Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil.

Abstract

This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources.