基于故障树方法的机动车燃油大气环境风险评价:以杭州市为例
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摘要
由于城市化、工业化和机动车数量的快速增长,灰霾天气已成为中国许多大城市亟待解决的严重环境污染问题。大量石油燃料消耗产生的机动车尾气排放可能是引起城市灰霾污染的一个关键因素。以长江三角洲的代表性城市杭州市为具体案例,探索将安全工程领域的故障树方法应用在机动车燃油尾气排放大气环境风险评价和与灰霾天气的致因机理分析上。通过辨识导致城市机动车尾气过量排放的关键风险因子,构建了杭州市"灰霾天气–机动车尾气过量排放"的故障树。另外采用结构、概率以及临界重要度分析,对关键风险因子对顶上事件"灰霾天气–机动车尾气过量排放"的贡献和影响程度进行了定性和定量分析。分析结果表明,过量机动车使用,严重的交通堵塞、高污染机动车的不当使用以及监管不严是对杭州市机动车尾气过量排放影响较大的关键风险因子。可为城市机动车燃油环境风险因子评价以及管理提供一种简洁有效的方法和思路。
Due to the urbanization, industrialization and rapid growth of vehicles, haze weather has become a serious environmental problem that needs to be controlled urgently in many Chinese megacities. Vehicle exhaustemissions from large amounts of petroleum fuel consumption may be a key factor in causing urban ash pollution. Inthis work, the fault tree analysis(FTA) method is investigated and employed for the risk assessment and causationmechanism of urban haze related to vehicle emissions in Hangzhou. After identifying all important risk factors, ahaze fault tree system of "haze weather–excess emission of vehicle exhausts" is established by using the deductiveFTA method. Based on the structure, probability and critical importance degree analysis, the contribution and effectof basic risk factors to the top event "haze weather-excess emission of vehicle exhausts" in Hangzhou is also carriedout. The analysis results showed that "excess vehicles", "severe traffic jam", "high pollution vehicle's using" and "supervision defect" were the most important risk factors for causing excess emission of vehicle exhausts in Hangzhou. This study may provide a concise and effective method for environmental risk assessment and management of oil consuming by vehicles related to urban haze in China.
Due to the urbanization, industrialization and rapid growth of vehicles, haze weather has become a serious environmental problem that needs to be controlled urgently in many Chinese megacities. Vehicle exhaustemissions from large amounts of petroleum fuel consumption may be a key factor in causing urban ash pollution. Inthis work, the fault tree analysis(FTA) method is investigated and employed for the risk assessment and causationmechanism of urban haze related to vehicle emissions in Hangzhou. After identifying all important risk factors, ahaze fault tree system of "haze weather–excess emission of vehicle exhausts" is established by using the deductiveFTA method. Based on the structure, probability and critical importance degree analysis, the contribution and effectof basic risk factors to the top event "haze weather-excess emission of vehicle exhausts" in Hangzhou is also carriedout. The analysis results showed that "excess vehicles", "severe traffic jam", "high pollution vehicle's using" and "supervision defect" were the most important risk factors for causing excess emission of vehicle exhausts in Hangzhou. This study may provide a concise and effective method for environmental risk assessment and management of oil consuming by vehicles related to urban haze in China.
引文
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[31]黄卫清,徐平如,钱宇.基于事故树方法的城市灰霾的致因机理分析:以天津市为例[J].化工学报, 2018, 69(3):982-991.Huang W Q, Xu P R, Qian Y. Causation mechanism analysis of urban haze based on FTA method:taking Tianjin as a case study[J]. CIESC Journal, 2018, 69(3):982-991.
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[2] Gao M, Guttikunda S K, Carmichael G R, et al. Health impacts and economic losses assessment of the 2013 severe haze event in Beijing area[J]. Sci. Total Environ., 2015, 511:553-561.
[3] Gao J, Woodward A, Vardoulakis S, et al. Haze, public health and mitigation measures in China:a review of the current evidence for further policy response[J]. Sci. Total Environ., 2017, 578:148-157.
[4] Zhang Q, Quan J, Tie X, et al. Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China[J]. Sci. Total Environ., 2015, 502:578-584.
[5] Huang G. PM2.5opened a door to public participation addressing environmental challenges in China[J]. Environ. Pollut., 2015, 197:313-315.
[6] Li M, Zhang L. Haze in China:current and future challenges[J].Environ. Pollut., 2014, 189:85-86.
[7] Zhang H, Wang S, Hao J, et al. Air pollution and control action in Beijing[J]. J. Clean. Prod., 2016, 112:1519-1527.
[8] Fu H, Chen J. Formation, features and controlling strategies of severe haze-fog pollutions in China[J]. Sci. Total Environ., 2017,578:121-138.
[9] Liu G, Yang Z, Chen B, et al. Prevention and control policy analysis for energy-related regional pollution management in China[J]. Appl. Energy, 2016, 166:292-300.
[10]黄卫清,李秀喜,钱宇.含滞后及不确定参数动态过程系统的定量优化方法与应用[J].化工学报, 2009, 60(1):83-89.Huang W Q, Li X X, Qian Y. Quantitative optimization method and application of dynamic process systems with time delay and parametric uncertainty[J]. CIESC Journal, 2009, 60(1):83-89.
[11] Huang W Q, Fan H B, Qian Y, et al. Assessment and computation of the delay tolerability for batch reactors under uncertainty[J].Chem. Eng. Res. Des., 2017, 124:78-84.
[12] Huang W Q, Qian Y, Shao Y Y, et al. Delay sensitivity analysis for typical reactor systems with flexibility consideration[J]. 2014,53:14721-14734.
[13] Zhang Y, Cao F. Is it time to tackle PM2.5air pollutions in China from biomass-burning emissions?[J]. Environ. Pollut., 2015, 202:217-219.
[14] Zhou S, Yang L, Gao R, et al. A comparison study of carbonaceous aerosols in a typical North China Plain urban atmosphere:seasonal variability, sources and implications to haze formation[J].Atmos. Environ., 2017, 149:95-103.
[15] Yang Y, Liu X, Qu Y, et al. Formation mechanism of continuous extreme haze episodes in the megacity Beijing, China, in January2013[J]. Atmos. Res., 2015, 5:192-203.
[16] Tie X, Zhang Q, He H, et al. A budget analysis of the formation of haze in Beijing[J]. Atmos. Environ., 2015, 100:25-36.
[17] Huang R, Zhang Y, Bozzeti C, et al. High secondary aerosol contribution to particulate pollution during haze events in China[J]. Nature, 2014, 514:218-222.
[18] Zhang R, Sun X, Shi A, et al. Secondary inorganic aerosols formation during haze episodes at an urban site in Beijing, China[J]. Atmos. Environ., 2018, 177:275-282.
[19] Huang W Q, Fan H B, Qiu Y F, et al. Application of fault tree approach for the causation mechanism of urban haze in Beijing—considering the risk events related with exhausts of coal combustion[J]. Sci. Total Environ., 2016, 544:1128-1135.
[20] Huang W Q, Fan H B, Qiu Y F, et al. Causation mechanism analysis for haze pollution related to vehicle emission in Guangzhou, China by employing the fault tree approach[J].Chemosphere, 2016, 151:9-16.
[21] Escher B I, Fenner K. Recent advances in environmental risk assessment of transformation products[J]. Environ. Sci. Technol.,2011, 45:3835-3847.
[22] Sexton K. Cumulative health risk assessment:finding new ideas and escaping from the old ones[J]. Hum. Ecol. Risk. Assess.,2015, 21:934-951.
[23] Tsang M P, Bates M E, Madison M, et al. Benefits and risks of emerging technologies:integrating life cycle assessment and decision analysis to assess lumber treatment alternatives[J].Environ. Sci. Technol., 2014, 48:11543-11550.
[24] Bedford T, Cooke R. Probabilistic Risk Analysis:Foundations and Methods[M]. Cambridge:Cambridge University Press, 2001.
[25] Placca L, Kouta R. Fault tree analysis for PEM fuel cell degradation process modeling[J]. Int. J. Hydrogen. Energ., 2011,36:393-405.
[26] Cheng S, Li Z, Mang H, et al. Application of fault tree approach for technical assessment of small-sized biogas systems in Nepal[J]. Appl. Energy, 2014, 113:1372-1381.
[27] Lindhe A, Norberg T, Rosen L. Approximate dynamic fault tree calculations for modelling water supply risks[J]. Reliab. Eng. Syst.Safe., 2012, 106:61-71.
[28] Abdul R F, Varuttamaseni A, Kintner-Meyer M, et al. Application of fault tree analysis for customer reliability assessment of a distribution power system[J]. Reliab. Eng. Syst. Safe., 2013, 111:76-85.
[29]唐桂忠,张广明,巩建鸣.基于模糊粗糙集和事例推理的凝汽器真空故障诊断[J].化工学报, 2011, 62(8):2227-2231.Tang G Z, Zhang G M, Gong J M. A new method of fault diagnosis of condenser vacuum based on fuzzy rough set and case-based reasoning[J]. CIESC Journal, 2011, 62(8):2227-2231.
[30]姜周曙,翁翔彬,王剑,等.反渗透海水淡化系统"脱盐率与产水量下降故障树"分析[J].化工学报, 2014, 65(6):2172-2178.Jiang Z S, Weng X B, Wang J, et al. Fault tree analysis on decreases of desalination rate and permeate flow rate of seawater reverse osmosis desalination system[J]. CIESC Journal, 2014, 65(6):2172-2178.
[31]黄卫清,徐平如,钱宇.基于事故树方法的城市灰霾的致因机理分析:以天津市为例[J].化工学报, 2018, 69(3):982-991.Huang W Q, Xu P R, Qian Y. Causation mechanism analysis of urban haze based on FTA method:taking Tianjin as a case study[J]. CIESC Journal, 2018, 69(3):982-991.
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