P.1 

OAR Box 1808 

Prepped by Charmelle Mathews 

Document Number: 

4) ll-A-14 

Docket Number: 

A-90-07 

Printed 11/20/2008 10:28:18 AM Sheet 4 of 91 
P.2 

I . - .0- 0 . 

•U-A -/</ 

EPA-450/5-83-004 

The NAAQS Exposure Model (NEM) 

Applied to Carbon Monoxide 

Ted Johnson and Roy A. Paul 

PEDCo Environmental, Inc. 

505 South Duke Street. Suite 503 

Durham, North Carolina 27701 

FEB? "199? 

Prepared for 

U.S. ENVIRONMENTAL PROTECTION AGENCY 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Research Triangle Park. NC 2771 1 

Thomas McCurdy, Task Manager 

December 1983 
P.3 

DISCLAIMER 

This report was furnished to the U.S. Environmental Protection Agency by PEDCo Environmental, Inc., in partial fulfillment 

of Contract No. 68-02-3390, Work Assignments No. 13 and 16. The 

contents of this report are reproduced herein as received from 

the contractor. The opinions, findings, and conclusions expressed 

are those of the authors and not necessarily those of the Environmental Protection Agency. 

This draft report is being circulated for review and comment. 

Anyone interested in commenting or providing information concerning the material should address their communications to Thomas 

Feagans, Ambient Standards Branch, MD-12, U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711. 

If you would like to discuss the report, please call him at 

(919)541-5655 (FTS 629-5655). 

ill 
P.4 

CONTENTS 

Page 

Figures vii 

Tables . ix 

Acknowledgment xvii 

1. Introduction 1-1 

2. Overview of the Exposure Model 2-1 

2.1 The exposure district version of NEM 2-1 

2.2 The neighborhood version of NEM 2-3 ' 

2.3 References 2-5 

3. Simulation of Population Movement 3-1 

3.1 Composition of cohort files 3-1 

3.2 Types of neighborhoods 3-8 

3.3 Estimation of cohort populations 3-10 

3.4 References 3-17 

4. Preparation of Air Quality Data 4-1 

4.1 Selection of representative data sets 4-1 

4.2 Validation of air quality data 4-4 

4.3 Simulation of missing values in hourly 

average CO data sets 4-9 

4.4 References 4-18 

5. Simulation of Air Quality Expected at Fixed 

Monitoring Site Under Alternative Carbon 

Monoxide Standards 5-1 

5.1 The rollback model 5-1 

5.2 Air quality indicators 5-2 

5.3 Background concentrations 5-15 

5.4 References 5-18 
CONTENTS (continued ) 

P.5 

6. Simulation of Carbon Monoxide Levels in the 

Microenvironment 

P a ?e 

6-1 

6.1 Work-school microenvironment 

6.2 Home-other microenvironment 

6.3 Transportation vehicle microenvironment 

6.4 Roadside microenvironment 

6.5 Other outdoor locations 

6.6 Summary 

6.7 References 

6-3 

6-9 

6-18 

6-25 

6-26 

6-26 

6-29 

7. Exposure Estimates for Adults with Cardiovascular 

Disease in Four Urban Areas 7-1 

7.1 "Best estimate" results 

7.2 Male/female comparisons 

7.3 The significance of indoor sources 

7.4 Uncertainty in NEM exposure estimates 

7.5 References 

7-1 

7-39 

7-42 

7-42 

7-55 

8. Nationwide Extrapolations 

8.1 Extrapolation procedure 

8.2 Extrapolation results 

8.3 Uncertainty of the nationwide estimates 

Appendix A 

Appendix B 

Appendix C 

Activity Patterns by Age-Occupation 

Subgroup 

Cohort Populations by Study Area 

Discussion of Air Quality Indicators 

Used in the NEM Analysis and Estimated 

Concentrations Used in the Regulatory 

Analysis 

8-1 

8-2 

8-4 

8-16 

A-1 

B-l 

C-l 

vi 
P.6 

FIGURES 

Number Page 

2-1 Flow Diagram of Exposure District Version 

of NEM 2-4 

2-2 Flow Diagram of Neighborhood Version of NEM 2-6 

3-1 Simulated Movements Between Exposure Districts 

In The Los Angeles-San Bernardino Study Area 3-2 

3-2 Pattern of Neighborhood Types in a Portion of 

The Philadelphia Study Area 3-3 

3-3 Representation of People-Movement in the 

Neighborhood Version of NEM 3-4 

3-4 Contents of Cohort Activity File 3-11 

4-1 Hourly Average 1978 Carbon Monoxide Data 

Reported by Monitoring Site 261040001G01 

in St. Louis 4-15 

4-2 Hourly Average 1978 Carbon Monoxide Data for 

Monitoring Site 261040001G01 in St. Louis 

After Initial Simulation of Missing Values 4-16 

4-3 Hourly Average 1978 Carbon Monoxide Data for 

Monitoring Site 261040001G01 in St. Louis 

After Final Simulation of Missing Values 4-17 

VI1 
P.7 

2. James L. Repace, Wayne R. Ott, and Lance A. Wallace, "Total 

human exposure to air pollution," paper 80-61.6, presented 

at the 73rd Annual Meeting of the Air Pollution Control 

Association, Montreal, June 1980. 

3. William F. Biller, Thomas B. Feagans, Ted R. Johnson, 

George M. Duggan, and James E. Capel, Estimated Exposure 

to Ambient Carbon Monoxide Concentrations Under Alternative 

Air Quality Standards^ Strategies and Air Standards Division, 

Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, N.C. 

27711, January 1981. 

4. Ted Johnson and Roy Paul, The NAAQS Exposure Model (NEM) and 

Its Application to Nitrogen Dioxide, prepared by PEDCo Environmental, Inc., for Strategies and Air Standards Division, 

Office of Air Quality Planning and Standards, Environmental 

Protection Agency, Research Triangle Park, N.C. 27711, 

August 1981. 

5. Ted Johnson and Roy Paul, The NAAQS Exposure Model (NEM) and 

Its Application to Particulate Matter, prepared by PEDCo 

Environmental, Inc., for Strategies and Air Standards Division, Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, 

N.C. 27711, August 1981. 

6. William F. Biller, Thomas B. Feagans, Ted R. Johnson, George 

M. Duggan, Roy A. Paul, Thomas McCurdy,, and Henry C. Thomas, 

"A general model for estimating exposure associated with 

alternative NAAQS," paper 81-18.4, presented at the 74th 

Annual Meeting of the Air Pollution Control Association, 

Philadelphia, June 1981. i 

xix 
P.8 

TABLES 

Number Page 

3-1 Description and Apportionment of Activity 

Pattern Subgroups 3-6 

3-2 Neighborhood Types (NT's) and Codes 3-9 

3-3 Geographic Classification of Exposure Districts 3-12 

3-4 A-0 Group Population By Residential Neighborhood Classifications in Four Study Areas 3-13 

3-5 Assumptions Concerning Work NT's of A-0 Groups 3-15 

3-6 Collapsed Home-To-Work Trip Tables, Expressed 

As Number Of Trips And Fraction of Trips 3-16 

3 

4-1 Data. Sets (mg/m ) Selected For Analysis of 

Population Exposure To Carbon Monoxide in 

Four Cities 4-2 

4-2 Anomalies Flagged by Data Screening 4-8 

5-1 Results of Fitting Weibull and Lognormal 

Distributions to 1978 CO Data (ppm) From 

St. Louis, MO by Least Squares Method 5-10 

5-2 Results of Fitting Weibull and Lognormal 

Distributions by Maximum Likelihood Procedure 

to Upper 50 Percent of Daily Maximum 1-Hour 

CO Data 5-11 

5-3 Results of Fitting Weibull and Lognormal Distributions by Maximum Likelihood Procedure 

to Upper 50 Percent of Daily Maximum 8-Hour 

Running Average CO Data 5-12 

5-4 Results of Fitting Weibull and Lognormal Distributions by Maximum Likelihood Procedure 

to Upper 20 Percent of Daily Maximum 1-Hour 

CO Data 5-13 

IX 
P.9 

TABLES (continued) 

Number Page 

5-5 Results of Fitting Weibull and Lognormal Distributions by Maximum Likelihood Procedure 

to Upper 20 Percent of Daily Maximum 8-Hour 

Running Average CO Data 5-14 

5-6 Air Quality Indicators For CO Data 5-16 

5-7 Estimated Annual Average Background Levels 5-17 

6-1 Studies Considered in Developing CO Microenvironment Factors 6-2 

6-2 Estimates of CO Concentrations in 

an Office With Smokers 6-5 

6-3 Weekday CO Measurements at Street Canyon Site 6-6 

6-4 CO Concentrations (ppm) at Two Office Sites 

Recorded by Moschandreas, et al. 6-7 

6-5 Indoor-Outdoor CO Ratios Determined for Two 

Office Buildings by Yocum, et al. 6-7 

6-6 Average Indoor/Outdoor CO Ratios Recorded by 

Yocum, et al. 6-9 

6-7 Air Exchange Rates Determined by Moschandreas, 

et al. 6-12 

6-8 Indoor/Outdoor CO Data Recorded by Cote, et al. 6-15 

6-9 Average Differences Between Kitchen, Living 

Room, and Outside CO Concentrations 6-16 

6-10 Average CO Levels in Various Structures 6-18 

6-11 Ratios of Mean Personal CO Exposures to Mean 

CO Concentrations at Fixed Monitors 6-23 

6-12 Ratios of CO in Motor Vehicles Concurrent to 

CO at Camp Stations 6-24 

6-13 Ratios of Mean CO Concentrations at Experimental 

Sites and at Fixed Sites 6-26 

6-14 Estimates of Additive Microenvironmental 

Factors (a .) 6-27 

m, t 
P.10_ 

Number 

TABLES (continued) 

Pa^e 

6-15 Estimates of Multiplicative Microenvironmental Factor (b ) 6-28 

7-1 Estimates of Occurrences for Adults With 

Cardiovascular Disease of 1-Hour CO Exposures 

Above Selected Concentration Values Assuming 

9 PPM/1 EXEX Standard is Attained 7-2 

7-2 Estimates of Adults With Cardiovascular 

Disease Who Have 1-Hour CO Exposures Above 

Selected Concentration Values Assuming 

9 PPM/1 EXEX Standard is Attained 7-3 

7-3 Estimates of Adults With Cardiovascular 

Disease Whose Maximum 1-Hour CO Exposure 

Occurs in Selected Concentration Ranges 

Assuming 9 PPM/1 EXEX Standard is Attained 7-4 

7-4 Estimates of Occurrences for Adults With 

Cardiovascular Disease of 8-Hour CO 

Exposures Above Selected Concentration 

Values Assuming 9 PPM/1 EXEX Standard 

is Attained 7-5 

7-5 Estimates of Adults With Cardiovascular 

Disease Who Have 8-Hour CO Exposures Above 

Selected Concentration Values Assuming 

9 PPM/1 EXEX Standard is Attained 7-6 

7-6 Estimates of Adults With Cardiovascular 

Disease Whose Maximum 8-Hour CO Exposure 

Occurs in Selected Concentration Ranges 

Assuming 9 PPM/1 EXEX Standard is Attained 7-7 

7-7 Estimates of Occurrences for Adults With ' 

Cardiovascular Disease of COHb Levels 

Exceeding Selected Values Assuming 

9 PPM/1 EXEX Standard is Attained 7-8 

7-8 Estimates of Adults With Cardiovascular 

Disease Who Experience COHb Levels Exceeding 

Selected Values Assuming 9 PPM/1 EXEX 

Standard is Attained 7-9 

7-9 Estimates of Adults With Cardiovascular Disease 

Whose Maximum COHb Level Occurs in Selected 

Ranges Assuming 9 PPM/1 EXEX Standard is 

Attained 7-10 

xi 

i 
P.11 

TABLES (continued) 

Number Page 

7-10 Estimates of Occurrences for Adults With 

Cardiovascular Disease of 1-Hour CO 

Exposures Above Selected Concentration 

Values Assuming 12 PPM/1 EXEX Standard 

is Attained 7-11 

7-11 Estimates of Adults With Cardiovascular 

Disease Who Have 1-Hour CO Exposures Above 

Selected Concentration Values Assuming 

12 PPM/1 EXEX Standard is Attained 7-12 

7-12 Estimates of Adults With Cardiovascular Disease 

Whose Maximum 1-Hour CO Exposure Occurs in 

Selected Concentration Ranges Assuming 

12 PPM/1 EXEX Standard is Attained 7-13 

7-13 Estimates of Occurrences for Adults With 

Cardiovascular Disease of 8-Hour CO 

Exposures Above Selected Concentration 

Values Assuming 12 PPM/1 EXEX Standard is 

Attained 7-14 

7-14 Estimates of Adults With Cardiovascular 

Disease Who Have 8-Hour CO Exposures Above 

Selected Concentration Values Assuming 

12 PPM/1 EXEX Standard is Attained 7-15 

7-15 Estimates of Adults With Cardiovascular 

Disease Whose Maximum 8-Hour CO Exposure 

Occurs in Selected Concentration Ranges 

Assuming 12 PPM/1 EXEX Standard is Attained 7-16 

7-16 Estimates of Occurrences for Adults With 

Cardiovascular Disease of COHb Levels 

Exceeding Selected Values Assuming 12 PPM/1 

EXEX Standard is Attained 7-17 

7-17 Estimates of Adults With Cardiovascular 

Disease Who Experience COHb Levels Exceeding 

Selected Values Assuming 12 PPM/1 EXEX 

Standard is Attained 7-18 

7-18 Estimates of Adults With Cardiovascular Disease 

Whose Maximum COHb Level Occurs in Selected 

Ranges Assuming 12 PPM/1 EXEX Standard is 

Attained 7-19 

X II 
P.12 

TABLES (continued) 

Number Page 

7-19 Estimates of Occurrences for Adults With 

Cardiovascular Disease of 1-Hour CO Exposures 

Above Selected Concentration Values Assuming 

15 PPM/1 EXEX Standard is Attained 7-20 

7-20 Estimates of Adults With Cardiovascular Disease 

Who Have 1-Hour CO Exposures Above Selected 

Concentration Values Assuming 15 PPM/1 EXEX 

Standard is Attained 7-21 

7-21 Estimates of Adults With Cardiovascular Disease 

Whose Maximum 1-Hour CO Exposure Occurs in 

Selected Concentration Ranges Assuming 

15 PPM/1 EXEX Standard is Attained 7-22 

7-22 Estimates of Occurrences for Adults With 

Cardiovascular Disease of 8-Hour CO Exposures 

Above Selected Concentration Values Assuming 

15 PPM/1 EXEX Standard is Attained 7-23 

7-23 Estimates of Adults With Cardiovascular Disease 

Who Have 8-Hour CO Exposures Above Selected 

Concentration Values Assuming 15 PPM/1 EXEX 

Standard is Attained 7-24 

7-24 Estimates of Adults With Cardiovascular Disease 

Whose Maximum 8-Hour CO Exposure Occurs in 

Selected Concentration Ranges Assuming 

15 PPM/1 EXEX Standard is Attained 7-25 

7-25 Estimates of Occurrences for Adults With 

Cardiovascular Disease of COHb Levels 

Exceeding Selected Values Assuming 

15 PPM/1 EXEX Standard is Attained 7-26 

7-26 Estimates of Adults With Cardiovascular Disease 

Who Experience COHb Levels Exceeding Selected 

Values Assuming 15 PPM/1 EXEX Standard is 

Attained 7-27 

7-27 Estimates of Adults With Cardiovascular Disease 

Whose Maximum COHb Level Occurs in Selected 

Ranges Assuming 15 PPM/1 EXEX Standard is 

Attained 7-28 

7-28 Algorithm Used to Calculate Carboxyhemoglobin 

in Blood of Cohorts 7-31 

x m 
___A_ 

TABLES (continued) 

Number Page 

7-29 Values Assigned to Variables in Algorithm 

Used to Estimate Carboxyhemoglobin " 7-32 

7-30 Percentage of Adults With Cardiovascular 

Disease Experiencing COHb Levels Exceeding 

Selected Values Assuming 9 PPM/1 EXEX 

Standard is Attained 7-35 

7-31 Percentage of Adults With Cardiovascular 

Disease Experiencing COHb Levels Exceeding 

Selected Values Assuming 12 PPM/1 EXEX 

Standard is Attained 7-37 

7-32 Percentage of Adults With Cardiovascular 

Disease Experiencing COHb Levels Exceeding 

Selected Values Assuming 15 PPM/1 EXEX 

Standard is Attained 7-38 

7-33 Estimates of Adult Females With Cardiovascular 

Disease Who Experience COHb Levels Exceeding 

Selected Values Assuming 9 PPM/1 EXEX Standard 

is Attained 7-40 

7-34 Estimates of Adult Males With Cardiovascular 

Disease Who Experience COHb Levels Exceeding 

Selected Values Assuming 9 PPM/1 EXEX Standard 

is Attained 7-41 

7-35 Estimates of Adults With Cardiovascular Disease 

Who Have 1-Hour CO Exposures Above Selected 

Values Under 9 PPM/1 EXEX Standard With Indoor Sources Omitted 7-4 3 

7-36 Estimates of Adults With Cardiovascular Disease 

Who Have 8-Hour CO Exposures Above Selected 

values Under 9 PPM/1 EXEX Standard With Indoor Sources Omitted 7-44 

7-37 Estimates of Occurrences for Adults With 

Cardiovascular Disease of COHb Levels 

Exceeding Selected Values Under 9 PPM/1 

EXEX Standard With Indoor Sources Omitted 7-45 

7-38 Estimates of Adults With Cardiovascular Disease 

Who Experience COHb Levels Exceeding Selected 

Values Under 9 PPM/1 EXEX Standard With Indoor Sources Omitted 7-46 

xiv 
Number 

7-

7-

7-

7-

7-

-39 

-40 

-41 

-42 

-43 

TABLES (continued) 

Estimates of Adults with Cardiovascular Disease 

whose Maximum COHb Level Occurs in Selected 

Ranges under 9 PPM/1 EXEX Standard with Indoor 

Sources Omitted 

Estimates of Adults with Cardiovascular Disease 

Chicago with 1-Hour Carbon Monoxide Exposures 

Above Selected Values Under 12 PPM/1 EXEX Standard Using Best, Lower, and Upper Microenvironment Factors 

Page 

7-47 

7-49 

Estimates of Adults with Cardiovascular Disease 

in Chicago with 8-Hour Carbon Monoxide Exposures 

above Selected Values under 12 PPM/1 EXEX Standard using Best, Lower, and Upper Microenvironment Factors 7-50 

Estimates of Occurrences for Adults with Cardiovascular Disease in Chicago of COHb Levels 

Exceeding Selected Values under 12 PPM/1 EXEX 

Standard using Best, Lower, and Upper Microenvironment Factors 

Estimates of Adults with Cardiovascular Disease 

in Chicago Experiencing COHb Levels Exceeding 

7-51 

P.14 

Selected Values Under 12 PPM/1 EXEX Standard 

Using Best, Lower, and Upper Microenvironment 

Factors 7-52 

7-44 Estimates of Adults with Cardiovascular Disease 

in Chicago whose Maximum COHb Level Occurs in 

Ranges under 12 PPM/1 EXEX Standard Using Best, 

Lower, and Upper Microenvironment Factors 7-53 

7-45 Sensitivity of COHb Estimates for Chicago to 

Variations in Two Physiological Variables 7-54 

8-1 Urbanized Area Population Data Used to Extrapolate Model Results 8-4 

8-2 Estimates of Occurrences in the Cardiovascular 

Adult Urban U.S. Population of 1-Hour Average 

CO Exposures above Selected Concentration Values 

under Alternative Air Quality Assumptions 8-5 

xv 
P.15 

TABLES (continued) 

Number Page 

8-3 Estimates of Cardiovascular Adults in Urban U.S. 

with 1-Hour Average CO Exposures above Selected 

Concentration Values under Alternative Air 

Quality Assumptions 8-6 

8-4 Estimates of Adults in Urban U.S. Whose Maximum 

1-Hour Average CO Exposure Occurs in Selected 

Concentration Ranges Under Alternative Air 

Quality Assumptions 8-7 

8-5 Estimates of Occurrences in the Cardiovascular 

Adult Urban U.S. Population of 8-Hour Average 

CO Exposures above Selected Concentration 

Values Under Alternative Air Quality Assumptions 8-8 

8-6 Estimates of Cardiovascular Adults in Urban U.S. 

with 8-Hour Average CO Exposure Above Selected 

Concentration Values Under Alternative Air 

Quality Assumptions 8-9 

8-7 Estimates of Cardiovascular Adults in Urban U.S. 

Whose Maximum 8-Hour Average CO Exposure Occurs 

in Selected Concentration Ranges Under Alternative Air Quality Assumptions 8-10 

8-8 Estimates of Occurrences among Cardiovascular 

Adults in Urban U.S. of COHb Levels Exceeding 

Selected Values under Alternative Air Quality 

Assumptions 8-11 

8-9 Estimates of Cardiovascular Adults in Urban U.S. 

Experiencing COHb Levels Exceeding Selected 

Values under Alternative Air Quality Assumptions 8-12 

8-10 Estimates of Cardiovascular Adults in Urban U.S. 

Whose Maximum COHb Level Occurs in Selected 

Concentration Ranges Under Alternative Air 

Quality Assumptions 8-13 

8-11 Percentage of Cardiovascular Adult Urban U.S. 

Population Experiencing COHb Levels Exceeding 

Selected Values under- Alternative Air Quality 

Assumptions 8-14 

xvi 
P.16 

TABLES (continued) 

Number Page 

8-12 Estimates of Cardiovascular Adults in Urban U.S. 

Experiencing COHb Levels Exceeding Selected 

Values a Given Number of Days Assuming 9 PPM/1 

EXEX Standard is Attained 8-15 

C-l Estimated Concentrations (EC's) Developed by 

EPA and Corresponding Air Quality Indicators 

(AQI's) from Table 5-6 (concentrations in 

parts per million) C-4 

x v n 
P.17 

ACKNOWLEDGMENTS 

The development of a general model for assessing population 

exposures associated with possible National Ambient Air Quality 

Standards (NAAQS's) and the application of that model to carbon 

monoxide (CO) reported in this document have involved the efforts 

of many people. The following persons associated with PEDCo 

Environmental, Inc., played important roles in conducting this 

study. Coauthor Roy Paul developed the population movement 

algorithms and created the population data files. Coauthor Ted 

Johnson developed the statistical models used to process air 

quality data and managed the development of air quality and 

population data bases. Irene Griffin assisted in the compilation 

of population and air quality data. James Capel created and 

debugged the computer programs which processed air quality data. 

Barbara Biagun developed estimates of background pollutant concentrations. Dianne Gupton and Dian Dixon typed the report. 

George M. Duggan, Strategies and Air Standards Division (SASD), 

U.S. Environmental Protection Agency (EPA), designed and implemented the computer programs used to calculate exposure estimates. 

Thomas McCurdy, SASD, EPA, facilitated conduct of the study and 

co-managed the SASD exposure assessment program with Henry Thomas. 

Much of the cohort data used in the model is based upon work done 

by SRI International.1 

Dr. William F. Biller, EPA contractor, and Thomas B. Feagans, 

SASD, developed the general exposure model and the method used to 

extrapolate exposure estimates for individual study areas to the 

nation. The general model makes use of ideas developed by EPA's 

Office of Research and Development and others.2 The model was 

first applied to carbon monoxide at an early stage of its development.

 3 A later version of the model was applied to nitrogen 

dioxide and particulate matter.1*'5 An exposition of the general 

model is available from EPA.6 

REFERENCES 

1. Marc F. Roddin, Hazel T. Ellis, and Waheed M. Siddiqee, 

Background Data for Human Activity Patterns, Vols. 1 and 2, 

Draft final report prepared for Strategies and Air Standards 

Division, Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, 

N.C. 27711, August 1979. 

xvi 11 
P.18 

SECTION 1 

INTRODUCTION 

Under the Clean Air Act, the Environmental Protection Agency 

(EPA) is responsible for establishing National Ambient Air Quality 

Standards (NAAQS's) and for reviewing them periodically to determine their adequacies on the basis of recent experience and 

research. In view of these responsibilities, the Strategies and 

Air Standards Division (SASD) of the Office of Air Quality Planning and Standards (OAQPS) is exploring the use of quantitative 

methods for assessing health risks associated with proposed air 

quality standards. 

An important aspect of health risk assessment is the estimation of population exposure. For the past few years, SASD has 

been engaged in the development of an exposure model suitable for 

evaluating alternative ambient air standards. The model is known 

as NEM, an acronym for NAAQS Exposure Model. 

Two versions of NEM have been developed. The exposure district version of NEM simulates the pollutant concentration expected 

to occur in selected exposure districts within a study area under 

user-specified regulatory scenarios. It then adjusts these 

estimates to account for an exhaustive set of microenvironments 

and simulates typical movements of population subgroups, called 

cohorts, through the districts and microenvironments. Outputs of 

the simulation program are population exposure estimates at 

specified pollutant levels. Three indices of exposure are used 

currently, and more are being investigated. 

The other version of NEM substitutes "neighborhood types" 

for exposure districts. This report describes this alternative 

model and its application to four U.S. urban areas (Chicago, IL, 

St. Louis, MO-IL, Philadelphia, PA, and Los Angeles, CA) to 

estimate population exposures associated with alternative NAAQS's 

1-1 
P.19 

proposed for carbon monoxide (CO). Results of these analyses are 

included in this report. The contribution of indoor CO sources 

to total population exposure is also evaluated. 

1-2 
P.20 

SECTION 2 

OVERVIEW OF THE EXPOSURE MODEL 

Analysis of population exposure under present and proposed 

National Ambient Air Quality Standards (NAAQS's) requires that 

the significant factors contributing to total human exposure be 

taken into account. Consequently, these factors have been incorporated into the NAAQS Exposure Model (NEM), a simulation 

model capable of estimating human exposure in selected urbanized 

areas under user-specified regulatory scenarios. The general 

model has been designed so that pollutants as diverse as ozone 

and CO can be accommodated without making changes to the basic 

NEM program. Instead, input data files are developed to reflect 

the assumed spatial characteristics of the specific pollutant 

being analyzed. In one version of the model, air quality data 

are selected to represent a small number of discrete exposure 

districts. This version has been applied to NO- and particulate 

matter. Another version incorporating the concept of "neighborhood types" is the basis of the CO exposure analysis described in 

this report. This section briefly discusses the differences 

between these two versions of NEM. 

2.1 THE EXPOSURE DISTRICT VERSION OF NEM 

In the exposure district version of NEM, land areas within a 

selected study area are represented by large, bounded "exposure 

districts." The population within each exposure district is 

assigned to a single discrete point, the population centroid. 

The air quality level within each exposure district is represented 

by the air quality level at the population centroid, which is 

estimated for each hour of the year by using monitoring data from 

adjacent monitoring sites. Because pollutants in the air can be 

2-1 
modified considerably when entering a building or vehicle, thes 

ambient air quality estimates are adjusted to account for five 

different microenvironments: indoors at work or school, indoor. 

at home or other locations, inside a transportation vehicle, 

outdoors near a roadway, and other outdoor locations. NEM simulates hour-by-hour movements of representative population groups 

through different districts of the city and through different 

microenvironments, accumulating the resulting exposure over a 

period of one year. 

Because degree of exposure and susceptibility to effects of 

pollution vary with age, occupation, and intensity of exercise; 

the total population of each study area is divided into ageoccupation (A-0) groups, and each A-0 group is further subdivided 

into three or more subgroups. For each subgroup, a typical pattern of activity through the five microenvironments is establishec 

and an exercise level (high, medium, or low) within each is specified . 

Units of population analyzed by NEM are called cohorts. 

Each cohort is identified by exposure district of residence, by 

exposure district of employment, by A-0 group, and by activitypattern subgroup. During each hour of the year, each cohort is 

assigned to a particular exposure district and a particular 

microenvironment. Since NEM simulates hour-by-hour air quality 

in each district and each microenvironment, the hourly exposures 

of each cohort may be summed over a one year period. Annual 

cohort exposures are summed to provide exposure estimates for 

each A-0 group, which, in turn, are summed for all groups to 

provide an estimate of total population exposure for a particular 

study area. Output of NEM is a series of tables showing frequency distributions of total exposure at different averaging 

times (e.g., 1 hour, 8 hours, 1 year) using different measures of 

exposure (e.g., number of persons with exposures above selected 

pollutant levels). 

In developing data bases for this version of NEM, PEDCo had 

to establish exposure districts within each study area that would 

accommodate the available breakdowns of transportation and census 

2-2 
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1 

IN 
P.23 

ANALYST SELECTS 

( 1 ) TEST CITY 

(2) NAAQS 

START PROGRAM 

• SELECT NEXT COHORT 

DETERMINE COHORT 

POPULATION 

COHORT POPULATION 

FILE 

NEXT HOUR 

~r~ 

DETERMINE EXPOSURE OISTRICT 

MICROENVIRON, ACTIVITY LEVEL 

COHORT LOCATION 

FILE 

DETERMINE AMBIENT AIR QUALITY 

(AQ) IN EXPOSURE DISTRICT 

c > 

DETERMINE ADJUSTED AQ 

PER NAAOS 

1 

— 

HOURLY AIR QUALITY 

FILE 

ROLL-BACK FACTORS 

DETERMINE AQ IN 

MICROENVIRONMENT 

MICROENVIRONMENT 

FACTORS 

ACCUMULATE 1 HOUR OF EXPOSURE 

AT Afl LEVEL IN A REGISTER 

-B LAST HOUR? 

YES 

NO END OF 

A-0 GROUP? 

YES 

. 

SUMMARIZE COHORT EXPOSURES FOR 

AGE-OCCUPATION (A-O) GROUPS 

NO ALL GROUPS 

COMPLETE? 

YES 

(OUTPUT) 

FREQUENCY DISTRIBUTION OF 

EXPOSURES BY A-0 GROUP 

Figure 2-1. Flow diagram of exposure district version of NEM. 

2-4 
P.24 

The neighborhood version of NEM is particularly appropriate 

for analyzing CO exposure. Since suburban neighborhoods generally 

have lower population densities and lower traffic densities than 

center-city neighborhoods, they generally have lower CO levels. 

Likewise, commercial and industrial neighborhoods generally have CO 

source patterns different from residential neighborhoods. 

Figure 2-2 shows the flow diagram for the neighborhood version 

of NEM. The principal differences between the two versions lie 

in the concept of people-movement and in the structure of data 

files used as inputs. The concept of neighborhood-type and 

methods of creating population data bases are described in the 

next section. 

2.3 REFERENCES 

1. Ted Johnson and Roy Paul, The NAAQS Exposure Model (NEM) 

and Its Application to Nitrogen Dioxide, prepared by PEDCo 

Environmental, Inc., for Strategies and Air Standards Division, Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, 

N. C. 27711, May 1981. 

2. Ted Johnson and Roy Paul, The NAAQS Exposure Model (NEM) 

and Its Application to Particulate Matter, prepared by PEDCo 

Environmental, Inc., for Strategies and Air Standards Division, 

Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, N. C. 

27711, August 1981. 

2-5 
P.25 

ANALYST SELECTS 

(1) TEST CITY 

(2) NAAQS 

START PROGRA )GRAM 

SELECT NEXT COHORT 

DETERMINE COHORT 

POPULATION 

NEXT HOUR 

~l 

DETERMINE NEIGHBORHOOD TYPE 

MICROENVIRON, ACTIVITY LEVEL 

DETERMINE AMBIENT AIR QUALITY 

(AQ) IN NEIGHBORHOOD TYPE 

3 COHORT POPULATION 

FILE 

COHORT LOCATION 

FILE 

DETERMINE ADJUSTED AQ 

PER NAAQS 

DETERMINE AQ IN 

MICROENVIRONMENT 

I 

ACCUMULATE I HOUR OF EXPOSURE 

AT Aq LEVEL IN A REGISTER 

ig—j LAST HOUR? 

HOURLY AIR QUALITY 

FILE 

ROLL-BACK FACTORS 

MICROENVIRONMENT 

FACTORS 

NO ENO OF 

A-0 GROUP? 

YES 

SUMMARIZE COHORT EXPOSURES FOR 

AGE-OCCUPATION (A-O) GROUPS 

NO ALL GROUPS 

COMPLETE? 

YES 

(OUTPUT) 

FREQUENCY DISTRIBUTION OF 

EXPOSURES BY A-0 GROUP 

Figure 2-2. Flow diagram of neighborhood 

2-6 

version of NEM. 
P.26 

SECTION 3 

SIMULATION OF POPULATION MOVEMENT 

NEM simulates movement of small, homogeneous groups called 

cohorts through zones of varying air quality. This section 

describes the development of computer files delineating cohort 

movements and the allocation of study area population among the j 

cohorts. 

3.1 COMPOSITION OF COHORT FILES 

A cohort is defined as a group of individuals having a 

statistical factor in common in a demographic study. In the 

neighborhood version of NEM, all members of a particular cohort 

(1) live in the same neighborhood type (NT), 

(2) work in the same NT, 

(3) are members of the same age-occupation group, and 

(4) are members of a subgroup with^a specified daily 

activity pattern. 

Consequently, a computer file describing a cohort is labeled as 

to home NT, work NT, age-occupation group, and activity pattern. 

In the exposure district version of NEM, the activity pattern for a cohort contains hourly assignments to predetermined 

exposure districts. Cohort movement can be visualized as transfers between geographically connected districts. For example, 

Figure 3-1 illustrates movement between various districts of the 

Los Angeles-San Bernardino study area. By contrast, the activity 

pattern for a cohort in the neighborhood version of NEM contains 

hourly assignments to predetermined NT's. A particular NT may be 

scattered over an entire study area and mixed with other NT's, as 

shown in Figure 3-2. Movement from one NT to another may represent a significant change in air quality level, without involving 

a significant change in geographic location. 

3-1 ! 
CJ 

I 

Fi9ure 31

 'ffCssriiKK: ans-?"* " -
P.28 

MILES 

Figure 3-2. Pattern of neighborhood types in a 

portion of the Philadelphia study area. 

3-3 
Assignment 

for Hour _1 

Neighborhood-Type 5 

(suburban residential) 

Midroenvironment 2 

(indoors, non-work) 

i Exercise level 1 

*- (low) 

COHORT 

Defined by: 

(1) Neighborhood-Type 

of Residence (5) 

(2) Neighborhood-Type 

for Work (2) 

(3) Age-Occupation 

Group (2) 

(4) Activity Pattern 

Subgroup (1) 

Assignment 

for Hour #2 

Neighborhood-Type 2 

(center-city commercial) 

Microenvironment 1 

(indoors-at-work) 

Exercise level 1 

(low) 

Figure 3-3. Representation of people-movement in 

the neighborhood version of NEM. 
P.30 

In addition to NT assignments, the activity pattern file 

contains hourly assignments of each cohort to a microenvironment 

and an exercise level for typical weekdays, Saturdays, and Sundays. 

Exposure to CO during a particular hour is determined by adjusting 

ambient CO for the assigned NT according to the assigned microenvironment. Because high exercise levels may induce a higher 

uptake of airborne chemicals into the body, NEM keeps track of 

exercise levels so that exposure distributions can be calculated 

for each exercise level separately. ' 

In developing activity patterns appropriate for analyzing 

CO exposure, we divided each A-0 group into three to six subgroups which could be described by one or more demographic variables affecting exposure (see Table 3-1). These variables include commuting time, work shift, work location (e.g., inside, 

outside, in motor vehicle), age, and degree of mobility. The 

population of each age-occupation group was apportioned among 

its constituent subgroups according to demographic statistics 

obtained from the Bureau of Census and other sources.

1 Whenever 

possible, the activity patterns developed for the subgroups were 

based on actual human activity data. Because such data are 

limited to a small number of studies initiated for other purposes, 

many simplifying assumptions were made in constructing the activity 

patterns. For example, retired persons with limited mobility 

were assigned to the outdoor microenvironment for fewer hours 

than retired persons with full mobility. Housewives with schoolage children at home were assigned to the transportation vehicle 

microenvironment more often than housewives with no children at 

home. In each case, an attempt was made to construct an activity 

pattern which was consistent with our intuitive expections of 

what members of that subgroup would do on a typical weekday, 

Saturday, or Sunday. The resulting activity patterns are contained 

in a separate document.2 Sample activity patterns are presented 

in Appendix A. 

3-5 
P.31 

TABLE 3-1. DESCRIPTION AND APPORTIONMENT OF 

ACTIVITY PATTERN SUBGROUPS 

Age-occupation group 

Students 18 and over 

Managers and professionals 

Sales workers 

Clerical and kindred workers 

Craftsmen and kindred 

workers 

Operatives and laborers 

Codea 

Oil 

012 

013 

014 

021 

022 

023 

024 

031 

032 

033 

034 

035 

041 

042-

043 

044 

045 

046 

051 

052 

053 

054 

055 

056 

061 

062 

063 

064 

065 

066 

Subqroup 

Description 

<30 min commute, 8 a.m. 

<30 min commute, 9 a.m. 

>30 min commute, 8 a.m. 

>30 min commute, 9 a.m. 

<30 min commute, single 

house 

<30 min commute, others 

>30 min commute, single 

house 

>30 min commute, others 

class 

class 

class 

class 

family 

family 

Indoor work, <30 min commute 

Indoor work, >30 min commute 

Outdoor work 

Indoor and outdoor work 

Traveling 

Indoor work, 1st shift, 

commute 

Indoor work, 1st shift, 

commute 

Indoor work, 2nd shift, 

commute 

Indoor work, 2nd shift, 

commute 

Outdoor work 

Indoor and outdoor work 

Indoor work, 1st shift, 

commute 

Indoor work, 1st shift, 

commute 

Indoor work, 2nd shift 

Indoor work, 3rd shift 

Outdoor work 

Indoor and outdoor work 

Indoor work, 1st shift, 

commute 

Indoor work, 1st shift, 

commute 

Indoor work, 2nd shift 

Indoor work, 3rd shift 

Outdoor work 

Work in motor vehicle 

<30 min 

>30 min 

<30 min 

>30 min 

<30 min 

>30 min 

<30 min 

>30 min 

Percent 

23 

45 

11 

21 

47 

21 

22 

10 

43 

21 

5 

9 

22 

56 

26 

9 

4 

1 

4 

50 

24 

10 

2 

4 

10 

39 

18 

6 

3 

18 

16 

(continued) 

3-6 
TABLE 3-1 (continued) 

Aqe-occupation group 

Service, military, and 

private household workers 

Housewives 

Unemployed and-retired 

Children less than 5 

Children 5 to 17 

Code3 

081 

082 

083 

084 

085 

086 

091 

092 

093 

101 

102 

103 

104 

105 

106 

111 

112 

113 

114 

121 

122 

123 

124 

125 

126 

Subqroup 

Description 

Service, day time work, <30 min 

commute 

Service, day time work, >30 min 

commute 

Service, night time 

Service, in motor vehicle 

Military 

Private household 

No children at home 

Some children <13 

No children <13, some 13 to 18 

Unemployed, job hunting 

Unemployed, not job hunting 

Disabled 

Retired, full mobility 

Retired, limited mobility 

Retired, confined indoors 

0 to 12 months 

13 to 24 months 

25 to 36 months 

37 to 60 months 

Elementary school, <30 min 

commute 

Elementary school, >30 min 

commute, walk or bike 

Elementary school, >30 min 

commute, vehicle 

High school, <30 min commute 

High school, >30 min commute, 

walk or bike 

High school, >30 min commute, 

vehicle 

Percent 

36 

17 

22 

3 

14 

8 

42 

49 

9 

20 

24 

20 

30 

4 

2 

21 

20 

20 

39 

56 

4 

7 

26 

2 

5 

First two digits indicate age-occupation group, third digit indicates 

subgroup. 

3-7 
_____ 

3.2 TYPES OF NEIGHBORHOODS 

Like the exposure district version of NEM, estimation of 

human exposure to CO using the neighborhood version involves a 

simple two-way trip model. This model assumes that each cohort 

is located either in its home NT or its work NT during each hour 

of the year. To use this model, it is necessary to determine 

hourly ambient CO concentration in home NT's and work NT's. 

In reviewing air quality data available from CO monitors, 

PEDCo found that information about neighborhood settings of monitors is.very limited, except for two National Air Monitoring 

Systems (NAMS) monitors in each city where sites have been surveyed in detail. For most monitors, neighborhood data are limited 

to the station-type (ST) identifiers listed in the Storage and 

Retrieval of Aerometric Data (SAROAD) system. The ST identifiers 

classify monitors according to geographic location and land use. 

Geographic location categories include 

1. Center City - core area of the city, not its incorporated 

limits 

2. Suburban 

3. Rural 

4. Remote - far enough from any activity to measure background levels. 

Land use categories vary with geographic location as follows: 

1. Center City and Suburban 

a. Industrial - product-oriented establishments such 

as manufacturing concerns, utilities, mining, and 

graineries. 

b. Commercial - service-oriented establishments such 

as retail establishments, shopping centers, gas 

stations, and laundromats. 

c. Residential - because other areas are also used 

residentially, this category is used only in the 

absence of a dominating industrial or commercial 

influence. 

d. Mobile - sites located in airports, truck or bus 

terminals, or an expressway cloverleaf. Sites 

placed near parking lots would probably be better 

categorized as industrial or commercial. 

3-8 
P.34 

2. Rural 

3. 

a. Industrial - same as center city and suburban 

industrial. 

b. Commercial - same as center city and suburban 

industrial. 

c. Near urban - sites located in a rural area, yet 

close enough to a major urban center to be affected: 

by the urban area. 

d. Agricultural - sites located near orchards, crop 

raising, cattle, and sheep grazing. 

Remote 

To simplify the selection of monitors to represent NT's, we 

established NT's to conform to the SAROAD ST's. Since all study 

areas were urbanized, ST's with rural and remote categories were 

dropped from consideration. The remaining eight ST's provide the 

basis for eight NT's used for classifying cohorts in a population 

exposure analysis (see Table 3-2). These NT's are coded with 

numbers 1 through 9 to facilitate their use in computer files. 

TABLE 3-2. NEIGHBORHOOD TYPES (NT's) AND CODES 

NT 

Center-city residential 

Center-city commercial 

Center-city industrial 

Center-city mobile 

Suburban residential 

Suburban commercial 

Suburban industrial 

Suburban mobile 

Not used or "other" 

Computer 

code 

1 

2 

3 

4 

5 

6 

7 

8 

9 

3-9 
'.35 

Once NT's are established, they can be used like exposure 

districts within an activity pattern computer file. District 

codes 1 to 9 can be replaced with NT codes 1 to 9 in both the 

definition of cohorts and in hour-by-hour assignments, as shown 

in Figure 3-4. The concept of "home" now refers to home NT 

rather than home district, and "work" refers to work NT rather 

than work district. The 2-way trip model is retained; commuters 

move from a home NT to a, work NT in the morning and return in 

the evening. Consequently, activity patterns developed for the 

neighborhood version of NEM use the same format as activity patterns developed for the exposure district version. 

3.3 ESTIMATION OF COHORT POPULATIONS 

There were several problems associated with establishing 

cohorts and determining cohort populations in the neighborhood 

version of NEM. First, NT's did not provide a convenient 

unit for assembling population data. As shown on a previous map 

(Figure 3-2) neighborhoods- occur in irregular patterns scattered 

throughout a city. Boundaries of neighborhoods do not correspond 

to the boundaries of census tracts or any other unit used by 

the Bureau of Census to organize population data. Second, even 

if it were theoretically possible to collect population data for 

each NT, such an effort would require a significant expenditure 

of resources which were not available for this purpose. Consequently, we made certain simplifying assumptions so that population data that had already been assembled for the exposure 

district version of NEM could be used in CO exposure analysis. 

Population data assembled for previous NEM analyses consisted 

of the numbers of people in 12 A-0 groups who resided in each of 

the exposure districts comprising each of four study areas. The 

first step in using this data was to determine the number of 

people in each A-0 group residing in center-city neighborhoods 

and the number of people residing in suburban neighborhoods. 

3-10 
P.36 

Dummy for 

city name 

Day-of-week 

code 

Cohort 

ID 

\ 

I I 1 T CITY).XXXH042W 

CITYXXXXH042W 12 Wll 

TA. A 

AM-PM 

code 

| , , 

1 H2-1H21H21H21H21H21431W11W11W11W11W11 

12-hr 

location assignments 

Home — 

NT 

A-0 groupJ 

Pattern — 

(Subgroup 

1-6) 

Work NT 

AAA 

1W11W11W31H21H21H21H21H21H21H21 

L-Activity level code (2nd hr) 

1—Microenvironment code (2nd hr) 

' Location assignment (2nd hr) 

Work(W) or Home(H) 

Neighborhood Types (NT) 

Day-of-week codes: 

1 = weekday 

2 = Saturday 

3 = Sunday 

AM-PM codes: 

Activity codes: 

1 = low activity level 

2 = medium activity level 

3 • high activity level 

Microenvironment codes: 

1 = AM, hrs 1-12 

2 = PM, hrs 1-12 

1 - indoors at work 

2 = indoors, other 

3 = transportation vehicle 

4 = transportation, other 

5 = outdoors 

Figure 3-4. Contents of cohort activity file. 

3-11 
P.37 

This was possible because each exposure district could be classified center-city or suburban by its geographic location and predominant land use pattern. Classifications of exposure districts 

were established in study areas used for previous NEM analyses, 

as shown in Table 3-3. 

TABLE 3-3. GEOGRAPHIC CLASSIFICATION OF EXPOSURE DISTRICTS 

Study area 

Chicago 

Philadelphia 

St. Louis 

Los Angeles 

Exposure districts 

Center-city 

1,2,3,4,5 

1 

1,2,3,4 

1 

Suburban 

6,7,8 

2,3,4,5,6 

5,6,7 

2,3,4,5,6,7 

Using this breakdown, the A-0 group residence data could be 

classified into suburban or center-city, as listed in Table 3-4. 

The next step was to further subdivide these general neighborhood 

classifications into specific NT's. For this breakdown, it was 

necessary to use transportation data and certain assumptions 

about movements of A-0 groups and their place of residence. 

To simplify the problem of NT assignments, we assumed 

that all people live in a residential neighborhood. This might 

be considered obvious by definition, but some neighborhoods are 

better characterized by predominant land uses. For example, ..some 

people live in a predominantly industrial neighborhood. However, 

it is not important to use pure definitions of neighborhoods, but 

to differentiate high pollution residential neighborhoods from 

presumably low pollution residential neighborhoods. Using the 

NT's listed in Table 3-2, this dichotomy can be approximated by 

center-city residential (high pollution) and suburban residential 

(low pollution). In St. Louis, for example, we approximated 

the population living in center-city residential NT's by the persons living within neighborhoods classified center-city in Table 

3-3, and the population living within suburban-residential NT's 

3-12 
TABLE 3-4. A-0 GROUP POPULATION BY RESIDENTIAL NEIGHBORHOOD 

CLASSIFICATIONS IN FOUR STUDY AREAS 

co 

i 

oo 

Age 

1. 

2. 

3. 

4. 

N 5. 

6. 

7. 

8. 

9. 

10. 

11. 

12. 

-occupation group 

Students 18+ 

Professionals/Managers 

Sales workers 

Clerical and kindred 

workers 

Craftsmen, foremen, 

kindred 

Operatives and 

laborers 

Farm workers 

Military, service 

household 

Married housewives 

Unemployed or retired 

Children under 5 

Children 5-17 

TOTALS 

Chi 

CenterCity 

83,982 

387,741 

82,701 

269,599 

174,648 

206,363 

-

137,201 

170,209 

57,399 

50,295 

120,703 

1,704,843 

cago 

Suburban 

25,639 

119,336 

25,482 

66,877 

60,795 

55,626 

-

30,803 

92,439 

16,385 

38,455 

92,292 

624,115 

Philadelphia 

CenterCity 

18,429 

43,002 

14,398 

64,792 

39,052 

98,768 

862 

57,695 

93,110 

96,979 

75,481 

209,444 

812,009 

Suburban 

75,594 

214,878 

74,567 

199,801 

116,303 

161,890 

1,891 

103,172 

290,814 

212,055 

166,766 

508,243 

2,125,970 

St. 

CenterCity 

16,319 

35,848 

11,729 

50,088 

24,115 

63,188 

685 

46,636 

65,402 

87,668 

49,973 

147,659 

599,320 

Louis 

Suburban 

23,191 

70,853 

23,063 

48,284 

27,664 

40,018 

843 

26,155 

81,385 

54,180 

50,029 

163,026 

608,690 

Los Angeles 

CenterCity 

75,047 

155,876 

46,364 

151,100 

66,589 

134,679 

978 

71,972 

166,345 

202,601 

115,731 

267,644 

1,454,924 

Suburban 

266,634 

645,758 

198,847 

490,555 

333,381 

478,973 

12,847 

321,552 

810,286 

563,154 

538,814 

1,617,642 

6,278,412 
P.39 

by the number of people living in all areas classified suburban. 

This allocation procedure allowed the population of each study 

area to be divided into residential neighborhood categories which 

are also geographic areas. The resulting breakdown of population 

is shown in Table 3-4. 

We next determined work NT's by making simple, intuitive 

assumptions about the work environment of A-0 groups. For example, 

we assumed that children under 5 years did not go to work, but 

stayed at home. We assumed that children 5-17 years of age went 

to "work" at a School, but that the school was located in the 

same NT as their residence. We assumed that operatives and 

laborers worked primarily in industries located within an industrial NT. The full roster of assumptions is listed in Table 3-5; 

these statements allowed the work NT of each A-0 group to be 

classified residential, commercial, or industrial, but they did 

not determine whether NT's are center-city or suburban. For this 

purpose, transportation data were used in the form of home-towork trip tables. 

As documented in other reports,3'

1* home-to-work trip tables 

were previously developed for each study area based on data provided by regional transportation planning agencies. These trip 

tables may be visualized as an n x n array which lists the number 

of trips taken during a typical day from each transportation zone 

of a city to every other zone. In previous NEM analyses, arrays 

were reduced in size by aggregating transportation zones into 

exposure districts. The resulting smaller arrays showed the 

number of trips from each exposure district to every other exposure district. 

In the CO analysis, each study area trip table was further 

condensed into a 2 x 2 array, because the only geographic 

elements used were suburban and center city NT classifications. 

In other words, all exposure districts were aggregated into either 

a suburban super-district or a center-city super-district. The 

resulting collapsed trip tables are listed in Table 3-6. As in 

previous NEM analyses, we assumed that the fraction of all trips 

3-14 
P.40 

TABLE 3-5. ASSUMPTIONS CONCERNING WORK NT'S OF A-0 GROUPS 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

A-0 Group 

Students 18+ 

Professional and 

Sales workers 

Clerical workers 

Craftsmen 

administrative 

Operatives and laborers 

Farmers 

Assumptions 

Work NT is same as home NT. 

All work in a commercial neighborhood; some work in suburban areas, 

others in center-city. 

Work neighborhood is suburbancommercial or center-city commercial. 

Work neighborhood is suburbancommercial or center-city commercial. 

Work neighborhood is suburbanindustrial or center-city industrial. 

Work neighborhood is suburbanindustrial or center-city industrial. 

There are no farmers in urbanized 

area. 

8. Service, military and household 

9. Housewives 

10. Unemployed and retired 

11. Children under 5 

12. Children 5 to 17 

Work neighborhood is same as 

home. 

Work neighborhood is same as home. 

Work neighborhood is same as 

home. 

Work neighborhood is same as home. 

Work neighborhood is same as home. 

3-15 
P.41 

TABLE 3-6. COLLAPSED HOME-TO-WORK TRIP TABLES, EXPRESSED AS 

NUMBER OF TRIPS AND FRACTION OF TRIPS 

Study 

area 

Chicago 

Philadelphia 

St. Louis 

Los Angeles 

From home 

Center-city 

Suburban 

Center-city 

Suburban 

Center-city 

Suburban 

Center-city 

Suburban 

To 

Center city 

3,182,820(0.935)a 

524,260(0.843) 

270,517(0.718) 

405,867(0.353) 

114,627(0.793) 

76,977(0.435) 

246,970(0.570) 

581,480(0.133) 

work 

Suburban 

221,720(0.065) 

97,300(0.157) 

106,507(0.283) 

743,723(0.647) 

29,920(0.207) 

99,957(0.565) 

1,861,400(0.430) 

3,800,720(0.867) 

Number in parenthesis is fraction of trips. 

taken from one super-distric t to another could be used to repre -

sent tri p distribution s for al l A-0 groups. These fractions were 

used in a modified 2-way tri p model to calculat e number of persons 

in each cohort, as follows: 

* _ ___. IAI 1 

(3-1) 

where 

PH,A,S,W (P

H,A/ ( F S ) 

"H-W 

"H / 

H,A,S,W 

H,A 

'H-W 

"H 

= home NT 

= age-occupation category 

= subgroup specified by activity pattern 

= work NT 

= population of a cohort which is defined by the 

subscripts 

= population of an age-occupation category in a 

home NT (center-city residential or suburban 

residential) 

= fraction of persons in A-0 group allocated to 

subgroup 

= number of trips from a home super-district to 

a work super-district 

= total number of trips by all A-0 groups from a 

home super-district to both super-districts 

(suburban or center-city). 

3-16 
P.42 

Calculation of cohort populations may be illustrated by an 

example. Using the modified trip model, one cohort may be defined 

as those persons residing in suburban-residential NT in St. Louis | 

who are managers or professionals (A-0 Group 2), who follow typical activity patterns of subgroup 2, and who are located in center-! 

city commercial NT during working hours. This definition is consistent with the list of assumptions in Table 3-5. This table 

shows that 70,853 people live in suburban residential NT's in '• 

St. Louis and belong to A-0 Group 2. From Table 3-1 we find that 

the fraction of A-0 Group 2 belonging to subgroup 2 is 21 percent. : 

From the collapsed trip table, Table 3-6, we find that 110,046 I 

people out of a total of 397,200 people from suburban residential ! 

NT's go to a center-city NT for work. Thus', we may calculate the 

cohort population as follows: J 

P5,2,2,2 - (70,853) (0.21) -f^§§ff = 4,122 persons. \ 

j 

Populations of all cohorts were calculated similarly; a complete ' 

list of cohorts and estimated cohort populations is provided in j 

Appendix B. 

Once methods for representing people movement were established, and methods were devised for calculating numbers of 

people following various movement patterns, there remained the 

task of estimating air quality levels to which cohorts were 

exposed. Development of air quality data is the subject of subsequent sections. 

3.4 REFERENCES 

1. Memorandum from Ted Johnson, PEDCo Environmental, to Thomas 

McCurdy, Strategies and Air Standards Division, U.S. Environmental Protection Agency, Research Triangle Park, 

North Carolina 27711, April 6, 1982. 

2. Ted Johnson, Activity Patterns for NEM Analysis of Carbon 

Monoxide Exposure, prepared by PEDCo Environmental, Inc., 

for Strategies and Air Standards Division, Office of Air 

Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, N. C , October 1982. 

3-17 
P.43 

3. Ted Johnson and Roy Paul, The NAAQS Exposure Model (NEM) 

and Its Application to Nitrogen Dioxide, prepared by PEDCo 

Environmental, Inc., for Strategies and Air Standards 

Division, Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle 

Park, N. C , May 1981. 

4. Ted Johnson and Roy Paul, The NAAQS Exposure Model (NEM) 

and Its Application to Particulate Matter, prepared by PEDCo 

Environmental, Inc., for Strategies and Air Standards 

Division, Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle 

Park, N. C., August 1981. 

3-18 
P.44 

SECTION 4 

PREPARATION OF AIR QUALITY DATA 

NEM requires representative outside air quality data for 

each neighborhood type in the form of a complete year of hourly 

average values. This section describes the procedures used for 

i 

selecting appropriate data sets, for validating these data, and 

for filling in missing values. 

4.1 SELECTION OF REPRESENTATIVE DATA SETS 

To simplify the computer simulation, air quality in all 

neighborhoods classified as belonging to a specific NT was assumed 

to be a linear function of air quality monitored at a single representative monitoring site. Consequently, for each study area 

we had to select one monitoring site per NT. We originally considered using the 8 NT's listed in Table 3-1; however, evaluation of data availability indicated that there was an insufficient 

number of center-city mobile and suburban mobile sites to include 

these NT's in the exposure analysis. The remaining NT's were: 

o center-city residential (CR), 

o center-city commercial (CC), 

o center-city industrial (CI), 

o suburban residential (SR), 

i 

o suburban commercial (SC), and 

o suburban industrial (SI). 

Table 4-1 lists data sets selected to represent these NT's. 

These data sets were selected according to the following procedure. 

(1) Data for the same year were used for all sites in a i 

study area. The year was selected to maximize the 

number of NT's that could be represented by acceptable 

4-1 
P.45 

TABLE 4-1. DATA SETS (mg/mJ) SELECTED FOR ANALYSIS OF POPULATION 

EXPOSURE TO CARBON MONOXIDE IN FOUR CITIES 

Study area 

and year 

Chicago (1979) 

Los Angeles (1977) 

Philadelphia (1978) 

St. Louis (1978) 

NT 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

-CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

ST 

CC 

CC 

CI 

SR 

SC 

SC 

CR 

CC 

SI 

SR 

SC 

SI 

CC 

CC 

SI 

SR 

SR 

SI 

CR 

CC 

CI 

SR 

SC 

CI 

SAROAD code 

141220039F01 

141220040F01 

141220026H01 

140780002G01 

145680001G01 

147160005G01 

056400003F01 

053900001101 

050900002101 

058720001101 

050230001101 

050900002101 

397140026H01 

397140026H01 

397140022H01 

3-97140004H01 

397140004H01 

397140021H01 

264280007H01 

264280064H01 

264280061H01 

260200002601 

261040001G01 

264280061H01 

Probe 

height 

(ft) 

11 

11 

26 

15 

15 

39 

32 

20 

15 

15 

15 

15 

13 

13 

13 

17 

17 

13 

35 

1 

12 

13 

14 

12 

1-hour 

obs. 

6172 

8392 

7615 

8664 

7662 

8425 

7921 

7940 

7775 

7777 

7888 

7775 

7773 

7773 

8088 

8193 

8193 

8485 

5944 

7466 

7539 

6817 

7010 

7539 

Geo. 

mean 

1.8 

2.9 

2.6 

1.3 

1.4 

1.4 

1.8 

2.6 

3.9 

3.4 

3.0 

3.9 

2.2 

2.2 

1.3 

0.8 

0.8 

1-. 

? 

5.4 

2.5 

2.6 

2.6 

2.5 

99th 

percentile 

7.0 

15.8 

9.8 

6.7 

7.0 

.6.4 

9.2 

18.4 

17.3 

13.8 

13.8 

17.3 

10.4 

10.4 

5.8 

5.8 

5.8 

8.1 

6.4 

10.7 

11.8 

9.3 

9.8 

11.9 

Notes 

b 

a 

b 

a 

b 

a 

a 

a.b 

a 

a,b 

a 

a,c 

a,d 

a 

b 

detailed site location description available at MDAD. 

site with matching ST not available. 

site with matching ST not appropriate. 

geometric mean not calculated because data completeness <75 percent. 

4-2 
P.46 

data sets. A data set was considered acceptable if it 

contained at least 58 39 observations (66% completeness). 

(2) When possible, an NT was represented by a data set with 

a corresponding ST (e.g., the center-city commercial NT 

for St. Louis was represented by site 264280064H01, 

which has a center-city commercial ST). 

(3) If two or more sites with the required ST were available, 

the site judged most representative of the NT was selected. In 11 cases, degree of representation was determined 

from photographs of site surroundings and detailed local 

land use descriptions on file in the Monitoring and Data 

Analysis Division (MDAD), EPA. Summary statistics, 

probe location, and data completeness were also considered in selecting sites. 

(4) If no site with the required ST was available, the site 

with the most similar ST was selected. Whenever possible, the MDAD photographs and land use descriptions 

were used in selecting alternate sites. 

In six cases, NT's and ST's do not match because a site with 

the appropriate ST was not available. For the Chicago center-city 

residential NT, we selected a center-city commercial site with 

low geometric mean and 99th percentile values. For the Chicago 

suburban industrial NT, we selected a suburban commercial site 

located in Skokie, a town near Chicago generally characterized 

as industrial. In Los Angeles, we used a suburban industrial 

site (the only industrial site in the study area) to represent 

the center-city industrial NT. A suburban commercial site in 

Afton was used for the St. Louis suburban industrial NT because 

Afton is located near an industrial area. The center-city residential NT in Philadelphia was represented by a center-city 

commercial site which photographs revealed to be surrounded by 

apartments. Similarly, the center-city industrial NT in 

Philadelphia was represented by a suburban industrial site because 

photographs suggested that the site is actually located in a 

heavily urbanized area. 

4-3 
P.47 

In one case, a site with the correct ST was not used because 

photographs revealed a site with a different ST was more appropriate 

The only suburban commercial site in Philadelphia (397140024H01) 

is surrounded on all sides by Philadelphia International Airport. 

Site 397140004H01, although labeled suburban residential, was used 

instead because it had commercial establishments nearby. 

4.2 VALIDATION OF AIR QUALITY DATA 

Air quality data sets containing erroneous values can bias 

the results of exposure studies, especially if the errors occur 

as extreme values. To ensure good data quality, the data sets 

listed in Table 4-1 were screened for anomalous values using 

three methods: (1) the gap test, (2) the patterns test, and (3) 

visual inspection. 

4.2.1 The Gap Test 

The Monitoring and Reports Branch (MRB) of MDAD has developed 

a standardized data review program called the MRB Validation Report.1 This program uses two different concepts to identify 

anomalies (unexpected data patterns) in hourly data sets. The 

first approach, called the Gap Test, is a statistical analysis 

of data over a 1-month period. This analysis assumes that the 

data can be modeled reasonably well by a smooth probability distribution curve. Two exponential curves are fit to the data 

using the 50th and 95th percentiles of the data for one fit and 

the 50th and 99.9th percentiles for the other fit. Both fits 

emphasize the upper tail of the distribution. All data values 

are arranged in order of magnitude and the program examines "gaps" 

in the data, i.e., large differences between succeeding ordered 

values. Using the fitted distribution functions, the program 

calculates the probability that a gap of magnitude "x" could be 

obtained by chance. A gap that is greater than would be expected 

from the underlying assumptions is flagged as a data anomaly. 

The strength of the Gap Test is that the criteria for identifying anomalies are based on an analysis of the data set itself. 

4-4 
Its weakness is that the assumed distribution is not always appropriate; consequently some false failures may occur. 

In this study, the output from the gap test was evaluated to 

determine whether or not the flagged data were plausible from 

other points of view. It was desired that data not be rejected 

unless a strong case for error couldW-be-established. Flagged 

data were usually retained if the tested data set contained a 

large number of missing values, only one of the two- fitted curves 

indicated an anomaly, or there were more than five observations 

above the gap. Data flagged by the gap test were more ."likely to 

be rejected if they were flagged by other tests. 

4.2.2 The Pattern Test 

The second test in the MRB Validation Program is called the 

Pattern Test. It is composed of 5 subtests carried out for each 

24-hour period, as follows: 

(1) High-value test - the test flags an hourly value that 

exceeds a predetermined limit. For carbon monoxide, the criterion 

depends on whether or not the measurement was taken during rush 

hours (7-11 a.m. or 4-9 p.m.). During rush hours the limit is 

66 ppm and at other times it is 44 ppm. 

(2) Adjacent-hour difference test - this test assumes that 

data take the form of an auto-correlated time series, i.e., a 

large jump or drop in the values within 1 hour is not expected. 

If there is a jump or drop greater than 22 ppm, the suspect value 

is flagged. 

(3) Dixon-ratio test - this is a statistical analysis of 

the highest and lowest value found during the day. If A is the 

difference between the two highest values and B is the range of 

values for the day, then the Dixon Ratio is A/B. If this ratio 

is statistically significant, the suspect values-are flagged. 

(4) Spike-test - the differences between a suspect hourly 

value and the preceding and following hourly value is measured. i 

If either of these two differences is greater than 20 ppm, or if 

4-5 
P.49 

the suspect value is 500 percent greater than either of the 

adjacent values, the suspect value is flagged. 

(5) Consecutive high values - it is unusual for a series 

of hourly measurements to remain at a high level. If four consecutive hourly values are greater than 40 ppm, the data are 

flagged. 

PEDCo evaluated the results of applying MRB Validation Program to SO- data in four cities.2 This analysis led to the 

development by PEDCo of a revised validation program. This new 

program (denoted here as MRB-2) contains improvements in the 

pattern tests and an option for graphical output of flagged data. 

The Dixon Ratio Test was enhanced for all pollutants in order to 

incorporate the recommendations of a recent EPA report.3 In the . 

new version, the formulas for testing the high and low hourly 

values from each 24-hour period vary according to the number of 

hourly measurements recorded during the period. The spike test 

was enhanced so that an individual hourly value is compared to 

the two (rather than one) preceeding values and the two succeeding 

values. In addition, MRB-2 incorporates a preliminary screening 

test, so that if all the values in a 24-hour period are low, 

the pattern tests are skipped. By passing over data that do 

not require testing, the computer program runs more quickly. 

The strength of the pattern test program is that it uses 

more than one test to identify possible anomalies. Its weakness 

is that the criteria for identifying anomalies is predetermined 

for all tests except the Dixon Ratio Test. The pattern test 

program could be improved by adjusting test criteria according 

to the data reported by each individual monitoring site. 

4.2.3 Visual Inspection 

George Duggan of SASD has developed a computer program which 

will plot all hourly values in a year of data on a single graph. 

Such data plots are useful in identifying unusually high values 

and long strings of identical values. All data sets used in the 

4-6 
P.50 

CO exposure analysis were plotted using this program and reviewed 

by PEDCo. Values which appeared anomalous were flagged for 

further investigation. 

4.2.4 Results of Data Screening 

CO levels recorded at monitoring sites can be drastically 

altered by short-term changes in local emissions. Exhaust from 

a delivery truck idling near a monitor may result in a high reading which appears totally inconsistent with other CO values 

recorded that day. However, this reading should not be excluded 

from the exposure analysis since it represents an exposure situation which may occur frequently throughout the study area. Consequently, we decided to retain flagged data unless they appeared 

to be the obvious result of instrument malfunction or transcription 

error. As discussed below, few of the flagged data fell into these 

categories. 

Table 4-2 lists data anomalies flagged by one or more of the 

screening methods. Of the anomalies detected, most were flagged 

by the gap test or visual inspection. No values were flagged by 

the high value test, the spike test, or the consecutive hour 

test. 

Eight data anomalies were identified by visual inspection. 

In two cases, Chicago site 145680001G01 and St. Louis site 

261040001G01, long strings of values equal to 0.1 ppm or zero 

were found. We assumed in both cases that these values were 

incorrectly entered in place of missing values and removed them 

from the data. The other six anomalies involved unusually high 

values. Analysis showed that the flagged high values occurred 

simultaneously at the two Chicago sites and at the two Philadelphia 

sites. We considered this sufficient corroboration to retain 

these values. 

We also retained the values flagged by visual inspection at 

sites 260200002G01 and 264280064H01 in St. Louis. In the first 

case, four large values (20.8, 26.3, 26.1, and 26.6 ppm) were preceded and followed by values of 3.1 ppm. This episode was unusual 

4-7 
TABLE 4-2. ANOMALIES FLAGGED BY DATA SCREENING 

i 

00 

Study 

area 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

SAROAD code 

140780002G01 

145680001G01 

053900001101 

397140021H01 

397140022H01 

397140026H01 

260200002G01 

261040001G01 

264280064H01 

NT 

SR 

SC 

CC 

SI 

CI 

CR.CC 

SR 

SC 

CC 

Date 

12-29-79 

12-29-79 

2-79 

2-07-77 

2-17-77 

6-78 

1-25-78 

1-25-78 

6-78 

7-24-78 

12-78 

4-09-78 

Anomaly 

9 values 2l 10 ppm 

9 values _> 10 ppm 

long string of 

values near zero 

large value 

large value 

large gap 

6 values >_ 10 ppm 

12 values >_ 10 ppm 

large gap 

4 values > 20 ppm 

long string of 

values near zero 

2 values > 15 ppm 

Gap 

X 

X 

X 

X 

X 

X 

Screening procedure 

Adjacent 

hour 

X 

Dixon 

ratio 

X 

X 

X 

Visual 

inspect. 

X 

X 

X 

X 

X 

X 

X 

X 
P.52 

enough to cause the day to be also flagged by the gap test using ; 

50th and 95th percentiles and the adjacent hour test. However,

 j 

the day was not flagged when the gap test was repeated using the 

50th and 99.9 percentiles. In the other case, two large values [ 

(15.2 and 16.5 ppm) were preceded and followed by smaller values, j 

These data were also flagged by the 50/95 gap test but not by the 

50/99.9 gap test. 

The 50/95 gap test also flagged data at site 397140021H01 in [ 

Philadelphia and at site 260200002G01 in St. Louis. Since these j 

data were not flagged by any other test, including the 50/99.9 | 

gap test, they were accepted. i 

Three days of data were flagged by the Dixon Ratio test. j 

Because analysis of these data indicated that the assumption of 

normality was not valid, we repeated the Dixon Ratio test using 

the logarithms of the recorded values as recommended by Nelson, 

et al.3 This time no values were flagged by the test. We con- i 

eluded there was no probable cause under an assumption of lognormality for rejecting the data. 

In summary, we investigated 12 cases of data flagged by 

various screening procedures. We retained the anomalies in j 

all but the two cases where visual inspection had identified long 

strings of values equal to 0.1 ppm or zero. These values were 

removed from the data sets. The next section describes the 

methodology used to fill in these and other missing values. 

4.3 SIMULATION OF MISSING VALUES IN HOURLY AVERAGE CO DATA SETS 

NEM requires air quality data sets with values for every 

hour of the year. Since absolutely complete data sets were not 

available, gaps were filled in using a time series model developed 

by Johnson and Wijnberg.1* 

4-9 
P.53 

4.3.1 The Time Series Model 

A complete year of hourly average data takes the form of a 

time series x,, x_, ..., x., ..., x where n = 8760. We can fit l & t_ n 

this series exactly by the model 

4380 

x. = x + I R.cos(co.t + 9.) (4-1) 

z

 j=l J .

 J 3 

where x is the arithmetic mean of the series, R. and 9. are 

amplitude and phase angle values determined by Fourier analysis, 

and OJ • = 2TTJ/8760. Omission of one or more of the 4380 Fourier 

cosine terms will yield an approximate fit. Because Fourier 

cosine functions are orthogonal and because the contribution of 

each cosine function to the representation of the original time 

series is proportional to its amplitude R., we can provide a least 

squares fit to the original time series with m cosine terms by 

using the cosine terms with the m largest amplitudes. We denote 

each term of this estimated time series as x. where 

t 

« _ m 

x. = x + _ R.cos (w.t + 6.) (4-2) 

r i=l x ' 1 

and R., co., and 9. are the parameters of the Fourier term having 

the ith largest amplitude. For convenience, we will refer to the 

m Fourier terms in Equation 4-2 as the essential cyclical component (ECC). 

The differences between the x. series and the x series 

comprise the d. series, i.e., 

d t = x t " X t ' (4~3) 

We can define how well the x. series represents the x. series 

by the goodness of fit statistic 

4-10 
P.54 

0 / 4380 -, 

I R- / I Rr (4-4) 

V j=i 3 

.2 As m increases, r increases and the goodness of fit improves. 

2 

Note that r = 1 when m = 4380. 

If the x. series exhibits autocorrelation, the d. series is 

likely to exhibit autocorrelation. One means of characterizing 

a series which exhibits autocorrelation is to use an AR(p) process 

(i.e., an autoregressive process of order p). In this case, each 

d. term can be expressed as 

dt = at + <j,1dt_1 + <D2dt_2 +..."•+ *pdt_p (4-5) 

where a. is a normally distributed random variate with mean 0 and 

c 2 variance a . 

CX 

Estimates of <J>, , <J>,, ..., <J> can be obtained by first estimating each autocorrelation p. , using the relationship p. = r. 

where 

ck 

rv = -£ k = 1, 2, ..., p (4-6) 

and / 

8760 / 

c. = (1/8760) Z (d. - d) (d. , - d). (4-7) 

K k+ - t t-JC 

From these estimates, the Yule-Walker estimates of the autoregressive parameters can be obtained.5 

Autocorrelation of the d. series will decrease as m increases 

since an increasing portion of the xfc series autocorrelation is 

explained by the cosine functions. We assumed that most of the 

autocorrelation in the data corresponding to k >_ 3 would be contained in the ECC we selected and that an AR(2) process would 

suffice to characterize the d. series. In this case, 

r. (1 - r5) 

4>i = — j , (4-8) 

1 " r l 

4-11 
P.55 

r - r 2 

r2 r, 

., = — ^ , (4-9) 

1 " rl 

and 

aa = c 0( 1 " *lrl " *2r2)# (4-10) 

This AR(2) process represents a stationary time series if it meets 

certain conditions described by Box and Jenkins.s 

A theoretical AR(2) process will have non-zero values of p. 

for k > 2 that decrease gradually according to the relationship 

pk = *lpk-l + *2pk-2 k > 0 (4-11) 

until a point is reached where the distribution of r, is approximately normal with mean zero and standard error 

a (rk) = ^ | (1 + 2p.2 + 2p2

2 ). (4-12) 

The values of p. and p- are estimated by r, and r-. No more than 

5% of the values of r, for large values of k should deviate from 

zero by more than two standard errors.7 

If we can select an ECC such that the autocorrelations in 

the d. series corresponding to k > 2 are consistent with Equations 

4-11 and 4-12, then an AR(2) process should suffice to characterize the.d, series. To select this ECC, we can start by determining 

the d. series that corresponds to m = 1. We then calculate r, for 

values of k that are likely to be significant. These include k = 

3, 4, 6, 8, 12, 24, and 168 for typical air quality data. If the 

r, values are not consistent with Equations 4-11 and 4-12, we 

determine the d. for m = 2 and repeat the analysis. We continue 

increasing m until the r, values for k > 2 meet our criteria. At 

this stage, we should have a combination of ECC and AR(2) process 

that will adequately characterize the data. 

4-12 
4.3.2 Initial Treatment of Missing Values 

Fourier analysis cannot be applied to a time series if one 

or more values are missing. If air quality data to be analyzed 

are incomplete, some method of estimating missing values must be 

used prior to analysis. Bloomfield8 recommends replacing each 

missing observation by a linear combination of its neighbors if 

most of the missing values tend to occur in small, isolated 

groups. If a gap containing b-l missing values occurs between 

values x and x . , each missing value x. can be estimated by 

linear interpolation as 

x t = x a + E ( t " a)(x a + b " x a} ' (4 "1 3 ) 

However, linear interpolation may not yield reasonable estimates 

of missing one-hour values for large gaps, especially if they are 

bounded by extreme values. In these cases, the arithmetic mean 

(x) may be a better estimate of each missing value. Inspection 

of data sets to be used in the CO population exposure analysis 

suggested that the arithmetic mean should be used to fill in gaps 

whenever gap length exceeded 72 hours and/or one of the boundary 

values exceeded the arithmetic mean by more than two standard 

deviations. In other cases, linear interpolation produced reasonable results. 

4.3.3 Procedure for Simulating Missing Values 

The time series model described above was the basis for the 

following procedure for simulating missing values. 

(1) The mean and standard deviation of each data set were 

calculated. 

(2) Gaps with lengths exceeding 72 hours and/or with boundary values exceeding the arithmetic mean by more than 

two standard deviations were identified. These gaps 

were filled in with the arithmetic mean. 

(3) Linear interpolation was used to fill in the remaining 

gaps. 

4-13 
P.57 

(4) Fourier analysis was applied to the augmented time series 

created in steps (2) and (3). 

(5) An ECC was constructed which contained the smallest 

number of cosine terms required to produce a d series 

consistent with Equations 4-11 and 4-12. 

(6) The d. series was represented by an AR(2) process by 

t •_ 

using Equations 4-8, 4-9, and 4-10 to determine <f>, , 

_ r\ 1 

(j) -, and c . 

(7) An a. series was formed by dividing each term in a 

N(0,1) random series by a . For consistency, the same 

cl 

random series was used in each case. 

(8) Missing d. values were simulated using the relationship 

K - *A-i+ ^ t - 2 + v (4-

i4) 

(9) Missing x. values were filled in using the model 

r\ m A 

x. = x + Z R.cos(o).t + 9.) + d. (4-15) 

t i= 1 1 1 i t 

to create the final augmented data set. 

Note that the final simulation (Equation 4-15) uses information 

concerning the cyclical, autoregressive, and stochastic properties 

of the time series which are omitted in the initial estimates made 

in steps (2) and (3). 

Figure 4-1 shows a data set which is missing 1750 values. 

Figure 4-2 shows the augmented data set after the initial simulation of missing values [steps (1) through (3)] . Figure 4-3 

shows the final augmented data set with missing terms filled in 

by adding an appropriate AR(2) process to the most significant 

Fourier cosine functions [steps (4) through (9)] . This two-step 

process simulates missing terms which are consistent with both 

the cyclical and the random character of the known values. 

4-14 
o 

o 

Si 

o 

o 

10. 

I 

M 

tn 

--f—r—frrrrr.—n r.~;—1 :rr 

MIR np« mi JUN JUL AUG SEP 

SI LOUIS CO I-HOUR - SUOURBfiN COIUIERCIfiL 

Figure 4-1. Hourly average 1978 carbon monoxide data reported by 

monitoring site 261040001G01 in St. Louis. 
o 

o 

• 

o 

N 

O 

O 

* 

10. 

I 

cn 

. r 

WR fiPR IM . JUN JUL AUG SEP OCI 

SI LOUIS CO I-HOUR - SUBURBfiN COMERCML 

Figure 4-2. Hourly average 1978 carbon monoxide data for monitoring site 261040001G01 

in St. Louis after initial simulation of missing values. 
o 

o 

I 

SI LOUIS CO I-HOUR - SUBURBAN COHHERCIflL 

Figure 4-3. Hourly average 1978 carbon monoxide data for monitoring site 261040001G01 

in St. Louis after final simulation of missing values. 
P.61 

4.4 REFERENCES 

1. Screening Procedures for Ambient Air Quality Data, publication 

no. EPA-450/2-78-037, U.S. Environmental Protection Agency, 

Research Triangle Park, N.C, July 1978. 

2. Roy A. Paul, R. B. Faoro, W. F. Hunt, "Screening S02 data 

anomalies from four test cities," Technical Conference 

Transactions, American Society for Quality Control, 

Milwaukee, Wisconsin, 1981. 

3. A. C. Nelson, D. W. Armentrout, and T. R. Johnson, Validation 

of Air Monitoring Data, publication no. EPA-600/4-80-030, 

U.S. Environmental Protection Agency, Research Triangle 

Park, N.C , June 1980. 

4. Ted Johnson and Luke Wijnberg, "Time series analysis of 

hourly average air quality data," paper no. 81-33.5, presented at the 74th Annual Meeting of the Air Pollution 

Control Association, Philadelphia, Pennsylvania, June 21-26, 

1981. 

5. G.E.P. Box and G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, 1976, p. 55. 

6. Box and Jenkins, p. 58. 

7. Box and Jenkins, pp. 35 and 59. 

8. Peter Bloomfield, Fourier Analysis of Time Series: An Introduction, John Wiley and Sons, New York, 1976, p. 245. 

4-18 
P.62 

SECTION 5 

SIMULATION OF AIR QUALITY EXPECTED AT FIXED MONITORING SITE 

UNDER ALTERNATIVE CARBON MONOXIDE STANDARDS 

The augmented data sets described in Section 4.3 were assumed 

to represent the current status of air quality at a representative 

monitor in each neighborhood type (NT). To represent air quality 

expected under the current NAAQS for CO and under proposed CO 

standards, these data sets were adjusted using a modified form of 

the EPA rollback model. 

5.1 THE ROLLBACK MODEL 

Each augmented data set is a time series containing 8760 

hourly values, i.e., 

xx, x2, ..., xfc, ..., Xg76Q. 

We assumed that y., the difference between each x. and an assumed 

constant background level x., would increase or decrease in proportion to the changes in emissions dictated by a given air quality 

standard, as long as x. > x, . If xfc < x, , we assumed x. would not 

be affected by changes in emissions. We further assumed that all 

emissions would change in proportion to the change in emissions required to bring the most polluted NT in the study area into 

compliance. 

Air quality in each NT was characterized by air quality 

indicators (AQI's) which varied according to the form of the air 

quality standard. We assumed the most polluted NT to be the one 

with the largest AQI with respect to the standard being considered. 

To simulate the air quality expected in each NT under a 

standard, we created an adjusted data set 

r , Xfc , ..., Xg 7 6 0 

5-1 
P.63 

where 

xt' = pyt + xb (5-1) 

and p is a rollback factor. Consistent with the assumptions 

above, values of p were calculated according to the formulas 

x - x, 

p = — - if y. > 0 (5-2) 

xmax' xb fc 

and 

p = 1 if yt < 0 , (5-3) 

where x is the highest concentration permitted by the standard 

for the stated averaging time and x is the corresponding 

AQI for the most polluted NT. The rollback model assumes reasonable estimates of x . and x. are available; Sections 5.2 anc 

max a 

5.3 describe how these estimates were developed. 

5.2 AIR QUALITY INDICATORS 

Use of the rollback model to adjust air quality data requires 

parameters for characterizing data which are related to the form 

of each standard under consideration. At the time of the CO population exposure analyses, four types of parameters were considered for proposed standards: the daily maximum 1-hour value expected to be exceeded once per year, the daily maximum 1-hour 

value expected to be exceeded five times, the daily maximum 8-

hour running average expected to be exceeded once, and the daily 

maximum 8-hour running average expected to be exceeded five 

times. Reasonable estimates of the 1-hour parameters can be made 

by fitting a cumulative distribution [F(x)] to the daily maximum 

values of an augmented data set and then calculating the values 

b, ,cc and b, _,cc such that 1,365 5,365 

F<£v,n> " X " _ <5-"> 

5-2 
P.64 

where v is the number of permitted exceedances and n is the number 

of possible daily maximum values. Similarly, reasonable estimates 

of the 8-hour parameters can be made by fitting a cumulative 

distribution to the daily maximum 8-hour running averages of an 

augmented data set and again calculating b, ,,, and br ,,.,-. In 

1 , JOD D,job 

statistical theory, b, is known as the characteristic largest 

value and bc is known as the characteristic fifth largest 5,n 

value. * 

Selection of an appropriate cumulative distribution to fit 

the data is important in determining a reasonable characteristic 

largest value. Two distributions which often provide close fits 

to ambient air quality data are the Weibull and the lognormal.2 

The Weibull distribution is defined as 

F(x) = 1 - exp [-(|)k] (5-5) 

where 6 is the scale parameter and k is the shape-parameter. The '• 

lognormal distribution is defined as 

1 Tw 

V2T J -co 

where 

F(x) =./=- / exp (-t 2/2) dt (5-6) 

w = m ; - ^ (5 . 7 ) 

2 

and ln x is distributed normally with mean u and variance a . 

From Equations 5-4 and 5-5, the characteristic vth largest 

value of the Weibull distribution can be estimated as 

V n - « (ln v")1A (5"

8) 

if good estimates of 6 and k are available. Similarly, the characteristic vth largest value of the lognormal distribution can be 

estimated as 

\,r. " exP

 {

»

 + °zv,n> (5"9) i 

if good estimates of u and a are available. The value of z 

v, n 

is determined from the normal distribution such that the area 

under the standard normal curve from z to °° is v/n. Approxiv, n 

mate values for z, 36- and z- 3g 5 are 2.7774 and 2.2058. 

5-3 
P.65 

The results of fitting distributions to a large number of 

ambient air quality data sets suggest that the characteristic vth 

largest value can be better estimated if the upper tail of the 

data is emphasized in the fit. PEDCo Environmental has used two 

methods to fit distributions to data censored on the left (i.e., 

data from which low values have been excluded): the method of 

least squares and the method of maximum likelihood. 

5.2.1 Fitting Distributions by the Method of Least Squares 

The least squares method requires that the equation defining 

the distribution under consideration be expressed as a linear 

relationship of the form y = az + b. Equations 5-5 and 5-6 

can be rewritten in linear form using the following identities 

where x is the mth ranked value in ascending order, 

m — 

Distribution y_ a_ z_ b 

lognormal In x„ a z„ _. \i 

m m,n 

Weibull ln xm i ln[ln( "t1 J ] ln 6 

m K n+i-m 

These identities follow Gumbel's recommendation3 that F (x ) = -

m-r 

m n+± 

when fitting distributions to empirical data. Values of z for 

^ * m,n 

the lognormal distribution are determined such that the area under 

the standard normal curve from -°° to z is m/(n+l). 

m, n 

A linear regression analysis of data transformed by these 

identities yields a regression line with an equation in the form 

of y = az + b. Parameters of the corresponding distribution can 

be determined from the values of a and b using the following 

equations: 

Weibull distribution 

6 = exp b (5-10) 

k = r (5-11) 

a 

Lognormal distribution ' 

. = b (5-12) 

a = a (5-13) 

5-4 
P.66 

5.2.2 Fitting Distributions by the Method of Maximum Likelihood 

In an earlier analysis'* of population exposure to NO-, the 

least squares method described in Section 5.2.1 was the sole 

method used to fit distributions to air quality data. The method 

of maximum likelihood was not used because no procedure was then 

available for applying it to the upper tail of a data set. During 

the PM exposure analysis,5 Louis Wijnberg of PEDCo Environmental 

(extending the work of Cohen6/7) developed the following maximum 

likelihood procedure which can fit Weibull and lognormal distriburtions to any portion of the upper tail of a data set. 

The n values in an augmented data set are ranked from smallest 

to largest to yield an ordered series 

Xi xm' . - , __ 2 , .. m _ „ n 

where x indicates the mth ranked value. We are interested in ob^-

m — 

taining maximum likelihood estimates (MLE's) of the parameters 9. 

and 9- of a two-parameter distribution F(x;9,,92) fitting the 

n, - n-c+1 values that equal or exceed x . Letting f(x;9.,9_) 

denote the density function of a two-parameter distribution and 

F(x ) be the value of the cumulative distribution at x , 

c c 

L = n! 

(n-nf)! 

n 

n 

m=c 

f(xm;91,92) F(xc) 

n-n. 

(5-14) 

is the likelihood function of interest. MLE's of 9, and 9. are 

determined by simultaneously solving the likelihood equations 

9 

39. 

•(log L) = 0 (5-15) 

and 

gf-dog L) = 0, (5-16) 

In the case of the Weibull distribution, the likelihood equations 

are 

5-5 
and 

i i n x_. 5 ' n x- *, 

^

 + ^_ E

c

ln( ^ ' •= ; z ( 3r' ln( ^ ' = 0 

f m=c c f m=c c c 

P.67 

(5-17) 

1 1 " XTT,

 n_n . 

£ + ±_ y (—) + -

? n f m=c x c n f (ex ^ " 1 } 

= 0, (5-18) 

where 

. = <-J) . (5-19) 

When fitting the lognormal distribution, the likelihood equations 

are: 

(y - . ) -

(n-nf)a<j>(zc) 

n<s*(z ) t c 

= 0 (5-20) 

and 

2 — 2 2 

s

z + (y - v) - CT 

1 + 

(n-nf)a <J> (zc)' 

nf«(zc) = 0 

where 

- 1 n 

y = — __ y , 

f m=c 

2 1 " — n 2 

s = HI _,£<**-*> • 

r m=c 

(5-21) 

/ 

(5-22) 

(5-23) 

"(5-24) 

(5-25) 

_> denotes the standard normal distribution, and <i> is the standard 

normal density function. 

The likelihood equations were solved by using the least square 

procedure described in Section 5.2.1 to make initial estimates of 

the parameters and then improving these estimates using an iterative process (the Secant method) until an optimal solution was 

reached. 

5-6 

Ym m = I n 

. -

y

« 

c 

m' 

- u 

a 
P.68 

The method of maximum likelihood has several advantages over 

the method of least squares. In particular, maximum likelihood 

estimates (MLE's) of parameter values have minimum variance and 

they asymptotically approach a normal distribution about the "true" 

parameter value as the number of observations increases. It is 

also possible to construct confidence intervals for MLE's. Parameter estimates developed by the least squares method have none of 

these properties. Consequently, we decided to use maximum likelihood to fit Weibull and lognormal distributions to the CO data. 

5.2.3 Determining Goodness-of-Fit 

Following the recommendations of Stephens8, and Green and 

Hegazy,9 we investigated the use of the Cramer-von Mises (W ) and 

2 

Anderson-Darling (A ) statistics to determine goodness of fit. 

These statistics are defined by the expressions 

W

2

 = l/12n + i Z [F(x)-(2m-l)/2n]2 (5-26) 

m=i 

and 

A

2

 = -I Z (2m-l) | ln F (xJ + ln [1-F (xn+1_.m) ] [ - n. (5-27) 

m=l 

The null hypothesis (HQ) is that the sample comes from a population 

with distribution function F(x). HQ is rejected at a given significance level if a goodness-of-fit statistic exceeds a critical 

value corresponding to that significance level. 

In our analysis, we were interested in which of two distributions (Weibull and lognormal) better fits a specified portion of 

2 2 

a sample data set. Unfortunately, the W and A statistics of a 

Weibull distribution fit to data cannot be directly compared with 

2 2 

the W and A of a lognormal fit to the same data. The corresponding significance levels can be compared, but tables which list 

significance levels for censored data or for Weibull distributions 

2 2 

are not currently available. Consequently, W and A can only be 

used to characterize the fit of a lognormal distribution to an 

uncensored data set. 

5-7 
P.69 

We also investigated various statistics based on the absolute 

differences between the sample data set and the fitted distribution. 

The mth absolute difference is 

£_, = lx- ~ x„l m = c, c+1, ..., n (5-28) 

m m m 

r\ where x_, is the mth ranked value, x is the estimate of x 

m — m m 

determined by the parameters of the fitted distribution, and c is 

the rank of the smallest data value used in the fit. In the case 

of a fitted Weibull distribution with parameters 5 and k, 

»_- « tln( H^"1 A - <5"

29> 

Similarly, 

/\ y\ 

xm = exp (y + a zm ) (5-30) 

m m,n 

for a fitted lognormal distribution with parameters y and a. 

The statistics considered were: 

max diff = max e , (5-31) 

mean diff = ^ ^ Z _m, (5-32) 

m=c 

1

 n 2 

mean diff**2 = n_*+1 Z e , (5-33) 

m=c 

A. 

max reldiff = max [2e /(x + x )], and (5-34) 

m m m 

mean reldiff = -^^r Z [2c / (xm + x )]. (5-35) m—c+i _ m m m 

m=c 

The closer any one of these statistics is to zero, the better the 

distribution fits the data. The bracketed term in Equations 5-34 

and 5-35 is termed the relative difference; it is the absolute 

difference divided by the mean of the observed and estimated 

values. 

5-3 
P.70 

We initially evaluated these statistics as alternatives to 

2 

the R statistic used for characterizing goodness-of-fit for the 

least squares method. Table 5-1 summarizes the results of fitting 

Weibull and lognormal distributions to 1978 CO data from St. Louis 

site 261040001G01. Fits were made to the upper 50%, 20%, 5%, and 

1% of the data by the least squares method. In each of the four 

cases, all six statistics indicate the same distribution as the 

best fit. The Weibull distribution provides a better fit in the 

5% case; the lognormal distribution is superior in the other three 

cases. According to most of the statistics, the two best fits are 

lognormal/50% and Weibull/5%. The estimated characteristic high, 

b , is close to the recorded maximum value of 14.9 ppm for both 

n 

fits. The two worst fits are Weibull/50% and Weibull/20%; in both 

instances, b is significantly different from the recorded maximum 

value. 

We ultimately selected mean reldiff as our goodness-of-fit 

statistic. This statistic is particularly robust (i.e., not significantly affected by outliers) and weights each value used in 

the fit equally. Max diff, mean diff**2, and max reldiff can be 

significantly affected by outliers. Max diff, mean diff, and 

mean diff**2 weight higher values more heavily than lower values. 

Weibull and lognormal distributions were first fit to the 

upper 50 percent of the daily maximum values in each augmented 

data set (Tables 5-2 and 5-3). Evaluation of the results indicated that the closer fitting distribution did not always yield 

a close fit to the five largest values. Repeating the analysis 

using the upper 20 percent of the daily maximum values in^ach 

augmented data set produced similar reldiff statistics and 

superior fits to the five largest values (Tables 5-4 and 5-5). 

Consequently, we decided to use the upper 20 percent of the 

augmented data set for all fits. The general procedure used for 

determining characteristic largest and fifth largest values is 

described below. 

(1) Maximum daily 1-hour and 8-hour running average values 

in each augmented data set were ranked from lowest to 

highest. 

5-9 
TABLE 5-1. RESULTS OF FITTING WEIBULL AND LOGNORMAL DISTRIBUTIONS 

TO 1978 CO DATA (ppm) FROM ST. LOUIS, MOa BY LEAST SQUARES METHOD. 

I 

Statistic 

R

2 

Max diff 

Mean dif f 

Mean diff**2 

Max reldiff 

Mean reldiff 

6 

k 

_A. 

0 

St 

b

n 

Upper 

Weibull 

0.979 

4.608 

0.138 

0.091 

0.38 

0.029. 

3.115 

1.829 

-

-

10.401 

• 50% 

lognormal 

0.998 

1.574 

0.043 

0.009 

0.12 

0.008 

-

-

0.982 

0.460 

14.540 

Data fi t by 

Upper 20% 

Weibull 

0.980 

2.818 

0.150 

0.055 

0.22 

0.026 

2.692 

1.432 

-

-

12.565 

lognormal 

0.995 

0.955 

0.074 

0.010 

0.07 

0.013 

-

-

0.939 

0.486 

15.366 

distribution 

UPF 

Weibull 

0.997 

1.026 

0.063 

0.009 

0.07 

0.008 

2.089 

1.123 

- • ' 

-

14.890 

>er 5% 

lognormal 

0.995 

•1.415 

0.084 

0.020 

0.09 

0.010 

-

-

0.875 

0.520 

16.315 

Upp 

Weibull 

0.987 

1.090 

0.109 

0.034 

0.08 

0.010 

2.160 

1.147 

-

-

14.783 

>er 1% 

lognormal 

0.990 

0.946 

0.096 

0.026 

0.07 

0.009 

-

-

1.055 

0.453 

15.226 

Recorded maximum value: 14.9 ppm. 
P.72 

TABLE 5-2. RESULTS OF FITTING WEIBULL AND LOGNORMAL DISTRIBUTIONS BY 

MAXIMUM LIKELIHOOD PROCEDURE TO UPPER 50 PERCENT OF 

DAILY MAXIMUM l-HOUR CO DATA 

Study area 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

I r •*••__________= _ 

NT 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

Weibull 

Mean 

reldiff 

0.0347 

0.0241 

0.0197 

0.0331 

0.0406 

0.0179 

0.0425 

0.0321 

0.0315 

0.0282 

0.0305 

0.0315 

0.0897 

0.0897 

0.0932 

0.0850 

0.0850 

0.0505 

0.0519 

0.0185 

0.0331 

0.1214 

0.0433 

0.0331 

Characteristic 

values, ppm 

bl,365 

10.0 

23.5 

15.7 

11.6 

11.7 

10.0 

14.6 

29.0 

26.3 

20.0 

21.8 

26.3 

16.6 

16.6 

8.9 

11.5 

11.5 

12.2 

8.8 

12.7 

17.6 

16.4 

13.0 

17.6 

. •,—,_..•, , ,_....„ 

b5,365 

8.4 

20.0 

13.5 

9.2 

9.5 

8.3 

12.1 

23.1 

21.2 

16.3 

17.2 

21.2 

13.4 

13.4 

7.4 

8.6 

8.6 

10.2 

7.3 

11.4 

14.7 

12.9 

11.0 

14.7 

Loqnormal 

Mean 

reldiff 

0.0186 

0.0121 

0.0112 

0.0153 

0.0153 

0.0254 

0.0535 

0.0351 

0.0485 

0.0388 

0.0435 

0.0485 

0.0598 

0.0598 

0.0678 

0.0777 

0.0777 

0.0472 

0.0338 

0.0118 

0.0147 

0.0758 

0.0214 

0.0147 

Characteristic 

values, ppm 

bl,365 

11.2 

26.4 

17.4 

13.5 

13.1 

11.6 

17.0 

35.0 

31.6 

23.6 

26.9 

31.6 

17.3 

17.3 

9.0 

13.3 

13.3 

13.9 

10.0 

13.4 

19.9 

17.4 

14.3 

19.9 

b5,365 

8.7 

20.9 

14.1 

9.6 

9.8 

8.7 

12.8 

24.9 

22.8 

17.4 

18.7 

22.8 

13.0 

13.0 

7.1 

8.9 

8.9 

10.7 

7.6 

11.5 

15.2 

12.6 

11.3 

15.2 

5-11 
P.73 

TABLE 5-3. RESULTS OF FITTING WEIBULL AND LOGNORMAL DISTRIBUTIONS BY 

MAXIMUM LIKELIHOOD PROCEDURE TO UPPER 50 PERCENT OF 

DAILY MAXIMUM 8-HOUR RUNNING AVERAGE CO DATA 

Study area 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

NT 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

W 

Mean 

reldiff 

0.0383 

0.0110 

0.0211 

0.0588 

0.0702 

0.0609 

0.0208 

0.0290 

0.0251 

0.0278 

0.0231 

0.0251 

0.0954 

0.0954 

0.0684 

0.0509 

0.0509 

0.0359 

0.0512 

0.0082 

0.0570 

0.0901 

0.0394 

0.0570 

eibull 

Characteristic 

values, ppm 

A 

bl,365 

6.2 

14.6 

10.4 

7.8 

8.0 

6.8 

9.2 

21.5 

19.5 

15.1 

16.3 

19.5 

11.4 

11.4 

6.4 

6.8 

6.8 

9.1 

5.3 

10.4 

12.0 

10.1 

9.9 

12.0 

As 

b5,365 

5.4 

12.7 

9.1 

6.2 

6.4 

5.5 

7.8 

16.7 

15.8 

12.3 

12.9 

15.8 

9.3 

9.3 

5.3 

5.2 

5.2 

7.6 

4.5 

9.5 

10.1 

8.4 

8.5 

10.1 

Loq 

Mean 

reldiff 

0.0178 

0.0156 

0.0112 

0.0247 

0.0362 

0.0310 

0.0393 

0.0518 

0.0383 

0.0219 

0.0248 

0.0383 

0.0521 

0.0521 

0.0340 

0.0310 

0.0310 

0.0172 

0.0271 

0.0138 

0.0314 

0.0519 

0.0218 

0.0314 

normal 

Characteristic 

values, ppm 

A* 

bl,365 

6.8 

16.3 

11.4 

8.8 

8.7 

7.5 

10.8 

27.0 

23.0 

17.8 

19.8 

23.0 

11.9 

11.9 

6.6 

8.1 

8.1 

10.2 

5.8 

11.2 

13.1 

10.5 

10.9 

13.1 

A, 

b5,365 

5.5 

13.3 

9.4 

6.3 

6.4 

5.6 

8.3 

18.4 

16.9 

13.1 

14.0 

16.9 

9.0 

9.0 

5.1 

5.5 

5.5 

7.8 

4.6 

9.8 

10.2 

8.2 

8.8 

10.2 

5-12 
TABLE 5-4. RESULTS OF FITTING WEIBULL AND LOGNORMAL DISTRIBUTIONS BY 

MAXIMUM LIKELIHOOD PROCEDURE TO UPPER 20 PERCENT OF 

DAILY MAXIMUM 1-HOUR CO DATA 

Study area 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

NT 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

Mean 

reldiff 

0.0165 

0.0116 

0.0139 

0.0317 

0.0386 

0.0231 

0.0360 

0.0289 

0.0252 

0.0278 

0.0282 

0.0252 

0.0782 

0.0782 

0.0843 

0.0688 

/0.0688 

0.0439 

0.0234 

0.0249 

0.0353 

0.0802 

0.0225 

0.0353 

Weibull 

Characteristic 

values, ppm 

As 

bl,365 

10.7 

24.9 

16.3 

12.5 

12.9 

10.2 

14.1 

29.3 -

25.0 

19.6 

21.0 

25.0 

19.0 

19.0 

9.9 

12.2 

12.2 

12.8 

9.8 

13.2 

19.0 

21.1 

14.5 

19.0 

As. 

b5,365 

8.7 

20.7 

13.9 

9.6 

10.1 

8.3 

11.8 

23.3 

20.6 

16.1 

16.8 

20.6 

14.3 

14.3 

7.8 

8.9 

8.9 

10.5 

7.8 

11.6 

15.3 

14.6 

11.7 

15.3 

Loqnormal 

Mean 

reldiff 

0.0196 

0.0164 

0.0126 

0.0210 

0.0250 

0.0177 

0.0432 

0.0288 

0.0230 

0.0300 

0.0316 

0.0230 

0.0607 

0.0607 

0.0672 

0.0718 

0.0718 

0.0390 

0.0327 

0.0169 

0.0263 

0.0608 

0.0189 

0.0263 

Characteristic 

values, ppm 

bl,365 

11.4 

26.4 

17.2 

13.3 

13.6 

10.8 

14.9 

31.4 

26.2 

20.8 

22.3 

26.2 

19.2 

19.2 

9.7 

13.2 

13.2 

13.4 

10.6 

13.4 

20.1 

22.8 

15.4 

20.1 

A 

b5,365 

8.8 

20.9 

13.9 

9.6 

10.0 

8.4 

12.0' 

23.5 

20.6 

16.2 

16.9 

20.6 

13.8 

13.8 

7.4 

. 8.9 

8.9 

10.5 

7.9 

11.5 

15.3 

14.6 

11.8 

15.3 

5-13 
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^1 
P.76 

(2) The upper 20 percent of the daily maximum values were 

fit by Weibull and lognormal distributions using the 

maximum likelihood method described above. 

(3) The reldiff statistics of the two fits were compared 

and the parameters of the better fitting distribution 

(i.e., the one with the smaller reldiff value) were 

used to determine the characteristic largest and fifth 

largest values. 

Table 5-6 lists characteristic values developed using this procedure. Appendix B discusses the relationship between these 

values and the expected concentration (EC) values developed by 

EPA for the four study areas. 

5.3 BACKGROUND CONCENTRATIONS 

NEM requires a city-specific average background level in 

order to calculate the rollback factor applied to ambient pollutant concentrations in each study area. This background value 

should represent the average hourly concentration of a given 

pollutant being transported into the urban area, a value unaffected 

by any control strategies imposed upon the urban area. . The monitoring sites selected to determine CO background should ideally 

be located sufficiently upwind from the urban area in a nonlowlying location, within no less than five degrees of alignment 

with extended straight highway segments. Also, each site should 

be in an area with sufficient ventilation so that air is not 

likely to stagnate. Sites established to monitor regional concentrations are preferred to those established to monitor local 

concentrations. PEDCo identified monitoring sites which satisfied ' 

these criteria through an evaluation of (1) regional office and 

local agency recommendations, (2) local wind profiles, and (3) 

local land use. It should be noted that the CO background concentration being transported into an urbanized area may in fact 

be higher on occasion than some of the reported values within the 

area. This phenomenon is due to dispersion and dilution and is 

dependent upon the siting objectives and spatial distribution of 

CO monitors across the study area. ! 

5-15 
P.77 

TABLE 5-6. AIR QUALITY INDICATORS FOR CO DATA 

Study area 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

NT 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

CR 

CC 

CI 

SR 

SC 

SI 

Daily maximum 1-hour 

averac 

Char. 

larqest 

10.7 

24.9 

17.2 

13.3 

13.6 

10.8 

14.1 

31.4 

26.2 

19.6 

21.0 

26.2 

19.2 

19.2 

9.7 

12.2 

12.2 

13.4 

9.8 

13.4 

20.1 

22.8 

15.4 

20.1 

es (ppm) 

Char. 

5th larqest 

8.7 

20.7 

13.9 

9.6 

10.0 

8.4 

11.8 

23.5 

20.6 

16.1 

16.8 

20.6 

13.8 

13.8 

7.4 

8.9 

8.9 

10.5 

7.8 

11.5 

15.3 

14.6 

11.8 

15.3 

Daily maximum 8-hour 

running averaqes (ppm) 

Char. 

larqest 

7.2 

15.6 

10.8 

9.5 

10.0 

8.3 

8.8 

20.1 

20.3 

15.5 

16.6 

20.3 

14.3 

14.3 

7.2 

7.9 

7.9 

10.4 

6.1 

10.7 

14.7 

12.5 

11.5 

14.7 

Char. 

5th larqest 

5.7 

12.9 

9.3 

6.6 

6.9 

6.0 

7.6 

16.1 

15.7 

12.5 

13.1 

15.7 

9.9 

9.9 

5.3 

5.5 

5.5 

7.9 

4.8 

9.5 

10.9 

9.0 

9.0 

10.9 

5-16 
P.78 

Contact with the local EPA Regional Office resulted in identification of the Chicago Heights site (SAROAD code: 141240001G01) 

as an appropriate background site for the Chicago study area. The 

site is located at a high school sufficiently far from areas with 

high traffic concentrations. 

A rural site near the urban area of St. Louis (SAROAD code: 

264300006G01) was selected as the indicator for background CO 

levels for that study area. CO levels measured at this site are 

similar to those reported by a site predominantly upwind of the 

metropolitan area. 

As a result of diurnal wind cycling caused by land-sea 

breezes, each station in the South Coast Air Basin is occasionally 

upwind and downwind of the center city core. Consequently, predominant wind direction was not considered a valid criterion for 

identifying a background site for the Los Angeles area. A ruralagricultural site fairly removed from urban influence (SAROAD 

code: 055160001101) was selected. 

The Philadelphia local agency recommended a site in Northwest 

Philadelphia (SAROAD code: 397140014H01) as the most appropriate 

indicator for average background concentrations. 

The average hourly concentration was calculated for a recent 

year at each site to estimate annual average background for the 

corresponding study area. These values are listed in Table 5-7. 

TABLE 5-7. ESTIMATED ANNUAL AVERAGE BACKGROUND LEVELS 

Study area 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

Year 

1979 

1977 

1978 

1978 

CO background concentration 

mq/m3 

1.5 

2.0 

1.1 

2.6 

ppm1 

1.31 

1.75 

0.96 

2.27 

Converted at STP using 1 ppm = 1145 ug/m3 

5-17 
P.79 

5.4 REFERENCES 

1. E. J. Gumbel, Statistics of Extremes, Columbia University 

Press, New York, 1958, p. 82. 

2. T. Johnson, "A comparison of the two-parameter Weibull and 

lognormal distributions fitted to ambient ozone data," Proc. 

of Specialty Conference on Quality Assurance in Air Pollution 

Measurement, Air Pollution Control Association, 1979. 

3. Op. cit., Gumbel, p. 34. 

4. T. Johnson and R. Paul, The NAAQS Exposure Model (NEM) and 

Its Application to Nitrogen Dioxide, prepared by PEDCo'Environmental, Inc., for Strategies and Air Standards Division, 

Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, N.C. 

27711, August 1981. 

5. Ted Johnson and Roy Paul, The NAAQS Exposure Model (NEM) 

and Its Application to Particulate Matter, prepared by PEDCo 

Environmental, Inc., for Strategies and Air Standards Division, 

Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, N. C. 

27711, August 1981. 

6. A. C. Cohen, Jr., "Simplified estimators for the normal distribution when samples are singly censored or truncated," 

Technometrics, Vol. 1, No. 3, August 1959. 

7. A. C. Cohen, Jr., "Maximum likelihood estimation in the 

Weibull distribution based on complete and on censored 

samples," Technometrics, Vol. 7, No. 4, November 1965. 

8. M. A. Stephens, "EDF statistics for goodness of fit and some 

"comparisons," Journal of the American Statistical Association, 

Vol. 69, No. 347, September 1974. 

9. J. R. Green and Y. A. S. Hegazy, "Powerful modified-EDF 

goodness-of-fit-tests," Journal of the American Statistical 

Association, Vol. 71, No. 353, March 1976. 

5-18 
P.80 

SECTION 6 

SIMULATION OF CARBON MONOXIDE LEVELS 

IN THE MICROENVIRONMENT 

A basic assumption of NEM is that each member of the study 

area population can be assigned during each hour of the day to 

one of five microenvironments: indoors (work or school), indoors 

(home or other), inside a transportation vehicle, outdoors near a 

roadway, or other outdoor locations. In applying NEM to CO, we 

initially assumed that air quality in each microenvironment (x_ .) 

of a given neighborhood type could be estimated by the expression 

x_ . = am + b x. ' (6-1) 

m, t m, t m t 

where a . is the pollutant concentration generated by a particular source in the microenvironment, x " is the monitor-derived 

air quality estimated for the neighborhood type, and b is a multiplicative factor. Consequently, estimates of a^ , and b (denoted 

„ /s ^ m, t m 

a_ . and b ) appropriate for CO were needed for each microenvironm, t m 

ment. We assumed that a . will vary with microenvironment, CO 

m, t 

source, and time of day; and that b will vary only with microenvironment. Equation 6-1 was later revised to account for observed lags between indoor and outdoor CO. 

PEDCo reviewed 75 reports with key words or abstracts suggesting they contained information useful in estimating a . and 

b —jointly referred to as microenvironment factors (MF's). The 

m

 J 

review indicated that 26 of these reports contained data applicable to our analysis. These reports are categorized by microenvironment in Table 6-1. In the following discussion, results 

of these studies are used to estimate MF's and, in some cases, to 

develop alternatives to Equation 6-1. 

6-1 
P.81 

TABLE 6-1. STUDIES CONSIDERED IN DEVELOPING 

CO MICROENVIRONMENT FACTORS 

Microenvironment 

Indoors: work or school 

•*!•••*' 

Indoors: home or other 

Transportation vehicle 

Roadside 

Study. 

Harke1 

Penkala and Oliveira2 

Moschandreas, et al.3 

Yocum, et al .* 

General Electric5 

Derham, et al.6 

Godin, et al.7 

Thompson, et al.8 

Yocum, et al.1* 

Moschandreas, et al.9 

Cote, et al.1 1 

Bridge and Corn12 

Sterling and Kobayashi13 

Penkala and Oliveira2 

Repace and Lowrey16 

Spengler, et al.1 7 

Sterling and Sterling18 

Spengler, et al.2 0 

Godin, et al.7 

Elliot and Rowe21 

Thompson, et al.8 

Ott and Will its2 2 

Ziskind, et al.2 3 

Colwill and Hickman21* 

Wallace25 

Cortese28 

Brice and Roesler27 

Petersen and Sabersky28 

Harke, et al.1 

Wilson and Schweiss29 

Wilson and Schweiss30 

Jabara, et al.31 

6-2 
P.82 

6.1 WORK-SCHOOL MICROENVIRONMENT 

Smoking has been identified by several studies as affecting 

CO levels in enclosed working areas. The contribution of smoking 

does not appear to be very significant, however. CO was monitored 

for 18 days by Harke1 in two office buildings, one air-conditioned, 

the other not. Harke found that no significant increase in CO 

occurred after employees started to smoke. In another experiment, 

Harke found that CO did not exceed 10 ppm in an unventilated 

office room (30 m ) when an occupant smoked at a rate of 2 cigarettes per hour. Background and outdoor CO levels are not mentioned 

in either study. Using test chamber data, Penkala and Oliveira2 

estimate that CO in a 400 ft room occupied by one smoker consuming 

3 

1.25 cigarettes per hour will average 18.6 mg/m per hour at 0 

air changes per hour. At recommended ventilation rates (2.1 to 

7.5 air changes per hour), CO should average 1.2 to 3.6 mg/m . 

Moschandreas, et al.,3 studied CO in two office buildings in 

Boston. They hypothesized that indoor sources of CO are largely 

damped by the diffusive effect of the air handling systems. 

Elevated CO concentrations related to smoking were not observed. 

Yocum, et al.,1* suggest that daytime indoor-outdoor ratios above 

1.00 observed in two office buildings in Hartford, CT, are the 

result of smoking by occupants and visitors but do not provide 

useful data for estimating a . . 

m, t 

The report by Penkala and Oliveira is the most useful of 

these four studies. The following excerpt describes their model 

and discusses their assumptions. 

Assume a smoker and a nonsmoker occupy the same office 

with a total volume of 400 ft . Ventilation rates in forced 

3 

ventilations systems are usually between 7 and 25 ft of 

fresh air per minute per room occupant. These ventilation 

rates are equivalent to 2.1-7.5 air changes per hour, and 

can be attained by normal leakage around windows and doors. 

A typical smoker consumes one pack of 20 cigarettes 

per day (16 waking hours). Each cigarette is smoked in 

6-3 
______ 

about 10 minutes, creating a high concentration of CO and 

SPM in the room, and then the ventilation system and other 

removal mechanisms (as measured in this study) lower those 

concentrations somewhat during a rest period (40 minutes) 

before the next cigarette is lit. The concentrations can 

be time-averaged by considering the room to be in a cycle 

consisting of a rapid concentration rise and a slower exponential decay. The decay rate depends upon the ventilation 

rate and the measured gas removal rate. Both can be represented by the equivalent air changes per hour, and converted 

to a time constant, x, representing the minutes per equivalent air change. 

Then C2 = C-J-1/T] 

C- is concentration at time t 

C, is an initial concentration 

x is the number of minutes per equivalent air,change 

t is the average time of one cigarette smoke plus 

following rest period 

Note that (C, - C-) is the concentration added by smoking a 

cigarette. 

A cycle ends with the room at concentration C- and is 

raised to a new concentration C, through smoking a cigarette. 

Combining the equations allows computing C, and C- for any 

equivalent air change rate. The average concentration C, 

can be found by integration over a smoking period plus rest 

period.2 

The 400 ft room volume is based on a ASHRAE recommendation 

of 200 ft per office building occupant. Repace and Lowrey15 

estimate that one-third of adults smoke. They also state that 

the recommended occupancy density for general.office space is 

10 persons per 1000 square feet. Assuming an 8-foot ceiling, we 

can estimate that there is one smoker per 2400 ft . Since Penkala 

and Oliveira assume there is one smoker per 400 ft , their estimates can be multiplied by 400/2400 to yield the CO levels expected 

6-4 
P.84 

in an office with one smoker per 2400 ft . Both sets of estimates 

are listed in Table 6-2. 

TABLE '6-2. ESTIMATES OF CO CONCENTRATIONS IN AN 

OFFICE WITH SMOKERS 

a ir changes/hour 

0 

1 

2.1 

7.5 

mean CO (mq/m ) 

400 ft0 per 

smoker 

2400 ftJ per 

smoker 

3.10 

1.03 

0.60 

0.20 

Based on these results, reasonable bounds for a . during working 

3 o. 

hours would be 0.20 mg/m (0.17 ppm) and 0.6 mg/mJ (0.52 ppm); a 

3 

reasonable best estimate for a . would be 0.35 mg/m (0.30 ppm), 

the geometric mean of the bounds. 

The relationship 

b„, = (xm - a «.)/x •«. m m, t m, t a, t (6-2) 

where. x, . represents ambient CO levels reported by a fixed monitor, 

a, t 

can be used to estimate b. if good data for determining x ., x . *. m ? m, t a, t 

and a . are available. Two studies—Moschandreas, et al.,3 and 

m, t 

. . and x_ data. Yocum, et al., 

m, t a, t 

None of these studies list values 

General Electric5—provide x 

provide x ,/x . values, 

m, t a, t 

directly relating to a .. General Electric measured CO inside 

and outside of two buildings in New York. One building was an 

air rights building above the Trans Manhattan Expressway; the 

other was a more conventional high rise structure on one side of 

a street canyon in midtown Manhattan. The following excerpt is 

taken from their conclusions. 

Concentrations indoors at the building base vary with 

outdoor concentrations. Indoor concentrations lag changes 

in outdoor CO levels. It is suspected that this time delay 

is a variable that is a function of both wind conditions as 

seen at the building and the direction of change in outdoor 

concentrations. 

6-5 
P.85 

Average concentrations inside and outside the buildings 

reduce exponentially with height above ground level. The 

rate of change with height is essentially constant outdoors 

for both heating and non-heating seasons. However, indoors 

the decay in average concentrations with height is greater 

during the non-heating season than during the heating 

season. This variation is the result of changes in the 

roof wind angle from the non-heating to the heating season. 

Indoor concentrations normally are lower than outdoor 

concentrations at all heights above the roadway when outdoor 

concentrations are high. Conversely, indoor concentrations 

are higher than outdoor concentrations when outdoor concentrations are low.5 

Because the air-rights building is atypical of urban work 

places, data for the street canyon building should receive primary 

attention. This building was not air-conditioned; ventilation, 

especially during the summer months, was achieved by opening 

windows. Table 6-3 lists average weekday CO concentrations at 

9 feet above street level, third floor, fifth floor, 11th floor, 

and 19th floor. 

TABLE 6-3. WEEKDAY CO MEASUREMENTS AT STREET CANYON SITE5 

season 

heating 

non-heating 

location 

9 feet 

3rd floor 

5th floor 

11th floor 

19th floor 

9 feet 

3rd floor 

5th floor 

11th floor 

19th floor. 

average C 

outside 

11.2 

9.9 

7.7 

6.6 

5.4 

11.2 

10.3 

8.1 

4.8 

4.2 

0 (ppm) 

inside 

9.5 

7.8 

6.9 

6.8 

• 

8.2 

7.1 

4.7 

3.8 

6-6 
P.86 

Inside CO concentrations are generally the same as outside CO 

concentrations at the same building height. CO decreases with 

height so that the ratio of inside CO to CO 9 feet above streetlevel varies from 0.85 at the third floor to 0.61 at the 19th 

floor during the heating season. In the non-heating season, the 

ratio ranges from 0.73 at the third floor to 0.34 at the 19th 

floor. The contribution of indoor sources to indoor CO is unknown 

but is probably small in proportion to the ambient CO levels. 

Moschandreas, et al.,3 measured CO inside and outside of two 

office buildings in Boston. Their results are listed in Table 6-4. 

TABLE 6-4. CO CONCENTRATIONS (ppm) AT TWO OFFICE SITES RECORDED 

BY MOSCHANDREAS, ET AL.3 

building 

new 

old 

mean indoor 

3.18 

2.16 

max indoor 

11.35 

14.36 

mean 

outdoor/indoor 

1.02 

0.88 

Note they reported outdoor/indoor ratios rather than indoor/outdoor 

ratios. Figure 3 in Moschandreas, et al., shows indoor CO tracking outdoor CO at the new building. 

Table 6-5 lists indoor-outdoor ratios for two air-conditioned 

office buildings in Hartford, CT, determined by Yocum, et al.1* 

TABLE 6-5. IND00R-0UTD00R CO RATIOS DETERMINED FOR TWO OFFICE 

BUILDINGS BY YOCUM, ET AL.* 

Building 

100 CP 

250 CP 

Season 

Summer 

Fall 

Winter 

Suitmer 

Fall 

Winter 

Daytime ratio 

1.31 

1.32 

1.13 

1.05 

0.96 

0.76 

Nightime ratio 

1.00 

1.25 

1.21 

1.02 

1.04 

' 0.96 

CP: Constitution Plaza 

6-7 
P.87 

Inside CO was measured on the second floor at 100 CP and the 

third floor at 250 CP. The authors suggest that the start-up of 

building ventilation during rush hour is the primary cause of 

summer and fall daytime ratios greater than 1.00 at 100 CP. 

They further suggest smoking may have elevated ratios in the 

winter. 

Derham, et al.,6 monitored CO inside and outside a building 

in Los Angeles. They found that indoor levels of CO reflect 

directly the levels outdoors but with a phase lag that can be 

explained by means of a simple analytical model which accounts 

for ventilation' rates but neglects any chemical reactions. They 

do not provide simultaneous indoor/outdoor readings and smoking 

is not discussed as a possible CO source. 

Godin, et al., measured CO levels inside and outside a downtown office in Toronto with the windows closed. They summarize 

their findings as follows: 

At 150 College St., about a mile from the city center, 

outdoor values were 2.7 + 1.8 ppm, while the corresponding 

values for the first and third floors were, respectively, 

2.2 + 1.3 ppm and 2.8 + 1.5 ppm. Values in taller downtown 

buildings apparently depended on the level of air intake for 

the floor in question; at the Toronto Dominion Centre, the 

sidewalk concentration was 6.4 ppm, figures for the first 

and third floors were 4.6 and 4.0 ppm, respectively, but 

the 54th floor (with a much higher air intake) has a level 

of only 2.4 ppm.7 

Godin, et al., conclude that indoor CO concentrations mirror 

outdoor concentrations, with a lag of one to two hours. 

These studies suggest that a reasonable model for hourly 

average CO in the workplace is 

*_,(t) =

 a

m .

 + T^ ^..(t) + x -(t-1)] . (6-3) 

m m, t _ c c 

The indoor CO at time t is equal to the indoor generated CO at 

time t plus b times the average of the outdoor CO at time t and 

6-8 
P.88 

at time t-1. This model assumes that building ventilation dampens 

variations in indoor CO and causes a slight lag between indoor and 

outdoor concentrations. A reasonable "best" estimate of b for 

m 

buildings of 3 stories or less is 0.85, the ratio of third floor 

CO to outside ground floor CO in the General Electric study. A 

reasonable range for b is 0.60'(unairconditioned highrise) to 

1.05 (ventilation system of 250 CP). 

The microenvironment under consideration includes schools as 

well as work places. Only one study—Thompson, et al.8—measured 

indoor and outdoor CO levels at a school. Accuracy of their CO 

analyzer, +1. 0 ppm, prevents a critical comparison of the low 

values which were measured. Since NEM treats indoors work and 

indoors school as the same microenvironment, we used the model 

already developed for indoors at work for the combined work-school 

microenvironment. 

6.2 HOME-OTHER MICROENVIRONMENT 

The value of b for homes can be estimated by comparing 

indoor and outdoor CO levels of homes with no indoor CO sources. 

Yocum, et al.,1* measured indoor and outdoor CO at two residences 

in Hartford, CT. Neither home had a gas stove or habitual smoker. 

Average indoor/outdoor ratios are listed in Table 6-6. 

TABLE 6-6. AVERAGE IND00R/0UTD00R CO RATIOS RECORDED BY 

YOCUM, ET AL." 

Residence 

Blinn.St. 

Carol! St. 

Season 

Summer 

Fall 

Winter 

Summer 

Fall 

Winter 

Time of day 

Day 

Night 

Day 

Night 

Day 

Night 

Day 

Night 

Day 

Night 

Day 

Night 

Ratio 

1.02 

1.07 

1.03 

1.08 

1.07 

1.08 

1.04 

1.02 

1.03 

1.08 

0.96 

1.08 

6-9 
P.89 

Note that all ratios are close to unity. Yocum, et al., do not 

provide data useful iri determining if indoor CO lags outdoor CO. 

Figure 4 from Moschandreas, et al.,9 suggests a lag of one hour 

in a conventional residence in Baltimore. The following is an 

excerpt from their study. 

Indoor concentration peaks of CO tend to lag behind 

outdoor CO peaks. Due to the CO emissions, this behavior 

may be shortened in houses with indoor sources. The 

observed large fluctuations of the hourly CO concentrations 

display a local structure without a general pattern. However, examination of the CO data base from several weekdays 

leads to identification of a typical pattern with respect 

to 3-h averages. Typically, the time periods 0800-1000 and 

1900-2100 exhibit the highest observed CO levels. These 

3-h indoor peaks correspond to outdoor peaks caused by 

automobile traffic during the typical urban rush hours 

(0600-0800 and 1700-1900). The association of rush-hour 

traffic and typical indoor high level periods reflect the 

time lag monitored earlier. Figure 4 illustrates the 

indoor and outdoor variation of CO concentrations for a 

typical day, in a dwelling with indoor CO sources. The 

indoor peak at hours 1400 to 1600 is not a typically observed 

elevation of the indoor concentrations.9 

These results suggest that Equation 6-3 is applicable to the home 

microenvironment as well as the work microenvironment. Based 

solely on the results of Yocum et al., a preliminary estimate of 

b would be 1.00. However, analysis by Feagans10 indicates that 

1.00 is probably too high. Feagans suggests 0.85 as a more 

appropriate best estimate of b and 0.70 to 1.10 as a reasonable 

*% m A 

range for b . Appropriate values of a , for different indoor 

m m, t 

sources are developed below. 

CO sources in the home include smoking, gas stoves, gas 

furnaces, coal furnaces, and attached garages. CO from these 

sources combined with CO from outside have resulted in indoor 

levels exceeding the CO NAAQS. 

6-10 
P.90 

Three studies—Cote, et al.,11 Moschandreas, et al.,9 and 

Bridge and Corn12—mention smoking as an indoor CO source in the 

home. Cote, et al., monitored indoor and outdoor CO in four 

homes in Hartford, CT. Unfortunately, the homes with smokers 

also had gas appliances so that the contribution of smoking to 

indoor CO cannot be determined separately. Moschandreas, et al., 

monitored CO levels in 15 homes. Persons living in these houses 

were polled as to smoking habits. Unfortunately, the report by 

Moschandreas, et al., provides only a few sample days of CO data 

and no smoking data. Bridge and Corn measured CO at two experimental "parties." Sterling and Kobayashi provide the following 

summary of this study. 

In one 5120 ft room containing 50 people, 25 people consumed 50 cigarettes and seven cigars in 1*5 hours. With a 

room air exchange rate of seven times per hour, CO averaged 

7 ppm during the course of the party. During the second 

experiment in a 3750 ft room containing 7 3 people, 36 

smokers consumed 63 cigarettes and 10 cigars in 1*5 hours 

and the average CO content was 9 ppm.x 3 

These results suggest that 7 ppm is a worst case a value for 

smoking that would not be exceeded in the typical home except 

during occasional social functions. 

The three studies described above are not useful in determining a typical a for smoking. However, with suitable assumptions we can use the model developed by Penkala and Oliveira to 

estimate a if we have good estimates of air exchange rates, 

in § u 

Table 6-7 lists air exchange rates determined by Moschandreas, 

et al., for residences of various kinds. 

6-11 
P.91 

TABLE 6-7. AIR EXCHANGE RATES DETERMINED BY 

MOSCHANDREAS, ET AL.9 

location 

Washington 

Baltimore 

Denver 

Chicago 

Pittsburg 

residence type 

experimental 

conventional 

experimental 

conventional 

conventional 

conventional 

experimental 

mobile 1 

mobile 2 

low-rise 1 

low-rise 2 

low-rise 3 

high-rise 1 

high-rise 2 

high-rise 3 

exchanqes/h 

0.5 - 1.0 

0.2 - 0.8 

0.5 - 1.2 

0.6 - 2.0 

0.8 - 1.0 

0.6 - 1.0 

0.1 - 0.3 

0.4 - 1.0 

0.3 - 1.1 

0.3 - 0.8 

0.7 - 1.4 

1.6 - 1.7 

0.9 - 1.4 

0.9 - 1.4 

0.9 - 1.2 

Air exchange rates range from 0.1 to 2.0. The mean of the midpoints of the 15 ranges listed in Table 6-7 is 0.9. The mean of 

the midpoints of the particular residence types are listed below. 

residence type 

experimental 

conventional 

mobile 

low-rise 

high-rise 

exchanqes/h 

0.6 

0.9 

0.7 

1.1 

1.1 

These results suggest a typical ventilation rate for a nonexperimental home of one exchange per hour. Penkala and Oliveira estimate 

that one smoker per 400 ft in an enclosed space will add 6.2 mg/m 

(5.4 ppm) to indoor CO if there is one air exchange per hour. According to U.S. Census data,11* the average number of rooms in a 

living unit is 5.1. Assuming the typical five room house has a 

floor area of 1300 square feet and a ceiling 8 feet high, we can 

estimate that the typical living unit has a volume of 10,400 ft . 

6-12 
P.92 

Housing data indicate that the average living unit has 2.1 adults.15 

Repace and Lowrey15 estimate that one third of adults smoke. 

Since some teenagers smoke, the average living unit has at least 

3 3 

0.7 smokers per 10,400 ft or 0.027 smokers per 400 ft . Smokergenerated CO would be at least (0.027)(5.4 ppm) = 0.15 ppm. A 

house with 10,400 ft and two smokers would have a smoker-generated 

CO concentration of 0.42 ppm. These levels are negligible. In 

fact, the number of smokers must be increased to five per 10,400 

ft for the smoker-generated CO concentration to exceed 1.00 ppm. 

In a sample of 69 homes, Spengler, et al.,17 found 32 percent 

had one smoker and 13 percent had two or more smokers. From 

these data we can estimate the average house with smokers has 

3 3 

about 1.3 smokers per 10,400 ft or 0.05 smoker per 400 ft . 

Smoker-generated CO concentration in such a house would be about 

0.3 ppm. Consequently, we used 0.3 ppm as our best estimate of 

A 

a_, . for smoking households from 7 a.m. to 9 a.m. and from 5 p.m. 

m,t * c 

to 11 p.m. A smaller a ., 0.2 ppm, was considered appropriate 

ill / C A. 

from 9 a.m. to 5 p.m. We assumed a__, . = 0 from 11 p.m. to 7 a.m. 

m,t 

In the CO exposure analysis we are particularly interested 

in kitchen and living room CO levels generated by gas stoves. 

Peak home CO exposure is expected to occur in the kitchen during 

and immediately after meal preparation. We assume that typical 

home CO exposure is better represented by CO levels in the living 

room. Data useful in estimating a for gas stoves are provided 

by several studies performed by Research Corporation of New 

England. Yocum, et al.,1* measured CO in two houses with gas 

stoves and gas furnaces. They found that "the heating system had 

no measurable effect on the indoor or outdoor CO levels; however, 

the gas fired stoves in each house had a significant influence on 

indoor CO levels." Figure 4 in Yocum, et al., shows kitchen levels 

in house G-1 exceeding outside levels by 3 ppm during meal prepara^ 

tion; living room levels exceeded outside levels by about 1.5 ppm. 

In house G-2, kitchen and family room levels during meal preparation exceeded outdoor levels by 3.0 to 4.5 ppm and 1.0 to 1.5 ppm, 

6-13 
P.93 

respectively. In a later study by Cote, et al.,11 indoor and outdoor CO levels were measured at four homes in Hartford, CT. 

2 House 1 is a 2,000 ft split-level with well-ventilated kitchen 

occupied by a married couple and two children. The wife smokes a 

pack a day. Yocum, et al., found that an attached garage made a 

significant contribution to indoor CO at this house. House 2 is 

2 

a 1500 ft two-story home with well-ventilated kitchen. A 

single adult lives there who seldom uses the stove. House 3 is a 

2 

1,000 ft apartment with a small, unventilated kitchen. A nonsmoking couple and their 2 children live there. House 4 also has 

2 

two adults and two children. It is a 1500 ft ranch-style house 

with kitchen open to other areas of the house. Table 6-8 lists 

the seasonal means of daily average CO concentrations measured in 

various areas of the four houses. Average kitchen and living 

room CO values in house 1 exceed outside CO by 1010 yg/m (0.88 

ppm) and 590 yg/m (0.52 ppm), respectively. The contribution of 

the attached garage is difficult to quantify. Data from house 2 

are probably atypical because of the infrequent stove use. Houses 

3 and 4 are not as well ventilated as house 1 and may be more 

appropriate for determining typical a values. Average kitchen 

3 CO exceeds average outside CO by 3040 yg/m (2.66 ppm) and by 

3 3 

4120 yg/m (3.60 ppm) in house 3 and by 6590 yg/m (5.76 ppm) in 

house 4. Average living room CO exceeds average outside CO by 

3 3 

980 yg/m (0.86 ppm) and 1690 yg/m (1.48 ppm) in house 3 and by 

5780 yg/m (5.05 ppm) in house 4. Closer examination of 2-hour 

CO values included in the report reveals that the differencebetween inside (kitchen and living room) and outside CO levels is 

usually greatest from 1600 to 1800 (4 p.m. to 6 p.m.) and is 

usually smallest from 400 to 600 (4 a.m. to 6 a.m.). Typical 

mealtime CO levels seem to occur between the hours 1200 and 1400 

(noon and 2 p.m.). Table 6-9 lists average differences between 

inside and outside CO levels during these 2-hour periods for 

houses 1, 3, and 4. 

6-14 
P.94 

TABLE 6-8. INDOOR/OUTDOOR CO DATA RECORDED BY COTE, ET AL.11 

House 

1 

2 

3 

4 

Season 

Spring-summer 

Fall-winter 

Fall-winter 

Spring-summer 

Spring-summer 

Fall-winter 

Fall-winter 

Fall-winter 

Mean dai 

Stove 

4190 

4790 

3000 

4310 

7820 

7130 

9070 

ly averaqe 

Kitchen 

4490 

3520 

4210 

-

6420 

6620 

9000 

CO concentration, 

Living 

Room 

4070 

3230 

3080 

3210 

5070 

8190 

Bedroom 

4170 

3830 

2900 

2680 

5500 

.-

yg/m3 

Outside 

3480 

1670 

2310 

2940 

2230 

3380 

2500 

2410 

Avg. 

stove 

usage 

(min) 

198 

106 

? 

43 

37 

66 

115 

201 

Sterling and Sterling18 studied the rate of CO buildup and 

dissipation in kitchens, dining rooms, and living rooms of nine 

homes in Burnaby, British Columbia. Kitchen levels of CO in 

house 1 increased from 6 ppm to 36 ppm in 30 minutes, depending 

on the number of burners on. 

Burners on 

1 

2 

3 

4 

CO increase, 

ppm per minute 

0.2 

0.63 

0.73 

1.20 

Rates of increase for the other eight homes varied from 0.7 to 

3.3 (number of burners on was not specified). The average rate 

of CO increase for the nine homes was about 2 ppm. Operating a 

stove at this rate for 30 minutes would yield an hourly average 

of 30 ppm if CO decayed immediately. Sterling and Sterling 

found that CO decayed very slowly in the test homes and that it 

diffused rapidly throughout the houses. 

An increase in kitchen CO of 30 ppm during meal preparation 

is probably atypical since it is based on the use of three to 

four burners continually for 30 minutes. None of the studies by 

Research Corporation of New England suggest meal-time CO levels 

6-15 
TABLE 6-9. AVERAGE DIFFERENCES BETWEEN KITCHEN, 

LIVING ROOM, AND OUTSIDE CO CONCENTRATIONS 

House 

1 

3 

4 

Season 

Spring/summer 

Fall/winter 

Spring/summer 

Fall/winter 

Fa11/winter 

Time of 

day 

4-6 

12-14 

16-18 

4-6 

12-14 

16-18 

4-6 

12-14 

16-18 

4-6 

12-14 

16-18 

4-6 

12-14 

16-18-

Difference in CO 

Kitchen-outside 

624 (ll)a 

1109 (12) 

1742 (12) 

1941 (11) 

2247 (12) 

3380 (14) 

-

3284 (21) 

3372 (23) 

3622 (22) 

2704 (9) 

7123 (7) 

12,424 (9) 

3 

concentration, yq/m 

Livinq room-outside 

385 (11) 

743 (12) 

832 (13) 

1236 (5) 

1154 (6) 

2105 (8) 

844 (16) 

1062 (16) 

915 (16) 

1757 (9). 

2189 (12) 

1045 (10) 

2256 (9) 

6177 (7) 

11,328 (9) 

Numbers in parentheses indicate number of days with data. 

6-16 
this high. If the data provided by Yocum and Cote are assumed 

to be more typical, a reasonable model for kitchen a .in gas 

~ m, t 

stove homes would be a__, . = 4.0 ppm during meal-time hours, and 

_ m, t 

a . = 2.5 ppm other times. Reasonable living room estimates 

m, t A ^ 

would be a_ , = 2.0 ppm during meal-time hours and a . = 1.0 ppm 

m,t

 cc 3 m,t rr 

other times. Meal-time hours would be defined as the 2-hour 

periods 600 to 800, 1100 to 1300, and 1700 to 1900. 

Although the home-other microenvironment includes nonresidential locations such as shopping malls, a single set of a ,_ and 

m, v 

b values is used for the combined microenvironment. Spengler19 

m 

cites work by Chapin which suggests that 92 percent of people's 

time characterized as spent in home-other microenvironments is 

spent in the home. Consequently, using the indoor home values 

for the combined microenvironment should not significantly bias 

exposure estimates. We can assume that a cohort is at home 

whenever its activity pattern places them in the home-other micro--

environment during a meal-time hour. Using the gas stove estimates for a_, . in these situations is reasonable. At other times 

m, t 

of the day, home-other could indicate visits to a library, 

courthouse, shopping center, sports arena, or doctor's office. 

The principal CO source in these enclosed areas is probably ' 

cigarette smoke, although Spengler, et al.,20 have found that ice 

cleaning machines at hockey rinks can produce one-hour CO levels 

exceeding 35 ppm. Godin, et al.,7 reported that CO in a theater 

foyer where smoking was permitted exceeded CO in the auditorium 

by 2 ppm. Elliot and Rowe20 found an average CO concentration of 

25 ppm in a sports arena (not air conditioned) where smoking was 

permitted. CO levels of 9 ppm were recorded in two other arenas 

with posted "No Smoking" signs. Average CO during periods of 

nonactivity was 3 ppm in all three arenas. Thompson, et al.,8 

recorded average daytime CO levels in a hospital, YMCA pool, 

department store, and shopping mall (see Table 6-10). 

6-17 
P.97 

TABLE 6-10. AVERAGE CO LEVELS IN VARIOUS STRUCTURES3 

Kind of structure 

community hospital 

YMCA pool 

department store 

shopping mall 

CO, ppm 

Out 

2.1 

0.5 

6.4 

2.7 

In 

1.7 

1.0 

3.3 

3.1 

Thompson, et al., state that the inaccuracy of their analyzer, 

+1.0 ppm, prevented critical comparison of most of the rather 

low values obtained with the possible exception of the CO levels 

measured at the department store. Thompson, et al., suggest the 

following explanation for the relatively low indoor/outdoor 

ratio. Because auto exhaust emissions near the building would be 

minimal at night, a mass of air with a minimal level of CO would 

accumulate during the night. If daytime ventilation rates are 

low, the inside air would fail to come to equilibrium with outside CO. 

None of these studies provide dependable data on typical CO 

levels in the "other" microenvironment. Consequently, we used 

the factors determined for "home" as the factors for the combined 

home-other microenvironment. 

6.3 TRANSPORTATION VEHICLE MICROENVIRONMENT 

The most commonly used transportation vehicle in the four 

study areas is the automobile. The principal internal sources of 

CO in automobiles are'probably cigarette smoking and leaky exhausts. 

In the absence of these sources, available data indicate that 

average interior CO is equal to or less than average exterior CO, 

although exterior CO shows greater fluctuations. Ott and Willits22 

concluded that the average value of the interior CO concentration is approximately equal to the average value of the exterior 

CO concentration if the averaging time, T, was much greater than 

the time constant T. They estimate x = 4.5 minutes for a test 

6-18 
P.98 

vehicle moving on residential side streets at 20 mph with windows 

closed. Since T decreases as speed increases or windows are open, 

we can assume T>>x for most moving vehicles. 

Ziskind, et al.,23 studied buses, cabs, and police cars in 

Denver and Boston. They found that interior concentrations "rise 

and fall with exterior concentrations, yet are almost always 

lower." They hypothesize that the relatively small difference 

between interior and exterior levels provides too small a driving 

force for diffusion of CO into the vehicle. Furthermore, there 

is insufficient time for the two concentrations to equilibriate, 

since the external source is constantly changing as long as the 

vehicle keeps moving. Ziskind, et al., found that all vehicles 

in their study having interior concentrations in excess of exterior concentrations had both exhaust system leaks and pathways 

through to the passenger area. Since most of the vehicles which 

were monitored continuously in their study were selected because 

of high interior CO levels, their results cannot be applied to the 

general vehicle population. 

Colwill and Hickman2* measured interior and exterior CO 

levels of 11 new cars driven around a 35 km route in London. 

They report inside/outside ratios of 0.35 to 0.75 with a mean 

ratio of 0.55. Although they did not relate inside CO levels 

to stationary monitor readings, Colwill and Hickman state that 

occupants of vehicles moving in heavy traffic are exposed to CO • 

levels higher than those recorded at curbside. 

Several studies provide data which relate interior CO 

levels to fixed monitoring data directly. When Ziskind, et al., 

compared personal sampler data with fixed site data, they found 

that total exposures exceeded fixed site concentrations by an 

average of 13.9 ppm. An average ratio was not determined. 

Ziskind, et al., also list average interior CO as measured by 

continuous monitors in 9 vehicles (8 buses and 1 cab) and the 

average CO levels at corresponding fixed sites. Interior/fixed 

site ratios vary from 1.0 to above 7.0 with a median of 2.7. 

Ziskind, et al., are uncertain how much of the difference between 

6-19 
P.99 

interior and fixed site CO "was due to vehicle self-contamination 

and how much was due to the inherent lack of representativeness 

of the fixed site monitoring station readings." However, they 

make the following inconsistent statement in their section listing overall study conclusions: 

Typically the CO level measured inside or immediately outside the vehicle significantly exceeded the value recorded 

by the nearest fixed site monitoring station. Vehicle 

self-contamination does not appear to be. the cause of this 

disparity. Rather, it is postulated that the proximity of 

the vehicle to the emission sources accounts for the difference between vehicle and fixed site monitor concentrations.

 2 3 

Wallace25 measured CO levels in cars and buses on 37 runs 

(27 by bus, 10 by car) around Washington, D.C. . Mean bus CO was 

11.7 ppm, excluding one outlier; mean car CO was 13.8 ppm. These 

values are three to four times higher than mean CO measured simutaneously at a stationary monitor at 427 New Jersey Avenue, N.W. 

However, Wallace found no significant relationship between ambient 

concentrations and interior vehicular concentrations. His results 

suggest that factors associated with particular vehicles—power 

source, design, and maintenance—may effect interior CO levels 

more than exterior CO levels. 

A doctoral thesis by Cortese26 provides more definitive results. In this study, population exposure to CO was measured 

by equipping volunteers living and working in the metropolitan 

Boston area with portable CO monitors. The monitored cohort 

consisted of 66 nonsmoking volunteers who carried a portable 

monitor for 3 to 5 days during commuting and working activities. 

Participants* commuting mode and route, residential and occupational location, exposure to cigarette smoke, and daily activities 

were documented. Volunteers were chosen from populations without 

significant occupational exposures to CO so that measured exposures resulted from ambient air contamination. Population 

6-20 
P.1 

exposure data, as measured fay personal monitoring, were compared 

to CO concentrations measured at 6 fixed location monitoring 

stations operated by the Massachusetts Bureau of Air Quality 

Control. Two of the fixed location monitoring stations are 

located in downtown Boston. These urban stations approach 

Federal siting criteria for monitoring maximum 1-hour exposure 

to CO. The other four stations are located in suburban areas. 

These stations approach federal siting criteria for monitoring 

8-hour average CO exposure but are not located close enough to 

heavily traveled roadways to monitor maximum 1-hour exposure. 

The following conclusions were drawn by Cortese. 

o Measurements at 6 fixed locations in metropolitan 

Boston underestimated mean 1-hour CO exposure during 

commuting by a factor of 1.8 to 2.0. 

o Measurements at the two urban monitoring stations, 

whose characteristics approach Federal criteria for 

monitoring maximum 1-hour exposures, underestimated 

the mean 1-hour CO exposure during commuting by a 

factor of 1.4. Because Boston pedestrians can be 

closer to automobile traffic than the two urban 

stations, measurements from the stations would also 

underestimate pedestrian exposure to CO. 

o Measurements at the four suburban monitoring stations 

underestimated mean 1-hour CO exposure during commuting 

by a factor of 2.1. This result is significant because 

a large portion of the average commuting trip in this 

study occurred in suburban areas. 

o Analysis of the highest 5-7% of the personal exposure 

and fixed location measurements, which are of greatest 

public health importance, indicated that fixed location 

measurements were better estimates of the higher commuting exposures than of the entire range of commuting 

exposures. Nevertheless, the mean 1-hour personal 

6-21 
P.2 

exposure concentration was 1.6 times the mean concentration at all fixed stations and 1.3 times the mean 

concentration at urban stations. 

10 to 15% of the difference between commuting exposures 

and the concentrations measured by fixed location monitors was attributed to an observed reduction in CO 

concentrations with increased sampling height between 

personal monitors at or near breathing zone (5.5 feet) 

and fixed location monitors at a height of 15 feet. 

The remainder of the difference was attributed to---

commuters being closer to CO emission sources than 

fixed location monitors. 

No consistent relationship was observed between personal 

exposure during commuting and fixed location measurements over the entire range of values encountered. 

This result made it impossible to develop a predictive 

relationship between personal exposure and fixed location measurements'. 

Mode of travel (automobile, mass transit, split mode, 

i.e., part auto, part transit) and route of travel were 

the significant factors influencing personal exposure 

to CO during commuting. Cigarette smoke is the only 

other significant source of CO to which a commuter may 

be exposed. 

Total travel by automobile resulted in a mean CO exposure nearly twice that of rail mass transit commuting 

and approximately 1.6 times that of split mode commuting. 

Automobile commuting on 4-lane, heavily traveled 

arterial roads resulted in a mean CO exposure approximately 1.4 times the mean exposure during automobile 

commuting on other types of roads. 

Wind speed, wind direction, season, and automobile age 

did not influence commuter population exposure to CO.26 

6-22 
P.3 

Pertinent data from the Cortese study are summarized in Table 6-11. 

These results suggest 1.4 <_ b <_ 2.1 for unspecified Boston trans-, 

portation vehicles during commuting hours. Ziskind's median 

ratio of 2.7 may be the result of using some vehicles known to 

have leaky exhausts and not using any rail transit. 

TABLE 6-11. RATIOS OF MEAN PERSONAL CO EXPOSURES TO MEAN CO 

CONCENTRATIONS AT FIXED MONITORS26 

mode of travel 

all vehicles 

all vehicles 

all vehicles 

fixed monitors 

6 urban sites 

2 urban sites 

meeting EPA criteria 

4 suburban sites 

mean personal 

exposure 

mean monitor CO 

1.8 to 2.0 

1.4 

2.1 

An earlier study by Brice and Roesler27 compared CO in motor 

vehicles moving in moderate, to heavy traffic with concurrent concentrations measured at CAMP sites in six cities. Table 6-12 lists 

results of the study. The mean of the five ratios is 3.5; the 

median is 2.4. Ratios of vehicles moving in light to moderate 

traffic would probably be lower. Most CAMP sites were located 

in downtown areas; probes were usually positioned 15 feet off 

the street. Brice and Roesler state that the low ratio in Chicago 

corresponds to a high average concentration of CO at the CAMP 

site, which is attributed to the close proximity of that site to 

high-density traffic routes. In-vehicle data for Cincinnati is 

heavily weighted toward downtown street canyons. The average 

ratio for major arteries in Cincinnati is 4.8. 

Petersen and Sabersky28 measured CO inside a car driving a 

route in Los Angeles that included a business district, a residential district, a part of a generally uncrowded freeway, and a 

part of a congested freeway. During a 50-minute drive from 1:52 

p.m. to 2:42 during the summer, average CO varied from 15 to 20 

ppm. The maximum reading reported by APCD for the day was 8 ppm 

6-23 
P.4 

and may not have occurred concurrently. Consequently, the ratio 

of interior CO to fixed site CO is at least 1.9. These data are 

too limited to make any firm estimates of b for Los Angeles, 

however. 

TABLE 6-12. RATIOS OF CO IN MOTOR VEHICLES CONCURRENT 

TO CO AT CAMP STATIONS27 

City 

Chicago 

Cincinnati 

Denver 

St. Louis 

Washington, D.C. 

Interior CO/ 

CAMP CO 

1.3 

6.8 

2.4 

2.1 

4.7 

The above studies suggest that b for the transportation 

microenvironment should fall between 1.3 and 4.7. In the NEM 

analysis, we used 2.1, the upper range of Cortese's estimates, 

since it incorporates movement by motor vehicles and trains. We 

assumed reasonable bounds for b_ would be 1.4, the smallest ratio 

m 

in Table 6-11, and-T3.5, the "mean of.the ratios in Table 6-12. 

There are few data on typical levels of CO from cigarette 

smoke in transportation vehicles. Ziskind, et al.,23 report that 

chi-square analysis of taxicab data showed that CO levels were 

not significantly higher when drivers and/or passengers smoked. 

However, Harke, et al.,1 measured CO levels of 30 ppm in an unventilated car with an outside windspeed of 50 km/hour when 9 

cigarettes were smoked intermittently. CO levels averaged 5 to 

6 ppm in a well-ventilated car with three people smoking continuously. Unfortunately, Harke does not give outside CO levels. 

Information on the percentage of automobiles that contain 

smokers and the average cigarette^generated CO levels on buses 

and trains is unavailable. Consequently, we let a +. = 0 for 

m, t 

smoking and assumed that our estimate b = 2.1 incorporates some 

of the smoker-generated CO to which commuters in Cortese's study 

6-24 
P.5 

were exposed. We made the same assumption concerning CO from 

leaky exhausts, since some of Cortese's subjects probably commuted 

in cars with leaking exhaust systems. 

6.4 ROADSIDE MICROENVIRONMENT 

Persons walking near roadways are usually closer to the 

automobiles that produce CO than the nearest fixed CO monitor. 

Consequently, fixed monitors usually underestimate roadside CO 

levels. If we assume a_ +. = 0, then b_ must exceed unity for 

reasonable estimates of roadside levels. 

Two studies by Wilson and Schweiss29'

3° provide data useful 

in estimating b . In 1977, Wilson and Schweiss measured 8-hour 

m 

(10 am - 6 pm) CO values at 33 sites in the central business 

district and 7 sites in nearby areas of Boise, Idaho, during 

November and December, the season when high CO levels frequently 

occur. These values were compared to 8-hour values recorded at 

the only continuous CO monitor in Boise. The fixed site was 

located in the center of the downtown business district. Most of 

the 40 study sites were near roadways (but not "hotspot" locations). 

Sample probes were mounted 3.5 meters above the ground. Roadside/ 

fixed monitor ratios ranged from 0.3 to 1.5. The mean ratio was 

0.92; the median ratio was 0.90. These results suggest that the 

fixed station may have been purposely sited in an area of Boise 

with particularly high CO levels. 

Wilson and Schweiss conducted a similar study in Seattle, 

collecting data from 36 outside samplers and 4 fixed-site monitors. Table 6-13 summarizes their results. In this case, roadside/fixed monitor ratios range from 0.69 to 2.22 and average 

about 1.15. 

Jabara, et al.,31 measured the occupational exposure of 

Denver traffic officers to CO during eight hour work shifts and 

compared the results to ambient levels at fixed site monitors. 

The ratio of mean dosimeter reading to mean fixed site reading 

was 21.7/6.4 = 3.39. Since traffic officers work in areas of 

6-25

 ! 
P.6 

TABLE 6-13 . RATIOS OF MEAN CO CONCENTRATIONS AT 

EXPERIMENTAL SITES AND AT FIXED SITES3 0 

Fixed site 

Pike St. 

University St. 

James St. 

Fire station 

Smejcor St. 

mean 

Study site CO/fixed site CO 

Nearest 

study site 

0.69 

1.07 

0.89 

2.22 

0.98 

1.16 

2nd nearest 

study site 

1.03 

1.10 

1.25 

1.26 

1.09 

1.15 

congested traffic , this ratio is probably high for the typical 

pedestrian. A more reasonable estimate of b would be 1.2, as 

m 

suggested by the Seattle data of Wilson and Schweiss. 

We assumed 

that b should fall between 0.7 and 2.3, and used b = 1.2 as our 

m m 

best estimate. 

6.5 OTHER OUTDOOR LOCATIONS 

We assumed that CO levels at outdoor locations away from 

roads could be approximately represented by x ', the monitorderived CO concentration, with no lag time or additive factor. 

Consequently, we used Equation 6-1 to estimate CO levels in this 

microenvironment. Following the recommendations of Feagans,10 

we assumed a . = 0 and that a reasonable range for b, would be 

m, t m 

0.90 to 1.00. Feagans' best estimate for b was 0.95. 

m 

6.6 SUMMARY 

Tables 6-14 and 6-15 summarize the estimates of a . and b 

m, t m 

for CO according to microenvironment, room, CO source, and time 

of day. Equation 6-3 was used to estimate CO levels in the 

work-school and home-other microenvironments. Equation 6-1 was 

used to estimate CO levels in the other three microenvironments. 

6-26 
P.7 

vBLE 6-14. ESTIMATES OF ADDITIVE MICROENVIRONMENTAL FACTORS (am t) 

Microenvironment 

Indoors: work or 

school 

Indoors: home or 

other 

Transportation 

vehicle 

Roadside 

Other outdoor 

locations 

Pollutant 

source 

none 

smoking 

none 

smoking 

gas stove 

none 

none 

none 

Room 

all 

all 

kitchen 

living 

room 

NA 

NA 

NA 

Hours ending 

all 

all 

all 

8,9,18-23 

10-17 

7,8,12,13,18, 

19 

1-6,9-11, 

14-17,20-24 

7,8,12,13,18, 

19 

1-6,9-11, 

14-17,20-24 

all 

all 

all 

Estimated val 

low 

0 

0.2 

0 

0.1 

0.1 

1.0 

0.5 

0.7 

0.3 

0 

0 

0 

best 

0 

0.3 

0 

0.3 

0.2 

4.0 

2.0 

2.5 

1.0 

0 

0 

0 

ue (ppm) 

hiqh 

0 

0.5 

0 

0.5 . 

0.5 

11.0 

3.0 

10.0 

2.0 

0 

0 

0 

6-27 
P.8 

TABLE 6-15. ESTIMATES OF MULTIPLICATIVE MICROENVIRONMENTAL FACTOR (b )' 

m 

Microenvironment 

Indoors: work or school 

Indoors: home or other 

Transportation vehicle 

Roadside 

Other outdoor locations 

Estimated value 

low 

0.60 

0.70 

1.40 

0.70 

0.90 

best 

0.85 

0.85 

2.10 

1.20 

0.95 

high 

1.05 

1.10 

3.50 

2.30 

1.00 

6-28 
P.9 

6.7 REFERENCES 

1. H. P. Harke, "The problem of passive smoking. I. The 

influence of smoking on the CO concentration of office 

rooms," International Archives Arbeitsmedizin, Vol. 33, 

1974, pp. 199-204. 

2. S. J. Penkala and G. De Oliviera, "The simultaneous analysis 

of carbon monoxide and suspended particulate matter produced 

by cigarette smoking," Environmental Research, Vol. 9, 1975, 

pp. 99-114. 

3. D. J. Moschandreas, J. Zabransky, Jr., and D. J. Pelton, 

"Indoor air quality characteristics of the office environment," Paper no. 80-61.2, presented at the 73rd Annual Meeting 

of the Air Pollution Control Association, Montreal, Quebec, 

June 22-27, 1980. 

4. J. Yocom, et al., A Study of Indoor-Outdoor Air Pollutant 

Relationships. Volume I and II, Publication number 

APTD-0592, Research Corporation of New England, Hartford, 

Connecticut, May 1970. 

5. General Electric Company, Indoor-Outdoor Carbon Monoxide 

Pollution Study, Publication number EPA-R4-73-020, U.S. 

Environmental Protection Agency, Research Triangle Park, 

North Carolina, December 1972. 

6. R. L. Derham, G. Peterson, R. H. Sabersky, and F. H. Shair, 

"On the relation between the indoor and outdoor concentrations of nitrogen oxides," Journal of the Air Pollution 

Control Association, Vol. 24, No. 2 (February 1974), pp. 

158-161. 

7. G. Godin, G. Wright, and R. J. Shepard, "Urban exposure to 

carbon monoxide," Archives of Environmental Health, Vol. 25, 

1972, pp. 305-313. 

8. C. R. Thompson, E. G. Hensel, and G. Kats, "Outdoor-indoor 

levels of six air pollutants," Journal of the Air Pollution 

Control Association, Vol. 23, No. 10 (October 1973). pp. 

881-886. 

9. D. J. Moschandreas, J. Stark, J. E. McFadden, and S. S. 

Morse, Indoor Air Pollution in the Residential Environment, 

Vol. I. Data Collection, Analysis, and Interpretation, ' 

Publication number EPA-600/7-78-229a, U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina, 

December 1978. 

6-29 
P.10 

10. Personal communication to Ted Johnson, PEDCo Environmental, 

from Thomas B. Feagans, Strategies and Air Standards Division, U.S. Environmental Protection Agency, Research Triangle 

Park, North Carolina 27711, December 1981. 

11. W. A. Cote, W. A. Wade III, and J. E. Yocum, A Study of 

Indoor Air Quality, Publication No. EPA-650/4-74-042, U.S. 

Environmental Protection Agency, Research Triangle Park, 

North Carolina, September 1974. 

12. D. P. Bridge and M. Corn, "Contributions to the assessment 

of non-smokers to air pollution from cigarette and cigar 

smoke in occupied spaces," Environmental Research, Vol. 5, 

1972, pp. 215-220.

 : 

13. T. D. Sterling and D. M. Kobayashi, "Exposure to Pollutants 

in Enclosed Living Spaces," Environmental Research, Vol. 13, 

pp. 1-35. 

14. U.S. Census data. 

15. U.S. Housing data. 

16. J. S. Repace and A. H. Lowrey, "Indoor air pollution, 

tobacco smoke, and public health," Science, Vol. 208, 

May 2, 1980. 

17. John D. Spengler, et al., Summary of Air Pollution Measurements , Air Quality Assessment Group, Harvard School of Public 

Health, Boston, Massachusetts. 

18. T. D. Sterling and E. Sterling, "Carbon monoxide levels in 

kitchens and homes with gas cookers," Journal of the Air 

Pollution Control Association, Vol. 29, No. 3 (March 1979), 

pp. 238-241. 

19. J. D. Spengler, B. G. Ferris, Jr., and D. W. Dockery, 

"Sulfur dioxide and nitrogen dioxide levels inside and 

outside homes and implications on health effects research," 

Environmental Science and Technology, Vol. 13, No. 10 

(October 1979), pp. 1276-1280. 

20. J. D. Spengler, K. R. Stone, and F. W. Lilley, "High carbon 

monoxide levels measured in enclosed skating rinks," Journal 

of the Air Pollution Control Association, Vol. 28, No. 8 

(August 1978), pp. 776-779. 

21. L. P. Elliot and D. R. Rowe, "Air quality during public 

gatherings," Journal of the Air Pollution Control Association, 

Vol. 25, No. 6 (June 1975), pp. 635-636. 

6-30 
P.11 

22. w. Ott and N. Willits, "Modeling the dynamic response of an 

automobile for air pollution exposure studies," Environmetries 

81, Summaries of Conference Presentations, 1981, pp. 104-105. 

23. R. A. Ziskind, M. B. Rogozen, I. Rosner, and T. Carlin, 

Carbon Monoxide Intrusion in Sustained-Use Vehicles, 

Publication number SAI-068-80-535, U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina, 

November 15, 1979. 

24. D. M. Colwill and A. J. Hickman, "Exposure of drivers to 

carbon monoxide," paper no. 79-59.3, 72nd Annual Meeting 

of the Air Pollution Control Association, Cincinnati, Ohio, 

June 24-29, 1979. 

25. Lance Wallace, "Use of personal monitor to measure commuter 

exposure to carbon monoxide in vehicle passenger compartments," 

paper no. 79-59.2, 72nd Annual Meeting of the Air Pollution 

Control Association, Cincinnati, Ohio, June 24-29, 1979. 

26. A. D. Cortese, Ability of Fixed Monitoring Stations to 

Represent Personal Carbon Monoxide Exposure, thesis submitted to the Faculty of the Harvard School of Public Health, 

Boston, Massachusetts, April 1976. 

27. R. M. Brice and J. F. Roesler, "The exposure to carbon monoxide of occupants of vehicles moving in heavy traffic," 

Journal of the Air Pollution Control Association, Vol. 16, 

No. 11 (November 1966), pp. 597-600. 

28. G. A. Peterson and R. H. Sabersky, "Measurements of pollutants inside an automobile," Journal of the Air Pollution 

Control Association, Vol. 25, No. 10 (October 1975), pp. 

1028-1032. 

29. C. B. Wilson and J. W. Schweiss, Part 1. Carbon Monoxide 

Study - Boise, Idaho, November 25 - December 22, 1977, 

Publication number EPA-910/9-78-055a, U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina, 

September 1975. 

30. C. B. Wilson and J. W. Schweiss, Carbon Monoxide Study, 

Seattle, Washington, October 6 - November 2, 1977, publication 

no. EPA 910/9-78-054, U.S. Environmental Protection Agency, 

Region X, Seattle, Washington, December 1978. 

31. J. W. Jabara, T. J. Keefe, H. J. Beaulieu, and R. M. Buchon, 

"Carbon monoxide: dosimetry in occupational exposures in 

Denver, Colorado," Archives of Environmental Health, Vol. 

35, No. 4 (July/August 1980). 

6-31 
SECTION 7 

EXPOSURE ESTIMATES FOR ADULTS 

WITH CARDIOVASCULAR DISEASE IN FOUR URBAN AREAS 

The computer output of NEM provides estimates of population 

exposure for various measures of exposure and averaging times. 

In the case of CO, NEM also estimates carboxyhemoglobin (COHb) 

levels, an important indicator of the physiological effects of 

CO on the exposed population. In this section the results of NEM 

analyses of CO exposure in the four study areas under various air 

quality assumptions are summarized. Extrapolations of these results to the nation are presented in Section 8. 

The exposure estimates presented in this report are for 

adults with cardiovascular disease. Adults are defined to be 

those at least 18 years old. Adults with peripheral vascular 

disease are included in the subpopulation considered to have 

cardiovascular disease. Based on the currently available evidence, 

this subpopulation is judged to be the most sensitive group of 

persons with respect to CO-induced adverse health effects. 

Estimates for three alternative standards are presented in 

Section 7.1. A comparison of male and female estimates is made 

in Section 7.2. The impact on the exposure estimates of omitting 

indoor sources from the analyses is discussed in Section 7.3. A 

brief discussion concerning the uncertainty about the accuracy of 

the estimates is provided in Section 7.4. 

7.1 "BEST ESTIMATE" RESULTS 

Tables 7-1 through 7-27 contain selected printouts of a NEM 

analysis of exposure of adults with cardiovascular disease to 

CO in the four study areas under various air quality assumptions. 

Each table is identified as to CO/COHb indicator and air quality 

standard being simulated. CO exposure estimates are provided for 

both 1- and 8-hour average CO concentrations. In each case, the 

7-1 
P.13 

TABLE 7-1. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR 

DISEASE OF 1-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION 

VALUES ASSUMING 9 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 

1 EXCEEDED 1 

1 (PPM) I 

1 60.0 1 

1 55.0 1 

1 50.0 1 

1 45.0 1 

1 40.0 1 

1 35.0 1 

1 30.0 1 

1 25.0 I 

1 20.0 I 

t 15.0 1 

1 12.0 I 

1 9.0 1 

1 7.0 1 

1 0.0 1 

1 MAX. CONCENTRATION 1 

1 ENCOUNTERS AT MAX. I 

CHICAGO I 

1,250 1 

25.300 1 

223.000 1 

828.000 1 

3.080.000 1 

10.600.000 1 

1,070.000,000 1 

25.6 1 

523 I 

LOS ANGELES 

5,790 

145,000 

1,310,000 

4,930,000 

22,100,000 

2,670,000.000 

21.6 

5.790 

PHILADELPHIA 

-

1,280 

1,280 

3,800 

22,900 

141,000 

389,000 

1,140,000 

4,120,000 

1,020,000,000 

36.0 

1,270 

ST LOUIS 1 

709 1 

6,910 1 

13,500 i 

39,200 1 

216,000 1 

878,000 1 

2,880,000 1 

416,000,000 1 

32.0 I 

707 I 

7-2 
P.14 

TABLE 7-2. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO HAVE 

1-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 1 i 1 | 

1 EXCEEDED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) 1 I I | | 

1 60.0 1 I I I 1 

1 55.0 I I 1 I | 

1 50.0 1 1 1 I I 

1 45.0 1 1 1 1 I 

1 40.0 1 1 1 1 I 

1 35.0 1 1 I 1,270 1 1 

1 30.0 1 I I 1,270 1 707 I 

1 25.0 I 1,250 I I 3.800 1 6.200 1 

1 20.0 1 11,400 1 ' 5,790 1 21,600 1 7,270 1 

1 15.0 1 22,300 1 62,200 1 36,800 1 19,500 1 

1 12.0 I 54,900- 1 188,000 1 69,300 1 28,800 1 

1 9.0 I 109,000 I 256,000 1 85,400 1 36,600 1 

1 7.0 1 121,000 1 304,000 1 110,000 1 44,800 1 

1 0.0 I 122,000 1 305,000 1 116,000 1 47,500 1 

1 MAX. CONCENTRATION 1 25.6 1 21.6 1 36.0 1 32.0 1 

1 PEOPLE AT MAXIMUM 1 523 1 5,790 1 1,270 1 707 1 

7-3 
P.15 

TABLE 7-3. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

1-HOUR CO EXPOSURE OCCURS IN SELECTED CONCENTRATION RANGES 

ASSUMING 9 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 

1 RANGE 

1 (PPM) 

1 60.0 

1 55.0 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

100.0 

60.0 

55.0 

50.0 

45.0 

40.0 

35.0 

30.0 

25.0 

20.0 

15.0 

12.0 

9.0 

7.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

CHICAGO 

1,250 

10.100 

10.900 

32,600 

54,500 

11,700 

494 

25.6 

523 

LOS ANGELES 

5,790 

56,400 

126,000 

68,000 

47,600 

1,360 

21.6 

5,790 

PHILADELPHIA 

1,280 

2,520 

17,900 

15,100 

32,500 

16.000 

25,100 

5,910 

36.0 

1,270 

ST LOUIS 1 

709 I 

5,490 1 

1,070 1 

12,200 1 

9,240 1 

7,860 1 

8,160 1 

2,730 1 

32.0 1 

707 1 

7-4 
TABLE 7-4. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR DISEASE 

OF 8-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

P.16 

I CONCENTRATION 1 I I 1 | 

1 EXCEEDED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) 1 1 1 1 1 

1 60.0 1 1 I I | 

1 55.0 1 1 1 1 1 

1 50.0 I I 1 | I 

1 45.0 1 1 1 1 1 

1 40.0 1 1 1 1 1 

1 35.0 1 1 1 | I 

1 30.0 1 I I I 1 

1 25.0 1 I I 1 1 

1 20.0 1 1 - 1 1 I 

1 15.0 1 1 / 1 I 1 

1 12.0 1 122 1 1 24,600 1 1 

1 9.0 1 107,000 I 153,000 1 322,000 1 66,500 1 

1 7.0 1 2.070.000 1 2,170,000 1 1,030,000 1 429,000 1 

1 0.0 1 1,070,000,000 1 2,670,000,000 1 1,020,000,000 1 416,000,000 1 

1 MAX. CONCENTRATION 1 12.0 1 10.6 1 14.0 1 11.5 1 

1 ENCOUNTERS AT MAX. 1 54 1 4,460 1 29 1 11 1 

7-5 
P.17 

TABLE 7-5. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO HAVE 

8-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 1 1 1 1 

1 EXCEEDED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA I ST LOUIS 1 

1 (PPM) I I I 1 1 

1 60.0 1 II I 1 

1 55.0 1 I I I I 

1 50.0 1 1 1 1 1 

1 45.0 I I | | | 

1 40.0 1 1 1 1 1 

1 35.0 1 1 1 1 1 

1 30.0 I I I I | 

1 25.0 1 I I 1 1 

1 20.0 1 1 1 1 1 

1 15.0 1 1 1 1 1 

1 12.0 1 120 1 1 11,900 1 I 

1 9.0 1 5,260 I 5,790 1 34,300 1 13,300 1 

1 7.0 1 67,200 1 147,000 1 48,300 1 24,200 1 

1 0.0 1 122,000 I 305,000 1 116,000 1 47,500 1 

1 MAX. CONCENTRATION 1 12.0 1 10.6 1 14.0 1 11.5 1 

1 PEOPLE AT MAXIMUM 1 54 1 4,460 1 29 1 11 1 

7-6 
P.18 

TABLE 7-6. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

8-HOUR CO EXPOSURE OCCURS IN SELECTED CONCENTRATION RANGES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 1 1 1 I 

1 RANGE 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) 1 I I I I 

1 60.0 < C <= 100.0 1 I I I 1 

1 55.0 < C <= 60.0 1 1 1 | < 1 

1 50.0 < C <= 55.0 1 1 1 1 I 

1 45.0 < C <= 50.0 1 1 1 I I 

1 40.0 < C <- 45.0 I I 1 I I 

1 35.0 < C <= 40.0 1 1 1 I I 

1 30.0 < C <= 35.0 1 I I 1 1 

1 25.0 < C <= 30.0 I I I I | 

1 20.0 < C <= 25.0 1 1 - 1 I | 

1 15.0 < C <= 20.0 1 1 1 1 I 

1 12.0 < C <= 15.0 1 122 1 1 11.900 1 1 

1 9.0 < C <= 12.0 1 5.140 I 5,790 1 22,400 1 13,300 1 

1 7.0 < C <= 9.0 I 61,900 I 141,000 1 14,000 1 10,900 1 

1 0.0 < C <= 7.0 I 54,400 1 158,000 1 68,000 1 23,300 1 

1 MAX. CONCENTRATION 1 12.0 1 10.6 1 14.0 1 11.5 1 

1 PEOPLE AT MAXIMUM 1 54 I 4,460 I 29 1 11 1 

7-7 
P.19 

TABLE 7-7. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR DISEASE 

OF COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 1 1 1 1 1 

1 EXCEEOED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PERCENT) 1 1 1 1 1 

1 3.70 1 1 I I 1 

1 3.50 1 I I 1 1 

1 3.30 1 1 1 1 1 

1 3.10 1 1 1 I I 

1 3.00 1 I I I I 

1 2.90 1 1 1 1 1 

1 2.70 1 I I 1 1 

1 2.50 1 1 1 1 1 

1 2.30 1 1 1 71 1 1 

1 2.10 1 1 I 1,490 1 1 

1 2.00 1 1 1 5,150 1 21 1 

1 1.50 1 78,300 1 115,000 1 236,000 1 53,400 1 

1 1.00 1 8,600,000 1 19,800,000 1 2,540,000 1 2,000,000 1 

1 0.00 1 1,070,000,000 I 2,670,000,000 1 1,020,000,000 1 416,000,000 1 

1 MAX. COHB CONC. 1 1.92 1 1.87 1 2.31 1 2.02 1 

1 ENCOUNTERS AT MAX. I 54 1 701 1 15 1 5 1 

7-8 
P.20 

TABLE 7-8. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO EXPERIENCE 

COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 1 1 1 I I 

1 EXCEEOED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PERCENT) 1 1 1 I | 

1 3.70 I I I I 1 

1 3.50 1 1 I | | 

1 3.30 1 1 1 1 I 

1 3.10 1 1 1 1 1 

1 3.00 1 1 1 1 I 

1 2.90 I I I | | 

1 2.70 1 I I 1 I 

1 2.50 1 1 1 1 I 

1 2.30 1 1 I 35 1 1 

1 2.10 1 I I 685 1 I 

1 2.00 1 I I 2,730 1 21 1 

1 1.50 1 9,680 1 5,790 1 34,300 1 15,700 1 

1 1.00 1 99,000 I 253,000 1 86,200 1 34,000 1 

1 0.00 1 122,000 | 305,000 1 116,000 1 47,500 1 

1 MAX. COHB CONC. 1 1.92 1 1.87 1 2.31 1 2.02 1 

1 PEOPLE AT MAXIMUM 1 54 1 701 1 15 1 5 1 

7-9 
P.21 , 

TABLE 7-9. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

COHb LEVEL OCCURS IN SELECTED RANGES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 1 1 1 1 1 

1 RANGE 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PERCENT) 1 1 1 1 1 

1 3.70 < C <= 10.00 1 1 I I 1 

1 3.50 < C <= 3.70 1 | I | | 

1 3.30 < C <= 3.50 1 1 1 1 I 

1 3.10 < C <= 3.30 1 1 1 I I 

1 3.00 < C <= 3.10 1 1 1 1 1 

I 2.90 < C <= 3.00 1 1 1 1 1 

1 2.70 < C <= 2.90 1 I I I | 

1 2.50 < C <= 2.70 1 I I 1 1 

1 2.30 < C <= 2.50 1 1 1 36 1 1 

1 2.10 < C <= 2.30 1 1 I 651 1 1 

1 2.00 < C <= 2.10 I I 1 2,040 1 21 1 

1 1.50 < C <= 2.00 1 9,680 1 5,790 1 31,600 1 15,700 1 

I 1.00 < C <= 1.50 1 89,300 1 248,000 1 51,900 1 18,300 1 

1 0.00 < C <= 1.00 I 22,600 1 52,000 1 30.200 1 13,500 1 

1 MAX. COHB CONC. 1 1.92 1 1.87 1 2.31 1 2.02 1 

1 PEOPLE AT MAXIMUM 1 54 1 701 1 15 1 5 1 

7-10 
P.22 

TABLE 7-10. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR DISEASE 

OF 1-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 

1 EXCEEDED 1 

1 (PPM) I 

1 60.0 1 

1 55.0 1 

1 50.0 1 

1 45.0 I 

1 40.0 1 

1 35.0 1 

1 30.0 1 

1 25.0 1 

1 20.0 1 

1 15.0 1 

1 12.0 1 

1 9.0 I 

1 7.0 1 

1 0.0 1 

1 MAX. CONCENTRATION 1 

1 ENCOUNTERS AT MAX. 1 

2 

9 

25 

1,070 

CHICAGO 

11.200 

50,500 

223,000 

987,000 

,640,000 

,320,000 

,700,000 

,000,000 

34.6 

523 

LOS ANGELES 

17,400 

145,000 

1.310,000 

4,840,000 

13,900,000 

55,800,000 

2,670,000.000 

29.1 

5,790 

PHILADELPHIA 

1,280 

1,280 

1,280 

3,800 

33,100 

141,000 

397,000 

1,070,000 

3,700,000 

8,990,000 

1,020,000,000 

49.0 

1,270 

ST LOUIS 1 

709 1 

6,910 1 

6,910 1 

24,000 1 

36,100 1 

248,000 1 

628,000 1 

2,000,000 1 

5,430,000 1 

416,000,000 1 

44.0 1 

707 1 

7-11 

. 
P.23 

TABLE 7-11. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO HAVE 

1-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 

1 EXCEEOEO 

1 (PPM) 

1 60.0 

1 55.0 

1 50.0 

1 ^ 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

CHICAGO 

• 

11,200 

11,400 

22,300 

59,200 

108,000 

119,000 

122,000 

122,000 

34.6 

523 

LOS ANGELES 

5,790 

62,200 

188,000 

256,000 

296,000 

305,000 

305,000 

29.1 

5,790 

PHILADELPHIA 

1,270 

1,270 

1,270 

3,800 

23,000 

36,800 

69,300 

85,400 

109,000 

116,000 

116,000 

49.0 

1,270 

ST LOUIS 1 

707 1 

6,200 1 

6,200 I 

16,100 1 

19,900 1 

28,800 1 

32,400 1 

43,700 1 

47,500 1 

47,500 1 

44.0 1 

707 1 

7-12 
P.24 

TABLE 7-12. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

1-HOUR CO EXPOSURE OCCURS IN SELECTED CONCENTRATION RANGES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 1 1 1 | 

1 RANGE 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) 1 1 1 1 1 

1 60.0 < C <= 100.0 I I I I | 

1 55.0 < C <= 60.0 1 I I | I 

1 50.0 < C <= 55.0 1 1 I | ' 1 

1 45.0 < C <= 50.0 1 1 1 1,280 1 1 

1 40.0 < C <= 45.0 1 1 1 1 709 1 

1 35.0 < C <= 40.0 1 1 1 j 5,490 1 

1 30.0 < C <= 35.0 1 11,200 1 1 2,520 1 1 

1 25.0 < C <= 30.0 1 182 1 5,790 1 19,200 1 9,900 1 

1 20.0 < C <= 25.0 1 10,900 1 - 56,400 1 13,800 1 3,840 1 

1 15.0 < C <= 20.0 I 36,900 1 126,000 1 32,500 1 8,830 1 

1 12.0 < C <= 15.0 1 48,700 1 68,000 1 16,000 1 3,590 1 

1 9.0 < C <= 12.0 1 11,000 I 39,500 1 23,200 1 11,300 1 

1 7.0 < C <= 9.0 1 2,670 1 8,790 1 7,850 1 3,770 1 

1 0.0 < C <= 7.0 I 68 1 714 1 1 38 1 

1 MAX. CONCENTRATION 1 34.6 1 29.1 1 49.0 1 44.0 1 

1 PEOPLE AT MAXIMUM 1 523 1 5,790 1 1,270 1 707 1 

7-13 
P.25 
P.26 

TABLE 7-14. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO HAVE 

8-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 1 1 1 1 

1 EXCEEDED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) 1 1 1 I | 

1 60.0 1 1 I I 1 

1 55.0 1 1 | | | 

1 50.0 I I 1 I | 

1 45.0 I I I | | 

1 40.0 1 1 1 1 1 

1 35.0 1 1 1 1 I 

1 30.0 1 I I | | 

1 25.0 1 1 1 1 1 

1 20.0 I I I | | 

1 15.0 1 1,900 I I 17,300 I 27 1 

1 12.0 I 5,260 I 5,790 1 33,500 1 8,800 1 

1 9.0 1 67,300 I 95,600 1 44,300 1 23,700 1 

1 7.0 1 93,300 1 252,000 1 104,000 1 29,000 1 

1 0.0 I 122,000 I 305,000 1 116,000 1 47,500 1 

1 MAX. CONCENTRATION 1 16.0 1 13.6 1 18.5 1 15.0 1 

1 PEOPLE AT MAXIMUM 1 54 1 4,460 1 29 1 11 | 

7-15 
E^2_. 

TABLE 7-15. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

8-HOUR CO EXPOSURE OCCURS IN SELECTED CONCENTRATION RANGES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 1 1 1 1 

1 RANGE 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) 1 1 1 1 1 

1 60.0 < C <= 100.0 1 1 I I 1 

1 55.0 < C <= 60.0 1 I 1 I I 

1 50.0 < C <= 55.0 1 I I I 1 

1 45.0 < C <= 50.0 1 I I I I 

1 40.0 < C <= 45.0 1 1 1 1 1 

1 35.0 < C <= 40.0 1 1 1 I 1 

1 30.0 < C <- 35.0 1 I I I 1 

1 25.0 < C <= 30.0 1 I I 1 1 

1 20.0 < C <= 25.0 1 1 1 1 1 

1 15.0 < C <= 20.0 1 1,910 1 1 17,300 1 29 1 

1 12.0 < C <= 15.0 1 3,360 1 5,790 1 16,200 1 8,770 1 

1 9.0 < C <= 12.0 1 62,000 1 89,800 1 10,800 1 14,900 1 

1 7.0 < C <= 9.0 1 26.100 1 156,000 1 59,600 1 5,310 1 

1 0.0 < C <= 7.0 I 28,300 i 53.500 1 12.400 1 18,500 1 

1 MAX. CONCENTRATION 1 16.0 1 13.6 1 18.5 1 15.0 1 

1 PEOPLE AT MAXIMUM 1 54 I 4,460 1 29 1 11 1 

7-16 
P.28 

TABLE 7-16. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR DISEASE 

OF COHb. LEVELS EXCEEDING SELECTED VALUES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 

1 EXCEEDED 

1 (PERCENT) 

1 3.70 

1 3.50 

1 3.30 

1 3.10 

1 3.00 

1 2.90 

1 2.70 

1 2.50 

1 2.30 

1 2.10 

1 2.00 

1 1.50 

1 1.00 

1 0.00 

1 MAX. COHB CONC. 

1 ENCOUNTERS AT MAX. 

j 

CHICAGO 1 

134 1 

2.460 1 

16.600 1 

39.700 1 

1.240.000 1 

32.700.000 1 

1,070.000.000 1 

2.52 1 

24 1 

LOS ANGELES 

5,190 

18,200 

35,900 

927,000 

75,600,000 

2,670,000,000 

2.38 

1,560 

PHILADELPHIA 

71 

329 

1,840 

8,850 

42,300 

114,000 

163,000 

861,000 

7,140,000 

1,020,000,000 

3.03 

15 

ST LOUIS 1 

187 I 

3,980 1 

16,400 I 

31,500 I 

243.000 I 

5.490,000 I 

416,000,000 1 

2.59 I 

5 1 

7-17 
P.29 

TABLE 7-17. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO EXPERIENCE 

COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 

1 EXCEEOEO 

1 (PERCENT) 

1 3.70 

1 3.50 

1 3.30 

1 3.10 

1 3.00 

1 2.90 

1 2.70 

1 2.50 

1 2.30 

1 2.10 

1 2.00 

1 1.50 

1 1.00 

1 0.00 

1 MAX. COHB CONC. 

1 PEOPLE AT MAXIMUM 

CHICAGO 

78 

1,250 

3,770 

8,910 

60,000 

120,000 

122,000 

2.52 

24 

LOS ANGELES 

2,470 

5,090 

5,790 

65,400 

300,000 

305,000 

2.38 

1,560 

PHILADELPHIA 

35 

156 

776 

4,670 

17,100 

29,400 

32,100 

36,800 

114,000 

116,000 

3.03 

15 

ST LOUIS 1 

186 1 

2.780 1 

7,500 1 

12,600 1 

24,300 1 

43,100 1 

47,500 1 

2.59 1 

5 1 

7-18 
P.30 

TABLE 7-18. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

COHb LEVEL OCCURS IN SELECTED RANGES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

1 3.70 

1 3.50 

1 3.30 

1 3.10 

1 3.00 

1 2.90 

1 2.70 

1 2.50 

1 2.30 

1 2.10 

1 2.00 

1 1.50 

1 1.00 

1 0.00 

COHB LEVEL 

RANGE 

(PERCENT) 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

< 

1 MAX. 

1 PEOPLE 

C <= 

C <= 

C <= 

c <= 

c <= 

c <= 

c <-

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

10.00 

3.70 

3.50 

3.30 

3.10 

3.00 

2.90 

2.70 

2.50 

2.30 

2.10 

2.00 

l.SO 

1.00 

COHB CONC. 

AT MAXIMUM 

CHICAGO 

79 

1,180 

2.510 

5.150 

51.100 

59,600 

1,930 

2.52 

24 

LOS ANGELES 

2,470 

2,620 

700 

59,600 

234,000 

5,660 

2.38 

1,560 

PHILADELPHIA 

36 

-~ 121 

621 

3,890 

12,400 

12,300 

2,730 

4,680 

77,500 

2,050 

3.03 

15 

ST LOUIS 

187 

2,590 

4,720 

5,130 

11,700 

18,800 

4,460 

2.59 

5 

7-19 
P.31 

TABLE 7-19. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR DISEASE 

OF 1-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 

1 EXCEEDED 1 

1 (PPM) 1 

1 60.0 1 

1 55.0 1 

1 50.0 1 

1 45.0 1 

1 40.0 1 

1 35.0 1 

1 30.0 1 

1 25.0 1 

1 20.0 1 

1 15.0 I 

1 12.0 1 

1 9.0 I 

1 7.0 I 

1 0.0 I 

1 MAX. CONCENTRATION 1 

1 ENCOUNTERS AT MAX. I 

CHICAGO 

11,200 

21,900 

63,300 

212,000 

667,000 

2,520,000 

6,190,000 

19,800,000 

49,800,000 

1,070,000,000 

43.6 

523 

LOS ANGELES 

5,790 

23,200 

145,000 

849,000 

3,440,000 

8,030,000 

31,500,000 

109,000,000 

2,670,000,000 

37.0 

5,790 

PHILADELPHIA 

1,280 

1,280 

1,280 

1,280 

3,800 

22,900 

55,000 

141,000 

277,000 

1,050,000 

2,090,000 

5,750,000 

13,300,000 

1,020,000,000 

61.5 

1,270 

ST LOUIS 1 

709 1 

709 1 

1,420 1 

6,910 1 

13,500 I 

24,100 1 

32,800 1 

181,000 1 

513,000 I 

1,130,000 I 

3,270,000 1 

9,220,000 1 

416,000,000 1 

56.1 1 

707 1 

7-20 
P.32 

TABLE 7-20. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO HAVE 

1-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 

1 EXCEEDED 

1 (PPM) 

1 60.0 

1 55.0 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

CHICAGO 

11,200 

11,200 

19,200 

22,300 

53,200 

108,000 

118,000 

122,000 

122,000 

122,000 

43.6 

523 

LOS ANGELES 

5,790 

5,790 

62,200 

167,000 

256,000 

287,000 

305,000 

305,000 

305,000 

37.0 

5,790 

PHILADELPHIA 

1,270 

1,270 

1,270 

1,270 

3,800 

21,600 

26,500 

36,800 

50,400 

85,400 

93,600 

116,000 

116,000 

116,000 

61.5 

1,270 

ST LOUIS 1 

707 I 

707 1 

707 I 

6,200 1 

7,270 1 

16,300 I 

18,600 1 

26,300 1 

32,300 1 

40,000 1 

46,600 1 

47,500 1 

47,500 1 

56.1 1 

707 I 

7-21 
P.33 

TABLE 7-21. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

1-HOUR CO EXPOSURE OCCURS IN SELECTED CONCENTRATION RANGES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 

1 RANGE 

1 . (PPM) 

1 60.0 

1 55.0 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

< C <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

100.0 

60.0 

55.0 

50.0 

45.0 

40.0 

35.0 

30.0 

25.0 

20.0 

15.0 

12.0 

9.0 

7.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

CHICAGO 

11,200 

8,020 

3,050 

30,900 

54,600 

10,400 

3,350 

68 

43.6 

523 

LOS ANGELES 

5,790 

56,400 

105,000 

89,100 

30,500 

17,700 

714 

37.0 

5,790 

PHILADELPHIA 

1,280 

2,520 

17,900 

4,880 

10,300 

13,600 

35,000 

8,270 

22,700 

61.5 

1,270 

ST LOUIS I 

709 I 

5,490 1 

1,070 1 

8,990 1 

2,380 I 

7,670 I 

6,010 1 

7,640 1 

6,660 1 

880 1 

56.1 1 

707 | 

7-22 
P.34 

TABLE 7-22. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR DISEASE 

OF 8-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 

1 EXCEEDED 

I (PPM) 

1 60.0 

1 55.0 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

1 MAX. CONCENTRATION 

1 ENCOUNTERS AT MAX. 

CHICAGO 1 

122 1 

53,500 I 

939,000 1 

7.390,000 I 

29,100,000 1 

1,070,000,000 1 

20.0 1 

120 1 

LOS ANGELES 

55,900 

647,000 

8,150,000 

43,200,000 

2,670,000.000 

17.0 

4,460 

PHILADELPHIA 1 

12,600 I 

243,000 1 

704,000 1 

2,000,000 1 

5,940,000 1 

1,020,000,000 1 

23.0 1 

29 1 

ST LOUIS 1 

38,900 I 

186,000 I 

1,020,000 1 

3,670,000 1 

416,000,000 1 

18.5 1 

11 1 

7-23 
P.35 

TABLE 7-23. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO HAVE 

8-HOUR CO EXPOSURES ABOVE SELECTED CONCENTRATION VALUES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 I I 1 | 

1 EXCEEDED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) 1 1, 1 1 I 

1 ' II I I | 

1 60.0 1 1 1 I I 

1 55.0 1 1 1 | I 

1 50.0 1 1 1 1 1 

1 45.0 1 1 1 1 1 

1 40.0 1 1 1 | | 

1 35.0 1 1 1 1 I 

1 30.0 1 I I | | 

1 25.0 1 1 1 1 1 

1 20.0 1 120 1 1 5,870 1 1 

1 15.0 1 5,260 1 5.790 1 33,500 1 8,300 1 

1 12.0 I 54,500 1 12.200 1 36,600 1 23,100 1 

1 9.0 I 84,200 1 234,000 1 81,800 1 28,800 1 

1 7.0 I 118,000 1 298,000 1 116,000 1 36,100 1 

1 0.0 1 122,000 1 305,000 1 116,000 1 47,500 1 

1 MAX. CONCENTRATION 1 20.0 1 17.0 1 23.0 1 18.5 1 

1 PEOPLE AT MAXIMUM 1 120 1 4,460 1 29 1 11 1 

7-24 
P.36 

TABLE 7-24. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

8-HOUR CO EXPOSURE OCCURS IN SELECTED CONCENTRATION RANGES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 CONCENTRATION 1 1 1 I | 

1 RANGE 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) 1 I 1 j j 

1 60.0 < C <= 100.0 1 1 1 I I 

1 55.0 < C <= 60.0 I I I | | 

1 50.0 < C <= 55.0 1 1 1 I | 

1 45.0 < C <= 50.0 1 1 1 1 I 

1 40.0 < C <= 45.0 1 1 1 1 1 

1 35.0 < C <= 40.0 1 1 I | | 

1 30.0 < C <= 35.0 1 I I I | 

1 25.0 < C <= 30.0 I I 1 1 I 

1 20.0 < C <= 25.0 1 122 1 1 5.870 1 1 

1 15.0 < C <= 20.0 1 5,140 1 5,790 1 27,600 1 8,300 1 

1 12.0 < C <= 15.0 1 49,300 1 6,440 1 3,050 1 14,800 1 

1 9.0 < C <= 12.0 1 29,700 1 222,000 1 45,300 1 5,730 1 

1 7.0 < C <= 9.0 1 33,500 1 64,400 1 34,500 1 7,260 1 

1 0.0 < C <= 7.0 1 3.890 1 7,130 1 1 11,500 1 

1 MAX. CONCENTRATION 1 20.0 1 17.0 1 23.0 1 18.5 1 

1 PEOPLE AT MAXIMUM 1 120 1 4,460 1 29 1 11 1 

7-25 
_P.37 

TABLE 7-25. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR DISEASE 

OF COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 1 

1 EXCEEDED 1 

1 (PERCENT) 1 

1 3.70 I 

1 3.50 1 

1 3.30 1 

1 3.10 1 

1 3.00 1 

1 2.90 1 

1 2.70 1 

1 2.50 I 

1 2.30 I 

1 2.10 1 

1 2.00 1 

1 1.50 1 

1 1.00 1 

1 0.00 1 

1 MAX. COHB CONC. 1 

1 ENCOUNTERS AT MAX. 1 

5 

67 

1,070 

CHICAGO I 

67 1 

240 1 

1,130 1 

6,330 1 

21,300 1 

71,200 I 

180,000 1 

361.000 1 

,120,000 1 

,400,000 1 

,000.000 I 

3.10 1 

54 f 

LOS ANGELES 1 

1.560 1 

8,650 1 

21.400 1 

61,000 1 

193,000 1 

319,000 f 

6,320,000 1 

166,000,000 1 

2,670,000,000 1 

2.89 1 

701 1 

PHILADELPHIA 1 

1 

15 

1,020 

79 1 

833 1 

1,910 1 

6,040 1 

11,900 1 

24,300 1 

65,400 I 

131,000 1 

216,000 1 

371,000 I 

493,000 I 

,850,000 I 

,100,000 1 

,000,000 1 

3.75 1 

15 1 

12 

416 

ST LOUIS 1 

120 I 

332 1 

1,640 1 

7,900 I 

20,400 I 

46,300 1 

84,800 1 

108,000 1 

789,000 1 

,500,000 1 

,000,000 I 

3.16 1 

5 1 

7-26 
P.38 

TABLE 7-26. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO EXPERIENCE 

COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 

1 EXCEEDED 

1 (PERCENT) 

1 3.70 

1 3.50 

1 3.30 

1 3.10 

1 3.00 

1 2.90 

1 2.70 

1 2.50 

1 2.30 

1 2.10 

1 2.00 

1 1.50 

1 1.00 

1 0.00 

1 MAX. COHB CONC. 

1 PEOPLE AT MAXIMUM 

CHICAGO 

66 

81 

800 

2,390 

4,200 

9,170 

20,100 

32,500 

85,300 

122,000 

122,000 

3.10 

54 

LOS ANGELES 

1,560 

2,600 

5,630 

5,790 

8,900 

16,300 

230,000 

305,000 

305,000 

2.89 

701 

PHILADELPHIA 

43 

398 

822 

2,810 

6,380 

11,300 

22,800 

30,700 

34,300 

35,300 

35,400 

58,400 

116.000 

116.000 

3.75 

15 

ST LOUIS 1 

119 | 

331 | 

1.240 1 

4,160 1 

9,510 I 

15,700 I 

21,600 I 

23,000 I 

28,700 I 

43,900 1 

47,500 1 

3.16 1 

5 1 

7-27 
P.39 

TABLE 7-27. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

COHb LEVEL OCCURS IN SELECTED RANGES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 1 1 1 1 1 

1 RANGE 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PERCENT) 1 I I I I 

1 3.70 < C <= 10.00 1 1 I 44 1 I 

1 3.50 < C <s 3.70 1 I I 356 1 1 

1 3.30 < C <= 3.50 1 I I 423 1 ' 1 

1 3.10 < C <= 3.30 1 67 1 I 1,980 1 120 1 

1 3.00 < C <= 3.10 1 15 1 I 3,570 1 212 1 

1 2.90 < C <= 3.00 1 720 I 1,560 1 4,880 1 909 1 

1 2.70 < C <= 2.90 1 1,590 1 1,040 1 11,500 1 2,920 1 

1 2.50 < C <= 2.70 1 1,810 1 3,030 1 7,870 1 5,350 1 

1 2.30 < C <= 2.50 I 4,970 1 161 1 3,640 1 6,160 1 

1 2.10 < C <= 2.30 1 10,900 1 3,110 1 981 1 5,930 1 

1 2.00 < C <= 2.10 1 12,400 1 7,360 1 132 1 1,400 1 

1 1.50 < C <= 2.00 1 52,800 1 214,000 1 23,000 1 5,730 1 

1 1.00 < C <= 1.50 1 36,200 1 74,700 1 57,900 1 15,200 1 

1 0.00 < C <= 1.00 1 70 1 639 1 1 3,590 1 

1 MAX. COHB CONC. 1 3.10 1 2.89 1 3.75 1 3.16 1 

1 PEOPLE AT MAXIMUM 1 54 1 701 1 15 1 5 1 

7-28 

J 
P.40 

"best-estimate" microenvironment factors developed in Section 6 

were used to simulate the contribution of gas stoves and smoking 

to total CO exposure. 

7.1.1 Attainment of 9 ppm/1 ExEx Standard 

NEM estimates in Tables 7-1 through 7-9 were developed by 

adjusting the air quality data for each study area using the 

roll-back formula described in Section 5.1 so that the most polluted neighborhood type just meets a "9 ppm/1 ExEx" standard, 

i.e., one specifying that the expected number of 8-hour CO values 

exceeding 9 ppm shall not be greater than one per year. Table 

7-1 provides estimates of the number of occurrences for adults 

with cardiovascular disease of 1-hour exposures to CO concentrations exceeding selected values. (Exposures exactly equal to 

zero are counted as exceeding zero.) Thus, each column in Table 

7-1 presents a cumulative frequency distribution in which the 

number of 1-hour exposures increases as CO concentration decreases; 

the distribution reaches a maximum at a CO concentration of zero. 

This maximum is the number of adults with cardiovascular disease 

used in the simulation times the number of possible occurrences 

in a year (8760). Although NEM yields individual frequency distributions for cohorts who are at low, medium, and high activity levels when a given CO concentration is encountered, only the total 

frequency distribution for all activity levels is presented in 

Table 7-1. According to these estimates, none of the four study 

areas would have more than 6,910 occurrences of 1-hour CO exposures above 25 ppm if a 9 ppm/1 ExEx standard were just attained. 

Table 7-2 uses an alternative exposure indicator, adults with 

cardiovascular disease with 1-hour exposures. This is the number 

of adults with cardiovascular disease in the study area that 

experience one or more 1-hour exposures per year to CO concentrations that exceed a specified value. This exposure indicator is 

also expressed as a cumulative frequency distribution. The number 

of adults with cardiovascular disease exposed at zero concentration (or above) is the total population of the study area. 

7-29 
P

1 

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fl) Oi p3 f t 

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tr 

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fl) 

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fl) 

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d i i co 

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fl) 

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i U l TJ 

H 

O 

< P- Oi (D CQ 

rt) CA rt p3 

P rt 

fl) CQ 

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tr 

rt) 

3 

c 

g 3* fl) 

n 

o Hi 

p O J 

P P

1 

r t CQ 

€ p- rt t r 
P.42 

TABLE 7-28. ALGORITHM USED TO CALCULATE CARBOXYHEMOGLOBIN 

IN BLOOD OF COHORTS 

1. Given the altitude, calculate average barometric pressure (Pg ): 

PB = 760*exp(-0.0000386*Alt) 

2. Calculate capillary oxygen pressure (Pc 02 ): 

pc 02 = 0.209*(PB-47) - 46.9 

3. Calculate quantity B: 

B = (1/DL) + (PB -47)/VA 

4. Let (%02Hb) = 100 

5. Calculate quantity A: 

A = pc02/(M*(%02Hb)) 

6. Calculate quantity F: 

F = exp(-t*A*60*104/(1.38*Hb*Vb*B)) 

7. Calculate trial (%C0Hb) value: 

(%C0Hb) = (%C0Hb)Q*F + (B*VCQ + (Pg-47)*10"6*(C0))*(1-F)/A 

8. Calculate (%02Hb) value for next iteration: 

New (%02Hb) = I00*(«02Hb)/((%02Hb) + (%C0Hb)) 

9. Starting with the new value of (%02Hb) repeat Steps 5 through 8. Compare the (%C0Hb) calculated with that from the previous iteration. 

Repeat cycle until two successive COHb values agree within the desired 

accuracy. 

7-31 
P.43 

TABLE 7-29. VALUES ASSIGNED TO VARIABLES IN ALGORITHM 

USED TO ESTIMATE CARBOXYHEMOGLOBIN 

Variable 

Haldane constant 

Hemoglobin concentration 

Endogenous CO 

production rate 

Altitude 

CO diffusion rate 

Blood volume 

Ventilation rate 

Category 

All 

Females 

Males 

Females 

Males 

All 

Females 

Males 

Females 

Males 

Low exercise 

Medium exercise 

High exercise 

Value 

218.0 

13.8 grams/100 ml 

15.7 grams/100 ml 

0.0062 ml/min 

0.0081 ml/min 

0 

31 ml/min/torr 

34 ml/min/torr 

4,800 ml 

5,800 ml 

8,000 ml/min 

20,000 ml/min 

35,000 ml/min 

7-32 
P.44 

cardiovascular disease, values were not developed for the two age/ 

occupation groups consisting of children. 

The value used for the endogenous CO production rate (VrfJ 

is 0.0081 ml/min for adult males and 0.0062 ml/min for adult 

females. These values are simple weighted (by the number of subjects) averages of the results of six studies for males'*"9 and 

four studies for females5-8 reported in the literature. 

The value used for the CO diffusion rate in the lung is 34 

ml (min«mm Hg) for adult males and 31 ml (min'mm Kg) for adult 

females. These values are taken from Joumard, et al.10 

The values used for blood volume are 5,800 ml for adult males 

and 4,800 ml for adult females. Each of these values was calculated by multiplying two other values: the 74 ml/kg body weight 

for average males and 73 ml/kg body weight for average females 

reported by Sjostrand11 multiplied by 78 kg average weight for 

adult males and 65 kg average weight for females, respectively. 

The latter two values are based on data provided by a publication 

of the U.S. National Center for Health Statistics.12 

Ventilation rates used for both adult males and adult females 

are 8 liters/min for a low exercise level, 20 liters/min for a 

medium exercise level, and 35 liters/min for a high exercise level. 

The basis for these values is a study by Niinimaa, et al.13 The 

low exercise level value represents sleeping and sitting, the 

medium exercise level value represents walking and other light 

forms of exercise, and the high exercise level value represents 

forms of exercise more strenuous than walking. Obviously these 

three categories represent a partitioning of a continuum of exercise levels (see Table A-l of the Office of Air Quality Planning 

and Standards Staff Paper on Sulfur Oxides11*). 

In essence, the algorithm presented in Table 7-28 estimates 

the COHb levels of an individual at the end of every hour of the 

year. Although COHb levels are, strictly speaking, the result 

of CO exposure, they can be described using concepts similar to 

those used for CO exposure. For example, Table 7-7 lists the 

7-33 
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p f t p0 (D o. O i 

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03 

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1 

rr 3* 

^ 

M O i 

d 

o 

p f t po 3 <» 

P 

3 

d 33 

fl) P» H i 

P 

H 

fl) 0-

P f t 

P 

• 

»-• U l 

H 

3 rt tr p- CQ 

p TJ TJ P

1 

P-

o 

p rt pO 

3 

0 H i 

p O i 

P 

M rt 

3 P 

M 

fl) CQ Ln 

• 00 

<*P 

. l ^ 

tr fl> (0 

fl> 

< P pd n> CO 

P 

H 

fl) tr P (A 

(t> O i 

o 3 

a 

• U) 

• 

a fl> TJ 

P 

n 

s 

fl) 

3 rt 

o H i 

o H i 

P CL 

d 

M f t H i 

fl) 

3 P P* 

fl) CO ps - rt tr 

o P 

H o. po 

< P CO 

o d p» 

P 

n 0- p- CQ 

fl) 

P CQ 

fl) 

* P (0 *» 

• to 

dP 

P 

3 O i H i 

o H 

P 

H 

fl) H i 

0 

H 

O 

P 11 O i P0 

< P CO 

o d 

*-> P 

n 

P O i 

d 

H rt CQ 

• 

H tr fl> 

< 

p 

M 

d 

rt) CQ 

d CO n> OJ H i 

O 

H f t tr fl) TJ 

fl> 

n 

o fl) tag

3 e 

> (A TJ 

H 

fl) 

< P

-

O 

d CO p« *< 

3 

0 rt 

fl) 0. 

> rt tr fl) 

fl) (0 rt p3 P rt 

fl) (A TJ 

H 

fl) CQ 

fl) 

3 rt 

rt> OJ p3 rt tr fl) rt 

P tr t-> (D CQ 

H i 

0 11 T) tr pH 

p O i 

fl) P

1 

TJ tr pP 

^ 

P 

3 O i ro 

• 

o 

M TJ 

fl) 

H 

O 

fl) 

3 rt H i 

O 

n CO rt 

« 

f 

0 

d p

-

CQ 

• 

TJ CD 11 

O 

fl) 

3 rt H i 

o i i 

n tr po P vQ 

0 

^ P

-

• 00 

- ~ j T) 

rt) H 

O 

fl) 

3 rt H i 

0 i i 

tr> 

0 CQ 

S vQ 

fl) P» 

fl) CQ 

^ to 

t co P« TJ 

fl) 

H 

O 

fl) 

3 rr 

O 

O 

w tr Hfl) <! fl) P« CQ 

fl) 

X 

O 

fl) 

fl) O i p3 U3 ro 

• 

o 

o TJ 

fl) 

n 

o co 3 rt 

• 

£ 

P 

X P

-

3 

g 

n 

o 

s tr 

M co 

< A) 

M CQ 

P 

n fl> p i 

• VO ro 

rt 3* 

fl) TJ 

tr pM 

P O i 

fl) P» TJ 3* PP 

P OJ 

d 

r-> 

Ct CQ 

« P

-

f t 3* 

O 

P 

h O i PO 

< P CQ 

O 

d p« 

P 

n O i p- CO 

ro P CO 

fl) 

fl) T1 

fl) 

n pfl) 

3 

o fl) 

d 

3 O i 

fl) 

H rt 3* 

fl) vo TJ TJ 

3 \ P* 

W 

N td 

X (A rt 

P 

3 O i 

P i i 0. 

* 

> TJ TJ 

n 0 

X p

-

3 P rt 

fl) p* *< 

M 

• •O TJ 

fl) 

M 

O 

fl) 

3 rt 

o H i 

O 

P 

n O i p0 

< P CO 

o d P> 

P 

n o< p- (A 

fl) 

P CQ 

fl) 

K p- rt 3" 

O 

O 

W tr 

*-> (D 

< rt) P* CO 

fl) 

X 

o fl) 

fl) O i p3 uQ CO 

• 

o TJ 

fl) 

n 

o n> 3 rt 

H 

P tr P« rp » j 

i co 

o CQ tr 

o S CQ rt tr P rt 

3 

O 

3 

fl) 

O H i rt tr fl> CO rt 

d O i »< 

P i i 

fl> 

P CO 

tr P 

< fl> 

P O i 

d 

M rt CA 

*, pel- s' 

P O i 

d 

M rt (A 

t. p- f t 3* 

o 

p 

H O i Po 

< 

p CA 

O 

d 

T-> 

P 

H O i P- CO 

fl) 

P CO 

fl) p3 rt tr fl) CO rt 

d O i ^< 

p 

n CD 

P TJ 

o TJ 

d P

1 

p rt PO 

3 

• 

0- P- H i H i 

rt) 

n fl) 

3 rt 

O 

O 33 tr P

1 

(D 

< fl> P

1 

(A rt 

O rt tr fl) 

o 

o 

n 

n fl) (A TJ 

o 3 O i p3 vQ TJ 

fl) 11 

O 

fl) 

3 rt 

P vQ 

fl) 

O H i rt 

O rt 

P p* 

rt tr 

rt) 

fl) CO rt p3 P rt 

fl) CA 

O H i 

P O i 

d 

T~> 

ct CO 

£ p

-

rt 3* 

o 

p 

n O i po 

< P (A 

o d p« 

P 

n O i p- CO 

fl) 

P CO 

fl) 

fl) 

X TJ 

fl) 

H Prt) 

3 

O P3 

CQ rt 

d O i K; 

P 

n (D 

P (A 

o P 

3 tr 

rt> 

o 

o 

3 TJ 

P 

H 

fl) O i tr *< 

3 

O 

H 

g 

P P

-

p-

N 

P f t P0 

3 

^ P

-

• 

rt) 

• 

^ tr • < 

o 

o 3 

< fl) 

H 

fl in- g 

H tr (D 

n 

rt> PP rt p-

< ro H i 

n ID uQ 

p 

fl> 

3 

o pfl) CQ 

o H i tr p- vQ tr 

n 

o ta tr P« 

ro 

< to p» CO 

p 

3 O 

3 

uO. f t 3* (D H i 

O 

d 

n 

P- ct 

3 P-

rt < 

fl) fl) 

i i < Oi P P- P1

 CO 

co ri-

l l p

-

tr d rt po 3 CO 

^ 

C tr pM 

fl> n3 

P tr \-> (D - J 

1 vo P« P- CA 

il- ea 

H 

rt) CQ 

d p« rt co p3 O i P- CO 

O 

H 

fl) rt 

fl) 

< 

P H

1 

d 

fl) (A 

• 

> CA 

€ 

O 

P P> O J tr fl> n> X TJ 

fl) 

O f t 

rt> O i 

^ 

r-3 

P tr p> 

fl) CQ ~J 

1 

- J 

p 

3 O J 

--J 

1 

00 TJ 

H 

fl) CQ 

fl) 

3 f t 

O 

g 

g d P

1 

P 

I 

fl) 

X TJ 

fl) 11 Pfl) 

3 

O 

fl) rt 3-

fl) Pn 

tr p- vQ tr 

fl) CO rt 

n O 

a tr 

M 

fl) 

< fl) 

M 

^ P- rt 

tr p3 CQ CD P" 

fl) 

o f t 

fl) O i 

n 

p 

3 

m (D CQ 

o H i COHb 

• ^ j 

i VO 

r-> P- CQ 

ci-

co rt 

tr fl) 

3 

g 

g 

J5* fl> 

n 

o H i 

P 

a d p* 

fl-

ea 

£ p

-

rt S' 

O 

P 

n o. po 

< 

p CO 

o d p* 

P i i O i p- CO 

fl) 

p CQ 

fl) 

K 

tr 

O 

ct 

tr P rt 

rt> 

X TJ 

fl) 

H P

-

fl) 

3 

O (D 

O 

O 

W tr p< (D 

< 

rt> P« CO 

t. 

tr pa 

tr co X 

o fl) 

fl) O i CQ 

rt) M 

rt) 

O f t 

rt> O i 

< 

p 

M 

d 

fl) ca 

• •^ P tr P

1 

fl> 

^3 

P tr M 

rt) ^J 

1 00 P* P- (A f t (A f t tr fl) 

3 

g 

g 5* fl) 

H 

o H i 

P O i 

d P

1 

f l-

ea 

S P

-

rt 

tr 

o P 

n O J po <; P CO 

o d 

M 

p H. O i P- (A (D 

P (A 

fl) 

3 

c 

g o* rt) H 

o H i 

o O 

O 

d 

H 

H 

fl) 

3 

O 

fl) CO 

o H i 

O 

o 

a tr Pfl> 

< fl> P- CO rt tr P rt 

fl) 

X 

o fl) 

fl) o. CQ 

fl) P" 

rt) 

o rt 

fl) O i 

< P P

1 

d 

fl) eo 

• 
P.46 

TABLE 7-30. PERCENTAGE OF ADULTS WITH CARDIOVASCULAR DISEASE EXPERIENCING 

COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

COHb level 

exceeded 

(percent) 

3.00 

2.90 

2.80 

2.70 

2.60 

2.50 

2.40 

2.30 

2.10 

2.00 

1.50 

1.00 

0.00 

Max. COHb 

cone. 

Percent 

at maximum 

Chicago 

7.93 

81.15 

100.00 

1.92 

0.04 

Los Angeles 

1.90 

82.95 

100.00 

1.87 

0.23 

Philadelphia 

0.03 

0.59 

2.35 

29.57 

74.31 

100.00 

2.31 

0.01 

St. Louis 

0.04 

33.05 

71.58 

100.00 

2.02 

0.01 

7-35 
I CO 

as 

Hi 

o 

n rt t r n> VO TJ TJ 

3 \ P« 

w X 

w X 

o 

p CO 

fl) 

• 

co 

• 

•~J cn TJ 

fl) 

H 

O 

fl) 

3 f t 

^ 

O 

0 

3 TJ 

P 

H 

fl) Oi 

« P- f t 3" 

P 

3 P 

X P

-

3 

g 

O Hi ro 

• co P* TJ 

rt) 

H 

O 

fl) 

3 f t 

fl) (A rt p3 P rt 

fl) 0 . 

fl) 

X 

o fl) 

fl) Oi ro 

• 00 VO TJ 

fl) 

n 

o (D 

3 e+ 

• TJ 3* P- P

1 

P Oi 

fl) P

1 

TJ t r PP 3* 

P CQ 

P 

3 P 

X p

-

3 

g 

O 

O 33 cr P * n> 

< n> P * 

o Hi 

CQ rt 

P 

3 OJ 

P 

h 

a CQ 

• 

> p- p* Hi 

o d 

n CO rt 

d Oi 

>< 

P 

H 

rt) 

P (A t r 

P 

< rt) 

o 

o 33 cr P

1 n> 

< 

ro P * (A 

K tr po 3" n> nQ 

d 

p p" 

o 11 

TJ TJ 

3 \ P* 

w X 

w X CA f t 

p 

3 OJ 

P i i OJ rt 3* 

P 

3 

d 

3 OJ 

fl) 

n rt t r 

rt) P

1 

ro TJ *2 3 \ p" 

w X 

w X 

p 

3 Oi vo TJ TJ 

3 \ p* rd 

X 

w 

X 

p-

3 •3 

P tr p* 

rt> 

-o. 

1 CO ro 

• 

> (A CD 

X TJ 

rt) 

O f t CD OJ 

^ 

n 

o 33 t r P

1 

rt> 

< n> p- CO 

p 

n (D t r p- vQ 3* 

rt) 

h 

d 

3 Oi 

rt) 

n rt 3* 

rt) 

T-> cn 

P . 

3 

P P

1 

*< N 

fl) Oi 

• 

z 0 

3 

p 

H p

-

N 

fl) Oi 

o 

o 33 t r 

fl) CO rt P3 P rt (D eo Hi 

O 

n rt t r p- (A eo rt 

P 

3 Oi 

P 

H Oi 

P 

H 

fl) P* p- teCQ d 

TJ TJ 

3 \ P» 

M 

X 

w X 

3 

CA f t 

p 

3 Oi 

P 

H Oi P- eo 

el- s' fl) P

1 

rt) 

P CQ rt (A rt 

n p3 vQ 

fl) 

3 rt 

o Hi 

el- s' fl) rt 3* 

H 

rt) 

fl) CQ rt 

P 

3 ard Oi s 

< 

P p* 

d fD CQ 

fl) 

X 

o co n> 0 - p3 vQ P" cn TJ TJ 

3 (A 3* 

P 

H p* 

3 

o rt 

fl) 

X 

o (D 

fl) Oi 

O 

3 

fl) TJ 

fl) 

n ^< fl) 

p 

h 

• 

»-3 3* 

fl) 

5 

P" cn 

3 

fl) 

fl) rt 

P (A f t 

P 

3 Oi 

P 

H Oi CO TJ 

fl) 

O P

-

Hi *< P3 vQ 

el- s' P f t f t t r 

fl) 

fl) 

X TJ 

fl) 

O rt 

fl) Oi 

3 

c 

g tr (D 

h 

o Hi 00 

I 

tr 

o P 

n 

n 

o 

P CO CO 

p 

g TJ f t PO 

3 f t t r 

P rt rt t r (D 

3 

o (A f t TJ 

O P" P« 

d rt 

fl) Oi 

3 

fl) P- vQ 3" t r 

o 11 3* 

O 

o 

a rt 

*< TJ 

fl) 

< P- P* P« 

C J . 

d CO rt 

23 

M 

£ 

fl) CO rt p3 P rt (D (A p3 

•-3 

P cr p< (D (A ^ j 

i 

M vo f t t r 

H 

o d vQ 3* ^1 

1 

ro 

-o. 

P 

h 

rt) t r 

P (A n> o. 

o 3 rt t r (D 

- j 

• P* 

• CJ 

> rt rt 

P p

-

3 

g 

rt) 

3 rt 

O Hi P« cn TJ TJ 

3 \ P« 

M 

X 

w X cn rt 

P 

3 Oi 

P 

H o. 

rt 

tr 

fl) 

3 P 

X p

-

3 

g 

o 

o 33 cr 

H n> 

< fl> P« p3 T1 

tr pM 

P Oi 

rt> P> TJ tr pP p- (A co 

• o co TJ (D 

n 

o 

ro 3 f t 

• 

P

1 

fl) 

< (D 

M CO 

« t r po t r ro 

iO 

d 

P po h 

fl> 

X 

o fl) 

fl) Oi ro 

• co 0 0 TJ 

rt) 

n 

o fl> 

3 rt 

p 

3 

a rt) 11 rt t r p- eo 

P eo eo 

d 

3 TJ rt pO 

3 

M 

X 

w X (A rt 

P 

3 o. P 

h Oi 

• 23 

O rt CD rt t r 

P rt 

P p« P * (A rt 

d OJ 

^< 

P 

h 

fl> 

P CO 

tr 

p 

< fl> 

3 P 

X p

-

3 •3 

n 

o 33 t r 

t-3 

P t r P< (D ~ j 

i CO p« TJ 

n 

o 

< p- OJ 

fl) CO 

3 

o 

y 

p p« pN 

fl) Oi 

o 

o 33 t r 

fl> CA rt p3 P rt 

fl) (A Hi 

o 

n rt 3" 

fl) 

M ro TJ TJ 

3 \ p« 

fl) CO rt p3 P rt 

rt) (A ^« 

P 

3 Oi 

H 

P cr P « fD CQ ^4 

1 P

1 

cn rt 3* 

H 

o d vQ 3* 

-o. 

l P« 0 0 TJ 

n 

o <J p- Oi 

fl) 

o 

o 33 tr (D (A rt p3 P rt 

fl) (A 

• 

fl) CQ rt p3 P rt 

fl> (A ^» 

i-3 

P cr P -

fl> (A ^ j 

i P

1 

co rt t r 

h 

o d vO 3" > J 

1 P* Cn TJ 

n 

o 

< p- Oi 

fl) 0 0 

1 3* 

o d 11 

fl) 

X TJ 

o CA 

d 

H (D 

w 

X 

w X CO rt 

p 

3 Oi 

p H. Oi 

• H3 

P cr P « (D CO ^ J 

1 p" 

o 

el- s' H 

O 

d 

iQ t r ^4 

1 

M ro TJ 

n 

o 

< p- OJ (D P

1 

1 

t r 

o d 

n 

fl> 

X TJ 

0 (A 

d 

n 

fl> 

n 3* p- co 

s 

P

1 

ro TJ TJ 

3 \ P* 

M 

X 

w X 

3 

eo rt 

P 

3 OJ 

P 

h o. p- (A P

1 

fl) CO (A (A f t 

h P

-

3 u3 

fl) 

3 f t rt 3* 

P 

3 rt 3* (D vo TJ TJ 

3 P" 

00 

1 

tr 

o 

p 

n 

o 

o 

< 

p p« 

d (D (A (D 

X 

o fD 

fl) Oi P3 vO P* K) TJ TJ 

3 eo 3 -

P 

M P« 

3 

o rt (D 

X 

o rt) 

rt) Oi 

o 3 

rt) TJ 

fl) 

n ^< rt) 

p 11 

• 

p 

n fl> 

P 

C J . 

d CO rt 

3 fl) 

fl) 

fl-

ea 

P CO rt 

P 

3 OJ 

P 

H Oi CO TJ 

fl) 

O P

-

Hi ^< P3 vQ rt 3* 

P rt 

el- s' (D (D 

X TJ 

fl) 

O rt 

fl) Oi 

3 

g 5* fl) 

H 

O Hi 

P (A CO 

C 

g TJ rt po 3 rt 3* 

P rt 

el- s' rt) 

3 

o (A r t TJ 

o P- P

1 

d rt (D Oi 

3 

fl) P- vQ t r t r 

o 

n t r 

o 

o Oi rt *< TJ (D p3 

fl) 

p 

o 3" CO rud

t y 

•3 

P cr t-> (D CO ~ j 

i 

P < 

o rt S* 

n 

o d vQ t r - j 

i 

M 0 0 TJ 

n 

o <3 P- Oi 

rt) 2S 

2 

fl) (A r t p3 P rt 

rt) CO cr P CO CD Oi 

O 

3 

el- s' 

fl) 

^ J 

• M 

• ro 

> rt rt 

P p

-

p 

g 

rt) 

3 rt 

o Hi P

1 

f>J TJ TJ 

3 \ P * 

W 

X 

M 

X cn rt 

P 

S 

a P 

h O

-

M 3 

P 

M f t P1

 fl) 

4>. M 

K 

X 

fl) 

P* 43 

d 

o P -O P« ro rt 

O ^^ P> 

• o 

M 

»—» « j 

P 

3 Oi P

1 

• P

1 

vo cn P- ea 

P TJ TJ 

H 

o X P3 P rt 

fl) P

1 

*< 

rt) 

iQ 

d 

P P> rt 

o 

vQ 

H 

o S rt 3* 

rt) 

P 

O 

tr 

*< fl> 

P 

n Hi i i 

o 3 

M VO 0 0 

o f t 

o p* vo 0 0 «J 

• 

n 3* 

P rt P- (A 

^ P" 

• 

o ~J ro p- (A 

P TJ TJ 

H 

o 

X P

-

1 

P 

3 OJ P" 

• 

o - 4 to 

• » 

P vQ 

n 

o £ rt s* Hi 

P 

O f t 

o H 

O 

o H 

n <D eo TJ 

o 3 Oi P3 

UJ. rt 

o 

P TJ H. 

o C J . 

fl) 

O rt 

rt) Oi p> TJ 

fl) 11 

O ent 

i i 

P rt pO 

o Hi p i vo 0 0 

o rt 

o rt 

P P

1 

a • cn 

• TJ 

o TJ 

d 

t-> 

P f t Po 3 rt 

o 

M VO -o 

o f t 

o rt 

P P

-

a • cn 

• TJ 

o TJ 

d 

M 

P r,ion

t 

, 

i-3 

tr 

rt) P * 

• P « vo cn 

3 d 

T-' 

ct P- TJ 

M P

-

O 

P rt p-

< fl) Hi 

P 

O rt 

O 

M P

-

(A rt t r 

rt) TJ 

n 

o Oi 

d 

o rt 

o Hi P> 

• P" 

M cn 

^ rt 3* 

fl) 
P.48 

TABLE 7-31. PERCENTAGE OF ADULTS WITH CARDIOVASCULAR DISEASE EXPERIENCING 

COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

12 PPM/1 EXEX STANDARD IS ATTAINED 

COHb level 

exceeded 

(percent) 

3.00 

2.90 

2.70 

2.50 

2.30 

2.10 

2.00 

1.50 

1.00 

0.00 

Max. COHb 

cone. 

Percent 

at maximum 

Chicago 

0.06 

1.02 

3.09 

7.30 

49.18 

98.36 

100.00 

2.52 

0.02 

Los Angeles 

0.81 

1.67 

1.90 

21.44 

98.36 

100.00 

2.38 

0.51 

Philadelphia 

0.03 

0.13 

0.67 

4.03 

14.74 

25.34 

27.67 

31.72 

98.28 

100.00 

3.03 

0.01 

St. Louis 

0.39 

5.85 

15.79 

26.53 

51.16 

90.74 

100.00 

2.59 

0.01 

7-37 
TABLE 7-32. PERCENTAGE OF ADULTS WITH CARDIOVASCULAR DISEASE EXPERIENCING 

COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

15 PPM/1 EXEX STANDARD IS ATTAINED 

COHb level 

exceeded 

(percent) 

3.70 

3.50 

3.3.0 

3.10 

3.00 

2.90 

2.70 

2.50 

2.30 

2.10 

2.00 

1.50 

1.00 

0.00 

Max. COHb 

cone. 

Percent 

at maximum 

Chicago 

0.05 

0.07 

0.66 

1.96 

3.44 

7.52 

16.48 

26.64 

69.92 

100.00 

100.00 

3.10 

0.04 

Los Angeles 

0.51 

0.85 

1.85. 

1.90 

2.92 

5.34 

75.41 

100.00 

100.00 

2.89 

0.23 

Philadelphia 

0.04 

0.34 

0.71 

2.42 

5.50 

9.74 

19.66 

26.47 

29.57 

30.43 

30.52 

50.34 

100.00 

100.00 

3.75 

0.01 

St. Louis 

0.25 

0.70 

2.61 

8.76 

20.02 

33.05 

45.47 

48.42 

60.42 

92.42 

100.00 

3.16 

0.01 

7-38 
- J 

1 CO VO 

O 

O 

3 TJ 

p 

h p

-

eo 

O 

3 CO tr fl> rt 

K fl> 

fl> 

3 rt 3* (D rt 

K 

0 

3 O 

< rt) cc 

o £ 

3 p3 

O 

O 33 cr 

M (D 

< fl) P

1 

• 

H 3* 

fl) 

iQ •1 (D 

P f t 

fl) 

M 

3 

g 

g 5* fl) 

n 

o Hi 

o P 

n OJ po <J P CQ 

o 

d p« 

P 

i 

3 

P p> 

rt) (A tr fl> vQ p3 (A f t 

o o. 

o 

3 p3 

P rt 

fl> 

P CQ 

n 

ro CO 

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TABLE 7-33. ESTIMATES OF ADULT FEMALES WITH CARDIOVASCULAR DISEASE 

WHO EXPERIENCE COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 1 i 1 1 1 

1 EXCEEDED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS I 

1 (PERCENT) 1 I I | | 

1 3.70 1 I I | | 

1 3.50 1 I | | | 

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1 3.10 1 1 I I | 

1 3.00 1 1 1 I | 

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1 1 ' t 1 I 1 

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1 2.30 1 1 I 66 1 1 

1 2.10 1 1 1 1.040 1 1 

1 2.00 1 1 I 4,480 | 39 1 

1 1.50 1 11,800 1 4,910 | 29,700 1 15,300 1 

1 1.00 1 85,500 1 219,000 1 74,500 1 29,300 1 

1 0.00 1 103,000 1 259,000 1 98,800 1 40,400 1 

1 MAX. COHB CONC. 1 1.93 1 1.88 1 2.32 1 2.03 1 

1 PEOPLE AT MAXIMUM 1 103 1 1,330 1 30 1 11 1 

7-40 

J 
P.52 

TABLE 7-34. ESTIMATES OF ADULT MALES WITH CARDIOVASCULAR DISEASE WHO 

EXPERIENCE COHb LEVELS EXCEEDING SELECTED VALUES ASSUMING 

9 PPM/1 EXEX STANDARD IS ATTAINED 

1 COHB LEVEL 1 1 1 1 | 

1 EXCEEDEO 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PERCENT) I j | j | 

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1 1.00 1 114,000 1 292,000 1 99,400 1 39,500 1 

1 0.00 1 142,000 1 357,000 1 136,000 1 55,800 1 

1 MAX. COHB CONC. 1 1.83 1 1.75 1 2.19 1 1.86 1 

1 PEOPLE AT MAXIMUM 1 142 1 4,070 1 41 1 15 1 

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P.54 

TABLE 7-35. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO HAVE 

1-HOUR CO EXPOSURES ABOVE SELECTED VALUES UNDER 9 PPM/1 EXEX 

STANDARD WITH INDOOR SOURCES OMITTED 

1 CONCENTRATION 

1 EXCEEDED 

1 (PPM) 

1 60.0 

1 55.0 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

CHICAGO 

1,250 

11,400 

22,300 

53,200 

108,000 

119,000 

122,000 

25.7 

523 

LOS ANGELES 

5,790 

62,200 

; 

188,000 

256,000 

299,000 

305,000 

21.7 

5,790 

PHILADELPHIA 

1,270 

1,270 

3,800 

21,600 

36,800 

69,300 

85,400 

99,000 

116,000 

36.0 

1,270 

ST LOUIS 1 

708 1 

6,200 I 

7,280 1 

19,500 1 

28,800 1 

36,600 1 

43,400 1 

47,500 1 

32.0 I 

708 1 

7-43 
P.55 

TABLE 7-36. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO HAVE 

8-HOUR CO EXPOSURES ABOVE SELECTED VALUES UNDER 9 PPM/1 EXEX 

STANDARD WITH INDOOR SOURCES OMITTED 

1 CONCENTRATION 1 1 | | 1 

1 EXCEEDED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PPM) I I 1 1 | 

1 60.0 1 1 1 1 I 

1 55.0 1 1 1 1 1 

1 50.0 1 1 1 1 I 

1 45.0 1 1 I I I 

1 40.0 1 1 1 1 1 

1 35.0 I I 1 I I 

1 30.0 1 1 I I | 

1 25.0 1 1 1 1 1 

1 20.0 1 1 I I 1 

1 15.0 1 1 I I | 

1 12.0 1 I | 2,160 1 1 

1 9.0 1 5,260 1 5,790 1 32,300 1 7,220 1 

1 7.0 1 54,200 1 13,400 1 36,300 1 22,500 1 

1 0.0 1 122,000 1 305,000 1 116,000 1 47,500 1 

1 MAX. CONCENTRATION 1 11.6 1 10.5 1 13.0 1 10.7 1 

1 PEOPLE AT MAXIMUM I 147 1 5,790 1 100 I 45 1 

7-44 
P.56 

TABLE 7-37. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR 

DISEASE OF COHb LEVELS EXCEEDING SELECTED VALUES UNDER 9 PPM/1 

EXEX STANDARD WITH INDOOR SOURCES OMITTED 

1 COHB LEVEL 1 

1 EXCEEDED 1 

1 (PERCENT) 1 

1 3.70 1 

1 3.50 I 

1 3.30 1 

1 3.10 I 

1 3.00 1 

1 2.90 1 

1 2.70 1 

1 2.50 1 

1 2.30 1 

1 2.10 1 

1 2.00 1 

1 1.50 1 

1 1.00 1 

1 0.00 1 

1 MAX. COHB CONC. I 

1 ENCOUNTERS AT MAX. 1 

CHICAGO 1 

45,200 1 

3,720,000 1 

1,070,000,000 f 

1.89 1 

66 1 

LOS ANGELES 

60,100 

3,540,000 

2,670,000,000 

1.82 

2.030 

PHILADELPHIA 

367 

1,870 

140,000 

1,340,000 

1,020,000,000 

2.22 

53 

ST LOUIS 1 

21 1 

22.100 1 

775,000 I 

416,000,000 1 

2.00 1 

21 1 

7-45 
TABLE 7-38. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHO 

EXPERIENCE COHb LEVELS EXCEEDING SELECTED VALUES UNDER 

9 PPM/1 EXEX STANDARD WITH INDOOR SOURCES OMITTED 

P.57 

1 COHB LEVEL 1 I I 1 1 

1 EXCEEDED 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PERCENT) 1 1 1 1 1 

1 3.70 1 | I | | 

1 3.50 1 | I | | 

1 3.30 1 1 1 1 1 

1 3.10 1 1 1 I I 

1 3.00 1 1 1 1 1 

1 2.90 1 1 1 I 1 

1 2.70 1 I I 1 1 

1 2.50 1 I I | I 

1 2.30 1 I I I I 

1 2.10 I I 1 156 1 1 

1 2.00 1 1 1 864 I 21 | 

1 1.50 1 9,170 1 5,790 1 33,000 1 9,110 1 

1 1.00 1 75,800 1 175,000 1 47,100 1 26,000 1 

1 0.00 1 122,000 1 305,000 1 116,000 1 47,500 1 

1 MAX. COHB CONC. 1 1.89 1 1.82 1 2.22 1 2.00 1 

1 PEOPLE AT MAXIMUM I 66 1 2,030 1 53 1 21 1 

7-46 
P.58 

TABLE 7-39. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE WHOSE MAXIMUM 

COHb LEVEL OCCURS IN SELECTED RANGES UNDER 9 PPM/1 EXEX STANDARD 

WITH INDOOR SOURCES OMITTED 

1 COHB LEVEL 1 1 1 1 1 

1 RANGE 1 CHICAGO 1 LOS ANGELES 1 PHILADELPHIA 1 ST LOUIS 1 

1 (PERCENT) 1 1 1 1 1 

1 3.70 < C <= 10.00 1 II I 1 

1 3.50 < C <= 3.70 1 1 1 I I 

1 3.30 < C <= 3.50 1 I I 1 1 

1 3.10 < C <= 3.30 1 1 1 1 1 

1 3.00 < C <= 3.10 1 1 1 1 1 

1 2.90 < C <= 3.00 1 1 1 1 1 

1 2.70 < C <= 2.90 1 I I 1 1 

1 2.50 < C <= 2.70 1 1 I I 1 

1 2.30 < C <= 2.50 1 1 - 1 1 1 

1 2.10 < C <= 2.30 1 1 1 157 1 I 

1 2.00 < C <= 2.10 1 1 1 709 I 21 I 

1 1.50 < C <= 2.00 1 9,170 1 5,790 1 32,200 1 9,090 1 

1 1.00 < C <= 1.50 1 66,600 1 169,000 1 14,100 1 16,900 1 

1 0.00 < C <= 1.00 I 45,800 1 130,000 1 69,300 1 21,500 1 

1 MAX. COHB CONC. 1 1.89 1 1.82 1 2.22 1 2.00 1 

1 PEOPLE AT MAXIMUM 1 66 1 2,030 1 53 1 21 1 

7-47 
P.59 

relevant data bases often are incomplete and/or inaccurate, 

professional judgment plays a significant role in selecting 

monitors to represent neighborhood types, in validating air 

quality data, in estimating cohort populations, and in determining cohort movements. 

Ideally, the uncertainty in each significant factor affecting exposure would be addressed formally within the exposure 

model so that a formal representation of the uncertainty in each 

exposure estimate would be part of the output of the model. 

Formal techniques for characterizing the uncertainty in estimates 

generated by applying the NEM model are under development. Due 

to limitations of time and resources, these techniques were not 

available for this application. Instead, several sources of 

uncertainty were investigated via a limited sensitivity analysis. 

Several values were used for some of the input quantities to see 

how sensitive selected exposure estimates are to this variation. 

The inputs chosen are the microenvironment factors, the largest 

source of uncertainty in estimating exposure, and the physiological 

variables used in determining blood COHb levels from exposure 

patterns. 

Lower, best, and upper estimates of microenvironmental factors are presented in Section 6.0. These differing estimates of 

microenvironmental factors were used to calculate exposure estimates for adults with cardiovascular disease in Chicago, assuming 

a 9 ppm/1 ExEx standard is just met. Exposure estimates for 

1-hour average and 8-hour average CO concentrations are presented 

in Tables 7-40 and 7-41. The results indicate that the difference 

between the lower estimates and the upper estimates is appreciable. 

This large variation primarily results from the large differences 

between lower and upper estimates of multiplicative microenvironment factors, particularly those for transportation vehicles and 

roadsides. 

Tables 7-42 through 7-44 provide COHb level estimates corresponding to the same lower, best, and upper estimates of microenvironment factors. The resulting variation in COHb levels is 

7-48 
P.60 

TABLE 7-40. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE IN CHICAGO 

WITH 1-HOUR CARBON MONOXIDE EXPOSURES ABOVE SELECTED VALUES UNDER 12 PPM/1 

EXEX STANDARD USING BEST, LOWER, AND UPPER MICROENVIRONMENT FACTORS 

1 CONCENTRATION 

1 EXCEEDEO 

1 (PPM) 

1 60.0 

1 55.0 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

BEST ESTIMATE 1 

11,200 I 

11,400 1 

22,300 I 

59,200 1 

108,000 1 

119,000 1 

122,000 I 

122,000 1 

34.6 1 

523 1 

1 LOWER ESTIMATE 

1 11,200 

1 22,000 

1 51,100 

1 99,900 

1 120,000 

1 122,000 

1 23.0 

1 523 

UPPER ESTIMATE 1 

1,250 1 

11,200 1 

11,400 1 

19,500 1 

22,300 1 

51,100 1 

59,200 1 

99,600 1 

121,000 1 

122,000 I 

122,000 I 

122,000 I 

122.000 I 

58.0 1 

523 1 

7-49 
P.61 

TABLE 7-41. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE IN CHICAGO 

WITH 8-HOUR CARBON MONOXIDE EXPOSURES ABOVE SELECTED VALUES UNDER 12 PPM/1 

EXEX STANDARD USING BEST, LOWER, AND UPPER MICROENVIRONMENT FACTORS 

1 CONCENTRATION 1 1 1 1 1 

1 EXCEEDED 1 BEST ESTIMATE 1 1 LOWER ESTIMATE 1 UPPER ESTIMATE 1 

1 (PPM) 1 1 1 1 1 

1 60.0 1 I I I | 

1 55.0 1 1 1 1 1 

1 50.0 1 I | I I 

1 45.0 1 1 I 1 1 

1 40.0 1 I | I | 

1 35.0 1 1 1 1 1 

1 30.0 1 I I 1 1 

1 25.0 1 1 I I 776 1 

1 20.0 1 I . I | 5,260 1 

1 15.0 I 1,900 1 I | 31,000 1 

1 12.0 1 5,260 I I 1 86,400 1 

1 9.0 I 67,300 1 I 4,670 1 115,000 1 

1 7.0 1 93,300 I I 61,600 1 121,000 1 

1 0.0 1 122,000 I I 122,000 1 122,000 1 

1 MAX. CONCENTRATION | 16.0 1 1 10.6 1 29.0 1 

1 PEOPLE AT MAXIMUM 1 54 1 1 335 I 54 I 

7-50 
TABLE 7-42. ESTIMATES OF OCCURRENCES FOR ADULTS WITH CARDIOVASCULAR DISEASE 

IN CHICAGO OF COHb LEVELS EXCEEDING SELECTED VALUES UNDER 12 PPM/1 

EXEX STANDARD USING BEST, LOWER, AND UPPER MICROENVIRONMENT FACTORS 

P.62 

1 COHB LEVEL 

1 EXCEEDED 

1 (PERCENT) 

1 3.70 

1 3.50 

1 3.30 

1 3.10 

1 .3.00 

1 2.90 

1 2.70 

1 2.50 

1 2.30 

1 2.10 

1 2.00. 

1 1.50 

1 1.00 

1 0.00 

1 MAX. COHB CONC. 

1 ENCOUNTERS AT MAX. 

BEST ESTIMATE 1 

134 I 

2,460 1 

16,600 1 

39,700 1 

1,240,000 1 

32,700,000 I 

1,070,000,000 1 

2.52 1 

24 1 

LOWER ESTIMATE 1 

14,700 1 

2,890,000 1 

1,070,000,000 1 

1.78 1 

67 I 

UPPER ESTIMATE 1 

3,980 I 

9,390 1 

24,100 I 

53,000 1 

83,300 I 

120,000 I 

257,000 1 

562,000 I 

1,210,000 1 

2,480,000 1 

3,580,000 I 

31,500.000 1 

409,000,000 1 

1,070,000,000 1 

4.55 1 

24 I 

7-51 
P.63 

TABLE 7-43. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE IN CHICAGO 

EXPERIENCING COHb LEVELS EXCEEDING SELECTED VALUES UNDER 12 PPM/1 EXEX 

STANDARD USING BEST, LOWER, AND UPPER MICROENVIRONMENT FACTORS 

1 COHB LEVEL 

1 EXCEEDED 

1 (PERCENT) 

1 3.70 

1 3.50 

1 3.30 

1 3.10 

1 3.00 

1 2.90 

1 2.70 

1 2.50 

1 2.30 

1 2.10 

1 2.00 

1 1.50 

1 1.00 

1 0.00 

1 MAX. COHB CONC. 

1 PEOPLE AT MAXIMUM 

BEST ESTIMATE 1 

1 

3 

8 

60 

120 

122 

78 I 

250 1 

770 1 

910 1 

000 1 

000 1 

000 1 

2.52 1 

24 1 

1 LOWER ESTIMATE 

1 3,760 

1 78,300 

1 122,000 

1 1.78 

1 67 

UPPER ESTIMATE 1 

1,310 1 

2.670 1 

3.550 1 

5,090 I 

7,380 1 

8,550 1 

12,100 I 

24,300 I 

55,600 1 

75,100 1 

85,800 1 

117,000 1 

122,000 1 

122,000 I 

4.55 1 

24 1 

7-52 
P.64 

TABLE 7-44. ESTIMATES OF ADULTS WITH CARDIOVASCULAR DISEASE IN CHICAGO 

WHOSE MAXIMUM COHb LEVEL OCCURS IN SELECTED RANGES UNDER 12 PPM/1 EXEX 

STANDARD USING BEST, LOWER, AND UPPER MICROENVIRONMENT FACTORS 

1 COHB LEVEL 1 1 1 1 1 

1 RANGE 1 BEST ESTIMATE 1 1 LOWER ESTIMATE 1 UPPER ESTIMATE 1 

1 (PERCENT) 1 1 1 1 1 

1 3.70 < C <= 10.00 1 1 1 I 1,310 1 

1 3.50 < C <= 3.70 1 1 1 1 1,360 1 

1 3.30 < C <= 3.50 1 I I | 881 1 

1 3.10 < C <= 3.30 I I I | 1,540 1 

1 3.00 < C <= 3.10 til l 2,290 1 

1 2.90 < C <= 3.00 1 1 1 1 1.170 1 

1 2.70 < C <= 2.90 1 1 1 I 3.550 1 

1 2.50 < C <= 2.70 I 79 I | | 12,200 1 

1 2.30 < C <= 2.50 1 1,180 1 1 1 31,300 1 

1 2.10 < C <= 2.30 1 2,510 1 1 1 19,600 1 

1 2.00 < C <= 2.10 I 5.150 1 1 I 10,600 1 

1 1.50 < C <= 2.00 1 51,100 1 I 3,760 1 30,900 1 

1 1.00 < C <= 1.50 1 59,600 1 1 74,600 1 4,940 f 

1 0.00 < C <= 1.00 1 1,930 1 1 43,300 1 1 

1 MAX. COHB CONC. 1 2.52 1 1 1.78 1 4.55 1 

1 PEOPLE AT MAXIMUM 1 24 1 1 67 1 24 1 

7-53 
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7.5 REFERENCES 

1. Memorandum from Harvey Richmond, Ambient Standards Branch, 

to Mike Jones, Chief of the Ambient Standards Branch, U.S. 

Environmental Protection Agency, Research Triangle Park, 

North Carolina 27711. September 8, 1982. 

2. F. L. Rodkey, J. D. O'Neal, and H. A. Collison, "Oxygen and 

carbon monoxide equilibria of human adult hemoglobin at 

atmospheric and elevated pressure," Blood, Vol. 33, No. 1, 

1960, pp. 57-65. 

3. U.S. Department of Health, Education, and Welfare, Public 

Health Service, Hemoglobin and Selected Iron-Related Findings of Persons 1-74 Years of Age; United States, 1971-1974. 

Advance data number 46, January 26, 1979. 

4. R. F. Coburn, et al, "Endogenous carbon monoxide production 

in man," J. of Clin. Invest., Vol. 42, 1963, pp. 1172-1178. 

5. S. R. Lynch and A. L. Moede, "Variation in the rate of 

endogenous carbon monoxide production in normal human 

beings," J. Lab. Clin. Med., Vol. 79, 1972, pp. 85-95. 

6. P. D. Berk, et al, "Comparison of plasma bilirubia turnover 

and carbon monoxide production in man," J. Lab. Clin. Med., 

Vol. 83, 1974, pp. 29-37. 

7. M. Delivoria-Papadopoulos, R. F. Coburn, and R. E. Forster, 

"Cyclic variation of rate of carbon monoxide production in 

normal women," J. Appl. Physiol., Vol. 36, 1974, pp. 49-51. 

8. R. P. Brouillard, M. E. Conrad, and T. A. Bensinger, "Effect 

of blood in the gut on measurements of endogenous carbon 

monoxide production," Blood, Vol. 45, 1975, pp. 67-69. 

9. C. A. Coltman and G. M. Dudley, "The relationship between 

endogenous carbon monoxide production and total heme mass in 

normal and abnormal subjects," Am. J. Med. Sci., Vol. 258, 

1969, pp. 374-385. 

10. R. Joumard, et al, "Mathematical models of the uptake of 

carbon monoxide on hemoglobin at low carbon monoxide levels," 

Env. Health Persp., Vol. 41, 1981, pp. 277-289. 

11. T. Sjostrand, "Blood volume," Handbook of Physiology, Vol. 1, 

Section 2, Chap. 4, 1962, pp. 51-62. 

12. U. S. National Center for Health Statistics, Advance Data. 

No. 3, November 19, 1976, and No. 14, November 30, 1977. 

7-55 
P.67 

13. V. Niinimaa, P. Cole, S. Mintz, and R. J. Shephard, "Oral 

nasal distribution of respiratory airflow," Resp. Physiol, 

Vol. 43, 1981, pp. 69-75. 

14. Review of the National Ambient Air Quality Standards for 

Sulfur Oxides: Assessment of Scientific and Technical Information, Strategies and Air Standards Division, Office of Air 

Quality Planning and Standards, U. S. Environmental Protection 

Agency, Research Triangle Park, N.C. 27711, November 1982. 

15. U. S. Department of Health, Education, and Welfare, Public 

Health Service, Coronary Heart Disease in Adults. United 

States; 1960-1962, Vital and Health Statistics Series 11, 

No. 10, December 1975. 

7-56 
SECTION 8 

NATIONWIDE EXTRAPOLATIONS 

The exposure model described in the preceding sections is 

applied directly to individual urbanized areas. To obtain CO 

exposure and COHb distributions for all urbanized areas directly 

from the model would require that the model be applied to each 

urbanized area separately and the distributions obtained be 

summed according to the expression 

n 

E(C) = Z e. (C) , (8-1) 

i=l

 x 

where E(C) is the total number of exposures to a concentration 

above C for all urbanized areas and e.(C) is the exposure disPh 

tribution for the i — area of n urbanized areas. Analogous 

expressions can be written for the number of people with exposures above selected concentrations and the number of people 

whose maximum exposure occurs in selected ranges. To carry out 

these computations would require the development of pollutant 

concentration and human activity data bases for each urbanized 

area in the U.S. Such an analysis is not feasible at the present 

time. Accordingly, rough estimates of national exposure for 

adults with cardiovascular disease were made by extrapolating 

the exposure and COHb estimates obtained from modelling the four 

study areas discussed in previous sections, namely, Chicago, Los 

Angeles, Philadelphia, and St. Louis. 

The extrapolation procedure used is described in Section 

8.1. Results of the extrapolation are presented in Section 8.2. 

A discussion of the uncertainty about the accuracy of these 

estimates is given in Section 8.3. 

8-1 
P.69 

8.1 EXTRAPOLATION PROCEDURE 

Equation 8-1 can be rewritten, in terms of exposures per 

person in the population, as 

n 

E(C) = Z P.e?(C) (8-2) 

i=l

 1 L 

where e?(C) is the exposure distribution per person in the popu1 j.U 

lation and P. is the population of the i — urbanized area. As 

with Equation 8-1, analogous equations can be written for each 

of the exposure and COHb distributions provided by the model. 

The effect of factoring out the population is to bring the e.(C) 

values for different areas just meeting a given alternative standard into closer agreement. There will continue to be significant 

differences, however, and the basic assumption of the extrapolation is that the e.(C) for the four base study areas are sufficient 

to represent these differences exhaustively. Therefore, the first 

step in applying the method was to assign each urbanized area to 

one of the four base areas. The value of n in Equation 8-2 was 

set equal to four, and P. became the total population of urbani j_t_ 

ized areas assigned to the i — base area. 

The ultimate goal of the extrapolation is to estimate what 

the exposure of the sensitive population (i.e., adults with 

cardiovascular disease) would be in 1987 under each of three air 

quality assumptions. These assumptions are that the three air 

quality standards discussed in Section 7 are just met in all 

urban areas. Since some urban areas are expected to have cleaner 

air in 1987 than required by the given standards, NEM estimates 

which are based on just meeting the standards are higher than 

they would be if they had been based on estimated 1987 quality. 

The CO exposure and COHb distributions for each of the four 

base areas are divided by their respective adult population values 

to obtain the e.(C) distributions. To obtain the urban population 

estimates to associate with each of the base areas, each of the 

urbanized areas with populations greater than 200,000 is assigned 

8-2 
8-3 

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H CD 

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P.71 

TABLE 8-1. URBANIZED AREA POPULATION DATA USED TO EXTRAPOLATE 

MODEL RESULTS 

Area 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

Totals 

Associated 

urbanized 

areas 

22 

26 

25 

32 

105 

1970 

Pop. of associated 

urbanized areas 

with pop. >200,000 

38,894,365 

26,339,249 

20,553,523 

17,350,712 

103,137,849 

1987 

Sensitive pop. 

of associated 

urbanized areas 

1,886,000 

1,277,000 

997,000 

841,000 

5,001,000 

By using an expression, which is mathematically equivalent 

to the per person approach described above, the desired extrapolated distributions can be calculated directly from the exposure 

distributions which are calculated for the four study areas. 

That is, E(C) can be calculated from the e. (C) for the four 

study areas by using the expression, 

E(C) = 

where 

Total Population (1970) 

Total Population > 200,000 (197Q) 

4 

E 

i=l 

fi e i( C ) (8-3) 

f = Total Pop, of i-type urban areas 

i Total Pop. of ith urban area 

8.2 EXTRAPOLATION RESULTS 

The results of the nationwide extrapolation are presented in 

Tables 8-2 through 8-12. The first nine tables can be divided 

into three sets of three tables. Tables 8-2, 8-3, and 8-4 present 

exposure estimates for a one-hour averaging time. Estimates of 

occurrences during 1987 among adults with cardiovascular disease 

of 1-hour average CO exposures above selected concentration 

values during 1987 under four alternative air quality assumptions 

are presented in Table 8-2. Estimates of the number of adults 

with cardiovascular disease in the urban U.S. who would incur 1-

hour average CO exposures above the same set of selected 

8-4 
P.72 

TABLE 8-2. ESTIMATES OF OCCURRENCES IN THE CARDIOVASCULAR ADULT URBAN U.S. 

POPULATION OF 1-HOUR AVERAGE CO EXPOSURES ABOVE 

SELECTED CONCENTRATION VALUES UNDER ALTERNATIVE 

AIR QUALITY ASSUMPTIONS 

CONCENTRATION 1 

EXCEEDED 1 

(PPM) I 

50.0 1 

45.0 I 

40.0 1 

35.0 1 

30.0 1 

25.0 1 

20.0 1 

15.0 1 

12.0 I 

9.0 1 

7.0 1 

0.0 1 

MAX. CONCENTRATION 1 

ENCOUNTERS AT MAX. 1 

9 PPM 8HR 1EXEX 112 

10.200 1 

21.900 I 

168.000 1 

915.000 I 

6.620,000 1 

27.700,000 I 

102.000.000 I 

370.000.000 1 

45.900.000.000 1 

36.0 1 

10.200 1 

PPM 8HR 1EXEX 

45 

10.200 

21.900 

124.000 

359.000 

1,700,000 

6,570.000 

31.300,000 

88.500.000 

296.000,000 

873,000,000 

,900,000,000 

49.0 

10,200 

15 PPM 8HR 1EXEX 1 

21,900 I 

33,600 1 

359,000 1 

849,000 1 

2,140.000 1 

6,310.000 1 

21,300.000 1 

78,700.000 1 

185.000.000 1 

602,000.000 1 

1,640,000.000 1 

45.900,000.000 1 

61.5 1 

10.200 1 

8-5 
P.73 

TABLE 8-3. ESTIMATES OF CARDIOVASCULAR ADULTS IN URBAN U.S. WITH 1-HOUR 

AVERAGE CO EXPOSURES ABOVE SELECTED CONCENTRATION 

VALUES UNDER ALTERNATIVE AIR QUALITY ASSUMPTIONS 

1 CONCENTRATION 

1 EXCEEDED 

1 (PPM) 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

I 12.0 

1 9.0 

1 7.0 

1 0.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

9 PPM 8HR 1EXEX 112 

10,200 1 

21,900 1 

157,000 1 

535,000 1 

1,290,000 1 

2,820,000 1 

4,390,000 1 

5,140,000 I 

5.240,000 1 

36.0 1 

10.200 1 

PPM 8HR 1EXEX 

10,200 

21,900 

113,000 

348,000 

691,000 

1,290,000 

2,900.000 

4,290.000 

5,030,000 

5,240,000 

5,240,000 

49.0 

10,200 

15 PPM 8HR 1EXEX 1 

21,900 1 

21,900 1 

348,000 I 

531,000 I 

872,000 1 

1,270,000 1 

2,510,000 1 

4,290,000 1 

4,800,000 1 

5,220,000 1 

5,240,000 1 

5,240,000 I 

61.5 1 

10,200 1 

8-6 
-POTABLE 8-4. ESTIMATES OF CARDIOVASCULAR ADULTS IN URBAN U.S. WHOSE MAXIMUM 

1-HOUR AVERAGE CO EXPOSURE OCCURS IN SELECTED CONCENTRATION 

RANGES UNDER ALTERNATIVE AIR QUALITY ASSUMPTIONS 

1 CONCENTRATION 

1 RANGE 

1 (PPM) 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

t 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

i 0.0 

< C <= 

< c <= 

< C <= 

< C <= 

< c <= 

< c <= 

< C <s 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

55.0 

50.0 

45.0 

40.0 

35.0 

30.0 

25.0 

20.0 

15.0 

12.0 

9.0 

7.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

9 PPM 8HR 1EXEX 

10,200 

11,700 

135,000 

378,000 

753,000 

1,530,000 

1,570,000 

747,000 

107,000 

36.0 

10,200 

12 PPM 8HR 1EXEX 

10,200 

11,700 

90,600 

235,000 

344,000 

603.000 

1.610,000 

1.390,000 

738,000 

211,000 

4,730 

49.0 

10.200 

15 PPM 8HR 1EXEX 1 

326,000 I 

184,000 1 

341.000 1 

401.000 t 

1,240.000 1 

1,770,000 1 

510,000 1 

426,000 1 

18,600 1 

61.5 1 

10,200 1 

8-7 
P.75 

TABLE 8-5. ESTIMATES OF OCCURRENCES IN THE CARDIOVASCULAR ADULT URBAN U.S. 

POPULATION OF 8-HOUR AVERAGE CO EXPOSURES ABOVE 

SELECTED CONCENTRATION VALUES UNDER ALTERNATIVE 

AIR QUALITY ASSUMPTIONS 

1 CONCENTRATION 

1 EXCEEDED 

1 (PPM) 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

1 MAX. CONCENTRATION 

1 ENCOUNTERS AT MAX. 

9 PPM 8HR 1EXEX |12 PPM 8HR 1EXEX 

1 429,000 

200,000 I 4,290,000 

6.340.000 1 54.900.000 

63,500.000 1 322,000,000 

45,900,000,000 1 45,900,000,000 

14.0 1 18.5 

232 1 232 

15 PPM 8HR 1EXEX 1 

104,000 1 

3,830,000 1 

29,200.000 1 

206.000,000 1 

835,000,000 t 

45,900,000,000 1 

23.0 I 

232 1 

8-8 

J 
P.76 

TABLE 8-6. ESTIMATES OF CARDIOVASCULAR ADULTS IN URBAN U.S. WITH 8-HOUR 

AVERAGE CO EXPOSURES ABOVE SELECTED CONCENTRATION 

VALUES UNDER ALTERNATIVE AIR QUALITY ASSUMPTIONS 

1 CONCENTRATION 

1 EXCEEDED 

1 (PPM) 

1 50.0 

1 45.0 

1 40.0 

I 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

9 PPM 8HR 1EXEX 112 PPM 8HR 1EXEX 1 15 

1 176.000 1 

98.000 1 538.000 1 

618,000 1 2,410,000 1 

2,650,000 1 4,090,000 1 

5,240,000 1 5,240,000 1 

14.0 1 18.5 1 

232 1 232 1 

PPM 8HR 1EXEX 1 

49,400 1 

529,000 I 

1,770,000 I 

3,660,000 1 

4,950,000 1 

5,240,000 I 

23.0 t 

232 1 

8-9 
P.77 

TABLE 8-7. ESTIMATES OF CARDIOVASCULAR ADULTS IN URBAN U.S. WHOSE MAXIMUM 

8-HOUR AVERAGE CO EXPOSURE OCCURS IN SELECTED CONCENTRATION 

RANGES UNDER ALTERNATIVE AIR QUALITY ASSUMPTIONS 

1 CONCENTRATION 

1 RANGE 

1 (PPM) 

1 50.0 

1 45.0 

1 40.0 

1 35.0 

1 30.0 

1 25.0 

1 20.0 

1 15.0 

1 12.0 

1 9.0 

1 7.0 

1 0.0 

< C <= 

< C <= 

< C <= 

< C <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

< c <= 

55.0 

50.0 

45.0 

40.0 

35.0 

30.0 

25.0 

20.0 

15.0 

12.0 

9.0 

7.0 

1 MAX. CONCENTRATION 

1 PEOPLE AT MAXIMUM 

9 PPM8HR 1EXEX 112 PPM 8HR 1EXEX 1 15 

1 176,000 1 

98,100 i 362,000 1 

520,000 1 1,870,000 I 

2,030,000 1 1,680,000 1 

2,590,000 1 1,160,000 1 

14.0 1 18.5 1 

232 1 232 I 

PPM 8HR 1EXEX 1 

49,400 I 

480,000 1 

1,240,000 1 

1,890,000 1 

1,290,000 1 

291,000 1 

23.0 1 

232 1 

8-10 
P.78 

TABLE 8-8. ESTIMATES OF OCCURRENCES AMONG CARDIOVASCULAR ADULTS IN URBAN U.S. 

OF COHb LEVELS EXCEEDING SELECTED VALUES UNDER ALTERNATIVE 

AIR QUALITY ASSUMPTIONS 

1 COHB LEVEL 

1 EXCEEDED 

1 (PERCENT) 

1 3.70 

1 3.50 

1 3.30 

1 3.10 

1 3.00 

I 2.90 

1 2.70 

1 2.50 

1 2.30 

1 2.10 

1 2.00 

1 1.50 

1 1.00 

1 0.00 

1 MAX. COHB CONC. 

1 ENCOUNTERS AT MAX. 

9 PPM 8HR lEXEX 112 PPM 8HR lEXEX 

4 

296 

45,900 

12 

41 

730 

000 

,000 

1 570 

1 2,650 

1 14,800 

1 76,700 

570 1 473,000 

,000 1 1,580,000 

,700 1 2,730,000 

,000 1 38,300,000 

,000 1 1,070,000,000 

,000 1 45,900,000,000 

2.30 1 3.02 

120 1 120 

15 PPM 8HR lEXEX 1 

637 1 

6,690 1 

15,300 1 

51.800 1 

106,000 1 

250,000 1 

810.000 1 

1.880,000 1 

4,100,000 1 

8,580.000 1 

13,900,000 I 

151.000,000 1 

2,270,000,000 1 

45,900.000,000 1 

3.75 1 

120 1 

, 1 

8-11 
P.79 

TABLE 8-9. ESTIMATES OF CARDIOVASCULAR ADULTS IN URBAN U.S. EXPERIENCING 

COHb LEVELS EXCEEDING SELECTED VALUES UNDER ALTERNATIVE 

AIR QUALITY ASSUMPTIONS 

1 COHB LEVEL 1 

1 EXCEEDEO I 

1 (PERCENT) I 

1 3.70 1 

1 3.50 1 

1 3.30 1 

1 3.10 1 

1 3.00 1 

1 2.90 1 

1 2.70 1 

1 2.50 1 

1 2.30 1 

1 2.10 1 

1 2.00 I 

1 1.50 1 

1 1.00 1 

1 0.00 1 

1 MAX. COHB CONC. 1 

1 PEOPLE AT MAXIMUM 1 

9 PPM 8HR lEXEX 112 

281 1 . 

5,500 1 

22,200 1 

742,000 1 

4,140,000 1 

5,240.000 1 

2.30 1 

120 1 

PPM 8HR lEXEX 1 15 

281 1 

1.250 1 

6.230 1 

42.000 1 

217,000 I 

452.000 I 

660.000 I 

2.100,000 I 

5.090.000 I 

5,240.000 1 

3.02 1 

120 1 

PPM 8HR lEXEX 1 

345 1 

3.200 1 

6.600 I 

25,800 1 

58.200 1 

132,000 1 

308,000 1 

506,000 1 

732,000 1 

1,060,000 1 

1,350,000 I 

3,480.000 I 

5.180.000 1 

5.240,000 I 

3.75 1 

120 1 

8-12 
P.80 

TABLE 8-10. ESTIMATES OF CARDIOVASCULAR ADULTS IN URBAN U.S. WHOSE MAXIMUM 

COHb LEVEL OCCURS IN SELECTED CONCENTRATION RANGES 

UNDER ALTERNATIVE AIR QUALITY ASSUMPTIONS 

1 COHB LEVEL 

1 RANGE 

1 (PERCENT) 

1 3.70 < 

1 3.50 < 

1 3.30 < 

1 3.10 < 

1 3.00 < 

1 2.90 < 

1 2.70 < 

1 2.50 < 

1 2.30 < 

1 2.10 < 

1 2.00 < 

1 1.50 < 

1 1.00 < 

1 0.00 < 

1 MAX. 

1 PEOPLE 

C <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

c <= 

10.00 

3.70 

3.50 

3.30 

3.10 

3.00 

2.90 

2.70 

2.50 

2.30 

2.10 

2.00 

1.50 

1.00 

COHB CONC. 

AT MAXIMUM 

9 PPM 8HR lEXEX 

5 

16 

720 

3,400 

1,100 

285 

,230 

,700 

,000 

,000 

,000 

2.30 

120 

12 PPM 8HR lEXEX 

285 

972 

4,990 

35,900 

175,000 

235,000 

208,000 

1,440,000 

2,990,000 

149,000 

3.02 

120 

15 PPM 8HR lEXEX 1 

351 1 

2,860 1 

3,400 1 

19,200 I 

32,400 1 

74,100 I 

175,000 I 

198,000 I 

227,000 1 

327,000 1 

291,000 1 

2,130,000 I 

1,700,000 I 

63,000 1 

3.75 1 

120 1 

8-13 
P.81 

TABLE 8-11. PERCENTAGE OF CARDIOVASCULAR ADULT URBAN U.S. POPULATION 

EXPERIENCING COHb LEVELS EXCEEDING SELECTED VALUES 

UNDER ALTERNATIVE AIR QUALITY ASSUMPTIONS 

COHb level 

exceeded 

(percent) 

3.70 

3.50 

3.30 

3.10 

3.00 

2.90 

2.70 

2.50 

2.30 

2.10 

2.00 

1.50 

1.00 

0.00 

9 ppm 8 hr 

1 ExEx 

0.01 

0.10 

0.42 

14.16 

79.01 

100.00 

12 ppm 8 hr 

1 ExEx 

0.01 

0.02 

0.12 

0.80 

4.14 

8.63 

12.60 

40.08 

97.14 

100.00 

15 ppm 8 hr 

1 ExEx 

0.01 

0.06 

0.13" 

0.49 

1.11 

2.52 

5.88 

9.66 

13.97 

20.23 

25.76 

66.41 

98.85 

100.00 

8-14 
P.82 

TABLE 8-12. ESTIMATES OF CARDIOVASCULAR ADULTS IN URBAN U.S. 

EXPERIENCING COHb LEVELS EXCEEDING SELECTED VALUES A GIVEN 

NUMBER OF DAYS ASSUMING 9 PPM/1 EXEX STANDARD IS ATTAINED 

CONCENTRATION 

EXCEEDED 

(PERCENT) 

3.70 

3.50 

3.30 

3.10 

3.00 

2.90 

2.70 

2.50 

2.30 

2.10 

2.00 

1.50 

1.00 

0.00 

1 

NUMBE R O F TIME S 

DAY I 2- 4 DAYS 1 5-2 5 DAYS 1 

281 1 1 1 

5,500 1 1 1 

21.800 1 1,120 1 I 

372.000 1 656,000 1 909,000 1 

673,000 1 2,570,000 1 25,300,000 1 

> 25 DAYS 1 

39,200,000 I 

1,910,000,000 1 

8-15 
P.83 , 

concentrations under the same assumptions are presented in Table 

8-3. Estimates of the number of urban U.S. adults whose maximum 

1-hour average CO exposure would occur in various concentration 

ranges are presented in Table 8-4. 

Analogous estimates for 8-hour average CO exposures are presented in Tables 8-5, 8-6, and 8-7 respectively. Similar estimates for COHb levels resulting from CO exposure are presented 

in Tables 8-8, 8-9, and 8-10. The absolute numbers presented in 

Table 8-9 are presented in percentage form in Table 8-11. 

Estimates of the number of adults with cardiovascular disease 

who would have their blood COHb levels elevated above selected 

concentrations for various numbers of days if an 8-hour average 

9 ppm/1 ExEx standard were just met in all urban areas are 

presented in Table 8-12. The table indicates the frequency of 

repeated peak COHb levels. The table indicates, for example, 

that of the 5,500 adults with cardiovascular disease who are 

estimated to have their blood COHb level exceed 2.1 percent under 

the 9 ppm/1 ExEx standard, none would have it occur more than one 

day. 

8.3 UNCERTAINTY OF THE NATIONWIDE ESTIMATES 

The uncertainty of the CO exposure and COHb estimates made 

for the four base cities was discussed in Section 7.4. The nationwide estimates are even more uncertain because of the additional 

uncertainty introduced by the extrapolation of exposure estimates 

for these four cities to all urban areas in the U.S. 

Formal means of dealing with the uncertainty of nationwide 

estimates are under development, but were not available for this 

analysis. Hence, no attempt was made to formally represent the 

uncertainty of the estimates presented in Section 8.2. The analyses discussed in Section 7.4 indicate that uncertainty is already 

great at the city level. That even greater uncertainty exists 

in the nationwide estimates should be recognized when considering 

the estimates presented in Tables 8-2 through 8-12. 

8-16 
P.84 

APPENDIX A 

Section 3.1 describes the development of activity patterns 

for the 56 population subgroups used in the NEM analysis. Reference 2 of Section 3.4 contains these 56 activity patterns. This 

appendix contains three examples of these activity patterns. At 

the top of each table is a label indicating the age-occupation 

group, the subgroup, and the percentage of the age-occupation 

group falling into the subgroup. In the body of the table are 

hourly assignments to locations, microenvironments, and activity 

levels for weekdays, Saturdays, and Sundays. Note that the hour 

designated "1 AM" is the hour which ends at 1 AM. 

A-l 
P.85 

ACTIWITT PATTERNS BT AGE-OCCUPATION SUBGilOUP 

A-0 6R0UP: 4 — Clerica l workers SUB6ftOUP:2 PCT IN SU86R0UP:26 

£>AY OF 

WEEK 

WEEKDAYS 

SATURDAY 

SUNDAY 

TIME 

OF DAY 

AM 

PM 

AM 

PM 

AM 

PM 

LOCATION/MICRCENVIRONMENT/ACTIVITY1 

H 

2 

1 

y 

2 

1 

H 

2 

1 

N 

2 

1 

H 

2 

1 

H 

2 

1 

2 

H 

2 

1 

y 

1 

1 

H 

2 

T 

H 

2 

1 

H 

2 

1 

H 

2 

1 

3 

H 

2 

1 

V 

1 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

4 

H 

2 

1 

ii 

1 

1 

H 

2 

1 

H 

2 

2 

H 

2 

1 

N 

2 

1 

5 

H 

2 

1 

y 

1 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

5 

3 

6 

H 

2 

1 

y 

3 

1 

H 

2 

1 

H 

2 

1 

N 

2 

1 

H 

4 

2 

7 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

8 

H 

3 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

9 

y 

1 

1 

H 

2 

1 

H 

2 

1 

H 

3 

1 

H 

2 

1 

H 

2 

1 

LEVEL 

10 

y 

1 

1 

H 

2 

1 

H 

2 

2 

H 

2 

1 

H 

2 

1 

H 

2 

1 

BY 

11 

y 

1 

1 

H 

2 

1 

H 

5 

2 

H 

2 

1 

H 

3 

1 

H 

2 

1 

HO 

12 

y 

1 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

N 

2 

1 

H 

2 

1 

LOCATION CODES; H-hoae W=work 

MICROENVIRONMENT CODES: 

1 = work or school 2 = bo«e or othe r 3 = transport vehicl e 

4 s roadside 5 = outdoors 6 - kitche n 

ACTIVITY LEVELS: 1-lou 2=aediua 3=hig h 

A-2 
ACTIVITY PATTERNS BY AGE-OCCUPATION SUBGROUP 

A-0 GROUP: 6—Operatives ^Laborers SUB6R0UP:6 PCT I N SU86R0UP:16 

DAY OF 

MEEK 

hEEKOAYS 

TIME 

OF DAY 

AM 

PM 

SATURDAY AM 

PM 

SUNDAY AM 

PM 

LOCATION/MICRQENVIRQNNENT/ACTIVITY-LEVEL BY HOUR 

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

H 

2 

1 

y 

3 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

y 

3 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

y 

2 

1 

H 

2 

1 

H 

5 

2 

H 

2 

1 

H 

2 

1 

H 

2 

1 

u 

3 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

y 

3 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

S 

2 

H 

2 

1 

H 

2 

1 

H 

2 

1 

U 

2 

1 

H 

2 

1 

H 

2 

1 

y 

3 

1 

H 

2 

1 

N 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

y 

3 

1 

H 

' 2 

1 

H 

2 

1 

H 

4 

2 

H 

2 

1 

H 

2 

1 

y 

3 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

y 

3 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

2 

y 

4 

2 

H 

2 

1 

H 

2 

2 

H 

2 

1 

H 

3 

1 

H 

2 

1 

y 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

LOCATION CODES: H=hoae W=*ork 

MICROENVIRONMENT CODES: 

1 - work o r schoo l 2 - hoae o r othe r 

4 = roadsid e 5 = outdoor s 

ACTIVITY LEVELS: 1-lo w 2=aediu a 3-hig h 

3 = transpor t vehicl e 

6 - kitche n 

A-3 
ACTIVITY PATTERNS 6Y A6E-OCCuPATION SUBGROUP 

A - 0 6R0UP: 9—Housewives SUBGROUP:1 PCT I N SUBGROUP:42 

DAY OF TIME LOCATION/MICROENVIRONMENT/ACTIVITY-LEVEL BY HOUR 

MEEK OF DAY 1 2 3 4 5 6 7 8 9 10 11 12 

WEEKDAYS AM 

PH 

SATURDAY AM 

PM 

SUNDAY AM 

PH 

H 

2 

1 

H 

2 

1 

H 

2 

1 

if 

c 

1 

H 

2 

1 

H 

6 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

tt 

2 

1 

H 

2 

1 

H 

2 

1 

tt 

3 

1 

tt 

2 

1 

H 

5 

2 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

•H 

2 

1 

H 

2 

1 

H 

2 

2 

H 

2 

1 

H 

2 

1 

H 

2 

1 

tt 

2 

1 

tt 

2 

1 

H 

5 

2 

H 

2 

1 

H 

6 

1 

H 

2 

1 

N 

2 

1 

H 

2 

1 

H 

2 

1 

tt 

6 

1 

tt 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

6 

1 

H 

2 

2 

H 

2 

1 

H 

6 

1 

H 

4 

2 

H 

6 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

2 

H 

3 

1 

tt 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

tt 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

2 

H 

2 

1 

tt 

2 

1 

H 

2 

1 

H 

2 

1 

H 

2 

1 

H 

5 

1 

H 

2 

1 

tt 

2 

1 

H 

2 

1 

H 

3 

1 

H 

2 

1 

LOCATION CODES: H-hoae y=wor k 

MICROENVIRONMENT CODES: 

1 = work o r schoo l 2 = taoae o r othe r 3 = transpor t vehicl e 

4 = roadsid e S = outdoor s 6 - kitche n 

ACTIVITY LEVELS: 1 = lov 2=aediua 3-high 

A-4 
P.88 

APPENDIX B 

COHORT POPULATIONS BY STUDY AREA 

Cohort description 

A-0 qroup 

Students 18+ 

01 

Professionals 

02 

Sales workers 

03 

Home 

NTa 

CR 

1 

SR 

5 

CR 

1 

CR 

1 

SR 

5 

SR 

5 

CR 

1 

CR 

1 

Work 

NTa 

CR 

1 

SR 

5 

CC 

2 

SC 

6 

CC 

2 

SC 

6 

CC 

2 

SC 

6 

Subgroup 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

5 

1 

2 

3 

4 

5 

Chicago 

19,316 

37,792 

9,238 

17,636 

5,897 

11,538 

2,820 

5,384 

170,393 

76,133 

79,758 

36,254 

11,845 

5,293 

5,545 

2,520 

47,282 

21,126 

22,132 

10,060 

8,806 

3,934 

4,122 

1,874 

33,250 

16,238 

3,866 

6,959 

17,011 

2,312 

1,129 

269 

484 

1,183 

Cohort population 

Philadelphia 

4,239 

8,293 

2,027 

3,870 

17,387 

34,017 

8,315 

15,875 

14,501 

6,479 

6,788 

3,085 

5,710 

2,551 

2,673 

1,215 

35,661 

15,933 

16,692 

7,587 

65,332 

29,191 

30,581 

13,901 

4,442 

2,169 

517 

930 

2,273 

1,749 

854 

203 

366 

895 

St. Louis 

3,753 

7,344 

1,794 

3,427 

5,334 

10,436 

2,551 

4,870 

13,364 

5,971 

6,256 

2,834 

3,489 

1,559 

1,633 

742 

14,490 

6,474 

6,782 

3,083 

18,812 

8,405 

8,805 

4,002 

4,000 

1,953 

465 

837 

2,046 

1,044 

510 

121 

219 

534 

Los 

Angeles 

17,261 

33,771 

8,255 

15,760 

61,326 

119,985 

29,330 

55,993 

41,759 

18,658 

19,547 

8,885 

31,503 

14,076 

14,746 

6,702 

40,366 

18,036 

18,895 

8,589 

263,140 

117,573 

123,172 

55,987 

11,364 

5,550 

1,321 

2,378 

5,814 

8,573 

4,187 

997 

1,794 

4,386 

(continued) 

B-l 
P.89 

Cohort description 

A-0 group 

Sales workers 

03 (cont.) 

Clerical 

workers 04 

Home 

NTa 

SR 

5 

SR 

5 

CR 

1 

CR 

1 

SR 

5 

SR 

5 

Work 

NTa 

CC 

2 

SC 

6 

CC 

2 

SC 

6 

CC 

2 

SC 

6 

Subgroup 

1 

2 

3 

4 

5 

1 

2 

3 

4 

5 

1 

2 

3 

4 

5 

6 

1 

2 

3 

4 

5 

6 

1 

2 

3 

4 

5 

6 

1 

2 

3 

4 

5 

6 

Chicago 

9,244 

4,514 

1,073 

1,932 

4,722 

1,719 

839 

200 

360 

879 

141,163 

65,540 

22,687 

10,083 

2,520 

10,083 

9,813 

4,556 

1,577 

701 

175 

701 

31,571 

14,658 

5,074 

2,255 

564 

2,255 

5,880 

2,730 

945 

420 

105 

420 

Cohort population 

Philadelphia 

11,322 

5,529 

1,317 

2,370 

5,792 

20,742 

10,130 

2,412 

4,341 

10,612 

26,033 

12,087 

4,184 

1,860 

465 

1,860 

10,250 

4,759 

1,647 

732 

183 

732 

39,508 

18,343 

6,349 

2,822 

706 

2,822 

72,380 

33,605 

11,633 

5,170 

1,293 

5,170 

St. Louis 

4,315 

2,107 

502 

903 

2,208 

5,602 

2,736 

651 

1,173 

2,866 

22,242 

10,327 

3,575 

1,589 

397 

1,589 

5,806 

2,696 

933 

415 

104 

415 

11,765 

5,462 

1,891 

840 

210 

840 

15,274 

7,092 

2,455 

1,091 

273 

1,091 

Los 

Angeles 

11,372 

5,554 

1,322 

2,380 

5,818 

74,133 

36,204 

8,620 

15,516 

37,928 

48,231 

22,393 

7,751 

3,445 

861 

3,445 

36,385 

16,893 

5,848 

2,599 

650 

2,599 

36,537 

16,963 

5,872 

2,609 

652 

2,609 

238,175 

110,175 

38,278 

17,013 

4,253 

17,013 

(continued) 

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ro •-• ro cn 4^ 

oo •-• tn oo vj o 

O ro cn o ro •-• 

ro I-* o ro cn o 

!-• I—• 4^ vo 

vo vi co vo tn tn 

!-• cn oo i—• vo cn 

ro tn ro ro o ro 

i—• 

C OM CO VO 00 

vi cn vi vi »-• vo 

vo !—• tn vo CD vi 

cn oo vo cn vo oo 

A-0 group 

Home NTa 

Work 

NTa -1 CO O C C CT O 1 Chicago Phila- delphia St. Louis Los Angeles 

Cohort description Cohort population 

\ 
o 

o 

3 

rt 

at 

CX. 

00 

I 4» 

Retired 10 

!-• ct 

za 

ri ct 

TO 

cn cn 4* co ro !-• 

ri 1—• 1—• 1—• 

r-i ro VI 1—• to 1—• 

!-• ro ro 4a vi 4a 

4a VO !-• 00 v| oo 

oo cn vo o cn o 

ro •-• ro !-• 

r-i to VO vo to VO 

vo oo o co ro to 

4a v| vo vO vl vo 

o vo to cn tn cn 

ro I1 ro !-• 

ri CO CTl vl ri V I 

vi cn co cn O tn tn o o co 4* to to v| o 4» O 4* cn 4=» 4* 4a 

*030000 0 o i—• vi cn cn cn tn C D oo ro ro ro ro 4* !-• O 4* o 

tn co 

za 

tn co 

73 

co ro !-• 

4* CO 

oo tn oo 

to ro oo 

ro vo ro 

o tn 4* 

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v| vo 4^ to vo ro to CO vl vo 4* CO 00 !-• ro vi oo tn vo ro to CO vl vo 4* ro vi o vo o to ro 4a ro cn o o 

Housewives 

09 

za 

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I—• CO vl cn to •-• 

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co cn !-• 

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vi o cn 

r-i vo tn 

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i — • 

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i — • 

ro 4a vi cn i-i 

tn tn vo O 4* tn 

vi CD cn vi cn vi 

ro M 4> 4» Ol Ul 

4a vj vl i—• 4a VO 

Service 

workers 08 

!-• ct 

73 

ri Ct 

73 

as cn 4a to ro i—• 

ii i—i to ro 4 ^ 

o VO 4* o to vo 

vo ro •-• !-• to co 

vi o !-• oo ro vo 

cn oo cn 4* 4* to 

•-• ro 

4^ oo i-i ro vo o 

cn o vi cn oo vi 

1-* vl CO VO O vl 

cn vi !-• co oo o 

to cn !-• o vi cn 

vi cn co ro vo vi 

to ro vo cn ro oo 

ri VO VO O 00 VO 

ri ri ri rO 

tn o ro tn ro tn 

vi o !-• oo ro vo 

tn vi tn to co i—• 

oo cn vo 4a cn C D 

tn co 

73 

vl CO 

1—• 

cn cn 4a co ro !-• 

r1 »—• !—• CO 

co tn ro tn tn 4a 

vo vi cn ro vi C D 

vi ro ro 4a ro cn 

ri 1—• ri 4 a 

cn oo to cn oo o 

vi oo i-i ro oo oo 

tn tn 4a 00 cn 4a 

cn r-i ro 4a r-i co 

CO 4a )-• 4a 00 

cn CD cn co o oo 

i—• cn vi cn cn i-i 

vi vo oo cn vo vi 

i 

i — • 

cn vi i-i ro vi cn 

CTl 4a ro 4a 4a i-i 

4a vl 4a vo vj vo 

CO 4a cn r-i 4=» 4a 

IO 4a vl 4a 4a cn 

Laborers 06 

(cont.) cn co 

73 

CO O 

1—1 

cn tn 4a co ro 1—• 

r-i 

VI 00 !-• ro 00 00 

tn 4a 4a 00 4a ro 

0 4a 0 II 4a 00 

CO ri CTl tO ri VO 

!—• 1—• ro 

vo 0 i-i to 0 ro 

!-• ro vi 4a ro ro 

4a 00 !-• CO 00 VO 

cn vo tn O vo 4a 

ro co 1—i to cn 

VIHOIOH M 00 co ro 4a CO VO cn 4a ro tn 4a r-» 

ri •—• ri rO 

O •-• 1—• to 1—• 4 a 

!-• 4a vo CO 4a 00 

vo cn 1—• ro cn 4a 

ro cn !-• ro cn co 

A-0 group 

Home NTa 

Work 

NTa x» -J CO 0 c c cr 0 1 Chicago Phila- delphia St. Louis Los Angeles 

Cohort description Cohort population 

CO 
P.92 

Cohort description 

A-0 group 

Retired 10 

(cont.) 

Children <5 

11 

Children 5-17 

12 

Home 

NTa 

SR 

5 

CR 

1 

SR 

5 

CR 

1 

SR 

5 

Work 

NTa 

SR 

5 

CR 

1 

SR 

5 

CR 

1 

SR 

5 

Subgroup 

1 

2 

3 

4 

5 

6 

1 

2 

3 

4 

1 

2 

3 

4 

1 

2 

3 

4 

5 

6 

1 

2 

3 

4 

5 

6 

Chicago 

3,277 

3,933 

3,277 

4,916 

655 

327 

10,562 

10,059 

10,059 

19,615 

8,076 

7,691 

7,691 

14,997 

67,594 

4,828 

8,449 

31,383 

2,414 

6,035 

51,683 

3,692 

6,460 

23,996 

1,846 

4,615 

Cohort population 

Philadelphia 

42,411 

50,893 

42,411 

63,617 

8,482 

4,241 

15,851 

15,096 

15,096 

29,438 

35,021 

33,353 

33,353 

65,039 

117,289 

8,378 

14,661 

54,455 

4,189 

10,472 

284,616 

20,330 

35,577 

132,143 

10,165 

25,412 

St. Louis 

10,836 

13,003 

10,836 

16,254 

2,167 

1,084 

10,494 

9,995 

9,995 

19,489 

10,506 

10,006 

10,006 

19,511 

82,690 

5,906 

10,336 

38,391 

2,953 

7,383 

91,294 

6,521 

11,412 

42,387 

3,261 

8,151 

Los 

Angeles 

112,631 

135,157 

112,631 

168,946 

22,526 

11,263 

24,304 

23,146 

23,146 

45,135 

113,151 

107,763 

107,763 

210,137 

149,881 

10,706 

18,735 

69,587 

5,353 

13,382 

905,879 

64,706 

113,235 

420,587 

32,353 

80,882 

B-5 
P.93 

APPENDIX C 

DISCUSSION OF AIR QUALITY INDICATORS 

USED IN THE NEM ANALYSIS AND ESTIMATED 

CONCENTRATIONS USED IN THE REGULATORY ANALYSIS 

A number of reviewers of early drafts of this report have 

asked how air quality indicators (AQI's) used in the NEM analyses 

of CO compare with estimated concentrations (EC's) used in the 

regulatory impact analysis of alternative CO NAAQS's. This appendix discusses how EC's are determined and why they differ from 

AQI's. 

For regulatory impact analysis purposes, EPA characterizes 

air quality levels in urbanized areas by a single value. This 

value, the EC, is determined from existing air quality data 

according to the same criteria that states would use to determine 

whether or not an area attains a proposed NAAQS. These criteria 

vary according to the "form" of the standard being analyzed and 

the allowed violation rate. In the case of CO, forms under consideration include one-hour and eight-hour daily maximum standards 

with allowed violation rates of one and five expected exceedances 

per year over a three year period. 

The EC for a given urbanized area is usually based on air 

quality data from the monitor which reported the highest air 

quality values over a two or three year period. According to 

current EPA guidance, the EC may be determined by applying the 

simple formula 

(

number \ / allowed \ 

of years J lexceedance] + 1 (B-l) 

analyzed/ \ rate J 

to a multi-year data set from this monitor. Thus if the permitted 

exceedance rate is five and three years of data are considered, 

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the NEM analysis. Because no more than six sites (one per neighborhood type) are used to represent CO levels across a NEM study 

area and because the boundary of the study area is smaller than 

the corresponding urbanized area, the monitor used for determining 

the AQI is often different from that used to determine the EC. 

C-3 
P.96 

TABLE C-1. ESTIMATED CONCENTRATIONS (EC'S) DEVELOPED BY EPA AND 

CORRESPONDING AIR QUALITY INDICATORS (AQI'S) FROM TABLE 5-6 

(concentrations in parts per million) 

Study area 

Chicago 

Los-Angeles 

Philadelphia 

St. Louis 

1-h average value, 

1 expected exceed. 

EC 

30.9 

37.8 

32.9 

27.9 

AQI 

24.9 

31.4 

19.2 

22.8 

8-h running average val 

1 expected exceed. 

EC 

18.0 

24.4 

14.7 

17.0 

AQI 

.. 15.6 

20.3 

14.3 

14.7 

je 

5 expected exceed. 

EC 

14.0 

17.0 

11.0 

10.0 

AQI 

12.9 

16.1 

9.9 

10.9 

C-4 
P.97 

TECHNICAL REPORT DATA 

(Please read Instructions on the reverse before completing) 

1. REPORT NO. 

EPA-450/5 84 003 

2. 

4. TITLE AND SU8TITLE 

The NAAQS Exposure Model (NEM) 

Applied to Carbon Monoxide 

7. AUTHOR(S) 

Ted Johnson and Roy A. Paul 

9. PERFORMING ORGANIZATION NAME AND ADDRESS 

PEDCo Environmenta, Inc. 

505 South Duke Street, Suite 503 

Durham. North Carolina 27701 

12..SPONSDRING AGENCY NAM£ AND ADDRESS. 

TJ.S, Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 27711 

3. RECIPIENT'S ACCESSION NO. 

5. REPORT DATE 

December 1983 

6. PERFORMING ORGANIZATION CODE 

8. PERFORMING ORGANIZATION REPORT NO. 

10. PROGRAM ELEMENT NO. 

11. CONTRACT/GRANT NO. 

13.-TYPE OF REPORT AND PERIOD COVERED 

Final 

14. SPONSORING AGENCY CODE 

15. SUPPLEMENTARY NOTES 

16. ABSTRACT 

This report presents a version of the National Ambient Air Quality Standard 

(.NAAQS) Exposure Model (NEM) suitable for assessing carbon monoxide (CO) exposure and 

presents the results of applying i t to CO, MEM is a simulation model that simulates 

the intersection of a population with pollutant concentrations over space and 

time to estimate exposures that would obtain i f various alternative NAAQS were 

just met. Estimates are presented for adults with cardiovascular disease in four 

urban study areas and for a nationwide extrapolation. 

17. . KEY WORDS AND DOCUMENT ANALYSIS ~~~ 

a. DESCRIPTORS 

Carbon Monoxide 

Ai> Pollution 

Exposure Assessment 

18. DISTRIBUTION STATEMENT 

Release to Public 

b.lDENTIFIERS/OPEN ENDED TERMS 

Air Quality Standards 

19. SECURITY CLASS (This Report) 

Unclassified 

20. SECURITY CLASS (This page) 

c. COSATI Field/Group 

' 

2 1 . NO. OF PAGES 

197 

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(a) DESCRIPTORS - Select from the Thesaurus of Engineering and Scientific Terms the proper authorized terms that identify the major 

concept of the research and are sufficiently specific and precise to be used as index entries for cataloging. 

(b) IDENTIFIERS AND OPEN-ENDED TERMS - Use identifiers for project names, code names, equipment designators, etc. Use openended terms written in descriptor form for those subjects for which no descriptor exists. 

(c) COSATI FIELD GROUP - Field and group assignments are to be taken from the 1965 COSATI Subject Category List. Since the majority of documents are multidisciplinary in nature, the Primary Field/Group assignment(s) will be specific discipline, area of human 

endeavor, or type of physical object. The application(s) will be cross-referenced with secondary Field/Group assignments that will follow 

the primary posting(s). 

18. DISTRIBUTION STATEMENT 

Denote releasability to the public or limitation for reasons other than security for example "Release Unlimited." Cite any availability to 

the public, with address and price. 

19. & 20. SECURITY CLASSIFICATION 

DO NOT submit classified reports to the National Technical Information service. 

21. NUMBER OF PAGES 

Insert the total number of pages, including this one and unnumbered pages, but exclude distribution list, if any. 

22. PRICE 

Insert the price set by the National Technical Information Service or the Government Printing Office, if known. 

E PA Form 2220- 1 (Rev. 4-77 ) (Reverse)