GIS-based approach to zoning the risk associated with municipal solid waste management: application to regional scale.

The objective of this paper is to offer an approach to assess the risk associated with Municipal Solid Wastes, in a geospatial context. Initially, a risk index including hazard, vulnerability and other important variables was built. The built model is based on multi-criteria evaluation techniques and geographic information systems. Subsequently, the constructed index was used to model possible damage in various municipalities of the state of Chiapas, Mexico. The results indicate that the highest levels of risk are found in places with unfavorable conditions, such as high rates of waste generation, low waste collection coverage, steep slopes, etc. that cover 6.22% of the study area. The areas with high risk level are mainly found in the southeast of the municipalities of Villa Corzo and Villaflores, and cover 27.06% of the study area. The places of low and very low risk levels are concentrated in the center and northeast of the study area, in the municipalities of Suchiapa, Chiapa de Corzo and Acala, and cover 38.6% of the area. At the municipal level, Berriozábal, Villaflores and Villa Corzo have the highest levels of risk in most of their territory; the high levels of risk presented in Berriozábal are due to the limited territorial area that it occupies in the study area. In Villaflores and Villa Corzo, the high levels of risk are due to the high population dispersion. A large part of Tuxtla Gutiérrez territory presents low and medium risk levels, especially within the population settlement. The peripheral areas show the highest levels of risk, because the waste collection service is not provided very often. Finally, the Cohen's kappa statistic used to validate the precision of the model gave a value of 0.34, which means that the spatial model can be considered acceptable despite its low value. Although this work is only a general approach to spatial risk modeling at a regional scale, it provides interesting information. Moreover, it adds to the few efforts that exist in the literature to model the risk associated with wastes.