| Abstrakt: |
In this study, the groundwater contamination degree in the unconfined coastal aquifer Jerba under arid climate (Southeastern Tunisia) was investigated, and a new index, groundwater contamination index (GCI), was calculated and combined with the geostatistical analysis and the self-organizing maps (SOM). The groundwater samples used in this research were collected from 79 groundwater wells during the dry season (August–October 2014). Hydrochemical parameters (Cl−, Br−, Na+, Ca2+ , Mg2+, K+, SO42−, NO3−, NO2−, HCO3−, Li+, and F−) and indicator bacteria of fecal contamination (total coliforms, thermotolerant coliforms, and Escherichia coli) were analyzed. The GCI was calculated based on selected indicator parameters notably NO3− and Li+, measured seawater fraction based on chloride balance, and fecal bacteria tracers. Geostatistical modeling was used for assessing and mapping groundwater contamination degrees. Ordinary Kriging was adopted for spatial interpolation to study the spatial pattern of the groundwater hydrochemical variables over the island using GIS software ArcGIS 10.1. The SOM method was adopted to analyze the relationship between ions and identify processes controlling groundwater salinization. According to the GCI, most of the unconfined aquifer (76%) comes under the significant pollution zone (high to moderate pollution), and the other areas (24%) are defined as areas with low degrees of pollution. The self-organizing maps (SOM) indicated that Cl−, Br−, and Na+ emanate mainly from seawater intrusion and Mg2+, Ca2+, and Li+ are mostly derived from rock-water interactions. Results show that the new index is robust and gives the best classification of groundwater quality. [ABSTRACT FROM AUTHOR] |
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