Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/50788
Título: Principal component analysis for heavy metals in hydrographic sub-basins of the rivers Capivari and Mortes/MG
Título(s) alternativo(s): Análise de componentes principais para metais pesados ​​em sub-bacias hidrográficas dos rios Capivari e Mortes/MG
Palavras-chave: Anthropogenic interference
Pollution load
Heavy metals
Interferência antropogênica
Carga de poluição
Metais pesados
Data do documento: 2021
Citação: AM NCIO, D. V. et al. Principal component analysis for heavy metals in hydrographic sub-basins of the rivers Capivari and Mortes/MG. Revista Ibero-Americana de Ciências Ambientais, [S.l.], v. 12, n. 4, 2021.
Resumo: Population growth and industrialization are correlated with the contamination of water resources by the release of untreated effluents into water sources. The objective of this work was to characterize heavy metals in sub-basins of the rivers Capivari and Mortes and the variability using principal component analysis (PCA). Three points were sampled at GD1 (P - I at Ingai – Minduri River, P - II at Capivari River and P - III at Ingai – Luminarias River) and three points at GD2 (P - IV at Mortes River, P - V at Peixe River and P - VI at Ribeirao dos Tabuoes). The monitoring period was from April 2015 to February 2016. Analysis of Aluminum, Bromine, Copper, Hexavalent Chromium, Iron, Manganese, Nickel and Zinc were evaluated. We compared the results with the Maximum Allowed Value in agreement with class 2, according to DN COPAM CERH 01/08. We also observed variables above the allowed value due to the discharge of domestic and industrial effluents, interference from precipitation and the contact between livestock and water sources. The principal components analysis (PCA) revealed that on average, the principal component 1 corresponds to 62.2% of the total variability of the data considering GD 1, and, in GD 2, PC 1 is responsible for a higher average percentage of the total variability of the data, corresponding to 73.4%, hence being more representative.
URI: http://repositorio.ufla.br/jspui/handle/1/50788
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