Department of Ecology, Evolution, and Natural Resources, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
Department of Statistics, Columbia University, New York, NY 10027, USA.
Sci Total Environ. 2023 Jan 20;857(Pt 1):159360. doi: 10.1016/j.scitotenv.2022.159360. Epub 2022 Oct 11.
Exposure to arsenic through private drinking water wells causes serious human health risks throughout the globe. Water testing data indicates there is arsenic contamination in private drinking water wells across New Jersey. To reduce the adverse health risk due to exposure to arsenic in drinking water, it is necessary to identify arsenic sources affecting private wells. Private wells are not regulated by any federal or state agencies through the Safe Drinking Water Act and therefore information is often lacking. To this end, we have developed machine learning algorithms including Random Forest Classification and Regression to decipher the factors contributing to higher arsenic concentration in private drinking water wells in west-central New Jersey. Arsenic concentration in private drinking water wells served as a response variable while explanatory variables were geological bedrock type, soil type, drainage class, land use/cover, and presence of orchards, contaminated sites, and abandoned mines within the 152.4-meter (500 ft) radius of each well. Random Forest Classification and Regression achieved 66 % and 55 % prediction accuracies for arsenic concentration in private drinking water wells, respectively. Overall, both models identify that bedrock, soil, land use/cover, and drainage type (in descending order) are the most important variables contributing to higher arsenic concentration in well water. These models further identify bedrock subgroups at a finer scale including Passaic Formation, Lockatong Formation, Stockton Formation contributing significantly to arsenic concentration in well water. Identification of sources of arsenic contamination in private drinking water wells at such a fine scale facilitates development of more targeted outreach as well as mitigation strategies to improve water quality and safeguard human health.
通过私人水井饮用水暴露于砷会在全球范围内对人类健康造成严重威胁。水质测试数据表明，新泽西州各地的私人饮用水井都存在砷污染。为了降低饮用水中砷暴露对健康的不利风险，有必要确定影响私人水井的砷源。私人水井不受《安全饮用水法》规定的任何联邦或州机构的监管，因此信息通常匮乏。为此，我们开发了机器学习算法，包括随机森林分类和回归，以破译导致新泽西州中西部私人饮用水井中砷浓度升高的因素。私人饮用水井中的砷浓度作为响应变量，而解释变量为地质基岩类型、土壤类型、排水等级、土地利用/覆盖以及每个井的 152.4 米（500 英尺）半径内果园、污染场地和废弃矿山的存在。随机森林分类和回归对私人饮用水井中砷浓度的预测准确率分别为 66%和 55%。总体而言，这两个模型都表明基岩、土壤、土地利用/覆盖和排水类型（按降序排列）是导致井水砷浓度升高的最重要变量。这些模型还进一步确定了更精细尺度的基岩亚组，包括帕塞伊克组、洛克托组和斯托克顿组，它们对井水砷浓度有显著贡献。在如此精细的尺度上确定私人饮用水井中砷污染的来源，有助于制定更有针对性的外展和缓解策略，以改善水质和保护人类健康。
