Prevention of landfill pollution by multicriteria spatial decision support systems (MC-SDSS): development,...

Area of Science:

• Environmental Science
• Geographic Information Systems (GIS)
• Decision Science

Background:

• Improper municipal solid waste (MSW) landfill siting poses significant environmental risks due to potential pollutant release.
• MSW landfill site selection is a complex, multi-faceted decision-making challenge involving numerous environmental, social, and economic criteria.
• Existing methods often struggle to integrate diverse and conflicting objectives in spatial decision-making processes.

Purpose of the Study:

• To propose and validate a novel framework for municipal solid waste (MSW) landfill site selection.
• To integrate multiple criteria decision analysis (MCDA) with geographical information systems (GIS) for enhanced spatial decision support.
• To develop suitability maps for identifying optimal landfill locations based on a comprehensive set of criteria.

Main Methods:

• Development of a framework utilizing Multicriteria Spatial Decision Support Systems (MC-SDSS).
• Integration of Multiple Criteria Decision Analysis (MCDA) techniques with Geographical Information Systems (GIS).
• Selection and categorization of 14 criteria into environmental, sociocultural, and economic groups for analysis.
• Generation of detailed suitability maps based on the MC-SDSS analysis.

Main Results:

• The MC-SDSS framework was successfully applied to a case study in Arak city, Iran.
• Suitability mapping revealed distinct zones: 10% least suitable, 23% low suitable, 37% moderate suitable, 19% suitable, and 11% most suitable.
• Three optimal alternative sites were identified for the final municipal solid waste (MSW) landfill location.

Conclusions:

• The proposed MC-SDSS framework provides a robust and effective approach for complex MSW landfill site selection.
• The integration of MCDA and GIS offers a powerful tool for balancing conflicting objectives in environmental planning.
• The study demonstrates the practical applicability of the developed method in environmentally sensitive regions, aiding informed decision-making.