Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater

Detalhes bibliográficos
Autor(a) principal: de Araújo, Karen Vendramini
Data de Publicação: 2024
Outros Autores: Freire, George Satander Sá, Cavalcante, Itabaraci Nazareno, de Oliveira, Rafael Mota, Freire, Diolande Ferreira Gomes, Gomes, Maria da Conceição Rabelo, Peixoto, Filipe da Silva
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anuário do Instituto de Geociências (Online)
Texto Completo: https://revistas.ufrj.br/index.php/aigeo/article/view/49797
Resumo: The objective of the present study was to identify the most influent parameters in the composition of groundwater in the municipality of Icapuí, Ceará - Brazil, seeking correlations with the composition of the percolating aquifer formations that can be associated with the sources of these components. For this purpose, multivariate statistical techniques were applied by means of a Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The PCA allowed a reduction of physicochemical parameters and determined the two components responsible for approximately 86% of total variance in the data for both sampling periods (rainy and dry). The first component is represented by variables that indicate natural rock weathering processes, and the second comprises seasonality and pollution indicators. Samples were also correlated through HCA according to compositional similarities, which were associated with possible natural or human sources.
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spelling Multivariate Statistics Applied to the Identification of Compositional Control Parameters for GroundwaterFactorial analysisPrincipal component analysisHierarchical clusterThe objective of the present study was to identify the most influent parameters in the composition of groundwater in the municipality of Icapuí, Ceará - Brazil, seeking correlations with the composition of the percolating aquifer formations that can be associated with the sources of these components. For this purpose, multivariate statistical techniques were applied by means of a Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The PCA allowed a reduction of physicochemical parameters and determined the two components responsible for approximately 86% of total variance in the data for both sampling periods (rainy and dry). The first component is represented by variables that indicate natural rock weathering processes, and the second comprises seasonality and pollution indicators. Samples were also correlated through HCA according to compositional similarities, which were associated with possible natural or human sources.Universidade Federal do Rio de Janeiro2024-04-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/4979710.11137/1982-3908_2024_47_49797Anuário do Instituto de Geociências; v. 47 (2024): Anuário do Instituto de GeociênciasAnuário do Instituto de Geociências; Vol. 47 (2024): Anuário do Instituto de Geociências1982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/49797/40474Copyright (c) 2024 Anuário do Instituto de Geociênciashttps://creativecommons.org/licenses/by/4.0/deed.eninfo:eu-repo/semantics/openAccessde Araújo, Karen VendraminiFreire, George Satander SáCavalcante, Itabaraci Nazarenode Oliveira, Rafael MotaFreire, Diolande Ferreira GomesGomes, Maria da Conceição RabeloPeixoto, Filipe da Silva2024-04-03T15:59:24Zoai:ojs.pkp.sfu.ca:article/49797Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2024-04-03T15:59:24Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
title Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
spellingShingle Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
de Araújo, Karen Vendramini
Factorial analysis
Principal component analysis
Hierarchical cluster
title_short Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
title_full Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
title_fullStr Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
title_full_unstemmed Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
title_sort Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
author de Araújo, Karen Vendramini
author_facet de Araújo, Karen Vendramini
Freire, George Satander Sá
Cavalcante, Itabaraci Nazareno
de Oliveira, Rafael Mota
Freire, Diolande Ferreira Gomes
Gomes, Maria da Conceição Rabelo
Peixoto, Filipe da Silva
author_role author
author2 Freire, George Satander Sá
Cavalcante, Itabaraci Nazareno
de Oliveira, Rafael Mota
Freire, Diolande Ferreira Gomes
Gomes, Maria da Conceição Rabelo
Peixoto, Filipe da Silva
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv de Araújo, Karen Vendramini
Freire, George Satander Sá
Cavalcante, Itabaraci Nazareno
de Oliveira, Rafael Mota
Freire, Diolande Ferreira Gomes
Gomes, Maria da Conceição Rabelo
Peixoto, Filipe da Silva
dc.subject.por.fl_str_mv Factorial analysis
Principal component analysis
Hierarchical cluster
topic Factorial analysis
Principal component analysis
Hierarchical cluster
description The objective of the present study was to identify the most influent parameters in the composition of groundwater in the municipality of Icapuí, Ceará - Brazil, seeking correlations with the composition of the percolating aquifer formations that can be associated with the sources of these components. For this purpose, multivariate statistical techniques were applied by means of a Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The PCA allowed a reduction of physicochemical parameters and determined the two components responsible for approximately 86% of total variance in the data for both sampling periods (rainy and dry). The first component is represented by variables that indicate natural rock weathering processes, and the second comprises seasonality and pollution indicators. Samples were also correlated through HCA according to compositional similarities, which were associated with possible natural or human sources.
publishDate 2024
dc.date.none.fl_str_mv 2024-04-03
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/49797
10.11137/1982-3908_2024_47_49797
url https://revistas.ufrj.br/index.php/aigeo/article/view/49797
identifier_str_mv 10.11137/1982-3908_2024_47_49797
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/49797/40474
dc.rights.driver.fl_str_mv Copyright (c) 2024 Anuário do Instituto de Geociências
https://creativecommons.org/licenses/by/4.0/deed.en
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 Anuário do Instituto de Geociências
https://creativecommons.org/licenses/by/4.0/deed.en
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
dc.source.none.fl_str_mv Anuário do Instituto de Geociências; v. 47 (2024): Anuário do Instituto de Geociências
Anuário do Instituto de Geociências; Vol. 47 (2024): Anuário do Instituto de Geociências
1982-3908
0101-9759
reponame:Anuário do Instituto de Geociências (Online)
instname:Universidade Federal do Rio de Janeiro (UFRJ)
instacron:UFRJ
instname_str Universidade Federal do Rio de Janeiro (UFRJ)
instacron_str UFRJ
institution UFRJ
reponame_str Anuário do Instituto de Geociências (Online)
collection Anuário do Instituto de Geociências (Online)
repository.name.fl_str_mv Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)
repository.mail.fl_str_mv anuario@igeo.ufrj.br||
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