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Spatial and multivariate statistics in assessing water quality in the North Sea

2024-07Documento de conferência Acesso aberto
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datacite.subject.fosCiências Naturais
datacite.subject.fosEngenharia e Tecnologia
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg13:Ação Climática
datacite.subject.sdg14:Proteger a Vida Marinha
dc.contributor.authorOdy, Christopher
dc.contributor.authorRamos, Maria do Rosário
dc.contributor.authorCarolino, Elisabete Teresa Mata Almeida
dc.contributor.editorGervasi , O.
dc.contributor.editorMurgante , B.
dc.contributor.editorGarau , C.
dc.contributor.editorTaniar, D.
dc.contributor.editorRocha , A. M. A. C.
dc.contributor.editorLago , M. N. Faginas
dc.date.accessioned2026-02-16T09:55:50Z
dc.date.available2026-02-16T09:55:50Z
dc.date.issued2024-07
dc.description.abstractThe Southern North Sea region plays a vital role for both the economy and society of the surrounding countries. Analyzing the quality of your water is a critical process that involves an assessment of physical, chemical, and biological parameters, essential to guarantee environmental sustainability and the health of local communities and marine ecosystems. Using Multivariate and Spatial Statistics methods, this study seeks to identify spatial patterns and autocorrelations to assess water quality in that region. The data set used was taken on a scientific cruise carried out in December 2020 aboard the RV Meteor vessel, led by a team of German researchers. The raw data went through pretreatment guided by the Data Quality Control protocol of SeaDataNet, an international oceanography project aimed at making European maritime data available. Spike and gradient tests were performed, in addition to data standardization and imputation through inverse distance weighting interpolation. For a better understanding of the scientific area, the data were aggregated by zones for certain analyses, and were sometimes considered globally. An exploratory spatial data analysis (ESDA) was carried out in order to summarize its main characteristics. A reduction in the dimensionality of the original data was carried out through principal component analysis as an auxiliary tool for spatial analysis. The Spatial autocorrelation is analyzed by calculating global and local Moran’s I Statistics. The outcomes indicate a significant spatial autocorrelation for all variables considered in the freshwater areas and a notable range flattening of the variables in the open sea areas, which possibly caused the lack of significant spatial autocorrelation in those areas.eng
dc.identifier.citationOdy C, Ramos MR, Carolino E. Spatial and multivariate statistics in assessing water quality in the North Sea. In: Gervasi O, Murgante B, Garau C, Taniar D, Rocha AM, Faginas Lago MN, editors. Computational science and its applications – ICCSA 2024 Workshops. Lecture notes in computer science, Vol. 14816, Cham: Springer; 2024. p. 177-93.
dc.identifier.doi10.1007/978-3-031-65223-3_12
dc.identifier.isbn978-3-031-65223-3
dc.identifier.urihttp://hdl.handle.net/10400.2/21304
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Publishing Company
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-031-65223-3_12
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectExploratory spatial data analysis
dc.subjectPrincipal components
dc.subjectSpatial correlation
dc.subjectWater quality
dc.subjectMultivariate statistics
dc.subjectNorth Sea
dc.subjectImputation
dc.titleSpatial and multivariate statistics in assessing water quality in the North Seaeng
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferenceDate2024
oaire.citation.endPage193
oaire.citation.startPage177
oaire.citation.titleComputational science and its applications – ICCSA 2024. Workshops. Lecture notes in Computer Science
oaire.citation.volume14816
oaire.versionhttp://purl.org/coar/version/c_fa2ee174bc00049f
person.familyNameRamos
person.familyNameCarolino
person.givenNameMaria do Rosário
person.givenNameElisabete Teresa Mata Almeida
person.identifier206757
person.identifier.ciencia-id3A1F-E648-078D
person.identifier.ciencia-id1216-EFA3-1E0F
person.identifier.orcid0000-0001-9114-0807
person.identifier.orcid0000-0003-4165-7052
person.identifier.ridP-4530-2015
person.identifier.scopus-author-id54403639500
relation.isAuthorOfPublicationc6cdb7b7-607d-4775-947f-d153ff6dded2
relation.isAuthorOfPublication421273fb-f38a-4cbd-9991-fd65e527674c
relation.isAuthorOfPublication.latestForDiscoveryc6cdb7b7-607d-4775-947f-d153ff6dded2

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Matemática e Estatística | Comunicações em congressos, conferências e seminários / Communications in congresses, conferences