Surface water quality assessment in the semi-arid area by a combination of heavy metal pollution indices and statistical approaches for sustainable management

Abstract

The current study consists of evaluating surface water pollution using an integrated approach based on pollution assessment indices and statistical approaches (principal component analysis (PCA), logistic regression (LR) analysis, and cluster analysis (CA)). Thirty-four water samples from the High Ziz basin (semi-arid area), occupied by abandoned mine sites in SE Morocco, were collected and analysed for physicochemical parameters and heavy metals (Cu, Zn, Fe, Pb, Ni, and Cd). The mean concentrations of Pb, Zn, Cu, and Fe in the water samples were 41.9 mg/L, 14.8 mg/L, 20.1 mg/L, and 8.1 mg/L, respectively. The large part of the samples is in a near neutral high metal zone based on the plot of heavy metals (Fe+Cu+Pb+Zn+Ni) versus pH. The HPI, HEI, and CI pollution indices were found to average around 586.2, 321.8, and 315.8 respectively, these show strong correlations with heavy metals. The HPI, CI, and HEI values were above 100 as critical pollution index value. These indices indicate that the surface water samples are critically polluted with respect to studied heavy metals. Therefore, the high metal content of the studied samples should be of great concern for the irrigation activity. Moreover, the water quality index (WQI) shows that 76% of the water samples are unsuitable for drinking. The CA analysis grouped 34 samples into four clusters according to the similarity proprieties observed in water quality. Furthermore, the total variance is explained by 75.7% of the water quality with four components according to PCA analysis. Based on LR analysis, the hazard area is strongly correlated with the river system near abandoned mine sites, agricultural and urban areas. The water quality in the study area is controlled by natural/geogenic processes according to the interpretation of pollution indices and statistical approaches (PCA, CA, and LR). The distribution intensity of Pb, Zn, Cu, and Fe metals in the study area's surface water constitutes an environmental concern and needs a monitoring network for sustainable water resources management.