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Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

机译:基于NRT交通数据的时空分辨率高的机动车排放清单的开发及其对北京空气污染的影响-第1部分:机动车排放清单的开发和评估

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This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near-real-time traffic data on road segments to develop a vehicle emission inventory with high temporal–spatial resolution (HTSVE) for the Beijing urban area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NOsubix/i/sub, CO, HC and PM were 10.54??×??10sup4/sup, 42.51??×??10sup4/sup and 2.13??×??10sup4/sup and 0.41??×??10sup4/sup?Mg respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The grid-based emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Based on the results of this study, improved air quality simulation and the contribution of vehicle emissions to ambient pollutant concentration in Beijing have been investigated in a companion paper (He et al., 2016).
机译:本文提出了一种基于局部排放因子的自下而上的方法,并补充了广泛使用的计算道路交通排放的计算机程序(COPERT)模型的排放因子以及路段的近实时交通数据以开发车辆北京市区的高时空分辨率(HTSVE)排放清单。为了准确地模拟现实世界中的车辆排放,根据该路段的行驶周期(交通速度)将道路划分为多个路段。结果表明,NO x ,CO,HC,PM的机动车排放分别为10.54××10 10 sup> 4 ,42.51? ×Mg分别为××10 10 sup> 4和2.13××10 10 sup>和0.41××10 10 sup> 4 ?该模型估算的车辆排放量和燃油消耗量与《中国车辆排放控制年度报告》及之后的燃油销量进行了比较。还将基于网格的排放与通过宏观方法开发的车辆排放清单进行了比较。该方法表明,自下而上的方法比依赖于更多信息的宏观方法更好地估计了车辆排放的水平和空间分布。根据这项研究的结果,在一篇伴随论文中研究了北京改善的空气质量模拟以及车辆排放对环境污染物浓度的贡献(He et al。,2016)。

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