2013

 EJ Screening Report for Clay Ceramics
Contents

Executive Summary................................................................................................................................................................. 4

About the Data........................................................................................................................................................................ 6

Demographic Data ..................................................................................................................................................... 7

Near Source Data ..................................................................................................................................................................12

Tribal Land Proximity ............................................................................................................................................................14

Statistical Summary...............................................................................................................................................................16

Category Totals, Wilcoxon Signed-Rank and Binomial Tests................................................................................... 17

Map and Charts..................................................................................................................................................................... 23

2
U.S. Environmental Protection Agency 

Environmental Justice Screening Report

National Emissions Standards for

Hazardous Air Pollutants From

Brick and Structural Clay and Clay Ceramics

Docket: EPA–HQ–OAR– 

RIN: 2060–AP69

Office of Air and Radiation,

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

3
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 

Clay Ceramics 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 Clay Ceramics 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 identify 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 disproportionately 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 

environmental 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 HAP 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 environmental 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. Consistent 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 among 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).

4
The EPA 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. Additionally, 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.

5
About the Data

6
Demographic Data

This report includes race, ethnicity, and age data derived from the 2010 Census SF1 dataset1

, and economic and 

education data from the Census Bureau’s 2006-2010 American Community Survey (ACS) 5-Year Estimates2

. 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. Social Vulnerability Data

Social Vulnerability Indicators (Census 2010 data):

• Minority/Hispanic (%)

• Low Income (%)

• Under Age 5 (%)

• Age 65 and up (%)

• Adults without a high 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 2006-2010 ACS 5-Year Estimates. These six subgroups of the population are included 

7
as social vulnerability indicators because there exists a substantive and easily-understood relationship between the 

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

indicators in environmental justice-related analyses. 

“Minority” 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 is 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 

susceptibility 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 subpopulation 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 considered 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 age 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 information 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 

8
The percentage of residents experiencing linguistic isolation is considered a relevant indicator of vulnerability 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 sites, 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 discrimination 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 percentage 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 

Populationab

SF1 Tables P1 and P3 Block

People < 18 years of ageb SF1 Table P12 Block 

People ≥ 65 years of ageb 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 2000 data are used for these 

areas.

9
Data on age distributions in the U.S. and Puerto Rico were obtained at the census block 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 for 

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 feature 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://www.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. http://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 the 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/PMC1240572/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 Allegations of Environmental Injustice. 

November 3; Bullard, Robert 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. June 11. 

http://understandingkatrina.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. Environmental 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 Cylinder. 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 Environmental 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 French 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 Ambient 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 Lung Cell Proliferation Following Short-Term Exposure to Ultrafine Soot and Iron Particles in Neonatal Rats: Key to 

Impaired Lung 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. September 9. http://www.nejm.org/doi/full/10.1056/NEJMoa040610?ck=nck&#articleTop; Neidell, Matthew J. 2004. Air Pollution, Health, and Socioeconomic Status: the Effect of Outdoor Air Quality on Childhood Asthma. Journal of Health Economics 23: pp.1209–1236.

10
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 population-based cohort study. BMC 

Public Health 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://www.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):2037-45; 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 Education 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.gov/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, 114:29-33.; 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 Immigrants 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. 1996. 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 Environmental 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.pdf. 

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 Stitt, Sr., Office of Air and Radiation, OAQPS, 109 TW Alexander Drive, Research Triangle Park, NC 

27711.

11
Near Source Data

12
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 %

Monarch Ceramic Tile Inc. NEI8517 4 AL 38,095 74.6% 19.9% 0.5% 5.1% 26.7% 1.3% 5.9% 19.9% 17.7% 16.9% 45.1%

Kohler Spartanburg Plant NEI41474 4 SC 13,950 73.1% 20.9% 0.2% 5.8% 27.9% 3.0% 5.6% 22.6% 17.1% 12.8% 23.6%

Florim USA NEITNRCC123 4 TN 13,742 67.9% 20.7% 0.9% 10.5% 36.7% 1.5% 10.1% 29.6% 4.9% 7.4% 38.1%

EPA Region Total 65,787 72.9% 20.3% 0.5% 6.4% 29.1% 1.7% 6.7% 22.5% 14.9% 14.2% 39.1%

Kohler Wisconsin Plant NEIWI1179993 5 WI 38,124 86.8% 1.1% 0.4% 11.6% 16.8% 2.3% 6.5% 23.7% 16.1% 10.8% 26.8%

EPA Region Total 38,124 86.8% 1.1% 0.4% 11.6% 16.8% 2.3% 6.5% 23.7% 16.1% 10.8% 26.8%

Dal Italia NEIOK1015305 6 OK 13,401 56.3% 13.5% 17.2% 13.1% 45.4% 1.0% 7.6% 27.4% 13.7% 17.6% 46.2%

Marazzi Sunnyvale Facility NEI12943 6 TX 58,007 60.8% 21.3% 0.8% 17.2% 53.7% 4.3% 7.3% 30.1% 8.2% 15.8% 28.4%

Kohler Brownwood Plant NEI13131 6 TX 12,306 85.0% 4.1% 0.5% 10.3% 28.0% 3.1% 7.4% 25.6% 16.6% 15.7% 38.4%

Dal-Tile El Paso Plant NEI41683 6 TX 6,759 76.9% 6.1% 1.1% 15.9% 78.0% 11.0% 10.3% 34.9% 4.8% 24.5% 50.0%

EPA Region Total 90,473 64.6% 16.6% 3.2% 15.6% 50.8% 4.2% 7.6% 29.5% 9.9% 16.7% 34.0%

13
Tribal Land Proximity

14
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

Dal Italia NEIOK1015305 2,299 71.73%

Marazzi Sunnyvale Facility NEI12943 445 13.88%

Monarch Ceramic Tile Inc. NEI8517 173 5.40%

Kohler Wisconsin Plant NEIWI1179993 169 5.27%

Florim USA NEITNRCC123 119 3.71%

Grand Total 3,205 100.00%

15
Statistical Summary

16
Category Totals, Wilcoxon Signed-Rank and Binomial Tests

The EPA compares 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 – Median demographics 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 demographic proportions within the study areas exceed the U.S. average?

Table 2 – Hypothesis Test for Question 1 (Wilcoxon Signed-Rank test for population median)1

• Null Hypothesis (H0): The proportion of the population in demographic category X within a given study area is a 

random variable with median EQUAL TO the U.S. average.

• Alternative Hypothesis (H1): The proportion of the population in demographic category X within a given study area is 

a random variable with median NOT EQUAL TO the U.S. average. 

• Test Statistic: The Wilcoxon Signed-Rank statistic (W) is the sum of the signed ranks of the study areas where the 

study area demographics exceed the U.S. average. If there are N study areas, W has range 0 to N*(N+1)/2. The pvalue is the probability that H0 is true given the value of W calculated from the demographic data.

• Statistical Conclusions:

o If the p-value is large (≥ 0.05): The median 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 W > N*(N+1)/4: The median proportion of the study area population in 

demographic category X is significantly greater than the U.S. average.

o If the p-value is small (< 0.05) and W < N*(N+1)/4: The median proportion of the study area population in 

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

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

Table 3 – Hypothesis Test for Question 2 (Binomial test for occurrence)1 

• Null Hypothesis (H0): The probability of a given study area having population in demographic category X greater than 

the U.S. average is EQUAL TO 50%.

• Alternative Hypothesis (H1): The probability of a given study area having population in demographic category X 

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

• Test statistic: B = # of study areas with population greater than the U.S. average

• If H0 is true and there are N study areas, then B/N has a Binomial distribution with mean = 50%. The p-value is the 

probability that H0 is true given the value of B calculated from the demographic data.

• Statistical Conclusions:

o If the p-value is large (≥ 0.05): The probability of a given study area 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 B > N/2: The probability of a given study area 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 B < N/2: The probability of a given study area 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 Wilcoxon Signed-Rank and Binomial tests are designed for use when the number of study areas is small. Other tests may be used in place of these tests for Tables 2 

and 3 when there are more than 20 study areas.

17
Statistical Data

Category Totals, Wilcoxon Signed‐Rank and Binomial Tests

Table 1 ‐ Category‐wide Total

Population White African American Native American Other or Multiracial Minority / Hispanic Linguistically Isolated Age 0 ‐ 4 Age 0 ‐ 17 

194,384 139,493 28,826 3,369 22,696 71,451 5,765 13,714 50,504 

72% 15% 2% 12% 37% 3% 7% 26% 

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 

72% 13% 1% 14% 37% 6% 7% 24% 

Table 2 ‐ Hypothesis Test 1 (Wilcoxon Signed‐Rank Test for Median Near‐Source Population)

H0: Median % of Population Near Sources in Category X = National % in Category X # of Stu

H1: Median % of Population Near Sources in Category X ≠ National % in Category X

White African American Native American Other or Multiracial Minority / Hispanic Linguistically Isolated Age 0 ‐ 4 Age 0 ‐ 17 

20 20 8 6 16 7 28 28 

0.7930 0.7930 0.1719 0.0938 0.7930 0.1289 0.1719 0.1719 

Table 3 ‐ Hypothesis Test 2 (Binomial Test for Occurrence)

H0: Prob. (Study area with Nearby Population in Category X is above National Average) = 50%

H1: Prob. (Study area with Nearby Population in Category X is above National Average) ≠ 50%

White African American Native American Other or Multiracial Minority / Hispanic Linguistically Isolated Age 0 ‐ 4 Age 0 ‐ 17 

5 512 3 155

0.5078 0.5078 0.0391 0.1797 0.5078 0.0391 0.5078 0.5078 

STATISTICAL SIGNIFICANCE LEVEL

No Significance (P >= 0.05)Significant (P < 0.05 and W > N*(N+1)/4 or B > N/2)

Significantly Less (P < 0.05 and W < N*(N+1)/4 or B < N/2)

STATISTIC

# STUDY AREAS > NATIONAL AVG (B)

P‐VALUE

STATISTIC

WILCOXON STATISTIC (W)

P‐VALUE

Demographic Summary

NEAR SOURCE TOTAL

% OF NEAR SOURCE TOTAL

NATIONAL TOTAL% OF NATIONAL TOTAL
Statistical Summary (Category Totals, Wilcoxon Signed‐Rank and Binomial Tests)

Demographic Category Minority / Hispanic

Summary of the data:

Demographic Category Linguistically Isolated

Summary of the data:

Table 3 indicates that 1 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) of Linguistically Isolated populations living in the study areas is less than the

national average; however, the median proportion (Table 2) is not significantly different than the national

average. Furthermore, 1 study areas have Linguistically Isolated populations greater than the national average

(Table 3). This suggests that the median proportion of Linguistically Isolated populations living in the study areas

is not significantly different than the national average, and significantly less than 50% of study areas have

Linguistically Isolated populations greater than the national average.

Table 1 indicates that nationally, 37% of the people living within 3 miles of the sources (study areas) are

Minority / Hispanic. This is approximately equal to the national average of 37% in 2010.

Table 2 indicates that the Wilcoxon Signed‐Rank statistic for Minority / Hispanic populations is 16. The value of

16 is not significant when compared to the national average.

Table 3 indicates that 3 study areas have a greater Minority / Hispanic 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 median proportion (Table 2) of Minority / Hispanic populations living in

the study areas are not significantly different than the national average. Furthermore, 3 study areas have

Minority / Hispanic populations greater than the national average (Table 3). This suggests that the median

proportion of Minority / Hispanic populations living in the study areas is not significantly different than the

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

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

Linguistically Isolated. This is ‐3 point(s), or ‐50%, compared to the national average of 6% in 2010.

Table 2 indicates that the Wilcoxon Signed‐Rank statistic for Linguistically Isolated populations is 7. The value of

7 is not significant when compared to the national average.

19
Statistical Summary (Category Totals, Wilcoxon Signed‐Rank and Binomial Tests)

Demographic Category Age 0 ‐ 4

Summary of the data:

Demographic Category Age 65+

Summary of the data:

Table 2 indicates that the Wilcoxon Signed‐Rank statistic for Age 65+ populations is 15. The value of 15 is not

significant when compared to the national average.

Table 3 indicates that 5 study areas have a greater Age 65+ 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 median proportion (Table 2) of Age 65+ populations living in the study

areas are not significantly different than the national average. Furthermore, 5 study areas have Age 65+

populations greater than the national average (Table 3). This suggests that the median proportion of Age 65+

populations living in the study areas is not significantly different than the national average, and there is a large

amount of variability among the study areas.

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 the Wilcoxon Signed‐Rank statistic for Age 0 ‐ 4 populations is 28. The value of 28 is not

significant when compared to the national average.

Table 3 indicates that 5 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) and median proportion (Table 2) of Age 0 ‐ 4 populations living in the study

areas are not significantly different than the national average. Furthermore, 5 study areas have Age 0 ‐ 4

populations greater than the national average (Table 3). This suggests that the median proportion of Age 0 ‐ 4

populations living in the study areas is not significantly different than the national average, and there is a large

amount of variability among the study areas.

Table 1 indicates that nationally, 13% of the people living within 3 miles of the sources (study areas) are Age

65+. This is approximately equal to the national average of 13% in 2010.

20
Statistical Summary (Category Totals, Wilcoxon Signed‐Rank and Binomial Tests)

Demographic Category Without a HS Diploma

Summary of the data:

Demographic Category Low Income

Summary of the data:

The national proportion (Table 1) of Low Income populations living in the study areas is greater than the

national average; however, the median proportion (Table 2) is not significantly different than the national

average. Furthermore, 5 study areas have Low Income populations greater than the national average (Table 3).

This suggests that the median proportion of Low Income populations living in the study areas is not significantly

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

Table 3 indicates that 5 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 median proportion (Table 2) of Without a HS Diploma populations living in

the study areas are not significantly different than the national average. Furthermore, 5 study areas have

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

proportion of Without a HS Diploma populations living in the study areas is not significantly different than the

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

Table 1 indicates that nationally, 34% of the people living within 3 miles of the sources (study areas) are Low

Income. This is 2 point(s), or 6.3%, greater than the national average of 32% in 2010.

Table 2 indicates that the Wilcoxon Signed‐Rank statistic for Low Income populations is 28. The value of 28 is

not significant when compared to the national average.

Table 3 indicates that 5 study areas have a greater Low Income 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.

Table 1 indicates that nationally, 15% of the people living within 3 miles of the sources (study areas) are Without

a HS Diploma. This is approximately equal to the national average of 15% in 2010.

Table 2 indicates that the Wilcoxon Signed‐Rank statistic for Without a HS Diploma populations is 19. The value

of 19 is not significant when compared to the national average.

21
Aggregated Data / Statisical Summary

Hypothesis 1 Hypothesis 2

Median Near‐Source Population

Wilcoxon Signed‐Rank Test

(Table 2)

Occurrence

Binomial Test

(Table 3)

Population 312,861,256 194,384

White 72% 72% 0% Not Significant Not Significant

African American 13% 15% 15% Not Significant Not Significant

Native American 1% 2% 100% Not Significant Significantly Less

Other or Multiracial 14% 12% ‐14% Not Significant Not Significant

Minority / Hispanic 37% 37% 0% Not Significant Not Significant

Linguistically Isolated 6% 3% ‐50% Not Significant Significantly Less

Age 0 ‐ 4 7% 7% 0% Not Significant Not Significant

Age 0 ‐ 17 24% 26% 8% Not Significant Not Significant

Age 65+ 13% 13% 0% Not Significant Not Significant

Without a HS Diploma 15% 15% 0% Not Significant Not Significant

Low Income 32% 34% 6% Not Significant Not Significant

Demographic National Near Source

(Table 1)

% Change

Near vs. National

22
Map and Charts

23
24
72%

37%

72%

37%

26%

73%

39%

27%

30%40%50%60%70%80%

Percentage Comparisons to National Averages

Category Total % is calculated by summing all of the study area demographic data.

Category Average % is calculated by averaging the demographic data by the number of study areas where the total population >= 100.

13%

1%

14%

6% 7%

24%

13%

15% 

2%

12%

3%

7%

26%

13%

13%

3%

11%

3%

8%

1

0%

10%20%

WHITE AFRICAN

AMERICAN

NATIVE

AMERICAN

OTHER /

MULTIRACIAL

MINORITY LING.

ISOLATED

AGE 0 ‐ 4 AGE 0 ‐ 17 AGE 65

NATIONAL AVERAGE % CATEGORY TOTAL % CATEGORY AVERAGE %
The national average = 37%

0‐10% 10‐20% 20‐30% 30‐40% 40‐50% 50‐60% More

Frequency 0131111

0

0.5

1

1.5

2

2.5

3

3.5

Frequency

Minority / Hispanic Demographic Histogram

2.5

3

3.5

Linguistically Isolated Demographic Histogram

The national average = 6%

0‐2% 2‐4% 4‐6% 6‐8% 8‐10% 10‐12% More

Frequency 3310010

0

0.5

1

1.5

2

Frequency

26
The national average = 7%

0‐2% 2‐4% 4‐6% 6‐8% 8‐10% 10‐12% More

Frequency 0024020

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Frequency

Age 0 ‐ 4 Demographic Histogram

3.5

4

4.5

Age 65+ Demographic Histogram

The national average = 13%

0‐4% 4‐8% 8‐12% 12‐16% 16‐20% 20‐24% More

Frequency 0211400

0

0.5

1

1.5

2

2.5

3

Frequency

27
The national average = 15%

0‐4% 4‐8% 8‐12% 12‐16% 16‐20% 20‐24% More

Frequency 0113201

0

0.5

1

1.5

2

2.5

3

3.5

Frequency

No High School Diploma Demographic Histogram

3

3.5

Low Income Demographic Histogram

The national average = 32%

0‐10% 10‐20% 20‐30% 30‐40% 40‐50% 50‐60% More

Frequency 0032210

0

0.5

1

1.5

2

2.5

Frequency

28
The national average = 1%

0‐0.2% .2‐0.6% .6‐1% 1‐1.4% 1.4‐1.8% 1.8‐2.2% More

Frequency 1321001

0

0.5

1

1.5

2

2.5

3

3.5

Frequency

Native American Demographic Histogram

29