Drivers of Spatiotemporal Variability in Drinking Water Quality in the United States

Approximately
10% of community water systems in the United States
experience a health-based violation of drinking water quality; however,
recently allocated funds for improving United States water infrastructure
($50 billion) provide an opportunity to address these issues. The
objective of this study was to examine environmental, operational,
and sociodemographic drivers of spatiotemporal variability in drinking
water quality violations using geospatial analysis and data analytics.
Random forest modeling was used to evaluate drivers of these violations,
including environmental (e.g., landcover, climate, geology), operational
(e.g., water source, system size), and sociodemographic (social vulnerability,
rurality) drivers. Results of random forest modeling show that drivers
of violations vary by violation type. For example, arsenic and radionuclide
violations are found mostly in the Southwest and Southcentral United
States related to semiarid climate, whereas disinfection byproduct
rule violations are found primarily in Southcentral United States
related to system operations. Health-based violations are found primarily
in small systems in rural and suburban settings. Understanding the
drivers of water quality violations can help develop optimal approaches
for addressing these issues to increase compliance in community water
systems, particularly small systems in rural areas across the United
States.

美国约10%的社区供水系统（community water systems）存在饮用水水质不符合健康标准的违规行为；然而，近期划拨用于美国供水基础设施升级的500亿美元资金，为解决此类问题提供了重要契机。本研究旨在通过地理空间分析与数据分析手段，探究饮用水水质违规行为时空变异性的环境、运营及社会人口学驱动因素。研究采用随机森林建模（random forest modeling）方法，对各类违规行为的驱动因素进行评估，涵盖环境类（如土地覆被、气候、地质）、运营类（如水源、系统规模）以及社会人口学类（社会脆弱性、乡村属性）驱动因素。随机森林建模结果显示，违规行为的驱动因素因违规类型而异。例如，砷与放射性核素违规行为主要集中在美国西南部及中南部地区，该区域气候多为半干旱；而消毒副产物准则（disinfection byproduct rule）违规行为则主要出现在美国中南部，与供水系统的运营模式密切相关。基于健康标准的违规行为主要集中在乡村及郊区的小型供水系统中。明确饮用水水质违规行为的驱动因素，有助于制定针对性优化方案以解决相关问题，提升社区供水系统的合规率，尤其是美国乡村地区的小型供水系统。