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ABSTRACT: Chemical fractionation of seven heavy metals (Cd, Cr, Cu, Mn, Ni, Pb and Zn) was studied using a modified three-step sequential procedure to assess their impacts in the sediments of the Seyhan River, Turkey. Samples were collected from six representative stations in two campaigns in October 2009 and June 2010, which correspond to the wet and dry seasons, respectively. The total metal concentrations in the sediments demonstrated different distribution patterns at the various stations. Cadmium was the only metal that was below detection at all stations during both sampling periods. Metal fractionation showed that, except for Mn and Pb, the majority of metals were found in the residual fraction regardless of sampling time, indicating that these metals were strongly bound to the sediments. The potential mobility of the metals (non-residual fractions) is reflected in the following ranking: Pb > Mn > Zn > Cu > Ni > Cr in October 2009 and Mn > Pb > Zn > Cu > Ni > Cr in June 2010. The second highest proportion of metals was bound to organic matter/sulfides, originating primarily from anthropogenic activities. Non-residual metal fractions for all stations were highest in June 2010, which may be linked to higher organic matter concentrations in the sediment samples with 1.40% and 15.1% in October 2009 and June 2010, respectively. Potential sediment toxicity was evaluated using the Risk Assessment Code (RAC). Based on RAC classification, Cd and Cr pose no risk, Cu and Ni pose low risk, Pb and Zn were classified as medium risk metals, while the environmental risk from Mn was high. In addition, based on the sediment quality guidelines (SQG), the Seyhan River can be classified as a river with no, to moderate, toxicological risks, based on total metal concentrations.
Journal of Environmental Management 09/2011; 92(9):2250-9. · 3.24 Impact Factor
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CLEAN - Soil Air Water 02/2011; 39(2):185 - 194. · 2.18 Impact Factor
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CLEAN - Soil Air Water 12/2010; 38(12):1137 - 1145. · 2.18 Impact Factor
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CLEAN - Soil Air Water 03/2010; 38(3):221 - 224. · 2.18 Impact Factor
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ABSTRACT: Two anaerobic filters, one mesophilic (35 degrees C) and one thermophilic (55 degrees C), were operated with a papermill wastewater at a series of organic loadings. The hydraulic retention time (HRT) ranged from 6 to 24 h with organic loading rates (OLR) 1.07-12.25 g/l per day. At loading rates up to 8.4 g COD/l d, there was no difference in terms of the removal of soluble COD (SCOD) and gas production. At the higher organic loading rate, the SCOD removal performance of thermophilic digester was slightly better compare to mesophilic digester. Similar trend was also observed in terms of the daily methane production. The stability of thermophilic digester was also better than mesophilic digester particularly for the higher organic loadings. Volatile fatty acid accumulation was observed in the effluent of the mesophilic filter at the higher organic loading rates. The Stover-Kincannon model was applied to both digesters and it was found that model was applicable to both digesters for papermill wastewater. K(B) and U(max) constants from the Stover-Kincannon model were also derived.
Bioresource Technology 02/2008; 99(1):156-63. · 4.98 Impact Factor
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ABSTRACT: In this study the performance of the upflow anaerobic filter (UAF) reactor treating cyanide was simulated using three different neural network techniques (ANNs) – multi-layer perceptron (MLP) neural network, radial basis neural network (RBNN), and generalized regression neural network (GRNN). The performance of UAF reactor over a period of 130 days at different cyanide concentrations was evaluated with these robust models. Influent chemical oxygen demand (CODin), hydraulic retention time (HRT), and influent cyanide concentration (CNin) were the inputs of the models, whereas the output variable was effluent chemical oxygen demand (CODeff). The models’ results were compared with each other using four statistical criteria – root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and determination coefficient (R2). The results showed that the MLP neural network with Levenberg–Marquardt algorithm was found to be better than the RBNN and GRNN techniques.
Advances in Engineering Software.
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ABSTRACT: Hydrochemical characterization of the Silifke Plain coastal aquifer was accomplished in this research in order to investigate the water quality of groundwater. The aquifer is located along the Mediterranean coast and forms one of the most productive aquifers in Turkey. Groundwater samples were collected from the aquifer as monthly between December 2007 and November 2008 for 12 months. Twenty one artesian wells were chosen and water sampling was made once a month. The values of electrical conductivity (EC) and pH as well as main anions and cations were determined twice for each month. Based on the observations in the water wells, the EC, sodium adsorption rate (SAR), and chloride (Cl−) concentrations varied between 437 and 3480 µS/cm, 0.13 and 23.1, and 25 and 1661 mg Cl−/L, respectively. Deterioration of water quality was observed in some areas very close to the sea due to seawater intrusion and intense use of groundwater for irrigation.
Desalination. 253:164-169.
