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

2024-07Journal article Embargoed access
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dc.contributor.authorOdy, Christopher
dc.contributor.authorRamos, M. Rosário
dc.contributor.authorCarolino, Elisabete
dc.date.accessioned2024-11-18T15:06:57Z
dc.date.embargo2026-11-18
dc.date.issued2024-07
dc.description.abstractThe Southern North Sea region plays a vital role in 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 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
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.pt_PT
dc.identifier.doi10.1007/978-3-031-65223-3_12pt_PT
dc.identifier.isbn9783031652233
dc.identifier.urihttp://hdl.handle.net/10400.21/17925
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-031-65223-3_12pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectExploratory spatial data analysispt_PT
dc.subjectPrincipal componentspt_PT
dc.subjectSpatial correlationpt_PT
dc.subjectWater qualitypt_PT
dc.subjectMultivariate statisticspt_PT
dc.subjectNorth Seapt_PT
dc.titleSpatial and multivariate statistics in assessing water quality in the North Seapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage193pt_PT
oaire.citation.startPage177pt_PT
oaire.citation.volume14816pt_PT
person.familyNameCarolino
person.givenNameElisabete
person.identifier.ciencia-id1216-EFA3-1E0F
person.identifier.orcid0000-0003-4165-7052
person.identifier.ridF-1012-2015
person.identifier.scopus-author-id25821697000
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication77930d39-ed34-44dc-a4a6-9bf833e5e688
relation.isAuthorOfPublication.latestForDiscovery77930d39-ed34-44dc-a4a6-9bf833e5e688

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