Detection of untreated sewage discharges to watercourses using machine learning

Abstract 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 inciden...

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Autores principales:	Peter Hammond, Michael Suttie, Vaughan T. Lewis, Ashley P. Smith, Andrew C. Singer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/be9a784f725d44ebb3f233f79c7c5d1a
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spelling	oai:doaj.org-article:be9a784f725d44ebb3f233f79c7c5d1a2021-12-02T13:30:10ZDetection of untreated sewage discharges to watercourses using machine learning10.1038/s41545-021-00108-32059-7037https://doaj.org/article/be9a784f725d44ebb3f233f79c7c5d1a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41545-021-00108-3https://doaj.org/toc/2059-7037Abstract 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.Peter HammondMichael SuttieVaughan T. LewisAshley P. SmithAndrew C. SingerNature PortfolioarticleWater supply for domestic and industrial purposesTD201-500ENnpj Clean Water, Vol 4, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Water supply for domestic and industrial purposes TD201-500
spellingShingle	Water supply for domestic and industrial purposes TD201-500 Peter Hammond Michael Suttie Vaughan T. Lewis Ashley P. Smith Andrew C. Singer Detection of untreated sewage discharges to watercourses using machine learning
description	Abstract 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.
format	article
author	Peter Hammond Michael Suttie Vaughan T. Lewis Ashley P. Smith Andrew C. Singer
author_facet	Peter Hammond Michael Suttie Vaughan T. Lewis Ashley P. Smith Andrew C. Singer
author_sort	Peter Hammond
title	Detection of untreated sewage discharges to watercourses using machine learning
title_short	Detection of untreated sewage discharges to watercourses using machine learning
title_full	Detection of untreated sewage discharges to watercourses using machine learning
title_fullStr	Detection of untreated sewage discharges to watercourses using machine learning
title_full_unstemmed	Detection of untreated sewage discharges to watercourses using machine learning
title_sort	detection of untreated sewage discharges to watercourses using machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/be9a784f725d44ebb3f233f79c7c5d1a
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Detection of untreated sewage discharges to watercourses using machine learning

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