Utilizing chemometric techniques to evaluate water quality spatial and temporal variation. A case study: Bahr El-Baqar drain — Egypt

Abstract

This study employed factor analysis (FA)/Principal component analysis (PCA), and hierarchical cluster analysis (HCA), as chemometric techniques in Bahr El-Baqar drain, Egypt to nominate and assess the most representative parameters of the present water quality status, and to assort the sampling sites according to the similarity of the selected parameters, in summer and winter seasons. The water quality data divided the drain into two datasets (before and after El-Salam canal) according to calculated values of the means percentage difference (%) and paired t-test. FA/PCA, selected four factors as indicators of water quality, explaining the cumulative variance ranged (76.0 - 81.1)%. The HCA grouped the sampling sites before the El-Salam canal into four, and five clusters during summer, and winter, respectively while the sampling sites after the El-Salam canal were grouped into three, and four clusters during summer, and winter, respectively showing that the sampling sites are influenced by the seasonal variation. It was revealed that the major sources of variability in Bahr El-Baqar drain are foremost from an anthropogenic origin and thereafter governed by biogenic and organic activities followed by agricultural activities. Finally, this study emphasizes chemometric techniques as a powerful and useful tool to understanding the water quality spatial and temporal variations and their causes. This can support cost, effort, and time reduction, thereby reinforcing regular water quality monitoring plans and may assist in the establishment of guidelines and regulatory plans. • FA/PCA, HCA, and Pearson’s correlation were adopted as chemometric techniques. • Two datasets were created according to the means % difference and paired t-test. • 4 factors explained water quality indicators with cumulative var. (76.0 - 81.1)%. • Sources of variability are anthropogenic/biogenic, organic and agricultural. • Chemometrics evaluated water quality spatial and temporal variations powerfully.