Fault tree analysis of the causes of urban smog events associated with vehicle exhaust emissions: A case study in Jinan, China

摘要

The excessive consumption of fuels associated with rapid industrialization, urbanization, and modernization has caused serious smog events in many Chinese cities. Vehicle exhaust is one of the primary causes of smog events due to the rapid growth of motor vehide ownership and increased fuel consumption. In this study, fault tree analysis (FTA) was used as a relatively simple but effective way to analyze the causes of smog associated with vehide exhaust emissions in Jinan, China. First, after the identification of the top event, intermediate events, and basic events, a comprehensive fault tree system for urban smog associated with vehicle exhaust emissions was constructed. Then, during the qualitative analysis stage, minimal cut sets (MCSs) were grouped using Boolean algebra operations, and the original fault tree was simplified to an equivalent tree based on 6 MCSs. Finally, during the quantitative analysis stage, the effects of the 12 basic events on the top event were evaluated and ranked according to the structural importance, probabilistic importance, and critical importance of their analytical measures. Our results indicated that traffic congestion, superabundance of vehicles. poor supervision, and yellow-label vehicles with long use ages had the greatest impact on smog events, with importance degrees of 0.52930, 0.52920, 022719, and 0.22716, respectively. These results are consistent with common sense. Although different basic events exert different influences, all of the basic events should be comprehensively taken into consideration and corresponding precautionary measures developed. This research provides a good case study of the application of FTA in the analysis of the causes of urban smog events associated with vehicle exhaust emissions. Our study further demonstrates that FTA is a relatively simple but effective method for the causal analysis of smog. as well as an effective tool for environmental risk management. (C) 2019 Elsevier B.V. All rights reserved.