Risk Assessment of Ambient Air Quality by Stochastic-Based Fuzzy Approaches

Abstract

Abstract

A stochastic-based fuzzy risk assessment approach was developed by integrating stochastic simulation, expert involvement, and fuzzy logic within a general framework for systematically examining both the probabilistic and possibilistic uncertainties associated with land cover, environmental guidelines, and health evaluation criteria in an ambient air quality management system. The developed approach was applied to a case study in which sulfur dioxide (SO₂) was of interest. Based on the SO₂ dispersion modeling results from Monte Carlo simulation, an in-depth fuzzy risk assessment was further employed to quantify the environmental guideline-based risk and health risk due to SO₂ inhalation. General risk levels were obtained through fuzzy membership functions and rule bases acquired from a comprehensive questionnaire survey. Scenarios with different air quality guidelines were also analyzed, leading to the variations of risk levels. Results indicated that the developed approach would offer an effective tool for quantifying uncertainties existing in air quality modeling parameters, evaluating their effects in risk levels and providing realistic support to related decision making in air quality management.