p.1 

OAR Box 1112 

Prepped by Ryan Dugan 

Document Number: 

43) ll-C-5c[ 

Docket Number: 

A-83-22 

Printed 4/16/2008 9:12:07 AM Sheet 43 of 97 
P.2 

\ 

DEMOGRAPHIC DATA FOR LEAD 

EXPOSURE ANALYSES 

ft - S- 3 - Z. Z. 

77 - C - srat 

by 

Roy A. Paul 

PEI Associates, Inc. 

505 South Duke Street, Suite 503 

Durham, North Carolina 27701-3196 

Contract No. 68-02-4309 

Work Assignment No. 14 

PN 3659-14 

Project Officer 

Richard B. Atherton 

Work Assignment Manager 

Jeff Cohen 

STRATEGIES AND AIR STANDARDS DIVISION 

U.S. ENVIRONMENTAL PROTECTION AGENCY 

RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711 

January 1986 
P.3 

DISCLAIMER 

This report was prepared by PEI Associates, Inc., Cincinnati, Ohio, under 

Contract No. 68-02-4309, Work Assignment No. 14. It has been reviewed by the 

Strategies and Air Standards Division of the Office of Air Quality Planning 

and Standards, U.S. Environmental Protection Agency, and approved for publication. Approval 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 endorsement 

or recommendation for use. 

n 
P.4 

CONTENTS 

Figures iv 

Tables v 

Acknowledgment vi 

1. Introduction 1 

2. Geographic Units of Analysis 3 

3. Population Data 4 

Bureau of census data 4 

Age groups 4 

Lead in homes 5 

Secondary occupational exposure 7 

Pregnant women 8 

Population projections 13 

4. Outputs from the demographic program 15 

References 24 

Appendix 

Source code for demographic program 25 

m 
P.5 

FIGURES 

Number Page 

1 Sample of Air Quality Data Used by Program MATCH-AQ 16 

2 Pregnancy Data Used in Program MATCH-AQ 17 

3 User-Specified Inputs for Program MATCH-AQ 18 

4 Sample of Geographic Data Table for Census Tracts Around a 

Point Source 20 

5 Sample of Air Quality Data Table for Census Tracts Around a 

Point Source 21 

6 Sample of Demographic Data Table for One Age Group 22 

7 Sample of Output File Records 23 

iv 
P.6 

TABLES 

Number Page 

1 Estimates of Percentage of .Homes With Lead-Based Paint. .. . 6 

2 Birth Rates by Age Group of Mother and by Race of Child, 

1981 9 

3 Fetal Death Ratios by Age Group of Mother and Race of Child, 

1978 10 

4 Number of Fetal Deaths by Specified Periods of Gestation for 

States Requiring Reporting of All Fetal Deaths, 1978. .. . 11 

5 BEA Population Projections and Corresponding Projection 

Fractions .' 14 
P.7 

ACKNOWLEDGMENT 

This report was prepared for the U.S. Environmental Protection Agency by 

PEI Associates, Inc., Cincinnati, Ohio. Mr. Jeff Cohen was the EPA Work 

Assignment Manager. Mr. David Dunbar served as the Project Director; Mr. Roy 

Paul was the Project Manager. The principal author was Mr. Roy-Paul. The 

author would like to thank Mr. Jeff Cohen for his guidance and direction on 

this work assignment. The author would also like to acknowledge Mr. Ted 

Johnson and Ms. Alicia Ferdo for their assistance and cooperation. 

vi 
P.8 

SECTION 1 

INTRODUCTION 

Under the Clean Air Act, the U.S. Environmental Protection Agency (EPA) 

is responsible for establishing National Ambient Air Quality Standards (NAAQS) 

and for reviewing them periodically to determine their adequacy on the basis 

of recent experience and research. In line with these responsibilities, the 

Strategies and Air Standards Division (SASD) of the Office of Air Quality 

Planning and Standards (OAQPS) is exploring the use of quantitative methods 

for assessing health risks associated with proposed air quality standards. 

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

an exposure model suitable for evaluating alternative ambient air standards. 

The model is known as NEM, an acronym for NAAQS Dcposure Model. 

Although different versions of NEM have been developed for different 

pollutants, the various versions are similar in that each considers the 

effect of a person's activity patterns on his or her exposure. Activity 

patterns are usually represented as a sequence of assignments to a geographic 

location, to a microenvironment (e.g., indoors), and to an activity level. 

The exposure at each point in time is determined from these assignments. 

The SASD is currently assessing health risks associated with alternative 

NAAQS for lead. Estimation of population exposure to lead is a four-step 

process involving 1) development of air quality data, 2) development of 

activity data, 3) development of demographic data, and 4) calculation of 

exposures. Analyses of lead exposures have been simplified somewhat because 

dispersion model estimates are used in lieu of air quality monitoring data 

and because activity patterns are simplified to the average times spent in 

various microenvironments. The bases for these simplifications are discussed 

in an OAQPS staff paper. 

The GCA Corporation provided PEI Associates, Inc. (PEI) with air quality 

estimates for selected geographic locations around point sources. The model 
P.9 

used for these estimates was the Industrial Source Complex - Short-Term 

(ISC-ST) dispersion model, a standard model frequently used to estimate levels 

of control for point-source emissions. The ISC was used to estimate monthly 

average lead concentrations at the centers of census tracts located near 

given point sources. Point sources were located within one of four Standard 

Metropolitan Statistical Areas (SMSA's): Chicago, Dallas, Nashville, or 

Tampa-St.. Petersburg. 

Given air quality estimates at selected locations, PEI developed a computer program (MATCH-AQ) to calculate the number of children and pregnant 

women that may be exposed to estimated lead concentrations at each location. 

The number of children in each census tract was further subdivided by year 

of age. Age groups were further subdivided into three housing types: 

1) lead-based-painted houses (LPH's) with deteriorated or peeling paint, 

2) LPH's with sound paint, and 3) non-LPH homes (no lead-based paints). 

Housing-type groups were further subdivided into two groups representing 

high and low probability of experiencing secondary occupational exposure. 

These data are used by a second computer program that calculates uptake of 

lead into children's bodies. The uptake program is covered in another report,' 

which describes the population program, the geographic units used, the demographic data used, and the data generated by the program. 
P.10 

SECTION 2 

GEOGRAPHIC UNITS OF ANALYSIS 

Census tracts are relatively small units of geography that can be used 

effectively in population exposure analyses. Census tracts are statistical 

subdivisions of a county that generally have stable boundaries. When established, they are designed to be relatively homogeneous with regard to population characteristics, economic status, and living conditions. Although the 

land area of census tracts may vary, they usually have between 2500 and 8000 

4 / v 

residents. All standard metropolitan statistical areas (SMSA's) recognized 

for the 1980 census can be defined as groups of census tracts. Consequently, 

census tracts are particularly useful geographic units for population exposure 

analyses. PEI and EPA have developed a library of detailed census tract data 

from the 1980 Census of Population and Housing. 

The location of the population within a census tract may conveniently be 

referenced by a single point, the population centroid, i.e., the average 

latitude and longitude of all the people in the census tract. Population 

centroids are usually estimated by the population-weighted average of all 

geographic centers of smaller units within the census tract, such as block 

groups. If the population can be assumed to be distributed homogeneously 

within the census tract, the geographic center of the census tract also may 

be used to estimate the population centroid. 

For this study, geographic centers of census tracts were readily available on a special computer tape prepared by Geographic Data Technology, Inc. 

(GDT) in 1981. This special file was developed as a side product of digitizing latitude and longitude values representing boundaries of census tracts in 

SMSA's. Geographic centers were defined by the average longitude and latitude 

of tract boundaries. In 1983, PEI provided GCA Technology, Inc., with lists 

of census tract centers for selected SMSA's for use in lead air quality 

modeling. 
P.11 

SECTION 3 

POPULATION DATA 

3.1 BUREAU OF CENSUS DATA 

PEI and EPA have established a library of 1980 census data that resides 

on a series of 72 computer tapes at the National Computer Center (NCC). These 

data include summary tape file 3A (STF3A), one of a series of summary data 

files created by the Bureau of Census (BOC) for use in analyses requiring detailed information. STF3A contains summary computer records consisting of 

12,096 characters (numbers and codes). One record exists for each one of a 

hierarchy of geographic units, such as State, county, minor civil divisions, 

places, census tracts, and block groups or enumeration districts. For example, 

there is one record for each one of the approximately 45,000 census tracts in 

g 

the United States. 

Each STF3A record contains a series of identification codes and flags 

plus 150 tables relating to various demographic characteristics. These include age, sex, race, family status, types and ages of housing, places of 

work, commuting time and distance, school enrollment, disabilities, types of 

industries, types of employment, income and poverty status, household 

facilities, value of houses, and householder characteristics. In conducting 

population analyses, only selected tables are used for a particular geographic 

area. 

3.2 AGE GROUPS 

The EPA wishes to analyze lead exposures of seven age groups of children, 

age 0 through 6. The principal population data for this analysis is from 

STF3A Table 15, Sex by Age. The age categories listed in Table 15 for 

children under 7 are 1) under 1 year, 2) 1 and 2 years, 3) 3 and 4 years, 

4) 5 years, and 5) 6 years. Each age year is determined by whether a person 

has reached the birthday indicated when the census is taken. 
P.12 

Because BOC age categories did not correspond exactly to the needed age 

groups, they were estimated when necessary. Child age group 1 corresponds 

exactly to BOC Category 1. Child age group 2 (1 to 1.99 years) was estimated by dividing BOC group 2 (age 1 and 2 years) by 2. To avoid rounding 

error, age group 3 (2 to 2.99 years) was estimated as BOC group 2 minus age 

group 2. Likewise, age group 4 was estimated by dividing BOC Category 3 by 

2; age group 5 was estimated as BOC Category 3 minus age group 4. Age groups 

6 and 7 correspond to BOC Categories 4 and 5, respectively. 

Other age data are used in estimating numbers of pregnant women. The 

female age groups are 15 to 19, 20 to 34, and 35 to 44 years. The BOC age 

categories include exact subdivisions of these groups; therefore, the BOC 

categories were simply aggregated to the larger age groups. 

3.3 LEAD IN HOMES 

PEI developed a model for estimating the number of children 6 years and 

under who live in LPH's. The model also estimates the number of homes that 

have chalking, flaking, or peeling paint (referred to as unsound homes). 

Both of these models were incorporated into the demographic program to estimate LPH-unsound, LPH-sound, and non-LPH homes in each census tract. 

Pope reviewed literature on lead-based paint to select estimators of LPH. 

The most reliable independent variable was found to be the age of the home. 

Table 1 presents a range of estimates (from Pope) of percentages of homes 

built in various years that are LPH's. Table 109 in census tape STF3A lists 

the numbers of homes in each census tract that were built in each decade after 

1940. These decade data were then aggregated into the same age categories of 

homes shown in Table 1. The number of homes in each age category times the 

fraction of LPH's in each category (PEI's best estimate) yields an estimate 

of the number of LPH's in each census tract. 

Assuming that LPH's were distributed evenly across all age groups, the 

fraction of children living in LPH's was assumed to be the same as the fraction 

of all housing units in the census tract that are LPH's. 
P.13 

TABLE 1. ESTIMATES OF PERCENTAGE OF HOMES 1 

Study 

EPA 

Criteria 

Document 

OAQPS 

Draft Lead 

Staff 

Paper 

Billick 

and Gray 

Lin-Fu 

Shier and 

Hall 

Arizona 

Health 

Department 

Minneapolis 

Gilsinn 

PEI's 

Best 

Estimate 

PEI's 

Range 

Amount of 

lead in painted 

surface 

>1.5 mg/cm 

• 2 

>1.5 mg/cm 

2 

>1.5 mg/cm 

2 

>2.0 mg/cm 

2 

>1.0 mg/cm 

2 

>1.0 mg/cm 

2 

>1.0 mg/cm 

>1% 

2 

>0.7 mg/cm 

2 

>0.7 mg/cm 

Pre1940 

75 

77 

100 

100 

100 

99 

75-100 

1940 to 1959 

50 

43a 

26 

90 

50 

70 

26-90 

dlTH LEAD-BASED PAINT 

1960 to 1974 

20 

22 

20 

12a 

15 

0 

20 

0-22 

1975 to 1980 

0 

Estimate for 1975. 
P.14 

3.4 SECONDARY OCCUPATIONAL EXPOSURE 

PEI developed a method for estimating the secondary occupational exposure 

(SOE) of children 6 years or younger. SOE occurs when a member of a family 

works in a production facility involving lead, for example, a lead smelter or 

lead battery plant. Particles of lead may be embedded in the clothing, skin, 

or hair of the employee and transferred to other members of the employee's 

family by personal contact. 

The number of employees occupationally exposed to lead was estimated as 

the number of lead production workers at the point source of lead being 

analyzed. These data were determined by contacting either the company or the 

local chamber of commerce (if the company did not provide data). The distribution of the homes of these workers could not be determined directly from 

census data, so it was estimated. Table 66 of Census tape STF3A lists numbers 

of employed persons by broad categories of industry. Durable goods manufacturing (DGM) is an industrial category that includes various primary and secondary 

smelters and production facilities, including smelters and lead battery production. Thus, lead-exposed employees were assumed to be geographically distributed around the point source in the same manner as DGM employees, as 

follows: 

Lt = (LT)(DGMt/DGMT) (1) 

where L. = Lead-exposed employees in census tract t 

LT = Total lead-exposed employees around point source 

DGM. = Employees in durable goods manufacturing in census tract t 

DGMj = Total employees in durable goods manufacturing. 

The number of children with SOE may differ from the number of exposed 

employees because the number of children per employee will vary. To find 

the number of SOE children, the child/employee ratio was calculated for each 

census tract: 

6 

CE. = Z C./E t (2) 

1

 y=Q

 ty U 
P.15 

where CE. = Child-employee ratio for census tract t 

C. = Number of children age y in census tract t 

Et = Total employees in census tract t. 

Thus, the fraction of all children age 0 to 6 with SOE could be estimated 

as follows: 

Ft = (Lt)(CEt)/Ct (3) 

where F. = Fraction of children with SOE in census tract t 

Ct = Total children age 0 to 6 in census tract t 

L. = Lead-exposed employees in census tract t 

CE. = Child-employee ratio for census tract t. 

Assuming all age groups and housing groups of children had the same probability of SOE, each group was multiplied by the same fraction (Ft) to estimate 

the number of children in each group with a high probability of having SOE. 

At the point sources under study, only a few children received SOE. When 

these were distributed over a large number of census tracts (e.g., 100), 

seven age groups, and three housing categories, only a small fraction (<<0.5) 

of a child was estimated for most demographic subgroups. Rounded off, it was 

usually zero. Thus, it was necessary to adjust the distributions of SOE 

children around the point source by multiplying the number of children in each 

SOE subgroup by an empirically determined adjustment fraction. The subgroup 

populations were repeatedly adjusted until the total of rounded numbers of 

all children in SOE subgroups was exactly equal to the estimated number of 

SOE children around the point source. 

3.5 PREGNANT WOMEN 

The number of pregnant women in a census tract may be estimated by 1) the 

specific race and age distribution of the census tract, and 2) national race 

and age-specific birth rates and fetal death rates. Pregnancy may lead to 

either a live birth or a fetal death, thus both rates are required. Methodo 

ology for these estimates was developed by PEI in 1983. 
P.16 

A key source of statistics about birth rates is Vital Statistics of the 

9 10 U.S., produced by the National Center for Health Statistics. ' Data are 

accumulated from birth certificates from every State in the Nation. As a 

result of birth registration laws that have been in effect since 1915, data 

on births are better than 90 percent complete in all States and average 99.6 

percent complete. Complete natality statistics were available for the year 

1978, however, an advance report for 1980 provided sufficient data needed 

for estimating cohort populations. For example, birth rates can be obtained 

by race of child and by age of mother, as shown in Table 2. Note that birth. 

rates are substantially higher for blacks than for nonblacks in all age 

categories. 

TABLE 2. BIRTH RATES BY 

Age 

group 

15-19 

20-34 

35-44 

Race 

Black 

Nonblack 

Black 

Nonblack 

Bl ack 

Nonblack 

AGE GROUP OF MOTHER AND BY RACE OF CHILD, 1981 

Number 

of women 

1,503,530 " 

8,914,602 

3,699,630 

29,360,935 

1,489,196 

11,542,075 

Number of 

live births 

150,353 

4011808 

409,645 

2,475,200 

23,621 

140,262 

Birth 

ratea 

100.0 

45.1 

110.7 

84.3 

15.9 

12.2 

Number of births per 1000 women in race and age group. 

Another key source of data is fetal death certificates, a standard form 

used since 1978 to record any kind of pregnancy termination. Fetal deaths 

are consistently reported from all States for interrupted pregnancies with a 

gestation period greater than 20 weeks in length. Statistics on fetal death 

ratios can be obtained by specific age and race groups, as shown in Table 3. 

Fetal death ratios (based on reported fetal deaths), vary significantly for 

different races. For example, in 1978 the fetal death ratio was 8.5 per 

1000 live births for "white" babies and 14.7 per 1000 for "all others." 

These fetal death ratios can be used to estimate the number of pregnancies 

that result in fetal death. 
P.17 

TABLE 3. 

Age 

group 

15-19 

20-34 

35 up 

FETAL DEATH RATIOS BY AGE GROUP OF MOTHER AND RACE OF CHILD, 1978 

Race 

White 

All others 

White 

All others 

White 

All others 

Totals: 

Number of 

live births 

227,158 

105,417 

1,290,604 

269,933 

67,567 

17,123 

1,977,802 

Number of 

fetal deaths 

2,158 

1,518 

9,951 

3,802 

1,057 

533 

19,019 

Fetal 

death. 

ratio 

9.5 

14.4 

7.7 

14.1 

15.6 

31.1 

9.6 

Number of reported fetal deaths, gestation 20 weeks plus. 

Fetal death ratio is number of fetal deaths per 1000 live births in 

specific group. 

Data on fetal deaths are fairly accurate for those fetal deaths attended 

by a physician where reporting is required by law; however, laws on registering fetal deaths vary from State to State. Only six States require the reporting of fetal deaths occurring in the first 16 weeks of pregnancy; most States 

do not require reporting in the first 20 weeks. This practice of nonreporting eliminates most abortions from fetal death statistics and may substantially 

alter the accuracy of the data. 

An estimate of the number of identifiable fetal deaths in early pregnancy 

may be gleaned from those States that require reporting of all fetal deaths. 

Table 4 shows, by mother's age, the number of fetal deaths occurring under 20 

weeks' gestation and the number occurring after a gestation period of 20 weeks 

or more. Data represent five States that require fetal death reporting for 

all gestation periods. An underreporting adjustment factor was calculated 

for each age group by using the ratio of total deaths to deaths after 20 weeks, 

or more gestation. If all areas of the United States experience the same 

ratio of deaths above and below 20 weeks, these underreporting factors may 

be used to adjust upward the actual number of fetal deaths where a fetal 

death ratio is based on deaths reported after 20 weeks' or more gestation 

periods, such as the ratios in Table 3. 

10 
P.18 

TABLE 4. NUMBER OF FETAL DEATHS BY SPECIFIED PERIODS OF GESTATION 

FOR 

Age of 

mother, yr 

Under 15 

15 to 19 

20 to 24 

25 to 29 

30 to 34 

35 to 39 

40 to 44 

45 to 49 

Totals: 

STATES REQUIRING REPORTING OF ALL FETAL DEATHS, 1978° 

Gestation period 

20 weeks 

and over 

28 

797 

1,560 

1,510 

860 

376 

109 

8 

5,248 

Under 20 

weeks 

77 

2,254 

4,965 

5,237 

3,217 

1,332 

416 

55 

17,553 

Not 

stated 

9 

225 

532 

474 

275 

118 

36 

7 

1,676 

Totals 

114 

3,276 

7,057 

7,221 

4,352 

1,826 

561 

70 

24,477 

Under 

reporting 

adjustment 

factorb 

4.071 

4.110 

4.524 

4.782 

5.060 

4.856 

5.147 

8.750 

4.664 

Reporting States include Arkansas, Colorado, Georgia,.New York, Rhode Island, 

and Virginia. Arkansas not included in data for technical reasons 

(Reference 10). 

Underreporting adjustment factor is the ratio of total deaths to deaths 

reported for gestation periods over 20 weeks. 

Fertility of women varies greatly with age. The 1980 data shows the 

greatest fertility among women occurs at age 20 to 29, but teenage (age 15 

to 19) fertility was also significantly high, comparable to that of the 30 

to 34 age group. Fertility of women age 35 to 39 was lower, and fertility 

of women.age 40 to 44 was very low. Thus, age-specific birth rates are used' 

in estimating numbers of pregnant women. 

The following procedure was used to estimate the population of pregnant 

women in each census tract: 

1. The SASD library of 1980 demographic data was used to find the 

number of women in each of three age groups. Age groups were further 

subdivided by race (black and nonblack). 

2. The expected number of live births was calculated for each group of 

women by using age and race-specific live birth rates from Table 2. 

Eart = (Wart>(Far>(100°) (4) 

11 
P.19 

where E . = Expected live births for a.specific age (a), race 

"art (r), and in census tract (t) 

W . = Number of women in age group (a) and race (r) in 

census tract (t) 

F = Birth rate per 1000 women in age group (a) and race 

a r (r) from Table 2. 

3. The total number of births (Bt) in the census tract in 1980 was 

determined by the number of children age 0 to 1. 

4. The expected live births by mother's age and child's race were 

adjusted up or down so that total births from census data (B.) was 

equal to total of births for all races and ages. 

(Bt-EEart> 

Bart " tEart]

 \ r [ <5> 

5. The number of fetal deaths was estimated by using an age- and racespecific fetal death ratio from Table 3 and the underreporting 

adjustment factor from Table 4 as follows: 

D _ (BartHRarHUa> 

Dart IOOO (6) 

where Dav. = Fetal deaths for age group (a) and race (r) in 

art census tract (t) 

B . = Live births from Equation 5 

R = Fetal death ratio per 1000 live births for age 

group (a) and race (r) 

U, = Underreporting adjustment factor for age group (a), 

a 

The number of pregnancies in 1 year (= number of fetuses) for each 

race, age, and poverty level was estimated by adding the estimated 

number of fetal deaths to estimated live births. 

Part " Bart + Dart (7) 

where P = Pregnancies for mother's age group (a) and child's 

ar race (r) in census tract (t), 

12 
P.20 

3.6 POPULATION PROJECTIONS 

Estimated demographic group populations around a point source were based 

on 1980 census data. Control strategies for lead, however, may not become 

effective until future years (e.g., 1990 or 1995). Over this time period the 

population may change, especially in rapidly growing urban areas. 

Estimates of population of children in future years were based on 1980 

projections by the U.S. Bureau of Economic Analysis (BEA) previously called 

"OBERS" projections. The BEA projections are based on 1) projections of 

future employment for persons in the labor pool and 2) projections of population factors estimated by the BOC. The BEA studied historical trends of a 

State's share of basic industries whose products are exported or exportable. 

For service industries, trends of service industry location quotients were 

studied. For population projections at the national level, BEA relied on the 

BOC projections of three factors: 

1) Amount of future net immigration by age, sex, and race 

2) Age-, sex-, and race-specific mortality rates 

3) Age- and race-specific fertility rates. 

PEI used overall results of SMSA-specific population projections by BEA 

as shown in Table 5. The selected projections assume no change in each 

industry's share of the State's employment. Growth or decline depends on an 

SMSA having a greater or lesser portion of the State's fast-growing industries. 

A projection fraction was developed for each projection year, defined as the 

ratio of the projected-year population to the base-year population. Note that 

the base year 1978 is different from base year 1979 used by the BOC. Futureyear population of a particular demographic subgroup around a point source was 

estimated by multiplying the base-year population of the subgroup by the 

corresponding SMSA-projection fraction. 

The procedure developed for this program was simple to implement. A 

somewhat more resource-intensive approach, however, could take into account 

the characteristics of net migration, age, and race in the census tracts 

around the point source. These data could be used to adjust the city-wide 

BEA projections to produce projections that are more specific to the area 

around the point source. 

13 
P.21 

I ' " 1 

TABLE 5. BEA POPULATION PROJECTIONS AND CORRESPONDING PROJECTION FRACTIONS 

SMSA 

Dallas-Ft. Worth 

Chicago 

Tampa-St. Petersburg 

Projection year 

Base 1978 

2,719,853 

7,029,602 

1,396,261 

1990 

3,457,730 

(1.271) 

7,428,395 

(1.057) 

1,820,692 

(1.304) 

2000 

3,986,199 

(1.466) 

7,736,542 

(1.101) 

2,084,093 

(1.493) 

14 
P.22 

SECTION 4 

OUTPUTS FROM THE DEMOGRAPHIC PROGRAM 

PEI has developed a computer program (MATCH-AQ) which performs the population calculations described in Section 3. Execution of MATCH-AQ requires 

the following inputs: 

1) An air quality file containing monthly lead concentrations by census 

tract 

2) A data file on pregnancy rates by age and sex 

3) A tape file of STF3A census data for all States in the chosen SMSA 

4) User-specified data to be introduced in the runstream. 

Air quality data must be in the format shown in Figure 1. Each line 

(record) contains a 2-digit State code, a 3-digit county code, and a 7-digit 

census tract code. Federal Information Processing Standards (FIPS) codes 

are used, consistent with BOC data files. Locations of centers of census 

tracts are given in latitude and longitude. [Program converts to Universal 

Transverse Mercator (UTM) coordinates.] Each of the last 12 columns represents 

average lead concentrations in nanograms/cubic meter for one of 12 months. 

Pregnancy data (file SASD*PREG-DATA) used in the program are printed in 

an output table for purposes of documentation, as shown in Figure 2. 

User-specified data are also documented in the output, as shown in Figure 

3. These data include name of study area city, point source name, SMSA FIPS 

code, and the number of census tracts (which should correspond to the number 

of lines in the air quality data file). The location of the plant stack(s) 

is expressed in UTM X and Y coordinates and UTM zone. The estimated number 

of persons employed in lead production is provided, as well as the projection 

year and projection fraction. If the desired year is 1980 (base), the projection fraction is 1.00. The BOC Region code is given along with four fractions 

indicating the fraction of houses in each of the four BOC regions that are 

unsound. 

15 ' 
Al R aUALlI f ESTIMATES 

4 7 03 7 

4 7 C37 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

4 7 03 7 

0119.0 0 

0123.3 0 

0121.0 0 

0122.0 0 

0123.3 0 

0124.0 0 

012S.0 0 

0141.0 0 

0145.3 3 

0146.0 0 

C147.0 O 

0148.0 0 

0149.0 0 

0158.3 0 

0159.3 0 

0160.0 0 

0161.3 0 

0162.0 0 

0163.0 0 

0164.0 0 

0165.3 0 

01 6-3. CO 

0169.0 0 

0173.3 0 

Cl 71.0 0 

Cl 72.3 0 

0173.0 0 

01 74.0 0 

EOR CENSUS TRACTS: 

4003.606 0 

4003.219 2 

4003.755 5 

4002.790 9 

4 002.298 7 

4002.496 8 

4002.661 6 

4002.524 1 

4000.704 7 

4001.671 7 

4001.186 3 

40C0.567 1 

4000.616 5 

3998.194 5 

3998.907 8 

3999.856 2 

3998.790 3 

3999.020 5 

3999.440 3 

3999.051 1 

4000.323 9 

3998.715 7 

3997.618 9 

3998.055 1 

3997.290 7 

3997.051 3 

3996.643 7 

3995.617 3 

521.618 4 

522.168 0 

523.659 4 

523.824 0 

522.215 3 

521.567 3 

520.946 3 

519.040 0 

518.855 0 

519.761 4 

52C.913 9 

521.707 0 

523.640 9 

526.113 0 

523.438 7 

521.249 9 

52-T.82C 6 

519.623 4 

518.875 7 

518.408 6 

517.065 5 

517.066 7 

517.727 8 

519.364 6 

519.834 3 

521.787 6 

522.877 7 

524.752 6 

41 

19 

8 

7 

22 

45 

24 

5 

4 

7 

23 

29 

t 

2 

9 

37 

21 

7 

T 

5 

2 

2 

1 

t 

(? 

17 

16 

L 

12 

4 9 

4 9 

1 3 

1 1 

0 

0 

0 

1 

1 

6 

2 1 

4 

V 

3i 

2 

21 

2 

1! 

H 

4" 

1 

I 17 

1 5 1 

I 9 

1 7 

1 5 1 

i 15 

1 3 

I 0 

» 0 

I 1 

; 3 

! 16 

J 9 

. 2 

, 11 

1 6 

1 3' 

I 1 

> 1 

! 0 

0 

0 

0 

! 0 

I . 1 

I 10 

r 21 

4 

59 

9 8 

85 

4 9 

7 2 

16 

4 9 

18 

i 

3. 

il 

51 

31 

3 

1( 

M 

I J 

1 4 8 

. 108 

1 8 

I 7 

100 

3 9 

> 5 

1 1 

0 

! 1 

> 5 

! 6 1 

) 8 . 

2 

. 10 

. 1 1 

r 4 

» 1 

c 

r 

0 

r 

0 

> 1 

» i 

4C 

l 23 

4 

•t2 

62 

9 

8 

to 

86 

27 

25 

h 

11 

45 

( 2 

47 

7 1 

4 2 

6 0 

14 

1 2 

6 1 

4C 

1 7 

3 

3 

5 

1 6 

4C 

14 

4 

17 

2 6 

14 

5 

1 

3 

2 

? 

2 

4 

•s 

2 3 

3<" 

7 

6 3 

2 7 

5 

5 

2 6 

6 6 

3 7 

6 

7 

1 2 

3 6 

5 5 

5 

1 

7 

5 5 

3 2 

1 1 

t 

6 

4 

4 

5 

U 

1 3 

3 1 

l i 

i. 

13 

13 

22 

26 

2 0 

12 

10 

46 

1 1 

2 

66 

2 3 

2 0 

74 

3 

2 

i . 

0 

1 

2 

2 

23 

6 

27 

3 

2 

1 

1 

k 

6 

c 

49 

11 

Figure 1. Sample of air quality data used by program MATCH-AO. 
PREGNANCY DATA FROM NATIONAL FILE 

WOMEN 

AGE 

GROUP 

UNDERREPORTING 

FACTOR(A) 

FETAL 

BIRTH DEATH 

RACE PA7E(B) RATIO(C) 

15 - 19 4.110 

20 - 24 4.524 

25 - 29 4.782 

30 - 34 5.C60 

35 - 44 4.922 

BLACK 

NON-BLACK 

BLACK 

NON-BLACK 

BLACK 

NON-BLACK 

BLACK 

NON "BLACK 

BLACK 

NON-BLACK 

100.00 

45.10 

146.30 

109.50 

1Q9«10 

112.40 

62.90 

6Q.AO 

15.90 

12.20 

14.40 

9.50 

13.20 

7.5P 

13.90 

7.40 

1 7.50 

8.9P 

31.10 

15.60 

NOTES: 

(A) APPLIES TO FETAL DEATH R/|TIO. 

(3) RATE PER 1,000 FEMALES IN GROUP. 

(C) PATE PER 1,000 LIVE BIPTHS. 

Figure 2. Pregnancy data used in program MATCH-AQ. 
INPUT DAIA FOR PROGRAM MATCH-AQ4 

oo 

STUDY AREA='DALLAS, TX ' ; 

SOURCE_NAME='GOULD INC . ' ; 

SMSA C0DE=*=1?2'; 

N_TRACTS = 6; 

SOURCE X= 7.0246799E-»002; 

S0URCE~Y = 3.66e7129E*003; 

ZONE= 14; 

EMPL_PB_PROD= 1 .2700 G0GE + 002; 

PR0J_YRT0)= 1980 

PROJ~FRACT= 1.0000000E + 000; 

B0C_REG10N= 3-

F RACT_UNS0UHD<1)= 6.GQOOOOCE-002 

fRACT~UNS0UND(3)= 3.600000GE"002 

PP0J_YR(1)= 198C; 

FRACT.UNSOUND(2)= 3.8999999E-C02; 

FRACT.UNSOUND (4) = 3 .8 OOOOOOE-002 ; 

Figure 3. User-specified inputs for program MATCH- AQ. 
P.26 

Outputs from the program include one table of geographic data for census 

tracts, one table of air quality data, and a series of eight tables of demographic data (one for pregnant women and one for each children's age group). 

Figure 4 is an example showing the table of geographic data for the census 

tracts around a particular point source. State, county, and census tract 

codes follow the census tract sequence number. The sequence number may be 

used as a cross reference between the geographic table, the air quality table, 

and each of the demographic tables. The distance from the census tract center 

to the point source (in meters) is listed next. The last three columns show 

the UTM X and Y coordinates and zone for the census tract center. 

Figure 5 shows air quality data by census tract sequence number, census 

tract code, and 12 monthly averages in nanograms/cubic meter. Also shown are 

quarterly averages in the same units, where each quarter consists of three 

calendar months. 

Figure 6 shows a sample output table for one children's age-group for 

census tracts around a point source. Census tract sequence number, State 

code, county code, and census tract code are listed, followed by populations 

of six demographic subgroups within the age category. For example, the first 

column shows numbers of children <1 year living in unsound LPH's who are estimated to have a high probability of secondary occupational exposure. The last 

column shows the total of all children <1 year in the census tracts (the sum 

all six subgroups). The same table is produced for pregnant women and children 

1 to 1.99, 2 to 2.99, 3 to 3.99, 4 to 4.99, 5 to 5.99, and 6 to 6.99 years of 

age. 

Data from the demographic and air quality tables are also placed in an 

output file. This file may be used as input to other programs, e.g., the 

3 

lead-uptake model described in a separate report. Figure 7 shows samples of 

output file records for two census tracts. On the first line, the census 

tract code is followed by the population of pregnant women in each of the six 

demographic subgroups shown in Figure 6. The second line (or record) contains 

the six demographic subgroup populations for children <1 year of age. Data 

for each succeeding age group follows on each succeeding line up to age group 

6 to 6.99 years. The ninth line lists monthly average lead concentrations in 

nanograms/cubic meter. The same pattern is repeated for the second census 

tract. More information concerning program MATCH-AQ may be found in the source 

code listing (Appendix A). 

19 
o 

CENSUS TRACTS AROUND POINT SOURCE GOULD INC . 

Meter s UTM Coordinates 

Tract FlpS FIPS Census to 

Seq. Stat e County Tract Source X Y Zone 

1 48 085 0304.00 1993 704.4259 3669.0866 14 

2 48 085 0305.00 8044 71o.O?15 3671.4797 14 

3 48 C85 0316.04 12140 709.9499 3659.1523 14 

4 48 085 0316.06 10025 710.2266 3662.3644 14 

5 48 G85 0316.07 10470 706.6524 3659.1154 14 

6 48 121 0215.03 8824 694. 1PQ4 3665.6828 14 

Figure 4. Sample of geographic data table for census tracts around a point source. 
ro 

CENSUS TRACT LEAD CONCENTRATIONS 

1 ract 

Seq. 

1 

2 

3 

4 

5 

6 

Census 

Tract 

03C4.op 

03 05. 00 

0316.04 

0316.06 

0316.07 

0215.03 

1 

35 

7 

8 

6 

8 

3 

2 

18 

10 

10 

10 

19 

12 

3 

16 

5 

4 

3 

8 

6 

Konthly 

4 

8 

3 

6 

2 

6 

3 

Averages(Nanogr aas/cu.a 

5 

3 

2 

0 

5 

1 

4 

6 

8 

3 

1 

5 

4 

' 8 

7 

10 

1 

1 

1 

1 

3 

8 

21 

4 

0 

1 

2 

8 

.) 

9 

8 

2 

1 

1 

2 

9 

10 

2V 

18 

8 

9 

13 

11 

11 

55 

12 

12 

48 

10 

9 

5 

8 

2 

Cuarterl y Averages 

1 2 3 4 

23 6 13 44 

7 3 2 12 

7 2 1 8 

6 4 1 7 

12 4 2 11 

7 5 7 6 

Figure 5. Sample of air quality data table for census tracts around a point source. 
ESTIMATED 1980 CENSUS TRACT POPULATIONS OF CHILDREN < 1 YEA* 

Tract 

Seq. 

1 

2 

3 

4 

5 

6 

FIPS 

Stat e 

48 

48 

48 

48 

48 

48 

FIPS 

Coun ty 

085 

085 

085 

C8 5 

08 5 

121 

Census 

Tract 

C304.L0 

0305.00 

0316.04 

0316.06 

0316.07 

0215.03 

LPH/Un 

SOE 

High 

0 

G 

G 

C 

0 

0 

soun d 

SOE 

Low 

1 

0. 

0 

0 

0 

1 

LPH/S 

S0E 

High 

-u 

0 

0 

0 

0 

0 

ound 

SCE 

Low 

35 

11 

2 

0 

3 

15 

NonSOE 

High 

C 

1 

5 

0 

0 

1 

-LP H 

SOE 

Lou 

67 

21 

178 

6 

47 

379 

Tota l 

103 

33 

185 

6 

•' 5C 

396 

LPH = livin g i n lead-base-j painte d home. 

Unsound LPHs estimate d by regional factor s provide d hy PEI Associates, Inc . 

SOE-Hi/low indicate s probabilit y of secondary occupational exposure. 

Figure 6. Sample of demographic data table for one age group. 
r-o 

CO 

a ED,R 

READ-ONLY 

ED 16R1C 

EDIT 

0304.00, 

0304.00, 

0304.00, 

0304.00, 

0304.00, 

0304.00, 

0304.00, 

0304.00, 

3 5, 

0305.00, 

0305.00. 

0305.00. 

0305.00, 

0305.00, 

0305.00, 

0305.00, 

0305.00, 

7, 

GOULDMODE -8C . 

THU-39/26/85-16: 

0, 

0, 

0, 

3. 

3, 

0, 

0, 

D. 

16. 

0, 

0. 

0, 

0. 

0, 

0, 

0. 

• j , 

1u, 

1 6, 

0, 

0, 

0, 

o, 

0. 

0, 

c, 

o, 

5, 

Figu 

53: 

8, 

3, 

re 

:28-<C. ) 

0, 

0. 

0, 

0, 

0, 

0, 

0, 

0, 

3. 

0, 

0, 

0, 

o, 

0. 

0, 

0, 

0, 

2, 

7. Sample 

2 0. 

3 5. 

1 9. 

1 9, 

2 9, 

2 9, 

1 9. 

3 0, 

&. 

6, 

1 1 . 

1 0. 

1 0, 

2, 

2« 

6, 

4, 

3, 

of output 

0, 

0. 

0, 

0. 

0, 

o, 

0, 

o. 

1 0. 

0, 

I t 

0. 

o, 

0, 

0, 

0, 

0. 

1 . 

file 

37 

67 

36 

36 

55 

55 

36 

58 

2 1 . 

10 

21 

17 

17 

4 

4 

12 

7 

4. 

records. 

R. 

2 . 

2 9, 

1 8, 

5 5, 

9. 

' 

4S 

_i 

/ 

U 

TJ 

CO 

o 
P.31 

REFERENCES 

1. Pope, A. Development of Activity Patterns for Population Exposure to 

Ozone. Memorandum from PEI Associates, Inc., to Mr. Tom McCurdy, U.S. 

Environmental Protection Agency, Research Triangle Park, North Carolina. 

June 1985. 

2. U.S. Environmental Protection Agency. Review of the National Ambient 

Air Quality Standards for Lead: Assessment of Scientific and Technical 

Information. OAQPS Draft Staff Paper. Research Triangle Park, North 

Carolina, April 1985. 

3. Johnson, T. Lead Uptake Model. Memorandum from PEI Associates, Inc., 

to Mr. Jeff Cohen,_ U.S.^Environmental Protection Agency, Research Triangle Park, North Carolina. August 1985. 

4. Bureau of the Census. Census of Population and Housing, 1980: Geographic 

Identification Code Scheme, PHC80-R5. April. 1983. 

5. Geographic Data Technology, Inc. 1980. Census Tract Area Calculation 

File - Technical Description. September 1982. 

6. Bureau of the Census. Census of Population and Housing, 1980: Summary 

Tape File 3 Technical Documentation. Prepared by Data User Services 

Division, Washington, D. C., 1981. 

7. Pope, A. Exposure of Children to Lead-Based Paints. .(Draft) Prepared 

by PEI Associates, Inc., for U.S. Environmental Protection Agency under 

Contract No. 68-02-4309, Work Assignment No. 9 (Draft). Research Triangle Park, North Carolina. September 1985. 

8. Paul, R. Pregnant Women and Fetuses. Memorandum from PEI Associates, 

Inc., to Mr. Tom McCurdy, U.S. Environmental Protection Agency, Research 

Triangle Park, North Carolina. August 1983. 

9. National Center for Health Statistics. Vital Statistics of the U.S., 

Vol. I, Natality. DHHS Pub. No. (PHS) 82-1100. Public Health Service, 

Washington, D.C. 1982. 

10. National Center for Health Statistics. Vital Statistics of the U.S., 

Vol. II, Mortality. DHHS Pub. No. (PHS) 83-1101. Public Health Service, Washington, D.C. 1982. 

11. National Center for Health Statistics. Advance Report of Final Natality 

Statistics, 1980. Monthly Vital Statistics Report, Vol. 31, No. 8, 

Supplement. DHHS Pub. No. (PHS) 83-1120. Public Health Service, 

Hyattsville, Maryland. November 30, 1982. 

24 
P.32 

APPENDIX A 

SOURCE CODE FOR DEMOGRAPHIC PROGRAM 

25 
P.33 

8 £0,8 

READ-ONLY 

EO 16R1C 

EDIT 

1 

2 

3 

4 

s 

6 

7 

a 

9 

10 

11 

12 

13 

14 

15 

16 

17 

IS 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

33 

39 

40 

41 

42 

43 

44 

45 

46 

47 

48 

49 

50 

51 

52 

S3 

SA SD'PR.KATCH-AOA/PB 

MODE 

ME 0-09/25/ 8 5-16 : 49 :46- ( 46, ) 

MATCH_AQ4: PROCEDURE OPTIONS(MAIN); 

This program aatches population oata with a set o 

data Inputs fro a ISC-LT . Population data is for 

of childre n and estiaate d number's of pregnant woa 

are subdivided into 3 groups of housing and by wh 

is a high probabilit y of secondary occupationa l e 

Nine output table s provide geographic data on 

around a given point source, aonthly AQ data on t 

and a serie s of population tables , one for each a 

OCL ( STUDY_AREA, S0URCEJIAME ) CHAR(15) STATIC, 

f Pb air qua tit y 

selected groups 

e n. Populations 

ether or not there 

xposure. 

census tract s 

he saae tracts , 

ge group. *»/ 

/ • 

S0URCE_STATE<3) 

SMSA_C00E 

S0URCE.X 

S0UBCE_T 

EMPL_P8_PR00 

fractio n durable 

PR0J.FRACT 

PROJ~YR<0:1> 

FRACT_UNSCUN0<4) 

/* 1*Northeast, 

3 0C REGION 

N TRACTS 

/* 

/* 

UTN h 

UTM v 

oriz . coordinat e */_ 

ert . coordinate »/ 

FIXED STATIC, 

CHARU) STATIC, 

FLOAT STATIC, 

FLOAT STATIC, 

FLOAT STATIC, 

goods »fg . workers i n pb-relate d production . * / 

FLOAT STATIC, 

FIXED STATIC, 

FLOAT STATIC INIT(.06 , .0 

2*H1dvest, 3'South , 4=west 

FIXED STATIC , / * Se e B.O 

FIXED STATIC ; 

/ * ?=1983 , 1=orojeetio n yr * / 

I39 , .033 , .078) , 

*/ 

i.C. Suide •»/ 

/* Input 

OCL ( 

data fo r pregnant woaen: • / 

BIRTH_RATE<5,2), FETA L.DJJ ATI 0 <5 ,2 ) , /*» 

UN0ER_RPT_FACT0R(5), EXP_BIRTHS(5,2) ) FLOA 

PRE6DATA 

RACE_NA*E<2) 

'BLACK*, 'NON-BLACK' 

AGE GRP NAME<5) 

'15~ - 19' , *20 - 24' 

SNGLS2<52) 

*25 

INPUT FILE, 

CHASdO) INI T 

CHARdO) INI T 

• 29* . '30 - 34' , 

CHARM) INIT <<5 

By age, race • / 

T STATIC, 

'3 5 - 4 4 ' ) , 

2)('-') ) ; 

/ * Inputs froa aonthly AQ file : • / 

OCL (X0 , TO, LAT, LONG ) 

MO.AQM2) 

TRACTDATA 

H0NTHLYAQ 

( EOF , HC0 DONE ) 

FLOAT, 

FLOAT, 

OUTPUT FILE, 

INPUT FILE, 

8IT11 ) STATIC INIT C '3) ; 

Inputs froa a .0 . C. t ape s : 

OCL STF3A FILE VARIABLE, 

I TAPE1, TAPE2, TAPE3 ) FILE INPUT ENV 

( ANSI, FB, RECSIZEM2096), BLKS I I E (1 2096) ), 

/*•> 

/• 

B0C_RACE(3) 

Subscript 1 » 

LA8R.FRCE <8) 

B0C_W0HEN(11) 

Subsrcipt 1 = 

7 = 21 yrs , 8 

BOCCHILD <5) 

FLOAT 

total , 2 *•* white , 

FLOAT 

FLOAT 

15 yrs , 2 = 16 , 3 

= 22-24 , 9 » 25 

STATIC, 

3 = black 

STATIC, 

STATIC, 

= 17, 4 = 13 , 

2 9 , 10 = 30-34 

• / 

5 = 19. 6 = 20 , 

, 11 = 35-4 4 */ 

FLOAT STATIC, 

26 
P.34 

54 

55 

56 

57 

58 

59 

60 

61 

62 

63 

64 

65 

66 

6 7 

6 8 

6 9 

7 0 

71 

72 

73 

74 

75 

76 

77 

78 

79 

30 

81 

82 

83 

8 4 

85 

86 

87 

aa 

89 

90 

91 

92 

93 

94 

95 

96 

97 

98 

99 

100 

101 

102 

103 

104 

105 

106 

107 

108 

109 

110 

/ * Subscrip t 1 = unoe r 1 year , 2 • 1 8 2 years , 3=384 , 

4 * 5 years , 5= 6 year s 

< HS_UNITS<7) , 

*/ 

•0MEM5.2) 

C.TRACT 

RCD.LEVEL , 

COUNTY 

BTRACT 

S.FLA6 1 

TR 

/ * Tabl e 109 - Tota l */ 

FLOAT STATIC , 

CHAR<7) , 

STATE, SUFFIX ) CHAR(2) , 

CHAR13) , 

CHARU) , 

CHARd) , / * Dat a suppressio n fla g * / 

FIXED STATIC ; 

OCL TRACT<130) 

2 T STATE 

T.CNTY 

T~C0DE 

H.TO.SRC 

Tjrrnx 

T"UTBY 

DCL 1 

OCL 1 

STATIC , 

CHARC2), 

CHAR(3) , 

CHAR (7) , 

FLOAT, 

FLOAT, 

FLOAT; 

AQ_DATA(130 ) STATIC , 

2 " MONTHLY( 12) , 

3 AO FLOAT, 

2 QTRLYU) , 

3 AO FLOAT; 

POP_DATA<130) STATIC , 

2 PR0JECTI0N(0:1) , 

3 A6E_6RP(0:7 ) , 

4 BY_HOUSE(3) , 

/ * subscrip t 1 = livin g 

subscrip t 2 = livin g 

subscrip t 3 = no t i n 

5 BY_SOE<2) , 

6 ~ POP 

4 SUBTOT 

/ * 198 0 •• 1 Projectio n y r 

/ * Preg . • 7 chiI d jroup s 

*/ 

• / 

DCL TR_OATA<130) STATIC , 

2 E.1PL_DUR_G00DS FLOAT, 

2 T0T_HSNG~ FLOAT, 

2 P3.HSNG FLOAT, 

2 T0T_CHIL D FLOAT, 

2 T0T_E"PL FLOAT, 

2 SOE.CHILO FLOAT; 

i n LPH , unsound 

i n LPH , sound 

LPH » / 

FLOAT, 

FLOAT; 

/ * Census trac t data : *l 

I * Employed i n ouraol e goods afg . 

/ * Tota l housin g unit s i n tract . 

/ * Housin g . it h leade d paint . 

/ * Tota l childre n age 0--5.9 9 yr s 

/ * Tota l employee s i n tract . 

/ * Childre n expose d t o SOE. * / 

/ • Variable s used i n 

OCL ( UTHY, 

SAI subroutine s -* 

UTWX, ALON, ALAT ) 

I ZONE 

NAPGTU ENTRY( FLOAT, FLOAT, 

EXTERNAL OPTIONS(FTN) , 

HAfUTG ENTRY( FLOAT, FLOAT, 

EXTERNAL OPTIONSlFTN) , 

VV6TU1 ENTRY EXTERNAL OPT IONS(FTN) ; 

REL.SAI~-»OUTINES 

FLOAT BI N STATIC , 

FIXED BI N STATIC , 

FIXED , FLOAT, FLOAT 

• / 

FIXED , FLOAT, FLOAT ) 

Used i n MATCH-AQ2 subroutin e calculations : * / 

OCL ( AGE1 , AGE2 , F_0EATHS, PR_WOI*EN, BL.FRACT , 

RACE.FRACT, TOT_E*PL ) FLOAT STATIC , 

( PB_HSN6 , T0T_H£NG ) FLOAT STATIC , 

27 
P.35 

'«7 

'<? 

'<3 : 

U<: 

'<5 ; 

U6-

u?. 

nl; 

150-

'57 ; 

'53 ; 

'54.. 

'55 . 

75-j. 

158-

159 : 

160: 

'67 

U2 

'63 ; 

76< . 

'65-* 

166 : 

16?. 

'12.-

" < . 

776-

V

7

' 

"?. -

120. 

121; 

,

7

*

2

-* '23 ; 

'24 

7

?5* 

'*6 : 

7 ?fl . 

'29 : 

730 j 

'31 . 

'33 : 

13 5: 

736; 

'j? : 

7Jfl. 

'39 

J<0 . 

U S T a. 

t4**f"ifi?,F

*-*CT 

•s«*r

 J o 

Tn» ' 

o.o-

"Sffi SMS'; 

ii$ym^: "* '* 

•?; "'• ::s "S!L"A 

r/r- - "-^•-. M S 0 

J 4-;t5", l*#*s«E*si..r

*. "«"vof ....?.'"••-*" „ . . ' • * • 

:::***a?^****'*-**r-•••*•... 

'

S

'-*=»P.-.„„ • 5*".«),"**««o.i 

• •*• « .. . 

es 
-» 

> 

-* 

a 

163 : 

169 : 

170 : 

171 : 

172 : 

173 : 

174 : 

175 : 

176 : 

177 : 

178 : 

179 : 

180 : 

181 : 

182 : 

183 

184 : 

185 : 

186 : 

187 : 

188 

189 

190 

191 : 

192 : 

193 

194 : 

195 

196 : 

197 

198 : 

199 

200 

201 

;C2 

203 

204 

205 

206 

207 

208 

209 

210 

211 

212 

213 

214 

215 

216 

217 

218 

219 

22 U 

221 

222 

223 

22 4 

(COL(IO).A , C0L(2C ) ,F(7,3 ) ) ; 

DO R » 1 TO 2 ; 

FUT EDI T (RACE_NA«E(R) , BIRTH_RATE(A,R) , 

FETAL 0 RATIO(A,R ) ) 

(C0L(30),A , C0L(40),F(1C,2) , COL(51 ) ,F(1 0 ,2)) ; 

END;E»D ; 

PUT SKIP(2 ) EDI T (SN6L52 ) (COL(10),52 A ) ; 

PUT SKIP EDIT ('NOTES:' ) ( C0LOG),A) ; 

PUT SKIP EDIT ( ' (A ) APPLIES TO FETAL DEATH RATIO.' ) (COL(10),A) ; 

PUT SKIP EDIT ( ' (B ) RATE PER 1,00 0 FEMALES IN GROUP.') (COLdO).A) ; 

PUT SKIP EDIT ( ' (C ) RATE PER 1,00 0 LIVE BIRTHS.' ) (C0L(10),A) ; 

A«_INPUTS : / * Fro a ISC-L T output s *»/ 

PUT SKIP(4 ) EDI T ('AI R QUALITY ESTIMATES FOR CENSUS TRACTS:' ) (A) ; 

PUT SKIP ; 

00 T » 1 TO N.TRACTS; 

GET FILE(«0NTHLYAO ) EDIT (T_STATE(T) , T_CNTY( T) ,. T.CODE ( T) , 

LAT, LONG, (MONTHLY.AQ <T ,M) 00 H=1 TO *» 2 7 J (C CL(3 ) TA ( 2) , 

X(1),A(3) , CCL(1C),A(7) , C0L(26),2F(9,4 ) , COL(44),12F(7,0)) ; 

/ • Convert l a t-lon g to UTM coordinates . * / 

ALAT » LAT; 

ALON • LONG; 

1Z0NE = ZCNE; 1* Froa runstrea a * / 

I F ALON > 0. 0 THEN ALON = -ALON ; / • Subr need s - • / 

CALL MAP6TU(AL0N,ALAT,IZ0N E ,UTMX,t»TMY); 

T_UTPX(T ) = UTMX; / « From subroutin e * / 

T~UT*Y(T ) = UTMY; /*» From subroutin e * / 

PUT EDIT (T_STATE(T) , T_CNTY(T) , T_COOE(T), ' 

UTNY, UTMX, (MONTHLY. AQ(T,M)DO N» 1 TO 12) ) ( COL (3 ) ,A ( 2) , 

X(1),A(3) , CaL(1C),A(7) , COL(24),2F(1C,4) , CO L(44 ) ,1 2 F (7 , C) ) ; 

/ * Calculat e distanc e t o source . * / 

XO = SCURCE_X - ITNX ; 

YO = SOURCE.Y - UTMY; 

fl TO SRC(T) = ( £QRT(XD** 2 • YD**?) ) • 1000.0 ; 

END; " 

: CIA FES: /-*** Read deaographi c dat a fro a census tapes : • / 

OC BOCTAPE = 1 TO N.TAPES; 

I F BCCTAFE - 1 THEN STF3A = TAPE1 ; 

ELSE I F BOCTAPE * 2 THEN STF3A = TAPE2 ; 

: ELSE I F BOCTAPE = 3 THEN STF3A = TAPE3 ; 

PUT PAGE EDIT ( 'BEGI N READING STF3A TAPE ' , BOCT*PE ) 

( A , F ( 1 ) ) ; 

: EOF a 'C'B ; 

ON EN0FILE(STF3A ) EOF = '1'8 ; 

T P : DO WHILE ("EOF) ; 

: GET FILE(STF3A ) EDIT ( RCO.LEVEL , STATE, SMSA, COUNTY, 

BTRACT, SUFFIX , S FLAG1 ) ( COLdO) , A(2) , C0L(34) , 

: A(2) , A(4) , A(3) , COL(SO) , »(4) , A(2) , C0L1205) , A (1 ) ) ; 

IF RCO.LEVEL * = '14 ' / * trac t * / THEN GO TO NEXT_B0C_RECORD; 

: IF SUFFIX = ' ' IHEN SUFFIX = 'CO' ; 

C_TRACT = BTRACT •! *. ' ! ! SUFFIX ; 

: 00 TR = 1 TO N_TBACTS; 

: IF (C.TRACT=T.CO0E(TR ) 8 STATE = T_ST*TE(TR ) 

: & COUNTY = T.CNTY(TR ) ) THEN GO TO~SUP_FLAG; 

: ENO; 

: GO TO N£XT_BOC_RECOR 0; / * no AO dat a t o Batch . * / 

29 

P.36 
P.37 

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226 

22 7 

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241 

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251 

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27C 

271 

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28C 

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SUP. 

IF S 

I* 

TAP 

OIT 

GET 

GET 

GET 

GET 

GET 

GET 

CALL 

PUT 

• t 

PUT 

CALL 

UR a 

NEXT 

GET 

I F U 

END 

END CTAP 

ES.DONE : 

CALL SUB 

CALL NOR 

CALL PRO 

FLAG: 

.FLAG 

FUT E 

STATE 

<CCL( 

PUT S 

GO TO 

END; 

FILE ( 

FILE ( 

FILE i 

FILE ( 

FILE ( 

FILE ( 

PREG 

SKIP 

SUFF 

EDIT 

TRAC 

UR ** 

BOC_ 

SKIP( 

* * 1 

R >= 

T P; 

E S; 

01 vl D 

ML. S 

JECTI 

/ * Dat a suppressio n fla g *l 

1 » *1 * THEN 00 ; / * i.e . i f < 3G person s 

OIT ( 'Les s tha n 3C person s i n censu s trac t 

, COUNTY, C TRACT, ' - - dat a suppressed. ' ) 

1 ) , A,A(3 ) ,A(4),2 A ) ; 

KIP ; 

NEXT_BOC_RECOR0; 

in t rac t 

record ", 

*/ 

STF3A) 

STF3A) 

STF3A) 

STF3A) 

STF3A) 

STF3A) 

WOMEN 

EDIT ( 

IX, ' 

(' '. 

T CALC 

1 ; 

BECORD 

1) FIL 

EDIT (BOC.RACE) (COL(«31) , 3F(9 ) ) ; 

EDIT (BOC CHILD) (C0L(676) , 5F(9 ) ) ; 

EDIT (80C.W0MEN) <C0L(9S2) , 11F(9 ) ) ; 

EOIT (LABR FRCE) (C0L(508C) , 8F(9 ) ) ; 

EDIT (EMPL~DUR_GOOOS(TR)) (COL(5518) , 

EOIT (HS UMTS ) (COL(3682) , 7 F (9 ) ) ; 

F(9 ) ) , 

'RECORD USED : ' , STATE, COUNTY, BTRACT, 

' ) ( COL(5),2A, X (1) , A,X(1) , 4A , ) ; 

BOC.CHILD , ' ' ) (A , 5F(9) , A) ; 

E (STF3A) ; 

I THEN GO TO TAPES.DONE; TEST * / 

/ • End of readin g record s on one tap e * / 

/ * End o f readin g al l censu s tape s * / 

E POP; 

CE; 

CN; 

FUTS: 

OCL LINEK89 ) CHARd ) INIT ( (89)('»*)) , 

LINE1A(89 ) CHARd ) IN I T( 09 ) ('-') ) , 

LINE2(117 ) CHASd ) Ihl?((117){'-')) , 

LINE3169 ) CMRd ) INI T ((69 ) ('-*)) , 

6R01P_NA*»E(C:7 ) CHAR(25 ) VARIABLE; 

PUT PAGE EDIT ('CENSUS TRACTS AROUND POINT SOURCE * , 

SOURCE NAME ) (COL( 12) ,2A) ; 

PUT SKIP(2 ) EDIT (LINE3 ) (69A) ; 

PUT SKIP EDITCMeters* , *UTM Coordinates' ) (COL(35),A , C0i(5?),A> ; 

PUT SKIP EOIT ('Trac t FIPS FIPS Census to ' , 

- ) ( A f C0L(44), A ) ; 

PUT SKIP EOIT ('Seq . Stat e County Trac t Source' , 

'X' , *Y' , 'Zore ' ) (A, C0L(48),A , C0L(58),A , COL(65), A ) ; 

PUT SKIP EOIT (LINE! ) (69A) ; 

DO TR = 1 TO H.TRACTS; 

FUT EDIT (TR , TRACT(TR) , ZONE) (C0Ld),F(3) , C0L(9),A , 

C0L(17),A , C0L(25),°A , CO L (32 ) , F( 8 , 0 ) , X ( 2 ) , 2 F (1 1 ,4 ) , F(4 ) 

END; 

QTRLY.AQ = O.C ; 

DO TR = 1 TO » TRACTS; 

00 0 = 1 TO 4 ; 

OC * - 1 TO 3 ; 

) ; 

30 
•» *. 

P.38 

2S2 

283: 

284: 

285: 

286: 

287: 

288: 

289: 

290: 

291 

292: 

293: 

294: 

295: 

296: 

297: 

298: 

299: 

200: 

201 

202: 

203: 

2C4: 

205 : 

206: 

207: 

208: 

209; 

210: 

211 

212 

213: 

!14 : 

!15. 

216: 

217: 

218: 

119: 

220: 

221 

222: 

223: 

224: 

22 5 

22 6: 

22 7: 

22 8: 

229: 

230: 

231 

232 

233: 

234: 

235: 

236: 

237: 

238 : 

QTRLY(TR,Q).AQ • MONTHLY(TR,NM).AQ; 

/ 3.0 ; 

MP » (Q-1 ) • 3 • N; 

Q1RLY(TR,Q) . AO 

EI.O; 

QTRLY(TR,Q).AO = OTRLYITR ,0).AQ 

END;ENO; 

PUT PAGE EOIT CCENSliS TRACT LEAD CONCENTRATIONS ' ) (C0L(45X,A); 

PUT SKiP(2) EOIT (LINE2) (117A); 

PUT SKIP EDIT ('Trac t census' , 

'Monthly Averages(Nanagr'as/cu.m.)' , 'Quarterl y Averages') 

(COL(1),A , C0L(39),A , C0L(98).A ) ; 

PUT SKIP EOIT ('Seq . Trac t * , * 1 2 3 4' , 

5 6 7 8 9 1C 11 12* . 

' 1 2 3 4' ) (C0L(DtA , C0L(24),2A , C0l(93), A ) ; 

PUT SKIP EDIT (L1NE2) (117A); 

OS- T8 * 1 TO (.TRACTS; 

PUT EDIT (TR, T CODE(TR), 

((MOhTHLY(TR,M).AQ)DO M=1 TO 12) , ( (QTRLY (TR,0 ) .AO)D0 Q=1 TO 4) ) 

( C0L(1),F(3) , X(6),A(7) , C0L(20),12F(6) , 

X(2 ) , 5F( i ) ) ; 

ENO; 

= 'PREGNANT WOMEN'; 

= 'CHILDREN < 1 YEAR*; 

= 'CHILOREN 1 TO 1.99 YEARS'; 

» 'CHILDREN 2 TO 2.99 YEARS'; 

=» 'CHILDREN 3 TO 3.99 TEARS'; 

« 'CHILOREN 4 TO 4.99 YEARS'; 

*» 'CHILDREN 5 TO 5.99 YEARS'; 

* 'CHILDREN 6 TO 6.99 YEARS'; 

7; 

THEN PUT PAGE EDIT ( ' ' ) (C0L(8),A) ; 

F a 1 THEN PUT PA6E EDIT ( ' *) (COL(14) ,A) ; 

F >» 2 THEN PUT PAGE EDIT ( ' ' ) (C0L(11),A) ; 

('ESTIMATED ' , PROJ YR(1) , ' CENSUS TRACT', 

IONS OF ' , GROUP.NAME(GP)) ( A, F (4 ) , 3A ) ; 

2) EDIT (LINED 7SSA); 

GROUP.NAHE(O) 

GROUP.NArEd ) 

GR0UP_NAPE(2) 

GROUP NAPE (3 ) 

GROUP NACE (4 ) 

6R0UP_NAFE(5 ) 

GR0UP_NAPE(6) 

GROUP.NA |<E(7) 

P0P.T8LS : 

00 GP » C TO 

IF 6P = 0 

ELSE I F G 

ELSE I F G 

PUT EDIT 

* POFULAT 

PUT SKIP( 

PUT SKIP 

(C0L(33 ) , 

PUT SKIP 

(C0L(38) , 

PUT SKIP 

SOE 

PUT SKIP 

* Lew 

PUT SKIP 

DO TF a 1 

FUT E 

( COL 

FUT E 

END; 

PUT SKIP( 

PUT SKIP 

FUT SKIP 

* rejicn a 

PUT SKIP 

' seconda 

'LPH/Unsound LPH/Sound Non-LPH') 

FIPS 

EDIT ( 

* > ; 

EDIT ( ' 

> ) ; 

EDIT ('Trac t f IP S 

SOE SOE SOE 

EDIT (' Seq. State 

High Low High 

EOIT (LINE1A) (39A); 

TO N TRACTS; 

OIT ^T( , T STATE(TR), T CNTY(TR), T COOE(TR) ) 

(1),F(3) , X(6),A(2) , X(67,A(3) , X(5),A(7 ) ) ; 

OIT ( AGE GRP(TR,1 ,GP)) ( 3(X (? ) ,2 (P'ZZ Z, ZZ?') ) 

Census SOE*, 

SOE' ) (COLO) ,2A ) ; 

County Trac t High 

Low Tot al' ) (C0L(1),2 A 

); 

2) EDIT (LINE D (89A); 

EDIT ( ' LPH 3 I iving 

EDIT ( ' Unsound LPHs estiaate d by 

I factor s 

EOIT ( ' SOE 

ry occup ati cnal exposure. 

# 

in lead-based painte d hone.' ) (A) ; 

sound LPHs estiaate d by' , 

proviaed by PEI Associates , Inc.' ) (2A) ; 

E-Hi/low indicate s probabilit y of . 

') <?A); 

31 
P.39 

239 

240 

241 

242 

243 

244 

245 

246 

347 

248 

249 

250 

251 

252 

253 

354 

ECF :254 

EtO ED. 

END POP.TBLS; 

* Output file s for UPTAKE -frograa: * / 

OCL 0U1FL OUTPUT FILE; 

DO T 3 1 TO N.TRACTs; 

DO GF - 0 TO 7 ; 

FUT FILE(OUTFL) EDIT (T COOE(T), ((*,* , P 0P( T, 1 ,G P,H S .S) 

00 S»1 TO 2)00 HS31 TO I ) ) (C0L(1),A , 6(A,F(7) ) ) ; 

END; 

FUT SKIP FILE(OUTFL) EDIT 

( (( MONTHLY.AO(T.M) , *,*)0 0 M=1 TO 11) ) ( 11(F(6).A ) ) ; 

FUT FILE(OUTFL) EOIT (MONTH LY . AQ (T , 12) ) (F(6>) ; 

END; 

fOt.END: 

»D MATCH.A04; 

hC CURRECTIOS APPLIED 

• FIU 

32