High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

• Published in: Atmospheric chemistry and physics 2019-07, Vol.19 (13), p.8831-8843
• Main Authors: Yang, Daoyuan, Zhang, Shaojun, Niu, Tianlin, Wang, Yunjie, Xu, Honglei, Zhang, K. Max, Wu, Ye
• Format: Article
• Language: English
• Subjects: Air pollution; Analysis; Area; Automotive emissions; Black carbon; Carbon monoxide; Computer simulation; Concentration gradient; Datasets; Emission inventories; Emissions; Fleets (Automotive); Heavy duty trucks; Local government; Mapping; Metropolitan areas; Nitrogen compounds; Nitrogen oxides; Oxides; Particulate emissions; Particulate matter; Photochemicals; Pollutants; Pollution; Pollution levels; Resolution; Roads; Spatial discrimination; Spatial resolution; Suspended particulate matter; Traffic; Traffic engineering; Traffic models; Trucks; Urban areas; Vehicle emissions
• ISSN: 1680-7324; 1680-7316; 1680-7324
• DOI: 10.5194/acp-19-8831-2019

On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet), based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOx), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that nonlocal HDTs could account for 29 % and 38 % of estimated total on-road emissions of NOx and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOx concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 µg m−3 of NOx in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.