Stochastic-based fuzzy risk assessment of SO2 emission from a power station

Electricity production from fossil-fuel power plants is one of the main sources of air pollution. Risk assessment is a significant component for power plant air emission management. The most important output from risk assessment is the capacity to provide the basis of a decision-making process. The results of such decisions should be presented to environmental managers and the public in plain language rather than complex calculations. Uncertainty is inherent in any environmental risk assessment, and has 2 main origins, notably stochastic and cognitive. Research is under way to explore techniques that can incorporate these 2 kinds of uncertainties into the assessment process. The probability theory, or Monte Carlo simulation, is commonly used for stochastic uncertainties, while the possibilistic theory, or fuzzy logic, is used for cognitive uncertainties. In this study, a stochastic-based fuzzy risk assessment method was developed for a risk assessment of ambient air quality. The objective was to accurately predict contaminant transport and fate in ambient air and effectively assess the associated environmental and health risks. The proposed stochastic-based fuzzy risk assessment model was able to systematically quantify both probabilistic and possibilistic uncertainties associated with terrain conditions, environmental guidelines, and health criteria in air quality management systems. The results obtained from the improved simulation, uncertainty analysis and risk assessment are expected to provide more realistic decision support and bring significant environmental and economic benefits. 27 refs., 2 tabs., 7 figs.