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Detection of untreated sewage discharges to watercourses using machine learning
npj Clean Water ( IF 11.4 ) Pub Date : 2021-03-11 , DOI: 10.1038/s41545-021-00108-3
Peter Hammond , Michael Suttie , Vaughan T. Lewis , Ashley P. Smith , Andrew C. Singer

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.



中文翻译:

使用机器学习检测未经处理的污水排放到水道

英格兰环境局的职责是监测和调节英国水体中废水污染的排放量。识别和报告废水处理厂的污染事件是操作员的责任。尽管如此,2018年公众仍报告了英格兰超过400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。操作员报告的未经处理的污水排放事件补充了两个污水处理厂的日常污水流向。使用机器学习,已知的泄漏事件用作训练数据。正确分类随机选择的“溢漏”和“无溢漏”出水模式对的可能性高于96%。在7160天中,没有操作员报告的泄漏事件,926被归类为“溢出”事件。该分析还表明,两个污水处理厂在2009年至2020年之间均排放了未处理的污水。这种基于原理的机器学习技术检测未处理的污水排放可以帮助自来水公司识别出故障的处理厂,并告知代理商不满意的处理厂。监管监督。实时,开放的访问流,警报数据和分析方法将使专业人士和市民科学审查未经处理的废水排放的频率和影响,特别是操作员未报告的废水排放。机器学习的原理证明是用于检测未经处理的废水排放,可以帮助自来水公司识别出故障的处理厂,并告知机构监管监督不力。实时,开放的访问流,警报数据和分析方法将使专业人士和市民科学审查未经处理的废水排放的频率和影响,特别是操作员未报告的废水排放。这种机器学习的原理证明可用于检测未经处理的废水排放,可以帮助自来水公司识别出故障的处理厂,并告知机构监管监督不力。实时,开放的访问流,警报数据和分析方法将使专业人士和市民科学审查未经处理的废水排放的频率和影响,特别是操作员未报告的废水排放。

更新日期:2021-03-11
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