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Assessing the importance of transportation activity data for urban emission inventories

机译：评估运输活动数据对城市排放清单的重要性

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The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NOx, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NOxand −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.
机译：这项研究的目的是实施一种基于GPS的建模方法，以改善城市区域内车速空间变化的特征，并将产生的排放与使用广泛的排放清单编制方法进行比较。这项研究的最终目标是评估和了解活动数据对于改善城市道路运输排放清单的重要性。为此，使用了三种数值工具，即：（i）微观模拟交通模型（VISSIM）； （ii）介观排放模型（TREM）； （iii）空气质量模型（URBAIR）已关联并应用于欧洲中型城市（葡萄牙阿威罗）。作为替代，通过假设根据驾驶模式的车速值来计算基于广泛使用的方法的交通排放。基于GPS的详细建模方法可降低市区的道路交通总排放量（分别占PM10，NOx，CO和VOC日排放总量的7.9％，5.4、4.6和3.2％）。此外，在分析整个市区的第5个和第95个百分位排放值的幅度时，观察到所有污染物的排放都有重要变化，CO的变化范围为-15至49％，VOC的变化范围为14至31％，-19对于NOx为46％，对于PM10为−22至52％。与基于行驶模式汇总的车速数据相比，基于GPS的拟议方法揭示了解决市区内车速的时空变化的优势，证明了其在量化和减少道路运输清单不确定性方面的有用性。

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