昆明市高速公路机动车温室气体排放清单及时空性分布特征

陈振瑜; 何超; 李加强; 付明亮; 徐加臣; 李菊

doi:10.12153/j.issn.1674-991X.20230226

昆明市高速公路机动车温室气体排放清单及时空性分布特征

doi: 10.12153/j.issn.1674-991X.20230226

陈振瑜^1,,
何超^{1, 2, ,},
李加强^{1, 2},
付明亮³,
徐加臣^{1, 2},
李菊^{1, 2}

1.
西南林业大学机械与交通学院
2.
云南省高校高原山区机动车环保与安全重点实验室
3.
中国环境科学研究院

基金项目: 云南省教育厅科学研究基金项目（2020J0418）；云南省高层次人才项目（YNWR-QNBJ-2018-066，YNQR-CYRC-2019-001）

详细信息

作者简介:
陈振瑜(1999—)，男，硕士研究生，主要从事机动车排放研究，chenzhenyu1999@126.com

通讯作者:
何超(1980—)，男，教授，博士，主要从事机动车排放研究，hcsmile@163.com

中图分类号: X51
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- HTML全文浏览量: 109
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- 被引次数: 0
出版历程
- 收稿日期: 2023-03-22
- 录用日期: 2023-07-26
- 修回日期: 2023-04-18

Inventory and spatiotemporal distribution characteristics of greenhouse gas emissions from vehicles on expressways in Kunming City

CHEN Zhenyu^1
,,
HE Chao^{1, 2
, ,},
LI Jiaqiang^{1, 2},
FU Mingliang³,
XU Jiachen^{1, 2},
LI Ju^{1, 2}

1.
School of Mechanics and Transportation, Southwest Forestry University
2.
Key Laboratory of Vehicle Environmental Protection and Safety in Plateau Mountain Area of Yunnan University
3.
Chinese Research Academy of Environmental Sciences

摘要

摘要:
为研究昆明市高速公路机动车的CO、CO₂、N₂O、CH₄温室气体排放清单，使用2021年昆明市高速公路客车交通流量数据、机动车GPS信息数据获得了高速公路网上的车型构成、车流量等基础数据，应用本土化修正后MOVES模型计算了昆明市高速公路的机动车CO、CO₂、N₂O、CH₄排放因子。基于实际交通流量数据、温室气体排放因子和昆明市高速公路实际道路信息，构建了昆明市高速公路机动车温室气体排放清单，并对其排放特征以及空间分布特征进行分析。结果表明：昆明市2021年高速公路机动车CO、CO₂、N₂O和CH₄的排放量分别为20 337.1、2 575 677.1、33.8和72.9 t，总计CO₂当量为2 626 212.5 t。按排放标准划分，国Ⅳ排放标准的机动车是4种温室气体排放的主要贡献车型；按车辆类型划分，小型客车是CO、CO₂、N₂O排放的主要贡献车型，大型客车是CH₄排放的主要贡献车型；按燃料类型划分，汽油车是CO、CO₂、N₂O的主要贡献车型，柴油车是CH₄排放的主要贡献车型。昆明市高速公路机动车温室气体排放时间分布特征为排放强度与不同时间段的交通流量呈正相关，在24 h内呈现双高峰变化；空间分布特征为排放强度与路网密度和区域交通流量密切相关，路网密度较高和交通流量较高的区域排放强度较高。
- MOVES模型 /
- 温室气体排放 /
- 排放清单 /
- 时空性特征 /
- 高速公路
Abstract:
In order to study the emission inventory of greenhouse gases (GHGs) such as CO, CO₂, N₂O and CH₄ from motor vehicles on expressways in Kunming City, the expressway passenger traffic flow data and vehicle GPS information data in Kunming City in 2021 were used to obtain the basic data such as vehicle type composition and vehicle flow on the expressway network. The localized modified MOVES model was applied to calculate the emission factors of CO, CO₂, N₂O and CH₄ of vehicles on the expressways. Based on the actual traffic flow data, GHG emission factors and the actual road information of expressways, the GHG emission inventory of the expressways in Kunming City was constructed, and its emission characteristics and spatial distribution characteristics were analyzed. The results showed that the emissions of CO, CO₂, N₂O and CH₄ from expressway vehicles in Kunming City in 2021 were 20 337.1, 2 575 677.1, 33.8 and 72.9 t, respectively, with a total CO₂ equivalent of 2 626 212.5 t. According to emission standards, the vehicles with national stage Ⅳ emission standards were the main contributors to the four types of GHG emissions. According to vehicle types, passenger cars were the main contribution models to CO, CO₂ and N₂O emissions, while large buses were the main contribution models to CH₄ emissions. Divided by fuel type, gasoline vehicles were the main contribution models of CO, CO₂ and N₂O, while diesel vehicles were the main contribution models of CH₄ emission. The temporal distribution characteristics showed that the emission intensity had a positive correlation with the traffic flow in different time periods, and the GHG emission intensity of motor vehicles on expressways in Kunming City showed a "bimodal" change within 24 h. The spatial distribution of emission intensity was closely related to road network density and regional traffic flow. The region with higher road network density and higher traffic flow had higher emission intensity.
- MOVES model /
- greenhouse gas emission /
- emission inventory /
- temporal-spatial characteristics /
- expressway

HTML全文

图 1 各车型的车龄占比

Figure 1. The age proportion of each vehicle type

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图 2 各车型的年交通流量

Figure 2. Annual traffic flow of each vehicle type

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图 3 按3种类型划分的温室气体排放贡献率

注：TGHG为CO、CO₂、N₂O、CH₄这4种温室气体的排放总量。

Figure 3. Contribution rate of greenhouse gas emission divided by three types

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图 4 高速公路交通流量及温室气体排放总量时间层面分布特征

Figure 4. Distribution characteristics of total greenhouse gas emissions and expressway traffic flow at time level

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图 5 各县（市、区）的总交通流量

Figure 5. Total traffic flows of each district and county

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图 6 CO排放量空间分配

注：底图源自云南省地理信息公共服务平台（https://yunnan.tianditu.gov.cn/MapResource）下载的《昆明市地图1∶87.5万》，审图号为云S(2021)63号。全文同。

Figure 6. Spatial distribution map of total CO emissions

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图 7 CO₂排放量空间分配

Figure 7. Spatial distribution map of total CO₂ emissions

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图 8 N₂O排放量空间分配

Figure 8. Spatial distribution diagram of total N₂O emission

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图 9 CH₄排放量空间分配

Figure 9. Spatial distribution diagram of total CH₄ emission

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表 1 昆明市每月平均气温及相对湿度

Table 1. Monthly mean temperature and relative humidity in Kunming City

月份	平均气温/℃	相对湿度/%
1	7.4	70.3
2	10.7	61.3
3	15.2	42.0
4	17.3	53.7
5	20.2	59.1
6	19.4	76.7
7	19.4	81.8
8	19.8	80.6
9	18.7	75.2
10	19.3	76.0
11	10.5	78.6
12	15.2	70.5

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表 2 车型对照

Table 2. Vehicle comparison table

本研究车型	MOVES选取车型
客一（小型客车）	passenger car
客二（中型客车）	transit bus
客三、客四（大型客车）	intercity bus
轻型货车	passenger truck
中型货车	light commercial truck
重型货车	combination long-haul truck

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表 3 不同排放标准的机动车占比

Table 3. The proportion of motor vehicles according to emission standards %　

车型	燃料类型	车辆占比
车型	燃料类型	国Ⅰ	国Ⅱ	国Ⅲ	国Ⅳ	国Ⅴ	国Ⅵ
小型客车	汽油	—	1.6	8.2	42.4	35.1	12.7
中型客车	汽油	—	14.2	32.4	53.4	—	—
大型客车	柴油	—	3.5	26.0	38.3	21.9	10.3
轻型货车	汽油	—	6.9	14.4	37.6	20.2	20.9
中型货车	柴油	—	44.3	40.8	14.9	—	—
重型货车	柴油	—	5.4	35.0	23.7	11.1	24.8
注：—表示无占有率。

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表 4 燃料参数

Table 4. Fuel parameters

燃油类别	雷德蒸汽压/kPa	硫含量百分比/%	烃含量百分比/%	芳烃含量百分比/%	十六烷值	多环芳烃含量百分比/%
汽油	64.8	10	18	35	0	0
柴油	0	10	0	0	46	7

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表 5 昆明市及其他地区的机动车温室气体排放因子

Table 5. Greenhouse gas emission factors of motor vehicles in Kunming City and some cities g/(km·辆)　

城市	模型	年份	车型	平均排放因子				数据来源
城市	模型	年份	车型	CO	CO₂	N₂O	CH₄	数据来源
昆明市	MOVES	2021	小型客车	2.517	227.166	0.003	0.002	本研究
			中型客车	6.583	814.881	0.012	0.006
			大型客车	0.600	930.209	0.004	0.084
			轻型货车	2.217	437.831	0.004	0.002
			中型货车	1.249	599.602	0.004	0.042
			重型货车	0.830	1669.190	0.006	0.122
渭南市	MOVES	2019	小型客车	2.799	318.741	0.008	0.010	文献[7]
			中型客车	6.248	781.236	0.011	0.024
			大型客车	1.892	961.717	0.015	0.568
			轻型货车	2.799	468.552	0.009	0.021
			中型货车	2.576	790.832	0.004	0.019
			重型货车	2.307	1324.703	0.002	0.075
渭南市	MOVES	2018	小型客车	2.818	—	—	0.041	文献[32]
			中型客车	5.372	—	—	0.019
			大型客车	1.738	—	—	0.112
			轻型货车	3.646	—	—	0.063
			中型货车	2.900	—	—	0.125
			重型货车	2.026	—	—	0.008
关中城市群	MOVES	2019	小型客车	—	—	0.007	0.016	文献[33]
			中型客车	—	—	0.013	0.019
			大型客车	—	—	0.011	0.011
			轻型货车	—	—	0.009	0.018
			中型货车	—	—	0.009	0.015
			重型货车	—	—	0.005	0.018
注：—表示无数据。

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表 6 昆明市高速公路温室气体排放清单

Table 6. Greenhouse gas emission inventory of expressways in Kunming City t　

车型	排放量
车型	CO	CO₂	N₂O	CH₄	CO₂当量
小型客车	18 064.2	1 630 366.1	25.5	16.1	1 672 689.6
中型客车	809.3	100 180.2	2.2	1.2	102 403.5
大型客车	288.1	447 053.1	2.4	40.2	449 320.7
轻型货车	1 066.2	210 478.1	2.3	1.1	213 216.8
中型货车	20.2	9 588.2	0.1	1.2	9 686.4
重型货车	89.1	178 011.4	1.3	13.1	178 895.6
总计	20 337.1	2 575 677.1	33.8	72.9	2 626 212.5

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