Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil)
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
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/54180 |
Resumo: | The Marizal/São Sebastião aquifer system is the main water supply of the municipality of Alagoinhas in the state of Bahia. However, anthropic interventions contribute to soil and groundwater pollution, increasing the need for related research. Multivariate statistical analysis is a widely used tool, helping in the investigation of groundwater quality while being capable of simultaneously evaluating diverse variables of a sample set. In this study, factor analysis and multivariate cluster analysis methodologies were applied. Ten of the most influential variables for groundwater quality were selected and then grouped into two factors. The first factor included electrical conductivity, salinity, calcium, chloride, sulfate, manganese, and iron, which are indicators of water salinity. The second factor encompassed pH, bicarbonate, and phosphate, indicating anthropic interventions and alkalinity in the environment. The multivariate cluster analysis was applied to the parameters of both factors, resulting in dendrograms with four clusters. The present study showed that the multivariate statistical analysis is an efficient tool for monitoring and can contribute to the management of groundwater quality. |
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Anuário do Instituto de Geociências (Online) |
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Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil)Sedimentary aquiferFactor analysisCluster analysisThe Marizal/São Sebastião aquifer system is the main water supply of the municipality of Alagoinhas in the state of Bahia. However, anthropic interventions contribute to soil and groundwater pollution, increasing the need for related research. Multivariate statistical analysis is a widely used tool, helping in the investigation of groundwater quality while being capable of simultaneously evaluating diverse variables of a sample set. In this study, factor analysis and multivariate cluster analysis methodologies were applied. Ten of the most influential variables for groundwater quality were selected and then grouped into two factors. The first factor included electrical conductivity, salinity, calcium, chloride, sulfate, manganese, and iron, which are indicators of water salinity. The second factor encompassed pH, bicarbonate, and phosphate, indicating anthropic interventions and alkalinity in the environment. The multivariate cluster analysis was applied to the parameters of both factors, resulting in dendrograms with four clusters. The present study showed that the multivariate statistical analysis is an efficient tool for monitoring and can contribute to the management of groundwater quality.Universidade Federal do Rio de Janeiro2023-06-27info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/vnd.openxmlformats-officedocument.wordprocessingml.documenthttps://revistas.ufrj.br/index.php/aigeo/article/view/5418010.11137/1982-3908_2023_46_54180Anuário do Instituto de Geociências; v. 46 (2023)Anuário do Instituto de Geociências; Vol. 46 (2023)1982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/54180/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/54180/39142Copyright (c) 2023 Anuário do Instituto de Geociênciasinfo:eu-repo/semantics/openAccessda Costa, Maíra SampaioGomes, Maria da Conceição Rabelode Morais Nascimento, Sérgio Augusto2023-06-27T14:01:01Zoai:ojs.pkp.sfu.ca:article/54180Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2023-06-27T14:01:01Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false |
dc.title.none.fl_str_mv |
Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil) |
title |
Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil) |
spellingShingle |
Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil) da Costa, Maíra Sampaio Sedimentary aquifer Factor analysis Cluster analysis |
title_short |
Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil) |
title_full |
Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil) |
title_fullStr |
Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil) |
title_full_unstemmed |
Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil) |
title_sort |
Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil) |
author |
da Costa, Maíra Sampaio |
author_facet |
da Costa, Maíra Sampaio Gomes, Maria da Conceição Rabelo de Morais Nascimento, Sérgio Augusto |
author_role |
author |
author2 |
Gomes, Maria da Conceição Rabelo de Morais Nascimento, Sérgio Augusto |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
da Costa, Maíra Sampaio Gomes, Maria da Conceição Rabelo de Morais Nascimento, Sérgio Augusto |
dc.subject.por.fl_str_mv |
Sedimentary aquifer Factor analysis Cluster analysis |
topic |
Sedimentary aquifer Factor analysis Cluster analysis |
description |
The Marizal/São Sebastião aquifer system is the main water supply of the municipality of Alagoinhas in the state of Bahia. However, anthropic interventions contribute to soil and groundwater pollution, increasing the need for related research. Multivariate statistical analysis is a widely used tool, helping in the investigation of groundwater quality while being capable of simultaneously evaluating diverse variables of a sample set. In this study, factor analysis and multivariate cluster analysis methodologies were applied. Ten of the most influential variables for groundwater quality were selected and then grouped into two factors. The first factor included electrical conductivity, salinity, calcium, chloride, sulfate, manganese, and iron, which are indicators of water salinity. The second factor encompassed pH, bicarbonate, and phosphate, indicating anthropic interventions and alkalinity in the environment. The multivariate cluster analysis was applied to the parameters of both factors, resulting in dendrograms with four clusters. The present study showed that the multivariate statistical analysis is an efficient tool for monitoring and can contribute to the management of groundwater quality. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06-27 |
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/54180 10.11137/1982-3908_2023_46_54180 |
url |
https://revistas.ufrj.br/index.php/aigeo/article/view/54180 |
identifier_str_mv |
10.11137/1982-3908_2023_46_54180 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistas.ufrj.br/index.php/aigeo/article/view/54180/pdf https://revistas.ufrj.br/index.php/aigeo/article/view/54180/39142 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Anuário do Instituto de Geociências info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Anuário do Instituto de Geociências |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/vnd.openxmlformats-officedocument.wordprocessingml.document |
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. 46 (2023) Anuário do Instituto de Geociências; Vol. 46 (2023) 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|| |
_version_ |
1797053535739707392 |