HGBEnviroScreen: Enabling Community Action through Data Integration in the Houston–Galveston–Brazoria Region
2. Materials and Methods
- Social Vulnerability, as described by the Center for Disease Control (CDC), “refers to the resilience of communities when confronted by external stresses on human health”, particularly with respect to natural or human-caused disasters or disease outbreaks, and typically consists of socioeconomic and demographic factors. Social vulnerability is relevant to environmental vulnerability in the HGB area because of the prevalence of disasters, both natural (e.g., flooding and hurricanes) and manmade (e.g., chemical spills, oil spills, and fires). We included the four main themes (socioeconomic status, household composition and disability, minority and language, and housing and transportation) in addition to one auxiliary indicator (percent without health insurance) from the CDC’s Social Vulnerability Index (SVI) in our analysis. We also added three indicators related to food/nutrition access and security. Existing EJ screening tools typically include individual indicators of socioeconomic status, but we used SVI both because it provides a more comprehensive portrait of social vulnerability, as well as because it has been widely used and validated .
- Baseline Health uses indicators related to health status and health care access to capture the extent to which members of a community may be more vulnerable to effects from the environment, similar to the “sensitive population indicators” used in CalEnviroScreen . Four of these indicators are of disease prevalence, specifically for coronary heart disease, stroke, childhood asthma, and chronic obstructive pulmonary disease, as estimated . It is posited that higher rates of these diseases suggest a larger population that is more susceptible to the health impacts from environmental factors. Additionally, life expectancy is taken as an aggregate measure of baseline health, with lower values indicating greater vulnerability . Finally, as a measure of access to health care, we counted the number of hospitals within 5 km of each census tract, again with lower numbers indicating greater vulnerability.
- Environmental Exposures and Risks utilize three well-established sources of indicators of pollutant exposures and risk. First, the EPA Risk-Screening Environmental Indicators provide a screening-level metric for the potential for chronic human health risks due to toxic releases from facilities that report to the Toxics Release Inventory (TRI) . Additionally, we utilized the most recently available U.S. EPA National Air Toxics Assessment calculations for cancer risk, respiratory effects, and reproductive effects . Third, we included two separate estimates of PM2.5 concentrations: one based on the last three years available from the U.S. EPA’s Community Multiscale Air Quality (CMAQ) model,  and one based on satellite imagery . Each of these indicators is available at the census tract level.
- Environmental Sources are indicators related to the proximity of each census tract to sources of environmental emissions. Specifically, these indicators provide metrics for potential environmental exposure, rather than measured or predicted exposure or risk levels. A large number of different types of facilities were included. Point sources where exposure was expected to be more localized, such as Superfund sites and leaking petroleum storage tanks, were counted if located in each census tract. Industrial facilities such as cement batch plants and concrete crushers (combined), petroleum and oil refineries (combined), metal recyclers, and powerplants, where some transport of pollutants would be expected, were counted if within 1 km of each census tract. Additionally, proximity to major roads was also measured with a 1 km buffer distance. Finally, to incorporate indicators related to accident risks, facilities with registered Risk Management Plans were included using three metrics: facility counts, number of accidents in the five years before 30 April 2018, and total number of shelter-in-place events in the five years before 30 April 2018. These facilities were counted if located in each census tract.
- Flooding reflects risks related to inundation by floodwaters and includes both frequency and severity metrics. The recent history of frequent flooding in the HGB area emphasizes the importance of this domain to evaluating environmental vulnerability. The fractions of the census tract area within 100- and 500-year FEMA flood plains were used as frequency indicators. For severity, the number of FEMA damage assessments at various levels (minimal, major, affected, and destroyed), as well as the percentage of households filing damage claims, were used as indicators.
3.1. Prioritization Across Census Tracts
3.2. Sensitivity Analysis: Primary Contributors to Vulnerability
3.3. Case Example: Galena Park
- Incorporating data on key, multifaceted vulnerabilities, including both common EJ concerns such as social vulnerability and air pollution, as well as HGB-specific issues such as flooding and proximity to large numbers of industrial facilities;
- Aggregating five key domains of vulnerability into an overall ToxPi score at the census tract level, thereby facilitating identification and prioritization of highly vulnerable communities;
- Visualizing both the overall ToxPi score as well as the relative contribution of its component indicators, so as to enable communities to target advocacy and interventions that create the greatest value in terms of reducing vulnerability.
Conflicts of Interest
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|Domain (% Weight of Overall Score) Indicator (Fractional Weight within Domain)||Indicator Unit for Each Census Tract||Ref.|
|Social Vulnerability (20%)|
|Socioeconomic status (1/6)||% ile index|||
|Household composition and disability (1/6)||% ile index|||
|Minority status and language (1/6)||% ile index|||
|Housing and transportation (1/6)||% ile index|||
|Without health insurance (1/6)||% of population|||
|Modified food retail environment index (1/18)||100—MFREI Score 1|||
|Food desert low access (1/18)||Sum of two low access flags|||
|Low food security (1/18)||% of population|||
|Baseline Health (20%)|
|Childhood asthma (1/6)||% crude prevalence|||
|Stroke (1/6)||% crude prevalence|||
|Chronic obstructive pulmonary disease (1/6)||% crude prevalence|||
|Coronary heart disease (1/6)||% crude prevalence|||
|Life expectancy (1/6)||100—Life exp in years 1|||
|Proximity to hospitals (1/6)||100—# within 5 km 1|||
|Environmental Exposures and Risks (20%)|
|Risk-screening environmental indicators (1/3)||Aggregated 2015–2017 Score|||
|National Air Toxics Assessment (NATA) Cancer (1/9)||Cancer risk|||
|NATA Respiratory Tract (1/9)||Hazard index|||
|NATA Reproductive (1/9)||Hazard index|||
|PM2.5 Community Multiscale Air Quality (CMAQ) (1/6)||μg/m3|||
|PM2.5 Satellite (1/6)||μg/m3|||
|Environmental Sources (20%)|
|Major roads (1/10)||% ile based on # within 1 km|||
|Cement batch plants (1/10)||% ile based on # within 1 km|||
|Metal recyclers (1/10)||% ile based on # within 1 km|||
|Petrochemical and oil refineries (1/10)||% ile based on # within 1 km|||
|Power plants (1/10)||% ile based on # within 1 km|||
|Superfund sites (1/10)||% ile based on # in census tract|||
|Leaking petroleum storage tanks (1/10)||% ile based on # in census tract|||
|Facilities with risk management plans (RMP) (1/10)||% ile based on # in census tract|||
|Accident events reported in RMP (1/10)||% ile based on # in census tract|||
|Shelter-in-place events reported in RMP (1/10)||% ile based on # in census tract|||
|100 year flood plain (1/6)||% ile based on fraction of area|||
|500 year flood plain (1/6)||% ile based on fraction of area|||
|Harvey damage assessment “Affected” (1/12)||% ile based on # in census tract|||
|Harvey damage assessment “Minimal” (1/12)||% ile based on # in census tract|||
|Harvey damage assessment “Major” (1/6)||% ile based on # in census tract|||
|Harvey damage assessment “Destroyed” (1/6)||% ile based on # in census tract|||
|Families filing Harvey damage claims (1/6)||% of population|||
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Bhandari, S.; Lewis, P.G.T.; Craft, E.; Marvel, S.W.; Reif, D.M.; Chiu, W.A. HGBEnviroScreen: Enabling Community Action through Data Integration in the Houston–Galveston–Brazoria Region. Int. J. Environ. Res. Public Health 2020, 17, 1130. https://doi.org/10.3390/ijerph17041130
Bhandari S, Lewis PGT, Craft E, Marvel SW, Reif DM, Chiu WA. HGBEnviroScreen: Enabling Community Action through Data Integration in the Houston–Galveston–Brazoria Region. International Journal of Environmental Research and Public Health. 2020; 17(4):1130. https://doi.org/10.3390/ijerph17041130Chicago/Turabian Style
Bhandari, Sharmila, P. Grace Tee Lewis, Elena Craft, Skylar W. Marvel, David M. Reif, and Weihsueh A. Chiu. 2020. "HGBEnviroScreen: Enabling Community Action through Data Integration in the Houston–Galveston–Brazoria Region" International Journal of Environmental Research and Public Health 17, no. 4: 1130. https://doi.org/10.3390/ijerph17041130