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    Heavy metal contamination in groundwater and surface water due to active Pb-Zn-Cu mine tails and water-rock interactions: A case study from the Kure mine area (Turkey)
    (Tubitak Scientific & Technological Research Council Turkey, 2020) Ekemen Keskin, Tulay; Ozler, Emre
    Ibis study aimed at elucidating the groundwater and surface water pollution resulting from water-rock interaction, mine tailings, and mining activities at Kure (Kastamonu) sulfidic Pb-Zn-Cu mine area and its vicinity. The study area has a surface area of approximately 2990 km(2). The Kure River drainage site covering the study area is approximately 440 km(2). The concentrations of SO4, Al, As, Ba, Mn, Ni, Sb, and Pb in some groundwater (K10, K20, K21, K27, K28, K29, K30), and surface water (KR3) exceed the maximum limit values with respect to the Turkish Standards for Water Intended For Human Consumption and World Health Organization Standards. The most polluted waters were K10, K27, and KR3. The K10 well was drilled for observation at the edge of the liquid mine tailing pond of the Kure Pb-Zn-Cu mine area. The K27 well was opened in alluvium outside of the study area and it cuts the clastic units and Mesozoic ophiolites. The KR3 measurement point is located on the Ersizler River, which drains the Kure Pb-Zn-Cu mining site and its dumps. The SO4, Al, As, Pb, and Sb concentrations of the K10, K27, and KR3 waters were 29.3, 0.8, and 15.1 meq/L; 1135, 1112, and 1186 ppb; 284, 255, and 271 ppb; 19.1, 19, and 18.5 ppb; and 18.7, 17.4, and 19.3 ppb, respectively, in the dry period. As revealed by the analysis, the study area had dual pollution source natural pollution caused by water-rock interactions (K27 and others) and anthropogenic pollution (K10 and KR3) caused by the mine tailings. Furthermore, the K10 water had high tritium (H-3)-high electrical conductivity (EC) values that likely indicated anthropogenic contamination. The K27 water had low H-3-high EC values, presumably referring to geogenic contamination. The current study also demonstrated that there was leakage from the liquid mine tailing pond into the groundwater (K10), possibly implying discharge of the liquid mine tailings into the river (KR3).
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    Prediction of electrical conductivity using ANN and MLR: a case study from Turkey
    (Springer Int Publ Ag, 2020) Keskin, TUlay Ekemen; Ozler, Emre; Sander, Emrah; Dugenci, Muharrem; Ahmed, Mohammed Yadgar
    The study areas are located in Turkey (Kastamonu, Bartin, Karabuk, Sivas) and contain very different rock types, various mining and agricultural activity opportunities. So, the areas have groundwaters that have different chemical compositions and electrical conductivity (EC) values. The EC can be measured using EC meter, and it must be measured in situ. But, the measurement of EC in situ is laborious, time-consuming, expensive, and difficult in arduous terrain environments. In recent years, machine learning models have been a primary focus of interest for a lot of study by providing often highly accurate forecast for solutions of such problems. The aim of the study is to forecast EC of groundwater using artificial neural networks (ANN) and multiple linear regressions (MLR). Twelve different hydrochemical parameters, which affect the EC, such as major/minor ions and trace elements, were used in the analysis. Multilayer feed-forward ANN trained with backpropagation in Python machine learning libraries was used in this study. In order to obtain the most appropriate ANN architecture, trial-and-error procedure was used and different numbers of hidden layers, neurons, activation functions, optimizers, and test sizes were constructed. This study also tests the usability of input parameters in EC prediction studies. As a result, comparisons between the measured and predicted values indicated that the machine learning models could be successfully applied and provide high accuracy and reliability for EC and similar parameters forecasting.