Russak, A., Sivan, O., & Yechieli, Y. (2016). Trace elements (Li, B, Mn and Ba) as sensitive indicators for salinization and freshening events in coastal aquifers. Chemical Geology, 441, 35–46.
Abstract: The current global intrusion of seawater into coastal aquifers causes salinization of groundwater and thus significant degradation of its quality. This study quantified the effect of seawater intrusion and freshening events in coastal aquifers on trace elements (Li, B, Mn and Ba) across the fresh-saline water interface (FSI) and their possible use as indicators for these events. This was done by combining field data and column experiments simulating these events. The experiments enabled quantification of the processes affecting the trace element composition and examination of whether salinization and freshening events are geochemically reversible, which has been seldom investigated. The dominant process affecting trace element composition during salinization and freshening is ion exchange. The results of the experiments show that the concentrations of major cations and Li+ were reversible during salinization and freshening, whereas B, Mn2+ and Ba2+ were not. During salinization, Li+ and B were depleted due to sorption by 10 and 100μmol·L−1, respectively, to about half of their expected conservative concentrations. The relative depletion of Li+ increased with distance from the shore, representing the propagation of salinization. Ba2+ and Mn2+ were desorbed from the sediment during salinization and enriched by tenfold in the aqueous phase compared to their concentration in seawater ( 0.1 μeq·L−1). During freshening both were depleted by almost tenfold compared to their concentration in fresh groundwater ( 0.7 μeq·L−1). The depletion of Mn2+ is a sensitive marker for freshening because Mn2+ has a strong affinity to the solid phase. Moreover, this study shows that both Mn2+ and Ba2+ can be used as sensitive hydrogeochemical tools to distinguish between salinization and freshening events in the FSI zone in coastal aquifers.
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Kisi, O., Heddam, S., Parmar, K. S., Yaseen, Z. M., & Kulls, C. (2024). Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data. Stochastic Environmental Research and Risk Assessment, 38(6), 2489–2519.
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