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Pesticide concentration monitoring: Investigating spatio‐temporal patterns in left censored data

hal.structure.identifierStatistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
dc.contributor.authorLaroche, Clément
HAL ID: 177120
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorOlteanu, Madalina
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRossi, Fabrice
dc.date.accessioned2023-02-06T16:22:29Z
dc.date.available2023-02-06T16:22:29Z
dc.date.issued2022
dc.identifier.issn1099-095X
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/23983
dc.language.isoenen
dc.subjectpesticide concentration monitoringen
dc.subjectleft censored dataen
dc.subjectchange-pointdetectionen
dc.subjectanomaly detectionen
dc.subjectPareto fronten
dc.subjectwater pollutionen
dc.subjectprosulfocarben
dc.subject.ddc519en
dc.titlePesticide concentration monitoring: Investigating spatio‐temporal patterns in left censored dataen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversitytrue
dc.description.abstractenMonitoring pesticide concentration is very important for public authorities given the major concerns for environmental safety and the likelihood for increased public health risks. An important aspect of this process consists in locating abnormal signals, from a large amount of collected data. This kind of data is usually complex since it suffers from limits of quantification leading to left censored observations, and from the sampling procedure which is irregular in time and space across measuring stations. The present manuscript tackles precisely the issue of detecting spatio-temporal collective anomalies in pesticide concentration levels, and introduces a novel methodology for dealing with spatio-temporal heterogeneity. The latter combines a change-point detection procedure applied to the series of maximum daily values across all stations, and a clustering step aimed at a spatial segmentation of the stations. Limits of quantification are handled in the change-point procedure, by supposing an underlying left-censored parametric model, piece-wise stationary. Spatial segmentation takes into account the geographical conditions, and may be based on river network, wind directions, etc. Conditionally to the temporal segment and the spatial cluster, one may eventually analyse the data and identify contextual anomalies. The proposed procedure is illustrated in detail on a data set containing the prosulfocarb concentration levels in surface waters in Centre-Val de Loire region.en
dc.relation.isversionofjnlnameEnvironmetrics
dc.relation.isversionofjnldate2022
dc.relation.isversionofjnlpages31en
dc.relation.isversionofdoi10.1002/env.2756en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2023-02-06T16:17:29Z
hal.author.functionaut
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