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

机译:基于大型现实世界交通数据集的大气污染物车辆排放的高分辨率绘图

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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 (16400km2). 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 (10m×10m in the entire municipality and 1m×1m 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 10μgm?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.
机译:在路上?车辆排放是人口众多地区的空气污染水平升高的主要贡献者。我们开发了车辆污染物的链路级排放清单,称为EMBEV-LINK(链路级排放因子模型在北京车队)的基础上,从粗放型道路交通提取的多个数据集监测网络覆盖北京整个直辖市,中国(16400km2)。我们在各种交通场景下雇用了Embef-Link模型,由于现实世界交通动态和当地政府实施的交通限制,在时间和空间上捕捉车辆排放的显着变化。结果揭示了城区的高碳一氧化物(CO)和全烃(THC)排放(即第五环道内)和高峰时间,包括乘用车交通。相比之下,在城市地区超出城市地区,由于重型卡车(HDTS)不允许穿过城市的重型卡车(HDTS),相当大的氮氧化物(NOx),细颗粒物质(PM2.5)和黑碳(BC)排放量的氮氧化物(NOx)和黑碳(BC)排放量白天的区域。 Embef-Link模型表明,非本体HDTS可以占NOx和PM2.5估计总通路排放的29%和38%,这在以前的常规排放库存中被忽略。我们进一步将embef-Link排放库存和计算有效的分散模型迅速地组合起来,以模拟精细分辨率的车辆NOx浓度(在整个市内的10m×10m,热点中1m×1m)。模拟结果表明与地面观察和从线索到环境区域的敏锐浓度梯度达成紧密一致。在夜间升起HDT交通限制期间,HDT可能负责市区约为10μg的NOx。通过与Embef-Link排放清单进行比较,还讨论了传统的自上而下分配方法的不确定性,这些方法被广泛用于增强车辆排放的空间分辨率。

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