Background and Objective: Phenol presence and its derivatives in water and waste water on human health and the environment is one the major concerns. Because of the toxicity of phenol and also because of the presence of even low concentrations in natural resources, water disinfection and oxidation processes can lead to the formation of additional components. This material is one of the most common organic pollutants in water. In this research, adsorption of phenol from wastewater by sawdust was simulated using intelligent techniques. Method: Intelligent techniques including multi-layer Perceptron, radial basis functions network and support vector regression were used. To design the network structure as well as the training and testing of 125 sets of experimental data is used. Performance evaluation criteria and stop network consists of % AARE and R2, which is used for all three models. Findings: All models compared results showed that the support vector regression with 0. 5132 and 0. 979, respectively, for %AARE and R2 is the best model. All models are better results than the quadratic polynomial model showed. Discussion and Conclusion: Models showed good agreement with experimental data. The optimum conditions for the removal of phenol were 127. 6 mg/l of initial phenol concentration, 0. 84 g/l of adsorbent dose, natural pH value of 3. 62 and 146. 9 min of contact time, under these conditions the maximum removal efficiency was 91. 23%.

Background and Objective: Due to the high potential of Hamedan city for agricultural products and subsequentlly effects of urban wastewaters and agricultural activities such as the use of fertilizers and chemical pesticides on soil quality, plant, and human health, performing this research was necessary. This research was done by purpose of investigating the effect of wastewater on accumulation of metals Pb and Cd in soil and vegetables. Method: For this purpose, sampling from soil and vegetables was performed randomly in the city of Hamedan. Sampling was done in an area irrigated with well water and in an area irrigated with wastewater. Vegetable Samples were prepared after washing and drying, powdered and prepared for experiment. Also soil samples after air-drying, sieve, and prepared for experiment. Then samples digested with acid mixture and heavy metals measured by Atomic Adsorption Spectroscopy (AAS). Finally obtained data were analyzed by SPSS 20. Findings: Results showed that wastewater had important effect on accumulation of Pb and Cd in soil and vegetables which means concentration of these metals in the soil and vegetables that was irrigated with wastewater was higher than those irrigated with well water. Also this is identified that the concentrations of Pb and Cd in studied vegetables was higher or equal to standards and Cd in the soil of both control and treatment were or equal to standards but Pb were lower than standards. Discussion and Conclusion: Since, in the treatment area, the mean concentration of Cd in scallion vegetable and mean concentration of Pb in leek vegetable is higher than standard. Therefore, in order to prevent creating problems for citizens’ health, irrigation of fields by wastewater must be prevented.

Background and Objective: Having information about qualitative and quantitative parameters distribution of groundwater supplies is one of most important parameters in integrated groundwater management. Thus, in this study it has been attempted to determine a proper model and input combination for estimation of quality parameters including electrical conductivity (EC), calcium (Ca) and sodium (Na) ions in aquifers of Guilans plain. Method: In this study, the data from 132 observation wells during 2001 to 2013 were used and artificial neural network (ANN) and support vector model (SVM) were applied. In the first approach, estimations were conducted according to five different combinations, including water level, distance from see, total precipitation of six months and coordinates of observation wells. In the second approach, estimations were conducted based on combination of the selected qualitative parameters of gamma test with combinations of the best input in the first part. Findings: Comparison of the results from the first part indicated that SVM model outperformed the ANN mode in the estimation of Ca, Na and EC parameters. Support vector machine error values for estimating Ca, Na and EC variables at the test period were 1. 218 (meq/l), 0. 867(meq/l), and 175. 742 (μ mos/cm), while for artificial neural network these values were 1. 268 (meq/l), 0. 933 (meq/l), and 186/448 (μ mos/cm) respectively. The results from this part showed that adding the distance from see input improves the estimation of models in all cases. In the second part, using gamma test for measuring the nine quality parameters, the best combination of quality parameters was determined to estimate the three parameters: Ca, Na and EC. The results from the second part show that both ANN and SVM models have an excellent performance in the estimation of the three qualitative parameters. ANN model error values in estimating Ca, Na and EC variables in validation period were 0. 662 (meq/l), 0. 305(meq/l), and 47. 346 (μ mos/cm), while these values were 0. 671 (meq/l), 0. 356 (meq/l), and 55. 412 (μ mos/cm) for SVM model respectively. Obviously, the results from ANN model in this section were better than those from SVM model. Discussion and Conclusion: Results showed that both ANN and SVM models have a great ability in predicting qualitative parameters in the aquifers. Also, in less inputs, the results of SVM model are better than those of ANN model and in more inputs it is vice versa. Results of the second section showed that gamma test is fully practical and accurate in determining the effective input combinations.