Navigation – Plan du site
2020
941

Schools near Toxics Release Inventory Sites: An Environmental Justice Study for Schoolchildren in Boston, MA

Écoles près des sites d'inventaire des substances toxiques : étude sur la justice environnementale pour des écoliers à Boston, MA
Escuelas próximas a sitios inventariados de emisiones y sustancias tóxicas: Un estudio de justicia ambiental en escuelas de Boston, MA
Yunliang Meng

Résumés

Des recherches antérieures sur la justice environnementale ont démontré une charge disproportionnée de risques environnementaux pour les résidents à faible revenu et appartenant à des minorités. Cette étude utilise des méthodes statistiques et d'analyse SIG pour examiner s'il y a, à Boston, des disparités dans la proximité des écoles publiques avec les sites de l'inventaire des rejets de substances toxiques selon les caractéristiques au niveau de l'école, telles que les statuts raciaux et socio-économiques des écoliers, les ressources éducatives et académiques réalisations. Les résultats de la recherche montrent que les écoles avec des pourcentages élevés de minorités (c'est-à-dire les écoliers noirs) et les écoliers économiquement défavorisés sont plus susceptibles d'être situées plus près des sites Toxics Release Inventory à Boston, ce qui indique l'existence d'un problème d'injustice environnementale dans la ville. De plus, les écoliers des écoles situées plus près des sites de l'inventaire des rejets de substances toxiques ont tendance à avoir de moins bons résultats scolaires. Cette recherche suggère qu'une attention particulière devrait être accordée à l'exposition inégale des écoles publiques aux sites dangereux de Boston et appelle les décideurs à dépenser des fonds rares pour promouvoir la justice environnementale. De tels changements pourraient avoir des effets positifs sur les résultats scolaires des écoliers.

Haut de page

Entrées d’index

Palabras claves :

justicia ambiental, SIG, escuela, Boston
Haut de page

Texte intégral

1. Introduction

1The U.S. Environmental Protection Agency (or U.S. EPA) defines environmental justice as the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies (U.S. EPA, 2019a); on the other hand, environmental injustice often refers to the disproportionate exposure of racial minorities and low income people to environmental hazards at both the individual and community level (Gerrard, 2001). In 1994, President Clinton signed Executive Order 12898 to direct the U.S. EPA and other federal agencies to integrate environmental justice considerations into their policies, programs and decision-making. The executive order required federal agencies to consider and address the ways in which their policies affect the health and environment of low-income communities and communities of color. Consequently, environmental justice became a legally operational notion in the US, as demonstrated by the recent “Plan EJ 2014” implemented by the U.S. EPA. Although environmental justice research has drawn much attention to whether socio-economically disadvantaged groups are disproportionately burdened by environmental hazards, only a few studies in the field have investigated how they relate to children (Pastor et al., 2002; Green et al., 2004; Gaffron and Niemeier, 2015).

2The impact of the environment on human health, especially children’s health, cannot be underestimated. According to WHO, environmental factors are responsible for 24 percent of the total burden of disease worldwide and 23 percent of all premature deaths (Prüss-Üstün and Corvalán, 2006). Among children aged between 0 and 14 years old, environmental factors account for as high as 36 percent of all deaths (Prüss-Üstün and Corvalán, 2006). Children consume more calories, drink more water, and breathe more air per pound of body weight than adults (Bearer, 1995). In addition, because children’s organ systems are in a constant state of development, “children absorb, metabolize, detoxify and excrete poisons differently from adults” (Center for Health, Environment, and Justice, 2001, p. 14). As a result, children are more vulnerable than adults to environmental hazards. Minority and low-income children in the U.S. are at even greater risk, since they are more likely to have limited or no access to healthcare services (Williams and Rucker, 2000; Riley, 2012). Therefore, there is a need for more empirical studies which focus on whether minority and socio-economically disadvantaged children are disproportionately burdened by environmental hazards.

3Previous environmental justice research shows that poor families and families of color are more likely to live in enumeration units (e.g., census tracts, block-groups) that are situated close to high-polluting industries, hazard waste facilities, and incinerators (Maantay et al., 2010; Brender et al., 2011; Johnson et al., 2016). This type of research has increased our understanding of race and class-based environmental inequality in many important ways, but it often ignores the fact that many people study and work outside of their home enumeration units during different times of the day. This weakness can be particularly problematic when examining environmental inequality issues for schoolchildren, since they spend a significant number of daytime hours in schools that can be located miles away from their homes. The National Center for Education Statistics (2018) shows that every school day, nearly 50.7 million school-aged children spend at least six hours at one of the approximately 99,000 public schools in the U.S. The average distance to school in U.S. is 4.4 miles, with elementary students having shorter average trip lengths (3.6 miles) than high school students (5.5 miles) (McDonald et al., 2011). However, some schools are located on or dangerously close to environmental hazards such as Toxic Release Inventory (TRI) sites that threaten the health and safety of schoolchildren as well as teachers, administrators, and others who work at schools (Salvesen et al., 2008). Consequently, school locations represent one of the most important spatial factors to be considered in environmental exposure analysis (Schwab et al., 1992). They can also be a key determinant of exposure to toxic pollutants for schoolchildren because they spend most of their daytime at school. As such, it is very important to examine the spatial distribution of public schools in relation to TRI sites and determine whether there are disparities in the proximity of schools to TRI sites based on the schoolchildren’s characteristics. Therefore, the primary objective of this study is to examine whether there are disparities in the proximity of schools to TRI sites based on school-level characteristics, such as schoolchildren’s racial and socioeconomic statuses, educational resources, and academic achievements.

4This paper is organized as follows. The second section provides a brief review of GIS-based methodologies utilized in environmental justice studies; schools, schoolchildren, and hazardous facilities; and the Toxics Release Inventory Program in the U.S. Section 3 outlines research methods, along with a description of the study area, data procedures, and analytic approach. Section 4 presents findings and discussion of the findings followed by conclusions in Section 5.

2. Literature Review

2.1. Environmental Justice Research using GIS Technology

5Environmental justice is a relatively new field for environmental advocacy. Based on the U.S. EPA definition, it is “fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies” (U.S. EPA, 2019b). It should be noted that fair treatment means that “no group of people, including a racial, ethnic or a socioeconomic group, should bear a disproportionate share of the negative environmental consequences from industrial, municipal and commercial operations or the execution of federal, state, local and tribal programs and policies” (U.S. EPA, 2019b). In Europe, environmental justice has a similar definition: that environmental benefits and burdens must be shared fairly (Laurent, 2010). The European Union is trying to promote environmental justice by emphasizing that all people have a right to a healthy environment. The Stockholm Declaration (Sohn, 1973), the 1987 Brundtland Commission’s Report (Burton, 1987), the Rio Declaration (United Nations, 1992), and Article 37 of the Charter of Fundamental Rights of the European Union (European Parliament, 2001), are the political documents and declarations that guide the European countries toward environmental justice.

6Despite the EPA’s awareness of what might be termed environmental injustice, as well as former President Clinton’s Executive Order 12898, which addresses the EPA’s responsibilities and strategies concerning environmental justice (U.S. EPA, 2019c), environmental injustice and related disparities continue to plague some areas in the United States (Wilson et al., 2012). By nature, environmental injustice issues are place-based and the key to addressing them is to examine whether socio-economically disadvantaged and racial minority dominated communities are disproportionately subjected to environmental hazards. This type of spatial analysis requires the examination of the spatial relationship between people’s socio-economic and racial statuses and points of pollution exposure at different scales.

7Geographic Information Systems (GIS) is a hardware and software system for storing, capturing, analyzing and displaying geographical data. It has been widely used in environmental justice fields because of its unique ability to store, manage, and analyze spatially referenced data sets and visually display complex spatial patterns including health risk, health outcomes, and pollutant emissions. There are two most common GIS approaches used in environmental justice research. One common GIS approach compares the socio-economic characteristics of the residential population in enumeration units (e.g., census tracts, block-groups) which contain environmental hazards (e.g., industrial toxic emissions, toxic waste sites) with the characteristics of the population living in enumeration units which do not contain such environmental hazards (Sheppard et al. 1999). However, there is a major weakness associated with this method: it assumes that the negative impacts caused by the environmental hazards do not go beyond the artificial man-made boundaries of enumeration units.

8The other common GIS approach generates buffers at pre-defined critical distances (i.e. 0.5 or 1 mile) around hazardous facilities (Sheppard et al. 1999). The buffers constructed for environmental justice studies are usually based on distances established as standards by environmental agencies (Bouwes and Hassur, 1997) or used most often by other researchers as the area of greatest potential impact from sources (Maantay, 2007; Mohai et al., 2009; Chakraborty et al., 2011). After buffers are created, three spatial query methods are frequently used as part of the GIS analyses to identify which enumeration units are within or outside the buffers: the “Intersect” method, the “Have their centroid within” method, and the “Containment” method (Sheppard et al. 1999). Each of these GIS-based selection methods involves a different approach to identifying the enumeration units encompassed by the buffers. The “Have their centroid in” method is the most accurate, but it requires target polygons to have regular geometric shapes. The Intersect method tends to largely overestimate the impacted population, whereas the containment technique tends to significantly underestimate the impacted population. After the GIS-based selections are made, differences in population characteristics within and outside the buffer zones are calculated to determine whether vulnerable populations are more likely to be located within buffer zones that are adjacent to hazardous sites (Glickman et al., 1995; Wilson et al., 2012). In addition, the distance measured between target polygons and TRI facilities is used in the study of environmental justice. It is hypothesized that exposure to toxic pollutants declines linearly and non-linearly (e.g. square root or natural logarithm) with increasing distance from the emitting source (Pollock and Vittas, 1995). The distance decay method seems better than buffering methods. However, the distance decay rate - the negative impacts of an environmental hazard decline with increasing distance is usually calculated based on assumptions rather than on precise knowledge (Chakraborty, 2018) and it is more frequently used in the study of exposure to air-based pollutants (Maheswaran and Elliott, 2003; Fisher et al., 2006).

2.2 Schools, Schoolchildren and Hazardous Facilities

9The location of schools is a very important factor that may influence family residential decisions (Taylor, 1981). Additionally, the physical environment associated with a school is likely to impact how people perceive the school and its associated neighborhood (Stretesky and Lynch, 2002). In some cases, negative environmental factors associated with a school can generate neighborhood deterioration by “destabilizing a neighborhood economically and socially” (Rosenbaum, 1996, p. 403). For example, the presence of a waste-to-energy facility (or abandoned toxic waste site) near a school will make that school less desirable, reduce property values of nearby housing (Ketkar, 1992; Nelson et al., 1997), create greater residential turnover (Pebley, 1998), and reduce the probability that desirable businesses will enter into the area.

10A few environmental justice studies that have examined schoolchildren or schools have found race and class inequities in the distribution of schoolchildren and schools around environmental hazards. For example, Stretesky and Lynch’s (2002) research examines the association between school segregation score (% of Black and Hispanic schoolchildren), economical disadvantage (% of schoolchildren eligible for free lunch) and school proximity to TRI sites among 84 public grade schools between the years of 1987 and 1999 in the Hillsborough County (Florida) School District. After controlling for the percentage of schoolchildren eligible for free lunch, the results demonstrate that predominantly Black and Hispanic schools are located closer to hazardous waste sites than are predominantly White schools. Similarly, Pastor et al.’s (2002) research combines GIS with multivariate statistics (OLS and logit regression) to compare enrollment and demographic information (i.e. race) for students who attend district schools with the spatial pattern of TRI sites. This study shows that minority schoolchildren are more likely than their White counterparts to attend schools that are close to hazardous waste facilities in the Los Angeles Unified School District (LAUSD). Green et al.’s research (2004) explores the characteristics (i.e. race/ethnicity, % of schoolchildren eligible for free or discounted meals, and % of English learners) of public schools (grades K–12) in California by the proximity to major roads as defined by a 150m buffer. The statistical analysis findings demonstrate that as the traffic exposure of schools increases, the percentage of both non-Hispanic black and Hispanic schoolchildren attending the schools also increases substantially. In their research, Gaffron and Niemeier (2015) test the correlations between the PM2.5 emission loads of a public-school location and the socio-economic characteristics (i.e. race/ethnicity, academic performance index, % of English learner, and % of schoolchildren eligible for discounted or free meals) of the public school (grades K-12) in the Sacramento, California Region. The results suggest that PM2.5 emissions from road traffic affecting a school site are significantly positively correlated with the following metrics: percent of Black, Hispanic, multi-ethnic, and subsidized meal eligible schoolchildren.

2.3 Toxic Release Inventory Sites

11After the leak of methyl isocyanate gas in Bhopal, India on December 2, 1984 and a chemical release at a plant in Institute, West Virginia in the following year, the general public was concerned about local preparedness for chemical emergencies and the availability of information on hazardous substances (U.S. EPA, 2013). In response to the concerns, the TRI Program was created in 1986 to track the management of certain toxic chemicals that are emitted by industrial facilities and pose a threat to human health and the environment. In general, chemicals covered by the TRI Program are those that could cause the following: a) cancer or other chronic human health effects, b) significant adverse acute human health effects or c) significant adverse environmental effects (U.S. EPA, 2019d). In total, the current TRI toxic chemical list contains 595 individually listed chemicals and 33 chemical categories. TRI facilities include companies and organizations from a wide range of industries (e.g. manufacturing, metal mining, electric power generation, chemical manufacturing, hazardous waste treatment etc.) that produce more than 25,000 pounds or handle more than 10,000 pounds of a listed toxic chemical (U.S. EPA, 2019d). All U.S. facilities that meet reporting criteria must submit TRI data to the EPA each year (U.S. EPA, 2019d).

12TRI sites threaten the surrounding natural environment or adversely affect people’s health. There are various health effects associated with the chemicals released by TRI sites, including cancer development and respiratory disorders, as well as neurological, reproductive, and developmental problems (Choi et al., 2006). Some toxic air pollutants such as mercury can penetrate soils or surface waters, where they can be later taken up by vegetation and eventually magnified up the food chain (Langlois et al., 2009). Since chemicals released by the TRI facilities can cause acute and chronic human health effects, and schoolchildren are more vulnerable to the chemicals, it is worthwhile to determine and compare the disparities in proximity of schools to TRI facilities by their school-level characteristics.

3. Methodology

3.1 Study Area

13The city of Boston is the capital of Massachusetts and, as New England’s economic and cultural hub, it is the unofficial capital of the region. Boston is located on Shawmut Peninsula on Boston Harbor, covering 48 square miles (See Figure 1). The city is home to roughly 694,583 people in 2018, making it the largest city in New England and the 24th largest city in the United States. The city is also the anchor of a substantially larger metropolitan area called Greater Boston, home to 4.5 million people and the tenth-largest metropolitan area in the country. According to the Census Bureau’s 2015 estimate, 53% of Boston’s population reported themselves as being part of a racial/ethnical minority group (see Table 1 for details). The City of Boston’s racial/ethnic minority population has increased by 9.5% since 2000, and by 12% since 1990. In addition, previous research shows that the Boston metropolitan region suffers from a persistently high level of racial segregation. For example, a recent study of segregation trends across 52 U.S. metropolitan areas between 1970 and 2010 illustrates that Boston is consistently among the set of hyper-segregated cities for Black residents - meaning that Black people were highly segregated on at least four of the five dimensions of population distribution (evenness, exposure, clustering, centralization, and concentration) used by the U.S. Census Bureau to measure racial and ethnic segregation within a given area (Massey and Tannen, 2015). According to Tucker’s (2019) findings, Black and Hispanic populations are highly segregated in the center north and southeast of the city, while predominantly White neighborhoods are located at the northeast, west and southwest edges of the city. Bloch et al. (2018) demonstrated that the census tracts with high level of poverty rate are clustered in the center north, south and southeast of the city. In other words, the spatial distribution of poverty patterns in Boston is largely correlated with race/ethnicity. A big metropolitan city – Boston was chosen as the study area due to the following reasons: 1) in the United States, disadvantaged populations are more likely to share more environmental burdens than their advantaged counterparts in metropolitan areas (Downey et al., 2008); 2) Boston has a large but diverse population with a history of racial segregation, 125 public schools, and 5 TRI facilities located within a relatively small geographic area (see Figure 1) ; 3) it is unknown whether spatial disparities exist in regards to proximity of public schools in Boston to TRI facilities by the school-level characteristics. 

Table 1. Race/Ethnicity Composition in Boston

Race/Ethnicity

2015 Population Estimates

Black

24.7 %

Native American

0.8 %

Asian

9.1 %

Hispanic

22.1 %

Non-Hispanic Whites

46.2 %

Two or more races

3.1 %

Figure 1. Study Area

Figure 1. Study Area

3.2 Data and Variables

14The data for this study came from the U.S. EPA and the BPS system. Data on TRI sites was collected for 2016 using the EPA’s TRI Explorer database (https://www.epa.gov/​toxics-release-inventory-tri-program). The TRI file consists of the data fields most frequently requested, including TRI facility name, address, latitude & longitude coordinates, chemical identification, classification information, on-site release quantities, publicly owned treatment works transfer quantities, off-site transfer quantities for release/disposal, further waste management, and summary pollution prevention quantities. In total, there are 5 TRI facilities located in the city of Boston (See Figure 1). Their locations were geocoded based on latitude & longitude coordinates using ArcMap 10.6 (Esri, 2018). In 2016, the 5 facilities (see Table 2 for details) managed a combined production-related waste of 354.3 thousand pounds, released 9.7 thousand pounds hazards to the air, and transferred 1.1 thousand pounds waste to other facilities.

Table 2. A Summary of TRI Facilities’ Name, Address, Established Year, Industry Type, and Major Pollutants

Facility Name and Address

Established Year

Industry Type

Major Pollutants

Soluteck Corp. 94 Shirley Street, Boston, MA

1961

Photographic film and chemical manufacturing

Hydroquinone

Massachusetts Bay Brewing Co, 306 Northern Ave, Boston, MA

1986

Beverages

Nitric acid

MATEP LLC, 474 Brookline Ave, Boston, MA

1906

Electric utilities

Ammonia

New Balance Athletics Inc., 145 Newton St, Brighton, MA

1906

Leather and footwear

Toluene diisocyanate

The Gillette LLC., 1 Gillette Park, Boston, MA

1901

Fabricated metals

Ammonia, Nickel, Chromium

15As the birthplace of public education in this nation, the Boston Public Schools (BPS) system is the oldest public-school system in America, founded in 1647. The city is also the home of the nation’s first public school, Boston Latin School, founded in 1635. The Mather School opened in 1639 as the nation’s first public elementary school, and English High School, the second public high school in the country, opened in 1821. In the school year 2016-2017, there were 125 schools in the Boston Public Schools network with a total enrollment of 55,843 children from pre-kindergarten to K12 (see Table 3).

Table 3. SY2016-2017 Schoolchildren Enrollment in BPS

Level

SY2017 Schoolchildren Enrollment

Schoolchildren in pre-kindergarten

3,006

Schoolchildren in kindergarten-grade 5

25,007

Schoolchildren in grades 6-8

10,668

Schoolchildren in grades 9-12

17,162

16The indices used to quantify the characteristics of BPS schools include race, economic status, class size, and student to teacher ratio. They have been chosen based on a review of previous literature in the environmental justice field (Stretesky and Lynch, 2002; Green et al., 2004; Pastor et al., 2006; Kweon et al., 2018). BPS characteristic data were collected for the year 2016, directly from the BPS website (https://www.bostonpublicschools.org/​domain/​175). The data includes each school’s pupils’ race composition (e.g. percent of Black, Hispanic, Asian, and White schoolchildren), class size, student to teacher ratio, economic status defined by the percent of schoolchildren coming from economically disadvantaged families, and academic achievement in English language arts, math, and science. The academic achievement is quantified by the Composite Performance Index (CPI) which is a number from 1-100 that represents the extent to which all schoolchildren are progressing toward proficiency in a given subject such as English language arts (ELA), math, or science. For example, when all schoolchildren in a school demonstrate proficiency on the Massachusetts Comprehensive Assessment System (MCAS) in math, the math CPI is 100 for the school. For reference purposes, schoolchildren demographics and school characteristics for BPS are summarized in Table 4. BPS location data in shapefile format (see Figure 2) was directly downloaded from the City of Boson’s website (https://data.boston.gov/​).

Table 4. SY 2016-2017 BPS Schoolchildren Demographics and School Characteristics

SY 2016-2017 BPS Schoolchildren Demographics and School Characteristics

Race:

Hispanic (%)

42%

Black (%)

35%

White (%)

14%

Asian (%)

9%

Economic Status:
Economically disadvantaged families (%)


70%

Average class size

13.4

Average student to teacher ratio

16.9

Academic performance:

Average English language art CPI score

56.7

Average math CPI score

50.9

Average science CPI score

52.8

17It should be noted that, according to National Center for Education statistics, 4,753 children enrolled in 18 private schools in Boston in the 2017 calendar year, which only accounts for 7.8% of the total school age children in the city. In addition, most private schools in Boston do not release the economic condition of pupils’ families nor do they participate in the student assessment system that is used by BPS, so the private school schoolchildren’s academic performance and economic conditions are not comparable with their counterparts in BPS. As a result, this study just focuses on the schools and schoolchildren in BPS.

Figure 2. Boston Public Schools School Year 2016-2017

Figure 2. Boston Public Schools School Year 2016-2017

(Website Source: https://www.bostonpublicschools.org/​domain/​175)

3.3 Analysis

18Data on schoolchildren’s racial background, class size, student to teacher ratio, percent of schoolchildren coming from economically disadvantaged families, and academic achievement in English language, arts, math, and science were compiled at the school levels and later joined with the school location shapefile. Then, the identification of which schools are proximate to the TRI sites is determined using a relatively straightforward buffering technique. The radius of circular buffers in environmental justice research has ranged from 100 yards (Sheppard et al., 1999) to 5 miles (Wilson et al., 2012). Distances of 0.5 and 1.0 mile from hazardous facilities have been adopted in this study for the following reasons: 1) they are the most widely used cut-off values in the field (Glickman, 1994; Chakraborty and Armstrong, 1997; Neumann et al., 1998; Bolin et al., 2000; Baden and Coursey, 2002; Maantay, 2007; Mohai et al., 2009; Chakraborty et al., 2011); 2) according to EPA internal evaluation, the mobility of chemicals is considered high or medium within 0.5 or 1 mile away from TRI sites when they are released on land (Bouwes and Hassur, 1997); and 3) a significantly increased disease (i.e. breast cancer, brain cancer, and asthma) rate was found for populations living within 0.5 or 1 mile away from TRI sites (Choi et al., 2006; Maantay, 2007; Acosta, 2016). Therefore, two critical distance buffers (0.5 and 1 mile) were generated around each TRI site in this study (see Figure 3). Later, the “Select by Location” function in the ArcGIS menu was used to identify which schools lie inside and outside the impacted area (labeled as 1 and 0 respectively). Only those schools spatially within the impacted area were identified as being within it.

Figure 3. Buffers around TRI Sites

Figure 3. Buffers around TRI Sites

19To compare the schoolchildren’s racial, economic, educational resource, and academic achievement characteristics for schools within the impacted area to those outside it, a standard, two-sample statistical testthe Mann-Whitney U test (Mann and Whitney, 1947) at a 0.05 significance level was conducted in SPSS 22. A non-parametric procedure Mann-Whitney U test was used in this research due to the lack of normality, equal variances, and independence which is common in socio-economic data. For each variable, the mean characteristic value for schools outside the impacted area was compared to the mean value for schools inside it, and the extent to which the two means differed was computed by SPSS 22 (IBM, 2017). The ten variables analyzed using the Mann-Whitney U test were: 1) percent of Black schoolchildren, 2) percent of Hispanic schoolchildren, 3) percent of Asian schoolchildren, 4) percent of White schoolchildren, 5) percent of schoolchildren coming from economically disadvantaged families, 6) class size, 7) student to teacher ratio, 8) English language art CPI score, 9) math CPI score, and 10) science CPI score.

4. Results and Discussion

20The number of public schools located within the 0.5-mile and 1-mile buffers is 6 (or 4.8%) and 38 (or 30.4%) respectively (see Table 5). The number of schoolchildren located within the 0.5-mile and 1-mile buffers are 3,550 (or 6%) and 18,650 (or 31.7%) respectively (see Table 5). It should be noted that almost 1 in 3 public schools in Boston is located within 1 mile of the TRI sites and nearly 1 in 3 schoolchildren attends a public school within the 1-mile buffer. In addition, the descriptive statistics of BPS schoolchildren’s demographics and school characteristics in BPS are shown in Table 6.

Table 5. Descriptive Statistics of Public-School Number and Schoolchildren Enrollment in Boston, MA

Buffer Distance

Public-School Number
(2016-2017)

Schoolchildren Enrollment
(2016-2017)

Inside
Buffer

Outside
Buffer

Inside
Buffer

Outside
Buffer

0.5 mile

6

119

3,550

55,293

1 mile

38

87

18,650

40,193

Table 6. Descriptive Statistics of BPS Schoolchildren’s Demographics and School Characteristics

Buffer Distance

Variables

Minimum

Maximum

Median

Standard Deviation

Inside
Buffer

Outside
Buffer

Inside
Buffer

Outside
Buffer

Inside
Buffer

Outside Buffer

Inside
Buffer

Outside Buffer






0.5 mile

Hispanic (%)

12.1

13.5

48.7

93.3

35.3

38.7

12.0

18.6

Black (%)

8.0

0.7

61.0

75.1

36.5

36.0

20.6

19.0

White (%)

1.4

0.5

46.9

58.6

18.1

7.4

19.4

14.1

Asian (%)

0.0

0.0

29.0

57.1

4.0

2.5

10.9

9.2

Economically disadvantaged (%)

40.2

20.4

62.8

84.5

50.8

60.3

7.4

13.5

Class size

12.0

5.9

25.3

29.0

18.5

17.6

4.5

4.1

Student to teacher ratio

8.9

2.7

20.2

29.0

13.1

13.6

4.0

3.5

English language art CPI score

54.4

28.0

100.0

100.0

57.7

52.0

23.1

27.4

Math CPI score

54.4

26.0

100.0

99.5

53.4

49.0

17.6

23.7

Science CPI score

48.8

35.0

89.5

93.6

71.5

53.7

14.9

20.5





1 mile

Hispanic (%)

12.1

17.5

75.5

93.3

37.4

39.0

15.1

21.0

Black (%)

4.1

0.7

72.8

75.1

37.8

33.1

15.6

21.7

White (%)

5.0

1.0

58.6

58.1

5.5

8.9

13.2

15.2

Asian (%)

0.0

0.0

57.1

38.9

2.5

2.2

11.1

6.8

Economically disadvantaged (%)

20.4

20.4

84.5

74.5

62.6

50.7

13.1

12.8

Class size

7.5

5.9

29.0

22.5

18.0

17.0

4.6

3.77

Student to teacher ratio

6.9

2.7

29.0

22.6

13.6

13.7

3.7

3.4

English language art CPI score

28.0

29.0

100.0

98.3

54.4

62.0

27.3

27.8

Math CPI score

26.0

32.0

100.0

95.3

53.0

56.5

23.6

23.6

Science CPI score

35.7

35.0

89.5

93.6

50.2

55.7

19.5

21.7

21The Mann Whitney U analysis results are shown in Table 7. In addition to reporting Mann Whitney U results in a two-tailed manner, it is necessary to compare the mean rank values of the variables inside and outside the buffers to determine whether justice or injustice was indicated in those instances where a significant difference between the two groups was found (p < 0.05). Given the definition of environmental justice adopted here, an injustice exposure is suggested when the proportion of minorities, the proportion of economically disadvantaged schoolchildren, class size, or student to teach ratio is statistically significantly higher within the impacted area than outside of it, or when schoolchildren’s CPI scores are statistically significantly lower. On the other hand, evidence of environmental justice is found when there is no statistically significant difference between schools within and outside the impacted area with respect to schoolchildren’s race, economic status, and academic performance, class size, and student to teacher ratio. As shown in Table 7, unjust exposure to TRI sites is found in Boston, MA. For the impacted area defined by a 1-mile buffer around TRI sites, the percentage of Black schoolchildren and the proportion of economically disadvantaged schoolchildren is statistically significantly higher (p < 0.05) within the impacted area than outside of it. These findings are consistent with previous research (i.e. Stretesky and Lynch, 2002; Green et al., 2004). Meanwhile, consistent with previous research (i.e. Kweon et al., 2018), English language art, math, and science CPI scores are statistically significantly lower (p < 0.05) within the area. There appear to be no statistically significant variations in exposure to TRI sites with the percentage of Hispanic schoolchildren, the percentage of White schoolchildren, the percentage of Asian schoolchildren, class size, or student to teacher ratio.

Table 7. Mann-Whitney U Analysis of BPS Schoolchildren’s Demographics and School Characteristics

Buffer Distance

Variables

Mean Rank
Inside Buffer

Mean Rank
Outside Buffer

Mann-Whitney U

2-tailed p value






0.5 mile

Hispanic (%)

47.26

50.58

282.5

0.727

Black (%)

65.92

66.53

374.5

0.969

White (%)

65.98

77.5

312

0.471

Asian (%)

74.42

66.12

330.5

0.604

Economically disadvantaged (%)

47.48

45.92

254.5

0.817

Class size

68.67

65.92

305

0.425

Student to teacher ratio

57.50

66.93

324

0.555

English language art CPI score

59.42

61.33

163

0.630

Math CPI score

59.40

61.75

160.5

0.828

Science CPI score

59.47

60.42

168.5

0.735





1 mile

Hispanic (%)

64.32

67.38

1703

0.677

Black (%)

73.42

63.70

1523

0.023

White (%)

59.72

69.24

1528.5

0.196

Asian (%)

73.13

63.82

1534

0.186

Economically disadvantaged (%)

79.63

61.19

1287

0.012

Class size

65.75

66.80

1757.5

0.886

Student to teacher ratio

59.47

69.34

1519

0.180

English language art CPI score

59.15

75.56

1345.5

0.032

Math CPI score

52.51

67.13

1290.5

0.020

Science CPI score

59.47

90.42

1268.5

0.035

22For the impacted area defined by a 0.5-mile buffer around TRI sites, there appears to be no statistically significant variations in exposure to TRI sites with all variables in the study. In other words, environmental injustice was not detected by using a 0.5-mile buffer around TRI sites. The inconsistent results obtained by using 0.5- and 1-mile buffers can be caused by the representation of TRI sites in this study and the conflicting results are consistent with Pastor et al.’s (2013) findings. While TRI facilities are represented as points with no area dimension in EPA’s database, such representation can distort facilities that cover a large area. In such instances, a 0.5-mile buffer drawn from the point representing the facility may underestimate the proximity of schools and schoolchildren in this study. As a result, an environmental injustice result is not discovered when a 0.5-mile buffer was applied but is valid when a 1-mile buffer is used. Therefore, this study calls for using multiple buffering methods in future environmental justice research when pollution sources are represented as points in the analysis.

23Across the nation, politicians, parents, and community leaders are repetitively emphasizing the need to improve the American public educational system in order to better prepare future generations for the ever-changing world. Special attention is often given to those disadvantaged schoolchildren who have been traditionally left behind (i.e. minority schoolchildren, schoolchildren from low income families, and English learners). While most of the critical interventions are focused on the issues such as low teaching quality, the absence of after-school programs, little parental engagement, and lack of study resources, this research suggests that special attention to environmental injustice issues associated with public school locations should be a key part of the mix.

24School locations are ultimately decided by local school boards. In general, such decisions are shaped by numerous factors, including the spatial distribution of current and future residential population, land availability and cost, and nearby infrastructure, as well as state and local planning policies and practices. In the U.S., twenty states have no policies of any kind affecting the siting of schools in relation to environmental hazards (Rhode Island Legal Services, 2006). Twenty-one states, including Massachusetts, have adopted policies that direct school officials to avoid siting schools on or near specified manmade or natural environmental hazards, or direct the school district to consider those hazards when selecting school sites (Rhode Island Legal Services, 2006). However, there is little quantitative research into whether state or city policies are enough to prevent schools, especially those with disproportionately higher percentage of disadvantaged schoolchildren, from being sited near environmental hazards.

25The Massachusetts government has been taking aggressive actions towards promoting environmental justice in recent years. In 2014, Executive Order 552 was issued, requiring the State Secretariats to take action in promoting environmental justice. In 2017, the Massachusetts government announced updates to the Executive Office of Energy and Environmental Affairs’(EEA) Environmental Justice Policy, which delivers the necessary resources to aggressively combat against environmental burdens unduly placed onto low income communities and communities of color. However, little attention was paid to the school locations and there was almost no discussion regarding this important issue. The patterns revealed in this research should encourage additional thought the need for strict environmental standards for future school construction in Boston. Building new schools or relocating schools in urban areas may necessarily involve “brownfield” lands or locations near pollution sources (Blume, 2000). The US EPA developed a Healthy School Environments Assessment Tool (U.S. EPA, 2006) that provides school districts with guidance for implementing effective environmental management systems in the context of health and safety issues facing new and existing school sites. This process would establish guidelines encouraging school districts to proceed with caution to ensure that children’s environmental health concerns are adequately addressed in siting, construction, and remediation projects (Pastor et al., 2004). In addition, any future construction or relocation plans should measure and seek to minimize disparities in environmental hazard distributions among minority schoolchildren in the city, particularly given the overall poorer health status of these populations (U.S. EPA, 1992) and the existing tendency of predominantly minority and poor schools to underperform academically (Pastor et al., 2004). The impact of the environmental injustice should also go beyond ‘who gets what?’ or, normatively, ‘Who ought to get what?’ (Wicks and Crompton, 1986). This research raises the concerns about the impact of environmental hazards on the educational achievement of schools with disproportionately high percentage of the disadvantaged schoolchildren. Previous research shows that, in some communities, parents have complained of decreased school performance among their children due to health effects associated with toxic pollution (Perera et al., 1999; Diette et al., 2000; Kaplan and Morris, 2000). This study confirms that schoolchildren in schools located closer to TRI sites are more likely to academically perform poorly. Consequently, this research calls for further in-depth research to better explain the cause behind the potential association between increased toxic exposures and diminished school academic achievement.

26Numerous limitations are associated with this study. First, this study is limited to Boston Massachusetts, making it challenging to generalize the empirical findings outside this area. In addition, the results provide insights regarding to spatial distribution of TRI sites in the city, so the injustice indications can not be applied to other polluting sites or amenities such as parks. Second, the measures of school performance used in this study are English language art, math, and science CPI scores. The standard test scores cannot capture all aspects of schoolchildren’s learning and academic performance. Third, schoolchildren’s demographic, socio-economic and academic performance data was collected or measured at the school level rather than an individual level. Such measurements are increasingly popular as parents and policymakers demand school-level accountability and for environmental condition which stick with the school (Pastor et al., 2004). However, the school level measurements may hide problems associated with individual schoolchildren and cause the ecological fallacy issue. Fourth, pupils enrolled in the private schools in Boston were not included in the study due to 1) the fact only 7.8% of the total school-age children are enrolled in private schools in Boston and 2) the economic condition of their families and student assessment results are not comparable with those in BPS. However, it would be beneficial to include them in future studies if the data comparison and consistency problem can be resolved, which may change the research results. Fifth, still, the study begs the question: how close is too close? In this study, 0.5- and 1-mile buffers were used to establish minimum setbacks for all 5 TRI sites. However, the use of the “containment” method is not without limitation. It creates abrupt or sudden value changes at the buffer border and assumes that adverse effects are limited within the buffer border and equal and uniform in all directions (isotropic) from the hazard. Additionally, more research should be done to identify the critical distance of exposure for each TRI site and how each individual TRI site affects nearby schoolchildren, since each site processes, transfers, and releases different amount of toxic materials each year.

5. Conclusions

27This paper has illustrated the utility of GIS as a means of visualizing and measuring levels of public schools’ exposure to TRI sites. As one of the few studies focusing on school locations in the field of environmental justice, this study’s findings contribute to the empirical literature of exposure disparities among schoolchildren by its focus on a major New England city: Boston. Although there are only five TRI sites located in Boston, they are not well distributed relative to the locations of public schools. Schools with disproportionately high percentages of minority schoolchildren (i.e. Black or Hispanic) and high percentages of economically disadvantaged schoolchildren are more likely to be located closer to TRI sites, indicating the existence of an environmental injustice issue in the city. In addition, environmental injustice is associated with low academic performance at the school level. These results can be incorporated into the larger dialogue related to the equitable siting of hazardous facilities. State and city regulations should limit clustering of TRI facilities close to schools with high percentages of minority and economically disadvantaged schoolchildren. In addition, state and city agencies can also improve enforcement of existing regulations to which hazardous facilities must follow. Many environmental factors are already considered when siting schools within Massachusetts, such as proximity to hazardous waste sites and stationary sources of toxic air contaminants (Rhode Island Legal Services, 2006). Our results suggest that policy makers, particularly those interested in environmental justice and children’s health, should recognize that spending scarce funds on promoting environmental justice could have positive effects on schoolchildren’s academic achievement and wellbeing.

Haut de page

Bibliographie

Acosta E.P., Barbosa J.S., Pérez E.S., Ortíz A.P., Matta J., 2016, Association between environmental carcinogen emissions reported to the Toxic Release Inventory and breast cancer in Puerto Rico, proceedings at 2016 National Training Conference.

Baden B.M. and Coursey D., 2002, "The locality of waste within the city of Chicago: a demographic, social, and economic analysis”, Resource and Energy Economics, Vol.24, No.1-2, 53-93.

Bearer C.F., 1995, "Environmental health hazards: How children are different from adults", The Future of Children, Vol.5, No.2, 11-26.

Bloch M., Ericson M. and Giratikanon T., 2019, Mapping poverty in America, retrieved from http://www.nytimes.com/newsgraphics/2014/01/05/poverty-map/index.html (last visited on July 6, 2019).

Blume H., 2000, "No vacancy: the school district’s space crunch is much worse than you know. And no one has a plan that will fix it", LA Weekly, Vol.9, 5-7.

Bolin B., Matranga E., Hackett E. J., Sadalla E. K., Pijawka K. D., Brewer D., Sicotte D., 2000, "Environmental equity in a sunbelt city: The spatial distribution of toxic hazards in Phoenix, Arizona", Environmental Hazards, Vol.2, No.1, 11-24.

Bouwes N.W., Hassur S.M., 1997, Toxics Release Inventory Relative Risk-Based Environmental Indicators Methodology. Washington, DC: U.S. Environmental Protection Agency.

Brender J., Maantay J., Chakraborty J., 2011, "Residential proximity to environmental hazards and adverse health outcomes", American Journal of Public Health, Vol.101, 37-52.

Burton I., 1987, "Report on reports: Our common future: The world commission on environment and development," Environment: Science and Policy for Sustainable Development, Vol 29, No.5, 25-29.

Center for Health, Environment, and Justice, 2001, Poisoned Schools: Invisible threats, Visible Actions. A Report of the Child Proofing Our Communities, retrieved from http://chej.org/wp-content/uploads/Poisoned-Schools-Invisible-Threats-Visible-Actions-REP-001.pdf (last visited on July 6, 2019).

Chakraborty J., Armstrong M. P., 1997, "Exploring the use of buffer analysis for the identification of impacted areas in environmental equity assessment", Cartography and Geographic Information Systems, Vol.24, No.3, 145-157.

Chakraborty J., Maantay J. A., Brender J. D., 2011, "Disproportionate proximity to environmental health hazards: Methods, models, and measurement", American Journal of Public Health, Vol.101, No.S1, 27-36.

Chakraborty J., 2018, "Spatial representation and estimation of environmental risk", in Holifield R., Chakraborty J., and Walker G. (editors), The Routledge Handbook of Environmental Justice, Routledge: New York, 175-189.

Choi H. S., Shim Y. K., Kaye W. E., Ryan P. B., 2006, "Potential residential exposure to toxics release inventory chemicals during pregnancy and childhood brain cancer", Environmental Health Perspectives, Vol.114, No.7, 1113-1118.

Diette G.B., Markson L., Skinner E. A., Nguyen T. J., Algatt-Bergstrom P., Wu A.W., 2000, "Nocturnal asthma in children affects school attendance, school performance, and parents’ work attendance", Archives of Pediatrics and Adolescent Medicine, Vol.154, No.7923- 929.

Downey L., Dubois S., Hawkins B., Walker M., 2008, "Environmental Inequality in Metropolitan America", Organization & Environment, Vol.21, No.3, 270-294.

ESRI, 2018, ArcMap 10.6, Redlands, California: Environmental Systems Research Institute, Inc.

European Parliament, 2001, The Charter of Fundamental Rights of the European Union, retrieved from http://www.europarl.europa.eu/charter/default_en.htm (last visited on July 6, 2019).

Fisher J.B., Kelly M., Romm J., 2006, "Scales of environmental justice: combining GIS and spatial analysis for air toxics in West Oakland, California," Health and Place, Vol.12, No.4, pp. 701-714.

Gaffron P. and Niemeier D., 2015, "School locations and traffic emissions - environmental (in)justice findings using a new screening method", International Journal of Environmental Research and Public Health, Vol.12, No.2, 2009-2025.

Gerrard M. B., 2001, "Reflections on environmental justice", Albany Law Review, Vol.65, No.2, 357-365.

Glickman T. S., 1994, "Measuring environmental equity with Geographical Information Systems", Renewable Resources Journal, Vol.12, No.3, 17-21.

Glickman T. S., Golding D., Hersh R., 1995, "GIS-Based environmental equity analysis - A case study of TRI facilities in the Pittsburgh area." In Beroggi G.E.G.,Wallace W.A. (eds), Computer Supported Risk Management, Netherlands: Kluwer Academic 95-114.

Green R. S., Smorodinsky S., Kim J. J., McLaughlin R., Ostro B., 2004, "Proximity of California public schools to busy roads", Environmental Health Perspectives, Vol.112, No.1, 61-66.

IBM, 2017, SPSS Statistics 22, retrieved from https://www.ibm.com/products/spss-statistics (last visited on July 6, 2019).

Johnson R., Ramsey-White K., Fuller C.H., 2016, "Socio-demographic differences in Toxic Release Inventory siting and emissions in Metro Atlanta", International Journal of Environmental Research and Public Health, Vol.13, No.8, 747-759.

Kaplan S. Morris J., 2000, "Kids at risk: chemicals in the environment come under scrutiny as the number of childhood learning problems soars," US News and World Report, Vol.1, A11.

Ketkar K., 1992, "Hazardous waste sites and property values in the State of New Jersey", Applied Economics, Vol.24, No.6, 647-659.

Kweon B.S., Mohai P., Lee S., Sametshaw A.M., 2018, "Proximity of public schools to major highways and industrial facilities, and students’ school performance and health hazards", Environment and Planning B, Vol 45, No.2, pp.312-329.

Langlois P. H., Brender J. D., Suarez L., Zhan F. B., Mistry J. H., Scheuerle A., and Moody K., 2009, "Maternal residential proximity to waste sites and industrial facilities and conotruncal heart defects in offspring", Paediatric and perinatal epidemiology, Vol.23, No.4, 321-331.

Laurent E., 2010, "Environmental justice and environmental inequalities: A European perspective," ETUI Policy Brief, European Social Policy, retrieved from https://www.ofce.sciences-po.fr/pdf/dtravail/WP2010-05.pdf (last visited on July 6, 2019).

Maantay J. A., 2007, "Asthma and air pollution in the Bronx: methodological and data considerations in using GIS for environmental justice and health research", Health and Place, Vol.13, No.1, 32-56.

Maantay J., Chakraborty J., and Brender J., 2010, Proximity to Environmental Hazards: Environmental Justice and Adverse Health Outcomes, Environmental Protection Agency, Washington, DC.

Maheswaran R. and Elliott P., 2003, "Stroke mortality associated with living near main roads in England and wales: a geographical study," Stroke," Vol.34, No.12 pp.2776-2780.

Mann H. B. and Whitney D.R., 1947, "On a test of whether one of two random variables is stochastically larger than the other", Annals of Mathematical Statistics, Vol.18, No.1, 50-60.

Massey D.S. and Tannen J., 2015, "A research note on trends in Black hyper-segregation," Demography, Vol.52, No.3, 1025-34

McDonald N. C., Brown A. L., Marchetti L. M., Pedroso M. S., 2011, "U.S. school travel, 2009 an assessment of trends", American Journal of Preventive Medicine, Vol.41, No.2, 146-51.

Mohai P., Lantz P. M., Morenoff J. and House J. S., 2009, "Mero RP. Racial and socioeconomic disparities in residential proximity to polluting industrial facilities: evidence from the Americans’ Changing Lives Study", American Journal of Public Health, Vol.99, No.S3, 649-655.

National Center for Education Statistics, 2018, Fast facts, retrieved from https://nces.ed.gov/fastfacts/display.asp?id=372 (last visited on July 6, 2019).

Nelson A., Genereux J., and Genereux M., 1997, "Price effects of landfills on different house value strata", Journal of Urban Planning and Development, Vol.123, No.3, 59-67.

Neumann C. M., Forman D. L. and Rothlein J. E., 1998, "Hazard screening of chemical releases and environmental equity analysis of populations proximate to toxic release inventory facilities in Oregon", Environmental Health Perspectives, Vol.106, No.4, 217-226.

Pastor M., Sadd J., Morello-Frosch R., 2002, "Who's minding the kids? Pollution, public schools, and environmental justice in Los Angeles", Social Science Quarterly, Vol.83, No.1, 263-280.

Pastor M., Sadd J. L., and Morello-Frosch R., 2004, "Reading, writing, and toxics: Children’s health, academic performance, and environmental justice in Los Angeles," Environment and Planning C: Government and Policy, Vol.22, No.2, 271-290.

Pastor M., R. Morello-Frosch, Sadd J.L., 2006, "Breathless: Schools, Air Toxics, and Environmental Justice in California", Policy Studies Journal, Vol.34, No.3, pp. 337-362.

Pastor M., Morello-Frosch R., Sadd J., and Scoggins J., 2013, "Risky Business: Cap-andtrade, Public Health, and Environmental Justice," in Boone C.G. and Fraigkias M. (editors), Urbanization and Sustainability, Springer: Netherlands, pp. 75–94.

Pebley A., 1998, "Demography and the environment", Demography, Vol.35, No.4, 377-389.

Perera F. P., Jedrychowski W., Rauh V., Whyatt R. M., 1999, "Molecular epidemiological research on the effects of environmental pollutants on the fetus", Environmental Health Perspectives, Vol.107, No.3, 451-460.

Pollock P.H. and Vittas M.E. 1995, "Who bears the burden of environmental pollution? race, ethnicity, and environmental equity in Florida", Social Science Quarterly, Vol.76 No.2, pp. 294-310.

Prüss-Üstün A. and Corvalán C., 2006, Preventing Disease through Healthy Environments: Towards an Estimate of the Environmental Burden of Disease. Geneva: World Health Organization.

Riley W. J., 2012, "Health disparities: Gaps in access, quality and affordability of medical care", Transactions of the American Clinical and Climatological Association, Vol.123, 167-174.

Rhode Island Legal Services, 2006, Not in My Schoolyard: Avoiding Environmental Hazards at School Through Improved School Site Selection Policies, retrieved from https://www.nylpi.org/wp-content/uploads/bsk-pdf-manager/49_EJ_-_NOT_IN_MY_SCHOOLYARD_-_IMPROVING_SITE_SELECTION_PROCESS.PDF (last visited on July 6, 2019).

Rosenbaum E., 1996, "Racial/ethnic differences in home ownership and housing quality, 1991," Social Problems, Vol.43, No.4, 403-426.

Salvesen D., Zambito P., Hamstead Z., and Wilson B., 2008, Safe Schools: Identifying Environmental Threats to Children Attending Public Schools in North Carolina. The Center for Sustainable Community Design, Institute for the Environment University of North Carolina at Chapel Hill.

Schwab M., McDermott A., Spengler J. D., 1992, "Using longitudinal data to better understand children’s activity patterns in an exposure context: data from the Kanaway County health study", Environment International, Vol.18, No.2, 173-189.

Sheppard E., Leitner H., McMaster R. B., and Hongguo T., 1999, "GIS based measures of environmental equity: exploring their sensitivity and significance", Journal of Exposure Analysis and Environmental Epidemiology, Vol.9, No.1, 18-28.

Sohn L.B., 1973, "The Stockholm Declaration on the Human Environment," Harvard International Law Journal, Vol.14, 423-424.

Taylor G., 1981, "Racial preferences, housing segregation, and the causes of school segregation: Recent evidence from a social survey used in civil litigation." Review of Public Data Use Vol 9, 267-282.

Tucker R., 2019, Examining Racial Segregation in Boston at Different Geographic Scales, retrieved from https://www.northeastern.edu/csshresearch/bostonarearesearchinitiative/2019/02/08/examining-racial-segregation-in-boston-at-different-geographic-scales/ (last visited on July 6, 2019).

United Nations, 1992, The Rio Declaration on Environment and Development, retrieved from https://legal.un.org/avl/pdf/ha/dunche/rio_ph_e.pdf (last visited on July 6, 2019).

U.S. Environmental Protection Agency, 2006, Healthy School Environments Assessment Tool, retrieved from http://www.epa.gov/schools/healthyseat/index.html (last visited on July 6, 2019).

U.S. Environmental Protection Agency, 2013, The Emergency Planning and Community Right-to-Know Act, retrieved from https://www.epa.gov/sites/production/files/2013-08/documents/epcra_fact_sheet.pdf (last visited on July 6, 2019).

U.S. Environmental Protection Agency, 2019a, EPA Environmental Justice Research, retrieved from https://www.epa.gov/healthresearch/epa-environmental-justice-research (last visited on July 6, 2019).

U.S. Environmental Protection Agency, 2019b, EJ 2020 Glossary, retrieved from https://www.epa.gov/environmentaljustice/ej-2020-glossary (last visited on July 6, 2019).

U.S. Environmental Protection Agency, 2019c, Summary of Executive Order 12898 - Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations, retrieved from https://www.epa.gov/laws-regulations/summary-executive-order-12898-federal-actions-address-environmental-justice (last visited on July 6, 2019).

U.S. Environmental Protection Agency, 2019d, Toxics Release Inventory (TRI) Program: TRI-Listed Chemicals, retrieved from https://www.epa.gov/toxics-release-inventory-tri-program/tri-listed-chemicals (last visited on July 6, 2019).

Wicks B. E. and Crompton, J. L., 1986, "Citizen and administrator perspectives of equity in the delivery of park services", Leisure Sciences, Vol.8, No.4, 341-365.

Williams D. R. and Rucker T.D., 2000, "Understanding and addressing racial disparities in health care," Health Care Financing Review, Vol.21, No.4, 75-90.

Wilson S. M., Fraser-Rahim H., Williams E., Zhang H., Rice L., Svendsen E., and Abara W. 2012, "Assessment of the distribution of toxic release inventory facilities in Metropolitan Charleston: An environmental justice case study", American Journal of Public Health, Vol.102, No.10, 1974-1980.

Haut de page

Table des illustrations

Titre Figure 1. Study Area
URL http://journals.openedition.org/cybergeo/docannexe/image/34682/img-1.png
Fichier image/png, 387k
Titre Figure 2. Boston Public Schools School Year 2016-2017
Crédits (Website Source: https://www.bostonpublicschools.org/​domain/​175)
URL http://journals.openedition.org/cybergeo/docannexe/image/34682/img-2.jpg
Fichier image/jpeg, 312k
Titre Figure 3. Buffers around TRI Sites
URL http://journals.openedition.org/cybergeo/docannexe/image/34682/img-3.png
Fichier image/png, 540k
Haut de page

Pour citer cet article

Référence électronique

Yunliang Meng, « Schools near Toxics Release Inventory Sites: An Environmental Justice Study for Schoolchildren in Boston, MA », Cybergeo : European Journal of Geography [En ligne], Environnement, Nature, Paysage, document 941, mis en ligne le 29 avril 2020, consulté le 09 juillet 2020. URL : http://journals.openedition.org/cybergeo/34682 ; DOI : https://doi.org/10.4000/cybergeo.34682

Haut de page

Auteur

Yunliang Meng

Associate Professor, Department of Geography, Central Connecticut State University, 1615 Stanley Street, New Britain, CT, USA 06089. United States
Email: mengy@ccsu.edu

Articles du même auteur

Haut de page

Droits d’auteur

Licence Creative Commons
La revue Cybergeo est mise à disposition selon les termes de la Licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 3.0 non transposé.

Haut de page