High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data

Abstract. On-road vehicle emissions play a crucial role in affecting fine-scale air quality and exposure equity in traffic-dense urban areas. They vary largely in both spatial and temporal scales due to the complex distribution patterns of vehicle types and traffic conditions. With the deployment of traffic cameras and big data approaches, we established a bottom-up model that employed interpolation to obtain a spatially continuous on-road vehicle emission mapping for the main urban area of Jinan, revealing fine-scale gradients and emission hotspots intuitively. The results show that the hourly average emissions of nitrogen oxides, carbon monoxide, hydrocarbons, and fine particulate matters from on-road vehicles in urban Jinan were 345.2, 789.7, 69.5, and 5.4 kg, respectively. The emission intensity varied largely with a factor of up to 3 within 1 km on the same road segment. The unique patterns of road vehicle emissions within urban area were further examined through time series clustering and hotspot analysis. When spatial hotspots coincided with peak hours, emissions were significantly enhanced, making them key targets for traffic pollution control. Based on the established emission model, we predicted that the benefits of vehicle electrification in reducing vehicle emissions could reach 40 %–80 %. Overall, this work provides new methods for developing a high-resolution vehicle emission inventory in urban areas, and offers detailed and accurate emission data and fine spatiotemporal variation patterns in urban Jinan, which are of great implications for air pollution control, traffic management, policy making, and public awareness enhancement.