# Risk and Technology Review

-
Analysis of Socio

- Economic Factors for Populations Living Near Petroleum Refineries Post Control Scenario

Prepared by:

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

EPA Contract No. EP

- W

- 12

- 01 1 Work Assignment No. 1

- 0 3

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

January 7

, 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 rev iew 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 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

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

, and economic groups within the population living near petroleum refineries in the United States , after proposed controls are implemented (i.e., “post control scenario”)

. This work was carried out in support of the U.S. Environmental Protection Agency’s Residual Risk and Technology Review (RTR) for petroleum refineries source category emissions subject to Maximum Available Control Technology (MACT) requirements under 40 CFR 63 Subpart s CC and UUU

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

- 3 ) 1

, 2

, 3 was used to estimate cancer risks due to the inhalation of HAP for the populations residing within 50 kilometers of petroleum refineries in the country

. HEM

- 3 e stimates 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 proposed National Emission Standards for Hazardous Air Pollutant Emissions: Petroleum Refinery Operations (post control scenario) rulemaking . The docket provides a report covering the inputs and specific assumptions, and addressing uncertainties.

In the current analysis, cancer risk estimates from the petroleum refineries HEM

- 3 post

control modeling effort were linked to detailed census dat a in order to evaluate the distribution of risks for different demographic groups (including racial, 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

1

. EC/R. November 2013 . 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. February 2013. The 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/data/hem/hem3_users_guide.pdf

3. EC/R. February 2013. 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. http://www.epa.gov/ttn/fera/data/hem/multi_hem

- 3_users_guide.pdf2 • Hispanic or Latino

• 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 dipl oma

• 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 estimated level of cancer risk that would be experienced by an individua l 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 petroleum refineries post control emissions is compared to the de mographic 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. All of the data are from the 20 1 0 Decennial census. Race

, ethnicity and age da ta are 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 census 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, Amer ican 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 s ingle category. 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 were retrieved from similar tables in the Virgin Islands Summary File based on Census 2000 5 , because Census 2010 data is not yet available for the Virgin Islands.

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 2000 Data for the U.S. Virgin Islands: http://www.census.gov/census2000/usvi.html prepared by the U.S. Census Bureau.3 Data on education level, household income

, poverty status, and linguistic isolatio n 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) 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

Hous eholds earning below the national median 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 w ithin 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 t he census blocks modeled for the petroleum refineries

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 the p etroleum refineries source category (post control scenario) . For comparison, the nationwide 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 result s table

.

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

- 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 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 data 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 Populat ion

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 bin s 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 petroleum refineries 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 c ases 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 tota l 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” (estimat ed 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 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 senio r. 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 no5 schooling, the numbers of males and females who have completed nursery school through 4 th grade, up to the nu mbers 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, th e 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 mil lion for different demographic groups among the population living near the single petroleum refineries facilit y is shown in Table 2

. For comparison purposes , T able 2 also provides the nationwide percentages of the se various demographic groups. D etailed d emographic 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 indica te that there are approximately 3

, 80 0 ,000 people exposed to a cancer risk greater tha n o r equal to 1

- in

- 1 million as a result of petroleum refineries (post control) emissions . The specific demographic results indicate th e population potentially impacted by petroleum refineries (post control) emissions is greater than its corresponding nationa l percentage for every demographic category except Native American and Ages 65 and up. T he M inority and African American percentages are approximately twice as high as the national percentages for these categories ( 5 1 % Minority compared to 28% nationwide, and 31% African American compared to 13% nationwide ). Furthermore, the Hi spanic and Latino percentage (24 % compared to 17%), the Othe r and Multiracial percentage (19 % compared to 14%), the Below the Poverty Line perce ntage (22 % compared to 14%), the Over 2 5 Without a High School diploma percentage (2 3 % compared to 1 5 %), and the Lingui stically Isolated percentage (9 % compared to 6.5%) are all elevated compared to their national percentages. The Ages 0 to 17 percentage and the Ages 18 to 64 percentage are onl y slightly elevated (by 1 percentage point) compare d to their national percentages

.8 Table 2. Summary of Demographic Assessment of Risk Results for the Petroleum Refineries 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 Demographic 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 With Cancer Risk Greater Than or Equal to 1 in 1 million 3

Source Category (Post Control) 1 6 0 3

, 765,225 5 1 % 3 1 % 1 9 % 2 4 % 1.0 % 2 5 % 6 4 % 1 1 % 22 % 23 % 9 %

Notes:

1 Post Con trol s ource c ategory emissions were estimated based on 201 3 RTR data and proposed control options

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

3 Population figures are for the population residing within 50 km from the cent er of these facilities whose cancer risks are estimated to be greater than or equal to 1 in a million

.9 

## 5. Uncertainty Discussion

Our analysis of the distribution of risks across various demographic groups is subject to the typical uncertainties associ ated 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 pov erty 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 inhal ation 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 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

- 1 

### Appendix AA

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

Latino c

0 to 1 84,354,634 53,534,402 12,569,681 874,549 17,376,002 18,653,798
1 to 5 3,380,612 1,680,900 1,005,707 33,916 660,089 858,190
5 to 10 306,576 140,610 122,682 2,272 41,012 45,771
10 to 20 59,790 29,136 23,051 543 7,060 7,741
20 to 30 16,689 8,822 5,802 141 1,924 2,782
30 to 40 1,126 709 327 6 84 109
40 to 50 389 149 220 3 17 8
50 to 60 43 18 25 0 0 5
60 to 100 0 0 0 0 0 0 Total number 88,119,859 55,394,746 13,727,495 911,430 18,086,188 19,568,404

0.2154 0.1880 0.3579 0.1829 0.1929 0.2205

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 petroleum refineries post control emissions were modeled at the census block level. Modeled risk from the Petroleum Refineries source category (post control scenario) Numbers of people in different ranges for lifetime cancer risk bA

- 3 Total population Ages 0 thru
17 Ages 18 thru
64 Ages 65 and

up

0 to 1 84,354,634 20,386,683 53,867,466 10,100,485
1 to 5 3,380,612 845,861 2,165,655 369,096
5 to 10 306,576 77,969 192,295 36,312
10 to 20 59,790 16,282 36,274 7,234
20 to 30 16,689 4,576 10,280 1,833
30 to 40 1,126 178 841 107
40 to 50 389 97 253 39
50 to 60 43 16 26 1
60 to 100 0 0 0 0 Total number 88,119,859 21,331,662 56,273,090 10,515,107

0.2154 0.2211 0.2150 0.2060

Notes: Table A-2. Distribution of Risk for Different Age Groups

Modeled risk from the Petroleum Refineries source category (post control scenario)

b Distributions by age are based on modeled risk and age data at the census block level. Range of lifetime individual cancer risk (chance in one million) a Numbers of people in different ranges for lifetime cancer

risk b

Average risk (chances in one million)

a Modeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and not adjusted for exposure factors.A

- 4 Total population Total number 25 and older Number 25 and older without a high school diploma

0 to 1 84,354,634 55,596,956 8,875,553
1 to 5 3,380,612 2,126,438 475,943
5 to 10 306,576 196,059 48,133
10 to 20 59,790 37,638 9,718
20 to 30 16,689 10,447 2,733
30 to 40 1,126 792 264
40 to 50 389 254 94
50 to 60 43 21 5
60 to 100 0 0 0 Total number 88,119,859 57,968,605 9,412,443

0.2154 0.2097 0.2627
Notes: Table A-3. Distribution of Risk for Adults with and without a High School Diploma

Modeled risk from the Petroleum Refineries source category (post control scenario)

b Distributions by education level are based on modeled risk at the census block level, and education data at the census tract level. All census blocks in a tract are assumed to have the same education level distribution. Range of lifetime individual cancer risk (chance in one million) a Numbers of people in different ranges for lifetime

cancer risk b

Average risk (chances in one million)

a Modeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and not adjusted for exposure factors.A

- 5 Total population People living in households below
the national median

income c People living below the poverty line

0 to 1 84,354,634 38,063,304 11,285,611
1 to 5 3,380,612 2,000,179 728,112
5 to 10 306,576 203,650 77,856
10 to 20 59,790 40,820 17,064
20 to 30 16,689 12,158 4,428
30 to 40 1,126 746 197
40 to 50 389 284 124
50 to 60 43 27 12
60 to 100 0 0 0 Total number 88,119,859 40,321,168 12,113,404

0.2154 0.2581 0.2982
Notes:

c The median income is the national median household income in 2009, about $50,000. Modeled risk from the Petroleum Refineries source category (post control scenario)

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 and poverty status are based on modeled risk at the census block level, income data at the block group level, and poverty status at the census tract level. All census blocks in a block group or tract are assumed to have the same income distribution or poverty status, respectively. 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 bA

- 6 Total population People living in linguistic isolation

0 to 1 84,354,634 7,310,963
1 to 5 3,380,612 317,260
5 to 10 306,576 21,339
10 to 20 59,790 3,260
20 to 30 16,689 1,152
30 to 40 1,126 89
40 to 50 389 13
50 to 60 43 1
60 to 100 0 0 Total number 88,119,859 7,654,077

0.2154 0.2128
Notes:

a Modeled risks are for a 70-year lifetime, based on the predicted outdoor concentration and not adjusted for exposure factors.

b Distributions of linguistic isolation are based on modeled risk at the census block level, and linguistic isolation data at the block group level. All census blocks in a block group are assumed to have the same linguistic isolation population distributions. Table A-5. Distribution of Risk for People Living in Linguistic Isolation

Range of lifetime individual cancer risk (chance in one million) a Numbers of people in different ranges for lifetime cancer risk b

Modeled risk from the Petroleum Refineries source category (post control scenario)

Average risk (chances in one million)