A Comprehensive 2018-Based Vehicle Emission Inventory and Its Spatial–Temporal Characteristics in the Central Liaoning Urban Agglomeration, China

Rapid economic expansion and urbanisation have seriously affected the atmospheric environmental quality of the Central Liaoning Urban Agglomeration (CLUA). This study aimed to establish a detailed vehicle emission inventory of the CLUA with a 3 km × 3 km gridded spatiotemporal distribution. A top-down methodology using vehicle kilometres travelled annually, emission factors, and activity data of each city was established. Carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO 2 ), ammonia (NH 3 ), volatile organic compounds (VOCs), particulate matter with an aerodynamic diameter less than 2.5 μm (PM 2.5 ), particulate matter with an aerodynamic diameter less than 10 μm (PM 10 ), Black Carbon (BC), and organic carbon (OC) emissions were 291.0, 221.8, 3.6, 2.2, 42.8, 9.3, 10.3, 5.2, and 1.6 Gg in 2018, respectively. The contribution of diesel heavy-duty trucks to NOx, SO 2 , PM 2.5 , PM 10 , BC, and OC emissions was greater than 54.5%, the largest contribution of all vehicles. Gasoline small passenger vehicles were the primary contributor to CO, VOC, and NH 3 emissions, contributing 37.3%, 39.5%, and 75.3% of total emissions, respectively. For emission standards, Pre-China 1 vehicles were the largest contributor to CO and VOC emissions and China 3 vehicles contributed the largest amount of NOx, SO 2 , PM 2.5 , PM 10 , BC, and OC emissions. The spatial distribution of pollutants showed “obvious lines” and grids with high emissions were concentrated in expressways, national highways, and provincial highways. The temporal variation showed morning–evening peaks during diurnal variations, which was consistent with resident behaviour. This work can help us understand vehicular emission characteristics of the CLUA and provide basic data for air quality modelling. Future research should investigate traffic flow by vehicle types and emission factors at a local level, which will be helpful for transport management planning.