Assessing the groundwater quality of El Fahs aquifer (NE Tunisia) using multivariate statistical techniques and geostatistical modeling
Journal
Applied Water Science
Date Issued
July 9, 2024
Abstract
This study is the first attempt to characterize the quality status of El Fahs aquifer by combining graphical tools, multivariate statistical techniques and traditional geostatistical methods. Water samples are collected from thirty-six observation wells during April 2016 to characterize the physicochemical properties of the aquifer. Subsequently, these samples are partitioned into three hydrochemically distinct water classes (i.e., C1, C2, and C3) using the K-means clustering method. Principal Component Analysis is used to reduce the dimensionality of the dataset prior performing the clustering computations, resulting in clusters of higher quality than the non-reduced case in terms of Silhouette coefficient. Piper diagram is used to display the chemical composition of the samples, revealing the dominant role of Mg–Ca–Cl water type for all three classes, whereas Sodium and Sulfate were found to be the second most important cations and anions respectively. Indicator kriging (IK) is used to identify the probability of occurrence of the hydrochemical classes beyond the sampling locations. It is found that Class 1, associated with fresh groundwater component, is most probable to occur at the central part of the plain, mainly due to the presence of a dense hydrological network, whereas Classes 2 (agricultural activities) and 3 (dissolution of evaporate geological formations) are expected to occur at the southern and northern regions respectively. IK also identified the regions associated with high levels of uncertainty, mostly occurring in a large portion of the northern area due to the absence of available hydrochemical information. The results showed that integration of graphical methods, multivariate statistical techniques and geostatistical modeling, is an efficient approach for characterizing the hydrochemical status of the aquifer system, to spatially optimize the groundwater monitoring well networks and quantify the uncertainty levels of the water classes in a systematic way.
Funding
This work was funded through the EXCELSIOR Teaming project (Grant Agreement No. 857510, www.excelsior2020.eu, accessed on 28 June 2024) that has received funding from the European
Union's Horizon 2020 research and innovation programme and from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development. Availability of data and materials Available upon request.
Code availability Available upon request. Declarations Conflict of interest On behalf of all authors, the corresponding author states that there is no known conflict of interests, no competing financial
interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical approval We verify that the current manuscript presents part of the work done within the frame of EXCELSIOR project. Consent to participate and publish We provide consent to participate and publish our work, which is jointly contributed by all authors.
Union's Horizon 2020 research and innovation programme and from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development. Availability of data and materials Available upon request.
Code availability Available upon request. Declarations Conflict of interest On behalf of all authors, the corresponding author states that there is no known conflict of interests, no competing financial
interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical approval We verify that the current manuscript presents part of the work done within the frame of EXCELSIOR project. Consent to participate and publish We provide consent to participate and publish our work, which is jointly contributed by all authors.
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