Simultaneous removal of fluoride and nitrate from synthetic aqueous solution and groundwater by the electrochemical process using non-coated and coated anode electrodes: A human health risk study.

Co-presence of fluoride (F-) and nitrate (NO3-) in water causes numerous health complications. Thus, they should be eliminated by an appropriate method like the EC process. In this research, simultaneous removal of F- and NO3- from synthetic aqueous solution and groundwater has been considered by the EC technique under operational parameters like anode materials (un-coated (Al and Fe) and synthesized coated (Ti/TiRuSnO2 and Ti/PbO2)), cathode materials (Cu, St, and Gr), current density (12, 24, and 36 mA/cm2), inter-electrode distance (0.5, 1, and 2 cm), pH (5.5, 7, and 8.5), NaCl concentrations (0.5, 1, and 1.5 g/L), electrolysis time (15, 30, 45, 60, 90, and 120 min), NO3- concentrations (75, 150, and 225 mg/L), and F- concentrations (2, 4, 6, and 8 mg/L) for the first time in this research. The results proved that Al as non-coated anode and Cu as cathode electrodes were more effective in the co-removal of F- and NO3-. The maximum removal efficiencies of 94.19 and 95% were observed at the current density of 36 mA/cm2, 1 cm of inter-electrode distance, pH 7, 1 g/L of NaCl, and 90 min electrolysis time by Al-Cu electrode for F- (2 mg/L) and NO3- (75 mg/L), respectively. The higher efficiency of Al-Cu electrodes was due to the simultaneous occurrence of electrocoagulation, electroreduction, and electrooxidation processes. Al-Cu electrode application considerably diminished f- and NO3- concentrations in the groundwater. Health risk assessment proved that HQ of F- was significantly decreased after treatment by the Al-Cu electrode. Thus, the EC process using an appropriate and effective electrode is a promising technique for treating aqueous solutions containing F- and NO3-.

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method.

Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.