In this paper, a support vector machine (SVM) model was developed to predict nitrate concentration in groundwater of Arak plain, Iran. The model provided a tool for prediction of nitrate concentration using a set of easily measurable groundwater quality variables including water temperature, electrical conductivity, groundwater depth, total dissolved solids, dissolved oxygen, pH, land use, and season of the year as input variables. The data set comprised of 160 water samples representing 40 different wells monitored for 1 year. The associated parameters for the optimum SVM model were obtained using a combination of 4-fold cross-validation and grid search technique. The optimum model was used to predict nitrate concentration in Arak plain aquifer. The SVM model predicted nitrate concentration in training and test stage data sets with reasonably high correlation (0.92 and 0.87, respectively) with the measured values and low root mean squared errors of 0.086 and 0.111, respectively. Finally, the map of nitrate concentration in groundwater was prepared for all four seasons using the trained SVM model and a geographic information system (GIS) interpolation scheme and compared with the results with a physics-based (flow and contaminant) model. Overall, the results showed that SVM model could be used as a fast, reliable, and cost-effective method for assessment and predicting groundwater quality.

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The moving bed biofilm reactor (MBBR) technology is a proven standalone and add-on technology for carbon and nutrient removal from municipal wastewaters. The key challenge of the carbon removal MBBR...

In this study, a municipal lagoon with high wintertime effluent total ammonia nitrogen (TAN) concentrations was upgraded with a pilot-scale nitrifying-nitrifyingdenitrifying (NIT-NIT-DENIT) moving bed biofilm reactor (MBBR) treatment train to characterize its effluent over wintertime operation, investigate the feasibility of upgrading lagoons to achieve substantial biological total nitrogen removal across ultra-low temperatures (0.6 -3.0°C) and investigate nitrification inhibition pathways in facultative lagoon systems at ultra-low temperatures. Throughout the study, it was observed that the system substantially reduced total nitrogen (TN) and total phosphorus (TP) effluent concentrations by an average of 69.8 ± 24.5% and 74.7 ± 20.1%, respectively. Furthermore, it was observed that sulfide toxicity may play an important role in the inhibition of nitrifying organisms in lagoons. Finally, the MBBR treatment technology has emerged as a suitable and sustainable upgrade technology for existing lagoon and waste stabilization pond facilities operating in temperate, northern, and cold climate countries.

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