Quantitative Risk and Exposure Assessment 

for Carbon Monoxide - Amended 

EPA-452/R-10-009 

July 2010 

Quantitative Risk and Exposure Assessment 

for Carbon Monoxide - Amended 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, North Carolina 

DISCLAIMER

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 Air Quality Planning and Standards, U.S. 

Environmental Protection Agency. Any opinions, findings, conclusions, or recommendations are 

those of the authors and do not necessarily reflect the views of the EPA Mention of trade names 

or commercial products is not intended to constitute endorsement or recommendation for use. 

Any questions or comments concerning this document should be addressed to Dr. Stephen 

Graham, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, 

C504-06, Research Triangle Park, North Carolina 27711 (email: graham.stephen@epa.gov ). 

Elements of this report have been provided to the U.S. Environmental Protection Agency (EPA) 

by Abt Associates, Inc. and TRJ Environmental, Inc. in partial fulfillment of Contract No. EP-D08-100, Work Assignments 0-08 and 1-15. 

PREFACE 

This amended Quantitative Risk and Exposure Assessment for Carbon Monoxide (CO 

REA) contains revised results reflecting corrections to one of the input variables used in the dose 

model to generate estimates of carboxyhemoglobin (COHb). Modeling results presented in the 

May 2010 CO REA were generated with erroneous values for altitude for both the Los Angeles 

and Denver study areas. This error has been corrected and the model simulations repeated 

resulting in increases to the COHb estimates in both study areas. These increases, in terms of the 

ambient contribution to COHb levels, were small for the Denver study area and negligible for the 

Los Angeles study area, with somewhat larger increases to the total COHb estimates. Because 

altitude was not used in calculating exposure estimates, none of the exposure results previously 

presented were affected by this error. The sections of the REA revised to reflect the corrected 

dose estimates include several tables and associated text in chapters 5, 6, 7 and 8 and Appendix 

B. The specific areas of the report containing revisions to address this error include section 

5.10.4 (Table 5-27), section 6.2 (Tables 6-15 through 6-23, Figures 6-5 and 6-6), section 6.3 

(Tables 6-24 and 6-25), section 7.2.1.2 (Table 7-4), section 7.2.2 (Tables 7-5 through 7-8, 7-10, 

7-11, and 7-14), chapter 8 and Appendix B.6 (Tables B-4 through B-6, Figures B-2 through B-5). 

In addition, the presentation of exposure estimates has been modified to reflect a correction to 

the rounding convention for percentages of the population less than 0.1%. Previously, values 

between 0.05 and 0.09% were rounded upwards to 0.1%. This correction is reflected in the 

presentation of exposure estimates in Tables 6-4, 6-7, 6-10, 6-13, and 6-14. 

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 AMBIENT 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 Disease-related Effects ................................................. 2-12

2.5.2 Other Effects .......................................................................................... 2-14

2.6 RISK CHARACTERIZATION 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 FOR CURRENT ASSESSMENT..................... 3-20 

3.3 KEY OBSERVATIONS..................................................................................... 3-20 

3.4 REFERENCES ................................................................................................... 3-22 

 
ii

4 OVERVIEW OF APEX MODELING SYSTEM FOR ESTIMATING CO 

EXPOSURE AND COHB DOSE LEVELS................................................................. 4-1 

4.1 PURPOSE............................................................................................................. 4-1 

4.2 MODEL OVERVIEW.......................................................................................... 4-1 

4.3 MODEL HISTORY AND EVOLUTION ............................................................ 4-2 

4.4 MODEL SIMULATION PROCESS.................................................................... 4-3 

4.4.1 Characterize Study Area .......................................................................... 4-6 

4.4.2 Generate Simulated Individuals............................................................... 4-6 

4.4.3 Construct Activity Sequences.................................................................. 4-8 

4.4.4 Calculate Microenvironmental 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-Hour 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................... 5-26 
iii

5.9 MICROENVIRONMENTS MODELED ........................................................... 5-27 

5.9.1 The Micronenvironmental Model as Implemented by APEX4.3 .......... 5-27 

5.9.2 Microenvironmental Mapping ............................................................... 5-30 

5.9.3 Selection of Microenvironmental 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 Microenvironmnet Contribution to At-Risk Population 

Exposure Levels..................................................................................... 5-34 

 5.10.2 Estimate of Microenvironment-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 Exposure 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 meet 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 meet 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 ESTIMATES OBTAINED FROM THE 2000 

PNEM/CO AND 2010 APEX/CO ASSESSMENTS ........................................ 6-35 

6.4 KEY OBSERVATIONS..................................................................................... 6-40 

6.5 REFERENCES ................................................................................................... 6-43 

 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 Characterizing 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 ........................................................... 8-1 
iv

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. Microenvironmental 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-1 
v

List of Figures 

Figure 3-1 Spatial and temporal trends in the 2nd highest 1-hour (top) and 8-hour average 

(bottom) CO ambient monitoring concentrations 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 (weekdays-left; weekends-right), 

 years 1995 (top) and 2006 (bottom) .......................................................................... 3-9 

Figure 3-3 Diurnal distribution of 1-hour CO concentrations in Los Angeles (monitor 

060371301) by day-type (weekdays-left; weekends-right), years 1997 (top) 

 and 2006 (bottom). .................................................................................................. 3-10 

Figure 3-4 Comparison of high concentration year (1997) versus 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 data flow for estimating population exposure 

 and dose using APEX4.3........................................................................................... 4-5 

Figure 5-1 Ambient monitor locations, air districts (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 districts (black circles), meteorological zones 

 (blue and pink circles), and study area (red circle) for the Los Angeles exposure 

modeling domain....................................................................................................... 5-6 

Figure 5-3 Relationship between microenvironment-to-ambient concentration ratios using 

 estimated indoor-residential concentrations (left panel) and inside-vehicle 

 concentrations (right panel) in Denver – as is air quality........................................ 5-35 

Figure 6-1 Estimated microenvironmental contributions to time spent at or above selected 

 exposure concentrations using the Denver CHD population – as is air quality. ....... 6-5 

Figure 6-2 Estimated microenvironmental contributions to time spent at or above selected 

 exposure concentrations using the Los Angeles CHD population 

 – as is air quality....................................................................................................... 6-9 

Figure 6-3 Estimated microenvironmental contributions to time spent at or above selected 

exposure concentrations using the Denver CHD population – air quality just 

 meeting the current 8-hour standard ........................................................................ 6-13 

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 standard ........................................................................ 6-17 

Figure 6-5 Estimated percent of the CHD population in Denver (top) and Los Angeles 

 (bottom) experiencing repeated COHb levels – as is air quality ............................. 6-26 

Figure 6-6 Estimated percent of the CHD population in Denver (top) and Los Angeles 

 (bottom) experiencing repeated COHb levels – air quality just meeting the 

 current 8-hour standard............................................................................................ 6-30 
vi

Figure 7-1. Comparison of commute and travel times for persons residing in Denver 

 and Los Angeles counties to those persons surveyed in CHAD. ............................ 7-17 

List of Tables 

Table 3-1 Within monitor temporal variability 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 – 99th 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 variability in Denver using historical (1995-97) and 

 recent (2005-07) air quality data – 99th 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 studies 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 parameters 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 distributions defined for proximity factors used 

 in applications of APEX3.1 to Los Angeles (Johnson and Capel, 2003)................ 4-30 

Table 5-1. Attributes of fixed-site monitors 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 diagnosed coronary heart disease by age range ......... 5-8 

Table 5-4 National prevalence rates for diagnosed coronary heart disease by gender.............. 5-9 

Table 5-5 Estimated national prevalence rates for 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, stratified by age and gender............................. 5-10 

Table 5-7 National prevalence rates for all types of diagnosed heart disease by age range. .. 5-11 

Table 5-8 National prevalence rates for all types of diagnosed heart disease by gender........ 5-11 

Table 5-9 Estimated national prevalence rates for all types of diagnosed heart disease, 

 stratified by age and gender.................................................................................... 5-11 

Table 5-10 Estimated national prevalence 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 alternative standard forms and levels informed by modeled exposure 

 scenarios in Denver................................................................................................ 5-15 
vii

Table 5-12 Array of alternative standard forms and levels informed by modeled exposure 

 scenarios in Los Angeles ....................................................................................... 5-16 

Table 5-13 Descriptive statistics for hourly carbon monoxide concentrations before and 

 after estimation of missing values – Denver 1995 .................................................. 5-18 

Table 5-14 Descriptive statistics for hourly carbon monoxide concentrations before and 

 after estimation of missing values – Denver 2006 .................................................. 5-18 

Table 5-15 Descriptive statistics for hourly carbon monoxide concentrations before and 

 after estimation of missing values – Los Angeles 1997.......................................... 5-19 

Table 5-16 Descriptive statistics for hourly carbon monoxide concentrations 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 carbon monoxide concentrations after adjusting 

 to just meet the current 8-hour standard – Denver (adjusted 1995 data). ............... 5-24 

Table 5-19 Descriptive statistics for hourly carbon monoxide concentrations after adjusting 

 to just meet the current 8-hour standard – 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 distributions of indoor air exchange rates used in Denver ................... 5-32 

Table 5-25 Lognormal distributions of indoor air exchange rates 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 output 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 for simulated at-risk 

populations in the Denver study area – as is air quality............................................ 6-3 

Table 6-2 Estimated distribution of microenvironment-to-ambient concentration ratios using 

 the Denver CHD population – as is air quality ......................................................... 6-6 

Table 6-3 Estimated distribution of microenvironmental 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 microenvironment-to-ambient concentration ratios using 

 the Los Angeles CHD population – as is air quality............................................... 6-10 

Table 6-6 Estimated distribution of microenvironmental 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 quality just meeting the current 8-hour 

standard. ................................................................................................................ 6-12 

Table 6-8 Estimated distribution of microenvironment-to-ambient concentration ratios 

 using the Denver CHD population – air quality just meeting the current 8-hour 

standard ................................................................................................................ 6-14 
viii

Table 6-9 Estimated distribution of microenvironmental concentrations using the Denver 

 CHD population – air quality just meeting the current 8-hour standard................. 6-14 

Table 6-10 Estimated daily maximum 1-hour or 8-hour exposure for 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 distribution of microenvironment-to-ambient concentration ratios using 

 the Los Angeles CHD population – air quality just meeting the current 8-hour 

 standard ................................................................................................................ 6-18

Table 6-12 Estimated distribution of microenvironmental concentrations using the Los Angeles 

CHD population – air quality just meeting 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 – alternative 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 simulated 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 simulated at-risk populations in the Los Angeles study area 

 Estimated to experience a daily maximum end-of-hour COHb at or above 

 specified levels – as is air quality............................................................................ 6-23 

Table 6-17 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 alone at or above specified levels 

– as is air quality ..................................................................................................... 6-27 

Table 6-18 Portion of the simulated 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 simulated 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 the current 8-hour standard................... 6-29 

Table 6-20 Percentage of simulated CHD populations in the Denver and Los Angeles study 

areas estimated to experience daily maximum 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 simulated 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 potential alternative standards ........................................ 6-33 

Table 6-22 Portion of the simulated 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 potential alternative standards ........................................ 6-34 

Table 6-23 Percentage of simulated CHD populations in the Denver and Los Angeles study 

 areas estimated to experience daily maximum end-of-hour COHb contribution from 

ambient exposure alone at or above specified levels – air quality just meeting a 

 99th percentile daily maximum 8-hour average concentration of 5.0 ppm............... 6-35 
ix

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 ........................................................................................... 6-39 

Table 6-25 Percentage of Los Angeles 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 ........................................................................................... 6-39 

Table 7-1. Summary of how variability was incorporated into the assessment. ........................7-2 

Table 7-2. Characterization of key uncertainties 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 alternative standards. ............................................................7-15 

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 concentrations – as is air quality. ..................................7-24 

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...........7-24 

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........................................................................................................ 7-25 

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 monitor concentrations – air quality just meeting the current 

 8-hour standard........................................................................................................ 7-25 

Table 7-9. Estimated alternative prevalence rates 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 maximum end-of-hour COHb at or above 

specified levels using base and alternative 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 maximum end-of-hour COHb at or above 

specified levels using base and alternative 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 statistics 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 maximum end-of-hour COHb at or above 

specified levels when sampling from the base and anemic hemoglobin content 

distributions – air quality just meeting the current standard. ..................................7-31 

1-1

1 INTRODUCTION 

This document, Quantitative Risk and Exposure Assessment for Carbon Monoxide,

describes the 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). Given the significant time 

constraints of this review,1 results of the analyses are provided in this document without 

substantial interpretation. Rather, interpretative discussion of these results 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 Act (Act) govern the establishment and periodic 

review of the NAAQS. These standards are established for certain pollutants that may 

reasonably be anticipated to endanger public health and welfare, and whose presence in the 

ambient air results from numerous or diverse mobile or stationary sources. The NAAQS are to 

be based on air quality criteria, which are to accurately reflect the latest scientific knowledge 

useful in indicating the kind and extent of identifiable effects on public health or welfare 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 Administrator is to make revisions in the criteria and 

standards, and promulgate any new standards, as may be appropriate. The Act also requires that 

an independent scientific review committee advise the Administrator as part of this NAAQS 

review process, a function performed by the Clean Air Scientific Advisory Committee 

(CASAC). 

The current NAAQS for CO includes two primary standards to provide protection for 

exposures to carbon monoxide. In 1994, EPA retained 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 were based primarily on the clinical evidence 

relating carboxyhemoglobin (COHb) levels to various adverse health endpoints and exposure 

modeling relating CO exposures to COHb levels. With the 1994 decision, EPA also reaffirmed 

an earlier decision that the 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 review 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 Episodes 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 regarding management of CO air pollution, cold start 

emissions standards, oxygenated fuels, and CO monitoring. Following completion of this NRC 

report, EPA did not conduct rulemaking to complete the review. 

EPA initiated the current review of the NAAQS for CO on September 13, 2007, with a 

call for information from the public (72 FR 52369) requesting the submission of recent scientific 

information on specified topics. A workshop was held on January 28–29, 2008 (73 FR 2490) to 

discuss policy-relevant scientific and technical information to inform EPA’s planning for the CO 

NAAQS review. Following the workshop, EPA outlined the science-policy questions that would 

frame this review, outlined the process and schedule 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. This assessment, the Integrated Science Assessment 

for Carbon Monoxide (hereafter, “ISA”) (US EPA, 2010b), includes an evaluation of the 

scientific evidence on the health effects of CO, including information on exposure, physiological 

mechanisms by which CO might adversely impact human health, an evaluation of the clinical 

evidence for CO-related morbidity, and an evaluation of the epidemiological evidence for COrelated morbidity and mortality associations.2

 

EPA’s Office of Air Quality Planning and Standards (OAQPS) has developed this Risk 

and Exposure Assessment (REA) describing the 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, observations, and related uncertainties associated with 

the quantitative analyses performed. The REA builds upon the health effects 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 the effects of CO on public 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 Methods Plan”) (US EPA, 2009a) and on the first 

and second draft REA documents (US EPA, 2009b; US EPA, 2010c). This final REA was 

completed by May 28, 2010, consistent with the court order governing the schedule for 

completion of this review. The court order also specified that EPA sign for publication notices 

of proposed and final rulemaking concerning its review of the CO NAAQS no later than October 

28, 2010 and May 13, 2011, respectively. 

The ISA and REA are used to inform the policy assessment and rulemaking steps that 

lead to final decisions on the CO NAAQS. The 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 the Administrator prior to rulemaking (US EPA, 

2010a). The PA integrates and interprets information 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 appropriate to retain or revise the existing standards. 

Development of the PA is also intended to facilitate elicitation of CASAC’s advice to the 

Administrator on the adequacy of existing standards, 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 percent of the modeled population that exceeded 

several potential health effect benchmarks, expressed in terms of COHb levels. The COHb 

levels of interest in these reviews 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 studies 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 narrowed 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 typically have additional ischemic episodes that are asymptomatic (2000 AQCD, 

section 7.7.2.1). 
1-4

In the review completed in 1994, this characterization was performed for the population 

of interest in the city of Denver, Colorado (US 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 cardiovascular disease4

 experiencing exposures to ambient CO at or above 9 

ppm for 8 hours decreased by an order of magnitude or more as compared to the proportion 

under then-existing ambient CO levels, down to less than 1 percent of the total person-days in 

that population. Likewise, just meeting the current 8-hour standard reduced the proportion of the 

nonsmoking cardiovascular-disease population person days at or above COHb levels of concern 

by an order of magnitude or more relative to then-existing ambient CO levels. More specifically, 

upon just meeting the 8-hour standard, EPA estimated 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 inclusion 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 consulted with CASAC (Mauderly, 1999) on a draft 

exposure analysis methodology document, Estimation of Carbon Monoxide Exposures and 

Associated Carboxyhemoglobin Levels 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 modeling component of EPA’s Total Risk Integrated 

Methodology (TRIM). A subsequent draft technical report (Johnson et al., 2000) was produced 

documenting the application of the CO exposure 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 heart 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 standard, 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 exposure and dose estimates were obtained by applying 

pNEM/CO version 2.1, a predecessor to the currently used Air Pollutants Exposure Model 

(APEX), to adults with IHD residing 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 study 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 existing conditions in the 

study area. Because the second highest non-overlapping 8-hour average CO concentration 

equaled 9.5 ppm, the existing conditions in Denver 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 monitors in the Los Angeles area, 

though air quality data for 1997 were adjusted downwards 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 accounting for the infiltration of outdoor (ambient) 

CO, air exchange rates, as well as CO emissions from two indoor sources (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-risk 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 ADVICE 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 consultation with CASAC to solicit comments on the 

draft Scope and Methods Plan during 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 implemented a simplified, screening-level approach 

to assess population exposure and dose in two 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 monitor9

 in each 

location. Only two microenviroments were simulated; 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 representative of upper level exposure and doses experienced 

by a portion of the simulated at-risk population rather than the simulated at-risk population as a 

whole. 

Following the review of the first draft REA by CASAC (Brain and Samet, 2010a) and by 

public commenters, we made a number of modifications to our initial approach and developed 

the second draft REA (US EPA, 2010c) to better estimate population exposure and dose 

distributions in each location modeled.11 Specifically in the second draft assessment, we 1) 

expanded each of the two original modeling domains to include a greater number of ambient 

monitors used as input to APEX, 2) increased the number of microenvironments modeled from 

two to eight, 3) improved the representation of variability in estimated microenvironmental 

concentrations, including in-vehicles, 4) included an algorithm that adjusts for spatial 

heterogeneity in estimated outdoor concentrations across each model domain, 5) implemented 

the mass-balance model for estimating concentrations in all indoor microenvironments, 6) 

implemented the algorithm that allows commuters 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 2nd highest 8-hour (or 1-hour) average CO concentration). 

10 In the first draft REA, in-vehicle concentrations were 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 submitted to the docket for this review and also presented 

in March, 2010 at the CASAC second draft review meeting. 
1-7

ambient concentrations, and 7) expanded the at-risk population to also 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 the public. The approach used to estimate 

population CO exposure and COHb levels 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 simulated at-risk population based on 

prevalence rates for all types of heart disease (as well as including the previous 

estimates of persons with undiagnosed CHD); 

 Evaluation of endogenous CO production and ambient CO exposure separately 

and their contributions to individual and population COHb levels in a larger 

and more representative population subset; 

 Identification of the specific microenvironments 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 microenvironmental factors used to estimate 

exposure concentrations in response to concerns regarding the application of 

the microenvironmental algorithm; and 

 Performance of additional sensitivity 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; and 

o An evaluation of the potential impact to estimated COHb dose levels 

experienced by a hypothetical anemic CHD population by using 

alternative hemoglobin content distributions. 

The chapters and appendices that follow describe the technical details in the exposure and 

dose modeling approach used for this assessment, as well as the data analysis results. More 

specifically, 

 Chapter 2 provides a conceptual overview 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 general trends in CO ambient monitoring 

across the U.S. and presents additional air quality analyses relevant to the two 

urban areas of focus in this REA; 

 Chapter 4 provides a technical overview 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-specific data used for the application of 

APEX to the two study areas assessed in this REA; 

 Chapter 6 provides the exposure and dose results; 

 Chapter 7 presents an analysis of how 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 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 from Drs. J.D. Brain 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. Brain 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. Brain 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. EPA, 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/fera/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/fera/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/naaqs/ozone/ozonetech/laxton.htm. 

Mauderly J. (1999). Letter from Dr. Joe Mauderly, Chair, 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 Denver 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/fera/human_related.html. 

US EPA. (1979). Air Quality Criteria for Carbon Monoxide. Research Triangle Park, NC: Office of Health and 

Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-79-022. 
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 Planning 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 Monoxide. 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/recordisplay.cfm?deid=18163. 

US EPA. (2008a). Draft 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/452D-08-001. Also 

known as Draft Integrated Review Plan. Available at: 

http://www.epa.gov/ttn/naaqs/standards/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/naaqs/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. Environmental Protection Agency, Research Triangle Park, NC, 

report no. EPA-452/R-09-004. Available at: http://www.epa.gov/ttn/naaqs/standards/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 External Review Draft. U.S. Environmental Protection 

Agency, Research Triangle Park, NC, report no. EPA-452/P-09-008. Available at: 

http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_rea.html. 

US EPA. (2010a). Policy Assessment to Support the Review of the Carbon Monoxide Primary National Ambient 

Air Quality Standards. U.S. Environmental Protection 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 External Review Draft. U.S. Environmental Protection 

Agency, Research Triangle Park, NC, report no. EPA-452/P-19-004. Available at: 

http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_rea.html. 
2-1 .

2 CONCEPTUAL OVERVIEW: ASSESSING 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 different components of the model 

including identification of the key emission sources to ambient concentrations (section 2.1), 

exposure pathways and key microenvironments (section 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). Section 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 photochemical reactions in the atmosphere. The amount of CO 

emitted from these reactions, relative to the amount of carbon dioxide (CO2) generated, is 

sensitive to conditions in the combustion zone. CO production relative to CO2 generally 

decreases with any increase in fuel oxygen (O2) content, burn temperature, 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 optimized fuel consumption conditions make boiler 

combustion highly efficient. In contrast, internal combustion engines commonly used to power 

mobile sources have widely varying operating conditions. 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 accounted 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 stationary sources may be important contributors to ambient CO 

concentrations. 

Sources of CO inside buildings include infiltration 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 the cabin, which can be substantial under air 

ventilation modes that limit inflow from outside 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 experienced 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 ambient CO in two urban areas 

(details on site selection are provided in chapter 3 below). 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 qualitatively draw upon 

available information regarding potential indoor source contributions to estimated population 

exposure and dose. 

2.2 EXPOSURE PATHWAYS AND IMPORTANT MICROENVIRONMENTS 

Human exposure to CO involves the contact (via inhalation) between a person and the 

pollutant in the various locations (or microenvironments) 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 percentage 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 over 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 influence CO exposures typically include residential indoor 

environments and other indoor locations, near-traffic outdoor microenvironments and other 

outdoor locations, and inside vehicles. Consideration of microenvironmental exposures 

illustrates the variability in the relationship between personal exposure and ambient 

concentrations. For example, one study summarized the relationship between personal CO 

exposure concentrations in five broadly defined microenvironments (i.e., indoor residence, 

indoor other, outdoor near road, outdoor other, and in-vehicle) and ambient CO concentrations2

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-ambient concentration ratios were about one, though 

most of the individual ratios observed across this set of indoor and outdoor microenvironments 

were less than one. With the exception of ratios for the in-vehicle microenvironments, which as 

a group had most of the ratio distribution (as well as the mean ratio) 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 the expected stability of CO as it infiltrates 

indoor microenvironments from outdoor air and the lack of significant removal mechanisms of 

CO in outdoor microenvironments, it is likely that the variability in personal- or 

microenvironmental-to-ambient monitor and outdoor-to-ambient monitor concentration ratios is 

the result of variability 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 concentrations are experienced while inside vehicles. 

Because motor vehicle emissions continue to be important contributors to ambient CO 

concentrations, both the time spent in motor vehicles and the elevated CO concentrations 

occurring on and near heavily trafficked roads continue to be important contributors to personal 

exposures. For example, in the study summarized above on personal exposures 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 summer, 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 exposure occurs within an in-vehicle microenvironment (e.g., Bruinen 

de Bruin et al., 2004; Scotto di Marco et al., 2005). 

Similarly, the influence of mobile sources to microenvironmental concentrations and 

personal exposurs was observed in the CO population exposure studies conducted in Denver CO 

and Washington, DC during the winter of 1982 and 1983 (Akland et al., 1985).4

 In both cities, 

when comparing the distribution of measured CO concentrations from the monitoring network to 

measured personal exposures, two common phenomena were observed. At the lowest 

percentiles of each distribution, ambient CO concentrations were consistently greater than the 

personal exposures. At the highest percentiles of each distribution, ambient concentrations were 

consistently lower than the personal exposures (US EPA, 2000). These studies determined that 

the highest average CO concentrations occurred when subjects were in a mobile sourceinfluenced microenvironment (e.g., inside parking garages, in-vehicles). Commute time was also 

a factor; those who commuted 6 hours or more per week had higher average exposures than 

 

3

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

4

 Both studies collected measurements and activity pattern diaries from a random sample of the population, 

defined as including non-institutionalized, non-smoking residents, 18 to 70 years of age, who lived in each 

respective city’s metropolitan area (Akland et al., 1985). 
2-4 .

those who commuted fewer hours per week. Furthermore, mean CO concentrations within invehicle microenvironments (ranging from 7.0 to 9.8 ppm) were greater than common outdoor 

locations (ranging from 1.4 to 3.2 ppm) (US EPA, 2000). In considering the results from the 

Denver and Washington personal exposure studies it is important to recognize that CO emissions 

from motor vehicle sources have declined dramatically since the early 1980’s when these studies 

were conducted. Consequently, both ambient fixed-site CO concentrations and in-vehicle CO 

concentrations have also been reduced significantly since that time period. 

Given their influence on ambient exposures, exposures to CO near roadways and in 

vehicle microenvironments are of particular importance in this assessment. Data from several 

studies that have compared concentrations inside vehicles to concentrations immediately outside 

vehicles indicate that indoor/outdoor concentration (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 Flachsbart (1999) regarding other 

studies published between 1982 and 1992 that measured interior and exterior CO concentrations 

simultaneously during motor vehicle trips and reported I/O ratios just below unity (Petersen and 

Allen, 1982; Koushi et al., 1992). Some studies reported no effect of ventilation setting on I/O 

ratios, while others reported an effect. For example, 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 attributed this finding to unaccounted sources of 

CO that caused increases in CO concentrations within the vehicle cabin under those conditions 

(ISA, section 3.6.6.2; Abi Esber and El-Fadel, 2008). 

In general, the above results suggest that the I/O ratio tends toward unity when there are 

no interior sources of CO, the automobile engine does not contribute directly to its own interior 

concentrations, and the measurement probes are properly installed on the vehicle. This 

conclusion is consistent with theoretical expectations for a non-reactive pollutant. For example, 

CO concentrations inside vehicles can be estimated as a function of outside CO concentration, 

air exchange rate, a penetration factor, and the emission rates of indoor sources (e.g., exhaust 

leaks, smoking). If one assumes that (1) steady-state ventilation conditions exist, (2) the indoor 

removal rate (k) is zero (i.e., no loss of CO as it moves from outside to inside the vehicle), and 

(3) there are zero emissions from interior sources, then the CO concentration inside a vehicle can 

be simplified to a function of outside CO concentrations and the penetration rate (i.e., infiltration 

is generally equivalent to penetration).5

 Under these stated conditions, the I/O ratio would 

ultimately converge to unity. 

 

5

 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 studies 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 ambient CO concentrations obtained at fixed-site 

monitors within the cities studied. For example, 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 measurements 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 measurement when compared with that of the ambient 

monitor. More specifically, Shikiya et al. (1989) measured in-vehicle 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 than that of the true fixed-site 

concentrations that occurred during the 33 minute commute, perhaps resulting in an 

overestimation of the concentration ratios when using this data. Furthermore, Shikiya et al. 

(1989) reported seasonal differences for the in-vehicle CO concentrations (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 ratios as was done above, the 

winter I/O ratio may be overestimated while the 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 concentration, such as the nearby roadway traffic 

density, the monitor siting characteristics (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 stratified by such factors. Thus, a general 

range of two to five may be adequate to represent the total variability for this particular 

relationship, recognizing that there are limitations in the available 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 particular concern for such at-risk populations as individuals with 
2-6 .

cardiovascular disease, among others (see section 2.4 below). For example, some assessments 

performed for previous reviews have included 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 indicated that the impact of such 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 reflects 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 health 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 healthy 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 vary with several physiological characteristics 

(e.g., slower COHb elimination with increasing age), as well as some disease states, which can 

lead to higher endogenous levels in some individuals (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 metabolism 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-time for COHb, can lead to increased COHb levels 

 

6

 As has been recognized in previous CO NAAQS reviews, 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 nonambient9, can contribute to CO uptake 

and increased levels of COHb. As recognized 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 higher baseline COHb levels, smokers may exhale more CO into 

the air than they inhale from the ambient environment when not smoking. Tobacco smoking can 

also contribute to increased CO exposures and associated COHb levels in nonsmokers (2000 

AQCD, p. 7-4). In order to focus on the impact of ambient CO sources on population COHb 

levels, exposure modeling for this REA does not include indoor CO sources; the impact of 

indoor sources has been evaluated in previous 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 solution of the Coburn-Forster-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 response to CO can potentially result from many 

factors, including pre-existing medical disorders or disease states, age, gender, lifestyle or 

increased exposures (ISA, section 5.7). For example, medical disorders that limit the flow of 

oxygenated blood to the tissues have the potential to make an individual 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 individuals 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 contributing to elevated COHb levels is 

tobacco smoking (e.g., ISA, Figure 4-12). 

10 For example, in addition to COHb estimates from previous assessments discussed in sections 2.1 and 2.2, 

indoor source-related exposures, such as faulty furnaces or other combustion 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 individuals with CAD, 

which is supported by results from recent epidemiologic 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 the 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 pectoris (cardiac chest pain) 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 ambient CO exposure when compared to the general 

population (ISA, section 5.7). 

Other types of cardiovascular disease12 may also potentially contribute 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-specific cardiovascular disease, although more 

limited for peripheral vascular and cerebrovascular disease, indicates that “the continuous nature 

of the progression of CAD and its close relationship with other forms of cardiovascular disease 

suggest that a larger population than just those individuals with a prior diagnosis of CAD may be 

susceptible to health effects from CO exposure” (ISA, p. 5-117). 

 

11 Ischemic heart disease is a category of cardiovascular 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 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, as identified by ST-segment depression, is asymptomatic (i.e., silent). Also, patients 

who experience angina typically have additional ischemic episodes that are asymptomatic (2000 AQCD, section 

7.7.2.1). In addition to such chronic conditions, CHD can include myocardial infarction (ISA, p. 5-24). 

12 Cardiovascular disease comprises many types of medical disorders, including heart disease, 

cerebrovascular disease (e.g., stroke), hypertension (high blood pressure), and peripheral vascular diseases. Heart 

disease, in turn, comprises several types of disorders, including ischemic heart disease (i.e., CHD, CAD, myocardial 

infarction, and angina), congestive heart failure, and disturbances 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 populations are listed in the paragraphs below. 

However, little empirical evidence is available by which to specify health effects associated with 

ambient or near-ambient CO exposures in these potentially 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 affect oxygen-carrying capacity 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 

anemia13 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-American 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 susceptible to CO includes those persons that may 

have increased endogenous production of CO and potentially higher endogenous COHb levels 

such as diabetics, for which a few epidemiological studies provide suggestive evidence of 

increased risk for cardiovascular emergency department visits and hospital admissions compared 

to non-diabetics in response to short-term CO concentrations (ISA, section 5.7.1.3). 

Additionally, older adults, especially those with compromised cardiovascular function, represent 

a potentially susceptible population (ISA, section 5.7.2.1). 

The developing young (e.g., gestational development and newborns) may also represent a 

population potentially susceptible to CO-induced health effects (ISA section 5.7.2.2; 2000 

AQCD, section 7.7.1). For example, although the effects of CO on maternal-fetal relationships 

are not well understood, fetal circulation is likely to have a higher COHb level than the maternal 

circulation because of differences in uptake and 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 consumption 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 delayed 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 physiological 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 association of short-term ambient CO exposure with pre-term birth and 

birth defects, and weak evidence suggests an association 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 may induce those effects and at what exposure 

levels is lacking (ISA, section 5.4.3). 

Other populations that may be potentially susceptible due to impacts on endogenous CO 

production, uptake and elimination of CO, or increased exposure concentrations include visitors 

to high-altitude locations, persons using medicinal or recreational drugs with central nervous 

system depressant properties or that that increase endogenous formation 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 sensitive endpoint which is the focus of this 

quantitative assessment is exacerbation of myocardial 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 IHD or CAD), both diagnosed and undiagnosed;14 and (2) 

adults with diagnosed heart disease (HD) which includes CHD as well as other conditions (e.g., 

arrhythmias), along with undiagnosed CHD. 

As mentioned above, there is little empirical evidence currently available by which to 

specify health effects associated with relevant CO exposures in the other, potentially at-risk 

groups identified above. Such evidence characterizing 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 these potentially susceptible populations. Quantitative evidence 

relating exposure or an applied dose to an adverse health outcome is requisite to the conduct of a 

quantitative exposure and risk assessment. As a result, while we continue to recognize the 

potential susceptibility of the larger cardiovascular disease population to health effects of CO, as 

has been recognized in past reviews, as well as the potential susceptibility of several other 

populations identified above (ISA, section 5.7), the at-risk populations 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 disease 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 (ISA, 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 associated health effects, as described in section 2.3 

above. 

The best characterized health effect associated with CO levels of concern is hypoxia 

(reduced oxygen availability) induced by increased COHb levels 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. Accordingly, CO is especially 

harmful in individuals with impaired cardiovascular systems (as discussed in section 2.4 above) 

and the clearest evidence of causal relationships with CO exists for cardiovascular effects. In 

characterizing the combined evidence, the ISA concluded that cardiovascular effects are likely 

causally related to short-term exposures to CO at relevant concentrations, 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 disease (CHD) (ISA, sections 5.2.4 and 5.2.6). 

Other potential effects of CO which are less well characterized at relevant exposure 

concentrations are those on the central nervous system, reproduction and prenatal development, 

and the respiratory system (ISA, section 2.5). These additional health endpoints, for which the 

limited available evidence is suggestive of causal relationships (ISA, sections 5.3, 5.4 and 5.5), 

are also considered in this review and are discussed in detail in the ISA and summarized briefly 

in section 2.5.2 below. Across the health endpoints identified here, however, the focus of the 

quantitative analysis described in this document is on cardiovascular 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 electrocardiogram that are indicative 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 disease-related effects associated with CO are 

markers of myocardial ischemia observed in studies of controlled CO exposures of CHD 

patients15 and effects on exercise duration and maximal aerobic capacity observed in controlled 

exposure studies of healthy adults.16 As noted in the ISA, the 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 athletes, although they are considered to provide 

coherence with the exercise-induced cardiovascular effects of greater concern that have been 

demonstrated in CHD patients. The controlled human exposure studies involving individuals 

with preexisting CHD provide strong 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 individuals 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 importance 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 electrocardiogram17 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 technique18 (ISA, section 5.2.4, from Allred et al., 1989a, 1989b, 1991). 

In this study, subjects on three separate occasions 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 concentration 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 congestive heart failure and cardiovascular disease as 

a whole (which includes IHD), although this evidence is not as consistent among 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 the electrocardiogram, depression of which is an indication of insufficient 

oxygen supply to the heart muscle tissue 

18 As stated in the ISA, the gas chromatographic technique for measuring COHb levels “is known to be 

more accurate than spectrophotometric measurements, particularly for samples 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 are reported to overestimate COHb 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 percent change in time to onset of angina and time 

to ST endpoint between the first and second exercise tests was determined. 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%19 were shown to decrease the 

time required to induce ST-segment changes by 5.1% (p=0.01) and 12.1% (p<0.001), 

respectively. These changes were well correlated with the onset of exercise-induced angina the 

time to which was shortened by 4.2% (p=0.027) and 7.1% (p=0.002), respectively, for the two 

CO exposures (ISA, section 5.2.4; Allred et al., 1989a, 1989b, 1991). 

No human clinical studies have been specifically designed to evaluate 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 the 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 the authors, the results comparing “the effects 

of increasing COHb from baseline levels (0.6%) to 2 and 3.9% COHb showed that each 

produced further changes in objective ECG measures of ischemia” implying that “small 

increments in COHb could adversely affect myocardial function and produce 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 angina 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 (Adams et al., 1988; Anderson et al., 1973; 

Kleinman et al., 1989, 1998) involving individuals with stable angina have confirmed the Allred 

et al. findings at COHb concentrations between 3 and 6% (as measured by CO-oximeter) (ISA, 

section 5.2.4). Among the evidence is also a study 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 subsequent study conducted by the same laboratory 

reported a significant increase in number of ventricular arrhythmias 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 clear 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 informative pooled or quantitative 

meta-analysis of the dose-response relationship across studies (ISA, section 5.2.4). Although the 

subjects evaluated in the controlled human exposure studies described above are not necessarily 

representative of the most sensitive population, the level of disease in these individuals ranged 

from moderate to severe, with the majority either having a history of myocardial infarction or 

having ≥ 70% occlusion of one or more of the coronary arteries (ISA, p. 5-43). 

We also note that, in the current review, a number of epidemiological studies are now 

available that investigate associations 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, sections 5.2.1.9). In considering the epidemiological 

evidence in the case of CO, we recognize 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 cardiovascular system (primarily for ischemia-related events). As discussed 

in the ISA, the epidemiological studies reported associations of CO concentrations at ambient 

monitors with emergency department visits and/or hospital admissions for IHD and other 

cardiovascular disease-related outcomes that are plausibly related to the effects on physiological 

indicators of myocardial ischemia (e.g., ST-segment 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 O2 availability lends 

biological plausibility to ischemia related health 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 relationships 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 central nervous system 

(CNS) function”, although the evidence does not include such effects associated with exposures 

close to ambient CO concentrations (ISA, p. 5-49). Further, the evidence indicates that healthy 

adults may be protected against such CNS effects at ambient levels through compensatory 

responses such as increased cardiac output and cerebral blood flow, although these compensatory 

mechanisms may be impaired among certain groups, such as those with reduced cardiovascular 

function (ISA, section 5.3.3). 
2-15 .

Epidemiological and toxicological studies provide limited evidence of CO effects on the 

developing fetus and newborn infants, as summarized in section 2.4 above. For example, some 

epidemiological studies have reported associations of CO exposure during early pregnancy with 

pre-term births and cardiac birth defects, with animal toxicological studies providing some 

support and coherence for these effects at prolonged exposure concentrations ranging from 60-

500 ppm (ISA, section 5.4.3, pp. 5-80 and 5-120). The 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 fetal growth, and infant mortality; with animal 

toxicological studies providing support and coherence for those effects. A clear understanding of 

the mechanisms by which CO may induce those effects is still lacking (ISA, section 2.5.3). 

New epidemiologic studies report positive associations for CO-induced lung-related 

health outcomes, although interpretation is affected by uncertainties including with regard to the 

biological mechanism that could explain CO-induced 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 the risk of cardiovascular effects of exposures 

to ambient CO, we considered 1) approaches employed in previous assessments, 2) the currently 

available evidence regarding associations between CO concentrations and cardiovascular 

outcomes, and 3) advice from CASAC (Brain, 2009; Brain and Samet, 2009, 2010a, 2010b). As 

summarized in section 1.2, the last CO NAAQS review included analyses of exposure to ambient 

CO and associated internal dose, in terms of COHb levels, which were used to characterize risks 

for the population of interest (US EPA, 1992). The prior risk characterization considered the 

percent of the modeled population that exceeded COHb levels of interest which were drawn from 

the evidence of COHb levels associated with a decrease in time to exercise-induced angina in 

controlled human exposure studies involving short-term (shorter than 8 hours) exposures of 

patients with diagnosed CAD20 to elevated CO concentrations (US EPA, 1991). 

In the current review, the controlled human 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 quantitative information linking CO exposures through COHb levels with 

 

20 Study subjects met certain criteria with respect to evidence of coronary artery disease, often also called 

CHD or IHD. 
2-16 .

these effects. Among these studies, the multilaboratory study of Allred et al. (1989a, 1989b, 

1991) continues to be the principal study informing 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 more broadly continues to support the use of 

COHb level as the internal dose 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 associations between ambient monitor CO concentrations 

and increases in emergency department visits and hospital admissions for cardiovascular disease 

(ISA, sections 5.2.1.9). These studies are coherent with the controlled human exposure studies 

(ISA, section 5.2.6), however, a number 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 Assessment. Accordingly, in light of the 

longstanding body of evidence that links exposures to effects through the internal dose metric, 

COHb, we have characterized health risk of ambient CO exposures 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 controlled human exposure studies to inform the 

characterization of potential CO risk in this assessment, 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 electrocardiogram and to exercise-induced onset of 

angina, were observed in response to CO exposures involving subjects with preexisting CAD. Staff gives primary focus here 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 resulted in a mean level of 0.6% COHb (postexercise), exposures to CO resulting in post-exercise mean COHb levels of 2.0% and 

 

21 While not used for the purposes of this quantitative 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 required to induce ST-segment changes by 

5.1% (p=0.01) and 12.1% (p<0.001), respectively. These changes were well correlated 

with the onset of exercise-induced angina, the 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 from baseline mean levels of 0.6% to 2 and 

3.9% COHb showing changes in the monitored 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 angina and 3.9% in time to exercise-induced 

ST-segment change per 1% increase in COHb concentration in persons with preexisting CAD (ISA, section 5.2.4; Allred et al., 1989a, 1989b, 1991). 

 Studies have not been designed to evaluate similar effects of exposures to increased 

CO concentrations eliciting average COHb levels below the 2% target level of Allred 

et al. (1989a, 1989b, 1991). In addition, these studies 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 conclusion that at this time there are insufficient 

controlled human exposure data to support the development of quantitative doseresponse relationships which would be required in order to conduct a quantitative risk 

assessment for this health endpoint, rather than the benchmark level approach. 

In drawing on this information, staff recognize the uncertainty associated with 

interpretation of COHb levels estimated to result from CO exposure concentrations in this 

assessment that are much lower than the CO exposure 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 levels, which represent the combined influence of 

ambient CO exposures and endogenous CO production, and the ambient CO contribution to 

COHb levels, derived by subtracting the COHb produced in the absence of any CO exposure 

from the total COHb level (see section 6.2 below). Results from the model simulations are 

reported in terms of percent of population expected to experience 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 potential health effects 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 average 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 identified benchmark levels of 1.5%, 2.0%, 2.5% and 

3% COHb based on consideration of the evidence from controlled human studies of CHD 

patients discussed above, and is inclusive of the range of levels considered in the review 

completed in 1994 (US EPA, 1992). This range extends below the lowest mean COHb level 

(e.g., 2.0% post-exercise in Allred et al., 1989b) resulting from controlled 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 actual 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 effects of controlled short-term CO exposures resulting in COHb 

levels below study mean 2.0-2.4% and the lack of evidence 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 results for this COHb level in this REA. In 

considering this advice, we recognize, however, that a level of 1% COHb overlaps with the 

upper part of the range of endogenous levels in health individuals as characterized in the ISA 

(ISA, p. 2-6) and with the upper part of the range of baseline COHb levels in the study by Allred 

et al. (1989b, Appendix B). As a result, while noting population dose estimates in relation to this 

level, we have not placed weight on this level 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 response to specific increases in COHb level over the subjects’ preexposure or air exposure, with the increased COHb resulting from short-term controlled CO 

exposure exposures of persons with pre-existing 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 contribution 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 effects benchmarks, which for this ambient 

contribution (or increment) to daily maximum end-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), 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), respectively, for the two CO exposures. A dose-response analysis in which the individual 

regressions of study subject responses at baseline COHb and at the two increased 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 concentration 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 interpret 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 cardiovascular 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 expected 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 scenarios employed to inform the current 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 implications 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 reactions in the atmosphere, with on-road 

mobile sources representing significant sources of CO to ambient air. 

 Microenvironments influenced by on-road mobile 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 potential for health effects of concern. 

 An individual’s COHb levels reflect their endogenous CO production, as well as CO 

taken into the body during exposure to ambient 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 where 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 the population with greatest susceptibility to short-term 

exposure to CO, and the population for which the current evidence indicates health 

effects occurring at the lowest exposures. The evidence further indicates a potential for 

other underlying cardiovascular conditions, particularly other types of heart disease, to 
2-20 .

contribute susceptibility to CO effects. Other populations potentially at risk include 

those with diseases such as chronic obstructive 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 include effects on the central nervous system, 

reproduction and prenatal development, 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, specific changes to the ST-segment of an 

electrocardiogram. 

 Risk is characterized in this REA through evaluation of COHb estimated in simulations 

involving ambient CO exposures experienced by two target populations: (1) 

individuals with CHD (including 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 terms of percent of the simulated at-risk 

population expected to experience 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 potential health effects benchmarks. 

 For daily maximum end-of-hour COHb levels (absolute), these benchmarks 

range from 1.5%, which is below the lowest study mean COHb level resulting 

from experimental CO exposure in controlled 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 include the range from 1.4% up to 2.4%, 

which are the COHb increments associated with effects in those studies. 

 Beyond the at-risk populations and myocardial ischemia-related effects that are the 

focus of this quantitative REA, the current evidence regarding other potentially 

susceptible populations and other health effects 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'Neil 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). Acute effects of carbon monoxide exposure on individuals with coronary 

artery disease. Cambridge, MA: Health Effects Institute; research 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 carbon 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/downloadable/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 Administrator Lisa Jackson: Clean Air Scientific Advisory 

Committee’s (CASAC) Peer Review of the Agency’s 1st Draft Carbon Monoxide Integrated Science 

Assessment. EPA-CASAC-09-011. June 24, 2009. 

Brain JD and Samet JM. (2010a). Letter from Drs. J.D. Brain 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. Brain 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. 

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 NO2 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 monoxide 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, Vandagriff RB, Caiozzo VJ, Whittenberger JL. (1989). Effects of short-term 

exposure to carbon monoxide in subjects 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 European 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 function in patients with ischemic heart disease. Arch 

Environ Health. 42:108-116. 

Sheps DS, Herbst MC, Hinderliter AL, Adams KF, Ekelund LG, O'Neil JJ, Goldstein GM, Bromberg PA, Dalton 

JL, Ballenger MN, 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 Rules, South Coast Air Quality Management District. 

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. 
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 Monoxide. National Center for Environmental Assessment, 

Office of Research and Development, U.S. Environmental Protection Agency, 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 Air Quality Planning and Standards, 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/standards/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 Quality 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 estimate exposure concentrations. How well the ambient air quality is 

represented in a particular location is dependent on a number of factors including the ambient 

monitoring network design relative to the spatial and temporal characteristics of the pollutant as 

well understanding the concentration contribution from important local source emissions. This 

chapter summarizes findings about the current air quality conditions and their temporal and 

spatial distribution, with particular focus on aspects informative to the design and conduct of this 

assessment and including descriptions of CO measurement 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 quality/study locations most 

useful in meeting the objectives of the REA. Finally, key observations 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 detection issues (section 3.1.2). Ambient CO 

concentrations and their spatial and temporal variability are characterized 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 anthropogenic emissions in 

continental North America are presented in section 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 comparison to the NAAQS is the State and Local Air 

Monitoring Stations (SLAMS) network. The subsections below provide specific information 

regarding the methods used for obtaining ambient 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, SO2, NO2, and Pb) where the 

current measured levels were all well below the applicable NAAQS. CO monitoring activities 
 3-2

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 represent one of the following spatial scales.1

 Microscale: Data represent concentrations within a 100 meter (m) radius of the 

monitor. For CO, microscale monitors are sited 2 – 10 m from a roadway. 

Measurements are intended to represent the near-road or street canyon environment. 

 Middle scale: Data represent concentrations averaged over areas defined by 100 – 500 

m radii. Measurements are intended to represent several city blocks. 

 Neighborhood scale: Data represent concentrations averaged over areas defined by 0.5 

– 4.0 km radii. Measurements are intended to represent extended portions of a city. 

In addition to monitoring required for determining compliance with the NAAQS, the 

EPA is currently in the process of implementing plans for a new network of multi-pollutant 

stations, called NCore, that is intended to meet multiple monitoring objectives. A subset of the 

SLAMS network, these NCore stations are intended to address integrated air quality 

management needs to support long-term trends analysis, model evaluation, health and ecosystem 

studies, as well as the more traditional objectives of NAAQS compliance and Air Quality Index 

reporting.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 include additional pollutant and meteorological 

measurements. Each state will contain at least one NCore station, and 46 of the states plus 

Washington, D.C. will have at least one urban station. CO will be measured using high 

sensitivity monitors (see section 3.1.2 below), as will SO2, NO, and NOY.

3

 The majority of 

NCore stations will be sited to represent neighborhood, urban, and regional scales, consistent 

with the NCore network design objective of representing exposure expected across urban and 

rural areas in locations that are not dominated by local sources. 

3.1.2 Analytical Sensitivity 

To promote uniform enforcement of the air quality standards set forth under the CAA, 

EPA has established provisions in the Code of Federal Regulations (CFR) under which analytical 

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 NO2 and SO2 follow the same general spatial scales. 

2

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

3

 NCore sites must measure, at a minimum, PM2.5 particle mass using continuous and integrated/filter-based 

samplers, speciated PM2.5, PM10-2.5 particle mass, speciated PM10–2.5, O3, SO2, CO, NO/NOY, wind speed, wind 

direction, relative humidity, and ambient temperature (http://www.epa.gov/ttn/amtic/ncore/index.html). 
 3-3

FEMs.4 Specifications for CO monitoring are designed to help states utilize equipment that has 

met performance criteria utilized in the FRM or FEM approval process; operational parameters 

are documented in 40 CFR Part 53, Table B-1. Given the levels of the current CO NAAQS (35 

ppm, 1-hour; 9 ppm, 8-hour average), the required 1.0 ppm lower detectable limit (LDL)5

 is well 

below the NAAQS levels and is therefore determined 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 distribution of monitoring data because a large 

percentage of these measurements are below the LDL of conventional monitors. For this reason, 

a new generation of ambient CO monitors has been designed that provides measurements with 

improved sensitivity at or below the typical ambient CO levels measured in most urban and all 

rural locations. Additionally, the higher sensitivity CO measurements are needed to support 

additional objectives such as validating the inputs to chemical transport models and assessing the 

role of transport between urban and rural areas because policy relevant background CO 

concentrations on the order of 0.1 ppm are well below the LDL of conventional monitors. 

Newer GFC instruments have been designed for automatic zeroing to minimize drift (US EPA, 

2000). 

Currently, a total of 13 approved 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 sensitivity 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 (GFC) high sensitivity instruments, manufacturerdeclared LDLs range from 0.02 – 0.04 ppm, with 24-hour zero drift varying between 0.5% 

 

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 include the gas filter correlation 

(GFC) methodology. An extensive and comprehensive review of NDIR, GFC, and alternative, non-FRM techniques 

for CO detection was included in the 2000 CO AQCD (US EPA, 2000). 

5

 Defined in 40 CFR Part 53.23 as the minimum pollutant concentration which produces 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 greater 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

within 1 ppm and 0.1 ppm, and precision varying from 0.5% to 0.1 ppm. EPA performed MDL 

testing on several high sensitivity CO monitors in 2005 and 2006 following the 40 CFR Part 136 

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 sensitivity CO monitors have reported data with the majority of these monitors 

currently active. Most of these active monitors are associated with the implementation of the 

NCore network. The extent to which high sensitivity 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 monitoring requirements for CO 

might either encourage or require increased sensitivity. 

3.1.3 General Patterns of CO Concentrations 

As discussed in the ISA, the spatial and temporal patterns of ambient CO concentrations 

are heavily influenced by the patterns associated with mobile source emissions (ISA, section 

3.2.1). Based on the 2002 National Emissions Inventory (NEI; US EPA, 2006), on-road mobile 

sources comprise about half of the total anthropogenic 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 (ISA, section 3.2). When considering all mobile 

sources (non-road and on-road combined), the contribution to total CO emissions is roughly 80% 

nationwide and can be higher in some metropolitan 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 national-scale anthropogenic CO emissions have decreased 35% 

between 1990 and 2002. Nearly all the national-level CO reductions since 1990 are the result of 

emission reductions in on-road vehicles (ISA, Figure 3-2). 

Nearly 400 ambient monitoring stations report continuous hourly averages of CO 

concentrations across the US. Over the period 2005-2007, 291 out of 376 monitors met a 75% 

completeness requirement, spread among 243 counties, cities, or municipalities (ISA, section 

3.4.2.2). No violations of the NAAQS were reported at these monitoring sites during this time 

period. For example, in 2007, none of the monitors reported a second-highest 1-hour CO 

concentration above 35 ppm, the level of the current 1-hour NAAQS, while only two sites 

reported 2nd highest 1-hour CO concentrations between 15.1 and 35.0 ppm (ISA section 3.5.1.1). 

Only five counties reported a 2nd highest 8-hour CO concentration 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 
 3-5

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 Angeles ambient concentrations, for 

several selected monitors within the urban core of each area during 1993 through 2008. Note 

that there is a significant decrease in the 2nd 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 monitors 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 data can be a useful indicator of near-road air 

quality. Such data analyzed in the ISA were 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 across 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 neighborhood scale monitors (ISA, Table 3-12). In 

general, similar patterns were present in the 1-hour daily max, 1-hour daily average, and 8-hour 

daily max distributions (ISA, Table 3-12). These patterns are also consistent with findings 

presented by other researchers regarding the relative 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; Baldauf 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 vehicle density (ISA, Figures 3-29 and 3-30). 
3-6

Figure 3-1. Spatial and temporal trends in the 2nd highest 1-hour (top) and 8-hour average (bottom) CO 

concentrations in Denver, Colorado (left) and Los Angeles, California (right), Years 1993 – 2

02468

10121416182022

2nd Highest 1-hour Average CO (ppm)

080310002080310013080310014080590002

Denver Los Angeles 

02468

10121416

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Year

2nd Highest 8-hour Average CO (ppm)

080310002080310013080310014080590002

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year
3-7

While recognizing that monitoring site attributes are not available for all monitors in the 

current network and that data for some attributes may not reflect 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 monitors 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 several roads with AADT > 100,000. The 

AADT ranged from 160,000-178,000 for the near-road monitors used in the aforementioned 

study by Zhu et al. (2002) where CO concentrations were up to 10 times greater than monitors 

sited at 300 m from a major road.9 Existing microscale sites near roads having only moderate 

traffic count data (<100,000 AADT) may record concentrations that are not substantially 

different from those obtained from neighborhood scale measurements (ISA, section 3.5.1.3). 

Within a specific urban area, however, consideration of only monitor scale or other 

attributes reported in AQS, such as AADT estimates, may be of limited use in efforts to 

characterize the monitoring data as to its representation of local near-road CO concentrations. 

For example, of the five monitors meeting a 75% completeness criterion 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 Denver measured the highest hourly ambient CO 

concentrations (ID 080310002), another microscale 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 microscale monitor within the urban core (ID 

080310002, AADT=17,200) was lower than that listed for the microscale monitor outside the 

urban core (ID 080130009, AADT=20,000) (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 likely that the higher CO concentrations measured 

at the downtown monitor reflect influences of the denser roadway network surrounding that 

monitor in the downtown Denver area (ISA, Figure 3-17).10

Thus, to better characterize the representation of near-road CO concentrations for many 

of the existing ambient monitors, additional analyses would need to be performed that go beyond 

 

8

 Recorded AQS monitoring site attributes are not always available for each monitor or may not always 

reflect potential source influences. For example, of 24 CO monitors in the Los Angeles CSA, AQS had no 

information regarding 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 well the AQS estimated AADT reflects current conditions 

at this monitor site. 
3-8

the AQS standard list of monitoring site attributes. Such analyses could include local-scale 

meteorological data, using GIS to determine detailed 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 traffic count data for all roads. 

Carbon monoxide also exhibits hourly 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-of-day that occur within urban locations. In 

general, in recent years observed mean and median concentrations for all hours of the day and 

across all monitors within urban areas demonstrated limited variability, however 90th and 95th

percentile hourly concentrations generally exhibit early-morning 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 occurring during weekdays as expected due to the 

relative absence of commuter vehicle 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 few locations (e.g., Phoenix, Los Angeles, Seattle) did have a more 

pronounced late afternoon peak (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 highest ambient CO concentrations 

in the Denver area. The monitor in Los Angeles is a middle scale monitor located in Lynwood; 

it is also the monitor measuring the highest ambient CO concentrations in the Los Angeles area. 

Figure 3-2 indicates that on average, peak ambient CO concentrations that occur during typical 

commute times in Denver ranged from about 1 to 5 ppm during weekdays in 1995, while 

currently, ambient CO concentrations during morning and afternoon commutes range from about 

1 to 2 ppm. Weekends tend to exhibit less variability throughout the day. On average, CO 

ambient concentrations generally ranged from 1 to 3 ppm throughout the day in 1995, while 

current weekend concentrations are less than 1 ppm 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 ranged from 2 to 10 ppm in 1995, while when 

considering more recent ambient monitor concentration (2006), most commuting times are 

associated with hourly concentrations ranging from 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. 
3-9

Figure 3-2. Diurnal distribution of 1-hour CO concentrations in Denver (Monitor 080310002) by day-typ

weekends-right), Years 1995 (top) and 2006 (bottom). The box encompasses concentrations fro

percentiles or Interquartile range (IQR), the line bisecting the box is the median, the solid dot withmean, the whiskers represent 1.5 times the IQR, and concentrations outside the whiskers are indica

Note there are differences in the y-axis scale for the two monitoring years. 
3-10

Figure 3-3. Diurnal distribution of 1-hour CO concentrations in Los Angeles (Monitor 060371301) by da

left; weekends-right), years 1997 (top) and 2006 (bottom). The box encompasses concentration

percentiles or IQR, the line bisecting the box is the median, the solid dot within the box is the meanrepresent 1.5 times the IQR, and concentrations outside the whiskers are indicated by open circles. differences 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 assessments that focus on the risks associated 

with ambient levels of a pollutant that are in excess of policy-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 anthropogenic emissions in the US, 

Canada, and Mexico. 

Over the continental US (CONUS), the 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 Alaskan sites is estimated to range from 0.127 to 

0.135 ppm, and from 0.095 to 0.103 ppm in two Hawaiian monitoring locations. The estimated 

PRB concentrations exhibit significant within-location seasonal variation, with minimum 

concentrations observed in the summer and fall and maximum concentrations occurring in the 

winter and spring. For example, PRB in two California 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 concentrations of interest in this REA are well above the estimated 

PRB levels discussed above and, thus the contribution of PRB to overall ambient CO 

concentrations is very small, EPA is characterizing risks associated with 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 consideration in determining how best to address alternative air quality 

conditions. These alternative air quality conditions are useful in evaluating how varying 

distributions of air quality might affect different exposure scenarios. In other recent NAAQS 

reviews for NO2 (US EPA, 2008) and SO2 (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 concentrations across the entire 

distribution at a single monitor changed in equivalent amounts over time. We needed the 

relationship to adjust current air quality because, at the time of the NAAQS reviews, the current 

ambient NO2 and SO2 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 for use in some of the exposure scenarios investigated in this 

REA. Ambient CO concentration data were obtained from AQS for several monitors in Los 
3-12

Angeles for two years: 1997 – representing a high concentration year and 2006 – representing a 

low concentration year. In Denver, the year 1995 was selected to represent a high concentration 

year, while 2006 was selected to represent a low 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 100th percentiles of the distribution were calculated (by 1% increments). Then the 

percentiles for the low concentration year were paired and plotted against those calculated for the 

high concentration year at each individual monitor. 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 calculated and plotted, along with the regression 

slope, intercept, and fit statistic (R2

). As shown by the relationships and fit statistics in each 

location, there is a very strong linear relationship when comparing each year of data within each 

monitor. In general, the regression slopes and intercepts are similar for monitors within each 

location, indicating a similarity in the rate of change in concentration 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 daily 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 addition, many of the estimated 

regression intercepts are positive, though most are ≤ 0.1 ppm. A positive intercept also indicates 

a larger percentage decline between high and low year concentrations in the upper end of the 

distribution relative to that of the middle and lower percentiles. However, given that there are a 

limited number of points deviating from linearity and that regression slopes and intercepts are 

similar for most of the monitors within each location and having mainly small intercepts, this 

analysis provides adequate support for adjusting 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. 
3-13

Figure 3-4. Comparison of a high concentration year (1995) versus a low concentration year (2006) at fou

in Denver. The 0 through 100th percentiles of the 1-hour daily maximum CO are plotted for 

080310002

y = 0.2846x + 0.2799

R2 = 0.9761

012345678

0 5 10 15 20 25

1995 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm)

(0-100 percentile) 

080310013

012345

0 3 6 

1995 Daily Maximum CO (p

2006 Daily Maximum CO (ppm)

(0-100 percentile) 

080310014

y = 0.3487x + 0.1085

R2 = 0.9932

01234

0 2 4 6 8 10 12

1995 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm)

(0-100 percentile) 

080590002

01234

0 2 4 6 

1995 Daily Maximum CO (p

2006 Daily Maximum CO (ppm)

(0-100 percentile) 
3-14

Figure 3-5. Comparison of a high concentration year (1997) versus a low concentration year (2006) at fou

in Los Angeles. The 0 through 100th percentiles of the 1-hour daily maximum CO are plotted 

year.

060370113

y = 0.4105x + 0.143

R2 = 0.992

01234

012345678

1997 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm)

(0-100 percentile) 

060371103

y = 

01234

0 2 4 6 

1997 Daily Maximum CO (ppm, 0-100 percentil

2006 Daily Maximum CO (ppm)

(0-100 percentile) 

060371301

y = 0.4106x + 0.1019

R2 = 0.9945

02468

10

0 5 10 15 20

1997 Daily Maximum CO (ppm, 0-100 percentile)

2006 Daily Maximum CO (ppm)

(0-100 percentile) 

060591003

y = 0

01234

0123456

1997 Daily Maximum CO (ppm, 0-100 percenti

2006 Daily Maximum CO (ppm)

(0-100 percentile) 
3-15

We were also interested in estimating the within-monitor temporal variability for three air 

quality metrics. The first air quality metric was the current design value, that is, the 2nd highest 

8-hour average CO concentration in a year. The next two air quality metrics we compared were 

the 99th percentile 1-hour and 8-hour daily maximum CO concentrations. We evaluated the 

within-monitor temporal variability using two comparisons: one using historical versus current 

air quality data and the other comparing year-to-year variability of these upper percentile 

concentrations within the air quality distribution. Two three-year periods (1995-1997 and 2005-

2007) were chosen to represent historical and recent air quality, respectively. We limited the 

analysis to four monitors within the Denver CSA and ten monitors within the Los Angeles CSA, 

with all monitor data meeting standard requirements 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 the selected monitors in each area. 

Tables 3-1 and 3-2 provide results for the current design value in Denver and Los 

Angeles, respectively. As shown by the Tables, there is a wide range in the temporal variability 

of the 2nd highest 8-hour average CO concentration 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 historical 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 selected three-year periods in both Denver and Los 

Angeles (and of course is consistent with Figures 3-1 and 3-2), though this trend is more 

prevalent when considering the historical air quality data. In addition, the magnitude of the 

spatial variability tends to vary from year-to-year as indicated by the COV, though there are 

differences in the historical versus recent air quality pattern by location. In Denver, there was 

generally less spatial variability in the 2nd highest 8-hour concentration 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 99th percentile 1-hour daily maximum 

concentrations when comparing historical versus recent air quality (Tables 3-3 and 3-4 for 

Denver and Los Angeles, respectively). The temporal variability in the recent air quality data 

was also less than that of the prior air quality metric (i.e., the 2nd highest 8-hour average), 

averaging about 4% COV in Denver and 7% COV in Los Angeles across that 3-year period. The 

 

12 The COV is calculated here by dividing the standard deviation (std) by the mean, then multiplying by 

100. 
3-16

year-to-year spatial variability for this metric is also consistent with that stated above. In 

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 spatial variability in Los Angeles 

when considering the two air quality periods. Results for the 99th percentile 8-hour daily 

maximum concentrations were more similar to the results for the 2nd highest 8-hour average 

concentration than the 99th percentile 1-hour daily maximum (Tables 3-5 and 3-6, for Denver and 

Los Angeles, respectively).
3-17

Table 3-1. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) 

2nd highest 8-hour average. 

Historical Air Quality – 2nd highest 8-hour average Recent Air Quality – 2nd highest 8-ho

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 31-0002 9.5 7.3 5.5 7.4 2.0 27 2.6 3.1 2.8 2.8 31-0013 6.2 5.2 4.7 5.4 0.8 14 2.4 2.5 2.4 31-0014 5.9 5.7 6.2 5.9 0.2 4 2.1 3.0 2.5 59-0002 4.6 4.3 4.9 4.6 0.3 7 1.8 2.0 1.9 

mean 6.6 5.6 5.3 5.8 2.2 2.6 2.8 2.4 

std 2.1 1.3 0.6 1.2 0.3 0.5 0.4 

COV 32 22 12 20 14 19 16 

Table 3-2. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (200

data - 2nd highest 8-hour average. 

Historical Air Quality – 2nd highest 8-hour average Recent Air Quality – 2nd highest 8-ho

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 37-0113 9.4 8.5 4.1 7.3 2.8 39 1.9 1.9 1.6 1.8 37-1002 10.9 8.5 7.2 8.9 1.9 21 3.2 3.4 2.7 3.1 37-1103 7.9 7.5 5.9 7.1 1.1 15 2.6 2.5 2.1 2.4 37-1201 9.4 6.7 7.7 7.9 1.3 17 3.4 3.4 2.7 3.2 37-1301 11.7 14.3 15.0 13.6 1.7 13 5.6 5.6 4.9 5.3 37-2005 8.6 6.9 5.4 7.0 1.6 23 2.8 2.7 2.2 2.6 37-4002 6.3 6.2 6.4 6.3 0.1 2 2.9 3.3 2.5 2.9 

59-0001/7 7.3 6.1 5.4 6.3 1.0 16 3.1 2.9 2.3 2.8 

59-1003 5.3 6.5 5.0 5.6 0.8 14 3.1 2.5 2.5 2.7 59-5001 6.4 6.3 5.7 6.1 0.4 6 2.9 2.9 2.5 2.8 

mean 8.3 7.7 6.8 7.6 3.1 3.1 2.6 3.0 

std 2.1 2.5 3.1 2.3 0.9 1.0 0.9 0.9 

COV 25 32 45 31 30 32 33 31 
3-18

Table 3-3. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) 

99th percentile 1-hour daily maximum. 

Historical Air Quality – 99th percentile 1-hour daily maximum Recent Air Quality – 99th percentile 1-hou

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 31-0002 13.5 13.4 9.1 12.0 2.5 21 3.8 4.5 4.4 4.2 31-0013 11.1 9.0 8.6 9.6 1.3 14 3.5 3.7 3.6 31-0014 8.2 7.3 7.8 7.8 0.5 6 3.3 3.2 3.2 59-0002 8.6 6.8 7.2 7.5 0.9 12 3.4 3.4 3.4 

mean 10.4 9.1 8.2 9.2 3.5 3.7 4.4 3.6 

std 2.5 3.0 0.9 2.1 0.2 0.6 0.4 

COV 24 32 10 22 6 16 12 

Table 3-4. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (200

data – 99th percentile 1-hour daily maximum. 

Historical Air Quality – 99th percentile 1-hour daily maximum Recent Air Quality – 99th percentile 1-hou

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 37-0113 13.9 7.5 6.1 9.2 4.2 45 2.6 2.7 2.1 2.5 37-1002 11.6 9.7 8.2 9.9 1.7 17 3.9 4.1 3.6 3.9 37-1103 9.0 9.4 7.4 8.6 1.1 13 3.1 2.9 2.6 2.9 37-1201 10.6 8.4 8.4 9.1 1.3 14 4.0 3.9 3.4 3.8 37-1301 16.2 20.2 18.5 18.3 2.0 11 7.1 7.4 6.8 7.1 37-2005 10.3 8.8 6.2 8.4 2.0 24 3.4 3.3 3.0 3.2 37-4002 7.6 8.4 7.6 7.9 0.5 6 3.8 3.8 3.1 3.6 

59-0001/7 9.1 8.2 7.7 8.4 0.7 9 3.6 3.6 3.2 3.5 

59-1003 7.3 8.4 6.9 7.5 0.8 11 3.6 3.2 3.2 3.3 59-5001 10.7 11.6 10.3 10.9 0.7 6 5.2 5.4 5.2 5.3 

mean 10.6 10.1 8.7 9.8 4.0 4.0 3.6 3.9 

std 2.8 3.7 3.6 3.1 1.3 1.4 1.4 1.3 

COV 26 37 42 32 32 35 38 34 
3-19

Table 3-5. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) 

99th percentile 8-hour daily maximum. 

Historical Air Quality – 99th percentile 8-hour daily maximum Recent Air Quality – 99th percentile 8-hou

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 31-0002 7.3 7.2 5.2 6.6 1.2 18 2.4 2.8 2.7 2.6 31-0013 5.4 5.2 4.7 5.1 0.4 7 2.2 2.1 2.2 31-0014 5.7 5.5 5.8 5.7 0.1 2 2.1 2.8 2.4 59-0002 4.1 3.8 4.8 4.2 0.5 12 1.8 1.8 1.8 

mean 5.6 5.4 5.1 5.4 2.1 2.4 2.7 2.3 

std 1.3 1.4 0.5 1.0 0.3 0.5 0.4 

COV 24 26 10 18 12 21 16 

Table 3-6. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (200

data – 99th percentile 8-hour daily maximum. 

Historical Air Quality – 99th percentile 8-hour daily maximum Recent Air Quality – 99th percentile 8-hou

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 37-0113 8.6 5.2 3.7 5.8 2.5 43 1.9 1.8 1.5 1.8 37-1002 9.7 8.3 6.8 8.3 1.4 17 3.0 3.2 2.6 3.0 37-1103 7.5 7.0 5.6 6.7 1.0 15 2.6 2.4 2.0 2.4 37-1201 9.0 6.7 7.3 7.6 1.2 15 3.2 3.1 2.6 2.9 37-1301 11.2 13.9 13.1 12.7 1.4 11 4.9 5.1 4.5 4.8 37-2005 8.5 6.8 5.0 6.7 1.8 26 2.8 2.6 2.1 2.5 37-4002 5.9 6.2 5.9 6.0 0.2 3 2.9 2.7 2.4 2.7 59-0001 6.5 5.7 5.1 5.8 0.7 12 2.7 2.7 2.1 2.5 59-1003 4.7 6.4 4.9 5.3 0.9 17 3.0 2.2 2.4 2.6 59-5001 6.3 5.9 5.3 5.8 0.5 9 2.6 2.7 2.5 2.6 

mean 7.8 7.2 6.3 7.1 3.0 2.9 2.5 2.8 

std 2.0 2.5 2.6 2.2 0.8 0.9 0.8 0.8 

COV 25 35 42 31 25 31 31 29 
3-20

3.2 STUDY AREAS SELECTED FOR CURRENT CO REA 

We identified several criteria to select the exposure assessment study areas drawing from 

information discussed in the earlier 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 susceptibility 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 criteria, the two locations were ranked 1st (Los Angeles) 

and 2nd (Denver) regarding percent of elderly population within 5, 10, and 15 km of monitor 

locations, and ranked 1st (Los Angeles) and 5th (Denver) regarding number 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 resulting from the air quality considerations. 

 Mobile sources (i.e., gasoline powered vehicles) are the primary contributor to CO 

emissions, particularly in urban areas due to greater vehicle and roadway densities. 

 Recent (2005-2007) ambient CO concentrations across the 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 lower detectable limit of 1 ppm. 

 The currently available information for CO monitors indicates that siting of microscale 

and middle scale monitors in the current network is primarily associated with roads 

having moderate traffic density (<100,000 AADT), however, factors other than 

reported AADT (e.g., orientation with regard to dense urban roadway networks) can 

contribute to sites reporting higher CO concentrations. 

 Ambient CO concentrations are highest at monitors sited closest to roadways (i.e., 

microscale and middle scale monitors) and exhibit 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) concentrations 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 distributions with concentrations at or above the current 8-hour 

standard and those reflecting current (as is) conditions. 
3-21

 The temporal variability in selected upper percentile ambient concentrations (e.g., 99th

percentile 1-hour daily maximum) at individual monitors in Denver and Los Angeles is 

relatively small across a three-year monitoring 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 selected upper percentile ambient concentrations 

(e.g., 99th 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: Summary 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, Dayton 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 Monoxide. National Center for Environmental Assessment, 

Office of Research and Development, U.S. Environmental Protection Agency, 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 Air Quality Planning and Standards, 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 NO2 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 SO2 Primary National Ambient Air 

Quality Standard. EPA-452/R-09-007. August 2009. Available at: 

http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf. 

US EPA. (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/standards/co/s_co_cr_isa.html. 

Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C. (2002). Study of ultrafine particles near a major highway with 

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 description 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 (APEX), 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 reviews (section 4.3). Section 4.4 provides a 

generalized description of the APEX simulation process, though having detailed focus on a few 

of the important approaches used for modeling CO exposure and COHb dose. This includes 

expanded discussion on the approach used to estimate microenvironmental concentrations 

(section 4.4.4) and COHb dose 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 pollutants at the local, urban, and 

consolidated metropolitan levels. APEX, also known as TRIM.Expo, is the human inhalation 

exposure module of EPA’s Total Risk Integrated Methodology (TRIM) model framework (US 

EPA, 1999), a modeling system with multimedia capabilities for assessing human health and 

ecological risks from hazardous and criteria air pollutants.1

APEX estimates human exposure using a 

stochastic, microenvironmental approach (see caption). 

The model randomly selects data for a sample of 

hypothetical individuals from 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 her exposure to a pollutant. 

APEX can account for travel to and from work locations 

(i.e., commuting) and provide estimates of exposures at 

both home and work locations for individuals who work away from home. 

 

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 microenvironment is 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 Ambient Air Quality Standards (NAAQS) 

Exposure Model (NEM) series of models. The NEM series was developed to estimate 

population exposures to the criteria pollutants (e.g., CO, ozone). In 1988, OAQPS first 

incorporated probabilistic elements into the NEM methodology and used activity pattern data 

based on available human activity diary studies to create an early version of probabilistic NEM 

for ozone (i.e., pNEM/O3). In 1991, a probabilistic version of NEM was developed for CO 

(pNEM/CO) that included a one-compartment mass-balance model to estimate CO 

concentrations in indoor microenvironments. The 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 Activities Database (CHAD) (McCurdy et al., 2000; 

US EPA, 2002) and enhanced algorithms for simulating gas stove usage, estimating alveolar 

ventilation rate (a measure of human respiration), and modeling home-to-work commuting 

patterns. A draft report by Johnson et al. (2000) describes the 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 approach rather than the 

previously used cohort simulation approach. APEX3 introduced a number of new features 

including automatic site selection from national databases, a series of new output tables 

providing summary exposure and dose statistics, and a thoroughly reorganized method of 

describing microenvironments and their variable parameters. Johnson 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 chapter 5 of this document. This version was 

also recently used to estimate O3 exposures in 12 urban areas for the O3 NAAQS review (US 

EPA, 2007), to estimate population exposures to nitrogen dioxide (NO2) in Atlanta as part of the 

NO2 NAAQS review (US EPA, 2008c), and to estimate sulfur dioxide (SO2) exposures for 

asthmatics and asthmatic children in two study areas in Missouri as part of the SO2 NAAQS 

review (US EPA, 2009a). There have been several recent enhancements to APEX since the prior 

1994 CO NAAQS review, including: 

 Algorithms for the assembly of multi-day (longitudinal) activity diaries that model intraindividual variance, inter-individual variance, and 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 exposure statistics for population groups and lifestages such as children and active people at varying ventilation rates; 

 The inclusion of tract-level commuting 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 criteria and air toxic pollutants 

at local, urban, and regional scales. The user specifies the geographic area to be modeled and the 

number of individuals to be simulated to represent a population of interest. APEX4.3 then 

generates a personal profile for each simulated person that specifies various parameter values 

required by the model to estimate their exposure and dose. The model next uses diary-derived 

time/activity data matched to each personal profile 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 location, 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 ambient (outdoor) pollutant concentration, penetration 

factor, air exchange rate, decay/deposition rate, and proximity to emission sources, each 

depending on the microenvironment, available data, 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 distribution of modeled individual exposures can then be 

extrapolated to the larger 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 the 
 4-4

user-defined center and radius of the study area and availability of air quality and 

meteorological input data for 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 employment probabilities or disease prevalence) 

(section 4.4.2) 

 Construct activity sequences: APEX4.3 constructs an exposure event sequence (timelocation-activity pattern) spanning the simulation 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 database). The model then calculates timeaveraged concentrations (e.g., hourly) of each pollutant in each of the 

microenvironments for each simulated person for the period of simulation based on the 

user-provided ambient air quality data (section 4.4.4). 

 Estimate energy expenditure and ventilation rates: APEX4.3 constructs a timeseries of energy expenditures for each individual’s exposure profile based on the 

sequence of activities performed. The sequence of energy expenditures are adjusted to 

ensure that they are physiologically 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 the event and the person’s activity. These 

values are time-averaged (e.g., hourly) to produce a sequence of exposures spanning 

the specified exposure period (typically one year). The hourly values may be further 

aggregated to produce 8-hour, daily, monthly, and annual average exposure values 

(section 4.4.6). 

 Calculate dose: APEX4.3 optionally calculates hourly, daily, monthly, and annual 

average dose values for each of the simulated individuals. For the application of 

APEX to CO, a module within the model estimates the percent COHb level in the 

blood at the end of each hour based on the time-series of CO concentrations and 

alveolar ventilation rates experienced by the simulated person (section 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 diagram illustrating the implementation of APEX4.3 

to estimate CO exposure and dose. The following sections provide additional 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 applying APEX4.3 to CO for the current assessment 

are further described in section 5.1.
4-5

Air Exchange Rates

(AER) & Prevalence

Time

US Census

Tract-Level Population

& Commuting Data

Ambient Air Quality

1-Hour Ambient Monitor

CO Concentrations

Microenvironmental

Factors/Distributions

Exposure Algorithms

(mass balance or factors)

Dose Al

(CFK 

Outdoor CO Concentration

Algorithm

(spatial & temporal adjustment)

Persons & Person-Days

At or Above

Exposure Levels

Persons & P

At or A

Benchmark 

USDHHS/CDC

Disease Prevalence

Meteorological Data

Temperature

Energy Exp

Ventilation 

(alveolar res

Air Exchange Rates

(AER) & Prevalence

Time

US Census

Tract-Level Population

& Commuting Data

Ambient Air Quality

1-Hour Ambient Monitor

CO Concentrations

Microenvironmental

Factors/Distributions

Exposure Algorithms

(mass balance or factors)

Dose Al

(CFK 

Outdoor CO Concentration

Algorithm

(spatial & temporal adjustment)

Persons & Person-Days

At or Above

Exposure Levels

Persons & P

At or A

Benchmark 

USDHHS/CDC

Disease Prevalence

Meteorological Data

Temperature

Energy Exp

Ventilation 

(alveolar res

Figure 4-1. Conceptual model and simplified data flow for estimating population exposure and dose usin
 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 center (latitude/longitude) and radius of the study area. Then 

APEX4.3 calculates the distances to the center of the study area of all the sectors included in the 

sector location database and selects the sectors within the radius of the study area. APEX4.3 

then maps the user-provided air quality and meteorological data for specified monitoring districts 

to the selected sectors. The sectors identified as having acceptable air quality and meteorological 

data within the radius of the study area are selected 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-specified number of simulated persons to 

represent the population in the study area. Each simulated person 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 based on sets of distribution data (e.g., air 

conditioning prevalence); 

 Physiological variables that are generated based on age- and gender-specific distribution 

data (e.g., blood volume, body mass, resting metabolic rate); and 

 Daily varying variables that are generated based on distribution data that change daily 

during the simulation period (e.g., daily work status). 

APEX4.3 first selects and calculates demographic, residential, and physiological 

variables (except for daily values) for each of the user-specified number of simulated individuals. 

APEX4.3 then follows each simulated individual 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 detailed description can be found in section 5 of 

US EPA (2008b). An overview of the data sources 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, population counts by area and 
 4-7

employment probability estimates are used to develop representative profiles of hypothetical 

individuals for the simulation. 

APEX4.3 is flexible in the resolution of population data provided. As long as the data are 

available, any resolution can be used (e.g., county, census tract, census block). For this 

application of the model, census tract level data were used. Census tract level population counts 

are obtained from the 2000 Census of Population and 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 inputs, it is important to integrate working 

patterns into the assessment. 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/age 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 employment 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 Census Transportation Planning Package (CTPP) (US DOT, 2007). The 

data used contain counts of individuals 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 data 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 development 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 more widely separated flows would commute 

daily. This means that the list of destinations 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 relationship between commute flows and distance 

traveled up to 120 km. 

 Eliminated Records: Tract-to-tract pairs representing 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 workers) were eliminated from the 

database. An additional 515 workers in the commuting database whose data were 
 4-8

missing from the original files, possibly due to privacy concerns or errors, were also 

deleted. 

 Commuting outside the study area: APEX4.3 allows for some flexibility in the 

treatment of persons in the modeled population 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 input concentrations based 

on the available ambient concentration data within the model domain. For the second 

option (i.e., “KeepLeavers = No”) , people who work inside the study area but live 

outside of it are not modeled, 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 used to generate results for variables related to 

simulated individuals. While certain settings for individuals are generated automatically by 

APEX4.3 based on other input files, including demographic characteristics, 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 gender-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 maximal oxygen uptake, body mass, resting metabolic 

rate (RMR), and blood hemoglobin content. Appendix A provides an evaluation of a few 

important variables used by APEX4.3 in this exposure and dose assessment as well as their 

updated values or distributions (e.g., new age-gender body mass distributions derived from 1999-

2004 National Health and Nutrition Examination Survey data). Details regarding any other 

physiology variable distributions and their parameters not discussed 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 daily activities and use such data 

in the exposure model. EPA’s Consolidated Human Activity Database (CHAD) provides diaryderived data indicating where people spend time and the activities they perform at each location 

(US EPA, 2002). CHAD was designed to provide a basis for conducting multi-route, multimedia exposure assessments (McCurdy et al., 2000). The data contained within CHAD originate 
 4-9

from multiple activity pattern surveys with varied structures (Table 4-1), however the surveys 

have commonality in that they contain daily diaries of human activities performed, locations 

visited, and the personal attributes of survey participants (e.g., age and gender). 

There are four CHAD-related input files used 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 personal data file, and a 

metabolic information file, each of which are discussed briefly below. The fourth CHAD-related 

input file maps the five-digit 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 microenvironments 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 individual 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 Johnson (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 contained 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 week 
 4-11

 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 requested in survey) 

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

4.4.3.2 Diary Events File 

The human activity diary data are contained in the events file that is distributed with 

APEX4.3. This file contains the locations visited 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 individuals. 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 distributional forms and parameters for the activityspecific metabolic equivalents (METs) used to quantitatively assign exertion levels to each 

activity performed by simulated individuals (McCurdy, 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 values to simulate the variable nature of activity 

levels among different people. The CHAD User’s guide provides details on the distributions 

used, parameters, and sources for each activity (US EPA, 2002). 

4.4.3.4 Longitudinal Diary Processing 

APEX4.3 probabilistically creates a composite 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 with 
 4-12

APEX4.3 for this purpose. A composite diary is a sequence of events that simulates the 

movement of a modeled person through varying geographical locations and microenvironments 

for the duration of the simulation period. Each diary event is defined by geographic location, 

start time, duration, microenvironment visited, and an activity performed. 

The activity database input to APEX4.3 contains the following information for each diary 

day: age, gender, employment status, occupation, day-of-week (or day-type), and maximum 

hourly average temperature. This information enables APEX4.3 to select data from the activity 

database that tend to match the characteristics of the simulated person, the study area, and the 

specified time period. APEX4.3 develops a composite diary for each of the simulated 

individuals according to the following steps. 

 Divide diary days in the CHAD database into user-defined 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 the 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 diary 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 distribution of each specific activity. The METs 

values are used to calculate an activity-specific 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 diary days into a sequential longitudinal diary for a simulated 

individual covering all days in the simulated period. 

APEX4.3 provides an optional longitudinal diary-assembly algorithm that enables the 

user to create composite diaries that reflect the 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 particular microenvironment (e.g., in 

a motor vehicle). The D statistic reflects the relative importance 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 diaries 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 estimated pollutant concentrations account for the 

effects of ambient (outdoor) pollutant concentration, penetration 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 the 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 specific to each microenvironment. In typical 

applications, APEX4.3 calculates hourly concentrations in all the microenvironments at each 

hour of the simulation for each of the simulated individuals, based on the hourly ambient air 

quality data specific to the geographic locations visited by the individual. APEX4.3 provides 

two methods for calculating microenvironmental concentrations: the mass balance method and 

the transfer factors method (each are described briefly below). The user is required to specify a 

calculation method for each of the microenvironments; there are no restrictions on the method 

specified for each microenvironment (e.g., some microenvironments can use the mass balance 

method while the others can use the transfer factors 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 estimate 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 vicinity of each microenvironment 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 expected with distance from the ambient monitor 

location. There can be different ways to derive the ambient concentration 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 statistically-based approach that 

addresses the variability in concentrations in a similar manner as a dispersion model, only that 

important physical factors that influence concentration levels are represented by and/or possibly 

combined with a series of regression equation coefficients and are related to an ambient monitor 

CO concentration. Ultimately, it is this estimated outdoor CO concentration that is then used as 

input to the microenvironmental algorithm (either 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 vicinity of each microenvironment based on the 
 4-14

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 concentrations to observed concentrations at a fixedsite ambient monitor. The approach was developed 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 assessment (Johnson et al., 2000). This approach was 

proposed as a method to address spatial and temporal variability in outdoor and 

microenvironmental concentrations in the draft scope and methods plan (US EPA, 2009b), 

though not fully described there as is done here. 

To provide both historical perspective and context regarding the current application, this 

microenvironmental algorithm and the data that were used in the past with pNEM/CO to estimate 

values for the algorithm variables is described in section 4.4.4.3. The pNEM/CO approach was 

then adapted and implemented in APEX3.1, a model 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 assessment 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 microenvironment due to deposition, filtration, and/or 

chemical degradation; and 

 Emissions from sources of a pollutant inside the microenvironment. 

Table 4-2 lists the parameters required by the mass balance method to calculate 

concentrations in a microenvironment. The proximity factor (fproximity) is used to account for 

differences in ambient concentrations between the geographic location represented by the 

ambient air quality data (e.g., a fixed-site monitor) and the geographic location of the 

microenvironment (e.g., near a roadway). 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 resulting from the source (these are not 

used in the current assessment. The factor Rremoval is defined as the removal rate of a pollutant 
 4-15

from a microenvironment due to deposition, filtration, and chemical reaction. The air exchange 

rate (Rair exchange) is expressed in air changes per hour. 

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 Rremoval ≥ 0 

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

V Volume of 

microenvironment 

m3 V > 0 

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

differential equation 

 Cin  Cout  Cremoval  Csource dt

dC ME (t) (4-1) 

where: 

 dCME(t) = Change in concentration in a microenvironment at time t (ppm), 

Cin = Rate of change in microenvironmental concentration due to influx 

of air (ppm/hour), 

Cout = Rate of change in microenvironmental concentration due to outflux 

of air (ppm/hour), 

Cremoval = Rate of change in microenvironmental concentration due to 

removal processes (ppm/hour), and 

Csource = Rate of change in microenvironmental concentration due to an 

emission source inside the microenvironment (ppm/hour). 
 4-16

Within the time period of an hour each of the rates of change, Cin, Cout, Cremoval, and 

Csource, is assumed to be constant. The change in microenvironmental concentration due to 

influx of air is represented by the following equation: 

ambient proximity penetration air exchange

in

in C x f x f x R

dt

dC t C   ( ) (4-2) 

where: 

Cambient = Ambient hourly outdoor concentration (ppm) 

fproximity = Proximity factor 

 fpenetration = Penetration factor 

Rair exchange = Air exchange rate (1/hour) 

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

 ( ) ( ) R C t

dt

dC t C air exchange ME

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: 

(R R R )C (t) R C (t) dt

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

where: 

Rdeposition = Removal rate of a pollutant from a microenvironment due to 

deposition (1/hour) 

Rfiltration = Removal rate of a pollutant from a microenvironment due to 

filtration (1/hour) 

Rchemical = Removal rate of a pollutant from a microenvironment due to 

chemical degradation (1/hour) 

Rremoval = Removal rate of a pollutant from a microenvironment due to 

overall removal (1/hour) 

As discussed in Section 2.2, EPA has not modeled indoor emissions of CO in the current 

exposure assessment; consequently, the optional term Csource was uniformly set equal to 0.0 for 

this study. 
 4-17

Combining equation 4-1 with equations 4-2, 4-3, and 4-4 yields 

( ) ( ) dt

dC ME (t) C R C t R C t   in  air exchange  ME  removal  ME (4-5) 

The solution to this differential equation is 

( ) ( (0) ) exp( R t) R

C C

R

C C t combined

combined

in

ME

combined

in

ME  

    (4-6) 

where: 

CME(0) = Concentration of a pollutant in a microenvironment at the 

beginning of a hour (ppm) 

 CME(t) = Concentration of a pollutant in a microenvironment at time t within 

the time period of a hour (ppm) 

Rcombined = Rair exchange + Rremoval (1/hour) 

Based on equation 4-6, the following three hourly concentrations in a microenvironment 

are calculated: 

 

combined

in

ME

equil

ME R

C C C t

  (  ) (4-7) 

( (0) ) exp( ) combined

equil

ME ME

equil

ME

hourly end CME C  C C R (4-8) 

combined

equil combined

ME ME

equil

ME

hourlymean

ME R

R C C C

dt

C t dt

C 1 exp( ) ( (0) )

( )

1

0

1

0      





 (4-9) 

where: 

equil CME = Equilibrium concentration in a microenvironment (ppm) 

CME(0) = Concentration in a microenvironment at the beginning of an hour 

(ppm) 
 4-18

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

hourlymean CME = 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 ending, and hourly mean concentrations. APEX4.3 

reports hourly mean concentration as the hourly concentration for a specific hour. The 

calculation continues to the next hour by using hourly end C ME for the previous hour as CME(0). 

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 estimates the concentration in a microenvironment as a 

linear function of ambient concentration of that hour, regardless of the concentration in the 

microenvironment during the preceding hour. Table 4-3 lists the parameters required by the 

factors model approach to calculate concentrations in a microenvironment 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: 

ambient proximity penetration

hourlymean

ME C C  f  f (4-10) 

where 

hourlymean CME = Hourly concentration in a microenvironment (ppm) 

Cambient = Hourly concentration in ambient environment (ppm) 

fproximity = Proximity factor (unitless) 

fpenetration = Penetration factor (unitless) 

The proximity factor (fproximity) is used to account for differences in ambient 

concentrations between the geographic location represented by the ambient air quality data (e.g., 

a fixed-site monitor) and the geographic location of the particular microenvironment. For 

example, persons travelling inside motor vehicles may be located on a heavily-trafficked 
 4-19

roadway, whereby the ambient air outside the vehicle would likely have elevated levels of 

mobile source pollutants such as carbon monoxide relative to the ambient monitor. In this case, 

a value greater than one for the proximity factor would be appropriate to represent the increase in 

concentrations outside the vehicle relative to the ambient monitor. Additionally, for some 

pollutants the process of infiltration may remove a fraction of the pollutant from the air. The 

fraction that is retained in the indoor/enclosed microenvironment is given by the penetration 

factor (fpenetration) and is dependent on the particular pollutant’s physical and chemical removal 

rates. 

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 

COout(c,m,d,h) = M(m) × L(c, m, d) × T(c,m,d,h) × [COmon(d,h)]

A

 (4-11) 

where, 

COout(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, 

COmon(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 microenvironment 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 (GML) equal to 1.0 and geometric standard deviation (GSDL) 
 4-20

equal to 1.5232. The natural logarithms of this distribution can be characterized by a normal 

distribution with an arithmetic mean (μL) equal to 0 and an arithmetic standard deviation (σL) 

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 distribution with geometric mean (GMT) equal to 1.0 and 

geometric standard deviation (GSDT) equal to 1.6289. The natural logarithms of this distribution 

follow a normal distribution with an arithmetic mean (μT) equal to 0 and an arithmetic standard 

deviation (σT) equal to 0.4879. 

The COout(c, m, d, h) term is interpreted as the outdoor CO concentration in the 

immediate vicinity of microenvironment m in district d during hour h. COmon(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 change in CO concentration associated ambient 

(outdoor) air as it moves into a microenvironment, though the CO concentration within the 

microenvironment will be affected by inputs for air exchange rate and indoor sources. 
 4-21

Table 4-4. Estimated values of distribution parameters and variables in equation 4-11 as 

implemented in the application of pNEM/CO to Denver and Los Angeles 

(Johnson et al., 2000). 

Microenvironmenta Parameter Estimates for Equation 4-11 

Code 

General 

location Specific location 

Activity diary 

locations included in 

microenvironment A σL σT 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 15), commercial (3 

through 7), and vehicle (12 through 14).
 4-22

4.4.4.3.1 Data Used To Estimate pNEM/CO Microenvironmental Algorithm 

Parameters 

The parameter values for the location factor (σL), time-of-day (σT), and ambient 

concentration exponent (A) were based on data collected during a residential monitoring study 

described by Wilson et al. (1995). Ten-minute CO concentrations were measured outside 293 

residences throughout California in 1992 including customers of Pacific Gas and Electricity 

(PG&E) (129 residences in Northern California), San Diego Gas and Electric Company (89 

residences in the San Diego area), and Southern California Gas Corporation (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 available, analysts used a remaining subset of 156 

residences, 70 from Los Angeles and 86 from San Diego, as the basis for estimating values of σL, 

σT, 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 concentrations reported by nearby fixed-site 

monitors. 

An assumption was made by the original data analysts to maximize the number of hourly 

averaged outdoor residential samples available to use in determining 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 occurrence of negative and zero values in the hourly-averaged data 

(which could not be used in fitting equation 4-11), analysts added a constant offset of 0.5 ppm to 

all hourly-averaged values measured outside a residence. In addition, seventeen (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 resulting 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 

 

2

 Note these same coefficient values derived from the 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. 
 4-23

which each pair was indexed by date, time, residence identifier, fixed-site monitor identifier, and 

fixed-site monitor scale (e.g., neighborhood scale). 

The parameters for the microenvironmental factors (or M(m)) in equation 4-11 were 

derived from data generated through the Denver Personal Monitoring Study (Akland et al, 1985; 

Johnson, 1984). During this study, each of approximately 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 concentration 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 which 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 provide more stable estimates than those based on 

the very limited amount of data available for 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, whereas 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 data analyses that suggested that the 

relationship between COout(c, m, d, h) and COmon(d, h) should account for the specific 

microenvironment, the geographic location of the microenvironment, and the time-of-day. 

Analysts recognized that numerous statistical algorithms could 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 with the need to represent the patterns in 

concentration variability observed in the available data. The bulk of the algorithm development 

was based on the Wilson et al. (1995) database, that is, hourly average 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 microenvironment in the district d. 
 4-24

The district d was initially taken to be the entire study region where measurements were 

collected (i.e., San Diego and Los Angeles areas). 

Analysts began by considering a simple linear regression model of the form 

COout(c,m,d,h) = a(m,d) + A × [COmon(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 and parsimony, the slope coefficient A was assumed to be the 

same for all microenvironments (m) and districts (d). 

Although the coefficient of determination (R2

) for this linear regression model was 

moderate (0.53),3

 the model was found to be unacceptable 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 from 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 concentrations, 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, analysts evaluated an alternative model where the 

natural logarithm of outdoor concentration was expressed as a linear function of the natural 

logarithm of monitor concentration: 

LN[COout(c,m,d,h)] = a(m,d) + A × LN[COmon(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 the 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[COout(c,m,d,h)] = a(c,m,d) + A × LN[COmon(d,h)] + e(c,m,d,h) (4-14) 

 

3

 Note that the R2 goodness-of-fit statistic is not an appropriate measure of model adequacy when the true, 

underlying errors are highly correlated. 
 4-25

Exponentiating both sides of equation 4-14 yields the equivalent formulation to that 

presented above in equation 4-11: 

COout(c,m,d,h) = M(m) × L(c,m,d) × T(c,m,d,h) × [COmon(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 model predictions were influenced by the past 

history of CO concentrations at the monitor 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 did 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 similar 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 when the model was applied to monitoring data 

obtained from the Denver Broadway site (ID 08310002). These high values were found to be a 

direct result of setting A = 1, which forced the geometric standard 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 employed restricted maximum likelihood estimation. 
 4-26

The fit yielded estimates of σL = 0.4208, σT = 0.4879, and A = 0.621, the values subsequently 

used in the pNEM/CO runs described by Johnson et al. (2000). The fitted value of M(m), 

representing residences in Los Angeles during 1992, was actually 0.9706. An alternative value 

(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 specified by four parameters [M(m), σL, σT, and A]. Note that σL, σT, 

and A are defined to be independent of the microenvironment, whereas M(m) is 

microenvironment-specific. At the time of the initial algorithm development, researchers were 

unable to find a single data source capable of providing estimates of all four parameters. 

Consequently, values for σL, σT, and A were estimated by analyzing 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 (Akland et al, 1985; Johnson, 1984). 

Researchers conducted a series of sensitivity analyses to evaluate the potential effects on 

parameter estimates of variations in the regional location and scale 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 monitor (based on the estimated maximum distance from 

the monitor represented by the measured concentrations: micro, middle, neighborhood, or urban 

scale). The fitted values of σL, σT, 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 other 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 σL, σT, 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 COout(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 concentrations could be reliably associated with 

particular indoor microenvironments. Consequently, 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 

 

4

 Because the simplified approach was also less sensitive 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 analysis to one-hour PEM values would have significantly reduced the pool of 

usable data. 
 4-27

source of the pollutant is the outside air. In such cases, the mean indoor concentration 

approximates the mean outdoor concentration, with the instantaneous indoor concentration 

exhibiting a lower degree of variability than the corresponding outdoor concentration. 

When equation 4-11 is expressed in a logarithmic form (i.e., as in equation 4-14) and 

averaged over cohorts, one obtains the equation 

Mean{LN[COout(c, m, d, h)} 

= Mean[a(c, m, d)] + A × Mean{LN[COmon(d, h)]} + Mean[e(c, m, d, h)] 

 = LN[M(m)] + A × Mean{LN[COmon(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 microenvironment within an aggregate. 

4.4.4.4 The Micronenvironmental Algorithm as Implemented by APEX3.1 

As discussed in section 4-3, the pNEM/CO model effectively evolved into what is known 

today as the APEX model. In APEX3.1, the portion of the outdoor concentration affecting the 

indoor concentration is determined by the formula 

COout = 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 = [COmon(d,h)]

A (4-18) 

Proximity = M(m) × L(c, m, d) (4-19) 

Penetration = T(c,m,d,h) (4-20) 

which yields 
 4-28

COout = M(m) × L(c, m, d) × T(c,m,d,h) × [COmon(d,h)]

A (4-21) 

and is identical to equation 4-11 above. 

To obtain results from APEX3.1 that are comparable to that generated by pNEM/CO, 

Johnson and Capel (2003) preprocessed the hourly ambient monitor data assigned to the district 

containing the microenvironment using the formula 

Ambient = [COmon(d,h)]

0.621 (4-22) 

where COmon(d,h) is expressed in ppm. For each profile, a value for the Proximity term was 

selected for each microenvironment from a lognormal distribution with geometric mean equal to 

M(m) and geometric standard deviation equal to 1.5232. The natural logarithms of this 

distribution were characterized by a normal distribution with an arithmetic mean (μL) equal to 

LN[M(m)] and an arithmetic standard deviation (σL) equal to 0.4208. Consistent with the 

pNEM/CO algorithm, Proximity values were not permitted to fall below the 5th percentile of the 

specified distribution or above the 95th percentile of the distribution. Table 4-5 lists the 

parameter values applicable to the 15 microenvironments defined by Johnson and Capel (2003). 

Penetration values were randomly selected for each hour from a lognormal distribution 

with geometric mean (GMT) equal to 1.0 and geometric standard deviation (GSDT) equal to 

1.6289. As indicated above, the natural logarithms of this distribution followed a normal 

distribution with an arithmetic mean (μT) equal to zero and an arithmetic standard deviation (σT) 

equal to 0.4879. In agreement with the pNEM/CO algorithm, Penetration values were not 

permitted to fall below the 5th percentile of the specified distribution (0.4482) or above the 95th

percentile of the distribution (2.2313).5

 

5

 Note the Penetration factor was not used according to its originally 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 microenvironment. 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). 
 4-29

Table 4-5. Parameters of bounded lognormal distributions 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 

(5th pct)

Upper 

Bound 

(95th 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 Restaurant 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 5.9345 
 4-30

4.4.5 Estimate Energy Expenditure and Ventilation Rates 

APEX4.3 includes a module that estimates COHb levels in the blood as a function of 

alveolar ventilation rate, the CO concentration of the respired air, endogenous CO production 

rate, and various physiological variables such as blood volume and pulmonary CO diffusion rate. 

Alveolar ventilation rate is estimated as a function of oxygen uptake rate, which in turn is 

estimated as a function of energy expenditure rate. This section provides a brief summary of the 

algorithm used to estimate alveolar ventilation rate. A detailed description of the algorithm, 

based on the nonlinear solution to the Coburn-Forster-Kane (CFK) equation (Coburn et al., 

1965), together with the distributions 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 ventilation (respiration) rate 

be estimated as functions of energy expenditure rate. The energy expended by an individual 

during a particular activity can be expressed as 

EE = METS × RMR (4-23) 

where EE is the average energy expenditure rate (kcal min-1) during the activity and RMR is the 

resting metabolic rate of the individual expressed in terms of number of energy units expended 

per unit of time (kcal min-1). METS (i.e., metabolic equivalent of work) is a ratio specific to the 

activity and is dimensionless. 

The METS concept provides a means for estimating the alveolar ventilation rate 

associated with each activity. For convenience, let EE(i,j,k) indicate the energy expenditure rate 

associated with the i

th activity on day j for person k. Equation 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 particular activity performed by person k. 

4.4.5.2 Oxygen Requirements for Energy Expenditure 

Energy expenditure requires oxygen which is supplied by ventilation (respiration). 

ECF(k) represents an energy conversion factor defined as the volume of oxygen required to 

produce one kilocalorie of energy in person k. The oxygen uptake rate (VO2) associated with a 

particular activity can be expressed as 

VO2(i,j,k) = ECF(k) × EE(i,j,k) (4-25) 
 4-31

where VO2(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 VO2(i,j,k) can now be determined from MET(i,j,k)

by substituting equation 4-24 into equation 4-25 to produce the relationship 

VO2(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 mismatch, an individual’s energy needs are met by 

anaerobic processes. The magnitude of the mismatch between expenditure and consumption is 

termed the oxygen deficit. During heavy exercise, further oxygen deficit (in addition to that 

associated with the start of exercise) may be accumulated. At some point, oxygen deficit reaches 

a maximum value, and performance and energy expenditure deteriorate. After exercise ceases, 

ventilation and oxygen consumption will remain elevated above baseline levels. This increased 

oxygen consumption was historically labeled the oxygen debt or recovery oxygen consumption. 

However, the term excess post-exercise oxygen consumption (EPOC) has been adopted here to 

represent this phenomenon. APEX4.3 has an algorithm for adjusting the MET values to account 

for EPOC. This algorithm is described in detail in section 7.2 of US EPA (2008b). 

4.4.5.4 Alveolar Ventilation Rate 

Alveolar ventilation (VA) represents the portion of the minute ventilation that is involved 

in gaseous exchange with the blood. VO2 is the oxygen uptake that occurs during this exchange. 

The absolute value of VA is known to be affected by total lung volume, lung dead space, and 

respiration frequency – parameters that vary according to the person and/or exercise rate. 

However, it is reasonable to assume that the ratio of VA to VO2 is relatively constant regardless of 

a person’s physiological characteristics or energy expenditure rate. Consistent with this 

assumption, APEX4.3 converts each estimate of VO2(i,j,k) to an estimate of VA(i,j,k) by the 

proportional relationship 

VA(i,j,k) = 19.63 × VO2(i,j,k) (4-27) 

where both VA and VO2 are expressed in units of liters min-1. This relationship was obtained 

from Joumard et al. (1981), who based it on research by Galetti (1959). Equation 4-15 can also 

be expressed by the equivalent equation 

VA(i,j,k) = 19.63 × METS(i,j,k) × ECF(k) × RMR(k) (4-28) 
 4-32

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-location-activity data obtained 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 probabilistic equations specific to age and 

gender using equations reported by Schofield (1985). A value of ECF is selected for each 

individual from a uniform distribution (minimum = 0.20, maximum = 0.21) based on data 

provided by Esmail et al. (1995). Using equation 4-28 and these inputs, APEX4.3 calculates a 

sequence of VA values for each simulated individual. These values are provided to the algorithm 

that estimates the percent COHb in the blood resulting from the simulated exposure (see section 

4.4.7 and Appendix B). 

4.4.6 Calculate Exposure 

APEX4.3 calculates exposure as a time series of exposure concentrations that a simulated 

individual experiences during the simulation period. APEX4.3 determines the exposure using 

hourly ambient air concentrations, calculated concentrations in each microenvironment based on 

these ambient air concentrations, and the minutes spent in a sequence of microenvironments 

visited according to the composite diary. The hourly exposure concentration at any clock hour 

during the simulation period is determined using the following equation: 

T

C t

C

N

j

j

hourlymean

ME j

i

  1

( ) ( )

 (4-29) 

where 

Ci = 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 CME( j) = Hourly mean concentration in microenvironment j (ppm) 

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

T = 60 minutes 

From the hourly exposures, APEX4.3 calculates time series of 8-hour and daily average 

exposure concentrations that a simulated individual 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. 
 4-33

4.4.7 Calculate Dose 

Using time-location-activity pattern data obtained from several diary studies, APEX4.3 

constructs a composite diary for each simulated 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 microenvironment, 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’ alveolar 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 simulated individual, to estimate the percent COHb in 

the blood at the end of each event. The percent COHb calculation is based on the solution to the 

nonlinear Coburn-Forster-Kane (CFK) equation (Coburn et al., 1965), as detailed in Appendix B. 

Briefly, the CFK module in APEX4.3 describes the rate of change in COHb blood levels as a 

function of the following quantities: 

 Inspired CO pressure; 

 COHb level; 

 Oxyhemoglobin (O2Hb) level; 

 Hemoglobin (Hb) content of blood; 

 Blood volume; 

 Alveolar ventilation rate; 

 Endogenous CO production rate; 

 Mean pulmonary capillary oxygen pressure; 

 Pulmonary diffusion rate of CO; 

 Haldane coefficient (M); 

 Barometric pressure; and 

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

If all of the listed quantities except COHb level are constant over some time interval, the 

CFK equation has a linear form over the interval and is readily integrated. The solution to the 

linear form gives reasonably accurate results for lower levels of COHb (ISA section 4.2.1). 

However, CO and oxygen can compete for binding with the available hemoglobin and, therefore, 

are not independent of each other. If this dependency is taken into account, the resulting 

differential equation is no longer linear. Peterson and Stewart (1975) proposed a heuristic 

approach to account for this dependency which assumed the linear form and then adjusted the 
 4-34

O2Hb level iteratively based on the assumption of a linear relationship between COHb and 

O2Hb. This approach was used in the COHb module of the original CO-NEM exposure model 

(Biller and Richmond, 1982; Johnson and Paul, 1983). 

Alternatively, it is possible to determine COHb at any time by numerical integration of 

the nonlinear CFK equation if one assumes a particular relationship between COHb and O2Hb. 

Muller and Barton (1987) demonstrated that assuming a linear relationship between COHb and 

O2Hb leads to a form of the CFK equation equivalent 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 employed a linear relationship between COHb and 

O2Hb which was consistent with the basic assumptions of the CFK model. The approach 

differed from the linear forms used by other modelers in that the Muller and Barton (1987) 

solution was employed. However, instead of the simple binary search described in the Muller 

and Barton paper, a combination of the binary search and Newton-Raphson root finding methods 

was used to solve for COHb (Press et al., 1986). 

As mentioned above, the current COHb module included in 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 O2Hb and COHb. The CFK equation does 

not have an explicit solution, so an iterative solution or approximation is needed to calculate each 

percent COHb value. APEX4.3 solves the CFK equation using a 4th-order Taylor’s series with 

subintervals. This method, first incorporated in APEX3 (Glen, 2002), is summarized in 

Appendix B. The selected method (4th-order Taylor series with subintervals) was chosen 

because of its simplicity, fast execution speed, and ability to produce relatively accurate 

estimates of percent COHb at both low and high levels of CO exposure. 

While there may be other approaches proposed 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 most widely accepted and validated approaches 

used to estimate COHb levels (ISA, section 4.2.3). Before any such future module modifications 

could be planned and implemented, a more thorough and balanced evaluation of the uncertainties 

needs to be performed to include 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 five 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 module, a number of variables would need to be 

introduced. Some of these variables may have data or equations available in the extant literature 
 4-35

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 modifications, 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 most relevant) exposure 

concentration level evaluated (see Figure 7 of Bruce and Bruce, 2003). This preliminary 

comparison indicates that while adding multiple compartments 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 CFK 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 evaluate the impact to exposure and dose results. This is being used in 

this REA to develop estimates of the contribution 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 all respects except their 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 simulations are performed: the first 

is a typical simulation that generates exposure and dose in the presence of ambient CO and the 

second simulation uses ambient concentrations equal to zero at all monitors and all hours of the 

day. In this first simulation, the exposures persons 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 exposure 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 endogenous CO production alone. The 

difference in the event-level time series for each individual (and entire population) can thus be 

used to approximate the contribution from ambient CO for all exposure events throughout the 

simulation period. See section 5.10 and Appendix B for details. 
 4-36

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 User’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 concentrations 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 expenditures. For example, both the hourly and events APEX files 

were needed to estimate a distribution of microenvironmental-to-ambient concentration ratios 

(see section 5.10). However, given the potential size of the files that can be generated for a large 

population and assessment duration, it is not common to generate event-level files outside of 

research purposes. Specific outputs 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 related 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 criteria pollutants including CO, O3, SO2, and NO2. 

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, sensitivity 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. 
 4-37

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Carboxyhemoglobin Levels in Denver Residents Using a Probabilistic Version of NEM. Report prepared 

for the Office of Air Quality Planning and Standards, 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. 

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Respondents from a Standpoint of Exposure Assessment. Washington, DC: U.S. Environmental Protection 

Agency, Office of Research and Development. EPA/600/R-96/074. 

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assessment of particulate matter for susceptible populations in Seattle. Environ Health Persp. 111:909-

918. 

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exposures. Resource Energy Econ. 28:215-228. 

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Triangle Park NC: Research Triangle Institute. 

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

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exposures. J Appl Physiol. 39(4):633-638. 

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Clin Nutr. 39C(S1):5-41. 

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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 

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US Census Bureau. (2007). Employment Status: 2000- Supplemental Tables. Available at: 

http://www.census.gov/population/www/cen2000/phc-t28.html. 

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(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. 

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5-1

5 APPLICATION OF APEX4.3 IN THIS ASSESSMENT 

5.1 PURPOSE 

This chapter presents detailed information regarding 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 study areas. In particular, this chapter (and its 

associated appendices) describes the: 

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

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

 study area population, the modeled at-risk population and associated 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 generated for this particular assessment. 

Note that the APEX model version used in this assessment 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 previous 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 metropolitan 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 the effects of geographic location, a diverse set 

of microenvironments, commuting within the study area, and selected indoor sources (e.g., 

passive smoking, gas stoves). In the specific application 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 detailed approach presented here was designed in 

consideration of comments and recommendations made by the CASAC and public regarding the 

earlier draft CO REAs (US EPA, 2009a; 2010). 

The general description of APEX, the standard databases used, 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 probabilities 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 activity-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 Users Guide (US EPA, 2008a) and the APEX Technical 

Support Document (US EPA, 2008b). The typical 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 generated 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 Denver, Colorado, and Los Angeles, California, 

were selected for the current exposure and dose assessment. 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 having 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 Tables 5-1 (Denver) and 5-2 (Los Angeles). 

The actual study areas were defined as including 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) represented by data from the associated CO monitor. Note that all air 

districts have the same radius (10 km), a value specified by the “AirRadius” input parameter of 

APEX. Any tracts residing within overlapping monitor radii were assigned to the closest 

monitor. 

In addition to defining the air districts, the model user must specify a location for the 

center of the study area and a value for “CityRadius.” The circular area defined by the city 

center location and the value of “CityRadius” must be large enough to include all census tracts 

included in the air districts. For Denver, staff used the location of monitor ID 08310014 (Denver 

-Carriage) for the city center and set the “CityRadius” equal to 20 km (Figure 5-1). Staff used 

the location of monitor ID 06371103 (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 years 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 monitor concentrations were near or exceeding the 8-

hour average CO NAAQS of 9 ppm. Staff judged that these historical monitoring data would be 

most useful in representing air quality that just meets the current or alternative CO standards and, 

following an appropriate concentration level adjustment, would represent 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 031-0002a

 031-0013a 031-0014a 059-0002a

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 2nd

Highest 8-hour 

avg. CO (ppm) 9.5 6.2 5.9 4.6 

2006 2nd

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). 
5-4

1 Table 5-2. Attributes of fixed-site monitors selected for the Los Angeles study area. 

Monitor ID 037-0113a 037-1002a 037-1103a 037-1201 037-1301a 037-2005a 037-4002a 059-0001/7a,City West LA Burbank 

Los 

Angeles Reseda Lynwood Pasadena 

Long 

Beach Anaheim 

Local Name - - - - - - - - Latitude 34.05111 34.17605 34.06659 34.19925 33.92899 34.1326 33.82376 33.83062 Longitude -118.45636 -118.31712 -118.22688 -118.53276 -118.21071 -118.1272 -118.18921 -117.93845 Elevation (m) 91 168 87 226 27 250 6 45 Scale - - - - Middle - - NeighborhooObjective Unknown Unknown Unknown Unknown 

Highest 

Conc. Unknown Unknown 

Population 

Exposure 

1997 2nd

Highest 8-hour avg. CO (ppm) 4.1 7.2 5.9 7.7 15 5.4 6.4 5.4 

2006 2nd

Highest 8-hour avg. CO (ppm) 1.9 3.4 2.5 3.4 5.6 2.7 3.3 2.9 

Notes: a Identified monitor was used in the 2000 pNEM/CO analysis (Johnson et al., 2000). b When considering the two monitoring periods (1997 and 2006), two separate ambient monitor IDs were noted (059-0001 

effectively the locations of both monitors were the same. 
5-5

Figure 5-1. Ambient monitor locations, air districts (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 districts (black circles), meteorological zones 

(blue and pink circles), and study area (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 was first restricted to persons aged 18 years 

or older. Next, the populations were adjusted to remove residents commuting outside of the 

study area. The resulting population for the Denver study area was 617,020 persons (or 81.1% 

of the total population ≥18 years of age residing in modeled 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 referred to below as the total simulated 

population in each area. To obtain adequate representation of the simulated population while 

also keeping the model runs tractable, fifty-thousand exposure profiles (or 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), consistent with the previous CO exposure 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 subpopulations using disease prevalence rates characterized in the National 

Health Interview Survey (NHIS): (1) the potential population comprised of persons with 

“coronary heart disease” and (2) the potential population comprised of persons with “all types of 

heart disease”. 

This first category (i.e., coronary heart disease) 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 EPA, 2010). The second category (i.e., all types 

of heart disease) is inclusive of those with diagnosed coronary heart disease, angina pectoris, 

heart attack, and any other heart condition or disease (CDC, 2009), in addition to an estimate of 

those persons with undiagnosed coronary heart disease. The specific data and method used to 

estimate the prevalence rates for 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 individuals 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 levels for subpopulations in the two study areas 

having the demographic characteristics (i.e., age and gender) of the two at-risk populations of 

interest, we note that the simulation does not include any other characteristics specific to the atrisk populations of interest. For example, the CHAD diaries used to represent the simulated 

individuals are not exclusively drawn from a pool of diaries of persons identified by the original 

activity pattern survey as having the disease state(s) of interest.3

 This limitation and its effect on 

exposure and dose estimates for representing the simulated at-risk subpopulations are discussed 

in chapter 7 below. 

5.5.1.1 Coronary Heart Disease 

For estimates of adults with diagnosed CHD, 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 rates. Although we desired the prevalence 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 provides CHD stratified by gender. The genderonly data were used to estimate gender-specific adjustment factors to apply to the age-only data 

set. For males, the adjustment factor = 0.080/0.061 = 1.31; for females, the adjustment factor = 

0.045/0.061 = 0.74. Table 5-5 provides the estimated national prevalence rates for CHD by age 

range adjusted for gender using 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 

diseasea

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 completed 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 

diseasea

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 Malesa

 Femalesb

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 expanded 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 IHD5

 were stratified by age and sex (Adams and Marano, 

1995) and constituted approximately 8.0 million individuals 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 silent 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 persons 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 

 

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 population (i.e., 3-4 million persons) is the same as that 

reported by AHA (2003). 
5-10

prevalence for use in estimating the undiagnosed prevalence. Consequently, this factor can be 

interpreted as the undiagnosed cases may be 43.8% of the diagnosed prevalence. 

Table 5-6 lists the results 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 disease (diagnosed and undiagnosed combined) stratified 

by gender was used by APEX in estimating the 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 simulated 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 simulated population) within the CHD simulated at-risk population. 

Table 5-6. Estimated national prevalence rates for 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 Undiagnoseda Total Diagnosed Undiagnoseda

 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) were multiplied by 0.438 to estimate the undiagnosed 

prevalence. This calculation assumed CHD and IHD are 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 prevalence 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 

 

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

prevalence data for HD by age and Table 5-8 provides HD stratified by gender. These genderonly data were used to estimate gender-specific adjustment factors to apply to the age-only data 

set. For males, the adjustment factor = 0.125/0.112 = 1.12; for females, the adjustment factor = 

0.102/0.112 = 0.91. Table 5-9 provides the estimated national prevalence rates for HD by age 

range adjusted for gender using 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 

diseasea

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 heart 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 diseasea

Age range Total Males Females 

18+ 0.112 0.125 0.102 

Notes: 

a

 Source: Statistics for all types of heart 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 5-9. 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 diseasea

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 
5-12

percent of persons with undiagnosed coronary heart disease in addition to the estimate of 

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 APEX to estimate the second simulated at-risk 

population. 

When using these prevalence rates in the APEX 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 simulated 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 disease, stratified by age and gender. 

Prevalence rate (fraction) for all types of heart disease 

Males Females 

Age range 

Diagnosed 

heart 

disease 

Undiagnosed 

coronary 

heart 

diseasea Total 

Diagnosed 

heart 

disease 

Undiagnosed 

coronary 

heart 

diseasea 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 above age 17 in the CHAD database 

were used. 

5.5.3 Construction of Longitudinal Diaries 

As discussed in section 4.4.3.4, APEX provides a longitudinal diary assembly algorithm 

that enables the user to create composite diaries that reflect the tendency of individuals to repeat 

day-to-day activities (Glen et al., 2008). The user specifies 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 
5-13

quantifies the day-to-day autocorrelation in the selected key daily variable. APEX then 

constructs composite diaries that exhibit the statistical properties 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 microenvironment. Parameter settings of D = 0.31 and A = 0.19 

were specified to control the day-to-day repetition of time spent in motor vehicles in the 

constructed composite diaries. These particular D and A values were obtained from Isaacs et al. 

(2009) (see Appendix C). 

In selecting particular diaries to represent the simulated population, the CHAD data are 

categorized or separated by APEX into data pools. The pools were 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 and weekday); thus, there were 3 × 2 = 6 diary pools. The 

window for age was set at 15%. For example, diaries 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 conditions considered for 

each APEX simulation. Staff evaluated five exposure 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 levels at or above selected benchmarks when 

considering current air quality conditions. The next four exposure scenarios used ambient data 

from a high concentration year in each location (i.e., the 1995 monitoring 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 the number of persons that may experience COHb 

levels at or above selected benchmark levels when 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 adjusted air quality exposure scenarios considered ambient 

concentrations adjusted to just meet the current 8-hour CO NAAQS of 9 ppm. The 8-hour 

standard was selected when considering the two current standards (8-hour and 1-hour) because it 
5-14

is the controlling standard.9 The second of these exposure scenarios using the historical 

monitoring data also considered the form of the current 8-hour 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 forms of potential alternative standards, consistent 

with the alternative standards investigated for other criteria pollutants (e.g., NO2 (US EPA, 

2008c); and SO2 (US EPA, 2009b)). The first of these potential percentile forms considered a 

99th percentile daily maximum 8-hour average CO concentration of 5.0 ppm, while the second 

considered the same form though with a 1-hour averaging time and a 1-hour level of 8.0 ppm. 

Details regarding the concentration adjustments associated with each of the current and potential 

alternative standards are provided 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 Angeles, respectively. An array was constructed 

using the varying air quality scenarios to indicate how a single APEX run using a particular air 

quality input data set might reflect different levels 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 second highest non-overlapping 8-hour average (Table 

5-11). The generated results would also represent exposures and doses experienced considering 

a 99th percentile 1-hour daily maximum concentration of 4.5 ppm. 

 

9

 The controlling standard by definition would be the standard that allows air quality to have either a 2nd

highest 8-hour average concentration of ≤ 9.4 ppm (i.e., the 8-hour standard is the controlling standard) or to have a 

2nd highest 1-hour concentration of ≤ 35.4 ppm (i.e., the 1-hour standard is the controlling standard). 
5-15

Table 5-11. Array of alternative standard forms and levels informed by modeled exposure 

scenarios in Denver. 

Denver Design Values (ppm) 

Averaging Time & Form 8-hour 1-hour 

Air Quality Scenario 

2nd highesta

99th percentile daily max 

2nd highesta

99th percentile 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 

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

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

99th 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 and levels informed by modeled exposure 

scenarios in Los Angeles. 

Los Angeles Design Values (ppm) 

Averaging Time & Form 8-hour 1-hour 

Air Quality Scenario 

2nd highesta

99th percentile daily max 

2nd highesta

99th percentile 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 

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

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

99th 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 allows 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 important input in estimating CO exposure and 

dose. Descriptive statistics were generated for 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 concentration year (1995 or 1997) are about a factor of 

two or greater than the more recent year (2006) of ambient monitoring data in either study area. 

In general, there is similarity in the concentration distribution for both study areas within a given 

year, with the following exceptions. There is one monitor in Los Angeles (ID 06371301) that 

consistently reported exceptionally high concentrations when compared with the other Los 

Angeles monitors for either year. In addition, there is a sharper rate of increase in the upper 

percentile concentrations (i.e., ≥95th percentiles) in Denver when compared 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-year of monitoring data be complete (i.e., it is free of hourly 

gaps in concentration levels). The missing values in each data set were estimated by the 

sequential application of the following four methods. 

1) If the data gap was less than six continuous 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 for a particular time of day was the model 

that maximized the variance explained (R2

) for that hour, subject to the constraints that 

regression model R2

 was greater than 0.5 and the number 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 interpolation between the valid values at the ends of 

the gap. 

4) All remaining missing values were substituted 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 statistics for 1-hour CO concentrations in 

each data set, before and after estimating missing values, and considering the two years of 

ambient monitoring data in each study area. The excellent agreement between concentrations at 

the various percentiles of the distribution (before and after substitution) indicates that the 

addition of the estimated missing-value concentrations did not significantly affect the overall 

distributions of the hourly CO concentrations.
5-18

Table 5-13. Descriptive statistics for hourly carbon monoxide concentrations before and after estimation o

Denver 1995. 

1-hour values CO concentration (ppm) 

Monitor (n) Percentile 

ID 

Missing 

values 

filled? Present Missing Mean SD Min 25th 50th 75th 90th 95th 

No 8697 63 1.50 1.20 0.0 0.8 1.2 1.8 2.7 3.4 

31-0002 

Yes 8760 0 1.50 1.20 0.0 0.8 1.2 1.8 2.7 3.4 

No 8647 113 1.25 1.08 0.1 0.6 0.9 1.5 2.5 3.4 

31-0013 

Yes 8760 0 1.25 1.08 0.1 0.6 0.9 1.5 2.5 3.4 

No 8701 59 1.09 1.05 0.0 0.5 0.7 1.3 2.3 3.2 

31-0014 

Yes 8760 0 1.09 1.05 0.0 0.5 0.7 1.3 2.3 3.2 

No 8680 80 0.96 0.93 0.1 0.4 0.6 1.1 2.0 2.7 

59-0002 

Yes 8760 0 0.96 0.93 0.1 0.4 0.6 1.1 2.0 2.7 

Table 5-14. Descriptive statistics for hourly carbon monoxide concentrations before and after estimation o

Denver 2006. 

1-hour values CO concentration (ppm) 

Monitor (n) Percentile 

ID 

Missing 

values 

filled? Present Missing Mean SD Min 25th 50th 75th 90th 95th 

No 8672 88 0.62 0.39 0.0 0.4 0.5 0.7 1.0 1.3 

31-0002 

Yes 8760 0 0.62 0.39 0.0 0.4 0.5 0.7 1.0 1.3 

No 8635 125 0.49 0.36 0.0 0.3 0.4 0.6 0.9 1.2 

31-0013 

Yes 8760 0 0.49 0.36 0.0 0.3 0.4 0.6 0.9 1.2 

No 8557 203 0.47 0.38 0.0 0.3 0.4 0.5 0.9 1.2 

31-0014 

Yes 8760 0 0.47 0.38 0.0 0.3 0.4 0.5 0.9 1.2 

No 8603 57 0.40 0.37 0.0 0.2 0.3 0.5 0.8 1.1 

59-0002 

Yes 8760 0 0.40 0.37 0.0 0.2 0.3 0.5 0.8 1.1 
5-19

Table 5-15. Descriptive statistics for hourly carbon monoxide concentrations before and after estimation o

Los Angeles 1997. 

1-hour values CO concentration (ppm) 

Monitor (n) Percentile 

ID 

Missing 

values 

filled? Present Missing Mean SD Min 25th 50th 75th 90th 95th 

No 8360 400 0.84 0.86 0.0 0.2 0.6 1.2 2.0 2.6 

37-0113 

Yes 8760 0 0.84 0.85 0.0 0.2 0.6 1.2 2.0 2.6 

No 8025 735 1.75 1.27 0.0 0.9 1.4 2.2 3.5 4.5 

37-1002 

Yes 8760 0 1.73 1.24 0.0 0.9 1.4 2.1 3.5 4.4 

No 8292 468 1.36 1.19 0.0 0.5 0.9 1.9 3.1 3.9 

37-1103 

Yes 8760 0 1.36 1.17 0.0 0.5 1.0 1.9 3.0 3.8 

No 8245 515 1.15 1.25 0.0 0.4 0.7 1.5 2.8 3.8 

37-1201 

Yes 8760 0 1.17 1.24 0.0 0.4 0.7 1.5 2.8 3.8 

No 8302 458 2.35 2.19 0.0 1.1 1.7 2.8 4.9 6.8 

37-1301 

Yes 8760 0 2.34 2.17 0.0 1.1 1.7 2.8 4.9 6.7 

No 8250 510 1.11 0.84 0.0 0.6 0.9 1.4 2.1 2.8 

37-2005 

Yes 8760 0 1.10 0.83 0.0 0.6 0.9 1.4 2.1 2.8 

No 8347 413 1.11 1.10 0.0 0.4 0.7 1.3 2.7 3.6 

37-4002 

Yes 8760 0 1.11 1.11 0.0 0.4 0.7 1.4 2.7 3.6 

No 8354 406 1.11 0.91 0.0 0.6 0.8 1.4 2.3 2.9 

59-0001/7 

Yes 8760 0 1.11 0.90 0.0 0.6 0.8 1.4 2.3 2.9 

No 8325 435 0.74 1.01 0.0 0.2 0.3 0.9 2.1 3.0 

59-1003 

Yes 8760 0 0.74 1.00 0.0 0.2 0.3 0.9 2.1 3.0 

No 8230 530 1.36 1.21 0.0 0.6 1.0 1.7 2.8 3.7 

59-5001 

Yes 8760 0 1.36 1.19 0.0 0.6 1.0 1.7 2.8 3.7 
5-20

Table 5-16. Descriptive statistics for hourly carbon monoxide concentrations before and after estimation o

Los Angeles 2006. 

1-hour values CO concentration (ppm) 

(n) Percentile 

Monitor 

Missing 

values 

filled? Present Missing Mean SD Min 25th 50th 75th 90th 95th 9

No 8365 395 0.42 0.37 0.0 0.2 0.3 0.6 0.9 1.2 

37-0113 

Yes 8760 0 0.43 0.37 0.0 0.2 0.3 0.6 0.9 1.2 

No 8345 415 0.67 0.61 0.0 0.3 0.5 0.8 1.5 2.0 2

37-1002 

Yes 8760 0 0.67 0.61 0.0 0.3 0.5 0.8 1.5 2.0 2

No 8265 495 0.55 0.50 0.0 0.2 0.4 0.7 1.3 1.6 2

37-1103 

Yes 8760 0 0.56 0.50 0.0 0.2 0.4 0.8 1.3 1.6 2

No 8375 385 0.55 0.54 0.0 0.2 0.4 0.6 1.2 1.7 2

37-1201 

Yes 8760 0 0.56 0.53 0.0 0.2 0.4 0.7 1.2 1.7 2

No 8275 485 1.00 0.89 0.0 0.5 0.7 1.1 2.0 2.9 4

37-1301 

Yes 8760 0 1.01 0.90 0.0 0.5 0.7 1.1 2.0 2.9 4

No 8258 502 0.73 0.49 0.0 0.4 0.6 1.0 1.4 1.7 2

37-2005 

Yes 8760 0 0.73 0.49 0.0 0.4 0.6 1.0 1.3 1.7 2

No 8216 544 0.74 0.55 0.0 0.4 0.6 0.9 1.5 1.9 2

37-4002 

Yes 8760 0 0.75 0.54 0.0 0.4 0.6 0.9 1.5 1.9 2

No 8342 418 0.43 0.47 0.0 0.1 0.3 0.5 1.0 1.4 2

59-0001/7 

Yes 8760 0 0.43 0.47 0.0 0.1 0.3 0.5 1.0 1.4 2

No 8358 402 0.33 0.45 0.0 0.1 0.1 0.4 0.9 1.4 2

59-1003 

Yes 8760 0 0.33 0.45 0.0 0.1 0.1 0.4 0.9 1.4 2

No 8227 533 0.64 0.57 0.0 0.3 0.4 0.7 1.3 1.8 

59-5001 

Yes 8760 0 0.64 0.56 0.0 0.3 0.4 0.7 1.3 1.8 2
5-21

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 resulting 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 used 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 adjustment of all hourly values (section 3.1.5). Consequently, the 

following adjustment equation was employed: 

 COadj(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 COadj(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 exceeded 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 calculating 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 equally to all 8,760 hourly ambient CO concentrations at that 

monitor is thus 9.4/9.5, or 0.989. In a similar manner, 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 nonoverlapping 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.pdf
5-22

Staff evaluated three additional air quality scenarios considering potential alternative 

standard levels, averaging times, and forms. Assuming a similar form and averaging time of the 

current 8-hour standard (2nd highest non-overlapping 8-hour average CO concentration), staff 

selected a level of 5 ppm for the first 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 identifying potential levels associated with alternative 

standards. The second potential alternative standard considered by staff also uses an 8-hour 

average concentration, though having a 99th percentile daily maximum CO concentration of 5.0 

ppm.

12 The final potential alternative standard that staff evaluated was a 99th percentile daily 

maximum 1-hour CO concentration of 8.0 ppm. Table 5-17 summarizes the adjustment factors 

that were developed from equation 5-1 and used to adjust the high 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 Valuea

(ppm) 

Adjustment 

Factor 

9 0.989b

2nd highest 5 9.5 

8-hour 0.568 

99th pct daily max 5.0 7.3 0.685 

Denver 

1-hour 99th pct daily max 8.0 13.5 0.593 

9 0.627b

2nd highest 5 15 

8-hour 0.360 

99th pct daily max 5.0 13.1 0.382 Los Angeles 

1-hour 99th 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 the current 8-hour average CO standard. 

 

 

11 Note that this would allow a 2nd 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 99th percentile 

1-hour daily maximum is exactly 8.0 ppm. 
5-23

Tables 5-18 and 5-19 provide the descriptive statistics for the Denver and Los Angeles 

ambient monitor 1-hour CO concentrations, respectively, after applying the appropriate 

adjustment factor to simulate just meeting the current standard. As expected, the adjusted 

monitoring concentrations for Denver 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 ppm. 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 section 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 just meet the current 8-hour CO NAAQS. When 

comparing the adjusted concentrations in Denver and Los Angeles, there is still a sharper rate of 

increase in CO concentrations at and above the 95th percentiles of the distribution, only now all 

of the Denver monitors have greater CO concentrations at these upper percentiles when 

compared with concentrations observed at all of the Los Angeles monitors (excluding 

concentrations at the Los Angeles design monitor). 

Given the proportional approach used to adjust ambient concentrations for each of the 

other exposure scenarios (e.g., 99th percentile daily maximum 1-hour concentration of 8.0); 

similar patterns in concentrations were expected and are therefore not summarized here. 
5-24

Table 5-18. Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just meet t

standard – Denver (adjusted 1995 data). 

Hourly-average CO concentration (ppm) 

Monitor 

ID Mean SD 25.0 50.0 75.0 90.0 95.0 99.0 99.5 99.9 

31-0002 1.5 1.2 0.8 1.2 1.8 2.7 3.4 6.0 7.6 13.0 31-0013 1.2 1.1 0.6 0.9 1.5 2.5 3.4 5.4 6.4 8.7 31-0014 1.1 1.0 0.5 0.7 1.3 2.3 3.2 5.3 6.4 7.7 59-0002 1.0 0.9 0.4 0.6 1.1 2.0 2.7 4.8 5.7 7.4 

Table 5-19. Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just meet t

standard – Los Angeles (adjusted 1997 data). 

Hourly-average CO concentration (ppm) 

Monitor 

ID Mean SD 25.0 50.0 75.0 90.0 95.0 99.0 99.5 99.9 

37-0113 0.5 0.5 0.1 0.4 0.8 1.3 1.6 2.3 2.6 3.2 37-1002 1.1 0.8 0.6 0.9 1.3 2.2 2.8 3.8 4.1 4.8 37-1103 0.9 0.7 0.3 0.6 1.2 1.9 2.4 3.4 3.6 4.5 37-1201 0.7 0.8 0.3 0.4 0.9 1.8 2.4 3.7 4.3 5.2 37-1301 1.5 1.4 0.7 1.1 1.7 3.1 4.2 7.0 8.5 10.8 37-2005 0.7 0.5 0.4 0.6 0.9 1.3 1.8 2.6 2.9 3.8 37-4002 0.7 0.7 0.3 0.4 0.8 1.7 2.3 3.3 3.7 4.5 59-0001 0.7 0.6 0.4 0.5 0.9 1.4 1.8 2.9 3.4 4.3 59-1003 0.5 0.6 0.1 0.2 0.6 1.3 1.9 2.9 3.2 3.9 59-5001 0.9 0.7 0.4 0.6 1.1 1.8 2.3 3.8 4.5 6.1 
5-25

5.8 METEOROLOGICAL DATA 

A few algorithms within APEX require meteorological data (primarily temperature) from 

stations located within the study area. For example, in selecting a CHAD diary to simulate an 

individual’s daily activities, a range 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 temperatures are used by APEX to select from 

an appropriate air exchange rate distribution to estimate indoor microenvironmental 

concentrations (section 5.8). For 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 stations 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 years (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 approximately 3.6 km from the 37-4002 station. 

The same two stations (31-0002 and 59-0002) were 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 will be represented by the temperature data 

provided by the station. In this assessment, we set this to a value that includes all census tracts 

within the air districts. A radius of 15.5 km met this requirement for Denver (Figure 5-1), while 

Los Angeles required a larger radius of 70.5 km (Figure 5-2). 

Table 5-20. Locations of meteorological stations 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 51.5 
5-26

Table 5-21. Locations of meteorological stations 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) meteorological 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 two MET stations for use in 1995 and 2006. All 

missing values in year 2006 were filled using linear 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 values), linear interpolation was judged as 

inappropriate because this method would likely not 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 approach 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 and 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 sites identified here as site 1 (ID 037-4002) and 

site 2 (located at Daugherty Field). Both locations reported temperature in both years of interest; 

however, the degree of completeness for each varied. 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 greatest 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 temperature from Site 2 was used to fill the missing 

value for 2006. For 1997, there were 2,263 missing values 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 Site 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 after 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, variance and percentile statistics. 

5.9 MICROENVIRONMENTS MODELED 

This section briefly discusses the approach and specific factors used to estimate CO 

microenvironmental concentrations in the current assessment. As described in section 4.4.4.3, 

the approach was originally developed for pNEM/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 

 COout= fproximity x fpenetration x COambient (5-2) 

Since this is effectively equivalent to the method used by APEX3.1 described in section 

4.4.4.4, we used the same method here with respect to application of the proximity and 

penetration factors in APEX4.3 to implement equation 4-11. First, to obtain 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 fproximity term 

would be sampled for each microenvironment from a lognormal distribution with geometric 

mean (GM) equal to M(m) and geometric standard deviation (GSD) equal to 1.5232. A value for 

fpenetration for each hour would also be sampled from a lognormal distribution with geometric 

 

14 This was done in SAS using a procedure, PROC EXPAND, along with the JOIN option. 
5-28

mean (GMT) equal to 1.0 and geometric standard deviation (GSDT) equal to 1.6289. As 

described in section 4.4.4.4, the fpenetration 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 application of APEX to Los Angeles and Denver. These eight 

microenvironments were selected rather than the 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 microenvironmental approach (see section 5.9.5). 

Note that when this algorithm is implemented within the APEX framework, the 

application of Equation 4-11 will produce a “compression” effect in which the ratio of COout to 

COmon tends to become smaller (on average) as COmon increases. This effect is consistent with 

data reported by field studies such as Wilson et al. (1995) which have compared outdoor 

concentrations with simultaneously measured fixed-site concentrations. Note also that the 

effective microenvironment-to-ambient concentration ratios will differ from the proximity factor 

distributions provided in Table 5-22 given the influence of other 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 distributions 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 

(5th pct)

Maximum 

(95th pct)

1 Indoors Residence Indoors - residence 1.034 1.5232 0.5175 2.0661 

2 Indoors Service 

station and 

auto repair 

Service station 

Auto repair 

2.970 1.5232 1.4864 5.9345 

3 Indoors Other 

indoor 

locations A 

Other repair shop 

Shopping mall 

Other indoor location 

Auditorium 

Store 

Office 

Other public building 

Bars 

Restaurants 

1.213 1.5232 0.6071 2.4237 

4 Indoors Other 

indoor 

locations B 

Health care facility 

School 

Church 

Manufacturing facility

0.989 1.5232 0.4950 1.9762 

5 Outdoors Near road 

locations 

Bus stop 

Bicycle 

Motorcycle 

Other near road 

1.607 1.5232 0.8042 3.2110 

6 Outdoor Public 

parking or 

fueling 

facility 

Indoor parking 

garage 

Outdoor parking 

garage 

Outdoor parking lot 

Outdoor service 

station 

2.970 1.5232 1.4864 5.9345 

7 Outdoors Other 

outdoor 

locations 

Outdoor res. garage 

Construction site 

Residential grounds 

School grounds 

Sports arena 

Park or golf course 

Other outdoor 

1.436 1.5232 0.7187 2.8693 

8 Vehicle Automobile 

and mass 

transit 

Automobile 

Truck 

Bus 

Train/subway 

Other vehicle 

3.020 1.5232 1.5114 6.0344 
5-30

5.9.2 Microenvironmental Mapping 

In APEX, microenvironments represent the exposure locations for simulated individuals. 

For exposures to be estimated accurately, it is important to have realistic microenvironments that 

match closely to the locations where 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 missing (X) or the location is classified 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 permits 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 ambient 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 designations 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 the CHAD location code). This list is inserted in 

the “CustomWork” parameter found in the simulation control file. The default list of work 

activity codes, which were used in this application, includes codes 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 associated 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 concentrations obtained from a single monitor. To 

avoid this effect, staff chose to specify the option 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 modeled 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 Outdoors 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. Air exchange rate data used for the indoor 

residential microenvironment were the same used in APEX for the most recent O3 NAAQS 

review (US EPA, 2007). As part of that earlier review, AER data were reviewed, compiled, and 

evaluated from the extant literature to generate location-specific AER distributions15 categorized 

by influential factors, namely temperature and presence of air conditioning. In general, 

lognormal distributions provided the best fit, and are defined by a geometric mean (GM) and 

standard deviation (GSD). To avoid unusually extreme simulated AER values, bounds of 0.1 

and 10 were selected for minimum and maximum AER, respectively. Tables 5-24 and 5-25 

 

15 There were AER measurement data specific to the Los 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 modeling indoor exposures in Denver and Los 

Angeles, respectively, each classified by A/C prevalence and temperature categories. For all 

other indoor microenvironments, the AER distributions used here (Tables 5-24 and 5-25) were 

based on data provided by an indoor air quality study (Persily et al., 2005). These are the same 

AER distributions used for the APEX assessments in the most recent O3 NAAQS review (US 

EPA, 2007), NO2 REA (US EPA, 2008c) and SO2 REA (US EPA, 2009b). 

Because the selection of an air 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 simulated 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 homes, 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 distribution and dependent on temperature. 

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

Classification category Parameters of bounded lognormal distributiona

Microenvironment A/C present? 

Mean Temp 

(degrees F) GM GSD Minimumc

 Maximumc

≤ 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 

Yesb

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 distributiona

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 

Yesb

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 0.1c 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 complete time-series of exposure and dose for 

each simulated individual based on the microenvironmental concentrations they contact and the 

activities they perform. Because there are usually 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 typical APEX output files. The 

APEX events and hourly files provide event-level and hourly-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 Contribution to At-Risk Population Exposure 

Levels 

The first analysis using the individual-level 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 aggregated into five microenvironments to evaluate 

time spent by simulated persons at selected exposure levels. The aggregation was based on 

microenvironments having similar proximity factors (see Table 5-22), resulting in aggregated 

microenvironments defined as follows: indoors-low (indoor-residence and indoor-other A&B), 

indoors-high (indoors-auto service/repair), outdoors-low (outdoors-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-level exposure and dose output was designed to 

estimate the effective microenvironmental factors, or the ratio of modeled microenvironmental 

concentrations to associated ambient monitor concentrations. The distributions of these effective 

microenvironmental factors are then compared to 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 earlier, the series of factors in 

equation 4-11 are designed to spatially and temporally adjust the ambient concentrations to 

reflect concentrations immediately proximal to the microenvironment. The proximity factors 

listed in Table 5-22 would effectively reflect microenvironment-to-spatially and temporally 

adjusted ambient concentration ratios, not microenvironment-to-fixed site ambient monitor 

concentration ratios commonly reported in the extant human exposure literature. 

The microenvironment-to-ambient concentration ratios were calculated 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 associated with ambient concentrations 

 

16 The standard APEX summary output table only generates microenvironment contributions for the 

general population (i.e., it is not necessarily representative 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 concentration in the APEX hourly file does account for when a person might 

experience ambient concentrations outside 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 average event-level ambient concentration. 
5-35

less than 1 ppm. These calculated ratios using the event-level modeled concentrations tended to 

be extremely large, particularly when considering the as is air quality, as a result of dividing by 

extremely low ambient concentrations rather than the calculation method employed in estimating 

the microenvironmental concentrations. Inclusion of these ratios (while valid) would be of little 

practical use in interpreting the microenvironment-to-ambient concentration ratios and high 

microenvironmental concentrations because the design of the microenvironmental algorithm 

results in the highest concentrations 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 estimated microenvironmental 

concentration. Any large proximity 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 extreme ratio and high concentration 

combinations in estimating the microenvironmental concentrations. Although the calculated 

ratios can reach extremely high values (> 100), they are not responsible for estimating the 

highest microenvironmental concentrations. The 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 microenvironment-to-ambient concentration ratios 

using estimated indoor-residential concentrations (left panel) and insidevehicle concentrations (right panel) in Denver – as is air quality. 
5-36

5.10.3 Estimate of Ambient Exposure Contribution to Total COHb Level 

In the third analysis, we were interested in the contribution of ambient CO exposure to 

each individual’s COHb dose level above their endogenous CO production. As summarized in 

section 4.4.7 and described fully in Appendix B, we estimated COHb levels in each simulated 

individual using the CFK dose module within APEX. Theoretically, in the absence of ambient 

concentrations or other sources 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 concentrations 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 levels 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 contribution 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 dose profile, only in this 

instance it is the daily maximum end-of-hour contribution to COHb associated 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, APEX generated the complete time-series of 

exposure and dose for 5,000 persons, of which there 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 meeting 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 demonstrate the representativeness of the smaller sample 

run size. The summary exposure and dose results from the smaller sample size runs (i.e., the 

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 exposure scenario and study area. Table 5-

26 summarizes the exposure output using as is air quality, while Table 5-27 summarizes the 

summary dose output for air quality just meeting the current standard; as noted above, little 

difference can be observed when comparing the 50,000 person simulation to the 5,000 person 

 

18 Of the 5,000 person model simulations, APEX simulated 438 CHD persons in Denver and 394 CHD 

persons in Los Angeles. 
5-37

simulation for either exposure scenario. Therefore, the time-series results generated for the 

smaller sample size simulations are considered representative of the larger modeled CHD 

population.19

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 Populationa,b,c Los Angeles CHD Populationa,b,c

Percent of Persons 

Percent of 

Person-days Percent of Persons 

Percent of 

Person-days 

Daily 

Maximum 

Exposure 

(ppm) 50K Rund

 5K Run 50K Rund 5K Run 50K Rune 5K Run 50K Rune 5K Run 

≥ 0 100 100 100 100 100 100 100 100 

≥ 3 99.5 99.3 11.2 10.5 99.9 100 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 < 0.1 

≥ 20 0.1 0 < 0.1 0 0.6 1.0 < 0.1 < 0.1 

≥ 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 57.8 55.3 1.0 0.9 76.9 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 ischemia 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. d

 Exposure results obtained from Table 6-1. 

e

 Exposure results obtained from Table 6-4. 

 

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 (and 

microenvironmental factors) estimated from the smaller sample size CHD population simulations are considered 

representative estimated exposures for 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 persons – just meeting the current 

standard. 

 Denver CHD Populationa,b,c Los Angeles CHD Populationa,b,c

Percent of 

Persons 

Percent of 

Person-days 

Percent of 

Persons 

Percent of 

Person-days 

COHb 

Level 

(%) 50K Rund

 5K Run 50K Rund 5K Run 50K Rune 5K Run 50K Rune 5K Run 

0.0 100 100 100 100 100 100 100 100 

1.0 82.3 82.6 6.8 6.1 41.2 44.4 1.8 2.1 

1.5 23.4 21.2 0.4 0.4 4.7 5.1 < 0.1 < 0.1 

1.75 10.8 10.0 < 0.1 < 0.1 1.6 1.3 < 0.1 < 0.1 

2.0 4.2 4.1 < 0.1 < 0.1 0.6 0.8 < 0.1 < 0.1 

2.5 0.8 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 < 0.1 0 < 0.1 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 ischemia developed by EPA (see section 5.5.1). c

 Ambient concentrations from 1995 (Denver) and 1997 (Los Angeles) were adjusted to just meet a 

2nd highest 8-hour average concentration of 9.4 ppm. d

 Dose results obtained from Table 6-18. 

e

 Dose results obtained from Table 6-19. 

5.11 KEY OBSERVATIONS 

The following presents the key observations for this chapter: 

 Two exposure model domains (Denver and Los Angeles study areas) were defined by 

overlaying ambient monitor locations having 10 km radii with US census tract 

population data. Monitors selected comprised the bulk of the urban core in each 

location, where ambient monitoring data exist. 

 Two simulated at-risk subpopulations were identified by combining the census tractspecific age and gender population distributions with HD and CHD prevalence rates, 

each also stratified by age and gender. In using this approach, staff can represent the 

variability that exists in the simulated at-risk HD and CHD subpopulations that reside 

in each census tract and within each study area. 

 Both simulated at-risk subpopulations include an estimate of persons with 

undiagnosed CHD. 

 To represent spatial variability in ambient concentrations in Denver, a total of four 

monitors were used; in Los Angeles, the total number of monitors was ten. Temporal 

variability was represented by use of hourly ambient concentrations in each study area. 
5-39

 The exposure and dose model simulations included 8 microenvironments in each 

location to represent the expected variability in microenvironmental CO 

concentrations. 

 All indoor microenvironments were modeled using a mass balance model to represent 

temporal variability in indoor CO concentrations with respect to the outdoor CO 

concentration variability. In addition, distributions of microenvironmental factors were 

used for all microenvironments rather than point estimates. Using distributions of 

microenvironmental factors will better represent both spatial and temporal variability in 

estimated microenvironmental CO concentrations. 

 Additional analyses using output from individual-level simulations were performed to 

provide information on the microenvironments most influential to population exposure 

at different exposure levels. This included an analysis of the effective ratios of 

microenvironment to ambient concentrations and the contribution of ambient CO 

exposure to total COHb level estimates. The smaller sample sizes generated for these 

analyses were found to be representative of the larger simulations employed for 

estimating exposure and dose in the different air quality exposure scenarios. 
5-40

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Scientific and Technical Information. Office of Air Quality Planning and Standards 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/fera/data/apex322/apexusersguidevoli4-24-03.pdf 

US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. July 2007. 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 Standards, U.S. Environmental Protection Agency, Research 

Triangle Park, NC. 
5-41

US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO2 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/COREA1stDraftOct2009.pdf

US EPA. (2009b). Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air 

Quality Standard. Report no. EPA-452/R-09-007. August 2009. Available at 

http://www.epa.gov/ttn/naaqs/standards/so2/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 Review Draft. Report no. EPA-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 Residential 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 described 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 2nd highest 8-hour average concentration of 5 ppm, (4) adjusted to just meet a 99th

percentile daily maximum 8-hour average of 5.0 ppm, and (5) adjusted to just meet a 99th

percentile daily maximum 8-hour average of 8.0 ppm (see section 5.7 for details regarding the air 

quality adjustment procedure). This chapter is divided into four main sections, with each 

described briefly below. 

The first section (6.1) summarizes the estimated 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 subpopulation includes individuals with 

diagnosed CHD as well as those persons 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 includes individuals with diagnosed heart 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 include the number and percent of persons at or 

above staff-selected exposure levels and the corresponding number of person-days.2

 Two 

exposure averaging times were also selected: 1-hour and 8-hour daily maximum exposures. 

Section 6.2 summarizes the estimated COHb levels for persons in the simulated at-risk 

population residing in each study area. The primary dose metric of interest in this REA and 

generated by APEX includes the number and percent 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 COHb level was reported. This section also 

 

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 heart attack) and all types of heart disease (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 opportunities for individuals to 

experience more than one day in the year above a selected exposure concentration, hence use of the term persondays. 
 6-2

presents an evaluation of the ambient contribution to COHb levels for APEX simulated 

individuals for three of the air quality scenarios. 

In section 6.3, staff compares the dose estimates in this CO REA with those estimated in 

the 2000 exposure assessment (Johnson et al., 2000). Finally, key observations 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 results 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 scenarios 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 APEX 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. Additional exposure output data sets (i.e., 

microenvironmental contributions to selected exposure levels and the evaluation of 

microenvironmental factors distributions) were generated using a smaller simulation size (i.e., 

5,000 persons) and the CHD population for 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 size and at-risk population used. 

6.1.1 Air Quality “As Is” 

As described in section 5.6, ambient monitoring data from each study area for the year 

2006 were used to represent the as is air quality. Table 6-1 summarizes the distribution 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 simulated 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 daily 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 exposure concentrations of less than 3 ppm. 
 6-3

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 population, consistent with the larger size of that 

population, there was little difference when considering the percentages of each population at or 

above selected exposure levels and for either averaging time. For example, about 20% of both 

the CHD and HD populations experienced 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 Diseasea,c All Heart Diseaseb,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 coronary heart disease, angina pectoris, heart attack, and any 

other heart condition or disease (CDC, 2009). 

c

 Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1). 

These exposure results are consistent with the ambient concentration distributions used to 

represent this scenario, where upper 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 concentrations (e.g., exposures occurring while inside vehicles 

or immediately near roads) that, in general, may be a factor of two to five times higher than 
 6-4

ambient CO concentrations at monitors that are not immediately near roads (2000 AQCD; ISA 

section 3.6.1). 

As mentioned above, a smaller subset of the at-risk CHD population was simulated to 

generate additional exposure results. First, we were interested in determining the important 

microenvironments that contribute to CHD population exposures at each of the selected levels.3

 

Figure 6-1 illustrates such an analysis, 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 exposure concentration decreases 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 than 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 million minutes in the year simulated where the 

population was exposed at or above a level of 20 ppm. Note that when considering the zero 

exposure concentration level, the distribution of microenvironmental contributions effectively 

approximates the time spent by the population in each of the microenvironments across the 

simulation period. Not surprisingly, the simulated population spends over 85% of their time 

within indoor microenvironments and is consistent with the reported activity pattern survey data 

(Graham and McCurdy, 2004). At the lowest exposure levels (e.g., < 2 ppm), much of the 

estimated population exposure occurs within indoor microenvironments. For exposure 

concentrations at or above 2 ppm, most of the population exposure occurs while inside vehicles 

or during time spent within outdoor high-concentration microenvironments. These two 

aggregate microenvironments predominantly contribute to exposure concentrations at or above 4 

ppm (> 90% of all personal time at these levels). 

These smaller CHD population simulations also served to approximate the effective 

microenvironmental factors, the distributions of which are more useful to compare with literature 

reported microenvironment-to-ambient concentration 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 reported personal exposure to ambient concentration ratios 

(ISA, Figure 3-46). The distribution of estimated microenvironmental concentrations (Table 6-

3) also generally reflect the range of concentrations reported in personal exposure studies, 

 

3

 The default APEX summary output table only generates microenvironmental contributions for the general 

population. 
 6-5

particularly those where measurements were made inside-vehicles and near-roadways (ISA 

section 3.6.6.2). 

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 or

above Exposure Concentration (%)

inside-vehicles

outdoors (low)

outdoors (high)

indoors (low)

indoors (high)

2.3 E+85.5 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+0

Total Minutes at or 

above Exposure

Concentration

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 microenvironment-to-ambient concentration ratios 

using the Denver CHD population – as is air quality. 

Distribution Percentilesc,d (unitless) 

Microenvironmenta

Eventsb,c

(n) 

Mean 

Ratioc,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 experienced an exposure event in that microenvironment. c

 Data set used to calculate the ratios represent the CHD population extracted from a 5,000 person 

simulation and was screened to eliminate ambient concentrations less than 1 ppm. See section 5.10 

for details. 

d The mean, standard deviation, and percentiles (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 6-3. Estimated distribution of microenvironmental concentrations using the Denver 

CHD population – as is air quality. 

Distribution Percentilesc,d (ppm) 

Microenvironmenta Eventsb,c

(n) 

Mean 

COc,d 

(ppm) Stdc,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 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 percentiles (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. 
 6-7

In Los Angeles, there was a greater number of individuals experiencing 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-risk population. This is expected given that the 

exposure modeling domain in Los Angeles encompasses a larger area than Denver and therefore 

comprises a larger total simulated population. The 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, approximately 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 experienced by approximately 20% of the CHD population 

(Table 6-1). This result is likely driven by the differences noted in the as is air quality data, 

where in Los Angeles, the 2006 ambient concentrations 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 estimated to be at or above 

30 ppm (but less than 40 ppm) in the Los Angeles 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 less 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 wider in Los Angeles when compared with that of 

Denver when considering the as is air quality scenario. 

Similar to that estimated for either at-risk population in Denver, over 98% of the persondays in Los Angeles were associated with 1-hour daily maximum exposures below 6 ppm and 

very few persons (<1%) experienced 8-hour daily maximum exposures at or above 9 ppm. 

These exposure results are also consistent with the distributions of ambient air quality used to 

represent this scenario, where the ambient monitor upper percentile 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 Diseasea,c All Heart Diseaseb,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 76.9 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 coronary heart disease, angina pectoris, heart attack, and any 

other heart condition or disease (CDC, 2009). 

c

 Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1). 

The microenvironmental contributions to time spent at or above selected exposure levels 

in Los Angeles (Figure 6-2) are also similar to that estimated for Denver, though indoor 

microenvironments contribute a somewhat greater percentage in Los Angeles at each of the 

exposure levels. This is likely the result of generally higher as is ambient concentrations across 

the entire distribution in Los Angeles when compared with the Denver ambient concentrations 

distribution (Table 5-14). As expected, the distributions of event-level microenvironment-toambient concentration ratios (Table 6-5) are also similar to those calculated for Denver (Table 6-

3), though estimated microenvironmental concentrations are slightly higher in Los Angeles 

(Table 6-6). This is also a function of the generally higher ambient concentrations 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 or

above Exposure Concentration (%)

inside-vehicles

outdoors (low)

outdoors (high)

indoors (low)

indoors (high)

2.1 E+86.5 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+0

Total Minutes at or 

above Exposure

Concentration

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 microenvironment-to-ambient concentration ratios 

using the Los Angeles CHD population – as is air quality. 

Distribution Percentilesc,d (unitless) 

Microenvironmenta

Eventsb,c

(n) 

Mean 

Ratioc,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 population experienced an exposure event in that 

microenvironment. 

c

 Data set used to calculate the ratios represents the CHD population extracted from a 5,000 

person simulation and was screened to eliminate ambient concentrations less than 1 ppm. See 

section 5.10 for details. 

d The mean, standard deviation, and percentiles (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 microenvironmental concentrations using the Los 

Angeles CHD population – as is air quality. 

Distribution Percentilesc,d (ppm) 

Microenvironmenta Eventsb,c

(n) 

Mean 

COc,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 population 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 percentiles (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 meet the current 8-hour standard 

As described in section 5.6, historical ambient monitoring data from each study area were 

adjusted to represent air quality that just meets the current 8-hour standard. For both Denver 

(year 1995) and Los Angeles (year 1997), air quality data were adjusted downwards to meet a 2nd

highest 8-hour average concentration of 9.4 ppm. Note that even with a downward proportional 

adjustment, these adjusted ambient concentrations remain much higher than as is ambient air 

quality. Table 6-7 summarizes the exposure results for the simulated at-risk populations in the 

Denver study area when using these adjusted ambient CO concentrations as an input to APEX 

and using the same modeling assumptions and parameter 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 nearly a factor of 10 greater than that estimated 

when using the as is air quality (Table 6-1). The highest 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 experienced by less than 0.2% of the simulated atrisk populations. Thus, there is a wider range in the exposure levels experienced by the 

simulated at-risk populations when considering this exposure scenario. 

The number and percent of persons experiencing 8-hour daily maximum exposures is 

also greater for this scenario when compared with corresponding levels using the as is air 

quality. Nearly 10% of the simulated at-risk population was estimated to experience an 8-hour 

daily maximum exposure at or above 9 ppm (Table 6-7) when considering 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 concentration at that same level when considering the as is 

air quality scenario (Table 6-1). 

Indoor microenvironments simulated in Denver were estimated to contribute to a greater 

percentage of time spent at each selected exposure level using the adjusted air quality when 

compared with the results using as is air quality, though the difference is most notable at 

exposures less than 9 ppm (Figure 6-3). For example, about 50% of exposures 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 the 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 Diseasea,c All Heart Diseaseb,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 2nd 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 coronary heart disease, angina pectoris, heart attack, and 

any other heart condition or disease (CDC, 2009). 

c

 Includes estimate of persons with undiagnosed ischemia developed 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 or

above Exposure Concentration (%)

inside-vehicles

outdoors (low)

outdoors (high)

indoors (low)

indoors (high)

2.3 E+81.3 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+0

Total Minutes at or 

above Exposure

Concentration

Denver: AQ Just Meeting Current Standard

Figure 6-3. Estimated microenvironmental contributions to time spent at or above 

selected exposure concentrations using the Denver CHD population – air 

quality just meeting the current 8-hour standard. The total minutes spent at 

or above each exposure concentration are presented above each bar. 
 6-14

Table 6-8. Estimated distribution of microenvironment-to-ambient concentration ratios 

using the Denver CHD population – air quality just meeting the current 8-hour 

standard. 

Distribution Percentilesc,d (unitless) 

Microenvironmenta Eventsb,c

(n) 

Mean 

Ratioc,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 experienced an exposure event in that microenvironment. c

 Data set used to calculate the ratios represents the CHD population extracted from a 5,000 person 

simulation and was screened to eliminate ambient concentrations less than 1 ppm. See section 5.10 

for details. 

d The mean, standard deviation, and percentiles (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 6-9. Estimated distribution of microenvironmental concentrations using the Denver 

CHD population – air quality just meeting the current 8-hour standard. 

Distribution Percentilesc,d (ppm)

Microenvironmenta Eventsb,c

(n) 

Mean 

COc,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 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 percentiles (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. 
 6-15

Similarly in Los Angeles, the number and percent of persons exposed above selected 

exposure concentrations is greater when considering 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-hour 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 estimate 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 average duration shorter than an hour.4

 For a time period 

closer to the present scenario, Rodes et al. (1998) reported maximum in-vehicle and on-road CO 

concentrations of only 7.6 and 9.0 ppm, respectively during Los Angeles commutes in 1997. 

Note however the scripted commutes in this latter 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 exposure distribution for Los Angeles to Denver 

for this exposure scenario, there are greater percentages of persons and person-days estimated for 

the Denver simulated at-risk populations at each corresponding exposure level. For example, 

only 2.7% of the CHD population was estimated to experience an 8-hour daily maximum 

exposure at or above 9 ppm in Los 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 have ambient concentrations 

adjusted to just meet the same 8-hour average CO concentration of 9.4 ppm. 

 

4

 On average, the commute time associated with the collection of these 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 Diseasea,c All Heart Diseaseb,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 2nd 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 coronary heart disease, angina pectoris, heart attack, and 

any other heart condition or disease (CDC, 2009). 

c

 Includes estimate of persons with undiagnosed ischemia developed 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 or

above Exposure Concentration (%)

inside-vehicles

outdoors (low)

outdoors (high)

indoors (low)

indoors (high)

2.1 E+88.5 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+0

Total Minutes at or 

above Exposure

Concentration

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 standard. 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 meeting the current standard. The Los Angeles data differ in that upperlevel exposure concentrations only extend upwards 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 (occurring for about 1,100 event-level minutes). Not 

surprisingly, estimated microenvironment-to-ambient concentration ratios for this scenario 

(Table 6-11) were also similar to those for the Denver air quality scenarios (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-vehicle microenvironment are within the maximum 

measured peak level (one minute average) concentration 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 microenvironment-to-ambient concentration ratios 

using the Los Angeles CHD population – air quality just meeting the current 8-

hour standard. 

Distribution Percentilesc,d (unitless) 

Microenvironmenta Eventsb,c

(n) 

Mean 

Ratioc,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 experienced an exposure event in that microenvironment. c

 Data set used to calculate the ratios represents the CHD population extracted from a 5,000 person 

simulation and was screened to eliminate ambient concentrations less than 1 ppm. See section 5.10 

for details. 

d The mean, standard deviation, and percentiles (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 6-12. Estimated distribution of microenvironmental concentrations using the Los 

Angeles CHD population – air quality just meeting the current 8-hour 

standard. 

Distribution Percentilesc,d (ppm) 

Microenvironmenta Eventsb,c

(n) 

Mean 

COc,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 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 percentiles (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. 
 6-19

6.1.3 Air quality adjusted to just meet alternative air quality scenarios 

Three potential alternative air quality scenarios were investigated to observe how the 

averaging times, forms, and levels for the simulated alternative standards would affect the 

estimated exposure concentrations (section 5.6). The data for the 1-hour and 8-hour daily 

maximum exposure concentrations are presented here, 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 quality adjusted to just meet the current 8-hour 

standard, the distributions of the microenvironment–to-ambient concentration ratios are expected 

to be the same and are thus not provided here for the alternative air quality scenarios. The air 

quality associated with these alternative standards is generally similar to as is conditions. 

Therefore, the microenvironmental contributions to exposure and microenvironmental 

concentrations associated with these 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 scenarios 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 simulated at-risk populations 

are observed at the same level. This was by general design, that is, to investigate differing forms 

of the potential alternative 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 (Table 6-13) when compared with those of Los Angeles (Table 6-

14) when considering the same potential alternative standard, consistent with the differing 

distribution of CO concentrations. There are also consistent patterns 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 considering a particular 

level, form, and study area. For example, the 2nd highest 8-hour CO concentration of 5 ppm 

most limited the number and percent of exposed persons in each location when compared to 

results for the other potential alternative standard, though in Denver there were still a few 

persons estimated to experience a 1-hour daily maximum at or above 30 ppm (Table 6-13). In 

Los Angeles, the upper level of the 1-hour daily maximum exposure concentration experienced 

by the simulated CHD population was just at or above 20 ppm, though below 30 ppm. 
6-20

Table 6-13. Estimated daily maximum 1-hour and 8-hour exposures for simulated at-risk populations in th

area – alternative air quality scenarios. 

2nd highest 8-hour average of 5 ppm 99th pct 8-hour Daily Max of 5.0 ppm 99th pct 1-hour Dai

CHD Personsa,c HD Personsb,c CHD Personsa,c HD Personsb,c CHD Personsa,c 

Daily 

Maximum 

Exposure 

(ppm) 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 ≥ 3 53,656 100 85,926 100 53,656 100 85,926 100 53,656 100 ≥ 6 47,264 88.1 75,671 88.1 50,534 94.2 80,928 94.2 48,239 89.9 ≥ 9 25,174 46.9 40,489 47.1 32,147 59.9 51,645 60.1 26,877 50.1 

≥ 12 11,082 20.7 18,140 21.1 16,326 30.4 26,384 30.7 12,192 22.7 ≥ 15 4,850 9.0 7,886 9.2 7,676 14.3 12,501 14.5 5,282 9.8 ≥ 20 975 1.8 1,802 2.1 1,888 3.5 3,209 3.7 1,222 2.3 ≥ 30 62 0.1 74 < 0.1 136 0.3 247 0.3 74 0.1 ≥ 40 0 0 0 0 0 0 0 0 0 0 

1-hour 

≥ 60 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 ≥ 3 44,574 83.1 71,488 83.2 48,819 91.0 78,189 91.0 45,808 85.4 ≥ 6 6,590 12.3 10,637 12.4 10,650 19.8 17,412 20.3 7,380 13.8 ≥ 9 839 1.6 1,296 1.5 1,555 2.9 2,369 2.8 926 1.7 

≥ 12 111 0.2 173 0.2 296 0.6 432 0.5 123 0.2 ≥ 15 25 < 0.1 37 < 0.1 49 < 0.1 74 < 0.1 25 < 0.1 ≥ 20 0 0 0 0 0 0 0 0 0 0 ≥ 30 0 0 0 0 0 0 0 0 0 0 ≥ 40 0 0 0 0 0 0 0 0 0 0 

8-hour 

≥ 60 0 0 0 0 0 0 0 0 0 0 

Notes: Ambient concentrations from 1995 were adjusted to just meet the level of the potential alternative standard indicated uderived 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 coronary heart disease, angina pectoris, heart attack, and any other heart 

disease (CDC, 2009). 

c Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1).
6-21

Table 6-14. Estimated daily maximum 1-hour and 8-hour exposures for simulated at-risk populations in th

area – alternative air quality scenarios. 

2nd highest 8-hour average of 5 ppm 99th pct 8-hour Daily Max of 5.0 ppm 99th pct 1-hour Dai

CHD Personsa,c HD Personsb,c CHD Personsa,c HD Personsb,c CHD Personsa,c 

Daily 

Maximum 

Exposure 

(ppm) 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 ≥ 3 378,624 98.8 624,183 99.0 379,728 99.1 626,090 99.3 381,535 99.6 ≥ 6 215,454 56.2 348,720 55.3 229,302 59.9 373,105 59.1 260,913 68.1 ≥ 9 68,540 17.9 105,469 16.7 77,571 20.3 120,321 19.1 98,244 25.6 

≥ 12 19,769 5.2 30,507 4.8 24,285 6.3 36,728 5.8 34,721 9.1 ≥ 15 6,322 1.7 9,533 1.5 7,827 2.0 11,841 1.9 10,738 2.8 ≥ 20 903 0.2 1,606 0.3 1,305 0.3 2,208 0.3 2,709 0.7 ≥ 30 0 0 0 0 0 0 0 0 0 0 ≥ 40 0 0 0 0 0 0 0 0 0 0 

1-hour 

≥ 60 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 ≥ 3 214,149 55.9 350,626 55.6 230,807 60.3 376,918 59.8 264,425 69.0 ≥ 6 17,060 4.5 26,192 4.2 20,672 5.4 31,811 5.0 28,801 7.5 ≥ 9 903 0.2 1,204 0.2 1,204 0.3 1,706 0.3 2,007 0.5 

≥ 12 301 < 0.1 502 < 0.1 301 < 0.1 502 < 0.1 301 < 0.1 ≥ 15 0 0 0 0 100 < 0.1 100 < 0.1 100 < 0.1 ≥ 20 0 0 0 0 0 0 0 0 0 0 ≥ 30 0 0 0 0 0 0 0 0 0 0 ≥ 40 0 0 0 0 0 0 0 0 0 0 

8-hour 

≥ 60 0 0 0 0 0 0 0 0 0 0 

Notes: Ambient concentrations from 1997 were adjusted to just meet the level of the potential alternative standard indicated uderived 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 coronary heart disease, angina pectoris, heart attack, and any other heart 

disease (CDC, 2009). 

c Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1).
 6-22

6.2 ESTIMATED COHB LEVELS 

Consistent with section 6.1, this 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 CHD and HD populations. For all five air quality scenarios, we 

report the number and percentage of persons and 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 selected COHb level. For three of the scenarios 

(i.e., as is conditions, air quality adjusted to just meet the current 8-hour standard, and air quality 

adjusted to just meet a 99th percentile daily maximum 8-hour average concentration of 5.0 ppm), 

additional dose output data sets were generated using a smaller simulation size (i.e., 5,000 

persons) and for the CHD population, to estimate the contribution of ambient CO exposure alone 

to selected COHb levels. See section 5.10 for details on the approach used and evaluation of the 

representativeness of the simulation size and at-risk population used. 

6.2.1 Air Quality “As Is” 

Table 6-15 provides the COHb levels (%) for the simulated at-risk populations in Denver 

when considering the as is air quality. No persons were estimated to have experienced a daily 

maximum end-of-hour COHb level at or above 2.5%, while only a few (<0.1%) were estimated 

to have experienced a COHb level ≥2.0%. Nearly 99% of the simulated at-risk populations had 

their highest daily maximum end-of-hour COHb level below 1.5%. Most of the simulated 

person-days (about 97.5%) were associated with daily maximum end-of-hour COHb levels 

below 1.0%. 

Similarly in Los Angeles, very few persons (1.6%) 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). Similar to that observed for the at-risk population in Denver, very few persons 

(<0.1%) were estimated to experience at least one daily maximum end-of-hour COHb levels at 

or above 2.0% in Los Angeles. Of these few hundred simulated individuals, all were estimated 

to have only one person-day per person at that level (i.e., to have experienced this COHb level on 

only one day in the year). As was observed with the Denver dose results, the majority of the 

simulated person-days (>98%) were limited to COHb levels at or below 1.0%. 
 6-23

Table 6-15. Portion of the simulated 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. 

Coronary Heart Diseasea,c All Heart Diseaseb,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 10,773 20.1 494,600 2.5 17,807 20.7 781,100 2.5 

≥ 1.5 654 1.2 17,170 < 0.1 1,074 1.2 29,050 < 0.1 

≥ 1.75 111 0.2 2,616 < 0.1 234 0.3 3,579 < 0.1 

≥ 2.0 12 < 0.1 86 < 0.1 12 < 0.1 86 < 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 coronary heart disease, angina pectoris, heart attack, and 

any other heart condition or disease (CDC, 2009). 

c

 Includes estimate of persons with undiagnosed ischemia developed 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 maximum end-of-hour COHb at or above 

specified levels – as is air quality. 

Coronary Heart Diseasea,c All Heart Diseaseb,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 99,348 25.9 1,725,000 1.2 165,880 26.3 3,112,000 1.4 

≥ 1.5 6,021 1.6 91,120 < 0.1 9,834 1.6 165,200 < 0.1 

≥ 1.75 1,907 0.5 18,870 < 0.1 3,011 0.5 28,100 < 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 coronary heart disease, angina pectoris, heart attack, and 

any other heart condition or disease (CDC, 2009). 

c

 Includes estimate of persons with undiagnosed ischemia developed 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 experiences a daily maximum end-of hour COHb at 

or above a selected level. As observed in the above analyses, there were little differences 

between the two simulated at-risk populations in the percentage estimated to experience a 

%COHb at or above selected levels. Therefore, we have chosen to use the CHD population as a 

base case for this analysis. Figure 6-5 presents the percent of the CHD populations in Denver 

(top) and Los Angeles (bottom) experiencing repeated COHb levels using as is air quality. As a 

point of reference in the figure, the percent of persons with the number of occurrences ≥ 1 

corresponds to the data summarized in Tables 6-15 and 6-16.5

 Note that we have included only 

those COHb levels between 1.5 and 2.0%, though in 0.1% COHb increments. 

Consistent with the summary tables, a small percentage of the CHD population (about 

1.2% in Denver; 1.6% in Los Angeles) was estimated to experience a single COHb level at or 

above 1.5% (Figure 6-15). Even fewer persons experienced two or more occurrences of COHb 

above 1.5% (about 0.7% of the CHD population in Denver; 0.5% in Los Angeles), generally 

about a factor of two or three lower than when considering persons experiencing at least one 

COHb dose at or above that level. There were very few persons experiencing 3 or more COHb 

levels at or above 1.5% in either study area. Even fewer persons experienced multiple 

occurrences of higher COHb levels. For example less than 0.1% of the population experienced 3 

or more COHb levels at or above 1.8% in either study area. 

As discussed in chapter 2, we also evaluated the contribution of ambient CO exposure 

alone to each simulated person’s COHb level. The complete time-series of exposure for each 

individual was used to generate each person’s maximum end-of-hour COHb level attributable to 

ambient CO exposure. This analysis also focused on the CHD population as a base-case in each 

study area, given the limited differences between the percentage 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 1.8% 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% COHb for approximately 99% of the 

simulated CHD population. This is consistent with the above results for total COHb (Tables 6-

15 and 6-16)6

 and the finding that endogenous CO production, on average, can contribute to an 

 

5

 The number of occurrences in Figure 6-5 corresponds to the number of days per year each person(s) 

experienced a maximum end-of-hour COHb dose at or above the given level. 

6

 We used the term total COHb here as the combined dose from ambient CO exposure and endogenous CO 

production. 
 6-25

end-of-hour COHb level of approximately 0.3% for the simulated at-risk population in either 

study area (Appendix B, Table B-4).
 6-26

1.2

0.7

0.3

< 0.1 < 0.1 < 0.1

0.7

0.3

0.1 < 0.1 < 0.1 < 0.1

0.5

0.2

0.1 < 0.1 < 0.1 < 0.1

0.5

0.2

0.1 < 0.1 < 0.1 < 0.1 0.4

0.2

< 0.1 < 0.1 < 0.1 < 0.1 0.4

0.2

< 0.1 < 0.1 < 0.1 < 0.1

0.0

0.5

1.0

1.5

2.0

Percent of CHD Population 

at or Above COHb Dose 

Level (%)

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

1.6

0.9

0.6

0.4

0.2

< 0.1

0.5

0.3 0.2 0.1 < 0.1 0

0.2

0.2 0.1 0.1 < 0.1 < 0.1 0

0.1 0.1 < 0.1 < 0.1 0

0.1 0.1 < 0.1 < 0.1 < 0.1 0

0.1 0.1 < 0.1 < 0.1 < 0.1 0

0.0

0.5

1.0

1.5

2.0

Percent of CHD Population 

at or Above COHb Dose 

Level (%)

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 population in Denver (top) and Los Angeles 

(bottom) experiencing repeated COHb 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 alone at or above specified levels – as is

air quality. 

Percent of CHD Populationa Ambient-Exposure 

Contribution to 

COHb Level (%) Denver Los Angeles 

≥ 1.0 2.1 7.1 

≥ 1.1 1.4 5.3 

≥ 1.2 0.9 3.0 

≥ 1.3 0.7 1.5 

≥ 1.4 0.2 0.5 

≥ 1.5 0.2 0.5 

≥ 1.6 0.2 0.5 

≥ 1.7 0 0.3 

≥ 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 concentrations, COHb levels estimated to be 

experienced by the simulated at-risk populations in each study area were greater when 

considering exposures associated with air quality adjusted to just meet the current standard than 

when using as is air quality. For example, in Denver, just over 4% 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). Note there were fewer than 0.1% of persons in Denver estimated to 

have experienced COHb at a level above 2.0% based on estimated ambient exposures associated 

with as is air quality (Table 6-15). A similar pattern is observed for the simulated at-risk 

population in Los Angeles (Table 6-19), though a lower percentage of persons (0.6%) 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 estimated to have 

experienced a daily maximum end-of-hour COHb levels as high as 3.0%. However, most of the 

persons that did experience these higher COHb levels (≥2.0%) experienced them for fewer than 

2 days in a year (Table 6-19). A general pattern in experiencing multiple days at or above 

selected COHb levels is evident in Figure 6-6. The percent of the population that experiences 

two or more occurrences of %COHb at or above selected levels is reduced by about a factor of 

two when compared with those experiencing at least one. Even fewer persons experience three 
 6-28

or more days per year, also reduced by about a factor of about 2 when compared with those 

experiencing at least two. When considering three or more occurrences in a year, the reduction 

rate in the percent of persons at or above the selected COHb levels lessens with increasing 

number of occurrences. In general, this pattern indicates a few simulated individuals, given 

specific conditions of their estimated exposure and dose, experienced multiple days (e.g., 

possibly 6 or more) at or above selected COHb levels.
 6-29

Table 6-18. Portion of the simulated 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. 

Coronary Heart Diseasea,c All Heart Diseaseb,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 44,166 82.3 1,330,000 6.8 71,710 83.5 2,125,000 6.8 

≥ 1.5 12,563 23.4 79,050 0.4 21,028 24.5 124,800 0.4 

≥ 1.75 5,800 10.8 15,240 < 0.1 9,502 11.1 24,820 < 0.1 

≥ 2.0 2,258 4.2 3,677 < 0.1 3,826 4.5 6,047 < 0.1 

≥ 2.5 444 0.8 494 < 0.1 802 0.9 901 < 0.1 

≥ 3.0 111 0.2 111 < 0.1 222 0.3 247 < 0.1 

≥ 4.0 12 < 0.1 12 < 0.1 25 < 0.1 25 < 0.1 

Notes: 

Ambient concentrations from 1995 were adjusted to just meet a 2nd 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 coronary heart disease, angina pectoris, heart attack, and 

any other heart condition or disease (CDC, 2009). 

c

 Includes estimate of persons with undiagnosed ischemia developed 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 maximum end-of-hour COHb at or above 

specified levels – air quality just meeting the current 8-hour standard. 

Coronary Heart Diseasea,c All Heart Diseaseb,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 

≥ 1.0 157,852 41.2 2,584,000 1.8 260,612 41.3 4,598,000 2.0 

≥ 1.5 17,963 4.7 125,400 < 0.1 31,410 5.0 227,400 < 0.1 

≥ 1.75 6,222 1.6 30,510 < 0.1 10,537 1.7 50,280 < 0.1 

≥ 2.0 2,107 0.6 3,211 < 0.1 3,613 0.6 4,716 < 0.1 

≥ 2.5 301 < 0.1 401 < 0.1 401 < 0.1 502 < 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 2nd 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 coronary heart disease, angina pectoris, heart attack, and 

any other heart condition or disease (CDC, 2009). 

c

 Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1). 
 6-30

23.4 17.3

12.6

9.1

6.2

4.2 11.2

7.2

4.5

3.0 2.1 1.4

7.0

4.7

2.7 1.6 0.9 0.4

5.0

3.1 1.6

0.9 0.5 0.2 4.0

2.4 1.3 0.6 0.2 < 0.1

3.3

1.8 1.0 0.3 < 0.1 < 0.1

0

5

10

15

20

Percent of CHD Population 

at or Above COHb Dose 

Level (%)

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

4.7

3.0

2.0

1.3 0.9 0.6

2.2 1.1 0.6 0.5 0.4 0.2

1.1 0.6 0.4 0.3 0.1 < 0.1

0.7 0.4 0.3 0.2 < 0.1 < 0.1

0.5 0.4 0.2 0.1 < 0.1 < 0.1

0.5 0.2 0.2 0.1 < 0.1 < 0.1

0

5

10

15

20

Percent of CHD Population 

at or Above COHb Dose 

Level (%)

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 population in Denver (top) and Los Angeles 

(bottom) experiencing repeated COHb levels – air quality just meeting the 

current 8-hour standard. 
 6-31

Table 6-20. Percentage of simulated CHD populations in the Denver and Los Angeles study 

areas estimated to experience daily maximum 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 Populationa Ambient-Exposure 

Contribution to 

COHb Level (%) Denver Los Angeles 

≥ 1.0 43.6 17.3 

≥ 1.1 30.4 11.2 

≥ 1.2 21.0 7.6 

≥ 1.3 16.0 4.1 

≥ 1.4 12.8 2.0 

≥ 1.5 9.4 1.8 

≥ 1.6 6.8 1.3 

≥ 1.7 4.1 0.8 

≥ 1.8 3.4 0.8 

≥ 1.9 3.0 0.5 

≥ 2.0 2.7 0.5 

Notes: 

Ambient concentrations from were adjusted to just meet a 2nd 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 current 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 given COHb level. Approximately 9% of the 

Denver population is estimated to have experienced 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 had 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 described above, the percentage of persons estimated 

to experience a daily maximum end-of-hour COHb at or above the selected levels is generally 

similar across the three potential alternative standard scenarios. For example, in Denver most of 

the population (≥99%) was estimated to not experience a daily maximum end-of-hour COHb 

level at or above 2.0% (Table 6-21). Further, the potential alternative standard of a 2nd highest 8-
 6-32

hour average of 5 ppm resulted in the lowest number 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 expected, the corresponding 

estimated percent of the CHD population experiencing 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 99th percentile daily 

maximum 8-hour average CO concentration of 5.0 ppm, 1.0% of the CHD population in the 

Denver study area was estimated to have experienced an estimated daily maximum end-of-hour 

COHb level at or above 2.0% (Table 6-21); in Los Angeles this dose level was estimated to have 

been experienced by fewer than 0.1% of the CHD population (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., greater spatial variability in 

ambient concentrations in the larger Los Angeles study area) and the large differences in altitude 

between the two study areas. 

Table 6-23 summarizes the percentage of the CHD population in both study areas 

estimated to have experienced selected levels of COHb, 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 a 99th percentile daily maximum 8-hour average 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 the CHD population estimated to have experienced a given COHb 

level. Approximately 3.2% of the Denver population 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. 
6-33

Table 6-21. Portion of the simulated at-risk populations in the Denver study area estimated to experience 

end-of-hour COHb at or above specified levels– air quality just meeting potential alternative s

2nd highest 8-hour average of 5 ppm 99th pct 8-hour Daily Max of 5.0 ppm 99th pct 1-hour Daily M

CHD Personsa,c HD Personsb,c CHD Personsa,c HD Personsb,c CHD Personsa,c H

COHb 

Level (%) 

Number Percent Number Percent Number Percent Number Percent Number Percent Nu

≥ 0.0 53,656 100 85,926 100 53,656 100 85,926 100 53,656 100 8≥ 1.0 24,212 45.1 39,773 46.3 31,690 59.1 51,459 59.9 25,656 47.8 4≥ 1.5 2,764 5.2 4,640 5.4 4,961 9.2 8,256 9.6 3,171 5.9 

≥ 1.75 802 1.5 1,382 1.6 1,703 3.2 2,789 3.2 1,074 2.0 

≥ 2.0 284 0.5 494 0.6 555 1.0 975 1.1 284 0.5 ≥ 2.5 62 0.1 86 0.1 111 0.2 210 0.2 74 0.1 ≥ 3.0 0 0 12 < 0.1 12 < 0.1 37 < 0.1 12 < 0.1 ≥ 3.5 0 0 0 0 0 0 12 < 0.1 0 0 ≥ 4.0 0 0 0 0 0 0 0 0 0 0 

Notes: Ambient concentrations from 1995 were adjusted to just meet the level of the potential alternative standard indicated usinderived 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 coronary heart disease, angina pectoris, heart attack, and any other heart con

disease (CDC, 2009). 

c Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1).
6-34

Table 6-22. Portion of the simulated at-risk populations in the Los Angeles study area estimated to experie

maximum end-of-hour COHb at or above specified levels – air quality just meeting potential a

standards. 

2nd highest 8-hour average of 5 ppm 99th pct 8-hour Daily Max of 5.0 ppm 99th pct 1-hour Daily M

CHD Personsa,c HD Personsb,c CHD Personsa,c HD Personsb,c CHD Personsa,c H

COHb 

Level (%) 

Number Percent Number Percent Number Percent Number Percent Number Percent Nu

≥ 0.0 383,040 100 630,807 100 383,040 100 630,807 100 383,040 100 63≥ 1.0 54,491 14.2 91,319 14.5 62,017 16.2 102,258 16.2 78,776 20.6 13≥ 1.5 2,509 0.7 4,917 0.8 3,613 0.9 6,121 1.0 5,319 1.4 

≥ 1.75 803 0.2 1,305 0.2 1,004 0.3 1,706 0.3 1,706 0.4 

≥ 2.0 301 < 0.1 401 < 0.1 301 < 0.1 401 < 0.1 401 0.1 ≥ 2.5 0 0 0 0 0 0 0 0 0 0 ≥ 3.0 0 0 0 0 0 0 0 0 0 0 ≥ 3.5 0 0 0 0 0 0 0 0 0 0 ≥ 4.0 0 0 0 0 0 0 0 0 0 0 

Notes: Ambient concentrations from 1997 were adjusted to just meet the level of the potential alternative standard indicated usinderived 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 coronary heart disease, angina pectoris, heart attack, and any other heart con

disease (CDC, 2009). 

c Includes estimate of persons with undiagnosed ischemia developed by EPA (see section 5.5.1.1).
 6-35

Table 6-23. Percentage of simulated CHD populations in the Denver and Los Angeles study 

areas estimated to experience daily maximum end-of-hour COHb contribution 

from ambient exposure alone at or above specified levels – air quality just 

meeting a 99th percentile daily maximum 8-hour average concentration of 5.0 

ppm. 

Percent of CHD Populationa Ambient-Exposure 

Contribution to 

COHb Level (%) Denver Los Angeles 

≥ 1.0 18.0 2.8 

≥ 1.1 13.2 1.8 

≥ 1.2 8.9 1.3 

≥ 1.3 5.9 0.8 

≥ 1.4 3.7 0.5 

≥ 1.5 3.2 0.3 

≥ 1.6 2.7 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 99th 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, population exposure and dose were estimated in 

2000 using pNEM/CO, a predecessor 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 regard 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 corresponding 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 COHb levels using a largely similar approach, 
 6-36

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: 

 number of ambient monitors used (e.g., previously six in Denver versus four used 

here),9

 

 location of ambient monitors used (e.g., eight 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 used a mass balance approach) 

 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 not listed, staff still did 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 concentrations, microenvironmental approach, CFK 

module used, etc.). Table 6-24 presents estimates for the percentage of Denver adults with CHD 

estimated to experience a daily maximum end-of-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 quality 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 emissions) 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 indoor source emissions are included in the 

exposure modeling simulation. It is clear by comparing 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 contributions, as the percent of persons at selected COHb levels increased 

by large margins (about 10-30 percentage points) where data are available and comparable from 

both model simulations. The range of COHb levels also extends upwards to at or above 6.0% 

 

7

 When considering the exposure scenario that uses air quality just meeting the current standard. 

8

 When considering the CHD population. 

9

 The actual number of monitors used in the 2000 assessment was seven, though air quality data from two 

monitors in Boulder CO (monitor IDs 080130010 and 080131001) were averaged to create a composite air quality 

district (Johnson et al., 2000). 
 6-37

COHb for 0.2% of the CHD population when considering 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 the current dose assessment results. This is 

because the data used for simulating the indoor source emissions, while some of it may be 

readily available for use in the currently used APEX model, are considered not necessarily 

reflective of current conditions. For example, the indoor gas stove emissions data used were 

generated at a time where pilot lights (a continuous low-level combustion 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 

been substantially reduced in public buildings and likely within many residential 

microenvironments. It is these important changes in indoor source emissions, the limited 

availability of current and relevant input data, and the limited time and resources allocated for 

this assessment, that preclude a current quantitative assessment of the impact of indoor source 

emissions on population COHb levels. 

The range of dose estimates without simulated indoor sources are generally similar in 

both study areas when comparing results from the 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 4.2% 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. The corresponding value estimated in the Johnson et al. (2000) 

assessment was approximately 0.5% of the IHD population. One factor contributing to the 

difference in the results for Denver at this benchmark level is the air quality data used for each 

assessment. The two additional air quality districts used in the 2000 assessment had consistently 

lower hourly CO concentrations when compared with concentrations measured at the other four 

ambient monitors that were consistent for both assessments. For example, at selected upper 

percentiles of the air quality distribution for these two monitoring sites, concentrations are 

reduced by a factor of 1.5 to 6.8 (Table 3-8 of Johnson et al., 2000). As a result, the simulated 

persons residing within these low CO concentration air quality districts10 would have 

consistently lower estimated exposure concentrations and thus experience lower COHb levels. 

While there were differences in two of the ten monitors used to define the Los Angeles study 

 

10 These persons comprised 20.6% of the total simulated IHD population in the Denver study area (see 

Table 2-8 of Johnson et al., 2000). 
 6-38

areas in each assessment, a 1.5 to 6.8 factor difference in the upper percentiles of the air quality 

distributions used is not present. 
 6-39

 

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. 

Percentage of CHD Adults at or Above COHb Level 

Johnson et al. (2000) pNEM/COa 2010 REA APEX/COb

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 82.3 

≥ 1.5 37.6 6.7 23.4 

≥ 2.0 19.9 0.5 4.2 

≥ 2.5 10.4 0.2 0.8 

≥ 3.0 5.5 < 0.1 0.2 

≥ 4.0 1.6 0 < 0.1 

≥ 5.0 0.6 0 na 

≥ 6.0 0.2 0 na 

Notes: 

a

 Used Denver 1995 CO ambient concentrations with no adjustment (2nd 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). 

na - benchmark level was not evaluated in the current exposure and dose simulations. 

 

Table 6-25. Percentage of Los Angeles 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. 

Percentage of CHD Adults at or Above COHb Level 

Johnson et al. (2000) pNEM/COa 2010 REA APEX/COa

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 41.2 

≥ 1.5 32.3 5.2 4.7 

≥ 2.0 16.8 0.5 0.6 

≥ 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 

Notes:

a

 Los Angeles 1997 ambient CO concentrations adjusted to just meet the current 8-

hour standard (9.4 ppm). 
 6-40

6.4 KEY OBSERVATIONS 

Presented below are key observations resulting from the exposure and dose assessment 

for ambient CO. 

 Ambient CO exposures and resulting COHb levels in the blood of two simulated at-risk 

populations in the Los Angeles and Denver study 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, and air quality adjusted to just meet three potential 

alternative standards. 

 The two at-risk populations simulated were: (1) persons with diagnosed CHD, 

including those estimated to have undiagnosed CHD, and (2) the larger group of 

persons with any type of HD including those estimated to have undiagnosed CHD. 

While the number of persons and person-days at or above selected 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 microenvironments to exposure concentrations was 

generally similar between the two 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 contributed to most exposures at or above 3 

ppm (70 – 100%). In comparison, when considering air quality just meeting the 

current standard, the percent contribution from indoor microenvironments was 

generally higher for low level exposures (about 65 – 85% of exposure concentrations at 

or above 1 ppm and 2 ppm), though again higher level exposures were dominated by 

the contributions from inside-vehicle microenvironments. 

 The relationship between the two study areas with regard to estimated distribution of 

maximum end-of-hour COHb levels differed with 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 slightly higher distribution of maximum endof-hour COHb levels than the Denver populations. Under conditions of air quality 

adjusted from historical air quality data to just meet the current 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 simulated populations. A lower percentage of 

the simulated at-risk populations in Denver 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 highest incremental contribution of 

ambient CO exposure to maximum end-of-hour COHb levels estimated in the 

simulated populations was 1.7% COHb, and more than 99% of both study area 

populations were estimated to have ambient CO contributions to COHb below 
 6-41

1.4%. As with estimates of total COHb (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 percentage of the population estimated to 

experience ambient contributions to COHb 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 95% of both simulated at-risk populations in the 

Los Angeles study area, and was below 2% COHb for 99.4% 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 maximum end-of-hour COHb below 2% was approximately 95%. 

 As with estimates of total COHb (i.e., COHb from endogenous CO production 

and ambient exposure together), the percentage of the simulated population 

estimated to experience the higher ambient contributions to maximum end-of 

hour COHb was appreciably greater in Denver as compared to Los Angeles. 

While estimated ambient CO contributions 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 about 

87%. 

 In addition to the simulations for as is and just meeting the current 8-hour standard air 

quality conditions, three simulations of air quality just meeting potential alternative 

standards were performed. The alternatives comprise different combinations of form, 

averaging time and level which were expected 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 simulated include: (1) a second-highest 8-hour 

average of 5 ppm, (2) a 99th percentile daily maximum 8-hour average of 5.0 ppm, and 

(3) a 99th percentile daily maximum 1-hour average of 8.0 ppm. 

 These three simulations generated generally 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 selected COHb levels than did simulations for air 

quality adjusted to just meet the current 8-hour standard. For example, about 

1% or less of the Denver populations had a highest daily maximum end-ofhour COHb at or above the 2.0% COHb level in the alternative standards’ 

simulations as compared to approximately 4.2% in simulations of air quality 

just meeting the current standard. When considering the potential alternative 

standards in Los Angeles, generally fewer than 0.1% of the simulated at-risk 

populations were estimated to experience a maximum end-of-hour COHb level 
 6-42

at or above 2.0 % COHb, compared to 0.5% at that same COHb 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 the level of public health protection that might be provided 

by alternative standard levels associated with 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 estimates 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 employed similar approaches, similar, 

although not identical air quality data for this scenario, and they used different 

exposure models (APEX vs. pNEM). Results were similar for the 1.5% and 2% COHb 

level for the simulated Los Angeles study area population and somewhat different for 

the Denver study area population. For example, the two assessments’ Los Angeles 

estimates of population percentages with highest daily maximum end-of hour COHb at 

or above these COHb levels were similar and within about 10% of one another. For 

the Denver simulated study populations, however, the estimates for these two COHb 

levels were about 3 and 8 times greater, respectively, in the current assessment when 

compared with that estimated in the prior assessment. 
 6-43

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 Rules, South Coast Air Quality Management District. 

October 1989. 
 7-1

7 VARIABILITY ANALYSIS AND UNCERTAINTY 

CHARACTERIZATION 

An important issue associated with any population exposure or risk assessment is the 

characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in 

a population or variable of interest (e.g., residential air exchange rates). 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 purpose of the assessment (i.e., scenario uncertainty). Uncertainty is, 

ideally, reduced to the maximum extent possible through improved measurement of key 

parameters and iterative model refinement. The approaches used to assess variability and to 

characterize uncertainty in this REA are discussed in the following two sections. Each section 

also contains a concise summary of the identified 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 ambient CO concentrations, population 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 estimated benchmark exceedances or 

health risk outputs. For example, variability 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 potential 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 obtained 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 incorporated 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 using 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 has been designed to account for variability in 
 7-2

some of the input data, including the physiological variables that are important inputs to 

determining ventilation rates and COHb levels. As a result, APEX addresses much of the 

variability in factors that affect human exposure and dose. Important sources of the variability 

accounted for in this analysis are summarized in Table 7-1. 

Table 7-1. Summary of how variability was incorporated 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 probabilistically 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 each 

individual 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 microenvironments 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 approach, 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 (NHANES), for the years 1999-

2004 (Isaacs and Smith (2005) in Appendix A). 

Height 

Values randomly sampled from distribution based on 

equations developed for each gender developed 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 values by accounting for 

variability inherent to influential 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 process of gathering data, developing models, and estimating 

exposures and risks, given the goals of the assessment, scale of the assessment performed, and 

limitations of the input data available. However, significant uncertainty often remains and 

emphasis is then placed on characterizing the nature 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 uncertainty were identified as important in 

improving the approach used to estimate exposure 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 approach modifications and analyses conducted throughout the 

evaluation of this final assessment included the following: 

 Expanding the number of monitors used to better address spatial variability in ambient 

CO concentrations; 

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

 Using distributions of proximity factors 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 estimates 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 microenvironmental factors used to estimate exposure 

concentrations, and; 

 Performing sensitivity analyses including 

 Evaluating the impact of additional monitoring data on estimated exposures 

and COHb levels; 
 7-5

 Evaluating the impact of varying undiagnosed prevalence rates by gender to 

estimated population COHb levels, and; 

 Evaluating the impact of using alternative hemoglobin content distributions to 

represent a hypothetical population with anemia. 

These additional analyses and approaches used are not without their own uncertainties, 

and following this iterative process, these uncertainties also need to be characterized. This 

characterization of uncertainty can include either qualitative or quantitative evaluations, or a 

combination of both. The approach can also be tiered; that is, the analysis can begin with a 

simple qualitative uncertainty characterization and then progress to a complex probabilistic 

uncertainty analysis. This second level of analysis may be appropriate when a lower tier analysis 

indicates there is a high degree of uncertainty for certain identified sources, the sources of 

uncertainty are highly influential 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 quantitative uncertainty analyses should always be performed in all 

exposure and risk assessments. The decision regarding the type of uncertainty characterization 

performed is also informed by the intended scope and purpose of the assessment, whether the 

selected analysis will provide additional information to the overall decision regarding health 

protection, whether sufficient data are available to conduct a complex quantitative analysis, and 

whether time and resources are available for higher tier characterizations (US EPA, 2004; WHO, 

2008). 

The primary purpose of the uncertainty characterization approach selected in this CO 

REA is to identify and compare the relative impact that important sources of uncertainty may 

have on the estimated potential health effect endpoints. The approach used to characterize 

uncertainty was adapted from guidelines outlining how to conduct a qualitative uncertainty 

characterization (WHO, 2008) and applied in the most recent NO2 (US EPA, 2008) and SO2

NAAQS reviews (US EPA, 2009). While it may be considered ideal to follow a tiered approach 

in the REA to quantitatively characterize all identified uncertainties, staff selected the mainly 

qualitative approach given the extremely limited data available to inform probabilistic analyses. 

The qualitative approach used in this REA varies from that of WHO (2008) in that a 

greater focus was placed on evaluating the direction and the magnitude1

 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 direction of the influence on the assessment results 

for each of these identified sources of uncertainty. 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 between 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 quality 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 there may be alternative methods 

available. However, staff judged that the quantitative comparison of the data sets indicates that 

there would likely be little influence 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 proportional) effect on the results. For example, 

the magnitude of uncertainty associated with using the historical data to represent a hypothetical 

future scenario was rated as low-medium. While we do not have information regarding how the 

ambient CO concentration distribution might look in the future, we do know however what the 

distribution might look like based on historical trends and the primary 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 would be judged as likely low

or having negligible impact on the 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 exposure scenarios, staff observed mainly proportional 

differences (e.g., a factor of two or three) in the estimated exposure and dose levels. Assuming 

that these types of ambient concentration adjustments could reflect 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 historical 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 results, potentially an order of magnitude or more. This rating 

would be used where the model was extremely sensitive to the identified source of uncertainty. 

In addition to characterizing the magnitude of uncertainty, staff also included the 

direction of influence, indicating how the source of uncertainty was judged to affect estimated 
 7-7

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 judge the directional nature of uncertainty 

associated with the particular source. Staff 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 regarding 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 characterization 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 characterization results. We identified sixteen 

sources of uncertainty associated with this approach for modeling CO population exposure and 

dose and associated potential health risk, each summarized in Table 7-2. Section 7.2.1 describes 

the sources more fully and provides support for the ratings ultimately selected by staff, while 

section 7.2.2 includes model sensitivity analyses referenced in section 7.2.1. As mentioned in 

earlier chapters, given the significant time constraints of this review, results of the 

characterization are provided in this document without substantial interpretation. Rather, 

interpretative discussion of these results, including further consideration 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 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. 
 7-9

7.2.1 Considerations in Characterizing Sources of Uncertainty 

Staff considerations in reaching 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 identified regarding the ambient monitoring 

concentrations used to estimate 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 the ambient concentration data 

sets, potentially influencing the number of benchmark dose-level exceedances. Note also that 

any uncertainty regarding low level concentrations at or near the monitoring detection limit is 

unlikely to influence high COHb levels, the levels 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 ambient 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 are being used in a manner consistent with one of 

the defined objectives of ambient monitoring. Therefore, we judge uncertainty associated with 

the knowledge base as low. 

Missing Data Substitution Method 

There were very few missing hourly concentration values when considering the years of 

ambient monitoring data used in this assessment. This is because we first screened all available 

monitoring data for a minimum of 75% completeness regarding hours/day and days/year for all 

four quarters within each year. In meeting the 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 monitoring data set, with an average 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 depending on the nature of the data gaps, including 

the development of linear regression models and interpolation between two points, with each 

method considering important factors that may influence 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 distribution of estimated concentrations would be similar to those generated here 

given that most of these potential alternative data substitution methods are also informed by the 

existing measurement data. Note also that there 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 missing 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 this were the case, the selected 

substitution method would lead to an underestimation in exposure concentrations and COHb 

levels experienced by the simulated at-risk populations. This also assumes that the substitution 

of missing low-level concentration 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 values equivalent to zero ppm, indicating 

that the monitor was in operation, though concentrations 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 are typically 0.5 ppm (section 3.1.2). This indicates 

that reported concentrations between 0.1 and 0.5 ppm were quantified, though likely having 

greater uncertainty in their assigned values compared 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 other value. In Denver, there were very few 

instances where a zero concentration was reported for 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 less than 0.1 ppm would 

change the number or percent of persons exposed or experiencing %COHb above levels of 
 7-11

interest in this REA. There were, however, a greater number of instances where a zero ppm 

monitoring concentration was reported in Los Angeles (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 levels of interest are driven exclusively by upper 

percentile ambient and microenvironmental exposure concentrations (For example, see Appendix 

B.6). 

Table 7-3. Frequency of CO concentrations reported 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 attributes (hours per day, days per year). The 

hourly ambient CO concentrations are used to estimate event-level exposure concentrations that 

can range from 1 minute to 1 hour in duration. The relationships used to estimate these exposure 

concentrations are commonly derived from hourly measurements, thus are generally consistent 

with how the ambient monitor data are being used in this REA. In addition, the 

microenvironmental algorithm employed a factor that adjusts for temporal differences in outdoor 

concentrations with respect to that measured at an ambient monitor (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, given the observed individual doses in 

response to highly variable ambient/exposure concentrations (see Figure B-2 in Appendix B.6), 

staff judges that there would likely be low impact 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 likely that while CO 

concentrations have changed dramatically, the 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 have considered the impact of the improvements 

made in representing spatial variability in ambient CO levels throughout each study area for this 

REA as compared with the simplified approach used in the first draft CO REA. 

Before considering the impact improving spatial representation of monitoring 

concentrations had on dose estimates, we first considered the spatial variability in the air quality 

data used for the study areas. Analysis of the full set of monitoring data indicates spatial 

variability in monitoring concentrations across each area is relatively limited, particularly when 

considering more recent years (Tables 3-1 through 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 measure similar (though still temporally variable) CO 

concentration levels. Note also that the microenvironmental algorithm we used in this 

assessment to estimate outdoor microenvironmental concentrations has an adjustment factor to 

address spatial variability in ambient concentrations 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 relationships between monitor and outdoor 

concentrations, it is possible that there would be a low magnitude of influence on estimated 

COHb levels in the presence of an alternative or supplemental monitoring network. 

Staff also considered the impact on dose estimates of using the full monitoring data set to 

represent the spatial variability in each study area, as compared 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 ambient concentrations across a larger study area (see section 7.2.2.1). 

Based on these sensitivity results, staff judged improving spatial representativeness of the 

monitoring network to potentially have a medium magnitude of impact on the estimated 
 7-13

exposures and COHb levels of interest if the existing monitoring network does not adequately 

represent high ambient concentrations to which 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 associated with representation of higher ambient 

concentrations. Conversely, a similar hypothetical scenario could be constructed to investigate 

the potential impact associated with representation of lower ambient concentrations. Such a 

comparison might find a similar, medium, magnitude of influence to the estimated number and 

percent of persons at or above COHb levels (albeit lower COHb levels experienced 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 monitoring network has specific objectives and monitoring scale that 

may not appropriately capture the true spatial variability in CO concentrations. In the absence of 

1) a monitoring network designed to better measure spatial 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, staff judge the uncertainty in the knowledge base as medium.

7.2.1.2 Adjustment of Air Quality to Simulate Just Meeting the Current and 

Potential Alternative Standards 

Two elements of uncertainty were identified regarding the ambient monitoring 

concentrations used to estimate 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. There is uncertainty in 

how the temporal and spatial distribution 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 uncertainty regarding how 

influential factors such as emission levels per vehicle, vehicular traffic, and meteorology 

associated with the historical monitoring data set might influence air quality conditions in future 

situations which the simulated scenarios have been designed to represent. 

We have noted differences between the two study areas in the percent of simulated 

populations estimated to experience a daily maximum end-of-hour COHb level at or above 2.0% 

COHb level when considering conditions just meeting the current standard (e.g., Tables 6-18 and 

6-19). In these simulations involving adjusted historical hourly concentrations, there are a 
 7-14

greater number of high 1-hour concentrations (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 ambient concentrations at the 

upper percentiles of the distribution likely have a strong influence on the number and percent of 

persons experiencing the higher COHb levels. Whether these conditions that 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 (Figures 3-4 and 3-5) and 

the results generated using the adjusted 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 scenarios, though these patterns would not account for the 

influence of a new CO emission source(s). Therefore, 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 monitoring data to just meet a 2nd

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 ambient monitors in Los Angeles (Figure 3-4) and Denver (Figure 3-

5), a strong proportional relationship is present when comparing the recent and historic CO 

concentrations. In general, the regression slopes and intercepts were similar for each of the 

monitors used to represent the air quality within each study area, indicating 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 quality scenarios is not 

uncommon. A similar proportional adjustment approach was judged adequate in simulating air 

quality conditions just meeting the 8-hour CO NAAQS in prior assessments (US EPA, 1992; 

Johnson et al., 2000). A proportional approach was also used in evaluating exposure scenarios 

associated with just meeting the current and several alternative standards in the most recent NO2

and SO2 NAAQS reviews (US EPA, 2008; US EPA, 2009). 

In addition, the simulations involving Los Angeles data indicate little difference in the 

percentage of persons estimated to experience daily maximum end-of-hour COHb at or above 

selected COHb levels between simulations using the historical data adjusted downwards to 

conditions similar to as is air quality and simulations using the 2006 as is air quality. This is 
 7-15

shown in Table 7-4 where COHb population 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 presentation 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 2nd highest non-overlapping 8-hour concentration at 

the design monitor in Los Angeles was equal to 5.6 ppm (i.e., consistent with air quality just 

meeting an 8-hour standard with a form of 2nd highest and level of 5.6; see Table 5-12). Table 7-

4 indicates, based on this Los Angeles 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 indicating the lack of a strong or unrealistic 

influence of the air quality adjustment procedure on the results. 

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 alternative standards. 

 

Form 

 Level 

(ppm) ≥ 2.0 % COHb ≥ 1.75 % COHb ≥ 1.5 % COHb 

5.7 < 0.1 0.3 0.9 

5.6* < 0.1 0.5 1.6 

Second Highest Nonoverlapping 8-hour 

Concentration 5.4 < 0.1 0.2 0.7 

5.7 0.1 0.4 1.4 

5.1* < 0.1 0.5 1.6 

99th Percentile of 8-

hour Daily Maximum 

Concentration 5.0 < 0.1 0.3 0.9 

Second Highest 1-hour 8.2* < 0.1 0.5 1.6 

concentration 8.1 0.1 0.4 1.4 

8.0 0.1 0.4 1.4 

7.4* < 0.1 0.5 1.6 

99th percentile of 1-

hour Daily Maximum 

Concentration 7.1 < 0.1 0.3 0.9 

Notes: 

* Asterisk indicates simulation used as is (2006) air quality. 

7.2.1.3 APEX Input Data and Algorithms 

Eight elements of uncertainty were identified regarding the APEX input data and 

algorithms used to simulate population activity. These include the population and activity 

pattern databases, the longitudinal profile algorithm, meteorological data, microenvironmental 

algorithm and input data, commuting algorithm, at-risk prevalence rates and physiological 

variables. 

 
 7-16

Population Database 

Population data (tract population density, age/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 influence on exposure and %COHb results. 

Activity Pattern Database 

Data are actual records of the time spent in specific locations while performing specific 

activities. While not specific to a particular study area, the activity patterns of a population are 

generally well represented by the mainly population-based and nationally-representative survey 

data. There is, however, uncertainty in how well the CHAD data represent the intended 

simulated at-risk population, given that there may be local geographic 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 contributed 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 represented persons residing within each study 

area regarding commute times, we obtained information on travel time to work for workers ages 

16 years2

 and over specific to Denver County, Colorado 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 between 6 am and 9 am. The distributions of 

commute times associated with these data are illustrated in Figure 7-1 (see Appendix E, Table E1 for the details). This comparison indicates that the available CHAD diaries3

 reasonably 

represent typical commute times in both the urban locations modeled in this assessment. Not 

surprisingly, the percentage of CHAD diaries containing longer duration commute times (> 40 

minutes) are more representative of Los Angeles than Denver, likely a function of the number of 

CHAD diaries from that geographic 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 actual 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 involve 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 times 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 condition4 and are assumed to represent the activities of persons with normal health status 

as well as those with certain health conditions that may not significantly affect general activity 

patterns. While some of the diary days are potentially reflective of the simulated at-risk 

population, the majority of the diaries may be from persons identified as having a normal health 

status. Thus, if there are differences in activities performed and locations visited that depend on 

health status, there would be uncertainty 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 subset of the CHAD data where persons were 

specifically asked whether they had angina (see Appendix F of CO REA 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 using selected exertion level metrics and time spent outdoors or 

inside vehicles. The percentages of time spent outdoors or in a vehicle were generally not 

statistically significantly different between angina and non-angina subjects. While there were 

statistically significant differences in the exertion 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 adjust for an angina/non-angina difference in 

activity patterns. 

In characterizing the knowledge base uncertainty, 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, adjustments are made to represent the population distribution in a 

specific area (using age and gender), and temperature 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 available to represent the large simulated at-risk 

populations residing within each study area. 

Longitudinal Profile Algorithm 

Much of this assessment focused on persons having 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 could range upwards to a medium 

level. In developing the longitudinal method, the evaluation indicated that both the D and A

statistics are reasonably reproduced for the population (Glen et al., 2008). The approach was 

also compared with two other independent methods used for constructing longitudinal activity 

patterns (see Appendix B, Attachment 4 of US EPA, 2009). This particular comparison 

indicated that, depending on the longitudinal profile method selected, the number of persons 

experiencing multiple exposure events at or above 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 

 

5

 Note there was a very large sample 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

population, though a few persons surveyed have longitudinal diary profiles (Table 4-1). 

Therefore, the knowledge-base uncertainty is judged by staff as medium. 

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 CHAD diaries to simulate 

the at-risk population. The temperature bin ranges that were used (see section 5.5.3) are wide 

such that any erroneous temperatures that do exist within the data set would likely have limited 

impact to estimated exposure and dose results. Daily mean temperatures are used when selecting 

air exchange rates (section 5.9.4). Given the overlap of the AER 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 magnitude of influence on COHb levels as low. 

Microenvironmental Algorithm and Input Data 

In this REA, the number of microenvironments selected captures the likely locations 

persons spend time and where CO exposures would occur. Using distributions of proximity 

factors derived from measurement data in Denver and applied to estimate microenvironmental 

concentrations is judged by staff to be a reasonable approach. However, the extent to which 

these Denver study data reflect similar relationships in Los Angeles likely has greater 

uncertainty. Additionally, the Denver measurement data were collected in the 1980’s; therefore, 

there is also uncertainty as to how these data might reflect relationships observed for other years 

modeled in this assessment (i.e., 2006). However, the distributions of microenvironmental 

concentrations (Tables 6-8 and 6-11) and the effective microenvironmental-to-ambient 

concentration ratios (e.g., Tables 6-9 and 6-12), in particular those used to estimate highexposure microenvironments, were found comparable to other measurement data and 

relationships available (albeit limited in number) and generally support the 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 quality-assured source. The data are used in 

addressing home-to-work travel, certainly within the bounds of the objectives associated with the 

original data collection; therefore, staff judged the knowledge-base uncertainty as low. 

While there may be some uncertainties associated with the application of the database 

(see US EPA, 2009), they are limited in the potential magnitude of influence they might have on 

estimated exposures and COHb levels. For example, although several of the APEX 

microenvironments account for time spent in travel, the travel is assumed to always occur in 
 7-20

basically a composite of the home- and work-tracts. No other provision is made for the 

possibility of passing through other census tracts during travel. This could contribute to either 

over- or under-estimating exposure concentrations, dependent on the number and identity of 

tracts the simulated individual would actually traverse and the spatial variability of the 

concentration across different tracts. Given that most persons would likely experience ambient 

concentrations from within their home air district (i.e., encompassing all tracts within a 10 km 

radius of the monitor location), tracts existing between 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 are traveling on during the commute) is not accounted for. From a 

practical perspective though, if staff was to consider multi-tract commuting, further complexity 

would need to be added to the modeling while also requiring additional input data that are not 

readily available (e.g., commuting route data for simulated individuals). These model 

adjustments would come with a number of additional uncertainties and would require additional 

time and resources not available for the assessment. 

At-risk Prevalence Rates 

Data are from the Centers for Disease Control, 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 and gender. Staff used gender-specific 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 gender 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 population with undiagnosed 

IHD. The percent of the population with undiagnosed CHD (i.e., 43.8%) was applied to the 

diagnosed CHD prevalence, without any difference between genders. In comments on the 

second draft REA, it was suggested that women with undiagnosed heart disease may be 

underrepresented by this approach. In the limited time available, extant literature regarding this 

topic were reviewed with regard to the suitability for developing an improved estimate of the 

undiagnosed prevalence rate. Relevant 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 values 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 
 7-21

levels. Only the total number of persons was affected by using the alternative prevalence rates. 

This suggests that, if new data were uncovered with improved representation of either the 

diagnosed or undiagnosed prevalence rates, there would be little change to the percent of persons 

at or above selected COHb levels. 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 estimated number of persons at or above selected COHb 

levels, though the observed impact was proportional across the range of selected COHb levels. 

Physiological Variables 

Many of the parameters used to estimate the physiological attributes of the simulated atrisk population were developed from healthy individuals; 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 health conditions that might affect CO uptake and 

elimination, most of the identified health conditions that could affect the physiological variables 

used in the CFK model may not necessarily 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 20th century) or uncertainty as to their representativeness. As an example, alveolar 

ventilation is represented in the simulations as a single point estimate of 19.63 and applied 

directly to activity-specific oxygen consumption rates. This was based on an analysis by 

Joumard et al. (1981), of data generated by Galetti (1959), which did not include measurements 

made at elevated exertion levels, although theoretically one would expect there to be a nonlinear 

relationship between VA and VO2 given the non-linear relationship of dead space volume (VD) to 

tidal volume (VT) with increasing breathing rate.7 Thus, the point estimate of 19.63 used may 

not adequately represent the VA to VO2 relationship at higher ventilation rates. We note however 

that most of the upper level exposure concentrations in this assessment are associated with time 

spent inside vehicles, where it is expected that the exertion level and breathing rate would be at a 

relatively low level. Therefore, it may be that the point estimate is appropriately used for these 

activities and the estimated maximum end-of-hour COHb associated with the in-vehicle 

microenvironment may not be adversely affected. As described in this example, it is possible 

that most of the data and or relationships still remain appropriate in modeling the current 

population; however, in the absence of conducting a comprehensive review and comparing the 

historical data to recent measurements, staff judges the knowledge base uncertainty as medium. 

 

7

 Dead space volume (VD) will remain relatively constant, increasing only slightly with increasing 

ventilation rate. Tidal volume (VT) consistently increases with increasing ventilation rate. Alveolar ventilation (VA) 

will approach that of total ventilation (VE) at higher ventilation rates, given that VA = VE (1-VD/VT). Note also, VE

is not linear with respect to VO2 (see Graham and McCurdy, 2005). 
 7-22

The potential influence of another physiological parameter – hemoglobin content – was 

evaluated in response to comments received on earlier 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 populations8

 in this REA were identified (in section 2.6) based on data from a 

well-conducted multi-center controlled human exposure study demonstrate cardiovascular effects 

in subjects with moderate to severe 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 specifically 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 baseline 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 level” and, thus, there is greater uncertainty concerning the lowest 

benchmark level identified (i.e., 1.5%). There is also uncertainty about whether individuals with 

the most severe CHD are adequately represented. Additionally the COHb levels estimated in 

this assessment result from CO exposure concentrations much lower than the experimental 

exposure concentrations used to increase study subject COHb levels to the study targets (e.g., 

2.0%) and with which the responses were associated. Given that the evidence supporting the 

choice of benchmark levels is based on controlled 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 referenced 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 data and algorithms. 

 

8

 Discussion here focuses on the uncertainty associated 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 

We performed sensitivity analyses to illustrate the quantitative impact of the 

improvements made by better representing the spatial variability in ambient CO concentrations. 

Of particular interest was how expanding the number of monitors used in this 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 spatially limited approach applied all hourly 

concentrations 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 

are 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 

concentrations in each study area and that there are restricted opportunities for persons to 

experience ambient CO concentrations lower than that of the design monitor. 

As shown in Tables 7-5 through 7-8, the use of multiple monitors (versus using the 

design monitor alone) to represent the air quality input to the exposure 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 when comparing air quality scenarios within 

each study area, although expanding spatial variability in ambient monitoring concentrations had 

a greater impact 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, which is 

larger, having generally greater spatial variability in ambient monitoring concentrations (i.e., 

from ten monitors) when compared with that provided by the four monitors representing air 

quality in the Denver study area. The spatial heterogeneity in ambient concentrations in the Los 

Angeles study area allowed simulated persons to experience a wider range of ambient 

concentrations when compared with the simulated persons Denver. As a result of the limited 

spatial heterogeneity in Denver ambient concentrations (given that only four monitors were 

selected, their close proximity to one another, and having similar concentration levels), a lesser 

difference in the percent of persons at or above selected COHb levels was observed when using 

the design monitor to represent all ambient concentrations in the model domain. This difference 

 

9

 In the first 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 Angeles were retained to define the respective exposure 

modeling domains. All of these monitoring locations (and hence the air quality districts) used the CO 

concentrations from the single design monitor for each respective study area (080310002 in Denver and 060371301 

in Los Angeles). 
 7-24

in spatial variability between the two study areas may also play a role in the study area 

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 at higher exposure concentrations, note Tables 6-7 and 6-9) than compared 

with simulated persons in Los Angeles (Tables 6-10 and 6-12). 

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 concentrations – as is air quality. 

COHB Number of HD Persons Percent of HD Persons 

Level 

(%) All Monitorsa

Design Monitor 

onlyb All Monitorsa

Design Monitor 

onlyb Ratioc

≥ 0.0 85,926 85,926 100 100 1.0 

≥ 1.0 17,807 25,125 20.7 29.2 1.4 

≥ 1.5 1,074 1,715 1.2 2.0 1.6 

≥ 1.75 234 444 0.3 0.5 1.9 

≥ 2.0 12 99 < 0.1 0.1 8.3 

≥ 2.5 0 0 0 0 - 

≥ 3.0 0 0 0 0 - 

≥ 4.0 0 0 0 0 - 

Notes: 

a

 Each monitor site used ambient concentrations from that site (dose results 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. 

COHB Number of HD Persons Percent of HD Persons 

Level 

(%) All Monitorsa

Design Monitor 

onlyb All Monitorsa

Design Monitor 

onlyb Ratioc

≥ 0.0 630,807 630,807 100 100 1.0 

≥ 1.0 165,880 406,020 26.3 64.4 2.4 

≥ 1.5 9,834 61,816 1.6 9.8 6.3 

≥ 1.75 3,011 22,378 0.5 3.5 7.4 

≥ 2.0 502 7,526 < 0.1 1.2 15.0 

≥ 2.5 0 401 0 < 0.1 - 

≥ 3.0 0 0 0 0 - 

≥ 4.0 0 0 0 0 - 

Notes: 

a

 Each monitor site used ambient concentrations from that site (dose results 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. 

COHB Number of HD Persons Percent of HD Persons 

Level 

(%) All Monitorsa

Design Monitor 

onlyb All Monitorsa

Design Monitor 

onlyb Ratioc

≥ 0.0 85,926 85,926 100 100 1.0 

≥ 1.0 71,710 81,743 83.5 95.1 1.1 

≥ 1.5 21,028 38,206 24.5 44.5 1.8 

≥ 1.75 9,502 19,276 11.1 22.4 2.0 

≥ 2.0 3,826 9,329 4.5 10.9 2.4 

≥ 2.5 802 2,295 0.9 2.7 2.9 

≥ 3.0 222 592 0.3 0.7 2.7 

≥ 4.0 25 74 < 0.1 < 0.1 3.0 

Notes: 

a

 Each monitor site used ambient concentrations from that site (dose results 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 monitor concentrations – air quality just 

meeting the current 8-hour standard. 

COHB Number of HD Persons Percent of HD Persons 

Level 

(%) All Monitorsa

 Design Monitorb All Monitorsa Design Monitorb Ratioc

≥ 0.0 630,807 630,807 100 100 1.0 

≥ 1.0 260,612 558,955 41.3 88.6 2.1 

≥ 1.5 31,410 168,289 5.0 26.7 5.4 

≥ 1.75 10,537 68,941 1.7 10.9 6.5 

≥ 2.0 3,613 29,302 0.6 4.6 8.1 

≥ 2.5 401 4,817 < 0.1 0.8 12.0 

≥ 3.0 100 602 < 0.1 < 0.1 6.0 

≥ 4.0 0 0 0 0 - 

Notes: 

a

 Each monitor site used ambient concentrations from that site (dose results 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 the prevalence rate used for undiagnosed CHD 

might have on the estimated COHb levels, we performed simulations using alternative values for 

undiagnosed CHD prevalence rates. The alternative values were based on assuming that the total 

CHD prevalence (diagnosed and undiagnosed combined) 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 described for these scenarios were 

retained. 

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

Prevalence rate (fraction) for coronary heart disease 

Malesa Females 

Age range Diagnosed Undiagnosed Total Diagnoseda

 Undiagnosedb 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 for females was equal to that of males. 

The outputs generated from these new simulations were compared with output from the 

previous simulations that applied 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 the expansion of the simulated at-risk population 

(Tables 7-10 and 7-11). However, there is little observed difference in the percentage of CHD 

population at or above the selected COHb levels. 

Interestingly, this was the same outcome when we expanded the simulated at-risk 

population to include all persons with diagnosed heart disease (HD) along with undiagnosed 

CHD. That is, the percentage of the HD population 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 three at-risk population simulations not only contain differing total 

prevalence of a particular health condition, but contain variable prevalence when comparing 

across age groups and the two genders. The variation in the prevalence rates used have only 

resulted in differences in the number of persons exposed, indicating that if additional prevalence 
 7-27

rates were available that varied across age groups and the two genders, there may be little impact 

to the percentage of persons at or above selected COHb levels. 

Table 7-10. Comparison of the portion of the simulated 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 Prevalencea,b,c Alternative Prevalencea,d

COHb 

Level 

(%) Number Percent Number Percent 

≥ 0.0 53,656 100 71,093 100 

≥ 1.0 10,773 20.1 15,512 21.8 

≥ 1.5 654 1.2 963 1.4 

≥ 1.75 111 0.2 185 0.3 

≥ 2.0 12 < 0.1 12 < 0.1 

≥ 2.5 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

 Dose results obtained from Table 6-15. 

d

 Effectively, a hypothetical undiagnosed CHD prevalence was used to 

generate an equivalent total CHD prevalence for both genders. See Table 7-9. 
 7-28

Table 7-11. Comparison of the portion of the simulated 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 meeting the current standard. 

CHD persons 

Base Prevalencea,b,c Alternative Prevalencea,d

COHb 

Level 

(%) Number Percent Number Percent 

≥ 0.0 53,656 100 71,093 100 

≥ 1.0 44,166 82.3 58,851 82.8 

≥ 1.5 12,563 23.4 16,721 23.5 

≥ 1.75 5,800 10.8 7,663 10.8 

≥ 2.0 2,258 4.2 2,999 4.2 

≥ 2.5 444 0.8 580 0.8 

≥ 3.0 111 0.2 148 0.2 

≥ 3.5 62 0.1 62 < 0.1 

≥ 4.0 12 < 0.1 12 < 0.1 

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

 Dose results obtained from Table 6-18. 

d

 Effectively, a hypothetical undiagnosed CHD prevalence was used to 

generate an equivalent total CHD prevalence for both genders. See Table 7-9. 

7.2.2.3 Physiological Variables 

Sensitivity analyses performed on one potentially influential physiological variable –

hemoglobin content - is described here. This physiological variable was identified and evaluated 

in response to a comment suggesting that the some of the physiological variables used in the 

simulations may not necessarily be adequately representing the at-risk populations or subgroups 

having characteristics that may contribute to increased susceptibility. One variable identified 

within the CFK module that could have an impact on estimated COHb levels, and for which data 

were available to evaluate variation among population subgroups, is an individual’s hemoglobin 

content. For any simulated individual, the model currently samples from age- and genderstratified distributions developed from the 1999-2004 NHANES (see Appendices A and B). 

These NHANES data are population-based; as such, represent a collection 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 drawn 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 alternative distributions could be developed 

based on influential factors such as race and health condition. Descriptive statistics were 

generated using the hemoglobin content (Hb) of four 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 (KS) tests were performed using the SAS 

procedure NPAR1WAY to test for differences between the subgroups and dataset comprised of 

all other persons. All of these tests were significant (p<0.0001), indicating that the Hb 

distributions were different for the compared groups. 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 using the WHO (2001) thresholds and develop 

distributions from these samples, we elected to apply the WHO (2001) thresholds to the existing 

hemoglobin distributions when sampling for that variable within APEX. This way the original 

population distribution shape is preserved, though truncated at the portion of the distribution of 

interest to represent an anemic person’s hemoglobin 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 et al. (2001) thresholds used here are given in Table 7-12. 
 7-30

as described previously, using the Denver study area, the CHD at-risk 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) (Table 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 results from the two simulations was not large when considering 

differences in the percentage points, with the smallest differences observed at the upper COHb 

levels. As far as the magnitude of the difference expressed as a percent increase in persons, there 

was a 50 – 100% increase in the percent of persons at or above selected COHb levels when 

considering this hypothetical anemic CHD population. 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 overall magnitude of the effect is dependent on the benchmark level considered. 

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 and 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 19.7 
 7-31

Table 7-14. Comparison of the portion of the simulated CHD population in the Denver 

study area estimated to experience a daily maximum end-of-hour COHb at or 

above specified levels when sampling from the base and anemic hemoglobin 

content distributions – air quality just meeting the current standard. 

CHD Personsa,b

Base Hemoglobinc Anemic Hemoglobind

COHb 

Levels 

(%) Number Percent Number Percent 

0.0 53,656 100 53,656 100 

1.0 44,166 82.3 46,918 87.4 

1.5 12,563 23.4 15,845 29.5 

1.75 5,800 10.8 7,639 14.2 

2.0 2,258 4.2 3,344 6.2 

2.5 444 0.8 827 1.5 

3.0 111 0.2 197 0.4 

3.5 62 0.1 74 0.1 

4.0 12 < 0.1 25 < 0.1 

Notes: 

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. 

Dose results were obtained from Table 6-18. 

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 sources of uncertainty in the 

assessment approach, selections made regarding input data, and algorithms used, and their 

characterization as to direction and magnitude of influence on exposures and doses, staff 

consider the exposure and dose estimates 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 each 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 

concentration frequency (underestimation) - and all were judged to have a low magnitude 

of influence on estimated exposures and doses. 

 Thirteen of the identified sources of uncertainty 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 sources (i.e., spatial representation, historical data 

used, activity pattern 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 and doses (i.e., database quality, missing data substitution method, zero 

concentration frequency, proportional approach used, population 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: spatial and temporal representation, historical data used, activity pattern 

database, longitudinal profile algorithm, microenvironmental algorithm and input data, 

at-risk population prevalence rates, 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 influence. This included the proportional 

approach used in adjusting air quality conditions, the population database, 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 substitution, and zero concentration frequency. 

The ratings of the knowledge-base uncertainty can indicate the need for 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 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, microenvironmental 

algorithm and input data, and physiological factors) remain as the most important uncertainties in 

this assessment. 
 7-34

7.4 REFERENCES 

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US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air 

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 8-1

8 SUMMARY OF KEY OBSERVATIONS 

This document describes the 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 Ambient Carbon Monoxide Exposure and Risk 

 Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 

carbon-containing fuels and photochemical reactions in the atmosphere, with on-road 

mobile sources representing significant sources of CO to ambient air. 

 Microenvironments influenced by on-road mobile 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 potential for health effects of concern. 

 An individual’s COHb levels reflect their endogenous CO production, as well as CO 

taken into the body during exposure to ambient 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 where 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 the population with greatest susceptibility to short-term 

exposure to CO, and the population for which the current evidence indicates health 

effects occurring at the lowest exposures. The evidence further indicates a potential for 

other underlying cardiovascular conditions, particularly other types of heart disease, to 

contribute susceptibility to CO effects. Other populations potentially at risk include 

those with diseases such as chronic obstructive 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 
 8-2

is suggestive of causal relationships include effects on the central nervous system, 

reproduction and prenatal development, 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, specific changes to the ST-segment of an 

electrocardiogram. 

 Risk is characterized in this REA through evaluation of COHb estimated in simulations 

involving ambient CO exposures experienced by two target populations: (1) 

individuals with CHD (including 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 terms of percent of the simulated at-risk 

population expected to experience 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 potential health effects benchmarks. 

 For daily maximum end-of-hour COHb levels (absolute), these benchmarks 

range from 1.5%, which is below the lowest study mean COHb level resulting 

from experimental CO exposure in controlled 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 include the range from 1.4% up to 2.4%, 

which are the COHb increments associated with effects in those studies. 

 Beyond the at-risk populations and myocardial ischemia-related effects that are the 

focus of this quantitative REA, the current evidence regarding other potentially 

susceptible populations and other health effects 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 vehicles) are the primary contributor to CO 

emissions, particularly in urban areas due to greater vehicle and roadway densities. 

 Recent (2005-2007) ambient CO concentrations across the 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 lower detectable limit of 1 ppm. 

 The currently available information for CO monitors indicates that siting of microscale 

and middle scale monitors in the current network is primarily associated with roads 

having moderate traffic density (<100,000 AADT), however, factors other than 
 8-3

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 exhibit 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) concentrations 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 distributions with concentrations at or above the current 8-hour 

standard and those reflecting current (as is) conditions. 

 The temporal variability in selected upper percentile ambient concentrations (e.g., 99th

percentile 1-hour daily maximum) at individual monitors in Denver and Los Angeles is 

relatively small across a three-year monitoring 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 selected upper percentile ambient concentrations 

(e.g., 99th 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 Estimating Co Exposure and COHb Dose Levels 

 APEX, an EPA human exposure and dose model, has a long history of use in 

estimating exposure and dose for many of the criteria pollutants including CO, O3, 

SO2, and NO2. 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, sensitivity 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 Los Angeles study areas) were defined by 

overlaying ambient monitor locations having 10 km radii with US census tract 

population data. Monitors selected comprised the bulk of the urban core in each 

location, where ambient monitoring data exist. 

 Two simulated at-risk subpopulations were identified by combining the census tractspecific age and gender population distributions with HD and CHD prevalence rates, 

each also stratified by age and gender. In using this approach, staff can represent the 

variability that exists in the simulated at-risk HD and CHD subpopulations that reside 

in each census tract and within each study area. 

 Both simulated at-risk subpopulations include an estimate of persons with 

undiagnosed CHD. 
 8-4

 To represent spatial variability in ambient concentrations in Denver, a total of four 

monitors were used; in Los Angeles, the total 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 included 8 microenvironments in each 

location to represent the expected variability in microenvironmental CO 

concentrations. 

 All indoor microenvironments were modeled using a mass balance model to represent 

temporal variability in indoor CO concentrations with respect to the outdoor CO 

concentration variability. In addition, distributions of microenvironmental factors were 

used for all microenvironments rather than point estimates. Using distributions of 

microenvironmental factors will better represent both spatial and temporal variability in 

estimated microenvironmental CO concentrations. 

 Additional analyses using output from individual-level simulations were performed to 

provide information on the microenvironments most influential to population exposure 

at different exposure levels. This included an analysis of the effective ratios of 

microenvironment to ambient concentrations and the contribution of ambient CO 

exposure to total COHb level estimates. The smaller sample sizes generated for these 

analyses were found to be representative of the larger simulations employed for 

estimating exposure and dose in the different air quality exposure scenarios. 

Simulated Exposure and COHb Dose Results

 Ambient CO exposures and resulting COHb levels in the blood of two simulated at-risk 

populations in the Los Angeles and Denver study 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, and air quality adjusted to just meet three potential 

alternative standards. 

 The two at-risk populations simulated were: (1) persons with diagnosed CHD, 

including those estimated to have undiagnosed CHD, and (2) the larger group of 

persons with any type of HD including those estimated to have undiagnosed CHD. 

While the number of persons and person-days at or above selected 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 microenvironments to exposure concentrations was 

generally similar between the two 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 contributed to most exposures at or above 3 

ppm (70 – 100%). In comparison, when considering air quality just meeting the 

current standard, the percent contribution from indoor microenvironments was 

generally higher for low level exposures (about 65 – 85% of exposure concentrations at 

or above 1 ppm and 2 ppm), though again higher level exposures were dominated by 

the contributions from inside-vehicle microenvironments. 
 8-5

 The relationship between the two study areas with regard to estimated distribution of 

maximum end-of-hour COHb levels differed with 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 slightly higher distribution of maximum endof-hour COHb levels than the Denver populations. Under conditions of air quality 

adjusted from historical air quality data to just meet the current 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 simulated populations. A lower percentage of 

the simulated at-risk populations in Denver 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 highest incremental contribution of 

ambient CO exposure to maximum end-of-hour COHb levels estimated in the 

simulated populations was 1.7% COHb, and more than 99% of both study area 

populations were estimated to have ambient CO contributions to COHb below 

1.4%. As with estimates of total COHb (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 percentage of the population estimated to 

experience ambient contributions to COHb 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 95% of both simulated at-risk populations in the 

Los Angeles study area, and was below 2% COHb for 99.4% 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 maximum end-of-hour COHb below 2% was approximately 95%. 

 As with estimates of total COHb (i.e., COHb from endogenous CO production 

and ambient exposure together), the percentage of the simulated population 

estimated to experience the higher ambient contributions to maximum end-of 

hour COHb was appreciably greater in Denver as compared to Los Angeles. 

While estimated ambient CO contributions 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 about 

87%. 

 In addition to the simulations for as is and just meeting the current 8-hour standard air 

quality conditions, three simulations of air quality just meeting potential alternative 
 8-6

standards were performed. The alternatives comprise different combinations of form, 

averaging time and level which were expected 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 simulated include: (1) a second-highest 8-hour 

average of 5 ppm, (2) a 99th percentile daily maximum 8-hour average of 5.0 ppm, and 

(3) a 99th percentile daily maximum 1-hour average of 8.0 ppm. 

 These three simulations generated generally 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 selected COHb levels than did simulations for air 

quality adjusted to just meet the current 8-hour standard. For example, about 

1% or less of the Denver populations had a highest daily maximum end-ofhour COHb at or above the 2.0% COHb level in the alternative standards’ 

simulations as compared to approximately 4.2% in simulations of air quality 

just meeting the current standard. When considering the potential alternative 

standards in Los Angeles, generally fewer than 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 COHb 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 the level of public health protection that might be provided 

by alternative standard levels associated with 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 estimates 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 employed similar approaches, similar, 

although not identical air quality data for this scenario, and they used different 

exposure models (APEX vs. pNEM). Results were similar for the 1.5% and 2% COHb 

level for the simulated Los Angeles study area population and somewhat different for 

the Denver study area population. For example, the two assessments’ Los Angeles 

estimates of population percentages with highest daily maximum end-of hour COHb at 

or above these COHb levels were similar and within about 10% of one another. For 

the Denver simulated study populations, however, the estimates for these two COHb 

levels were about 3 and 8 times greater, respectively, in the current assessment when 

compared with that estimated in the prior assessment. 
 8-7

Variability Analysis and Uncertainty Characterization 

 Based on an overall qualitative judgment of the identified sources of uncertainty in the 

assessment approach, selections made regarding input data, and algorithms used, and 

their characterization as to direction and magnitude of influence on exposures and 

doses, staff consider the exposure and dose estimates 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 each 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 concentration frequency (underestimation) - and 

all were judged to have a low magnitude of influence on estimated exposures 

and doses. 

 Thirteen of the identified sources of uncertainty 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 sources (i.e., spatial representation, 

historical data used, activity pattern 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 and doses (i.e., database quality, 

missing data substitution method, zero concentration frequency, 

proportional approach used, population 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: spatial and temporal representation, historical data used, 

activity pattern database, longitudinal profile algorithm, microenvironmental 

algorithm and input data, at-risk population prevalence rates, physiological 

factors, and the benchmark levels for the simulated at-risk populations. 

 The knowledge-base uncertainty was judged as low for four of the identified 

sources having either unknown or bidirectional influence. This included the 

proportional approach used in adjusting air quality conditions, the population 

database, meteorological data, and commuting data. 
 8-8

 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 substitution, and zero 

concentration frequency. 

 The ratings of the knowledge-base uncertainty can indicate the need for 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 

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, microenvironmental 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 technical memo provided by Isaacs and Smith (2005) in its 

original format. Staff included page numbers 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 Smith, Alion Science and Technology

DATE: December 20, 2005 

SUBJECT: New Values for Physiological Parameters for the Exposure Model Input 

File Physiology.txt.

Table of Contents 

List of Figures..................................................................................................................... 3 

1. Introduction.................................................................................................................... 4 

2. Evaluation of the Current Physiology File Data ............................................................ 4 

2.1 Normalized Maximal Oxygen Uptake (nvo2max)............................................... 4 

2.2 Body Mass. .......................................................................................................... 5 

2.3 Resting Metabolic Rate......................................................................................... 5 

2.4 Hemoglobin Content and Blood Volume Factor. ................................................ 5 

2.5 Summary of Findings............................................................................................ 5 

3. Derivation of New Distributions for Body Mass........................................................... 6 

3.1 The NHANES Body Mass Dataset...................................................................... 6 

3.2 Calculation of the New Sampling Weights for the Combined NHANES Dataset.

....................................................................................................................... 7 

3.3 Fitting the Body Mass Data. ................................................................................. 7 

4. Derivation of New Distributions for Normalized Vo2max .......................................... 13 

4.1 The Nvo2max Data ............................................................................................. 13 

4.2 Determining the NVo2max Distributions........................................................... 17 

5. Derivation of New Distributions for Hemoglobin Content (Hemoglobin Density) ..... 25 

6. Blood Volume as a Function of Height and Weight.................................................... 28 

References......................................................................................................................... 29 

Appendix A. SAS Code for Estimating the Body Mass Distributions............................. 40 

Appendix B. SAS Code for Estimating the Normalized Vo2Max Distributions ............. 41 

Appendix C. SAS Code for Estimating the Hemoglobin 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 Lognormal Distributions for Body Mass as a 

Function of Age, Derived from NHANES 1999-2004 Study Data. ................................... 9 

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........ 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 Females, Derived from 

Literature Studies and Experimental Measurements. ....................................................... 14 

Figure 6. Grouped Mean Nvo2max Measurements for Males and Females, Derived from 

Literature Studies.............................................................................................................. 15 

Figure 7. Nvo2max Standard Deviations for Males and Females, Derived from Literature 

Studies............................................................................................................................... 16 

Figure 8. Combined Nvo2max Group Means for Males and Females............................ 19 

Figure 9. Combined Nvo2max Group Standard Deviations............................................. 20 

Figure 10. Nvo2max Normal Distribution Fits: Raw Fit Means and Smoothed Fits. ..... 21 

Figure 11. Nvo2max Normal Distribution Fits: Raw Fit Standard Deviations and 

Smoothed 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 Hemoglobin Content as Derived from the 1999-2002 

NHANES Dataset, with Comparison to Current Physiology.txt Values.......................... 26 

Figure 15. Values of Hemoglobin Content Standard 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 present 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-based information for several physiological 

parameters used in human exposure modeling. This information includes 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 present 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 the current state of the 

physiology file is presented, followed by the derivation of new physiological data for 

body mass, normalized vo2max, and hemoglobin content. Portions of the SAS code used 

for analysis are included (Appendices A-C), as is the new Physiology.txt file (Appendix 

D). The final appendix (Appendix E) contains tables of all the derived physiological 

parameters. 

2. EVALUATION OF THE CURRENT PHYSIOLOGY FILE 

DATA 

The physiology.txt file was originally generated 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 references for the data in the file were provided. An 

evaluation of the data in the file was included 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 nvo2max data were derived from a number of sources. The data for males, 

especially, were pieced together from a variety of studies (a total of 6), leading to 

discontinuities in the distributional parameters. However, in each age and gender cohort, 

the distributions parameters were derived 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-4 
from a study from 1960 [1]. Data for males aged 0-8 and 16-19, and females 0-19 came 

from a figure in a textbook from 1977 [2], which in turn was based on limited earlier 

data. An additional issue with the 1977 data is (according to the report mentioned above) 

that values for certain ages (very young or elderly) were acquired by simple tangential 

extrapolation of the data in the figure. 

In addition, in some cases it was not clear how the parameters were derived from the 

referenced studies. For example, Heil et al. [3] was referenced as the source of the values 

for females aged 66-100. However, an examination of that study provided no clues as to 

how the values were actually determined. As far as can be determined, in no place did 

the authors break down the means and SDs of their data into groups separated by both 

gender and age simultaneously. 

2.2 BODY MASS. 

The current body mass data were derived from an in-depth analysis [4, 5] of the second 

CDC National Health and Nutrition Examination Survey (NHANES II) body mass data 

[6]. The data were relatively comprehensive, and the methods used to generate the 

lognormal distributions were sound. However, the NHANES II data were compiled for 

the years 1976-1980, so an analysis of more recent data is necessary to accurately 

account for changes in human activity patterns in adults and especially children. 

2.3 RESTING METABOLIC RATE. 

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

2.4 HEMOGLOBIN CONTENT AND BLOOD VOLUME FACTOR.

The original references for the hemoglobin content or blood volume factor values given 

in the current physiology.txt file could not be identified. Therefore, their validity could 

not be evaluated and it was desirable that new statistics be calculated. 

2.5 SUMMARY OF FINDINGS 

 In some cases, especially for nvo2max, the data are unnecessarily 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 equations for the BM/RMR regression, parameter 

distributions at each age and gender cohort were derived from data from a single 

study. 

 A-5 
 Many of the studies used are very old (ex. 1960, 1977). 

 Some 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 content 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 equations should be developed based on thorough, compiled data 

from appropriate studies. 

3. DERIVATION OF NEW DISTRIBUTIONS FOR BODY MASS 

3.1 THE NHANES BODY MASS DATASET. 

New body mass distributions were generated from data from the National Health and 

Nutrition Examination Survey (NHANES). This 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 the American population for risk 

assessment and policy support purposes [7]. 

Older NHANES data (for the years 1976-1980) have been used previously to develop 

population estimates of body mass distributions [4,5]. The current Physiology.txt file 

body mass distributions are based on this work. However, the analysis presented here is 

based on the most recent NHANES data, for the years 1999-2004 [8]. 

Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES 

studies were downloaded from the NHANES website. 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 datasets contained the body weights for each 

subject. The combined dataset comprised 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-6 
3.2 CALCULATION OF THE NEW SAMPLING WEIGHTS FOR THE 

COMBINED NHANES DATASET. 

In the analysis of the NHANES data, sampling weights must be used to ensure that the 

data are weighted to appropriately represent the national population. Sampling weights 

for the combined NHANES body mass dataset were derived as recommended by the 

documentation provided with the most recent NHANES release [9]. Specifically, the 

sampling weight for each subject was calculated as: 

20042003 3

1

wcombined  w  (1) 

20021999 3

2

wcombined  w  (2) 

where wcombined is the sampling weight for the combined dataset, w2003-2004 is the weight 

for the subjects in the most recent study, and w1999-2002 is the weight for subjects in 

combined 4-year (1999-2000 and 2001-2002) NHANES dataset. (Both weights are 

provided with the appropriate NHANES release. The combined 1999-2002 weight, 

which is not a simply half of that for the corresponding 2-year periods, was explicitly 

calculated for researcher use by CDC since the two 2-year periods use different census 

data.) 

By using the sampling weights, once can consider any 2-year NHANES dataset or any 

combination of datasets as a nationally representative sample. 

3.3 FITTING THE BODY MASS DATA. 

In the current physiology file, body mass is modeled as a two-parameter lognormal 

distribution. The NHANES body mass data were fit to several types of distributions 

(including normal, beta, and three-parameter lognormal distributions). It was determined 

that overall, the distribution that provided the best combination of good behavior over 

ages and good fit to the data was a two-parameter lognormal distribution. 

The data were fit to the lognormal distributions using the SAS PROC UNIVARIATE 

procedure. The FREQ option of the procedure was used to apply the sampling weights. 

The SAS code used to generate the body mass 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-7 
3.4 Body Mass Results. 

Geometric means and standard deviations (SD) for the best-fit lognormal 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 file, which were derived from earlier NHANES 

studies. The new means also capture the trend towards decreasing body weight in older 

persons that was previously neglected in the Physiology.txt file. 

The maximum and minimum values for the distributions are presented in Figures 3 and 4. 

The minimums and maximums were calculated as the 1st and 99th percentile of the raw 

body mass data for the cohort. (Note that these values differ from the 1st and 99th

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 parameters (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 may desirable to use age-grouped data or running 

averages over years in these age ranges. While the attached prepared Physiology.txt file 

uses the “raw” parameters, smoothed results using 5-year running averages are provided 

in the attached data tables (Appendix E, plots not shown). These could be used at the 

direction of EPA; changing the “official” release Physiology.txt file would be trivial. 

 A-8 
MALES: Body Mass Geometric Mean

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

80.000

90.000

100.000

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

FEMALES: Body Mass Geometric Mean

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

80.000

90.000

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

Figure 1. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a 

Function of Age, Derived from NHANES 1999-2004 Study Data.

 A-9 
MALES: Body Mass GSD

1.000

1.050

1.100

1.150

1.200

1.250

1.300

1.350

1.400

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

FEMALES: Body Mass GSD

1.000

1.050

1.100

1.150

1.200

1.250

1.300

1.350

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

Figure 2. Geometric Standard Deviations for the Best-fit Lognormal Distributions for Body 

Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.

 A-10 
FEMALES: Body Mass Minimum

0

10

20

30

40

50

60

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology File

MALES: Body Mass Minimum

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

Figure 3. Minimums (1st Percentile) for Body Mass as a Function of Age, Derived from 

NHANES 1999-2004 Study Data.

 A-11 
MALES: Body Mass Maximum

0

50

100

150

200

250

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

FEMALES: Body Mass Maximum

0

20

40

60

80

100

120

140

160

180

200

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

Figure 4. Maximums (99th 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 vo2max in individuals. However, the NHANES 

vo2max values are estimated values, i.e. they are not measured directly. Such estimated 

values are not appropriate for use in this context (as per discussion with the WA-COR). 

Therefore, nvo2max distributional shapes were determined from a large database of 

experimental and literature vo2max measurements for different age/gender cohorts. 

A PubMed-based literature search located a number of studies in which vo2max was 

directly measured. In addition, a large number of scientific 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 broad or contained both males and females were discarded. 

Also discarded were any studies in which vo2max was not normalized by body mass, or 

for which no age data were reported. Data for 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 comprised age versus nvo2max data for 1949 men and 

1558 women. The data were pulled from either tables or graphs in 20 published studies 

[11-30]. Additional raw experimental data were provided by the WA-COR [31]. In the 

case of the graphical data, the original source was digitized and the data points were 

pulled from the digital figure using graphics software. (This was accomplished by 

calibrating the pixels of the digitized image with the range of age and nvo2max values.) 

The individual nvo2max data for males and females are shown in Figure 5. 

The grouped mean and SD data were derived from 136 studies [32-167]. These data 

comprised approximately 550 means and SDs for 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 for 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 values 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) nn

n

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-14 
MALES: Study Means, Nvo2max

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

0.0 20.0 40.0 60.0 80.0

Age (Years)

nvo2max (ml/kg

FEMALES: Study Means, Nvo2max

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

Age (Years)

nvo2max (ml/kg

Figure 6. Grouped Mean Nvo2max Measurements for Males and Females, Derived from 

Literature Studies. 

 A-15 
MALES: Study Standard Deviations: Nvo2max

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

0.0 20.0 40.0 60.0 80.0

Age (Years)

nvo2max (ml/kg

FEMALES: Study Standard Deviations: Nvo2max

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

Age (Years)

nvo2max (ml/kg

Figure 7. Nvo2max Standard Deviations for Males and Females, Derived from Literature 

Studies. 

 A-16 
4.2 Determining the NVo2max Distributions

Both the grouped mean and the individual datasets 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 (weighted by the number of subjects in each 

study) and retransforming the variances to SDs. The combined group means and SDs are 

given in Figures 8 and 9. 

The combined group means were fairly well-behaved 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 included 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 several types of distributions (including normal, 

beta, and lognormal distributions). It was determined that the normal distribution fit the 

data best. The parameters (means and standard deviations) of the best-fit distributions 

were obtained using the SAS PROC 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 considerably. 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 raw and smoothed fits for means are given in 

Figure 10; analogous data for SD is given in Figure 11. The raw nvo2max parameters 

were not as clean across ages as the body mass data (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-17 
The results for the nvo2max means were in fact quite close to those in the current file. 

However, the values exhibited much more consistent 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 longer demonstrate nonsensical discontinuities 

across ages. 

The minimum and maximum nvo2max values were assumed to be the 1st and 99th

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 appropriate for use as minimum and maximum. 

As the nvo2max data cohorts had much smaller N than the NHANES studies, the raw 

percentiles were less appropriate.) The maximum and minimum values are shown in 

Figures 12 and 13. 

 A-18 
MALES: Nvo2max, Combined Group Means

0

10

20

30

40

50

60

0 20 40 60 8

Age (Years)

nv02max (ml/k

0

g

FEMALES: Nvo2max, Combined Group Means

0

10

20

30

40

50

0 10 20 30 40 50 60 70

Age (Years)

nv02max (ml/k

g

Figure 8. Combined Nvo2max Group Means for Males and Females 

 A-19 
MALES: Nvo2max, Combined Group SD

0

2

4

6

8

10

12

14

16

0 20 40 60 8

Age (Years)

nv02max (ml/k

0

g

FEMALES: Nvo2max, Combined Group SD

0

2

4

6

8

10

0 10 20 30 40 50 60 70

Age (Years)

nv02max (ml/k

g

Figure 9. Combined Nvo2max Group Standard Deviations. 

 A-20 
MALES: MEAN Nvo2max

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Fit Values

Smoothed Fit Values

Current Physiology.txt

FEMALES: MEAN Nvo2max

0

10

20

30

40

50

60

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Fit Values

Smoothed Fit Values

Current Physiology.txt

Figure 10. Nvo2max Normal Distribution Fits: Raw Fit Means and Smoothed Fits. 

 A-21 
MALES: Nvo2max Standard Deviation

0

2

4

6

8

10

12

14

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Values

Fit Values

Current Physiology.txt

FEMALES: Nvo2max Standard Deviation

0

2

4

6

8

10

12

0 20 40 60 80 100

Age (Years)

Nvo2max (ml/kg

Raw Values

Fit Values

Current Physiology.txt

Figure 11. Nvo2max Normal 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. 1st Percentile of the Best-fit 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/k

g

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/k

g

New Values

Current Physiology.txt

Figure 13. Nvo2max Maximums. 99th 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, previously 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 dataset comprised 20,321 individuals; appropriate 

sample weights were used for the combined 4-year (1999-2002) dataset as provided with 

the NHANES 2001-2002 data release. Similarly to the body mass data, the hemoglobin 

content values were analyzed in SAS. The age and hemoglobin datasets were merged 

and fit to normal distributions using the SAS PROC UNIVARIATE procedure. The 

FREQ option of the procedure was used to apply 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 were smooth in this age range. For persons 20 

and over, the data were grouped in 5-year cohorts (20-24, 25-29, etc.) No blood count 

data were available for subjects 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 the mean values, the age 0 value was assumed 

equal to the age 1 value, and the age 91-95 and 96-100 value was assumed equal to the 

age 90-94 value. 

The resulting means and standard deviations for the best-for normal distributions for 

hemoglobin content are given 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 Physiology.txt input file 

overestimates mean hemoglobin content in children 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 smooth over ages; EPA may elect to continue to 

use the current values. It should be noted that the original reference 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-25 
MALES: Mean Hemoglobin Content

10

11

12

13

14

15

16

17

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

New Values

Current Physiology.txt

FEMALES: Mean Hemoglobin Content

10

11

12

13

14

15

16

17

0 20 40 60 80 10

Age (years)

Hb Content (g/dL

0

)

Figure 14. Mean Values of Hemoglobin Content as Derived from the 1999-2002 NHANES 

Dataset, with Comparison to Current Physiology.txt Values 

 A-26 
 

MALES: Hemoglobin Content Standard Deviation

0

0.5

1

1.5

2

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

New Values

Current Physiology.txt

FEMALES: Hemoglobin Content Standard Deviation

0

0.5

1

1.5

2

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

Figure 15. Values of Hemoglobin Content 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 function of height and weight by the following 

equation: 

30 3 V BVF*Weight+ K*Height - blood 

where Vblood 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 dependent BVF and adjusted for the 

given units. 

As previously mentioned, the data upon which the BVF values in the physiology file 

were based could not be identified. 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 for use with the Allen equations. It should be 

noted however, that these equations 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 specifically in children [170,171] or body surface area [172]. In 

particular, Linderkamp et al. [170] derived prediction equations for blood volume as a 

function of a number of physiological parameters for children in three different age 

groups. It is recommended that further analysis of this study 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 specific improvements to the current BVF values 

in the physiology file can be made. 

 A-28 
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 A-39 
Appendix A. SAS Code for Estimating 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 conform to the current APEX read formats. That 

is, the number of decimal places for each parameter 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-43 
 59 NA Normal 31.5 4.9 23.5 42.7 

 60 NA Normal 31.1 5.3 21.0 41.8 

 61 NA Normal 30.7 5.3 21.0 41.8 

 62 NA Normal 30.4 5.3 21.0 41.8 

 63 NA Normal 30.0 5.3 21.0 41.8 

 64 NA Normal 29.7 5.3 21.0 41.8 

 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-44 
 39 NA Normal 31.9 6.2 17.4 46.4 

 40 NA Normal 31.4 6.1 17.3 45.6 

 41 NA Normal 31.0 6.0 17.1 44.8 

 42 NA Normal 30.5 5.8 17.0 44.0 

 43 NA Normal 30.1 5.7 16.8 43.3 

 44 NA Normal 29.6 5.6 16.6 42.6 

 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-45 
 16 CDC LN 72.5 1.267 45.8 139.1 

 17 CDC LN 73.1 1.248 49.9 136.6 

 18 CDC LN 75.1 1.243 51.2 144.2 

 19 CDC LN 77.2 1.245 52.6 134.5 

 20 CDC LN 78.0 1.250 50.5 130.0 

 21 CDC LN 78.2 1.297 46.8 199.2 

 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-46 
 97 CDC LN 67.1 1.140 48.5 79.5 

 98 CDC LN 66.5 1.140 48.3 77.6 

 99 CDC LN 65.9 1.140 48.1 75.6 

 100 CDC LN 65.3 1.140 47.9 73.6 

 0 CDC LN 7.4 1.304 3.7 12.1 

 1 CDC LN 11.1 1.163 7.4 15.3 

 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-47 
 77 CDC LN 70.1 1.240 40.3 119.8 

 78 CDC LN 66.4 1.211 44.1 109.8 

 79 CDC LN 67.8 1.200 46.2 98.4 

 80 CDC LN 62.2 1.255 41.2 121.4 

 81 CDC LN 65.4 1.184 42.7 91.4 

 82 CDC LN 64.8 1.260 40.6 120.0 

 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-48 
 54 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 55 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 56 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 57 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 58 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 59 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 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-49 
 34 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 35 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 36 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 37 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 38 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 39 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 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-50 
 11 17.0 13.7 0.7 

 12 17.0 14.0 1.0 

 13 17.0 14.3 1.0 

 14 17.0 14.7 1.0 

 15 17.0 15.1 1.0 

 16 17.0 15.4 1.0 

 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-51 
 92 20.4 13.8 1.8 

 93 20.4 13.8 1.8 

 94 20.4 13.8 1.8 

 95 20.4 13.8 1.8 

 96 20.4 13.5 1.8 

 97 20.4 13.5 1.8 

 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-52 
 72 14.6 13.8 1.1 

 73 14.6 13.8 1.1 

 74 14.6 13.8 1.1 

 75 14.6 13.8 1.1 

 76 14.6 13.8 1.3 

 77 14.6 13.8 1.3 

 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-54 
 MALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit Values

Raw 

Fit Values

Smoothed

 Fit Values (1st Pctl) (99th Pctl)

41.00 39.72 40.24 9.96 8.10 21.39 59.09

42.00 35.58 39.66 9.85 7.96 21.14 58.18

43.00 39.98 39.09 6.46 7.82 20.89 57.28

44.00 38.65 38.53 7.60 7.69 20.64 56.41

45.00 40.15 37.98 6.59 7.56 20.40 55.56

46.00 40.67 37.44 7.89 7.43 20.16 54.73

47.00 41.51 36.92 9.68 7.31 19.91 53.92

48.00 38.92 36.40 10.52 7.19 19.68 53.12

49.00 34.65 35.90 7.68 7.07 19.44 52.35

50.00 33.85 35.41 6.49 6.96 19.21 51.60

51.00 32.52 34.92 4.51 6.86 18.98 50.87

52.00 36.31 34.45 7.08 6.75 18.75 50.16

53.00 36.23 34.00 7.31 6.65 18.52 49.47

54.00 33.91 33.55 5.29 6.56 18.30 48.79

55.00 33.40 33.11 5.08 6.46 18.08 48.14

56.00 31.68 32.69 6.52 6.37 17.86 47.51

57.00 32.47 32.27 6.33 6.29 17.64 46.90

58.00 33.24 31.87 6.32 6.21 17.43 46.31

59.00 33.05 31.48 6.45 6.13 17.22 45.74

60.00 29.02 31.10 3.59 6.06 17.01 45.19

61.00 31.68 30.73 6.95 5.99 16.80 44.66

62.00 29.72 30.37 5.09 5.92 16.60 44.14

63.00 30.90 30.02 8.06 5.86 16.40 43.65

64.00 30.65 29.69 5.32 5.80 16.20 43.18

65.00 29.86 29.36 6.90 5.75 16.00 42.73

66.00 28.60 29.05 5.51 5.70 15.80 42.30

67.00 29.47 28.75 5.25 5.65 15.61 41.89

68.00 28.95 28.46 5.63 5.61 15.42 41.50

69.00 31.13 28.18 6.43 5.57 15.23 41.13

70.00 27.12 27.91 3.44 5.53 15.05 40.78

71.00 27.65 5.50 14.86 40.45

72.00 28.56 27.41 5.71 5.47 14.68 40.13

73.00 27.62 27.17 5.03 5.45 14.50 39.84

74.00 27.84 26.95 6.27 5.43 14.33 39.57

75.00 26.74 5.41 14.15 39.32

76.00 25.05 26.54 6.68 5.40 13.98 39.09

77.00 23.74 26.35 4.99 5.39 13.81 38.88

78.00 26.17 5.38 13.65 38.69

79.00 26.00 5.38 13.48 38.52

80.00 25.84 5.39 13.32 38.37

81.00 23.68 25.70 5.88 5.39 13.17 38.22

82.00 25.57 5.39 13.04 38.09

83.00 25.44 5.39 12.92 37.97

84.00 25.33 5.39 12.81 37.86

85.00 25.23 5.39 12.70 37.76

86.00 25.14 5.39 12.62 37.67

 A-55 
 MALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit Values

Raw 

Fit Values

Smoothed

 Fit Values (1st Pctl) (99th Pctl)

87.00 25.06 5.39 12.54 37.59

88.00 25.00 5.39 12.47 37.52

89.00 24.94 5.39 12.42 37.47

90.00 24.90 5.39 12.37 37.42

91.00 24.86 5.39 12.34 37.39

92.00 24.84 5.39 12.32 37.37

93.00 24.83 5.39 12.31 37.36

94.00 24.83 5.39 12.31 37.36

95.00 24.84 5.39 12.32 37.37

96.00 24.87 5.39 12.34 37.39

97.00 24.90 5.39 12.37 37.43

98.00 24.95 5.39 12.42 37.47

99.00 25.00 5.39 12.48 37.53

100.00 25.07 5.39 12.54 37.60

Table 2. Nv02max Values for Females: Raw and Smoothed Fits 

 FEMALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit 

Values 

Raw 

Fit 

Values 

Smoothed

 Fit 

Values 

(1st 

Pctl) (99th Pctl) 

0.00 35.88 5.90 22.15 49.61

1.00 36.21 6.00 22.26 50.17

2.00 36.54 6.09 22.37 50.72

3.00 36.87 6.19 22.48 51.27

4.00 37.20 6.28 22.59 51.82

5.00 37.54 6.38 22.70 52.37

6.00 37.87 6.47 22.81 52.93

7.00 38.20 6.57 22.92 53.48

8.00 38.53 6.66 23.03 54.03

9.00 30.56 38.86 9.90 6.76 23.14 54.58

10.00 45.53 39.19 6.27 6.85 23.25 55.13

11.00 43.88 39.52 5.26 6.95 23.36 55.69

12.00 43.03 39.85 6.88 7.04 23.47 56.24

13.00 42.00 40.18 7.48 7.14 23.58 56.79

14.00 37.57 40.51 6.79 7.23 23.69 57.34

15.00 39.57 40.85 5.43 7.33 23.80 57.89

16.00 35.51 41.18 5.36 7.42 23.91 58.45

17.00 38.22 41.51 8.86 7.52 24.02 59.00

18.00 45.67 41.84 8.53 7.61 24.13 59.55

19.00 43.87 42.17 7.83 7.71 24.24 60.10

 A-56 
 FEMALES

 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-57 
 FEMALES

 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-58 
Table 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-59 
 

 MALES 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-60 
 

 MALES 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-61 
 

 MALES 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-62 
 

 MALES 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 107.0343 62.74189138 1.218822 42.59095 104.7926

86.00 73.81250877 1.177977 48.50949 104.4969 62.16041309 1.208932 42.80295 100.844

87.00 73.43871959 1.179377 47.90759 102.5276 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 97.15354 61.29487188 1.209189 41.46516 99.35077

90.00 72.11983988 1.162177 50.00172 95.19446 60.74212987 1.207753 41.27036 98.44462

91.00 71.4976219 1.159818 49.81292 93.23538 60.18938785 1.206317 41.07556 97.53846

92.00 70.87540393 1.157459 49.62412 91.27631 59.63664584 1.20488 40.88075 96.63231

93.00 70.25318595 1.1551 49.43532 89.31723 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 81.48092 56.87293577 1.197699 39.90674 92.10154

98.00 67.14209607 1.143307 48.49132 79.52185 56.32019375 1.196263 39.71193 91.19538

99.00 66.5198781 1.140948 48.30252 77.56277 55.76745174 1.194826 39.51713 90.28923

100.00 66.20876911 1.139769 48.20812 76.58323 55.49108073 1.194108 39.41973 89.83615

 A-63 
 A-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 1.552685662 
 B-1

Appendix B 

COHb Module for APEX4.3 

 This appendix describes the probabilistic carboxyhemoglobin (COHb) module used in the 

current APEX4.3 model. The approach described 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 appendix also describes the principal changes 

made to the COHb module when it was incorporated into APEX4.3, including a change in the 

method used to solve the Coburn-Forster-Kane (CFK) equation (Coburn et al., 1965). Lastly, 

section B.6 summarizes outputs that were generated in performing event-level model 

simulations. 

B.1 Base Physiological Model for Computing COHb Levels 

 

Using time/activity data obtained from various diary studies, APEX constructs a composite diary 

for each simulated person in the specified population at risk. 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, a duration, a geographic location, a 

microenvironment, and an activity. Using various algorithms described in Section 4 of the 

Quantitative Risk and Exposure Assessment for Carbon Monoxide (CO REA), APEX4.3 

provides estimates of CO concentration and alveolar ventilation rate for each event in the 

composite diary. APEX4.3 then uses these data, together with estimates of various physiological 

parameters specific to the simulated individual, 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 model 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 (O2Hb) 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 and is readily integrated. The solution to the 

linear form gives reasonably accurate results for lower levels of COHb. However, CO and 

oxygen compete for the available hemoglobin and are, therefore, not independent of each other. 

If this dependency is taken into account, the resulting differential equation is no longer linear. 

Peterson and Stewart (1975) proposed a heuristic approach to account for this dependency which 

assumed the linear form and then adjusted the O2Hb level iteratively based on the assumption of 

a linear relationship between COHb and O2Hb. This approach was used in the COHb module of 

the original CO-NEM exposure model (Biller and Richmond, 1982; Johnson and Paul, 1983). 

Alternatively, it is possible to determine COHb at any time by numerical integration of 

the nonlinear CFK equation (e.g., by use of the Runge-Kutta method) if one assumes a particular 

relationship between COHb and O2Hb. Muller and Barton (1987) demonstrated that assuming a 

linear relationship between COHb and O2Hb leads to a form of the CFK equation equivalent to 

the Michaelis-Menten kinetic model which 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 employed a linear relationship between COHb and 

O2Hb which was consistent with the basic assumptions of the CFK model. The approach 

differed from the linear forms used by other modelers in that the Muller and Barton (1987) 

solution was employed. However, instead of the simple binary search described by Muller and 

Barton (1987), a combination of the binary search and Newton-Raphson 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 Peterson and Stewart (1975) method but was shown 

to be faster than fourth-order Runge-Kutta numerical integration. 

APEX4.3 employs a different approach in which the CFK equation is solved using a 

fourth-order Taylor’s series expansion with subintervals. This method, first incorporated in 

Version 3 of APEX, is described in Section B.2 of this appendix. A more 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

MBV O Hb

P COHb

BV

P

V

V

dt

d COHb

b

CO

b

Ico

b

co     (Eq. B-1) 

where, 

 

A

B H O

Lco V

P P

D

B 

1 2    (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 carboxyhemoglobin (COHb) in blood at 

time t 

ml CO per ml blood at STPD 

[O2Hb] Concentration of oxyhemoglobin (O2Hb) in blood at time t ml O2 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] + [O2Hb] 

%[COHb] [COHb] expressed as percent of [RHb]0 % 

%[O2Hb] [O2Hb] expressed as percent of [RHb]0 % 

%[COHb]0 [COHb] at t = 0 % 

%[COHB]∞ [COHb] at t = ∞ % 

PICO

Pressure of inspired CO in air saturated with water vapor 

at body temperature 

torr 

PCCO

Mean pulmonary capillary CO pressure torr 

PCO2

Mean pulmonary capillary O2 pressure torr 

PB Barometric pressure torr 

P H O2

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 

DLCO

Pulmonary CO diffusion rate ml/min/torr, STPD 

M Haldane coefficient 

k Equilibrium constant for reaction O2 + RHb = O2Hb 

Vb 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 [COHb], the CFK equation 

is linear and can be readily integrated. However, since oxygen (O2) and CO compete for the 

available Hb, [COHb] and [O2Hb] must be related. Increasing [COHb] will result in decreasing 

[O2Hb]. 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 over a wide range of COHb levels. 

 Various linear relationships between [COHb] and [O2Hb] have been used (see Marcus, 

1980; McCartney, 1990; Muller and Barton, 1987; and Tikuisis et al., 1987). A relationship not 

previously used follows directly from the basic assumptions of the CFK model. The CFK model 

employs the Haldane coefficient, which is the equilibrium constant associated with the following 

reaction representing the replacement of O2 in O2Hb by CO: 

 CO  O Hb  O  COHb 2 2 (Eq. B-3) 

Equation B-4, the Haldane relationship, applies approximately at equilibrium conditions: 

 M

Pc O Hb

Pc COHb

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 difference between two competing chemical reactions: 

 CO  RHb  COHb (Eq. B-5) 

 

 O RHb O Hb 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

Pc RHb

O Hb

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 (O2Hb) and the remainder (3%) as the 

reduced form (RHb). It is also known that at one atmosphere barometric pressure, the mean 

pulmonary capillary oxygen pressure 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 Hb COHb RHb THb O [ ] [ ] [ ] [ ] 2    (Eq. B-8) 

Eliminating [RHb] between (B-7) and (B-8) and solving for [O2Hb] yields: 

 ([ ] [ ]) 1 [ ] 2 0

2

2 THb COHb

kPc

kPc

O Hb

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 McCartney (1990), but replaces the constant in the 

McCartney equation by the term in (B-9) involving the mean pulmonary capillary oxygen 

pressure and the equilibrium constant k. Substituting (B-9) into (B-1) yields a CFK equation free 

of [O2Hb] and fully consistent with Coburn, Forster, and Kane’s original derivation. 

 

b

O

b O

I

b

CO

kMBV

kPc

THb COHb

COHb

BV

P

V

V

dt

d COHb CO 2 1

[ ] [ ]

[ ] [ ] 

     

 (Eq. B-10) 

In working with the CFK model it is convenient to express COHb as a percent of [RHb]0. 

Multiplying (B-10) by 100 and dividing by [RHb]0 yields the expression 

O b

CO

b

I

b

CO

O k RHb MBV

kP

COHb

COHb

BV

P

V

V

dt THb

d COHb CO

[ ]

100(1 )

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

d COHb

O    (Eq. B-12) 

where, 

 ( ) [ ]

100

b

I

b

CO

O

O BV

P

V

V

THb

C CO    (Eq. B-13) 

 

O b

CO

k THb MBV

kP

C

[ ]

100(1 ) 2

1

  (Eq. B-14) 

 Given values for the atmospheric pressure and the physiological variables 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 fourth-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 integrated. The integration yields: 

0

%[ ] %[ ]

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

      







O

t

O t O COHb COHb

COHb COHb C C t COHb COHb COHb (Eq. B-15) 

The equation for %[COHb]∞ is obtained by setting equation (B-12) equal to zero and 

solving for %[COHb], which is now equal to %[COHb]∞: 

 ( )

100 %[ ] C C1C 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 binary search and Newton-Raphson methods 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) employs an alternative approach in which the 

CFK equation is solved using a fourth-order Taylor’s series expansion with subintervals. This 

method, first incorporated in Version 3 of APEX, is described in detail in the Programmer’s 

Guide for the APEX3 Model by Glen (2002). This reference also includes the results of various 

tests conducted on 10 candidate methods for solving the CFK equation. The selected method 

(fourth-order Taylor series with subintervals) was chosen because of its simplicity, fast execution 

speed, and ability to produce relatively accurate estimates of %COHb at both low and high levels 

of CO exposure. Additional information concerning the %COHb calculation method and its 

theoretical basis can be found in Section 10.2 of US EPA (2008a). 

In developing the fourth-order Taylor Series 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) = C0 - C1 N(t) / (100 - N(t) ) (Eq. B-17) 

where C0 and C1 are constants (at least over the duration of one event) that depend on physical 

and physiological parameters and on the CO concentration 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: 

 D0 = 1- N(0) / 100 (Eq. B-18) 

 A0 = C0 / (C0 + C1) (Eq. B-19) 

 A1 = C1 / (C0 + C1) (Eq. B-20) 

 D = D0 - A1 (Eq. B-21) 

 

z = (C0 + C1) t / (100×D0

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 expansion. 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 D0

2

 A0 – 100 D0

2

 A1 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) z2

 / 2 + N```(0) z3 / 6 + Niv(0) z4/ 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 A1 D0 D (A1

 2 - 8 D A1 + 6 D 2) z4

 / 24 (Eq. B-25) 

where 

 T3(z) = N(0) +100 D0 D z -100 A1 D0 D z2

 / 2 + 100 A1 D0 D (A1 - 2D) z3 / 6 (Eq. B-26) 

Tests showed that the fourth-order Taylor series 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 correspond to N(0) values or %COHb below 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 events (“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) recommends 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 finite set of simulated 

individuals residing within a given 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 body weight. The values of these factors are used 

to derive values for blood volume, menstrual 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 sequence of exposure events which span the time period of interest. 

Each exposure event represents a time interval of 60 minutes or less during which the individual 

resides in a single environment and engages in a single activity. To permit calculation of hourly 

average exposures, exposure events are not permitted to fall in more than one clock hour. 

Consequently, the passage from one exposure event to the next is indicated by a change in 

microenvironment, activity, or clock hour. Algorithms within APEX4.3 calculate an average CO 
B-8

concentration for each exposure event according to the time, district, and microenvironment 

specified for the event. As the exposure events for a simulated individual are contiguous, the 

model can combine these concentrations to generate output distributions of one-hour and running 

eight-hour exposures for each individual. The exposures calculated for the simulated individuals 

can then be population-weighted to produce exposure distributions for population groups of 

particular interest (e.g., people with coronary heart disease). 

 APEX4.3 constructs a year-long time/activity pattern for each simulated individual by 

sampling 24-hour activity patterns from the Consolidated Human Activity Data Base (CHAD) 

(McCurdy et al., 2000; US EPA, 2002), which is described 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 characteristics of the individual and the 

season, day type (weekday/weekend), and maximum temperature of each day in the specified 

exposure period. 

The COHb Module

 The COHb module in APEX4.3 currently employs the version of the CFK model 

represented by equations (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 the CFK model. In addition, the COHb level at the beginning of 

the exposure event must be known. This latter quantity is usually the COHb level computed at 

the end of the previous contiguous exposure event. To obtain the initial 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 times ranging from one hour to one day. 

Assignment of CFK Model Input Data for an Exposure Event

 Section B.4 describes the equations and procedures used by the APEX4.3 COHb module 

to obtain the values of the input variables for equations (B-2) and (B-13) through (B-16). A brief 

overview is given here. 

 The actual inspired CO level can change significantly during an exposure event. The 

model supplies an average exposure concentration for the event, which is used as the CO input. 

The time constant for the change in COHb is sufficiently large that the use of concentrations 

based on averaging times up to one hour can be used in place of the instantaneous concentrations 

over the averaging time period with little loss of accuracy in estimating the COHb level at the 

end of the exposure event. Furthermore, applying the average concentrations to a contiguous 

sequence of exposure events does not cause an accumulation of error. 

 The COHb model presently used in APEX4.3 does not account for changing barometric 

pressure. It uses a constant barometric pressure calculated for each study 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 model are all physiological parameters. While 
 B-9

the Haldane coefficient, the equilibrium constant k, and average pulmonary capillary oxygen 

pressure are treated as having the same constant values for all individuals, the remaining 

physiological input variables will vary among individuals. The next section describes the 

methods used to generate the various physiological input variables for each combination of 

individual and calendar day 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 VE 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 parameters; 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., age and gender), season, 

day type (weekday/weekend), and maximum daily temperature. 

 A UES is a contiguous set of exposure events spanning 24 hours. Each event is 

characterized by start time, duration, home/work status, microenvironment, and activity. All 

exposure events are constrained to occur entirely within a clock 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 O2 

 Atmospheric pressure (torr) 

 Blood volume (ml) 

 Total potential reduced hemoglobin content of blood (ml CO/ml STPD) 

 Pulmonary CO diffusion rate (ml/min/torr STPD) 

Endogenous CO production rate (ml/min STPD) 
B-10

Table B-2. Principal parameters included in the physiological profile for adults for applications of APE

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter Method Used to Estimate Parameter Value 

Age COHb 

Ventilation rate 

Demographic group Randomly selected from population-weighted distribution spe

group 

Gender COHb 

Ventilation rate 

Demographic group Randomly selected from population-weighted distribution spe

group 

Body Weight COHb 

Ventilation rate 

Gender Age 

Randomly selected from population-weighted lognormal distrgender-specific geometric mean (GM) and geometric standarderived from data from the National Health and Nutrition Exa(NHANES), for the years 1999-2004 (Isaacs and Smith, 2005

Height COHb Weight 

Gender 

Estimated using equations developed by Johnson (1998) usindata provided by Brainard and Burmaster (1992).  Males: height = 34.43 inches + (6.67)[ln(weight)] + (2.3 Females: height = 48.07 inches + (3.07)[ln(weight)] + (2.4The z term is randomly selected from a unit normal [N(0,1)] d

height (inches), weight (lbs). 

Menstrual phase COHb Gender 

Age 

If gender = female, menstrual phase was randomly assigned cycles according to the following age-specific probabilities.  Age < 12 or >50: 100% premenstrual  Age 12 through 50: 50% premenstrual, 50% postmenstrua
B-11

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter Method Used to Estimate Parameter Value 

Blood volume COHb Gender 

Weight Height 

Blood volume (Vb) was determined according to gender by th

developed from Allen et al. (1956) which were modified to accheight and weight.

 Males: Vb = (20.4)(weight) + (0.00683)(H3) - 30 

 Females: Vb = (14.6)(weight) + (0.00678)(H3) - 30 

Units: blood volume (ml), weight (lbs), height (inches). 

Hemoglobin content of the blood, Hb 

COHb Gender 

Age 

Randomly selected from normal distribution with arithmetic marithmetic standard deviation (ASD) determined by gender an

obtained from the National Health and Nutrition Examination for the years 1999-2004 (see Isaacs and Smith (2005) in the Units: grams of Hb per deciliter of blood 
B-12

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter Method Used to Estimate Parameter Value 

Pulmonary CO diffusion rate, 

DL

CO

COHb Gender 

Height Age 

Pulmonary CO diffusion rate (DL) was determined according age according to the following equations obtained from a pap

and modified to conform to the units used in the COHb modu Males:

DL

CO = (0.361)(height) - (0.232)(age) + 16.3 ml/min/torr 

 Females:

DL

CO = (0.556)(height) - (0.115)(age) - 5.97 ml/min/torr 

Units:

DL

CO (ml/min/torr), height (inches), age (years). 

Given the alveolar ventilation rate for the exposure event the pulmonary diffusion rate is calculated as: 

( )  ( )  0.000845  5.7 DLCO Adjusted DLCO Base VA



Endogenous CO production rate 

COHb Gender 

Age Menstrual phase 

Endogenous CO production rate was randomly selected from distribution with geometric mean (GM) and geometric standa

determined according to the following equations specific to agmenstrual phase groupings (see data in Table B-3).  Males, 18+: GM = 0.473, GSD = 1.316  Females, 18+, premenstrual: GM = 0.497, GSD = 1.459  Females, 18+, postmenstrual: GM = 0.311, GSD = 1.459 Units: GM (ml/hr), GSD (dimensionless). 
B-13

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter Method Used to Estimate Parameter Value 

Resting metabolic rate (RMR) 

Ventilation rate 

Gender Age Body Weight 

See Section 4.4.5 of CO REA and Chapter 5 of US EPA (200

Energy conversion factor (ECF) 

Ventilation rate 

Gender See Section 4.4.5 of CO REA and Chapter 5 of US EPA (200

NVO2max Ventilation 

rate 

Gender Age

See Section 4.4.5 of CO REA and Chapter 5 of US EPA (200

VO2max Ventilation 

rate 

NVO2max

Body Weight 

See Section 4.4.5 of CO REA and Chapter 5 of US EPA (200
B-14

Demographic Profile of

Simulated Individual

Gender Weight Age

Height

Menstrual Phase

Endogenous CO

Production Rate

Blood Volume

Pulmonary CO

Diffusion Rate

Maximum Normalized

Oxygen Uptake Rate

(NVO2max)

Maximum Oxygen Uptake

Rate (VO2max)

Resting Metabolic Rate

(RMR)

Total Hemoglobin

Content

of the Blood

Energy Conversion Factor

(ECF)

Figure B-1. Flow diagram for physiological profile generator. Input data is supplied at 

the start of the APEX4.3 computation. 

 
 B-15

Given these data as inputs, the module computes the percent COHb at the end of the 

exposure event. This value is used by the module as the initial percent COHb for the next 

contiguous exposure event. The module also computes the average percent COHb value for each 

exposure event. The main program retains these 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 during an APEX4.3 computer run and, therefore, 

need to be supplied to the computer program only once 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 therefore 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: 

 P 760 exp( 0.0000386 Altitude) B     (Eq. B-27) 

where altitude is the average height (in feet) of the study area above sea 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 approximation 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: 

 0.209( 47) 49 2 PcO  PB   (Eq. B-28) 

where 0.209 is the mole fraction of O2 in dry air and 47 is the vapor pressure of water at body 

temperature. This expression was used in an investigation of the CFK equation by Tikuisis et al. 

(1987). A value of 100 torr is commonly used since 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 coefficient. While measured values in the 

range 210 to 270 have been reported in the extant literature, most researchers 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 (Friedlander, 1982) that the most careful work done in 

this area was that by Rodkey et al. (1969), who determined a value of 218. This value was 

selected for use in the COHb module of the earlier CO-NEM exposure model. Other researchers 

using values in the range 218 to 220 include Peterson and Stewart (1970), Marcus (1980), Collier 

and Goldsmith (1983), and Muller and Barton (1987). As the value 218 falls within the range 

currently used by researchers, we have elected to continue using this value in APEX4.3. 

Equilibrium Constant for the Reaction of O2 and RHb

 This quantity was estimated in Section B.2 to have the value 0.32 based on the 
B-16

observation that %[RHb] is about 3% in individuals breathing air which is free of CO and a 

value of 100 torr for CO2 P . 

Total Reduced Hemoglobin in the Absence of O2 and CO 

 The quantity [THb]0 is expressed as equivalent milliliters of O2 or CO at STPD per 

milliliter of blood. Total Hb blood levels are customarily expressed as grams per deciliter of 

blood. The total Hb level in the absence of COHb and O2Hb would consist principally of RHb 

which can react with O2 or CO and MetHb which cannot. Total 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, adjust for the effect of altitude, and convert to equivalent 

milliliters of CO at STPD. The later conversion is based on the observation that a gram of 

reduced Hb can react with a maximum of 1.39 ml of O2 or CO at STPD. The application of 

these three factors yields the equation: 

 



 



     

100 [ ] 1.39 (100 % ) 1 HbAlt RHb O Hb MetHb (Eq. B-29) 

where HbAlt is the percent increase in Hb due to exposure to altitude and is given by (US EPA, 

1978): 

Altitude HbAlt e0.0001249  2.76

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 deviation both dependent on gender and age class (see 

entry in Table B-2 for the distributions of Hb by age and gender). Given the hemoglobin content 

of the blood based on the distributions listed in Table B-2, [THb]0 is calculated using equation 

(B-29). The weight percent MetHB, %MetHB, is taken 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 Health and Nutrition Examination Survey for the 

years 1999-2004 (Isaacs and Smith, 2005). Distribution parameters were estimated for singleyear age cohorts for both genders for ages 0-85. As the NHANES 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 inches)(z) (Eq. B-30) 

 females: height = 48.07 inches + (3.07)[ln(weight)] + (2.48 inches)(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: D  0.361 height  0.232 age 16.3 LCO (Eq. B-32) 

 Women: 97 D  0.556 height  0.115 age  5. LCO (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 module. The Salorinne data were obtained for nonexercising individuals. Tikuisis et al. (1992), working with eleven male subjects at various 

exercise levels, showed significant increase in lung diffusivity of CO with increasing alveolar 

ventilation rate. Regression analyses of data provided by Tikuisis et al. (1992) for the individual 

subjects in the study showed the relationship to be linear. From this relationship and the heights 

and ages of the subjects in the Tikuisis et al. (1992) study, it was determined that the Salorinne 

(1976) equations for male subjects correspond to an alveolar ventilation rate of 6.69 l/min STPD. 

In the absence of other data it is assumed that this same value applies to women. Thus, for each 

twenty-four hour period equations B-32 and B-33 are used to compute lung diffusion rates of CO 

for a base case alveolar ventilation rate of 6.69 1/min STPD. As will be seen, this value is 

adjusted to account for the actual ventilation rate experienced 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 difference depending on whether 

she is in her premenstrual or postmenstrual phase. Table B-2 presents 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 premenstrual; consequently, APEX4.3 specifies a 

single value for endogenous CO production rate to be used for all days of the year. The specified 

values are randomly selected from the appropriate distributions presented in Table B-2. A 

random number, z, is sampled from the standardized normal distribution, N(0,1) to make each 

selection. The appropriate endogenous CO production rate is then obtained from: 

 

 z VCO  0.01667 (geom.mean) (geom.S.D.)  (Eq. B-34) 

The constant term converts ml/hr to ml/min. 

 A probabilistic algorithm within APEX4.3 assigns a menstrual phase to each day of the 

year for female individuals aged 12 to 50 years. The algorithm randomly assigns a number 
B-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 numbered 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-19

Table B-3. Literature data used to derive lognormal distributions used to estimate endogenous CO produ

Reference Gender 

Menstrual

Cycle 

(pre/post) Endogenous CO Production Rate (ml/hr) 

Berk et al. (1974) Male NA 0.43 0.58 0.52 0.59 0.80 0.72 Brouillard et al. (1975) Male NA 0.81 0.57 0.33 0.70 Coburn et al. (1963) Male NA 0.35 0.40 0.39 0.43 0.35 0.51 

0.45 

Coltman and Dudley, 0.58 0.38 0.51 0.55 0.37 0.49 (1969) 

Male NA 

0.33 0.45 0.36 

Male NA 0.46 0.26 0.60 0.45 0.39 0.40 

Pre 0.57 0.54 0.72 0.99 0.48 0.53 

Delivoria-Papadoppulos et al. (1974) Female 

Post 0.23 0.51 0.34 0.41 0.26 0.16 

Luomanmaki and Coburn (1969) 

Male NA 0.38 0.42 0.41 0.54 0.38 

Male NA 0.40 0.81 0.26 0.65 0.55 0.62 

Pre 0.72 0.37 0.23 0.33 0.42 0.44 

Lynch and Moede (1972) Female 

Post 0.48 0.23 0.25 0.20 0.22 0.15 

Merke et al. (1975) Female Pre 0.64 0.86 0.35 0.52 0.80 0.54 

Female Post 0.40 0.47 0.23 0.24 0.55 0.32 

Werner and Lindahl (1980) 

Male NA 0.54 0.76 0.48 0.31 0.69 0.70 
B-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 quantity is converted to pressure via: 

 6 ( ) ( 47) 10 PI  CO  Pb   CO (Eq. B-35) 

 

Initial Percent COHb Level at Start of Exposure Event

 The program retains the percent COHb computed at the end of the previous exposure 

event and uses this value as the initial percent COHb 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 activity day of the overall computation since the 

program starts the day before the overall period covered by the APEX4.3 computation. 

Alveolar Ventilation Rate

 The main program supplies the COHb module 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 the exposure event the associated adjusted 

pulmonary diffusion rate can be calculated from: 

( )  ( )  0.000845  5.7 DLCO Adjusted DLCO Base VA

 (Eq. B-36) 

B.6 Analysis of Selected 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 event-level and hourly-level 

exposure and dose profiles for all simulated individuals (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 historical2

 air quality adjusted to just meet the current 8-hour standard. 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 99th percentile 8-hour daily maximum of 5.0 

ppm, though because these endogenous CO production runs were based on the same historical years of data in each 
 B-21

simulations were run for each scenario and study area; 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 concentrations set to zero (i.e., to 

estimate COHb resulting from endogenous CO production alone). See CO REA chapters 4 and 5 

for details regarding all other relevant APEX model simulation settings. 

First, an analysis of the contribution of endogenous CO production to COHb levels is 

provided. This is followed by an analysis of the ambient concentration profile and dose time 

series for a few selected individuals. Finally, the total maximum end-of-hour COHb levels are 

evaluated with respect to the maximum COHb contribution from ambient 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 the relative contribution of endogenous CO 

production to an individual’s total 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 concentration input values equal 

to zero, endogenous CO production will be entirely responsible for the calculated COHb levels 

for each simulated person. 

We note here that while the ambient CO concentration 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 individual’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 are linked to the activity pattern profile. 

Therefore, we expect there to be small differences in the contribution of endogenous CO 

production to COHb level when comparing the two exposure scenarios within each study area. 

Furthermore, we expect the contribution of endogenous COHb production to be greater in 

Denver when compared with that of simulated persons in Los Angeles, given the large 

differences in altitude between the two study areas. 

 

First, we generated descriptive statistics for each individual’s 8,760 hourly average 

COHb dose values resulting from endogenous CO production alone. Then annual means were 

calculated for each individual and used to generate a population distribution of annual means for 

each study area and air quality scenario. These population statistics are summarized in Table B4. The annual average hourly COHb level for the population was 0.32% for the simulated CHD 

population in Denver and about 0.27% for the simulated CHD population in Los Angeles. There 

was variability in the population means as indicated by the range of individual annual mean 

values that extended from a low of about 0.11% COHb, to a high of about 1.1% COHb. Overall 

the population variability in annual average COHb, as measured by the coefficient of variability 

(COV), was about 42% for both study areas and for both exposure scenarios. 

The variability in each individual’s annual mean COHb, as measured by an average of 

each person’s respective COV for the year, was also calculated and found generally consistent 

for each simulated individual, and when considering the two study areas and both exposure 

 study area, the generated time-series data for the endogenous CO contribution would be identical. These exposure 

scenarios are described more fully in section 5.6 of the CO REA. 
B-22

scenarios. On average, an individual’s hourly COHb was estimated to vary by about 22-23% of 

their annual average hourly COHb. Note the standard deviation of the individual-based COVs 

equaled about 5%. When compared to the population variability statistics, it can be seen that 

there is greater inter-personal variability in the contribution of endogenous CO production to 

COHb levels than intra-personal variability. This finding is generally as expected, given the 

wide range of interpersonal attributes that might influence the COHb level including age, gender, 

body mass, blood hemoglobin content, 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 this analysis does not include variability in COHb due to diseases and 

other medical conditions, some of which may increase or decrease the endogenous CO 

production rate (e.g., hemolytic anemia and infection) (ISA, Section 4.5). 

B.6.2 Time-Series of COHb Levels In Individuals 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 was to 

illustrate the variation in COHb levels occurring over time, with respect to endogenous CO 

production and ambient CO concentrations3

 (which of course would ultimately be related to 

ambient CO exposure). We purposefully selected these three simulated persons 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 included the hours where the ambient 

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 endogenous CO production rates, and therefore 

different total COHb levels over the time period of interest. 

The person designated as ‘low’ had a mean total 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 used 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 low total COHb levels across this time period, actually has a relatively high 

contribution from 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 

average contributing to a COHb level of about 0.2%). 

The person designated as ‘mid’ also had a mean COHb dose level of about 0.6% (Figure 

B-2, middle) though with greater variability in hourly COHb level when compared with the 

‘low’ person. The COHb profile for the ‘mid’ person extended upwards to a peak COHb level of 

about 1.2% on several hourly excursions, largely in response to exposure to ambient CO 

concentrations. The estimated contribution to COHb levels resulting from endogenous CO 

production for the ‘mid’ person was 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. 

 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

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 total COHb 

level of 1.0% across the illustrated 1-week period, though 1-hour total COHb levels peaked just 

above 2.9% following the upwards spiking of associated hourly ambient CO concentrations of 

between 10 and 12 ppm. Note that COHb levels resulting from endogenous CO production 

alone for this 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 high 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 COHb 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 the two air quality scenarios: as is

air quality and just meeting the current 8-hour standard. Consistent with the standard output 

generated for the CO REA, we generated a single maximum end-of-hour COHb level for each 

simulated CHD person, resulting from both 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 concentrations, to estimate a 

maximum COHb level resulting from endogenous CO production alone. And finally, the timeseries of COHb levels resulting from endogenous CO production were subtracted from the timeseries of total COHb (i.e., from endogenous production and ambient exposure), to estimate the 

maximum COHb level resulting from ambient CO exposure alone. In considering the results, we 

note that the focus is on ambient CO contribution 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 

percentile 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 the distribution of annual mean 

COHb levels resulting from endogenous CO production, the maximum contribution is nearly 

identical when comparing the air quality scenarios within the study areas. In general, over 98% 

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 endogenous CO production when comparing Denver to 

Los Angeles; the maximum extends upwards to a COHb level of about 1.5% 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 importance of ambient CO exposure; however, it is not always the case 

that the maximum end-of-hour total COHb level occurred simultaneously with the maximum 

contribution from ambient exposure. About half of the time, this was the case (Table B-6), 

though it varied based on the study area and air quality 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 average was between 51-66% of a person’s maximum 

total COHb.
B-24

Table B-4. Descriptive statistics for annual average hourly COHb levels resulting from endogenous CO p

using an APEX simulated CHD population. 

CHD Population Annual Average Hourly COHb Levels (%)a

Indiv

in 

Study 

Area 

Air 

Quality 

Scenario Mean stdev min p1 p5 p50 p95 p99 max COV 

Mea

CO

As Is 0.315 0.132 0.121 0.136 0.161 0.285 0.573 0.799 1.074 42.1 20

Denver Current 

Standard 0.315 0.133 0.122 0.136 0.161 0.285 0.576 0.804 1.087 42.3 20

As Is 0.268 0.111 0.095 0.105 0.139 0.246 0.506 0.675 0.695 41.5 22

Los Angeles Current Standard 0.268 0.111 0.095 0.106 0.139 0.245 0.504 0.665 0.695 41.5 22Notes: a An annual hourly average COHb (i.e., the 8,760 hourly values) for each simulated individual was first calculated. Then the 

deviation, and selected percentiles of this annual hourly average were calculated for the simulated population (i.e., 438 perso394 persons in Los Angeles). b The COV (mean/standard deviation) was calculated for each individual using each person’s 8,760 hourly values. Then the 

standard deviation of these individual COVs was calculated for each simulated population

Table B-5. Selected percentiles of the maximum end-of-hour COHb levels using an APEX simulated CHD 

considering the maximum contribution from endogenous CO production, the maximum contriambient CO exposure and the maximum total COHb level. 

Maximum End-of-Hour COHb Level (%)a

Study Endogenous Contribution Ambient Contribution Total 

Area 

Air 

Quality 

Scenario min med max min med max min med max 

As Is 0.19 0.43 1.55 0.24 0.50 1.70 0.46 0.80 1.85 

Denver Current 

Standard 0.19 0.43 1.54 0.43 0.94 2.74 0.64 1.21 2.95 

As Is 0.14 0.38 1.04 0.24 0.60 1.70 0.46 0.84 1.92 Los 

Angeles Current Standard 0.14 0.37 1.04 0.30 0.72 2.77 0.48 0.97 3.04 Notes: a Selected percentiles correspond to COHb levels in Figure B-3. 
 B-25

0.0

0.5

1.0

1.5

2.0

2.5

3.0

COHb Level (%)

0

2

4

6

8

10

12

Ambient 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

COHb Level (%)

0

2

4

6

8

10

12

Ambient 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 Year

COHb Level (%)

0

2

4

6

8

10

12

Ambient CO (ppm)

COHb contribution 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 quality just meeting the current standard.

Person 1: 

Low COHb 

Person 2: 

Mid COHb 

Person 3: 

High COHb 
B-26

Denver: As Is Air Quality

0

102030405060708090

100

Population Percentile

Maximum Contribution from Ambient CO Exposure AloneMaximum Contribution from Endogenous CO Production AloneMaximum (Ambient Exposure + Endogenous CO Production)

Los Angeles: As Is Air Quality

Maximum Contribution from Ambient CO ExMaximum Contribution from Endogenous CMaximum (Ambient Exposure + Endogenou

Denver: Just Meeting the Current Standard

0

102030405060708090

100

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Maximum End-of-Hour COHb (%)

Population Percentile

Maximum Contribution from Ambient CO Exposure AloneMaximum Contribution from Endogenous CO Production AloneMaximum (Ambient Exposure + Endogenous CO Production)

Los Angeles: Just Meeting the Current Standar

0.0 0.5 1.0 1.5 2.0 2.5 

Maximum End-of-Hour COHb (%)

Maximum Contribution from Ambient CO ExMaximum Contribution from Endogenous CMaximum (Ambient Exposure + Endogenou

Figure B-3. Maximum end-of-hour COHb levels using an APEX simulated CHD population in Denver (lef

(right), considering as is air quality (top) and air quality adjusted to just meet the current 8-ho(bottom).
 B-27

Table B-6. Evaluation of the ambient contribution 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 COHba Study 

Area 

Air 

Quality 

Scenario 

Maximum Ambient COHb 

Contribution COHb Occurred 

Simultaneously with 

Maximum Total COHb (n) (%) min mean max 

Yes 183 41.8 32.5 73.5 95.6 As Is 

No 255 58.2 7.5 50.6 86.2 

Yes 320 73.0 57.6 79.9 97.6 

Denver 

Current 

Standard No 118 27.9 23.4 65.9 89.9 

Yes 213 54.1 43.5 77.0 93.7 As Is 

No 181 45.2 20.1 57.7 90.1 

Yes 229 58.1 43.5 79.6 95.2 

Los 

Angeles Current 

Standard No 165 41.9 26.2 63.6 90.5 

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-of-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 maximum 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 relationships exist when the maximum ambient contribution 

occurred at the same time as the maximum total COHb level, regardless of air quality scenario or 

study area. The coefficient of determination (R2

 values) ranged from about 0.80 to 0.92, with 

linear regression slopes very close to unity, supporting the importance of the ambient 

contribution to these persons’ maximum total COHb level. Regression intercepts were also 

similar to one another, ranging from a value about 0.21 to 0.31%, and generally approximate the 

average endogenous contribution to COHb. 

Relatively weaker relationships were exhibited when the maximum total COHb level did 

not correspond with the maximum ambient contribution. The coefficient of determination 

ranged from about 0.11 to 0.48 with regression slopes ranging from 0.4 to 0.7. The relative 

greater importance of the endogenous contribution to COHb for these persons is indicated by the 

higher regression intercepts, estimated to range from about 0.51 to 0.65% COHb. 

The most important message conveyed by these figures illustrating simulations for which 

exposure included only contribution from ambient CO sources is that for all simulated persons 

having a maximum end-of-hour COHb level at or above 2.0%, and for most persons having a 

maximum end-of-hour COHb level at or above 1.5%, the ambient contribution is a more 

important factor than endogenous CO production. This is indicated by the relatively few data 

points that greatly deviate from the bounded regression lines at or above these selected COHb 

levels, regardless of whether the maximum total COHb occurred at the same time as the 

maximum ambient contribution.
B-28

Denver: As Is Air Quality

Max Ambient Contribution Did Not Occur at Same Time as Max Total COHb

y = 0.4318x + 0.6526

R2 = 0.1125

0.00.51.01.52.02.53.03.5

Maximum End-of-Hour COHb (%)

Denver: Just Meeting the Current Standard

Max Ambient Contribution Did Not Occur at Same Time as Max To

y = 0.7

R2 

Denver: As Is Air Quality

Max Ambient Contribution Occurred at Same Time as Max Total COHb

y = 0.9561x + 0.2473

R2 = 0.7975

0.00.51.01.52.02.53.03.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Ambient Contribution to Maximum End-of-hour COHb (%)

Maximum End-of-Hour COHb (%)

Denver: Just Meeting the Current Standard

Max Ambient Contribution Occurred at Same Time as Max Tota

y = 0.9

R

0.0 0.5 1.0 1.5 2.0 2.5 

Ambient Contribution to Maximum End-of-hour 

Figure B-4. Relationship between ambient contribution to COHb level and the corresponding maximum to

COHb level in Denver. Air quality as is (left) and just meeting the current standard (right). Mcontribution did not occur with maximum total COHb (top) and maximum ambient contributmaximum total COHb (bottom). 
B-29

Figure B-5. Relationship between ambient contribution to COHb level and the corresponding maximum to

COHb level in Los Angeles. Air quality as is (left) and just meeting the current standard (righ

ambient contribution did not occur with maximum total COHb (top) and maximum ambient coccur with maximum total COHb (bottom). 

Los Angeles: As Is Air Quality

Max Ambient Contribution Did Not Occur at Same Time as Max Total COHb

y = 0.5914x + 0.5396

R2 = 0.2344

0.00.51.01.52.02.53.03.5

Maximum End-of-Hour COHb (%)

Los Angeles: Just Meeting the Current Standard

Max Ambient Contribution Occurred at Same Time as Max Tota

y = 0.9

R

0.0 0.5 1.0 1.5 2.0 2.5 

Ambient Contribution to Maximum End-of-hour 

Los Angeles: Just Meeting the Current Standard

Max Ambient Contribution Did Not Occur at Same Time as Max To

y = 0.7

R2 

Los Angeles: As Is Air Quality

Max Ambient Contribution Occurred at Same Time as Max Total COHb

y = 0.9684x + 0.2366

R2 = 0.8453

0.00.51.01.52.02.53.03.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Ambient Contribution to Maximum End-of-hour COHb (%)

Maximum End-of-Hour COHb (%)
B-30

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 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 the Isaacs et al. (2009) presentation to allow for 

easier reading. The poster, included 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 Longitudinal Time-Activity Data for Use in EPA Exposure Models

Kristin Isaacs1

, Thomas McCurdy2, April Errickson3, Susan Forbes3, Graham Glen1, Stephen 

Graham4, Lisa McCurdy5, Melissa Nysewander1, Luther Smith1, Nicolle Tulve2, and Daniel 

Vallero2

 

1

Alion 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, 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 Protection 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 pollutant exposures in human exposure models. In 

EPA’s exposure models, longitudinal activity diaries for simulated persons are constructed from 

the 1-day cross sectional activity diaries in the Consolidated Human Activity Database (CHAD). 

Recently, new algorithms have been developed to construct longitudinal diaries from CHAD 

diaries based on realistic variance and autocorrelation properties of diary characteristics relevant 

to pollutant exposure. Characteristics of particular interest include time spent in particular 

microenvironments and time spent in activities that produce high ventilation rates. However, 

few multi-day data are currently available for estimating accurate statistical properties for these 

quantities. Results from a recent time-activity study of 10 adults and one newborn child are 

presented here. The participants recorded their 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 recording periods for these same individuals are 

expected in the future. The diaries for all subjects were assessed to calculate the between-person 

variance, the within-person variance, and the autocorrelation for various lags in the time spent in 

outdoor, residence, indoor (non-residence), and vehicle microenvironments, as well as for time 

spent performing high-METS activities. The effectiveness 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 activity patterns is analyzed. These data have the 

potential to aid in the development of improved input variance and autocorrelation statistics for 

longitudinal diary assembly algorithms in EPA’s human exposure models. 

INTRODUCTION 

Recently, new methods of assembling multi-day diaries in human exposure models from 

cross-sectional single-day diaries have been proposed that are based on the variance and 

autocorrelation statistics of the simulated population (Glen et al. 2008). Appropriately modeling 

intra- and interindividual variability using such algorithms may be essential in producing 

appropriate estimates of exposure. In addition, reproducing realistic autocorrelations in key diary 

properties may be required for the modeling of episodic exposure patterns. 

Previously, longitudinal time activity-location data collected in children 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 study in adults are now presented. 
 C-3

BACKGROUND 

Exposure models require construction of human activity diaries that cover the entire 

simulation period of a model run. This period is often several months, a year, or even longer. In 

EPA’s models, human activity diaries are usually 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 from 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 properties 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 between-person variances 

for this diary property; and 

 3) Specify the 1-day lag autocorrelation in this diary property. 

The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxics 

models. The new method allows the modeler to apportion the total variance in the key diary 

property into the within- and between-person variances σw2 and σb2 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, whereas 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 tendency for similar diaries 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 exposure 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

j 1

2

j 1 j

N 1

j 1

j A y y y y / y y
 C-4

METHODS

Activity Diary Study

Activity-location data were collected 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 periods 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 maternity leave). The data were recorded 24 hours 

a day, in increments as small as one minute. In this preliminary analysis, the time spent 

outdoors, indoors, in travel, and performing hard work each day were calculated from the diaries. 

“Hard work” was self-reported by each individual, as defined as activities requiring heavy 

breathing and/or sweating. Daily high temperatures and precipitation amounts were acquired for 

each day of the study. 

Variance and Autocorrelation Statistics

Variance and lag-one autocorrelation statistics were calculated for the studied 

individuals. Variance statistics were estimated for both the raw 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 the total variance (the sum of the between- and within-person 

variance) was calculated for the population. This ratio, calculated using 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 both 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 examined locations/activities were assessed as a 

function of day of the week (weekday versus weekend), day type (workday versus nonworkday), season, temperature, precipitation, and gender. These analyses were undertaken to 

assess the utility of different diary pool definitions. Optimal definitions of diary pools can 

adequately capture temporal patterns 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 rank test (for 2 groups) or the Kruskal-Wallis 

test (for more than 2 groups). The Wilcoxon rank sum (two-sample) test was used to test 

differences between genders. 

RESULTS AND DISCUSSION 

Individual Variability

Figure 1 shows an example of the individual variability in time spent in different 

locations/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 large amount 

of intra-individual variability that can be seen in longitudinal activity 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 variability in behavior. 

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

Variance and Autocorrelation Statistics

D, ICC, and A values for the population for time 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 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 versus 0.36 in 

this study). These differences reflect the increased heterogeneity in these variables in the studied 

adults versus the (relatively homogenous) studied 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 temperature and day types were notable, even considering 

the small number subjects in this study. There were gender differences observed in D; the 

mechanism of these differences is unclear, but is likely influenced by the activity patterns of the 

female who was not a worker. 

There were observed differences in A by temperature, but especially by day type. This is 

not unexpected, 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 or 

exposure is of interest. In general, the values of D are much more relevant to exposure. 
 C-7

Time Spent in Different Locations/Activities

The time spent in different locations/activities for different day types, seasons, 

temperature categories are presented in Figures 3-6. The effects of gender and precipitation 

were also studied. There were no significant differences for these categories, and 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 first 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 interquartile 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 the larger, cross-sectional database of diaries 

from The National Human Activity Pattern Survey (NHAPS, data not shown). That analysis 

indicated that a workday/non-workday was a better discriminator of time spent outside than a 

weekday/weekend split. As such, further comparisons are also presented for both workdays and 

non-workdays. 
 C-8

Figure 3. Time spent in different locations/activities 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 particular group of workers. 

Figure 4. Time spent in different locations/activities 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 greater than or equal to 75 degrees, colder= 

maximum temperature less than 75 degrees. Temperature category was better than or as good as 

season in discriminating behavior in time spent outdoors, even when day type was considered. 

Figure 5. Time spent in different locations/activities as a function of temperature category 

 (colder: max temp< 75 degrees, warmer: max temp 75 degrees) and day type. 

CONCLUSIONS 

• The diversity (D) and autocorrelation (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 temperature, 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 aid in the fine-tuning of the longitudinal diary 

algorithm. 

• The analysis of the time spent in locations was consistent with recent findings from crosssectional diary studies indicating that workdays/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 may eliminate the need for diary pools for 

different seasons, providing larger 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 periodically. Data will be compared to/combined 

with analyses of other available longitudinal time/location/activity studies. 

DISCLAIMER 

The information in this document has been funded wholly (or in part) by the U. S. 

Environmental Protection Agency (EPA contract 68-D-00-206). It has been subjected to review 

by the EPA and approved for publication. Approval 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. (2000). 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, Langstaff 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-11

Isaacs et al. (2009) in original poster format: 

BACKGROUNDExposure models require construction of human activity diaries that cover the entire simulation period of a model run. This period is often several months, a year, or even longer. In EPA’s models, human activity diaries are usually 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 from 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 properties 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 betweenperson variances for this diary property; and 3) specify the 1-day lag autocorrelation in this diary property.

The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxics models.The new method allows the modeler to apportion the total variance in the key diary property into the within- and between-person variances σw2 and σb2 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, whereas 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 tendency for similar diaries to occur on consecutive days. The lag-one autocorrelation in a variable y is 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 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., 2007). 

INTRODUCTIONRecently, new methods of assembling multi-day diaries in human exposure models from cross-sectional single-day diaries have been proposed that are based on the variance and autocorrelation statistics of the simulated population (Glen et al. 2007). Appropriately modeling intra- and interindividual variability using such algorithms may be essential in producing appropriate estimates of exposure. In addition, reproducing realistic autocorrelations in key diary properties may be required for the modeling of episodic exposure patterns.Previously, longitudinal time activity-location data collected in children in the Southern California Chronic Ozone Exposure Study (Geyh et al. 1999) have been analyzed to obtain estimates of appropriate measures of variance and autocorrelation for use in the longitudinal algorithm. Data from a new study in adults are now presented.

CO• The highesome expo•Whidiverconfialgor• The crossgrou•Tempopubreaseasother FU• We other 

Statistical Properties of Longitudinal Time-Activity Data for Use in EPA Exposu

Kristin Isaacs1, Thomas McCurdy2, April Errickson3, Susan Forbes3, Graham Glen1, Stephen Graham4, Lisa McCurdy5, Melissa Nysewander1, Luther Smith1, Nicolle 

1Alion Science and Technology, Research Triangle Park, NC, 2Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle 

Library 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, 5Home

ABSTRACTRealistic simulation of longitudinal activity patterns is necessary for appropriately reproducing the frequency 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 sectional activity diaries in the Consolidated Human Activity Database (CHAD). Recently, new algorithms have been developed to construct longitudinal diaries from CHAD diaries based on realistic variance and autocorrelation properties of diary characteristics relevant to pollutant exposure. Characteristics of particular interest include time spent in particular microenvironments and time spent in activities that produce high ventilation rates. However, few multi-day data are currently available for estimating accurate statistical properties for these quantities. Results from a recent time-activity study of 10 adults and one newborn child are presented here. The participants recorded their 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 recording periods for these same individuals are expected in the future. The diaries for all subjects were assessed to calculate the between-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 as for time spent performing high-METS activities. The effectiveness 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 activity patterns is analyzed. These data have the potential to aid in the development of improved input variance and autocorrelation statistics for longitudinal diary assembly algorithms in EPA’s human exposure models.

DISThe incontrathe coendor

METHODSActivity Diary StudyActivity-location data were collected 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 periods 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 maternity leave). The data were recorded 24 hours a day, in increments as small as one minute. In this preliminary analysis, the time spent outdoors, indoors, in travel, and performing hard work each day were calculated from the diaries. “Hard work” was self-reported by each individual, as defined as activities requiring heavy breathing and/or sweating. Daily high temperatures and precipitation amounts were acquired for each day of the study. Variance and Autocorrelation StatisticsVariance and lag-one autocorrelation statistics were calculated for the studied individuals. Variance statistics were estimated for both the raw measured variables (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 total variance (the sum of the between- and within-person variance) was calculated for the population. This ratio, calculated using 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 both 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/ActivitiesThe longitudinal data were assessed to support decisions on optimal diary pools for exposure modeling. Time spent in each of the examinedlocations/activities were assessed as a function of day of the week (weekday versus weekend), day type (workday versus nonworkday), season, temperature, precipitation, and gender. These analyses were undertaken to assess the utility of different diary pool definitions. Optimal definitions of diary pools can adequately capture temporal patterns 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 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 The ttempe

precipcategThe pmidlinby the quartResuversutime/asectiodata ndiscricompThe eeffecthard wrelateThe e

definemaximas godayty

REFEGeyh assesPerspGlen GmodelXue Jyear o

McCuActivit

2

w

2

b

2

b

22

b

σ σ

σ

σσ D



 

    









   

N

j 1

2

j 1 j

N 1

j 1

A y j y y y / y y

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 variability in behavior.

Figure 2. Distributions of time spent in different activities for all days for all subjects. Figure 3. Time spent in different locations/activities as a function of day of the week, and daytype (workday versus nonworkdays).

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

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

daytype.

RESULTS AND DISCUSSIONIndividual VariabilityFigure 1 shows an example of the individual variability in time spent in different locations/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 large amount of intra-individual variability that can be seen in longitudinal activity studies. Distributions of time spent in locations/activities for the population is shown in Figure 4. 

Variance and Autocorrelation StatisticsD, ICC, and A values for the population for time 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 daytypes (Table 4) where possible.

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 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 versus 0.36 in this study). These differences reflect the increased heterogeneity in these variable in the studied adults versus the (relatively homogenous) studied 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 temperature and daytypes were notable, even considering the small number subjects in this study. There were gender differences observed in D; the mechanism of these differences is unclear, but are likely influenced by the activity patterns of the female who was not a worker. There were observed differences in A by temperature, but especially by daytype. This is not unexpected, 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 or exposure is of interest. In general, the values of D are much more relevant to exposure.

P<0.002 P<0.04 NS

P<0.02 P<0.006 NS NS NS NS

P<0.003 P<0.001 NS

P<0.001 P<0.001 NS

NS NS P<0.0001

P<0.02 NS NS P<0.001 P<0.005 P<0.001

BACKGROUNDExposure models require construction of human activity diaries that cover the entire simulation period of a model run. This period is often several months, a year, or even longer. In EPA’s models, human activity diaries are usually 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 from 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 properties 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 betweenperson variances for this diary property; and 3) specify the 1-day lag autocorrelation in this diary property.

The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxics models.The new method allows the modeler to apportion the total variance in the key diary property into the within- and between-person variances σw2 and σb2 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, whereas 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 tendency for similar diaries to occur on consecutive days. The lag-one autocorrelation in a variable y is 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 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., 2007). 

INTRODUCTIONRecently, new methods of assembling multi-day diaries in human exposure models from cross-sectional single-day diaries have been proposed that are based on the variance and autocorrelation statistics of the simulated population (Glen et al. 2007). Appropriately modeling intra- and interindividual variability using such algorithms may be essential in producing appropriate estimates of exposure. In addition, reproducing realistic autocorrelations in key diary properties may be required for the modeling of episodic exposure patterns.Previously, longitudinal time activity-location data collected in children in the Southern California Chronic Ozone Exposure Study (Geyh et al. 1999) have been analyzed to obtain estimates of appropriate measures of variance and autocorrelation for use in the longitudinal algorithm. Data from a new study in adults are now presented.

CO• The highesome expo•Whidiverconfialgor• The crossgrou•Tempopubreaseasother FU• We other 

Statistical Properties of Longitudinal Time-Activity Data for Use in EPA Exposu

Kristin Isaacs1, Thomas McCurdy2, April Errickson3, Susan Forbes3, Graham Glen1, Stephen Graham4, Lisa McCurdy5, Melissa Nysewander1, Luther Smith1, Nicolle 

1Alion Science and Technology, Research Triangle Park, NC, 2Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle 

Library 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, 5Home

ABSTRACTRealistic simulation of longitudinal activity patterns is necessary for appropriately reproducing the frequency 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 sectional activity diaries in the Consolidated Human Activity Database (CHAD). Recently, new algorithms have been developed to construct longitudinal diaries from CHAD diaries based on realistic variance and autocorrelation properties of diary characteristics relevant to pollutant exposure. Characteristics of particular interest include time spent in particular microenvironments and time spent in activities that produce high ventilation rates. However, few multi-day data are currently available for estimating accurate statistical properties for these quantities. Results from a recent time-activity study of 10 adults and one newborn child are presented here. The participants recorded their 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 recording periods for these same individuals are expected in the future. The diaries for all subjects were assessed to calculate the between-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 as for time spent performing high-METS activities. The effectiveness 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 activity patterns is analyzed. These data have the potential to aid in the development of improved input variance and autocorrelation statistics for longitudinal diary assembly algorithms in EPA’s human exposure models.

DISThe incontrathe coendor

METHODSActivity Diary StudyActivity-location data were collected 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 periods 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 maternity leave). The data were recorded 24 hours a day, in increments as small as one minute. In this preliminary analysis, the time spent outdoors, indoors, in travel, and performing hard work each day were calculated from the diaries. “Hard work” was self-reported by each individual, as defined as activities requiring heavy breathing and/or sweating. Daily high temperatures and precipitation amounts were acquired for each day of the study. Variance and Autocorrelation StatisticsVariance and lag-one autocorrelation statistics were calculated for the studied individuals. Variance statistics were estimated for both the raw measured variables (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 total variance (the sum of the between- and within-person variance) was calculated for the population. This ratio, calculated using 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 both 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/ActivitiesThe longitudinal data were assessed to support decisions on optimal diary pools for exposure modeling. Time spent in each of the examinedlocations/activities were assessed as a function of day of the week (weekday versus weekend), day type (workday versus nonworkday), season, temperature, precipitation, and gender. These analyses were undertaken to assess the utility of different diary pool definitions. Optimal definitions of diary pools can adequately capture temporal patterns 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 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 The ttempe

precipcategThe pmidlinby the quartResuversutime/asectiodata ndiscricompThe eeffecthard wrelateThe e

definemaximas godayty

REFEGeyh assesPerspGlen GmodelXue Jyear o

McCuActivit

2

w

2

b

2

b

22

b

σ σ

σ

σσ D



 

    









   

N

j 1

2

j 1 j

N 1

j 1

A y j y y y / y y

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 variability in behavior.

Figure 2. Distributions of time spent in different activities for all days for all subjects. Figure 3. Time spent in different locations/activities as a function of day of the week, and daytype (workday versus nonworkdays).

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

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

daytype.

RESULTS AND DISCUSSIONIndividual VariabilityFigure 1 shows an example of the individual variability in time spent in different locations/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 large amount of intra-individual variability that can be seen in longitudinal activity studies. Distributions of time spent in locations/activities for the population is shown in Figure 4. 

Variance and Autocorrelation StatisticsD, ICC, and A values for the population for time 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 daytypes (Table 4) where possible.

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 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 versus 0.36 in this study). These differences reflect the increased heterogeneity in these variable in the studied adults versus the (relatively homogenous) studied 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 temperature and daytypes were notable, even considering the small number subjects in this study. There were gender differences observed in D; the mechanism of these differences is unclear, but are likely influenced by the activity patterns of the female who was not a worker. There were observed differences in A by temperature, but especially by daytype. This is not unexpected, 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 or exposure is of interest. In general, the values of D are much more relevant to exposure.

P<0.002 P<0.04 NS

P<0.02 P<0.006 NS NS NS NS

P<0.003 P<0.001 NS

P<0.001 P<0.001 NS

NS NS P<0.0001

P<0.02 NS NS P<0.001 P<0.005 P<0.001
 D-1

Appendix D 

Microenvironmental Mapping 

Figure D-1 presents how CHAD codes are mapped to the eight microenvironments used to model exposure 

in the 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 File 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 H 
 D-2

30300 Residential garage or carport = 1 H 

30310 ..., indoor = 1 H 

30320 ..., outdoor = 7 H 

30330 Your garage or carport = 1 H 

30331 ..., indoor = 1 H 

30332 ..., outdoor = 7 H 

30340 Other residential garage or carport = 1 H 

30341 ..., indoor = 1 H 

30342 ..., outdoor = 7 H 

30400 Residence, none of the above = 1 H 

31000 Travel, general = 8 O 

31100 Motorized travel = 8 O 

31110 Car = 8 O 

31120 Truck = 8 O 

31121 Truck (pickup or van) = 8 O 

31122 Truck (not pickup or van) = 8 O 

31130 Motorcycle or moped = 5 O 

31140 Bus = 8 O 

31150 Train or subway = 8 O 

31160 Airplane = 0 O 

31170 Boat = 7 O 

31171 Boat, motorized = 7 O 

31172 Boat, other = 7 O 

31200 Non-motorized travel = 7 O 

31210 Walk = 7 O 

31220 Bicycle or inline skates/skateboard = 7 O 

31230 In stroller or carried by adult = 7 O 

31300 Waiting for travel = 7 O 

31310 ..., bus or train stop = 5 O 

31320 ..., indoors = 4 O 

31900 Travel, other = 8 O 

31910 ..., other vehicle = 8 O 

32000 Non-residence indoor, general = 3 O 

32100 Office building/ bank/ post office = 3 O 

32200 Industrial/ factory/ warehouse = 4 O 

32300 Grocery store/ convenience store = 3 H 

32400 Shopping mall/ non-grocery store = 3 O 

32500 Bar/ night club/ bowling alley = 3 O 

32510 Bar or night club = 3 O 

32520 Bowling alley = 3 O 

32600 Repair shop = 3 O 

32610 Auto repair shop/ gas station = 2 O 

32620 Other repair shop = 3 O 

32700 Indoor gym /health club = 3 O 

32800 Childcare facility = 4 O 

32810 ..., house = 1 O 

32820 ..., commercial = 4 O 

32900 Large public building = 3 O 

32910 Auditorium/ arena/ concert hall = 3 O 

32920 Library/ courtroom/ museum/ theater = 3 O 

33100 Laundromat = 3 H 
 D-3

33200 Hospital/ medical care facility = 4 O 

33300 Barber/ hair dresser/ beauty parlor = 3 H 

33400 Indoors, moving among locations = 3 O 

33500 School = 4 O 

33600 Restaurant = 3 O 

33700 Church = 4 H 

33800 Hotel/ motel = 3 O 

33900 Dry cleaners = 3 H 

34100 Indoor parking garage = 6 O 

34200 Laboratory = 3 O 

34300 Indoor, none of the above = 3 O 

35000 Non-residence outdoor, general = 7 O 

35100 Sidewalk, street = 5 O 

35110 Within 10 yards of street = 5 O 

35200 Outdoor public parking lot /garage = 6 O 

35210 ..., public garage = 6 O 

35220 ..., parking lot = 6 O 

35300 Service station/ gas station = 2 O 

35400 Construction site = 7 O 

35500 Amusement park = 7 O 

35600 Playground = 7 H 

35610 ..., school grounds = 7 O 

35620 ..., public or park = 7 H 

35700 Stadium or amphitheater = 7 O 

35800 Park/ golf course = 7 O 

35810 Park = 7 O 

35820 Golf course = 7 O 

35900 Pool/ river/ lake = 7 O 

36100 Outdoor restaurant/ picnic = 7 O 

36200 Farm = 7 O 

36300 Outdoor, none of the above = 7 O 

Table D-1. CHAD Work Related Activity Codes Used To identify Work Air Districts. 

<10> Work and Other Income Producing Activities 

10000: work and other income producing activities, general 

10100: work, general 

 10110: work, general, for organizational activities 

 10111: work for professional/union organizations 

 10112: work for special interest identity organizations 

 10113: work for political party and civic participation 

 10114: work for volunteer/ helping organizations 

 10115: work of/ for religious groups 

 10116: work for fraternal organizations 

 10117: work for child/ youth/ family organizations 

 10118: work for other organizations 

 10120: work, income-related only 

 10130: work, secondary (income-related) 

10200: unemployment 

10300: breaks 
 E-1

Appendix E 

Analysis of CHAD Diaries for Time Spent in Vehicles. 

The US Census Bureau (2009) provides an on-line facility for 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 specific to Denver County, Colorado and Los Angeles, 

CA (US Census Bureau, 2009, Table P31). Staff converted the counts listed in Table P31 for 

trips to work places other than home into the percentages listed in Columns 2 and 3 of Table E-1. 

Although the P31 statistics apply to people 16 years or older, staff assumed 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 reported at least one minute in a motor vehicle between 6 am and 9 

am. The number of these diaries that had in-vehicle times corresponding to the bins listed in 

Table E-1 are given in Column 4 and were converted to the percentages listed in Column 5. 

Table E-1. Comparison of Denver and LA commuting characteristics (US Census, 2009) 

to time spent in motor vehicles using CHAD Diaries (US EPA, 2002). 

 

24-hour diaries meeting 

inclusion criteriaa

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. Census 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 coefficients 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 subjects were evaluated for several summary 

statistics: average and 95th 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 exertion 
F-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 analyses 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 angina 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 questions about cardiovascular disease and so 

could not be used for these analyses comparing 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 their 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 to 
F-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 comparison 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 basal 
F-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. 

 95th 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. The 

significance 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 percent level of significance. By the central limit 

theorem, the p-values for the t test comparisons should be reasonably accurate for the large 

samples 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 applied 

two 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 for angina population might be shifted by some 
F-7

constant value, and thus might have a different 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 exertion 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 explained by the greater propensity for females to get 

angina, rather than the direct effect of angina. 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 95th 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, the 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-angina 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 show 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, just 

significant 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 functions 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 different, so are the two distribution functions. The 

discrepancy between the F and Kolmogorov-Smirnov tests is partly explained by the fact that the 

F 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 Exertion 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 95th 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 patterns 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 95th 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 statistically 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 functional 
F-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 Metabolic 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-13

Table 1. Distribution of subjects according to their age, gender and disease status 

Males Females 

Age Group Angina (%) Non-angina 

(%)

All Angina (%) Non-angina 

(%)

All Angina (%) No

(%)

0-54 35 (1.0) 3307 (98.9) 3342 17 (0.5) 3570 (95.5) 3587 52 (0.8) 68

55-64 28 (6.5) 400 (93.5) 428 28 (5.4 ) 491 (94.6) 519 56 (5.9) 8965-74 23 (7.9) 267 (92.1) 290 48 (9.6) 450 (91.4) 498 71 (9.0) 71

75+ 17 (10.7) 142 (89.3) 159 47 (14.4) 279 (85.6) 326 64 (13.2) 42

Total 103 (2.4) 4116 (97.6) 4219 140 (2.8) 4790 (97.2) 4930 243 (2.6) 89

This table was modified by staff on 2-22-10 from the below original version due to issues related to the conversion from WoWord. 

 Gender Age group 

 Males Angina(%) Non-angina(%) All 

 Females Angina(%) Non-angina(%) All 

 All Angina(%) Non-angi

35 (1.0) 3307 (98.9) 3342 

17 (0.5) 3570 (95.5) 3587 

52 (0.8) 6877 (996929 

28 (6.5) 400 (93.5) 428 

28 (5.4 ) 491 (94.6) 519 

56 (5.9) 891 (94947 

23 (7.9) 267 (92.1) 290 

48 (9.6) 450 (91.4) 498 

71 (9.0) 717 (91788 

17 (10.7) 142 (89.3) 159 

47 (14.4) 279 (85.6) 326 

64 (13.2) 421 (86485 

103 (2.4) 4116 (97.6) 4219 

140 (2.8) 4790 (97.2) 4930 

243 (2.6) 8906 (979149 
F-14

Table 2. Statistical Tests for the Association between Angina and Various Variables RepresentiExertion. All 

Variable T Test Comparison of 

Means Mean Mean P-valueAngina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

Average maximum 8hr exertion (Mcal) 

1.28 1.40 0.00 0.48 0.49 0.68 0.00 

Ninety fifth percentile of maximum 8hr exertion (Mcal) 

1.87 2.25 0.00 0.97 1.13 0.00 0.00 

Percentage of time spent outdoors 

6.73 6.74 0.99 12.87 11.63 0.02 0.22 

Percentage of time spent in vehicle 

4.55 5.55 0.01 6.19 7.13 0.00 0.00 

Percentage of time spent outdoors or in vehicle 

11.27 12.29 0.27 14.33 13.45 0.15 0.00 

Average percentage of time with exertion above 2.39 kcal/min  = 0.010MJ/min (light) 

19.98 23.53 0.00 13.56 13.78 0.75 0.00 

Average percentage of time with exertion above 5.97 kcal/min  = 0.025 MJ/ min (moderate) 

2.17 2.78 0.01 3.68 3.56 0.46 0.00 

Average percentage of time with exertion above 9.55 kcal/min 

0.213 0.406 0.00 0.554 0.761 0.00 0.00 
F-15

 = 0.040 MJ/ min (heavy) 

Table 3. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. MalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

Average maximum 8hr exertion (Mcal) 

0-54 55-64 65-74 75+ 

1.85 1.59 0.00 1.48 1.77 0.00 1.39 1.49 0.36 1.27 1.20 0.52 

0.49 0.55 0.34 0.48 0.48 0.86 0.47 0.48 0.96 0.44 0.42 0.65 

 0.02  0.01  0.41  0.51 

Ninety fifth percentile of maximum 8hr exertion (Mcal) 

0-54 55-64 65-74 75+ 

2.94 2.68 0.17 2.40 2.90 0.04 1.91 2.17 0.14 1.73 1.67 0.71 

1.06 1.30 0.13 1.21 1.14 0.61 0.78 0.94 0.31 0.69 0.76 0.68 

 0.22  0.02  0.26  0.55 

Percentage of time spent outdoors 

0-54 55-64 65-74 

16.86 8.85 0.02 9.28 10.02 

19.16 13.75 0.00 14.26 13.86 

 0.01  0.65  0.12 
F-16

Table 3. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. MalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

75+ 0.79 

6.43 10.40 0.13 8.23 7.09 0.72 

0.78 11.38 14.34 0.20 12.63 10.05 0.16 

 0.58 

Percentage of time spent in vehicle 

0-54 55-64 65-74 75+ 

5.96 6.09 0.92 3.99 6.87 0.00 7.20 5.88 0.51 2.29 3.34 0.14 

7.55 8.10 0.63 3.78 9.63 0.00 9.04 7.80 0.29 2.54 3.94 0.05 

 0.89  0.18  0.82  0.44 

Percentage of time spent outdoors or in vehicle 

0-54 55-64 65-74 75+ 

22.83 14.94 0.02 13.27 16.89 0.24 13.63 16.29 0.45 10.51 10.43 0.98 

19.28 15.52 0.05 15.30 16.18 0.76 15.87 15.91 1.00 12.86 10.39 0.19 

 0.02  0.17  0.19  0.69 

Average 0-54 34.76 27.78 13.33 15.18 0.02 
F-17

Table 3. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. MalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

percentage of time with exertion above 2.39 kcal/min = 0.010MJ/min (light) 

55-64 65-74 75+ 

0.00 24.60 30.07 0.06 21.92 23.32 0.63 18.24 15.87 0.41 

0.34 14.51 12.03 0.14 13.23 12.48 0.64 11.13 10.37 0.63 

 0.06  0.67  0.39 

Average percentage of time with exertion above 5.97 kcal/min = 0.025 MJ/ min (moderate) 

0-54 55-64 65-74 75+ 

6.63 4.46 0.05 3.43 5.44 0.01 2.27 3.27 0.08 2.02 1.62 0.53 

6.37 4.31 0.00 3.55 4.41 0.17 2.47 3.46 0.06 2.42 2.41 0.92 

 0.02  0.01  0.20  0.43 

Average percentage of time with exertion above 9.55 kcal/min = 0.040 MJ/ min (heavy) 

0-54 55-64 65-74 75+ 

0.662 0.735 0.59 0.565 0.846 0.19 0.155 0.388 0.01 0.132 0.157 

0.792 0.986 0.11 1.068 1.222 0.40 0.361 0.716 0.00 0.331 0.512 

 0.55  0.05  0.06  0.55 
F-18

Table 3. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. MalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

0.79 0.05 

Table 4. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. FemalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

Average maximum 8hr exertion (Mcal) 

0-54 55-64 65-74 75+ 

1.30 1.27 0.69 1.21 1.27 0.33 1.05 1.10 0.29 0.96 0.98 0.63 

0.31 0.38 0.340.32 0.33 1.000.30 0.31 0.940.33 0.30 0.34 

 0.72  0.56  0.31  0.44 

Ninety fifth 0-54 1.98 2.01 0.82 0.91 0.99 
F-19

Table 4. Statistical Tests for the Association between Angina and Various Variables Represen

Exertion. FemalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

percentile of maximum 8hr exertion (Mcal) 

55-64 65-74 75+ 

0.86 1.79 1.92 0.41 1.42 1.51 0.26 1.27 1.31 0.59 

0.69 0.80 0.77 0.68 0.53 0.57 0.57 0.56 0.52 0.43 

 0.33  0.31  0.43 

Percentage of time spent outdoors 

0-54 55-64 65-74 75+ 

3.64 5.11 0.42 4.31 4.59 0.88 2.84 4.14 0.14 3.79 2.27 0.40 

7.29 9.58 0.20 9.53 8.22 0.23 5.51 7.34 0.02 12.04 4.60 0.00 

 0.43  0.23  0.49  0.76 

Percentage of time spent in vehicle 

0-54 55-64 65-74 75+ 

4.54 5.35 0.55 4.21 5.60 0.35 5.26 4.15 0.23 2.82 2.94 0.86 

5.48 5.98 0.72 7.53 7.23 0.71 5.94 6.18 0.77 4.37 4.34 0.91 

 0.40  0.06  0.15  0.72 

Percentage of time 0-54 8.18 10.46 9.93 11.27 0.18 
F-20

Table 4. Statistical Tests for the Association between Angina and Various Variables Represen

Exertion. FemalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

spent outdoors or in vehicle 

55-64 65-74 75+ 

0.36 8.52 10.18 0.48 8.10 8.29 0.88 6.61 5.21 0.45 

0.57 12.15 10.42 0.22 8.21 9.38 0.26 12.36 6.25 0.00 

 0.04  0.99  0.77 

Average percentage of time with exertion above 2.39 kcal/min = 0.010MJ/min (light) 

0-54 55-64 65-74 75+ 

23.50 21.40 0.49 19.04 21.04 0.33 13.97 15.46 0.26 11.31 11.84 0.73 

12.28 12.15 0.86 10.34 10.47 1.00 8.45 9.53 0.31 9.75 8.61 0.24 

 0.41  0.51  0.43  0.51 

Average percentage of time with exertion above 5.97 kcal/min = 0.025 MJ/ min (moderate) 

0-54 55-64 65-74 75+ 

1.44 1.59 0.71 0.79 1.31 0.05 0.65 0.68 0.87 0.75 0.43 0.23 

1.66 1.97 0.44 1.27 2.07 0.00 1.35 1.56 0.22 1.73 1.31 0.01 

 0.73  0.04  0.51  0.84 

Average 0-54 0.101 0.184 0.170 0.324 0.63 
F-21

Table 4. Statistical Tests for the Association between Angina and Various Variables Represen

Exertion. FemalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

percentage of time with exertion above 9.55 kcal/min = 0.040 MJ/ min (heavy) 

55-64 65-74 75+ 

0.06 0.095 0.093 0.96 0.028 0.030 0.89 0.025 0.016 0.44 

0.00 0.277 0.198 0.01 0.076 0.110 0.00 0.075 0.077 0.90 

 0.67  0.85  0.28 
F-22

Table 5. General Linear Models for the Association between Angina and Various Variables RepresentiExertion. 

Variable 

Angina Coefficient1 

Standard Error 

P-value R s

Average maximum 8hr exertion (Mcal) -0.0445 0.0237 0.0608 0Ninety fifth percentile of maximum 8hr exertion (Mcal) 

-0.1553 0.0581 0.0075 0

Percentage of time spent outdoors +0.6975 0.7648 0.3618 0Percentage of time spent in vehicle -0.4805 0.4679 0.3045 0Percentage of time spent outdoors or in vehicle +0.2170 0.8777 0.8047 0Average percentage of time with exertion above 2.39 kcal/min = 0.010 MJ/min (light) 

-0.7359 0.6996 0.2929 0

Average percentage of time with exertion above 5.97 kcal/min = 0.025 MJ/min (moderate) 

-0.1730 0.1910 0.3650 0

Average percentage of time with exertion above 9.55 kcal/min = 0.040 MJ/min (heavy) 

-0.0933 0.0439 0.0334 0

1. The angina coefficient is the expected difference (angina minus non-angina) between the summary statistic non-angina subjects of the same age and gender.
United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-10-009 

Environmental Protection Health and Environmental Impacts Division July 2010 Agency Research Triangle Park, NC