Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam,Algeria |
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| Authors: | Somia Hamil Siham Arab Amin Chaffai Mounia Baha Abdeslem Arab |
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| Institution: | 1.Laboratory of Dynamic and Biodiversity (LaDyBio),FSB, USTHB,Algiers, Bab Ezzouar,Algeria;2.Laboratory of Eco-Biology Animals (L.E.B.A.),école Normale Supérieure de Kouba Bachir El Ibrahimi,Algiers,Algeria;3.FST/GAT,USTHB,Bab-Ezzouar,Algeria |
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| Abstract: | This study explores the water quality status and pollution sources in Ghrib Dam, Algeria. It allows us to obtain more accurate information on water quality by applying a series of multivariate statistical techniques, including principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (CA), and multiple regression analysis (MRA). On 19 physicochemical parameters dataset over 5 years and from 6 different sites located in and around the lake. One-way analysis of variance (ANOVA) was used to investigate the statistically considerable spatial and seasonal differences. The results of ANOVA suggest that there exist a statistically significant temporal variation in the water quality of the dam for all parameters. On the other hand, only organic matter has a statistically significant spatial variation. In the multiple linear models, an association between organic and inorganic parameters was found; their origin comes from the mechanical erosion process of agricultural lands in the watershed. The PCA/FA identifies five dominant factors as responsible of the data structure, explaining more than 94.96% of the total variance in the water quality dataset. This suggests that the variations in water compounds’ concentration are mainly related to the multiple anthropogenic activities, as well as natural processes. The results of cluster analysis demonstrate that the sampling stations were divided in two similar groups, which indicates spatial homogeneity. While seasonal grouping has showed that the source of pollution was related to the level of runoff in the seasons. |
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