p.1 

OAR Box 1846 

Prepped by Ollie Stewart 

Document Number: 

1) ll-A-18 

Docket Number: 

A-90-07 

Printed 11/18/2008 7:30:14 AM Sheet 1 of 93 
P.2 

IT-fVlS 

DRAFT 

APPLICATION OF THE HAZARDOUS AIR 

POLLUTANT EXPOSURE MODEL (HAPEM) TO MOBILE 

SOURCE POLLUTANTS 

by 

Ted Johnson, Roy A. Paul, and James E. Capel 

International Technology Corporation 

Air Quality Services 

3710 University Drive, Suite 201 

Durham, North Carolina 27707-6208 

Contract Number 68-DO-0062 

Work Assignment Number 6 

JTN 830013-06 

Richard B. Atherton, Project Director 

Office of Air Quality Planning and Standards 

Air Quality Management Division 

U. S. Environmental Protection Agency 

Research Triangle Park, North Carolina 27711 

Penny M. Carey, Work Assignment Manager 

Emissions Control Technology Division 

U. S. Environmental Protection Agency 

Ann Arbor, Michigan 48105 

January, 1992 
P.3 

ACKNOWLEDGEMENTS 

This report describes the development of a new version of the Hazardous Air 

Pollutant Exposure Model (HAPEM) and its application to pollutants emitted by mobile 

sources. This effort was conducted by IT Air Quality Services (ITAQS) for the Office of 

Mobile Sources (OMS) of the U.S. Environmental Protection Agency (EPA). Mr. Joe 

Somers and Ms. Alva Edwards of the Emissions Control Technology Division (ECTD) 

of OMS provided the primary technical supervision for this project. Ms. Penny Carey 

of ECTD provided administrative direction. 

Project work was funded under Contract No. 68-DO-0062 with the Air Quality 

Management Division (AQMD) of EPA's Office of Air Quality Planning and Standards. 

Mr. Richard Atherton of AQMD was the Project Director. Mr. Thomas McCurdy of 

AQMD coordinated between AQMD and ECTD in the development and use of 

exposure modeling techniques. The original version of HAPEM was developed by 

ITAQS for the Pollutant Assessment Branch of AQMD. 

Mr. Ted Johnson of ITAQS developed the general methodology incorporated in 

HAPEM and was the principal author of this report. Mr. James Capel was the 

principal programmer on the project. Inez VanArsdall developed the carbon monoxide 

data base for 334 cities. Mr. Roy Paul served as the ITAQS project manager. 
P.4 

DISCLAIMER 

This report was prepared by International Technology Corporation, Air Quality 

Services, under Contract Number 68-DO-0062, Work Assignment Number 6. It has 

been reviewed by the Office of Mobile Sources, U. S. Environmental Protection 

Agency, and approved for publication. Approval for publication does not signify that 

the contents necessarily reflect the views and policies of the U. S. Environmental 

Protection Agency. Mention of trade names or commercial products is not intended to 

constitute an endorsement of their use. 

in 

L 
P.5 

TABLE OF CONTENTS 

Acknowledgement ii 

Disclaimer iii 

Figures v 

Tables vi 

1.0 Executive Summary 1 

2.0 Introduction 3 

3.0 HAPEM Methodology 5 

4.0 Recommendations for Further Research 38 

References 40 

Appendix A 41 

Appendix B 48 

IV 

fc 
P.6 

FIGURES 

1. Excerpt From Sample HAPEM Output Listing Cancer 

Incidence by Cohort and Study Area Total 25 

2. EExcerpt From Sample HAPEM Output Presenting Study Area 

Exposure Distribution and Identifying the Cohort With 

Highest Annual Average Exposure 26 

3. Excerpt From REDE Printout Listing .Exposure Estimates for 

Philadelphia Entered as Input Data 31 

4. Excerpt From REDE Printout Listing Area-specific Exposure 

Estimates for Pittsburgh, Pennsylvania 33 

5. Excerpt From REDE Printout Listing Exposure Estimates for 

the Region Represented by Philadelphia, Pennsylvania 34 
P.7 

TABLES 

1. City Centers and Residential Zones Defined for Model Study Areas 9 

2. Residential Populations, Exposure Periods, and Cohort Counts for Model 

Study Areas 10 

3. Carbon Monoxide Monitors Selected to Represent Model Study Areas 11 

4. Number of Cohorts Defined for Philadelphia Based on 9 Demographic 

Groups, 7 Home Districts, and 8 Work Districts 16 

5. Percentage of Time Spent in Each Combination of Geographic 

Location and Microenvironment by a Philadelphia Cohort Selected 

at Random From Each Demographic Group 18 

6. Microenvironment Factors 20 

7. Average Benzene Exposures by Study Area 28 

8. Summary Statistics by Region for Pollutant Adjustment Ratio Based 

on Data Reported by Cities with Carbon Monoxide Monitors 29 

9. Estimated Cancer Incidence Rates Associated With Estimated Benzene 

Exposure by Region 35 

10. Estimated Benzene Exposure of Rural U. S. Population Based on 

Exposure Estimates for Farmington, New Mexico and Paducah, Kentucky 35 

11. Estimated Benzene Exposure for Urban, Rural, and Total U. S. 

Populations 37 

12. Average Benzene Exposures by Region 37 

VI 
P.8 

1.0 EXECUTIVE SUMMARY 

The Hazardous Air Pollutant Exposure Model (HAPEM) is a computer-based 

model which provides estimates of human exposure to air pollution and characterizes 

the health risks associated with these exposures. HAPEM was originally developed by 

the U.S. Environmental Protection Agency as a means for evaluating hearth risks 

associated with hazardous pollutants emitted by stationary sources. This report 

describes a new version of HAPEM, designated HAPEM-MS, which is applicable to 

hazardous and non-hazardous pollutants emitted from mobile sources. 

To apply HAPEM-MS to a particular study area, the analyst divides the area's 

population into a comprehensive set of cohorts. The members of each cohort are 

assumed to have similar activity patterns and exposures over a one-year period. Each 

cohort is identified with respect to the district where its members live, the district where 

they work, and various demographic characteristics, such as age and type of work. 

Detailed census and commuting data are used to estimate the size of each cohort. 

HAPEM-MS uses detailed activity diary data from a study conducted in 

Cincinnati, Ohio, to develop an activity pattern for each cohort. This activity pattern 

specifies the fraction of time spent by the cohort in each of five microenvironments 

within each exposure district. The pollutant concentration in each microenvironment is 

assumed to be a linear function of the ambient (outdoor) concentration within the 

district. The ambient concentration in each district is derived from fixed-site monitoring 

data specific to the district. 

In a typical application, HAPEM-MS is used initially to estimate carbon monoxide 

(CO) exposures expected in a study area under existing conditions. These exposures 

are then multiplied by an adjustment factor to simulate the CO exposures expected 

under a particular control scenario. The adjustment factor is the ratio of a mobile 

source emission factor (gm/mile) representing the control scenario to a factor 

1 
P.9 

representing existing conditions. These emission factors are determined through 

application of the MOBILE4 model to the study area. 

The HAPEM-MS user can estimate exposure to Pollutant X, a hazardous 

pollutant emitted by mobile sources, by assuming that the concentration of Pollutant X 

is proportional to that of CO. In analyses of this type, the user provides a 

proportionality constant for the pollutant and a risk factor indicating the pollutant's 

potential for causing cancer. HAPEM-MS will then calculate both the annual average) 

exposure of each cohort to Pollutant X and the estimated annual cancer incidence 

associated with that exposure. These estimates are summed over all cohorts to 

provide exposure and cancer risk estimates for the entire study area. 

This report provides a detailed description of the HAPEM-MS methodology and 

the results of a three-step modeling effort conducted under the direction of OMS. In 

Step 1, HAPEM-MS was used to estimate the distribution of annual average carbon 

monoxide (CO) exposures in eleven urban study areas and two rural study areas. In 

Step 2, these CO distributions were converted to benzene distributions by assuming 

that the annual benzene exposure attributable to mobile sources was proportional to 

annual CO exposure. In Step 3, the Regional Exposure Distribution Estimator (REDE) 

program was used to extrapolate the benzene exposure distributions associated with 

the eleven urban study areas to 334 Metropolitan Statistical Areas, to eleven regional 

urban populations, and to the entire U.S. urban population. The two rural exposure 

distributions were extrapolated to the U.S rural population. 

The EPA unit risk factor for benzene was used to convert estimated benzene 

exposures to annual cancer incidence rates. The resulting annual incidence rates 

were 0.329 case per million persons for the U.S. urban population, 0.184 case per 

million persons for the U.S. rural population, and 0.296 case per million persons for 

the general U.S. population. 
P.10 

2.0 INTRODUCTION 

Within the U.S. Environmental Protection Agency (EPA), the Office of Mobile 

Sources (OMS) has responsibility for assessing health risks associated with human 

exposure to air pollutants emitted by mobile sources. In accordance with this 

responsibility, OMS continually evaluates and updates its exposure and risk 

assessment capabilities to reflect the current state of knowledge. 

Recently, OMS directed IT Air Quality Services (ITAQS) to develop a modeling 

methodology capable of estimating annual average carbon monoxide (CO) exposures 

in urban and rural areas under specified control scenarios. The annual average CO 

exposures would be used to estimate annual average exposures to various hazardous 

pollutants associated with mobile sources. In each case, OMS would assume that the 

annual average exposure to a particular hazardous pollutant (hereafter referred to as 

"Pollutant X") was proportional to annual average CO exposure under the same control 

scenario. 

Researchers identified two existing models which appeared to be applicable to 

the specified task: the National Ambient Air Quality Standards Exposure Model 

(NEM)1 and the Hazardous Air Pollutant EExposure Model (HAPEM)2. These models 

are similar in that both simulate the movements of population groups between home 

and work locations and through various microenvironments. They differ in the time 

resolution used in expressing exposure estimates. NEM provides minute-by-minute 

exposure estimates which can be averaged over longer time periods, whereas HAPEM 

provides only annual average exposure estimates. HAPEM includes a facility for 

estimating cancer incidence; NEM does not currently include such a facility. As the 

OMS task required estimates of annual average exposure and corresponding cancer 

incidence, HAPEM was judged the more appropriate model. 
P.11 

A special version of HAPEM applicable to pollutants associated with mobile 

sources was subsequently developed. This model, denoted HAPEM-MS, was tested 

by using it to estimate benzene exposures based on the 10-step methodology 

described in Section 3. Recommendations for further refinements of this model are 

presented in Section 4. 
P.12 

3.0 THE HAPEM METHODOLOGY 

This section provides a description of the methodology used to apply HAPEMMS to benzene. This general procedure is applicable to other hazardous pollutants in 

exposure assessments which assume that exposure to the hazardous pollutant is 

proportional to exposure to carbon monoxide emitted by mobile sources. 

3.1 Overview 

The primary goals of the modeling effort included 1) estimation of benzene 

exposures for each of 334 Metropolitan Statistical Areas defined by the Bureau of 

Census (BOC) in 1988 and 2) estimation of benzene exposures within the U.S. rural 

population, defined as all persons residing outside the 344 Metropolitan Statistical 

Areas. The modeling effort employed the following ten-step methodology. 

1. Select representative urban and rural areas. These areas constitute the 

Model Urban Areas (MUAs) and Model Rural Areas (MRAs), jointly 

referred to as the Model Study Areas. 

2. Define the population-at-risk within each Model Study Area, divide each 

Model Study Area into exposure districts, and specify an exposure 

period. 

3. Divide the population-at-risk into cohorts. 

4. Estimate the fraction of time spent by each cohort in each combination of 

geographic location and microenvironment. 

5. Estimate the annual average CO exposure of each cohort. Estimate the 

annual average exposure to Pollutant X by assuming Pollutant X 

concentration is proportional to CO concentration. 

6. Extrapolate the Pollutant X cohort exposures to the population-at-risk 

within each Model Study Area and estimate the associated risk. 
P.13 

7. Define a geographic region represented by each MUA. Within each 

region, determine the population and pollutant adjustment ratio for each 

city of significant size. 

8. Apply the exposure distribution and cancer incidence rate of each MUA 

to each city in the associated geographical region. Sum exposure and 

incidence estimates over all cities in the region to determine the regional 

exposure distribution and cancer incidence. Sum the regional 

estimates to determine the exposure distribution and cancer incidence for 

the U. S. urban population. 

9. Apply the average exposure distribution and incidence rate estimated for 

the MRAs to the U.S. rural population. 

10. Combine urban and rural estimates of exposure and incidence to 

produce a national exposure estimate. 

This approach provides exposure and incidence estimates for each of the 334 

Metropolitan Statistical Areas (Step 8), for regional urban populations (Step 8), for the 

total U.S.. urban population (Step 8), for the total U.S. rural population (Step 9), and for 

the total U.S. population (Step 10). 

The general methodology is quite flexible. Only minor program changes are 

required to develop exposure and incidence estimates for other populations of special 

interest to OMS, such as the nine cities which are currently using reformatted gasoline. 

Subsection 3.2 provides a detailed description of each of the ten steps using 

benzene as Pollutant X. Section 4 presents recommendations for further research. 

3.2 Application to Benzene 

3.2.1 Select Representative Urban and Rural Areas 

The following 11 urban study areas served as sample cities, or Model Urban 

Areas (MUA's), in the modeling effort. 
P.14 

Boston 

Denver 

Houston 

Los Angeles 

Minneapolis/St. Paul 

New York City 

Philadelphia 

Phoenix 

St. Louis 

Spokane 

Washington, D. C 

These 11 urban areas had been selected previously for an application of pNEM to CO 

based on perceived geographical representativeness and the availability of sufficient 

carbon monoxide monitoring data. 

To select representative rural areas for the modeling effort, researchers first 

compiled a list of all CO monitoring sites which met the following two criteria. 

1) The site reported valid annual average CO concentrations for 1988, 

based on the completeness criteria employed by EPA's Aerometric 

A Information Retrieval System (A|RS). 

2) The site was not located within a Metropolitan Statistical Area defined by 

the BOC. 

Nine sites met both criteria. Six of these were found to be located in counties 

associated with Metropolitan Statistical Areas, and one was found to have reported 

high CO concentrations suggesting high traffic density. These sites were considered 

unrepresentative of rural land use and were dropped from further consideration. The 

remaining two sites were located in Paducah, Kentucky, and Farmington, New Mexico. 

The land use surrounding each of these sites was considered to be generally 

representative of rural areas. Consequently, land within a 10 km radius of each site 

was designated as a Model Rural Area (MRA). 

3.2.2 Define Population-At-Risk, EExposure Districts, and EExposure Period 

The population-at-risk in each Model Study Area was defined as all residents of 

a specified residential zone. For the urban areas, this zone was defined so as to 

include most of the highly urbanized land area. In some cases, this zone could be 
P.15 

best described as all census tracts within a specified distance of a "center point" 

located within the central business district. In the remaining cases, it could best be 

described as one or more counties surrounding the center point. The residential 

zones defined for the rural areas included all census tracts within 10 km of the fixedsite CO monitor selected to represent each area. 

Table 1 provides a description of each residential zone. The location of each 

center point is defined in terms of the Universal Transverse Mercator (UTM) coordinate 

system. This system locates geographic points as distances north and east of an 

origin specific to a defined quadrant, the designated UTM zone. 

Table 2 lists the number of persons living in each residential zone according to 

the 1980 census. Populations range from over 26 million (New York City) to less than 

30,000 (Farmington). 

The workers within each residential zone were assumed to commute to work 

places located within a commuting zone, defined as all census tracts located within 50 

km of the center point (Table 1). The 50 km radius ensured that each commuting 

zone would include all potential work places within the associated residential zone and 

the most probable work places outside the residential zone. In general, all exposures 

of the population-at-risk were assumed to occur within the commuting zone when 

persons were at work and within the residential zone at other times. 

A set of fixed-site CO monitors were selected to represent the ambient CO 

levels in each Model Study Area. Calendar year 1988 was identified as the best recent 

year with respect to the general quality of data reported by these monitors. It was 

used as the exposure period for all study areas except Washington. Two of the 

downtown Washington monitors reported insufficient 1988 data to be used in the 

exposure analysis. As all of the Washington monitors reported sufficient data for the 

following year, 1989 was specified as the exposure period for the Washington study 

area. Table 3 lists the CO monitors selected to represent each Model Study Area, the 

exposure period (calendar year), the number of one-hour values reported by each 

monitor during the exposure period, and the annual average CO value for that year. 

8 
P.16 

TABLE 1. CITY CENTERS AND RESIDENTIAL ZONES DEFINED FOR MODEL STUDY AREAS 

Land use 

classification 

Urban 

Rural 

= 

Model study 

area 

Boston 

Denver 

Houston 

Los Angeles 

Minneapolis/ 

St. Paul 

New York City 

Philadelphia 

Phoenix 

St. Louis 

Spokane 

Washington, DC 

Farmington, NM 

Paducah, KY 

UTM" coordinates of area 

center 

Zone 

19 

13 

15 

11 

15 

18 

18 

12 

15 

11 

18 

12 

16 

Northing 

4691.5781 

4399.1328 

3294.0330 

3769.4038 

4979.8633 

4510.8672 

4422.2305 

3705.6689 

4279.1250 

5278.1953 

4308.4375 

4068.050 

4105.450 

Easting 

330.6902 

500.4460 

271.4929 

384.9932 

477.2979 

585.7268 

487.5796 

400.2964 

743.5127 

468.7935 

324.0149 

748.2000 

357.8500 

Description of 

residential zone 

20 km radius 

Adams Co., Denver Co., 

Jefferson Co., Arapahoe 

Co. 

20 km radius 

25 km radius 

Hennepin Co., Ramsey Co. 

Bergen Co., Essex Co., 

Union Co., Hudson Co., 

Bronx Co., Kings Co., 

New York Co., Queens 

Co., Richmond Co. 

Camden Co., Gloucester 

Co., Philadelphia Co. 

20 km radius 

St. Louis City, St. 

Louis Co. 

10 km radius 

District of Columbia, 

Montgomery Co., Prince 

George's Co., 

Alexandria, Fairfax, 

Falls Church, Manassas, 

Manasses Park, 

Arlington, Fairfax Co. 

10 km radius 

10 km radius 

Universal Transverse Mercator 
P.17 

TABLE 2. RESIDENTIAL POPULATIONS, EXPOSURE PERIODS, AND COHORT COUNTS 

FOR MODEL STUDY AREAS 

Land use 

classification 

Urban 

Rural 

Model study area 

Boston 

Denver 

Houston 

Los Angeles 

Minn./St. Paul 

New York City 

Philadelphia 

Phoenix 

St. Louis 

Spokane 

Washington, O.C. 

Farmi ngton 

Paducah 

1980 

Residential 

population 

1.742.721 

1.387.511 

1.671.041 

15.807,653 

1.385.759 

26.392,236 

8,645,446 

1,120,893 

1.417.505 

196,773 

2,754,625 

26,919 

95,518 

Exposure 

period 

1988 

1888 

1988 

1988 

1988 

1988 

1988 

1988 

1988 

1988 

1989 

1988 

1988 

Number of 

home 

districts 

4 

6 

6 

10 

7 

13 

7 

8 

8 

4 

11 

1 

1 

Number of 

work 

districts 

5 

7 

7 

11 

8 

14 

8 

9 

9 

5 

12 

2 

2 

Number 

of 

cohorts 

84 

162 

162 

390 

ao 

624 

ao 

264 

2164 

84 

462 

12 

12 

10 

L 
P.18 

TABLE 3. CARBON MONOXIDE MONITORS SELECTED TO REPRESENT MODEL 

STUDY AREAS 

Model study area 

Boston 

Denver 

Farmington 

Houston 

Los Angeles 

Minneapolis 

Year 

1988 

1988 

1988 

1988 

1988 

1988 

Monitor ID 

250250002 

250250016 

250250021 

080310002 

080310014 

080310013 

080590002 

080050002 

350450006 

482010024 

482010047 

482011034 

482011035 

482011037 

060370002 

060370113 

060371002 

060371103 

060371201 

060371301 

060372401 

060374002 

060375001 

270530056 

270530058 

270530059 

271230044 

271230048 

271230050 

Number 

of 1-h 

values 

6980 

8240 

8673 

8668 

8649 

8448 

8659 

8190 

8535 

7775 

8294 

7826 

8481 

8289 

8281 

8377 

8170 

8305 

8320 

8267 

8343 

8349 

8329 

7997 

8723 

8648 

8504 

7900 

8394 

Annual average carbon 

monoxide concentration, ppm 

1.58 

1.51 

0.98 

2.19 

1.65 

1.93 

1.20 

0.53 

0.74 

0.78 

1.21 

0.86 

0.77 

0.93 

1.22 

1.46 

2.56 

2.18 

1.55 

3.17 

1.76 

1.53 

1.66 

0.83 

1.46 

2.84 

2.07 

1.20 

2.36 

11 (continued) 
P.19 

TABLE 3. (continued) 

Model study area 

N ew York 

Paducah 

Philadelphia 

Phoenix 

St. Louis 

Year 

1988 

1988 

1988 

1988 

1988 

Monitor ID 

340040004 

340035001 

340130011 

340171002 

340390003 

360470071 

360470076 

360610010 

360610058 

360610062 

360610082 

360810054 

211451023 

340070003 

340071001 

421010004 

421010029 

421010047 

421010051 

040130013 

040130019 

040131004 

040132001 

040132004 

040133002 

040133003 

291890001 

291890006 

291893001 

291895001 

291897001 

295100080 

295100083 

Number 

of 1-h 

values 

8467 

8548 

8511 

8417 

8368 

7588 

7013 

6628 

8142 

8136 

7682 

6648 

8300 

8250 

8232 

8129 

8102 

6686 

6781 

8662 

8170 

7854 

8191 

8358 

8602 

8691 

8561 

8403 

8520 

7936 

8378 

8584 

8244 

Annual average carbon 

monoxide concentration, ppm 

2.06 

1.23 

1.02 

2.39 

2.23 

2.91 

0.92 

1.20 

3.03 

3.34 

4.63 

1.03 

0.65 

0.73 

0.38 

0.99 

1.14 

1.46 

2.11 

1.30 

2.18 

1.37 

1.12 

0.95 

1.66 

1.50 

0.68 

0.50 

0.89 

0.64 

0.86 

1.20 

1.55 

12 (continued) 
P.20 

TABLE 3. (continued) 

1 Model study area 

Spokane 

Washington, D.C. 

Year 

1988 

1989 

Monitor ID 

530630027 

530630028 

530630029 

530630040 

110010017 

110010023 

240311008 

240330001 

510130020 

510590018 

510591004 

Number 

of 1-h 

values 

8625 

8727 

8743 

8656 

7213 

7138 

8290 

8610 

8420 

8697 

8460 

Annual average carbon 

monoxide concentration, ppm 

2.20 

2.13 

2.04 

3.62 

1.56 

1.75 

1.54 

1.33 

0.78 

0.96 

0.66 

Each Model Study Area was divided into a nonoverlapping set of exposure 

districts. Each exposure district was defined as a collection of census tracts. The 

maximum number of exposure districts defined for a particular Model Study Area was; 

n + 2, where n was the number of fixed-site CO monitors selected to represent the 

Model Study Area. One exposure district was defined as a circular area surrounding 

each of the n monitoring locations; one exposure district was defined as the remaining 

land area within the residential zone (if any); and one exposure district was defined as 

the remaining land area within the commuting zone. 

The step-by-step procedure used for defining the exposure districts and for 

assigning monitoring data to each district is presented below. 

1. Identify the "city center" as a specific location and determine its UTM 

coordinates. The city center should be located in or near the area of 

highest traffic density in the central business district of the city. 

2. Define the "residential zone" as a collection of census tracts including 

and surrounding the city center. All cohorts are assumed to live within 

the residential zone. Avoid extending the residential zone into areas far 

13 
P.21 

from fixed-site monitors. Do not include census tracts that are more than 

50 km from the city center. 

3. Define the "commuting zone" as all census tracts within 50 km of the city 

center. All cohorts are assumed to work within the commuting zone. 

4. Identify the fixed-site monitor that best represents street canyon locations 

(Monitor SC). 

5. Define the high CO district as all census tracts that can be characterized 

as having street canyons and all census tracts in the central business 

district. Ambient CO levels in the high CO district are determined by 

Monitor SC. 

6. Define a district for each remaining fixed-site monitor as all census tracts 

within 10 km of the specified monitor that fall within the residential zone. 

Ambient CO levels in each of these districts are determined by the 

associated monitor. 

7. Define the inner residual district as all census tracts within the residential 

zone that are not included in one of the districts defined in Steps 5 and 

6. Ambient levels in this district are determined by averaging the 

concentrations at all fixed-site monitors. 

8. Define the outer residual district as all census tracts within 50 km of the 

city center (i.e., within the commuting zone) that are not included in one 

of the districts defined in Steps 5, 6, and 7. Ambient levels in this district 

are determined by averaging concentrations at all fixed-site monitors. 

The rationale behind this procedure is discussed in Reference 3. 

3.2.3 Divide the Population-At-Risk Into Cohorts 

Each population-at-risk was divided into a non-overlapping set of cohorts. Each 

cohort was assumed to contain persons with identical exposures during the specified 

calendar year. Each cohort was identified as a distinct combination of 1) demographic 

group, 2) home district, and 3) work district (if applicable). 

14 
P.22 

The demographic group specified the age and work status of the cohort (Table 

4). This information provided a means of linking cohort exposure to activity patterns, 

which are assumed to vary significantly with age and work status. The home district 

was defined as the exposure district containing the cohort's residence. The work 

district was similarly defined as the exposure district containing the cohort's place of 

employment (if applicable). Specifying the home and work district of each cohort 

provided a means of linking cohort exposure to ambient pollutant concentrations at 

home and at work. 

Table 4 lists the number of cohorts associated with each demographic group 

for Philadelphia, a Model Study Area with 7 home districts and 8 work districts. In this 

example, each of the six nonworking demographic groups is associated with 7 

cohorts, one for each possible home district. Each of the three working demographic 

groups is associated with 56 cohorts, one for each of the 7 x 8 possible combinations 

of home district and work district. The total number of cohorts is (6 x 7) + (3 x 56) or 

210. 

3.2.4 Estimate Time Spent by Cohorts in Each Combination of Location and 

Microenvironment 

HAPEM-MS provides an algorithm for estimating the fraction of time each 

cohort spends at home and at work in user-specified microenvironments. The 

algorithm accomplishes this task by assembling an exposure event sequence for each 

cohort spanning the exposure period. In the application described here, each 

sequence consisted of 365 person-days sampled from an activity diary data base 

prepared by ITAQS. 

15 
P.23 

TABLE 4. NUMBER OF COHORTS DEFINED FOR PHILADELPHIA BASED ON 9 DEMOGRAPHIC 

GROUPS, 7 HOME DISTRICTS, AND 8 WORK DISTRICTS 

| Demographic group 

I 1. Children 0 to 5 

2. Children 6 to 13 

3. Children 14 to 18 years 

4. Workers with low probability of outdoor 

work 

5. Workers with moderate probability of 

outdoor work 

6. Workers with high probability of 

outdoor work 

7. Nonworking adults under 35 years 

8. Nonworking adults 35 to 54 years 

9. Nonworking adults 55+ years 

Includes 

commuting 

cohorts? 

No 

No 

No 

Yes 

Yes 

Yes 

No 

No 

No 

Number of cohorts 

associated with 

group 

7 

7 

7 

56 

56 

56 

7 

7 

7 

210 

The activity diary data base was constructed from data collected during the 

Cincinnati Activity Diary Study4. During this study over 900 subjects completed threeday diaries and detailed background questionnaires. Each person-day of Cincinnati 

diary data was classified according to four indices: demographic group (Table 4), 

season (summer or winter), day type (weekday or weekend), and temperature range 

(high or low). The demographic group index was determined by the demographic 

characteristics of the subject filling out the diary. The season and day type indices 

were based on the calendar date of the diary data. The temperature range index was 

based on the daily maximum temperature in Cincinnati on that date. Additional 

information concerning the classification procedure is provided by Johnson et al1. 

To adjust for the effects of local climate on activities, a sequence of daily 

16 
P.24 

maximum temperatures was obtained for each Model Study Area for the specified 

exposure period (1988 or 1989). Based on these data, each calendar day of the 

exposure period was assigned to one of the temperature ranges used in classifying 

the Cincinnati diary data. The algorithm then constructed an exposure event 

sequence for each cohort in the Model Study Area by selecting a person-day of 

Cincinnati diary data for each calendar day according to the demographic group of the 

cohort and the season, day type, and temperature range associated with the calendar 

day. 

Each exposure event sequence consisted of a series of events with durations 

from 1 to 60 minutes. Each exposure event assigned the cohort to a particular 

combination of geographic location and microenvironment. The geographic location 

was either the home or work district associated with the cohort. The home/work 

assignment was based on the activity description for the event appearing in the 

associated activity diary. 

Five microenvironments were specified: 

0

 Indoors - residence 

0

 Indoors - other locations 

0

 Outdoors - near road 

0

 Outdoors - other locations 

0

 Inside Motor Vehicle. 

The microenvironment assignment was based on a microenvironmental description for 

the event appearing in the associated activity diary. 

The exposure event sequences were analyzed to determine the fraction of time 

spent by each cohort in each combination of geographic location and 

microenvironment. Table 5 lists these fractions as percentages for nine cohorts, one 

selected at random from each demographic group. The fractions reflect daily 

maximum temperature data reported for Philadelphia during 1988. 

17 
TABLE 5 . PERCENTAGE OF TIME SPENT IN EACH COMBINATION OF GEOGRAPHIC 

LOCATION AND MICROENVIRONMENT BY A PHILADELPHIA COHORT SELECTED AT 

RANDOM FROM EACH DEMOGRAPHIC GROUP 

Demographic 

group' 

1 1 

2 

3 

4 

5 

6 

7 

8 

9 

Percentage of time 

Home distr 

l

b 

82.4 

69.4 

65.8 

62.0 

61.7 

67.4 

76.6 

80.2 

85.3 

2 

6.2 

16.4 

21.3 

5.9 

7.7 

4.5 

11.3 

10.2 

6.4 

3 

3.3 

5.7 

3.3 

1.6 

1.9 

1.8 

2.8 

1.9 

2.3 

ct 

4 

4.8 

5.1 

4.5 

2.4 

3.1 

2.3 

2.7 

2.8 

2.5 

5 

3.3 

3.3 

4.9 

5.2 

7.7 

5.5 

6.6 

4.9 

3.5 

Work district 

1 

0 

0 

0 

1.7 

0.5 

0.5 

0 

0 

0 

2 

0 

0 

0 

19.7 

13.5 

6.6 

0 

0 

0 

3 

0 

0 

0 

0.3 

1.3 

1.7 

0 

0 

0 

4 

0 

0 

0 

0.4 

1.7 

5.7 

0 

0 

0 

5 

0 

0 

0 

0.8 

0.9 

4.0 

0 

0 

0 

"Defined in Table 4. 

Microenvironment (defined in Table 6). 

3.2.5 Annual Average Cohort [Exposures 

Tiie annual average exposure of each cohort was estimated by the expression 

C(d,h,w) = £ F(d,h,w,i,j) x M(i) xA'(j) ( l ) 

where C(d,h,w) = Annual average pollutant concentration to which the cohort was 

exposed 

d = Demographic group identifier 

h = Home location identifier 

w = Work location identifier (if applicable) 

18 
P.26 

F(d,h,w,i,j) = Fraction of time spent by the cohort in microenvironment i at 

location j 

M(i) = Microenvironment factor associated with microenvironment i 

A'(j) = Adjusted annual average ambient pollutant concentration for 

location j . 

The summation was carried out for all values of i and j . 

Values for F(d,h,w,i,j), the fraction of time spent in microenvironment i and district 

j , were developed in the previous step. The pollutant concentration in 

microenvironment i in district j was assumed to be proportional to a monitor-based 

pollutant level determined for the district. The proportionality factor is denoted by M(i). 

The Denver Personal Monitoring Study5 was identified as a good source of data 

for estimating M(i) values. In this study, each of 454 subjects carried a personal 

exposure monitor (PEM) which measured the CO concentrations to which the subject 

was exposed over a two-day period. The durations of the measured exposures varied 

from one to 60 minutes. The conditions associated with each exposure (time, 

location, microenvironment, etc.) were recorded in an activity diary carried by the 

subject. The study was conducted over a four-month period during the winter of 

1982-83. 

Analysts classified the Denver PEM measurements according to the five 

microenvironments listed in Table 6. The PEM CO concentrations associated with a 

particular microenvironment were regressed against simultaneous CO concentrations 

reported by fixed-site monitors to determine a regression equation for the 

microenvironment. The regression lines were constrained to pass through zero so 

that each regression equation is characterized by a slope value only (i.e., intercept = 

zero). PEM values obtained at locations with operating gas stoves or smokers were 

omitted from the regression analysis to better emphasize the effect of mobile sources 

on exposure. 

19 
_£.££-

Two sets of regression analyses were conducted. In the first set, PEM values 

identified as having been obtained at locations within 10 km of a Denver fixed-site 

monitor were regressed against the CO concentrations reported by the specified 

monitor. In the second set, PEM values identified as having been obtained at 

distances greater than 10 km from the nearest monitor were regressed against the 

average CO concentration for all monitors. 

The available data base for the first set of analyses was substantially larger than 

that available for the second set. As the slope value determined for each 

microenvironment by the second set of analyses was roughly equal to that determined 

by the first set, the decision was made to use the first set of regression slopes to 

estimate corresponding M(i) values. These M(i) values were applied in all districts, 

including districts where ambient levels were represented by monitoring data from a 

single monitor and districts where ambient levels were represented by the average of 

all monitors. These M(i) values are listed in Table 6. 

A different set of microenvironment factors were used in an earlier CO exposure 

analysis conducted by Johnson and Paul6. The earlier factors were based on a 1983 

TABLE 6. MICROENVIRONMENT FACTORS 

Code 

1 

2 

3 

4 

5 

''Number of PEM values usee 

Microenvironment 

Indoors - residence 

Indoors - other 

Outdoors - near road 

Outdoors - other 

Inside motor vehicle 

J to calculate microenvironme 

n

8 

11,725 

2,624 

303 

205 

3,726 

nt factor. 

Factor 

0.495 

0.619 

1.001 ! 

0.758 

1.554 

survey of the scientific literature. Most of the relevant research identified by this survey 

consisted of studies where monitors were placed at various locations inside and 

outside of selected buildings. Data from studies of this type are most appropriate for 

20 
estimating the ratio of indoor CO concentration to the concentration immediately 

outdoors. 

The 1983 survey failed to identify any data bases obtained from studies similar to 

the Denver PEM study, in which CO concentrations were measured simultaneously by 

PEM's and by nearby fixed-site monitors. Studies of this type are the preferred source 

of data for estimating the value of M(i), the average ratio of exposure in 

microenvironment i to the simultaneous outdoor CO concentration at a nearby fixedsite monitor. Recommendations for further research concerning the estimation of M(i) 

factors are presented in Section 4. 

The adjusted air quality concentration in Equation 1 was estimated by the 

expression 

A'(j) = k*D(j) * B (2) 

where 

D(j) = Mj) - B (3) 

AG) is the air quality assumed for district j under "as is" conditions, and B is a userdefined city-wide background pollutant concentration. The effects of controlling mobile 

source emissions is simulated through the use of the adjustment factor k. 

The "as is" air quality value [AQ)] is assumed to be the sum of two components, 

that is, 

A(j) = D{j) * B << > 

The D(j) term represents the contribution of emission sources that are affected by the 

control scenario being modeled. The background term (B) represents the contribution 

21 
P.29 

i t 

of sources that are not affected by the emission control scenario. In adjusting A(j) to 

simulate the effects of controlling mobile source emissions, only D(j) is multiplied by 

the adjustment factor k. For example, a city-wide 20 percent reduction in mobile 

source emissions would be modeled by setting k equal to 0.8 in Equation 2. 

All analyses described in this report used unadjusted air quality data; that is, k 

was assumed to equal 1.0. Consequently, the A'(j) term in Equation 1 was equal to 

AQ)-

The value of AQ) varies with district. For those districts where the district was 

defined around a fixed-site monitor, the value of AG) was set equal to the annual 

average CO concentration reported for the monitor. In the remaining districts, AQ) was 

set equal to the mean of the annual average CO concentrations for all monitors. 

The above procedure provides an estimate of the annual average CO exposure 

of each cohort. HAPEM-MS provides the user with the option of multiplying each 

cohort CO exposure by a constant which will convert the CO concentration to a 

corresponding Pollutant X concentration, based on the assumption that exposure to 

Pollutant X is directly proportional to CO exposure. 

In applying HAPEM-MS to benzene, researchers assumed that a constant of 

0.0033 would convert each CO exposure in micrograms per cubic meter to the 

corresponding benzene exposure in the same units. This conversion factor was 

developed by analyzing motor vehicle exhaust data obtained by the American 

Petroleum Institute (API) from 61 tests of late-model automobiles7. Benzene emissions 

were found to be highly correlated with CO emissions (the R-squared value equaled 

0.80); the median ratio of benzene to CO was 0.0033. 

3.2.6 Extrapolate Cohort Exposures to Population-At-Risk 

The cohort-specific exposure estimates developed in the previous step were 

extrapolated to the population-at-risk by estimating the size of each cohort. Through 

the use of a special computer program, cohort size was estimated directly from 

census and commuting data specific to each home district. These data were obtained 

from the Bureau of Census (BOC). 

22 
P.30 

•** * 

A two-step process was used to determine cohort population. Rrst, the 

population of each demographic group residing in a particular home district was 

calculated using detailed BOC data. As nonworking cohorts are defined by 

demographic group and home district, this step provided the populations of all 

nonworking cohorts. The populations of the working cohorts were then determined by 

further dividing the populations of the working demographic groups associated with 

each home district. The formula used in this step was: 

Com(d.h.w) = Pop(d,h) x F(h,w) (5) 

where 

Com(d,h,w) = Number of persons in the commuting cohort associated with 

demographic group d, home district h, and work district w 

P(d,h) = Number of persons in demographic group d in home district h 

F(h,w) = Fraction of all workers residing in home district h who commute 

to work district w. 

Values of F(h,w) were obtained from an origin-destination (O-D) table produced by a 

special commuting model. This model has been described by Johnson et ai2. 

The population exposure estimate for a particular cohort was converted to an 

expected annual cancer incidence [l(d,h,w)] using the expression 

I{d,h,w) = C(d,h,w) x P(d.h.w) x URE/70 (6) 

23 
P.31 

where C(d,h,w) = Annual average pollutant concentration associated with the 

cohort 

P(d,h,w) = Estimated population of the cohort 

URE = Lifetime (70-year) unit risk estimate for the pollutant. 

The current URE for benzene 8.3 x 10"6 case per /ig/m3. 

Figure 1 is an excerpt from the output of the Philadelphia HAPEM-MS run. The 

table in the printout lists the population of each cohort, the annual average benzene 

exposure estimated for the cohort, and the incidence value calculated by Equation 6 

At the bottom of the table, the cohort-specific incidence values are summed to yield a 

city-wide incidence value of 2.4066 cases. As this incidence occurred in an exposed 

population of 8,645,466 persons, the incidence rate for Philadelphia is estimated as 

0.278 case per million persons. 

Figure 2 provides additional output from the same HAPEM-MS run. The top 

table in the printout lists city-wide exposure estimates for Philadelphia. The exposures 

are expressed as the number of persons with annual average benzene exposures 

above specified concentration values. These data are also presented as population 

percentages in the same table. Below the table is listed the cohort with the highest 

annual average exposure. In this case, the cohort is defined as members of 

Demographic Group 6 (outdoor workers) who both reside and work in District 6. The 

cohort is estimated to contain 458 persons. Appendix A contains a facsimile of the 

entire printout. 

Table 7 presents the average benzene exposure concentration estimated by 

HAPEM-MS for each of the 13 model study areas. This value is the populationweighted average of the individual cohort exposures. The table also presents the 

incidence rate estimated for each area. These values range from 0.172 case per 

million (Paducah) to 0.652 case per million (Spokane). 

24 
P.32 

Wtl . 

**• * "•Mtfia.tiar. 

«i«k by C*ar.fa » 

'"•••aaaaaaaaaaaaaaa 

Annual 

Oi.tric t Bi.<ri«rt P^Utio * 

I M < Ua k 

<<i«/toaaS)aa-l) 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

19 

to 

>0 

to 

n 

19 

19 

19 

to 

to 

to 

tttts 

11990 

U99 t 

32987 

*»*s 

70M 

U t 

17 

t * 

um 

1791 

•19-

9*t 

9190 

1*13 

t007 

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9 U 

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1901 

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I M 

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t9t 

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272*2 

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1987 

S5S0 

7799 

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1710 

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23S10 

32403 

I9*t 

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t9f H 

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Study Araa Totals •**!** « 

Study i m than l»po«jrai 

l.M* 

1.433 

1.M7 

1.47S 

1.7*9 

1.7M 

l.* M 

1.S7* 

1.I7I 

1.801 

1.899 

1.99S 

1.900 

1.971 

t.109 

2.043 

t.l M 

t.SSO 

t.4St 

t.U» 

t.«89 

1.91* 

1.97S 

t.ll S 

1.8** 

<.M 7 

t.l M 

t.U 9 

2.1XS 

t.M 9 

2.444 

2.744 

t.«04 

t.7*S 

t.ai 7 

t.7*S 

t.920 

S.O** 

1.1*7 

J. 10* 

I.** 9 

t.S90 

t.7U 

t.824 

1.407 

t.7S9 

t.847 

2.M * 

t.«8S 

t.39 * 

2.3*8 

0.000008] 

0.0000081 

0.000008S 

0.000008S 

0.0000083 

0.000008S 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.«"»-

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

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0.0000083 

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0.0000083 

0.0000083 

0.0000083 

0.0000081 

0.0000083 

0.0000083 

0.0000083 

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0.0000083 

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0.0000083 

0.0000083 

0.0000083 

0.0000083 

Study Araa Canear Incidanea Rata • 

la I Study ATMi PHXUOUPMIA 

- trio Analysadi SASI CASI • 

•r ef h^aaura Pittrieta : 

0.17817 naN oata* 

P"* **•• * P«raor». 

t.OMlt 

•.004H 

o.oosii 

o.ootss 

0.0010* 

0.001*9 

0.00002 

0.00000 

0.00000 

o.ootss 

0.000*0 

0.000*0 

0.0014* 

0.00023 

0.0003* 

0.00230 

0.000J* 

0.000M 

o.ooias 

0.00029 

0.000*3 

0.00287 

0.00898 

0.00132 

0.00213 

0. 

0. 

0.00011 

0.00979 

0.001*3 

0.00233 

0.008*1 

0.001*1 

0.00202 

0.00822 

0.00138 

0.00200 

0.00291 

0.00048 

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.00742 

.01091 

.00122 

.00021 

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0.00872 

0.04*71 

•auaaaaaa 

t.40**0 

1988 

7 

FIGURE 1. EXCERPT FROM SAMPLE HAPEM OUTPUT CANCER INCIDENCE 

BY COHORT AND STUDY AREA TOTAL 

25 
P.33 

sssssssssssssssssssssssssssssssssssssssrrssssssss-iss 

POLLUTANT 

CONC 

ug/m*«3) 

5.00 

4.50 

4.00 

3.50 

3.00 

2.50 

2.00 

1.50 

1.00 

0.50 

0.00 

f of People 

•t or Above 

1 858 

1 61822 

1 114733 

1 123284 

451904 

1930816 

8372339 

8628433 

8633430 

8645446 

8645446 

V. of People 

ai or Above 

0.0099 

0.7151 

1.3271 

1.4260 

5.2271 

22.3333 

96.8410 

99.8032 

99.8610 

100.0000 

100.0000 

(a) Study Area: PHILADELPHIA 

Scenario Analyzed: BASE CASE - 1988 

Number of Exposure Districts: 7 

Cohort having highest 

annual average exposure: 

Demographic Group: 6 

Hone District: 6 

Work District: 6 

Group Population: 458 

Group Annual Average Exposure: 5.240 

Study Area: PHILADELPHIA 

Scenario Analyzed: BASE CASE - 1988 

FIGURE 2. EXCERPT FROM SAMPLE HAPEM OUTPUT PRESENTING STUDY 

AREA EXPOSURE DISTRIBUTION AND IDENTIFYING COHORT WITH 

HIGHEST ANNUAL AVERAGE EXPOSURE 

26 
P.34 

It should be noted that the exposure and incidence estimates presented in Table 

7 were developed using 1980 population data and 1988-89 CO monitoring data. 

Ideally, the application of HAPEM-MS to each area would have used 1988 population 

data to be consistent with the principal time period represented by the monitoring 

data. As census data of the required detail were available for 1980 but not for 1988, 

analysts employed 1980 data at this stage of the methodology. 

It is likely that the 1988 population in each area was larger than the 1980 

population. In this case, the use of 1980 population data will underestimate the 

number of persons in 1988 with benzene exposures above a specified concentration 

level. Cancer incidence (number of cases) will also be underestimated. The 

exposure/incidence statistics presented in Table 7 may be approximately correct, 

however. To the extent that the percent change in population from 1980 to 1988 was 

the same for all cohorts in an area, benzene exposure estimates expressed as 

averages and percentages will be correctly estimated. Incidence estimates expressed 

as rates (i.e., number of cases per million persons) will also be correctly estimated. 

Subsequent steps of the methodology draw on the exposure and incidence 

estimates determined by this step. In all cases, these later steps employ estimates 

expressed in the form of averages, percentages, or rates. 

3.2.7 Develop Data Bases for Geographic Regions 

ITAQS developed a list of 334 cities to represent the U.S urban population. This 

list included all cities designated by the BOC as Metropolitan Statistical Areas. The 

populations of these cities varied from 58,000 to 8,588,000 with a median population of 

240,000. OMS divided the U.S. into 11 geographical regions, one per MUA, so that 

each of the 334 cities could be assigned to one region. 

ITAQS developed a data base listing the following data for each of the 334 cities: 

Metropolitan Statistical Area identification number, region, 1988 population, and 

pollutant adjustment ratio. The pollutant adjustment ratio was calculated in two ways. 

27 
P.35 

TABLE 7. AVERAGE BENZENE EXPOSURES BY STUDY AREA 

Model study area 

Boston 

Denver 

Farmington 

Houston 

Los Angeles 

Minn./St. Paul 

New York 

Paducah 

Philadelphia 

Phoenix 

St. Louis 

Spokane 

Washington 

Average benzene 

exposure, Mg/m3 

3.21 

3.50 

1.64 

2.09 

4.42 

4.03 

3.14 

1.45 

2.34 

3.14 

2.00 

5.50 

2.69 

Incidence rate, cases 

million persons 

0.381 

0.415 

0.195 

0.248 

0.524 

0.478 

0.372 

0.172 

0.278 

0.372 

0.237 

0.652 

0.319 

per 

For cities possessing adequate CO monitoring data, the pollutant adjustment ratio was 

calculated as the ratio of the mean annual average CO concentration reported for the 

city to the mean annual average CO concentration in the associated MUA. These 

ratios are listed in Appendix B. For cities without CO monitoring data, the pollutant 

adjustment ratio was set equal to the median of the known ratios for the region 

containing the city. Table 8 lists the regional medians, means, and standard 

deviations. 

The combined 1988 population of the 334 cities was 188,696,000 persons. The 

remaining 1988 U.S. population (57,107,000 persons) was assumed to reside in rural 

areas. 

28 
P.36 

TABLE 8. SUMMARY STATISTICS BY REGION FOR POLLUTANT ADJUSTMENT 

RATIO BASED ON DATA REPORTED BY CITIES WITH 

CARBON MONOXIDE MONITORS 

Model study 

area 

Boston 

Denver 

Houston 

Los Angeles 

Minn./St. 

Paul 

New York 

City 

Philadelphia 

Phoenix 

St. Louis 

Spokane 

Washington, 

D.C. 

All regions 

Associated 

region 

5 

6 

10 

3 

4 

2 

8 

7 

11 

1 

9 

Number of cities in 

region 

Total 

46 

11 

84 

24 

35 

5 

16 

7 

63 

17 

26 

334 

With monioring data 

20 

8 

23 

21 

14 

5 

11 

7 

30 

13 

13 

165 

Pollutant adjustment ratio' 

Median 

0.950 

1.005 

1.070 

0.570 

0.790 

0.700 

0.870 

1.000 

1.170 

0.660 

1.000 

0.890 

Mean 

0.877 

1.107 

1.133 

0.612 

0.773 

0.750 

0.905 

1.061 

1.136 

0.736 

1.088 

0.940 

Standard 

deviation 

0.267 

0.340 

0.427 

0.190 

0.239 

0.215 

0.182 

0.489 

0.410 

0.159 

0.369 

0.371 

"Statistics based on cities with monitoring data. 

29 
P.37 

3.2.8 Estimate Urban Exposures and Incidence Values for Geographic 

Regions 

ITAQS developed the Regional EExposure Distribution Estimator (REDE) program 

as a means of applying the exposure distribution and cancer incidence rate 

determined for a particular MUA to all cities in the associated region. The program 

user enters the MUA exposure distribution into REDE as a set of paired pollutant 

concentration and percentage values. Each percentage value indicates the fraction of 

the population that experienced annual-average exposures above the specified 

pollutant concentration. The program user enters the MUA cancer incidence rate 

(either annual or lifetime) into REDE as the number of cases per million persons. 

These data are provided as output by HAPEM-MS. 

The REDE program also requires a file listing the population and pollutant 

adjustment ratio associated with each city in the region associated with the MUA. This 

file was created during the previous step (Subsection 3.2.7). 

To determine the exposure distribution for a particular city, the program multiplies 

each concentration value of the MUA exposure distribution by the city-specific 

adjustment factor and each associated percentage value by the population of the city. 

The program then interpolates where necessary to provide exposure estimates at 

pollutant concentration values specified by the user. 

To determine the incidence value (number of cases) for a particular city, the 

program multiplies the incidence rate (cases per million) of the MUA by the city 

population in millions. This product is then multiplied by the pollutant adjustment 

factor to account for the difference in pollutant levels between the city and the MUA. 

Figure 3 provides an excerpt from a sample run of the REDE program for an 

analysis of benzene exposure. The input exposure distribution and incidence rate for 

Philadelphia were obtained from the HAPEM-MS run associated with Figures 1 and 2. 

These data are listed in Figure 3 under the heading "INPUT DATA". In this case the 

user entered data into the program indicating that 0.0099 percent of Philadelphia 

residents experienced annual average exposures above 5 /.g/m3, 0.7151 percent 

30 
P.38 

INPUT DATA 

POLLUTANT: benzene 

MODEL CITY: Philadelphia 

POLLUTANT POPULATION 

CONC. PERCENTAGE 

6.00 

5.00 

4.00 

3.00 

2.00 

1.00 

0.00 

0.00 

0.01 

1. 33 

5.22 

96.84 

99.86 

100.00 

MODEL CITY INCIDENCE RATE = .278 case(s) per million 

FIGURE 3. EXCERPT FROM REDE PRINTOUT LISTING EXPOSURE ESTIMATES 

FOR PHILADELPHIA ENTERED AS INPUT DATA. 

31 
P.39 

above 4.50 Mg/m3, and so on. The assumed incidence rate was 0.278 case per 

million persons. 

Figures 4 and 5 present additional excerpts from the Philadelphia benzene 

analysis. Under the heading "AREA-SPECIFIC EXPOSURE ESTIMATES", the printout 

in Figure 4 presents the results of applying the Philadelphia exposure distribution and 

incidence rate to Pittsburgh. Similar results were obtained for each of 16 cities in the 

region represented by Philadelphia. Under the heading "REGIONAL EEXPOSURE 

ESTIMATES", the printout in Rgure 5 presents summaries of exposures and incidence 

for the entire region. 

REDE outputs for the 11 regions are available from ITAQS. Table 9 summarizes 

incidence data from these outputs. In the table are listed the 1988 population of each 

region, the estimated annual cancer incidence, and the incidence rate. Incidence rates 

vary from 0.267 case per million persons (Houston) to 0.484 case per million persons 

(Spokane). The total cancer incidence for all regions is 62.17 cases. The 

corresponding incidence rate is 0.329 case per million persons. 

3.2.9 Estimate Rural EExposures and Incidence Values 

HAPEM-MS was used to determine exposure distributions and incidence rates 

for each of the two representative rural areas (Farmington and Paducah). Following 

the directions of OMS, these results were then averaged to determine an overall rural 

exposure distribution and an overall rural incidence rate. Table 10 presents benzene 

exposure estimates for Farmington and Paducah together with the results of applying 

the overall rural exposure distribution for benzene to the 1988 rural population of the 

U.S., previously estimated to be 57,107,000 persons. Applying the average incidence 

rate for Farmington and Paducah, 0.184 case per million, to the rural U.S. population 

yields a rural incidence of 10.5 cases. 

32 
P.40 

AREA-SPECIFIC EXPOSURE ESTIMATES 

URBAN AREA ID = 6280 

POPULATION = 2094000 

ADJUSTMENT RATIO =1.03 

ESTIMATED INCIDENCE = .5995959 case(s) 

POLLUTANT 

CONC. POPULATION 

10.00 0 

9.00 0 

8.00 0 

7.00 0 

6.00 36 

5.00 4233 

4.00 37340 

3.00 276945 

2.00 2031513 

1.00 2091154 

0.00 2094000 

FIGURE 4. EXCERPT FROM REDE PRINTOUT LISTING AREEA-SPECIFIC BENZENE 

EEXPOSURE ESTIMATES FOR PITTSBURGH, PENNSYLVANIA (MSA code = 6280). 

CONCENTRATION CONCENTRATIONSS AP C IM ..- /- 3 ARE IN /<g/m3

. 

33 
P.41 

REGIONAL EXPOSURE ESTIMATES 

POLLUTANT 

CONC. 

10.00 

9.00 

8.00 

7.00 

6.00 

5.00 

4.00 

3.00 

2.00 

1.00 

0.00 

POPULATION 

0 

0 

0 

43 

3908 

18225 

153915 

1096520 

994*610 

11771940 

11813000 

Tota l populatio n = 1.1813E+07 

Tota l incidenc e = 3.17613 9 case(s ) 

Avq. incidenc e rat e = .268868 1 case(s). per millio n 

FIGURE 5. EEXCERPT FROM REDE PRINTOUT LISTING BENZENE EXPOSURE 

EXTIMATES FOR REGION REPRESENTED BY PHILADELPHIA, PENNSYLVANIA. 

CONCENTRATIONS ARE IN jig/m3. 

34 
P.42 

TABLE 9. ESTIMATED CANCER INCIDENCE RATES ASSOCIATED WITH 

ESTIMATED BENZENE EXPOSURE BY REGION 

Model urban 

area 

Boston 

Denver 

Houston 

Los Angeles 

Minn./St. Paul 

New York City 

Philadelphia 

Phoenix 

St. Louis 

Spokane 

Washington 

All 

Urbanized population 

of associated region 

19,051,000 

4,386,000 

38,227,000 

27,929,000 

18,490,000 

12,618,000 

11,813,000 

4,620,000 

30,323,000 

6,280,000 

14,959,000 

188,696,000 

Estimated annual incidence 

Number of cases 

6.48 

2.06 

10.20 

11.47 

6.23 

4.25 

3.18 

1.98 

8.11 

3.04 

5.17 

62.17 

Rate, cases per 

million persons 

0.340 

0.470 

0.267 

0.411 

0.337 

0.336 

0.269 

0.428 

0.267 

0.484 

0.346 

0.329 

TABLE 10. 

EXPOSUR 

Benzene 

concentration, 

M9/m3 

2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0 

ESTIMATED BENZENE EXPOSURE OF RURAL U.S. POPULATION BASED ON 

E ESTIMATES FOR FARMINGTON, NEW MEXICO AND PADUCAH, KENTUCKY 

Population at or above indicated benzene concentration 

Farmington' 

Number of 

persons 

0 

2,312 

17,001 

26,919 

26,919 

26,919 

26,919 

Percentage 

0 

8.59 

63.16 

100.00 

100.00 

100.00 

100.00 

Paducah" 

Number of 

persons 

0 

0 

6,475 

64,888 

95,518 

95,518 

95.518 

Percentage 

0 

0 

6.78 

68.04 

100.00 

100.00 

100.00 

Average 

percentage 

0 

4.30 

34.97 

84.02 

100.00 

100.00 

100.00 

Number of 

persons in 

rural U.S.0 

0 

2,456,000 

19,970,000 

47,981,000 

57,107,000 

57,107,000 

57,107,000 

"Estimated incidence rate • 0.195 cases p9r million persons. 

"Estimated incidence rale - 0.172 cases per million persons. 

Estimated incidence rate • 0.184 cases per million persons, estimated incidence • 10.51 cases. 

35 
P.43 

3.2.10 Combine Urban and Rural EExposure Estimates 

To determine the exposure distribution and incidence for the entire U.S., the 

estimates for the 11 regions defined by MUA's (Subsection 3.2.8) were added to the 

rural estimates developed in the previous step (Subsection 3.2.10). Table 11 provides 

these results for benzene. The total incidence is 72.68 cases in a population of 

245,803,000 persons, an incidence rate of 0.296 case per million persons. 

Tables 10 and 11 provide estimates of annual cancer incidence rates associated 

with benzene exposure for each of the 11 regions, for all urban areas, for all rural 

areas, and for the total U.S. population. To facilitate comparisons, these estimates are 

collected in Table 12. Analysts applied the EPA unit risk estimate for benzene to each 

incidence rate in Table 12 to determine a corresponding annual average benzene 

exposure concentration. The equation used in this calculation was 

C = IR x 70/URE, (7) 

where C is the estimated annual average benzene exposure, IR is the estimated 

annual incidence rate (cases per million persons), and URE is the unit risk estimate for 

benzene. Table 12 lists the resulting values of C under the heading "equivalent 

average benzene exposure." 

36 
P.44 

TABLE 11. ESTIMATED BENZENE EXPOSURE FOR URBAN, RURAL, AND 

TOTAL U.S. POPULATIONS 

Benzene concentration, 

M9/m3 

12 

11 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

0 

Number of persons at or above indicated benzene concentration 

Urban areas3 

0 

8,262 

73,691 

241,278 

421,519 

1,145,169 

3,462,811 

8,426,555 

28,478,840 

71,623,397 

139,502,987 

184,656,228 

188,696,000 

Rural areas'3 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

57,107,000 

57,107,000 

Totalc 

0 

8,262 

73,691 

241,278 

421,519 

1,145,169 

3,462,811 

8,426.555 

28,478,840 

71,623,397 

139,502,987 

241,763,228 

245,803,000 

Estimated incidence = 62.17 cases. 

b

 Estimated incidence = 10.51 cases. 

Estimated incidence = 72.68 cases. 

TABLE 12. AVERAGE BENZENE EXPOSURES BY REGION 

Model study area defining region 

Boston 

Denver 

Houston 

Los Angeles 

Minn./St. Paul 

New York 

Philadelphia 

Phoenix 

St. Louis 

Spokane 

Washington 

Total urban population 

Total rural population 

Total U. S. population 

Incidence rate, cases per million 

persons 

0.340 

0.470 

0.267 

0.411 

0.337 

0.336 

0.269 

0.428 

0.267 

0.484 

0.346 

0.329 

0.184 

0.296 

Equivalent average benzene esposure, 

J*g/m3 

2.87 

3.96 

2.25 

3.47 

2.84 

2.83 

2.27 

•J 

3.61 

2.25 

4.09 

2.92 

2.77 

1.55 

2.49 

37 
P.45 

4.0 RECOMMENDATIONS FOR FURTHER RESEARCH 

The methodology described in Section 3 was developed to provide reasonable 

exposure estimates within the scheduling and resource constraints specified for this 

project. The authors of this report recommend that EPA consider refining the 

methodology when additional resources are available. Four specific recommendations 

are discussed here. 

In the current methodology, monitoring data and activity data (i.e., time fractions) 

are expressed as annual averages. HAPEM-MS could better account for seasonal and 

diurnal patterns in exposure if these monitoring and activity data were averaged by 

season and time of day. For example, the use of four calendar quarters and four 

diurnal periods (midnight - 6:00 a.m., 6:00 a.m. - noon, noon - 6:00 p.m., and 6:00 

p.m. - midnight) would divide the year into 16 (i.e., 4x4) distinct time periods. 

Indexing the F(d,h,w,i,j) and A'(j) terms in Equation 1 according to these 16 time 

periods would greatly improve the temporal resolution of the model without 

significantly increasing program complexity or run time. 

Five microenvironments are specified in the current methodology (Table 6). 

These microenvironments are actually aggregate microenvironments in that each is 

defined as a combination of microenvironments selected from a list of over 30 

"location" codes appearing in the Cincinnati activity diary data base. These codes 

could be combined in other ways to yield aggregate microenvironments of particular 

interest to OMS. For example, the "indoors - other" microenvironment could be 

divided into two separate microenvironments, one that contains indoor locations which 

are likely to be directly affected by motor vehicles (e.g., indoors - service station) and 

one that contains indoor locations which are unlikely to be directly affected by motor 

vehicles (e.g., indoors • health care facility). 

The microenvironment factors [i.e., the M(i) values] specified by the HAPEM-MS 

user have a significant effect on the exposure estimates produced by the model. In 

38 
P.46 

the exposure analyses discussed in this report, the value of M(i) for each 

microenvironment was assumed to be constant regardless of time of year or 

geographical location. The M(i) values in Table 6 were estimated using data obtained 

from one city (Denver) over a four-month period during the winter of 1982-83. 

Although the Denver study was judged to be the best single source of data for 

estimating M(i) values, there is some degree of uncertainty as to whether the resulting 

M(i) estimates are applicable to other areas and other seasons. Alternative sources of 

data should be investigated with the goal of determining whether the M(i) values 

presented in Table 6 are generally applicable. 

HAPEM-MS can be considered a stochastic or "probabilistic" model in that it 

contains a source of random variation. The algorithm that determines the set of time 

fractions for each cohort [i.e., the F(d,h,w,i,j) values in Equation 1] employs a random 

number generator to select data from the Cincinnati activity diary data base. 

Consequently, two runs of the algorithm will yield two different sets of time fractions. 

In the applications discussed in this report, it has been assumed that the exposure 

estimates produced by HAPEM-MS are fairly robust; that is, the estimates are not 

sensitive to the random elements incorporated in the time fraction algorithm. This 

assumption should be tested by making multiple runs of HAPEM-MS with all other 

inputs held constant and comparing the results. 

39 
P.47 

REFERENCES 

1. Johnson, T. R., R. A. Paul, and J. E. Capel, 1991, "Estimation of Carbon 

Monoxide EExposures and Associated Carboxyhemoglobin Levels in Denver 

Residents Using a Probabilistic Version of NEM," prepared by IT Air Quality 

Planing and Standards, U. S. Environmental Protection Agency, Research 

Triangle Park, North Carolina. 

2. Johnson, T. R„ J. E. Capel, and D. M. Byrne, 1991, The Estimation of 

Commuting Patterns in Applications of the Hazardous Air Pollutant EExposure 

Model (HAPEM)," Paper No. 91-172.6, presented at the 84th Annual Meeting of 

the Air and Waste Management Association, Vancouver, Canada. 

3. Johnson, T. R., 1991, Letter to Tom McCurdy (May 1,1991), Office of Air Quality 

Planning and Standards, U. S. Environmental Protection Agency, Research 

Triangle Park, North Carolina. 

4. Johnson, T. R., 1987, "A Study of Human Activity Patterns in Cincinnati, Ohio," 

Electric Power Research Institute, Palo Alto. 

5. Johnson, T. R., 1984, "A Study of Personal .Exposure to Carbon Monoxide in 

Denver, Colorado," EPA-600/54-014, U. S. Environmental Protection Agency, 

Research Triangle Park, North Carolina. 

6. Johnson, T. R. and R. A. Paul, 1983, The NAAQS EExposure Model (NEM) 

Applied to Carbon Monoxide," EPA-450/5-83-C04, U. S. Environmental Protection 

Agency, Research Triangle Park, North Carolina. 

7. American Petroleum Institute, 1988, "Exhaust Benzene Emissions from Late 

Model Vehicles," Publication No. 841-44700, Washington, D. C. 

40 
P.48 

APPENDIX A 

SAMPLE OUTPUT OF HAPEM-MS 

41 
P.49 

Tabla 1. Sunmry of Cancer Risk by Cohort! •) 

•i>ii.::fZ3iiiiiBti>i:iiii>i<iiiiiii<i»>:iis»sHi<itisiitfiiniit i tiiniui»>i » 

Cohort Identification 

Demographic Hon* Hork Cohort 

Creep District District Population 

Annual Unit Risk Incidence 

Average Factor of Cancer 

Exposure ((ug/e*«3Hw-1) I m cases 

<ug/m*«3) par year) 

1 

2 

3 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

7 

8 

9 

1 

2 

3 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

0 

0 

0 

1 

1 

1 

2 

2 

2 

3 

3 

3 

4 

4 

4 

5 

5 

5 

6 

6 

6 

19 

19 

19 

20 

20 

20 

0 

0 

0 

0 

0 

0 

1 

1 

1 

2 

2 

2 

3 

3 

3 

4 

4 

4 

5 

5 

5 

6 

6 

6 

22223 

33998 

25992 

32987 

4963 

7050 

112 

17 

24 

11921 

1793 

2548 

6392 

962 

1366 

9390 

1413 

2007 

6351 

955 

1357 

12648 

1903 

2703 

797 

120 

170 

22963 

10117 

52005 

2086 

2703 

1398 

158 

26 

50 

910 

50 

288 

89 

15 

28 

60 

10 

19 

56 

9 

18 

29 

5 

9 

2933 i 

1.564 

1.633 

1.657 

1.675 

1.769 

1.788 

1.488 

1.574 

1.572 

1.801 

1.899 

1.993 

1.900 

1.975 

2.109 

2.063 

2.166 

2.358 

2.452 

2.528 

2.689 

1.916 

1.973 

2.113 

1.864 

1.969 

2.107 

1.655 

1.590 

1.540 

0.821 

0.843 

0.860 

1.070 

1.099 

1.236 

0.874 

0.918 

0.954 

1.198 

1.242 

1.346 

1.274 

1.320 

1.456 

1.501 

1.480 

1.640 

1.808 

1.828 

2.083 

MB 

1.432 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000063 

0.0000083 

0.0000083 

0.0000083 

0.0000063 

0.0000083 

0.0000083 

0.0000063 

0.0000083 

0.0000063 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000063 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 OOO OOO OOO OOO ooo ooo 

0.00412 

0.00658 

0.00511 

0.00655 

0.00104 

0.00149 

0.00002 

0.00000 

0.00000 

0.00255 

0.00040 

0.00060 

0.00144 

0.00023 

0.00034 

0.00230 

0.00036 

0.00056 

0.00185 

0.00029 

0.00043 

0.00287 

0.00045 

0.00068 

0.00018 

0.00003 

0.00004 

0.00451 

0.00191 

0.00949 

0.00020 

0.00027 

0.00014 

0.00002 

0.00000 

0.00001 

0.00009 

0.00002 

0.00003 

0.00001 

0.00000 

0.00000 

0.00001 

0.00000 

0.00000 

0.00001 

0.00000 

0.00000 

0.00001 

0.00000 

0.00000 

0.00044 

5.00008 

0.00016 
P.50 

4 

5 

6 

7 

8 

9 

1 

2 

3 

4 

5 

6 

4 

5 

6 

4 

5 

> "4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

7 

8 

9 

1 

2 

3 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

5 

6 

4 

2 

2 

2 

2 

2 

2 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

20 20 20 

0 

0 

0 

0 

0 

0 

1 

1 

1 

2 

2 

2 

3 

3 

3 

4 

4 

4 

5 

5 

5 

6 

6 

6 

19 19 19 20 20 20 

0 

0 

0 

0 

0 

0 

1 

1 

1 

2 

2 

2 

3 

3 

3 

4 

4 

4 

5 

5 

5 

6 

6 

6 

19 \l 

92 15 29 

1240 

531 

2710 

505747 

782074 

599671 

327112 

49555 

73059 

4563 

691 

1019 

600927 

91035 

134214 

281290 

42613 

62825 

165005 

24997 

36853 

147364 

22324 

32913 

377445 

57180 

84301 

39020 

5911 

8715 

519252 

226568 

1191441 

40685 

61359 

51245 

21437 

3381 

4312 

667 

105 

/*.. w «44 

3997 

5098 

27117 

4277 

5454 

17475 

2756 

3515 

10258 

1618 

2063 

43284 

2562 

1.254 

1.326 

1.462 

0.850 

0.622 

0.603 

2.140 

2.207 

2.243 

2.126 

2.232 

2.265 

1.925 

2.055 

2.062 

2.275 

2.409 

2.441 

2.341 

2.483 

2.500 

2.512 

2.664 

2.784 

2.910 

3.008 

3.227 

2.352 

2.451 

2.531 

2.338 

2.454 

2.560 

2.228 

2.148 

2.095 

2.434 

2.523 

2.553 

2.370 

2.562 

2.570 

2.186 

2.340 

2.311 

2.510 

2.689 

2.727 

2.619 

2.765 

2.764 

2.758 

2.912 

3.037 

3.237 

3.287 

3.463 

2.627 

f:f.l 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000063 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 

0.0000083 OOO ooo ooo ooo ooo ooo 

0.00001 

0.00000 

0.00001 

0.00013 

0.00005 

0.00026 

0.12830 

0.20468 

0.15948 

0.08246 

0.01312 

0.01962 

0.00104 

0.00017 

0.00025 

0.16207 

0.02601 

0.03884 

0.07809 

0.01255 

0.01862 

0.04915 

0.00790 

0.01217 

0.05085 

0.00796 

0.01260 

0.10525 

0.01662 

0.02530 

0.01082 

0.00172 

0.00265 

0.13721 

0.05822 

0.29591 

0.01174 

0.01836 

0.01551 

0.00602 

0.00103 

0.00131 

0.00017 

0.00003 

0.00004 

0.00754 

0.00128 

0.00165 

0.00842 

0.00140 

0.00179 

0.00572 

0.00095 

0.00127 

0.00394 

0.00063 

0.00085 

0.01348 

0.00224 rail 
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P.53 

Cohort having highest 

annual average exposure: 

Demographic Sro-p: 6 

Hone District: 6 

Hork District: 6 

Group Population: 458 

Group Annual Average Exposure: 5.240 

Study Area: PHILADELPHIA 

Scenario Analyzed: BASE CASE - 1988 
P.54 

Table 2. Number of People in the Study Aree 

Exposed et or above selected Pellutent 

Concentretions(e) 

SSS3aS8S8B8SSSaSB38BSSSSsasSSS8SSaBSS8SS88BSS8SSSBBS 

POLLUTANT 

CONC 

(ug/tn*#3) 

5.00 

4.50 

4.00 

3.50 

3.00 

2.50 

2.00 

1.50 

1.00 

0.50 

0.00 

f of People 

et or Above 

658 

61622 

114733 

123284 

451904 

1930816 

8372339 

8628433 

8633430 

8645446 

6645446 

'/. of People 

at or Above 

0.0099 

0.7151 

1.3271 

1.4260 

5.2271 

22.3333 

96.8410 

99.8032 

99.8610 

100.0000 

100.0000 

(a) Study Area: PHILADELPHIA 

Scenario Analyzed: BASE CASE - 1988 

Number of Exposure Districts: 7 
P.55 

APPENDIX B 

CITIES SELECTED TO REPRESENT U.S. URBAN POPULATION 

48 
P.56 

Region 

Code 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

MSA 

ID NSA/CMSA/PMSA 

7840 Spokane, WA MSA 

380 Anchorage, AK 

860 Bellingham, UA 

880 Billings, NT 

1080 Boise City, ID 

1150 Bremerton, UA 

2400 Eugene-Springfield, OR MSA 

3040 Great Falls, MT 

4890 Nedford, OR 

5910 Olynpia, UA 

6440 Portland, OR PMSA 

6740 Richtand-KenneMick-Pasco, UA 

7080 Salem, OR MSA 

7600 Seattle, UA PMSA 

8200 Tacoma, UA PMSA 

8725 Vancover, UA PMSA 

9260 Yakima, UA 

5600 New York, NY PMSA 

875 Bergen-Passaic, NJ PMSA 

3640 Jersey City, NJ PMSA 

5640 Newark, NJ PMSA 

8480 Trenton, NJ PMSA 

4480 Los Angeles-Long Beach, CA PMSA 

360 Anaheim-Santa Ana, CA PMSA 

680 Bakersfield, CA MSA 

1620 Chico, CA 

2840 Fresno, CA MSA 

3320 Honolulu, HI MSA 

4940 Merced, CA 

5170 Modesto, CA MSA 

5775 Oakland, CA PMSA 

6000 Oxnard-Ventura, CA PMSA 

6690 Redding, CA 

6780 Riverside-San Bernard!o, CA PMSA 

6920 Sacramento, CA MSA 

7120 Salinas-Seaside-Monterey, CA MSA 

7320 San Diego, CA MSA 

7360 San Francisco, CA PMSA 

7400 San Jose, CA PMSA 

7480 Santa Barbara-Santa Maria-Lompoc, CA MSA 

7485 Santa Cruz, CA PMSA 

7500 Santa Rosa-Petaluna, CA PMSA 

8120 Stockton, CA MSA 

8720 Vallejo-Fairfield-Napa, CA PMSA 

8780 Visalia-Tulare-Porterville, CA MSA 

9340 Yuba City, CA 

5120 Minneapolis-St. Paul, MN-UI MSA 

460 Appleton-Oshkosh-Neenah, UI MSA 

620 Aurora-Elgin, IL PMSA 

1010 Bismarck, ND 

op '88 

356000 

219000 

119000 

116000 

201000 

181000 

270000 

78000 

146000 

157000 

1188000 

146000 

270000 

1862000 

559000 

226000 

186000 

8567000 

1292000 

542000 

1886000 

331000 

8588000 

2257000 

520000 

174000 

615000 

838000 

170000 

341000 

2006000 

647000 

140000 

2278000 

1385000 

349000 

2370000 

1590000 

1432000 

343000 

227000 

366000 

456000 

421000 

298000 

118000 

2388000 

313000 

355000 

86000 

Avg An 

CO 

(PPm) 

2.50 

-99.00 

1.56 

-99.00 

1.58 

1.59 

1.48 

1.64 

2.50 

-99.00 

1.61 

-99.00 

1.22 

2.03 

2.08 

2.09 

2.06 

2.44 

1.65 

2.39 

1.70 

0.95 

1.79 

1.58 

1.02 

0.97 

0.99 

0.83 

-99.00 

0.95 

1.08 

0.94 

-99.00 

1.35 

1.33 

0.71 

1.38 

1.60 

1.28 

0.68 

0.37 

1.27 

0.84 

1.17 

0.91 

-99.00 

1.79 

-99.00 

-99.00 

-99.00 

CO 

Ratios 

1.00 

-99.00 

0.62 

•99.00 

0.63 

0.64 

0.59 

0.66 

1.00 

-99.00 

0.64 

-99.00 

0.49 

0.81 

0.83 

0.84 

0.82 

1.00 

0.68 

0.98 

0.70 

0.39 

1.00 

0.88 

0.57 

0.54 

0.55 

0.46 

-99.00 

0.53 

0.60 

0.53 

-99.00 

0.75 

0.74 

0.40 

0.77 

0.89 

0.72 

0.38 

0.21 

0.71 

0.47 

0.65 

0.51 

-99.00 

1.00 

-99.00 

-99.00 

-99.00 
P.57 

Region MSA 

Code ID NSA/CMSA/PMSA 

4 1360 Cedar Rapids, IA 

4 1600 Chicago, IL PMSA 

4 1680 Cleveland, OH PMSA 

4 2200 Dubuque, IA 

4 2240 Duluth, MN-UI 

4 2290 Eau Claire, UI 

4 2360 Erie, PA MSA 

4 2520 Fargo«Noorheed, ND-MN 

4 2985 Grand Forks, ND 

4 3080 Green Bay, UI 

4 3500 Iowa City, IA 

4 3620 Janesville-Beloit, UI 

4 3690 Joliet, IL PMSA 

4 3800 Kenosha, UI PMSA 

4 3870 La Crosse, UI 

4 3965 Lake County, IL PMSA 

4 4360 Lincoln, NE 

4 4440 Lorein-Elyria, OH PMSA 

4 4720 Madison, UI MSA 

4 5080 Milwaukee, UI PMSA 

4 6600 Racine, UI PMSA 

4 6660 Rapid City, SD 

4 6820 Rochester, MN 

4 7620 Sheboygan, UI 

4 7720 Sioux City, IA=NE 

4 7760 Sioux Falls, Sd 

4 6980 St. Cloud, MN 

4 8160 Syracuse, NY MSA 

4 8400 Toledo, OH MSA 

4 8920 Waterloo-Cedar Falls, IA 

4 8940 Uausau, UI 

5 1120 Boston, MA PMSA 

5 160 Albany-Schenectady-Troy, NY MS/ 

5 560 Atlantic City, NJ MSA 

5 730 Bangor, ME 

5 960 Binghamton, NY MSA 

5 1160 Bridgeport-Millford, CT PMSA 

5 1170 Bristol, CT PMSA 

5 1200 Brockton, MA PMSA 

5 1280 Buffalo, NY PMSA 

5 1305 Burlington, VT 

5 1930 Danbury, CT PMSA 

5 2335 Elmira, NY 

5 2480 Fall River, MA-RI PMSA 

5 2600 Fitchburg-Leominster, MA 

5 2975 Glens Fells, NY 

5 3280 Hartford, CT PMSA 

5 3610 Jamestown-Dunkirk, NY 

5 4160 Lawrence-Heverhill, MA-NH PMSA 

5 4240 Lewiston-Auburn, NE 

Pop <88 

172000 

6216000 

1845000 

91000 

241000 

138000 

277000 

Avg An 

CO 

(Ppm) 

0.65 

0.93 

1.55 

1.52 

2.14 

-99.00 

1.19 

CO 

Ratios 

0.36 

0.52 

0.87 

0.85 

1.20 

-99.00 

0.66 

148000 

70000 

191000 

87000 

136000 

379000 

123000 

96000 

495000 

212000 

270000 

353000 

1398000 

174000 

82000 

101000 

103000 

116000 

126000 

181000 

650000 

616000 

148000 

113000 

2845000 

98000 

309000 

85000 

260000 

444000 

79000 

187000 

959000 

129000 

191000 

92000 

154000 

98000 

116000 

755000 

141000 

381000 

87000 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.46 

-99.00 

1.14 

0.82 

1.86 

-99.00 

1.75 

-99.00 

-99.00 

-99.00 

-99.00 

1.36 

1.18 

-99.00 

-99.00 

1.36 

0.71 

-99.00 

-99.00 

-99.00 

1.39 

-99.00 

•99.00 

0.63 

1.23 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.61 

-99.00 

-99.00 

-99.00 

-99.00 

•99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

0.82 

-99.00 

0.64 

0.46 

1.04 

-99.00 

0.98 

-99.00 

-99.00 

-99.00 

-99.00 

0.76 

0.66 

-99.00 

-99.00 

1.00 

0.52 

-99.00 

-99.00 

-99.00 

1.02 

-99.00 

•99.00 

0.46 

0.90 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.18 

-99.00 

-99.00 

-99.00 
P.58 

Region 

Code 

NSA 

10 NSA/CMSA/PMSA 

4560 Lowell, MA-NH PMSA 

4760 Manchester, NH 

5015 Niddlesex-Somerset-Hunterdon, NJ PMSA 

5020 Middletown, CT PMSA 

5190 Monmouth-Ocean, NJ PMSA 

5350 Nashua, NH PMSA 

5380 Nassau-Suffolk, NY PMSA 

5400 New Bedford, MA 

5440 New Britain, CT PMSA 

5480 New Haven-Neriden, CT NSA 

5520 New London-Norwich, CT-RI MSA 

5700 Niagara Falls, NY PMSA 

5760 Norwalk, CT PMSA 

5950 Orange County, NY PMSA 

6060 Pawtucket-Uoonsocket-Attleboro, RI-MA PMSA 

6320 Pittsfield, NA 

6400 Portland, NE 

6450 PortsmouthaOover-ROchester, NH-NE 

6460 Poughkeepeie, NY MSA 

6480 Providence, RI PMSA 

6840 Rochester, NY MSA 

7090 Salem-Gloucester, MA PMSA 

8000 Springfield, NA NSA 

8040 Stamford, CT PMSA 

8680 Utica-Rome, NY NSA 

8880 Uaterbury, CT 

9240 Uorcester, NA NSA 

2080 Denver, CO PMSA 

1250 Boulder-Longmont, CO PMSA 

1350 Casper, UY 

1580 Cheyene, UY 

1720 Colorado Springs, CO MSA 

2670 Fort Collins-Loveland, CO 

3060 Greely, CO 

6520 Provo-Orem, UT 

6560 Pueblo, CO 

6720 Reno, NV 

7160 Salt Lake City-Ogden, UT MSA 

6200 Phoenix, AZ MSA 

200 Albuquerque, NM MSA 

2320 El Paso, TX MSA 

4100 Las Cruces, NN 

4120 Las Vegas, NV NSA 

7490 Santa FE, NM 

8520 Tucson, AZ NSA 

6160 Philadelphia, PA-NJ PMSA 

240 Allentown-Bethlehem, PA-NJ MSA 

280 Altoona, PA 

845 Beaver County, PA PMSA 

3240 Harrisburg-Lebanon-Carlisle, PA NSA 

op '88 

262000 

150000 

978000 

86000 

969000 

177000 

2639000 

168000 

148000 

524000 

259000 

217000 

126000 

294000 

325000 

79000 

212000 

220000 

262000 

647000 

980000 

259000 

523000 

192000 

313000 

216000 

416000 

1640000 

218000 

65000 

75000 

394000 

182000 

136000 

243000 

128000 

240000 

1065000 

2030000 

493000 

586000 

132000 

631000 

112000 

636000 

4920000 

677000 

133000 

190000 

591000 

Avg An 

CO 

(Ppm) 

1.36 

1.77 

1.41 

-99.00 

1.41 

1.15 

1.08 

•99.00 

-99.00 

1.55 

-99.00 

0.44 

-99.00 

-99.00 

-99.00 

-99.00 

1.30 

-99.00 

-99.00 

0.84 

0.68 

-99.00 

1.28 

1.54 

-99.00 

-99.00 

1.11 

1.35 

0.84 

-99.00 

-99.00 

1.36 

1.34 

1.07 

2.12 

-99.00 

1.99 

1.90 

1.28 

1.15 

1.69 

0.66 

2.56 

0.85 

1.32 

1.09 

0.94 

-99.00 

0.82 

1.45 

CO 

Ratios 

1.00 

1.30 

1.04 

-99.00 

1.04 

0.85 

0.79 

-99.00 

-99.00 

1.14 

-99.00 

0.32 

-99.00 

-99.00 

-99.00 

-99.00 

0.96 

•99.00 

-99.00 

0.62 

0.50 

-99.00 

0.94 

1.13 

-99.00 

-99.00 

0.82 

1.00 

0.62 

-99.00 

-99.00 

1.01 

0.99 

0.79 

1.57 

-99.00 

1.47 

1.41 

1.00 

0.90 

1.32 

0.52 

2.00 

0.66 

1.03 

1.00 

0.86 

-99.00 

0.75 

1.33 
P.59 

Region 

Code 

8 

8 

8 

8 

8 

8 

8 

8 

8 

8 

8 

MSA 

ID NSA/CNSA/PMSA 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

3680 Johnstown, PA NSA 

4000 Lancaster, PA NSA 

6280 Pittsburg, PA PMSA 

6680 Reading, PA NSA 

7560 Scranton-Wilkes-Barre, PA NSA 

7610 Sharon, PA 

8050 State Colege, PA 

8760 Vineland-Nillville-Bredgeton, NJ PMSA 

9140 Uilliamsport, PA 

9160 UiImington, DE-NJ-MD PMSA 

9280 York, PA NSA 

8840 Washington, DC-ND-VA NSA 

480 Asheville, NC 

720 Baltimore, ND NSA 

1300 Burlington, NC 

1480 Charleston, UV NSA 

1520 Charlotte-Gastonia-Rock Hill, NC-SC NSA 

1540 Charlottesville, VA 

1900 Cumberland, ND-UV 

1950 Danville, VA 

2560 Fayetteville, NC NSA 

3120 Greensboro-Win.Sal.-High Pt.,NC NSA 

3160 Greenville-Spertanburg, SC NSA 

3180 Hagerstown, ND 

3290 Hickory, NC 

3400 Huntington-Ashland, UV-KY-OH NSA 

3605 Jacksonville, NC 

3660 Johnson City-Kingspt.-Briston.TN-VA NSA 

4640 Lynchburg, VA 

5720 Norfolk-VA Beach-Hewport News, VA NSA 

6020 Parkersburg-Narietta, UV-OH 

6640 Raleigh-Ourham, NC NSA 

6760 Richmond-Petersburg, VA NSA 

6800 Roanoke, VA 

8080 Steubenville-Ueirton, OH-UV 

9000 Wheeling, UV-OH 

9200 UiImington, NC 

3360 Houston, TX PMSA 

40 Abilene, TX 

120 Albany, GA 

220 Alexandria, LA 

320 Amarillo, TX 

405 Anderson, SC 

450 Anniston.AL 

500 Athens, GA 

520 Atlanta, GA MSA 

600 Augusta, GA-SC MSA 

640 Austin, TX NSA 

760 Baton Rouge, LA NSA 

840 Beaumnt-Port Arthur, TX NSA 

op '88 

251000 

414000 

2094000 

329000 

737000 

122000 

116000 

138000 

118000 

573000 

410000 

3734000 

173000 

2342000 

106000 

261000 

1112000 

124000 

102000 

108000 

256000 

925000 

621000 

118000 

222000 

322000 

126000 

442000 

145000 

1380000 

154000 

683000 

844000 

222000 

148000 

172000 

117000 

3247000 

122000 

116000 

138000 

196000 

143000 

123000 

145000 

2737000 

396000 

748000 

537000 

364000 

Avg An 

CO 

(PP") 

0.88 

0.73 

1.12 

0.96 

0.95 

-99.00 

•99.00 

-99.00 

-99.00 

1.09 

0.82 

1.02 

-99.00 

1.52 

•99.00 

0.66 

1.22 

-99.00 

1.03 

-99.00 

-99.00 

1.01 

-99.00 

-99.00 

-99.00 

1.07 

-99.00 

0.91 

-99.00 

0.72 

-99.00 

1.81 

0.72 

-99.00 

1.79 

0.92 

-99.00 

0.91 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

0.76 

-99.00 

0.64 

0.74 

0.45 

CO 

Ratios 

0.81 

0.67 

1.03 

0.88 

0.87 

-99.00 

-99.00 

-99.00 

-99.00 

1.00 

0.75 

1.00 

•99.00 

1.49 

-99.00 

0.67 

1.20 

-99.00 

1.01 

-99.00 

-99.00 

0.99 

-99.00 

-99.00 

-99.00 

1.05 

-99.00 

0.89 

-99.00 

0.71 

-99.00 

1.77 

0.71 

-99.00 

1.75 

0.90 

-99.00 

1.00 

•99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

0.84 

-99.00 

0.70 

0.81 

0.49 
P.60 

Region 

Code 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

NSA 

ID MSA/CMSA/PMSA 

920 Blloxi-Bulfport, NS 

1000 Birmingham, AL NSA 

1140 Bradenton, FL 

1145 Brazoria, TX PMSA 

1240 Brownsville-Harlingen, TX 

1260 Bryan-College Station, TX 

1440 Charleston, SC NSA 

1760 Coluifcia, SC NSA 

1800 Colutbus, GA-AL 

1880 Corpus Christi, TX NSA 

1920 Dallas, TX PMSA 

2020 Daytona Beach, FL MSA 

2030 Decatur, AL 

2180 Dothan, AL 

2340 Enid, OK 

2650 Florence, AL 

2655 Florence, SC 

2700 Fort Myers-Cape Coral, FL 

2710 Fort Pierce, FL 

2750 Fort Walton Beach, FL 

2800 Fort Worth-Arlington, TX 

NSA 

NSA 

PMSA 

2680 Ft. Lauderdale-Hollywd-Poopano Beach,FL 

2880 Gadsden, AL 

2900 Gainesville, FL 

2920 Galveston-Texas City, TX 

3350 Houna-Thibodaux, LA 

3440 Huntsville, AL 

3560 Jackson, MS NSA 

3600 Jacksonville, FL NSA 

3810 KiIleen-Temple, TX 

3880 Lafayette, LA 

3960 Lake Charles, LA 

3980 Lakeland-Winter Haven, FL 

4080 Laredo, TX 

4200 Lawton, OK 

4420 Longview-Narshall, TX 

4600 Lubbock, TX 

4680 Macon-Uarner Robins, GA 

4880 NcAllen-Edinburg-Nission, 

PMSA 

MSA 

USA 

TX MSA 

4900 Nelbourne-Titusvilie-Palm Bay, FL MSA 

5000 Miami-Hialeah, FL PMSA 

5040 Midland, TX 

5160 Mobile, AL MSA 

5200 Monroe, LA 

5240 Montgomery, AL NSA 

5345 Naples, FL 

5560 New Orleans, LA MSA 

5790 Ocala, FL 

5800 Odessa, TX 

5880 Oklahoma City, OK NSA 

Pop '88 

205000 

923000 

187000 

185000 

264000 

117000 

511000 

456000 

247000 

358000 

2475000 

348000 

133000 

131000 

58000 

136000 

118000 

309000 

232000 

151000 

1291000 

1187000 

103000 

208000 

210000 

183000 

237000 

396000 

898000 

240000 

210000 

172000 

396000 

129000 

119000 

167000 

227000 

287000 

388000 

388000 

1814000 

107000 

486000 

144000 

301000 

139000 

1307000 

190000 

125000 

964000 

Avg An 

CO 

(PP") 

-99.00 

1.27 

-99.00 

-99.00 

•99.00 

-99.00 

0.99 

1.18 

-99.00 

-99.00 

0.66 

•99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

•99.00 

•99.00 

1.62 

0.95 

-99.00 

-99.00 

-99.00 

-99.00 

0.81 

1.39 

1.05 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

•99.00 

-99.00 

-99.00 

-99.00 

1.25 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.27 

-99.00 

•99.00 

1.04 

CO 

Ratios 

-99.00 

1.40 

-99.00 

-99.00 

-99.00 

-99.00 

1.09 

1.30 

-99.00 

-99.00 

0.73 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.78 

1.04 

-99.00 

-99.00 

-99.00 

-99.00 

0.89 

1.53 

1.15 

-99.00 

-99.00 

-99.00 

-99.00 

•99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.37 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.40 

-99.00 

-99.00 

1.14 
P.61 

Region 

Code 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

10 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

11 

ID NSA/CNSA/PNSA 

5960 Orlando, FL NSA 

6015 Panama City, FL 

6025 Pascagoula, NS 

6080 Pensacola, FL NSA 

6240 Pine Bluff, AR 

7200 San Angelo, TX 

7240 San Antonio, TX NSA 

7510 Sarasota, FL NSA 

7520 Savannah, GA 

7640 Sherman-Denison, TX 

7680 Shreveport, LA NSA 

8240 Tallahassee, FL 

8280 Tampa-St. Petersburg-Clearwater, FL NSA 

8360 Texarkana, TX-AR 

8560 Tulsa, OK NSA 

8600 Tuscaloosa, AL 

8640 Tyler, TX 

8750 Victoria, TX 

8800 Waco, TX 

8960 Uest Palm Beach-Boca Raton-Delray Beach, FL 

9080 Uichita Falls, TX 

7040 St. Louis, NO-IL MSA 

80 Akron, OH PMSA 

400 Anderson, IN 

440 Ann Arbor, NI PMSA 

780 Battle Creek, NI 

870 Benton Harbor, NI 

1020 Bloomington, IN 

1040 Bloomington-Normal, IL 

1320 Canton, OH NSA 

1400 Chaopaign-Urbana-Rantoul, IL 

1560 Chattanooga, TN-GA NSA 

1640 Cincinnati, OH-KY-IN PMSA 

1660 Clarksville-Hopkinsville, TN-KY 

1740 Columbia, NO 

1840 Colunbus, OM NSA 

1960 Davenport-Rock Isl.-Noline, IA-IL NSA 

2000 Dayton-Springfield, OM NSA 

2040 Decatur, IL 

2120 Des Moines, IA NSA 

2160 Detroit, NI PMSA 

2330 Elkhart-Goshen, IN 

2440 Evansville, In-KY MSA 

2580 Fayetteville-Springdale, AR 

2640 Flint, NI NSA 

2720 Fort Smith, AR-OK 

2760 Fort Wayne, IN NSA 

2960 Gary-Hammond, IN PMSA 

3000 Grand Rapids, NI MSA 

3200 Hamilton-Niddletown, OM PMSA 

op '88 

971000 

125000 

128000 

350000 

91000 

99000 

1323000 

261000 

244000 

133000 

359000 

229000 

1995000 

119000 

728000 

145000 

153000 

74000 

188000 

818000 

125000 

2467000 

653000 

132000 

268000 

139000 

167000 

103000 

125000 

401000 

172000 

438000 

1449000 

159000 

106000 

1344000 

364000 

948000 

124000 

392000 

4352000 

151000 

281000 

111000 

431000 

181000 

367000 

612000 

665000 

280000 

Avg An 

CO 

(Ppm) 

0.97 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.16 

2.27 

•99.00 

•99.00 

-99.00 

-99.00 

0.92 

-99.00 

0.80 

-99.00 

-99.00 

-99.00 

-99.00 

0.61 

-99.00 

0.90 

0.88 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

0.61 

-99.00 

-99.00 

1.11 

-99.00 

-99.00 

1.15 

1.04 

1.08 

-99.00 

0.72 

0.80 

-99.00 

0.64 

-99.00 

-99.00 

-99.00 

1.12 

0.92 

0.57 

•99.00 

CO 

Ratios 

1.07 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.27 

2.49 

-99.00 

•99.00 

-99.00 

-99.00 

1.01 

-99.00 

0.88 

-99.00 

-99.00 

•99.00 

•99.00 

0.67 

-99.00 

1.00 

0.98 

•99.00 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

0.68 

-99.00 

-99.00 

1.23 

-99.00 

•99.00 

1.28 

1.16 

1.20 

-99.00 

0.80 

0.84 

-99.00 

0.71 

-99.00 

-99.00 

-99.00 

1.24 

1.02 

0.63 

-99.00 
Region NSA 

Code ID NSA/CNSA/PNSA 

1 3480 Indianapolis, IN NSA 

1 3520 Jackson, NI 

1 3580 Jackson, TN 

1 3710 Joplin, NO 

1 3720 Kalamazoo, NI 

1 3740 Kankakee, IL 

1 3760 Kansas City, N0-KS NSA 

1 3840 Knoxville, TN NSA 

1 3850 Kokomo, IN 

1 3920 Lafayette-West Lafayette, IN 

1 4040 Lansing-East Lansing, NI NSA 

1 4150 Lawrence, KS 

1 4280 Lexington-Feyette, KY NSA 

1 4320 Lima, OH 

1 4400 Little ROck-N. Little Rock, AR NSA 

1 4520 Louisville, ICY-IN NSA 

1 4800 Mansfield, OH 

1 4920 Memphis, TN-AR-MS NSA 

1 5280 Nuncie, IN 

1 5320 Muskegon, NI 

1 5360 Nashville, TN NSA 

1 5920 Omaha, NE-IA NSA 

1 5990 Owensboro, KY 

1 6120 Peoria, IL NSA 

1 6880 Rockford, IL MSA 

1 6960 Saginaw-Bay City-Midland, MI NSA 

1 7800 South Bend-Nishewaka, IN 

1 7880 Springfield, IL 

1 7920 Springfield, NO 

1 7000 St. Joseph, NO 

1 8320 Terre Haute, IN 

1 8440 Topeka, KS 

1 9040 Wichita, KS NSA 

1 9320 Youngstown-Uarren, OH NSA 

Pop '88 

1237000 

149000 

78000 

136000 

218000 

98000 

1575000 

600000 

99000 

125000 

428000 

76000 

348000 

157000 

513000 

967000 

129000 

979000 

120000 

161000 

972000 

622000 

88000 

340000 

282000 

406000 

244000 

192000 

234000 

85000 

133000 

165000 

483000 

502000 

Avg An 

CO 

(Ppm) 

1.07 

•99.00 

-99.00 

-99.00 

-99.00 

-99.00 

0.91 

1.41 

-99.00 

0.28 

-99.00 

-99.00 

1.08 

-99.00 

-99.00 

1.54 

-99.00 

1.13 

-99.00 

-99.00 

1.52 

1.10 

0.99 

1.72 

2.02 

0.52 

0.93 

1.24 

0.66 

-99.00 

-99.00 

-99.00 

1.06 

-99.00 

CO 

Ratios 

1.19 

-99.00 

-99.00 

-99.00 

-99.00 

-99.00 

1.01 

1.57 

-99.00 

0.31 

-99.00 

-99.00 

1.20 

-99.00 

-99.00 

1.71 

-99.00 

1.26 

-99.00 

-99.00 

1.69 

1.22 

1.10 

1.91 

2.24 

0.58 

1.03 

1.38 

0.73 

-99.00 

-99.00 

-99.00 

1.18 

-99.00