Mathematical Modeling and Multivariate Statistics as Integrated Tools for Water Quality Management in a Watershed.

Abstract

This study aimed to simulate contamination scenarios in the hydrographic basin of Upper Paraopeba through water quality modeling and evaluate the water quality using multivariate statistical analysis. Two scenarios of intervention in the basin were simulated using the QUAL-UFMG model, considering the establishment of sewage treatment plants (STPs) in the basin municipalities by 2035. Principal components and cluster analysis were used for multivariate statistical analysis, applied to 19 variables at eight monitoring points. In mathematical modeling, it was possible to identify the point at which the pollutant load made the most significant contribution to the overall system. This point is situated near the most urbanized region of the hydrographic basin, at the confluence with the Maranhão River. The principal components analysis presented four components that explained 72.82% of the data variation. The variables that most affect water quality variability are biochemical oxygen demand, electrical conductivity, suspended solids, and dissolved solids. The cluster analysis grouped the eight monitoring stations into three clusters with similarities among the points. The clusters were separated based on physical, hydrological, and anthropogenic factors. One of the clusters consisted of the two stations located in the Maranhão River subbasin, which presented even more deteriorated conditions when compared to the rest of the basin. The results enabled us to conclude that wastewater discharge, surface runoff, and soil erosion all influence water quality. The implementation of secondary treatment was insufficient to meet the Escherichia coli concentration requirements set by local legislation, indicating a need for tertiary treatment. Using the tools in a complementary manner made it possible to indicate the critical point in the basin regarding the sources of contamination. This information is crucial for structuring actions to mitigate pollution and developing plans for effective water resource management.