An integrated approach for assessing surface water quality: Case of Beni Haroun dam (Northeast Algeria)

• Published in: Environmental monitoring and assessment Vol. 192; no. 10; p. 630
• Main Authors: Soltani, Ahmed Amin, Bermad, Abdelmalek, Boutaghane, Hamouda, Oukil, Amar, Abdalla, Osman, Hasbaia, Mahmoud, Oulebsir, Rafik, Zeroual, Sara, Lefkir, Abdelouahab
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-10-2020; Springer Nature B.V
• Subjects: Algorithms; Analysis; Aquatic organisms; Atmospheric Protection/Air Quality Control/Air Pollution; Cluster analysis; Clustering; Damsites; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental science; Factor analysis; Integrated approach; Learning algorithms; Machine learning; Monitoring/Environmental Analysis; Physicochemical processes; Physicochemical properties; Principal components analysis; Quality assessment; Surface water; Surface water quality; Vector quantization; Water pollution; Water quality; Water resources; Algeria; K-means; Ordinary least square; CCME-WQI; PCA/FA; Water quality; Beni Haroun dam
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-020-08572-z

In this paper, we use an integrated approach to carry out a comprehensive evaluation of water quality in the Beni Haroun (BH) dam, the largest surface water resource in Algeria. Several techniques have been employed under the same framework, including the Canadian Council Ministers Environment Water Quality Index (CCME-WQI), principal component analysis and factor analysis (PCA/FA), the K-means clustering, and the ordinary least square (OLS) analysis. A data set of 22 physicochemical parameters has been collected, over a period of 11 years, from three sampling stations: Ain Smara (ST1) and Menia (ST2), both located upstream of “Wadi Rhumel,” and BH dam station (ST3), located at the dam site. The PCA/FA enables the identification of seven key factors that influence significantly BH dam water quality. The average values of CCME indices at the BH dam were 17, 40, 42, and 32 for drinking, irrigation, industry, and aquatic life purposes, respectively, which indicate poor water quality, according to the CCME categorization scheme. Besides, the K-means algorithm has been proven to be a very useful machine learning tool to detect that the major source of BH dam pollution is “Wadi Rhumel.” Finally, OLS analysis, along with the Mann-Kendall test, highlighted the positive trend of BH dam’s water quality.