Comprehensive assessment of surface water quality and pollution sources in Al-Muzaynah dam lake, Syria: insights from multivariate statistical analysis

This paper comprehensively assesses water quality across artificial lakes in the Mediterranean environment, an area increasingly vulnerable to pollution from human activities. The intrusion of pollutants not only disrupts the ecological integrity of these lakes but also undermines their social and economic value to the surrounding communities. By analyzing the pollution process, this paper aims to enhance our understanding of the pollution mechanism and establish recommendations to mitigate the impact effects and restore the vital role of lakes subjected to pollution risk. The paper’s originality lies in its use of real dataset collected from the Al-Muzaynah dam lake in Homs, Syria, to explore the performances of several analysis methods for assessing lake pollution and to establish recommendations for its mitigation. The dataset includes 21 physical, chemical, and bacteriological parameters. It was collected through monthly water sampling from five strategically chosen sites around and within the lake from 2018 to 2019. The collected data underwent rigorous analysis utilizing robust multivariate statistical techniques such as multivariate analysis of variance (MANOVA), hierarchical cluster analysis (CA), principal components analysis (PCA), and factor analysis (FA). The CA successfully identified two distinct clusters representing different pollution sources. Cluster 1 comprised three monitoring stations, which exhibited higher pollution levels attributed to sewage discharge from nearby villages and industrial and tourist activities proximal to the lake. In contrast, Cluster 2 consisted of two monitoring stations associated with lower pollution levels resulting from villages, sewage, runoff, and agricultural sources. The FA revealed three principal components that accounted for an impressive 86.5% of the total temporal and spatial variations observed in the water-quality data. These findings offer valuable insights to water-management authorities, empowering them to make informed decisions regarding the utilization, modification, and determination of a water-quality index. Furthermore, this research contributes to future planning studies aimed at ensuring the provision of safe drinking water. The outcomes of this study serve as a crucial resource for policymakers, researchers, and stakeholders involved in water-resource management, facilitating effective strategies for pollution control, conservation, and sustainable water-management practices.