# Risk and Technology Review

-
Analysis of Socio

- Economic Factors for Populations Living Near Petroleum Refineries

Prepared by:

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

EPA Contract No. EP

- W

- 12

- 011 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 6

, 201 4ii 

### 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

- 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. Cens us 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 hazardo us air pollutants (HAP) in different social, demographic

, and economic groups within the population living near petroleum refineries 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 petroleum refineries source category emissions subject to Maximum Available Control Technology (MACT) requirements under 40 CFR 63 Subpart CC and UUU

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

- 3 ) 1

, 2

, 3 wa s used to estimate cancer risks due to the inhalation of HAP for the populations residing within 50 kilometers of petroleum refineries in the U.S., including the Virgin Islands

. HEM

- 3 estimates cancer risks at the level of census blocks using the AERMOD s tate

- 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 ris k analysis is available in the docket for the proposed National Emission Standards for Hazardous Air Pollutant Emissions: Petroleum Refinery Operations rulemaking . The docket provides a report covering the inputs and specific assumptions, and addressing u ncertainties.

In the current analysis, cancer risk estimates from the petroleum refineries 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) . 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. August 200 8 . 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. [October 2013 updated report expected]

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 Pr otection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/fera/data/hem/multi_hem

- 3_users_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

• 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 with in the block boundaries for 70 years. In this analysis, the demographic composition of the p opulation estimated to experience a risk greater than 1 in 1 million as a result of petroleum refineries emissions is compared to the demographic composition of the overall nationwide population

. The census data used in this analysis is described in Sect ion 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 data 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 bre akdown 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 category. The SF1 database also indicates the number of people in each tract that are of Hispanic or Latino ethni city. 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. Vi rgin 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 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) SF1 Tables P4 & P7 Ce nsus 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 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 bloc k 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 petroleum refineries

source category. W e also computed the average risk for all indiv iduals in each population category.

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

- 3 model results for the petroleum refineries source category . For comparison, the nationwide demographic composition (i.e., population percentag e in each demographic group for the country as a whole, based on the 20 1 0 Census ) is also provided in the results 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 th e 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

– rac ial

, 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, house hold 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 categories inv olved 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 t he 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 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 populati on 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 adul ts 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 no5 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 scho ol 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 mil lion for different demographic groups among the population living near petroleum refineries 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 and 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 5,2 0 0,000 people exposed to a cancer risk greater tha n or equal to 1

- in

- 1 million as a r esult of petroleum refineries emissions

. The specific demographic results indicate that the percentage of the population potentially impacted by petroleum refiner y emissions is greater than its corresponding national percentage for every demographic catego ry 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 ( 50 % M inority compared to 28% nationwide, and 2 8 % African American compared to 13% nationwide )

. Furthermore, t he Hispanic and Latino percentage (29% compared to 17%) , the Other and Multiracial percentage (21% compared to 14%) , the Below the Poverty Line percentage (21% compared to 14%) , the Over 25 Without a High School diploma percentag e (23% compared to 15%) , and the Linguistically Isolated percentage (10% 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 only slightly elevated compared to their na tional percentages, by 2 percentage points and 1 percentage point, respectively.8 Table 2. Summary of Demographic Assessment of Risk Results for the Petroleum Refineries Source Category

Emissions

Basis Demographic Group

Total Minority 2 Afric an 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 1 6 0 5,2 04 ,2 34 50 % 2 8 % 21 % 29 % 1 % 2 6 % 6 4 % 1 1 % 2 1 % 23 % 10 %

Notes:

1 Source Category emissio ns were estimated based on 201 3 RTR data.

2 Minority population is the 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 t o 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 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 dev eloping 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 82,915,625 52,793,544 12,292,133 858,836 16,971,112 18,071,022 1 to 5 4,657,445 2,345,485 1,255,262 46,746 1,009,952 1,383,516 5 to 10 437,319 200,754 140,922 4,611 91,032 94,738 10 to 20 79,050 38,063 29,782 758 10,447 13,232 20 to 30 27,220 15,021 8,446 404 3,349 5,462 30 to 40 2,570 1,604 636 69 261 392 40 to 50 481 243 202 5 31 29 50 to 70 149 32 112 1 4 13 70 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

0.2639 0.2274 0.4084 0.2453 0.2668 0.3009

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 emissions were modeled at the census block level. Modeled risk from the Petroleum Refineries source category 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 82,915,625 19,989,677 52,964,747 9,961,201 1 to 5 4,657,445 1,201,579 2,964,730 491,136 5 to 10 437,319 110,638 276,898 49,783 10 to 20 79,050 21,598 47,718 9,734 20 to 30 27,220 7,470 16,798 2,952 30 to 40 2,570 564 1,759 247 40 to 50 481 82 350 49 50 to 70 149 54 90 5 70 to 100 0 0 0 0 Total number 88,119,859 21,331,662 56,273,090 10,515,107

0.2639 0.2727 0.2631 0.2501

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

Modeled risk from the Petroleum Refineries source category

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 82,915,625 54,702,106 8,668,319 1 to 5 4,657,445 2,919,559 659,852 5 to 10 437,319 278,248 66,870 10 to 20 79,050 49,642 12,668 20 to 30 27,220 16,914 4,052 30 to 40 2,570 1,719 534 40 to 50 481 341 128 50 to 70 149 76 20 70 to 100 0 0 0 Total number 88,119,859 57,968,605 9,412,443

0.2639 0.2567 0.3266
Notes: Table A-3. Distribution of Risk for Adults with and without a High School Diploma

Modeled risk from the Petroleum Refineries source category

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 82,915,625 37,331,651 11,042,927 1 to 5 4,657,445 2,643,515 939,785 5 to 10 437,319 271,963 101,164 10 to 20 79,050 52,915 21,720 20 to 30 27,220 18,757 7,101 30 to 40 2,570 1,895 517 40 to 50 481 366 145 50 to 70 149 106 45 70 to 100 0 0 0 Total number 88,119,859 40,321,168 12,113,404

0.2639 0.3115 0.3558
Notes:

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

Average risk (chances in one million)

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

b Distributions by income 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 82,915,625 7,132,497 1 to 5 4,657,445 474,119 5 to 10 437,319 40,758 10 to 20 79,050 4,661 20 to 30 27,220 1,848 30 to 40 2,570 160 40 to 50 481 27 50 to 70 149 7 70 to 100 0 0 Total number 88,119,859 7,654,077

0.2639 0.2639
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

Average risk (chances in one million)