Land filling is the most common disposal method in most parts of the world and landfill site has always been the final destination in solid waste management hierarchy. Thus, the selection of landfill site is always an essential part in the management of solid waste. Selecting an appropriate site for landfill minimizes any unwarranted ecological and socio-economic effects. Hence, landfill site selection requires a detailed analysis of the area that must be able to meet the local authority requirement and criteria. The present study presents a feasibility assessment of landfill establishment for Khomeynishahr city in Isfahan, applying a multi criteria evaluation (MCE) method using GIS technique. Information layers related to topography, soil, water table, sensitive habitats, land use and geology maps were prepared and superposed using Boolean logic in GIS environment. Essential analysis and regulation, criteria and site selection assessment showed that because of many limitations khomeynishahr city doesn’t have adequate conditions for landfill site establishment. Khomeynishahr city has a dense population and limited area and is not suitable for landfill establishment. In this case consideration of adjacent cities and finding a common landfill site between two or more cities could be a viable solution of solving this problem.
In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.