Formal probabilistic risk analysis of accidental air pollution in a development zone using Bayesian networks

Abstract

The potential risk caused by the leakage of toxic and hazardous substances during air pollution accidents poses a great threat to local environmental safety, human heath, and even property security. Uncertainties and causalities are important components that should not be ignored in risk assessment. Compared with traditional non-probabilistic methods, Bayesian networks (BNs) are well suited to solving problems with high complexity and uncertainties. To utilize these advantages, a BN-based approach, coupled with diffusion simulation of risk substances, was proposed to estimate the risk of air pollution accidents in a probabilistic way at the local scale. This method was applied to analyze the risk levels of 24 risk sources and 24 risk subregions in Daya Bay, a national economic and technological development zone with a large petrochemical industry in Huizhou, Guangdong province. The results indicated that the Daya Bay area suffered a relatively low risk of accidental air pollution. Only five risk sources (20.8%) and two risk subregions (8.3%) were found to have high danger or risk. Ammonia, styrene, and sulfur dioxide were the most threatening substances in order. Acute exposure was the most sensitive factor, and made the highest contribution to risk level. This research provides an effective approach that can be used to characterize the air pollution accident risk quantitatively, probabilistically, and graphically. Probabilistic risk assessment can provide substantial support to local risk prevention, control, and emergency response for accidental air pollution, as well as to future risk management and policy decisions.