| Summary: | 博士 === 國立交通大學 === 環境工程系所 === 99 === During landfill site selection, a significant amount of spatial data with respect to various regulations, criteria, and rules must be processed, in order to avoid a site being built at an inappropriate location. An appropriate site should not have significant impacts on or risks to the surrounding environment. However, determining how to locate a site with low environmental impacts and risks is still a challenging task, especially when the temporal and spatial uncertainties of the environmental impacts and risks are considered. This study was thus initiated to develop methods and tools to deal with the uncertainties in making landfill siting decisions.
In evaluating a factor causing any environmental impact from a candidate landfill site, if the data for the factor exhibits significant temporal fluctuation or uncertainty, evaluating the factor based on its average value may be inappropriate and misleading. This study thus developed a method applying the Markov chain to evaluate the probability of occurrence and a fuzzy approach to reduce the effect of the uncertainty. The method was further integrated with the spatial analysis function provided by a geographical information system (GIS) for siting a landfill. This fuzzy-Markov-chain method was demonstrated by using it to select sites with low potential risks.
In addition to the temporal uncertainty, spatial uncertainties also exist for some siting factors. For instance, the distribution of air pollutants emitted from a landfill is greatly influenced by wind directions and speeds, causing different impacts depending on the direction and location. Exposure risks are also different for areas with different population densities. Therefore, this study applied an air quality model to simulate the pollutant concentration distribution and created a pollutant concentration layer for each direction of the candidate site. Then, a directional risk layer for each candidate site was produced from the pollutant concentration layer and the population density layer, using the spatial analysis function provided by a GIS. This directional risk method is expected to improve the quality of a siting decision and to avoid the problems that may arise from implementing a siting analysis primarily based on the prevailing wind direction.
The results obtained from a case study reveal that the fuzzy-Markov-chain method can identify sites with low potential risk. Furthermore, the directional risk method can identify both the areas with high concentration and high potential risk and the areas with low concentration but high potential risk. The proposed methods can deal with temporal and spatial uncertainties effectively and provide proper information for assessing the environmental impacts and risks posed by a candidate site. Consequently, the proposed methods are expected to improve the quality of a landfill siting decision.
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