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. Urban on-road vehicle emissions affect air quality and human health locallyand globally. Given uneven sources, they typically exhibit distinct spatialheterogeneity, varying sharply over short distances (10 m–1 km). However, all-around observational constraints on the emission sourcesare limited in much of the world. Consequently, traditional emissioninventories lack the spatial resolution that can characterize the on-roadvehicle emission hotspots. Here we establish a bottom-up approach to reveala unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current emissioninventories. We interconnect all-around traffic monitoring (includingtraffic fluxes, vehicle-specific categories, and speeds) via an intelligenttransportation system (ITS) over Xiaoshan District in the Yangtze RiverDelta (YRD) region. This enables us to calculate single-vehicle-specificemissions over each fine-scale (10 m–1 km) road segment. Thus,the most hyperfine emission dataset of its type is achieved, and on-roademission hotspots appear. The resulting map shows that the hourly averageon-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01,40.35, 8.13, and 1.68 kg, respectively. More importantly, widespread andpersistent emission hotspots emerged. They are of significantly sharpsmall-scale variability, up to 8–15 times within individualhotspots, attributable to distinct traffic fluxes, road conditions, andvehicle categories. On this basis, we investigate the effectiveness ofroutine traffic control strategies on on-road vehicle emission mitigation.Our results have important implications for how the strategies should bedesigned and optimized. Integrating our traffic-monitoring-based approachwith urban air quality measurements, we could address major data gapsbetween urban air pollutant emissions and concentrations.

Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system