# Risk and Technology Review

-

Analysis of Socio

- Economic Factors for Populations

Living Near Aerospace Facilities

Prepared by:

EC/R Incorporated 501 Eastowne Drive, Suite 250 Chapel Hill, NC 27514

EPA Contract No. EP

- W

- 12

- 011 Work Assignment No. 2

- 06

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

November 4

, 201 4ii 

### Disclaimer

Although the analysis described in this document has been funded wholly or in part by the United States Environmental Protection Agency contract EP

- W

- 12

- 011 to EC/R Incorporated, it has not been subject to the Agency's review and therefore do es not necessarily reflect the views of the Agency, and no official

endorsement should be inferred.iii 

## Contents

1. Introduction 1

2. C ensus Data 2

3. Calculation Methods 3

3.1 Racial, Ethnic and Age Categories and the Total Population 4

3.2 Level of Education 4

3.3 Household Income 5

3.4 Poverty Level 6

3.5 Linguistic Isolation 6

4. Results 7

5. Uncertainty Discussion 9

Appendix A A

- 1iv1



## 1. Introduction

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

associated with inhalation and air

- related exposures to haza rdous air pollutants (HAP) in

different social, demographic

, and economic groups within the population living near Aerospace

facilities 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 Aerospace source category emissions subject to Maximum Available Control Technology (MACT) requirements

under 40 CFR 63 Subpart GG

.

In the RTR analysis, the Human Exposure Model, Version 3 ( HEM

- 3 ) 1

, 2

, 3 was used to

estima te cancer risks due to the inhalation of HAP for the populations residing within 50

kilometers of Aerospace facilities in the U.S. HEM

- 3 estimates cancer 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 typically includes about 50 people. Additional information on the risk analysis is available in the docket for the N ational Emission Standards

for the Aerospace Manufacturing and Rework Industry rulemaking . The docket provides a report covering the inputs and specific assumptions, and addressing uncertainties.

In the current analysis, cancer risk estimates from the A erospace 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

, and linguistically

isolated population categories) . T he 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 1 7 years of age and under

1

. EC/R. January 2014 . Modeling for the Residual Risk and Technology Review Using the Human Exposure

Model 3

– AERMOD Version. Technical Support Document p repared by EC/R Incorporated for the U.S. Environmental Protection Agency, Research Triangle Park, NC.

2

. EC/R. April 2014 . The HEM

- 3 User’s Guide. Prepared by EC/R Incorporated for the U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www2.epa.gov/sites/production/files/2014

- 04/documents/hem3_users_guide.pdf

3. EC/R. April 2014 . Multi HEM

- 3 and RTR Summary Programs User’s Guide. Prepared by EC/R Incorporated for the U.S. Environmental Protection Agency, Research Triangle Park, NC.

h ttp://www2.epa.gov/sites/production/files/2014

- 04/documents/multi_hem

- 3_users_guide.pdf2

 Adults 1 8 to 64 years of age

 A dults 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

 Linguistically isolated people

The HEM

- 3 results for a particular census block reflect the estimat ed level of cancer risk that would be experienced by an individual residing with in the block boundaries for 70 years. In this analysis, the demographic composition of the population estimated to experience a risk greater

than 1 in 1 million as a result of Aerospace 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 th e analysis, showing the source of each dataset and the level of geographic resolution. Race

, ethnicity and age data are provi ded by the

Census Bureau at the census block level. Distributions regarding household income and linguistic isolation are provided at the block group level. Distributions regarding educational status and poverty status are at the tract level

. A census b lock contains about 50 people on average; and a block group contains about 2 8 blocks on average, or about 1, 40 0 people. A census tract is larger than a block group, with each tract containing an average of 3 block groups, or about 4,300 people.

Data on race
, ethnicity , and age were obtained from tables in the 2010 Census Summary File 1 (SF1). 4 SF1 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 catego ry. The SF1 database also indicates the number of people in each tract that are of Hispanic or Latino ethnicity. SF1 covers the 50 states, the District of Columbia, and Puerto Rico, but does not cover the Virgin Islands

. Data for the Virgin Islands can be retrieved from similar tables in the Virgin Islands Summary File

. 5

4

. 2010 Census Summary File 1

– United States: http://www2.census.gov/census_2010/04

- Summary_File _1/ prepared by the U.S. Census Bureau, 2011. See also Technical Documentation for the 2010 Census Summary File 1.

5

. Census 20 1 0 Data for the U.S. Virgin Islands htt p://www2.census.gov/census_2010/11

- Island_Areas_PUMS/Virgin_Islands/ prepared by the U.S. Census Bureau.3

Data on education level, household income

, poverty status, and linguistic isolation were obtained from tables in the Census’ American Community Survey (ACS) 5

- year estimates for

2005

- 2009. 6

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 categories SF1 Table P3 Census block Ethnic categories (Hispanic) SF 1 Tables P4 & P7 Census block Age groups SF 1 Table P 12 Census block

Level of education

- a dults without a high school diploma ACS Table B15002 Tract

Households earning below the national

median income ACS Table B1 9 00 1 Block group

People living below th e poverty line ACS Table B1 7 001 Tract Linguistically isolated people ACS Table B1 6 00 2 Block group



## 3. C alculation Methods

T he HEM

- 3 models the cancer risk and noncancer hazard at a point near the geographic

center of each census block and (optionally) at model user

- defined receptors representing populations located within each block

. 7 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 modeling re sults 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 popul ation 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 Aerospace source category. W e also computed the average risk for a ll individuals in each population category.

Distributions of risk and average risks were computed for the raw HEM

- 3 model results

for the Aerospace source category . For comparison, the nationwide demographic composition (i.e., population percentage i n each demographic group for the country as a whole, based on the

20 1 0 Census and 2009 ACS ) is also provided in the results table

.

6

. 2009 Five

- year American Community Survey

– 2005

- 2009, United States : http://www2.cen sus.gov/acs2009_5yr/summaryfile/ prepared by the U.S. Census Bureau, 2011.

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.4

Section 3.1 describe s the calculation method used for categories where block

- level data

were available from the Census Bu reau

– racial

, ethnic and age categories and the total population. Sections 3.2 through 3. 5 describe calculation methods for categories where block

level data had to be estimated from tract or block group data provided by the Census Bureau

– education sta tus, household income, poverty status, and linguistic isolation

.



### 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 cate gories involved a simple block

- by

- block accumulation of the 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 Aerospace 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 i s 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) ∑ 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) ∑ 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 B15002 of the 2005

- 2009 ACS dataset specifies the education status for men and

women age 25 and older for each census tract

, 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 n o5

schooling, 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 tract . Thus, the number of adults without a high school degree in each block was computed as follows:

N (nhs ,b/ tc ) = N( t ,b/ tc ) × N (nhs

, tc ) ∕ N( t

, tc ) ( 3 )

where: N(nhs ,b/ tc ) = numb er of adults without a high school diploma, in block “ b ” of tract “ tc ” N( t ,b/ tc ) = total number of people in block “ b ” of tract “ tc ” N(nhs

, tc ) = numb er of adults without a high school diploma in tract “ tc ” N( t

, tc ) = total number of people in tract “ tc ”

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

Tab le B19001 of the 2005

- 2009 ACS dataset estimates the numbers of households in each block group with income for the year 2009 in various ranges, generally divided into $5

, 000 increments (e.g. $10,000 to $14,999, $15

, 000 to $19,999, etc.). The median nation al income for

2009 was about $ 50 ,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

. The following equation was used to estimate the fraction of households below the national

median income within each census block group:

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

- 15 + .... + C 35

- 40 + C 40

- 45 + C 45

- 50 ] ∕ 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 = nu mber 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 4 5

- 50 = number of households with incomes from $4 5 ,000 to $4 9 ,999

C T = total number of households in block group “bg”

The fract ion 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.6 N( n m, b/bg) = F( n m,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”

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 below the

national median income

. It must be noted that t his app roach 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 B17001 of the 2005

- 2009 ACS dataset estimates the total number people in each

census tract 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 census tract . Thus, the pop ulation below the poverty line in each block was computed as follows:

N(p ,b/ tc ) = N(T,b / tc ) × N(p

, tc ) ∕ N(T, tc ) ( 6 )

where: N(p ,b/ tc ) = number of people below the poverty line in block “b” of tract “ tc ” N(T,b/ tc ) = total number of people in block “b” of tract “ tc ” N(p

, tc ) = number of people below the poverty line in tract “ tc ” N(T, tc ) = total number of people in tract “ tc ”

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 Linguistic Isolation

Table B16002 o f the 2005

- 2009 ACS 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) ( 7 )7

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 ri sk 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 mi llion for different demographic groups among the population living near Aerospace facilities

is shown in Table 2

. For comparison purposes
, T able 2 also provides the nationwide percentages

of the se various demographic groups. D etailed demographic s data an d analyses used to create

Table 2 can be found in Appendix A of this document.

The results of the analysis presented in Table 2 indica te that there are approximately

1 80 ,000 people exposed to a cancer risk greater tha n or equal to 1

- in

- 1 million as a re sult of Aerospace emissions

. The specific demographic results indicate that the percentage of the population potentially impacted by Aerospace emissions is greater than its corresponding national percentage for the minority population (36 % for the source c ategory compared to 28% nationwide) , the African American population (19% for the source category compared to 13% nationwide) and for the population below the poverty level (19 % for the source category compared to 1 4 % nationwide). Furthermore, other demogr aphic groups with source category percentages greater than the corresponding national percentage include : the population over 25 without a high school diploma ( 17 % compared to 15%) ; the o ther and m ulti

- racial population (16% compared to 14 % ) ; the Native Am erican population (1. 5 % compared to 1.1%) ; the populati on younger than 18 years old (26 % compared to 24%) ; and the population from 18 to 64 years of age (65% compared to 63%)

. The other demographic categories potentially impacted by Aerospace emissions (i. e., Hispanic or Latino, ages 55 and up, and the linguistically isolated) are less than or equal to the corresponding national percentage.8 Table 2. Summary of Demographic Assessment of Risk Results for the Aerospac e Source Category

Emissions

Basi s Demographic Group

Total Minority 1 African

American Other and

Multiracial Hispanic

or Latino Native

American Ages 0

to 1 7 Ages 1 8

to 64 Ages 65

and up Below the

Poverty

Level Over 25

Without a

HS Diploma Linguistic

Isolation

Nationwide n/a 31 2,861,265 28% 13% 14% 17% 1.1% 24% 63% 13% 14% 1 5 % 6.5 %

Maximum

Risk (in 1 million) Population With Cancer Risk Greater Than or Equal to 1 in 1 million 2

Source Category 1 0 179,054 36 % 19 % 16 % 16 % 1. 5 % 26 % 65 % 9 % 19 % 1 7 % 5.4 %

Notes:

1 Minority popul ation is the total population minus the white population.

2 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

.9



## 5. Uncertain ty 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 tract level data (e.g., education status and poverty status) and census block group data (e.g., income level and linguistic isolation ) 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 analyse s. We present these analyses, with their associated uncertainties, to EPA decision makers and the public as additional analyses to inform RTR decisions.10 This page intentionally left blankA

- 1 

### Appendix AA

- 2A

- 3A

- 4A

- 5A

- 6