Environmental Justice Screening Research Articles

Environmental health and justice screening tools: a critical examination and path forward

Environmental health and justice screening tools: a critical examination and path forward

Assessing the 2023 Canadian wildfire smoke impact in Northeastern US: Air quality, exposure and environmental justice

The Canadian wildfires in June 2023 significantly impacted the northeastern United States, particularly in terms of worsened air pollution and environmental justice concerns. While advancements have been made in low-cost sensor deployments and satellite observations of atmospheric composition, integrating dynamic human mobility with wildfire PM2.5 exposure to fully understand the environmental justice implications remains underinvestigated. This study aims to enhance the accuracy of estimating ground-level fine particulate matter (PM2.5) concentrations by fusing chemical transport model outputs with empirical observations, estimating exposures using human mobility data, and evaluating the impact of environmental justice. Employing a novel data fusion technique, the study combines the Weather Research and Forecasting model with Chemistry (WRF-Chem) outputs and surface PM2.5 measurements, providing a more accurate estimation of PM2.5 distribution. The study addresses the gap in traditional exposure assessments by incorporating human mobility data and further investigates the spatial correlation of PM2.5 levels with various environmental and demographic factors from the US Environmental Protection Agency (EPA) Environmental Justice Screening and Mapping Tool (EJScreen). Results reveal that despite reduced mobility during high PM2.5 levels from wildfire smoke, exposure for both residents and individuals on the move remains high. Regions already burdened with high environmental pollution levels face amplified PM2.5 effects from wildfire smoke. Furthermore, we observed mixed correlations between PM2.5 concentrations and various demographic and socioeconomic factors, indicating complex exposure patterns across communities. Urban areas, in particular, experience persistent high exposure, while significant correlations in rural areas with EJScreen factors highlight the unique vulnerabilities of these populations to smoke exposure. These results advocate for a comprehensive approach to environmental health that leverages advanced models, integrates human mobility data, and addresses socio-demographic disparities, contributing to the development of equitable strategies against the growing threat of wildfires.

Health Impacts of Environmental Inequalities in Redlined Areas

Chicago has a long history of redlining, a discriminatory housing process that has led to segregation in Chicago up to the modern day. This practice marginalized people of color and created a significant and still prevalent wealth gap between redlined and non-redlined communities. It is well known that poorer communities in Chicago are victims of negative environmental factors, such as industrial corridor proximity, and higher air pollution levels. There have been studies done on these things, but there are many overlapping factors that have led to Chicago’s prevalent inequality, creating a complex problem.
 Our study aimed to tackle some of this complexity and to analyze how various environmental indicators impacted the health outcomes of people based on the HOLC (Home Owners’ Loan Corporation) grade they lived in. We conducted a literature review to see which health impacts were tied to which significant environmental indicators and designed our technical portion to account for the complexity and inter-relation of the data, but we wanted to do more with the communities as well, rather than just analyzing data. A major part of our project was inspired by the work of Phillip Boda of UIC in regards to designing research centered around communities rather than around data. 
 We came to the conclusion that the scope of our project was larger and more complex than any two people sufficiently cover and decided to design a coding tool alongside our analysis that will allow for communities to both interpret our findings and run their own analyses in the ways they deem most useful. We hope that this can both provide communities with a more thorough understanding of the complex relations between the environment and health. We also hope to provide a tool that can be more useful than existing resources we’ve used and found to have issues, such as the EPA’s Environmental Justice Screening tool (EJScreen).
 We found that higher percentages of greenlined and bluelined areas had little significance to negative health outcomes, or lowered risk, while higher percentages of red and yellowlined areas had greater significance to the model, increasing risk of negative health outcomes with increasing area. The impact of environmental indicators on our model varied depending on the health impact being analyzed, but in the case of every health impact, there was a semi-linear positive trend when a model was created taking into account all of the independent variables and their coefficients.

Building a vision for more effective equity indices and planning tools

Recent years have seen a proliferation of equity indices and environmental justice screening tools to support more just environmental planning processes that attempt to quantify the concept of equity. While the equity index framework has proven important to advance the conversation around environmental equity and connect need to investments, we are concerned that these tools do not adequately address the intersectional nature of environmental justice concerns, effectively incorporate local knowledge on the lived experience of residents, or provide an actionable set of next steps to be taken. We see opportunities to rethink and expand on the equity index model to address issues of climate justice and preparedness through the development of Planning for Resilience and Equity through Accessible Community Technology (PREACT), a multipurpose and multi-scalar climate preparedness and neighborhood planning software application informed by both community need and community assets. This perspective article will discuss the theoretical and practical importance of adding these perspectives into screening tools and will describe our research in Philadelphia, PA aimed at understanding these challenges and developing a more inclusive and community-responsive methodology for effective tool development.

Chemical explosion, COVID-19, and environmental justice: Insights from low-cost air quality sensors

ObjectivesTo examine the impact of the Intercontinental Terminals Company (ITC) fire and COVID-19 on airborne particulate matter (PM) concentrations and the PM disproportionally affecting communities in Houston using low-cost sensors. MethodsWe compared measurements from a network of low-cost sensors with a separate network of monitors from the Environmental Protection Agency (EPA) in the Houston metropolitan area from Mar 18, 2019, to Dec 31, 2020. Further, we examined the associations between neighborhood-level sociodemographic status and air pollution patterns by linking the low-cost sensor data to EPA environmental justice screening and mapping systems. FindingsWe found increased PM levels during ITC fire and pre-COVID-19, and lower PM levels after the COVID-19 lockdown, comparable to observations from the regulatory monitors, with higher variations and a greater number of locations with high PM levels detected. In addition, the environmental justice analysis showed positive associations between higher PM levels and the percentage of minority, low-income population, and demographic index. ImplicationOur study indicates that low-cost sensors provide pollutant measures with higher spatial variations and a better ability to identify hot spots and high peak concentrations. These advantages provide critical information for disaster response and environmental justice studies. SynopsisWe used measurements from a low-cost sensor network for air pollution monitoring and environmental justice analysis to examine the impact of anthropogenic and natural disasters.

MD EJSCREEN v2.0: Visualizing Overburdening of Environmental Justice Issues Using the Updated Maryland Environmental Justice Screening Tool

MD EJSCREEN v2.0: Visualizing Overburdening of Environmental Justice Issues Using the Updated Maryland Environmental Justice Screening Tool

Intersections of disadvantaged communities and renewable energy potential: Data set and analysis to inform equitable investment prioritization in the United States

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. We sought to identify areas where disadvantaged community indicators and high generation potential from cost-effective renewable energy opportunities intersect and deployment could lead to economic development and job creation. Through a geospatial intersection of energy burden, environmental hazard, and sociodemographic data with technical generation potential and the levelized cost of energy for multiple renewable energy technologies, we calculated county-level correlations and identified trends across disadvantaged community indicators and renewable energy deployment potential. Data sources and tools included the Low-Income Energy Affordability Data (LEAD) tool, the Environmental Justice Screening and Mapping (EJSCREEN) tool, the State and Local Planning for Energy (SLOPE) platform, and the Renewable Energy Integration and Optimization (REopt®) model. Metrics include levelized costs and generation potential for utility-scale photovoltaics (PV), rooftop PV (residential and commercial), distributed PV plus storage, land-based wind, geothermal, and hydropower development. This research and the associated county-level data set are intended to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantaged communities.

Revealing the racial and spatial disparity in pediatric asthma: A Kansas City case study

Black and other socially disadvantaged children are disproportionately burdened by high rates of pediatric asthma. Intraurban variation in environmental risk factors and limited access to high-resolution health data make it difficult to identify vulnerable patients, communities, or the immediate exposures that may contribute to pediatric asthma exacerbation. This article presents a novel, interdisciplinary health disparities research and intervention strategy applied to the problem of pediatric asthma in Kansas City. First, address-level electronic health records from a major children's hospital in the Kansas City region are used to map the distribution of asthma encounters in 2012 at a high spatial resolution. Census tract Environmental Justice Screening Method (EJSM) indicators are then developed to scan for patterns in both the population health risks and vulnerabilities that may contribute to the burden of asthma in different communities. A Bayesian Profile Regression cluster analysis is used to systematically explore the complex relationships between census tract EJSM indicators and pediatric asthma incidence rates, helping to identify population characteristics and risk factors associated with both high and low rates of pediatric asthma throughout the region. The EJSM scanning exercise and BPR analysis demonstrate that each community faces a distinct set of risks and vulnerabilities that can contribute to the rate of acute pediatric asthma acute care encounters, providing targets for research and intervention. It is clear, however, that different forms of social disadvantage are driving high rates of pediatric asthma, which is closely tied to uneven development patterns and racial residential segregation. The results provide a starting point for designing place-based health disparities research and intervention strategies catered to the unique needs of vulnerable patients and communities; disparities-focused research and intervention strategies that leverage local knowledge and resources through community-based practices.

Identifying predictors of cancer prevalence at the neighborhood level in the United States: A Bayesian machine learning approach

BACKGROUND AND AIM: Cancer is the second leading cause of death in the United States (US). Individual- level factors including utilization of prevention, health behaviors, environmental exposure, and sociodemographic measures have been linked to individual-level cancer risks. However, risk factors of cancer at the neighborhood level remain understudied. To fill this research gap, we identify and rank important predictors of cancer prevalence at the neighborhood level in the US. METHODS: We developed a new neighborhood dataset by combining data from the Population Level Analysis and Community Estimates (PLACES), a dataset with population health data across all the US census tracts (n=72,337), with environmental exposure data from the Environmental Justice Screening database and sociodemographic factors from the American Community Survey. Our outcome of interest was tract-level adult cancer prevalence. We included 23 tract-level explanatory variables, including unhealthy behaviors (e.g., smoking, no leisure-time physical activity, drinking), prevention measures (e.g., cholesterol screening), environmental exposures (e.g., air toxics, lead paint), and sociodemographic factors (e.g., racial and ethnic composition, poverty, age 65 years and over). We used Bayesian additive regression trees (BART) to identify the most important predictors of cancer prevalence. RESULTS:The median prevalence of adults diagnosed with cancer was 6.7% (interquartile range: 5.4%-7.7%) across US census tracts. Based on local threshold criteria, we identified the five most important predictors of cancer prevalence: percentage of adults 65 years old or over, prevalence of routine checkup, percentage of non-Hispanic white, percentage of housing built before 1960, and percentage of individuals below the lower poverty level. CONCLUSIONS:Using an integrated neighborhood dataset with fine geographic resolution and a machine learning approach, we identified several important predictors of cancer prevalence at the neighborhood level in the US. The results may inform public health practitioners and policymakers to prioritize the improvement of environmental and neighborhood factors in reducing cancer burden. KEYWORDS: Cancer, Neighborhood-level analysis, Environmental exposures, Big data, Bayesian additive regression trees

Association of Air Pollutant Exposure and Sinonasal Histopathology Findings in Chronic Rhinosinusitis.

Ambient air pollution is well known to cause inflammatory change in respiratory epithelium and is associated with exacerbations of inflammatory conditions such as asthma and chronic obstructive pulmonary disease. However, limited work has been done on the impact of air pollution on pathogenesis of chronic rhinosinusitis and there are no reports in the literature of how pollutant exposure may impact sinonasal histopathology in patients with chronic rhinosinusitis. This study aims to identify associations between certain histopathologic characteristics seen in sinus tissue of patients with chronic rhinosinusitis (CRS) and levels of particulate air pollution (PM2.5) and ground-level ozone in their place of residence. A structured histopathology report was created to characterize the tissues of CRS patients undergoing sinus surgery. An estimate for each patient's exposure to air pollutants including small particulate matter (PM2.5) and ground-level ozone was obtained using the Environmental Protection Agency's (EPA) Environmental Justice Screening and Mapping Tool (EJSCREEN). Mean pollutant exposures for patients whose tissues exhibited varying histopathologic features were compared using logistic regression models. Data from 291 CRS patients were analyzed. Higher degree of inflammation was significantly associated with increased ozone exposure (p = 0.031). Amongst the patients with CRSwNP (n=131), presence of eosinophilic aggregates (p = 0.018) and Charcot-Leyden crystals (p = 0.036) was associated with increased ozone exposure. Exposure to ambient air pollutants may contribute to pathogenesis of CRS. Increasing ozone exposure was linked to both higher tissue inflammation and presence of eosinophilic aggregates and Charcot-Leyden crystals in CRSwNP patients.

A mixtures approach to assessing relationships between environmental and social determinants of health and neonatal size and body composition in the Healthy Start cohort

Background: Previous studies have identified neighborhood-level environmental and social exposures associated with birth weight, but no studies have quantified the effects of simultaneous exposures across these domains. Here we used Bayesian Kernel Machine Regression (BKMR) to estimate the independent and joint effects of environmental and social exposures on birth weight in the Healthy Start cohort in Denver, CO.Methods: The initial selection of exposures was guided by an environmental justice screening tool developed for California. Data on neighborhood-level environmental (e.g., fine particulate matter, traffic counts, impervious surfaces) and social (e.g., hospitalization rates, educational attainment, and crime rates) exposures were assigned to pregnant mothers based on their census tract at enrollment. Birth weight was abstracted from medical records. We used a two-step approach to fit our model. First, we used a Bayesian nonparametric variable selection model to reduce the number of candidate exposures. Second, we fit a BKMR model with the exposures, interaction terms for all exposure pairs, and individual-level covariates.Results: We had complete data on birth weight, exposures, and covariates for 792 infants. Out of 19 exposures originally considered, the non-parametric Bayesian model selected 9 (3 environmental and 6 social). BKMR results showed a mixture with all exposures above the median was associated with decreased birth weight. The mixture components selected most frequently by the model were respiratory hospitalization rate and the percentage of households in poverty. Respiratory hospitalization rates had the greatest effect on birth weight (β = -0.03, SE = 0.06) when all other exposures were held at the 75th percentile. For some exposures (e.g., fine particulate matter), relationships with birth weight appeared non-linear.Conclusions: We identified joint effects of environmental and social exposures on birth weight. Results suggest that analyses within one domain may limit the identification of important interactions that are associated with neonatal outcomes.

The Demographics of Citizen Science Participation and Its Implications for Data Quality and Environmental Justice

Citizen science programs have been growing in popularity in recent years and can provide various benefits to participants and their communities. We sought to determine whether these benefits are equitably accessible to all by examining potential demographic disparities in participation in our statewide citizen science program. We first explored whether Illinois RiverWatch participants are demographically and geographically representative of Illinois’s population. We conducted online surveys of current participants to gather information on demographics, including race, income, and education level, and then compared these responses with the population of Illinois as a whole using (US) census data. We also examined whether RiverWatch stream sites are disproportionately located in areas of low environmental justice concern, as determined by the US Environmental Protection Agency’s (US EPA’s) Environmental Justice screening tool. We used GIS to map all stream sites (n = 936) and determined whether they were located in areas of high or low environmental justice concern. The survey data indicate that RiverWatch participants are disproportionately white, highly educated, and affluent compared with the Illinois general population. In addition, we found RiverWatch sites tend to be located in areas of lower environmental justice concern, and that areas of high environmental justice concern are underrepresented. We discuss how disparities in demographics of participants can not only indicate need for improvement in the accessibility of a citizen science program but could also harm the quality of the river-monitoring data collected, as it leads to sampling that is not accurately representative.

Ranking sociodemographic, health behavior, prevention, and environmental factors in predicting neighborhood cardiovascular health: A Bayesian machine learning approach

Cardiovascular disease is the leading cause of death in the United States. While abundant research has been conducted to identify risk factors for cardiovascular disease at the individual level, less is known about factors that may influence population cardiovascular health outcomes at the neighborhood level. The purpose of this study is to use Bayesian Additive Regression Trees, a state-of-the-art machine learning approach, to rank sociodemographic, health behavior, prevention, and environmental factors in predicting neighborhood cardiovascular health. We created a new neighborhood health dataset by combining three datasets at the census tract level, including the 500 Cities Data from the Centers for Disease Control and Prevention, the 2011–2015 American Community Survey 5-Year Estimates from the Census Bureau, and the 2015–2016 Environmental Justice Screening database from the Environmental Protection Agency in the United States. Results showed that neighborhood behavioral factors such as the proportions of people who are obese, do not have leisure-time physical activity, and have binge drinking emerged as top five predictors for most of the neighborhood cardiovascular health outcomes. Findings from this study would allow public health researchers and policymakers to prioritize community-based interventions and efficiently use limited resources to improve neighborhood cardiovascular health.

Environmental justice screening tools: Implications for nursing.

Environmental justice (EJ) means socially and economically disadvantaged communities and citizens are entitled to an equally clean environment and resources to enjoy. EJ communities are mostly located close to polluters such as Superfund sites, coal-fired power plants and landfills and are more likely to be exposed to higher levels of environmental hazards than the U.S. population on average. It is often a difficult task to identify EJ communities and the environmental hazards in the communities without using specific EJ screening tools. Therefore, the goal of this manuscript is to introduce public health nurses to map-based EJ screening tools. This will help public health nurses to identify EJ communities and take appropriate actions. Public Health Nurses can also use other relevant web sites, such as the U.S. Environmental Protection Agency (EPA), National Institute of Environmental Health Sciences (NIEHS) and Toxicology Data Network (TOXNET) as resources concerning potential health effects of pollutants. Using the provided tools in this paper, nurses should be able to identify EJ communities and be united to recognize barriers of EJ communities and become promoters for EJ advocacy in practice, leadership and education.

Utilization of the Maryland Environmental Justice Screening Tool: A Bladensburg, Maryland Case Study

Maryland residents’ knowledge of environmental hazards and their health effects is limited, partly due to the absence of tools to map and visualize distribution of risk factors across sociodemographic groups. This study discusses the development of the Maryland EJSCREEN (MD EJSCREEN) tool by the National Center for Smart Growth in partnership with faculty at the University of Maryland School of Public Health. The tool assesses environmental justice risks similarly to the U.S. Environmental Protection Agency’s (USEPA) EJSCREEN tool and California’s tool, CalEnviroScreen 3.0. We discuss the architecture and functionality of the tool, indicators of importance, and how it compares to USEPA’s EJSCREEN and CalEnviroScreen. We demonstrate the use of MD EJSCREEN through a case study on Bladensburg, Maryland, a town in Prince George’s County (PG) with several environmental justice concerns including air pollution from traffic and a concrete plant. Comparison reveals that environmental and demographic indicators in MD EJSCREEN most closely resemble those in EPA EJSCREEN, while the scoring is most similar to CalEnviroScreen. Case study results show that Bladensburg has a Prince George’s environmental justice score of 0.99, and that National Air Toxics Assessment (NATA) air toxics cancer risk is concentrated in communities of color.

Validating and Refining EPA's Traffic Exposure Screening Measure.

Exposure to high air pollutant concentrations results in significant health risks. Many communities of color and low-income communities face disproportionately higher levels of air pollution exposure. Environmental justice (EJ) screening tools play a critical role in focusing early attention on areas with a high likelihood of disparate health impacts. In 2015, the United States Environmental Protection Agency (US EPA) released EJScreen, a screening tool with indicators of a range of pollution burdens across the US. However, little is known about the accuracy of the screening estimates of pollution exposure. This study compares EJScreen’s traffic proximity air quality metric to dispersion modeling results. Using the area around the Houston Ship Channel, we conduct fine-grained air pollution dispersion modeling to evaluate how closely EJScreen’s indicator approximates estimated roadway air pollution concentrations. We find low correlation between modeled concentrations and the EJScreen roadway air pollution indicator. We extend EJScreen’s roadway air pollution screening method in three ways: (1) using a smaller unit of analysis, (2) accounting for the length of each road segment, and (3) accounting for wind direction. Using the Houston region, we use two of the methods and show that the proposed extensions provide a more accurate transportation air pollution screening assessment at the regional and local level.

A Novel Environmental Justice Indicator for Managing Local Air Pollution.

Environmental justice efforts in the United States seek to provide equal protection from environmental hazards, such as air pollution, to all groups, particularly among traditionally disadvantaged populations. To accomplish this objective, the U.S. EPA has previously required states to use an environmental justice screening tool as part of air quality planning decision-making. The generally utilized approach to assess potential areas of environmental justice concern relies on static comparisons of environmental and demographic information to identify areas where minority and low income populations experience elevated environmental exposures, but does not include any additional information that may inform the trade-offs that sub-populations of varying socio-demographic groups make when choosing where to reside in cities. In order to address this limitation, job accessibility (measured by a mobility index defining the number of jobs available within a set commuting time) was developed as a novel environmental justice indicator of environmental justice priority areas at the local level. This approach is modeled using real-world data in Allegheny County, PA (USA), and identifies areas with relatively high levels of outdoor air pollution and low access to jobs. While traditional tools tend to flag the poorest neighborhoods for environmental justice concerns, this new method offers a more refined analysis, targeting populations suffering from the highest environmental burden without the associated benefits of urban living.

Investigating Links Among Urban Sprawl and Environmental Justice Indicators in US Territories

Sprawl often characterizes unsustainable, car-dependent, and low-density urban development at the edges of cities. Much research has documented the relationship among sprawl and air pollutant concentrations and many studies have addressed sprawl’s social implications, especially for low-income and minority groups. However, limited research has investigated the links between areas with increased levels of sprawl and air pollution, where vulnerable populations reside. This paper brings together the refined sprawl dataset from Smart Growth America and selected environmental justice indicators on air pollution-ozone and air toxics- from the US Environmental Protection Agency’s Environmental Justice Screening and Mapping Tool (EJSCREEN), in a national-level analysis of U.S. territories. Through Pearson correlations and a series of logistic regressions, the significant connection of sprawl and ozone concentrations is shown, in areas with more low-income, and less educated groups with higher percentages of children. On the other hand, while air toxics cancer risk is higher in areas with low-income, and linguistically isolated racial minorities, it has lower levels in more sprawled areas. Upon a closer look, it is shown that only selected dimensions of compactness link to higher cancer risk, while aspects such as a higher mix of jobs may have a reverse effect on it. These findings provide new directions in the ongoing discussion of sustainable urban development patterns and suggest that the focus should be on development that can promote better air quality, while simultaneously reducing social vulnerability to environmental challenges, with additional benefits for local innovation and community building.

A multivariate analysis of CalEnviroScreen: comparing environmental and socioeconomic stressors versus chronic disease

BackgroundThe health-risk assessment paradigm is shifting from single stressor evaluation towards cumulative assessments of multiple stressors. Recent efforts to develop broad-scale public health hazard datasets provide an opportunity to develop and evaluate multiple exposure hazards in combination.MethodsWe performed a multivariate study of the spatial relationship between 12 indicators of environmental hazard, 5 indicators of socioeconomic hardship, and 3 health outcomes. Indicators were obtained from CalEnviroScreen (version 3.0), a publicly available environmental justice screening tool developed by the State of California Environmental Protection Agency. The indicators were compared to the total rate of hospitalization for 14 ICD-9 disease categories (a measure of disease burden) at the zip code tabulation area population level. We performed principal component analysis to visualize and reduce the CalEnviroScreen data and spatial autoregression to evaluate associations with disease burden.ResultsCalEnviroScreen was strongly associated with the first principal component (PC) from a principal component analysis (PCA) of all 20 variables (Spearman ρ = 0.95). In a PCA of the 12 environmental variables, two PC axes explained 43% of variance, with the first axis indicating industrial activity and air pollution, and the second associated with ground-level ozone, drinking water contamination and PM2.5. Mass of pesticides used in agriculture was poorly or negatively correlated with all other environmental indicators, and with the CalEnviroScreen calculation method, suggesting a limited ability of the method to capture agricultural exposures. In a PCA of the 5 socioeconomic variables, the first PC explained 66% of variance, representing overall socioeconomic hardship. In simultaneous autoregressive models, the first environmental and socioeconomic PCs were both significantly associated with the disease burden measure, but more model variation was explained by the socioeconomic PCs.ConclusionsThis study supports the use of CalEnviroScreen for its intended purpose of screening California regions for areas with high environmental exposure and population vulnerability. Study results further suggest a hypothesis that, compared to environmental pollutant exposure, socioeconomic status has greater impact on overall burden of disease.

A Comparison of Major Environmental Justice Screening and Mapping Tools

The concept of Environmental Justice (EJ) has evolved in United Sates for more than 30 years. Since then most empirical studies have shown that low-income and minority neighborhoods are disproportionately exposed to environmental hazards. Across the world, communities are struggling to protect their land, air, water, forests, and their livelihoods from damaging projects and activities with heavy environmental and social impacts. A Number of tools already exist to identify and map those areas with potential environmental justice concerns. This paper presents a comparison of the three major EJ tools; EJSCREEN (version 2016), CalEnviroScreen 2.0, EJ Atlas and their methodologies. There are some common parameters across these tools in presenting Environmental Justice and in identifying environmentally burdened communities, socially burdened communities, or both. Environmental burdens can include any environmental pollutant, hazard or disadvantage that compromises the health of a community. The tools are expected to help in understanding and studying the distribution of environmental benefits and burdens, decision making for disadvantaged communities in certain areas and in setting up environmental policies and planning.