Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis

Abstract

ABSTRACT This study evaluated the efficiency of the water quality monitoring network of the Sergipe river basin, using multivariate data analysis, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). The PCA was applied to a data matrix consisting of 12 sampling stations and mean concentrations of 23 water quality parameters, obtained in four sampling campaigns from June/2013 to November/2015. All 12 sampling stations were considered as main (weight>0.7) and therefore should remain in the monitoring program. The PCA pointed out that of the 23 measured parameters, only 16 are essential for water quality assessment, in the dry period and 17 in the rainy season. The HCA separated the stations of the monitoring network in 4 groups according to the water quality characteristics, considering the natural and anthropogenic impacts. The main impacts were originated from natural sources (mineral constituents) and the anthropogenic contributions were associated with urban input, sewage, industrial dumps and surface runoff from agricultural areas.