An improved high-resolution passenger vehicle emission inventory for China using ride-hailing big data

Abstract. As the global automotive industry continues to grow rapidly, the increasing number of passenger vehicles has contributed to worsening air pollution. However, previous studies have insufficiently addressed nationwide hourly vehicle emissions. This study firstly utilized big data of ride-hailing services and traffic flow model to obtain nationwide hourly gridded speed and traffic volume. Then we established a high spatiotemporal resolution (0.01° × 0.01°; 1h) emission inventory by using multiple correction factors. The annual amount of CO, VOCs, NO_x, PM and NH₃ emitted from national passenger vehicles in 2019 were 4087.8, 1069.4, 211.7, 1.9, 77.5 kt, respectively. Despite occupying merely 0.8% of the national territory, urban areas generated 35.3% of the country's total vehicle emissions, due to high local traffic volumes and relatively low vehicle speeds. From a temporal perspective, passenger vehicle emissions exhibit significant holiday effect and weekend effect. In addition, hourly average emissions on workday exceeded those of weekend and holiday by 8% and 5% during the morning peak, with these differences increasing to 12% and 18% during the evening peak. Current traditional emission methodology might underestimate emissions by 31.5%. We also used the WRF-Chem model for simulation validation. This hourly-scale inventory provides quantitative support for the precise implementation of pollution control and early warning.