Quantity prediction of municipal solid waste (MSW) is crucial for design and programming municipal solid waste management system (MSWMS). Because effect of various parameters on MSW quantity and its high fluctuation, prediction of generated MSW is a difficult task that can lead to enormous error. The works presented here involve developing an improved support vector machine (SVM) model, which combines the principal component analysis (PCA) technique with the SVM to forecast the weekly generated waste of Mashhad city. In this study, the PCA technique was first used to reduce and orthogonalize the original input variables (data). Then these treated data were used as new input variables in SVM model. This improved model was evaluated by using weekly time series of waste generation (WG) and the number of trucks that carry waste in week of t. These data have been collected from 2005 to 2008. By comparing the predicted WG with the observed data, the effectiveness of the proposed model was verified. Therefore, in authors' opinion, the model presented in this article is a potential tool for predicting WG and has advantages over the traditional SVM model.

The irregular and non-normative development of cities and also weakness in the waste management system has created many problems especially in large cities. However, even many of the principles of engineering and environmental criteria about burying of wastes do not match in the landfill sites. Therefore, environmental impact assessment (EIA) for landfills to examine the effects of landfills on environment is essential. Rapid impact assessment matrix (RIAM) is one of the ways that it can be used to EIA. This method minimizes the elements of subjectivity and introduces some degree of transparency and objectivity. Using the RIAM, EIA has been carried out on different municipal solid waste disposal options in Tabriz landfill and it has been found that composting with the sanitary landfill is the best recommended option according to the existing circumstances.

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Industrial waste management generated by different petrochemical complexes at Pars Special Economic Energy Zone, located in the south of Iran, was investigated. All 10 active petrochemical complexes were visited and generated wastes were identified by a checklist. Petrochemical plants were classified regarding feeds, process, and products and nine representative wastes were sampled. Physicochemical characteristics were analyzed and appropriate management approaches were proposed according to the literature review and the results of waste characterization. The generated wastes were classified as hazardous and non-hazardous according to the Basel Convention and Environmental Protection Agency lists of waste classification. Also, the concentrations of organic compounds and heavy metals were measured to classify wastes characteristically. Comparing concentrations of the most important heavy metals in sampled wastes illustrated that sandblast with Cu concentration of 4295 mg kg–1, spent activated carbon with Hg concentration of 127 mg kg–1, and spent catalyst with 25% Ni content can be categorized as hazardous wastes, due to the exceeding Total Threshold Limit Concentration levels. Based on laboratory results, all industrial waste generated in the petrochemical complexes were categorized into three groups, namely Organic Waste with High Calorific Value, Non-organic Recyclable Waste, and Non-organic Non-recyclable Waste. Finally, management approaches, including material recycling, energy recovery (through incineration), and landfilling, were proposed and a conceptual model was suggested in order to show different routes and final destination for each kind of waste generated in all similar petrochemical complexes.

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