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Exploring multiscale variability in groundwater quality: a comparative analysis of spatial and temporal patterns via clustering

2023-04Journal article Open access
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dc.contributor.authorMohsine, Ismail
dc.contributor.authorKacimi, Ilias
dc.contributor.authorAbraham, Shiny
dc.contributor.authorValles, Vincent
dc.contributor.authorBarbiero, Laurent
dc.contributor.authorDassonville, Fabrice
dc.contributor.authorBahaj, Tarik
dc.contributor.authorKassou, Nadia
dc.contributor.authorTouiouine, Abdessamad
dc.contributor.authorJabrane, Meryem
dc.contributor.authorTouzani, Meryem
dc.contributor.authorEl Mahrad, Badr
dc.contributor.authorBouramtane, Tarik
dc.date.accessioned2023-05-20T12:07:14Z
dc.date.available2023-05-20T12:07:14Z
dc.date.issued2023-04
dc.description.abstractDefining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Cote d'Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters. The fractions of unexplained variance (FUV) and ANOVA R-2 were calculated to assess the performance of the method at each scale. For example, for the total dissolved load (TDS) parameter, the temporal variance was quantified at 0.36 and the clustering causes a loss of information with an R-2 going from 0.63 to 0.4 from the scale of the sampling point to that of the GWB cluster. The results show that the logarithmic transformation reduces the effect of outliers and improves the quality of the GWB clustering. The groups of GWBs are homogeneous and clearly distinguishable from each other. The results can be used to define specific management and protection strategies for each group. The study also highlights the need to take into account the temporal variability of groundwater quality when implementing monitoring and management programs.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/w15081603pt_PT
dc.identifier.eissn2073-4441
dc.identifier.urihttp://hdl.handle.net/10400.1/19603
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectGroundwater qualitypt_PT
dc.subjectEuropean Union Water Framework Directivept_PT
dc.subjectGroundwater Bodiespt_PT
dc.subjectHydrogeological clusterspt_PT
dc.subjectEnvironmental outlierspt_PT
dc.subjectPACA region of Francept_PT
dc.titleExploring multiscale variability in groundwater quality: a comparative analysis of spatial and temporal patterns via clusteringpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue8pt_PT
oaire.citation.startPage1603pt_PT
oaire.citation.titleWaterpt_PT
oaire.citation.volume15pt_PT
person.familyNameEl Mahrad
person.givenNameBadr
person.identifier702391
person.identifier.ciencia-id1414-4FFE-F235
person.identifier.orcid0000-0001-6485-0539
person.identifier.scopus-author-id57209271531
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationec0db23f-77b1-4d57-8011-8930aa8d0509
relation.isAuthorOfPublication.latestForDiscoveryec0db23f-77b1-4d57-8011-8930aa8d0509

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