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: 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.

Background and Objective: Surveying of Balekhloo River’ s water is necessary because it is one of the main branches of Gharasoo River in Ardabil and supplying water for Yamchi dam Due to the presence of contaminant sources such as wastewater, residential areas, and industrial, agricultural and mineral water, river morphology. Today evaluation of water quality based on macro invertebrates variety has completed chemical methods of pollution recognizes. Method: In this study water quality examination of Balekhloo River in Ardebil, macro invertebrates were sampled in 5 stations along the river in 2 times of year (low water season and high water season). For results’ simulation measuring of some physico chemical parametres were perfomed. Macro invertebrates were identified and evaluated with Shannon winner variety index, Margalef biotic index and Pielou similarity index. Findings: In this study, ten orders of invertebrates in 23 categories were identified and counted and result simulation laboratory measured of physico chemical parameters like DO, Nitrate, PH, Debi and velocity. Discussion and Conclusion: Results showed that there are similarities between physical, chemical and biological indexes and Margalef biotic index was known more suitable bio assessment index to determine water quality in Balekhloo River. Finally, by comparison of physical, chemical and bio index parameters of Balekhloo River’ s water quality was classified into three region low, moderate and high pollution.

Background and Objective: In this research concentration and extent of soluble contaminants plumes caused by probable leakage of petroleum materials from Khoy Oil Products Storages and also transporting mechanism of pollutants by groundwater was investigated. Method: Groundwater flow model for a steady and transient state was simulated using MODFLOW-2000 code. Then, flow model calibrated and the aquifer hydraulic parameters were estimated. Flow model output and calibrated parameters were used for simulation of BTEX and MTBE transported by MT3DMS code. Three scenarios were considered to predict transporting of pollutants under various conditions. Findings: The model results suggest that the plume MTBE in the condition of continuous source will be distributed up to 774 meters whereas it will distribute about 108m far from the source if the leakage stops after three years. According to the model prediction, the extension of BTEX will be less than MTBE in the case of continuous leakage. Under condition of the second scenario (non-continuous release of the pollutants) the plume extension of benzene reach will be 126 meter after 10 years and it can reach to the nearest abstraction well in 8. 5 years. MTBE plume size in this period reaches to 6 times of BTEX and will reach the well in 1. 5 year. Discussion and Conclusion: Average velocity of contaminant distribution is about 5 to 6 cm per day. It is predicted that the MTBE plume reaches earlier to the first pumping wells related to BTEX. So maintenance of the Oil tanks and monitoring of the downstream groundwater is a necessity. Average velocity of contaminant distribution is about 5 to 6 cm per day. It is predicted that the MTBE plume reaches earlier to the first pumping wells related to BTEX. So maintenance of the Oil tanks and monitoring of the downstream groundwater is a necessity.