# 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 27 711

January 3 , 201 1ii 

### Disclaimer

Although the analysis described in this document has been funded wholly or in part by the United St ates 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 101 

## 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 ec onomic 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

- of

the

- art air di spersion 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 availabl e in the docket for the proposed National Emission Standards for Hazardous Air Pollutant Emissions: Primary Lead Smelting Operations rulemaking wh ere 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 follow ing 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#guid e2 **•** 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 quotien ts 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, f or this source category, the “facility

- wide” risks are equivalent to the source category risks , so no such comparisons are pr ovided

. 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 dist ributions, 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 t ract 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 Socio

economic 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

- 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 group3 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 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. 4

Data on age dis tributions, 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 Ge oLytics. 5 SF3 data for Puerto Rico were obtained from the Census Department website, 6

4 and d ata 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 Table s P 8, 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 graduat e, 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. C alculation 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 b lock identification code s 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 bl ock. 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 t he 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, giv ing 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. W e 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 e mission sources at the facility (including primary lead smelting ) (e.g., “ facility

- wide ”), and in the country as a whole

.

Section 3.1 describe s 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 Ra cial and Ethnic Categorie s and the Total Population

Since race and ethnicity data were available a t the census block level, the calculation of risk distributions for these categories involved a simple block

- by

- block accumulation of the people in each sub 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 subcategory or for the total population was 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 )5 ∑ i refers to the summation over all blocks “í” modeled for the emission source categor y N(s ,i) and R i 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 ) = numb er 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 ) = numb er 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 compl eted 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.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) = numb er of adults without a high sch ool 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

.



### 3.4 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 increment were evenly d istributed 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 households below the nat ional median income within each census block group:

F(sm,bg ) = [C <10 + C 10

- 15 + .... + C 35

- 40 + (0.4×C 40

- 45 )] ∕ C T (5)

where: F ( sm ,bg) = fraction of households in block group “bg” with incomes below the median national income C <10 = number of household s 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 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) = to tal number of people in block “b” of 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. 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.



### 3.5 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 fraction of people below the poverty line was assumed to be the same for each block in t he 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 distribution s based on the bloc k

- level results, and Equation 2 was used to compute the average risk for adults without a high school diploma.



## 4. Results

T he 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 , T able 2 provides the nationwide percentages of the various demographic groups. For this source category, the facilit y

- 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 value s greater than 1, thus we did not perform a demographics analyses on noncancer risks for any pollutants other than lead

.8 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 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 demographi cs 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 the ir 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 H igh S chool Diploma

, ” there is a modest percentage incre ment 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

- in

1 million and 1,900 individuals in areas with lead concentrations abov e the NAAQS), we do not think th ese 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 gr eater 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 wit h 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 gu idance 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 ATotal population White African American Native American Other and multiracial Hispanic or
Latino c

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 risk b

A

‐ 2Total 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
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

A

‐ 3Total 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
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

‐ 4Total population People living in households below the national median
income c 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
risk b

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

‐ 5Total population White African American Native American Other and multiracial Hispanic or
Latino c

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 index a

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 indices b

A

‐ 6Total 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
index a Numbers of people in different ranges for lifetime hazard
indices b

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

‐ 7Total 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
index a Numbers of people in different ranges for lifetime hazard
indices b

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

‐ 8Total population People living in households below the national median
income c 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
index a Numbers of people in different ranges for lifetime hazard
indices b

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