# Risk and Technology Review

-

Analysis of Socio

- Economic Factors for Populations Living Near Ferroalloys 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. 3

- 0 7

Prepared for:

Air Toxics Assessment Group Health and Environmental Impacts Division

Office of Air Quality Planning and Standards U.S. Environmental Protection Agency Research Triangle Park, North Carolina 27 711

April 9, 2015ii 

### Discl aimer

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 does not necessarily reflect the views of the Agency, and no official

endorsement should be inferred.iii 

## Contents

1. Introduction 1

2. Census D ata 2

3. Calculation Methods 3

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

3.2 Level of Education 5

3.3 Household Income 5

3.4 Poverty Level 6

3.5 Linguistic Isolation 7

4. Results 7

5. Uncertainty Discussion 9

Appendix A A

- 0iv1



## 1. Introduction

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

noncancer 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 ferroalloys production facilities in the United States

. This work was carried out in support of the U.S. Environmental Protection Agenc y’s Residual Risk and Technology Review (RTR) for

F erroalloys P roduction emissions subject to Maximum Available Control Technology (MACT) requirements under 40 CFR 63 Subpart XXX

.

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

- 3 ) 1

, 2

, 3 wa s used to

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

within 50 kilometers of each F erroalloys facility in the country

. HEM

- 3 estimates cancer and

noncancer risks at the level of census blocks using the AER MOD 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 proposed National Emission Standards for Hazardous Air Pollutant Emissions: Ferroalloys Production Operations rulemaking . The docket provides a report covering the inputs and specific assumptions, and addre ssing uncertainties.

In the current analysis, cancer and noncancer risk estimates from the F erroalloys HEM

- 3 modeling effort were linked to detailed census data in order to evaluate the distribution of risks for different demographic groups (including ra cial, ethnic, age, economic

, educational

, 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

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 Incorpor ated 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 Incorpora ted for the U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www2.epa.gov/sites/production/files/2014

- 04/documents/multi_hem

- 3_u sers_guide.pdf2

 Children 1 7 years of age and under

 Adults 1 8 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

 Li nguistically 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 with in the block boundaries for 70 years

, and the estimated noncancer hazards experi enced 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 or a hazard index greater than 1 as a

result of F erroalloy s P roduction 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 pres ented 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 ar e provided 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 cens us block 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 Indi an 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 cate gory. 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

. Data for the Virgin Islands , American Samoa,

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 Docum entation for the 2010 Census Summary File 1.3

Northern Mariana Islands an d Guam (although not needed for this analysis) can be retrieved

from similar tables in the “Island Areas: 2010 Census Summary File”. 5

Data on education level, household income

, poverty status, and linguistic isolation were obtained from tables in the C ensus’ 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 categ ories SF1 Table P3 Census block Ethnic categories (Hispanic) SF1 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

me dian income ACS Table B1 9 00 1 Block group

People living below the 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 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 ferroall oys source category. W e also computed the average risk for all individuals in each population category.

5

. Census 2010 Data for the U.S. Virgin Islands http://www2.census.gov/census_2010/11

- Island_Areas_PUMS/Virgin_Islands/ p repared by the U.S. Census Bureau.

6

. 2009 Five

- year American Community Survey

– 2005

- 2009, United States : http://www2.census.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

- ris k point outside of the facility’s footprint.4

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

- 3 model results

for the ferroalloys source category . For comparison, the nati onwide demographic composition (i.e., population percentage in each demographic group for the country as a whole, based on the

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

.

Section 3.1 describe s the calculation method used for categories where bl ock

- level data

were available from the Census Bureau

– 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 dat a provided by the Census Bureau

– education status, 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 c alculation

of risk distributions for these categories 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 ap propriate 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 ferroalloys 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 h azard 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 above5



### 3.2 Level of Education

Table B15002 of the 2005

- 2009 ACS dataset specifies the ed ucation 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 no schooling, the numbers of males and females who have completed nursery school through 4 th
grade, up to the numb ers 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

. T hus, the n umber 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 nationa l 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 o f 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 $44,9996

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.

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 appr oach 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 th e 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. Th e 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 populati on 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

.7



### 3. 5 Linguistic Isolation

Table B16002 of 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 )

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 ris k 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 mil lion and noncancer hazard indices greater than 1 for different demographic groups among the population living near F erroalloys facilities is shown in Table 2

. For comparison purposes
, T able 2 also provides the nationwide percentages of the se various demogr aphic 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 indicate that there are approximately 40,750 people exposed to a cance r risk greater than or equal to 1

- in

- 1 million and approximately 1, 350 people exposed to noncancer hazard index greater than 1. The demographic results for those impacted by source category emissions are below or similar to (i.e., within a couple percentag e points of) their corresponding national percentages for all racial, ethnic, economic, educational, and linguistically isolated groups, as well as for most age groups. With respect to the “ages 65 and up” category potentially affected by F erroalloys emiss ions, there is a modest percentage increase

– of 5 percentage points for the cancer results and 6 percentage points for the noncancer results

– compared to the corresponding national percentage for this demographic (1 3 %). Overall, the demographic results d o not indicate significant environmental justice concerns for the F erroalloys source category for cancer risks and noncancer hazards.8

Table 2. Summary of Demographic Assessment of Risk Results for the Ferroalloys Source Category

Emissions

Basis Demographic Group

Total Minority 2 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 Demograph ic

Breakdown (2010 Census) n/a 312,861,265 2 8 % 1 3 % 1 4 % 1 7 % 1.1 % 2 4 % 6 3 % 1 3 % 1 4 % 1 5 % 6.5 %

Maximum

Risk (in 1 million) Population W ith Cancer Risk G reater Than or Equal to 1 in 1 million 3

Source Category 1 2 0 40,748 3 % 1 % 2 % 1 % 0.3 % 2 1 % 6 2 % 1 8 % 15 % 11 % 1 %

Maximum

Hazard

Index Population With Noncancer Hazard Index Greater Than 1 3

Source Category 1 4 1, 348 1% 0.2% 1% 0. 7 % 0.1% 22% 5 9 % 19 % 6 % 1 0 % 0%

Notes:

1 Source Category emissions were estimated based on 201 4 RTR data.

2 Minority population is t he total population minus the white population.

3 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

, or whose noncancer hazar d indices are estimated to be greater than 1.9



## 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 tr anscribing 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 le vel 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 ar e 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 devel oping 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.10 This page intentionally left blankA

- 0 

### Appendix AA

- 1A

- 2A

- 3A

- 4A

- 5A

- 6A

- 7A

- 8A

- 9A

- 10