# 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

- 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

July 2 9 , 201 1ii 

### D isclaimer

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

- D

- 06

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

endorsement should be inferred.iii 

## Contents

1. Introduction 1

2. Cens us Data 2

3. Calculation Methods 4

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

3.2 Level of Education 5

3.3 Household Income 6

3.4 Poverty Level 7

3.5 Disability 7

3.6 Linguistic Isolation 8

4. Results 8

5. Uncertainty Discussion 10

Appendix A A

- 1iv1



## 1. Introduction

This document describes the approach used to evaluate the pote ntial cancer and

noncancer risks associated with inhalation and air

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

, and economic groups within the population living near ferroalloys production facili t ies 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 ferroalloys production emissions subject to Maximum Available Control Technology (MACT) requirements under 40 CFR 63 S ubpart XXX

.

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

- 3 ) 1

, 2 was used to

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

within 50 kilometers of each ferroalloys 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 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 rulemaki ng 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 ferroalloys HEM

- 3 modeling effort were linked to detailed census data in orde r to evaluate the distribution of risks for 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

1

. EC/R. 2006. Modeling for the Residual Risk and Technology Review Using the Human Exposu re 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, Res earch Triangle Park, NC. http://www.epa.gov/ttn/fera/human_hem.html#guide2

Adults without a high school dipl oma Households earning under the national median income People living below the poverty line Working aged people (16

- 64) with a disability Linguistically isolated people

The HEM

- 3 results for a particular census block reflect the estimated level of cancer risk that would be experienced by an individual residing in the block for 70 years , and/or the estimated noncancer hazards experienced by an individual residing in the block due to chronic exposure

. In this analysis, the demographic composition of the popu lation estimated to experience a risk greater than 1 in 1 million or a hazard index greater than 1 as a result of ferroalloy production emissions is compared to the demographic composition of the overall nationwide population

.

The census data used in thi s 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 t his analysis, showing the source of each dataset and the level of geographic resolution. All of the data are from the 2000 Decennial

census. Race

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

, poverty status, household income , disabilities and linguistic

isolation were obtained at the block group level. A census block contains about 50 people on average; and a block group contains about 26 blocks on average, or about 1,350 people. (For

compa rison, a census tract 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 So urce 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 nation al

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 group3

Data on rac e 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 or more races. In the current analysis, the Asian, Native Hawaiian or other South Pacific Islander, and other race categories were combined in to a single category.

The Landview ® database also indicates the number of people in each tract that are of Hispanic or Latino ethnicity. Landview ® covers the 50 states, the District of Columbia, and Puerto Rico, but does not cover the Virgin Islands. Ra ce and ethnicity data on the Virgin Islands were obtained from the Virgin Islands Summary File. 4

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) Lon g Form. For the U.S. this file was accessed on a DVD version prepared by GeoLytics. 5 SF3 data for Puerto Rico were obtained from the Census Department website, 6 and d ata for the Virgin Islands were retrieved from similar tables in the Virgin Islands Sum mary File. 4

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 attai ned by men and women over 25 years of age (e.g. some high school but no high school diploma, high school graduate, some college, etc.) in each block group . Table P52 gives information on household income in 1999

. Table P87 estimates the number of people living below the poverty line in each block group. Table P42 estimates the number of people in different age categories with disabilities in each block group. And Table P20 estimates the fraction of households in each block group that are considered ling uistically isolated.

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.

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, Releas e 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.cen sus.gov4



## 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. 7 For the current analysis, this risk estimate was assumed to apply to all individuals residing in th e 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 f or the ferroalloys 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 ferroalloys operations . For comparison, t he 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 w here 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, respectiv ely.



### 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 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 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 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 withi n population subc ategory s , in block i

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

- r isk point outside of the facility’s footprint.5

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) an d 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 hi gh 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 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 )

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 bloc k 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

.6



### 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 me dian 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 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 estima te 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 + (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 i ncome for the block group.

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”

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 li ving in households below 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 indicati on of the risk level of people living below the national median income, relative to the population as a whole.7



### 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 t he 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

ass umed 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,b g) = 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 disability. 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 g roup “bg” N(T,bg) = total number of people in block group “bg”

Equation ( 1 ) was then used to generate risk distributions based on the block

- level results, and Equation ( 2 ) was used to compute the average risk for working age d people with a disability

.8



### 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 a ssumed 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 great er than or equal to 1 in a

million or noncancer hazard indices greater than 1 for different racial and ethnic groups among the population living near ferroalloys facilities is shown in Table 2

. For comparison purposes
, T able 2 provides the nationwide perce ntages 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 indicate that there are approximately 26

, 000 people exposed to a cancer risk greater than or equal to 1

- in

- 1 million and approximately 28 ,000 people exposed to a noncancer hazard index greater than 1. The demographic results for those impacted by source category emissions are below or similar to their corresponding national percentages for all racial, ethnic, educational, disability and linguistically isolated groups, and the “age 0 to 18” age group . With respect to the “ ages 65 and

up ” category potentially affected by ferroalloys emissions, the re is a modest percentage increase (of 4 percentage points ) for both cancer and noncancer results compared to th e corresponding national percentage (of 12%) for this demographic

. Additionally, the percentage of people ages 19 to 64 and potentially exposed to a cancer risk greater than or equal to 1 in 1 million or a noncancer hazard index greater than 1 from source category emissions is slightly higher (by 2

and 1 percentage points , respect i vely ) compared to the corresponding national percentage (of

60 %) f or this demographic. Overall, the demographic results do not indicate significant environmental justice concerns for the ferroalloys source category for cancer risks or noncancer

hazards

.9 Table 2. Summary of Demographic Assessment of Risk Results for th e Ferroalloys 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 Diplo ma 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 80 26,367 3% 1% 2% 0.7% 0.3% 22% 62% 16% 13% 11% 12% 0.3%

Maximum

Hazard

Index Population With Noncancer Hazard Index Greater Than 1

Source Category 90 28,190 3% 0.8% 2% 0.7% 0.3% 22% 61% 16% 13% 9% 10% 0.4%

Notes:

Source Category e missions were estimated based on Information Collection R equest (ICR) data from the fall of 2010

. 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, or whose noncancer hazard indices are estimated to be greater than 1.10



## 5. Uncertainty Discussion

Our analysis of the distribution of risks across variou s demographic groups is subject to the typical uncertainties associated with census data (e.g., errors in filling out and transcribi ng 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 i n 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 demog raphic analyses is still evolving. While this is our best attempt to provide useful information now, our thinking is continuously advancing. EPA is in the process of developing technical guidance for environmental justice analyses. We present

these anal yses, with their associated uncertainties, to EPA decision makers and the public as additional analyses to inform RTR decisions.A

- 1 

### Appendix AA

- 2A

- 3A

- 4A

- 5A

- 6A

- 7A

- 8A

- 9A

- 10A

- 11