Risk and Technology Review -

Updated 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. 4-01

Prepared for:

Regina Chappell, Work Assignment Manager

Community and Tribal Programs Group

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, North Carolina 27711

October 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.
1

Contents

1. Introduction 2

2. Census Data 3

3. Calculation Methods 5

3.1 Racial, Ethnic and Age Categories and the Total Population 5

3.2 Level of Education 6

3.3 Household Income 7

3.4 Poverty Level 8

3.5 Disability 8

3.6 Linguistic Isolation 9

4. Results 9

5. Uncertainty Discussion 12

Appendix A A-1
2

1. Introduction

During the public comment period for the proposed rule, the one facility in the Primary 

Lead Smelting source category, (The Doe Run Company) submitted substantially updated 

emissions, meteorological, facility boundary, and other relevant information with respect to 

performing a risk assessment (see docket number: EPA-HQ-OAR-2004-0305 for Doe Run’s 

public comments and the Residual Risk Assessment for the Primary Lead Smelting Source 

Category). As a result, the methods and data used to generate the baseline and post-control risk 

results presented in the final risk assessment document are largely based on this updated

information.1 Considering this updated information, the final risk assessment indicated that

cancer incidence appreciably increased relative to the modeling to support the proposed 

rulemaking. Thus, in this document, EPA updates its “Analysis of Socio-Economic Factors for 

Populations Living near Primary Lead Smelting Facilities” with respect to cancer risk.

2

This document describes the approach used to evaluate the potential cancer 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 operating 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 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 (HEM-3)

3,4 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. HEM-3

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 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.

 

1 Updated emission information received from the Doe Run Company during the public comment period was 

reviewed for accuracy by EPA engineers (see "Residual Risk Analysis - Development of the Revised 

Emissions Dataset for the Primary Lead Smelting Source Category" in the docket for this rulemaking for 

more information on this temporal profile). 

2 EPA did not update its “Analysis of Socio-Economic Factors for Populations Living Near Primary Lead 

Smelting Facilities” (located in the docket for the proposed rulemaking) with respect to lead risk because 

the results of the final risk assessment did not suggest the demographics analysis based on lead risk would 

be appreciably different from the analysis already entered into the docket for the proposed rule.

3 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. 

4 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
3

In the current analysis, cancer risk estimates from the primary lead smelting HEM-3

modeling effort were linked to detailed census data in order to evaluate the distribution of risks 

for different demographic groups (including racial, ethnic, age, economic, educational, disabled 

and linguistically isolated population categories). 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

• Adults without a high school diploma

• Households earning under the national median income

• People living below the poverty line

• Working aged people (16-64) with a disability

• Linguistically isolated people

The HEM-3 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 analysis, the demographic composition of the population estimated to experience a risk 

greater than or equal to 1 in 1 million as a result of primary lead smelting emissions is compared 

to the demographic composition of the overall nationwide population.

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, ethnicity and age data were obtained at the census block level. Distributions 

regarding educational status, poverty status, household income, disabilities and linguistic 

isolation 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.)
4

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 SF1 Table P12 Census block

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

Working age people (16-64) with a

 disability

SF3 Table P42 Block group

Linguistically isolated people SF3 Table P20 Block group

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

compiled by the Census Department.5 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.6

Data on age distributions in the U.S. and Puerto Rico for each census block were 

obtained from the 2000 Census of Population and Housing Summary File 1 (SF1) Short Form, 

Table P12. Data on poverty status, household income, education level, disabilities and linguistic

isolation for each block group 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.7

 SF3 data for Puerto Rico were obtained 

from the Census Department website,8 and data for the Virgin Islands were retrieved from 

similar tables in the Virgin Islands Summary File.4

 5. 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.

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

www.factfinder.census.gov

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

www.geolytics.com

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

www.factfinder.census.gov
5

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 P20, P37, P42, P52, 

and P87. 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.)

in each block group. Table P52 gives information on household income in 1999. Table P87 

estimates the number of people living below the poverty line in each block group. Table P42 

estimates the number of people in different age categories with disabilities in each block group. 

And Table P20 estimates the fraction of households in each block group that are considered 

linguistically isolated.

3. Calculation Methods

The HEM-3 models the cancer and noncancer risk at a point near the geographic center 

of each census block.9 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 HEM-3

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

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

analyzed the distribution of estimated inhalation risks within each population category, given the 

numbers of people within the category 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 category. 

Distributions of risk and average risks were computed for the raw HEM-3 model results 

for primary lead smelting operations. For comparison, the nationwide demographic composition 

(i.e., population percentage in each demographic group for the country as a whole, based on the 

2000 Census) is also provided in the results table. 

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

were available – racial, ethnic and age categories and the total population. Sections 3.2 

through 3.6 describe calculation methods for education status, household income, poverty status, 

disability and linguistic isolation, respectively. 

3.1 Racial, Ethnic and Age Categories and the Total Population

Since race, ethnicity and age data are available at the census block level, the calculation 

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

 7. HEM-3 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.
6

people in each category. 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 

category or for the total population was then 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)

∑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 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.

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) (3)
7

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. 

3.3 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(nm,bg) = [C<10 + C10-15 + .... + C35-40 + (0.4×C40-45)] ∕ CT (4)

where:

F(nm,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(nm,b/bg) = F(sm,bg) × N(t,b/bg) (5)

where:

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

the national median income

F(nm,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”
8

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 people living in households below the 

national median income. 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. 

3.4 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) (6)

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 people living below the poverty level. 

3.5 Disability

Table P42 of the SF3 dataset estimates the total number of people in each block group 

with disabilities, as well as the numbers of people with disabilities in different age groups. This 

analysis includes the total number of working aged people (16 through 64 years of age) with a 

disability. The fraction of working aged people with a disability was assumed to be the same for 

each block in the block group. Thus, the population of working aged people with a disability in 

each block was computed as follows:

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

where:

N(d,b/bg) = number of working aged people with a disability in block “b” of block group 

“bg”

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

N(d,bg) = number of working aged people with a disability in block group “bg”
9

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 working aged people with a disability.

3.6 Linguistic Isolation

Table P20 of the SF3 dataset estimates the fraction of households in linguistic isolation in 

each block group. For this analysis, the fraction of people living in linguistic isolation for each 

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

linguistic isolation for the block group. Thus, the population of linguistically isolated people in 

each block was computed as follows:

N(li,b/bg) = F(li,bg) × N(t,b/bg) (8)

where:

N(li,b/bg) = number of people in block “b” of block group “bg” living in linguistically 

isolated households

F(li,bg) = fraction of households in block group “bg” in linguistic isolation

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 people living in linguistic isolation.

4. Results

The distribution of estimated lifetime inhalation cancer risks greater than or equal to1 in a 

million for specific demographic groups living near the one remaining primary lead smelting

facility in the U.S. is shown in Table 2. For comparison purposes, Table 2 provides the 

nationwide percentages of the various demographic groups. 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 estimate that there are 

approximately 120,500 people exposed to a cancer risk greater than or equal to 1-in-1 million. 

The demographic results for those impacted by source category emissions are below or similar to 

their corresponding national percentages for all racial, ethnic, economic, educational, disability 

and linguistically isolated groups, as well as for most age groups. With respect to the “ages 19 to 

64” category potentially affected by primary lead smelting emissions, there is a small percentage 

increase (of 2 percentage points) compared to the corresponding national percentage (of 60%) 

for this demographic. Overall, we do not think that these updated results indicate any significant 

potential for disparate impacts to the specific demographic groups analyzed. Notably, this was 
10

the same conclusion reached with respect to cancer risk in the demographics analysis based on 

the risk modeling to support the proposed rule (see Analysis of Socio-Economic Factors for 

Populations Living near Primary Lead Smelting Facilities in the docket for the proposed rule). 
11

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

Working 

Age with

Disability

Linguistic 

Isolation

Nationwide 

Demographic 

Breakdown

n/a 285,339,128 25% 12% 12% 14% 0.9% 27% 60% 12% 13% 13% 12% 5.4%

Maximum 

Risk 

(in 1 

million)

Population With Cancer Risk Greater Than or Equal to 1 in 1 million

Source 

Category 20 120,516 3% 0.8% 2% 1% 0.3% 27% 62% 11% 6% 11% 10% 0.6%

Notes:

Source Category emissions were based on emissions submitted in 2009 by the facility during the public comment period for the proposed rule.

Minority population is the total population minus the white population.

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

million.
12

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
A-2

Total 

population White

African 

American

Native 

American

Other and 

multiracial

Hispanic or 

Latinoc

0 to 1 1,396,199 1,041,534 298,447 3,117 53,101 21,817

1 to 5 119,861 116,634 916 315 1,996 1,201

5 to 10 622 604 12 0 6 2

10 to 20 0 0 0 0 0 0

20 to 30 33 33 0 0 0 1

Total number 1,516,715 1,158,805 299,375 3,432 55,103 23,021

0.3348 0.3916 0.1304 0.3537 0.2516 0.2789

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)

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

lead smelting emissions were modeled at the census block level. 

Modeled risk from the primary lead 

smelting source category

Numbers of people in different ranges for lifetime cancer riskb
A-3

Total 

population

Ages 0 thru 

18

Ages 19 thru 

64

Ages 65 and 

up

0 to 1 1,396,199 355,483 848,682 192,034

1 to 5 119,861 31,949 74,628 13,284

5 to 10 622 159 392 71

10 to 20 0 0 0 0

20 to 30 33 6 21 6

Total number 1,516,715 387,597 923,723 205,395

0.3348 0.3392 0.3381 0.312

Notes:

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

Modeled risk from the primary lead smelting 

source category

b

Distributions by age are based on modeling data and age data at the census block level.

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

Total number 

25 and older

Number 25 and older 

without a high school 

diploma

0 to 1 1,396,199 907,879 162,817

1 to 5 119,861 76,787 13,612

5 to 10 622 380 119

10 to 20 0 0 0

20 to 30 33 22 7

Total number 1,516,715 985,068 176,555

0.3348 0.3351 0.3254

Notes:

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

Modeled risk from the primary lead smelting source 

category

b

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

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

have the same education level 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-5

Total 

population

People living in 

households below 

the national median 

incomec

People living 

below the 

poverty line

0 to 1 1,396,199 672,944 171,980

1 to 5 119,861 49,581 7,255

5 to 10 622 420 127

10 to 20 0 0 0

20 to 30 33 20 4

Total number 1,516,715 722,965 179,366

0.3348 0.3044 0.2325

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 

source 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 census blocks in a block group are assumed to have the 

same income distribution.
A-6

Total 

population

Working aged people 

with a disability

People living in 

linguistic isolation

0 to 1 1,396,199 147,725 19,744

1 to 5 119,861 12,326 704

5 to 10 622 72 6

10 to 20 0 0 0

20 to 30 33 3 0

Total number 1,516,715 160,126 20,454

0.3348 0.3164 0.243

Notes:

a

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

and not adjusted for exposure factors.

b

Distributions of disability and linguistic isolation are based on modeling data at the census 

block level, and demographic population data at the block group level. All census blocks 

in a block group are assumed to have the same disability and linguistic isolation population 

distributions.

Table A-5. Distribution of Risk for People Living with Disabilities and in 

Linguistic Isolation

Range of lifetime individual 

cancer risk (chance in one 

million)a

Numbers of people in different ranges for lifetime 

cancer riskb

Modeled risk from the primary lead smelting 

source category

Average risk (chances in one 

million)