Abstract. Urban on-road vehicle emissions affect air quality and human health locally and globally. Such emissions typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m ~ 1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at 1 ~ 3 orders of magnitude higher spatial resolution than current inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over the Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m ~ 1 km) road segment. Thus, a hyperfine emission dataset is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01 kg, 40.35 kg, 8.13 kg, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerge, of significantly sharp small-scale variability, up to 8 ~ 15 times, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on the on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.
Hyperfine-Resolution Mapping of On-Road Vehicle Emissions with Comprehensive Traffic Monitoring and Intelligent Transportation System