Métodos estatisticos aplicados a análise da qualidade da água da bacia do Rio Vermelho, Mato Grosso - MT
Data
2019-09-10
Autores
Silva, Mayara Oliveira da
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Universidade Federal de Mato Grosso
Resumo
The intrinsic structure of a water quality dataset describes a characteristic that is characteristic
and essential of a lotic system while the uniqueness describes a differential aspect, which may
be related to an environmental impact. This analysis allows the manager to distinguish, within
a set of variables, which ones can be elected as indicators of impact processes (singularities)
from those that are probably a little variable (intrinsic) system attribute. The aim of this study
is to identify and evaluate the intrinsic structure and singularities of the water body based on
the physical, chemical and biological water quality variables of the Rio Vermelho-MT Basin
using long-term monitoring data (2006 to 2017). The methodology consists of the use of
multivariate statistical methods, factor analysis (FA) and principal component analysis (PCA).
The results indicated that the statistical methods are efficient to rank the parameters, so that the
parameters: color, chemical oxygen demand, total nitrogen, total coliforms, chloride,
unfilterable residue, Escherichia coli, total phosphorus, turbidity, total residue have greater
weight in variance and are the characteristic variables of water body changes while: air
temperature, water temperature; ammoniacal nitrogen; nitrogen nitrite, pH; dissolved oxygen,
BOD5, orthophosphate, average flow; and nitrate may be characterized by low variance as the
intrinsic variables of the river, which are of the natural order of the water body. The
potentialities and limitations of the methodology are discussed.
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Citação
SILVA, Mayara Oliveira da. Métodos estatisticos aplicados a análise da qualidade da água da bacia do Rio Vermelho, Mato Grosso - MT. 2019. 58 f. Dissertação (Mestrado em Gestão e Tecnologia Ambiental) - Universidade Federal de Mato Grosso, Instituto de Ciências Agrárias e Tecnológicas, Rondonópolis, 2019.