化工学报 ›› 2019, Vol. 70 ›› Issue (2): 661-669.DOI: 10.11949/j.issn.0438-1157.20181007
收稿日期:
2018-09-10
修回日期:
2018-10-16
出版日期:
2019-02-05
发布日期:
2019-02-05
通讯作者:
黄卫清
作者简介:
黄卫清(1983—),男,博士,副教授,<email>huangwq@dgut.edu.cn</email>
基金资助:
Weiqing HUANG1,2(),Pingru XU1,Yu QIAN2
Received:
2018-09-10
Revised:
2018-10-16
Online:
2019-02-05
Published:
2019-02-05
Contact:
Weiqing HUANG
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。
中图分类号:
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.
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.
Item | Energy consumed×10?4/ (t standard coal) | ||||
---|---|---|---|---|---|
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 |
表1 2011—2015年中国能源消费概况
Table 1 Energy consumed situation in China during 2011—2015
Item | Energy consumed×10?4/ (t standard coal) | ||||
---|---|---|---|---|---|
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 | ||||
---|---|---|---|---|---|
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 |
表2 2011—2015年杭州市油品消费概况
Table 2 Oil consumed situation in Hangzhou during 2011—2015
Item | Oil consumed/t | ||||
---|---|---|---|---|---|
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 | 2013 | 2014 | 2015 |
---|---|---|---|
annual average concentration of NO2/(μg·m–3) | 53 | 50 | 49 |
annual average concentration of SO2/(μg·m–3) | 28 | 21 | 16 |
annual average concentration of PM2.5/(μg·m–3) | 70 | 64.4 | 57 |
days of good air quality /d | 217 | 228 | 242 |
rate of good air quality /% | 60 | 62.5 | 66.3 |
表3 2013—2015年杭州市大气污染概况
Table 3 Atmospheric pollution in Hangzhou during
Item | 2013 | 2014 | 2015 |
---|---|---|---|
annual average concentration of NO2/(μg·m–3) | 53 | 50 | 49 |
annual average concentration of SO2/(μg·m–3) | 28 | 21 | 16 |
annual average concentration of PM2.5/(μg·m–3) | 70 | 64.4 | 57 |
days of good air quality /d | 217 | 228 | 242 |
rate of good air quality /% | 60 | 62.5 | 66.3 |
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 |
表 5 风险因子以及相应的定性与定量分析结果
Table 5 Risk factors and the qualitative/quantitative analysis results
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 |
Risk factors | Probability | |||
---|---|---|---|---|
Expert 1 | Expert 2 | Expert 3 | Average | |
excess emission of vehicle exhausts (T) | — | — | — | — |
high pollution vehicle’s using (E 1) | — | — | — | — |
improper using (X 1) | 0.45 | 0.5 | 0.55 | 0.5 |
supervising defect (X 2) | 0.2 | 0.2 | 0.35 | 0.25 |
vehicle using with design defect (E 2) | — | — | — | — |
ignition device defect (X 3) | 0.2 | 0.3 | 0.25 | 0.25 |
lack of purification device (X 4) | 0.2 | 0.25 | 0.3 | 0.25 |
non-strict supervision (X 5) | 0.25 | 0.2 | 0.3 | 0.25 |
bad traffic (E 3) | — | — | — | — |
severe traffic jam (X 6) | 0.45 | 0.5 | 0.4 | 0.45 |
excess vehicles (X 7) | 0.65 | 0.6 | 0.55 | 0.6 |
unqualified oil using (E 4) | — | — | — | — |
production of inferior oil (X 8) | 0.25 | 0.4 | 0.3 | 0.3 |
interests driving (X 9) | 0.4 | 0.25 | 0.25 | 0.3 |
absence of supervision on oil quality (X 10) | 0.35 | 0.3 | 0.25 | 0.3 |
表4 故障树风险因子及相应的概率赋值
Table 4 Risk factors and probability values in fault tree
Risk factors | Probability | |||
---|---|---|---|---|
Expert 1 | Expert 2 | Expert 3 | Average | |
excess emission of vehicle exhausts (T) | — | — | — | — |
high pollution vehicle’s using (E 1) | — | — | — | — |
improper using (X 1) | 0.45 | 0.5 | 0.55 | 0.5 |
supervising defect (X 2) | 0.2 | 0.2 | 0.35 | 0.25 |
vehicle using with design defect (E 2) | — | — | — | — |
ignition device defect (X 3) | 0.2 | 0.3 | 0.25 | 0.25 |
lack of purification device (X 4) | 0.2 | 0.25 | 0.3 | 0.25 |
non-strict supervision (X 5) | 0.25 | 0.2 | 0.3 | 0.25 |
bad traffic (E 3) | — | — | — | — |
severe traffic jam (X 6) | 0.45 | 0.5 | 0.4 | 0.45 |
excess vehicles (X 7) | 0.65 | 0.6 | 0.55 | 0.6 |
unqualified oil using (E 4) | — | — | — | — |
production of inferior oil (X 8) | 0.25 | 0.4 | 0.3 | 0.3 |
interests driving (X 9) | 0.4 | 0.25 | 0.25 | 0.3 |
absence of supervision on oil quality (X 10) | 0.35 | 0.3 | 0.25 | 0.3 |
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