High-resolution mapping of regional traffic emissions by using land-use machine learning models

Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly-based, link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on the datasets of road traffic monitoring in the Beijing-Tianjin-Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than a Gaussian process regression (GPR) model. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. One notable finding is that NOX, fine particulate matter (PM2.5) and black carbon (BC) emissions from heavy-duty trucks (HDTs) in general have higher emission intensity on the highways connecting to regional ports. Even when traffic restrictions were implemented, a detour of the HDTs in Hebei was observed resulting in relatively lower emission reductions in Hebei than Beijing. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, which provides a platform to realize the near real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.