# 201 **3**

EJ Screening Report for Brick and Structural

Clay

## Contents

Executive Summary

................................................................................................ ................................................................ . 4

About the Data

................................................................................................ ........................................................................ 6

Demographic Data

................................................................................................ ..................................................... 7

Near Source Data

................................................................................................ .................................................................. 12

Tribal Land Proximity

................................................................................................ ............................................................ 1 8

Facility Clusters

................................................................................................ ..................................................................... 20

Statistical Summary

................................................................................................ ............................................................... 23

Category Totals and T

- Tests

................................................................................................ ..................................... 24

Map and Charts

................................................................................................ ..................................................................... 30

### U.S. Environmental Protecti on Agency

E nvironmental J ustice Screening Report

National Emissions Standards for

Hazardous Air Pollutants From

Brick and Structural Clay and Clay Ceramics

Docket: EPA –HQ– OAR –

RIN: 2060 –A P69

Office of Air and Radiation,

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

### Executive Summary

Under section 112 of the Clean Air Act (CAA) the U.S. Environmental Protection Agency is required to develop standards

for hazardous air pollutants (HAP) for both major and area sources that are listed under section 112(c). In the case of the

Brick and Structural Clay Products source category under section 112(d)(2) of the CAA

, standards are set based on the

maximum achievable control technology (MACT) available. Therefore, health based risks are not expressly considered in

this rule. As a result, the health impacts associated with this rule are not part of this report.

To gain a better understanding of the Brick and Structural Clay Products source category and near

- source populations,

the EPA conducted a proximity analysis at a study area of 3 miles of the source category for this rulemaking. This analysis

identifies, on a limited basis, the subpopulations that may be exposed to air pollution from the regulated sources and

thus are expected to benefit most from this regulation. This analysis does not identif y the demographic characteristics of

the most highly affected individuals or communities

, nor does it quantify the level of risk faced by those individuals or

communities. To the extent that any minority, low

- income or indigenous subpopulation is dispropor tionately impacted

by hazardous air emissions due to the proximity of their homes to sources of these emissions, that subpopulation also

stands to see increased environmental and health benefit from the emission reductions called for by this rule.

The EPA has determined that this proposed rule will not have disproportionately high and adverse human health or

environ mental effects on minority, low

- income and indigenous populations because the proposed rule increases the

level of environmental protection for all affected populations. These proposed standards may improve public health and

welfare, now and in the future, by reducing Hazardous Air Pollutant (HAP) emissions that may contribute to adverse

environmental and human health impacts. The reductions in H AP emissions associated with the rule are expected to

benefit all populations living near the sources affected by this rule.



### Background

Executive Order 12898 (59 FR 7629, February 16, 1994) establishes federal executive policy on environmental justice. Its

main provision directs federal agencies, to the greatest extent practicable and permitted by law, to make environmental

justice part of their mission by identifying and addressing, as appropriate, disproportionately high and adverse human

health or enviro nmental effects of their programs, policies and activities on minority populations and low income

populations in the United States.

E.O. 12898 informed the development and implementation of the EPA’s Environmental Justice ( EJ ) program and EJ

policies. Cons istent with the E.O. and the Presidential Memorandum that accompanies the E.O., the agency’s EJ policies

promote environmental protection by focusing attention and agency efforts on addressing the types of environmental

harms and risks that are prevalent a mong minority, low

- income and indigenous populations. E.O. 12898 and the

agency’s EJ policies do not mandate particular outcomes for an action, but they demand that decisions involving the

action be informed by a consideration of EJ issues. Where feasible, actions should prevent or address and mitigate EJ

concerns.

A key element of environmental justice is ensuring that all people have an opportunity for meaningful involvement in

the decision

- making process. Certain groups may not have participated in the decision

- making process historically

because of economic (e.g., income), social (e.g., language barriers, education levels, distrust of government) and

structural reasons. A critical concern is whether, and the extent to which, communities have the ability to influence the

types and number of regulated activities taking place in their community, as well as the requirements, conditions and

parameters by which such regulated activities must operate (e.g., permit conditions).

### The EP A aims to notify and engage the public in a variety of ways. Each rule is reviewed to identify appropriate

mechanisms to promote meaningful involvement, which may include notices on the EJAir website, community call

discussions, fact sheets, community meetings and/or webinars. Additi onally, when reliable source location data is

available, the EPA strives to provide a screening report of near source populations. It is important to note that while the

EPA provides near

- source data, whether or not a facility

- level analysis is appropriate depends on the Clean Air Act’s

(CAA) authority for the subject rule. For example, CAA sections 112 (d) and 112(f), which address hazardous air

pollutants, require the EPA to establish emission standards that apply to all sources in specified categories or

subcategories. In the case of the CAA 112(f), the EPA sets standards based on the maximum exposed individual risk

( MIR) or the facility in the source category that poses the highest risks to the public , and must consider specific statutory

factors, including costs, energy and safety.

# About the Data

### D **emographic Data**

Th is report includes race, ethnicity, and age data derived from the 2010 Census SF1 dataset 1 

### , and economic and

education data from the Census Bureau’s 2006

- 2010 American Community Survey (ACS) 5

- Year Estimates 2 

### . This data is

summarized in Table 1 , with Table 2 notating the six socioeconomic indictors of social vulnerability used within this

report

.

Table 1

. Census Derived Demographic Data

Race, Ethnicity, and Age Data (Census 2010 block

- level SF1 data)*

Parameter Definition

Population Total population in block

White Number of whites (may include Hispanics)

African Americans Number of African Americans (may include Hispanics)

Native Americans Number of Native Americas (may include Hispanics)

Other and multiracial Number of other race and multiracial (may include Hispanics)

Hispanic Number of Hispanics

Age 0 to 4 Number of people age 0 to 4

Age 0 to 17 Number of people age 0 to 17

Age 16 to 64 Number of people age 16 to 64

Age 18 to 64 Number of people age 18 to 64

Age 25 and up Number of people age 25 and up

Age 65 and up Number of people age 65 and up

Economic and Education Date (2006

- 2010 ACS)*

Parameter Definition

Poverty Number of people living in households with income below the poverty

line

2 x Poverty Number of people living in households with income below twice the

poverty line

Below median income Number of people living in households with income below the national

median income

Linguistic isolation Number of people linguistically isolated

*Census 2010 does not currently report this data for the Virgin Islands, Guam, American Samoa, and the Northern Marianas ; Census 2000 data are used for these

areas.



### Table 2

. S ocial Vulnerability Data

Social Vulnerability Indicators (Census 2010 data):

**•** Minority/Hispanic (%)

**•** Low Income (%)

**•** Under Age 5 (%)

**•** Age 65 and up (%)

**•** Adults without a hi gh school diploma (%)

**•** Linguistically isolated (%)

American Indian Tribal Land and Reservation Boundaries (EPA Shapefile)

The EPA uses the six socioeconomic demographic parameters shown in Table 2 from the 2010 Census as indicators of

social vulnerability. The economic and education indicators used in this group are at the block group level and were

derived from the Census Bureau’s 20 06

- 2010 ACS 5

- Year Estimates. These six subgroups of the population are included

### as social vulnerability indicators because there exists a substantive and easily

- understood re lationship between the

indicator and the concept of vulnerability, which is consi stent with the EPA guidance regarding the use of vulnerability

indicators in environmental justice

- related analyses.

“M inority” means a person, as defined by the U.S. Bureau of Census, who is a: (1) Black American (a person having

origins in any of the black racial groups of Africa); (2) Hispanic person (a person of Mexican, Puerto Rican, Cuban, Central

or South American, or other Spanish culture or origin, regardless of race); (3) Asian American or Pacific Islander (a

person having origins in any of the original peoples of the Far East, Southeast Asia, the Indian subcontinent, or the

Pacific Islands); or (4) American Indian or Alaskan Native (a person having origins in any of the original people of North

America and maintain cultural identification through tribal affiliation or community recognition). Because the Census

Bureau does not classify Hispanic as a race but as an ethnicity, the race minority field is computed by subtracting the

white population from the total population. A separate “Minority/Hispanic” field is calculated by adding the number of

white Hispanics to the race minority field count. The “Minority/Hispanic” field is used as one of the social vulnerability

indicators. Compared to non

- minority (white) populations in the U.S., numerous studies have found that minority

populations live in closer proximity to pollution sources, experience worse health, and have less ability to participate in

environmental decision making. 3



### The percentage of people living below the national poverty line i s defined by the Census as the percentage of residents

whose median household income is at or below the poverty guidelines updated periodically in the Federal Register by

the U.S. Department of Health and Human Services under the authority of 42 U.S.C. 9902(2)

. 4 

### Low income has been

linked in many studies to poor health, lack of access to health care, closer proximity to pollution sources, and greater

s usceptibility to illnesses caused by exposure to air pollution. 5 

### Low income in this report is defined as 2 X poverty line.

The percentage of residents whose age is less than 5 years includes infants and children

– all of whom are considered a

sensitive su bpopulation for many forms of environmental contaminants, including air pollution. 6 

### The reasons for

children’s increased sensitivity to air pollution are manifold and include: still

- developing respiratory and other bodily

systems; smaller body size in proportion to inhaled contaminants; and varying behavior patterns including longer

durations spent outdoors at high breathing rates compared to adults. 7 

### These factors lead to increased morbidity and

mortality risks for children from exposure to air pollutants

. 8 

### Furthermore, minority and low

- income children are at even

greater risk, as the increased susceptibility for each of these demographic groups compounds such a child’s overall

vulnerability. 9



### The percentage of residents of age 65 years and over is consi dered a sensitive subpopulation for many forms of

environmental contamination, including air pollution. 10 

### The increased susceptibility of this subpopulation stems not only

from their age, but also from their poorer health and lower fitness levels. 11 

### Those a ge 65 years and up have a higher

mortality risk

— both long

- term and short

- term

— than other populations from air pollution, as well as increased

morbidity risks including cardiovascular illness and respiratory disease. 12



### The percentage of residents lacking a high school diploma is considered a relevant indicator of vulnerability because lack

of education may indirectly increase the effects of environmental contamination, including air pollution. Low education

may decrease access to health care and informatio n about environmental risks and how to respond appropriately to

such risks. 13 

### Studies have revealed that low education appears to be a factor in health disparities, multi

- morbidity, and

mortality in general; and lower education may increase the relative risk of air pollution and premature mortality. 14 

### In

particular, this association was established after examining the relationship between an increase in particulate matter

and mortality among persons with lower education. 15

### The percentage of residents exper iencing linguistic isolation is considered a relevant indicator of v ulnerability because

linguistic isolation may render households less able to identify and mitigate environmental harms by limiting both access

to health care and access to information about environmental risks and how to respond appropriately to those risks. 16



### Studies have revealed that counties with higher concentrations of immigrants and linguistically isolated households have

more hazardous waste generators and more proposed Superfund si tes, compared to other counties. 17 

### For example, a

significant cancer risk was found between linguistically isolated households and exposure from Toxics Release Inventory

(TRI) releases in the San Francisco Bay area. 18 

### Finally, immigrants may encounter disc rimination and prejudice which

impact vulnerability.

As noted above, the EPA uses population data collected by the 2010 Census. Table 3 summarizes where precisely the

social vulnerability indicator data is located within the Census results, including the level of geographic resolution in the

original census data set. All data is stored at the block level. For those indicators available from the Census at the block

group, but not block level, the EPA assigns a block the same percentage as the block group of which it is a part. For

example, a block is assigned the same percen tage of people living below the national poverty line as the block group in

which it is contained. Nationally, 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 comparison, a census tract is larger than a block group, with each tract

containing an average of 3 block groups, or about 4,300 people.)

As shown in Table 3, data on race, ethnicity and age for all census blocks in the country except for the Virgin Islands,

Guam, American Samoa, and the Northern Marianas were obtained from the 2010 Census SF1 dataset. 19 

### This dataset

gives a breakdown of the population for each census block among different racial and ethnic classifications, including:

White, African American or Black, Hispanic or Latino, American Indian or Native Alaskan, Asian, Native Hawaiian or other

South Pacific Islander, other race, and two or more races. Race, ethnicity, and age data for census blocks in the Virgin

Islands, Guam, American Samoa, and the Northern Marianas are from the 2000 Census, and were derived from the year

2000 “Island Surveys” which were available through the Census Bureau’s American FactFinder. 20



### Table 3 . Summary of Social Vulnerability Indicators provided by Census 2010

Parameter Source of Census Data Level of Geographic Resolution

(Census Block or Block Group)

Minority/Hispanic

Population ab 

### SF1 Tables P1 and P3 Block

People < 18 years of age b 

### SF1 Table P12 Block

People ≥ 65 years of age b 

### SF1 Table P12 Block

Low Income 2006

- 2010 ACS Block Group

Adults without a High School

diploma 2006

- 2010 ACS Block Group

Linguistically Isolated People 2006

- 2010 ACS Block Group

a Minority/Hispanic population = Total population (P0010001) minus white population (P0030003)

b Census 2010 does not currently report this data for the Virgin Islands, Guam, American Samoa, and the Northern Marianas ; Census 200 0 data are used for these

areas.

### Data on age distributions in the U.S. and Puerto Rico were obtained at the census blo ck level from the 2010 Census of

Population and Housing Summary File 1 (SF1) short form, Table P12. SF1 contains the information compiled from the

questions asked of all people about every housing unit. Data on poverty status, education level, and linguistic isolation

in the U.S. and Puerto Rico were obtained at the block group level from the Census Bureau’s 2006

- 2010 ACS. 21 

### Data fo r

the Virgin Islands and other island territories (Guam, American Samoa, and the Northern Marianas) were retrieved from

similar tables which are available through the Census’ American Fact Finder internet portal, www.factfinder.census.gov

.

American Indian Tribal Land and Reservation Boundaries

Boundaries for American Indian Reservations and Federally Recognized Tribal Entities were obtained from a shapefile

provided by the U.S. EPA. 22 

### The shapefile depicts fe ature locations, selected demographics and other associated

data for the 562 Federally Recognized Tribal entities in the contiguous U.S. and Alaska. Categories included are:

American Indian Reservations (AIR), Federally Recognized Tribal Entities (FRTE) and Alaska Native Villages (ANV).

1 2010 Census Summary File 1 Delivered via FTP, http://www2.census.gov/census_2010/04

- Summary_File_1/

2 U.S. Census Bureau 2006

- 2010 American Community Survey 5

- Year Estimates, http://w ww.census.gov/acs/www/data_documentation/2010_release/

3 Brulle, Robert J. and David N. Pellow. 2006. Environmental Justice: Human Health and Environmental Inequalities. Annu. Rev. Public Health 27:103

– 24.

http://publhealth.annualreviews.org ; Cutter, Susan. 2006. The Geography of Social Vulnerability: Race, Class, and Catastrophe. Understanding Katrina. Social

Sciences Research Council. June 11. h ttp://understandingkatrina.ssrc.org/Cutter/ ; Gold, Diane R. and Rosalind Wright. 2005. Population Disparities in Asthma. Annu.

Rev. Public Health 26:89

– 113; Nelson DA, Johnson CC, Divine GW, Strauchman C, Joseph CL, Ownby DR. 1997. Ethnic Differences in t he Prevalence of Asthma in

Middle Class Children. Ann. Allergy Asthma Immunol. 78:21

– 26; O’Neill et al., 2003. Health, Wealth, and Air Pollution: Advancing Theory and Methods, 111

Environmental Health Perspectives 16: 1861, 1865, http://www.ehponline.org/docs/2003/6334/abstract.html ; Friebele, E. 1996. The Attack of Asthma. Env. Health

Persp. 104:1, January, pp. 22

- 25. ; Gwynn, R. Charon, and George D. Thurston. 2001. The Burden of Air Pollution: Impacts among Racial Minorities. Environ Health

Perspect. 109 (Suppl 4): 501

– 506 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC124057 2/pdf/ehp109s

- 000501.pdf ; American Lung Association. 2007. State of Lung

Disease in Diverse Communities. August.; USEPA. 2004. Office of Environmental Justice. Toolkit for Assessing Potential Allega tions of Environmental Injustice.

November 3 ; Bullard, Ro bert D. and et al. 2007. Toxic Wastes and Race at Twenty 1987

- 2007: Grassroots Struggles to Dismantle Environmental Racism in the United

States. Cleveland: The United Church of Christ. March.

4 U.S. Department of Health & Human Services. Frequently Asked Questions Related to the Poverty Guidelines and Poverty. http://aspe.hhs.gov/poverty/faq.shtml

5 USEPA. Office of Environmental Justice. 2004. Toolkit for Assessing Potential Allegations of Environmental Injustice. November 3; Cutter, Susan. 2006. The

Geography of Social Vulnerability: Race, Class, and Catastrophe. Understanding Katrina. Social Sciences Research Council. Ju ne 11.

http://understand ingkatrina.ssrc.org/Cutter/ ; Jiang Y, Hesser JE. 2009. Using Item Response Theory to Analyze the Relationship Between Health

- Related Quality of

Life and Health Risk Factors. Prev Chronic Dis. 6(1); Brulle, Robert J. and David N. Pellow. 2006. Environmenta l Justice: Human Health and Environmental Inequalities.

27:103

– 24. http://publhealth.annualreviews.org ; American Heart Association. Air Pollution, Heart Disease and Stroke

. http://www.americanheart.org/presenter.jhtml?identifier=4419 ; Friebele E. 1996. The Attack of Asthma. Env. Health Persp. 104:1, January, pp. 22

- 25; Gwynn, R.

Charon, and George D. Thurston. 2001. The Burden of Air Pollution: Impacts among Racial Minorities. Environ Health Perspect. August; 109 (Suppl 4):501

– 506

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1240572/pdf/ehp109s

- 000501.pdf ; USEPA. 2008. Assessment and Standards Division, Office of Transportation and

Air Quality. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters

Per Cyli nder. EPA420 -R- 08

- 001a, p. 2

- 57.

6 American Lung Association. 2009. Protect the Air we Breathe. February. http://www.lungusa.org/assets/documents/PROTECT_THE_AIR_WE_BREATHE.pdf

.

7 USEPA. 2004. Air Quality Criteria for Particulate Matter. RTP, NC. EPA/600/P

- 99/002aF

- bF. http://cfpub.epa.gov /ncea/cfm/recordisplay.cfm?deid=87903 ; USEPA

. Factors for Identifying and Assessing Disproportionate Environmental Health Impacts; USEPA. Framework for Cumulative Risk Assessment; National Resources

Defense Council. Our Children at Risk: The 5 Worst Enviro nmental Threats to Their Health. http://www.nrdc.org/health/kids/ocar/chap4.asp

8 USEPA. 2004. Air Quality Criteria for Particulate Matter. Research Triangle Park, NC. EPA/600/P

- 99/002aF

- bF.

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=87903 ; Host S; et al. 2007. Short

- term Associations Between Fine and Coarse Particles and Cardiorespiratory

Hospitalizations in Six F rench Cities. Occup Environ Med; Mar TF; et al. 2004. An Analysis of the Association Between Respiratory Symptoms in Subjects with Asthma

and Daily Air Pollution in Spokane, Washington. Inhal Toxicol 16:809

- 815; Mauad T; et al. 2008. Chronic Exposure to Am bient Levels of Urban Particles Affects Mouse

Lung Development. Am J Respir Crit Care Med 178: 721

- 728; Peel JL; et al. 2005. Ambient Air Pollution and Respiratory Emergency Department Visits. Epidemiology,

16:164

- 174; Pinkerton KE; et al. 2004. Reduced Lu ng Cell Proliferation Following Short

- Term Exposure to Ultrafine Soot and Iron Particles in Neonatal Rats: Key to

Impaired Lun g Growth? Inhal Toxicol 1:73

- 81; National Resources Defense Council. Our Children at Risk: The 5 Worst Environmental Threats to Their Health.

http://www.nrdc.org/health/kids/ocar/chap4.asp ; Gauderman, et al. 2004. The Effect of Air Pollution on Lung Development from 10 to 18 Years of Age. N Engl J Med

351:1057

- 1067. Septe mber 9. http://www.nejm.org/doi/full/10.1056/NEJMoa040610?ck=nck&#articleTop ; Neidell, Matthew J. 2004. Air Pollution, Health, and Socio

economic Status: the Effect of Outdoor Air Quality on Childhood Asthma. Journal of Health Economics 23: pp.1209

– 1236.9 O’Neill et al. 2003. Health, Wealth, and Air Pollution: Advancing Theory and Methods. Environmental Health Perspectives 16: pp.1861, 1865.

http://www.ehponline.org/docs/2003/6334/abstract.html ; Gold, et al. 1993. Race and Gender Differences in Respiratory Illness Prevalence and Their Relationship to

Environmental Exposures in Children 7 to 14 Years of Age. Am Rev Respir Dis.148(1):10

- 8; Neidell, Matthew J. 2004. Air Pollution, Health, and Socio

- economic Status:

the Effect of Outdoor Air Quality on Childhood Asthma. Journal of Health Economics 23: pp.1209

– 1236.

10 American Lung Association. 2009. Protect the Air we Breathe. February.

11 USEPA. Framework for Cumulative Risk Assessment.

12 Gerber et al. 2010. Cumulative Exposure to Air Pollution and long term outcomes after first acute myocardial infarction: A po pulation

- based cohort study. BMC

Public Heal th 10:369. http://www.biomedcentral.com/1471

- 2458/10/369 ; American Heart Association. Air Pollution, Heart Disease and Stroke.

http://www.americanheart.org/presenter.jhtml?identifier=4419 ; National Institute of Environmental Health and Sciences. Elderly Have Higher Risk for Cardiovascular,

Respiratory Disease From Fine Particle Pollution. http://www.nih.gov/news/pr/mar2006/niehs

- 08.htm ; National Resources Defense Council. Our Children at Risk: The

5 Worst Environmental Threats to Their Health. http://ww w.nrdc.org/health/kids/ocar/chap4.asp ; Franklin M; Zeka A; Schwartz J. 2007. Association Between PM2.5

and All

- Cause and Specific

- Cause Mortality in 27 US Communities. Journal of Exposure Science and Environmental Epidemiology, 17:279

- 87.;

http://www.scientificintegrityinstitute.org/JESEE2005.pdf ; Ostro B, et al. 2006. Fine Particulate Air Pollution and Mortality in Nine California Counties: Results from

CALFINE. Environ Health Perspect , 114:29

- 33.; http://cfpub.epa.gov/ncea/hero/index.cfm?action=search.view&reference_id=87991 ; Samoli E; et al. 2008. Acute

Effects of Ambient Particulate Matter on Mortality in Europe and North America: Results From the APHENA Study. Environ Health Perspect 116:1480

- 1486.

http://cfpub.epa.gov/ncea/hero/index.cfm?action=search.view&reference_id=188455 ; Zeka A; Zanobetti A; Schwartz J. 2006. Individual

- Level Modifiers of the Effects

of Particulate Matter on Daily Mortality. Am J Epidemiol, 163:849

- 859. http://cfpub.epa.gov/ncea/hero/index.cfm?action=search.view&reference_id=88749

.

13 USEPA. 2004. National Environmental Justice Advisory Council. Ensuring Risk Reduction in Communities with Multiple Stressors: Environmental Justice and

Cumulative Risks/Impacts.

14 DeWalt, Darren A., Marylee H. Dilling, Marjorie S. Rosenthal, and Michael P. Pignone. 2007. Low Parental Literacy Associated with Worse Asthma Care Measures in

Children. Ambul Pediatr 7(1): 25

– 31. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1797805/pdf/nihms17216.pdf ; Banks J, Marmot M, Oldfield Z, Smith JP. 2006.

Disease and disadvantage in the United States and in England. JAMA 295(17):2037 -4 5; Nagel, Gabriele, Richard Peter, Stefanie Braig, Silke Hermann, Sabine

Rohrmann and Jakob Linseisen. 2008. The Impact of Education on Risk Factors and the Occurrence of Multimorbidity in the EPIC

- Heidelberg Cohort. BMC Public

Health 8:384. http://www.biomedcentral.com/1471

- 2458/8/384 ; Adler NE, and K Newman. 2002. Socioeconomic Disparities in Health: Pathways and Policies. Heath

Aff (Millwood) 21(2):60

- 76; Kan, et al. 2008. Season, Sex, Age, and E ducation as Modifiers of the Effects of Outdoor Air Pollution on Daily Mortality in Shanghai, China:

The Public Health and Air Pollution in Asia (PAPA) Study. Environ Health Perspect, 116:1183

- 1188; Levy, Jonathon I., Susan L. Greco, John D. Spengler. 2002. The

Importance of Population Susceptibility for Air Pollution Risk Assessment: A Case Study of Power Plants Near Washington, DC. Environmental Health Perspectives,

Dec. http://www.ncbi.nlm.nih.go v/pubmed/12460806

.

15 Krewski D, Burnett RT, Goldberg MS, Hoover K, Siemiatycki J, Jerrett M, et al. 2000. Reanalysis of the Harvard Six Cities Study and the American Cancer Society

Study of Particulate Air Pollution and Mortality. Health Effects Institute. Cambridge, MA; Krewski D; Jerrett M; Burnett RT; Ma R; Hughes E; Shi Y; Turner MC; Pope AC

III; Thurston G; Calle EE; Thun MJ. 2009. Extended follow

- up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.

Health Effects Institute. Cambridge, MA. 140; Ostro B; Broadwin R; Green S; Feng W

- Y; Lipsett M. 2006. Fine particulate air pollution and mortality in nine California

counties: results from CALFINE. Environ Health Perspect, 1 14:29

- 33.; Ostro BD; Feng WY; Broadwin R; Malig BJ; Green RS; Lipsett MJ. 2008. The impact of

components of fine particulate matter on cardiovascular mortality in susceptible subpopulations. Occup Environ Med, 65: 750

- 756; Zeka A; Zanobetti A; Schwartz J.

2006. Individual

- level modifiers of the effects of particulate matter on daily mortality. Am J Epidemiol, 163: 849

- 859.

16 Asian Pacific Environmental Network. Environmental Justice and API Issues. http ://www.apen4ej.org/issues_api.htm ; West Coast Poverty Center. 2010. Ethnic

Residential Clustering and Health in West Coast States. Dialogues on Research and Policy. No. 2, March.

http://depts.washington.edu/wcpc/sites/default/files/DIALOGUE%20No.%202.Ethnic%20Enclaves.webFN_.pdf ; LeClere, E B., L. Jensen and A. E. Biddlecom. 1994.

Health Care Utilization, Family Context, and Adaptation among I mmigrants to the United States. Journal of Health and Social Behavior, 35 (4):370

- 384. December;

Arias, Beatriz. 2007. School Desegregation, Linguistic Segregation and Access to English for Latino Students. Journal of Education Controversy 2(1) Winter.

http://www.wce.wwu.edu/Resources/CEP/eJournal/v002n001/a008.shtml

.

17 San Francisco Department of Public Health. 2004. Unaffordable Housing: the Costs to Public Health. June.

http://www.sfphes.org/publications/reports/UnaffordableHousingJune04.pdf ; Pinderhughes, Racquel Rivera. 19 96. Social and Economic Inequality in San Francisco: A

Case Study of Environmental Risks in the City's Mission District. Based on a presentation made at the Conference on Urban Env ironmental Issues in the Bay Area.

Hastings West

- Northwest Journal of Environmental Law and Policy. Second Annual Environmental Law Symposium. March 23.

http://bss.sfsu.edu/raquelrp/pub/1996_march_pub.html ; Hunter, Lori M. 2000. The Spatial Association between U.S . Immigrant Residential Concentration and

Environmental Hazards. International Migration Review. 34(2): pp. 460

- 488. http://www.jstor.org/stable/2675910

.

18 Pastor, Manuel, James Sadd, Rachel Morello

- Frosch . 2007. Still Toxic After All These Years: Air Quality and Environmental Justice in the San Francisco Bay Area.

Center for Justice, Tolerance & Community. February. http://cjtc.ucsc.edu/docs/bay_final.p df

.

19 2010 Census Summary File 1 Delivered via FTP, http://www2.census.gov/census_2010/04

- Summary_File_1/

20 American Fact Finder, http://factfinder.census.gov/servlet/DatasetMainPageServlet?_ds_name=DEC_2000_SF1_U&_program=DEC&_lang=en

21 U.S. Census Bureau 2006

- 2010 American Community Survey 5

- Year Estimates, http://www.census.gov/acs/www/data_documentation/2010_release/

22 USEPA. 2010. Tribal Lands Shapefile Metadata, provided by Brian Sti tt, Sr., Office of Air and Radiation, OAQPS, 109 TW Alexander Drive, Research Triangle Park, NC

27711.

# Near Source Data

# Study Area Demographics

Near Source

Population Name ID EPA

Region State White % African

American % Native

American % Other /

Multiracial % Minority /

Hispanic % Age 0 - 4 % Age 0 - 17

% Age 65

and Up % No

Diploma % Low

Income % Linguistically

Isolated %

12,541 The Stiles and Hart Brick

Company NEIMAR119215 1 MA 91.0% 5.9% 0.3% 2.8% 11.8% 3.6% 19.1% 15.9% 11.1% 8.5% 1.0%

12,541 91.0% 5.9% 0.3% 2.8% 11.8% 1.0% 3.6% 19.1% 15.9% 11.1% 8.5% EPA Region Total

88,676 Glen-Gery Corporation-York NEIPA13331-0 3 PA 70.1% 16.4% 0.4% 13.1% 36.2% 7.2% 24.5% 13.5% 17.2% 41.4% 3.6%

35,834 McAvoy Brick NEIPA23-0852 3 PA 82.4% 5.9% 0.2% 11.5% 20.2% 7.3% 24.6% 11.4% 6.5% 14.6% 2.8%

11,422 Continental Clay-Kittanning NEIPARBSCP17 3 PA 98.1% 0.5% 0.1% 1.3% 2.3% 5.6% 20.0% 19.1% 12.7% 33.5% 0.1%

6,253 Watsontown Brick NEIPAT$1908 3 PA 96.6% 1.2% 0.2% 2.0% 3.9% 6.1% 21.1% 17.4% 9.1% 29.5% 0.0%

5,098 Glen-Gery Corporation-Bigler NEIPA03326-7 3 PA 99.0% 0.1% 0.3% 0.6% 1.5% 5.2% 21.0% 15.5% 24.0% 45.1% 0.1%

67,367 Glen-Gery Corporation-

Capitol NEIVA6830000 3 VA 59.8% 14.9% 0.9% 24.4% 53.2% 8.7% 28.6% 6.1% 18.9% 24.5% 13.6%

25,211 Old Virginia-Salem NEIVA7750000 3 VA 93.1% 3.3% 0.3% 3.3% 7.7% 4.4% 20.0% 18.2% 11.4% 19.9% 0.8%

17,757 Old Virginia-Madison Heights NEIVA00025 3 VA 70.7% 25.0% 0.7% 3.6% 30.3% 5.9% 21.5% 17.9% 15.7% 34.8% 0.7%

7,736 General Shale Brick-Plants 35

and 36 NEI151 3 VA 95.3% 2.9% 0.2% 1.6% 5.3% 4.9% 24.2% 13.9% 10.0% 15.4% 1.1%

1,630 General Shale Brick-Plants 6

and 28 NEIVA00011 3 VA 98.0% 0.2% 0.3% 1.5% 2.5% 5.3% 20.5% 16.5% 19.0% 39.3% 0.0%

32,213 Continental Brick-

Martinsburg NEIWVRBSCP64 3 WV 81.3% 12.2% 0.4% 6.2% 21.4% 7.4% 24.6% 12.4% 16.5% 35.5% 0.9%

299,197 75.4% 12.1% 0.5% 12.1% 30.2% 4.7% 7.1% 24.6% 12.5% 15.1% 30.4% EPA Region Total

18,590 Boral-Bessemer Plant #5 NEI8444 4 AL 40.4% 55.4% 0.3% 3.9% 60.5% 6.8% 23.9% 15.6% 14.1% 37.7% 0.7%

17,500 Henry Brick NEIAL0470005 4 AL 11.1% 87.5% 0.2% 1.2% 89.0% 8.6% 29.0% 12.2% 29.3% 64.3% 0.0%

15,774 Boral-Bessemer Plant #6 NEI8443 4 AL 44.6% 51.4% 0.3% 3.8% 56.2% 6.8% 23.9% 16.2% 12.8% 34.2% 0.8%

10,782 Boral-Phenix City-Plant 3 NEI8625 4 AL 22.6% 68.5% 0.8% 8.0% 80.1% 7.6% 26.6% 12.5% 24.4% 55.3% 3.9%

10,444 Jenkins-Coosada Plant NEIAL0510010 4 AL 70.2% 24.7% 0.4% 4.6% 31.7% 6.4% 26.5% 11.2% 12.0% 33.4% 1.6%

9,475 Boral-Phenix City-Plant 4 NEI8624 4 AL 23.8% 68.5% 1.0% 6.7% 78.2% 7.0% 26.0% 11.1% 23.6% 51.5% 3.0%

3,887 Dal-Tile Fayette Alabama NEIAL0007 4 AL 71.7% 25.9% 0.2% 2.2% 28.8% 5.9% 23.2% 17.4% 26.8% 44.4% 0.0%

1,214 Ragland Clay Products NEIALRBSCP15 4 AL 82.8% 14.4% 0.2% 2.6% 17.6% 6.1% 24.1% 16.8% 20.0% 46.9% 0.0%

55,218 Boral-Atlanta Plant NEI8041 4 GA 47.1% 42.8% 0.4% 9.7% 56.2% 10.4% 24.5% 6.8% 7.2% 28.3% 1.6%

35,620 Boral-Augusta Plant 6 NEI8121 4 GA 27.5% 68.5% 0.3% 3.8% 73.4% 7.9% 24.5% 13.2% 28.1% 60.7% 0.7%Near Source

Population Name ID EPA

Region State White % African

American % Native

American % Other /

Multiracial % Minority /

Hispanic % Age 0 - 4 % Age 0 - 17

% Age 65

and Up % No

Diploma % Low

Income % Linguistically

Isolated %

27,990 Cherokee Brick and Tile NEIGAT$3927 4 GA 22.2% 74.3% 0.2% 3.3% 78.9% 8.3% 25.6% 11.2% 36.2% 74.2% 1.1%

3,418 General Shale Brick-Plant 40 NEIBIA204 4 GA 90.6% 4.3% 0.2% 4.9% 10.9% 6.0% 22.6% 13.3% 16.0% 33.6% 0.0%

5,785 General Shale Brick-Corbin-

Plant 33 NEI11283 4 KY 97.5% 0.2% 0.2% 2.1% 3.1% 6.4% 25.0% 17.9% 29.3% 60.6% 3.2%

4,387 Hanson-Stanton NEIKY1068 4 KY 98.0% 0.3% 0.1% 1.6% 2.2% 7.0% 23.7% 14.9% 28.5% 55.3% 0.0%

1,757 Dal-Tile Lewisport Kentucky NEIKY091231 4 KY 95.0% 2.6% 0.0% 2.4% 6.7% 7.5% 24.6% 14.5% 7.9% 39.2% 1.0%

25,103 Columbus Brick Company NEIMS1773 4 MS 36.9% 60.5% 0.2% 2.4% 63.6% 7.2% 24.0% 15.1% 21.7% 54.6% 0.7%

7,903 Acme-Holly Springs Plant

(HSP) NEIMSRBSCP67 4 MS 19.3% 79.2% 0.2% 1.3% 80.9% 6.3% 22.2% 10.6% 31.7% 60.1% 0.0%

31,003 Hanson-Monroe NEI46595 4 NC 77.0% 12.3% 0.6% 10.2% 29.6% 8.2% 31.2% 8.3% 10.3% 23.2% 2.4%

26,901 Boral-Salisbury Plant NEINCT$3069 4 NC 49.1% 43.7% 0.5% 6.8% 53.2% 6.7% 22.6% 15.3% 19.3% 48.7% 1.4%

6,630 Pine Hall-Madison Plant NEI46185 4 NC 78.6% 16.7% 0.6% 4.1% 23.4% 5.4% 21.1% 17.2% 20.3% 39.0% 1.4%

5,426 Cunningham-Thomasville NEI44719 4 NC 96.9% 0.7% 0.4% 2.1% 3.9% 5.7% 22.3% 14.6% 29.1% 54.0% 1.7%

5,337 Lee Brick Sanford Facility NEI43670 4 NC 72.7% 18.8% 0.5% 8.0% 31.8% 6.9% 24.0% 12.1% 8.2% 23.8% 1.2%

4,461 Cunningham-Cleveland Co NEI44906 4 NC 79.0% 14.6% 0.4% 6.0% 22.5% 6.7% 26.8% 10.3% 19.5% 39.4% 0.0%

4,273 Hanson-Roseboro NEI46140 4 NC 76.2% 14.5% 1.4% 8.0% 26.0% 5.7% 23.0% 14.7% 21.5% 44.4% 0.0%

3,551 Statesville Brick NEI43753 4 NC 86.3% 9.0% 0.3% 4.4% 14.7% 5.4% 22.2% 16.5% 25.4% 31.9% 3.4%

1,863 Triangle Brick-Wadesboro

Plant NEI2NC700043 4 NC 31.6% 64.8% 0.6% 3.0% 68.8% 6.9% 25.8% 15.1% 29.1% 62.6% 0.0%

1,323 Triangle Brick-Merry Oaks

Plant NEI44992 4 NC 86.0% 9.4% 0.8% 3.8% 15.0% 6.7% 23.8% 14.9% 19.9% 18.2% 0.0%

0 General Shale Brick-Moncure

Facility NEI44985 4 NC 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

51,556 Hanson Columbia-Plants 2

and 4 NEI41452 4 SC 26.4% 69.4% 0.3% 3.9% 74.5% 7.4% 21.0% 8.0% 20.2% 57.3% 0.9%

1,999 Hanson-Ninety Six NEI41366 4 SC 87.2% 11.4% 0.2% 1.3% 13.1% 3.8% 17.5% 21.9% 14.1% 34.1% 0.0%

1,082 Marion Ceramics NEISCRBSCP50 4 SC 56.8% 40.8% 0.3% 2.1% 43.2% 4.9% 19.1% 15.8% 20.0% 48.4% 0.0%

3,160 Boral-Gleason Plant NEI11017 4 TN 96.8% 1.7% 0.2% 1.3% 4.4% 5.8% 23.4% 15.6% 22.6% 33.5% 0.1%

403,412 45.1% 49.3% 0.4% 5.2% 56.8% 1.2% 7.7% 24.6% 11.7% 19.5% 47.0% EPA Region Total

25,752 Richards Brick NEIIL119803A 5 IL 86.3% 8.9% 0.3% 4.5% 15.0% 5.3% 20.4% 11.6% 6.0% 24.3% 2.0%

6,972 Glen-Gery Corporation-

Marseilles NEIILT$9627 5 IL 96.6% 0.3% 0.4% 2.7% 6.3% 6.3% 23.6% 14.5% 13.8% 27.6% 0.2%Near Source

Population Name ID EPA

Region State White % African

American % Native

American % Other /

Multiracial % Minority /

Hispanic % Age 0 - 4 % Age 0 - 17

% Age 65

and Up % No

Diploma % Low

Income % Linguistically

Isolated %

1,210 Boral- Terre Haute Plant 1816700139 (AFS) 5 IN 97.5% 0.9% 0.1% 1.5% 2.6% 4.8% 21.2% 16.9% 7.2% 29.5% 0.0%

2,235 Acme-Ochs Brick Plant (AOP) NEIMN1189 5 MN 98.0% 0.1% 0.2% 1.7% 5.5% 6.4% 22.8% 25.8% 18.0% 30.5% 0.2%

48,006 Metropolitan Ceramics Inc. NEIOH1576051 5 OH 66.9% 26.3% 0.6% 6.2% 34.2% 8.0% 27.3% 11.3% 21.8% 58.2% 0.6%

24,901 Whitacre Greer NEI11563 5 OH 86.1% 9.3% 0.2% 4.4% 14.9% 6.4% 22.4% 15.1% 16.9% 42.4% 0.1%

11,413 Logan Clay Products NEIROHSCP142 5 OH 97.4% 0.7% 0.3% 1.5% 3.1% 6.7% 24.6% 16.0% 14.6% 45.1% 0.2%

10,353 Superior Clay Corporation-

Uhrichsville NEI40514 5 OH 97.0% 1.1% 0.2% 1.8% 3.6% 7.0% 25.9% 14.6% 17.7% 47.0% 0.0%

6,073 Certainteed Corporation-

Ludowici NEIROHSCP181 5 OH 98.2% 0.4% 0.2% 1.2% 2.2% 7.7% 27.2% 13.5% 17.3% 44.8% 0.0%

5,808 Belden-Plant 1 Strasburg NEIOHRBSCP11 5 OH 97.7% 0.2% 0.4% 1.6% 2.8% 5.5% 21.3% 17.6% 11.5% 26.5% 0.5%

5,056 Summitville Tiles Inc.-

Minerva Plant NEIOHT$6289 5 OH 97.2% 0.4% 0.4% 2.0% 3.2% 5.6% 23.1% 19.5% 14.0% 34.0% 0.4%

2,784 Belden-Plant #234689

Sugarcreek NEI2OHT16041-

NEIOHT$6305-

NEI2OHT$6304-

NEIOHT$6302- 5 OH 97.7% 0.1% 0.1% 2.1% 2.8% 7.0% 24.9% 16.0% 22.0% 26.3% 3.8%

1,274 Glen-Gery Corporation-

Caledonia NEI11415 5 OH 98.0% 0.8% 0.0% 1.3% 2.3% 6.3% 25.4% 14.8% 9.9% 38.9% 0.0%

151,837 84.0% 11.5% 0.4% 4.1% 17.0% 0.7% 6.8% 24.3% 13.7% 16.1% 42.8% EPA Region Total

53,972 Acme-Fort Smith Plant (FSP) NEI12908 6 AR 69.8% 9.2% 2.2% 18.8% 34.9% 7.3% 24.1% 13.5% 22.2% 52.1% 6.3%

12,241 Acme-Perla Plant (PEP/PWP) NEI12869 6 AR 70.8% 24.2% 0.6% 4.4% 30.9% 7.7% 24.3% 17.7% 19.9% 43.0% 0.0%

7,389 Acme-Eureka Plant (EBP) NEI1129 6 AR 79.7% 2.1% 1.1% 17.1% 26.3% 7.0% 24.2% 15.0% 24.8% 60.3% 4.2%

817 Acme-Wheeler Plant (WTP) NEIART$10892 6 LA 82.3% 15.3% 0.1% 2.3% 19.1% 4.9% 18.5% 17.9% 12.9% 58.9% 0.0%

42,910 American Eagle Brick Co NEINMRBSCP2 6 NM 79.3% 2.5% 0.7% 17.5% 81.9% 6.5% 23.2% 13.3% 21.6% 56.0% 19.4%

64,858 Acme-Tulsa Plant (TUP) NEIOK1008 6 OK 53.1% 18.5% 7.5% 21.0% 53.5% 9.2% 27.8% 8.9% 25.8% 60.3% 8.4%

1,440 Commercial Brick Corp NEIOKRBSCP13 6 OK 63.3% 6.3% 22.3% 8.1% 37.6% 4.9% 21.9% 17.9% 25.2% 50.1% 0.0%

824 Boral-Muskogee Plant NEI12979 6 OK 68.6% 1.8% 21.6% 8.0% 32.6% 4.2% 24.3% 14.3% 0.0% 37.7% 0.0%

14,242 Boral-Henderson Facility NEI6472 6 TX 61.3% 28.1% 0.4% 10.2% 46.7% 5.7% 19.0% 12.1% 20.6% 35.3% 2.5%

12,198 Hanson-Elgin NEITXRBSCP77 6 TX 76.0% 11.5% 0.9% 11.6% 42.0% 7.3% 29.7% 9.2% 4.0% 9.5% 2.0%

12,099 Hanson-Athens NEITXRBSCP14 6 TX 64.2% 18.1% 0.5% 17.2% 47.2% 7.5% 26.1% 15.0% 24.4% 54.2% 6.8%

8,877 Hanson-Mineral Wells East NEI6648 6 TX 71.0% 10.9% 0.6% 17.5% 38.8% 6.3% 22.1% 11.1% 28.5% 31.2% 5.2%

7,463 Acme-Bridgeport Plant (BPP) NEITXT$11439 6 TX 76.8% 3.8% 0.9% 18.6% 43.6% 8.2% 28.8% 9.4% 24.9% 38.8% 6.5%Near Source

Population Name ID EPA

Region State White % African

American % Native

American % Other /

Multiracial % Minority /

Hispanic % Age 0 - 4 % Age 0 - 17

% Age 65

and Up % No

Diploma % Low

Income % Linguistically

Isolated %

2,852 Acme-Texas Clay Plant (TCP) NEITXT$11205 6 TX 74.4% 15.7% 0.8% 9.1% 31.0% 6.0% 23.2% 24.2% 17.1% 40.4% 3.4%

1,999 Elgin Butler Plant NEITXRBSCP15 6 TX 67.6% 4.6% 0.8% 27.1% 50.0% 6.6% 28.0% 10.6% 33.6% 58.6% 12.7%

1,999 Acme-Elgin Plant (ELP/ENP) NEI6580 6 TX 67.6% 4.6% 0.8% 27.1% 50.0% 6.6% 28.0% 10.6% 33.6% 58.6% 12.7%

1,437 Acme-Bennett Plant

(BEP)/Bennett Tunnel Plant

(BTP) NEITXRPC0001 6 TX 90.7% 1.5% 0.8% 7.0% 13.2% 5.9% 24.5% 14.9% 17.8% 18.8% 0.0%

1,115 Acme-Garrison Plant (GAP) NEITXT$11347 6 TX 69.9% 23.7% 0.0% 6.5% 33.9% 7.2% 25.3% 20.5% 38.8% 47.9% 0.0%

248,732 67.4% 12.5% 3.0% 17.2% 49.5% 8.2% 7.5% 25.1% 12.3% 22.4% 50.3% EPA Region Total

5,413 Sioux City-Sergeant Bluff

Plant NEIIAT$8026 7 IA 93.3% 1.0% 0.7% 5.0% 8.4% 6.6% 30.4% 10.1% 8.6% 19.2% 0.3%

4,203 Sioux City-Adel Plant NEIIAT$7929 7 IA 97.7% 0.3% 0.4% 1.6% 3.7% 6.7% 26.6% 12.7% 6.1% 25.1% 0.0%

945 Glen-Gery Corporation-

Redfield NEI12110 7 IA 96.4% 0.3% 0.1% 3.2% 5.8% 6.7% 23.9% 18.0% 12.1% 25.6% 0.0%

2,871 General Finance-Cloud

Ceramics NEIKS0290000 7 KS 95.7% 0.2% 0.5% 3.6% 6.5% 7.9% 23.7% 18.6% 14.4% 38.2% 0.0%

1,264 Kansas Brick and Tile NEIKSRBSCP17 7 KS 94.5% 0.8% 0.7% 4.0% 8.1% 6.0% 24.8% 15.3% 12.9% 43.4% 2.0%

1,184 Acme-Kanapolis Plant (KAP) NEI12757 7 KS 96.9% 0.0% 0.6% 2.5% 8.4% 5.4% 16.7% 19.8% 6.7% 41.0% 0.0%

17,577 Kasten Clay Products NEIMO0006 7 MO 96.0% 1.6% 0.3% 2.2% 4.9% 7.3% 26.1% 14.5% 14.3% 28.5% 0.1%

29,487 Yankee Hill Brick and Tile NEI44504 7 NE 81.3% 6.1% 1.4% 11.3% 23.5% 7.9% 23.6% 6.7% 12.3% 30.5% 3.4%

1,323 Endicott Clay Products NEI642 7 NE 98.0% 0.1% 0.2% 1.7% 3.2% 4.6% 19.5% 22.0% 7.1% 28.6% 0.0%

64,267 89.2% 3.3% 0.8% 6.7% 13.8% 1.6% 7.4% 24.9% 10.9% 12.0% 29.3% EPA Region Total

137,889 General Shale Robinson Brick

Co.-Plant 2 NEI3669 8 CO 75.0% 2.2% 1.7% 21.1% 44.5% 7.1% 22.3% 11.2% 19.2% 39.8% 9.1%

65,290 Summit-Brick Plant and

Storage Yard NEI519 8 CO 74.1% 2.5% 2.7% 20.8% 55.7% 6.9% 23.2% 15.1% 18.3% 52.5% 3.5%

771 Hebron Brick Plant NEINDRBSCP14 8 ND 96.6% 0.0% 0.8% 2.6% 4.5% 7.8% 24.8% 20.2% 17.9% 41.6% 0.1%

51,331 Interpace Industries NEIUTRBSCP71 8 UT 85.5% 1.2% 1.0% 12.3% 22.0% 9.7% 31.5% 9.5% 10.6% 30.5% 3.2%

255,281 77.0% 2.0% 1.8% 19.2% 42.7% 6.5% 7.6% 24.4% 11.9% 17.4% 41.2% EPA Region Total

48,542 Higgins Brick NEICARBSCP63 9 CA 55.4% 6.7% 0.8% 37.2% 65.4% 6.3% 25.6% 5.9% 14.3% 13.5% 4.8%

23,202 McNear Brickyard NEI21639 9 CA 62.1% 1.5% 1.6% 34.7% 55.7% 7.2% 21.7% 12.8% 26.2% 39.0% 28.0%Near Source

Population Name ID EPA

Region State White % African

American % Native

American % Other /

Multiracial % Minority /

Hispanic % Age 0 - 4 % Age 0 - 17

% Age 65

and Up % No

Diploma % Low

Income % Linguistically

Isolated %

15,877 Pacific Clay Products-Lake

Elsinore NEI22439 9 CA 63.7% 5.1% 1.0% 30.3% 52.5% 7.9% 32.2% 5.1% 15.0% 16.8% 4.1%

87,621 58.7% 5.1% 1.0% 35.3% 60.5% 10.8% 6.8% 25.8% 7.6% 17.7% 20.8% EPA Region Total

71,368 Mutual Materials-Newcastle NEI46650 10 WA 64.7% 2.6% 0.7% 32.1% 38.3% 5.8% 24.4% 11.9% 4.3% 12.3% 7.6%

2,913 Mutual Materials-Mica NEIWA0122 10 WA 96.1% 0.3% 1.0% 2.7% 5.4% 4.1% 24.7% 12.3% 5.5% 7.4% 0.0%

74,281 65.9% 2.5% 0.7% 30.9% 37.1% 7.3% 5.8% 24.5% 11.9% 4.4% 12.2% EPA Region Total

# Tribal Land Proximity

### Study Area Intersect of Tribal Lands

NAME ID TRIBAL LAND

NAME CITY STATE

None

Native American Totals > or = 100

NAME ID Sum of NATIVE

AMERICAN % of NATIVE

AMERICAN TOTAL

Acme

‐ Tulsa Plant (TUP) NEIOK1008 4,847 30.99%

General Shale Robinson Brick Co.

‐ Plant 2 NEI3669 2,347 15.01%

Summit

‐ Brick Plant and Storage Yard NEI519 1,743 11.15%

Acme

‐ Fort Smith Plant (FSP) NEI12908 1,193 7.63%

Glen

‐ Gery Corporation

‐ Capitol NEIVA6830000 583 3.73%

Interpace Industries NEIUTRBSCP71 530 3.39%

Mutual Materials

‐ Newcastle NEI46650 472 3.02%

Yankee Hill Brick and Tile NEI44504 402 2.57%

Glen

‐ Gery Corporation

‐ York NEIPA13331

‐ 0 392 2.51%

McNear Brickyard NEI21639 370 2.37%

Higgins Brick NEICARBSCP63 365 2.33%

Commercial Brick Corp NEIOKRBSCP13 321 2.05%

American Eagle Brick Co NEINMRBSCP2 312 2.00%

Metropolitan Ceramics Inc. NEIOH1576051 311 1.99%

Boral

‐ Atlanta Plant NEI8041 194 1.24%

Boral

‐ Muskogee Plant NEI12979 178 1.14%

Hanson

‐ Monroe NEI46595 176 1.13%

Hanson Columbia

‐ Plants 2 and 4 NEI41452 169 1.08%

Pacific Clay Products

‐ Lake Elsinore NEI22439 157 1.00%

Boral

‐ Salisbury Plant NEINCT$3069 132 0.84%

Old Virginia

‐ Madison Heights NEIVA00025 119 0.76%

Continental Brick

‐ Martinsburg NEIWVRBSCP64 113 0.72%

Boral

‐ Augusta Plant 6 NEI8121 107 0.68%

Hanson

‐ Elgin NEITXRBSCP77 106 0.68%

Grand Total 15,639 100.00%

# Facility Clusters

## Facility Clusters

Cluster 1

ID Name

NEI6580 Acme

‐ Elgin Plant (ELP/ENP)

NEITXRBSCP15 Elgin Butler Plant

Total population: 1,999

White population: 1,352

African American population: 91

Native American population: 15

Other and multi

‐ racial population: 541

Hispanic population: 863

Total minority population: 647

Population in poverty: 480

Population age 0 to 17: 559

Population age 65 and up: 211

Number of adults 25 and over without a highschool diploma: 421

Linguistically isolated population: 253

Cluster 2

ID Name

NEI8624 Boral

‐ Phenix City

‐ Plant 4

NEI8625 Boral

‐ Phenix City

‐ Plant 3

Total population: 13,180

White population: 3,186

African American population: 8,891

Native American population: 113

Other and multi

‐ racial population: 990

Hispanic population: 948

Total minority population: 9,994

Population in poverty: 3,249

Population age 0 to 17: 3,500

Population age 65 and up: 1,603

Number of adults 25 and over without a highschool diploma: 1,963

Linguistically isolated population: 421 Facility clusters in this proximity analysis are formed when the study areas overlap and a Census

block group can be attributed to one or more of the facilities.

Facilities in Cluster

Demographics

Facilities in Cluster

Demographics

## Facility Clusters

Cluster 3

ID Name

NEI8443 Boral

‐ Bessemer Plant #6

NEI8444 Boral

‐ Bessemer Plant #5

Total population: 18,590

White population: 7,509

African American population: 10,308

Native American population: 57

Other and multi

‐ racial population: 716

Hispanic population: 709

Total minority population: 11,081

Population in poverty: 3,221

Population age 0 to 17: 4,451

Population age 65 and up: 2,894

Number of adults 25 and over without a highschool diploma: 1,775

Linguistically isolated population: 128 Facilities in Cluster

Demographics

# Statistical Summary

### Category Totals and **T- Tests** 

The EPA compare s aggregated study area demographics to U.S. demographics in three tables within this section:

**•** Table 1

– Demographic totals for the entire source category and U.S. demographic totals

**•** Table 2

– Demographics averaged across study areas vs. U.S. average demographics

**•** Table 3

– Proportion of study areas with populations exceeding U.S. average demographics

Table 1 is simply calculated by summing the demographic data in the study areas; however, urban study areas can often skew

the data. Therefore, Tables 2 and 3 use statistical inference to answer two additional and specific questions :

1. On average, how much do the demographics within the study areas differ from the U.S. population?

2. How often do the demographics within the study areas exceed U.S. average demographics?

Table 2

– Hypothesis Test for Questi on 1 (T

- test for population average) 1

**•** Null Hypothesis (H 0 ): The proportion of the population in demographic category X within a given study area is a

normally

- distributed random variable with mean EQUAL TO the U.S. average.

**•** Alternative Hypothesis (H

1 ): The proportion of the population in demographic category X within a given study area is

a normally

- distributed random variable with mean NOT EQUAL TO the U.S. average.

**•** Test statistic: T = (Study area average %

– U.S. average %)/Standard Error

**•** If H 0 is true, t hen T has a T

- distribution with mean = 0 and degrees of freedom = # study areas

– 1. The p

- value is the

probability that H 0 is true given the value of T calculated from the demographic data.

**•** Statistical Conclusions:

o If the p

- value is large (≥ 0.05): The a verage proportion of the study area population in demographic category

X is not significantly different from the U.S. average.

o If the p

- value is small (< 0.05) and the T

- statistic is POSITIVE: The average proportion of the study area

population in demograp hic category X is significantly greater than the U.S. average.

o If the p

- value is small (< 0.05) and the T

- statistic is NEGATIVE: The average proportion of the study area

population in demographic category X is significantly less than the U.S. average.

**•** Stud y areas with fewer than 100 total residents are ignored.

Table 3

– Hypothesis Test for Question 2 (T

- test for occurrence) 1

**•** Null Hypothesis (H 0 ): The proportion of study areas having population in demographic category X exceeding the U.S.

average is EQUAL TO 50%.

**•** Alternative Hypothesis (H

1 ): The proportion of study areas having population in demographic category X exceeding

the U.S. average is NOT EQUAL TO 50%.

**•** Test statistic: T = (% of study areas > U.S. average – 50%)/Standard Error

**•** If H 0 is true, then T has a T

- distribution with mean = 0 and degrees of freedom = # study areas

– 1. The p

- value is the

probability that H 0 is true given the value of T calculated from the demographic data.

**•** Statistical Conclusions:

o If the p

- value is large (≥ 0.05): The proportion of study areas having population in demographic category X

exceeding the U.S. average is not significantly different from 50%.

o If the p

- value is small (< 0.05) and T > 0: The proportion of study areas having population in demographic

category X exceeding the U.S. average is significantly greater than 50%.

o If the p

- value is small (< 0.05) and T < 0: The proportion of study areas having population in demographic

category X exceeding the U.S. average is significantly less than 50%.

**•** Study areas with fewer than 100 total residents are ignored.

A qualitative summary on the statistical significance immediately follows the tabular data in this section.

1 T- tests are designed for use when the number of study areas is large. Other tests may be used in place of T

- tests for Tables 2 and 3 when there are fewer than 50

study areas.StatisticalDataCategoryTotalsandT‐TestsTable1‐Category‐wideTotalPopulation WhiteAfricanAmericanNativeAmericanOtherorMultiracialMinority/HispanicLinguisticallyIsolatedAge0‐4 Age0‐17 Age65+WithoutaHSDiplomaLowIncome1,572,319 1,057,969 285,052 17,358 211,940 652,831 72,326 114,107 387,167 187,071 175,047 612,62967% 18% 1% 13% 42% 5% 7% 25% 12% 17% 39%312,861,256 226,405,205 39,475,216 3,646,995 43,333,840 115,989,918 19,196,507 20,465,065 75,217,176 40,830,262 30,952,789 101,429,43672% 13% 1% 14% 37% 6% 7% 24% 13% 15% 32%Table2‐HypothesisTest1(AveragePopulation)H0:Average%ofPopulationNearSourcesinCategoryX=National%inCategoryX#ofStudyAreas>=100totalpopulation(N) 92H1:Average%ofPopulationNearSourcesinCategoryX≠National%inCategoryXWhiteAfricanAmericanNativeAmericanOtherorMultiracialMinority/HispanicLinguisticallyIsolatedAge0‐4 Age0‐17 Age65+WithoutaHSDiplomaLowIncome76% 16% 1% 8% 29% 2% 7% 24% 14% 18% 39%2.347% 2.293% 0.338% 0.880% 2.565% 0.456% 0.130% 0.312% 0.409% 0.834% 1.529%1.4066 1.2716‐0.2911‐6.9542‐3.1533‐8.2285 3.3071 0.0163 2.9887 3.3234 4.07120.1630 0.2068 0.7716 < 0.0001 0.0022 < 0.0001 0.0014 0.9870 0.0036 0.0013 < 0.0001Table3‐HypothesisTest2(Occurrence)H0:%ofstudyareaswithNearbyPopulationinCategoryXaboveNationalAverage=50%H1:%ofstudyareaswithNearbyPopulationinCategoryXaboveNationalAverage≠50%WhiteAfricanAmericanNativeAmericanOtherorMultiracialMinority/HispanicLinguisticallyIsolatedAge0‐4 Age0‐17 Age65+WithoutaHSDiplomaLowIncome62% 35% 10% 17% 34% 12% 53% 50% 64% 59% 66%5.062% 4.966% 3.097% 3.952% 4.928% 3.383% 5.202% 5.213% 5.000% 5.133% 4.928%2.3622‐3.0646‐12.9848‐8.2518‐3.3086‐11.2466 0.6269 0.0000 2.8259 1.6939 3.30860.0203 0.0029 < 0.0001 < 0.0001 0.0013 < 0.0001 0.5323 1.0000 0.0058 0.0937 0.0013STATISTICALSIGNIFICANCELEVELNoSignificance(P>=0.05)0.05 notsignificantSignificant(P<0.05)significantSignificantlyLess(P<0.05andTStatisnegative)significantlylessDemographicSummaryNEARSOURCETOTAL%OFNEARSOURCETOTALNATIONALTOTAL%OFNATIONALTOTALSTATISTICAVERAGE%NEARFACILITIESSTANDARDDEVIATIONTSTATISTICP‐VALUESTATISTIC%STUDYAREAS>NATIONALAVGSTANDARDDEVIATIONTSTATISTICP‐VALUE

## Statistical Summary (Category Totals and T

‐ Tests)

Demographic Category Minority / Hispanic

Summary of the data:

Demographic Category Linguistically Isolated

Summary of the data: Table 3 indicates that 12% of study areas have a greater Linguistically Isolated population than the national

average. This is significantly less from the 50% we would expect if the sources were randomly distributed across

the U.S.

The national proportion (Table 1) and average proportion (Table 2) of Linguistically Isolated populations living in

the study areas are both less than the national average. Furthermore, 12% of the study areas have Linguistically

Isolated populations greater than the national average (Table 3). This suggests that the proportion of

Linguistically Isolated populations living in the study areas is significantly less than the national average. Table 1 indicates that nationally, 42% of the people living within 3 miles of the sources (study areas) are

Minority / Hispanic. This is 5 point(s), or 13.5%, greater than the national average of 37% in 2010.

Table 2 indicates that on average, 29% of the people living in the study areas are Minority / Hispanic. The

‐ 8

point difference in the average proportion is significantly less when compared to the national average.

Table 3 indicates that 34% of study areas have a greater Minority / Hispanic population than the national

average. This is significantly less from the 50% we would expect if the sources were randomly distributed across

the U.S.

The national proportion (Table 1) of Minority / Hispanic populations living in the study areas is greater than the

national average; however, the average proportion (Table 2) is significantly less than the national average.

Furthermore, 34% of the study areas have Minority / Hispanic populations greater than the national average

(Table 3). This suggests that the proportion of Minority / Hispanic populations living in the study areas is

significantly less than the national average.

Table 1 indicates that nationally, 5% of the people living within 3 miles of the sources (study areas) are

Linguistically Isolated. This is

‐ 1 point(s), or ‐ 16.7%, compared to the national average of 6% in 2010.

Table 2 indicates that on average, 2% of the people living in the study areas are Linguistically Isolated. The

‐ 4

point difference in the average proportion is significantly less when compared to the national average.

## Statistical Summary (Category Totals and T

‐ Tests)

Demographic Category Age 0

‐ 4

Summary of the data:

Demographic Category Age 65+

Summary of the data: Table 1 indicates that nationally, 7% of the people living within 3 miles of the sources (study areas) are Age 0

‐ 4.

This is approximately equal to the national average of 7% in 2010.

Table 2 indicates that on average, 7% of the people living in the study areas are Age 0

‐ 4. The 0 point difference

in the average proportion is significant when compared to the national average.

Table 3 indicates that 53% of study areas have a greater Age 0

‐ 4 population than the national average. This is

not significant from the 50% we would expect if the sources were randomly distributed across the U.S.

The national proportion (Table 1) of Age 0

‐ 4 populations living in the study areas is approximately equal to the

national average; however, the average proportion (Table 2) is significantly greater than the national average.

Furthermore, 53% of the study areas have Age 0

‐ 4 populations greater than the national average (Table 3). This

suggests that the average proportion of Age 0

‐ 4 populations living in the study areas is significantly greater

than the national average, but there is a large amount of variability among the study areas.

Table 1 indicates that nationally, 12% of the people living within 3 miles of the sources (study areas) are Age

65+. This is

‐ 1 point(s), or ‐ 7.7%, compared to the national average of 13% in 2010.

Table 2 indicates that on average, 14% of the people living in the study areas are Age 65+. The 1 point difference

in the average proportion is significant when compared to the national average.

Table 3 indicates that 64% of study areas have a greater Age 65+ population than the national average. This is

significant from the 50% we would expect if the sources were randomly distributed across the U.S.

The national proportion (Table 1) of Age 65+ populations living in the study areas is less than the national

average; however, the average proportion (Table 2) is significantly greater than the national average.

Furthermore, 64% of the study areas have Age 65+ populations greater than the national average (Table 3). This

suggests that the proportion of Age 65+ populations living in the study areas is significantly greater than the

national average.

## Statistical Summary (Category Totals and T

‐ Tests)

Demographic Category Without a HS Diploma

Summary of the data:

Demographic Category Low Income

Summary of the data: Table 1 indicates that nationally, 17% of the people living within 3 miles of the sources (study areas) are Without

a HS Diploma. This is 2 point(s), or 13.3%, greater than the national average of 15% in 2010.

Table 2 indicates that on average, 18% of the people living in the study areas are Without a HS Diploma. The 3

point difference in the average proportion is significant when compared to the national average.

The national proportion (Table 1) and average proportion (Table 2) of Low Income populations living in the

study areas are both greater than the national average. Furthermore, 66% of the study areas have Low Income

populations greater than the national average (Table 3). This suggests that the proportion of Low Income

populations living in the study areas is significantly greater than the national average. Table 3 indicates that 59% of study areas have a greater Without a HS Diploma population than the national

average. This is not significant from the 50% we would expect if the sources were randomly distributed across

the U.S.

The national proportion (Table 1) and average proportion (Table 2) of Without a HS Diploma populations living

in the study areas are both greater than the national average. Furthermore, 59% of the study areas have

Without a HS Diploma populations greater than the national average (Table 3). This suggests that the average

proportion of Without a HS Diploma populations living in the study areas is significantly greater than the

national average, but there is a large amount of variability among the study areas.

Table 1 indicates that nationally, 39% of the people living within 3 miles of the sources (study areas) are Low

Income. This is 7 point(s), or 21.9%, greater than the national average of 32% in 2010.

Table 2 indicates that on average, 39% of the people living in the study areas are Low Income. The 7 point

difference in the average proportion is significant when compared to the national average.

Table 3 indicates that 66% of study areas have a greater Low Income population than the national average. This

is significant from the 50% we would expect if the sources were randomly distributed across the U.S.

### Aggregated Data / Statisical Summary

Hypothesis 1 Hypothesis 2

Average Population

T

‐ Test

(Table 2) Occurrence

T

‐ Test

(Table 3)

Population 312,861,256 1,572,319

White 72% 67%

‐ 7% Not Significant Significant

African American 13% 18% 38% Not Significant Significantly Less

Native American 1% 1% 0% Not Significant Significantly Less

Other or Multiracial 14% 13%

‐ 7% Significantly Less Significantly Less

Minority / Hispanic 37% 42% 14% Significantly Less Significantly Less

Linguistically Isolated 6% 5%

‐ 17% Significantly Less Significantly Less

Age 0

‐ 4 7% 7% 0% Significant Not Significant

Age 0

‐ 17 24% 25% 4% Not Significant Not Significant

Age 65+ 13% 12%

‐ 8% Significant Significant

Without a HS Diploma 15% 17% 13% Significant Not Significant

Low Income 32% 39% 22% Significant Significant Demographic National Near Source

(Table 1) % Change

Near vs. National

# Map and Charts72%37%32%67%42%39%76%29%39%30%40%50%60%70%80%PercentageComparisonstoNationalAveragesCategoryTotal%iscalculatedbysummingallofthestudyareademographicdata.CategoryAverage%iscalculatedbyaveragingthedemographicdatabythenumberofstudyareaswherethetotalpopulation>=100.13%1%14%6%7%24%13%15%18%1%13%5%7%25%12%17%16%1%8%2%7%24%14%18%0%10%20%WHITE AFRICANAMERICANNATIVEAMERICANOTHER/MULTIRACIALMINORITY LING.ISOLATEDAGE0‐4 AGE0‐17 AGE65+ NOHIGHSCHOOLDIPLOMALOWINCOMENATIONALAVERAGE%CATEGORYTOTAL%CATEGORYAVERAGE%The national average = 37% 0

‐ 10% 10

‐ 20% 20

‐ 30% 30

‐ 40% 40

‐ 50% 50

‐ 60% More

Frequency 31 10 10 13 8 8 12 0 5 10 15 20 25 30 35Frequency 

## Minority / Hispanic Demographic Histogram

60 70 

## Linguistically Isolated Demographic Histogram

The national average = 6% 0

‐ 2% 2

‐ 4% 4

‐ 6% 6

‐ 8% 8

‐ 10% 10

‐ 12% More

Frequency 62 15 4 4 2 0 5 0 10 20 30 40 50FrequencyThe national average = 7% 0

‐ 2% 2

‐ 4% 4

‐ 6% 6

‐ 8% 8

‐ 10% 10

‐ 12% More

Frequency 0 2 26 56 7 1 0 0 10 20 30 40 50 60Frequency 

## Age 0

‐ 4 Demographic Histogram

40 45 

## Age 65+ Demographic Histogram

The national average = 13% 0

‐ 4% 4

‐ 8% 8

‐ 12% 12

‐ 16% 16

‐ 20% 20

‐ 24% More

Frequency 0 5 20 39 22 4 2 0 5 10 15 20 25 30 35FrequencyThe national average = 15% 0

‐ 4% 4

‐ 8% 8

‐ 12% 12

‐ 16% 16

‐ 20% 20

‐ 24% More

Frequency 2 10 10 17 20 12 21 0 5 10 15 20 25Frequency 

## No High School Diploma Demographic Histogram

25 30 

## Low Income Demographic Histogram

The national average = 32% 0

‐ 10% 10

‐ 20% 20

‐ 30% 30

‐ 40% 40

‐ 50% 50

‐ 60% More

Frequency 3 9 14 26 17 15 8 0 5 10 15 20FrequencyThe national average = 1% 0

‐ 0.2% .2

‐ 0.6% .6

‐ 1% 1

‐ 1.4% 1.4

‐ 1.8% 1.8

‐ 2.2% More

Frequency 20 39 22 4 2 0 5 0 5 10 15 20 25 30 35 40 45Frequency 

## Native American Demographic Histogram