# Risk and Technology Review

-

Updated Analysis of Socio

- Economic Factors for Populations Living n ear 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

- 0 1

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 27 711

October 201 1ii 

### 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

necessa rily 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 Discuss ion 12

Appendix A A

- 12 

## 1

. Introduction

D uring 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 c omments and the Residual Risk Assessment for the Primary Lead Smelting Source Category ) . As a result , t he methods and data used to generate the baseline and post

- control risk results presented in the final risk assessment document are largely based on thi s updated information. 1 Considering this updated information, the final risk assessment indicated that cancer incidence appreciably increased relative to the modeli ng to support the proposed rule making . 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

T his document describes the approach used to evaluate the potential cancer risks associated with inhalation and air

- related exposures t o hazardous air pollutants (HAP) in different social, demographic

, and economic groups within the population living near the one remaining primary lead smelting facili t y 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 Exposur e 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

- of

the

- art air dispersion model developed under the direction of the American Meteorological Society (AMS) / EPA Regulatory Model Improvement Committee (AERMIC). Each census block typica lly 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 wh ere a report is provided

, coverin g 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 d id not update its “Analysis of Socio

- Economic Factors for Populations Living N ear Primary Lead S melting Facilities ” (located in the docket for the proposed rulemaking) with respect to lead risk because the results of the final risk assessment did not su ggest the demographics analysis based on lead risk would be appreciably different from th e 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 Mode l 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 Tri angle Park, NC. http://www.epa.gov/ttn/fera/human_hem.html#guide3 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 fo r different demographic groups (including racial, ethnic, age, economic

, educational , disab led 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 indiv idual 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

grea ter 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 summarize s 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

r egarding 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 abou t 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 Socio

economic Groups

Type of population category Source of data Level of geographic resolution Racial and ethnic categories Landview ® Census block Age groups SF 1 Table P 12 Census block Level of education

- a dults 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 a ge p eople (16

- 64) with a d isabilit y SF3 Table P42 Block group

Linguistically i solated p eople 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 o r 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 Hispan ic 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 d ata 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.gov5 The SF3 data set consist s of over 800 separate tables , each providing information on a different subject. For the current analysis, data were obtained from Table s 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 estima tes 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

. C alculation Methods

T he 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 b lock identification code s to link the HEM

- 3

mo deling 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, giv en 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. W e 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 describe s 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 Categorie s and the Total Population

Since race

, ethnicity and age data are available a t 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 ou tside 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(R ab ,s ) = ∑ i (Ra≤Ri<Rb) [ N (s ,i ) ] (1)

where: H(R ab ,s ) = the population count for risk bin R ab , which is between R a and R b for population subgroup “s”
R i = 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 R i falls in bin R ab , between R a and R b N(s ,i ) = the number of people within population subc ategory 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) × R i ] ∕ ∑ 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 R i 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 school ing, the numbers of males and females who have completed nursery school through 4 th 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) = numb er 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) = numb er 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 distribution s 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 P 52 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

. W e assumed that the household incomes in the $40,000 to $44,999 i ncrement were evenly distributed over this range. Therefore, 40% of the households in the $40,000 to $4 4,999 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 ho useholds below the national median income within each census block group:

F( n m,bg ) = [C <10 + C 10

- 15 + .... + C 35

- 40 + (0.4×C 40

- 45 )] ∕ C T ( 4 )

where:
F ( n m ,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 C 10

- 15 = number of households with incomes from $10,000 to $14,999 C 35

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

- 45 = number of households with incomes from $40,000 to $44,999 C T = 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 g roup.

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

where:

N( n m,b/bg) = number of people in block “b” of block group “bg” living in households below the national median income F( n m,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 distribution s based on the block

- level results, and Equation ( 2 ) was used to compute the average risk for people living in households b elow the national median income

. It must be noted that t his approach neglects any potential relationship between household s ize 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 P 87 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 fract ion 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 distribution s 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 disa bility. The fraction of working age d people with a disability was assumed to be the same for each block in the block group. Thus, the population of working age d 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 age d 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 age d 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 age d people with a disability

.



### 3.6 Linguistic Isolation

Table P 20 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

T he distribution of estimated lifetime inhalation cancer risks greater than or equal to 1 in a million for specific demographic groups living near the one remaining primary lead smelting facilit y in the U.S. is shown in Table 2

. For comparison purposes , T able 2 provides the nationwide percentages of the various demographic groups. D etailed demographic s 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

, 5 00 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 increas e (of 2 percentage points ) compared to th e 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 ana lyzed. Notably, this was10 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 L ead S melting 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 2 0 120,516 3 % 0.8 % 2 % 1 % 0. 3 % 2 7 % 6 2 % 1 1 % 6 % 1 1 % 1 0 % 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 unce rtainties 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 disp ersion 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 use ful 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 AA

- 2 Total population White African
American Native
American Other and
multiracial Hispanic or

Latino c

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 risk bA

- 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

risk b

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 risk b

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

income c 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 risk b

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 risk b

Modeled risk from the primary lead smelting
source category

Average risk (chances in one million)