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Water Quality Assessment and Turbidity Prediction Using Multivariate Statistical Techniques: A Case Study of the Cheurfa Dam in Northwestern Algeria

  • ADDOUCHE, Amina (Laboratory of Advanced Materials and Physicochemistry for Environment and Health, Djillali Liabes University of Sidi Bel Abbes) ;
  • RIGHI, Ali (Laboratory of probability and statistics and stochastic processes, Djillali Liabes University of Sidi Bel Abbes) ;
  • HAMRI, Mehdi Mohamed (Computer Science Research Laboratory, Djillali Liabes University of Sidi Bel Abbes) ;
  • BENGHAREZ, Zohra (Laboratory of Advanced Materials and Physicochemistry for Environment and Health, Djillali Liabes University of Sidi Bel Abbes) ;
  • ZIZI, Zahia (Laboratory of Advanced Materials and Physicochemistry for Environment and Health, Djillali Liabes University of Sidi Bel Abbes)
  • Received : 2022.10.11
  • Accepted : 2022.11.24
  • Published : 2022.12.10

Abstract

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH4+), nitrate (NO3-), nitrite (NO2-), phosphate (PO43-), total suspended solids (TSS), biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO43-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R2 = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO3-, NO2-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.

Keywords

Acknowledgement

The authors would like to thank the Directorate General of Scientific Research and Technological Development (DGRSDT) and the Ministry of Higher Education and Scientific Research (MESRS), Algeria, for their support. Also, the authors are grateful to the staff of the National Agency for Water Resources (ANRH) and the National Agency for Dams and Transfers (ANBT) for providing some data used in this study.

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