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

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。

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

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.