Vehicle emission models and their applications in China: A comprehensive review

Road transport becomes the dominant driver behind the increase in global transport energy consumption, and consequently, vehicle emissions are a primary contributor to global climate change and atmospheric pollution. Despite extensive research into vehicle emissions, significant challenges persist in the refinement and application of emission models. Here, we review key techniques for developing emission factors and categorize vehicle emission models into macroscopic, mesoscopic, microscopic, and machine learning-based models. Specifically, we emphasize that an increasing number of scholars have been developing deep learning-based vehicle emission models owing to the rapid advancement of deep learning technology. We summarize the usages of vehicle emission models to compile emission inventories and discuss their applications in urban site selection, environmental pollution control, traffic management, and policy formulation. However, variations exist among vehicle emission models in terms of modeling accuracy, applicability, and computational complexity. Thus, future research should address the following challenges: (1) development of localized emission models that are suitable for different regions and scenarios; (2) fusion of deep learning-based emission models with physical models to develop novel models; (3) coupling of emission models with traffic simulation models to tackle urban sustainable transportation issues; (4) formulation of transportation policies for sustainable urban development. • A comprehensive and in-depth investigation of vehicle emission models. • Emphasizing the importance and urgency of developing deep learning-based models • Discussing a variety of applications of vehicle emission models in China. • Proposing potential research directions on vehicle emission models and policy recommendations.