Abstract
Abstract. 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.
Highlights
The rapid growth in vehicle use associated with socioeconomic development has triggered serious atmospheric pollution and adverse health impacts (Anenberg et al, 2017; Guo et al, 2014; Huang et al, 2014)
This study presents the development of a high-resolution emission inventory of vehicle emissions in Beijing (EMBEVLink) by using multiple large-scale traffic monitoring datasets
The vehicular nitrogen oxides (NOx) concentrations were simulated by using the RapidAir® model at high spatial resolutions, meshed into 10 m × 10 m cells in the entire municipality and further 1 m × 1 m cells in the hotspots
Summary
The rapid growth in vehicle use associated with socioeconomic development has triggered serious atmospheric pollution and adverse health impacts (Anenberg et al, 2017; Guo et al, 2014; Huang et al, 2014). Yang et al.: High-resolution mapping of vehicle emissions of atmospheric pollutants in 2017 was 58 μg m−3 This value was reduced by 35 % as opposed to that in 2013, it still significantly exceeded the limit of China’s national ambient air quality standard (35 μg m−3) by 66 % (Beijing MEEB, 2018a). Recent official source apportionment results indicated that vehicle emissions remained one of the most important pollution contributors, responsible for an average of 45 % of total PM2.5 concentrations from local sources (Beijing MEEB, 2018b). The exceedance of ambient nitrogen dioxide (NO2) concentrations represents another air quality problem in Beijing (UNEP, 2016; Beijing MEEB, 2018a), where nitrate aerosols have become one of the most important PM2.5 components, with an average mass fraction of up to 40 % (Beijing MEEB, 2018b; Li et al, 2018). Controlling vehicle emissions is one of the prioritized tasks remaining for local environmental protection authorities
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