TY - JOUR
T1 - An integrated approach for assessing surface water quality
T2 - Case of Beni Haroun dam (Northeast Algeria)
AU - Soltani, Ahmed Amin
AU - Bermad, Abdelmalek
AU - Boutaghane, Hamouda
AU - Oukil, Amar
AU - Abdalla, Osman
AU - Hasbaia, Mahmoud
AU - Oulebsir, Rafik
AU - Zeroual, Sara
AU - Lefkir, Abdelouahab
N1 - Funding Information:
The authors would like to thank Agence Nationale des Resources Hydrauliques (ANRH) for their assistance in providing the necessary data. They also are grateful to Professor Abderrahmane BOUDOUKHA of the University of Batna, Algeria, for his pertinent information and valuable advice.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - 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.
AB - 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.
KW - Beni Haroun dam
KW - CCME-WQI
KW - K-means
KW - Ordinary least square
KW - PCA/FA
KW - Water quality
KW - Canada
KW - Water Pollutants, Chemical/analysis
KW - Environmental Monitoring
KW - Water Resources
KW - Algeria
KW - Water Quality
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U2 - 10.1007/s10661-020-08572-z
DO - 10.1007/s10661-020-08572-z
M3 - Article
C2 - 32902799
AN - SCOPUS:85090394303
SN - 0167-6369
VL - 192
JO - Environmental Monitoring and Assessment
JF - Environmental Monitoring and Assessment
IS - 10
M1 - 630
ER -