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- Title
Hydrochemical characterization of groundwater using multivariate statistical analysis: the Maritime Djeffara shallow aquifer (Southeastern Tunisia).
- Authors
Ayed, Bachaer; Jmal, Ikram; Sahal, Samir; Mokadem, Naziha; Saidi, Salwa; Boughariou, Emna; Bouri, Salem
- Abstract
Worldwide, groundwater resources have been considered as the main sources of drinking, domestic uses, industrial and agriculture water demands, especially in arid and semiarid regions. Accordingly, the monitoring of the groundwater quality based on different tools and methods becomes a necessity. The aim of this study was to apply several approaches to assess the water quality and to define the main hydrochemical process which affect groundwater of the Maritime Djeffara shallow aquifer. In addition to the hydrochemical approach, two multivariate statistical analyses, hierarchical clusters analysis (HCA) and principal component analysis (PCA), were carried out to identify the natural and the anthropogenic processes affecting groundwater chemistry. Hydrochemical approach, based on 47 analyzed groundwater samples, shows that most of samples present a sulfate to mixed chloride, with sodi-potassic tendency facies. According to their chemically composition, the HCA revealed three different groups (C1, C2 and C3) according to their electrical conductivity (EC) values: C1 (average EC = 4500 µS/cm), C2 (average EC = 7040 µS/cm) and C3 (average EC = 9767 µS/cm). Furthermore, PCA results show two principal factors account 84.05% of the total variance: (1) F1 represents the natural component, and (2) F2 symbolizes the anthropic component. Moreover, the groundwater quality map of the Maritime Djeffara shows three categories: suitable, doubtful and unsuitable water for irrigation. These different results should be taken to protect water resources in arid and semiarid regions, especially at the alluvial coastal regions. Also, they help to make a suitable planning to manage and protect the groundwater resources.
- Subjects
HIERARCHICAL clustering (Cluster analysis); WATER chemistry; MULTIPLE correspondence analysis (Statistics); AQUIFERS; WATER quality
- Publication
Environmental Earth Sciences, 2017, Vol 76, Issue 24, p0
- ISSN
1866-6280
- Publication type
Article
- DOI
10.1007/s12665-017-7168-6