Detection of untreated sewage discharges to watercourses using machine learning
Detection of untreated sewage discharges to watercourses using machine learning
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DOI：
10.1038/s41545-021-00108-3
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发表时间：
2021-03-11
期刊：
影响因子：
11.4
通讯作者：
Singer, Andrew C.
中科院分区：
文献类型：
--
作者：
Hammond, Peter;Suttie, Michael;Singer, Andrew C.
监测和管理英格兰水体中的废水污染排放是环境署的职责。识别和报告污水处理厂的污染事件是运营商的责任。然而，在2018年，公众报告了英格兰400多起污水污染事件。我们提出了新颖的污染事件报告方法，以识别废水处理厂可能未经处理的污水泄漏。两个废水处理厂的每日污水流量模式得到了运营商报告的未经处理的污水排放事件的补充。使用机器学习，已知的泄漏事件作为训练数据。正确分类一对随机选择的“溢出”和“无溢出”的污水模式的概率是96%以上。在7160天没有操作员报告的泄漏事件中，926天被归类为“泄漏”。分析还表明，两家污水处理厂在2009年至2020年期间违规排放未经处理的污水。这种使用机器学习来检测未经处理的废水排放的原理证明可以帮助水务公司识别故障的处理厂，并告知监管机构不满意的监管监督。实时、开放的流量和警报数据以及分析方法将使专业人士和公民能够对未经处理的废水排放的频率和影响进行科学审查，特别是那些运营商未报告的废水排放。
Monitoring and regulating discharges of wastewater pollution in water bodies in England is the duty of the Environment Agency. Identification and reporting of pollution events from wastewater treatment plants is the duty of operators. Nevertheless, in 2018, over 400 sewage pollution incidents in England were reported by the public. We present novel pollution event reporting methodologies to identify likely untreated sewage spills from wastewater treatment plants. Daily effluent flow patterns at two wastewater treatment plants were supplemented by operator-reported incidents of untreated sewage discharges. Using machine learning, known spill events served as training data. The probability of correctly classifying a randomly selected pair of 'spill' and 'no-spill' effluent patterns was above 96%. Of 7160 days without operator-reported spills, 926 were classified as involving a 'spill'. The analysis also suggests that both wastewater treatment plants made non-compliant discharges of untreated sewage between 2009 and 2020. This proof-of-principle use of machine learning to detect untreated wastewater discharges can help water companies identify malfunctioning treatment plants and inform agencies of unsatisfactory regulatory oversight. Real-time, open access flow and alarm data and analytical approaches will empower professional and citizen scientific scrutiny of the frequency and impact of untreated wastewater discharges, particularly those unreported by operators.