Background: The effects of trace elements on human health and the environment gives importance to the analysis of heavy metals contamination in environmental samples and, more particularly, human food sources. Therefore, the current study aimed to predict arsenic and heavy metals (Cu, Pb, and Zn) contamination in the groundwater resources of Ghahavand Plain based on an artificial neural network(ANN) optimized by imperialist competitive algorithm (ICA).
Methods: This study presents a new method for predicting heavy metal concentrations in the groundwater resources of Ghahavand plain based on ANN and ICA. The developed approaches were trained using 75% of the data to obtain the optimum coefficients and then tested using 25% of the data. Two statistical indicators, the coefficient of determination (R2) and the root-mean-square error (RMSE), were employed to evaluate model performance. A comparison of the performances of the ICA-ANN and ANN models revealed the superiority of the new model. Results of this study demonstrate that heavy
metal concentrations can be reliably predicted by applying the new approach.
Results: Results from different statistical indicators during the training and validation periods indicate that the best performance can be obtained with the ANN-ICA model.
Conclusion: This method can be employed effectively to predict heavy metal concentrations in the groundwater resources of Ghahavand plain.

Background: This retrospective study aimed to investigate the physicochemical properties of drinking water sources in Ethiopia and compare the water quality with the health-based target. For this purpose, the water quality database of Ethiopian Public Health Institute (EPHI) from 2010 to 2016 was used.
Methods: The concentration and other properties of the water samples were analyzed according to the Standard Methods of Water and Wastewater analysis. Quality control and quality assurance were applied in all stages following our laboratory standard operation procedures (SOPs).
Results: The concentration of the selected parameters varied based on the type of water sources. The mean concentration of turbidity was higher in spring water (21.3 NTU) compared to tap (12.6 NTU) and well (3.9 NTU) water sources. The mean concentration of total dissolved solids (TDS), electrical
conductivity (EC), sodium (Na+), and sulfate (SO4-2) was found to be higher in spring water sources than tap and well water sources. Comparably, the concentration of hardness, calcium, and magnesium was found to be higher in well water sources than spring and tap water sources. The bivariate analysis indicated that out of 845 analyzed water samples, more than 50% of the samples from Oromia region had turbidity, pH, TDS, hardness, Ca++, K+, and Na+ within an acceptable limit. In addition, the logistic regression analysis showed that water quality parameters were strongly associated with the type of water sources and regional administration at P < 0.05.
Conclusion: More than 80% of the samples analyzed from drinking water sources were in agreement with WHO guidelines and national standards. However, the remaining 20% specifically, pH (25%), calcium (20%), hardness (18.1%), TDS (15.5%), and turbidity (13.3%) analyzed from improved water
sources did not comply with these recommendations. Due to objectionable or unpleasant taste, people may force to look for alternative unprotected water sources that lead to health concerns.

Background: Plasmatron is a hydrocarbon reformer of fuels and heavy oil sludge with high efficiency in gasification operation. The gasification of acidic sludge (AS) was investigated to assess the technical and financial demands in a model. AS is a by-product of used motor oil (UMO) recycling industries with a large quantity that its reprocessing, refining, and re-refining operations are not performed in Iran.
Methods: In this empirical study, the inventory of requirements for generating value-added gaseous products was tabulated based on the recent studies. To develop a techno-economic model, the costs of reactor configuration, equipment, and installation outlay, materials and product costs, facilities, staff salary, land and landscaping budget, and energy demand expenses were taken into consideration.
Results: The initial requirements of the project in the screening step were identified and a framework of the economic model was provided to develop and identify the technical dimension of the chemical vapor deposition (CVD) reactor.
Conclusion: The findings expanded the technology and its technical demands for identification of screening step of project prior to competing for development and establishment.

Background: Treatment of combined industrial wastewater from industrial parks is one of the most complex and difficult wastewater treatment processes. Also, the accuracy of biological models for the prediction of the performance of these processes has not been sufficiently evaluated. Therefore, in this study, the International Association on Water Quality (IAWQ(-Activated Sludge Model No. 1 (ASM1) was implemented for the Jey industrial park in Isfahan province, Iran.
Methods: The Jey IPWWTP process is a combination of anaerobic and aerobic biological processes. To evaluate the overall performance of IPWWTP, organic compounds, suspended solids, nutrients, attached biomass, and some operating parameters were measured during 6 months. Then, the biokinetic coefficients of aerobic processes were determined using Monod equations. Finally, the aerobic processes were modeled using ASM1 implemented in STOAT software.
Results: The values of the biokinetic coefficients K, Y, Ks, Kd, and µmax were calculated as 2.7d-, 0.34 mg VSS/mg COD, 133.36 mg/L COD, 0.03d-, and 0.93d-, respectively. Based on the default coefficients and conditions of the ASM model, the difference between the experiments and model prediction was about 2 to 98%. After calibrating the ASM model, the difference between the experiments and prediction in all parameters was reduced to less than 10%.
Conclusion: Investigations showed that the default coefficients and operation conditions of the ASM1 model do not have good predictability for complex industrial wastewaters and the outputs show a low accuracy compared to the experiments. After calibrating the kinetic coefficients and operating conditions, the model performance is acceptable and the predictions show a good agreement with the experiments.

Background: Modeling of wastewater treatment plants is necessary to predict their later works. In this research, three methods were compared to predict some parameters at the outlet of wastewater treatment plant in Hama city in Syria.
Methods: In this paper, three methods (linear regression, power regression, and regression trees) to model wastewater treatment plant in Hama city were compared to predict the parameters at the outlet of the plant (cBOD5out, CODout, TSSout) in terms of the parameters at the inlet of the plant (Qin, cBOD5in, CODin, TSSin).
Results: When predicting cBOD5out, the values of RMSE of the test data set were 4.4105, 4.3875, and 3.8418; when predicting CODout, the values of RMSE of the test data set were 6.9325, 6.8003, and 5.3232; and when predicting TSSout, the values of root mean squared error (RMSE) of the test data set were 3.7781, 3.6936, and 3.2391 using linear regression, power regression, and regression trees (RTs), respectively.
Conclusion: According to the results, the RTs outperforms in predicting cBOD5out, CODout, and TSSout because this method achieved the least RMSE of the test data set.