Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020.
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157541 https://doi.org/10.3390/hydrology10100196 |
Resumo: | Abstract: Research on water quality is a fundamental step in supporting the maintenance of environmental and human health. The elements involved in water quality analysis are multidimensional, because numerous characteristics can be measured simultaneously. This multidimensional character encourages researchers to statistically examine the data generated through multivariate statistical analysis (MSA). The objective of this review was to explore the research on water quality through MSA between the years 2001 and 2020, present in the Web of Science (WoS) database. Annual results, WoS subject categories, conventional journals, most cited publications, keywords, water sample types analyzed, country or territory where the study was conducted and most used multivariate statistical analyses were topics covered. The results demonstrate a considerable increase in research using MSA in water quality studies in the last twenty years, especially in developing countries. River, groundwater and lake were the most studied water sample types. In descending order, principal component analysis (PCA), hierarchical cluster analysis (HCA), factor analysis (FA) and discriminant analysis (DA) were the most used techniques. This review presents relevant information for researchers in choosing the most appropriate methods to analyze water quality data. |
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Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020.Multivariate analysisWater qualityMonitoringPrincipal component analysisAbstract: Research on water quality is a fundamental step in supporting the maintenance of environmental and human health. The elements involved in water quality analysis are multidimensional, because numerous characteristics can be measured simultaneously. This multidimensional character encourages researchers to statistically examine the data generated through multivariate statistical analysis (MSA). The objective of this review was to explore the research on water quality through MSA between the years 2001 and 2020, present in the Web of Science (WoS) database. Annual results, WoS subject categories, conventional journals, most cited publications, keywords, water sample types analyzed, country or territory where the study was conducted and most used multivariate statistical analyses were topics covered. The results demonstrate a considerable increase in research using MSA in water quality studies in the last twenty years, especially in developing countries. River, groundwater and lake were the most studied water sample types. In descending order, principal component analysis (PCA), hierarchical cluster analysis (HCA), factor analysis (FA) and discriminant analysis (DA) were the most used techniques. This review presents relevant information for researchers in choosing the most appropriate methods to analyze water quality data.Na publicação: Daphne H. F. Muniz; Eduardo C. Oliveira-Filho.DAPHNE HELOISA DE FREITAS MUNIZ, CPAC; EDUARDO CYRINO DE OLIVEIRA FILHO, CPAC.MUNIZ, D. H. de F.OLIVEIRA FILHO, E. C. de2023-10-26T18:33:10Z2023-10-26T18:33:10Z2023-10-262023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleHydrology, v. 10, n. 10, 2023. p.196.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157541https://doi.org/10.3390/hydrology10100196enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2023-10-26T18:33:10Zoai:www.alice.cnptia.embrapa.br:doc/1157541Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542023-10-26T18:33:10falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-10-26T18:33:10Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. |
title |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. |
spellingShingle |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. MUNIZ, D. H. de F. Multivariate analysis Water quality Monitoring Principal component analysis |
title_short |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. |
title_full |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. |
title_fullStr |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. |
title_full_unstemmed |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. |
title_sort |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. |
author |
MUNIZ, D. H. de F. |
author_facet |
MUNIZ, D. H. de F. OLIVEIRA FILHO, E. C. de |
author_role |
author |
author2 |
OLIVEIRA FILHO, E. C. de |
author2_role |
author |
dc.contributor.none.fl_str_mv |
DAPHNE HELOISA DE FREITAS MUNIZ, CPAC; EDUARDO CYRINO DE OLIVEIRA FILHO, CPAC. |
dc.contributor.author.fl_str_mv |
MUNIZ, D. H. de F. OLIVEIRA FILHO, E. C. de |
dc.subject.por.fl_str_mv |
Multivariate analysis Water quality Monitoring Principal component analysis |
topic |
Multivariate analysis Water quality Monitoring Principal component analysis |
description |
Abstract: Research on water quality is a fundamental step in supporting the maintenance of environmental and human health. The elements involved in water quality analysis are multidimensional, because numerous characteristics can be measured simultaneously. This multidimensional character encourages researchers to statistically examine the data generated through multivariate statistical analysis (MSA). The objective of this review was to explore the research on water quality through MSA between the years 2001 and 2020, present in the Web of Science (WoS) database. Annual results, WoS subject categories, conventional journals, most cited publications, keywords, water sample types analyzed, country or territory where the study was conducted and most used multivariate statistical analyses were topics covered. The results demonstrate a considerable increase in research using MSA in water quality studies in the last twenty years, especially in developing countries. River, groundwater and lake were the most studied water sample types. In descending order, principal component analysis (PCA), hierarchical cluster analysis (HCA), factor analysis (FA) and discriminant analysis (DA) were the most used techniques. This review presents relevant information for researchers in choosing the most appropriate methods to analyze water quality data. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-26T18:33:10Z 2023-10-26T18:33:10Z 2023-10-26 2023 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Hydrology, v. 10, n. 10, 2023. p.196. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157541 https://doi.org/10.3390/hydrology10100196 |
identifier_str_mv |
Hydrology, v. 10, n. 10, 2023. p.196. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157541 https://doi.org/10.3390/hydrology10100196 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
institution |
EMBRAPA |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503551252168704 |