Análise espacial e temporal de dados fisicos e químicos de águas superficiais, das sub-bacias do rio Turvo Sujo e do ribeirão São Bartolomeu, por quimiometria

• Ano de defesa: 2012
• Autor(a) principal: Fonseca, Rita Aparecida Dutra
• Orientador(a): Reis, Efraim Lázaro
• Banca de defesa: Neves, Antônio Augusto , Matos, Antonio Teixeira de , Fidencio, Paulo Henrique
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Doutorado em Agroquímica
• Departamento: Agroquímica analítica; Agroquímica inorgânica e Físico-química; Agroquímica orgânica
• País: BR
• Palavras-chave em Português: Águas naturais; Quimiometria; Mapas auto-organizáveis; Capacidade de complexação
• Palavras-chave em Inglês: Natural waters; Chemometrics; Self organizing maps; Complexation capacity
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA
• Link de acesso: http://locus.ufv.br/handle/123456789/220

This study aims to analyze the spatial and temporal variation of physical and chemical data of surface water collected at 23 local points where 15 of them along the sub-basin of the Turvo Sujo river and 8 along the sub-basin of the São Bartolomeu stream. The sampling was collected during nine months in the region known as Zona da Mata Mineira at the Minas Gerais state, Brasil. The physical-chemical data were analyzed for: the quantification of heavy metals in trace form by absorption spectrometry and atomic emission; determination of the capacity of complexation of the organic by the conditional constant stability in the presence of copper using stripping analysis of anodic voltammetry with differential pulse, and to obtain relevant parameters concerning the quality of surface waters. The exploratory analysis of the chemical data was performed applying chemometric methods as Principal Component Analysis (PCA), which aims reduction of the size of the original data set keeping as much information as possible; the Hierarchical Cluster Analysis (HCA) which has the purpose of organizing the samples into groups based on similarities between samples of the same group and dissimilarities between samples from different groups. The Euclidean distances are calculated between each sample and the application of the Kohonen network Self Organizing Maps (SOM) has the main characteristic of forming a two-dimensional topographic map of the standard input pattern in which samples of spatial locations in the grid are indicative of the intrinsic statistical characteristics from the standard input pattern. In this way models of seasonal and local behavior of this watershed basin can thus be establised.