Atmospheric environment risk analysis of oil consuming by vehicles based on FTA method: taking Hangzhou as a case study

doi:10.11949/j.issn.0438-1157.20181007

Abstract

Abstract:

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 exhaust emissions from large amounts of petroleum fuel consumption may be a key factor in causing urban ash pollution. In this work, the fault tree analysis (FTA) method is investigated and employed for the risk assessment and causation mechanism of urban haze related to vehicle emissions in Hangzhou. After identifying all important risk factors, a haze fault tree system of “haze weather–excess emission of vehicle exhausts” is established by using the deductive FTA method. Based on the structure, probability and critical importance degree analysis, the contribution and effect of basic risk factors to the top event “haze weather-excess emission of vehicle exhausts” in Hangzhou is also carried out. 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.

Key words: vehicle, haze pollution, systems engineering, sustainability, environment

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。

关键词: 机动车, 灰霾污染, 系统工程, 可持续性, 环境

CLC Number:

TQ 021.8

Weiqing HUANG, Pingru XU, Yu QIAN. Atmospheric environment risk analysis of oil consuming by vehicles based on FTA method: taking Hangzhou as a case study[J]. CIESC Journal, 2019, 70(2): 661-669.

黄卫清, 徐平如, 钱宇. 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[J]. 化工学报, 2019, 70(2): 661-669.

Figures/Tables 8

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Item	Energy consumed×10^?4/ (t standard coal)
Item	2011	2012	2013	2014	2015
total energy	387043.00	402138.00	416913	425806	429905.1
coal	271704.19	275464.53	280999.36	279328.74	273849.49
oil	65023.22	68363.46	71292.12	74090.24	78672.62
natural gas	17803.98	19302.62	22096.39	24270.94	25364.40
water, nuclear and wind energy	32511.61	39007.39	42525.13	48116.08	52018.51
transportation related energy	28535.50	31524.71	34819.02	36336.43	38317.66

Item	Energy consumed×10^?4/ (t standard coal)
Item	2011	2012	2013	2014	2015
total energy	387043.00	402138.00	416913	425806	429905.1
coal	271704.19	275464.53	280999.36	279328.74	273849.49
oil	65023.22	68363.46	71292.12	74090.24	78672.62
natural gas	17803.98	19302.62	22096.39	24270.94	25364.40
water, nuclear and wind energy	32511.61	39007.39	42525.13	48116.08	52018.51
transportation related energy	28535.50	31524.71	34819.02	36336.43	38317.66

Item	Oil consumed/t
Item	2011	2012	2013	2014	2015
diesel oil	186630	171080	162127	157836	148219
gasoline	51827	55293	49705	42974	45907
fuel oil	67662	51759	44097	32541	26072
other petroleum products	224673	244203	134188	71599	24964

Item	Oil consumed/t
Item	2011	2012	2013	2014	2015
diesel oil	186630	171080	162127	157836	148219
gasoline	51827	55293	49705	42974	45907
fuel oil	67662	51759	44097	32541	26072
other petroleum products	224673	244203	134188	71599	24964

Risk factors	Probability average values	Structure importance degree coefficients	Probability importance degree coefficients	Critical importance degree coefficients
excess emission of vehicle exhausts (T)	0.3888	—	—	—
high pollution vehicle’s using (E ₁)	—	—	—	—
improper using (X ₁)	0.5	0.5	0.175	0.225
supervising defect (X ₂)	0.25	0.5	0.350	0.225
vehicle using with design defect (E ₂)	—	—	—	—
ignition device defect (X ₃)	0.25	0.25	0.155	0.0997
lack of purification device (X ₄)	0.25	0.25	0.155	0.0997
non-strict supervision (X ₅)	0.25	0.25	0.155	0.0997
bad traffic (E ₃)	—	—	—	—
severe traffic jam (X ₆)	0.45	0.5	0.502	0.581
excess vehicles (X ₇)	0.6	0.5	0.377	0.581
unqualified oil using (E ₄)	—	—	—	—
production of inferior oil (X ₈)	0.3	0.25	0.157	0.121
interests driving (X ₉)	0.3	0.25	0.157	0.121
absence of supervision on oil quality (X ₁₀)	0.3	0.25	0.157	0.121