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<p>Demographic expansion together with fast-paced urbanization within hilly terrain of ecologically fragile areas such as Darjeeling in West Bengal complicated the process of managing municipal solid waste (MSW). A study develops a comprehensive geospatial method which combines remote sensing (RS) and geographic information systems (GIS) with multi-criteria decision analysis (MCDA) to locate sustainable zones for municipal solid waste disposal. The study examines the Darjeeling Municipality area alongside its 2-km surrounding zone which demonstrates steep topography and density as well as ecological risks. A spatial decision support system (SDSS) is developed using a multi-criteria RS-GIS framework to determine the suitable areas for municipal solid waste disposal site suitability (MSWDSS). The framework standardizes geospatial and urban planning criteria through quantitative evaluation of slope, elevation, land use/land cover, and areas around roads, water bodies, and settlements which are weighted using analytic hierarchy process (AHP). The weighted linear combination (WLC) technique is used to compute a composite suitability index, ensuring proportional influence from each criterion after normalization. For proximity-sensitive factors, a Gaussian decay function is applied to model nonlinear reductions in suitability near sensitive infrastructure. The parameters were weighted using AHP based on their influence on landfill site suitability, with land value (0.184), distance to settlement (0.135), and distance to road (0.123) receiving the highest weights. These reflect the prioritization of economic feasibility, public health, and operational efficiency. Spatial data layers were generated, reclassified, and overlaid in a GIS environment to produce a composite suitability map. The final map classified land into three suitability zones: high, moderate, and low, highlighting that high suitability zones are located in the southern and southwestern parts of Darjeeling Municipality, characterized by low population density, low land value, greater distance from sensitive sites, gentle slopes, and poor access to existing waste services. The composite MSWDSS index is classified using natural breaks (Jenks) into three suitability categories: high (≥ 0.66), moderate (0.33–0.65), and low (≤ 0.32), to support informed site selection under constrained urban conditions. Findings reveal that only a limited portion of the study area meets the environmental and infrastructural criteria for landfill development, owing to Darjeeling’s challenging topography and dense urban fabric. Nevertheless, the model successfully identifies zones with optimal accessibility, minimal ecological disruption, and reduced risks of leachate contamination and landslides. The findings show that the analysis produced the best results when applied to the study area, optimizing the balance between environmental, infrastructural, and economic factors.</p>

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Sustainable mapping identification of municipal solid waste disposal zones using RS-GIS-basedMCDA techniques: a case study in Darjeeling, West Bengal

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Rubeena Vohra,
  • Prachi Mishra

摘要

Demographic expansion together with fast-paced urbanization within hilly terrain of ecologically fragile areas such as Darjeeling in West Bengal complicated the process of managing municipal solid waste (MSW). A study develops a comprehensive geospatial method which combines remote sensing (RS) and geographic information systems (GIS) with multi-criteria decision analysis (MCDA) to locate sustainable zones for municipal solid waste disposal. The study examines the Darjeeling Municipality area alongside its 2-km surrounding zone which demonstrates steep topography and density as well as ecological risks. A spatial decision support system (SDSS) is developed using a multi-criteria RS-GIS framework to determine the suitable areas for municipal solid waste disposal site suitability (MSWDSS). The framework standardizes geospatial and urban planning criteria through quantitative evaluation of slope, elevation, land use/land cover, and areas around roads, water bodies, and settlements which are weighted using analytic hierarchy process (AHP). The weighted linear combination (WLC) technique is used to compute a composite suitability index, ensuring proportional influence from each criterion after normalization. For proximity-sensitive factors, a Gaussian decay function is applied to model nonlinear reductions in suitability near sensitive infrastructure. The parameters were weighted using AHP based on their influence on landfill site suitability, with land value (0.184), distance to settlement (0.135), and distance to road (0.123) receiving the highest weights. These reflect the prioritization of economic feasibility, public health, and operational efficiency. Spatial data layers were generated, reclassified, and overlaid in a GIS environment to produce a composite suitability map. The final map classified land into three suitability zones: high, moderate, and low, highlighting that high suitability zones are located in the southern and southwestern parts of Darjeeling Municipality, characterized by low population density, low land value, greater distance from sensitive sites, gentle slopes, and poor access to existing waste services. The composite MSWDSS index is classified using natural breaks (Jenks) into three suitability categories: high (≥ 0.66), moderate (0.33–0.65), and low (≤ 0.32), to support informed site selection under constrained urban conditions. Findings reveal that only a limited portion of the study area meets the environmental and infrastructural criteria for landfill development, owing to Darjeeling’s challenging topography and dense urban fabric. Nevertheless, the model successfully identifies zones with optimal accessibility, minimal ecological disruption, and reduced risks of leachate contamination and landslides. The findings show that the analysis produced the best results when applied to the study area, optimizing the balance between environmental, infrastructural, and economic factors.