Revealing uncertainties of on-road emissions in a mountainous city using multi-source data

Source abstract

Robust on-road traffic emission modeling supports carbon reduction, pollution control, and energy transition planning. While road grade surely affects individual vehicle emissions, its city-scale impact remains underexplored. This study integrates multi-source data from Hong Kong (HK)—dynamic fleet composition, spatially resolved traffic simulation, and geographic information—to capture spatiotemporal emission patterns. A grade-included link-level emission model is developed based on the local regulatory grade-invariant model and validated by extensive on-road plume-chasing experiments. Results show that incorporating road grade increases citywide carbon and air pollutants by 10–30% and substantially alters their spatial distribution, with over 50% of road segments deviating by more than 10% from grade-invariant estimates. Road grade’s emission effects cannot be fully offset in mountainous cities like HK, while the effects are both local and regional. Leveraging the high-resolution emission model, we further find an about several-year delay in achieving HK’s 2035 carbon reduction goal for on-road transportation.