北京市机动车排放趋势与协同控制情景分析

叶贺勇; 樊守彬; 李婷婷; 王亚洲; 龙腾

doi:10.12153/j.issn.1674-991X.20220917

北京市机动车排放趋势与协同控制情景分析

doi: 10.12153/j.issn.1674-991X.20220917

叶贺勇^{1, 2,},
樊守彬^{2, 3, ,},
李婷婷^{2, 3},
王亚洲^{2, 3},
龙腾^{2, 3}

1.
北京工业大学环境与生命学部
2.
北京市生态环境保护科学研究院
3.
国家城市环境污染控制工程技术研究中心

基金项目: 北京市科技计划项目(Z191100009119011)；大气重污染成因与治理攻关项目(DQGG0201)

详细信息

作者简介:
叶贺勇(1996—)，男，硕士，主要从事大气污染防治研究，yhy5956@163.com

通讯作者:
樊守彬(1981—)，男，研究员，博士，主要从事大气污染防治研究，fanshoubin@163.com

中图分类号: X701
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- 文章访问数: 119
- HTML全文浏览量: 115
- PDF下载量: 35
- 被引次数: 0
出版历程
- 收稿日期: 2022-09-16
- 录用日期: 2023-02-02
- 修回日期: 2022-11-15
- 网络出版日期: 2023-09-20

Scenario analysis of motor vehicle emission trends and synergistic control in Beijing

YE Heyong^{1, 2
,},
FAN Shoubin^{2, 3
, ,},
LI Tingting^{2, 3},
WANG Yazhou^{2, 3},
LONG Teng^{2, 3}

1.
Faculty of Environment and Life, Beijing University of Technology
2.
Beijing Municipal Research Institute of Eco-Environmental Protection
3.
National Engineering Technology Research Center of Urban Environmental Pollution Control

摘要

摘要:
为了分析北京市长时间尺度的机动车尾气排放趋势，研究机动车排放大气污染物和温室气体的协同控制效应。应用COPERT 5模型构建2005—2020年北京市机动车污染物CO、NO_x、VOCs、PM_2.5和CO₂、CH₄、N₂O的排放清单，以2020年为基准年，设置5种减排情景评估2025年各情景下的机动车污染物减排效果，并利用协同减排弹性系数法和坐标系法分别分析了大气污染物与温室气体的协同效应。结果表明：CO₂排放增长趋势显著，相比2005年，2020年其排放增长率达到85.25%，而其他污染物相比2005年均呈下降趋势。不同控制情景下，北京市机动车大气污染物和温室气体排放量相比于基准情景(BAU)均具有减排效果，综合控制情景(RIS)减排效果最好。从协同减排弹性系数法和坐标系法分析结果看，不同控制情景下大气污染物和温室气体均具有协同效应，且RIS下协同效应最优。未来北京市应积极采取综合控制对策，兼顾统筹各种减排措施，为尽快实现降碳减污协同治理和绿色低碳经济社会转型奠定基础。
- 排放清单 /
- 排放趋势 /
- 情景分析 /
- 减排效果 /
- 协同效应
Abstract:
In order to analyze the trends of motor vehicle emissions on the time scale of Beijing, the synergistic control effects of motor vehicle emissions of air pollutants and greenhouse gases were studied. COPERT 5 model was applied to construct the emission inventories of motor vehicle pollutants CO, NO_x, VOCs, PM_2.5 and greenhouse gases CO₂, CH₄ and N₂O in Beijing from 2005 to 2020, and five emission reduction scenarios were set up to evaluate the emission reduction effects of motor vehicle pollutants under each scenario in 2025 with 2020 as the base year, and the synergistic emission reduction elasticity coefficient method and coordinate system method were applied to analyze the synergistic effects of air pollutants and greenhouse gases, respectively. The results showed that CO₂ emissions increased significantly, with a growth rate of 85.25% in 2020 compared to 2005, while all the pollutants decreased compared to 2005. Under different control scenarios, the emissions of motor vehicle air pollutants and greenhouse gases in Beijing were reduced compared with the business-as-usual scenario (BAU), and the integrated control scenario (RIS) had the best emission reduction effect. From the results of the synergistic emission reduction elasticity coefficient method and coordinate system method, the synergistic effects of air pollutants and greenhouse gases under different control scenarios were observed, and the synergistic effects were optimal in the RIS scenario. In the future, Beijing should actively adopt comprehensive control measures and coordinate various emission reduction measures, laying the foundation for collaborative governance of pollution reduction and carbon reduction and green low-carbon economic and social transformation as soon as possible.
- emission inventories /
- emission trends /
- scenario analysis /
- emission reduction effects /
- synergistic effects

HTML全文

图 1 北京市机动车保有量

Figure 1. Number of motor vehicles in Beijing

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图 2 北京市各年份不同车型年均行驶里程

Figure 2. Average annual mileage of motor vehicles in Beijing

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图 3 机动车减排协同控制情景坐标系示意

Figure 3. Schematic diagram of coordinate system for co-control of motor vehicle emission reduction

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图 4 北京市历年不同类型机动车排放污染物和温室气体占比及排放量变化趋势

Figure 4. Proportion of pollutants and GHGs emitted by different types of motor vehicles and emission trends in Beijing over the years

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图 5 不同控制情景下各种污染物和温室气体的排放量

Figure 5. Emission reductions of various pollutants and GHGs under different control scenarios

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图 6 大气污染物和温室气体当量的协同减排效应坐标系

Figure 6. Coordinates of synergistic emission reduction effect of air pollutants and GHG equivalents

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表 1 机动车排放情景设置

Table 1. Descriptions of motor vehicle emission control scenarios

控制对策	控制情景	情景描述
单一对策	基准情景(BAU)	保持当前北京市实行的控制措施和排放标准与基准年一致，不实施其他减排措施且机动车保有量遵循自然淘汰更新规律
	淘汰高排放汽车(ESV)	依据《北京市“十三五”时期交通发展建设规划》，结合2020年国4排放标准车辆平均燃料消耗量和使用年限，到2025年基本淘汰国4及以下微小型汽油客车与轻型载货柴油车，根据车型占比，预计由32.9%国4以下微小型汽油客车和14.4%轻型载货柴油车淘汰更新为国6标准车辆
	推广新能源汽车(NEV)	依据《北京市“十四五”时期交通发展建设规划》，结合《北京市推广应用新能源汽车管理办法》，设置从2020年新能源车占比为6.3%到2025年占比达到28.2%，全市新能源汽车保有量达到200万辆，其中新能源客车占比95%，新能源货车占比5%
	发展公共交通 (DPT)	依据《北京城市总体规划（2016—2035年）》，结合《北京市“十四五”时期交通发展建设规划》中提到的北京中心城区公共交通占机动化出行比例预期提高至62.3%，设置到2025年微小型客车、摩托车年均行驶里程相比基准情景分别降低15%和20%
综合对策	综合控制情景(RIS)	将所有控制措施进行结合，以考虑其综合减排的潜力

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表 2 2025年北京市机动车大气污染物和温室气体排放量预测

Table 2. Prediction of emissions of air pollutants and GHGs from motor vehicles for the target year of 2025 in Beijing t　

年份	大气污染物排放量				温室气体排放量
年份	CO	NO_x	VOCs	PM_2.5	CO₂	CH₄	N₂O
2020	62 018.3	51 882.2	13 683.5	2 306.4	21 449 371.5	1 405.8	218.9
2025	78 959.1	57 810.9	18 206.6	2 878.3	26 504 480.6	1 804.7	286.3

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表 3 不同控制情景下温室气体对大气污染物的减排弹性系数

Table 3. Elasticity coefficient of GHG emission reduction on air pollutants under different control scenarios

控制对策	控制情景	$\mathrm{ELS}_{\left(\mathrm{CO}_2 \mathrm{e} / \mathrm{NO} x\right)} $	$\mathrm{ELS}_{\left(\mathrm{CO}_2 \mathrm{e} / \mathrm{PM}_{2.5} \right)} $	$\mathrm{ELS}_{\left(\mathrm{CO}_2 \mathrm{e} / \mathrm{VOCs} \right)} $	$\mathrm{ELS}_{\left(\mathrm{CO}_2 \mathrm{e} / \mathrm{CO} \right)} $	$\mathrm{ELS}_{\left(\mathrm{CO}_2 \mathrm{e} / \mathrm{AE} \right)}$
单一对策	ESV	0.37	0.51	0.74	0.60	0.59
	NEV	1.00	1.01	1.04	1.02	1.02
	DPT	7.92	1.28	1.11	0.83	0.87
综合对策	RIS	0.87	0.91	1.00	0.92	0.92

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