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Registro Completo |
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
26/10/2023 |
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Data da última atualização: |
26/10/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MUNIZ, D. H. de F.; OLIVEIRA FILHO, E. C. de. |
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Afiliação: |
DAPHNE HELOISA DE FREITAS MUNIZ, CPAC; EDUARDO CYRINO DE OLIVEIRA FILHO, CPAC. |
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Título: |
Multivariate statistical analysis for water quality assessment: a review of research published between 2001 and 2020. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Hydrology, v. 10, n. 10, 2023. p.196. |
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DOI: |
https://doi.org/10.3390/hydrology10100196 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Daphne H. F. Muniz; Eduardo C. Oliveira-Filho. |
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Conteúdo: |
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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Thesaurus Nal: |
Monitoring; Multivariate analysis; Principal component analysis; Water quality. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1157541/1/hydrology-10-00196.pdf
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Marc: |
LEADER 02081naa a2200205 a 4500
001 2157541
005 2023-10-26
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/hydrology10100196$2DOI
100 1 $aMUNIZ, D. H. de F.
245 $aMultivariate statistical analysis for water quality assessment$ba review of research published between 2001 and 2020.$h[electronic resource]
260 $c2023
500 $aNa publicação: Daphne H. F. Muniz; Eduardo C. Oliveira-Filho.
520 $aAbstract: 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.
650 $aMonitoring
650 $aMultivariate analysis
650 $aPrincipal component analysis
650 $aWater quality
700 1 $aOLIVEIRA FILHO, E. C. de
773 $tHydrology$gv. 10, n. 10, 2023. p.196.
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