AN ANALYTICAL APPROACH FOR ENVIRONMENTAL POLLUTION CONTROL THROUGH SOLID WASTE MANAGEMENT: A MODEL STUDY

The planning and management of integrated rural and urban solid waste disposal is complex, involves uncertainty, and requires judgment and diverse expertise. Different methods are created over the last few decades to assist in developing more efficient waste disposal allocation system. Most of the existing models of waste management, however, do not deal with the spatial and temporal dimensions of waste disposal in an integrated manner. Spatial models for waste disposal allocation are intrinsically static; and dynamic models for the design of waste disposal systems generally do not handle spatial allocation of waste disposal facilities. In view of this, it is proposed to develop Multi-Criteria Decision Support System for optimum management of solid waste. The broad objective of this research work is to bring out a comprehensive solution to the solid waste disposal problem and generation of a model to increase the economy out of the waste, which would create a better solution to urbanization problems through competent management and recycling of solid waste. The specific objectives are to design and develop DSS comprising of data management module, data validation and analysis module, module that is capable of analyzing the generated data in to deferent categories, transfer station capacity module, module that calculates Site Sensitivity Index (SSI), and selection criteria for disposal site evaluation. The MCDSS is developed and applied on a typical area comprising of both urban and rural conglomeration of the study area for its applicability so as to generalize the conceptual methodology for other areas which are analogous environmental settings. The significance of this research is the first attempt to compile a number of computer and GIS based tools to provide meaningful, understandable results to people involved in solid waste management decisions. These tools include multi-criteria decision analysis; plan development models, sensitivity analysis capabilities, and geographic presentation of results. The research paper includes spatial distribution maps, attribute database and computer based DSS.