Selection and grouping of indicators of water quality using Multivariate Statistics

Detalhes bibliográficos
Autor(a) principal: Bertossi, Ana Paula Almeida
Data de Publicação: 2013
Outros Autores: Menezes, João Paulo Cunha de, Cecílio, Roberto Avelino, Garcia, Giovanni de Oliveira, Neves, Mirna Aparecida
Tipo de documento: Artigo
Idioma: por
Título da fonte: Semina. Ciências Agrárias (Online)
Texto Completo: https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/10862
Resumo: Multivariate statistics techniques (Principal Component Analysis and Cluster Analysis) were employed to select the most important parameters that explain water quality variability at a rural watershed in the state of Espírito Santo (Brazil). In addition to group the waters studied for the similarity of features selected to verify the effect of type of soil cover (agriculture, livestock, forest and urban), water resource (surface and underground) and sampling period (rainy and dry seasons). Nineteen physico-chemical parameters of water quality were analyzed: pH, electrical conductivity, total solids, total dissolved solids, total suspended solids, turbidity, biochemical oxygen demand (BOD), ammoniacal nitrogen, nitrate, nitrite, total phosphorous, Ca, Mg, Fe, Na, K, Zn, Cu and total coliform. Application of Principal Component Analysis reduced the 19 parameters to three components that explained 87.53% of the total variance of data set. Water quality parameters that best explained variability of data were: electrical conductivity, total solids, total dissolved solids, turbidity, BOD, nitrate, Ca, Mg, and Na. Application of Cluster Analysis showed four different groups of water quality that differed in concentration of physicochemical characteristics and the type of water resource study, since the collection periods and the type of soil cover did not influence the segregation of groups formed.
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spelling Selection and grouping of indicators of water quality using Multivariate StatisticsSeleção e agrupamento de indicadores da qualidade de águas utilizando Estatística MultivariadaPrincipal component analysisCluster analysisWater quality.Análise de componentes principaisAnálise de agrupamentoQualidade da água.Ciências agráriasMultivariate statistics techniques (Principal Component Analysis and Cluster Analysis) were employed to select the most important parameters that explain water quality variability at a rural watershed in the state of Espírito Santo (Brazil). In addition to group the waters studied for the similarity of features selected to verify the effect of type of soil cover (agriculture, livestock, forest and urban), water resource (surface and underground) and sampling period (rainy and dry seasons). Nineteen physico-chemical parameters of water quality were analyzed: pH, electrical conductivity, total solids, total dissolved solids, total suspended solids, turbidity, biochemical oxygen demand (BOD), ammoniacal nitrogen, nitrate, nitrite, total phosphorous, Ca, Mg, Fe, Na, K, Zn, Cu and total coliform. Application of Principal Component Analysis reduced the 19 parameters to three components that explained 87.53% of the total variance of data set. Water quality parameters that best explained variability of data were: electrical conductivity, total solids, total dissolved solids, turbidity, BOD, nitrate, Ca, Mg, and Na. Application of Cluster Analysis showed four different groups of water quality that differed in concentration of physicochemical characteristics and the type of water resource study, since the collection periods and the type of soil cover did not influence the segregation of groups formed.No presente trabalho empregaram-se técnicas de Estatística Multivariada (Análise de Componentes Principais e Análise de Agrupamento Hierárquico) com o objetivo de selecionar as características físico-químicas mais importantes para explicar a variabilidade da qualidade das águas de uma sub-bacia hidrográfica rural no Sul do Estado do Espírito Santo, além de agrupar as águas estudadas quanto à similaridade das características selecionadas para verificar o efeito do tipo de cobertura do solo (agrícola, pecuário, florestal e urbano), de recurso hídrico (subterrâneo e superficial) e período de coleta (chuva e estiagem). A análise físico-química das águas foi feita por meio da determinação de pH, condutividade elétrica, sólidos totais, sólidos dissolvidos, sólidos suspensos, turbidez, demanda bioquímica de oxigênio (DBO), nitrogênio amoniacal, nitrato, nitrito, fósforo total, Ca, Mg, Fe, Na, K, Zn, Cu e coliformes totais. A Análise de Componentes Principais promoveu a redução de dezenove parâmetros de qualidade em três componentes que explicaram 87,53% da variância total. As características mais representativas da variabilidade da qualidade das águas estudadas foram: condutividade elétrica, sólidos totais, sólidos dissolvidos, turbidez, DBO, nitrato, Ca, Mg e Na. Na Análise de Agrupamento Hierárquico foram formados quatro grupos distintos de qualidade da água que diferiram quanto à concentração das características físico-químicas e quanto ao tipo de recurso hídrico estudado, já os períodos de coleta e o tipo de cobertura do solo não influenciaram na segregação dos grupos formados.UEL2013-10-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPesquisa experimentalapplication/pdfhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/1086210.5433/1679-0359.2013v34n5p2025Semina: Ciências Agrárias; Vol. 34 No. 5 (2013); 2025-2036Semina: Ciências Agrárias; v. 34 n. 5 (2013); 2025-20361679-03591676-546Xreponame:Semina. Ciências Agrárias (Online)instname:Universidade Estadual de Londrina (UEL)instacron:UELporhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/10862/pdf_4Bertossi, Ana Paula AlmeidaMenezes, João Paulo Cunha deCecílio, Roberto AvelinoGarcia, Giovanni de OliveiraNeves, Mirna Aparecidainfo:eu-repo/semantics/openAccess2015-11-19T18:36:23Zoai:ojs.pkp.sfu.ca:article/10862Revistahttp://www.uel.br/revistas/uel/index.php/semagrariasPUBhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/oaisemina.agrarias@uel.br1679-03591676-546Xopendoar:2015-11-19T18:36:23Semina. Ciências Agrárias (Online) - Universidade Estadual de Londrina (UEL)false
dc.title.none.fl_str_mv Selection and grouping of indicators of water quality using Multivariate Statistics
Seleção e agrupamento de indicadores da qualidade de águas utilizando Estatística Multivariada
title Selection and grouping of indicators of water quality using Multivariate Statistics
spellingShingle Selection and grouping of indicators of water quality using Multivariate Statistics
Bertossi, Ana Paula Almeida
Principal component analysis
Cluster analysis
Water quality.
Análise de componentes principais
Análise de agrupamento
Qualidade da água.
Ciências agrárias
title_short Selection and grouping of indicators of water quality using Multivariate Statistics
title_full Selection and grouping of indicators of water quality using Multivariate Statistics
title_fullStr Selection and grouping of indicators of water quality using Multivariate Statistics
title_full_unstemmed Selection and grouping of indicators of water quality using Multivariate Statistics
title_sort Selection and grouping of indicators of water quality using Multivariate Statistics
author Bertossi, Ana Paula Almeida
author_facet Bertossi, Ana Paula Almeida
Menezes, João Paulo Cunha de
Cecílio, Roberto Avelino
Garcia, Giovanni de Oliveira
Neves, Mirna Aparecida
author_role author
author2 Menezes, João Paulo Cunha de
Cecílio, Roberto Avelino
Garcia, Giovanni de Oliveira
Neves, Mirna Aparecida
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Bertossi, Ana Paula Almeida
Menezes, João Paulo Cunha de
Cecílio, Roberto Avelino
Garcia, Giovanni de Oliveira
Neves, Mirna Aparecida
dc.subject.por.fl_str_mv Principal component analysis
Cluster analysis
Water quality.
Análise de componentes principais
Análise de agrupamento
Qualidade da água.
Ciências agrárias
topic Principal component analysis
Cluster analysis
Water quality.
Análise de componentes principais
Análise de agrupamento
Qualidade da água.
Ciências agrárias
description Multivariate statistics techniques (Principal Component Analysis and Cluster Analysis) were employed to select the most important parameters that explain water quality variability at a rural watershed in the state of Espírito Santo (Brazil). In addition to group the waters studied for the similarity of features selected to verify the effect of type of soil cover (agriculture, livestock, forest and urban), water resource (surface and underground) and sampling period (rainy and dry seasons). Nineteen physico-chemical parameters of water quality were analyzed: pH, electrical conductivity, total solids, total dissolved solids, total suspended solids, turbidity, biochemical oxygen demand (BOD), ammoniacal nitrogen, nitrate, nitrite, total phosphorous, Ca, Mg, Fe, Na, K, Zn, Cu and total coliform. Application of Principal Component Analysis reduced the 19 parameters to three components that explained 87.53% of the total variance of data set. Water quality parameters that best explained variability of data were: electrical conductivity, total solids, total dissolved solids, turbidity, BOD, nitrate, Ca, Mg, and Na. Application of Cluster Analysis showed four different groups of water quality that differed in concentration of physicochemical characteristics and the type of water resource study, since the collection periods and the type of soil cover did not influence the segregation of groups formed.
publishDate 2013
dc.date.none.fl_str_mv 2013-10-17
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Pesquisa experimental
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/10862
10.5433/1679-0359.2013v34n5p2025
url https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/10862
identifier_str_mv 10.5433/1679-0359.2013v34n5p2025
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/10862/pdf_4
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv UEL
publisher.none.fl_str_mv UEL
dc.source.none.fl_str_mv Semina: Ciências Agrárias; Vol. 34 No. 5 (2013); 2025-2036
Semina: Ciências Agrárias; v. 34 n. 5 (2013); 2025-2036
1679-0359
1676-546X
reponame:Semina. Ciências Agrárias (Online)
instname:Universidade Estadual de Londrina (UEL)
instacron:UEL
instname_str Universidade Estadual de Londrina (UEL)
instacron_str UEL
institution UEL
reponame_str Semina. Ciências Agrárias (Online)
collection Semina. Ciências Agrárias (Online)
repository.name.fl_str_mv Semina. Ciências Agrárias (Online) - Universidade Estadual de Londrina (UEL)
repository.mail.fl_str_mv semina.agrarias@uel.br
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