U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division, Washington, DC, USA.
U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division, Corvallis, OR, USA.
Sci Total Environ. 2020 Jun 20;722:137661. doi: 10.1016/j.scitotenv.2020.137661. Epub 2020 Mar 5.
Excess nitrate in drinking water is a human health concern, especially for young children. Public drinking water systems in violation of the 10 mg nitrate-N/L maximum contaminant level (MCL) must be reported in EPA's Safe Drinking Water Information System (SDWIS). We used SDWIS data with random forest modeling to examine the drivers of nitrate violations across the conterminous U.S. and to predict where public water systems are at risk of exceeding the nitrate MCL. As explanatory variables, we used land cover, nitrogen inputs, soil/hydrogeology, and climate variables. While we looked at the role of nitrate treatment in separate analyses, we did not include treatment as a factor in the final models, due to incomplete information in SDWIS. For groundwater (GW) systems, a classification model correctly classified 79% of catchments in violation and a regression model explained 43% of the variation in nitrate concentrations above the MCL. The most important variables in the GW classification model were % cropland, agricultural drainage, irrigation-to-precipitation ratio, nitrogen surplus, and surplus precipitation. Regions predicted to have risk for nitrate violations in GW were the Central California Valley, parts of Washington, Idaho, the Great Plains, Piedmont of Pennsylvania and Coastal Plains of Delaware, and regions of Wisconsin, Iowa, and Minnesota. For surface water (SW) systems, a classification model correctly classified 90% of catchments and a regression model explained 52% of the variation in nitrate concentration. The variables most important for the SW classification model were largely hydroclimatic variables including surplus precipitation, irrigation-to-precipitation ratio, and % shrubland. Areas at greatest risk for SW nitrate violations were generally in the non-mountainous west and southwest. Identifying the areas with possible risk for future violations and potential drivers of nitrate violations across U.S. can inform decisions on how source water protection and other management options could best protect drinking water.
饮用水中过多的硝酸盐是一个对人类健康的关注问题，尤其是对幼儿而言。违反 10mg 硝酸盐氮/L 最大污染物水平 (MCL) 的公共饮用水系统必须在 EPA 的安全饮用水信息系统 (SDWIS) 中报告。我们使用 SDWIS 数据和随机森林模型来研究美国各地硝酸盐违规的驱动因素，并预测公共供水系统有哪些面临超过硝酸盐 MCL 的风险。作为解释变量，我们使用了土地覆盖、氮输入、土壤/水文地质和气候变量。虽然我们在单独的分析中研究了硝酸盐处理的作用，但由于 SDWIS 中的信息不完整，我们没有将处理作为最终模型的一个因素。对于地下水 (GW) 系统，分类模型正确分类了 79%的违规集水区，回归模型解释了硝酸盐浓度超过 MCL 的 43%的变化。GW 分类模型中最重要的变量是耕地百分比、农业排水、灌溉与降水比、氮盈余和盈余降水。预测 GW 硝酸盐违规风险的地区包括加利福尼亚中部山谷、华盛顿州、爱达荷州、大平原、宾夕法尼亚州皮埃蒙特和特拉华州沿海平原，以及威斯康星州、爱荷华州和明尼苏达州的部分地区。对于地表水 (SW) 系统，分类模型正确分类了 90%的集水区，回归模型解释了硝酸盐浓度变化的 52%。SW 分类模型最重要的变量主要是水文气候变量，包括盈余降水、灌溉与降水比和灌木百分比。地表水硝酸盐违规风险最大的地区通常在无山的西部和西南部。确定未来可能违规的地区和美国硝酸盐违规的潜在驱动因素，可以为水源保护和其他管理选项如何最好地保护饮用水的决策提供信息。
