conducted by the Agency to support the review of the primary CO standards. This document is a concise presentation of the methods, key results, observati ons, and related uncertain ties associated with the quantitative analyses performed. The REA bu ilds upon the health effect s evidence presented in the ISA, as well as CASAC advice (Brain, 2009; Brain and Samet, 2009; Brain and Samet,

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 has not developed a quantitative risk assessment for the secondary standard review.1-3 2010a; Brain and Samet, 2010b) and public comments on a scope and methods planning document for the REA (hereafter, “Scope and Me thods Plan”) (US EPA, 2009a) and on the first and second draft REA documents (US EPA, 20 09b; US EPA, 2010c). This final REA was completed by May 28, 2010, consistent with th e court order governing the schedule for completion of this review. The court order also specified that EPA si gn for publication notices of proposed and final rulemaking concerning its review of the CO NAAQS no later than October 28, 2010 and May 13, 2011, respectively.
The ISA and REA are used to inform the policy assessment and rulemaking steps that lead to final decisions on the CO NAAQS. Th e policy assessment is described in a Policy Assessment (hereafter, “PA”) document, which include staff analyses of the scientific basis for alternative policy options for consideration by th e Administrator prior to rulemaking (US EPA, 2010a). The PA integrates and interprets inform ation from the ISA and the REA to frame policy options for consideration by the Administrator. The PA is intended to link the Agency’s scientific and technical assessments, presented in the ISA and REA, to judgments required of the Administrator in determining whether it is appropri ate to retain or revise the existing standards. Development of the PA is also intended to f acilitate elicitation of CASAC’s advice to the Administrator on the adequacy of existing standa rds, and any new standards or revisions to existing standards as may be appropriate.

1.2 PREVIOUS REVIEWS AND ASSESSMENTS

Reviews of the CO NAAQS completed in 1985 and 1994 included analyses of exposure to ambient CO and associated internal dose, in terms of COHb levels, which were used to characterize risks for populations of interest (50 FR 37484; 59 FR 38906). These prior risk characterizations compared the numbers and pe rcent of the modeled population that exceeded several potential health effect benchmarks, expr essed in terms of COHb levels. The COHb
levels of interest in these revi ews were drawn from the evidence of COHb levels associated with reduction in time to exercise-induced angina and other indicators of myocardial ischemia in controlled human exposure studie s involving short-term (shorter than 8 hours) exposures of patients with diagnosed ischemic heart disease (IHD) 3 to elevated CO concentrations (US EPA, 1979; US EPA, 1984; US EPA, 1991).

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).1-4 In the review completed in 1994, this char acterization was performed for the population of interest in the city of Denver, Colorado (U S EPA, 1992; Johnson et al., 1992). That analysis
indicated that if the current 8-hour standard were just met, the proportion of the nonsmoking population with cardi ovascular disease 4 experiencing exposures to ambient CO at or above 9 ppm for 8 hours decreased by an order of magnit ude or more as compared to the proportion under then-existing ambient CO le vels, down to less than 1 percen t of the total person-days in that population. Likewise, just meeting the curren t 8-hour standard reduced the proportion of the nonsmoking cardiovascular-disease population person days at or a bove COHb levels of concern by an order of magnitude or more relative to th en-existing ambient CO le vels. More specifically, upon just meeting the 8-hour standard, EPA estim ated that less than 0.1% of the nonsmoking cardiovascular-disease population would experience a COHb level of about 2.1% as a result of exposure to ambient CO. 5 A smaller percentage of the at-risk population was estimated to exceed higher COHb levels. The analysis also considered additional exposure scenarios that
included certain indoor sources (e.g., passive smoking, gas stove usage). However, the indoor
sources were shown to contribute to total CO exposure to a much greater extent than ambient air CO sources, leading to a conclusion that inclus ion of indoor sources was of limited utility in considering risk related to CO in ambient air. Further, it was noted that these indoor source emissions would not be effectively mitigated by setting more stringent ambient air quality standards (59 FR 38914). In the review initiated in 1997, EPA consu lted with CASAC (Mauderly, 1999) on a draft exposure analysis methodology document, Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Le vels in Denver Residents (Johnson et al., 1999), using the Probabilistic NAAQS Exposure Model (pNEM/CO , Version 2.0). Although the EPA did not complete the review initiated in 1997, OAQPS continued work on the CO exposure assessment to further develop the exposure assessment mode ling component of EPA’s Total Risk Integrated Methodology (TRIM). A subsequent draft tech nical report (Johnson et al., 2000) was produced documenting the application of the CO expos ure and dose modeling methodology for two study

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 (US EPA, 1992).

5 In the 1992 assessment, the person-days (number of persons multiplied by the number of days per year exposed) and person-hours (number of persons multiplied by the number of hours per year exposed) were the reported exposure metrics. Upon meeting the 8-hour sta ndard, it was estimated that less than 0.1% of the total person-days simulated for the nonsmoking cardiovascular-disease population were associated with a maximum COHb level greater than or equal to 2.1% (US EPA, 1992; Johnson et al., 1992).1-5 areas (Denver and Los Angeles). The exposur e and dose estimates were obtained by applying pNEM/CO version 2.1, a predece ssor to the currently used Air Pollutants Exposure Model (APEX), to adults with IHD re siding within each urban area. 6 This report was subjected to an external peer review by three exposure modeling experts and convened by Science Applications International Corporation (SAIC, 2001). In the 2000 pNEM/CO assessment, the Denver st udy area was defined as all census tracts
located within 10 km of each of six fixed-site monitors within the Denver metropolitan area. Air quality data for 1995 reported by these monitors were used to represent ex isting conditions in the study area. Because the second highest non-ove rlapping 8-hour average CO concentration equaled 9.5 ppm, the existing conditions in Denve r for 1995 were considered to approximate just meeting the 8-hour average CO standard. 7 In a similar manner, the Los Angeles study area was
defined as all census tracts within 10 km of ten fixed-site mon itors in the Los Angeles area, though air quality data for 1997 were adjusted down wards so that the concentrations associated with the design monitor just met the 8-hour NAAQS . A total of 15 distinct microenvironments were modeled using a mass balance model accoun ting for the infiltration of outdoor (ambient) CO, air exchange rates, as well as CO emissions from two indoor source s (residential gas stoves and passive cigarette smoke). In the 2000 pNEM/CO assessment, approximately 0.5% of the non-smoking IHD population in both urban areas was estimated to experience a maximum end-of-hour COHb level of about 2.0% as a result of exposure to ambient CO under air quality conditions just meeting the
current 8-hour standard. 8 A smaller percentage of the at-r isk population was estimated to exceed higher COHb levels (e.g., <0.1% of persons were estimated to have COHb levels at or above 3.0% in either location). Indoor CO sources were a much greater contributor to COHb levels, with their inclusion impacting a much larger portion of the simulated population at the higher COHb levels (i.e., those persons with >1% COHb). For example, in Denver with indoor sources included, nearly 20% of persons with IHD were estimated to have a maximum end-of-hour COHb level of about 2.0%. In Los Angeles with indoor sources included, the estimated percent of persons having a COHb level at or above 2.0% was lower (i.e., about 17%), though still a

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

7 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 (Laxton, 1990).

8 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).1-6 much greater percentage than that estimated in the absence of indoor sources (i.e., <1% of the simulated at-risk population).

1.3 CURRENT REVIEW, CASAC ADVI CE AND PUBLIC COMMENT

In preparing the draft Scope and Methods Plan for the REA (US EPA, 2009a), we considered the scientific evidence presented in the ISA and the key science policy issues raised in the IRP (US EPA, 2008b). EPA held a cons ultation with CASAC to solicit comments on the draft Scope and Methods Plan dur ing a May 2009 CASAC meeting. Public comments were also requested (74 FR 15265). Those CASAC and public comments were considered in developing the first draft REA (US EPA, 2009b) which implem ented a simplified, screening-level approach to assess population exposure and dose in tw o urban study areas, Denver and Los Angeles. The current version of EPA’s exposure model for CO (APEX/CO) was used to estimate exposure and dose for a simulated at-risk population within 20 km of a single fixed-site monitor 9 in each location. Only two microenviroments were simula ted; one in-vehicle and the second comprising all other locations persons might visit. 10 In using this simplified approach, the results were considered by staff as likely more representati ve of upper level exposure and doses experienced by a portion of the simulated at-r isk population rather than the si mulated at-risk population as a
whole. Following the review of the first draft RE A by CASAC (Brain and Samet, 2010a) and by public commenters, we made a number of modifi cations to our initial approach and developed the second draft REA (US EPA, 2010c) to be tter estimate populati on exposure and dose
distributions in each location modeled. 11 Specifically in the second draft assessment, we 1) expanded each of the two original modeling domai ns to include a greate r number of ambient monitors used as input to AP EX, 2) increased the number of microenvironments modeled from two to eight, 3) improved the representation of variability in estimated microenvironmental concentrations, including in-v ehicles, 4) included an algorithm that adjusts for spatial heterogeneity in estimated outdoor concentratio ns across each model domain, 5) implemented the mass-balance model for estimating concentr ations in all indoor microenvironments, 6) implemented the algorithm that allows commut ers to experience home-tract and work-tract

9 The single monitor used in each location was the design monitor, that is, the monitor used to evaluate concordance with the NAAQS. This monitor would measure the highest CO concentrations pertaining to the NAAQS (i.e, the greatest 2 nd highest 8-hour (or 1-hour) average CO concentration).

10 In the first draft REA, in-vehicle concentrations we re estimated by applying a factor of 2.0 to ambient CO concentrations. All other microenvironmental concentrations (i.e., both outdoor and indoor) were assumed to be the same as measured at the single ambient monitor.

11 Public Comments on the first draft REA were submitte d to the docket for this review and also presented in March, 2010 at the CASAC s econd draft review meeting.1-7 ambient concentrations, and 7) expanded the at-risk population to al so include undiagnosed persons with CHD. This final REA was produced in consideration of comments received on the second draft REA from CASAC (Brain and Samet, 2010b) and th e public. The approach used to estimate population CO exposure and COHb le vels in this final REA has remained largely the same as that used in the second draft REA, with the following adjustments or additions:

 Inclusion of a further expanded si mulated at-risk population based on prevalence rates for all types of heart di sease (as well as including the previous estimates of persons with undiagnosed CHD);

 Evaluation of endogenous CO production a nd ambient CO exposure separately
and their contributions to individual and population C OHb levels in a larger and more representative population subset;

 Identification of the specific microenvir onments that contribute to low- and high-level exposures;

 Inclusion of estimates of persons experiencing multiple occurrences per year at or above selected COHb levels;

 Evaluation of the distribution of microe nvironmental factors used to estimate exposure concentrations in response to concerns rega rding the appl ication of the microenvironmental algorithm; and

 Performance of additional se nsitivity analyses including

o An evaluation of the impact additional monitors had on estimated COHb dose levels experienced by the at-risk populations

o An evaluation of the potential impact to estimated COHb dose levels experienced by the at-risk populations by varying undiagnosed prevalence rates by gender.

o An evaluation of the potential impact to estimated COHb dose levels experienced by a hypothetical an emic CHD population by using alternative hemoglobin content distributions. The chapters and appendices that follow descri be the technical details in the exposure and dose modeling approach used for this assessment, as well as the data anal ysis results. More specifically,

 Chapter 2 provides a conceptual overvie w of the assessment of CO exposure and risk with particular focus on aspects pertinent to this REA;1-8  Chapter 3 summarizes some of the genera l trends in CO ambient monitoring across the U.S. and presents additional ai r quality analyses re levant to the two urban areas of focus in this REA;

 Chapter 4 provides a technical overvi ew of EPA’s APEX model including model algorithms and databases common to most pollutant applications as well as the description of approaches used specifically for estimating CO exposure and dose;

 Chapter 5 details the site- and pollutant-spe cific data used for the application of APEX to the two study areas assessed in this REA;

 Chapter 6 provides the e xposure and dose results;

 Chapter 7 presents an analysis of ho w variability was addressed in this assessment and qualitatively characterizes how uncertainties in input data and model algorithms might affect exposure and dose results; and

 Chapter 8 summarizes the key observations associated with each chapter.1-9 1.4 REFERENCES

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

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

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

Brain JD and Samet JM. (2010b). Letter from Drs. J.D. Br ain and J.M. Samet to Administrator Lisa Jackson. Re: Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO) Primary National Ambient Air Quality Standards: Second External Review Draft. EPA-CASAC-10-012. May 19, 2010.

Johnson T, Capel J, Paul R, Wijnberg L. (1992). Estimation of Carbon Monoxide Exposure and Associated Carboxyhemoglobin Levels in Denver Residents Using a Probabilistic version of NEM. Report prepared by International Technology for U.S. EPA, Office of Air Quality Planning and Standards, Durham, NC, Contract No. 68-D0-0062.

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

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

Laxton, WG. (1990). Memorandum on “Ozone and Carbon Monoxide Design Value Calculations”. June 18, 1990. Available at: http://www.epa.gov/ttn/n aaqs/ozone/ozonetech/laxton.htm.

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

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

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

US EPA. (1979). Air Quality Criteria for Carbon Monoxi de. Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-79-022.1-10 US EPA. (1984). Review of the NAAQS for Carbon Monoxide: Reassessment of Scientific and Technical Information. Office of Air Quality Planning and Standards, report no. EPA-450/584-904. Research Triangle Park, NC.

US EPA. (1991). Air Quality Criteria for Carbon Monoxide. Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-90/045F.

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

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

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

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

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

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

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

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

US EPA. (2010a). Policy Assessment to Support the Re view of the Carbon Monoxide Primary National Ambient Air Quality Standards. U.S. Environmental Protectio n Agency, Research Triangle Park, NC, report no. EPA-452/R-10-007. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pa.html

US EPA. (2010b). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency, Washington, DC, report no. EPA/600/R-09/019F. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html..

US EPA. (2010c). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary National Ambient Air Quality Standards: Second Extern al Review Draft. U.S. Environmental Protection Agency, Research Triangle Park, NC, repo rt no. EPA-452/P-19-004. Available at: http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_rea.html2-1

. 

### 2 CONCEPTUAL OVERVIEW: A SSESSING AMBIENT CARBON MONOXIDE EXPOSURE AND RISK

In this chapter, we have summarized the conceptual model for assessing exposure to
ambient CO and associated health risk. Subsections focus on di fferent components of the model including identification of the key emission sources to am bient concentrations (section 2.1), exposure pathways and key microenvironments (s ection 2.2), exposure and dose metrics (section 2.3), at-risk populations (section 2.4) , health endpoints (section 2.5), and the risk characterization approach (section 2.6). Sec tion 2.7 presents the key observations for this chapter.

2.1 SOURCES OF CARBON MONOXIDE

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels and photoche mical reactions in the atmosphere. The amount of CO emitted from these reactions, relative to the amount of carbon dioxide (CO 2 ) generated, is
sensitive to conditions in the combus tion zone. CO production relative to CO 2 generally decreases with any increase in fuel oxygen (O 2 ) content, burn temperatur e, or mixing time in the combustion zone (ISA, section 3.2). As a result , CO emissions from large fossil-fueled power plants are typically very low because optimi zed fuel consumption conditions make boiler combustion highly efficient. In contrast, inte rnal combustion engines commonly used to power mobile sources have widely varying operating cond itions. Therefore, higher and more variable CO emission levels result from the operation of these mobile sources (ISA, section 3.2). In 2002, CO emissions from on-road vehicles account ed for a substantial majority of total emissions by individual source sectors in the U.S. (ISA, Figure 3-1). 1 As in previous NAAQS reviews, mobile sources continue to be a significant emission source of CO to ambient air, although in some areas, local sta tionary sources may be importan t contributors to ambient CO
concentrations. Sources of CO inside buildings include inf iltration of ambient air indoors, as well as, where present, indoor (nonambient ) sources such as gas stoves and tobacco smoke (ISA, section 3.6.5.2). In addition to infiltration of ambient air, CO inside motor vehicles may also receive
contributions from nonambient sources in th e cabin, which can be substantial under air
ventilation modes that limit inflow from outsi de the vehicle (ISA, p. 3-89). In past CO assessments, nonambient sources were estimated to have a substantially greater impact on the highest total exposures experien ced by the simulated population than have ambient sources (as

1 The 2002 National Emissions Inventory (NEI; US EPA, 2006) was the most recently available NEI meeting data quality objectives for the ISA (US EPA, 2010a) . The NEI includes data from various sources such as industries and state, tribal, and local air agencies (ISA, p. 3-1).2-2

. summarized in section 6.3 below). However, the focus of this REA, conducted to inform the current review of the CO NAAQS, is on sources of ambient CO. We provide quantitative estimates of population exposure and dose originating from ambi ent CO in two urban areas (details on site select ion are provided in chapter 3 belo w). The exposure modeling in this assessment does not quantitatively estimate the contribution of indoor sources to estimated population exposure and dose. In section 2.2 below, however, we qua litatively draw upon available information regardi ng potential indoor source contri butions to estimated population exposure and dose.

2.2 EXPOSURE PATHWAYS AND IMPO RTANT MICROENVIRONMENTS

Human exposure to CO involves the contact (via inhalation) between a person and the pollutant in the various locations (or microenvi ronments) in which people spend their time. Studies of personal exposure have generally found that the largest portion of the day is generally spent indoors and the largest percen tage of the time in which an individual is exposed to ambient CO occurs indoors (ISA, sections 2.3 and 3.6). As a result, CO concentrations in indoor microenvironments are an important determinant of an individual’s total CO exposure. Recent population exposure studies conducted in Milan, Italy support this conclusion (Bruinen de Bruin et al., 2004), indicating that ove r 80% of the population exposure to CO can occur in indoor microenvironments (ISA, Table 3-13). Taking into account the infiltration of ambient CO indoors, indoor CO concentrations are similarly an important determinant in an individual’s exposure to ambient CO. Microenvironments that may in fluence CO exposures typicall y include residential indoor environments and other indoor locations, near -traffic outdoor microenvironments and other outdoor locations, and inside ve hicles. Consideration of microenvironmental exposures illustrates the variability in the relationship between pe rsonal exposure and ambient concentrations. For example, one study summa rized the relationship between personal CO exposure concentrations in five broadly define d microenvironments (i.e ., indoor residence, indoor other, outdoor near road, outdoor other, and in -vehicle) and ambient CO concentrations 2 in Baltimore, MD (ISA, section 3.6.5.2; Chang et al., 2000). For most of the microenvironments,
the mean indoor-to-ambient and outdoor-to-ambi ent concentration ratio s were about one, though
most of the individual ratios observed across th is set of indoor and outdoor microenvironments
were less than one. With the ex ception of ratios for the in-vehic le microenvironments, which as a group had most of the ratio dist ribution (as well as the mean ra tio) above one, few ratios were

2 The ambient CO concentrations were those measured at a fixed site monitor (winter) or reflected average concentrations across three fixed-site monitors (summer) (Chang et al., 2000).2-3

. above unity (ISA, p. 3-85, Figure 3-46). Given th e expected stability of CO as it infiltrates
indoor microenvironments from outdoor air and th e lack of significant re moval mechanisms of CO in outdoor microenvironments, it is likel y that the variability in personal- or
microenvironmental-to-ambient m onitor and outdoor-to-ambient mon itor concentration ratios is
the result of variab ility in outdoor concentrations that are not correlated with simultaneously
measured ambient concentrations at fixed-site monitors. This lack of correlation is a function of the presence of local ambient and nonambient source emissions as well as local meteorology Typically the highest CO exposure concentratio ns are experienced while inside vehicles.
Because motor vehicle emissions continue to be important contributors to ambient CO concentrations, both the time spent in motor ve hicles and the elevated CO concentrations occurring on and near heavily trafficked roads co ntinue to be important contributors to personal exposures. For example, in the study summarized above on personal exposu res occurring within particular microenvironments (i.e., Chang et al., 2000), most in-vehicle CO exposure-to-ambient concentration ratios were greater than one, with the median being approximately 2.5. The average ratio was approximately 2.5 in summ er, but a few somewhat higher in-vehicle measurements in the winter period, contributed to a winter average of approximately 4 (ISA, section 3.6.5.2, Figure 3-46; Chang et al., 2000 Figure 5). 3 Given this relationship, it should not be surprising that while about 8% of a person’s time per day is spent in transit, approximately 13-17% of their total daily expos ure occurs within an in-vehic le microenvironment (e.g., Bruinen de Bruin et al., 2004; Scotto di Marco et al., 2005).
A similar influence of mobile source-influ enced microenvironments was observed in the CO population exposure studies cond ucted in Denver CO and Wash ington, DC during the winter of 1982 and 1983 (Akland et al., 1985). 4 In both cities, when co mparing the distribution of
measured CO concentrations from the monitori ng network to measured personal exposures, two common phenomena were observed. At the lowest percentiles of each distribution, ambient CO concentrations were consistently greater than th e personal exposures. At the highest percentiles of each distribution, ambient concentrations were consistently lower than the personal exposures (US EPA, 2000). These studies determined that the highest average CO concentrations occurred when subjects were in a mobile source-infl uenced microenvironment (e.g., inside parking garages, in-vehicles). Commute time was also a factor; those who co mmuted 6 hours or more per week had higher average exposures than those who commuted fewer hours per week.

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

4 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).2-4

. Furthermore, mean CO concentrations within in -vehicle microenvironments (ranging from 7.0 to 9.8 ppm) were greater than co mmon outdoor locations (ranging from 1.4 to 3.2 ppm) (US EPA, 2000). In considering the results from the De nver and Washington personal exposure studies it is important to recognize that CO emissions from motor vehicle sources have declined dramatically since the early 1980’s when these studies were conducted. Consequently, both
ambient fixed-site CO concentrations and in-vehic le CO concentrations have also been reduced significantly since that time period. Given their influence on ambient exposures, exposures to CO near roadways and in vehicle microenvironments are of particular import ance in this assessment. Data from several studies that have compared concen trations inside vehicles to c oncentrations immediately outside
vehicles indicate that indoor/outdoor concentratio n (I/O) ratios on average range from just above to just below unity (Chan et al., 1991; Rodes et al., 1998; Boulter and McCrae, 2005; Sharp and Tight, 1997). These studies are supported by a review by Flachs bart (1999) regarding other studies published between 1982 and 1992 that measured interior and exterior CO concentrations simultaneously during motor vehicle trips and report ed I/O ratios just below unity (Petersen and Allen, 1982; Koushi et al., 1992). Some studies reported no effect of ve ntilation setting on I/O ratios, while others reported an effect. For ex ample, one study described in the ISA indicated I/O ratios could exceed unity with the ventilation set to re-circulate vehicle air (Abi Esber and El-Fadel, 2008). However, the study authors at tributed this finding to unaccounted sources of
CO that caused increases in CO concentrations within the vehicle cab in under those conditions (ISA, section 3.6.6.2; Abi Esber and El-Fadel, 2008). In general, the above results suggest that th e I/O ratio tends toward unity when there are no interior sources of CO, the au tomobile engine does not contribut e directly to its own interior concentrations, and the measurement probes ar e properly installed on the vehicle. This
conclusion is consistent with th eoretical expectations for a non-re active pollutant. For example,
CO concentrations inside vehicles can be esti mated as a function of outside CO concentration, air exchange rate, a penetrati on factor, and the emission rates of indoor sources (e.g., exhaust leaks, smoking). If one assumes that (1) steady- state ventilation condition s exist, (2) the indoor removal rate ( k ) is zero (i.e., no loss of CO as it moves from outside to inside the vehicle), and (3) there are zero emissions from interior sources, then the CO concentration inside a vehicle can be simplified to a function of outside CO concentra tions and the penetration rate (i.e., infiltration is generally equivale nt to penetration). 5 Under these stated cond itions, the I/O ratio would ultimately converge to unity.

5 See section 3.6.2 of the ISA.2-5

. There are a few studies that have measured both in-vehicle and fixed-site monitoring
concentrations. The data from these studi es can also inform the development of microenvironmental factors used for estimating in -vehicle CO exposures. The ISA notes that
studies summarized in the 2000 CO AQCD found that in-vehicle CO concentrations were generally two to five times higher than ambien t CO concentrations obtained at fixed-site
monitors within the cities studied. For ex ample, Shikiya et al. (1989) reported such concentrations measured as part of a southern California study. When using the reported invehicle CO measurements, one could estimate concentration ratios ranging from 1.8 to 2.7, a range of ratios dependent on the time-of-year meas urements were collected. Note however that
there are several factors that could contribute to variability in reported or calculated concentration ratios. For example, often times in these measurement studies, the averaging time associated with the companion measurements differ, that is there may be a much shorter sampling interval for the in-vehicle measuremen t when compared with that of the ambient monitor. More specifically, Sh ikiya et al. (1989) measured in-v ehicle CO concentrations during commutes lasting, on average, 33 minutes, while fixed site monitoring values averaged over 4 hours. It is possible that the time -averaged concentrations are less th an that of the true fixed-site concentrations that occurred during the 33 minute commute, perhaps resulting in an
overestimation of the concentration ratios when us ing this data. Furthermore, Shikiya et al. (1989) reported seasonal differences for the in-v ehicle CO concentratio ns (winter averaged 10.1 ppm; summer averaged 6.5 ppm), but not for the fixed-site monitor (average for both seasons was 3.7 ppm). Typically ambient concentrations are greater in winter (e.g., ISA Figure 3-22 for Los Angeles). Therefore, when using the fixed- site seasonal average and in-vehicle seasonally stratified measurements from Shikiya et al. (1989) to calculate the ratio s as was done above, the winter I/O ratio may be overestimated while th e summer value could be underestimated. In
addition to the factors mentioned above, this relationship can vary based on several other factors that may influence the fixed-site monitor con centration, such as the nearby roadway traffic density, the monitor siting char acteristics (e.g., proximity to the roadway), and local scale meteorology (e.g., downwind), with each described in greater detail in chapter 3. Of the few studies reporting in-vehicle and companion fixed-site measurements, most do not measure all of the potentially influential factors or provide the data st ratified by such factors. Thus, a general range of two to five may be adequate to repr esent the total variability for this particular relationship, recognizing that there are limitations in the av ailable measurement data to better define this relationship. Although not the focus of this review, indoor sources such as gas stoves and environmental tobacco smoke can, where present, be important contributors to total CO exposure and may, consequently, be of par ticular concern for such at-risk populations as individuals with2-6

. cardiovascular disease, among othe rs (see section 2.4 below). For example, some assessments performed for previous reviews have include d modeling simulations both without and with indoor sources (gas stoves and tobacco smoke) to provide context for the assessment of ambient CO exposure and dose (e.g., US EPA, 1992; Johnson et al., 2000). The 2000 pNEM/CO
simulations with indoor sources i ndicated that the impact of su ch sources on the proportion of the population experiencing higher exposures and COHb levels can be substantial (Johnson et al., 2000), as summarized in section 1.2 above and in section 6.3 below. 6

2.3 EXPOSURE AND DOSE METRICS

Exposure concentration over a time period of interest (e.g., one hour or eight hours) is a common exposure metric which refl ects the integration of exposures to pollutant concentrations
that occur in each microenvironment in which time is spent (see section 4.4.6 below). In the case of CO, for which the common mechanism underlying biological response is binding to heme proteins, COHb level in blood is well recognized as an important internal dose metric used in evaluating CO exposure and the potential for heal th effects (ISA, p. 2-4, sections 4.1, 4.2, 5.1.1). Accordingly, COHb levels are used in this assessment. Carboxyhemoglobin occurs in the blood due to endogenous CO production from
biochemical reactions associated with normal breakdown of heme proteins, as well as in response to inhaled (exogenous) CO exposures (ISA, section 4.5). 7 Levels of COHb associated with endogenous CO production in h ealthy individuals have been described to range down to 0.3% and generally be less than 1% (ISA, pp. 4-9, 4-23, 2-6). However, the production of endogenous CO and levels of endogenous COHb va ry with several physio logical characteristics (e.g., slower COHb elimination with increasing age) , as well as some disease states, which can lead to higher endogenous levels in some indivi duals (ISA, section 4.5). Other factors affecting CO uptake and elimination include physical activity and altitude (ISA, section 4.4). The amount of COHb formed in response to exogenous CO is dependent on the CO concentration and duration of exposure, exercise (which increases the amount of air removed and replaced per unit of time for gas exchange), the pulmonary diffusing capacity for CO, ambient pressure, health status, and the specific metabo lism of the exposed individual (ISA, chapter 4; 2000 AQCD, chapter 5). The formation of COHb is a reversible process, but the high affinity of CO for Hb, which affects the elimination half-tim e for COHb, can lead to increased COHb levels

6 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.

7 The dosimetry and pharmacokinetics of CO are discussed in detail in chapter 4 of the ISA.2-7

. in some circumstances. 8 Exogenous CO, ambient and nonambient 9 , can contribute to CO uptake and increased levels of COHb. As recogni zed in sections 2.1 and 2.2 above, nonambient (indoor) sources of CO (ISA, section 3.6.5.2) can result in much greater CO exposures and
associated COHb levels than those associated with ambient sources. 10 Further, baseline COHb
levels in active smokers have been estimated to range from 3 to 8% for one- to two-pack-per-day
smokers. As a result of their hi gher baseline COHb levels, smokers may exhale more CO into the air than they inhale from the ambient e nvironment when not smoking. Tobacco smoking can also contribute to increased CO exposures a nd associated COHb levels in nonsmokers (2000 AQCD, p. 7-4). In order to focus on the imp act of ambient CO sources on population COHb levels, exposure modeling for this REA does not include indoor CO s ources; the impact of indoor sources has been evaluated in previ ous assessments (see section 6.3 below). As described in section 4.4.7 and Appendix B, blood levels of COHb have been estimated in this REA using a nonlinear solu tion of the Coburn-Fors ter-Kane (CFK) model (Coburn et al., 1965), which remains “the most extensively validated and applied model for COHb prediction (ISA, section 4.2.3).

2.4 AT-RISK POPULATIONS

The term ‘susceptibility’ (and the term “at-risk”) has been used to recognize populations that have a greater likelihood of experiencing effects related to ambient CO exposure (ISA, section 5.7). This increased likelihood of res ponse to CO can potentially result from many factors, including pre-existing me dical disorders or disease stat es, age, gender, lifestyle or increased exposures (ISA, section 5.7). For exam ple, medical disorders that limit the flow of oxygenated blood to the tissues have the potential to make an indivi dual more susceptible to the potential adverse effects of low levels of CO, especially during exercise. Based on the available evidence in the current review, coronary artery disease (CAD), also known as coronary heart disease (CHD) is the “most important susceptibility characteristic for increased risk due to CO exposure” (ISA, p. 2-11). While persons with a normal cardiovascular system can tolerate

8 Fortunately, mechanisms exist in normal, healthy i ndividuals to compensate for the reduction in tissue oxygen caused by increasing levels of COHb. Cardiac output increases and blood vessels dilate to carry more blood so that the tissue can extract adequate amounts of oxygen from the blood (ISA, chapter 4). As discussed in sections 2.4 and 2.5 below, however, there are several medical disorders that can make an individual more susceptible to the potential adverse effects of low levels of CO, especially during exercise.

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

10 For example, in addition to COHb estimates from prev ious assessments discussed in sections 2.1 and 2.2, indoor source-related exposure s, such as faulty furnaces or other combus tion appliances, have been estimated in the past to lead to COHb levels on the order of twice as high as those short-term exposures to ambient CO considered more likely to be encountered by the general public (2000 AQCD, p. 7-4).2-8

. substantial concentrations of CO if they vasodilate or increase cardiac output in response to the hypoxia produced by CO, those that are unable to vasodilate in response to CO exposure may
show evidence of ischemia at low concentrations of COHb (ISA, p. 2-10). There is strong evidence for this in controlled human exposure studies of exercising in dividuals with CAD, which is supported by results from recent epidem iologic studies reporting associations between short-term CO exposure and increased risk of emergency department visits and hospital admissions for individuals affected with ischemic heart disease (IHD) 11 and related outcomes (ISA, section 5.7). This combined evidence, briefly summarized in section 2.5.1 below and described in more detail in the ISA, supports th e conclusion that individuals with CAD represent the population most susceptible to increased risk of CO-induced health effects (ISA, sections 5.7.1.1 and 5.7.8). The 2007 estimate of the size of the U.S. population with coronary heart disease, inclusive of those with angina pect oris (cardiac chest pa in) and those who have experienced a heart attack (ISA, Table 5-26) is 13.7 million people, some fraction of whom have IHD (ISA, pp.5-117). Further, there are estimated to be several million additional people with silent ischemia or undiagnosed IHD (AHA, 2003). In combination this represents a large population that is more susceptible to ambien t CO exposure when compared to the general population (ISA, section 5.7). Other types of cardi ovascular disease 12 may also potentially co ntribute to increased susceptibility to the adverse effects of low levels of CO, especially during exercise (ISA, section 5.7.1.1). For example, some evidence with regard to other types of cardiovascular disease such as congestive heart failure, arrhythmia, and non-sp ecific cardiovascular di sease, although more limited for peripheral vascular and cerebrovascular disease, indicates that “the continuous nature of the progression of CAD and its close relations hip with other forms of cardiovascular disease suggest that a larger pop ulation than just those individuals wi th a prior diagnosis of CAD may be susceptible to health effects fr om CO exposure” (ISA, p. 5-117).

11 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 de pression, 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).

12 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 hear t disease (i.e., CHD, CAD, myocardial infarction, and angina), congestive heart failure, and distur bances of cardiac rhythm (dysrhythmias and arryhthmias) (2000 AQCD, p. 7-7).2-9

. Beyond persons with cardiovascular diseases , other populations may be potentially susceptible to CO-related health effects. These p opulations are listed in the paragraphs below. However, little empirical evidence is available by wh ich to specify health effects associated with ambient or near-ambient CO exposures in these potentiall y at-risk groups. Populations with other preexisting diseases , such as chronic obstructive pulmonary disease, diabetes or anemia have been identified as potentially susceptible to CO-induced health effects (ISA, p. 5-123). For example, although there are no controlled human exposure or epidemiological studies examining potential CO-induced effects in people suffering from hematologic diseases, such as anemia, that a ffect oxygen-carrying capacit y or transport in the blood, it is reasonable to assume that the potential combination of hypoxic effects of CO together with reduced oxygen availability and/or elevated baseline COHb levels in people suffering with
anemia 13 may make those with anemia susceptible to CO-induced effects (ISA, section 5.7.1.4). Included in this category of anemia diseases is sickle cell anemia, which is documented at a higher incidence in African-Ame rican populations (ISA, section 5.7.1.4). Asthma and COPD are other oxygen-limiting diseases which may be exacerbated by CO-related oxygen limitation. Another population that may be potentially suscep tible to CO includes those persons that may have increased endogenous production of CO an d potentially higher endogenous COHb levels such as diabetics, for which a few epidemiol ogical studies provide s uggestive evidence of increased risk for cardiovascular emergency depa rtment visits and hospital admissions compared to non-diabetics in response to short-term CO concentr ations (ISA, section 5.7.1.3). Additionally, older adults, especi ally those with compromised car diovascular func tion, represent a potentially susceptible population (ISA, section 5.7.2.1). The developing young (e.g., gestational developm ent and newborns) may also represent a population potentially susceptible to CO-i nduced health effects (ISA section 5.7.2.2; 2000 AQCD, section 7.7.1). For example, although the e ffects of CO on maternal -fetal relationships are not well understood, fetal circulat ion is likely to have a higher COHb level than the maternal circulation because of differences in uptake a nd elimination of CO from fetal Hb, which may contribute to an enhanced sensitivity to CO exposure during gestation (ISA, section 5.7.2.2). The comparatively higher rate of oxygen consum ption and lower oxygen-transport capacity for Hb in newborn infants as compared to adults may make them susceptible to CO-induced hypoxic effects (2000 AQCD, section 7.7.1). Data from laboratory animal studies on CO developmental toxicity suggest that prolonged exposure to high CO levels (>60 ppm) during gestation may produce reduction in birth weight , transient cardiomegaly and dela yed behavioral development,

13 Individuals affected with anemias of different etiologies may have low hematocrit, reduced capacity of the blood to carry oxygen, or increased COHb levels due to increased endogenous CO production, all of which would decrease the oxygen available for organs and tissues (ISA, pp. 118-119).2-10

. or may disrupt the normal phys iological roles of endogenous CO in the body (ISA, section 5.4.2.2); multiple-day prenatal animal exposures to exposures at or above 12 ppm indicated effects on the developing auditory system (ISA, pp. 5-75 to 5-76). Limited epidemiological evidence suggests some associati on of short-term ambient CO e xposure with pre-term birth and birth defects, and weak evidence suggests an asso ciation with reduction in birth weight and fetal growth, and infant mortality (ISA, section 5.7.2.2; 2000 AQCD, section 7.7.1), although a clear understanding of the mechanisms by which CO ma y induce those effects and at what exposure levels is lacking (ISA, section 5.4.3). Other populations that may be potentially su sceptible due to impacts on endogenous CO production, uptake and elimination of CO, or increas ed exposure concentrati ons include visitors to high-altitude locations, persons using medici nal or recreational drugs with central nervous system depressant properties or that that increase endogenous fo rmation of CO, and people that spend a substantial amount of time on or near heavily traveled roads which may contribute to higher CO exposures (ISA, section 5.7). As discussed in section 2.5 below, the sens itive endpoint which is the focus of this quantitative assessment is exacerbation of myocardi al ischemia. Based on the current evidence for this endpoint, two target populations have been identified for this REA: (1) adults with CHD (also known as ischemic heart disease I HD or CAD), both diagnosed and undiagnosed; 14 and (2) adults with diagnosed heart di sease (HD) which includes CHD as well as other conditions (e.g., arrhythmias), along with undiagnosed CHD. As mentioned above, there is little empirica l evidence currently available by which to specify health effects associated with relevant CO exposures in the othe r, potentially at-risk groups identified above. Such evidence characterizi ng the nature of specific health effects of CO in these populations is extremely limited and does not include COHb levels related to a particular
health effect identified in th ese potentially susceptible popula tions. Quantitative evidence relating exposure or an applied do se to an adverse health outcome is requisite to the conduct of a quantitative exposure and risk as sessment. As a result, while we continue to recognize the potential susceptibility of the larger cardiovascular disease population to he alth effects of CO, as has been recognized in past reviews, as well as the potential susceptibi lity of several other populations identified above (ISA, section 5.7), the at-risk pop ulations simulated in this assessment are individuals with CAD (diagnosed and undiagnosed and inclusive of individuals with angina pectoris and heart attacks), as well as the larger HD population. We additionally
note that the still broader cardiovascular diseas e population and the potential susceptibility of

14 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).2-11

. other populations is further considered with regard to the review of the CO NAAQS in the Policy Assessment document (US EPA, 2010b).

2.5 HEALTH ENDPOINTS

Carbon monoxide elicits various health effects by binding to heme proteins and altering the function of a number of heme proteins (I SA, section 2.4.2). The level of CO bound to hemoglobin as carboxyhemoglobin (COHb) in the blood is the best characterized dose metric for evaluating CO exposure and the potential for associat ed health effects, as described in section 2.3
above.
The best characterized health effect associ ated with CO levels of concern is hypoxia (reduced oxygen availabili ty) induced by increased COHb leve ls in blood (ISA, section 5.1.2).
The formation of COHb reduces the oxygen carrying capacity of the blood and impairs the release of oxygen from oxy-hemoglobin complexes to the tissues. Accordi ngly, CO is especially
harmful in individuals with im paired cardiovascular systems (a s discussed in section 2.4 above)
and the clearest evidence of causa l relationships with CO exists for cardiovascular effects. In characterizing the combined evidence, the ISA conc luded that cardiovascul ar effects are likely causally related to short-term exposures to CO at relevant concentrati ons, with “relevant CO concentrations” defined in the ISA as “generally within one or two orders of magnitude of ambient CO concentrations” (ISA, p. 2-5). The “most compelling evidence of CO-induced effects on the cardiovascular system comes from a series of controlled human exposure studies among individuals with coronary heart dise ase (CHD) (ISA, sections 5.2.4 and 5.2.6). Other potential effects of CO which are le ss well characterized at relevant exposure
concentrations are those on the central nervous system, reproducti on and prenatal development, and the respiratory system (ISA, section 2.5). Th ese additional health endpoints, for which the limited available evidence is suggestive of cau sal relationships (ISA, sections 5.3, 5.4 and 5.5),
are also considered in this review and are disc ussed in detail in the ISA and summarized briefly in section 2.5.2 below. Across the health endpoi nts identified here, howev er, the focus of the quantitative analysis described in this document is on cardiovascula r disease-related effects that have been observed in adults with CHD, most specifically decreased time to exercise-induced angina and changes to the ST-segment of an elec trocardiogram that are in dicative of myocardial ischemia. This focus is based on the strength of the evidence and availability of quantitative
information from human studies of controlled CO exposures in which the resulting COHb levels were associated with these effects (as discussed in sections 2.5.1 and 2.6 below).2-12

. 2.5.1 Cardiovascular Disease-related Effects

The best characterized cardiovascular diseas e-related effects associated with CO are markers of myocardial ischemia observed in studies of controlled CO exposures of CHD patients 15 and effects on exercise duration and maxi mal aerobic capacity observed in controlled exposure studies of healthy adults. 16 As noted in the ISA, th e decreases in exercise duration among healthy adults (associated with COHb levels from 3 up to 20%) were relatively small and only likely to be noticed by competing athlet es, although they are c onsidered to provide
coherence with the exercise-induced cardiovascula r effects of greater concern that have been demonstrated in CHD patients. The controlle d human exposure studie s involving individuals with preexisting CHD provide str ong evidence for an association between short-term exposure to CO and measures of ischemia (US EPA, 2000, section 6.2.2; ISA, section 5.2.4). Multiple controlled human exposure studies have shown that short-term exposure to CO and subsequent elevation of COHb to levels of approximately 2-6% reduces time to onset of exercise-induced myocardial ischemia in individua ls with preexisting CAD, with no evidence of a threshold at the lowest levels tested (ISA, section 5.2.4). The controlled exposure study of principal im portance is a large multi-laboratory study designed to evaluate myocardial ischemia, as documented by reductions in time to change in the ST-segment of an electrocardiogram 17 and in time to onset of angina, during a standard treadmill test, at CO exposures targeted to result in mean subject COHb levels of 2% and 4%, as measured by gas chromatographic technique 18 (ISA, section 5.2.4, from Allred et al., 1989a, 1989b, 1991). In this study, subjects on three separate occasi ons underwent an initial graded exercise treadmill test, followed by 50- to 70-minute exposures under resting conditions to average CO concentrations of 0.7 ppm (room air concentr ation range 0-2 ppm), 117 ppm (range 42-202 ppm)

15 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 (ISA, p. 2-14, section 5.2.6.1). Additional studies have shown associations between ambient CO and hospital admissions for cong estive heart failure and cardiovascular disease as a whole (which includes IHD), although this evidence is not as consistent am ong studies as the IHD evidence (ISA, sections 5.2.3 and 5.2.6.1).

16 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).

17 The ST-segment is a portion of th e electrocardiogram, depression of wh ich is an indication of insufficient oxygen supply to the heart muscle tissue

18 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).2-13

. and 253 ppm (range 143-357 ppm). After the 50- to 70-minute exposures, subjects underwent a second graded exercise treadmill test, and the per cent change in time to onset of angina and time to ST endpoint between the first and second exercise tests was dete rmined. 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 c oncentrations of 2.0% and 3.9% 19 were shown to decrease the time required to induce ST-segment cha nges by 5.1% (p=0.01) and 12.1% (p<0.001), respectively. These changes were well correlated with the onset of exercise-induced angina the time to which was shortened by 4.2% (p=0.027) and 7.1% (p=0.002), resp ectively, for the two CO exposures (ISA, section 5.2.4; Allred et al., 1989a, 1989b, 1991). No human clinical studies have been specifi cally designed to eval uate the effect of controlled exposures to CO resulting in study mean COHb levels lower than 2% (ISA, section 5.2.6). However, an important finding of th e multi-laboratory study was the dose-response relationship observed between COHb and ischemia without evidence of a measurable threshold effect (Allred et al., 1989b, 1991). As reported by th e authors, the results comparing “the effects of increasing COHb from base line levels (0.6%) to 2 and 3.9% COHb showed that each produced further changes in objective ECG m easures of ischemia” implying that “small increments in COHb could adversely affect m yocardial function and pr oduce ischemia” (Allred et al., 1989b, 1991). For each 1% increase in COHb resulting from the experimentally increased CO exposure concentrations the dose-response analysis performed by the authors indicated
decreases of 1.9% in time to exercise-induced an gina and 3.9% in time to exercised-induced STsegment change in persons with pre-existing CAD (ISA, section 5.2.4, from Allred et al., 1989a, 1989b, 1991). Other controlled human exposure studies (A dams et al., 1988; Anderson et al., 1973; Kleinman et al., 1989, 1998) involvi ng individuals with stable angi na have confirmed the Allred et al. findings at COHb concentrations betw een 3 and 6% (as measured by CO-oximeter) (ISA, section 5.2.4). Among the evidence is also a st udy of a small group of patients with CAD which reported no change in time to onset of angina or maximal exercise time following a 1 hour exposure targeted to result in 4% COHb. A s ubsequent study conducted by the same laboratory reported a significant increase in number of ventricular arrhythmia s during exercise relative to room air among individuals with CAD following a 1-hr CO exposure targeted to yield 6% COHb, but not following a 1-hr exposure targeted to yield a COHb level of 4% (ISA, p. 5-42; Sheps et al., 1987, 1990). Although there was no clea r pattern across the different studies with respect to the magnitude of the decreased time to onset of angina versus dose level, differences

19 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).2-14

. in study protocols and analytical methods do not allow for an info rmative pooled or quantitative meta-analysis of the dose-res ponse relationship across studies (ISA, section 5.2.4). Although the subjects evaluated in the controlled human expos ure studies described above are not necessarily representative of the most sensitive population, th e level of disease in these individuals ranged from moderate to severe, with the majority eith er having a history of my ocardial infarction or having ≥ 70% occlusion of one or more of th e coronary arteries (ISA, p. 5-43). We also note that, in the current review, a number of epidemiological studies are now available that investigate associ ations of cardiovascular morbidity with ambient measurements of CO (ISA, sections 5.2.4 and 5.2.5). These studies have observed associations between ambient
monitor CO concentrations and increases in emergency department visits and hospital admissions for cardiovascular disease (ISA, secti ons 5.2.1.9). In considering the epidemiological evidence in the case of CO, we r ecognize that there is coherence between the available clinical and much expanded epidemiological evidence since the prior review, with regard to the health
effects of CO in the cardiovascul ar system (primarily for ischemia -related events). As discussed in the ISA, the epidemiological studies reported associations of CO conc entrations at ambient monitors with emergency department visits and/or hospital admissions for IHD and other
cardiovascular disease-rela ted outcomes that are plausibly re lated to the effects on physiological indicators of myocardial ischemia (e.g., ST-seg ment changes) demonstrated in the controlled human exposure studies, providing coherence between the two sets of findings. Furthermore, in consideration of the epidemiological studies for cardiovascular outcomes in light of the larger body of evidence, the ISA notes that the “known role of CO in limiting O 2 availability lends biological plausibility to ischemia related he alth outcomes following CO exposure”, providing coherence between the two sets of findings.

2.5.2 Other Effects

Other health effects for which the evidence is suggestive of causal re lationships with CO exposures include some effects on the central nervous system, reproduction and prenatal development, and the respiratory system (ISA, section 2.5). High CO exposures have “long been known to adversely affect centr al nervous system (CNS) function”, although the evid ence does not include such effect s associated with exposures
close to ambient CO concentratio ns (ISA, p. 5-49). Further, the evidence indicates that healthy adults may be protected against such CNS eff ects at ambient levels through compensatory responses such as increased cardiac output and cerebral blood flow, althou gh these compensatory mechanisms may be impaired among certain grou ps, such as those with reduced cardiovascular function (ISA, section 5.3.3).2-15

. Epidemiological and toxicological studies pr ovide limited evidence of CO effects on the developing fetus and newborn infants, as summari zed in section 2.4 above. For example, some epidemiological studies have reported associatio ns of CO exposure durin g early pregnancy with pre-term births and cardiac birth defects, with animal toxicological studies providing some support and coherence for these effects at prol onged exposure concentrations ranging from 60- 500 ppm (ISA, section 5.4.3, pp. 5-80 and 5-120). Th e ISA notes that overall, there is limited though positive evidence for CO-induced effects on pre-term birth and birth defects, and weak evidence for a decrease in birth weight and fe tal growth, and infant mortality; with animal toxicological studies providing s upport and coherence for those effe cts. A clear understanding of the mechanisms by which CO may induce those e ffects is still lacking (ISA, section 2.5.3). New epidemiologic studies report positive a ssociations for CO-induced lung-related health outcomes, although interpretation is affected by uncertainties including with regard to the biological mechanism that could explain CO-indu ced respiratory outcomes (ISA, section 5.5.5). While only briefly summarized here, the evidence for the health effects identified here is further discussed and considered with regard to the review of the CO NAAQS in the Policy
Assessment.

2.6 RISK CHARACTERIZATION APPROACH

In identifying an approach to characterize th e risk of cardiovascular effects of exposures to ambient CO, we considered 1) approaches em ployed in previous assessments, 2) the currently available evidence regarding associations be tween CO concentrati ons and cardiovascular outcomes, and 3) advice from CASAC (Brai n, 2009; Brain and Samet, 2009, 2010a, 2010b). As summarized in section 1.2, the last CO NAAQS revi ew included analyses of exposure to ambient CO and associated internal dose, in terms of C OHb levels, which were used to characterize risks for the population of interest (US EPA, 1992). Th e prior risk characteri zation considered the percent of the modeled population th at exceeded COHb levels of in terest which were drawn from
the evidence of COHb levels associated with a decrease in time to exercise-induced angina in controlled human exposure studie s involving short-term (shorter than 8 hours) exposures of patients with diagnosed CAD 20 to elevated CO concentrations (US EPA, 1991). In the current review, the controlled huma n exposure studies among individuals with CAD continue to provide the clearest evidence of CO-induced effects on the cardiovascular system as the most sensitive endpoint. In contrast to epidemiological studies, human exposure studies also provide quantitativ e information linking CO exposures through COHb levels with

20 Study subjects met certain criteria w ith respect to evidence of coronary artery disease, often also called CHD or IHD.2-16

. these effects. Among these studies, the multila boratory study of Allred et al. (1989a, 1989b, 1991) continues to be the principal study inform ing our understanding of the effects of CO on individuals with pre-existing CAD at the low end of the range of COHb levels studied (US EPA, 1991, 2000, 2010a). The strength of the evidence mo re broadly continues to support the use of
COHb level as the internal dos e metric for assessing exposure to ambient levels of CO and characterizing associated potential for cardiovascular disease-related health risk. Thus, based on the strength of the evidence and the availability of quantitative information from controlled human exposure studies, this REA also focuses on estimates of the percent of the simulated atrisk population expected to experience maximum end-of-hour COHb levels of interest based on findings of those studies. As noted in section 2.5.1 above, a number of epidemiological studies are now available in
the current review that have observed associatio ns between ambient monitor CO concentrations and increases in emergency department visits and hospital admissions fo r cardiovascular disease (ISA, sections 5.2.1.9). These stud ies are coherent with the cont rolled human exposure studies (ISA, section 5.2.6), however, a numb er of uncertainties complicate their use for our purposes in a quantitative risk assessment (ISA, pp. 2-14 to 2-17, section 5.2.3). These uncertainties are
discussed and considered in greater detail in the Policy Assessm ent. Accordingly, in light of the longstanding body of evidence that links exposures to effects th rough the internal dose metric, COHb, we have characterized h ealth risk of ambient CO expos ures in this assessment using
estimates of associated COHb levels and a benchmark level approach, with benchmarks
identified in consideration of the controlled human exposure literature. 21 In drawing from the results of the contro lled human exposure studies to inform the characterization of potential CO risk in this as sessment, staff considered a number of factors,
listed below.

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

 Relative to clean-air exposure that resulte d in a mean level of 0.6% COHb (postexercise), exposures to CO resulting in pos t-exercise mean COHb levels of 2.0% and

21 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.2-17

. 3.9% 22 were shown to decrease the time re quired to induce ST-segment changes by 5.1% (p=0.01) and 12.1% (p<0.001), respectivel y. These changes were well correlated with the onset of exercise-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 exposures (Allred et al., 1989a, 1989b, 1991).

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

 Studies have not been designe d to evaluate similar effect s of exposures to increased CO concentrations eliciting average COHb le vels below the 2% target level of Allred et al. (1989a, 1989b, 1991). In addition, these st udies do not address the fraction of the population experiencing a specified health effect at various dose levels. These aspects of the evidence contributed to EPA’s conclusi on that at this time there are insufficientcontrolled human exposure data to support the development of quantitative doseresponse relationships which would be require d in order to conduct a quantitative risk assessment for this health endpoint, rather than the benchmark level approach. In drawing on this information, staff r ecognize the uncertainty associated with interpretation of COHb levels estimated to result from CO e xposure concentrations in this assessment that are much lower than the CO exposur e concentrations used in the clinical studies to elicit increases in participant’s COHb levels to target levels for the study. We have reviewed the daily maximum end-of -hour COHb estimates developed in this
REA with attention to both the total COHb leve ls, which represent the combined influence of ambient CO exposures and endogenous CO produc tion, and the ambient CO contribution to COHb levels, derived by subtracting the COHb produced in the absen ce of any CO exposure from the total COHb level (see section 6.2 belo w). Results from the model simulations are reported in terms of percent of population exp ected to experience daily maximum end-of-hour COHb levels (or ambient CO contribution to da ily maximum end-of-hour COHb levels) at or
above a series of levels that range as low as 1%. These resu lts are interpreted in the Policy Assessment document in light of pot ential health eff ects benchmarks.

22 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 after the subsequent exercise test (Allred et al., 1989a, 1989b, 1991).2-18

. With regard to total COHb, staff identif ied benchmark levels of 1.5%, 2.0%, 2.5% and
3% COHb based on consideration of the evid ence from controlled human studies of CHD patients discussed above, and is inclusive of th e range of levels cons idered in the review completed in 1994 (US EPA, 1992). This range extends below the lowe st mean COHb level (e.g., 2.0% post-exercise in Allred et al., 1989b) resulting from c ontrolled exposure to increased
CO concentration in the clinical evidence. This extension reflects comments from the CASAC CO panel on the draft Analysis Plan (Brain and Samet, 2009) and consideration of the uncertainties regarding the actua l COHb levels experienced in the controlled human exposure studies; that these studies did not include individuals with most severe cardiovascular disease; the lack of studies evaluating e ffects of controlled short-term CO exposures resulting in COHb levels below study mean 2.0-2.4% and the lack of evid ence of an effect threshold at these levels.
We note that CASAC comments on the first draft REA recommended the addition of a benchmark at 1% COHb and staff has presented re sults for this COHb level in this REA. In considering this advice, we r ecognize, however, that a level of 1% COHb overlaps with the upper part of the range of endogenous levels in he alth individuals as ch aracterized in the ISA (ISA, p. 2-6) and with the upper pa rt of the range of baseline COHb levels in the study by Allred et al. (1989b, Appendix B). As a result, while no ting population dose estimates in relation to this level, we have not placed weight on this leve l as a potential health effects benchmark in
discussions of the results below and in the Policy Assessment document. We additionally consider the assessment results in light of the multi-laboratory clinical study conclusions regarding respons e to specific increases in C OHb level over the subjects’ preexposure or air exposure, with the increased C OHb resulting from short-term controlled CO exposure exposures of persons with pre-existi ng CAD (ISA, section 5.2.4 ; Allred et al., 1989a, 1989b, 1991). 23 For this, we present the percentage of the simulated populations estimated to experience ambient CO contributi on or increment to daily maximum end-of hour COHb levels greater than a series of levels that range as low as 1%. These results are interpreted in the Policy Assessment document in light of potential health eff ects benchmarks, which for this ambient contribution (or increment) to daily maximum e nd-of-hour COHb levels include the range from

23 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).2-19

. 1.4% up to 2.4%, COHb increments associated with reduced time to exercise-induced angina and ST-segment change in those studies.
The benchmark levels identified are used to in terpret COHb levels estimated to occur in the modeled population in response to exposures to ambient CO in different air quality scenarios in light of the evidence discussed above for car diovascular effects observed in individuals with CHD when exposed to CO. More specifically, we have estimated the number of persons and percent of the simulated at-risk population expect ed to experience COHb levels below each of these potential health effect benchmark levels as a result of ambient CO exposures associated with a set of air quality scenar ios employed to inform the curren t review of the CO NAAQS (see chapter 5 below). As noted in chapter 1 above, given the significant time constraints of this review, results are provided in this document without substantial interpretation. Rather, discussion of health risk and public health impli cations of these results in the context of the NAAQS review is provided in the Policy Assessment.

2.7 KEY OBSERVATIONS

Presented below are key observations for this conceptual overview of the assessment of ambient CO exposure and health risk.

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

 Microenvironments influenced by on-road mob ile sources are important contributors to ambient CO exposures, particularly in urban areas. Where present, other (nonambient) CO sources can also be important influences on total CO exposure and on the impact of ambient CO exposure on COHb levels.

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

 An individual’s COHb leve ls reflect their endogenous CO production, as well as CO taken into the body during exposure to am bient and nonambient CO sources. CO uptake into the bloodstream during exposure is influenced by a number of variables including internal levels of CO and COHb, such that net uptake may be lower or negligible in instances wher e a preceding exposure has been substantially higher than the current one. Thus, the magnitude of the change in COHb level in response to ambient CO exposure may decrease with th e presence of concurrent or preceding nonambient CO exposure. .

 Individuals with CHD are th e population with greatest sus ceptibility to short-term exposure to CO, and the population for which the current evidence indicates health effects occurring at the lowest exposures. Th e evidence further indicates a potential for other underlying cardiovascular conditions, partic ularly other types of heart disease, to2-20

. contribute susceptibility to CO effects. Ot her populations potentia lly at risk include those with diseases such as chronic obstru ctive pulmonary disease (COPD), anemia, or diabetes, and those in prenatal or elderly life stages.

 Cardiovascular effects are the category of health effects for which the evidence is strongest and indicative of a likely causal relationship with relevant short-term CO exposures, particularly for people with CHD. Other endpoints for which the evidence is suggestive of causal relationships incl ude effects on the cent ral nervous system, reproduction and prenatal developmen t, and the respiratory system.

 The specific cardiovascular effects occurring at the lowest COHb levels studied in CHD patients are reduced time to exercise-induced angina and other markers of myocardial ischemia, in particular, speci fic changes to the ST-segment of an electrocardiogram.

 Risk is characterized in this REA through ev aluation of COHb estimated in simulations involving ambient CO exposures experien ced by two target populations: (1) individuals with CHD (incl uding undiagnosed CHD persons) and (2) individuals with HD, including CHD (diagnosed and undiagnosed).

 Two types of COHb estimates are considered for the two target populations: (1) daily maximum end-of-hour COHb levels and (2 ) ambient contribution to daily maximum end-of-hour COHb levels (i.e., the change in COHb associated with ambient CO exposure alone).

 Results from simulations are reported in te rms of percent of th e simulated at-risk population expected to expe rience daily maximum end-of-hour COHb levels (or ambient CO contribution to daily maximum end-of-hour COHb levels) at or above a series of levels that range as low as 1%. These results are interpreted in the Policy Assessment document in light of poten tial health effect s benchmarks.

 For daily maximum end-of-hour COHb le vels (absolute), these benchmarks range from 1.5%, which is below the lo west study mean COHb level resulting from experimental CO exposure in cont rolled human exposures of subjects with CAD, up to 3.0%, a level within the range associated with effects in those studies. For ambient contribution to daily maximum end-of-hour COHb levels, the comparison benchmarks incl ude the range from 1.4% up to 2.4%, which are the COHb increments associ ated with effects in those studies.

 Beyond the at-risk populations a nd myocardial ischemia-related effects that are the focus of this quantitative REA, the curre nt evidence regardin g other potentially susceptible populations and other health e ffects associated with CO exposures is discussed and considered with regard to the review of the CO NAAQS in the Policy Assessment.2-21

. 2.8 REFERENCES
Abi-Esber L and El-Fadel M. (2008). In-vehicle CO ingression: validation through field measurements and mass balance simulations. Sci Total Environ . 394:75-89.

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

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

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

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

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

. 91:89-132.

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

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

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

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

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

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

Brain JD and Samet JM. (2010b). Letter from Drs. J.D. Br ain and J.M. Samet to Administrator Lisa Jackson. Re: Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO) Primary National Ambient Air Quality Standards: Seco nd External Review Draf t. EPA-CASAC-10-012. May 19, 2010.

Bruinen de BruinY, Hanninen O, Carter P, Maroni M, Kephalopoulos S, Scotto De Marco G, Jantunen M. (2004). Personal carbon monoxide exposure levels: contribution of local sources to exposures and microenvironmental concentrations in Milan. J Expo Anal Environ Epidemiol . 14:312-322.2-22

. Chan C, Ozkaynak H, Spengler J, Sheldon L. (1991). Driver exposure to volatile organic compounds, CO, ozone, and NO 2 under different driving conditions. Environ Sci Technol . 25(5) :964-972.

Chang LT, Koutrakis P, Catalano PJ, Suh HH. (2000). Hourly personal exposures to fine particles and gaseous pollutants - results from Baltimore, Maryland. J Air Waste Manage Assoc . 50:1223-1235.

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

Flachsbart P. (1999). Human exposure to carbon monoxide from mobile sources. Chemosphere – Global Change Science . 1:301-329.

Petersen WB and Allen RA. (1982) Carbon monoxi de exposures to Los Angeles area commuters. J Air Pollution Control Assoc . 32:826-833.

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

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

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

. 53:388-397.

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

. 42:1603-1608.

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

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

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

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

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

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

US EPA. (1991). Air Quality Criteria For Carbon Mono xide. Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria an d Assessment Office; report no. EPA/600/8-90/045F.2-23

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

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

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

US EPA. (2010a). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency, Washington, DC, report no. EPA/600/R-09/019F. Available at: http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_isa.html.

US EPA. (2010b). Policy Assessment for the Review of the Carbon Monoxide National Ambient Air Quality Standards, External Review Draft. Office of Air Qu ality Planning and Standards Staff Paper, report no. EPA-452/P-10-005.3-1 

### 3 AIR QUALITY CONSIDERATIONS

Ambient air quality data can be used as an indicator of exposure or used in conjunction
with other information to estimat e exposure concentrations. How well the ambient air quality is represented in a particular location is depende nt on a number of factor s including the ambient monitoring network design relative to the spatial and temporal characteristics of the pollutant as well understanding the concentration contribution fr om important local s ource emissions. This chapter summarizes findings about the current air quality conditions and their temporal and spatial distribution, with particul ar focus on aspects informative to the design and conduct of this assessment and including desc riptions of CO measurem ent methods, monitor siting requirements, and monitor locations (section 3.1) . Section 3.2 then draws upon the information presented in sections 3.1, among other data, to select ambient air quali ty/study locations most useful in meeting the objectives of the REA. Finally, key ob servations of the chapter are presented in section 3.3.

3.1 AMBIENT CO MONITORING

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

3.1.1 Monitoring Network

Ambient CO concentrations are measured by monitoring networks that are operated by state and local monitoring agencies in the US, and are funded in part by the EPA. The main network providing ambient data for use in comp arison to the NAAQS is the State and Local Air Monitoring Stations (SLAMS) network. The su bsections below provide specific information regarding the methods used for obtaining ambien t CO measurements and the requirements that apply to states in the design of the CO network. Minimum monitoring requirements for CO were revoked in the 2006 revisions to ambient monitoring requirements (see 71 FR 61236, October 17, 2006). This action was made to allow for reductions in measurements of some criteria pollutants (CO, SO 2 , NO 2 , and Pb) where the
current measured levels were all well below th e applicable NAAQS. CO monitoring activities3-2 have been maintained at some SLAMS and these measurements of CO at these monitoring sites are required to continue until discontinuation is approved by the EPA Regional Administrator. CO monitors are typically sited to repr esent one of the following spatial scales. 1

 Microscale: Data represent concentrations wi thin a 100 meter (m) radius of the monitor. For CO, micros cale monitors are sited 2 – 10 m from a roadway. Measurements are intended to represent th e near-road or street canyon environment.

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

 Neighborhood scale: Data represent concentrations averaged over areas defined by 0.5 – 4.0 km radii. Measurements are intended to represent extended portions of a city. In addition to monitoring required for de termining compliance with the NAAQS, the EPA is currently in the process of implementing plans for a new network of multi-pollutant stations, called NCore, that is intended to m eet multiple monitoring objectives. A subset of the SLAMS network, these NCore stat ions are intended to address integrated air quality management needs to support long-term trends an alysis, model evaluation, health and ecosystem studies, as well as the more traditional objectiv es of NAAQS compliance and Air Quality Index reporting. 2 The complete NCore network, required to be fully implemented by January 1, 2011, will consist of approximately 63 urban and 20 rural stations and will include some existing
SLAMS sites that have been modified to in clude additional polluta nt and meteorological
measurements. Each state will contain at leas t one NCore station, and 46 of the states plus Washington, D.C. will have at least one urba n station. CO will be measured using high sensitivity monitors (see section 3.1.2 below), as will SO 2 , NO, and NO Y
. 3 The majority of NCore stations will be sited to represent nei ghborhood, urban, and regional scales, consistent with the NCore network design objective of representing exposu re expected across urban and
rural areas in locations that are not dominated by local sources.

3.1.2 Analytical Sensitivity

To promote uniform enforcement of the air quality standards set forth under the CAA, EPA has established provisions in the Code of Federal Regulations (CFR ) under which analytical methods can be designated as federal reference methods (FRMs) or federal equivalent methods (FEMs). Measurements for determinations of NAAQS compliance must be made with FRMs or

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).3-3 FEMs. 4 Specifications for CO monitoring are designe d to help states utilize equipment that has met performance criteria utilized in the FRM or FEM approval process; operational parameters are documented in 40 CFR Part 53, Table B-1. Gi ven the levels of the current CO NAAQS (35 ppm, 1-hour; 9 ppm, 8-hour average), the requ ired 1.0 ppm lower detectable limit (LDL) 5 is well below the NAAQS levels and is therefore de termined sufficient for demonstration of compliance. However, with ambient CO levels now routinely near or below 1 ppm, there is greater uncertainty in a larger portion of the distri bution of monitoring da ta because a large percentage of these measurements are below the LDL of conventional mon itors. For this reason, a new generation of ambient CO monitors has be en designed that provides measurements with improved sensitivity at or below the typical ambien t CO levels measured in most urban and all rural locations. Additionally, th e higher sensitivity CO measur ements are needed to support additional objectives such as validating the input s to chemical transport models and assessing the role of transport between ur ban and rural areas because policy relevant background CO concentrations on the order of 0.1 ppm are we ll below the LDL of conventional monitors. Newer GFC instruments have been designed for au tomatic zeroing to minimize drift (US EPA, 2000). Currently, a total of 13 appr oved FRMs are in use in the SLAMS network, based on a retrieval of data reported between 2005 and 2009. Among these methods, nine are “legacy” monitors with a federal method detection limit (MDL) 6 given as 0.5 ppm according to records in EPA’s Air Quality System (AQS). 7 As discussed in the ISA (US EPA, 2010), many of the reported concentrations in recent years are near or below these MDLs (ISA, section 3.5.1.2). Four of these new methods are high sensitivit y methods with a federal MDL of 0.02 ppm and there is a growing body of ambient data from high sensitivity CO instruments becoming available. Among newer gas filter correlation (G FC) high sensitivity inst ruments, manufacturerdeclared LDLs range from 0.02 – 0.04 ppm, w ith 24-hour zero drift varying between 0.5% within 1 ppm and 0.1 ppm, and precision varyi ng from 0.5% to 0.1 ppm. EPA performed MDL testing on several high sensitivity CO monito rs in 2005 and 2006 following the 40 CFR Part 136

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 correlation (GFC) methodology. An extensive and comprehensive review of NDIR, GFC, and alternative, non-FRM techniques for CO detection was included in the 2000 CO AQCD (US EPA, 2000).
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).3-4 procedures. Those tests demonstrated MDLs of approximately 0.017 – 0.018 ppm, slightly below the stated LDL of 0.02 – 0.04 ppm. Based on a retrieval of data reported to AQS for the time period between 2005 and 2009, a total of 36 high sensitiv ity CO monitors have reported data w ith the majority of these monitors currently active. Most of these active monitors are associated with th e implementation of the NCore network. The extent to which high sens itivity monitors become integrated into nonNCore SLAMS stations, however, will depend on the availability of funding for states to replace operating legacy CO monitors as well as the possibility that monito ring requirements for CO might either encourage or re quire increased sensitivity.

3.1.3 General Patterns of CO Concentrations

As discussed in the ISA, the spatial and tem poral patterns of ambient CO concentrations are heavily influenced by the patterns associat ed with mobile source emissions (ISA, section 3.2.1). Based on the 2002 National Emissions Inve ntory (NEI; US EPA, 2006), on-road mobile sources comprise about half of the total an thropogenic CO emissions, though in metropolitan
areas of the US the contribution is as high as 75% of all CO emissions due to greater motor vehicle density. For example, emissions in Denver county originating from on-road mobile sources is about 71% of total CO emissions (I SA, section 3.2). When considering all mobile sources (non-road and on-road co mbined), the contribu tion to total CO emi ssions is roughly 80% nationwide and can be higher in some metropolit an areas. Again using Denver County as an example, all mobile sources combined contribute to about 98% of the total CO emissions in the county. Temporally, the nationa l-scale anthropogenic CO em issions have decreased 35% between 1990 and 2002. Nearly all the national-leve l CO reductions since 1990 are the result of
emission reductions in on-road vehicles (ISA, Figure 3-2). Nearly 400 ambient monitoring stations re port continuous hourly averages of CO concentrations across the US. Over the pe riod 2005-2007, 291 out of 376 monitors met a 75% completeness requirement, spread among 243 countie s, cities, or municipalities (ISA, section 3.4.2.2). No violations of the NAAQS were repor ted at these monitoring sites during this time period. For example, in 2007, none of the moni tors reported a second-highest 1-hour CO concentration above 35 ppm, the level of th e current 1-hour NAAQS, while only two sites reported 2 nd highest 1-hour CO concentrations between 15.1 and 35.0 ppm (ISA section 3.5.1.1). Only five counties reported a 2 nd highest 8-hour CO concentra tion of 5.0 ppm or higher.
The current levels of ambient CO across the US reflect the steady declines in ambient concentrations that have occurred over the past several years. On average across the US the
decline has been on the order of 50% since the early 1990s (ISA, Figure 3-34). As an example, Figures 3-1 illustrate the trends observed in Denver and Los Ange les ambient concentrations, for3-5 several selected monitors within the urba n core of each area during 1993 through 2008. Note that there is a significa nt decrease in the 2 nd highest 1-hour and 8-hour average CO
concentrations since the last review. Ambient monitor siting characteristics can influence ambient CO concentrations. Microscale and middle scale monitors are commonly used to measure significant local source impacts, while neighborhood and urban scale mo nitors are designated for population-oriented monitoring (40 CFR Part 58 Appendix D). As CO concentrations primarily originate from vehicle emissions, the microscale and middle scale da ta can be a useful indicator of near-road air quality. Such data analyzed in the ISA we re concluded to be consistent with hourly concentrations reported in the literature for the near-road environment in the US (ISA, p. 3-57). Further, when considering monitoring scale acr oss ambient monitors in the US, the median hourly CO concentration measured at microscale monitors was about 25% higher than at middle scale monitors and 67% higher than at neighbo rhood scale monitors (ISA, Table 3-12). In general, similar patterns were present in the 1-hour daily max, 1-hour da ily average, and 8-hour daily max distributions (ISA, Table 3-12). Thes e patterns are also consistent with findings presented by other researchers regarding the rela tive decrease in concentration with increasing distance from roadways, though the magnitude of the relationship can vary. Two studies summarized in the ISA (Zhu et al., 2002; Bald auf et al., 2008) indicate that near-road CO
concentrations measured within 20 meters of an interstate highway can range from 2 – 10 times greater than CO concentrations measured as far as 300 meters from a major road possibly influenced by wind direction and on-road ve hicle density (ISA, Figures 3-29 and 3-30).3-6Figure 3-1.Spatial and temporal trends in the 2ndhighest 1-hour (top) and 8-hour average (bottom) CO ambient monitoringconcentrations in Denver, Colorado (left) andLos Angeles, California (right), Years 1993 – 2008.0246810121416182022

2 nd Highest 1-hour Average CO (ppm)080310002080310013080310014080590002060370113060371002060371103060371201060371301060372005060374002060590001060591003060595001Denver Los 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 2008Year0603701130603710020603711030603712010603713010603720050603740020605900010605910030605950013-7 While recognizing that monitoring site attribut es are not available fo r all monitors in the
current network and that data for some at tributes may not refl ect current conditions, 8 the ISA also evaluated the average annual daily traffic ( AADT) data available for each ambient monitor. The ISA noted that only two microscale monitors and two middle scale m onitors in the existing
network are sited at roads with ≥ 100,000 AADT, although it is not uncommon for roadways
within Consolidated Statistical Areas (CSAs) to have severa l roads with AADT > 100,000. The AADT ranged from 160,000-178,000 for the near-road monitors used in the aforementioned study by Zhu et al. (2002) where CO concentrations were up to 10 times greater than monitors sited at 300 m from a major road. 9 Existing microscale sites n ear roads having only moderate traffic count data (<100,000 AADT) may record c oncentrations that are not substantially different from those obtained from neighbor hood scale measurements (ISA, section 3.5.1.3). Within a specific urban area, however, cons ideration of only monitor scale or other attributes reported in AQS, such as AADT estim ates, may be of limited use in efforts to characterize the monitoring data as to its repres entation of local near-road CO concentrations. For example, of the five monitors meeting a 75% completeness criteri on in the Denver CSA, three were microscale and two were neighborhood scale (ISA, section 3.5.1.2). While one of the
microscale monitors sited within downtown De nver measured the highest hourly ambient CO concentrations (ID 080310002), another microsca le monitor (ID 080130009) located outside the urban core measured the lowest hourly ambient CO concentrations (ISA, Figure 3-19). Further, the AADT estimate for a major road near the mi croscale monitor within the urban core (ID 080310002, AADT=17,200) was lower than that listed for the microscale monitor outside the urban core (ID 080130009, AADT=20,000) (ISA, Table A- 2). And, a third microscale monitor
located 1.3 km from monitor ID 080310002, within the urban core, and measuring somewhat lower CO concentrations (but not lower than the monitor outside the urban core) had only 500 AADT listed for the nearest major road. It is like ly that the higher CO concentrations measured
at the downtown monitor reflect influences of the denser ro adway network surrounding that monitor in the downtown Denver area (ISA, Figure 3-17). 10 Thus, to better characterize the representati on of near-road CO concentrations for many of the existing ambient monitors, additional analys es would need to be performed that go beyond the AQS standard list of monitoring site attri butes. Such analyses c ould include local-scale

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 the monitoring scale for 16 monitors (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.
10 We also recognize there is uncertainty in how we ll the AQS estimated AADT reflects current conditions at this monitor site.3-8 meteorological data, using GIS to determine de tailed monitor-to-roadway characteristics (e.g., monitor distance from roadways, the number and type of roads within close proximity of the monitor), and obtaining current tra ffic count data for all roads.
Carbon monoxide also exhibits ho urly variability within a day, with two distinct temporal patterns noted for weekdays and weekends (ISA, section 3.5.2.2). The diurnal variation is inherently linked to the typical commute times-o f-day that occur within urban locations. In general, in recent years observed mean and medi an concentrations for all hours of the day and
across all monitors within urban areas de monstrated limited variability, however 90 th and 95 th percentile hourly concentrations generally exhibit early-morni ng and late afternoon peak CO concentrations during weekdays (ISA, Figure 3-36) . The weekend diurnal variation in ambient
CO concentrations was much lower than that oc curring during weekdays as expected due to the
relative absence of commuter vehi cle traffic during the morning and evening hours of the day. Most urban areas have relatively stable concentrations throughout weekend days at each of the selected percentiles, though a fe w locations (e.g., Phoenix, Los Ange les, Seattle) did have a more pronounced late afternoon p eak (ISA, Figure 3-37). We investigated local hourly variation at two separate CO monitors located in Denver and Los Angeles to illustrate similar trends. The monitor in Denver is a microscale monitor located in downtown Denver and expected to reflect concentrations resulting from dense downtown traffic in that city; it is the monitor measuring the highe st ambient CO concentrations in the Denver area. The monitor in Los Angele s is a middle scale monitor located in Lynwood; it is also the monitor measuring the highest ambient CO concentra tions in the Los Angeles area. Figure 3-2 indicates that on average, peak ambien t CO concentrations th at occur during typical commute times in Denver ranged from about 1 to 5 ppm during weekdays in 1995, while currently, ambient CO concentrations during mo rning and afternoon commutes range from about 1 to 2 ppm. Weekends tend to exhibit less va riability throughout the day. On average, CO ambient concentrations generally ranged from 1 to 3 ppm throughout the day in 1995, while current weekend concentrations are less than 1 pp m for most hours of the day. In Los Angeles,
both the concentration levels and variability are greater than when compared with similar years and times of day in Denver (Figure 3-3). Peak ambient CO concentrations are more prominent during morning commutes and generally ra nged from 2 to 10 ppm in 1995, while when considering more recent ambient monitor con centration (2006), most commuting times are associated with hourly concentrations ranging fr om between 1 and 5 ppm. The weekend profile exhibits some variation when considering either year, with maximum concentration levels and variability commonly exhibited during the late-night/early morning hours.3-9Figure 3-2.Diurnal distribution of 1-hour CO concentrationsin Denver (Monitor 080310002) by day-type (weekdays-left;weekends-right), Years 1995 (top) and 2006 (bottom).The box encompasses concentrations from the 25thto 75thpercentiles or Interquartilerange (IQR), the line bisecting the box is the median, the solid dot within the box is themean, the whiskers represent 1.5 times theIQR, and concentrations outside the whiskers are indicated by open circles.Note there are differences in the y-axisscale for the two monitoring years.3-10Figure 3-3.Diurnal distribution of 1-hour CO concentrations inLos Angeles (Monitor 060371301) by day-type (weekdaysleft; weekends-right), years 1997 (top) and 2006 (bottom).The box encompasses concentrations from the 25thto 75thpercentiles or IQR, the line bisectingthe box is the median, the solid dot within the box is the mean, the whiskersrepresent 1.5 times the IQR, andconcentrations outside the whiskers are indicated by open circles.Note there aredifferences in the y-axis scale for the two monitoring years.3-11 3.1.4 Policy-Relevant Background Concentrations

EPA has generally conducted NAAQS risk assessm ents that focus on the risks associated with ambient levels of a pollu tant that are in excess of po licy-relevant background (PRB). Policy-relevant background levels are defined, for purposes of this document, as concentrations of a pollutant that would occur in the US in the absence of anthropogeni c emissions in the US, Canada, and Mexico. Over the continental US (CONUS), th e 3-year (2005-2007) average CO PRB concentration is estimated to range from 0.118 to 0.146 ppm (ISA, section 3.5.4). Outside the CONUS, the 3-year average CO PRB in three Al askan sites is estimated to range from 0.127 to 0.135 ppm, and from 0.095 to 0.103 ppm in two Ha waiian monitoring locations. The estimated PRB concentrations exhibit significant within -location seasonal vari ation, with minimum
concentrations observed in the summer and fall and maximum concentrati ons occurring in the winter and spring. For example, PRB in two Calif ornia sites is estimated to range from about 0.085 to 0.170 ppm, and PRB in one site in Colorado ranged from about 0.080 to 0.140 ppm (ISA, Figure 3-43).
Given that ambient concentrati ons of interest in this REA are well above the estimated PRB levels discussed above and, thus the co ntribution of PRB to overall ambient CO concentrations is very small, EPA is characte rizing risks associated w ith ambient CO levels without regard to estimated PRB levels.

3.1.5 Within-Monitor CO Concentration Trends

The previous section addressed general trends in ambient concentrations. Of particular
interest in this assessment is how concentrations have changed at a specific monitor over time. This is an important considera tion in determining how best to address alternative air quality conditions. These alternative air quality condi tions are useful in evaluating how varying distributions of air quality might affect different exposure scen arios. In other recent NAAQS
reviews for NO 2 (US EPA, 2008) and SO 2 (US EPA, 2009) it was determined that the relationship between high concentration and low concentration years of ambient monitoring data was mainly proportional (Rizzo, 2008, 2009), that is all concen trations across the entire distribution at a single monitor changed in e quivalent amounts over time. We needed the relationship to adjust current ai r quality because, at the time of the NAAQS reviews, the current
ambient NO 2 and SO 2 concentrations were far below that expected to just meet the current
standards. Knowledge of this relationship for ambient CO concentrations is also needed to develop alternative air quality conditions fo r use in some of the exposure scenarios investigated in this
REA. Ambient CO concentration data were obt ained from AQS for several monitors in Los3-12 Angeles for two years: 1997 – representing a hi gh concentration year and 2006 – representing a low concentration year. In Denver, the year 199 5 was selected to represent a high concentration year, while 2006 was selected to represent a lo w concentration year. As was done for prior NAAQS reviews (Rizzo, 2008, 2009), 75% completeness criteria were applied in selecting valid monitoring data, 11 the 1-hour daily maximum concentration for each day was identified, and the 0 through 100 th percentiles of the distribution were cal culated (by 1% increments). Then the percentiles for the low concentration year were pa ired and plotted against those calculated for the high concentration year at each individual monito r. Figure 3-4 illustrates the results for four monitors in Denver, while Figure 3-5 illustrates a similar comparison for four monitors in Los Angeles. A simple linear regression was also ca lculated and plotted, al ong with the regression slope, intercept, and fit statistic (R 2 ). As shown by the relationsh ips and fit statistics in each location, there is a very strong line ar relationship when co mparing each year of data within each monitor. In general, the regr ession slopes and intercepts are si milar for monitors within each location, indicating a similarity in the rate of change in concentr ation occurring at the monitors
within each location. There are however, at most of the sites, instances where upper percentile values deviate from linearity (i.e., ≥ 99th percentile 1-hour dail y maximum concentration). Concentrations deviating above the best fit line indicate that these upper percentile
concentrations have not declined at the same rate as the middle of the distribution at that particular monitor (e.g., Figure 3-5, monitor ID 060370113). In addi tion, many of the estimated regression intercepts are positive, though most are ≤ 0.1 ppm. A positive intercept also indicates a larger percentage decline be tween high and low year concentr ations in the upper end of the
distribution relative to th at of the middle and lower percentiles. However, given that there are a limited number of points deviating from linearity a nd that regression slopes and intercepts are
similar for most of the monitors within each lo cation and having mainly small intercepts, this analysis provides adequate support for ad justing air quality by a proportional method.

11 Monitoring sites first had to have 75% of hours reported in each day to be considered as a valid day. Then each quarter had to have at least 75% valid days to be complete and all four quarters had to be complete across the year for the site to be retained.3-13Figure 3-4.Comparison of a high concentration year (1995) versusa low concentration year (2006)at four ambient monitorsin Denver. The 0 through 100thpercentiles of the 1-hour daily maximum CO are plotted for each monitor-year.080310002y = 0.2846x + 0.2799R2= 0.97610123456780 5 10 15 20 251995 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm) (0-100 percentile)080310013y = 0.3122x + 0.0931R2= 0.97860123450 3 6 9 12 151995 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm) (0-100 percentile)080310014y = 0.3487x + 0.1085R2= 0.9932012340 2 4 6 8 10 121995 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm) (0-100 percentile)080590002y = 0.349x + 0.0451R2= 0.9825012340 2 4 6 8 10 121995 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm) (0-100 percentile)3-14Figure 3-5.Comparison of a high concentration year (1997) versusa low concentration year (2006)at four ambient monitorsin Los Angeles. The 0 through 100thpercentiles of the 1-hour daily maximum CO are plotted for each monitoryear.060370113y = 0.4105x + 0.143R2= 0.992012340123456781997 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm) (0-100 percentile)060371103y = 0.4112x - 0.0053R2= 0.9901012340 2 4 6 8 101997 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm) (0-100 percentile)060371301y = 0.4106x + 0.1019R2= 0.994502468100 5 10 15 201997 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm) (0-100 percentile)060591003y = 0.4422x - 0.0148R2= 0.9927012340123456781997 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm) (0-100 percentile)3-15 We were also interested in estimating the w ithin-monitor temporal variability for three air quality metrics. The first air quality metric was the current design value, that is, the 2 nd highest 8-hour average CO concentration in a year. The next two air quality metrics we compared were
the 99 th percentile 1-hour and 8-hour daily maximu m CO concentrations. We evaluated the within-monitor temporal variability using two co mparisons: one using historical versus current air quality data and the other comparing year-t o-year variability of these upper percentile
concentrations within the air quality distribution. Two thr ee-year periods (1995-1997 and 2005- 2007) were chosen to represent hi storical and recent air quality, respectively. We limited the analysis to four monitors within the Denver CS A and ten monitors within the Los Angeles CSA, with all monitor data meeting standard requireme nts for data completeness. In addition to the temporal evaluation of the air quality metrics, a limited analysis of the spatial variability across the two periods is also provided for th e selected monitors in each area. Tables 3-1 and 3-2 provide results for th e current design valu e in Denver and Los Angeles, respectively. As shown by the Tables, ther e is a wide range in the temporal variability
of the 2 nd highest 8-hour average CO concentrati on in both locations, however, the relative variability, as indicated by the coefficient of variation (COV), 12 is slightly less for the recent air quality when compared with the historical air quality. For example, in Denver the COV ranges from 4-27 percent (mean = 13%) for the historic al data, while the recent data temporal COV ranges from 3-23 percent (mean = 10%) (Table 3-1) . Note also that the design value decreases with increasing monitoring year over the select ed three-year periods in both Denver and Los Angeles (and of course is consistent with Figures 3-1 and 3-2), t hough this trend is more prevalent when considering the historical air qua lity data. In addition, the magnitude of the spatial variability tends to vary from year-t o-year as indicated by the COV, though there are differences in the historical versus recent air qua lity pattern by location. In Denver, there was generally less spatial variability in the 2 nd highest 8-hour concentra tion when comparing the recent to historical air quality data. There was no apparent trend in year-to-year spatial variability for Los Angeles as both air quality periods had a mean COV of about 31% (Table 3- 2). Similar temporal trends are observed with the 99 th percentile 1-hour daily maximum concentrations when comparing historical vers us recent air quality (Tables 3-3 and 3-4 for Denver and Los Angeles, respectively). The tempor al variability in the recent air quality data was also less than that of the pr ior air quality metric (i.e., the 2 nd highest 8-hour average), averaging about 4% COV in Denver and 7% COV in Los Angeles across that 3-year period. The year-to-year spatial variability for this metric is also consistent with that stated above. In

12 The COV is calculated here by dividing the standard deviation (std) by the mean, then multiplying by 100.3-16 Denver, the COV on average was less for the recent air quality when compared to that of the
historical data. There was little difference in the year-to-year sp atial variability in Los Angeles when considering the two air quali ty periods. Results for the 99 th percentile 8-hour daily
maximum concentrations were more similar to the results for the 2 nd highest 8-hour average
concentration than the 99 th percentile 1-hour daily maximum (Tables 3-5 and 3-6, for Denver and Los Angeles, respectively).3-17Table 3-1.Within monitor temporal variability inDenver using historical (1995-97) and recent (2005-07) air quality data -2ndhighest 8-hour average.Historical Air Quality – 2ndhighest 8-hour average Recent Air Quality – 2ndhighest 8-hour averageMonitor1995 1996 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 qualitydata - 2ndhighest 8-hour average.Historical 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 0.9COV25324531303233313-18Table 3-3.Within monitor temporal variability inDenver using historical (1995-97) and recent (2005-07) air quality data –99thpercentile 1-hour daily maximum.Historical Air Quality – 99thpercentile 1-hour daily maximum Recent Air Quality – 99thpercentile 1-hour daily maximumMonitor1995 1996 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 qualitydata – 99thpercentile 1-hour daily maximum.Historical 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 1.3COV26374232323538343-19Table 3-5.Within monitor temporal variability inDenver using historical (1995-97) and recent (2005-07) air quality data –99thpercentile 8-hour daily maximum.Historical Air Quality – 99thpercentile 8-hour daily maximum Recent Air Quality – 99thpercentile 8-hour daily maximumMonitor1995 1996 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 qualitydata – 99thpercentile 8-hour daily maximum.Historical 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 0.8COV25354231253131293-20 3.2 STUDY AREAS SELECTED FOR CURRENT CO REA

We identified several criteria to select th e exposure assessment study areas drawing from
information discussed in the earlie r sections of this Chapter and additional scientific evidence in the ISA. We selected Denver and Los Angeles as areas to focus the current assessment because (1) both cities have been included in prior CO NAAQS exposure assessments and thus serve as an important connection with past assessments, (2 ) they have historically had among the highest CO ambient concentrations among urban areas in the U.S., and (3) Denver is at high altitude and represents a scenario of interest due to the potentially increased su sceptibility of visitors to high altitude locations from exposure to CO. In addition, of ten urban areas across the US having monitors meeting a 75% completeness crit eria, the two locations were ranked 1 st (Los Angeles)
and 2 nd (Denver) regarding pe rcent of elderly population with in 5, 10, and 15 km of monitor locations, and ranked 1 st (Los Angeles) and 5 th (Denver) regarding numbe r of 1-hour and 8-hour daily maximum CO concentration measurements (ISA, section 3.5.1.1).

3.3 KEY OBSERVATIONS

Presented below are key observations resul ting from the air quality considerations.

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

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

 The currently available information for CO m onitors indicates that siting of microscale and middle scale monitors in the current ne twork is primarily associated with roads having moderate traffic density (<10 0,000 AADT), however, factors other than reported AADT (e.g., orientation with regard to dense urban roadway networks) can contribute to sites reporting higher CO concentrations.

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

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

 Historical trends in ambient monitoring data indicate that at individual sites, ambient concentrations have generally decreased in a proportional manner. This comparison included air quality distributi ons with concentrations at or above the current 8-hour standard and those reflecting current ( as is ) conditions.3-21  The temporal variability in selected uppe r percentile ambient concentrations (e.g., 99 th percentile 1-hour daily maximum) at individua l monitors in Denver and Los Angeles is relatively small across a three-year monito ring period, particularly when considering recent air quality. Much of the within-monitor temporal variability is due to a trend in decreasing concentration from year-to-year.

 There is greater spatial variability in select ed upper percentile ambient concentrations (e.g., 99 th percentile 1-hour daily maximum) at ten selected monitoring sites in Los Angeles when compared with four selected monitoring sites Denver, particularly when considering the recent air quality.3-22 REFERENCES

Baldauf R, Thoma E, Hays M, Shores R, Kinsey J, Gullett B, Kimbrough S, Isakov V, Long T, Snow R, Khlystov A, Weinstein J, Chen FL, Seila R, Olson D, Gilmour I, Cho SH, Watkins N, Rowley P, Bang J. (2008). Traffic and meteorological impacts on near-road air quality: Su mmary of methods and trends from the Raleigh near-road study. J Air Waste Manag Assoc . 58: 865-878.

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

Rizzo M. (2008). Investigation of how distributions of hourly nitrogen dioxide concentrations have changed over time in six cities. Nitrogen Dioxide NAAQS Review Docket. Docket ID no. EPA–HQ–OAR–2006– 0922). Available at http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_rea.html

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

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

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

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

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

US EPA. (2010). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency, Washington, DC, report no. EPA/600/R-09/019F. Available at: http://www.epa.gov/ttn/naaqs/sta ndards/co/s_co_cr_isa.html.

Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C. (2002). Study of ultrafine particles near a major highway with heavy-duty diesel traffic. Atmos Environ . 36:4323-4335.4-1 

### 4 OVERVIEW OF APEX MODELING SYSTEM FOR ESTIMATING CO EXPOSURES AND COHB DOSE LEVELS

4.1 PURPOSE

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

4.2 MODEL OVERVIEW

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

1 Additional information on the TRIM modeling system , as well as downloads of the APEX Model, user guides (US 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.4-2 4.3 MODEL HISTORY AND EVOLUTION

APEX was derived from the National Am bient Air Quality Standards (NAAQS) Exposure Model (NEM) series of models. Th e NEM series was developed to estimate population exposures to the criteria polluta nts (e.g., CO, ozone) . In 1988, OAQPS first incorporated probabilistic elem ents into the NEM methodology and used activity pattern data based on available human activity diary studies to create an early versi on of probabilistic NEM for ozone (i.e., pNEM/O 3 ). In 1991, a probabilistic versi on of NEM was developed for CO (pNEM/CO) that included a one-compartment mass-balance model to estimate CO
concentrations in indoor microenvironments. Th e first application of this model to Denver, Colorado is summarized in Johnson et al. ( 1992). Between 1999 and 2001, updated versions of pNEM/CO (versions 2.0 and 2.1) were developed that relied on detailed activity diary data compiled in EPA’s Consolidated Human Activ ities Database (CHAD) (McCurdy et al., 2000; US EPA, 2002) and enhanced algorithms for si mulating gas stove usage, estimating alveolar ventilation rate (a measure of human respir ation), and modeling home-to-work commuting patterns. A draft report by J ohnson et al. (2000) describes the application of Version 2.1 of pNEM/CO to Denver and Los Angeles. The first version of APEX was essentially identical to pNEM/CO (version 2.0) except that it ran on a personal computer (PC) instead of a mainframe. The next version, APEX2, was substantially different, particularly in the use of a personal profile appr oach rather than the previously used cohort simulation approach. APEX3 introduced a number of new features including automatic site selection from nationa l databases, a series of new output tables providing summary exposure and dose statistics, and a thoroughly reorganized method of describing microenvironments and their variable parameters. J ohnson and Capel (2003) describe a case study in which the PC-based Version 3.1 of APEX was used to estimate population exposure to CO in Los Angeles. The current version of APEX (Version 4.3) (US EPA, 2008a; 2008b) was used to estimate CO exposure and dose as described in ch apter 5 of this document. This version was also recently used to estimate O 3 exposures in 12 urban areas for the O 3 NAAQS review (US EPA, 2007), to estimate population e xposures to nitrogen dioxide (NO 2 ) in Atlanta as part of the
NO 2 NAAQS review (US EPA, 2008c), a nd to estimate sulfur dioxide (SO 2 ) exposures for
asthmatics and asthmatic child ren in two study areas in Missouri as part of the SO 2 NAAQS review (US EPA, 2009a). There have been severa l recent enhancements to APEX since the prior 1994 CO NAAQS review, including:

 Algorithms for the assembly of multi-day (long itudinal) activity diaries that model intraindividual variance, inter-ind ividual variance, an d day-to-day autocorrelation in diary properties;4-3  Methods for adjusting diary-based energy expenditures for fatigue and excess postexercise oxygen (EPOC) consumption;

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

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

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

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

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

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

 Expanded options for modeling microenvironments.

4.4 MODEL SIMULATION PROCESS

APEX4.3 is designed to simulate population exposure to crite ria and air toxic pollutants at local, urban, and regional scales. The user sp ecifies the geographic area to be modeled and the
number of individuals to be simulated to re present a population of interest. APEX4.3 then generates a personal profile for each simulated pe rson that specifies various parameter values required by the model to estimate their exposure a nd dose. The model next uses diary-derived time/activity data matched to each personal profil e to generate an exposure event sequence (also
referred to as a time-location

- activity pattern or composite diary ) for the modeled individual that spans a specified time period, such as a calendar year. Each event in the sequence specifies a start time, exposure duration, a geographic lo cation, a microenvironment inhabited, and an activity performed. Probabilistic algorithms are used to estimate the pollutant concentration and ventilation (respiration) rate associated with each exposure event. The estimated pollutant
concentrations account for the effects of ambien t (outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate , and proximity to emission sources, each depending on the microenvironment, available da ta, and the estimation method selected by the
user. The ventilation rate is derived from an energy expenditure rate estimated for each individual when performing the specified activity. Because the simulated individuals represent a random sample of the population of interest and are proportionally derived from actual population distributions, the di stribution of modeled indivi dual exposures can then be extrapolated to the larg er population of interest. The model simulation generally includes up to seven steps as follows:

 Characterize study area: APEX4.3 selects sectors (e.g., census tracts) within a study area—and thus identifies the potentially exposed population — usually based on the4-4 user-defined center and radius of the st udy area and availability of air quality and meteorological input data fo r the area (section 4.4.1).

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

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

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

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

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

 Calculate dose: APEX4.3 optionally calcu lates hourly, daily, monthly, and annual average dose values for each of the simula ted individuals. For the application of APEX to CO, a module within the model estimates the pe rcent COHb level in the blood at the end of each hour based on the time-series of CO concentrations and alveolar ventilation rates experienced by the simulated person (section 4.4.7). The model simulation continues until exposures (and associated COHb dose levels) are calculated for the user-specified number of simulated individuals. Figure 4-1 presents a conceptual model and simplified data flow di agram illustrating the implementation of APEX4.3 to estimate CO exposure and dose. The follo wing sections provide ad ditional details on the general procedures and algorithms used in each of the seven simulation steps listed above, though more complete discussion can be found in US EPA (2008a, 2008b). The specific input data and microenvironmental factors used in ap plying APEX4.3 to CO for the current assessment
are further described in section 5.1.4-5Air 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)Figure 4-1.Conceptual model and simplified data flow for estimating population exposure and dose using APEX4.3.4-6 4.4.1 Characterize Study Area

An initial study area in an APEX4.3 assessment consists of a set of basic geographic units called sectors, typically defined by US census data reported at the census tract level. The user may provide the geographic cente r (latitude/longitude) and radius of the study area. Then
APEX4.3 calculates the distances to the center of the st udy area of all the sect ors included in the
sector location database and sele cts the sectors within the radi us of the study area. APEX4.3 then maps the user-provided air quality and mete orological data for specified monitoring districts
to the selected sectors. The sectors identified as having acceptable air qu ality and meteorological data within the radius of the study area are se lected to comprise a final study area for the APEX4.3 simulation analysis. This final study area determines the population make-up of the simulated persons (profiles) to be modeled.

4.4.2 Generate Simulated Individuals

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

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

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

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

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

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

4.4.2.1 Population Demographics

APEX4.3 takes population characteristics into account to develop accurate representations of study area demographics. Specifically, populati on counts by area and4-7 employment probability estimates are used to develop representative profiles of hypothetical
individuals for the simulation.
APEX4.3 is flexible in the reso lution of population data provided. As long as the data are available, any resolution can be used (e.g., c ounty, census tract, census block). For this application of the model, census tract level data were used. Ce nsus tract level population counts are obtained from the 2000 Census of Population a nd Housing Summary File 1 (SF-1). This file contains data compiled from the questions asked of all respondents and about every housing unit. As part of the population demographics input s, it is important to integrate working patterns into the assessm ent. In the 2000 US Census, estimates of employment were developed by census information (US Census Bureau, 2007). The employment statistics are broken down by gender and age group, so that each gender/ag e group combination is given an employment probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are: 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. Children under 16 years of age were assumed by the model to not be employed.

4.4.2.2 Commuting Database

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

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

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

 Eliminated Records: Tract-to-tract pairs representi ng workers who either worked outside of the US (9,631 tract pairs with 107,595 workers) or worked in an unknown location (120,830 tract pairs with 8,940,163 wo rkers) were eliminated from the database. An additional 515 workers in th e commuting database whose data were4-8 missing from the original files, possibly due to privacy conc erns or errors, were also deleted.

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

4.4.2.3 Profile Functions File

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

4.4.2.4 Physiology File

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

4.4.3 Construct Activity Sequences

Different human activities, such as spending time outdoors, indoors, or driving, will be associated with varying pollutant concentrations. Therefore, to accurately model individuals and their exposure to pollutants, it is critical to understand people’s da ily activities and use such data in the exposure model. EPA’s Consolidated Human Activity Database (CHAD) provides diaryderived data indicating where people spend time a nd the activities they perform at each location (US EPA, 2002). CHAD was designed to prov ide a basis for conducting multi-route, multimedia exposure assessments (McCur dy et al., 2000). The data c ontained within CHAD originate4-9 from multiple activity pattern surv eys with varied structures (T able 4-1), however the surveys have commonality in that they contain daily diaries of human activit ies performed, locations visited, and the personal attr ibutes of survey participants (e.g., age and gender). There are four CHAD-related input files us ed in APEX4.3. The first three can be considered standard input files for most model simulations; the user typically does not modify their contents. These include the human activity diaries file, the pers onal data file, and a metabolic information file, each of which are disc ussed briefly below. The fourth CHAD-related input file maps the five-d igit location codes used in the diary file to APEX4.3 microenvironments; this file is commonly modified by the user and is discussed in section 5.8 (i.e., it is most relevant for the specific micr oenvironments modeled in this CO REA). And finally, section 4.4.3.4 discusses how these diaries are linked together to form a continuous timelocation-activity pattern for each indivi dual across the entire simulation period.4-10 Table 4-1. Summary of activity pattern studies comprising the recent version of CHAD.

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, 2003b)

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)

4.4.3.1 Personal Information file

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

 The study, person, and diary day identifiers

 Day of week4-11  Gender

 Employment status

 Age in years

 Maximum temperature in degrees Celsius for the diary day

 Mean temperature in degrees Celsius for the diary day

 Occupation code (if re quested in survey)

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

4.4.3.2 Diary Events File

The human activity diary data ar e contained in the events file that is distributed with
APEX4.3. This file contains the locations visite d and the activities performed for the nearly 35,000 person-days of data with event intervals ranging from a minimum of one minute upwards to a one hour maximum duration. Typically, a study individuals’ diary can vary in length from one to 15 days (i.e., referring to the number of person-days) though a few recent surveys have upwards of hundreds of diary days for a few indi viduals. Nevertheless, the diary events file contains the following variables:

 The study, person, and diary day identifiers

 Start time of the event

 Number of minutes for the event

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

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

4.4.3.3 Activity-Specific Metabolic File

The metabolic file contains the distributi onal forms and parameters for the activityspecific metabolic equivalents (METs) used to quantitatively assign exertion levels to each activity performed by simulated individuals (McC urdy, 2000). Some activities are specified as a single point value (for instance, watching TV), while others , such as athletic endeavors or manual labor, are represented by normal, lognormal, or other statistical distributions. APEX4.3 samples from these distributions and calculates valu es to simulate the variable nature of activity levels among different people. The CHAD User’s guide provid es details on the distributions used, parameters, and sources fo r each activity (US EPA, 2002).

4.4.3.4 Longitudinal Diary Processing

APEX4.3 probabilistically creates a compos ite longitudinal diary for each of the simulated persons by selecting a 24-hour diary record – or diary day – from an activity database for each day of the simulation period. The EPA’ s CHAD data (US EPA, 2002) are supplied with4-12 APEX4.3 for this purpose. A co mposite diary is a sequence of events that simulates the movement of a modeled person through varying geographical lo cations and microenvironments for the duration of the simulation period. Each diary event is define d by geographic location, start time, duration, microenvironment vi sited, and an activity performed. The activity database input to APEX4.3 contai ns the following information for each diary day: age, gender, employment status, occupa tion, day-of-week (or day-type), and maximum hourly average temperature. This information en ables APEX4.3 to select data from the activity
database that tend to match the characteristic s of the simulated pers on, the study area, and the specified time period. APEX4.3 develops a composite diary for each of the simulated individuals according to the following steps.

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

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

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

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

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

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

 Concatenate the selected diar y days into a sequential longi tudinal diary for a simulated individual covering all days in the simulated period. APEX4.3 provides an optional longitudinal di ary-assembly algorithm that enables the user to create composite diaries that reflect th e tendency of individuals to repeat activities on a day-to-day basis. The user specifies values for two statistical variables (i.e., D and A ) that relate to a key daily variable, typically the time spent per day in a part icular microenvironment (e.g., in a motor vehicle). The D statistic reflects the relative impor tance of within-person variance and between-person variance in the key variable. The A statistic quantifies the lag-one (day-to-day)
variable autocorrelation. APEX4.3 then constructs composite di aries that exhibit the statistical properties defined by the specified values of D and A . The longitudinal diary assembly algorithm is described in greater detail by Glen et al. (2008) and in section 6.3 of US EPA (2008b).4-13 4.4.4 Calculate Microenvironmental Concentrations

Probabilistic algorithms are used by APEX4.3 to estimate the pollutant concentration associated with each exposure event. The es timated pollutant concentr ations account for the effects of ambient (outdoor) pollu tant concentration, penetrati on factor, air exchange rate, decay/deposition rate, and proximity to emission sources, depending on the microenvironment, available data, and the estimation method selected by the user.
APEX4.3 calculates air concentrations in th e various microenvironments visited by the simulated person by using the ambient air data for the relevant census tracts and the userspecified method and parameters that are spec ific to each microenvironment. In typical applications, APEX4.3 calculates hourly concentr ations in all the microenvironments at each
hour of the simulation for each of the simulate d individuals, based on the hourly ambient air quality data specific to the geographic locatio ns visited by the indivi dual. APEX4.3 provides two methods for calculating microenvironmental concentrations: the mass balance method and the transfer factors method (each are described brie fly below). The user is required to specify a
calculation method for each of the microenviron ments; there are no restrictions on the method specified for each microenvironment (e.g., some microenvironments can use the mass balance
method while the others can use the transfer f actors method). Each of these approaches is described in sections 4.4.4.1 and 4.4.4.2, respectively. When using an exposure model to estima te population exposures to CO based on exposures to concentrations in microenvironments , it is best to use estimates of the outdoor (ambient) CO concentration in the immediate vici nity of each microenvir onment to address the
ambient contribution to that microenvironment. These concentrations may need to be derived
because concentrations measured at a fixed-site monitor may not adequately represent the spatial and temporal heterogeneity in concentrations ex pected with distance from the ambient monitor location. There can be different ways to deri ve the ambient concentr ation in the immediate vicinity of the microenvironment. For example, one can use an emission-based dispersion model
to estimate ambient concentrations at a fine temporal (e.g., hourly) and spatial scale (e.g., census block-level or 500 meter grid cells). Another method is to use a st atistically-based approach that addresses the variability in concentrations in a similar manner as a dispersion model, only that important physical factors that influence concentr ation levels are repres ented by and/or possibly combined with a series of regression equation coe fficients and are related to an ambient monitor CO concentration. Ultimately, it is this estimated outdoor CO concen tration that is then used as input to the microenvironmental algorithm (eith er the mass balance model or factors method) employed to estimate CO microenvironmental concentrations. For this APEX application, staff selected a statistically-based approach to estimate
ambient concentrations in the immediate vi cinity of each microenvironment based on the4-14 ambient monitor concentrations. The approach was designed to reflect both the spatial and temporal variability expected to occur outside microenvironments, while also appropriately linking the estimated microenvironmental concentra tions to observed concentrations at a fixedsite ambient monitor. The approach was develo ped using personal exposure, fixed-site monitor,
and outdoor concentration measurement data and first implemented in the pNEM/CO model for use in the most recent CO exposure assessm ent (Johnson et al., 2000). This approach was proposed as a method to address spatial a nd temporal variabil ity in outdoor and
microenvironmental concentrations in the dr aft scope and methods plan (US EPA, 2009b), though not fully described there as is done here. To provide both historical pers pective and context regarding the curren t application, this microenvironmental algorithm and the data that we re used in the past with pNEM/CO to estimate values for the algorithm variable s is described in section 4.4.4 .3. The pNEM/CO approach was then adapted and implemented in APEX3.1, a mode l more similar in structure to the current version of APEX (version 4.3) than pNEM/CO. This approach as applied to APEX3.1 is then
described in section 4.4.4.4. The details regarding selection of specific microenvironments and parameters used by APEX4.3 in this a ssessment is provided in section 5.9.

4.4.4.1 Overview of the Mass Balance Model

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

 Inflow of air into the microenvironment;

 Outflow of air from the microenvironment;

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

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

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

f

proximity ) is used to account for
differences in ambient concentrations between the geographic locati on represented by the ambient air quality data (e.g., a fixed-site monitor) and the geographic location of the microenvironment (e.g., near a roadwa y). This factor could take a value either greater than or
less than 1. Emission source ( ES ) represents the emission rate for the emission source, and

concentration source ( CS ) is the mean air concentration resu lting from the source (these are not
used in the current assessment. The factor R removal is defined as the removal rate of a pollutant4-15 from a microenvironment due to deposition, filtration, and chemical reaction. The air exchange

rate ( R air exchange ) is expressed in air changes per hour.4-16 Table 4-2. Variables used by APEX4.3 in the mass balance model.

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

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

source removal out in C C C C   

 

   dt (t) dC ME (4-1)

where:

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

Within the time period of an hour each of the rates of change,  C in ,  C out ,  C removal , and

 C source , is assumed to be constant. The change in microenvironmental concentration due to influx of air is represente d by the following equation:

exchange air n penetratio proximity ambient in

in R x f x f x C dt t dC C    ) ( (4-2)

where:4-17 C

ambient = Ambient hourly outdoor concentration (ppm) f

proximity = Proximity factor f penetration = Penetration factor

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

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

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

out     (4-3)

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

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

where:

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

R

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

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

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

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

Combining equation 4-1 with equati ons 4-2, 4-3, and 4-4 yields

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

 

   (4-5)

The solution to this differential equation is4-18 ) exp( ) ) 0 ( ( ) ( t R R C C R C t C combined combined in
ME combined in
ME  

  

 (4-6)

where:

C

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

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

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

combined in ME equil ME R C t C C 

    ) ( (4-7)

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

    (4-8)

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)

where:

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

C

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

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

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

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

.4-19 4.4.4.2 Overview of the Factors Model

The factors model approach is conceptually simpler than the mass balance method and has fewer user-specified parameters. It estimat es the concentration in a microenvironment as a
linear function of ambient concen tration of that hour, regardless of the c oncentration in the microenvironment during the pre ceding hour. Table 4-3 lists th e parameters required by the factors model approach to calcu late concentrations in a micr oenvironment in the absence of
indoor emissions sources.

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

Variable Definition Units Value Range

f proximity Proximity factor unitless f proximity > 0
f penetration Penetration factor unitless 0 ≤ f penetration ≤ 1

The factors model approach uses the following equation to calculate hourly mean
concentration in a microenvironment from the user-provided hourly air quality data:

n penetratio proximity ambient hourlymean ME 

### f f C C    (4-10)

where

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

C

ambient = Hourly concentration in ambient environment (ppm) f

proximity = Proximity factor (unitless) f

penetration = Penetration factor (unitless)

The proximity factor ( f

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

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

rates.4-20 4.4.4.3 Description of the Original pNEM/CO Microenvironmental Algorithm

Version 2.1 of pNEM/CO determined the hourly outdoor CO concentration applicable to each microenvironment through a Monte Carlo process based on the following equation

CO out (c,m,d,h) = M(m) × L(c, m, d) × T(c,m,d,h) × [ CO mon (d,h) ] A (4-11)

where,

CO out (c,m,d,h) = outdoor CO concentration (ppm) for cohort c with respect to
microenvironment m in district d during hour h
, M(m) = multiplier (> 0) specific to microenvironment m , L(c,m,d) = location factor (> 0) specific to cohort c , microenvironment m , and
district d (held constant for all hours), T(c,m,d,h) = time-of-day factor (> 0) specific to cohort c , microenvironment m , district d , and hour h
, CO mon (d,h) = ambient monitor-derived CO concentration (ppm) for hour h in
district d , and
A = exponent (A > 0).

This equation was used to generate a year-long sequence of outdoor one-hour CO concentrations for each combination of cohort ( c ), microenvironment ( m ), and district ( d ) by Johnson et al. (2000). The exponent A was set equal to 0.621 and held constant for all sequences. The value of M(m) varied only with microenvironmen t as indicated in Table 4-4 [and
is identical to Table 2-6 in Johnson et al. (2000)].
A value of the location factor L(c, m, d) was specified for each individual sequence and held constant for all hours in the sequence. The value was randomly selected from a lognormal distribution with geometric mean (GM L ) equal to 1.0 and geometric standard deviation (GSD L ) equal to 1.5232. The natural logarithms of th is distribution can be characterized by a normal distribution with an arithmetic mean ( μ L ) equal to 0 and an arithm etic standard deviation ( σ L ) equal to 0.4208. A value of the time-of-day factor T(c, m, d, h) was randomly selected for each hour within a sequence from a lognormal di stribution with geometric mean (GM T ) equal to 1.0 and geometric standard deviation (GSD T ) equal to 1.6289. The natural l ogarithms of this distribution follow a normal distribution with an arithmetic mean ( μ T ) equal to 0 and an arithmetic standard deviation ( σ T ) equal to 0.4879.4-21 The CO out (c, m, d, h) term is interpreted as the ou tdoor CO concentration in the immediate vicinity of microenvironment m in district d during hour h

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

. The mass balance model in pNEM/CO included a penetration factor that was set equal to 1.0 for CO. Consequently, this predicts no ch ange in CO concentration associated ambient (outdoor) air as it moves into a microenvironm ent, though the CO concentration within the microenvironment will be affected by inputs for air exchange rate and indoor sources.4-22 Table 4-4. Estimated values of distribution paramet ers and variables in equation 4-11 as implemented in the application of pNEM/CO to Denver and Los Angeles (Johnson et al., 2000).

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).4-23 4.4.4.3.1 Data Used To Estimate pNEM/C O Microenvironmental Algorithm Parameters The parameter values for the location factor ( σ L ), time-of-day ( σ T ), and ambient concentration exponent (A) were based on data collected during a residential monitoring study described by Wilson et al. (1995) . Ten-minute CO concentrati ons were measured outside 293 residences throughout California in 1992 including customers of Pacific Gas and Electricity (PG&E) (129 residences in Northern Califor nia), San Diego Gas and Electric Company (89 residences in the San Diego area ), and Southern California Gas Co rporation (75 residences in the Los Angeles area). After excluding the PG&E data (i.e., not part of the Los Angeles study area)
and homes for which valid CO data were not ava ilable, analysts used a remaining subset of 156 residences, 70 from Los Angeles and 86 from San Diego, as the basis for estimating values of σ L ,

σ T , and A applicable to the Los Angeles study area. 2 This data subset contained 44,726 valid 10- minute averages measured outside of residences, of which less than 1% were negative (smallest value = -1.0 ppm), 14,817 (33%) were equal to 0 ppm, and the remainder were positive (maximum = 68.7 ppm). These valid 10-minute CO concentrations were then averaged by clock hour to permit comparison with hourly CO c oncentrations reported by nearby fixed-site
monitors. An assumption was made by the original data analysts to maximize the number of hourly averaged outdoor residential samp les available to use in determ ining the algorithm parameters. It was proposed that the negative concentrations in this data set were most likely caused by the subtraction of an offset from all measured values to account for monitor drift. To adjust for this offset and to prevent the occu rrence of negative and zero values in the hourly-averaged data (which could not be used in fitt ing equation 4-11), analysts added a constant offset of 0.5 ppm to all hourly-averaged values measured outside a resi dence. In addition, se venteen (0.2%) of the original hourly averages ≤ -0.5 ppm were removed from the data set (i.e., the offset adjustment would still yield a concentration of ≤ 0 ppm). Each of the resulti ng one-hour outdoor residential CO concentrations was paired with the one-hour CO concentration measured simultaneously at the nearest fixed-site monitor [based on data obtained from EPA’s Aerometric Information Retrieval System (AIRS)]. The fixed-site ambient monitoring data were used as reported. This approach yielded a final database containing 6,330 pairs of hourly average CO concentrations, in which each pair was indexed by date, time, residen ce identifier, fixed-site monitor identifier, and
fixed-site monitor scale (e.g., neighborhood scale).

2 Note these same coefficient values derived from th e California measurement data were also applied to estimate exposures in the pNEM/CO Denver study area, as researchers were unable to identify a usable data set specific to Denver.4-24 The parameters for the microenvironmental factors (or M(m) ) in equation 4-11 were derived from data generated through the Denve r Personal Monitoring Study (Akland et al, 1985; Johnson, 1984). During this study, each of a pproximately 450 subjects carried a personal exposure monitor (PEM) for two 24-hour periods. Each PEM measured CO concentration continuously. The PEM readings were averaged by exposure event such that each event was associated with a single microenvironment and a single clock hour (e.g., 1 pm to 2 pm). Event durations ranged from one minute to one hour. The microenvironment assigned to each PEM reading was determined from entries made in a real-time diary carried by the subject.
Researchers created a database in which each PEM CO concentration was matched to the corresponding hourly-average CO c oncentration reported by the nearest fixed-site monitor. The data were first processed by excluding data with missing measurements, where measurements failed a quality control check, and instances in wh ich applicable diary data indicated the presence of smokers or gas stoves. Each PEM CO concentration was then assigned to a microenvironment, m , based on entries in the activity dairy. In some cases, data for two or more similar microenvironments were aggregated to pr ovide more stable estimates than those based on the very limited amount of data available fo r specific microenvironments (see Table 4-4 footnote). For consistency with the above described Wilson et al. (1995) database, all cases with a zero PEM measurement were excluded, as were all cases in which the fixed-site monitor concentration was zero after rounding to the nearest integer ppm. Note that the Denver fixed-site data were recorded to the nearest 0.1 ppm, wh ereas the Los Angeles fixed-site data were only recorded to the nearest integer.

4.4.4.3.2 Development of the pNEM/CO Microenvironmental Algorithm Form

Equation 4-11 was based on the results of da ta analyses that suggested that the relationship between CO out (c, m, d, h) and CO mon (d, h) should account for the specific microenvironment, the geographic location of th e microenvironment, and the time-of-day. Analysts recognized that numer ous statistical algorithms coul d have been developed. In specifying the algorithm that was ultimately used (i.e., equation 4-11), the analysts attempted to balance the need for simplicity and parsimony w ith the need to repres ent the patterns in concentration variability observed in the available data. The bulk of the algorithm development was based on the Wilson et al. (1995) databa se, that is, hourly av erage 10-minute CO
concentrations measured outside residences in southern California paired with hourly average
CO concentrations measured at the nearest fixed-site monitor. For this case and consistent with equation 4-11 nomenclature, m represented the residence micr oenvironment in the district d

. The district d was initially taken to be the entir e study region where measurements were collected (i.e., San Diego and Los Angeles areas).4-25 Analysts began by considering a simple linear regression model of the form

CO out (c,m,d,h) = a(m,d) + A × [ CO mon (d,h) ] + e(c,m,d,h) (4-12)

where the residual term e(c,m,d,h) was assumed to be independent and normally distributed with a mean of zero. For simplicity a nd parsimony, the slope coefficient A was assumed to be the same for all microenvironments ( m ) and districts ( d ). Although the coefficient of determination (R 2 ) for this linear regression model was moderate (0.53), 3 the model was found to be unacceptabl e because it does not properly reflect the strong correlations that were observed between concentrations measured outside the same location. Instead, this form of regression model assumes that the residuals associated with a particular residential location are independent. In other words, this model does not properly separate out the variation between locations fr om the variation within locations. Analysts
identified two other deficiencies in this model: (1) large negative values of the randomly-selected e(c,m,d,h) term could produce negative outdoor concentr ations, an unrealistic exposure scenario, and (2) the model did not generate outdoor concentrations characterized by lognormal distributions. Various researchers (e.g., Ott, 1995) have demonstrated that ambient CO concentrations tend to be characterized by lognormal distributions rather than normal
distributions. To better address these latter concerns, analys ts evaluated an altern ative model where the natural logarithm of outdoor c oncentration was expressed as a linear function of the natural logarithm of monitor concentration:

LN[ CO out (c,m,d,h) ] = a(m,d) + A × LN[ CO mon (d,h) ] + e(c,m,d,h) (4-13)

In this equation and those that follow, LN[ ] indicates the natural logarithm of the quantity in brackets. To properly separate th e variability between and within locations, the intercept term a(m,d) was also permitted to vary with the cohort location, c , leading to the final selected algorithm:

LN[ CO out (c,m,d,h) ] = a(c,m,d) + A × LN[ CO mon (d,h) ] + e(c,m,d,h) (4-14)

Exponentiating both sides of equation 4-14 yiel ds the equivalent formulation to that presented above in equation 4-11:

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.4-26 CO out (c,m,d,h) = M(m) × L(c,m,d) × T(c,m,d,h) × [ CO mon (d,h) ] A (4-15)

where M(m) = exp{mean [ a(c,m,d) ]}, averaged over cohorts,

L(c,m,d) = exp{ a(c,m,d) - mean [ a(c,m,d) ]}, and

T(c,m,d,h) = exp[ e(c,m,d,h )].

Several alternative statistical models were considered by analysts during the development of the selected algorithm formulation. Early in the process, analysts evaluated a series of autoregressive time series models, in which m odel predictions were influenced by the past history of CO concentrations at the monito r and outdoors of the microenvironment. These models were rejected for several reasons: (1) they were inherently complex, (2) they yielded a
wide variation in model coefficients which di d not always produce reasonable estimates when applied to specific California residences, and (3 ) they required microenvironment-specific time
series data for coefficient estimation which were not readily available for non-residential
microenvironments. Analysts also evaluated algorithms simila r to equation 4-11 in which the exponent A varied with microenvironment. These algorithms were rejected due to the need for parsimony and perhaps more importantly, the lack of sufficient, suitable data for estimating microenvironment-specific values of A . A simpler model in which the exponent A is fixed at 1 was rejected because fits of equation 4- 11 to the California data indicated that A differed significantly from 1 (p<0.01). In addition, the assumption that A = 1 produced unrealistically high predictions for outdoor CO concentrations wh en the model was applied to monitoring data obtained from the Denver Broadway site (ID 083 10002). These high values were found to be a direct result of setting A = 1, which forced the geometric st andard deviation of the estimated outdoor concentrations to significantly exceed the geometric standard deviation of the monitor
values. Analysts ultimately arrived at equation 4-11 (equivalent to equation 4-15), which permits
the A exponent to differ from 1.0. The model was fitted using statistical software for a mixed (random and fixed effects) model which employe d restricted maximum likelihood estimation. The fit yielded estimates of σ L = 0.4208, σ T = 0.4879, and A = 0.621, the values subsequently used in the pNEM/CO runs described by J ohnson et al. (2000). The fitted value of M(m)
, representing residences in Lo s Angeles during 1992, was actually 0.9706. An alternative value4-27 (1.034), based on the additional analyses described below, was applied to the indoor-residence microenvironment in the pNEM/CO runs (see Table 4-4). This algorithm, considered a reasonable compromise between model simplicity and performance, is completely sp ecified by four parameters [ M(m)
, σ L , σ T , and A ]. Note that σ L , σ T , and A are defined to be independent of the microenvironment, whereas M(m) is microenvironment-specific. At the time of the in itial algorithm development, researchers were unable to find a single data source capable of providing estimates of all four parameters. Consequently, values for σ L , σ T , and A were estimated by analyzi ng data obtained from the Wilson et al. (1995) database, whereas the specific M(m) values were based on data provided by the Denver PEM database (Akl and et al, 1985; Johnson, 1984). Researchers conducted a series of sensitivity an alyses to evaluate the potential effects on parameter estimates of variations in the regional location and scal e of the fixed-site monitor. Equation 4-11 was fitted to a series of data subsets defined by region (Los Angeles or San Diego) or by the scale of the fixed-site mon itor (based on the estimated maximum distance from the monitor represented by the measured concen trations: micro, middle, neighborhood, or urban scale). The fitted values of σ L , σ T , A , and M(m) were very similar across the different subsets, supporting the assumption that these parameters can be assumed to be representative of concentration patterns outside residences in othe r regions and for other time periods, and can be chosen to be the same value for all monitoring scales. Due to a lack of additional suitable data,
the values of σ L , σ T , and A are also assumed to be applicable to concentrations outside all other microenvironments, although M(m) varies with the particular microenvironment (see below). In equation 4-11, the CO out (c, m, d, h) term represents the outdoor CO concentration associated with a particular microenvironment m , even when the microenvironment is an indoor
location. Few of the Denver outdoor PEM concen trations could be relia bly associated with particular indoor microenvironments. Cons equently, researchers employed a simplified procedure for estimating M(m) values which assumed that the mean of the indoor PEM values
associated with each indoor microenvironment was approximately equal to the mean of the
outdoor concentration for the microenvironment. 4 This assumption is consistent with the results of applying mass-balance modeling to non-reactive pollutants in enclosed spaces where the only source of the pollutant is the outside air. In such cases, the mean indoor concentration

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.4-28 approximates the mean outdoor concentration, wi th the instantaneous indoor concentration exhibiting a lower degree of variability than the corresponding outdoor concentration. When equation 4-11 is expressed in a logar ithmic form (i.e., as in equation 4-14) and averaged over cohorts, one obtains the equation

Mean{LN[ CO out (c, m, d, h )} = Mean[ a(c, m, d) ] + A × Mean{LN[ CO mon (d, h) ]} + Mean[ e(c, m, d, h) ] = LN[ M(m) ] + A × Mean{LN[ CO mon (d, h) ]}.

Therefore, the value of M(m) equals

M(m) = exp{Mean LN[ COout(c, m, d, h) ]

− A × Mean LN[ COmon(d, h) ]} (4-16)

where A = 0.621 (as above). This equation was then used to obtain estimates of M(m) for each particular microenvironment, or aggregate of microenvironments, as indicated in Table 4-4 using the available Denver PEM study data (Akland et al, 1985; Johnson, 1984). The same value of M(m) was applied to each specific microenvir onment within an aggregate.

4.4.4.4 The Micronenvironmental Algorit hm as Implemented by APEX3.1

As discussed in section 4-3, the pNEM/CO mo del effectively evolved into what is known today as the APEX model. In APEX3.1, the portion of the outdoor con centration af fecting the
indoor concentration is de termined by the formula

CO out = Ambient × Proximity × Penetration (4-17)

Note that we can represent Proximity and Penetration as distributions in APEX3.1. These distributions can be sampled hourly, daily , or yearly. Let us make the following
substitutions of the variables used to estimate the outdoor concentrations:

Ambient = [ CO mon (d,h) ] A (4-18) Proximity = M(m) × L(c, m, d) (4-19)
Penetration = T(c,m,d,h) (4-20)

which yields

CO out = M(m) × L(c, m, d) × T(c,m,d,h) × [ CO mon (d,h) ] A (4-21)4-29 and is identical to equation 4-11 above.
To obtain results from APEX3.1 that are co mparable to that generated by pNEM/CO, Johnson and Capel (2003) preprocessed the hourly am bient monitor data assigned to the district containing the microenvironment using the formula

Ambient = [ CO mon (d,h) ] 0.621 (4-22)

where CO mon (d,h) is expressed in ppm. For each profile, a value for the Proximity term was selected for each microenvironment from a lognorma l distribution with geom etric mean equal to M(m) and geometric standard deviation equal to 1.5232. The natural logarithms of this
distribution were characteriz ed by a normal distribution wi th an arithmetic mean ( μ L ) equal to LN[ M(m) ] and an arithmetic standard deviation ( σ L ) equal to 0.4208. Consistent with the pNEM/CO algorithm, Proximity values were not permitted to fall below the 5 th percentile of the specified distributi on or above the 95 th percentile of the distribu tion. Table 4-5 lists the parameter values applicable to the 15 micr oenvironments defined by Johnson and Capel (2003).
Penetration values were randomly selected for each hour from a lognormal distribution with geometric mean ( GM T

) equal to 1.0 and geometric standard deviation ( GSD T ) equal to 1.6289. As indicated above, the natural logar ithms of this distri bution followed a normal distribution with an arithmetic mean ( μ T ) equal to zero and an arithm etic standard deviation ( σ T ) equal to 0.4879. In agreemen t with the pNEM/CO algorithm, Penetration values were not permitted to fall below the 5 th percentile of the specified dist ribution (0.4482) or above the 95 th percentile of the distribution (2.2313). 5

5 Note the Penetration factor was not used according to its origin ally 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).4-30 Table 4-5. Parameters of bounded lognormal distri butions defined for proximity factors used in applications of APEX3.1 to Los Angeles (Johnson and Capel, 2003).

Microenvironment Parameters of bounded lognormal distribution

Code General Location Specific location Activity diary locations included in

microenvironment GM GSD Lower Bound (5 th pct) Upper Bound (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 5.93454-31 4.4.5 Estimate Energy Expenditure and Ventilation Rates

APEX4.3 includes a module that estimates C OHb levels in the blood as a function of alveolar ventilation rate, the CO concentration of the respir ed air, endogenous CO production rate, and various physiological variables such as blood volume and pulmonary CO diffusion rate.
Alveolar ventilation rate is estimated as a function of oxygen uptake ra te, which in turn is estimated as a function of energy expenditure rate . This section provides a brief summary of the algorithm used to estimate alveolar ventilation ra te. A detailed descri ption of the algorithm,
based on the nonlinear solution to the Coburn- Forster-Kane (CFK) equation (Coburn et al., 1965), together with the distributi ons and estimating equations used in determining the value of each parameter in the algorithm, can be found in Appendix B of this document.

4.4.5.1 Energy Expenditure

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

EE = METS × RMR (4-23)

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

EE(i,j,k) = METS(i,j,k) × RMR(k) (4-24)

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

.

4.4.5.2 Oxygen Requirements for Energy Expenditure

Energy expenditure requires oxygen which is supplied by ventila tion (respiration). ECF(k) represents an energy convers ion factor defined as the volume of oxygen required to produce one kilocalorie of energy in person k . The oxygen uptake rate ( VO 2 ) associated with a particular activity can be expressed as

VO 2 (i,j,k) = ECF(k) × EE(i,j,k) (4-25)4-32 where VO 2 (i,j,k) has units of liters oxygen min -1

, ECF(k) has units of liters oxygen kcal -1 , and EE(i,j,k) has units of kcal min -1 . The value of VO 2 (i,j,k) can now be determined from MET(i,j,k) by substituting equation 4-24 into equa tion 4-25 to produce the relationship

VO 2 (i,j,k) = ECF(k) × METS(i,j,k) × RMR(k) (4-26)

4.4.5.3 Excess Post-Exercise Oxygen Consumption

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

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

4.4.5.4 Alveolar Ventilation Rate

Alveolar ventilation ( V A ) represents the portion of the mi nute ventilation th at is involved in gaseous exchange with the blood. VO 2 is the oxygen uptake that o ccurs during this exchange.
The absolute value of V A is known to be affected by total lung volume, lung dead space, and respiration frequency – parameters that vary according to the person a nd/or exercise rate. However, it is reasonable to assume that the ratio of V A to VO 2 is relatively constant regardless of a person’s physiological characteristics or ener gy expenditure rate. Consistent with this assumption, APEX4.3 converts each estimate of VO 2 (i,j,k) to an estimate of V A (i,j,k) by the proportional relationship

V

A (i,j,k) = 19.63 × VO 2 (i,j,k) (4-27)

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

V

A (i,j,k) = 19.63 × METS(i,j,k) × ECF(k) × RMR(k) (4-28)4-33 If ECF and RMR are specified for an individual, then equation 4-28 requires only an activity-specific estimate of METS to produce an estimate of the energy expenditure rate for a given activity. APEX4.3 processes time-locati on-activity data obtaine d from the CHAD to create a sequence of activity-specific METS values for each simulated individual. APEX4.3
estimates RMR as a function of body mass based on proba bilistic equations specific to age and gender using equations reported by Schofield (1985). A value of ECF is selected for each
individual from a uniform dist ribution (minimum = 0.20, maxi mum = 0.21) based on data provided by Esmail et al. (1995). Using equati on 4-28 and these inputs, APEX4.3 calculates a sequence of V A values for each simulated individual. Th ese values are provided to the algorithm that estimates the percent COHb in the blood re sulting from the simulated exposure (see section 4.4.7 and Appendix B).

4.4.6 Calculate Exposure

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



### T t C
C N

j j hourlymean j ME

i 

# 

 

###  1 ) ( ) ( (4-29)

where
C

i = Hourly exposure concentration at clock hour i of the simulation period (ppm)
N

= Number of events (i.e., varied microenvironments visited/activities performed) in clock hour i of the simulation period.

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

t (j) = Time spent in microenvironment j (minutes)

T

= 60 minutes From the hourly exposures, APEX4.3 calculate s time series of 8-hour and daily average exposure concentrations that a simulated indi vidual would experience during the simulation period. APEX4.3 then statistically summarizes and tabulates the number of persons and persondays at or above selected hourly, 8-hour, and daily average exposure concentrations in a series of output tables.4-34 4.4.7 Calculate Dose

Using time-location-activity pattern data obt ained from several diary studies, APEX4.3 constructs a composite diary for each simula ted person in the specified population. The composite diary consists of a sequence of events spanning the specified period of the exposure assessment (typically one calendar year). Each event is defined by a start time, duration, a geographic location, a microenvir onment, and an activity. Using the algorithms described above in sections 4.4.4, 4.4.5, and 4.4.6, APEX4. 3 provides estimates of CO microenvironmental concentrations and the persons’ al veolar ventilation rate for each event in the composite diary,
for each simulated individual. APEX4.3 then uses these data, together with estimates of various physiological parameters specific to the simulate d individual, to estimate the percent COHb in the blood at the end of each event. The percen t COHb calculation is base d on the solution to the nonlinear Coburn-Forster-Kane (CFK ) equation (Coburn et al., 1965), as detailed in Appendix B. Briefly, the CFK module in APEX4.3 describes th e rate of change in COHb blood levels as a function of the following quantities:

 Inspired CO pressure;

 COHb level;

 Oxyhemoglobin (O 2 Hb) level;

 Hemoglobin (Hb) content of blood;

 Blood volume;

 Alveolar ventilation rate;

 Endogenous CO production rate;

 Mean pulmonary capillary oxygen pressure;

 Pulmonary diffusion rate of CO;

 Haldane coefficient (M);

 Barometric pressure; and

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

If all of the listed quantities except COHb level are constant over some time interval, the CFK equation has a linear form over the interval and is readily integrated. The solution to the linear form gives reasonably accurate results fo r lower levels of COHb (ISA section 4.2.1). However, CO and oxygen can compete for binding w ith the available hemoglobin and, therefore, are not independent of each othe r. If this dependency is taken into account, the resulting differential equation is no longer linear. Pete rson and Stewart (1975) proposed a heuristic approach to account for this dependency which a ssumed the linear form and then adjusted the4-35 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 mo dule of the original CO-NEM exposure model (Biller and Richmond, 1982; Johnson and Paul, 1983). Alternatively, it is possible to determine C OHb at any time by nume rical integration of the nonlinear CFK equation if one assumes a pa rticular relationship between COHb and O 2 Hb. Muller and Barton (1987) demons trated that assuming a linear relationship between COHb and
O 2 Hb leads to a form of the CFK equation equivale nt to the Michaelis-Menten kinetic model that 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 in the Muller and Barton paper, a combination of the binary search and Newton-Raphson root finding methods was used to solve for COHb (Press et al., 1986). As mentioned above, the current COHb m odule included in APEX4.3 is based on the solution to the nonlinear CFK equation using the assumption adopted by Muller and Barton (1987) which employs a linear relationship between O 2 Hb and COHb. The CFK equation does not have an explicit solution, so an iterative solution or approxima tion is needed to calculate each percent COHb value. APEX4.3 solves the CFK equation using a 4 th -order Taylor’s series with subintervals. This method, first incorporat ed in APEX3 (Glen, 2002), is summarized in Appendix B. The selected method (4 th -order Taylor series with subintervals) was chosen because of its simplicity, fast execution speed , and ability to produce relatively accurate estimates of percent COHb at both lo w and high levels of CO exposure. While there may be other approaches propos ed as improvements to the standard CFK equation (e.g., Bruce and Bruce (2003) multi- compartment model), at this time both the
nonlinear and linear CFK models remain the mostwidely accepted and validated approaches used to estimate COHb levels (ISA, section 4.2. 3). Before any such fu ture module modifications could be planned and implemented, a more thoro ugh and balanced evaluation of the uncertainties needs to be performed to incl ude those uncertainties that may be reduced, as well as those uncertainties introduced by the model modification,. Briefly as an example, the Bruce and Bruce (2003) model accounts for distribution of CO to fi ve modeled compartments: the lungs, arterial blood, mixed venous blood, muscle tissue, and other soft tissues. In accounting for these additional compartments in a new APEX/COHb modul e, a number of variables would need to be
introduced. Some of these variable s may have data or equations av ailable in the extant literature4-36 to parameterize the variable (e.g., Q or cardiac output), while others may not be measureable or are unknown (i.e., the distribution of Q between two tissue compartments). When data do become available to support such model modi fications, one would need to evaluate the appropriateness of these data sets for estimating the parameter values used for the selected at-risk population. Further, a comparison of estimated COHb levels using the standard CFK equation with that of the 5-compartment model indicated that, consistently, the estimated COHb levels would be lower when considering uptake and storage within muscle tissue, however these differences were very small, particularly at the lowest (and mo st relevant) exposure concentration level evaluated (see Figure 7 of Bruce and Bruce, 2003). This preliminary comparison indicates that while adding multiple co mpartments to the COHb model may be more physiologically representative, the extent of any overall benefit in adding such modeling complexity to the current approach used is unclear at this time. Given the extremely tight
timeframe for this assessment and the relative strength of the dose modeling approach used, we
elected to use the nonlinear CF K model to best approximate population-based end-of-hour COHb levels for this current CO NAAQS review. And finally, the current structure of APEX allows the user to control the random sampling of model input parameters, such that, the same persons, their personal attributes, and
microenvironmental factors will be identical from one simulation to another. Modelers can then vary a particular input to evalua te the impact to exposure and dose results. This is being used in this REA to develop estimates of the contributio n of ambient exposure to an individual’s COHb levels, an additional metric of interest in this current APEX application. Results for simulations
that are identical in a ll respects except thei r CO exposure can be used to separate the contribution of endogenous CO production to an individual’s maximum end-of-hour COHb level from that of the ambient exposure contribution. For such an analysis, two simu lations are performed: the first is a typical simulation that gene rates exposure and dose in the pr esence of ambient CO and the
second simulation uses ambient conc entrations equal to zero at al l monitors and all hours of the day. In this first simulation, the exposures pe rsons experience will be a result of their contact with ambient and microenvironmental CO concentrations, while end-of-hour dose levels will reflect both the contribution from CO exposur e and endogenous CO production. In the second simulation, exposure concentrations will be zero for all hours and for all persons, while end-ofhour COHb dose levels will be that resultant from endoge nous CO production alone. The
difference in the event-level time series for each individual (and entire po pulation) can thus be used to approximate the contribution from ambi ent CO for all exposure events throughout the simulation period. See section 5.10 and Appendix B for details.4-37 4.4.8 Model Output

All of the output files written by APEX4.3 are ASCII text files; the complete list and their descriptions can be found in Table 5-1 of the APEX4.3 Use r’s Guide (US EPA, 2008a). In general, the simulation output files most relevant to results generated for the assessment include summary tabulations of population exposure con centrations and maximum end-of-hour COHb levels. Detailed event-level (minute to 1-hour in duration) or hourly-average information can also be output for each of the exposure and dose metrics of interests as well as activity specific ventilation rates and energy expend itures. For example, both the hourly and events APEX files
were needed to estimate a distribution of micr oenvironmental-to-ambient concentration ratios (see section 5.10). However, given the potential size of the files th at can be generated for a large population and assessment duration, it is not common to generate event-level file s outside of research purposes. Specific out puts generated for the purposes of the current CO exposure and
dose assessment are discussed in section 6.1.

4.5 KEY OBSERVATIONS

Presented below are key observations relate d to the modeling system used for the population assessment of CO exposure and dose.

 APEX, an EPA human exposure and dose model, has a long history of use in estimating exposure and dose for many of the cr iteria pollutants including CO, O 3 , SO 2 , and NO 2
. Over time, EPA has improved and developed new model algorithms, incorporated newer available input data and para meter distributions, as well as performed several model evaluations, sensitivity analyses, and uncer tainty characterizations for the above pollutants. Based on this analysis, APEX wa s judged to be an appropriate model to use for assessing CO exposure and dose.4-38 4.6 REFERENCES

Akland GG, Hartwell TD, Johnson TR, Whitmore RW. (1985). Measuring human exposure to carbon monoxide in Washington, D. C. and Denver, Colorado during the winter of 1982-83. Environ Sci Technol . 19:911-918.

Biller WF and Richmond HM. (1982). Sensitivity Anal ysis on Coburn Model Pred ictions 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. Enviro nmental Protection Agency, Research Triangle Park, NC. November, 1982.

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

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

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

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

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

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

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

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

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

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

Johnson T and Paul R. (1983). The NAAQS Exposure Model (NEM) Applied to Carbon Monoxide. EPA Report No. 450/5-83-003. Prepared by PEDCo Environmental, Inc. for the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Johnson T. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Research Triangle Park, NC: U.S. Environmental Protection Agency, En vironmental Monitoring Sy stems Laboratory. EPA600/4-84-014.

Johnson T. (1989). Human Activity Patterns in Cincinnati, Ohio. Palo Alto, CA: Electric Power Research Institute. EPRI EN-6204.4-39 Johnson T, Capel J, Olaguer E, Wijnberg L. (1992). Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver Residents Using a Probabilistic Version of NEM. Report prepared for the Office of Air Quality Planning and Standard s, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.

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

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

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

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

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

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

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

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

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

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

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

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

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

Schofield WN. (1985). Predicting basal metabolic rate, new standards, and review of previous work. Hum Nutr Clin Nutr . 39C(S1):5-41.

Spier CE, Little DE, Trim SC, Johnson TR, Linn WS, Hackney JD. (1992). Activity patterns in elementary and high school students exposed to oxidant pollution. J Expo Anal Environ Epidemiol . 2:277-293.

Tsang AM and Klepeis NE. (1996). Descriptive Statistics Tables from a Detailed Analysis of the National Human Activity Pattern Survey (NHAPS) Data. U.S. Environmental Protection Agency. EPA/600/R-96/148.4-40 US Census Bureau. (2007). Employment Status: 2000- Supplemental Tables. Available at: http://www.census.gov/population/www/cen2000/phc-t28.html

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

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

. US EPA. (2002). EPA’s Consolidated Human Activities Database. Data and associated documentation available
at: http://www.epa.gov/chad/

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

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

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

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

US EPA. (2009a). Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009. Available at: http://www.epa.gov/ttn/naaqs/standards/s o2/data/200908SO2REAFinalReport.pdf

. US EPA. (2009b). Carbon Monoxide National Ambient Ai r Quality Standards: Scope and Methods Plan for Health Risk and Exposure Assessment. Report no. EPA-452/R-09-004. Available at: http://www.epa.gov/ttn/naaqs/st andards/co/s_co_cr_pd.html

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

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

Wiley JA, Robinson JP, Cheng Y-T, Piazza T, Stork L, Pladsen K. (1991b). Study of Children's Activity Patterns: Final Report. California Air Resources Board, Sacramento, CA. ARB-R-93/489.

Williams R, Suggs J, Creason J, Rode s C, Lawless P, Kwok R, Zweidinger R, Sheldon L. (2000). The 1998 Baltimore particulate matter epidemiology-exposure stud y: Part 2. Personal exposure associated with an elderly population. J Expo Anal Environ Epidemiol . 10(6):533-543.

Williams R, Suggs J, Rea A, Leovic K, et al. (2003a). The Research Triangle particulate panel study: PM mass concentrations relationships. Atmos Environ . 37:5349-5363.4-41 Williams R, Suggs J, Rea A, Sheldon L, et al. (2003b). The Research Triangle particulate panel study: modeling ambient source contributions to personal and residential PM mass concentrations. Atmos Environ

. 37:5365-5378.

Wilson AL, Colome SD, Tian Y. (1995). California Resi dential Indoor Air Quality Study. Volume III: Ancillary and Exploratory Analysis. Integrated Environmental Services, Irvine, California.5-1 

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

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

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

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

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

 exposure scenarios under evaluation,

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

 method used to estimate local outdoor and microenvironmental CO concentrations, and

 additional output data files generate d for this particular assessment.

Note that the APEX model version used in this asse ssment was APEX4.3, but for simplicity will be referred to as APEX in much of the discussion that follows.

5.2 OVERVIEW As summarized above in section 1.3, the pr evious analysis of population CO exposure employed the pNEM/CO model in Denver and Los Angeles study areas, comprising the majority
of census tracts within those me tropolitan areas (Johnson et al., 2000). In this earlier exposure assessment, air quality data were obtained from multiple fixed-site monitors within the study areas, and the exposure assessment accounted for th e effects of geographic location, a diverse set of microenvironments, commuting within the st udy area, and selected indoor sources (e.g., passive smoking, gas stoves). In the specific appl ication of APEX described in this CO REA, a similar exposure and dose modeling approach has been developed by staff, though without
inclusion of indoor source emissions. The detail ed approach presented here was designed in
consideration of comments a nd recommendations made by the CA SAC and public regarding the
earlier draft CO REAs (US EPA, 2009a; 2010). The general description of AP EX, the standard databases us ed, modeling capabilities, as well as the history of the pNEM /APEX series of exposure models, can be found in chapter 4.
This includes use of the national data files obtained from the US Census Bureau (i.e., the 2000 Census data) for the following types of information:5-2  Population data and employment probabil ities by gender, age, and census tract;

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

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

Other default input files provided within APEX include tables of age- and gender-specific physiological parameters (e.g., body weight) and activ ity-specific metabolic equivalents (METs). The contents of each of these default files and their use were summarized in chapter 4. They are described in greater detail in the APEX User s Guide (US EPA, 2008a) and the APEX Technical Support Document (US EPA, 2008b). The typica l output files (e.g., number of persons at or above a selected exposure or dose level) were also summarized in chapter 4, though additional exposure and dose outputs were generate d for this assessment using the APEX hourly and events files (US EPA, 2008a, 2008b) and are described in section 5.10.

5.3 STUDY AREAS As discussed in section 3.2, areas within De nver, Colorado, and Los Angeles, California, were selected for the current exposure and dose a ssessment. Briefly, considerations in selection of these areas included: the prior analysis of these locations in CO NAAQS reviews, the areas having historically elevated CO concentrations, and the areas currently ha ving some of the most complete ambient monitoring data available. The monitors selected for use in defining the air quality in each urban area are listed in Ta bles 5-1 (Denver) and 5-2 (Los Angeles). The actual study areas were defined as includi ng all census tracts within 10 km of the
selected fixed-site monitors. These areas are illustrated in Figures 5-1 and 5-2, which indicate
the locations of the fixed-site monitors and the circular 10-km region surrounding each ambient monitor. Each 10 km region defines the aforementioned air district that includes the geographic area (i.e., the census tracts) represen ted by data from the associated CO monitor. Note that all air districts have the same radius (10 km), a valu e specified by the “AirRadius” input parameter of APEX. Any tracts residing with in overlapping monito r radii were assigned to the closest
monitor. In addition to defining the air districts, th e model user must specify a location for the center of the study area and a value for “CityRadi us.” The circular area defined by the city
center location and the value of “CityRadius” must be large e nough to include all census tracts
included in the air districts. For Denver, staff used the locati on of monitor ID 31-0014 (Denver - Carriage) for the city center and set the “CityRadius ” equal to 20 km (Figure 5-1). Staff used the location of monitor ID 37-1103 (Los Angeles) for the center city of Los Angeles and set the “CityRadius” equal to 65 km (Figure 5-2).5-3 5.4 EXPOSURE PERIODS EPA selected the following calendar year s as the study periods for each area:
Denver: 1995 and 2006 Los Angeles: 1997 and 2006 The year 2006 was selected for both cities because it was the most recent year of monitoring data that met the 75% completeness requirement for the ambient monitors listed above. Note, the CO levels reported for 2006 were well below the 8-hour NAAQS (see Tables 5-1 and 5-2) and are considered representative of the as is air quality in each study area for purposes of this assessment. The year 1995 for Denver and the year 1997 for Los Angeles were selected as periods for which the ambient monito r concentrations were near or exceeding the 8- hour average CO NAAQS of 9 ppm. Staff judged that these historical m onitoring data would be most useful in representing air qua lity that just meets the current or alternative CO standards and, following an appropriate concentrat ion level adjustment, would repr esent a particular air quality scenario (see sections 5.6 and 5.7.3).

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

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).5-4Table 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.5-5 Figure 5-1. Ambient monitor locations, air district s (black circles), meteorological zones (blue circles), and study area (red circle) for the Denver exposure modeling domain.5-6 Figure 5-2. Ambient monitor locations, air district s (black circles), meteorological zones (blue and pink circles), and study a rea (red circle) for the Los Angeles exposure modeling domain.5-7 5.5 STUDY POPULATION Population estimates were obtained from the 2000 US Census for Denver and Los Angeles study areas. 1 In light of the health outcome of interest and characteristics of the susceptible population, the population in each area wa s first restricted to persons aged 18 years or older. Next, the populations were adjusted to remove residents co mmuting outside of the study area. The resulting population for th e Denver study area was 617,020 persons (or 81.1% of the total population ≥ 18 years of age residing in modele d census tracts). The corresponding figure for Los Angeles was 5,017,551 persons (or 88.5% of the total population ≥ 18 years old
within the modeled census tracts). These populations are referre d to below as the total simulated population in each area. To obtain adequate re presentation of the simulated population while also keeping the model runs tractable, fifty-t housand exposure profiles (o r simulated individuals) were run by APEX for each study area and exposure scenario. 2

5.5.1 Simulated at-Risk Subpopulations

As mentioned above, the simulated at-risk populations within each study area focused on adults (ages 18 and older), consis tent with the previous CO e xposure assessment (Johnson et. al, 2000) and the completed 1994 CO NAAQS review (US EPA, 1992), as the incidence of heart disease in younger individuals is extremely small (CDC, 2009). For this assessment, we identified two at-risk subpopulati ons using disease prevalence rate s characterized in the National Health Interview Survey (NHIS): (1) the pot ential population comprise d of persons with “coronary heart disease” and (2) the potential po pulation comprised of pers ons with “all types of
heart disease”. This first category (i.e., coronary heart diseas e) is limited to those persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009) in addition to an estimate of those persons having undiagnosed CHD (US EP A, 2010). The second category (i.e., all types of heart disease) is inclusive of those with di agnosed coronary heart disease, angina pectoris, heart attack, and any other heart condition or di sease (CDC, 2009), in addition to an estimate of those persons with undiagnosed coronary heart di sease. The specific data and method used to estimate the prevalence rates fo r each of these subpopulations is provided in the following
subsections. The disease prevalence rates (stratified by age and gender) are used to generate a population-weighted representative sample of simulated individua ls that are then used to calculate exposure and associated COHb for the simulated at-risk subpopulations. While this

1 No adjustments were made to census estimates to reflect alternate years.
2 There were a few APEX simulations performed for purposes of obtaining the exposure and dose timeseries for each individual. These runs were 5,000 persons only. See section 5.10 for details.5-8 provides estimates of exposure and COHb le vels for subpopulations in the two study areas having the demographic characteristics (i.e., age and gender) of the two at-ri sk populations of interest, we note that the simula tion does not include any other ch aracteristics specific to the atrisk populations of interest. For example, th e CHAD diaries used to represent the simulated
individuals are not exclusiv ely drawn from a pool of diaries of persons identified by the original activity pattern survey as having th e disease state(s) of interest. 3 This limitation and its effect on exposure and dose estimates for representing the simulated at-risk subpopul ations are discussed in chapter 7 below.

5.5.1.1 Coronary Heart Disease

For estimates of adults with diagnosed C HD, staff obtained CHD prevalence data from the NHIS for 2007 (CDC, 2009). The CHD prevalence for the population at or above 18 years of age is about 6% (ISA, section 5.7.2.1). 4 We assumed the national prevalence rates for CHD were appropriate to use in each of the two study areas because there was a general similarity in the reported regional rate s. Although we desired the preval ence rates to be stratified by age and gender, the available data were stratified by age or gender. Table 5-3 provides national prevalence data for CHD by age and Table 5-4 pr ovides CHD stratified by gender. The genderonly data were used to estimate gender-specific ad justment factors to appl y to the age-only data set. For males, the adjustment factor = 0.080/0.06 1 = 1.31; for females, the adjustment factor = 0.045/0.061 = 0.74. Table 5-5 provides the estimate d national prevalence rates for CHD by age range adjusted for gender usi ng these adjustment factors.

Table 5-3. National prevalence rates for diagnosed coronary heart disease by age range.

Age range Prevalence rate (fraction) for diagnosed coronary heart disease a
18 to 44 0.009
45 to 64 0.067
65 to 74 0.186

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.

3 Note though, that the activity database did include a few activity pattern studies (e.g., NHAPS) where a disease state was identified (including having a lung or heart condition). Therefore, in sampling for diaries using attributes such as age and gender, some of the APEX simulated individuals would have their activity sequence constructed of diaries obtained from persons having a heart condition.
4 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).5-9 Table 5-4. National prevalence rates for diagnosed coronary heart disease by gender.

Prevalence rate (fraction) for diagnosed 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. Estimated national prevalence rates for diagnosed coronary heart disease, stratified by age and gender.

Prevalence rate (fraction) for diagnosed coronary heart disease Age range Males a Females b

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. b Values listed in Table 5-4 were multiplied by 0.74.

The selected at-risk population was then expa nded to also include undiagnosed cases of coronary heart disease using a method similar to that developed by OAQPS for use in the 2000 exposure assessment (see Appendix F of Johnson et al., 2000). Briefly, in the prior assessment the prevalence estimates of diagnosed IHD 5 were stratified by age and sex (Adams and Marano, 1995) and constituted approximately 8.0 million indi viduals in the civilian, non-institutionalized population. 6 In addition, as many as three to four million persons were estimated by the American Heart Association as having sile nt ischemia or undiagnosed IHD (AHA, 1990). 7 We used this information to provide estimates of the undiagnosed IHD population for use in the pNEM/CO model. We assumed 3.5 million pe rsons had undiagnosed IHD and assumed the prevalence to be distributed by age and gender in the same manner as diagnosed IHD. These data yield an adjustment factor of 0.438 (i.e., 3.5 million/8.0 million) to apply to the diagnosed prevalence for use in estimating the undiagnosed pr evalence. Consequently, this factor can be

5 The NHIS prevalence rates used in the 2000 assessment used the term IHD, rather than CHD (Adams and Marano, 1995).
6 These estimates did not include individuals in the military or individuals in nursing homes or other institutions.
7 Note that the size of this undiagnosed IHD populatio n (i.e., 3-4 million persons) is the same as that reported by AHA (2003).5-10 interpreted as the undiagnosed cases may be 43.8% of the diagnosed prevalence.
Table 5-6 lists the resu lts of applying the 0.438 factor to the age and gender stratified prevalence rates listed in Table 5- 5. This assumes that CHD and IHD are identical with respect to the ratio of undiagnosed cases to diagnosed cases and this ratio has not changed since reported in 1990 (and 2003) and assumes that undiagnosed prevalence rates would not vary by gender. 8 This total prevalence for coronary heart diseas e (diagnosed and undiagnosed combined) stratified by gender was used by APEX in estimating th e first simulated at -risk subpopulation. When using these CHD prevalence rates in the APEX model runs, there were 383,040 simulated persons (or 7.6% of the total simu lated population) with either diagnosed or undiagnosed CHD in the Los Angeles study area , while in Denver there were 53,656 simulated persons (or 8.7% of the total si mulated population) within the CHD simulated at-risk population.

Table 5-6. Estimated national prevalence rates fo r coronary heart disease, including diagnosed and undiagnosed cases, stratified by age and gender.

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 0.038 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 0.135 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. This calculation assumed CHD and IHD ar e identical with respect to the ratio of undiagnosed cases (3.5 million) to diagnosed cases (8.0 million), that this ratio has been constant since reported in 1990 and 2003, and that there is no gender difference in undiagnosed prevalence rates.

5.5.1.2 All Heart Disease

For estimates of adults with heart disease ( HD), we also obtained pr evalence data from the NHIS for 2007 (CDC, 2009). The HD prevalence for the population above 18 years of age is about 11% (Table 2, CDC, 2009). The national prevalence rates for HD were used in each of the two study areas because there was a general similarity in the reported regional rates. As described in section 5.5.2.1, although staff desired the prevalence rates to be stratified by age and gender, the available data were stratified by age or gender. Table 5-7 provides national prevalence data for HD by age and Table 5-8 provides HD stratified by gender. These gender-

8 Specific data on which to base development of differing prevalence estimates by gender for undiagnosed CHD as compared to diagnosed CHD were not identified in the limited time available for this assessment.5-11 only data were used to estimate gender-specific ad justment factors to appl y to the age-only data set. For males, the adjustment factor = 0.125/0.11 2 = 1.12; for females, the adjustment factor = 0.102/0.112 = 0.91. Table 5-9 provides the estima ted national prevalen ce rates for HD by age range adjusted for gender usi ng these adjustment factors.

Table 5-7. National prevalence rates for all types of diagnosed heart disease by age range.

Age range Prevalence rate (fraction) for all types of diagnosed heart disease a
18 to 44 0.041
45 to 64 0.122
65 to 74 0.271

75+ 0.358
Notes:

a Source: Statistics for all types of hea rt disease listed in Table 2 of NHIS (CDC, 2009), which include coronary heart disease, angina pectoris, heart attack, or any other heart condition or disease.

Table 5-8. National prevalence rates for all types of diagnosed heart disease by gender.

Prevalence rate (fraction) for all types of diagnosed heart disease a Age range Total Males Females
18+ 0.112 0.125 0.102
Notes:

a Source: Statistics for all types of hea rt disease listed in Table 2 of NHIS (CDC, 2009), which include corona ry heart disease, angina pectoris, heart attack, or any other heart condition or disease.

Table 5-9. Estimated national prevalence rates for all types of diagnosed heart disease, stratified by age and gender.

Prevalence rate (fraction) for all types of diagnosed heart disease a Age range Males Females

18 to 44 0.046 0.037
45 to 64 0.137 0.111
65 to 74 0.304 0.247

75+ 0.401 0.326
Notes:

a Values listed in Table 5-7 were multiplied by 1.12 for males and 0.91 for females using data from Table 5-8.

The same approach described in section 5.5.2.1 was used to include an estimate of the percent of persons with undiagnos ed coronary heart disease in addition to the estimate of5-12 population estimated having HD. The undiagnosed CHD prevalence from Table 5-6 was simply added to the HD to generate the prevalence rates summarized in Table 5-10. The total prevalence listed for each gender was used by AP EX to estimate the second simulated at-risk population. When using these prevalen ce rates in the AP EX model runs, there were 630,807 simulated persons (or 12.6% of the total simulated population) with HD in the Los Angeles study area, while in Denver there were 85,926 simula ted persons (or 13.9% of the total simulated population) comprising the same simulated at-risk population.

Table 5-10. Estimated national prevalence rates for all types of diagnosed heart disease plus undiagnosed coronary heart diseas e, stratified by age and gender.

Prevalence rate (fraction) for all types of heart disease
Males Females

Age range Diagnosed heart disease Undiagnosed coronary

heart disease a Total Diagnosed heart disease Undiagnosed coronary

heart disease a Total
18 to 44 0.046 0.005 0.051 0.037 0.003 0.040
45 to 64 0.137 0.038 0.175 0.111 0.022 0.133
65 to 74 0.304 0.107 0.410 0.247 0.060 0.307

75+ 0.401 0.135 0.536 0.326 0.077 0.402
Notes:

a Values obtained from Table 5-6 (i.e., undiagnosed CHD).

5.5.2 Time-Location-Activity Patterns

APEX constructs a 365-day longitudinal diary for each simulated individual by selecting
24-hour diaries from those available in CHAD. In performing the exposure assessments described in this report, all available diaries for persons a bove age 17 in the CHAD database
were used.

5.5.3 Construction of Longitudinal Diaries

As discussed in section 4.4.3.4, APEX provide s a longitudinal diar y assembly algorithm
that enables the user to create co mposite diaries that reflect the te ndency of individuals to repeat day-to-day activities (Glen et al., 2008). The user specif ies values for two statistical variables ( D
and A ) that relate to a key daily variable, typically the time spent per day in a particular microenvironment (e.g., in a motor vehicle). The D statistic reflects the relative importance of intra- and inter-personal variance within the selected key daily variable. The A variable quantifies the day-to-day autocorrelation in th e selected key daily va riable. APEX then5-13 constructs composite diaries that exhibit the statistical propertie s defined by the specified values
of D and A

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

5.6 EXPOSURE SCENARIOS In this CO REA, the exposure scenario refers to the air quality c onditions considered for each APEX simulation. Staff evaluated five expo sure scenarios for each study area. The first exposure scenario used unadjusted 2006 ambient air quality as input to APEX; this is designated
as the as is air quality exposure scenario. The purpose of this scenario is to determine the number of persons that may experience COHb le vels at or above selected benchmarks when considering current air quality c onditions. The next four exposure scenarios used ambient data from a high concentration year in each location (i.e., the 1995 mon itoring data in Denver and the 1997 monitoring data in Los Angeles) adjusted to represent different air quality conditions. The purpose of these scenarios is to determine th e number of persons that may experience COHb
levels at or above selected benchmark levels wh en considering air quality conditions that just meet a selected level, form, and averaging time of interest. This is not the same as considering exposures associated with the as is air quality conditions. The first of these four adju sted air quality exposure s cenarios considered ambient concentrations adjusted to just meet the cu rrent 8-hour CO NAAQS of 9 ppm. The 8-hour standard was selected when considering the tw o current standards (8-hour and 1-hour) because it is the controlling standard. 9 The second of these exposure scenarios using the historical

9 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).5-14 monitoring data also considered the form of the current 8-ho ur CO standard, but with the ambient concentrations in each study adjusted to meet an alternative standard level of 5 ppm. The next two scenarios considered percentile form s of potential alternative standards, consistent with the alternative standards investigat ed for other criteri a pollutants (e.g., NO 2 (US EPA, 2008c); and SO 2 (US EPA, 2009b)). The first of these po tential percentile forms considered a
99 th percentile daily maximum 8-hour average CO concentration of 5.0 ppm, while the second considered the same form though with a 1-hour av eraging time and a 1-hour level of 8.0 ppm. Details regarding the concentration adjustments asso ciated with each of the current and potential alternative standards are pr ovided in section 5.7.3. Tables 5-11 and 5-12 provide perspective on the selected levels and the air quality used to represent each scenario in Denver and Los A ngeles, respectively. An array was constructed using the varying air quality scenarios to indicat e how a single APEX run using a particular air quality input data set might reflect different leve ls and forms of potential alternative standards. For example, in Denver, the exposure and dose results for the as is scenario would be the same as a standard level of 3.1 ppm in terms of s econd highest non-overlapping 8-hour average (Table 5-11). The generated results would also repres ent exposures and doses experienced considering
a 99 th percentile 1-hour daily maximu m concentration of 4.5 ppm.5-15 Table 5-11. Array of alternative standard forms a nd levels informed by modeled exposure scenarios in Denver.

Denver Design Values (ppm)
Averaging Time & Form 8-hour 1-hour

Air Quality Scenario2ndhighesta99thpercentile daily max2ndhighesta99thpercentile daily max

As Is 3.1 2.8 4.6 4.5
Current 8-hour standard (9 ppm) b 9.4 7.2 16.2 13.3

2 nd highest 8-hour average (5 ppm) b 5.4 4.1 9.3 7.7

99 th percentile daily max 8-hour (5.0 ppm) 6.5 5.0 11.2 9.2

99 th percentile daily max 1-hour (8.0 ppm) 5.6 4.3 9.7 8.0

Notes:

a This is the form of the current standards. b Note that the rounding convention for the current standard allows for concentrations of up to the given standard level plus 0.4 ppm.5-16 Table 5-12. Array of alternative standard forms a nd levels informed by modeled exposure scenarios in Los Angeles.

Los Angeles Design Values (ppm)
Averaging Time & Form 8-hour 1-hour

Air Quality Scenario2ndhighesta99thpercentile daily max2ndhighesta99thpercentile daily max

As Is 5.6 5.1 8.2 7.4
Current 8-hour standard (9 ppm) b 9.4 8.2 11.8 11.6

2 nd highest 8-hour average (5 ppm) b 5.4 4.7 6.8 6.7

99 th percentile daily max 8-hour (5.0 ppm) 5.7 5.0 7.2 7.1

99 th percentile daily max 1-hour (8.0 ppm) 6.5 5.7 8.1 8.0

Notes:

a This is the form of the current standards. b Note that the rounding convention allo ws for concentrations of up to the given standard level plus 0.4 ppm.

5.7 AMBIENT AIR QUALITY DATA

5.7.1 Unadjusted 1-Hour Ambient Concentrations

Ambient monitoring data serve as an impor tant input in estimating CO exposure and dose. Descriptive statistics were generated fo r the hourly CO concentrations measured at the identified ambient monitors in each study area and monitoring year (Tables 5-13 to 5-16). As expected, CO concentrations in the high concentr ation year (1995 or 1997) are about a factor of two or more greater than the more recent year (2006) of ambient monitoring data in either study area. In general, there is sim ilarity in the concentration distri bution for both study areas within a given year, with the following exceptions. Th ere is one monitor in Los Angeles (ID 37-1301) that consistently reported exceptionally high con centrations when compar ed with the other Los Angeles monitors for either year. In addition, th ere is a sharper rate of increase in the upper percentile concentrations (i.e., ≥ 95 th percentiles) in Denver when co mpared with the Los Angeles ambient concentration distribution, for either year.5-17 5.7.2 Method for Estimating Missing 1- Hour Ambient Concentrations

APEX requires that each site-y ear of monitoring data be comp lete (i.e., it is free of hourly gaps in concentration levels). The missing va lues in each data set were estimated by the sequential application of the following four methods.
1) If the data gap was less than six conti nuous missing values, the missing values were estimated by linear interpolation using the valid values at the ends of the gap. 2) Where possible, data gaps of at least six hours were estimated as linear functions of hourly values reported by other ambient CO monitors in the area. Linear regression was used to develop a set of models that were specific to time-of-day and monitor. The model selected to estimate missing values fo r a particular time of day was the model that maximized the variance explained (R 2 ) for that hour, subject to the constraints that regression model R 2 was greater than 0.5 and the num ber of available measurements used in constructing the model was at least 50. 3) In cases where method 2 (above) could not be used (i.e., no regression models were available for a particular time-of-day) and the gap was less than nine hours, the missing values were estimated by linear interpolati on between the valid values at the ends of the gap. 4) All remaining missing values were substitute d with the 1-hour concentration from the same day and hour as the nearest monitor. The hourly concentration used was normalized to the respective monitors ’ monthly mean concentrations.

Tables 5-13 to 5-16 provide the descriptive st atistics for 1-hour CO concentrations in each data set, before and after estimating mi ssing values, and consider ing the two years of ambient monitoring data in each study area. The excellent agreement between concentrations at the various percentiles of the distribution (bef ore and after substitution) indicates that the addition of the estimated missing-value concentra tions did not significantl y affect the overall distributions of the hourly CO concentrations.5-18Table 5-13.Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing values –Denver 1995.CO 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 5-14.Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing values –Denver 2006.CO 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 3.65-19Table 5-15.Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing values –Los Angeles 1997.CO 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 11.95-20Table 5-16.Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing values –Los Angeles 2006.CO 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 6.05-21 5.7.3 Adjusted 1-Hour Ambient Concentrations

In addition to modeling exposures based on recent as is air quality (i.e., ambient monitoring data for year 2006), exposures and re sulting dose were estimated for air quality conditions that just meet the current 8-hour CO NAAQS and various alternative standards under
evaluation. Because CO concentrations in recent years were significantly lower than the current NAAQS, staff first selected an earlier year for each city (1995 for Denver and 1997 for Los Angeles) to represent air quality conditions that were near the current 8-hour CO standard. Consistent with the data adjustment approach employed in the previous draft CO exposure assessment (Johnson et al., 2000), and approaches us ed in prior REAs supporting other pollutant NAAQS reviews (e.g., US EPA, 2008c; US EPA, 2009b), staff concluded (1) that the policyrelevant background levels of CO were negligible in each area (section 3.1.4), and (2) that the fixed-site monitoring data could be adjusted to simulate just meeting the current CO standards by use of a simple proportional adju stment of all hourly values (s ection 3.1.5). Consequently, the following adjustment equation was employed:

CO adj ( m , h ) = (NAAQS/DV) x CO( m , h ). (5-1)

CO( m , h ) is the 1-hour CO concentration at hour h for monitor m . It follows that CO adj ( m , h ) is the adjusted CO concentration for hour h at monitor m through the use of the specific design value (DV) for monitor m . Although the current 8-hour NAAQS for CO specifies a maximum concentration of 9 ppm, which is not to be ex ceeded more than one time in a year, the NAAQS term in Equation 5-1 is equivalent to 9.4 ppm due to the application of a standard data rounding
convention used in calculati ng design values (DVs) for CO. 10 The DVs for Denver (1995) and for Los Angeles (1997) were 9.5 ppm and 15 ppm, respectively. The Denver DV is calculated as the second-highest 8-hour average CO concentration reported by monitor ID 080310002 for 1995. The adjustment factor (or NAAQS/DV) that was applied equa lly to all 8,760 hourly ambient CO concentrations at that monitor is thus 9.4/9.5, or 0.989. In a similar mann er, the DV used in Los Angeles is the secondhighest 8-hour average CO concentration reported at monitor ID 060371301 for 1997, giving an ambient concentration adjustment factor of 9.4/15, or 0.627 which was applied equally to all 8,760 hourly ambient CO concentrations from the Los Angeles monitor.

10 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-hour 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.pdf5-22 Staff evaluated three additional air quality scenarios considering potential alternative standard levels, averaging times, and forms. A ssuming a similar form and averaging time of the current 8-hour standard (2 nd highest non-overlapping 8-hour av erage CO concentration), staff selected a level of 5 ppm for the fi rst potential alternative standard. 11 As was done for other recent NAAQS reviews (US EPA, 2008c; US EPA, 2009b), staff selected percentiles of the air quality distribution and averaging times to identifyi ng potential levels associated with alternative standards. The second potential alternative standard considered by staff also uses an 8-hour average concentration, though having a 99 th percentile daily maximu m CO concentration of 5.0

ppm. 12 The final potential alternative sta ndard that staff evaluated was a 99 th percentile daily maximum 1-hour CO concentration of 8.0 ppm. Table 5-17 summarizes the adjustment factors that were developed from equati on 5-1 and used to adjust the hi gh concentration year air quality data in each study area.

Table 5-17. Design values and adjustment factors used to represent air quality just meeting the current and potential alternative standards.

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 0.382 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.

Tables 5-18 and 5-19 provide the descriptiv e statistics for the Denver and Los Angeles ambient monitor 1-hour CO concentrations, respectively, after applying the appropriate

11 Note that this would allow a 2 nd highest non-overlapping 8-hour concentration up to 5.4 ppm (hence the design value).
12 It was assumed that there are an infinite number of zeros, that is, the level is exactly 5.0 ppm. This rounding convention also applies to the other potential alternative standard selected; the level of the 99 th percentile 1-hour daily maximum is exactly 8.0 ppm.5-23 adjustment factor to simulate just meeting th e current standard. As expected, the adjusted monitoring concentrations for De nver 1995 are very similar to the unadjusted data set given that the adjustment factor used was close to unity. For example, the maximum concentration at the design monitor was reduced from 24.5 ppm to 24.2 pp m. The change in CO concentrations was much greater in Los Angeles compared with that of Denver as a result of differences in the adjustment factor used in each study area. For example, the maximum CO concentration at the design monitor in Los Angeles was reduced from 19.2 ppm to 12.0 ppm. Considering the patterns described above in se ction 5.7.1 for the unadjusted air quality and given that the concentration adjustment was proportional, additional remarks can be made regarding differences in the air quality adjusted to ju st meet the current 8-hour CO NAAQS. When comparing the adjusted concentrations in Denver a nd Los Angeles, there is still a sharper rate of
increase in CO concentra tions at and above the 95 th percentiles of the distribution, only now all of the Denver monitors have greater CO con centrations at these upper percentiles when compared with concentrations observed at a ll of the Los Angeles monitors (excluding concentrations at the Los Angeles design monitor). Given the proportional approach used to adju st ambient concentrations for each of the other exposure scenarios (e.g., 99 th percentile daily maximum 1- hour concentration of 8.0); similar patterns in concentrations were e xpected and are therefor e not summarized here.5-24Table 5-18.Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just meet the current 8-hourstandard – Denver (adjusted 1995 data).Hourly-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 4.5Table 5-19.Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just meet the current 8-hourstandard – Los Angeles (adjusted 1997 data).Hourly-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 3.65-25 5.8 METEOROLOGICAL DATA A few algorithms within APEX require meteorol ogical data (primarily temperature) from
stations located within the study area. For example, in sel ecting a CHAD diary to simulate an individual’s daily activities, a ra nge of daily maximum temperatures is used to categorize diaries for sampling purposes so as to best match the temperature observed on the simulation day within the study area (section 5.5.4). In addition, mean te mperatures are used by APEX to select from an appropriate air exchange rate distribu tion to estimate indoor microenvironmental concentrations (section 5.8). Fo r the analyses described in this report, hourly temperature data were obtained from meteorological stations located at or near the fixed-site CO monitor specified for each study area. Tables 5-20 and 5-21 list the meteorological st ations we used in modeling the Denver and Los Angeles study areas, respectively. Ideally, staff would have used the same station (Long Beach: 37-4002) matched for both monitoring year s (1997 and 2006) in Los Angeles. Because this station did not report a complete year of data for 1997, we substituted data reported by the Long Beach Daugherty Field station located ap proximately 3.6 km from the 37-4002 station. The same two stations (31-0002 and 59-0002) we re used for the Denver study area for 1995 and Denver 2006, because there were adequate data for both years for both sites. To run APEX, a “ZoneRadius” is specified by the user as the maximum radius for the region surrounding each meteorological station that wi ll be represented by the temperature data provided by the station. In this a ssessment, we set this to a valu e that includes a ll census tracts
within the air districts. A radius of 15.5 km met this requi rement for Denver (Figure 5-1), while Los Angeles required a larger radi us of 70.5 km (Figure 5-2).

Table 5-20. Locations of meteorological st ations selected for Denver.

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 51.55-26 Table 5-21. Locations of meteorological stat ions selected for Los Angeles.

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 Missing 1-Hour Temperature Data

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

13 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 temperature 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.5-27 data set. Because site 1 had fewer missing values than site 2 for 2006, site 1 was selected as the primary meteorological site to represent the Los Angeles area for that year. For the one missing value on Site 1 in 2006, the corresponding temperatur e from Site 2 was used to fill the missing value for 2006. For 1997, there were 2,263 missing va lues on Site 1 while only 9 missing values on Site 2. As a result, Site 2 was selected as the primary meteorological station for 1997. Two of the nine missing values from S ite 2 were available from Site 1. Therefore, these temperatures were directly substituted with values from the corresponding hours of the Site 1 data set. To fill the remaining seven missing values, we used linear interpolation by connecting successive straight line segments and fitting a continuous curve to the data. 14 The temperature distributions before and af ter filling missing values were compared at for each station in each year to assess the impact (if any) of the substitution method. Given the limited number of missing values in the original data sets, there were negligible differences when comparing mean, median, varian ce and percentile statistics.

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

5.9.1 The Microenvironmental Model as Implemented by APEX4.3

Section 8.2.2 of US EPA (2008b) indicates that the mass balance model in APEX4.3 models the portion of outdoor air that enters the microenvironment as

CO out = f proximity x f penetration x CO ambient (5-2)

Since this is effectively equivalent to th e method used by APEX3.1 described in section 4.4.4.4, we used the same method here with resp ect to application of the proximity and penetration factors in APEX4.3 to implement equa tion 4-11. First, to obt ain the appropriate CO concentrations outside each microenvironment, ambient CO concentration were adjusted by an exponential factor of 0.621 (see equation 4-22 ). Then for each profile, a value for f proximity term would be sampled for each microenvironment fr om a lognormal distribution with geometric mean (GM) equal to M(m) and geometric standard deviation (GSD) equal to 1.5232. A value for f

penetration for each hour would also be sampled from a lognormal distribution with geometric mean (GM T ) equal to 1.0 and geometric standard deviation (GSD T ) equal to 1.6289. As

14 This was done in SAS using a procedure, PROC EXPAND, along with the JOIN option.5-28 described in section 4.4.4.4, the f penetration values were bounded at the lower and upper tails of the distribution by 0.4482 and 2.2313, respectively. Table 5-16 presents the algorithm parameters proposed for the eight microenvironments currently defined for the app lication of APEX to Los Ange les and Denver. These eight
microenvironments were selected rather than th e fifteen selected in earlier assessments (see Tables 4-4 and 4-5) based on the locations having the same proximity factors and air exchange rates distributions, or when using a similar mi croenvironmental approach (see section 5.9.5). Note that when this algorithm is impl emented within the APEX framework, the application of Equation 4-11 will produce a “comp ression” effect in which the ratio of CO out to
CO mon tends to become smaller (on average) as CO mon increases. This effect is consistent with data reported by field studies such as Wilson et al. (1995) which ha ve compared outdoor
concentrations with simultaneousl y measured fixed-site concentr ations. Note also that the
effective microenvironment-to-ambient concentra tion ratios will differ from the proximity factor distributions provided in Table 5-22 given the influence of othe r variables used in equation 4-11. An analysis of these ratios (e .g., in-vehicle to ambient monitor concentration) is provided in
section 6.1.5-29 Table 5-22. Parameters of bounded lognormal distri butions defined for proximity factors used in the application of APEX4.3 to Los Angeles and Denver.

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 6.03445-30 5.9.2 Microenvironmental Mapping

In APEX, microenvironments repr esent the exposure locations for simulated individuals. For exposures to be estimated accu rately, it is important to have realistic microenvironments that match closely to the locations wher e actual people spend time on a daily basis. It is necessary to map the CHAD location codes to one of the eight specific microenvironments selected for this exposure assessment or to a supplemental category (either -1 or 0 ). As a reminder, these eight microenvironments were selected based on having suitable data to use for proximity factors and air exchange rates (when using a mass balance approach). The -1 code is assigned to events
where the location code is missi ng (X) or the location is classi fied as uncertain (U); the -1 code
instructs APEX to use the last known microenvironment for that person’s diary in determining the exposure concentration. The 0 code is assigned to an airplane microenvironment (CHAD location code: 31160) and instructs APEX to set the exposure concentration equal to 0 ppm. Figure D-1 in Appendix D describes the specific mapping of CHAD codes to
microenvironments. The microenvironment mapping file also perm its the user to assign a home/work/other (H/W/O) location to each CHAD location code. The home/work/other location determines the source of the hourly-average monitoring data that will represent the ambi ent CO concentration for the microenvironment: the home district monitor, the work district monitor, or other. The initial APEX assignments of H/W/O to the CHAD location codes were used as a starting point (see Figure D-1 in Appendix D) and modified using a few of the options available in APEX. First, staff overrode the H/W/O de signations listed in the microenvironment mapping file for selected activities by compiling a list of CHAD activity codes that will always be associated with the work district (regardless of th e CHAD location code). This list is inserted in the “CustomWork” parameter found in the simulati on control file. The default list of work activity codes, which were used in this a pplication, includes code s 10000 through 10300 (see Appendix D Table D-1). As a result of using this option, APEX will assign the simulated person
to the work district whenever the activity code falls between 10000 and 10300. This assignment will override the home/work/assignment a ssociated with the applicable CHAD location code

. There will still be exposure events in which the simulated person is assigned to the
“other” location. In the default mode, APEX uses an average of all monitor values to determine
the ambient concentration for these events. Note that this averaging approach will tend to smooth the data; that is, it will produce ambient CO concentrations that have slightly less variance than a comparable set of ambient concen trations obtained from a single monitor. To avoid this effect, staff chose to specify the opti on OtherDistricts = 1, so that only one monitor is used to represent “other.” The monitor used in the model application is randomly selected from
the set of all monitors.5-31 5.9.3 Selection of Microenvironmental Method Used

As discussed in chapter 4, the two approaches available in APEX for calculating pollutant levels within microenvironments are (1) the mass balance method and (2) the factors method.

Table 5-23 lists the microenvironments used in this study and the calculation method used.

Table 5-23. List of microenvironments modele d and calculation methods used.

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

5.9.4 Air Exchange Rates and Air Conditioning Prevalence

For the microenvironments using the mass balance method (i.e., all indoor microenvironments), air exchange rate (AER) and air conditioning prevalence data are needed to

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

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

and 10 were selected for mini mum and maximum AER, respec tively. Tables 5-24 and 5-25

15 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.5-32 summarize the AER distributions used in m odeling indoor exposures in Denver and Los Angeles, respectively, each classified by A/C pr evalence and temperature categories. For all
other indoor microenvironments, the AER distribu tions used here (Tables 5-24 and 5-25) were based on data provided by an indoor air quality st udy (Persily et al., 2005). These are the same

AER distributions used for the APEX assessments in the most recent O 3 NAAQS review (US EPA, 2007), NO 2 REA (EPA, 2008c) and SO 2 REA (US EPA, 2009b).

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

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

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

Table 5-24. Lognormal distributions of indoor ai r exchange rates used in Denver.

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

≤ 50 0.919 1.859 0.1 10.0

50 – 68 0.564 1.940 0.1 10.0

68 – 77 0.468 2.201 0.1 10.0

77 – 86 0.424 2.037 0.1 10.0 Yes b

86+ 0.567 1.945 0.1 10.0

≤ 50 0.926 2.084 0.1 10.0

50 – 68 0.733 2.330 0.1 10.0 Indoors - residence

No

68+ 1.378 2.276 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.5-33 Table 5-25. Lognormal distributions of indoor air exchange rates used in Los Angeles.

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.

5.10 ADDITIONAL EXPOSURE AND DOSE OUTPUT GENERATED USING REDUCED APEX SIMULATIONS In a typical model run, APEX generates a co mplete time-series of exposure and dose for
each simulated individual based on the microenviro nmental concentrations they contact and the activities they perform. Because there are us ually thousands of simulated persons and thus thousands of exposure and dose profiles, it is common practice that only summary output data are generated. As there are 8,760 hours in a year and potentially multiple events within an hour, the large size of these time-series profile files presents computational challenges for the data analyst, and increases the model run time. In this assessment however, we were interested in additional exposure and dose output data not summarized by the typi cal APEX output files. The
APEX events and hourly files provide event-level and hour ly-level exposure and dose profiles for all simulated individuals (US EPA, 2008a, 2008b) . The time-series of exposure and dose for each individual are useful in performing three important analyses in this assessment, each described in the following sections.5-34 5.10.1 Estimate of Microenvironment Contribut ion to At-Risk Population Exposure Levels The first analysis using the individual-leve l exposure and dose output was designed to identify the microenvironments that are most influential to different levels of population

exposure. 16 To simplify the presentation of the data in this analysis, the total time spent in each of the modeled eight microenvironments was aggreg ated into five microenvironments to evaluate time spent by simulated persons at selected e xposure levels. The aggregation was based on microenvironments having similar proximity fact ors (see Table 5-22), re sulting in aggregated
microenvironments defined as follows: indoor s-low (indoor-residence an d indoor-other A&B), indoors-high (indoors-auto servic e/repair), outdoors-low (o utdoors-other and near-road), outdoors-high (outdoor-parking/gas station), inside-vehicles. In addition, the total time the CHD population spent at or above each exposure level was calculated.

5.10.2 Estimate of Microenvironment-to-Ambient Concentration Ratios

The second analysis using the individual-le vel exposure and dose out put was designed to estimate the effective microenvironmental factors, or the ratio of modeled microenvironmental
concentrations to associated ambient monitor con centrations. The distribu tions of these effective microenvironmental factors are then comparedto commonly reported microenvironment-toambient concentration ratios that are based on measurement data. The use of these estimated microenvironmental factors for comparison is more informative than simply using the distribution of proximity factors given in Table 5-22. As noted ear lier, the series of factors in equation 4-11 are designed to spatially and temp orally adjust the ambient concentrations to
reflect concentrations immediatel y proximal to the microenvironm ent. The proximity factors listed in Table 5-22 would effectively reflect microenvironment-to-spa tially and temporally adjusted ambient concentration ratios, not mi croenvironment-to-fixed site ambient monitor concentration ratios commonly reported in the extant human exposure literature.
The microenvironment-to-ambient concentratio n ratios were calcula ted by dividing each
estimated event-level microenvironmental CO concentration by its corresponding hourly CO
ambient monitor concentration. 17 In summarizing these microenvironment-to-ambient concentration ratios, we excluded those ratios that were associat ed with ambient concentrations less than 1 ppm. These calculated ratios using the event-level modeled concentrations tended to

16 The standard APEX summary output table only generates microenvironment contributions for the general population (i.e., it is not necessarily representativ e of the demographics of the CHD or HD population).
17 Note that event-level ambient concentration is not a variable that can be output from APEX at the time of analysis. The 1-hour ambient concen tration in the APEX hour ly file does account for when a person might experience ambient concentrations outsid e their home tract (e.g., when commuting). When this does occur, the 1- hour concentration would be time-averaged based on the time spent in each air quality district for each event during the hour, effectively approximating an aver age event-level ambient concentration.5-35 be extremely large, particularly when considering the as is air quality, as a result of dividing by extremely low ambient concentrations rather th an the calculation met hod employed in estimating
the microenvironmental concentrations. Inclusion of these ratios (while valid) would be of little practical use in intepreting the microenvironm ent-to-ambient concentration ratios and high
microenvironmental concentrations because th e design of the microenvironmental algorithm results in the highest concentra tions being driven largely by the high ambient concentrations, not by these extreme ratios. For example, Figure 5-3b illustrates an inverse relationship between the
microenvironment-to-ambient concentration ratios and es timated microenvironmental concentration. Any large proxim ity factors that might have occurred when randomly sampling
from the distributions used in Table 5-22 were effectively modified by the ambient concentration exponential adjustment (equation 4- 11), thus controlling for extr eme ratio and high concentration combinations in estimating the microenvironmen tal concentrations. Al though the calculated ratios can reach extremely high values (> 100) , they are not responsible for estimating the highest microenvironmental concentrations. Th e full distribution representing all event-level
microenvironmental concentrations estimated to be experienced by the CHD population is also included in this analysis.

Figure 5-3. Relationship between microenvironmen t-to-ambient concentration ratios using estimated indoor-residential con centrations (left panel) and insidevehicle concentrations (right panel) in Denver – as is air quality.

5.10.3 Estimate of Ambient Exposure Co ntribution to Total COHb Level

In the third analysis, we were interested in the contribution of am bient CO exposure to
each individual’s COHb dose level above thei r endogenous CO production. As summarized in5-36 section 4.4.7 and described fully in Appendix B, we estimated COHb levels in each simulated individual using the CFK dose module within AP EX. Theoretically, in the absence of ambient
concentrations or other sour ces of CO, one can perform an APEX simulation to estimate endogenous CO production and its effect on COHb levels. Two APEX model simulations were performed for each location and air quality scenario evaluation: one using ambient concentrations and the second using ambient con centrations equal to zero. Each of the new runs simulated the complete dose time-series for each individual. By design, the simulated persons in each of these two model runs line up perfectly in terms of physiology and activities performed, enabling staff to compare the COHb le vels experienced across the two runs event-byevent. We calculated all event-by-event ambient contributions ( COHb ambient contribution ) to corresponding COHb levels (i.e, COHb ambient contribution = % COHb with ambient exposure minus % COHb for zero exposure), effectively giving the ambient cont ribution to estimated
COHb levels. Consistent with the dose metric of interest in this assessment, the daily maximum end-of-hour COHb level is calculated using each individual’s entire dos e profile, only in this instance it is the daily maximum end-of-hour cont ribution to COHb associ ated with ambient CO

exposure.

5.10.4 Comparison of the Exposure and Dose Results Generated Using 50,000 Persons Versus 5,000 Persons Simulation For each of these particular model runs, AP EX generated the complete time-series of exposure and dose for 5,000 persons, of which th ere were approximately 400 CHD simulated
individuals. 18 The CHD prevalence rates were used to simulate the at-risk population for two air quality scenarios in each study area: as is air quality and just meeti ng the current standard. The complete output and analysis of these data are provided in chapter 6; however, a comparison of the summary results is provided here to justify the representativeness of the smaller sample run size. The summary exposure and dose results from the smaller runs (i.e., percent of persons and person-days at or above selected levels) were very similar to the results generated using a 50,000 person simulation for either scenario and study ar ea. Table 5-26 summarizes the exposure output
for as is air quality, while Table 5-27 summarizes the summary dose output for air quality just meeting the current standard; little differen ce can be observed when comparing the 50,000 person simulation to the 5,000 person simulation. Therefore, it is likely that the time-series results generated for the smaller population are representative of the larger modeled CHD population. 19

18 Of the 5,000 person model simulations, APEX simulated 438 CHD persons in Denver and 394 CHD persons in Los Angeles.
19 Given that the results generated using the CHD and HD populations were also generally similar when comparing the percent of persons and person-days (e.g., see Table 6-1), the microenvironmental contributions (and5-37 Table 5-26. Comparison of exposure summary output generated when simulating 50,000
persons versus that of simulating 5,000 persons – as is air quality.

Denver CHD Population a,b,c Los Angeles CHD Population a,b,c Percent of

Persons Percent of Person-days Percent of Persons Percent of Person-days Daily Maximum Exposure

(ppm) 50K Run 5K Run 50K Run 5K Run 50K Run 5K Run 50K Run 5K Run
≥ 0 100 100 100 100 100 100 100 100
≥ 3 100 99.3 11.2 10.5 100 100 16.9 17.1
≥ 6 60.6 59.6 0.9 0.8 75.1 78.2 1.7 1.7
≥ 9 19.9 16.4 0.1 0.1 32.0 28.4 0.3 0.3
≥ 12 5.7 5.0 <0.1 <0.1 11.2 10.9 0.1 0.1
≥ 15 1.6 1.1 <0.1 <0.1 3.6 4.3 <0.1 0
≥ 20 0.1 0 <0.1 0 0.6 1 <0.1 0
≥ 30 0 0 0 0 <0.1 0 <0.1 0 1- hour

≥ 40 0 0 0 0 0 0 0 0
≥ 0 100 100 100 100 100 100 100 100
≥ 3 58 55.3 1.0 0.9 77.0 78.4 2.7 2.7
≥ 6 3.2 3.0 <0.1 <0.1 9.5 9.1 0.1 <0.1
≥ 9 0.1 0.2 <0.1 <0.1 0.8 0.8 <0.1 <0.1
≥ 12 <0.1 0 <0.1 0 0.1 0.3 <0.1 <0.1
≥ 15 0 0 0 0 0 0 0 0
≥ 20 0 0 0 0 0 0 0 0
≥ 30 0 0 0 0 0 0 0 0 8- hour

≥ 40 0 0 0 0 0 0 0 0 Notes:

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Includes estimate of persons with undiagnosed isch emia developed by EPA (see section 5.5.1).

c Unadjusted ambient concentrations from four monitors (Denver) and ten monitors (Los Angeles) in 2006 were used to represent the As Is air quality scenario.

microenvironmental factors) estimated from the CHD popul ation simulations are also likely representative of the simulated HD population.5-38 Table 5-27. Comparison of dose summary output generated when simulating 50,000
persons versus that of simulating 5,000 pers ons – just meeting the current standard.

Denver CHD Population a,b,c Los Angeles CHD Population a,b,c Percent of

Persons Percent of Person-days Percent of Persons Percent of Person-days COHb Level

(%) 50K Run 5K Run 50K Run 5K Run 50K Run 5K Run 50K Run 5K Run 0.0 100 100 100 100 100 100 100 100 1.0 76.3 74 4.2 3.9 40.5 44.2 1.8 2.0 1.5 19.1 18 0.2 0.2 4.6 4.8 0.1 <0.1 2.0 3.4 3.7 <0.1 <0.1 0.5 0.8 <0.1 <0.1 2.5 0.6 0.7 <0.1 <0.1 0.1 0.3 <0.1 <0.1 3.0 0.2 0 <0.1 0 <0.1 0.3 <0.1 <0.1 4.0 0 0 0 0 0 0 0 0 Notes:

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Includes estimate of persons with undiagnosed isch emia developed by EPA (see section 5.5.1).

c Ambient concentrations from 1995 (Denver) and 1997 (Los Angeles) were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm.

5.11 KEY OBSERVATIONS The following presents the key observations for this chapter:

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

 Two simulated at-risk subpopulations were id entified by combining the census tractspecific age and gender populat ion distributions with HD and CHD prevalence rates, each also stratified by age and gender. In us ing this approach, staff can represent the variability that exists in the simulated at-risk HD and CHD subpopul ations that reside in each census tract and with in each study area.

 Both simulated at-risk subpopulations in clude an estimate of persons with undiagnosed CHD.

 To represent spatial variability in ambient concentrations in Denve r, a total of four monitors were used; in Los Angeles, the to tal number of monitors was ten. Temporal variability was represented by use of hourly ambient concentrations in each study area.

 The exposure and dose model simulations in cluded 8 microenvironments in each location to represent the expected va riability in microenvironmental CO concentrations.5-39  All indoor microenvironments were modeled using a mass balance model to represent temporal variability in indoor CO concentr ations with respect to the outdoor CO concentration variability. In addition, distributions of mi croenvironmental factors were used for all microenvironments rather than point estimates. Using distributions of microenvironmental factors will better represen t both spatial and temporal variability in estimated microenvironmental CO concentrations.

 Additional analyses using output from indivi dual-level simulations were performed to provide information on the microenvironments most influential to population exposure at different exposure levels. This include d an analysis of the effective ratios of microenvironment to ambient concentrati ons and the contribution of ambient CO exposure to total COHb level estimates. Th e smaller sample sizes generated for these analyses were found to be representative of the larger simulations employed for estimating exposure and dose in the differe nt air quality exposure scenarios.5-40 5.12 REFERENCES

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

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

American Heart Association. (1990). Heart and Stroke Facts. American H eart Association, Dallas, TX. p.13. As cited by Johnson et al. (2000).

American Heart Association. (2003). Heart and Stroke Facts. American Heart Association, Dallas, TX. p.14. Available at: http://www.americanheart.org/down loadable/heart/1056719919740HSFacts2003text.pdf

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

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

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

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

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

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

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

. US EPA. (2007). Ozone Population Exposure Analysis fo r Selected Urban Areas. O ffice of Air Quality Planning and Standards, Research Triangle Park, NC. July 200 7. Report no. EPA-452/R-07-010. Available at: http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_07_o3_exposure_tsd.pdf

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

US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report no. EPA-452/B-08- 001b. Office of Air Quality Planning and Standard s, U.S. Environmental Protection Agency, Research Triangle Park, NC.5-41 US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO 2 Primary National Ambient Air Quality Standard. Report no. EPA-452/R-08-008a. November 2008. Available at: http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf

. US EPA. (2009a). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary National Ambient Air Quality Standard. First External Review Draft. Report no. EPA-452/P-09-008. Available at: http://www.epa.gov/ttn/naaqs/standards/ co/data/COREA1st DraftOct2009.pdf

US EPA. (2009b). Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009. Available at http://www.epa.gov/ttn/naaqs/standards/s o2/data/200908SO2REAFinalReport.pdf.

US EPA. (2010). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary National Ambient Air Quality Standard. Second External Revi ew Draft. Report no. EP A-452/P-10-004. Available at: http://www.epa.gov/ttn/naaqs/standards/co/data/COREA2ndDraftFeb2010.pdf

Wilson AL, Colome SD, Tian Y. (1995). California Resi dential Indoor Air Quality Study. Volume III: Ancillary and Exploratory Analysis. Integrated Environmental Services, Irvine, California.6-1 

### 6 SIMULATED EXPOSURE AND COHB RESULTS

This chapter summarizes the CO exposure and dose results for the Denver and Los Angeles study areas that were generated using EPA’s APEX model descri bed in chapters 4 and 5. Staff considered exposures associated with five air quality scenarios; air quality (1) unadjusted or as is , (2) adjusted to just meet the current 8- hour standard of 9 ppm, (3) adjusted to just meet a 2 nd highest 8-hour average con centration of 5 ppm, (4) adjusted to just meet a 99 th percentile daily maximum 8-hour average of 5.0 ppm, and (5) adjusted to just meet a 99 th percentile daily maximum 8-hour average of 8.0 pp m (see section 5.7 for details regarding the air quality adjustment procedure). This chapter is divided into f our main sections, with each described briefly below. The first section (6.1) summarizes the estima ted exposures associated with each of the five air quality scenarios. As described in section 5.5.1, two at-risk subpopulations were
simulated in this assessment. The first simulated at-risk subpopul ation includes individuals with diagnosed CHD as well as those pers ons with potentially undiagnosed CHD. 1 For simplicity, they will be combined and referred to as the CHD population in this chapter. The second simulated at-risk population include s individuals with diagnosed h eart disease (HD) as well as those persons with potentially undiagnosed CHD. For simplicity, this subpopulation will be
combined and referred to as the HD population in this chapter. The primary exposure metrics of interest in this REA and generated by APEX in clude the number and percent of persons at or above staff-selected exposure levels a nd the corresponding number of person-days. 2 Two exposure averaging times were al so selected: 1-hour and 8-hour daily maximum exposures.
Section 6.2 summarizes the estimated COHb leve ls for persons in the simulated at-risk population residing in each study area. The primar y dose metric of interest in this REA and generated by APEX includes the number and percen t of persons at or above staff selected COHb levels and the corresponding number of person- days. Consistent with prior CO exposure assessments, the daily maximum end-of-hour C OHb level was reported. This section also presents an evaluation of the ambient contri bution to COHb levels for APEX simulated individuals for three of the air quality scenarios.

1 As described in section 5.5.2, 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, which includes CHD, angina pectoris and hear t attack) and all types of heart diseas e (HD, which includes coronary heart disease, angina pectoris, heart attack, or any other heart condition or disease) provided by the National Health Interview Survey, each combined with 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

.6-2 In section 6.3, staff compares the dose estima tes in this CO REA with those estimated in the 2000 exposure assessment (Johnson et al., 2000). Finally, key observati ons are presented in the final section (6.4). As mentioned in Chapter 1, due to the extremely tight timeline for this NAAQS review, the exposure and risk resu lts are provided here without substantial interpretation. Rather, interpretative discussion of these results is provided in the CO Policy
Assessment.

6.1 ESTIMATED EXPOSURES

This section summarizes the estimated exposures for the simulated individuals in a series of tables, classified by the five air quality s cenarios and two study areas considered. Given the complexity of the simulations and output data requirements, a limited number of additional modeling runs were added to this final assessment. First, in expanding the at-risk population to include persons with all types of heart disease, the standard AP EX output data (i.e., number and percent of persons and person-days at selected exposure levels) were generated for all five air quality scenarios and summarized. Add itional exposure output data sets (i.e.,
microenvironmental contributions to selected exposure levels and the evaluation of
microenvironmental factors distri butions) were generated using a smaller simulation size (i.e., 5,000 persons) and the CHD population fo r two air quality scenarios (i.e., as is and just meeting the current standard). The smaller simulation size was used to reduce the size of the output file under analysis. See section 5.10 for details on the approach used and evaluation of the representativeness of the simulation si ze and at-risk population used.

6.1.1 Air Quality “As Is”

As described in section 5.6, ambient monitori ng data from each study area for the year 2006 were used to represent the as is air quality. Table 6-1 summari zes the distribu tion of the 1- hour and 8-hour daily maximum CO exposures experienced by the CHD and HD populations in the Denver Study area. About 80% of the simu lated CHD population did not experience a 1- hour daily maximum exposure at or above 9 ppm; 99.9% did not experience a 1-hour daily maximum exposure concentration at or above 20 ppm. Of the nearly 20 million CHD persondays, over 99% were associated with a 1-hour da ily maximum exposure below 6 ppm. Very few individuals were estimated to experience an 8- hour daily maximum exposure at or above 9 ppm (0.1% of the CHD population). Approximately 99% of simulated CHD person-days were associated with 8-hour daily maximum expos ure concentrations of less than 3 ppm. While there were a greater number of persons and person-days at each of the exposure levels for the HD as compared to the CHD popula tion, consistent with th e larger size of that population, there was little difference when consider ing the percentages of each population at or6-3 above selected exposure levels and for either av eraging time. For example, about 20% of both the CHD and HD populations experi enced a 1-hour daily maximum exposure at or above 9 ppm. About 89% of person-days for either population were associated with 1-hour daily maximum exposures of less than 3 ppm.

Table 6-1. Estimated daily maximum 1-hour or 8-hour exposure for simulated at-risk populations in the Denver study area – as is air quality.

Coronary Heart Disease a,c All Heart Disease b,c 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 85,926 100 31,360,000 100
≥ 3 53,397 99.5 2,188,000 11. 2 85,494 99.5 3,582,000 11.4
≥ 6 32,517 60.6 170,400 0.9 52,274 60.8 281,700 0.9
≥ 9 10,662 19.9 24,560 0.1 17,412 20.3 39,550 0.1
≥ 12 3,048 5.7 4,677 <0.1 5,010 5.8 7,441 <0.1
≥ 15 876 1.6 1,061 <0.1 1,431 1.7 1,666 <0.1
≥ 20 62 0.1 62 <0.1 99 0.1 99 <0.1
≥ 30 0 0 0 0 0 0 0 0 1- hour

≥ 40 0 0 0 0 0 0 0 0
≥ 0 53,656 100 19,580,000 100 85,926 100 31,360,000 100
≥ 3 31,036 57.8 189,500 1.0 50,361 58.6 309,400 1.0
≥ 6 1,715 3.2 2,851 <0.1 2,604 3.0 4,171 <0.1
≥ 9 62 0.1 86 <0.1 99 0.1 136 <0.1
≥ 12 12 <0.1 12 <0.1 12 <0.1 12 <0.1
≥ 15 0 0 0 0 0 0 0 0
≥ 20 0 0 0 0 0 0 0 0
≥ 30 0 0 0 0 0 0 0 0 8- hour

≥ 40 0 0 0 0 0 0 0 0 Notes: Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air quality scenario.

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Inclusive of those persons with diagnosed corona ry heart disease, angina pectoris, heart attack, and any other heart condition or disease (CDC, 2009).

c Includes estimate of persons with undiagnosed isch emia developed by EPA (see section 5.5.1).

These exposure results are consistent with the ambient concentration di stributions used to represent this scenario, where uppe r percentile concentrations range from about 2 to 6.4 ppm (see Table 5-14). Note also that the highest estimated 1-hour daily maximum exposures are likely a
function of microenvironmental co ncentrations (e.g., exposures occurring while inside vehicles or immediately near roads) that, in general, ma y be a factor of two to five times higher than
ambient CO concentrations at monitors that are not immediately near roads (2000 AQCD; ISA section 3.6.1).6-4 As mentioned above, a smaller subset of th e at-risk CHD populati on was simulated to generate additional exposure results. First, we were interested in determining the important microenvironments that contribute to CHD populati on exposures at each of the selected levels. 3 Figure 6-1 illustrates such an an alysis, beginning with the total minutes per year spent by the simulated CHD population in Denver at or above each exposure level. Note that total time spent at or above a particular exposur e concentration decrea ses with increasing exposure level. This pattern is consistent with the exposure results above (Table 6-1) ; that is, for most of the time, the population is exposed to concentrations less th an 6 ppm (about 99.9% of the total time), with very little time spent exposed to concentrations at or above 20 ppm. For this scenario, in fact,
there were 50 total minutes out of the 230 m illion minutes in the year simulated where the population was exposed at or abov e a level of 20 ppm. Note th at when considering the zero exposure concentration level, th e distribution of microenvironmen tal contributions effectively approximates the time spent by the population in each of the microenvironments across the simulation period. Not surprisingly, the simula ted population spends over 85% of their time
within indoor microenvironments and is consistent with the reported activit y pattern survey data (Graham and McCurdy, 2004). At the lowest ex posure levels (e.g., < 2 ppm), much of the estimated population exposure occurs within indoor micr oenvironments. For exposure concentrations at or above 2 ppm, most of the po pulation exposure occurs while inside vehicles or during time spent within outdoor high-c oncentration microenvironments. These two aggregate microenvironments predominantly contribu te to exposure concentrations at or above 4 ppm (> 90% of all personal time at these levels). These smaller CHD population simulations al so served to approximate the effective microenvironmental factors, the dist ributions of which are more usef ul to compare with literature reported microenvironment-to-ambient concentr ation ratios rather than simply using the distribution of proximity factors given in Table 5-22. Table 6-2 summarizes these ratios that were calculated by dividing the estimated event- level microenvironmental concentration by its corresponding ambient concentration. Values for the estimated microenvironmental ratios correspond reasonably well with re ported personal exposure to am bient concentration ratios (ISA, Figure 3-46). The distribut ion of estimated microenvironmen tal concentrations (Table 6- 3) also generally reflect the range of concentrations reporte d in personal exposure studies, particularly those where measurements were made inside-vehicles and near-roadways (ISA section 3.6.6.2).

3 The default APEX summary output table only generates microenvironmental contributions for the general population.6-5 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

≥ 0 ≥ 1 ≥ 2 ≥ 3 ≥ 4 ≥ 5 ≥ 6 ≥ 9 ≥ 12 ≥ 15 ≥ 20 ≥ 30 ≥ 40 ≥ 60 CO Exposure Concentration (ppm)Percent of Total Minutes Spent at orabove Exposure Concentration (%) inside-vehicles outdoors (low) outdoors (high) indoors (low) indoors (high)5.5 E+79.1 E+62.7 E+61.1 E+64.7 E+52.3 E+53.5 E+47.3 E+31.6 E+35.0 E+10.0 E+00.0 E+00.0 E+02.3 E+8Total Minutes at orabove ExposureConcentration Denver: AQ As Is

Figure 6-1. Estimated microenvironmental contributions to time spent at or above selected exposure concentrations using the Denver CHD population – as is air quality. The total minutes spent at or above each exposure concentration are presented above each bar.6-6 Table 6-2. Estimated distribution of microenviron ment-to-ambient concentration ratios using the Denver CHD population – as is air quality.

Distribution Percentiles c,d (unitless)

Microenvironment a Events b,c (n) Mean Ratio c,d (unitless) std min p1 p5 p50 p95 p99 max 1: In-Residence 401,098 0.9 0. 4 0 0.3 0.4 0.8 1.8 2.4 5.1 2: In-Service Station 1,425 2.9 1.3 0.5 0.9 1.3 2.7 5.4 6.6 10.0 3: In-Other A 39,348 1.1 0.5 0.1 0.4 0.5 1.0 2.1 2.6 4.2 4: In-Other B 15,956 0.9 0.4 0.1 0.3 0.4 0.9 1.7 2.2 4.2 5: Out-Near Road 8,822 1.6 0. 8 0.1 0.5 0.6 1.5 3.1 4.1 6.1 6: Out-Parking Lot/ Refueling 3,079 2.9 1.4 0.4 0.8 1.2 2.6 5.5 7.4 10.2 7: Out-Other 24,515 1.5 0.8 0 0.4 0.6 1.3 2.9 3.9 6.1 8: In-Vehicle 44,998 2.9 1.4 0 0.8 1.2 2.6 5.7 7.5 14.1 Notes:

a See section 5.9 and Table 5-22 for details. b This is the number of times the population exper ienced an exposure event in that microenvironment.

c Data set used to calculate the ratios represent the CHD population extr acted from a 5,000 person simulation and was screened to eliminate ambient co ncentrations less than 1 ppm. See section 5.10 for details. d The mean, standard deviation, and perc entiles (p) were calculated using all events regardless of event duration. Note that based on the activity pa ttern diaries used, the length of an event can range from 1 minute to 1 hour.

Table 6-3. Estimated distribution of microenvironme ntal concentrations using the Denver CHD population – as is air quality.

Distribution Percentiles c,d (ppm)

Microenvironment a Events b,c (n) Mean CO c,d (ppm) Std c,d min p1 p5 p50 p95 p99 Max 1: In-Residence 4,046,011 0.7 0. 4 0 0.1 0.2 0.6 1.5 2.2 7.8 2: In-Service Station 17,715 2.2 1.2 0 0.3 0.8 1.9 4.5 6.4 14.3 3: In-Other A 486,641 0.8 0. 5 0 0.1 0.3 0.7 1.7 2.5 7.3 4: In-Other B 183,535 0.7 0. 4 0 0.1 0.2 0.6 1.5 2.1 6.2 5: Out-Near Road 100,303 1.1 0.8 0 0 0.3 1.0 2.6 3.8 15.4 6: Out-Parking Lot/ Refueling 39,801 2.0 1.3 0 0 0.6 1.7 4.4 6.7 16.6 7: Out-Other 354,654 1.0 0. 7 0 0 0.3 0.8 2.2 3.4 12.3 8: In-Vehicle 546,573 2.0 1. 4 0 0 0.6 1.7 4.6 6.9 23.0 Notes:

a See section 5.9 and Table 5-22 for details. b This is the number of times the population exper ienced an exposure event in that microenvironment.

c These include all exposure events for the CHD popu lation extracted from a 5,000 person simulation. See section 5.10 for details. d The mean, standard deviation, and perc entiles (p) were calculated using all events regardless of event duration. Note that based on the activity pa ttern diaries used, the length of an event can range from 1 minute to 1 hour.6-7 In Los Angeles, there was a greater number of individuals experien cing exposures at or above each of the selected exposure levels (Table 6-4) when compared with exposures in Denver (Table 6-1) and considering either simulated at-r isk population. This is e xpected given that the exposure modeling domain in Los Angeles encompa sses a larger area than Denver and therefore comprises a larger total simulated population. Th e estimated percentage of persons exposed in Los Angeles is also greater when compared with the corresponding exposure levels evaluated for the Denver study area. For example, approxima tely 32% of the CHD population was estimated to experience a 1-hour daily maximum exposure of at least 9 ppm in Los Angeles (Table 6-6) while in Denver this same level was experien ced by approximately 20% of the CHD population (Table 6-1). This result is likely dr iven by the differences noted in the as is air quality data, where in Los Angeles, the 2006 ambient concen trations were generally higher than those observed for Denver (see section 5.7.1; Tables 5-14 and 5-16). In addition, the highest 1-hour daily maximum exposure was estim ated to be at or above 30 ppm (but less than 40 ppm) in the Los A ngeles study area, though limited to a small fraction of either simulated at-risk population (<0.1 %). The corresponding highest 1-hour daily maximum exposure in the Denver study area was at or above 20 ppm (but le ss than 30 ppm) and was experienced by approximately 0.1% of either simulated at-risk population. Therefore, the overall range of the exposure distribution was wide r in Los Angeles when compared with that of Denver when considering the as is air quality scenario.
Similar to that estimated for either at-ris k population in Denver, over 98% of the persondays in Los Angeles were associated with 1-hour daily maximum expos ures below 6 ppm and very few persons (<1%) experienced 8-hour dail y maximum exposures at or above 9 ppm. These exposure results are also c onsistent with the dist ributions of ambient air quality used to represent this scenario, where the ambient monitor upper percent ile concentrations extend from about 2 to 8.4 ppm (Table 5-16).6-8 Table 6-4. Estimated daily maximum 1-hour or 8-hour exposure for simulated at-risk populations in the Los Angeles study area – as is air quality.

Coronary Heart Disease a,c All Heart Disease b,c Persons Person-days Persons Person-days Daily Maximum Exposure

(ppm) Number Percent Number Percent Number Percent Number Percent
≥ 0 383,040 100 139,800,000 100 630,807 100 230,200,000 100
≥ 3 382,739 99.9 23,620,000 16. 9 630,305 99.9 38,680,000 16.8
≥ 6 287,606 75.1 2,423,000 1.7 471,951 74.8 3,880,000 1.7
≥ 9 122,428 32.0 408,300 0.3 194,681 30.9 651,500 0.3
≥ 12 42,850 11.2 83,990 0.1 65,730 10.4 133,500 0.1
≥ 15 13,949 3.6 20,170 <0.1 21,074 3.3 31,510 <0.1
≥ 20 2,208 0.6 2,509 <0.1 3,211 0.5 3,613 <0.1
≥ 30 100 <0.1 100 <0.1 100 <0.1 100 <0.1 1- hour

≥ 40 0 0 0 0 0 0 0 0
≥ 0 383,040 100 139,800,000 100 630,807 100 230,200,000 100
≥ 3 294,430 77.0 3,793,000 2.7 481,986 76.4 6,178,000 2.7
≥ 6 36,528 9.5 72,150 0.1 57,100 9.1 114,300 <0.1
≥ 9 3,011 0.8 3,412 <0.1 4,616 0.7 5,319 <0.1
≥ 12 301 0.1 301 <0.1 301 <0.1 301 <0.1
≥ 15 0 0 0 0 0 0 0 0
≥ 20 0 0 0 0 0 0 0 0
≥ 30 0 0 0 0 0 0 0 0 8- hour

≥ 40 0 0 0 0 0 0 0 0 Notes: Unadjusted ambient concentrations from ten monitors in 2006 were used to represent the As Is air quality scenario.

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Inclusive of those persons with diagnosed corona ry heart disease, angina pectoris, heart attack, and any other heart condition or disease (CDC, 2009).

c Includes estimate of persons with undiagnosed ischemia develop ed by EPA (see section 5.5.1.1).

The microenvironmental contributions to time sp ent at or above selected exposure levels in Los Angeles (Figure 6-2) are also simila r to that estimated for Denver, though indoor microenvironments contribute a somewhat greate r percentage in Los A ngeles at each of the exposure levels. This is likely the result of ge nerally higher as is ambient concentrations across the entire distribution in Los Angeles when co mpared with the Denver ambient concentrations distribution (Table 5-14). As expected, the di stributions of event-le vel microenvironment-toambient concentration ratios (Table 6-5) are also similar to thos e calculated for Denver (Table 6- 3), though estimated microenvironmental concentr ations are slightly higher in Los Angeles (Table 6-6). This is also a function of the ge nerally higher ambient con centrations measured at the Los Angeles monitors when compared with ambient monitor concentrations in Denver.6-9 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

≥ 0 ≥ 1 ≥ 2 ≥ 3 ≥ 4 ≥ 5 ≥ 6 ≥ 9 ≥ 12 ≥ 15 ≥ 20 ≥ 30 ≥ 40 ≥ 60 CO Exposure Concentration (ppm)Percent of Total Minutes Spent at orabove Exposure Concentration (%) inside-vehicles outdoors (low) outdoors (high) indoors (low) indoors (high)6.5 E+71.4 E+74.5 E+61.8 E+68.2 E+54.2 E+57.1 E+41.6 E+44.2 E+36.9 E+26.5 E+10.0 E+00.0 E+02.1 E+8Total Minutes at orabove ExposureConcentration Los Angeles: 2006 AQ As Is

Figure 6-2. Estimated microenvironmental contributions time spent at or above selected exposure concentrations using the Los Angeles CHD population – as is air quality. The total minutes spent at or above each exposure concentration are presented above each bar.6-10 Table 6-5. Estimated distribution of microenviron ment-to-ambient concentration ratios using the Los Angeles CHD population – as is air quality.

Distribution Percentiles c,d (unitless)

Microenvironment a Events b,c (n) Mean Ratio c,d (unitless) Std min p1 p5 p50 p95 p99 max 1: In-Residence 631,366 0.9 0. 4 0 0.3 0.4 0.9 1.8 2.3 5.7 2: In-Service Station 2,190 2.8 1.2 0.4 1.0 1.3 2.6 5.1 6.6 9.4 3: In-Other A 45,134 1.2 0.5 0 0.4 0.5 1.0 2.2 2.8 5.4 4: In-Other B 18,735 1.0 0.4 0.1 0.3 0.4 0.9 1.8 2.3 3.8 5: Out-Near Road 12,954 1.5 0. 8 0 0.3 0.6 1.4 3.0 3.9 9.0 6: Out-Parking Lot/ Refueling 4,307 2.9 1.5 0.3 0.7 1.1 2.6 5.9 7.6 10.8 7: Out-Other 40,000 1.4 0.7 0. 1 0.4 0.5 1.3 2.9 3.8 7.0 8: In-Vehicle 59,191 2.9 1.5 0 0.7 1.1 2.5 5.7 7.5 21.1 Notes:

a See section 5.9 and Table 5-22 for details. b This is the number of times the populat ion experienced an exposure event in that microenvironment.

c Data set used to calculate the ratios repres ents the CHD population extracted from a 5,000 person simulation and was screened to eliminate am bient concentrations were less than 1 ppm. See section 5.10 for details. d The mean, standard deviation, and perc entiles (p) were calculated using all events regardless of event duration. Note that based on the activity pattern diaries used, the length of an event can range from 1 minute to 1 hour.

Table 6-6. Estimated distribution of microenviron mental concentrations using the Los Angeles CHD population – as is air quality.

Distribution Percentiles c,d (ppm)

Microenvironment a Events b,c (n) Mean CO c,d (ppm) Std min p1 p5 p50 p95 p99 max 1: In-Residence 3,620,332 0.8 0.6 0 0 0.2 0.7 1.9 2.9 9.7 2: In-Service Station 19,825 2. 2 1.5 0 0 0.6 1.8 5.1 7.5 14.6 3: In-Other A 433,366 0.9 0. 6 0 0 0.2 0.7 2.0 3.0 10.7 4: In-Other B 164,167 0.8 0. 5 0 0 0.2 0.6 1.7 2.6 7.8 5: Out-Near Road 102,844 1.2 0.9 0 0 0.3 0.9 2.9 4.5 14.7 6: Out-Parking Lot/ Refueling 41,611 2.1 1.6 0 0 0.5 1.6 5.2 8.2 26.8 7: Out-Other 359,727 1.0 0. 8 0 0 0.2 0.8 2.6 4.1 14.1 8: In-Vehicle 523,967 2.1 1. 6 0 0 0.5 1.7 5.3 8.1 34.3 Notes:

a See section 5.9 and Table 5-22 for details. b This is the number of times the populat ion experienced an exposure event in that microenvironment.

c These include all exposure events for the CHD population extracted from a 5,000 person simulation. See section 5.10 for details. d The mean, standard deviation, and perc entiles (p) were calculated using all events regardless of event duration. Note that based on the activity pattern diaries used, the length of an event can range from 1 minute to 1 hour.6-11 6.1.2 Air quality adjusted to just me et the current 8-hour standard

As described in section 5.6, hi storical ambient monitoring da ta from each study area were adjusted to represent air quality that just m eets the current 8-hour st andard. For both Denver (year 1995) and Los Angeles (year 1997), air quality data were adjusted downwards to meet a 2 nd highest 8-hour average concentration of 9.4 ppm. Note that even with a downward proportional adjustment, these adjusted ambient con centrations remain much higher than as is ambient air quality. Table 6-7 summarizes the exposure result s for the simulated at-risk populations in the Denver study area when using these adjusted ambi ent CO concentrations as an input to APEX and using the same modeling assumptions and para meter distributions described in chapters 4
and 5. Over half of the Denver at-risk population was estimated to experience a 1-hour daily maximum exposure at or above 12 ppm. This is n early a factor of 10 great er than that estimated when using the as is air quality (Table 6-1). The highe st 1-hour daily maximum exposure was estimated to be at or above 40 ppm (but below 60 ppm) when considering air quality adjusted to just meet the current standard, though only expe rienced by less than 0.2% of the simulated atrisk populations. Thus, there is a wider rang e in the exposure levels experienced by the simulated at-risk populations when c onsidering this exposure scenario. The number and percent of persons experi encing 8-hour daily ma ximum exposures is also greater for this scenario when comp ared with corresponding levels using the as is air quality. Nearly 10% of the si mulated at-risk population was es timated to experience an 8-hour daily maximum exposure at or above 9 ppm (Tab le 6-7) when consider ing air quality just meeting the current 8-hour standard. Most of the CHD or HD population (>99%) would not experience an 8-hour daily maximum concentrati on at that same level when considering the as is air quality scenar io (Table 6-1).
Indoor microenvironments simulated in Denver we re estimated to contribute to a greater percentage of time spent at each selected expos ure level using the adju sted air quality when compared with the results using as is air quality, though the differe nce is most notable at exposures less than 9 ppm (Figure 6-3). For exam ple, about 50% of expos ures that occurred at or above 3 ppm were experienced within indoor microenvironments when air quality just meets the current standard (Figure 6-3), while indoor microenvironments account for less than 20% of exposures when considering as is air quality (Figure 6- 1). Indoor microenvironments would be expected to play a larger role in low level exposures when using air quality adjusted to just meet the current standard given the higher ambient concentrations across the entire air quality distribution when compared with as is air quality.6-12 As was observed for both locations using as is air quality, the distributions of microenvironment to ambient concentration ratios are largely the same when comparing microenvironments (Table 6-8) , likely a function of the same algorithm and parameter inputs
used for each scenario and location. Also as expected, the estimated microenvironmental concentrations (Table 6-9) are higher across th e entire distribution when compared with those estimated when considering the as is exposure scenario (Table 6-3).

Table 6-7. Estimated daily maximum 1-hour and 8-hour exposures for simulated at-risk populations in the Denver study area – air quality just meeting the current 8- hour standard.

Coronary Heart Disease a,c All Heart Disease b,c 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 85,926 100 31,360,000 100
≥ 3 53,656 100 8,638,000 44. 1 85,926 100 13,960,000 44.5
≥ 6 53,039 98.9 1,625,000 8.3 84,964 98.9 2,660,000 8.5
≥ 9 44,598 83.1 404,800 2.1 71,426 83.1 666,400 2.1
≥ 12 28,469 53.1 127,300 0.7 45,919 53.4 206,700 0.7
≥ 15 16,610 31.0 46,710 0.2 26,840 31.2 75,350 0.2
≥ 20 6,022 11.2 10,290 0.1 9,885 11.5 16,500 0.1
≥ 30 691 1.3 802 <0.1 1,283 1.5 1,493 <0.1
≥ 40 86 0.2 86 <0.1 136 0.2 136 <0.1 1- hour

≥ 60 0 0 0 0 0 0 0 0
≥ 0 53,656 100 19,580,000 100 85,926 100 31,360,000 100
≥ 3 52,706 98.2 2,690,000 13. 7 84,445 98.3 4,318,000 13.8
≥ 6 23,879 44.5 97,760 0.5 38,132 44.4 157,000 0.5
≥ 9 5,060 9.4 9,724 <0.1 7,861 9.1 14,850 <0.1
≥ 12 1,037 1.9 1,382 <0.1 1,666 1.9 2,197 <0.1
≥ 15 309 0.6 346 <0.1 457 0.5 531 <0.1
≥ 20 37 0.1 37 <0.1 62 0.1 62 <0.1
≥ 30 0 0 0 0 0 0 0 0
≥ 40 0 0 0 0 0 0 0 0 8- hour

≥ 60 0 0 0 0 0 0 0 0 Notes: Ambient concentrations from 1995 were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm using a relationship derived from the design monitor (ID 080310002).

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Inclusive of those persons with diagnosed cor onary heart disease, angina pectoris, heart attack, and any other heart condition or disease (CDC, 2009).

c Includes estimate of persons with undiagnosed ischemia develop ed by EPA (see section 5.5.1.1).6-13 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

≥ 0 ≥ 1 ≥ 2 ≥ 3 ≥ 4 ≥ 5 ≥ 6 ≥ 9 ≥ 12 ≥ 15 ≥ 20 ≥ 30 ≥ 40 ≥ 60 CO Exposure Concentration (ppm)Percent of Total Minutes Spent at orabove Exposure Concentration (%) inside-vehicles outdoors (low) outdoors (high) indoors (low) indoors (high)1.3 E+83.9 E+71.4 E+76.4 E+63.3 E+61.9 E+64.9 E+51.6 E+56.1 E+41.5 E+41.1 E+36.2 E+12.0 E+02.3 E+8Total Minutes at orabove ExposureConcentration Denver: AQ Just Meeting Current Standard

Figure 6-3. Estimated microenvironmental contributions to time spent at or above selected exposure concentrations us ing the Denver CHD population – air quality just meeting the current 8-hour st andard. The total minutes spent at or above each exposure concentration are presented above each bar.6-14 Table 6-8. Estimated distribution of microenviron ment-to-ambient concentration ratios using the Denver CHD population – air quali ty just meeting the current 8-hour standard.

Distribution Percentiles c,d (unitless)

Microenvironment a Events b,c (n) Mean Ratio c,d (unitless) Std min p1 p5 p50 p95 p99 max 1: In-Residence 1,633,520 0.9 0. 5 0.1 0.2 0.3 0.8 1.8 2.3 5.5 2: In-Service Station 7,342 2.8 1.3 0.4 0.8 1.2 2.6 5.3 7.0 10.5 3: In-Other A 202,514 1.1 0.5 0.1 0.3 0.5 1.0 2.1 2.7 6.2 4: In-Other B 76,797 0.9 0.4 0.1 0.3 0.4 0.8 1.7 2.3 6.0 5: Out-Near Road 43,108 1.5 0. 8 0.1 0.4 0.6 1.3 2.9 4.0 9.6 6: Out-Parking Lot/ Refueling 16,563 2.6 1.3 0.2 0.7 1.0 2.4 5.2 6.9 13.6 7: Out-Other 130,733 1.4 0.7 0.1 0.4 0.5 1.2 2.7 3.7 8.5 8: In-Vehicle 228,813 2.6 1.4 0 0.7 1.0 2.3 5.3 7.1 17.7 Notes:

a See section 5.9 and Table 5-22 for details. b This is the number of times the population exper ienced an exposure event in that microenvironment.

c Data set used to calculate the ratios represent s the CHD population extrac ted from a 5,000 person simulation and was screened to eliminate ambient concentrations were less than 1 ppm. See section 5.10 for details. d The mean, standard deviation, and perc entiles (p) were calculated using all events regardless of event duration. Note that based on the activity pa ttern diaries used, the length of an event can range from 1 minute to 1 hour.

Table 6-9. Estimated distribution of microenvironme ntal concentrations using the Denver CHD population – air quality just m eeting the current 8-hour standard.

Distribution Percentiles c,d (ppm)

Microenvironment a Events b,c (n) Mean CO c,d (ppm) Std min p1 p5 p50 p95 p99 max 1: In-Residence 4,047,786 1.2 0. 8 0 0.3 0.4 1.0 2.7 4.0 16.6 2: In-Service Station 17,781 3.7 2.2 0.4 1.0 1.3 3.2 8.0 11.4 25.0 3: In-Other A 489,984 1.4 0.9 0.1 0.4 0.5 1.2 3.2 4.7 16.6 4: In-Other B 186,897 1.2 0.8 0.1 0.3 0.4 1.0 2.6 3.9 11.4 5: Out-Near Road 100,520 2.0 1. 4 0.1 0.4 0.6 1.6 4.7 7.1 22.5 6: Out-Parking Lot/ Refueling 39,895 3.5 2.5 0 0.7 1. 0 2.8 8.0 12.4 63.4 7: Out-Other 351,163 1.7 1.3 0 0.3 0.5 1.3 4.1 6.4 25.5 8: In-Vehicle 548,978 3.5 2.5 0 0.8 1.1 2.8 8.2 12.8 56.9 Notes:

a See section 5.9 and Table 5-22 for details. b This is the number of times the population exper ienced an exposure event in that microenvironment.

c These include all exposure events for the CHD popu lation extracted from a 5,000 person simulation. See section 5.10 for details. d The mean, standard deviation, and perc entiles (p) were calculated using all events regardless of event duration. Note that based on the activity pa ttern diaries used, the length of an event can range from 1 minute to 1 hour.6-15 Similarly in Los Angeles, the number and pe rcent of persons expos ed above selected exposure concentrations is greater when consider ing the air quality adjusted to just meet the current standard than when using as is air quality. For example, nearly 50% of the CHD population was estimated to experience a 1-hou r daily maximum exposure of 9 ppm when considering air quality just meeting the current standard (Table 6-8), while only 32% were estimated to experience a similar concentration using as is air quality (Table 6-2). The range of the 1-hour daily maximum exposure distribution extends upward to 30 ppm, but less than 40 ppm for this scenario in Los Angeles. This esti mate of an upper level is below the maximum invehicle concentration of 46 ppm measured by Shikiya (1989) during 112 southern California commutes in wintertime in 1987-88, of aver age duration shorter than an hour. 4 For a time period closer to the present scenario, Rodes et al. (1998) reported maxi mum in-vehicle and on-road CO concentrations of only 7.6 and 9.0 ppm, respect ively during Los Angeles commutes in 1997. Note however the scripted commutes in this latte r study are not necessarily directly comparable to this modeled data, as the measurements were time-averaged for two hours, the sample size was limited to about 30 total samples, and data were collected over nine days in the fall. When comparing the overall population exposur e distribution for Los Angeles to Denver for this exposure scenario, ther e are greater percentage s of persons and person-days estimated for the Denver simulated at-risk populations at each corresponding exposure le vel. For example, only 2.7% of the CHD population was estimate d to experience an 8-hour daily maximum exposure at or above 9 ppm in Lo s Angeles (Table 6-10), while in Denver, the estimated percent of the CHD population exposed at this level was over a factor of three greater (9.4%) (Table 6- 7). This result is likely driven by differences observed at the upper tails of the air quality
distribution noted in section 5.7.3, even though both study areas ha ve ambient concentrations adjusted to just meet the same 8-hour average CO concentration of 9.4 ppm.

4 On average, the commute time a ssociated with the collection of th ese samples was 33 minutes. The reported mean 4-hour integrated ambient monitor concentrations was 3.6 ppm (std = 2.1; max = 8.6 ppm).6-16 Table 6-10. Estimated daily maximum 1-hour and 8-hour exposures for simulated at-risk populations in the Los Angeles study area – air quality just meeting the current 8-hour standard.

Coronary Heart Disease a,c All Heart Disease b,c Persons Person-days Persons Person-days Daily Maximum Exposure

(ppm) Number Percent Number Percent Number Percent Number Percent
≥ 0 383,040 100 139,800,000 100 630,807 100 230,200,000 100
≥ 3 383,040 100 36,430,000 26. 1 630,807 100 59,960,000 26.0
≥ 6 335,975 87.7 4,826,000 3.5 553,536 87.8 7,828,000 3.4
≥ 9 189,563 49.5 982,300 0.7 305,268 48.4 1,550,000 0.7
≥ 12 83,693 21.9 257,200 0.2 130,356 20.7 396,600 0.2
≥ 15 36,126 9.4 80,180 0.1 55,293 8.8 119,900 0.1
≥ 20 8,731 2.3 14,450 <0.1 13,547 2.1 20,970 <0.1
≥ 30 803 0.2 803 <0.1 1,004 0.2 1,004 <0.1
≥ 40 0 0 0 0 0 0 0 0 1- hour

≥ 60 0 0 0 0 0 0 0 0
≥ 0 383,040 100 139,800,000 100 630,807 100 230,200,000 100
≥ 3 342,598 89.4 8,655,000 6.2 562,166 89.1 14,190,000 6.2
≥ 6 75,966 19.8 262,600 0.2 122,328 19.4 405,100 0.2
≥ 9 10,336 2.7 18,670 <0.1 16,157 2.6 28,600 <0.1
≥ 12 1,505 0.4 2,308 <0.1 2,007 0.3 3,011 <0.1
≥ 15 301 0.1 401 <0.1 502 0.1 602 <0.1
≥ 20 100 <0.1 100 <0.1 100 <0.1 100 <0.1
≥ 30 0 0 0 0 0 0 0 0
≥ 40 0 0 0 0 0 0 0 0 8- hour

≥ 60 0 0 0 0 0 0 0 0 Notes: Ambient concentrations from 1997 were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm using a relationship derived from the design monitor (ID 060371301).

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Inclusive of those persons with diagnosed cor onary heart disease, angina pectoris, heart attack, and any other heart condition or disease (CDC, 2009).

c Includes estimate of persons with undiagnosed ischemia develop ed by EPA (see section 5.5.1.1).6-17 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

≥ 0 ≥ 1 ≥ 2 ≥ 3 ≥ 4 ≥ 5 ≥ 6 ≥ 9 ≥ 12 ≥ 15 ≥ 20 ≥ 30 ≥ 40 ≥ 60 CO Exposure Concentration (ppm)Percent of Total Minutes Spent at orabove Exposure Concentration (%) inside-vehicles outdoors (low) outdoors (high) indoors (low) indoors (high)8.5 E+72.4 E+78.2 E+63.4 E+61.7 E+68.9 E+51.9 E+55.2 E+41.7 E+43.3 E+31.8 E+20.0 E+00.0 E+02.1 E+8Total Minutes at orabove ExposureConcentration Los Angeles: AQ Just Meeting Current Standard

Figure 6-4. Estimated microenvironmental contributions to time spent at or above selected exposure concentrations using the Los Angeles CHD population – air quality just meeting the current 8-hour st andard. The total minutes spent at or above each exposure concentration are presented above each bar.

The percent contribution of the aggregated microenvironments to the selected exposure levels in Los Angeles (Figure 6- 4) is nearly identical to that estimated for Denver, given air quality adjusted to just meeti ng the current standard. The Los Angeles data differ in that upperlevel exposure concentrations only extend upw ards to about 30 ppm (occurring for about 180 event-level minutes), while in Denver there were a greater number of exposure events with concentrations at or above 40 ppm (occurri ng for about 1,100 event-level minutes). Not surprisingly, estimated microenv ironment-to-ambient concentration ratios for this scenario (Table 6-11) were also similar to those for the Denver air quality scenario s (Tables 6-2 and 6-8) and those derived for the Los Angeles as is air quality (Table 6-5). Note also that estimated upper level concentrations here for the in-veh icle microenvironment are within the maximum measured peak level (one minute average) concen tration reported by Rodes et al. (1998) of 67 ppm during rush hour commutes in Los Angeles (ISA, section 3.6.6.2).6-18 Table 6-11. Estimated distribution of microenviron ment-to-ambient concentration ratios using the Los Angeles CHD population – ai r quality just meeting the current 8- hour standard.

Distribution Percentiles c,d (unitless)

Microenvironment a Events b,c (n) Mean Ratio c,d (unitless) Std min p1 p5 p50 p95 p99 max 1: In-Residence 1,087,377 1.0 0. 5 0 0.3 0.4 0.9 1.8 2.4 6.6 2: In-Service Station 4,516 2.8 1.4 0.4 0.8 1.2 2.6 5.4 7.0 12.6 3: In-Other A 92,255 1.1 0.5 0 0.4 0.5 1.0 2.2 2.9 6.4 4: In-Other B 37,297 1.0 0.4 0 0.3 0.4 0.9 1.8 2.4 4.2 5: Out-Near Road 23,676 1.5 0. 8 0 0.3 0.5 1.3 2.9 4.0 7.4 6: Out-Parking Lot/ Refueling 8,614 2.9 1.5 0 0.7 1.1 2.5 5.8 7.7 16.9 7: Out-Other 78,546 1.4 0.7 0 0.3 0.5 1.2 2.9 3.8 10.8 8: In-Vehicle 112,592 2.8 1.5 0 0.7 1.0 2.5 5.7 7.7 18.9 Notes:

a See section 5.9 and Table 5-22 for details. b This is the number of times the population exper ienced an exposure event in that microenvironment.

c Data set used to calculate the ratios represent s the CHD population extrac ted from a 5,000 person simulation and was screened to eliminate ambient concentrations were less than 1 ppm. See section 5.10 for details. d The mean, standard deviation, and perc entiles (p) were calculated using all events regardless of event duration. Note that based on the activity pa ttern diaries used, the length of an event can range from 1 minute to 1 hour.

Table 6-12. Estimated distribution of microenviron mental concentrations using the Los Angeles CHD population – air quality just meeting the current 8-hour standard.

Distribution Percentiles c,d (ppm)

Microenvironment a Events b,c (n) Mean CO c,d (ppm) Std min p1 p5 p50 p95 p99 max 1: In-Residence 3,621,050 1.0 0.7 0 0 0.2 0.8 2.4 3.5 15.7 2: In-Service Station 19,803 2. 7 1.8 0 0 0.6 2.2 6.1 9.0 25.2 3: In-Other A 432,894 1.0 0. 7 0 0 0.2 0.9 2.4 3.6 12.0 4: In-Other B 162,031 0.9 0. 6 0 0 0.2 0.8 2.1 3.1 9.6 5: Out-Near Road 102,771 1.4 1.1 0 0 0.2 1.2 3.5 5.4 15.2 6: Out-Parking Lot/ Refueling 41,803 2.5 2.0 0 0 0.4 2.0 6.3 10.1 25.4 7: Out-Other 358,006 1.3 1. 0 0 0 0.2 1.0 3.1 4.9 22.5 8: In-Vehicle 522,406 2.6 2. 0 0 0 0.4 2.1 6.4 9.9 39.8 Notes:

a See section 5.9 and Table 5-22 for details. b This is the number of times the population exper ienced an exposure event in that microenvironment.

c These include all exposure events for the CHD popu lation extracted from a 5,000 person simulation. See section 5.10 for details. d The mean, standard deviation, and perc entiles (p) were calculated using all events regardless of event duration. Note that based on the activity pa ttern diaries used, the length of an event can range from 1 minute to 1 hour.6-19 6.1.3 Air quality adjusted to just meet alternative air quality scenarios

Three potential alternative air quality scenar ios were investigated to observe how the averaging times, forms, and le vels for the simulated alterna tive standards would affect the estimated exposure concentrati ons (section 5.6). The data for the 1-hour and 8-hour daily maximum exposure concentrations are presented he re, with a focus on the number and percent of persons exposed at selected concentrations. As observed in the above two sections summarizing exposures associated with as is air quality and air quali ty adjusted to just meet the current 8-hour standard, the distributions of th e microenvironment–to-ambient c oncentration ratios are expected to be the same and are thus not provided here fo r the alternative air quality scenarios. The air quality associated with these alternat ive standards is generally similar to as is conditions. Therefore, the microenvironmental contribu tions to exposure and microenvironmental
concentrations associated with th ese alternative standard scenarios are expected to be similar to
those estimated for the as is scenario and are not included here.
Table 6-13 summarizes the 1-hour and 8-hour daily maximum exposures for each of the
three alternative standards scen arios in the Denver study area, while Table 6-14 presents the same information for the Los Angeles study area. In comparing the exposure results for each potential alternative scenario within each study area and exposure averaging time, generally similar numbers of persons and their respective percentages of the simu lated at-risk populations
are observed at the same level. This was by general design, that is , to investigate differing forms of the potential alterna tive standards that would generate potentially similar exposure (and dose) results. Again, there is a wider range in the 1-hour exposure levels experienced by the simulated at-risk populations in Denver (Tab le 6-13) when compared with those of Los Angeles (Table 6- 14) when considering the same potential alternative standard, c onsistent with the differing
distribution of CO concentrations. There are also consistent patte rns in the estimated distribution of 8-hour daily maximum exposures experienced by the simulated at-risk populations, though the upper range of that 8-hour maximum exposure is of course less than that of the 1-hour daily maximum in each respective location. There is some variability in the percent of persons exposed when c onsidering a particular level, form, and study area. For example, the 2 nd highest 8-hour CO c oncentration of 5 ppm most limited the number and percent of exposed persons in each location when compared to results for the other po tential alternativ e standard, though in Denver there were still a few persons estimated to experience a 1-hour daily ma ximum at or above 30 ppm (Table 6-13). In Los Angeles, the upper level of the 1-hour dail y maximum exposure con centration experienced by the simulated CHD population was just at or above 20 ppm, though below 30 ppm.6-20Table 6-13.Estimated daily maximum 1-hour and 8-hour exposuresfor simulated at-risk populations in the Denver studyarea – alternative air quality scenarios.2ndhighest 8-hour average of 5 ppm 99thpct 8-hour Daily Max of 5.0 ppm 99thpct 1-hour Daily Max of 8.0 ppmCHD Personsa,cHD Personsb,cCHD Personsa,cHD Personsb,cCHD Personsa,cHD Personsb,cDailyMaximumExposure(ppm)Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent≥0 53,656 100 85,926 100 53,656 100 85,926 100 53,656 100 85,926 100≥3 53,656 100 85,926 100 53,656 100 85,926 100 53,656 100 85,926 100≥6 47,264 88.1 75,671 88.1 50,534 94.2 80,928 94.2 48,239 89.9 77,263 89.9≥9 25,174 46.9 40,489 47.1 32,147 59.9 51,645 60.1 26,877 50.1 43,241 50.3≥12 11,082 20.7 18,140 21.1 16,326 30.4 26,384 30.7 12,192 22.7 19,917 23.2≥15 4,850 9.0 7,886 9.2 7,676 14.3 12,501 14.5 5,282 9.8 8,688 10.1≥20 975 1.8 1,802 2.1 1,888 3.53,209 3.7 1,222 2.3 2,123 2.5≥30 62 0.1 74 0.1 136 0.3 247 0.3 74 0.1 111 0.1≥40 0 0 0 0 0 0 0 0 0 0 0 01-hour≥60 0 0 0 0 0 0 0 0 0 0 0 0≥0 53,656 100 85,926 100 53,656 100 85,926 100 53,656 100 85,926 100≥3 44,574 83.1 71,488 83.2 48,819 91.0 78,189 91.0 45,808 85.4 73,376 85.4≥6 6,590 12.3 10,637 12.4 10,650 19.8 17,412 20.3 7,380 13.8 11,995 14.0≥9 839 1.6 1,296 1.5 1,555 2.9 2,369 2.8 926 1.7 1,469 1.7≥12 111 0.2 173 0.2 296 0.6 432 0.5 123 0.2 197 0.2≥15 25 <0.1 37 <0.1 49 <0.1 74 0.1 25 <0.1 37 <0.1≥20 0 0 0 0 0 0 0 0 0 0 0 0≥30 0 0 0 0 0 0 0 0 0 0 0 0≥40 0 0 0 0 0 0 0 0 0 0 0 08-hour≥60 0 0 0 0 0 0 0 0 0 0 0 0Notes:Ambient concentrations from 1995 were adjusted to just meet thelevel of the potential alternative standard indicated using a relationshipderived from the design monitor (ID 080310002).aPersons with diagnosed coronary heart disease,angina pectoris, and heart attack (CDC, 2009).bInclusive of those persons with diagnosed coronary heart disease, angina pectoris, heart attack, and any other heart conditionordisease (CDC, 2009).cIncludes estimate of personswith undiagnosed ischemia developed by EPA (see section 5.5.1.1).6-21Table 6-14.Estimated daily maximum 1-hour and 8-hour exposures forsimulated at-risk populations in the Los Angeles studyarea – alternative air quality scenarios.2ndhighest 8-hour average of 5 ppm 99thpct 8-hour Daily Max of 5.0 ppm 99thpct 1-hour Daily Max of 8.0 ppmCHD Personsa,cHD Personsb,cCHD Personsa,cHD Personsb,cCHD Personsa,cHD Personsb,cDailyMaximumExposure(ppm)Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent≥0 383,040 100 630,807 100 383,040 100 630,807 100 383,040 100 630,807 100≥3 378,624 99.0 624,183 99.0 379,728 99.1 626,090 99.3 381,535 99.6 628,498 99.6≥6 215,454 56.2 348,720 55.3 229,302 59.9 373,105 59.1 260,913 68.1 426,090 67.5≥9 68,540 17.9 105,469 16.7 77,571 20.3120,321 19.1 98,244 25.6 154,641 24.5≥12 19,769 5.2 30,507 4.8 24,285 6.3 36,728 5.8 34,721 9.1 53,186 8.4≥15 6,322 1.7 9,533 1.5 7,827 2.0 11,841 1.9 10,738 2.8 16,759 2.7≥20 903 0.2 1,606 0.3 1,305 0.32,208 0.4 2,709 0.7 4,315 0.7≥30 0 0 0 0 0 0 0 0 0 0 0 0≥40 0 0 0 0 0 0 0 0 0 0 0 01-hour≥60 0 0 0 0 0 0 0 0 0 0 0 0≥0 383,040 100 630,807 100 383,040 100 630,807 100 383,040 100 630,807 100≥3 214,149 55.9 350,626 55.6 230,807 60.3 376,918 59.8 264,425 69.0 430,707 68.3≥6 17,060 4.5 26,192 4.2 20,672 5.4 31,811 5.0 28,801 7.5 44,355 7.0≥9 903 0.2 1,204 0.2 1,204 0.31,706 0.3 2,007 0.5 3,011 0.5≥12 301 <0.1 502 0.1 301 <0.1 502 0.1 301 <0.1 502 0.1≥15 0 0 0 0 100 <0.1 100 0 100 <0.1 201 <0.1≥20 0 0 0 0 0 0 0 0 0 0 0 0≥30 0 0 0 0 0 0 0 0 0 0 0 0≥40 0 0 0 0 0 0 0 0 0 0 0 08-hour≥60 0 0 0 0 0 0 0 0 0 0 0 0Notes:Ambient concentrations from 1997 were adjusted to just meet thelevel of the potential alternative standard indicated using a relationshipderived from the design monitor (ID 060371301).aPersons with diagnosed coronary heart disease,angina pectoris, and heart attack (CDC, 2009).bInclusive of those persons with diagnosed coronary heart disease, angina pectoris, heart attack, and any other heart conditionordisease (CDC, 2009).cIncludes estimate of personswith undiagnosed ischemia developed by EPA (see section 5.5.1.1).6-22 6.2 ESTIMATED COHB LEVELS

Consistent with section 6.1, th is section summarizes the estimated COHb levels for the simulated at-risk populations in a series of tables , classified by the air quality scenarios and study areas considered. As was done in presenting the exposure results, we summarized the dose results corresponding to both the C HD and HD populations. For all fi ve air quality scenarios, we report the number and percentage of persons a nd person-days estimated to have experienced
selected levels of the dose metric of interest (daily maximum end-of- hour COHb level). In addition, we include an evaluation of the number of days in the year that individuals are estimated to experience a dose at or above the sel ected COHb level. For three of the scenarios (i.e., as is conditions, air quality adjusted to just meet the curren t 8-hour standard, and air quality adjusted to just meet a 99 th percentile daily maximum 8-hour average concentration of 5.0 ppm), additional dose output data sets were genera ted using a smaller simulation size (i.e., 5,000 persons) and the CHD population, to approximate contribution of ambient CO exposure to
selected COHb levels. See section 5.10 for deta ils on the approach used and evaluation of the representativeness of th e simulation size and at-risk population used.

6.2.1 Air Quality “As Is”

Table 6-15 provides the COHb levels (%) for th e simulated at-risk populations in Denver when considering the as is air quality. No persons were esti mated to have experienced a daily
maximum end-of-hour COHb level at or abov e 2.0%, while only a few (<0.1%) were estimated to have experienced a COHb level ≥ 1.75%. Over 99% of the simulated at-risk population had their highest daily maximum end-of-hour COHb level below 1.5%. Most of the simulated person-days (>99%) were associated with da ily maximum end-of-hour COHb levels below
1.0%. Similarly in Los Angeles, very few persons (98.5%) were estimated to experience a daily
maximum end-of-hour COHb level at or above 1.5% when considering the as is air quality (Table 6-16). There were, however, a few pers ons (0.1%) estimated to have daily maximum
end-of-hour COHb levels at or above 2.0% in this study area. Of these few hundred simulated individuals, all were estimated to have only one person-day per pers on at that level (i.e., to have experienced this level on only one day in the year). As was observed with the Denver dose results, the majority of the person-days (about 99%) were limited to COHb levels at or below
1.0%.6-23 Table 6-15. Portion of the simulated a t-risk populations in the De nver study area estimated to experience a daily maximu m end-of-hour COHb at or above specified levels– as is air quality.

Coronary Heart Disease a,c All Heart Disease b,c Persons Person-days Persons Person-days COHb
Level

(%) Number Percent Number Percent Number Percent Number Percent
≥ 0.0 53,656 100 19,580,000 100 85,926 100 31,360,000 100
≥ 1.0 7,873 14.7 293,000 1.5 12,723 14.8 445,800 1.4
≥ 1.5 333 0.6 4,652 <0.1 592 0.7 7,330 <0.1
≥ 1.75 25 <0.1 346 <0.1 86 0.1 494 <0.1
≥ 2.0 0 0 0 0 0 0 0 0
≥ 2.5 0 0 0 0 0 0 0 0
≥ 3.0 0 0 0 0 0 0 0 0
≥ 4.0 0 0 0 0 0 0 0 0 Notes: Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air quality scenario.

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Inclusive of those persons with diagnosed cor onary heart disease, angina pectoris, heart attack, and any other heart condition or disease (CDC, 2009).

c Includes estimate of persons with undiagnosed ischemia develop ed by EPA (see section 5.5.1.1).

Table 6-16. Portion of the simulated at-risk populations in the Los Angeles study area estimated to experience a daily maxi mum end-of-hour COHb at or above specified levels – as is air quality.

Coronary Heart Disease a,c All Heart Disease b,c Persons Person-days Persons Person-days COHb
Level

(%) Number Percent Number Percent Number Percent Number Percent
≥ 0.0 383,040 100 139,800,000 100 630,807 100 230,200,000 100
≥ 1.0 98,043 25.6 1,645,000 1.2 162,769 25.8 2,975,000 1.3
≥ 1.5 5,820 1.5 86,800 0. 1 9,433 1.5 157,900 0.1
≥ 1.75 1,907 0.5 16,760 <0.1 3,011 0.5 24,890 <0.1
≥ 2.0 301 0.1 301 <0.1 502 0.1 502 <0.1
≥ 2.5 0 0 0 0 0 0 0 0
≥ 3.0 0 0 0 0 0 0 0 0
≥ 4.0 0 0 0 0 0 0 0 0 Notes: Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air quality scenario.

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Inclusive of those persons with diagnosed cor onary heart disease, angina pectoris, heart attack, and any other heart condition or disease (CDC, 2009).

c Includes estimate of persons with undiagnosed ischemia develop ed by EPA (see section 5.5.1.1).6-24 The population-based person-day dose metric was evaluated further by analyzing the number of days in the year each individual experi ences %COHb at or above a selected level. As observed in the above analyses, th ere were little differences be tween the two simulated at-risk populations in the percentage estimated to expe rience a %COHb at or above selected levels. Therefore, we have chosen to use the CHD population as a base-cas e for this analysis. Figure 6- 5 presents the percent of the CHD populations in Denver (top) and Los Angeles experiencing repeated COHb levels using as is air quality. As a point of refere nce in the figure, the percent of persons with a number of occurrences ≥ 1 corresponds to the data summarized in Tables 6-15
and 6-16. 5 Note that we have included only thos e COHb levels between 1.5 and 2.0 %, though
in 0.1% COHb increments. Consistent with the summary tables, a sma ll percentage of the CHD population (about 0.6% in Denver; 1.5% in Los Angeles) was estima ted to experience a single COHb level above 1.5% (Figure 6-15). Even fewer persons experien ced two or more occurrences of COHb above 1.5% (about 0.2% of the CHD population in Denver; 0.6% in Los Angeles). There is a pattern between experiencing at least one and experienci ng a greater number of days per year with a
maximum end-of-hour COHb abov e the selected levels, most evident with the Los Angeles results. The percentage of pers ons experiencing at leas t two or more occurrences of COHb at or above any of the selected levels is a factor of 3 lower than those experien cing at least one. About
a factor of 2-3 difference exists when co mparing the percentage of the CHD population experiencing two or more occurrenc es of COHb at or above any of the selected levels to those experiencing at least thr ee occurrences in a year. As discussed in chapter 2, we also evalua ted the contribution of ambient CO exposure alone to each simulated person’s COHb level. The time-series of expos ure for each individual was used to generate each person’s maximum end- of-hour COHb level attr ibutable to ambient CO exposure. This analysis also focused on the CHD population as a ba se-case in each study area, given the limited differences between the per centage of persons at or above specific COHb levels for either simulated at-risk population. Table 6-17 summarizes the estimated COHb levels experienced by the CHD population for both study areas, using the as is air quality. None of the persons experienced a COHb level at or above 1. 7% due to ambient CO exposure alone for this scenario in either study area. Estimated levels of maximum end-of-hour COHb attributable to ambient CO exposure were at or below 1.3 % for approximately 99% of the simulated CHD population. This is consistent with the above re sults for a persons tota l COHb (Tables 6-15 and

5 The number of occurrences corr esponds to the number of days per year a person(s) experienced a maximum end-of-hour COHb dose at or above the given level.6-25 6-16) 6 and given that endogenous CO production, on average, can cont ribute to a COHb level of approximately 0.27% for the simulated at-risk population in either study area (Appendix B, Table B-4).

6 We used the term total COHb here as the combined dose from ambient CO exposure and endogenous CO production.6-26 0.6

0.3

0.1

0.2

0.1
0.1

0.1

0.1 0.0 0.5 1.0 1.5 2.0Percent of CHD Populationat or Above COHb DoseLevel (%)

1.5 1.6 1.7 1.8 1.9 2.0 ≥ 6 ≥ 5 ≥ 4 ≥ 3 ≥ 2 ≥ 1

COHb Dose Level (%) Number of Occurrences Per Year Denver As Is Air Quality

1.5

0.9

0.6

0.4

0.2 0.1

0.5 0.3 0.2 0.1

0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1

0.1 0.1 0.1 0.1

0.1 0.1 0.1 0.0 0.5 1.0 1.5 2.0Percent of CHD Populationat or Above COHb DoseLevel (%)

1.5 1.6 1.7 1.8 1.9 2.0 ≥ 6 ≥ 5 ≥ 4 ≥ 3 ≥ 2 ≥ 1

COHb Dose Level (%) Number of Occurrences Per Year Los Angeles As Is Air Quality

Figure 6-5. Estimated percent of the CHD populatio n in Denver (top) and Los Angeles (bottom) experiencing repeated C OHb levels – as is air quality.6-27 Table 6-17. Percentages of the simulated CHD populations in the Denver and Los Angeles study areas estimated to experience a daily maximum end-of-hour COHb contribution from ambient exposure alon e at or above specified levels – as is air quality.

Percent of CHD Population a Ambient-Exposure

Contribution to COHb Level (%) Denver Los Angeles
≥ 1.0 2.1 7.1
≥ 1.1 1.1 5.3
≥ 1.2 0.9 2.8
≥ 1.3 0.9 1.5
≥ 1.4 0.2 0.5
≥ 1.5 0.2 0.5
≥ 1.6 0.2 0.5
≥ 1.7 0 0
≥ 1.8 0 0
≥ 1.9 0 0
≥ 2.0 0 0 Notes: Unadjusted ambient concentrations from 2006 were used to represent the As Is air quality scenario.

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1).

6.2.2 Air Quality Adjusted to Just Meet the Current 8-hour Standard

Consistent with the estimated exposure con centrations, COHb levels estimated to be experienced by the simulated at-risk populati ons in each study area were greater when considering exposures associated wi th air quality adjusted to just meet the current standard than when using as is air quality. For example, in Denver, just over 3% of the simulated at-risk populations were estimated to have experienced a daily maximum end-of- hour COHb level at or above 2.0% (Table 6-18). There were no persons in Denver estimated to have experienced
COHb at a level above 2.0% based on estim ated ambient exposures associated with as is air quality. A similar pattern is observed for the si mulated at-risk population in Los Angeles (Table 6-19), though a lower percentage of persons (0.5 %) were estimated to have experienced a daily
maximum end-of-hour COHb level at or above 2.0% when compared to the results for Denver. In both study areas, a few persons were estimatedto have experienced a daily maximum end-ofhour COHb levels as high as 3.0% . However, most of the pers ons that did experience these higher COHb levels ( ≥ 2.0%) experienced them for fewer than 2 days in a year (Table 6-19). The pattern in experiencing multiple days at or abov e selected COHb levels is readily evident in Figure 6-6, which shows that where a smaller per cent of the population expe riences two or more
occurrences of %COHb at or above selected le vels (reduced by about a factor of 3 when6-28 compared with those experiencing at least one). Even fewer experience three or more days per year (reduced by about a factor of 2 when co mpared with those expe riencing at least two).6-29 Table 6-18. Portion of the simulated a t-risk populations in the De nver study area estimated to experience a daily maximu m end-of-hour COHb at or above specified levels – air quality just meeting the current 8-hour standard.

Coronary Heart Disease a,c All Heart Disease b,c Persons Person-days Persons Person-days COHb
Level

(%) Number Percent Number Percent Number Percent Number Percent
≥ 0.0 53,656 100 19,580,000 100 85,926 100 31,360,000 100
≥ 1.0 40,921 76.3 829,300 4.2 66,465 77.4 1,326,000 4.2
≥ 1.5 10,267 19.1 35,520 0. 2 17,141 19.9 59,170 0.2
≥ 1.75 4,541 8.5 8,317 <0.1 7,614 8.9 13,760 <0.1
≥ 2.0 1,814 3.4 2,480 <0.1 3,110 3.6 4,233 <0.1
≥ 2.5 346 0.6 370 <0.1 679 0.8 753 <0.1
≥ 3.0 86 0.2 86 <0.1 197 0.2 210 <0.1
≥ 4.0 0 0 0 0 12 <0.1 12 <0.1 Notes: Ambient concentrations from 1995 were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm using a relationship derived from the design monitor (ID 080310002).

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Inclusive of those persons with diagnosed cor onary heart disease, angina pectoris, heart attack, and any other heart condition or disease (CDC, 2009).

c Includes estimate of persons with undiagnosed ischemia develop ed by EPA (see section 5.5.1.1).

Table 6-19. Portion of the simulated at-risk populations in the Los Angeles study area estimated to experience a daily maxi mum end-of-hour COHb at or above specified levels – air quality just meeting the current 8-hour standard.

Coronary Heart Disease a,c All Heart Disease b,c Persons Person-days Persons Person-days COHb
Level

(%) Number Percent Number Percent Number Percent Number Percent
≥ 0.0 383,040 100 139,800,000 100 630,807 100 230,200,000 100
≥ 1.0 155,243 40.5 2,472,000 1.8 256,297 40.6 4,417,000 1.9
≥ 1.5 17,561 4.6 120,100 0.1 31,008 4.9 218,600 0.1
≥ 1.75 5,820 1.5 27,200 <0.1 10,035 1.6 44,560 <0.1
≥ 2.0 2,007 0.5 3,111 <0.1 3,412 0.5 4,516 <0.1
≥ 2.5 301 0.1 401 <0.1 401 0.2 502 <0.1
≥ 3.0 100 <0.1 100 <0.1 100 <0.1 100 <0.1
≥ 4.0 0 0 0 0 0 0 0 0 Notes: Ambient concentrations from 1997 were adjusted to just meet a 2 nd highest 8-hour average concentration of 9.4 ppm using a relationship derived from the design monitor (ID 060371301).

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Inclusive of those persons with diagnosed cor onary heart disease, angina pectoris, heart attack, and any other heart condition or disease (CDC, 2009).

c Includes estimate of persons with undiagnosed ischemia develop ed by EPA (see section 5.5.1.1).6-30 19.1

14

10.1

7.1

4.6

3.4

8.3

5.2 3.3 2.2 1.6 0.9 4.9 3 1.7 0.9 0.6 0.3 3.4

1.8 0.9 0.5 0.2
2.5 1.3 0.6 0.2

2 0.9 0.4 0.1 0 5 10 15 20Percent of CHD Populationat or Above COHb DoseLevel (%)

1.5 1.6 1.7 1.8 1.9 2.0 ≥ 6 ≥ 5 ≥ 4 ≥ 3 ≥ 2 ≥ 1

COHb Dose Level ( % ) Number of Occurrences Per Year Denver (8-hr Std) Air Quality

4.6

3 2 1.3 0.9 0.5

2.1 1 0.6 0.5 0.3 0.2

1 0.6 0.4 0.3 0.1 0.7 0.4 0.3 0.2 0.1

0.5 0.4 0.2 0.1 0.1

0.4 0.2 0.2 0.1 0.1 0 5 10 15 20Percent of CHD Populationat or Above COHb DoseLevel (%)

1.5 1.6 1.7 1.8 1.9 2.0 ≥ 6 ≥ 5 ≥ 4 ≥ 3 ≥ 2 ≥ 1

COHb Dose Level ( % ) Number of Occurrences Per Year Los Angeles (8-hr Std) Air Quality

Figure 6-6. Estimated percent of the CHD populatio n in Denver (top) and Los Angeles (bottom) experiencing repeated COHb le vels – air quality just meeting the current 8-hour standard.6-31 Table 6-20. Percentage of simulated CHD population s in the Denver and Los Angeles study areas estimated to experience daily m aximum end-of-hour COHb contribution from ambient exposure alone at or above specified levels – air quality just meeting the current 8-hour standard.

Percent of CHD Population a Ambient-Exposure

Contribution to COHb Level (%) Denver Los Angeles
≥ 1.0 40.9 17.0
≥ 1.1 29.7 10.9
≥ 1.2 20.8 7.4
≥ 1.3 15.3 4.1
≥ 1.4 12.3 2.0
≥ 1.5 9.1 1.8
≥ 1.6 6.2 1.3
≥ 1.7 4.1 0.8
≥ 1.8 3.2 0.8
≥ 1.9 2.7 0.5
≥ 2.0 2.5 0.5 Notes: Ambient concentrations from were adjusted to just meet a 2 nd highest 8- hour average concentration of 9.4 ppm.

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1).

Table 6-20 summarizes the percentages of the CHD population for both study areas estimated to have experienced COHb levels at or above selected levels, when considering ambient CO exposure alone (i.e., COHb level in the absence of endogenous CO production) and using air quality adjusted to just meet the curr ent 8-hour standard. As observed above (Tables 6- 18 and 6-19), there are differences between the two study areas when comparing the percent of the CHD population estimated to experience a gi ven COHb level. Approximately 9% of the Denver population is estimated to have experien ced a daily maximum end-of-hour COHb level
at or above 1.5% due to ambient CO exposure alone, while it was estimated that about 2% of the simulated at-risk population in Los Angeles ha d at least one occurrence at this level.

6.2.3 Air Quality Adjusted to Just Meet Alternative Air Quality Scenarios

Consistent with the exposure results describe d above, the percentage of persons estimated to experience a level of daily maximum end-of -hour COHb at selected levels are generally similar across the three potential alternative standa rd scenarios. For example, in Denver most of the population (>99%) were estimated to not ex perience a daily maximum end-of-hour COHb level above 2.0% (Table 6-21). Further, th e potential al ternative standard of a 2 nd highest 8-hour6-32 average of 5 ppm resulted in the lowest num ber and percent of the simulated population experiencing at least one occurrence of %COHb at or above the selected levels. There are a few study area differences worthy of note. As expect ed, the corresponding estimated percent of the CHD population experi encing a particular dose level in Denver is greater than that estimated for Los Angeles, even when considering the same potential alternative standard form and air quality level. For example, when considering a 99 th percentile daily maximum 8-hour average CO concentration of 5.0 ppm, 0.8% of the CHD population in the Denver study area was estimated to have experi enced an estimated daily maximum end-of-hour COHb level at or above 2.0% (Tab le 6-21); in Los Ange les this dose level was estimated to have been experienced by only 0.1% of the CHD populati on (Table 6-22). Again, this is largely a
function of the differences observed between the upper percentile ambient concentrations used to simulate these air quality scenarios in each study area (i.e., a greater spatia l variability in ambient concentrations in Los Angeles). Table 6-23 summarizes the percentage of the CHD population in both study areas estimated to have experienced selected levels of COHb, when consider ing ambient CO exposure alone (i.e., COHb level in th e absence of endogenous CO produc tion) and using air quality adjusted to just meet a 99 th percentile daily maximum 8-hour av erage concentration of 5.0 ppm. As observed above (Tables 6-21 and 6-22), there is a similar pattern when comparing the two study areas and the percent of th e CHD population estimated to ha ve experienced a given COHb level. Approximately 2.7% of the Denver popula tion experienced a daily maximum end-of-hour
COHb level at or above 1.5% due to ambient CO exposure alone, while in Los Angeles, it was
estimated that about 0.3% of the simulated at-risk population had at least one occurrence at this
level.6-33Table 6-21.Portion of the simulated at-risk populations in the Denver study area estimated to experience a daily maximumend-of-hour COHb at or above specified levels– air quality just meeting potential alternative standards.2ndhighest 8-hour average of 5 ppm 99thpct 8-hour Daily Max of 5.0 ppm 99thpct 1-hour Daily Max of 8.0 ppmCHD Personsa,cHD Personsb,cCHD Personsa,cHD Personsb,cCHD Personsa,cHD Personsb,cCOHbLevel (%)Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent≥0.0 53,656 100 85,926 100 53,656 100 85,296 100 53,656 100 85,926 100≥1.0 19,560 36.5 32,394 37.7 26,692 49.7 43,722 50.9 21,040 39.2 34,825 40.5≥1.5 2,061 3.8 3,431 4.0 3,826 7.1 6,454 7.5 2,271 4.2 3,850 4.5≥1.75 444d0.8d1,012 1.2 1,271 2.4 2,123 2.5 568d1.1d1,234 1.4≥2.0 197 0.4 358 0.4 407 0.8 765 0.9 234 0.4 420 0.5≥2.5 62 0.1 86 0.1 86 0.2 173 0.2 62 0.1 86 0.1≥3.0 0 0 12 <0.1 12 <0.1 37 <0.1 0 0 12 <0.1≥4.0 0 0 0 0 0 0 0 0 0 0 0 0Notes:Ambient concentrations from 1995 were adjusted to just meet thelevel of the potential alternative standard indicated using a relationshipderived from the design monitor (ID 080310002).aPersons with diagnosed coronary heart disease,angina pectoris, and heart attack (CDC, 2009).bInclusive of those persons with diagnosed coronary heart disease, angina pectoris, heart attack, and any other heart conditionordisease (CDC, 2009).cIncludes estimate of personswith undiagnosed ischemia developed by EPA (see section 5.5.1.1).dThe 2ndhighest 8-hour average and 99thpct 1-hour daily max scenario results for the CHD pop presented here for 1.75 are actually for abin of at or above 1.8% COHb.6-34Table 6-22.Portion of the simulated at-risk populations in the Los Angeles study area estimated to experience a dailymaximum end-of-hour COHb ator above specified levels – air quality just meeting potential alternativestandards.2ndhighest 8-hour average of 5 ppm 99thpct 8-hour Daily Max of 5.0 ppm 99thpct 1-hour Daily Max of 8.0 ppmCHD Personsa,cHD Personsb,cCHD Personsa,cHD Personsb,cCHD Personsa,cHD Personsb,cCOHbLevel (%)Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent≥0.0 383,040 100 630,807 100 383,040 100 630,807 100 383,040 100 630,807 100≥1.0 53,086 13.9 88,811 14.1 60,913 15.9100,451 15.9 77,772 20.3 128,650 20.4≥1.5 2,408 0.6 4,616 0.7 3,312 0.9 5,820 0.9 5,319 1.4 9,132 1.4≥1.75 602d0.2d1,204 0.2 1,004 0.3 1,606 0.3 1,204d0.3d3,011 0.5≥2.0 301 0.1 401 0.1 301 0.1 401 0.1 401 0.1 502 0.1≥2.5 0 0 0 0 0 0 0 0 0 0 0 0≥3.0 0 0 0 0 0 0 0 0 0 0 0 0≥4.0 0 0 0 0 0 0 0 0 0 0 0 0Notes:Ambient concentrations from 1997 were adjusted to just meet thelevel of the potential alternative standard indicated using a relationshipderived from the design monitor (ID 060371301).aPersons with diagnosed coronary heart disease,angina pectoris, and heart attack (CDC, 2009).bInclusive of those persons with diagnosed coronary heart disease, angina pectoris, heart attack, and any other heart conditionordisease (CDC, 2009).cIncludes estimate of personswith undiagnosed ischemia developed by EPA (see section 5.5.1.1).dThe 2ndhighest 8-hour average and 99thpct 1-hour daily max scenario results forthe CHD population presented here for 1.75 areactually for a bin of at or above 1.8% COHb.6-35 Table 6-23. Percentage of simulated CHD population s in the Denver and Los Angeles study areas estimated to experience daily m aximum end-of-hour COHb contribution from ambient exposure alone at or above specified levels – air quality just meeting a 99 th percentile daily maximum 8-hour average concentration of 5.0
ppm.

Percent of CHD Population a Ambient-Exposure

Contribution to COHb Level (%) Denver Los Angeles
≥ 1.0 16.2 2.8
≥ 1.1 13.0 1.8
≥ 1.2 8.7 1.3
≥ 1.3 5.5 0.8
≥ 1.4 3.7 0.5
≥ 1.5 2.7 0.3
≥ 1.6 2.5 0.3
≥ 1.7 2.1 0.3
≥ 1.8 0.9 0.3
≥ 1.9 0.7 0.3
≥ 2.0 0.2 0.3 Notes: Ambient concentrations from were adjusted to just meet a 99 th percentile daily maximum 8-hour average concentration of 5.0 ppm.

a Persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1).

6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000 PNEM/CO AND 2010 APEX/CO ASSESSMENTS As described above in chapters 2 and 4, popul ation exposure and dose were estimated in 2000 using pNEM/CO, a predecesso r to APEX, for adults with ischemic heart disease(IHD) residing in a defined study area within the same two urban areas (Johnson et al., 2000). As described in section 1.2 above, IHD is also termed CHD, and with regard to characterizing the population of interest with rega rd to demographics (age and gender), the 2000 assessment, like the current assessment, drew from estimates of the prevalence provided by the NHIS (which includes CHD or IHD, angina pectoris, and heart attack) and corre sponding estimates of undiagnosed ischemia developed by EPA. As part of this current (2010) CO REA, staff has used APEX to estimate CO exposures and resulting C OHb levels using a largely similar approach, modeling domains, years of ambient concentration data, 7 and defined at-risk population. 8 There are some differences that exist when comparing details of the methodologies and data sets used:

7 When considering the exposure scenario that uses air quality just meeting the current standard.
8 When considering the CHD population.6-36  number of ambient monitors used (e.g., prev iously 6 in Denver versus 4 used here),

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

 number of microenvironments modeled (previously 15 versus the 8 modeled here),

 use of mass balance modeling (previously all 12 enclosed microenvironments used mass balance, here only indoor microenvironments use mass balance)

 use of a cohort approach (pNEM) versus individual approach (APEX), and

 inclusion of two indoor emission sources of CO in the 2000 pNEM/CO assessment for residential microenvironments: gas stoves and passive smoking. Despite these differences and a few others no t listed, staff still di d not expect to see greatly different results when comparing the two assessments given the similarities in the most likely influential variables (i.e., ambient CO co ncentrations, microenviro nmental approach, CFK module used, etc.). Table 6-24 presents estimate s for the percentage of Denver adults with CHD estimated to experience a daily maximum end-o f-hour %COHb at or above the selected level under the specified air quality conditions for 1995. Table 6-25 presents similar estimates for Los Angeles using the 1997 ambient air quali ty data adjusted to just meet the current 8-hour standard. Each table provides two sets of COHb level estimates for the 2000 pNEM/CO assessment (one
with and the other without indoor source emissi ons) and one set generated from the current (2010) APEX/CO REA. As expected, the estimated percent of persons at or above selected COHb levels from the 2000 pNEM/CO assessment is greatest when in door source emissions are included in the exposure modeling simulation. It is clear by co mparing the two estimates from Johnson et al. (2000) that the presence of indoor sources had a significant impact on COHb levels – much more
so than the ambient air c ontributions, as the percent of persons at selected COHb levels increased by large margins (about 10-30 percentage points) wh ere data are available and comparable from both model simulations. The range of COHb leve ls also extends upwards to at or above 6.0% COHb for 0.2% of the CHD population when c onsidering indoor source emissions and air quality adjusted to just meet the current 8-hour standard. While these results regarding indoor sources from Johnson et al. (2000) are generally informative, they cannot be directly applied to th e current dose assessment results. Furthermore, the data used for simulating the indoor sour ce emissions, while some of it may be readily available for use in the currently used APEX mode l, are considered not n ecessarily reflective of current conditions. For example, indoor gas stove emissions data were generated at a time where pilot lights (a continuous low-level combusti on scenario) and limited external ventilation conditions existed. In addition, while tobacco smoking prevalence rates have not necessarily changed much over the past two decades, the prevalence of smoking indoors has been6-37 substantially reduced in public buildings and likely within many residential microenvironments. It is these changes in indoor source emissions, the limited availabi lity of current and relevant input data, and the limited time and resources a llocated for this assessm ent, that preclude a current quantitative assessment of the impact of indoor source emissions on population COHb
levels. The range of dose estimates without simulate d indoor sources are generally similar in both study areas when comparing results from th e Johnson et al. (2000) assessment with those generated in the current CO REA. However at selected COHb levels in Denver, the current approach estimated a higher percent of the CHD population than when compared with the previous Johnson et al. (2000) assessment. For example, approximately 3.4% of the CHD population was estimated to have a daily maximum end-of-hour COHb level at or above 2.0% in
this current assessment. Th e corresponding value estimated in the Johnson et al. (2000) assessment was approximately 0.5% of the IHD population.6-38 Table 6-24. Percentage of Denver adults with corona ry heart disease (CHD) estimated to experience a daily maximum end-of-hour COHb level – air quality just meeting the current 8-hour standard.

Percentage of CHD Adults at or Above COHb Level Johnson et al. (2000) pNEM/CO a 2010 REA APEX/CO b COHb Level

(%) Includes Indoor Source Emissions No Indoor Source emissions No Indoor Source Emissions
≥ 0.0 100 100 100
≥ 1.0 83.2 65.0 76.3
≥ 1.5 37.6 6.7 19.1
≥ 2.0 19.9 0.5 3.4
≥ 2.5 10.4 0.2 0.6
≥ 3.0 5.5 < 0.1 0.2
≥ 4.0 1.6 0 0
≥ 5.0 0.6 0 0
≥ 6.0 0.2 0 0

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).

Table 6-25. Percentage of Los Angeles adults with coronary heart disease (CHD) estimated to experience a daily maximum end-of-h our COHb level – air quality just meeting the current 8-hour standard.

Percentage of CHD Adults at or Above COHb Level Johnson et al. (2000) pNEM/CO a 2010 REA APEX/CO a COHb Level

(%) Includes Indoor Source Emissions No Indoor Source emissions No Indoor Source Emissions
≥ 0.0 100 100 100
≥ 1.0 79.0 58.1 40.5
≥ 1.5 32.3 5.2 4.6
≥ 2.0 16.8 0.5 0.5
≥ 2.5 9.0 <0.1 0.1
≥ 3.0 5.1 <0.1 <0.1
≥ 4.0 2.2 0 0
≥ 5.0 0.9 0 0
≥ 6.0 0.3 0 0

a Los Angeles 1997 ambient CO concentrations adjusted to just meet the current 8- hour standard (9.4 ppm).6-39 6.4 KEY OBSERVATIONS

Presented below are key observations resu lting from the exposure and dose assessment
for ambient CO.

 Ambient CO exposures and resulting COHb le vels in the blood of two simulated at-risk populations in the Los Angeles and Denver stud y areas were estimated considering five air quality scenarios: as is air quality, air quality adjusted to simulate just meeting the current 8-hour CO NAAQS, a nd air quality adjusted to just meet three potential alternative standards.

 The two at-risk populations simulated we re: (1) persons with diagnosed CHD, including those estimated to have undia gnosed CHD, and (2) the larger group of persons with any type of HD including t hose estimated to have undiagnosed CHD. While the number of persons and person-da ys at or above sel ected COHb levels differed between the two populations, reflecting their differing size, the percentage of each population’s persons and person-days were similar.

 The relative contribution of various microenvi ronments to exposure concentrations was generally similar between the tw o study areas. When considering as is air quality, indoor microenvironments contributed mostly to low level exposures (at or above 1 ppm and 2 ppm), comprising between 40 – 80% of the time spent at those exposure levels, while time spent inside vehicles cont ributed to most exposures at or above 3 ppm (70 – 100%). In comparison, when considering air quality just meeting the current standard, the percent contribu tion from indoor microenvironments was generally higher for low level exposures (abou t 65 – 85% of exposure concentrations at or above 1 ppm and 2 ppm), though again hi gher level exposures were dominated by the contributions from inside -vehicle microenvironments.

 The relationship between the two study areas wi th regard to estimated distribution of maximum end-of-hour COHb levels differed w ith the different air quality scenarios. Under as is air quality conditions, the simulated at-risk populations in the Los Angeles study area were estimated to experience a sl ightly higher distribution of maximum endof-hour COHb levels than the Denver popul ations. Under conditi ons of air quality adjusted from historical air qua lity data to just meet the cu rrent or alternative standards, however, appreciably larger percentages of the Denver populations were estimated to experience COHb at or above specific levels than the Los Angeles populations.

 For as is air quality conditions, the highest daily maximum end-of-hour COHb estimated to be experienced over the course of the simulated year was below 1.5% for more than 98% of the at-risk populations simulated in each study area; it was below 2% COHb for more than 99.9% of these simu lated populations. A lower percentage of the simuated at-risk populations in Denve r were estimated to experience daily maximum end-of hour COHB below these benchmarks than were the populations in Los Angeles.

 Under as is air quality conditions, the highe st incremental contribution of ambient CO exposure to maximum end-of -hour COHb levels estimated in the simulated populations was 1.6% COHb, a nd more than 99% of both study area populations were estimated to have ambi ent CO contributions to COHb below6-40 1.4%. As with estimates of total C OHb (i.e., COHb from endogenous CO production and ambient exposure together), a larger percentage of the Los Angeles population was estimated to experience the higher ambient contributions to maximum end-of- hour COHb compared to the Denver population. For example, the percenta ge of the population estimated to experience ambient contributions to C OHb at or above COHb levels above 1.0% was approximately 2 to 3 times as high in Los Angeles than in Denver.

 For simulations of air quality adjusted to just meet the current 8-hour standard of 9 ppm, the highest estimated daily maximum end-of-hour COHb over the course of the simulated year was below 1.5% for more than 95% of both simulated at-risk populations in the Los Angeles study area, and was below 2% COHb for 99.5% of these populations. In contrast , the percentage of the simu lated at-risk populations in the Denver study area estimated to expe rience daily maximum end-of-hour COHb levels that did not exceed 1.5% was about 80%. The percentage of the Denver populations with their highest estimated da ily maximum end-of-hour COHb below 2% was approximately 92-93%.

 As with estimates of total COHb (i .e., COHb from endogenous CO production and ambient exposure together), the pe rcentage of the simulated population estimated to experience the higher ambi ent contributions to maximum end-of hour COHb was appreciably greater in De nver as compared to Los Angeles. While estimated ambient CO contribu tions to daily maximum end-of-hour COHb were below 1.4% for nearly 98% of the Los Angeles simulated population, the corresponding percentage of the Denver population was just under 88%.

 In addition to the simulations for as is and just meeting the curre nt 8-hour standard air quality conditions, three simula tions of air quality just me eting potential alternative standards were performed. The alternatives comprise different combinations of form, averaging time and level which were expect ed to achieve somewhat similar exposure and dose results. The combinations that were selected were based on consideration of exposure and dose results obtained for the as is air quality conditions and the higher just meeting the current 8-hour standard conditions. The combinations of form, averaging time and level that were simu lated include: (1) a second-highest 8-hour average of 5 ppm, (2) a 99 th percentile daily maximum 8-hour average of 5.0 ppm, and (3) a 99 th percentile daily maximu m 1-hour average of 8.0 ppm.

 These three simulations generated genera lly similar percentages of the at-risk populations estimated to be exposed at se lected concentrations and experience maximum end-of hour COHb levels at or above selected levels. Each of these three simulations generated fewer persons and a lower percent of the at-risk populations at or above se lected COHb levels than did simulations for air quality adjusted to just meet the cu rrent 8-hour standard. For example less than 1% of the Denver populations ha d a highest daily ma ximum end-of-hour COHb at or above the 2.0% COHb le vel in the altern ative standards’ simulations as compared to approximat ely 3.5% in simulations of air quality just meeting the current standard. When considering the potential alternative standards in Los Angeles, only 0.1% of the simulated at-risk populations were6-41 estimated to experience a maximum end- of-hour COHb level at or above 2.0 % COHb, compared to 0.5% at that same CO Hb level associated with air quality adjusted to just meet the current standard.

 Results for the five air quality scenarios are further analyzed in the Policy Assessment to inform consideration of th e level of public health protec tion that might be provided by alternative standard levels associated w ith different combinations of averaging time and form.

 Results generated in the current assessment for the air quality conditions just meeting the current NAAQS were compared with es timates from the assessment conducted in 2000 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study areas (section 6.3). The two assessments em ployed similar approaches and similar, although not identical, air quality data for this scenario and th ey used different versions of the exposure model (APEX vs. pNEM). Re sults were quite similar for the 1.5% and 2% COHb level for the simulated Los A ngeles study area population and somewhat different for the Denver study area population. For example, the two assessments’ Los Angeles estimates of population percentage s with highest daily maximum end-of hour COHb at or above these COHb levels we re similar within 10%. For the Denver simulated study populations, however, the estim ates for these two COHb levels were about 3 and 7 times greater, respectively, in the current assessment when compared with the prior assessment.6-42 6.5 REFERENCES
Graham, SE and McCurdy T. (2004). Developing meaningful cohorts for human exposure models. J Expos Anal Environ Epidemiol . 14(1)23-43.

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

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

Shikiya D, Liu C, Kahn M, Juarros J, Barcikowski W. ( 1989). In-Vehicle Air Toxics Characterization Study in the South Coast Air Basin. Office of Planning and Ru les, South Coast Air Quality Management District. October 1989.7-1 

### 7 VARIABILITY ANALYSIS AND UNCERTAINTY CHARACTERIZATION

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

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

7.1 ANALYSIS OF VARIABILITY

The purpose for addressing variability in this REA is to ensure that the estimates of exposure and risk reflect the variability of ambi ent CO concentrations, pop ulation characteristics, associated CO exposure and dose, and potential health risk across the study area and for the simulated at-risk populations. In this CO REA, there are several algorithms that account for variability of input data when generating the number of estimat ed benchmark exceedances or health risk outputs. For example, variabili ty may arise from differences in the population residing within census tracts (e.g., age distribution) and the activities that may affect population exposure to CO and the resulting dose (e.g., time spent inside vehicles, performing moderate or greater exertion level activities outdoors). A complete range of poten tial exposure levels and associated risk estimates can be generated when appropriately addressing variability in exposure and risk assessments; note however that the range of values ob tained would be within the constraints of the input parameters, algorithms, or modeling system used, not necessarily the complete range of the true exposure or risk values. Where possible, staff identified and incorporat ed the observed variability in input data sets to estimate model parameters within the exposure and dose assessment rather than employing standard default assumptions and/or usi ng point estimates to describe model inputs. The details regarding variability distributions used in data inputs are described in chapter 5. To the extent possible given the data available for the assessment, staff accounted for variability within the exposure and dose modeling. APEX ha s been designed to account for variability in7-2 some of the input data, including the physiologi cal variables that are important inputs to determining ventilation rates and COHb levels. As a result, APEX addresses much of the variability in factors that aff ect human exposure and dose. Impor tant sources of the variability accounted for in this analysis are summarized in Table 7-1.

Table 7-1. Summary of how variability was inco rporated into the assessment.

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 that people visit and the activities they perform, 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 microenvironment.

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.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 Regression equations for three age-group (18-29, 30- 59, and 60+) by two genders 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 distributions with age- and gender-specific geometric mean (GM) and geometric standard deviation (GSD) derived from data from the National Health and Nutrition Examination Survey (NHAN ES), 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 by Johnson (1998) using height and weight data from Brainard and Burmaster (1992) (see Appendix B for details).

Blood volume Values determined according to gender using equations developed from 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).7-4 7.2 CHARACTERIZATION OF UNCERTAINTY

While it may be possible to capture a range of exposure or risk valu es by accounting for variability inherent to in fluential factors, the true exposure or risk for any given individual within a study area is largely unknown. To characterize health risks, exposure and risk assessors commonly use an iterative pro cess of gathering data, deve loping models, and estimating exposures and risks, given the goals of the asse ssment, scale of the assessment performed, and limitations of the input data available. Howe ver, significant uncertainty often remains and emphasis is then placed on characterizing the na ture of that uncertainty and its impact on exposure and risk estimates. We have used such an iterative process in characterizing the uncertainty associated with the approach and data used in developing this final CO REA. Following a review of the draft REA’s by CASAC and the public, a few sources of un certainty were identified as important in improving the approach used to estimate e xposure and dose. These included spatial representation of the monitors used, the number of microenvironments and approach used to estimate exposure, and representation of the at-risk population, among a few others (e.g., Brain and Samet, 2010a). Major a pproach modifications and an alyses conducted throughout the evaluation of this final assessment included the following:

 Expanding the number of monitors used to be tter address spatial variability in ambient CO concentrations;

 Increasing the number of microenvironments modeled from two to eight;

 Using distributions of proximity fact ors to estimate all microenvironmental concentrations rather than simple point estimates;

 Expanding analysis of historical trends in ambient CO concentrations at individual monitors;

 Including two simulated at-risk populations based on prevalence rates for CHD and all types of heart disease (including esti mates of undiagnosed CHD prevalence);

 Evaluating endogenous CO production and the ambient contribution to individual and population COHb levels;

 Identifying the specific microenvironments that contribute to low- and high-level
exposures;

 Estimating the percent of simulated at-risk persons experiencing multiple occurrences per year at or above selected COHb levels;

 Evaluating the distribution of microenvironm ental factors used to estimate exposure concentrations, and;

 Performing sensitivity analyses including

 Evaluating the impact of additional m onitoring data on estimated exposures and COHb levels;7-5  Evaluating the impact of varying und iagnosed prevalence rates by gender to estimated population COHb levels, and;

 Evaluating the impact of using alterna tive hemoglobin content distributions to represent a hypothetical population with anemia. These additional analyses and approaches us ed are not without their own uncertainties, and following this iterative process, these uncerta inties also need to be characterized. This characterization of uncertainty can include either qualitative or quantit ative evaluations, or a combination of both. The approach can also be tiered; that is, the analysis can begin with a simple qualitative uncertainty characterizati on and then progress to a complex probabilistic uncertainty analysis. This second level of analysis may be appropria te when a lower tier analysis indicates there is a high degree of uncertainty for certain iden tified sources, the sources of uncertainty are highly influentia l variables in estimating the exposure and risk, and sufficient
information and other resources are available to conduct a quantitative uncertainty assessment. This is not to suggest that qua ntitative uncertainty analyses s hould always be performed in all exposure and risk assessments. The decision rega rding the type of uncer tainty characterization performed is also informed by the intended scope and purpose of the assessment, whether the selected analysis will provide additional inform ation to the overall decision regarding health protection, whether sufficient data are available to conduct a comp lex quantitative analysis, and whether time and resources are available for high er tier characterizations (US EPA, 2004; WHO, 2008). The primary purpose of the uncertainty charact erization approach selected in this CO REA is to identify and compare the relative imp act that important sources of uncertainty may have on the estimated potential health effect endpoints. The approach used to characterize uncertainty was adapted from guidelines outlin ing how to conduct a qua litative uncertainty characterization (WHO, 2008) and applied in the most recent NO 2 (US EPA, 2008) and SO 2 NAAQS reviews (US EPA, 2009). While it may be considered ideal to follow a tiered approach in the REA to quantitatively characterize all iden tified uncertainties, staff selected the mainly qualitative approach given the extremely limited data available to inform probabilistic analyses. The qualitative approach used in this REA va ries from that of WHO (2008) in that a greater focus was placed on evaluati ng the direction and the magnitude 1 of the uncertainty; that is, qualitatively rating how the source of uncertainty, in the presence of alternative information, may affect the estimated exposures and health risk results. In addition and consistent with the WHO (2008) guidance, staff discuss the uncertainty in the knowledge base (e.g., the accuracy of the data used, acknowledgement of data gaps) and decisions made where possible (e.g., selection

1 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2.7-6 of particular model forms), although qualitative ratings were assigned only to uncertainty regarding the knowledge base. First, staff identified the key aspects of the assessment approach that may contribute to uncertainty in the exposure and risk estimates and provided the rationale for their inclusion. Then, staff characterized the magnitude and dire ction of the influence on the assessment results
for each of these identified sources of uncertain ty. Consistent with the WHO (2008) guidance, staff subjectively scaled the overall impact of the uncertainty by considering the degree of uncertainty as implied by the relationship betw een the source of uncertainty and the exposure
concentrations and COHb levels. Where the magnitude of uncertainty was rated low , it was judged that large changes within the source of uncertainty would have only a small effect on the exposure results. For example, a statistical procedure was used to substitute missing ambient concentrations in each ambient data set. Staff compared the air qual ity distributions and found negligible differences
between the substituted data set and the one with missing values (e.g., Tables 5-13 through 5- 16). There is still uncertainty in the approach used, since th ere may be alternative methods available. However, staff judged that the quantita tive comparison of the data sets indicates that there would likely be little in fluence on exposure estimates by the data substitution procedure. A magnitude designation of medium implies that a change within the source of uncertainty would likely have a moderate (or pro portional) effect on the results. For example, the magnitude of uncertainty associated with usi ng the historical data to represent a hypothetical
future scenario was rated as low-medium . While we do not have in formation regarding how the
ambient CO concentration distri bution might look in the future , we do know however what the distribution might look like based on historical trends and the prim ary emission sources. If these
trends in observed concentrations and emissions remain consistent in the future, then the magnitude of the impact to estimated exposures in this assessment w ould be judged as likely low or having negligible impact on th e exposure and dose estimates. However, if there are entirely new emission sources, the magnitude of influence might be greater. When adjusting air quality in each location to simulate the various exposur e scenarios, staff observed mainly proportional differences (e.g., a factor of two or three) in th e estimated exposure and dose levels. Assuming that these types of ambient con centration adjustments could refl ect the addition of a new source in each area carries its own uncertainties; however , based on this information, staff also judged the magnitude of influence in using the historic al air quality data to represent a hypothetical
future scenario as medium . A characterization of high implies that a small change in the source would have a large affect on resu lts, potentially an order of ma gnitude or more. This rating would be used where the model was extremely sensitiv e to the identified source of uncertainty. In addition to characterizing the magnitude of uncertainty, staff also included the direction of influence, indicati ng how the source of uncertainty was judged to affect estimated7-7 exposures or risk estimates; either the estimated values were likely over - or under-estimated . In the instance where the component of uncertainty can affect the assessment endpoint in either direction, the influence was judged as both . Staff characterized the direction of influence as
unknown when there was no evidence available to j udge the directional nature of uncertainty associated with the particular source. Sta ff also subjectively scaled the knowledge-base uncertainty associated with each identified source using a three level scale: low indicated significant confidence in the data used and its applicability to the assessment endpoints, medium implied that there were some limitations regard ing consistency and completeness of the data used or scientific evidence presented, and high indicated the extent of the knowledge base was extremely limited. The output of the uncertainty characteri zation was a summary describing, for each identified source of uncertainty, the magnitude of the impact and the direction of influence the uncertainty may have on the exposure and risk ch aracterization results. We identified sixteen sources of uncertainty associated with this approach for modeling CO population exposure and dose and associated potential health risk, each su mmarized in Table 7-2. Section 7.2.1 describes the sources more fully and provides support for th e ratings ultimately selected by staff, while
section 7.2.2 includes model sensit ivity analyses referenced in section 7.2.1. As mentioned in earlier chapters, given the si gnificant time constraints of this review, results of the characterization are provided in this document without substantial interpretation. Rather, interpretative discussion of th ese results, including further considerati on of public health implications, is provided in the Policy Assessment.7-8 Table 7-2. Characterization of key uncertainties in the assessment

.

Sources of Uncertainty Influence of Uncertainty on Exposure/Dose Estimates Category Element Direction Magnitude Knowledge-base Uncertainty

Database Quality Over Low Low
Missing Data Substitution Method Under Low Low
Zero Concentration Frequency Under Low Low
Temporal Representation Both Low Medium Ambient Monitoring Concentrations

Spatial Representation Both Low – Medium Medium

Historical Data Used Unknown Low – Medium Medium Adjustment of Air Quality to Simulate Just Meeting the Current and Potential Alternative Standards Proportional Approach Used Both Low Low

Population Database Both Low Low

Activity Pattern Database Unknown Low – Medium Medium

Longitudinal Profile Algorithm Both Low – Medium Medium

Meteorological Data Both Low Low

Microenvironmental Algorithm and Input Data Unknown Medium Medium

Commuting Algorithm Both Low Low

At-Risk Prevalence Rates Both Low Medium APEX Input Data and Algorithms

Physiological Variables Unknown Low – Medium Medium

Potential Health Effect Benchmark Levels Simulated At-Risk Populations a Unknown Low Medium

Notes:

a This entry focuses on the uncertainty associated with the benchmark levels in their application to estimated COHb levels for the simulated at-risk populations (i.e., individuals with diagnosed HD or CHD combined with an estimate of undiagnosed CHD). With regard to other potentially susceptible populations (as described in section 2.4 above), we additionally not e the lack of studies that describe CO Hb levels and health effects that might be expected as a result of short-term el evations in CO exposure in those populations.7-9 7.2.1 Considerations in Characterizing Sources of Uncertainty

Staff considerations in reach ing the judgments summarized in Table 7-2 above are noted below in order of the presentation in Table 7-2.

7.2.1.1 Ambient Monitoring Concentrations

Five elements of uncertainty were id entified regarding th e ambient monitoring
concentrations used to estimat e at-risk population exposures and COHb levels. These include the following elements: database quality, the method used to substitute for missing ambient concentrations, use of reported zero ambient concentrations, and the temporal and spatial representation of the monitors used as input to the exposure model. Database Quality All ambient CO measurements available in AQS are quality-assured. There may be a limited number of poor-quality, high concentration data within th e ambient concentration data sets, potentially influencing the number of benchm ark dose-level exceedances. Note also that any uncertainty regarding low leve l concentrations at or near the monitoring detection limit is unlikely to influence high COHb levels, the leve ls of which are of greatest interest in this assessment. Based on this, we judge there to be potential for overestimation in the number and percent of persons at or above a given COHb level, though the magnitude of this potential
overestimation would be low. The source of am bient monitoring data used in the analyses, EPA’s Air Quality System, is of high quality. There is no other source of ambient monitoring data as comprehensive. In addition, the data ar e being used in a manner consistent with one of the defined objectives of ambient monitoring. Ther efore, we judge uncertainty associated with the knowledge base as low. Missing Data Substitution Method There were very few missing hourly concentra tion values when considering the years of ambient monitoring data used in this assessment. This is because we firs t screened all available monitoring data for a minimum of 75% complete ness regarding hours/day and days/year for all four quarters within each year. In meeting th e completeness criteria, they are (by definition), valid and appropriate for the purposes of this assessment. In Denver, the amount of missing hourly data ranged from a low of 0.7% to a high of 2.3% of the potentially reportable concentrations for a year (mean = 1.3%) (Tables 5- 7 and 5-8). There was a greater percentage of missing values in the Los Angeles ambient monitori ng data set, with an av erage of about 5.4% of the possible reportable concentrations for a year (minimum = 4.4%; maximum = 8.4%) (Tables 5-9 and 5-10). Therefore, both sets of data were well within the bounds set by the completeness
criteria.7-10 The method used to substitute for missing concentrations is informed by the measurements available within and among each of the monitors. A variety of standard techniques were used to fill missing values depe nding on the nature of the data gaps, including the development of linear regression models an d interpolation between two points, with each method considering important factors that may in fluence concentration such as time-of-day, dayof-week, or month-of-year. While a number of alternative methods might be available, it is likely that the distributio n of estimated concentrations would be similar to those generated here given that most of these potential alternative da ta substitution methods are also informed by the existing measurement data. Note also that ther e were negligible differences between the air quality distributions when comparing the before-substitution and after-substitution data sets (section 5.7.2). Assuming there is an equal probability of mi ssing either low or high concentration hourly values, and that substituted data are limited by the bounds of the data substitution algorithm (i.e., as defined by limits in the measurement data), there still may be a few instances where missing high concentration data would not be appropriately estimated. If th is were the case, the selected
substitution method would lead to an underestim ation in exposure concentrations and COHb levels experienced by the simulated at-risk popula tions. This also assumes that the substitution of missing low-level concentrati on data with potentially higher concentrations (though still within the bounds of the algorithm) does not affect exposure and COHb results of interest. Very few data values were substituted with respect to the number of measured values available in each
location. Zero Concentration Frequency The ambient monitoring data contain reported va lues equivalent to zero ppm, indicating that the monitor was in operation, though concen trations were below a quantitative detection limit. The minimum reported concentration in both study areas was 0.1 ppm, though reported detection limits for many of the instruments ar e typically 0.5 ppm (sectio n 3.1.2). This indicates that reported concentrations between 0.1 a nd 0.5 ppm were quantified, though likely having greater uncertainty in their assigned values comp ared with other reported concentrations > 0.5 ppm. There is a possibility that exposures and associated COHb levels are underestimated because a reported zero value may represent a non-zero concentration less than 0.1 ppm. Staff elected to use the reported monitoring data as a zero concentration rather than
substitute the zero concentrations with some ot her value. In Denver, there were very few instances where a zero concentration was reported fo r either monitoring year of data (Table 7-3).
Given the limited occurrence of zero concentrations in Denver, it is highly unlikely that a data substitution method employed to assign non-zero concentrations of le ss than 0.1 ppm would change the number or percent of persons e xposed or experiencing %COHb above levels of7-11 interest in this REA. Ther e were, however, a greater number of instances where a zero ppm monitoring concentration was reported in Los Angele s (Table 7-3). Staff judge that it would also be of little consequence if all of these values were substituted with a non-zero concentration less than or equal to 0.1 ppm, given that the COHb le vels of interest are dr iven exclusively by upper percentile ambient and microenvironmental exposure concentrations (For example, see appendix B.6).

Table 7-3. Frequency of CO concentrations report ed as zero in Denver and Los Angeles ambient monitoring data.

Frequency of Reported Zero CO Concentrations Denver Ambient Monitoring Data 1995 2006 Monitor ID n % n %

0310002 3 0.0 3 0.0 0310013 0 0.0 154 1.8 0310014 1 0.0 139 1.6 0590002 0 0.0 143 1.6 Los Angeles Monitoring Data 1997 2006 Monitor ID n % n %

0370113 980 11.2 6 0.1 0371002 81 0.9 95 1.1 0371103 126 1.4 236 2.7 0371201 393 4.5 3 0.0 0371301 74 0.8 1 0.0 0372005 430 4.9 307 3.5 0374002 206 2.4 41 0.5 0590001 7 0.1 290 3.3 0591003 908 10.4 1288 14.7 0595001 61 0.7 2 0.0

Temporal Representation As described above (section 7.2.1.2), staff used hourly ambient monitoring data that met completeness criteria largely defined by temporal at tributes (hours per day, days per year). The hourly ambient CO concentrations are used to es timate event-level exposur e concentrations that can range from 1 minute to 1 hour in duration. Th e relationships used to estimate these exposure concentrations are commonly derived from hourly measurements, thus are generally consistent
with how the ambient monitor data are bei ng used in this REA. In addition, the microenvironmental algorithm employed a factor th at adjusts for temporal differences in outdoor concentrations with respect to that measured at an ambient m onitor (section 4.4.4.3). Therefore,7-12 staff judges the hourly ambient monitoring data of appropriate time resolution when modeling an
individual’s maximum end- of-hour COHb level. Further, give n the observed individual doses in response to highly variable ambient/exposure con centrations (see Figure B-2 in Appendix B.6), staff judges that there would likely be low im pact to the estimated percent of persons experiencing elevated COHb levels with improved temporal representation (e.g., minute-byminute concentrations). Staff judges that there is a medium level of uncertainty in the knowledge base regarding the temporal representation. This is because much of the data used to derive the temporal adjustment factors are based on ambient, microenvironmental, and personal exposure measurements conducted in the 1980’ s, although it is likel y that while CO concentrations have changed dramatically, th e relationships among the measurements remain

constant. Spatial Representation In evaluating the uncertainty associated with representation of the spatial variability in ambient CO concentrations in this REA, we ha ve considered the impact of the improvements made in representing spatial variability in ambi ent CO levels throughout each study area for this REA as compared with the simplified appro ach used in the first draft CO REA. Before considering the impact improving spatial representa tion of monitoring concentrations had on dose estimates, we first cons idered the spatial variab ility in the air quality data used for the study areas. Analysis of the full set of monitoring data indicates spatial variability in monitoring concentr ations across each area is rela tively limited, particularly when considering more recent years (Tables 3-1 throug h 3-6). This could indicate that ambient monitored concentrations do not vary greatly across the study area or that the study area
monitors are sited in locations that measur e similar (though still temporally variable) CO
concentration levels. Note also that the mi croenvironmental algorithm we used in this
assessment to estimate outdoor microenvironmental concentrations has an adjustment factor to address spatial variability in ambient concentr ations expected to occur when modeling human exposures (section 4.4.4.3). Based on this limited analysis of air quality data from the existing ambient monitoring network and observed re lationships between monitor and outdoor concentrations, it is possible th at there would be a low magnit ude of influence on estimated COHb levels in the presence of an alterna tive or supplemental monitoring network. Staff also considered the impact on dose estima tes of using the full monitoring data set to represent the spatial variability in each study area, as comp ared to a more limited approach such as what was done for the first draft REA, in which a single high concentration ambient monitor was used to represent the ambien t concentrations across a larger study area (see section 7.2.2.1). Based on these sensitivity results, staff judge d improving spatial representativeness of the monitoring network to potentially have a me dium magnitude of im pact on the estimated7-13 exposures and COHb levels of interest if th e existing monitoring network does not adequately represent high ambient concentrations to whic h people might be exposed. We note that the limited, single monitor, approach described here assigned the highest monitor to all monitor locations, which illustrated a potential impact as sociated with represen tation of higher ambient concentrations. Conversely, a si milar hypothetical scenar io could be construc ted to investigate the potential impact associated with representati on of lower ambient concentrations. Such a comparison might find a similar, medium, magnit ude of influence to the estimated number and percent of persons at or above COHb levels (alb eit lower COHb levels e xperienced by the at-risk population compared with simulations that employed all monitors). In considering uncertainty in the knowledge base, we note that each ambient monitor comprising the existing monitori ng network has specific objectiv es and monitoring scale that may not appropriately capture the tr ue spatial variability in CO con centrations. In the absence of 1) a monitoring network designed to measure sp atial variability in CO concentrations, 2) performing air quality modeling to estimate fine scale spatial and temporal variability in CO concentrations, and 3) analysis of additional monitoring data that can potentially indicate spatial concentration gradients, sta ff judge the uncertainty in th e knowledge base as medium.

7.2.1.2 Adjustment of Air Quality to Simu late Just Meeting the Current and Potential Alternative Standards Two elements of uncertainty were iden tified regarding the ambient monitoring
concentrations used to estimat e at-risk population exposures and COHb levels. These include
the use of historical data to represent the hypothetical air quality scenarios (with adjustment) and the proportional approach that was used for adjustment.
Historical Data Used Even though the historical data represent air quality conditions that have existed in the past, the conditions simulated using these data are hypothetical scenarios. Th ere is uncertainty in how the temporal and spatial dist ribution of CO concentrations represented by these historical data might reflect the scenarios being simulated with these data given the air quality conditions
that affect ambient concentration levels. More specifically, there is un certainty regarding how influential factors such as emission levels pe r vehicle, vehicular tr affic, and meteorology associated with the historical monitoring data se t might influence air quality conditions in future
situations which the simulated scenario s have been designe d to represent.
We have noted differences between the two study areas in the pe rcent of simulated populations estimated to experience a daily maxi mum end-of-hour COHb le vel at or above 2.0% COHb level when considering conditions just m eeting the current standa rd (e.g., Tables 6-18 and 6-19). In these simulations involving adjusted historical hourly con centrations, there are a7-14 greater number of high 1-hour c oncentrations (e.g., above 8 ppm) in Denver (Tables 5-18 and 5- 19) as compared to the Los Angeles study area. It is evident that ambien t concentrations at the upper percentiles of the distribut ion likely have a strong influen ce on the number and percent of persons experiencing the higher COHb levels. Wh ether these conditions th at existed at the time
the Denver data were collected are appropriate to the modeled hypothetical scenario is largely unknown. However, based on observed trends in air quality over time (Figur es 3-4 and 3-5) and the results generated using the ad justed ambient concentrations, staff judges that, at most, the magnitude of potential influence to the estimated COHb levels in using these historical data could be a medium level. It is possible that these historical patterns can serve as a reasonable basis for predicting future air quality scenario s, though these patterns would not account for the influence of a new CO emission source(s). Th erefore, staff judges the magnitude of the knowledge-base uncertainty as medium. Proportional Approach Used The magnitude of the adjustment applied to historical ambient concentration data was wide ranging across the air quality scenarios. For example, in Denver, to simulate conditions just meeting the current standard, 0.989 was the adjustment applied to the 1995 ambient monitoring data. In comparison, in adjusting the 1997 ambient mon itoring data to just meet a 2 nd highest 8-hour average CO concentration of 9.4 in Los Angeles, a greater adjustment to the data was needed (i.e., a factor of 0.627). However, in comparing recent and historical ambient CO
concentrations for several ambi ent monitors in Los Angeles (F igure 3-4) and Denver (Figure 3- 5), a strong proportional relations hip is present when comparing the recent and historic CO concentrations. In general, the regression slop es and intercepts were similar for each of the monitors used to represent the air quality with in each study area, indicati ng a similarity in the rate of change in concentration occurring at the monitors. This finding suggests there are likely similar sources affecting each of the monitors and that their associated source emissions have also changed at a similar rate over time. The use of a proportional approach to simulate alternative air quali ty scenarios is not uncommon. A similar proportional adjustment appr oach was judged adequate in simulating air quality conditions just meeti ng the 8-hour CO NAAQS in prio r assessments (US EPA, 1992; Johnson et al., 2000). A proportional approach was also used in evalua ting exposure scenarios associated with just meeting the current and se veral alternative standards in the most recent NO 2 and SO 2 NAAQS reviews (US EPA, 2008; US EPA, 2009). In addition, the simulations involving Los Ange les data indicate lit tle difference in the percentage of persons estimated to experience daily maximum end-of-hour COHb at or above
selected COHb levels between simulations usi ng the historical data adjusted downwards to
conditions similar to as is air quality and simulations using th e 2006 as is air quality. This is7-15 shown in Table 7-4 where COHb popul ation estimates are presented for as is conditions (denoted by the asterisk) and contrasted to results for simulations of potential alternative standards that result in similar air quality conditions. As indicated by the presenta tion in Table 7-4, the as is conditions in Los Angeles can be considered to just meet several potential alternative standard of different form and level. For example, the 2 nd highest non-overlapping 8-hour concentration at the design monitor in Los Angele s was equal to 5.6 ppm (i.e., c onsistent with air quality just meeting an 8-hour standard with a form of 2 nd highest and level of 5.6; see Table 5-12). Table 7- 4 indicates, based on this Los Ange les analysis, that whether using as is air quality, or historical air quality adjusted to a level similar to as is air quality, the estimated percent of persons at or above selected COHb levels are similar, thus indi cating the lack of a stro ng influence of the air quality adjustment procedure on the results.

Table 7-4. Percentage of simulated at-risk CHD po pulation in Los Angeles with highest daily maximum end-of-hour COHb levels at or above indicated COHb level considering potential alternative standards.

Form Level (ppm) ≥ 2.0 % COHb ≥ 1.8 % COHb a ≥ 1.5 % COHb

5.7 0.1 0.2 0.9 5.6 * 0.1 0.4 1.5Second Highest Nonoverlapping 8-hour Concentration 5.4 0.1 0.2 0.6
5.7 0.1 0.3 1.4 5.1 * 0.1 0.4 1.5 99 th Percentile of 8- hour Daily Maximum Concentration 5.0 0.1 0.2 0.9 8.2 * 0.1 0.4 1.5 Second Highest 1-hour concentration 8.1 0.1 0.3 1.4 8.0 0.1 0.3 1.4 7.4 * 0.1 0.4 1.5 99 th percentile of 1- hour Daily Maximum Concentration 7.1 0.1 0.2 0.9 Notes: * Asterisk indicates simulation used as is (2006) air quality.

a The 1.8% COHb level was used in the model simula tions for the second draft CO REA. These data were retained for consistency in this Table. Estimates of CHD persons at or above selected COHb levels in that second draft are identical to this final REA; they only differ in the selected benchmark levels.

7.2.1.3 APEX Input Data and Algorithms

Eight elements of uncertainty were iden tified regarding the APEX input data and algorithms used to simulate population activit y. These include the population and activity pattern databases, the longitudi nal profile algorithm, meteorol ogical data, microenvironmental algorithm and input data, commuting algorithm, at-risk prevalence rates and physiological
variables.7-16 Population Database Population data (tract population density, ag e/gender distributions) are from the US Census Bureau, a reliable and quality-assured source. Data used are specifically for census tracts modeled in Denver and Los Angeles. Staff assumed any remaining uncertainties in the database would have negligible influen ce on exposure and %COHb results. Activity Pattern Database
Data are actual records of th e time spent in specific locations while performing specific activities. While not specific to a particular study area, the ac tivity patterns of a population are generally well represented by the mainly populati on-based and nationally -representative survey data. There is, however, uncertainty in how well the CHAD data represent the intended simulated at-risk population, given th at there may be local geographi c attributes that influence a person’s exposure that are not accounted for by CHAD. For example, in each study area it was
observed that the in-vehicle microenvironment cont ributed greatly to concentrations at or above 3-6 ppm (Figures 6-1 through 6-4). This indicates that in-vehicle exposures are likely important determinants in upper level exposures and, thus , the distribution of time spent commuting may be an important influential factor. To evaluate how well the CHAD data repr esented persons residing within each study area regarding commute times, we obtained informa tion on travel time to work for workers ages 16 years 2 and over specific to Denver County, Colo rado and Los Angeles, CA (US Census Bureau, 2009, Table P31). We next isolated persons in CHAD that were ≥ 18 years of age and spent at least one minute in a motor vehicle be tween 6 am and 9 am. The distributions of
commute times associated with these data are il lustrated in Figure 7-1 (see Appendix E, Table E1 for the details). This comparison i ndicates that the available CHAD diaries 3 reasonably represent typical commute times in both the urba n locations modeled in this assessment. Not surprisingly, the percentage of CHAD diaries containing longe r duration commute times (> 40 minutes) are more representative of Los Angeles than Denver, likely a function of the number of CHAD diaries from that ge ographic area (Table 4-1).

2 This is how these data are reported by the US Census. We assumed that the distribution of commute times for persons aged 18 years or older in Los Angeles and Denver counties would be similar to this.
3 Note, however, that this CHAD distribution is developed from an un-weighted sample of all possible diaries that could be used in each study area. The act ual diaries used, their frequency of use, and associated distribution of commute times may be different from that presented in Figure 7-1. To obtain this information is not a trivial computational undertaking and would invol ve generating the necessary data in the APEX daily output file (which was not done here).7-17 0 5 10 15 20 25 30 35

1 to 9 10 to 19 20 to 29 30 to 39 40 to 59 60 to 89 90+ Commute or Travel Time (minutes)Percent of Population (%) Denver County (US Census, 2009) LA County (US Census, 2009) CHAD (US EPA, 2002)

Figure 7-1. Comparison of commute and travel time s for persons residing in Denver and Los Angeles counties to those persons surveyed in CHAD.

We also recognize that the health status of an individual may affect their time-locationactivity patterns, and hence their estimated exposure and dose levels. This could include factors such as time spent in certain locations as well as exertion levels attained by simulated individuals. CHAD is comprised of data from individuals that have , or do not have, an identified
health condition 4 and are assumed to represent the activitie s of persons with normal health status
as well as those with certain health conditions th at may not significantly affect general activity patterns. While some of the diary days are pot entially reflective of the simulated at-risk population, the majority of the diaries may be fr om persons identified as having a normal health status. Thus, if there are differences in activ ities performed and locatio ns visited that depend on health status, there is uncertain ty in the representation of the simulated at-risk population activity patterns modeled in this CO REA by the CHAD diaries. A statistical analysis was performed on a subs et of the CHAD data where persons were specifically asked whether they had angina (s ee Appendix F of CO RE A and Johnson et al., 2000, for details). Activity patterns for persons with angina were compared to those for

4 Of the approximately 35,000 diary days within the CHAD master file, about 65% contain information on whether the person was identified as asthmatic (or not) or whether the person was identified as having a heart or lung condition (or not). The remaining 35% have unknown health status (i.e., the health status was not requested in the original survey questionnaire).7-18 individuals not having angina us ing selected exertion level metr ics and time spent outdoors or inside vehicles. The percentages of time spen t outdoors or in a vehicl e were generally not statistically significantly differe nt between angina and non-angina subjects. While there were statistically significant differences in the exertio n level attained between angina and non-angina subjects, 5 actual differences were generally numerically small compared to the mean values. The differences in activity and exertion level between angina and non-angina subjects, although statistically significant, were judged not large enough to severely impact the validity of APEX (or pNEM/CO) modeling results that do not adju st for an angina/non-angina difference in activity patterns. In characterizing the knowledge base uncer tainty, we note that the CHAD time-location activity diaries used are the most comprehensive source of such data and realistically represent where individuals are located and what they are doing. Features of an individual’s activity pattern are well represented, adju stments are made to represent the population distribution in a specific area (using age and gender), and temper ature is used to link CHAD diaries with the simulated individuals residing in a specific area. In addition, the CHAD data are from a reliable and quality-assured source (US EPA, 2002) and have recently incorporated several thousand new diary days (see Table 4-1). However, we judge the knowledge-base uncertainty as medium given the overall limited number of diary days av ailable to represent the large simulated at-risk populations residing within each study area. Longitudinal Profile Algorithm Much of this assessment focused on persons ha ving at least one exposure or dose above a selected level; therefore, the potential magnitude of influence from the longitudinal profile algorithm would be considered low. However, when considering multi-day exposures, staff judges the magnitude of potential influence to COHb levels c ould range upwards to a medium level. In developing the longitudinal method, the evalua tion indicated that both the D and A statistics are reasonably reproduced for the popu lation (Glen et al., 2008). The approach was also compared with two other independent met hods used for constructi ng longitudinal activity patterns (see Appendix B, Att achment 4 of US EPA, 2009). This particular comparison indicated that, depending on th e longitudinal profile method se lected, the number of persons experiencing multiple exposure events at or ab ove a selected level could differ by about 15 to
50%. 6 Note however, long-term diary profiles (i.e., monthly, annual) do not exist for a population, though a few persons surveyed have l ongitudinal diary profil es (Table 4-1). Therefore, the knowledge-base uncertai nty is judged by staff as medium.

5 Note there was a very large samp le size for the non-angina subjects.
6 The comparison used simulated persons in Atlanta and evaluated the number of persons experiencing three or more ozone 8-hour average exposures at or above 0.07 ppm concomitant with moderate or greater exertion.7-19 Meteorological Data Data are from the National Weather Service, a well-known and quality-assured source. The daily maximum temperatures are used when selecting appropriate C HAD diaries to simulate the at-risk population. The temper ature bin ranges that were us ed (see section 5.5.3) are wide such that any erroneous temperat ures that do exist within the data set would likely have limited impact to estimated exposure and dose results. Da ily mean temperatures are used when selecting air exchange rates (section 5.9.4). Given the overlap of the AE R distributions and the wide temperature ranges used to categorize them, it is also likely that there is limited impact by erroneous temperature data that may exist. Therefore, staff judges uncertainty regarding meteorological data and its potential magnit ude of influence on COHb levels as low. Microenvironmental Algorithm and Input Data In this REA, the number of microenvironmen ts selected captures the likely locations persons spend time and where CO exposures woul d occur. Using distributions of proximity
factors derived from measurement data in Denve r and applied to estimate microenvironmental concentrations is judged by staff to be a reasona ble approach. However, the extent to which these Denver study data reflect similar relati onships in Los Angeles likely has greater uncertainty. Additionally, the De nver measurement data were collected in the 1980’s; therefore, there is also uncertainty as to how these data mi ght reflect relationships observed for other years modeled in this assessment (i.e., 2006). Howe ver, the distributions of microenvironmental concentrations (Tables 6-8 and 6-11) and th e effective microenvironmental-to-ambient concentration ratios (e.g., Tables 6-9 and 6-12) , in particular those used to estimate highexposure microenvironments, were found comp arable to other measurement data and relationships available (alb eit limited in number) and gene rally support th e algorithm and distributions applied in this assessment. Commuting Algorithm In this REA, the commuting algorithm within APEX was implemented. Use of this algorithm better represents individual exposures across each modeling domain. The data are derived from the US Census, a well-known and qua lity-assured source. The data are used in addressing home-to-work travel, ce rtainly within the bounds of the objectives associated with the original data collection; th erefore, staff judged the knowle dge-base uncertainty as low. While there may be some uncertainties associ ated with the application of the database (see US EPA, 2009), they are limite d in the potential magnitude of influence they might have on estimated exposures and COHb levels. Fo r example, although se veral of the APEX microenvironments account for time spent in travel , the travel is assumed to always occur in basically a composite of the home- and work-t racts. No other provision is made for the possibility of passing through other census tracts during travel. Th is could contribute to either7-20 over- or under-estimating exposure concentratio ns, dependent on the number and identity of tracts the simulated individual would actually traverse and the spatia l variability of the
concentration across different tracts. Given that most persons would likely experience ambient
concentrations from within thei r home air district (i.e., encomp assing all tracts within a 10 km radius of the monitor location), tracts existing be tween home and work tracts for these persons would not have a different assigned ambient concentration. In addition, the commuting route (i.e., which roads individuals ar e traveling on during the commute) is not accounted for. From a practical perspective though, if staff was to c onsider multi-tract commuting, further complexity would need to be added to the modeling while al so requiring additional input data that are not readily available (e.g., commuti ng route data for simulated individuals). These model adjustments would come with a number of additi onal uncertainties and would require additional
time and resources not available for the assessment.
At-risk Prevalence Rates
Data are from the Centers for Disease Cont rol, a well-known and quality-assured source. Though prevalence data are not specific for each region, the national prevalence data were stratified by selected age-groups a nd gender. Staff used gender-spe cific ratios and applied them to all age groups uniformly even though there may be uncertainty in the accuracy of the prevalence estimates for specific age and gende r groups. In addition, potentially undiagnosed individuals with CHD were included to expand the total CHD population considered. This was based on several assumptions including using 1990 estimates of the popula tion with undiagnosed IHD. The percent of the population with undi agnosed CHD (i.e., 43.8%) was applied to the diagnosed CHD prevalence, without any diffe rence between genders. In comments on the second draft REA, it was suggested that wo men with undiagnosed heart disease may be underrepresented by this approach. In the limited time available, extant lit erature regarding this topic were reviewed with regard to the suitab ility for developing an improved estimate of the undiagnosed prevalence rate. Rele vant data that could be used to generate new undiagnosed prevalence rates and provide a greater degree of confidence than what was used in this
assessment were not identified. We recognize there is uncertainty associated with the undiagnosed prevalence rate used. To evaluate the potential impact the prevalence rate might have on the estimated COHb levels, we performed simulations using alternative va lues for undiagnosed CHD prevalence rates. These analyses are described in section 7.2.2.2 below. The COHb results generated from this sensitivity analysis, as well as when using different prevalence rates to estimate COHb levels for the HD population, had little impact to the percent of persons at or above the selected COHb levels. Only the total number of persons was a ffected by using the alternative prevalence rates. This suggests that, if new data were uncovere d with improved represen tation of either the7-21 diagnosed or undiagnosed prevalence rates, there would be little ch ange to the percent of persons
at or above selected COHb leve ls. Therefore, staff judges the magnitude of influence to COHb as low for the estimated percent of persons at or above selected levels. The prevalence rates do
have a medium level of influence to the estimate d number of persons at or above selected COHb levels, though the observed impact was proportiona l across the range of se lected COHb levels. Physiological Variables Many of the parameters used to estimate the physiological attributes of the simulated atrisk population were developed from healthy i ndividuals; there were no adjustments made to account for a particular health condition. While the ISA notes some variability in some parameters in individuals with specific hea lth conditions that might affect CO uptake and elimination, most of the identified health conditions that could a ffect the physiological variables used in the CFK model may not n ecessarily be associated with the simulated at-risk populations, i.e., HD or CHD individuals. In addition, there is uncertainty in some of the parameter values used in the COHb algorithm due to the age of source publications cited (e.g., dating back to the
mid 20 th century) or uncertainty as to their repres entativeness. As an example, alveolar ventilation is represented in the simulations as a single point estimate of 19.63 and applied directly to activity-specific oxyge n consumption rates. This was based on an analysis by Joumard et al. (1981), of data generated by Gale tti (1959), which did not include measurements made at elevated exertion levels, although theore tically one would expect there to be a nonlinear relationship between V A and VO 2 given the non-linear relationship of dead space volume (V D ) to tidal volume (V T ) with increasing breathing rate. 7 Thus, the point es timate of 19.63 used may not adequately represent the V A to VO 2 relationship at higher ventilat ion rates. We note however that most of the upper level exposure concentrati ons in this assessment ar e associated with time spent inside vehicles, where it is expected that the exertion level and breat hing rate would be at a relatively low level. Therefore, it may be that the point estimate is appr opriately used for these
activities and the estimated ma ximum end-of-hour COHb associated with the in-vehicle microenvironment may not be advers ely affected. As described in this example, it is possible that most of the data and or relationships st ill remain appropriate in modeling the current population; however, in the abse nce of conducting a comprehensiv e review and comparing the
historical data to recent measur ements, staff judges the knowledge base uncertainty as medium.

7 Dead space volume (V D ) will remain relatively constant, increasing only slightly with increasing ventilation rate. Tidal volume (V T ) consistently increases with increasing ventilation rate. Alveloar ventilation (V A ) will approach that of total ventilation (V E ) at higher ventilation rates, given that V A = V E (1-V D /V T ). Note also, V E is not linear with respect to VO 2 (see Graham and McCurdy, 2005)7-22 The potential influence of another physiol ogical parameter – hemoglobin content – was evaluated in response to comments received on earlie r drafts of this document. These analyses
are described in section 7.2.2.3 below.

7.2.1.4 Potential Health Effect Benchmark Levels for the Simulated At-risk Populations The potential health effect benchmark levels for considering the COHb estimates for the simulated at-risk populations 8 in this REA were identified (in section 2.6) based on data from a
well-conducted multi-center controlled human ex posure study demonstrate cardiovascular effects in subjects with moderate to se vere coronary artery disease at study mean COHb levels as low as
2.0-2.4% of which were increased from a baseline mean of 0.6-0.7% as a result of short (~1hour) experimentally controlled increases in CO exposures (study mean of 117 ppm CO). No laboratory study has been specificall y designed to evaluate the effect of experimentally increased exposure to CO resulting in an increase in COHb levels to a study mean below 2.0%. However, based on analysis of individual study subject responses at base line and at the two increased COHb levels, study authors concluded that each increase in COHb produced further changes in the study response metric, without evidence of a measurable threshold effect. There is no
established “no adverse effect leve l” and, thus, there is greater unc ertainty concerning the lowest benchmark level identified (i.e., 1.5%). There is also uncertainty about wh ether individuals with the most severe CHD are adequately represente d. Additionally the COHb levels estimated in this assessment result from CO exposure concen trations much lower than the experimental exposure concentrations used to increase study subject COHb le vels to the study targets (e.g., 2.0%) and with which the responses were associ ated. Given that the evidence supporting the
choice of benchmark levels is based on cont rolled human exposure data, staff judged the influence of this uncertainty on the risk characterization as being low.

7.2.2 Sensitivity Analyses

This section describes sensitivity analyses refe renced in section 7.2.1 with regard to three sources of uncertainty in the assessment, one in the category of ambient monitoring concentrations and two in the category of APEX input da ta and algorithms.

8 Discussion here focuses on the uncertainty associat ed with benchmark levels in their application to estimated COHb levels for the simulated at-risk populations (i.e., individuals with diagnosed HD or CHD combined with an estimate of undiagnosed CHD). With regard to other potentially susceptible populations (as described in section 2.4 above), we additionally note the lack of studies that describe COHb levels and health effects that might be expected as a result of short-term elevations in CO exposure in those populations.7-23 7.2.2.1 Spatial Representation

In evaluating the uncertainty associated with representation of the spatial variability in ambient CO concentrations in this assessment, we performed sensitivity an alyses that illustrate the quantitative impact of the impr ovements made in this representi on. Of particular interest was how expanding the number of monitors used in th is REA for both study areas compared with the simplified approach used in the first draft CO REA. In the analysis shown here, however, the more limited approach applied all hourly concentr ations from the single design monitor to all monitoring locations defined for each study area to ensure comparable modeling domains. 9 Four model simulations for the HD population ar e shown: two air quality conditions ( as is and just meeting the current 8-hour standard) in each of the two study areas (Denver and Los Angeles). The results for the more spatially-limited approach (single monitor) might be considered to provide an upper bound estimate of COHb levels for this modeling domain, given that the design monitor represents the highest measured concen trations in each study ar ea and that there are restricted opportunities for persons to experience concentrations lower than that of the design
monitor. As shown in Tables 7-5 through 7-8, the us e of multiple monitors (versus using the design monitor alone) to represent the air quality input to the e xposure model results in a lower number and percent of persons at or above a given COHb level. In general, there were larger differences in the number and percent of persons at or above the higher COHb levels (i.e., ≥ 1.75% COHb). A similar pattern was observed wh en comparing air quality scenarios within each study area, although the expanded spatial vari ability had a greater im pact on the number of persons at or above selected COHb levels in the Los Angeles study area (Tables 7-6 and 7-8). This is likely the result of the Los Angeles study area having gene rally greater spatial variability in ambient monitoring concentrat ions (i.e., ten monitors) when compared with that provided by the four monitors representing ai r quality in the Denver study area. The spat ial heterogeneity in ambient concentrations in the Los Angeles study area allowed simulated pe rsons to experience a wider range of ambient concentrations when comp ared with the simulated persons Denver. As a result of the limited spatial heterogeneity in Denver ambient concentrations (given the few monitors selected, their cl ose proximity to one another, and sim ilar concentration levels), a lesser difference in the percent of pers ons at or above selected COHb levels was observed when using the design monitor to represent al l ambient concentrations in the model domain. This difference in spatial variability between the two study areas may also pl ay a role in the study area

9 In the 1 st draft CO REA, the design monitor in each study area was used to represent air quality for all census tracts within a 20 km radius of the monitor. In this current investigation, the 10 km radii for the 4 monitor locations in Denver, and 10 monitor locations in Los An geles were retained to define the respective exposure modeling domains. All of these monito rs used the CO concentrations from the single design monitor for each respective study area (080310002 in Denver and 060371301 in Los Angeles).7-24 differences observed for the estimated percent of persons at or above the selected COHb levels when using air quality just meeting the current standard (e.g., Table 6- 18 and 6-19). This is because a greater proportion of the simulated persons will experience generally similar exposure levels in Denver (and higher con centrations, note Tables 6-7) than compared with simulated persons in Los Angeles (Table 6-10).

Table 7-5. Comparison of highest estimated daily maximum end-of-hour COHb levels for Denver HD population for two model simulations - all monitor concentrations versus the design monitor concen trations – as is air quality.

Number of HD Persons Percent of HD Persons COHB Level

(%) All Monitors a Design Monitor only b All Monitors a Design Monitor only b Ratio c

≥ 0.0 85,926 85,926 100 100 1.0
≥ 1.0 12,723 18,868 14.8 22.0 1.5
≥ 1.5 592 1,049 0.7 1.2 1.8
≥ 1.75 86 234 0.1 0.3 2.7
≥ 2.0 0 49 0.0 0.1
≥ 2.5 0 0 0.0 0.0
≥ 3.0 0 0 0.0 0.0
≥ 3.5 0 0 0.0 0.0

≥ 4.0 0 0 0.0 0.0 Notes:

a Each monitor site used ambient concentrat ions from that site (from Table 6-15). b Each monitor site used ambient concentrations from design site (080310002).

c Ratio = (value for design site scenario) / (value for all sites scenario).

Table 7-6. Comparison of highest estimated daily maximum end-of-hour COHb levels for Los Angeles HD population for two model simulations - all monitor concentrations versus the design monitor concentrations – as is air quality.

Number of HD Persons Percent of HD Persons COHB Level

(%) All Monitors a Design Monitor only b All Monitors a Design Monitor only b Ratio c

≥ 0.0 630,807 630,807 100 100 1.0
≥ 1.0 162,769 402,006 25.8 63.7 2.5
≥ 1.5 9,433 60,712 1.5 9.6 6.4
≥ 1.75 3,011 21,776 0.5 3.5 7.2
≥ 2.0 502 7,125 0.1 1.1 14.2
≥ 2.5 0 301 0.0 0.0
≥ 3.0 0 0 0.0 0.0
≥ 3.5 0 0 0.0 0.0

≥ 4.0 0 0 0.0 0.0 Notes:

a Each monitor site used ambient concentrat ions from that site (from Table 6-16). b Each monitor site used ambient concentrations from design site (060371301).

c Ratio = (value for design site scenario) / (value for all sites scenario).7-25 Table 7-7. Comparison of highest estimated daily maximum end-of-hour COHb levels for Denver HD population for two model simulations - all monitor concentrations versus the design monitor concentrations – air quality just meeting the current 8-hour standard.

Number of HD Persons Percent of HD Persons COHB Level

(%) All Monitors a Design Monitor only b All Monitors a Design Monitor only b Ratio c

≥ 0.0 85,926 85,926 100 100 1.0
≥ 1.0 66,465 79,275 77.4 92.3 1.2
≥ 1.5 17,141 32,245 19.9 37.5 1.9
≥ 1.75 7,614 16,018 8.9 18.6 2.1
≥ 2.0 3,110 7,614 3.6 8.9 2.4
≥ 2.5 679 2,011 0.8 2.3 3.0
≥ 3.0 197 555 0.2 0.6 2.8
≥ 3.5 74 234 0.1 0.3 3.2

≥ 4.0 12 74 0.0 0.1 6.2 Notes:

a Each monitor site used ambient concentrat ions from that site (from Table 6-18). b Each monitor site used ambient concentrations from design site (080310002).

c Ratio = (value for design site scenario) / (value for all sites scenario).

Table 7-8. Comparison of highest estimated daily maximum end-of-hour COHb levels for Los Angeles HD population for two model simulations - all monitor concentrations versus the design moni tor concentrations – air quality just meeting the current 8-hour standard.

Number of HD Persons Percent of HD Persons COHB Level

(%) All Monitors a Design Monitor b All Monitors a Design Monitor b Ratio c

≥ 0.0 630,807 630,807 100 100 1.0
≥ 1.0 256,297 557,149 40.6 88.3 2.2
≥ 1.5 31,008 164,776 4.9 26.1 5.3
≥ 1.75 10,035 67,837 1.6 10.8 6.8
≥ 2.0 3,412 28,700 0.5 4.5 8.4
≥ 2.5 401 4,716 0.1 0.7 11.8
≥ 3.0 100 602 0.0 0.1 6.0
≥ 3.5 0 0 0.0 0.0

≥ 4.0 0 0 0.0 0.0 Notes:

a Each monitor site used ambient concentrat ions from that site (from Table 6-19). b Each monitor site used ambient concentrations from design site (060371301).

c Ratio = (value for design site scenario) / (value for all sites scenario).7-26 7.2.2.2 At-risk Prevalence Rates

To evaluate the potential impact that th e prevalence rate used for undiagnosed CHD might have on the estimated COHb levels, we perf ormed simulations using alternative values for undiagnosed CHD prevalence rates. The alternative values were based on assuming that the total CHD prevalence (diagnosed and undiagnosed combin ed) for females equaled that of the males, in effect representing a greater proportion of undiagnosed CHD for females (Table 7-9). Two air quality scenarios were considered in Denver; as is air quality and air quality adjusted to just
meet the current standard. All standard model settings previously descri bed for these scenarios
were retained.

Table 7-9. Estimated alternative prevalence rates fo r CHD, stratified by age and gender.

Prevalence rate (fraction) for coronary heart disease

Males a Females Age range Diagnosed Undiagnosed Total Diagnosed a Undiagnosed b Total

18 to 44 0.012 0.005 0.017 0.007 0.010 0.017
45 to 64 0.088 0.038 0.127 0.050 0.077 0.127
65 to 74 0.244 0.107 0.351 0.138 0.213 0.351

75+ 0.310 0.135 0.446 0.175 0.271 0.446
Notes:

a Values obtained from Table 5-6. b Generated assuming total CHD prevalence fo r females was equal to that of males.

The outputs generated from these new simulations were compared with output from the previous simulations that appl ied an undiagnosed CHD uniformly to the two genders (termed
base-CHD ). As expected, there are a greater number of persons at or above selected COHb levels for both air quality scenarios due to th e expansion of the simu lated at-risk population (Tables 7-10 and 7-11). However, there is little observed diffe rence in the percentage of CHD population at or above the se lected COHb levels. Interestingly, this was the same outcome when we expanded the simulated at-risk population to include all persons with diagnosed heart diseas e (HD) along with undiagnosed CHD. That is, the percentage of the HD populat ion estimated to experience COHb at or above selected levels was nearly identical to that estimated for the CHD population (e.g., Tables 6-18 and 6-19). Note that these th ree at-risk population simulations not only contain differing total prevalence of a particular hea lth condition, but contai n variable prevalence when comparing across age groups and the two genders. The vari ation in the prevalence rates used have only resulted in differences in the number of persons exposed, indicating that if additional prevalence7-27 rates were available, there might be little impact to the percentage of pers ons at or above selected
COHb levels.

Table 7-10. Comparison of the portion of the si mulated CHD population in the Denver study area estimated to experience a daily maximum end-of-hour COHb at or above specified levels using base and alternative undiagnosed CHD prevalence rates – as is air quality.

CHD persons
Base Prevalence a,b Alternative Prevalence a,c COHb
Levels

(%) Number Percent Number Percent

≥ 0.0 53,656 100.0 71,093 100

≥ 1.0 7,873 14.7 11,032 15.5

≥ 1.5 333 0.6 494 0.7

≥ 1.75 25 <0.1 49 0.1

≥ 2.0 0 0 0 0 Notes: Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air quality scenario.

a Includes persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.2.1).

c Effectively, a hypothetical undiag nosed CHD prevalence was used to generate an eqivalent total CHD prevalenc e for both genders. See Table 7-9.7-28 Table 7-11. Comparison of the portion of the si mulated CHD population in the Denver study area estimated to experience a daily maximum end-of-hour COHb at or above specified levels using base and alternative undiagnosed CHD prevalence rates – air quality just m eeting the current standard.

CHD persons
Base Prevalence a,b Alternative Prevalence a,c COHb
Levels

(%) Number Percent Number Percent

≥ 0.0 53,656 100 71,093 100

≥ 1.0 40,921 76.3 54,532 76.7

≥ 1.5 10,267 19.1 13,587 19.1

≥ 1.75 4,541 8.5 5,973 8.4

≥ 2.0 1,814 3.4 2,382 3.4

≥ 2.5 346 0.6 457 0.6

≥ 3.0 86 0.2 123 0.2

≥ 3.5 49 0.1 49 0.1

≥ 4.0 0 0 0 0 Adjusted ambient concentrations from four monitors in 1995 were used to represent the air quality just meeting the current 8-hour standard.

a Includes persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.2.1).

c Effectively, a hypothetical undiag nosed CHD prevalence was used to generate an equivalent total CHD preval ence for both genders. See Table 7-9.

7.2.2.3 Physiological Variables

Sensitivity analyses performed on one potential ly influential physiological variable – hemoglobin content - is described here. This phy siological variable was identified and evaluated in response to a comment suggesting that the so me of the physiological variables used in the simulations may not necessarily be adequately representing the at-risk populations or subgroups having characteristics that may c ontribute to increased susceptibil ity. One variable identified within the CFK module that could have an impact on estimated COHb levels, and for which data
were available to evaluate va riation among population subgroups, is an individual’s hemoglobin content. For any simulated individual, the model currently samples from age- and genderstratified distributions deve loped from the 1999-2004 NHANES (see Appendices A and B). These NHANES data are population-ba sed; as such, represent a coll ection of individuals that may be based on population age, gender, and race groupings. In addition, persons having been diagnosed with anemia are represented in these data, although in a small fraction. Nevertheless,7-29 hemoglobin content estimates in this REA are dr awn from a sample that includes persons with
anemia. To evaluate the influence of hemoglobin on dose estimates, we obtained the most recent data available from NHANES (2005-2008), combined these data with the previously used data set (NHANES 1999-2004), and evaluated whether alte rnative distributions could be developed
based on influential factors such as race and he alth condition. Descriptive statistics were generated using the hemoglobin content (Hb) of f our data groups: African-Americans, pregnant women, persons identified using WHO et al. ( 2001) guidelines as potentially having anemia (Table 7-12), 10 and all other persons.

Table 7-12. Hemoglobin levels below which anemia is present in a population (from WHO et al., 2001).

Age or gender group Hb threshold (g/dl) Children (0.5–5.0 yrs) 11.0 Children (5–12 yrs) 11.5 Children (12–15 yrs) 12.0 Women, non-pregnant (>15yrs) 12.0 Women, pregnant 11.0 Men (>15yrs) 13.0

The hemoglobin data for the four groups, as represented by normal distributions, are provided in Table 7-13. Kolgomorov-Smirnov (K S) tests were performed using the SAS procedure NPAR1WAY to test for differences between the subgroups and dataset comprised of all other persons. All of th ese tests were significant (p< 0.0001), indicating that the Hb distributions were different for the compared gr oups. In knowing that the group of persons with anemia contained the lowest values for hemoglobin content, we decided that this group would be used to develop an alternative distribution for evaluating the effect it has on estimated COHb
levels. Rather than identify anemic persons us ing the WHO (2001) thresholds and develop distributions from these samples, we elected to apply the WHO (2001) thre sholds to the existing hemoglobin distributions when sampling for that va riable within APEX. This way the original population distribution shape is preserved, though tr uncated at the portion of the distribution of interest to represent an anemic person’s hemogl obin content. A model simulation was performed

10 While there were persons that responded to a question asking whether they were being treated for anemia, we judged that these persons might not best represent the anemic population based on the medical intervention. The WHO thresholds used here are given in Table 7-12.7-30 as described previously, using the Denver study area, the CHD at-ri sk population, and air quality just meeting the current standard. The COHb levels experienced by this new simulated population (i.e., CHD persons with anemic hemoglobin) were compared with the corresponding results generated when sampling for hemoglobin content from the full distribution (base hemoglobin) (T able 7-14). Using the anemic hemoglobin distribution resulted in a greater number of persons and hence a greater percent of persons at or above selected COHb levels when compared with the base hemoglobin simulation. The difference between the two simulations, how ever, was not large, with the smallest differences observed at the upper C OHb levels. This indicates that it is possible that the number and percent of persons estimated to experience %COHb at or above selected benchmarks may be underestimated when considering certain physiological attributes such as hemoglobin content, but the magnitude is likely to be small given the results provid ed in Table 7-14. A quantitative characterization is not possible at this time due to the lack of readily available information on the percent of HD or CHD population with anemia a nd regarding the extent to which persons with anemia might have been included in the CAD/COHb clinical studies.

Table 7-13. Descriptive statistics of blood hemoglobin content measured in various groups (from NHANES 1999-2008).

Blood Hemoglobin Content (g/dL) Group N mean stdev min max African-American 10,171 13.4 1.5 5.8 18.5 Anemia 2,853 11.3 1.0 5.8 12.9 Pregnant 659 12.5 1.1 8.6 16.4 All Other Persons 41,985 14.2 1.5 5.8 19.77-31 Table 7-14. Comparison of the portion of the si mulated CHD population in the Denver study area estimated to experience a daily maximum end-of-hour COHb at or above specified levels when sampling fr om the base and anemic hemoglobin content distributions – air quality just meeting the current standard.

CHD Persons a,b Base Hemoglobin c Anemic Hemoglobin d COHb
Levels

(%) Number Percent Number Percent 0.0 53,656 100 53,656 100 1.0 40,921 76.3 44,191 82.4 1.5 10,267 19.1 13,266 24.7 1.75 4,541 8.5 6,355 11.8 2.0 1,814 3.4 2,814 5.2 2.5 346 0.6 642 1.2 3.0 86 0.2 173 0.3 3.5 49 0.1 74 0.1 4.0 0 0.0 12 0.0 Adjusted ambient concentrations from four monitors in 1995 were used to represent the air quality just meeting the current 8-hour standard.

a Includes persons with diagnosed coronary heart disease, angina pectoris, and heart attack (CDC, 2009). b Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.2.1).

c Used hemoglobin distributions defined in Appendix A for general population. d Used hemoglobin distributions defined in Appendix A for general population, only truncated the distributions by upper limits defined in Table 7-13.7-32 7.3 KEY OBSERVATIONS

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 to be reasonable for the simulated population the assessment is intended to repr esent (i.e., the CHD or HD populati on residing within the urban core of each study area). This is because:

 Only three sources of uncertainty were associ ated with a potential directional influence - data base quality (overestimation), missing data substitution (undere stimation), and zero concentration frequency (underestimation) - a nd all were judged to have a low magnitude of influence on estimated exposures and doses.

 Thirteen of the identified sources of uncerta inty were judged by staff to have either bidirectional influence (eight sources ) or unknown direction (five sources):

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

 Five of the remaining twelve sources (i.e ., spatial representation, historical data used, activity pattern database, longit udinal profile algorithm, physiological factors) were judged as having low to me dium magnitude of influence, the level of which varied based on whethe r an identified condition existed.

 Ten of the sources were judged to have a low magnitude of influence on estimated exposures and doses (i.e., database quality, missing data substitution method, zero concentration frequency, proportional approach used, population database, meteorological data, commuting databa se and algorithm, at-risk population prevalence rates, and benchmark levels for the simulated at-risk population). There was a wide-ranging level of uncertainty in the knowledge base for the identified

sources:

 Nine sources were judged by staff as having medium knowledge-base uncertainty including: spatial and temporal representa tion, historical data used, activity pattern database, longitudinal profile algorithm, microenvironmenta l algorithm and input data, at-risk population prevalence rate s, physiological factors, and the benchmark levels for the simulated at-risk population.

 The knowledge-base uncertainty was judged as low for four of the identified sources having either unknown or bidirectional influe nce. This included the proportional approach used in adjusting air quality condi tions, the population data base, meteorological data, and commuting data.7-33  The knowledge-base uncertainty was also judged as low for the three sources identified above as being associated with either under- or overestimating exposures, i.e., the data base quality, missing data substitutio n, and zero concentration frequency. The ratings of the knowledge-base uncertainty ca n indicate the need fo r additional data or analyses to better characterize the uncertainty. When combined with the potential magnitude of influence associated with each identified source, a prioritization can be given to the higher rated
influential sources. Based on the results of this uncertainty characteri zation, staff judges that seven sources (i.e., the spatial a nd temporal representation of ambi ent monitoring data, historical data used, activity pattern da tabase, longitudinal profile algorithm, microenvironmental algorithm and input data, and physiological factors) remain as the most important uncertainties in
this assessment.7-34 7.4 REFERENCES

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

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

Brain JD and Samet JM. (2010b). Letter from Drs. J.D. Br ain and J.M. Samet to Administrator Lisa Jackson. Re: Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO) Primary National Ambient Air Quality Standards: Seco nd External Review Draf t. EPA-CASAC-10-012. May 19, 2010.

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.

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

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

Graham SE and T McCurdy. (2005). Revised ventilatio n rate (VE) equations for use in inhalation-oriented exposure models. Report no. EPA/600/X-05/008. Report is found within Appendix A of US EPA (2009). Metabolically Derived Human Ventilation Rates: A Revised Approach Based Upon Oxygen Consumption Rates (Final Report). Report no. EPA/600/R-06/129F. Appendix D contains “Response to peer-review comments on Appendix A”, prepared by S. Graham (US EPA). Available at: http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=202543.

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

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

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

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

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

McCurdy T. (2000). Conceptual basis for multi-route intake dose modeling using an energy expenditure approach. J Expos Anal Environ Epidemiol . 10:1-12.7-35 SAIC. (2001). Technical Peer Review of “Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO (version 2.1)” Prepared by Science Applications International Corporation under EPA Contract No. 68-D-98-113. Available at: http://www.epa.gov/ttn/fer a/human_related.html

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

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

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

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

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

. US EPA. (2004). An Examination of EPA Risk Assessmen t Principles and Practices. Staff Paper prepared by the US EPA Risk Assessment Task Force. Repo rt no. EPA/100/B-04/001. Available at: http://www.epa.gov/OSA/pdfs/ratf-final.pdf

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

. US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009. Available athttp://www.epa.gov/ttn/naaqs/standards /so2/data/200908SO2REAFinalReport.pdf.

WHO, UNICEF, UNU (2001). Iron Deficiency Anaemia: Pr evention, Assessment and Control. Report of a joint WHO/UNICEF/UNU consultation. World Health Organization, Geneva.

WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on characterizing and communicating uncertainty in exposure assessment. Available at: http://www.who.in t/ipcs/methods/harmonization/areas/exposure/en/.8-1 

### 8 SUMMARY OF KEY OBSERVATIONS

This document describes th e quantitative human exposure assessment and risk characterization being conducted to inform the U. S. Environmental Protection Agency’s (EPA’s)
current review of the National Ambient Air Quality Standards (NAAQS) for carbon monoxide (CO). An assessment of ambient CO exposure/dose was developed in an earlier phase of this review in the late 1990s. The design of this REA builds upon recommendations from CASAC, information presented in the final ISA, as well as comments made by the public. Presented together below are the key observations made in each of the chapters.

Conceptual Overview: Assessing Ambi ent Carbon Monoxide Exposure and Risk

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

 Microenvironments influenced by on-road mob ile sources are important contributors to ambient CO exposures, particularly in urban areas. Where present, other (nonambient) CO sources can also be important influences on total CO exposure and on the impact of ambient CO exposure on COHb levels.

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

 An individual’s COHb leve ls reflect their endogenous CO production, as well as CO taken into the body during exposure to am bient and nonambient CO sources. CO uptake into the bloodstream during exposure is influenced by a number of variables including internal levels of CO and COHb, such that net uptake may be lower or negligible in instances wher e a preceding exposure has been substantially higher than the current one. Thus, the magnitude of the change in COHb level in response to ambient CO exposure may decrease with the presence of concurrent or preceding nonambient CO exposure.

 Individuals with CHD are th e population with greatest sus ceptibility to short-term exposure to CO, and the population for whic h the current evidence indicates health effects occurring at the lowest exposures. Th e evidence further indicates a potential for other underlying cardiovascular conditions, partic ularly other types of heart disease, to contribute susceptibility to CO effects. Ot her populations potentia lly at risk include those with diseases such as chronic obstru ctive pulmonary disease (COPD), anemia, or diabetes, and those in prenatal or elderly life stages.

 Cardiovascular effects are the category of health effects for which the evidence is strongest and indicative of a likely causal relationship with relevant short-term CO exposures, particularly for people with CHD. Other endpoints for which the evidence8-2 is suggestive of causal relationships incl ude effects on the cent ral nervous system, reproduction and prenatal developmen t, and the respiratory system.

 The specific cardiovascular effects occurring at the lowest COHb levels studied in CHD patients are reduced time to exercise-induced angina and other markers of myocardial ischemia, in particular, speci fic changes to the ST-segment of an electrocardiogram.

 Risk is characterized in this REA through ev aluation of COHb estimated in simulations involving ambient CO exposures experien ced by two target populations: (1) individuals with CHD (incl uding undiagnosed CHD persons) and (2) individuals with HD, including CHD (diagnosed and undiagnosed).

 Two types of COHb estimates are considered for the two target populations: (1) daily maximum end-of-hour COHb levels and (2 ) ambient contribution to daily maximum end-of-hour COHb levels (i.e., the change in COHb associated with ambient CO exposure alone).

 Results from simulations are reported in te rms of percent of th e simulated at-risk population expected to expe rience daily maximum end-of-hour COHb levels (or ambient CO contribution to daily maximum end-of-hour COHb levels) at or above a series of levels that range as low as 1%. These results are interpreted in the Policy Assessment document in light of poten tial health effect s benchmarks.

 For daily maximum end-of-hour COHb le vels (absolute), these benchmarks range from 1.5%, which is below the lo west study mean COHb level resulting from experimental CO exposure in cont rolled human exposures of subjects with CAD, up to 3.0%, a level within the range associated with effects in those studies. For ambient contribution to daily maximum end-of-hour COHb levels, the comparison benchmarks incl ude the range from 1.4% up to 2.4%, which are the COHb increments associ ated with effects in those studies.

 Beyond the at-risk populations a nd myocardial ischemia-related effects that are the focus of this quantitative REA, the curre nt evidence regardin g other potentially susceptible populations and other health e ffects associated with CO exposures is discussed and considered with regard to the review of the CO NAAQS in the Policy Assessment. Air Quality Considerations

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

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

 The currently available information for CO m onitors indicates that siting of microscale and middle scale monitors in the current ne twork is primarily associated with roads having moderate traffic density (<10 0,000 AADT), however, factors other than8-3 reported AADT (e.g., orientation with regard to dense urban roadway networks) can contribute to sites reporting higher CO concentrations.

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

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

 Historical trends in ambient monitoring data indicate that at individual sites, ambient concentrations have generally decreased in a proportional manner. This comparison included air quality distributi ons with concentrations at or above the current 8-hour standard and those reflecting current ( as is ) conditions.

 The temporal variability in selected uppe r percentile ambient concentrations (e.g., 99 th percentile 1-hour daily maximum) at individua l monitors in Denver and Los Angeles is relatively small across a three-year monito ring period, particularly when considering recent air quality. Much of the within-monitor temporal variability is due to a trend in decreasing concentration from year-to-year.

 There is greater spatial variability in select ed upper percentile ambient concentrations (e.g., 99 th percentile 1-hour daily maximum) at ten selected monitoring sites in Los Angeles when compared with four selected monitoring sites Denver, particularly when considering the recent air quality. Overview of Approach Used for Estima ting Co Exposure and COHb Dose Levels

 APEX, an EPA human exposure and dose m odel, has a long history of use in estimating exposure and dose for many of th e criteria pollutants including CO, O 3 , SO 2 , and NO 2 . Over time, EPA has improved and developed new model algorithms, incorporated newer available input data and parameter distributions, as well as performed several model evaluations, se nsitivity analyses, and uncertainty characterizations for the above pollutants. Based on this analysis, APEX was judged to be an appropriate model to use for assessing CO exposure and dose. Application of APEX4.3 in this Assessment

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

 Two simulated at-risk subpopulations were id entified by combining the census tractspecific age and gender populat ion distributions with HD and CHD prevalence rates, each also stratified by age and gender. In us ing this approach, staff can represent the variability that exists in the simulated at-risk HD and CHD subpopul ations that reside in each census tract and with in each study area.

 Both simulated at-risk subpopulations in clude an estimate of persons with undiagnosed CHD.8-4  To represent spatial variability in ambient concentrations in Denve r, a total of four monitors were used; in Los Angeles, the to tal number of monitors was ten. Temporal variability was represented by use of hourly ambient concentrations in each study area.

 The exposure and dose model simulations in cluded 8 microenvironments in each location to represent the expected va riability in microenvironmental CO concentrations.

 All indoor microenvironments were modeled using a mass balance model to represent temporal variability in indoor CO concentr ations with respect to the outdoor CO concentration variability. In addition, distributions of mi croenvironmental factors were used for all microenvironments rather than point estimates. Using distributions of microenvironmental factors will better represen t both spatial and temporal variability in estimated microenvironmental CO concentrations.

 Additional analyses using output from indivi dual-level simulations were performed to provide information on the microenvironments most influential to population exposure at different exposure levels. This include d an analysis of the effective ratios of microenvironment to ambient concentrati ons and the contribution of ambient CO exposure to total COHb level estimates. Th e smaller sample sizes generated for these analyses were found to be representative of the larger simulations employed for estimating exposure and dose in the diffe rent air quality exposure scenarios. Simulated Exposure and COHb Dose Results

 Ambient CO exposures and resulting COHb le vels in the blood of two simulated at-risk populations in the Los Angeles and Denver stud y areas were estimated considering five air quality scenarios: as is air quality, air quality adjusted to simulate just meeting the current 8-hour CO NAAQS, a nd air quality adjusted to just meet three potential alternative standards.

 The two at-risk populations simulated we re: (1) persons with diagnosed CHD, including those estimated to have undia gnosed CHD, and (2) the larger group of persons with any type of HD including t hose estimated to have undiagnosed CHD. While the number of persons and person-da ys at or above sel ected COHb levels differed between the two populations, reflecting their differing size, the percentage of each population’s persons and person-days were similar.

 The relative contribution of various microenvi ronments to exposure concentrations was generally similar between the tw o study areas. When considering as is air quality, indoor microenvironments contributed mostly to low level exposures (at or above 1 ppm and 2 ppm), comprising between 40 – 80% of the time spent at those exposure levels, while time spent inside vehicles cont ributed to most exposures at or above 3 ppm (70 – 100%). In comparison, when considering air quality just meeting the current standard, the percent contribu tion from indoor microenvironments was generally higher for low level exposures (abou t 65 – 85% of exposure concentrations at or above 1 ppm and 2 ppm), though again hi gher level exposures were dominated by the contributions from inside -vehicle microenvironments.8-5  The relationship between the two study areas wi th regard to estimated distribution of maximum end-of-hour COHb levels differed w ith the different air quality scenarios. Under as is air quality conditions, the simulated at-risk populations in the Los Angeles study area were estimated to experience a sl ightly higher distribution of maximum endof-hour COHb levels than the Denver popul ations. Under conditi ons of air quality adjusted from historical air qua lity data to just meet the cu rrent or alternative standards, however, appreciably larger percentages of the Denver populations were estimated to experience COHb at or above specific levels than the Los Angeles populations.

 For as is air quality conditions, the highest daily maximum end-of-hour COHb estimated to be experienced over the course of the simulated year was below 1.5% for more than 98% of the at-risk populations simulated in each study area; it was below 2% COHb for more than 99.9% of these simu lated populations. A lower percentage of the simuated at-risk populations in Denve r were estimated to experience daily maximum end-of hour COHB below these benchmarks than were the populations in Los Angeles.

 Under as is air quality conditions, the highe st incremental contribution of ambient CO exposure to maximum end-of -hour COHb levels estimated in the simulated populations was 1.6% COHb, a nd more than 99% of both study area populations were estimated to have ambi ent CO contributions to COHb below 1.4%. As with estimates of total C OHb (i.e., COHb from endogenous CO production and ambient exposure together), a larger percentage of the Los Angeles population was estimated to experience the higher ambient contributions to maximum end-of- hour COHb compared to the Denver population. For example, the percenta ge of the population estimated to experience ambient contributions to C OHb at or above COHb levels above 1.0% was approximately 2 to 3 times as high in Los Angeles than in Denver.

 For simulations of air quality adjusted to just meet the current 8-hour standard of 9 ppm, the highest estimated daily maximum end-of-hour COHb over the course of the simulated year was below 1.5% for more than 95% of both simulated at-risk populations in the Los Angeles study area, and was below 2% COHb for 99.5%of these populations. In contrast, the percentage of the simulated at-risk populations in the Denver study area estimated to experience daily maximum end-of-hour COHb levels that did not exceed 1.5% was about 80%. The percentage of the Denver populations with their highest estimated daily ma ximum end-of-hour COHb below 2% was approximately 92-93%.

 As with estimates of total COHb (i .e., COHb from endogenous CO production and ambient exposure together), the pe rcentage of the simulated population estimated to experience the higher ambi ent contributions to maximum end-of hour COHb was appreciably greater in De nver as compared to Los Angeles. While estimated ambient CO contribu tions to daily maximum end-of-hour COHb were below 1.4% for nearly 98% of the Los Angeles simulated population, the corresponding percentage of the Denver population was just under 88%.8-6  In addition to the simulations for as is and just meeting the curre nt 8-hour standard air quality conditions, three simula tions of air quality just m eeting potential alternative standards were performed. The alternatives comprise different combinations of form, averaging time and level which were expect ed to achieve somewhat similar exposure and dose results. The combinations that were selected were based on consideration of exposure and dose results obtained for the as is air quality conditions and the higher just meeting the current 8-hour standard conditions. The combinations of form, averaging time and level that were simu lated include: (1) a second-highest 8-hour average of 5 ppm, (2) a 99 th percentile daily maximum 8-hour average of 5.0 ppm, and (3) a 99 th percentile daily maximu m 1-hour average of 8.0 ppm.

 These three simulations generated genera lly similar percentages of the at-risk populations estimated to be exposed at selected concentrations and experience maximum end-of hour COHb levels at or above selected levels. Each of these three simulations generated fewer persons and a lower percent of the at-risk populations at or above se lected COHb levels than did simulations for air quality adjusted to just meet the cu rrent 8-hour standard. For example less than 1% of the Denver populations ha d a highest daily ma ximum end-of-hour COHb at or above the 2.0% COHb le vel in the altern ative standards’ simulations as compared to approximat ely 3.5% in simulations of air quality just meeting the current standard. When considering the potential alternative standards in Los Angeles, only 0.1% of the simulated at-risk populations were estimated to experience a maximum end- of-hour COHb level at or above 2.0 % COHb, compared to 0.5% at that same CO Hb level associated with air quality adjusted to just meet the current standard.

 Results for the five air quality scenarios are further analyzed in the Policy Assessment to inform consideration of th e level of public health protec tion that might be provided by alternative standard levels associated w ith different combinations of averaging time and form.

 Results generated in the current assessment for the air quality conditions just meeting the current NAAQS were compared with es timates from the assessment conducted in 2000 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study areas (section 6.3). The two assessments em ployed similar approaches and similar, although not identical, air quality data for this scenario and th ey used different versions of the exposure model (APEX vs. pNEM). Re sults were quite similar for the 1.5% and 2% COHb level for the simulated Los A ngeles study area population and somewhat different for the Denver study area population. For example, the two assessments’ Los Angeles estimates of population percentage s with highest daily maximum end-of hour COHb at or above these COHb levels we re similar within 10%. For the Denver simulated study populations, however, the estim ates for these two COHb levels were about 3 and 7 times greater, respectively, in the current assessment when compared with the prior assessment. Variability Analysis and Uncertainty Characterization

 Based on an overall qualitative judgment of the identified so urces of uncertainty in the assessment approach, selections made rega rding input data, and algorithms used, and8-7 their characterization as to direction and magnitude of influence on exposures and doses, staff consider the exposure and dose es timates to be reasonable for the simulated population the assessment is intended to represent (i.e., the CHD or HD population residing within the urban core of ea ch study area). This is because:

 Only three sources of uncertainty were associated with a potential directional influence - data base quality (overestimation), missing data substitution (underestimation), and zero concentra tion frequency (underestimation) - and all were judged to have a low magnit ude of influence on estimated exposures and doses.

 Thirteen of the identified sources of un certainty were judged by staff to have either bidirectional influence (eight sources) or unknown direction(five sources):

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

- Five of the remaining twelve s ources (i.e., spatial representation, historical data used, activity pa ttern database, longitudinal profile algorithm, physiological factors) were judged as having low to medium magnitude of influence, the level of which varied based on whether an identified condition existed.

- Ten of the sources were judged to have a low magnitude of influence on estimated exposures an d doses (i.e., database quality, missing data substitution met hod, zero concentration frequency, proportional approach used, popula tion database, meteorological data, commuting database and algorithm, at-risk population prevalence rates, and benchmark levels for the simulated at-risk population).

 There was a wide-ranging level of uncertainty in the knowledge base for the identified

sources:

 Nine sources were judged by staff as having medium knowledge-base uncertainty including: spatia l and temporal representati on, historical data used, activity pattern database, longitudinal profile algorithm, microenvironmental algorithm and input data, at-risk popula tion prevalence rates, physiological factors, and the benchmark levels fo r the simulated at -risk populations.

 The knowledge-base uncertainty was judged as low for four of the identified sources having either unknown or bidirec tional influence. This included the proportional approach used in adjusti ng air quality conditions, the population database, meteorological da ta, and commuting data.

 The knowledge-base uncertainty was also judged as low for the three sources identified above as being associated with either under- or overestimating exposures, i.e., the data base qualit y, missing data substitution, and zero concentration frequency.8-8  The ratings of the knowledge-base uncertainty can indicate the need for additional data or analyses to better characterize the uncerta inty. When combined with the potential magnitude of influence associated with each identified source, a prioritization can be given to the higher rated infl uential sources. Based on the results of this uncertainty characterization, staff judges that seven sources (i.e., the spatial and temporal representation of ambient monitoring data , historical data used, activity pattern database, longitudinal profile algorithm, microenvironmenta l algorithm and input data, and physiological factors) remain as the most important uncertainties in this
assessment.

## APPENDICES

### 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 the purposes of inclusion into the CO REA appendices.

A-1

### 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 a nd Blood Volum e Factor. ................................................ 5 2.5 Summary of Findings ............................................................................................ 5 3. Derivation of New Di stributions for Body Mass ........................................................... 6 3.1 The NHANES Body Mass Dataset. ..................................................................... 6 3.2 Calculation of the New Sampling We ights for the Com bined NHANES Dataset. ....................................................................................................................... 7 3.3 Fitting the Body Mass Data. ................................................................................. 7 4. Derivation of New Distribu tion s for Normalized Vo2max .......................................... 13 4.1 The Nvo2max Data ............................................................................................. 13 4.2 Determining the NVo2max Distributions ........................................................... 17 5. Derivation of New Dist ributions for Hem oglobin Content (Hemoglobin Density) ..... 25 6. Blood Volume as a Function of Height and Weight .................................................... 28 References ......................................................................................................................... 29 Appendix A. SAS Code for Estim ating the Body Mass Distributions ............................. 40 Appendix B. SAS Code for Estimating the Norm alized Vo2Max Distributions ............. 41 Appendix C. SAS Code for Estima ting the Hem oglobin Content Data ........................... 42 Appendix D. The New Physiology.txt file ....................................................................... 43 Appendix E. All Derived Physiological Parameters ........................................................ 54

A-2

### LIST OF FIGURES

Figure 1. Geometric Means for the Best-fit Lognorm al Distributions for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data. ................................... 9 Figure 2. Geometric Standard Deviations fo r th e Best-fit Lognormal Distributions for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data. ....... 10 Figure 3. Minimums (1 Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data. st .................................................................................... 11 Figure 4. Maximums (99 Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data. th
........................................................................... 12 Figure 5. Individual Nvo2max Measurements for Males and Fe males, Derived from Literature Studies and Experimental Measurements. ....................................................... 14 Figure 6. Grouped Mean Nvo2max Measurements for Males and Fem ales, Derived from Literature Studies. ............................................................................................................. 15 Figure 7. Nvo2max Standard Deviations for Ma les and Fem ales, Derived from Literature Studies. .............................................................................................................................. 16 Figure 8. Combined Nvo2max Gr oup Means for Males and F emales ............................ 19 Figure 9. Combined Nvo2max Group Standard Deviations. ............................................ 20 Figure 10. Nvo2max Normal Distribution Fi ts: Raw Fit Means and Sm oothed Fits. ..... 21 Figure 11. Nvo2max Normal Distribution Fi ts: Raw Fit Standard Deviations and Sm oothed Fits. .................................................................................................................. 22 Figure 12. Nvo2max Minimums. 1 Percentile of the Best-fit Normal Distribution. st .... 23 Figure 13. Nvo2max Maximums. 99 Percentile of the Best-fit Normal Distribution. th .. 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 ................. 27

A-3

### 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 CURRENT PHYSIOLOGY 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 2.1 NORMALIZED MAXIMAL OXYGEN UPTAKE (NVO2MAX).

The nvo2m ax 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

A-4from 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 ad ditional 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.

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 fe males 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 m ass data were derive d from an in-depth analysis [ 4

, 5 ] of the second CDC National Health and Nutrition Exam ination Survey (NHANES II) body m ass data [ 6 ]. The data were relatively comprehensive, and the methods used to generate the lognorm al 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 VOL UM E 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 som e 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.

A-5 Many of the studies used are very old (ex. 1960, 1977).

 Som e the data is of questionable validity (for example, the extrapolation 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, new 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 m ass 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 estim ates of body mass distributions [ 4

, 5 ]. The current Physiology.txt file body m ass 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 ].

De mographic (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).

A-63.2 CALCULATION OF THE NE W SAMPLING WEIGHTS FOR THE COMBINE D 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

sam pling weight for each subject was calculated as:

20042003 3 1

  w w combined (1)

20021999 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 m odeled 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 procedur e was used to apply the sampling weights. The SAS code used to generate the body ma ss distributions is provided 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 based on the data for ages 60 and greater.

A-73.4 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 Physiology.txt f ile, 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 th 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, plot s not shown). These could be used at the direction of EPA; changing th e “official” release Physiology. txt file would be trivial.

A-8MALES: 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.

A-9MALES: 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.

A-10FEMALES: 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.

A-11MALES: 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.

A-12

### 4. DERIVATION OF NEW DISTRIBUTIONS FOR NORMALIZED VO2MAX

2.2 4.1 THE NVO2MAX DATA

The NHANES studies do report data for vo2ma x 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 da ta we re provided by the WA-COR [ 31 ]. In the case of the g raphical 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 we re derived from 136 studies [ 32

- 167 ]. These data

com prised 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.

A-13

### 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 N=1949

N=1558

Figure 5. Individual Nvo2max Measurements for Males and Females, Derived from Literature Studies and Experimental Measurements.

A-14MALES: 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.

A-15MALES: 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.

A-164.2 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 calculated 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. Calculating 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 ma ss data (probably 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.

A-17The results for the nvo2m ax 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.

A-18MALES: Nvo2max, Combined Group Means

0 10 20 30 40 50 60

0 20 40 60 8
Age (Years)nv02max (ml/k

0g

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 Females

A-19MALES: Nvo2max, Combined Group SD

0 2 4 6 8 10 12 14 16

0 20 40 60 8
Age (Years)nv02max (ml/k

0g

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.

A-20MALES: 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.

A-21MALES: 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 Distribution Fits: Raw Fit Standard Deviations and Smoothed Fits.

A-22

### 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.

A-23

### 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.

A-24

### 5. DERIVATION OF NEW DISTRIBUTIONS 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 hemoglobin 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 which 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 deviations 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 standa rd 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 content statistics affect only the CO dose algorithm calculations.

A-25MALES: 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 10
Age (years)Hb Content (g/dL

0)

Figure 14. Mean Values of Hemoglobin C ontent as Derived from the 1999-2002 NHANES Dataset, with Comparison to Current Physiology.txt Values

A-26MALES: 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 Standard Deviation as Derived from the 1999- 2002 NHANES Dataset, with Comparison to Current Physiology.txt Values

A-27

### 6. BLOOD VOLUME AS A FUNCTION 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 for females). This is a modification of Allen’s equation [ 168 ] to include the age/gender depe ndent B VF 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 equati ons were modified 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 specif ically in children [ 170

, 171 ] or body surface area [ 172 ]. In particular, L inderkamp et al. [ 170 ] derived prediction equations for blood volume as a function of a num ber of physiological parame ters for children in three different age groups. It is recommended that further analysis of this st udy and others be undertaken.

However, inclusion of new blood volume equations 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.

A-28

### 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 and 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 and 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 Center 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 a nd interpretation of aerobic fitness in children and adolescents.Exer c Sport Sci Rev. 1994;22:435-76. 13. Astrand I. Aerobic work capacity in men 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.

A-2915. Falgairette, G. 1989. Evol ution 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-te rm 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 oxygen 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, C line 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 ar m-leg ergometry. J Appl Physiol. 1975 May;38(5):774-9. 25. Ribisl PM, Kachadorian WA. Maximal 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 Ex erc. 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 Ex erc. 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 Communicat ion – 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 wo rk capacity in middle-aged Norwegian men. J Appl Physiol. 1965 May;20(3):432-6.

A-3034. 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 la boratory indices 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 preliminary 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 Ch ild. 1991 Feb;66(2):245-7. 40. Armstrong N, Davies B, Heal M. The specificity 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 Ma r-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 thre shold. 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, Sh earburn 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 Med Phys Fitn ess. 1983 Dec;23(4):445-9.

A-3151. Binkhorst 1984 (Ref??) From T. McCurdy 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, Bo ileau 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 P hysical 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, Wasserman K. Aerobic parameters of exercise as a function of body size during growth in chil dren.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, Ba ggley G. Failure 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 longitudi nal 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.

A-3268. 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 A ppl Physiol. 1968 May;24(5):700-6. 70. Davies CT. Metabolic cost of exercise and physical performan ce 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 congen ital heart disease and healt hy 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 st udents. Res Q Exerc Spor t. 1996 Dec;67(4):452-7. 80. Gilliam TB, Sady S, Thorland WG, Weltman 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.

A-3384. 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 Physio l. 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. Recovery of muscle power after highintensity short-term exerci se: comparing 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 Spor ts 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 tr aining 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.

A-34101. 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 a nd 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. Lo ndon: 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. 1972

A-35117. McDonough JR, Kusumi F, Bruce RA. Varia tions 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 anal ysis in boys aged 11 to 15 year s. 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 basi s of submaximum ergometric stress tests in children 10-14 ye ars old. Eur 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 adoles cent girls. Res Q Exerc S port. 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 American 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.

A-36133. 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 healt hy 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.

A-37150. Stoedefalke K, Armstrong N, Kirby BJ, Welsman JR. Ef fect 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 oxygen 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. Exercise 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 l actate 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 lip ids 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.

A-38166. Yoshizawa S, Honda H, Urus hibara M, Nakamura 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 (T okyo). 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, Bl och T. Prediction 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.

A-39

### 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** ;

A-40

### 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** ;

A-41

### 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** ;

A-42

### 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

A-4359 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 41.8 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
A-4439 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 42.6 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
A-4516 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 46.8 199.2 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
A-4697 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 15.3 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
A-4777 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 40.6 120.0 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
A-4854 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 7.3 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
A-4934 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 5.7 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
A-5011 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 1.0 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
A-5192 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 1.8 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
A-5272 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 1.3 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 1.6

A-53

### 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 60.02

A-54MALES 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 37.67

A-55MALES 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

A-56FEMALES MEAN MEAN SD SD MIN MAX

Age Raw Fit Values Smoothed Fit Values Raw Fit Values Smoothed Fit Values (1st Pctl) (99th Pctl)
20.00 42.52 42.50 7.69 7.80 24.35 60.65 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

A-57FEMALES MEAN MEAN SD SD MIN MAX

Age Raw Fit Values Smoothed Fit Values Raw Fit Values Smoothed Fit Values (1st Pctl) (99th Pctl)
65.00 20.26 22.74 3.83 4.39 12.53 32.95 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

A-58Table 3. Body Mass Raw Fits.

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

A-59MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
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 41.6 141.5 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

A-60MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
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 96.632 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

A-61MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
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 42.86 133.3 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

A-62MALES FEMALES

Age Geometric Mean GSD Min Max Geometric Mean GSD Min Max
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 45.18 114.68 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 99.66646 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 89.83615

A-63A-64 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 1.552685662B-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 ( 1982) and contained within 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 ap pendix also describes the principal changes made to the COHb module when it was incorp orated into APEX4.3, including a change in the method used to solve the Cobur n-Forster-Kane (CFK) equation (Coburn et al., 1965). Lastly, section B.6 summarizes outputs that were generated in pe rforming event-level model simulations.

B.1 Base Physiological Model for Computing COHb Levels

Using time/activity data obtained from various diar y studies, APEX constructs a composite diary for each simulated person in the specified populati on at risk. The composite diary consists of a sequence of events spanning the specified period of the expos ure assessment (typically one calendar year). Each event is defined by a start time, a duration, a geographic location, a microenvironment, and an activity. Using vari ous algorithms described in Section 4 of the Quantitative Risk and Exposure Assessmen t for Carbon Monoxide (CO REA), APEX4.3 provides estimates of CO concentration and alve olar ventilation rate for each event in the composite diary. APEX4.3 then uses these data, t ogether with estimates of various physiological parameters specific to the simulated individua l, to estimate the percent COHb in the blood (%COHb) as an average %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).B-2 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. 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 (Biller 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-Menten 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 B.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 CFK Model for Estimation of Carboxyhemoglobin

Table B-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)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)B-4 If the only quantity in equation (B-1) that varies with time is [C OHb], the CFK equation is linear and can be readily inte grated. 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 linear and requires the relationship between the two quantities to be known if it is to be accurately integrated ov er 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)

Equation B-4, the Haldane relationship, applies a pproximately 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:

]) [ ] ([ 1 ] [ 0 2

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

O O

   (Eq. B-9)B-5 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 MichaelisMenten kinetics model which can be in tegrated. The integration yields:

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

 

    



 

 O t O t 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] ∞ :

) ( 100 ] %[

1 C C C COHb

O O
   (Eq. B-16)B-6 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 S ection 10.2 of US EPA (2008a).

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 (B-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 2 ) (Eq. B-22)

The z variable is a re-scaled time variable that is dimensionless. It is used as the 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

.B-7 Expressing (B-17) as a function of z yields the expression

N`(z) = 100 D 0 2 A 0 – 100 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 corresp ond to N(0) values or %COHb be low forty 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 individual 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 COB-8 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 genera te output distributions of one-hour and running eight-hour exposures for each individual. The expos ures calculated for the simulated individuals can then be population-weighted to produce exposure distributi ons for 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) (McCurdy et al., 2000; US EPA, 2002), which is desc ribed in Section 4.4.3 of the CO REA. The sampling approach attempts to match the 24-hour activity patterns to the simulated individual and exposure period according to the demographic characteristic s of the individual and the season, day type (weekday/weekend), and maximu m 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.

The COHb model presently used in APEX4. 3 does not account for changing barometric pressure. It uses a constant barometric pressu re calculated for each st udy area as a function of the average elevation of the 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. WhileB-9 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.

B.4 Computation of Input Data for the COHb Module

As discussed in the previous section and in Sections 4.4.5 and 4.4.7 of the 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 (2008a). An algorithm within APEX4.3 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, home/wor k status, microenvironment, and activity. All exposure events are constrained to occur entirely within a cloc k hour. The CFK model within the COHb module is called for each exposure event. Each event requires the following data.

Time duration of event (minutes) 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 Coefficient (unitless) Equilibrium constant for the reaction of O 2 Atmospheric pressure (torr) Blood volume (ml) Total potential reduced hemoglobi n content of blood (ml CO/ml STPD) Pulmonary CO diffusion rate (ml/min/torr STPD) Endogenous CO production rate (ml/min STPD)B-10Table B-2. Principal parameters included in the physiological profile for adultsfor applications 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-weightedlognormal distributions with age- andgender-specific geometric mean (GM) and geometric standard deviation (GSD)derived from data from the National Health and Nutrition Examination Survey(NHANES), for the years 1999-2004 (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.B-11ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValueBlood volume COHb GenderWeightHeightBlood volume (Vb) was determined according to gender by the following equationsdeveloped from Allen et al. (1956) which were modified to accept the units used forheight 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 agebased on dataobtained from the National Health and Nutrition Examination Survey (NHANES),for the years 1999-2004 (see Isaacs and Smith (2005) in the CO REA, Appendix A)Units: grams of Hb per deciliter of bloodB-12ParameterAlgorithm(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Endogenous 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 groupings (see data in Table B-3).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).B-13ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValueRestingmetabolic rate(RMR)VentilationrateGenderAgeBody WeightSee Section 4.4.5 of CO REA andChapter 5 of US EPA (2008a).Energyconversion factor(ECF)VentilationrateGender See Section 4.4.5 of COREA and Chapter 5 of US EPA (2008a).NVO2maxVentilationrateGenderAgeSee Section 4.4.5 of CO REAand Chapter 5 of US EPA (2008a).VO2maxVentilationrateNVO2maxBody WeightSee Section 4.4.5 of CO REAand Chapter 5 of US EPA (2008a).B-14 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 physiological prof ile generator. Input data is supplied at the start of the APEX4.3 computation.B-15 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). A value of 100 torr is commonly used sin ce 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 extant 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 et al. (1969), who de termined a value of 218. This value was selected for use in the COHb m odule of the earlier CO-NEM expos ure model. Other researchers using values in the range 218 to 220 include Pete rson and Stewart (1970), Ma rcus (1980), Collier and Goldsmith (1983), and Mulle r and Barton (1987). As the va lue 218 falls within the range currently used by researchers, we 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 B.2 to have the value 0.32 based on theB-16 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.

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.B-17 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 analys es of data provided by Tikuisis et al. (1992) for the individual subjects in the study showed the re lationship to be linear. From this relationship and the heights and ages of the subjects in the Tikuisis et al. ( 1992) study, it was determ ined that the Salorinne (1976) equations for male subjects correspond to an alveolar ventil ation rate of 6.69 l/min STPD. In the absence of other data it is assumed that this same value applies to women. Thus, for each twenty-four hour period equations B-32 and B-33 are used to comput e 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 actua l ventilation rate expe rienced 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.

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 numberB-18 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.B-19Table B-3. Literature data used to derive lognormal distributions used to estimate endogenous CO production rate.Reference GenderMenstrualCycle(pre/post) Endogenous CO Production Rate (ml/hr)Berk et al. (1974) Male NA 0.43 0.58 0.52 0.59 0.80 0.72Brouillard et al. (1975) Male NA 0.81 0.57 0.33 0.700.35 0.40 0.39 0.43 0.35 0.51 0.42 0.57Coburn et al. (1963) Male NA0.450.58 0.38 0.51 0.55 0.37 0.49 0.45 0.50Coltman and Dudley,(1969)Male NA0.33 0.45 0.36Male NA 0.46 0.26 0.60 0.45 0.39 0.40Pre 0.57 0.54 0.72 0.99 0.48 0.53 0.43Delivoria-Papadoppuloset al. (1974)FemalePost 0.23 0.51 0.34 0.41 0.26 0.16 0.30Luomanmaki andCoburn (1969)Male NA 0.38 0.42 0.41 0.54 0.38Male NA 0.40 0.81 0.26 0.65 0.55 0.62 0.44Pre 0.72 0.37 0.23 0.33 0.42 0.44 0.29 0.48Lynch and Moede(1972)FemalePost 0.48 0.23 0.25 0.20 0.22 0.15 0.21Female Pre 0.64 0.86 0.35 0.52 0.80 0.54 0.68 0.28Merke et al. (1975)Female Post 0.40 0.47 0.23 0.24 0.55 0.32 0.43 0.35Werner and Lindahl(1980)Male NA 0.54 0.76 0.48 0.31 0.69 0.70 0.36 0.65B-20 B.5 Input Data Supplied By APEX 4.3 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.

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)

B.6 Analysis of Select ed APEX COHb Outputs

This section provides analysis of APEX outputs by using the event-level APEX simulations. The APEX events and hourly output files can provide ev ent-level and hourly-level exposure and dose profiles for all simulated in dividuals (US EPA, 2008a, 2008b). We generated the hourly time-series of dose for approximately 400 CHD simulated individuals, with the CHD population defined as described in section 5.5.1 of the CO REA. 1 Both the Los Angeles and Denver study areas were evaluated using two air quality scenarios: as is 2006 ambient air quality and historical 2 air quality adjusted to just meet th e current 8-hour st andard. Two model

1 For each of these particular model runs, APEX generated the complete time-series of exposure and dose for 5,000 persons. Of the 5,000 persons simulated, APEX generated 438 CHD persons in Denver and 394 CHD persons in Los Angeles based on the NHIS CHD prevalence rates.
2 1997 ambient monitoring data was used in Los Angeles, 1995 data was used in Denver. An additional air quality/exposure scenario included air quality adjusted to just meet a 99 th percentile 8-hour daily maximum of 5.0 ppm, though because these endogenous CO pr oduction runs were based on the same historical years of data in eachB-21 simulations were run for each scenario and study location; the first generated total COHb levels (i.e., COHb dose resulting from both ambient CO exposure and endogenous CO production) and the second simulation generated COHb levels with all exposure concentrati ons set to zero (i.e., resulting from endogenous CO production alone). See CO REA chapters 4 and 5 for details regarding all other relevant AP EX model simulation settings.

First, an analysis of the contribution of endogenous CO pr oduction to COHb levels is provided. This is followed by an analysis of the ambient concentrati on profile and dose time series for a few individuals. Finally, the tota l maximum end-of-hour COHb levels are evaluated with respect to the maximum COHb contribution from ambien t exposure and maximum COHb contribution from endogenous CO production.

B.6.1 Contribution of Endogenous CO Production to COHb Level We were interested in determining th e relative contributi on of endogenous CO production to an individual’s tota l COHb level. In this first analysis, we generated the hourly time-series of dose for the approximately 400 CHD simulated individuals in the absence of CO exposure. When an APEX model simulation is performed with c oncentration input values equal to zero, endogenous CO production will be entirely responsible for the calculated COHb levels reported for each person.

We note here that while the ambient CO con centration is set to zero for these runs, the temperature data associated with each simulation time period will affect the activity diaries sampled from CHAD used to construct each indivi dual’s longitudinal activity pattern profile. This will have a small impact on the estimated COHb doses due to, for example, differences in each simulated individual’s ventilation rates that ar e linked to the activity pa ttern profile.

First, we generated descriptive statistics for each individual’s 8,760 hourly average values for COHb dose values resulting from endogenous CO producti on alone. Then annual means were calculated for each individual and us ed to generate a population distribution of annual means for each study area and air quality scenario. These population statistics are summarized in Table B-4. The annual average hourly COHb level for the population was 0.28 % for the simulated CHD population in Denver and about 0.27% for the simulated CHD population in Los Angeles. There was variability in this population mean as indicated by the range of individual annual mean values that extended from a low of about 0.10 % COHb, to a high of about 0.96 % COHb. Overall th e population variability in annua l average COHb, as measured by the coefficient of variability (COV), wa s about 42% for both study areas and for both exposure scenarios.

The variability in each indivi dual’s annual mean COHb, as measured by an average of each person’s respective COV for the year, was al so calculated and found generally consistent for each individual. On averag e, an individual’s hourly COHb was estimated to vary by about 22-23% of their annual average hou rly COHb. Note the standarddeviation of this individualbased COV equaled about 5%. When compared to the population variability statistics, it can be seen that there is greater inter-personal va riability in the contribution of endogenous CO production to COHb levels than intra-personal vari ability. This finding is generally as expected,

study area, the generated time-series data for the endogenous CO contribution would be identical. These scenarios are described more fully in section 5.6 of the CO REA.B-22 given the wide range of interpersonal attributes that might influence the COHb level including age, gender, body mass, blood hemoglobin cont ent, endogenous CO production rate, etc., compared with the limited number of intra-personal attributes that might influence COHb level, such as alveolar ventilation rate. Note that th is analysis does not include variability due to diseases and other medical conditions, some of which increase the endogenous CO production rate (e.g., hemolytic anemia and infection) (ISA, Section 4.5).

B.6.2 Time-Series of COHb Levels In Indivi duals and Associated Ambient Concentrations The time-series of hourly average COHb levels was plotted along with the hourly ambient CO concentration for three simulated CHD persons in Los Angeles. The purpose of this was to illustrate the variation in COHb levels occurring over time, with respect to endogenous CO production and ambient CO concentrations 3 (which of course would ultimately be related to ambient CO exposure). The three persons were purposely selected to represent a low, mid, and high level COHb time-series. A full week of data was extracted from the air quality scenario of just meeting the current standard and include d the hours where the ambi ent CO concentration approached the 8-hour CO standard of 9.4 ppm. Note again that the total COHb in this analysis refers to the COHb dose resulting from both ambient CO exposure and endogenous CO production. The time-series for all three persons was for the 1-week period from December 17 through December 23 (Figure B-2) , though the individuals have entirely different ambient CO concentration profiles and endoge nous CO production rates, and th erefore different total COHb levels over the time period of interest.

The person designated as ‘low’ had a mean to tal COHb dose level of about 0.6 % across the time period, with hourly total COHb levels ranging from about 0.4 to 0.7 % (Figure B-2, top). Hourly ambient CO concentrations us ed in calculating this person’s exposure were generally low (i.e., less than 1 ppm) though, on occasion, ranged upwards to 4 ppm. This individual, while experiencing lo w total COHb levels across this time period, actually has a relatively high contribution fr om endogenous CO production (on average contributing to a COHb level of about 0.4 % over the 1-week period) with limited COHb contribution from ambient CO exposure (on aver age contributing to a COHb level of about 0.2 %).

The person designated as ‘mid’ also had a m ean COHb dose level of about 0.6% (Figure B-2, middle) though with greater variability in hourly COHb le vel when compared with the ‘low’ person. The COHb profile for the ‘mid’ person ex tended upwards to a peak COHb level of about 1.2% on several hourly excursions, largely in respons e to exposure to ambient CO concentrations. The estimated contribution to COHb levels resulting from endogenous CO production for the ‘mid’ person wa s within the lower range of the population average (Table B4), having an average hourly COHb level from endogenous CO of less than 0.2 % for this time period.

The response to ambient CO concentrations is more notable when observing the profile of the designated ‘high’ person (Figure B-2, bottom). On average, this person had a COHb level of 1.0% across the illustrated 1- week period, though 1-hour total COHb levels peaked to about 2.9% following the upwards spiking of associated hourly ambient CO con centrations of between 10 and 12 ppm. Note that COHb levels resul ting from endogenous CO production alone for this

3 This is the ambient concentration used as input to APEX, only adjusted for the air quality scenario. The 1997 Los Angeles monitoring data were adjusted by a factor of 0.627 to just meet the current 8-hour standard.B-23 individual were similar to the population average (Table B-4), at about 0.3% COHb for this time period.

This analysis suggests that moderate to hi gh total COHb levels result from ambient exposure rather than endogenous CO production, while individuals with lower total COHb levels have a greater contribution to total COHb from endogenous CO.

B.6.3 Evaluation of Maximum End-of-Hour CO Hb Levels with Respect to Contribution from Ambient CO Exposure and Endogenous CO Production In this third analysis, we calculated three dose metrics using the event-level output files generated for the CHD population in each study area and for two air quality scenarios: as is air quality and just meeting the current 8-hour sta ndard. Consistent with the standard output generated for the CO REA, we generated a si ngle maximum end-of-hour COHb level for each simulated CHD person, resulting from bot h ambient CO exposure and endogenous CO production (and termed total COHb in this document). Next, we used the COHb levels generated from APEX simulations employing zero exposure con centrations, to estimate a maximum COHb level resulting from endogenous CO production alone. A nd finally, the timeseries of COHb levels resulting from endogenous CO production were subtracted from the timeseries of total COHb (i.e., from endogenous pr oduction and ambient expo sure), to estimate the maximum COHb level resulting from ambient CO e xposure alone. In consid ering the results, we note that the focus is on ambient CO contributio n to exposure, although in some cases, other CO sources can play a more important role in COHb levels (as described in section 2.3 of CO REA).

The percentiles of the distribution of each of these metrics were calculated in 0.5% increments, the results of which are illustrated in Figure B-3. Selected percentiles of each distribution are provided in Table B-5. As noted above for th e distribution of m ean COHb levels resulting from endogenous CO pr oduction, the maximum contributi on is nearly identical when comparing the air quality scenar ios within the study ar eas. In general, over 98.5% of the simulated CHD population has a maximum end-of -hour COHb level resulting from endogenous CO production of less than 1.0%. There is a difference in the highest maximum end-of-hour COHb level contributed by endoge nous CO production when comparing Denver to Los Angeles; the maximum extends upwards to a COHb level of about 1.4% in Denver, while in Los Angeles the highest maximum end-of-hour COHb level is about 1.0%.

The distribution of the maximum end-of- hour COHb levels resulting from ambient exposure alone is consistently higher than that resulting from endogenous CO production, indicating the relative im portance of ambient CO exposure; ho wever, it is not always the case that the maximum end-of-hour total COHb leve l occurred simultaneously with the maximum contribution from ambient exposure. About half of the time, this wa s the case (Table B-6), though it varied based on the study area and air qu ality scenario considered. Even when a person’s maximum total COHb did not occur at the same time as the maximum ambient contribution, the ambient contribution, on aver age was between 53-67% of a person’s maximum total COHb.B-24Table B-4. Descriptive statistics for annual average hourly COHb levels resultingfrom endogenous CO production aloneand using an APEX simulated CHD population.Individual Annual Average Hourly COHb Levels (%)Individual Variabilityin Hourly COHbLevels (%)StudyAreaAirQualityScenarioMean stdev min p1 p5 p50 p95 p99 max COVMeanCOVStdevCOVAs Is 0.278 0.117 0.107 0.119 0.143 0.251 0.508 0.707 0.95342.2 22.0 5.2DenverCurrentStandard0.278 0.118 0.108 0.119 0.143 0.251 0.510 0.711 0.96542.3 22.0 5.1As Is 0.266 0.110 0.094 0.1040.138 0.244 0.501 0.669 0.688 41.522.9 5.6LosAngelesCurrentStandard 0.266 0.110 0.094 0.105 0.138 0.243 0.499 0.660 0.688 41.522.9 5.6Table B-5. Selected percentiles of the maximum end-of-hour COHb levels using an APEX simulated CHD population,considering the maximum contribution from endogenous CO production, the maximum contribution fromambient CO exposure and themaximum total COHb level.Maximum End-of-Hour COHb Level (%)aEndogenous Contribution Ambient Contribution TotalStudyAreaAirQualityScenariomin med max min med max min med maxAs Is 0.17 0.39 1.40 0.23 0.49 1.63 0.43 0.75 1.76DenverCurrentStandard 0.17 0.39 1.39 0.420.93 2.70 0.61 1.16 2.87As Is 0.14 0.37 1.03 0.24 0.60 1.70 0.46 0.84 1.92LosAngelesCurrentStandard0.14 0.37 1.03 0.29 0.72 2.77 0.48 0.97 3.03Notes:aSelected percentiles correspond to COHb levels in Figure B-3.B-25 0.0 0.5 1.0 1.5 2.0 2.5 3.0COHb Level (%)

0 2 4 6 8 10 12Ambient CO (ppm) COHb contribution from endogenous CO
COHb total (ambient CO exposure + endogenous CO)

Ambient monitor concentration

0.0 0.5 1.0 1.5 2.0 2.5 3.0COHb Level (%)

0 2 4 6 8 10 12Ambient CO (ppm) COHb contribution from endogenous CO
COHb total (ambient CO exposure + endogenous CO)

Ambient monitor concentration

0.0 0.5 1.0 1.5 2.0 2.5 3.0

8400 8424 8448 8472 8496 8520 8544 8568 Hour of Day in YearCOHb Level (%)

0 2 4 6 8 10 12Ambient CO (ppm) COHb contri bution from endogenous CO
COHb total (ambient CO exposure + endogenous CO)

Ambient monitor concentration

Figure B-2. Time-series profile of COHb levels and ambient CO for three simulated CHD persons in Los Angeles – air qualit y just meeting the current standard. Person 1: Low COHb

Person 2: Mid COHb

Person 3: High COHbB-26Figure B-3. Maximum end-of-hour COHblevels using an APEX simulated CHD population in Denver (left) and Los Angeles(right), consideringas isair quality and air quality adjusted tojust meeting the current 8-hour standard.Denver: As Is Air Quality0102030405060708090100 Population PercentileMaximum Contribution from Ambient CO Exposure AloneMaximum Contribution from Endogenous CO Production AloneMaximum (Ambient Exposure + Endogenous CO Production)Los Angeles: As Is Air QualityMaximum Contribution from Ambient CO Exposure AloneMaximum Contribution from Endogenous CO Production AloneMaximum (Ambient Exposure + Endogenous CO Production)Denver: Just Meeting the Current Standard01020304050607080901000.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Maximum End-of-Hour COHb (%)

Population PercentileMaximum Contribution from Ambient CO Exposure AloneMaximum Contribution from Endogenous CO Production AloneMaximum (Ambient Exposure + Endogenous CO Production)Los Angeles: Just Meeting the Current Standard0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Maximum End-of-Hour COHb (%)Maximum Contribution from Ambient CO Exposure AloneMaximum Contribution from Endogenous CO Production AloneMaximum (Ambient Exposure + Endogenous CO Production)B-27 Table B-6. Evaluation of the ambient cont ribution occurring simultaneously with a CHD person’s maximum total end-of-hour COHb in Denver and Los Angeles.

Persons Percent of Ambient COHb Contribution to Maximum Total COHb a Study Area Air Quality Scenario Maximum Ambient COHb Contribution COHb Occurred Simultaneously with Maximum Total COHb (n) (%) min mean max Yes 199 45.4 34.3 75.1 96.3 As Is No 239 54.6 9.3 52.9 85.9 Yes 326 74.4 59.7 82.1 97.9 Denver Current Standard No 112 25.6 24.2 67.0 89.7 Yes 216 54.8 43.7 77.1 93.8 As Is No 178 45.2 20.1 57.5 90.2 Yes 231 58.6 52.4 80.0 95.3 Los Angeles Current Standard No 163 41.4 26.3 63.7 90.2 Notes:

a The ambient COHb contribution that occurred at the same time as the persons maximum total COHb was used for this calculation.

Each simulated CHD person’s maximum end-o f-hour total COHb level was also plotted against their respective ambient contribution to that maximum COHb level. We separated each of the air quality scenarios, the study areas , and whether the maximu m ambient contribution coincided with the maximum total COHb level or did not. The results of this analysis are illustrated in Figures B-4 and B-5 for Denver and Los Angeles, respectively.

As expected, strong linear re lationships exist when the maximum ambient contribution occurred at the same time as the maximum total C OHb level, regardless of air quality scenario or study area. The coefficient of determination (R 2 values) ranged from about 0.81 to 0.93, with slopes very close to unity, s upporting the importance of the am bient contribution to these persons’ maximum total COHb level. Regression intercepts were also similar to one another, ranging from a value about 0.23 to 0.26%, and approximate the averag e endogenous contribution to COHb.

Relatively weaker relationships were exhibited when the ma ximum total COHb level did not correspond with the maximum ambient contribu tion. Correlation coefficients ranged from about 0.10 to 0.48 with regression slopes ranging from 0.4 to 0.5. The relative greater importance of the endogenous contribution to COHb for these persons is i ndicated by the higher regression intercepts, ranging from about 0.51 to 0.59% COHb.

The most important message conveyed by these figures illustrating simulations for which exposure included only contribution from ambi ent CO sources which is conveyed by these figures is that for all simulated persons having a maximum end-of-hour C OHb level at or above 2.0%, and for most persons having a maximum e nd-of-hour COHb level at or above 1.5%, the ambient contribution is a more important fact or than endogenous CO production. This is indicated by the very few data points that greatly deviate from the bounded regression lines at or above these COHb levels, regard less of whether the maximum total COHb occurred at the same time as the maximum ambient contribution.B-28Figure B-4. Relationship between ambient contribution toCOHb level and the corresponding maximum total end-of-hourCOHb level in Denver. Air qualityas is(left) and just meeting the currentstandard (right). Maximum ambientcontribution did not occur with maximum total COHb (top) and maximum ambient contribution did occur withmaximum total COHb (bottom).Denver: Just Meeting the Current StandardMax Ambient Contribution Did Not Occur at Same Time as Max Total COHby = 0.8059x + 0.512R2= 0.48150 001 012 023 03Denver: Just Meeting the Current StandardMax Ambient Contribution Occurred at Same Time as Max Total COHby = 0.9555x + 0.263R2= 0.9336050505050.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Ambient Contribution to Maximum End-of-hour COHb (%)Denver:As IsAir QualityMax Ambient Contribution Did Not Occur at Same Time as Max Total COHby = 0.4349x + 0.5935R2= 0.10920.00.51.01.52.02.53.03.50 001 012 023 03

Maximum End-of-Hour COHb (%)Denver:As IsAir QualityMax Ambient Contribution Occurred at Same Time as Max Total COHby = 0.9505x + 0.2266R2= 0.81430.00.51.01.52.02.53.03.50.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Ambient Contribution to Maximum End-of-hour COHb (%)

Maximum End-of-Hour COHb (%)B-29Figure B-5. Relationship between ambient contribution to COHb level and the corresponding maximum total end-of-hourCOHb level in Los Angeles. Air qualityas is(left) and just meeting the current standard (right). Maximumambient contribution did not occur with maximum total COHb (top) and maximum ambient contribution didoccur with maximum total COHb (bottom).Los Angeles: Just Meeting the Current StandardMax Ambient Contribution Did Not Occur at Same Time as Max Total COHby = 0.7472x + 0.5036R2= 0.4358Los Angeles: Just Meeting the Current StandardMax Ambient Contribution Occurred at Same Time as Max Total COHby = 0.9949x + 0.2066R2= 0.91740.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Ambient Contribution to Maximum End-of-hour COHb (%)Los Angeles:As IsAir QualityMax Ambient Contribution Did Not Occur at Same Time as Max Total COHby = 0.6004x + 0.5378R2= 0.23630.00.51.01.52.02.53.03.50 001 012 023 03

Maximum End-of-Hour COHb (%)Los Angeles:As IsAir QualityMax Ambient Contribution Occurred at Same Time as Max Total COHby = 0.9652x + 0.2367R2= 0.84270.00.51.01.52.02.53.03.50.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Ambient Contribution to Maximum End-of-hour COHb (%)

Maximum End-of-Hour COHb (%)B-30 B.7 References

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

Biller WF and Richmond HM. (1982). Sensitivity Analys is 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. Enviro nmental Protection Agency, Research 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.

Berk PD, Rodkey FL, Blaschke TF, Collison HA, Waggoner JG. (1974). Comparison of plasma bilirubin turnover and carbon monoxide production in man. J Lab Clin Med . 83:29-37.

Coburn RF, Forster RE, Kane RB. (1965). Considerations 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). Interactions of carbon monoxide and hemoglobin at high altitude. Atmos Environ . 17:723-728.

Coltman CA and Dudley III GM. 1969. The relationship between endogenous carbon monoxide and total heme mass in normal and abnormal subjects. Am J Med Sci . 258:374-385.

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

Friedlander SK. (1982). Letter from the Clean Air Scientific Advisory Committee to US EPA Administrator. August 31, 1982.

Glen G. (2002). Programmer’s Guide for the APEX3 Model. Prepared by ManTech Environmental Technology, Inc., for the U.S. Environmental Protection Ag ency, Research Triangle Park, NC. April 30.

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

Johnson T and Paul RA. (1983). The NAAQS Model (NEM) Applied to Carbon Monoxide. Report no. EPA 450/5-83-003. U.S. Environmental Protection Agency, Research Triangle Park, NC.

Johnson T. (1998). Memo No. 5: Equations for Converting Weight to Height Proposed for the 1998 Version of pNEM/CO. Memorandum Submitted 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 Using a Probabilistic Version of NEM. Prepared by TRJ Environmental, Inc., for the Office of Air Quality Pl anning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.B-31 Johnson T, Mihlan G, LaPointe J, et al. (2000). Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels in Residents of Denver and Los Angeles Using pNEM/CO (Version 2.1). Prepared by TRJ Environmental, Inc., for the Of fice of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Luomanmaki K and Coburn RF. (1969). Effects 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). Mathematical models for carboxyhemoglobin. Atmos Environ . 14:841-844.

McCartney ML. (1990). Sensitivity analysis applied to Coburn-Forster-Kane models of carboxhemoglobin rormation. Am Ind Hyg Assoc J . 51(3):169-177.

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

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 version of the Coburn, Forster and Kane model of blood carboxyhemoglobin. Atmos Environ . 21:1963-1967.

Peterson JE and Stewart RD. (1970). Absorption and e limination of carbon monoxide by inactive young men. Arch Environ Health . 21:165-171.

Peterson JE and Stewart RD. (1975). Predicting th e carboxhemoglobin levels resulting from carbon monoxide
exposures. J Appl Physiol . 39:633-638.

Press WH, Flannery BP, Teukolsky SA, Vettering WT. (1 986). Numerical Recipes: The Art of Scientific Computing. 1 st edition. Cambridge University Press, Cambridge MA.

Rodkey FL, O’Neal JD, Collison HA. (1969). Oxygen an d carbon monoxide equilibria of human adult hemoglobin at atmospheric and elevated pressure. Blood . 33:57-65.

Salorinne Y. (1976). Single-breath 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 Pollutio n. Report no. EPA-600/9-78-015. U.S. Environmental Protection Agency, Research Triangle Park, NC.

US EPA. (2002). EPA’s Consolidated Human Activities Database. Data and associated documentation available
at: http://www.epa.gov/chad/

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

.B-32 US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report no. EPA-452/B-08-001a. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Available at: http://www.epa.gov/ttn/f era/human_apex.html

. Werner B and Lindahl J. (1980). Endogenous carbon monoxide production after bicycle exercise in healthy subjects and in patients with hereditary spherocytosis. Scan J Lab Invest . 40:319-324.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.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.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 AC-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 ]

.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.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.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.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.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.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.C-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<0.005D-1 

### Appendix D

Microenvironmental Mapping

Figure D-1 presents how CHAD codes are mapped to the eight microenvironments used to model exposure in the CO REA. Table D-1 provides the CHAD activity codes used to identify 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 HD-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 HD-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: breaksE-1 

### 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).

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.E-2 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/

.F-1 

### 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 the CO REA.F-2 

## 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 exertionF-3 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 toF-4 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 basalF-5 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).F-6 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 someF-7 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.F-8 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).F-9 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-F-10 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 functionalF-11 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.F-12 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.F-13Table 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)9149F-14Table 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 0.000.000.00F-15= 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 13.860.010.650.120.011.000.13F-16Table 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 15.180.020.05F-17Table 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 0.5120.550.050.060.550.150.170.040.96F-18Table 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 0.910.990.86F-19Table 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 11.270.180.27F-20Table 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 0.3240.630.80F-21Table 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 0.0770.900.670.850.281.000.990.83F-22Table 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/R-10-006Environmental Protection Health and Environmental Impacts Division May 2010Agency Research Triangle Park, NC