Department of Water Resources and Drinking Water, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland.
Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK.
Science. 2020 May 22;368(6493):845-850. doi: 10.1126/science.aba1510.
Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated data points of measured groundwater arsenic concentration. Our global prediction map includes known arsenic-affected areas and previously undocumented areas of concern. By combining the global arsenic prediction model with household groundwater-usage statistics, we estimate that 94 million to 220 million people are potentially exposed to high arsenic concentrations in groundwater, the vast majority (94%) being in Asia. Because groundwater is increasingly used to support growing populations and buffer against water scarcity due to changing climate, this work is important to raise awareness, identify areas for safe wells, and help prioritize testing.
天然存在于地下水中的砷影响着全球数百万人。我们利用基于 11 个地理空间环境参数和超过 50,000 个聚合的地下水砷浓度测量数据点的随机森林机器学习模型,创建了一个地下水砷超过 10 微克/升的全球预测图。我们的全球预测图包括已知的砷污染地区和以前没有记录的关注地区。通过将全球砷预测模型与家庭地下水使用统计数据相结合,我们估计有 9400 万至 2.2 亿人可能接触到高浓度的地下水砷,其中绝大多数(94%)在亚洲。由于地下水越来越多地被用于支持不断增长的人口,并缓冲因气候变化导致的水资源短缺,因此这项工作对于提高认识、确定安全水井的区域以及帮助确定测试的优先级非常重要。