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dc.contributor.authorJi, Y
dc.contributor.authorZheng, F
dc.contributor.authorDu, J
dc.contributor.authorHuang, Y
dc.contributor.authorBi, W
dc.contributor.authorDuan, H
dc.contributor.authorSavic, D
dc.contributor.authorKapelan, Z
dc.date.accessioned2022-11-03T11:26:04Z
dc.date.issued2022-10-21
dc.date.updated2022-11-03T09:30:19Z
dc.description.abstractMost of the contamination source localization methods for water distribution systems (WDSs) assume the availability of accurate water quality models and multi-parameter online sensors, which are often out of reach of many water utilities. To address this, a novel manual grab-sampling method (MGSM) is developed to effectively and efficiently locate continuous contamination sources in a WDS using a dynamic and cyclical sampling strategy. The grab samples are collected at a pre-specified number of hydrants by the corresponding teams followed by laboratory tests. The MGSM optimizes the sampling plan at each cycle by making the probability of contamination source(s) in each sub-network as equal as possible, where sub-networks are determined by the selected hydrants and current flow pipe directions. The CS's size is reduced at each cycle by exploiting sample testing results obtained in the previous cycle until there are no further hydrants to sample from. Two real-world WDSs are used to demonstrate the effectiveness of the proposed MGSM. The results obtained show that the MGSM can significantly reduce the spatial range of the CS (to about 5% of the entire WDS) for a range of scenarios including multiple contamination sources and pipe flow direction changes. We found that an optimal number of sampling teams exists for a given WDS, representing a balanced trade-off between detection efficiency and sampling/testing budgets. Due to its relative simplicity, the proposed MGSM can be used in engineering practice straightaway and it represents a viable alternative to the methods associated with water quality models and sensors.en_GB
dc.description.sponsorshipExcellent Youth Natural Science Foundation of Zhejiang Province, Chinaen_GB
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_GB
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_GB
dc.description.sponsorshipHong Kong Research Grants Councilen_GB
dc.description.sponsorshipEuropean Research Councilen_GB
dc.identifier.citationVol. 58, No. 11, article e2022WR032784en_GB
dc.identifier.doihttps://doi.org/10.1029/2022wr032784
dc.identifier.grantnumberLR19E080003en_GB
dc.identifier.grantnumber51808497en_GB
dc.identifier.grantnumber51922096en_GB
dc.identifier.grantnumber15200719en_GB
dc.identifier.grantnumber951424en_GB
dc.identifier.urihttp://hdl.handle.net/10871/131591
dc.identifierORCID: 0000-0001-9567-9041 (Savic, Dragan)
dc.identifierScopusID: 35580202000 (Savic, Dragan)
dc.identifierResearcherID: G-2071-2012 | L-8559-2019 (Savic, Dragan)
dc.language.isoenen_GB
dc.publisherAmerican Geophysical Unionen_GB
dc.rights© 2022. American Geophysical Union. All Rights Reserved.en_GB
dc.subjectwater distribution systemsen_GB
dc.subjectmanual grab-sampling methoden_GB
dc.subjectcontamination sourcesen_GB
dc.subjectwater qualityen_GB
dc.titleAn effective and efficient method for identification of contamination sources in water distribution systems based on manual grab‐samplingen_GB
dc.typeArticleen_GB
dc.date.available2022-11-03T11:26:04Z
dc.identifier.issn0043-1397
dc.descriptionThis is the author accepted manuscript. The final version is available from American Geophysical Union via the DOI in this recorden_GB
dc.descriptionData Availability Statement: The data will eventually be deposited in the general repository Zenodo by the time the article is accepted, and the data are now available as Supporting Information for review purpose.en_GB
dc.identifier.eissn1944-7973
dc.identifier.journalWater Resources Researchen_GB
dc.relation.ispartofWater Resources Research, 58(11)
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2022-10-16
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2022-10-21
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-11-03T11:22:05Z
refterms.versionFCDAM
refterms.dateFOA2022-11-03T11:26:18Z
refterms.panelBen_GB
refterms.dateFirstOnline2022-10-21


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