Iranian Journal of

Background and Objective: Nowadays, the increasing use of antibiotics to control diseases and mismanagement and inappropriate disposal of medicinal wastes cause environmental problems and threatens human health. The present study was conducted to determine removal of antibiotic Metronidazole as one of the most used drugs during the process of composting.
Materials and Methods: In this experimental study, a mixture of livestock manure, fruit, straw and sludge were used for the preparation of compost. Duration of the process was 40 days. Metronidazole was added to the compost reactors at three concentrations of 20, 50 and 100 mg/kg. Totally, 42 Samples were taken weekly. HPLC was used to analyze the samples. The experiment was repeated twice. SPSS22 software was used to analyze the results.
Results: The rate of Metronidazole removal after day 21 and end of the thermophilic phase was 99.9, 96.73 and 93.48 % in the reactors contained 20, 50 and 100 mg/kg, respectively. Increasing concentrations in the reactors caused the removal rate to decrease, while increasing removal time caused to increase the removal rate. At the end of the process, the removal rate for all three reactors was 99.99 %. The physico-chemical properties of the final compost were within the national standard.
Conclusion: The use of an aerobic composting process to degrade antibiotic Metronidazole is an economical, effective and, environmentally friendly method. At the end of the process, 99.99 % of Metronidazole was degraded.
 

Background and Objective: In the present study, EC and TDS quality parameters of Karun River were modeled using data-mining algorithms including LSSVM, ANFIS, and ANN, at Mollasani, Ahvaz and Farsiat hydrometric stations.
Material and Methods: Eight different inputs including the combination of Cl^-1, Ca⁺², Na⁺¹, Mg⁺², K⁺¹, CO₃^2-, HCO₃, and SO₄^2- with discharge flow (Q) were selected as non-random and random calibration inputs for these algorithms. Then, in order to guarantee the accuracy of the results, the simulation was performed by random calibration and the results of the two methods were compared. In the next step, the EC and TDS parameters were modeled based on the four parameters of Na⁺¹, Cl^-1, Ca⁺², and Q and a lag time of zero to three months.
Results: Modeling results indicated that Na⁺¹, Cl^-1, and Ca⁺² have the highest influence on modeling of EC and TDS parameters. The LSSVM algorithm was the most accurate in modeling EC and TDS parameters. Among the studied stations, the highest precision for EC and TDS modeling belongs to Ahvaz and Mollasani station, which has 16% and 36% higher coefficient of determination. LSSVM has highest accuracy in modeling the oscillation and peak EC and TDS parameters in during times.
Conclusion: The methods and models applied in the present study especially the LSSVM algorithm, can be a useful decision-making tool for predicting and qualitative management of rivers, including rivers in the Karun catchment area. The results of modeling the quality parameters of the rivers were reliable and usable by using both non-random and random calibration methods. However, the accuracy of the random calibration method was slightly higher.