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Are average speed emission functions scale-free?
Atmospheric Environment ( IF 4.2 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.atmosenv.2020.117324
D. Lejri , L. Leclercq

Abstract Although emission models have been designed using vehicle data over driving cycles of a few minutes, they are often applied at large scale to estimate total emission (inventories). In between, there is a range of scales in use in traffic and environmental studies (road sections, sub-areas, etc.). Coupling a traffic microsimulation with COPERT emission factors at different scales reveals scaling biases. We compare network fuel consumption (FC) and nitrogen oxide (NOx) emissions resulting from emission calculations based on different spatial decompositions. The results show that for an area of Paris covering 3 km2, the differences due to the aggregation scale for emissions range from 5 to 17% depending on the pollutant, spatial partitioning and traffic conditions. These discrepancies can be reduced using a distance-weighted mean speed, which is not a scale-consistent definition of mean travel speed. They can almost be cancelled by using a correction term derived analytically in this paper, thus consistency can be guaranteed between emissions assessed at different scales. Finally, a case study shows that it is possible to evaluate FC and NOx emissions on a large-scale network from a sample of traffic data (probes), and obtain the corrective term to be applied to remove scaling bias. The most critical step is the accurate estimation of the total travel distance. The gaps were successfully reduced to a maximum of 8% in congestion for a penetration rate of about 20%.

中文翻译:

平均速度发射函数是无标度的吗?

摘要 虽然排放模型是使用几分钟驾驶周期内的车辆数据设计的,但它们通常被大规模应用于估算总排放量(清单)。在这两者之间,有一系列用于交通和环境研究(路段、分区等)的尺度。将交通微观模拟与不同尺度的 COPERT 排放因子相结合,揭示了尺度偏差。我们比较了基于不同空间分解的排放计算产生的网络燃料消耗 (FC) 和氮氧化物 (NOx) 排放。结果表明,对于巴黎面积为 3 平方公里的区域,根据污染物、空间分区和交通条件的不同,排放聚合规模的差异在 5% 到 17% 之间。可以使用距离加权平均速度来减少这些差异,这不是平均行驶速度的尺度一致定义。它们几乎可以通过使用本文分析得出的修正项来抵消,从而可以保证不同尺度评估的排放之间的一致性。最后,一个案例研究表明,可以根据流量数据样本(探​​针)评估大型网络上的 FC 和 NOx 排放,并获得用于消除比例偏差的校正项。最关键的一步是准确估计总行驶距离。在渗透率约为 20% 的情况下,这些差距成功地减少到最大 8% 的拥堵。因此可以保证在不同尺度上评估的排放之间的一致性。最后,一个案例研究表明,可以根据流量数据样本(探​​针)评估大型网络上的 FC 和 NOx 排放,并获得用于消除比例偏差的校正项。最关键的一步是准确估计总行驶距离。在渗透率约为 20% 的情况下,这些差距成功地减少到最大 8% 的拥堵。因此可以保证在不同尺度上评估的排放之间的一致性。最后,一个案例研究表明,可以根据流量数据样本(探​​针)评估大型网络上的 FC 和 NOx 排放,并获得用于消除比例偏差的校正项。最关键的一步是准确估计总行驶距离。在渗透率约为 20% 的情况下,这些差距成功地减少到最大 8% 的拥堵。
更新日期:2020-03-01
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