用数据科学方法了解弗林特的住宅水污染
A Data Science Approach to Understanding Residential Water Contamination in Flint
摘要：
当弗林特的居民得知铅污染了他们的供水系统时，当地政府免费为他们提供了水检测工具。市政府公布了这些测试的结果，创建了一个有价值的数据集，这是了解弗林特铅污染事件的原因和程度的关键。这是全国最大的关于城市供水系统中铅含量的数据集。在本文中，我们预测了每个家庭的供水铅污染，我们研究了弗林特的水问题，其中许多推广远远超出了这一个城市的几个相关方面。例如，我们表明，铅的风险升高可以（弱）从可观察到的家庭属性预测。然后我们探讨与铅升高相关的因素。这些风险评估部分是通过密歇根大学的众包预测挑战开发的。为了让弗林特居民了解这些评估，这些评估已被纳入由\texttt{Google.org}资助的网络和移动应用程序。我们还探讨了住宅测试计划中的自我选择问题，研究哪些因素与居民自愿取样的时间和频率有关。
英文摘要：
When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system. In this paper, we predict the lead contamination for each household's water supply, and we study several related aspects of Flint's water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by \texttt{Google.org}. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
