Analyzing the Pollution of Nine Points on the Paraíba do Sul River Using Multivariate Statistics

Objective: The aim of this study is to accurately analyze water quality at strategic monitoring points over a decade through the combined use of statistical methods in the Paraíba do Sul River Basin in the Fluminense region of the state of Rio de Janeiro, Brazil. Theoretical framework: PCA was invented in 1901 by Karl Pearson and today, it is most used as a tool for Exploratory Data Analysis and for making predictive models. PCA can be done by decomposing a covariance matrix into eigenvalues, usually after centering (and normalizing or using Z-scores) the data matrix for each attribute (Derksen et al., 2000; Febriani et al., 2020; Semagn et al., 2000). Method: The data was obtained from the INEA and ANA agencies over a decade at nine points on the Paraíba do Sul River and treated with Multivariate Statistics. Final Considerations: This study showed the adverse effects included the concentration of pollutants, the silting up of the river, the reduction in oxygen and the increase in water temperature, all reflected in sharp drops in the WQI. Implications of the research: The use cases of Multivariate Statistics are multiplying in the scientific literature and are proving to be highly effective in dealing with data where the assumptions of Normality are confirmed. Originality/value: Despite being well-known statistical tools, Factor Maps are widely used and can bring innovations to their application, as in the case of environmental variables.