Community-Level Analysis of Drinking Water Data Highlights the Importance of Drinking Water Metrics for the State, Federal Environmental Health Justice Priorities in the United States

Abstract

Research studies analyzing the geospatial distribution of air pollution and other types of environmental contamination documented the persistence of environmental health disparities between communities. Due to the shortage of publicly available data, only limited research has been published on the geospatial distribution of drinking water pollution. Here we present a framework for the joint consideration of community-level drinking water data and demographic data. Our analysis builds on a comprehensive data set of drinking water contaminant occurrence for the United States for 2014–2019 and the American Community Survey 5-year estimates (2015–2019) from the U.S. Census Bureau. Focusing on the U.S. states of California and Texas for which geospatial data on community water system service boundaries are publicly available, we examine cumulative cancer risk for water served by community water systems of different sizes relative to demographic characteristics for the populations served by these water systems. In both California and Texas, greater cumulative cancer risk was observed for water systems serving communities with a higher percentage of Hispanic/Latino and Black/African American community members. This investigation demonstrates that it is both practical and essential to incorporate and expand the drinking water data metrics in the analysis of environmental pollution and environmental health. The framework presented here can support the development of public policies to advance environmental health justice priorities on state and federal levels in the U.S.

A systematic review of geospatial representation of United States community water systems

“…For CWS‐level analysis, we used block group race, ethnicity, and poverty data obtained from the American Community Survey (ACS) 5‐year estimates (2006–2010 dataset) to match population characteristics to CWSs. For county‐level comparative analysis, we used the county‐level assignment for the aforementioned block group data (see Appendix S1 for ACS variables used and methodology to match to CWS ESAB (Uche et al, 2021). To compare systems that varied by size, we selected a very small CWS (i.e., PA2450054 serves 120 persons) that reported the highest number violations in the county at 22%.…”

“…Although city boundaries are a more refined spatial partition that utilize a different data configuration (Su et al, 2011) as compared to county-level aggregation, they do not accurately represent the populations served by CWSs. In a recent article, Uche et al (2021) assert that drinking water research should be operationalized at the service area boundary of a CWS as opposed to county level. While this study did not compare CWS versus county-level unit of analysis results, it is emphasized that the application of county-level characteristics will most likely result in sociodemographic spatial mismatch of populations served by a CWS.…”

Challenges, Opportunities and Future Paths: Environmental Governance of Big Data Initiatives in China

“…As mentioned above, the Facility Registration System (FRS) is an important grip for the integration of environmental big data in the U.S., and is centrally managed and maintained by the U.S. EPA's Office of Environmental Information Technology [55]. At the same time, the U.S. EPA has constructed the Environmental Facts Database (Envirofacts), an environmental data query system that provides the public with access to environmental information including air, water, waste, toxics, radiation, soil, maps, and the like [56,57]. The EPA's transmission and sharing of environmental data is enabled by the Central Data Exchange (CDX), a fast and accurate network for exchanging real-time environmental data, which is built with the latest and secure information technology to connect the federal government, local governments, businesses, and EPA's subdivisions [58].…”

Efficient Flood Risk Mitigation and Intersectional Equity Implications: A Case Study in New Orleans

“…This has the potential to worsen existing social inequity. 9,10 Substantial differences already exist between racial and income groups in air quality, [11][12][13] water quality, [14][15][16] and ood risk. [17][18][19] This potential for harm is increasingly salient; the growing adoption of analyses which characterize ood risk at the level of individual structures [20][21][22] makes it possible to algorithmically design interventions at the level of individual homes which (inadvertently) selectively protect expensive structures, the effects of which may then be masked by data aggregation.…”