Risk and Technology Review -

Analysis of Socio-Economic Factors for Populations 

Living Near Primary Lead Smelting Facilities

Prepared by:

EC/R Incorporated

501 Eastowne Drive, Suite 250

Chapel Hill, NC 27514

EPA Contract No. EP-D-06-119

Work Assignment No. 4-02

Prepared for:

Terri Hollingsworth, Work Assignment Manager

Sector Based Assessment Group

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, North Carolina 27711

January 3, 2011
ii

Disclaimer

Although the analysis described in this document has been funded 

wholly or in part by the United States Environmental Protection 

Agency contract EP-D-06-119 to EC/R Incorporated, it has not 

been subject to the Agency's review and therefore does not 

necessarily reflect the views of the Agency, and no official 

endorsement should be inferred.
Contents

1. Introduction 1

2. Census Data 2

3. Calculation Methods 3

3.1 Racial and Ethnic Categories and the Total Population 4

3.2 Age Categories 5

3.3 Level of Education 5

3.4 Household Income 6

3.5 Poverty Level 7

4. Results 7

5. Uncertainty Discussion 10
1

1. Introduction

This document describes the approach used to evaluate the potential cancer and 

noncancer risks associated with inhalation and air-related exposures to hazardous air pollutants 

(HAP) in different social, demographic, and economic groups within the population living near 

the one remaining primary lead smelting facility in the United States. This work was carried out 

in support of the U.S. Environmental Protection Agency’s Residual Risk and Technology 

Review (RTR) for primary lead smelting operations emissions subject to Maximum Available 

Control Technology (MACT) requirements under 40 CFR 63 Subpart TTT. 

In the RTR analysis, the Human Exposure Model, Version 3 (HEM3)1,2 was used to 

estimate cancer and noncancer risks due to the inhalation of HAP for the populations residing 

within 50 kilometers of the one remaining primary lead smelting facility in the country. HEM3 

estimates cancer and noncancer risks at the level of census blocks using the AERMOD state-ofthe-art air dispersion model developed under the direction of the American Meteorological 

Society (AMS) / EPA Regulatory Model Improvement Committee (AERMIC). Each census 

block typically includes about 50 people. Additional information on the risk analysis is available 

in the docket for the proposed National Emission Standards for Hazardous Air Pollutant 

Emissions: Primary Lead Smelting Operations rulemaking where a report is provided, 

covering the inputs and specific assumptions, and addressing uncertainties.

In the current analysis, cancer and noncancer risk estimates from the primary lead 

smelting HEM3 modeling effort were linked to detailed census data in order to evaluate the 

distribution of risks for different social, demographic and economic groups. The following 

population categories were included in this analysis:

• Total population

• White

• Minority

• African American (or Black)

• Native Americans

• Other races and multiracial

• Hispanic or Latino

• Children 18 years of age and under

• Adults 19 to 64 years of age

• Adults 65 years of age and over

 1. EC/R. 2006. Modeling for the Residual Risk and Technology Review Using the Human Exposure Model 

3 – AERMOD Version. Prepared by EC/R Incorporated for the U.S. Environmental Protection Agency, 

Research Triangle Park, NC. 

2. EC/R. 2008. HEM-3 User’s Guide. Prepared by EC/R Incorporated for the U.S. Environmental 

Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/fera/human_hem.html#guide
2

• Adults without a high school diploma

• Households earning under the national median income

• People living below the poverty line

The HEM3 results for a particular census block reflect the estimated level of cancer risk that 

would be experienced by an individual residing in the block for 70 years, and/or the estimated 

noncancer hazards experienced by an individual residing in the block due to chronic exposure. 

In this case, the latter includes the development of chronic hazard quotients for lead which are 

derived from estimated maximum 3-month rolling average exposures divided by the National 

Ambient Air Quality Standard (NAAQS) for lead. 

The distributions of source category risks across the various demographic groups are 

normally compared to those associated with HAP sources at the facilities containing the source 

category (i.e., “facility-wide”). However, for this source category, the “facility-wide” risks are 

equivalent to the source category risks, so no such comparisons are provided. 

The census data used in this analysis is described in Section 2. The algorithms used to 

compute the distributions of risk and exposure are presented in Section 3. The results of this 

analysis are presented in Section 4. 

2. Census Data

Table 1 summarizes the census data used in this analysis, showing the source of each 

dataset and the level of geographic resolution. All of the data are from the 2000 Decennial 

census. Race and ethnicity data were obtained at the census block level. Age distributions, data 

on educational status, and economic data were obtained at the block group level. A census block 

contains about 50 people on average; and a block group contains about 26 blocks on average, or 

about 1,350 people. (For comparison, a census tract is larger than a block group, with each tract 

containing an average of 3 block groups, or about 4,300 people.)

Table 1. Summary of Census Data used to Analyze Risks for Different Socioeconomic Groups

Type of population category Source of data

Level of 

geographic 

resolution

Racial and ethnic categories Landview® Census block

Age groups SF3 Table P8 Block group

Level of education - adults without a high 

school diploma

SF3 Table 37 Block group

Households earning below the national 

median income

SF3 Table 52 Block group

People living below the poverty line SF3 Table P87 Block group
3

Data on race and ethnicity were obtained primarily from the Landview® database 

compiled by the Census Department.3 Landview® gives a breakdown for the population of each 

census block among different racial classifications, including: White, African American or 

Black, American Indian or Native Alaskan, Asian, Native Hawaiian or other South Pacific 

Islander, other race, and two or more races. In the current analysis, the Asian, Native Hawaiian 

or other South Pacific Islander, and other race categories were combined into a single category. 

The Landview® database also indicates the number of people in each tract that are of Hispanic 

or Latino ethnicity. Landview® covers the 50 states, the District of Columbia, and Puerto Rico, 

but does not cover the Virgin Islands. Race and ethnicity data on the Virgin Islands were 

obtained from the Virgin Islands Summary File.4

Data on age distributions, poverty status, household incomes, and education level in the 

U.S. and Puerto Rico were obtained from the 2000 Census of Population and Housing Summary 

File 3 (SF3) Long Form. For the U.S. this file was accessed on a DVD version prepared by 

GeoLytics.5 SF3 data for Puerto Rico were obtained from the Census Department website,6

4

and 

data for the Virgin Islands were retrieved from similar tables in the Virgin Islands Summary 

File.

The SF3 data set consists of over 800 separate tables, each providing information on a 

different subject. For the current analysis, data were obtained from Tables P8, P37, P52, and 

P87. Table P8 gives the estimated numbers of men and women in different age categories for 

each census block group. Table P37 analyzes the level of education attained by men and women 

over 25 years of age (e.g. some high school but no high school diploma, high school graduate, 

some college, etc.). Table P52 gives information on household income in 1999, and Table P87 

estimates the number of people living below the poverty line in each block group. 

3. Calculation Methods

The HEM3 models the cancer and noncancer risk at a point near the geographic center of 

each census block.7

 3. Census. 2002. LandView 5 on DVD [electronic resource] : a viewer for EPA, Census and USGS data and 

maps. U.S. Census Bureau, Washington, D.C.

 For the current analysis, this risk estimate was assumed to apply to all 

individuals residing in the block. We used block identification codes to link the HEM3 modeling 

4. Census. 2008. Virgin Islands Summary File. U.S. Census Bureau, Washington, D.C. 

www.factfinder.census.gov

5. Census. 2004. Census DVD 2000 Long Form SF3, Release 2.2. Geolytics, Inc., East Brunswick, NJ. 

www.geolytics.com

6. Census. 2008. SF3 Data for Puerto Rico. U.S. Census Bureau, Washington, D.C. 

www.factfinder.census.gov

8. HEM3 generally uses the coordinates given by the census for the internal point, or “centroid” of each 

block. However, when the footprint of an industrial facility includes the block centroid, the model is 

designed to identify the highest-risk point outside of the facility’s footprint.
4

results for each block to the appropriate census statistics. This allowed us to estimate the 

numbers of people falling into different population subcategories within each block. We then 

analyzed the distribution of estimated inhalation risks within each population subcategory, giving 

the numbers of people within the subcategory that are exposed to different risk levels. Each 

distribution involved a tabulation of all the census blocks modeled for the primary lead smelting

source category. We also computed the average risk for all individuals in each population 

subcategory. 

Distributions of risk and average risks were computed for the raw HEM3 model results 

for primary lead smelting operations. For comparison, distributions of risk and average risks 

were also computed for all emission sources at the facility (including primary lead smelting) 

(e.g., “facility-wide”), and in the country as a whole. 

Section 3.1 describes the calculation method used for categories where block-level data 

were available -- racial and ethnic categories and the total population. Sections 3.2 through 3.5 

describe calculation methods for age categories, education status, household income, and poverty 

status, respectively. 

3.1 Racial and Ethnic Categories and the Total Population

Since race and ethnicity data were available at the census block level, the calculation of 

risk distributions for these categories involved a simple block-by-block accumulation of the 

people in each subcategory. We began by identifying a set of bins reflecting the level of risk. 

The population of each block was then assigned to the appropriate risk bin based on the modeled 

risk level in the block. The numbers of people in each risk bin were then added together for all 

of the blocks modeled for the primary lead smelting source category:

H(Rab,s) = ∑i

(Ra≤Ri<Rb) [N(s,i)] (1)

where:

H(Rab,s) = the population count for risk bin Rab, which is between Ra and Rb for population 

subgroup “s”

Ri = the modeled risk level in block “í” (estimated lifetime cases of cancer per 

million population or noncancer hazard index)

∑i

(Ra≤Ri<Rb) refers to the summation over all blocks i where Ri falls in bin Rab, between Ra

and Rb

N(s,i) = the number of people within population subcategory s, in block i

The same approach was used for the total population. The average risk for a given population 

subcategory or for the total population was calculated using the following equation:

A(S) = ∑i [N(s,i) × Ri] ∕ ∑i [N(s,i)] (2)

where:

A(s) = the average risk for population subgroup “s” (estimated lifetime cases of cancer 

per million population or noncancer hazard index)
5

∑i refers to the summation over all blocks “í” modeled for the emission source 

category

N(s,i) and Ri were defined above

3.2 Age Categories

Age data were retrieved from the Table P8 of the census SF3 Table, which contains data

on the numbers of males and females of different ages in each census block group. In processing 

the age data, we began by aggregating the categories in the census to the broader age groups 

studied in this analysis. For instance, the total number of children 18 years of age and under was 

calculated by adding together the number of girls under 1 year, the number of boys under 1 year, 

the number of 1-year-old girls, the number of 1-year-old boys, and so on up to and including 18-

year-old girls and boys. In this way, we calculated the number of children age 18 and under, the

number of adults from 18 to 64, and the number of adults 65 and older in each census block 

group.

The next step was to estimate the numbers of people in each age group at the block level. 

To make this calculation, we assumed that the fraction of people in each age group was the same 

for all blocks in a given block group. Thus, the number of people in a particular age group and 

within a particular census block was estimated as follows: 

N(a,b/bg) = N(t,b/bg) × N(a,bg) ∕ N(t,bg) (3)

where:

N(a,b/bg) = number of people within age group “a”, in block “b” of block group “bg”

N(t,b/bg) = total number of people in block “b” of block group “bg”

N(a,bg) = number of people within age group “a” in block group “bg”

N(t,bg) = total number of people in block group “bg”

Equation 1 was then applied to the block-level population estimates to generate risk distributions 

for different age groups, and Equation 2 was used to compute the average risk for each age 

group.

3.3 Level of Education

Table P37 of the SF3 dataset specifies the education status for men and women age 25 

and older for each census block group, based on the last grade completed. To obtain the total 

number of adults without a high school degree, we added together the numbers who had 

completed grades below a high school senior. Thus, the number of people without a high school 

degree equals the sum of the number of males with no schooling, the number of females with no 

schooling, the numbers of males and females who have completed nursery school through 4th

grade, up to the numbers of males and females who have completed some high school but not 

received a high school degree.
6

The number of adults without a high school degree as a fraction of the total population 

was assumed to be the same for each block in the block group. Thus, the number of adults 

without a high school degree in each block was computed as follows:

N(nhs,b/bg) = N(t,b/bg) × N(nhs,bg) ∕ N(t,bg) (4)

where:

N(nhs,b/bg) = number of adults without a high school diploma, in block “b” of block group 

“bg”

N(t,b/bg) = total number of people in block “b” of block group “bg”

N(nhs,bg) = number of adults without a high school diploma in block group “bg”

N(t,bg) = total number of people in block group “bg”

Equation 1 was then used to generate risk distributions based on the block-level results, and 

Equation 2 was used to compute the average risk for adults without a high school diploma. 

3.4 Household Income

Table P52 of the SF3 dataset estimates the numbers of households in each block group 

with income for the year 1999 in various ranges, generally divided into $5,000 increments (e.g. 

$10,000 to $14,999, $15,000 to $19,999, etc.). The median national income for 1999 was about 

$42,000 per year. Therefore, in order to determine the number of households with incomes 

under the median income, we added the estimates for the ranges below that level. We assumed 

that the household incomes in the $40,000 to $44,999 increment were evenly distributed over 

this range. Therefore, 40% of the households in the $40,000 to $44,999 income range were 

assumed to be below the national median income of about $42,000. The following equation was 

used to estimate the fraction of households below the national median income within each census

block group:

F(sm,bg) = [C<10 + C10-15 + .... + C35-40 + (0.4×C40-45)] ∕ CT (5)

where:

F(sm,bg) = fraction of households in block group “bg” with incomes below the median 

national income

C<10 = number of households with incomes under $10,000

C10-15 = number of households with incomes from $10,000 to $14,999

C35-40 = number of households with incomes from $35,000 to $39,999

C40-45 = number of households with incomes from $40,000 to $44,999

CT = total number of households in block group “bg”

The fraction of people living in households below the median income for each block 

within the block group was assumed to be the same as the fraction of households below the 

median income for the block group.

N(sm,b/bg) = F(sm,bg) × N(t,b/bg) (6)
7

where:

N(sm,b/bg) = number of people in block “b” of block group “bg” living in households below 

the national median income

F(sm,bg) = fraction of households in block group “bg” below the national median income

N(t,b/bg) = total number of people in block “b” of block group “bg”

Equation 1 was then used to generate risk distributions based on the block-level results, and 

Equation 2 was used to compute the average risk for adults without a high school diploma. It 

must be noted that this approach neglects any potential relationship between household size and 

income level within a particular block group. However, it is expected to provide a reasonable 

indication of the risk level of people living below the national median income, relative to the 

population as a whole. 

3.5 Poverty Level

Table P87 of the SF3 dataset estimates the total number people in each block group living 

below the poverty level, as well as the numbers of people below the poverty level in different age 

groups. The current study did not include an analysis of poverty status by age group, only of the 

total population below the poverty line. The fraction of people below the poverty line was 

assumed to be the same for each block in the block group. Thus, the population below the 

poverty line in each block was computed as follows:

N(p,b/bg) = N(T,b/bg) × N(p,bg) ∕ N(T,bg) (7)

where:

N(p,b/bg) = number of people below the poverty line in block “b” of block group “bg”

N(T,b/bg) = total number of people in block “b” of block group “bg”

N(p,bg) = number of people below the poverty line in block group “bg”

N(T,bg) = total number of people in block group “bg”

Equation 1 was then used to generate risk distributions based on the block-level results, and 

Equation 2 was used to compute the average risk for adults without a high school diploma. 

4. Results

The distribution of estimated lifetime inhalation cancer risks above 1 in a million or 

noncancer hazard indices above 1 for different racial and ethnic groups among the population 

living near primary lead smelting facilities is shown in Table 2. For comparison purposes, Table

2 provides the nationwide percentages of the various demographic groups. For this source 

category, the facility-wide emissions are equivalent to the source category emissions, thus a 

separate demographic analysis based on facility-wide emissions was not performed. Moreover, 

for HAP other than lead, there were no estimated noncancer HI values greater than 1, thus we did 

not perform a demographics analyses on noncancer risks for any pollutants other than lead. 
8

Detailed demographics data and analyses used to create Table 2 can be found in Appendix A of 

this document.

The results of the demographic analysis presented in Table 2 indicate that there are 

approximately 4,900 people exposed to a cancer risk greater than 1-in-1 million, and 1,900 

people in areas with ambient lead concentrations above the NAAQS for lead. In both instances, 

the demographics analysis estimates that about 4 percent of these populations can be classified as 

a minority, which is well below the national percentage. Similarly, in the cancer and lead 

demographic analyses, the percentage of “African American,” “Hispanic,” “Native American,” 

and “Other and Multiracial” population groups are well below their corresponding national 

percentages. With respect to the percentage of those “Below the Poverty Level,” in both 

demographic analyses there is a small (2 percent) increment above the corresponding national 

percentage. Additionally, with respect to the percentage of those “Over 25 Without a High 

School Diploma,” there is a modest percentage increment above the corresponding national 

percentage considering both cancer and lead risks (4% and 5%, respectively). However, given 

that the total population affected is small (i.e., 4,900 individuals for cancer risk greater than 1-in1 million and 1,900 individuals in areas with lead concentrations above the NAAQS), we do not 

think these results indicate a significant potential for environmental justice issues.
9

Table 2. Summary of Demographic Assessment of Risk Results for the Primary Lead Smelting Source Category

Emissions 

Basis

Demographic Group

Total Minority

African 

American

Other and

Multiracial

Hispanic

or Latino

Native 

American

Ages 0 

to 18

Ages 19 

to 64

Ages 65 

and up

Below 

the

Poverty 

Level

Over 25 

Without 

a HS 

Diploma

Nationwide 

Demographic 

Breakdown

n/a 285,339,128 25% 12% 12% 14% 0.9% 27% 60% 12% 13% 13%

Maximum 

Risk 

(in 1 

million)

Population With Risk Greater Than 1 in 1 million

Source 

Category 30 4,862 4% 2% 1% 1% 0.2% 30% 58% 12% 15% 17%

Maximum 

Hazard 

Index

Population With Ambient Lead Concentrations Exceeding the NAAQS

Source 

Category 50 1,922 4% 3% 0.8% 0.3% 0.0% 28% 56% 15% 15% 18%

Notes:

The Maximum Hazard Index refers to ratio of the maximum estimated off-site ambient concentration to the NAAQS for lead

Source Category emissions were estimated based on August 2010 NEI data.

Minority population is the total population minus the white population.

Population figures are for the population residing within 50 km of these facilities whose cancer risks are estimated to be greater than or equal to 1 in a million or whose 

noncancer hazard indices are estimated to be greater than 1.
10

5. Uncertainty Discussion

Our analysis of the distribution of risks across various demographic groups is subject to 

the typical uncertainties associated with census data (e.g., errors in filling out and transcribing 

census forms), which are generally thought to be small, as well as the additional uncertainties 

associated with the extrapolation of census-block group data (e.g., income level and education 

level) down to the census block level. 

The uncertainties in these risk estimates include the same uncertainties in emissions data 

sets, in air dispersion modeling, in inhalation exposure and in dose response relationships that are 

associated with our source category risk estimates.

The methodology for our demographic analyses is still evolving. While this is our best 

attempt to provide useful information now, our thinking is continuously advancing. EPA is in 

the process of developing technical guidance for environmental justice analyses. We present 

these analyses, with their associated uncertainties, to EPA decision makers and the public as 

additional analyses to inform RTR decisions.
A-1 

Appendix A
Total 

population White

African 

American

Native 

American

Other and 

multiracial

Hispanic or 

Latinoc

0 to 1 1,503,707 1,150,763 294,946 3,405 54,593 22,754

1 to 5 4,457 4,310 67 10 70 24

5 to 10 194 192 2 0 0 0

10 to 20 186 136 48 0 2 0

20 to 30 1 1 0 0 0 0

30 to 40 24 24 0 0 0 1

Total number 1,508,569 1,155,426 295,063 3,415 54,665 22,779

0.038 0.0423 0.0226 0.0351 0.0283 0.0307

Notes:

c

The Hispanic or Latino population is double-counted in this analysis, since different individuals within the 

category may classify themselves as White, African American, Native American, or other.

Table A-1. Distribution of Inhalation Cancer Risk for Racial and Ethnic Groups

a

Modeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and not adjusted for 

exposure factors.

Range of lifetime individual 

cancer risk (chance in one 

million)a

Average risk (chances in 

one million)

b

Distributions by race are based on demographic information at the census block level. Risks from primary lead 

smelting emissions were modeled at the block level, as were other NATA stationary source emissions. Mobile 

sources, area sources, and background risks were analyzed at the census block level in NATA. 

Modeled risk from the primary lead smelting 

category

Numbers of people in different ranges for lifetime cancer riskb

A‐2
Total 

population Ages 0 thru 18

Ages 19 thru 

64

Ages 65 and 

up

0 to 1 1,503,707 404,485 895,246 203,977

1 to 5 4,457 1,362 2,564 531

5 to 10 194 61 116 16

10 to 20 186 47 102 37

20 to 30 1 0 1 0

30 to 40 24 6 13 5

Total number 1,508,569 405,961 898,041 204,566

0.038 0.0393 0.0377 0.0362

Notes:

Table A-2. Distribution of Risk for Different Age Groups

Modeled risk from the primary lead smelting 

category

b

Distributions by age and education level are based on modeling data at the Census block level, 

and age and education data at the block group level. All blocks in a block group are assumed to 

have the same age and income distribution.

Range of lifetime individual cancer 

risk (chance in one million)a

Numbers of people in different ranges for lifetime cancer 

riskb

Average risk (chances in one 

million)

a

Modeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and not 

A‐3
Total 

population

Total number 25 

and older

Number 25 and older 

without a high school 

diploma

0 to 1 1,503,707 988,840 176,414

1 to 5 4,457 2,794 745

5 to 10 194 118 32

10 to 20 186 126 41

20 to 30 1 1 0

30 to 40 24 16 5

Total number 1,508,569 991,894 177,237

0.038 0.0375 0.0405

Notes:

Table A-3. Distribution of Risk for Adults with and without a High School Diploma

Modeled risk from the primary lead smelting category

b

Distributions by age and education level are based on modeling data at the Census block level, 

and age and education data at the block group level. All blocks in a block group are assumed to 

have the same age and income distribution.

Range of lifetime individual cancer 

risk (chance in one million)a

Numbers of people in different ranges for lifetime cancer 

riskb

Average risk (chances in one 

million)

a

Modeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and not 

adjusted for exposure factors.

A‐4
Total 

population

People living in 

households below the 

national median 

incomec

People living 

below the poverty 

line

0 to 1 1,503,707 716,427 177,768

1 to 5 4,457 2,595 674

5 to 10 194 127 48

10 to 20 186 115 21

20 to 30 1 1 0

30 to 40 24 15 3

Total number 1,508,569 719,280 178,514

0.038 0.0379 0.0351

Notes:

Table A-4. Distribution of Risk for People Living in Households below the 

National Median Income and Below the Poverty Line

Range of lifetime individual cancer 

risk (chance in one million)a

Numbers of people in different ranges for lifetime cancer 

riskb

c

The median income is the national median household income in 1999, about $42,000.

Modeled risk from the primary lead smelting 

category

Average risk (chances in one 

million)

a

Modeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and not 

adjusted for exposure factors.

b

Distributions by income are based on modeling data at the Census block level, and income 

data at the block group level. All blocks in a block group are assumed to have the same age 

and income distribution.

A‐5
Total 

population White

African 

American

Native 

American

Other and 

multiracial

Hispanic or 

Latinoc

0 to 0.2 1,493,502 1,141,017 294,716 3,380 54,389 22,671

0.2 to 1 12,718 12,145 283 32 258 102

1 to 1.5 427 416 5 3 3 0

1.5 to 2 0 0 0000

2 to 3 1,111 1,092 6 0 13 4

3 to 4 454 451 3 0 0 1

4 to 5 96 94 2 0 0 0

5 to 6 91 81 10 0 0 0

6 to 7 23 23 0 0 0 0

7 to 8 29 22 7 0 0 0

8 to 9 30 30 0 0 0 1

9 to 10 0 0 0 0 0 0

10 to 100 88 55 31 0 2 0

Total number 1,508,569 1,155,426 295,063 3,415 54,665 22,779

0.0274 0.0296 0.0199 0.0241 0.0217 0.0225

Notes:

c

The Hispanic or Latino population is double-counted in this analysis, since different individuals within the 

category may classify themselves as White, African American, Native American, or other.

Table A-5. Distribution of Inhalation Hazard Indices for Racial and Ethnic Groups

a

Modeled hazard indices are for a 70-year lifetime, based on the predicted outdoor concentration and not adjusted 

for exposure factors.

Range of lifetime individual 

hazard indexa

Average hazard index

b

Distributions by race are based on demographic information at the census block level. Risks from primary lead 

smelting emissions were modeled at the block level, as were other NATA stationary source emissions. Mobile 

sources, area sources, and background risks were analyzed at the census block level in NATA. 

Modeled hazard index from the primary lead 

smelting category

Numbers of people in different ranges for lifetime hazard indicesb

A‐6
Total 

population Ages 0 thru 18

Ages 19 thru 

64

Ages 65 and 

up

0 to 0.2 1,493,502 401,750 889,357 202,396

0.2 to 1 12,718 3,555 7,362 1,801

1 to 1.5 427 114 239 74

1.5 to 2 0000

2 to 3 1,111 318 624 169

3 to 4 454 128 257 70

4 to 5 96 30 58 8

5 to 6 91 24 51 15

6 to 7 23 6 13 4

7 to 8 29 7 16 6

8 to 9 30 8 16 6

9 to 10 0 0 0 0

10 to 100 88 22 48 18

Total number 1,508,569 405,961 898,041 204,566

0.0274 0.0278 0.0272 0.0275

Notes:

Table A-6. Distribution of Hazard Indices for Different Age Groups

Modeled hazard index from the primary lead 

smelting category

b

Distributions by age and education level are based on modeling data at the Census block level, 

and age and education data at the block group level. All blocks in a block group are assumed to 

have the same age and income distribution.

Range of lifetime individual hazard 

indexa

Numbers of people in different ranges for lifetime hazard 

indicesb

Average hazard index

a

Modeled hazard indices are for a 70-year lifetime, based on the predicted outdoor concentration 

and not adjusted for exposure factors.

A‐7
Total 

population

Total number 25 

and older

Number 25 and older 

without a high school 

diploma

0 to 0.2 1,493,502 982,051 174,908

0.2 to 1 12,718 8,315 1,916

1 to 1.5 427 285 70

1.5 to 2 0 0 0

2 to 3 1,111 717 182

3 to 4 454 293 89

4 to 5 96 58 16

5 to 6 91 60 19

6 to 7 23 15 5

7 to 8 29 20 6

8 to 9 30 20 7

9 to 10 0 0 0

10 to 100 88 59 19

Total number 1,508,569 991,894 177,237

0.0274 0.0272 0.0305

Notes:

Table A-7. Distribution of Hazard Indices for Adults with and without a High 

School Diploma

Modeled hazard index from the primary lead smelting 

category

b

Distributions by age and education level are based on modeling data at the Census block level, 

and age and education data at the block group level. All blocks in a block group are assumed to 

have the same age and income distribution.

Range of lifetime individual hazard 

indexa

Numbers of people in different ranges for lifetime hazard 

indicesb

Average hazard index

a

Modeled hazard indices are for a 70-year lifetime, based on the predicted outdoor concentration 

and not adjusted for exposure factors.

A‐8
Total 

population

People living in 

households below the 

national median 

incomec

People living 

below the poverty 

line

0 to 0.2 1,493,502 711,216 176,636

0.2 to 1 12,718 6,672 1,536

1 to 1.5 427 230 47

1.5 to 2 0 0 0

2 to 3 1,111 655 165

3 to 4 454 277 70

4 to 5 96 66 26

5 to 6 91 58 14

6 to 7 23 14 4

7 to 8 29 18 3

8 to 9 30 19 3

9 to 10 0 0 0

10 to 100 88 54 10

Total number 1,508,569 719,280 178,514

0.0274 0.0285 0.0277

Notes:

Table A-8. Distribution of Hazard Indices for People Living in Households below 

the National Median Income and Below the Poverty Line

Range of lifetime individual hazard 

indexa

Numbers of people in different ranges for lifetime hazard 

indicesb

c

The median income is the national median household income in 1999, about $42,000.

Modeled hazard index from the primary lead 

smelting category

Average hazard index

a

Modeled hazard indices are for a 70-year lifetime, based on the predicted outdoor 

concentration and not adjusted for exposure factors.

b

Distributions by income are based on modeling data at the Census block level, and income 

data at the block group level. All blocks in a block group are assumed to have the same age 

and income distribution.

A‐9