School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham, UK.
Institute for Transport Studies, Faculty of Environment, University of Leeds, Leeds, UK.
Sci Total Environ. 2023 Feb 1;858(Pt 2):159814. doi: 10.1016/j.scitotenv.2022.159814. Epub 2022 Oct 28.
It is often assumed that a small proportion of a given vehicle fleet produces a disproportionate amount of air pollution emissions. If true, policy actions to target the highly polluting section of the fleet could lead to significant improvements in air quality. In this paper, high-emitter vehicle subsets are defined and their contributions to the total fleet emission are assessed. A new approach, using enrichment factor in cumulative Pareto analysis is proposed for detecting high emitter vehicle subsets within the vehicle fleet. A large dataset (over 94,000 remote-sensing measurements) from five UK-based EDAR (emission detecting and reporting system) field campaigns for the years 2016-17 is used as the test data. In addition to discussions about the high emitter screening criteria, the data analysis procedure and future issues of implementation are discussed. The results show different high emitter trends dependent on the pollutant investigated, and the vehicle type investigated. For example, the analysis indicates that 23 % and 51 % of petrol and diesel cars were responsible for 80 % of NO emissions within that subset of the fleet, respectively. Overall, the contributions of vehicles that account for 80 % of total fleet emissions usually reduce with EURO class improvement, with the subset fleet emissions becoming more homogenous. The high emitter constituent was more noticeable for pollutant PM compared with the other gaseous pollutants, and it was also more prominent for petrol cars when compared to diesel ones.
通常认为，给定车辆车队中的一小部分车辆会产生不成比例的大量空气污染排放。如果这是事实，那么针对污染排放严重的车队采取政策措施，可能会显著改善空气质量。本文定义了高排放车辆子集，并评估了它们对车队总排放量的贡献。本文提出了一种新的方法，即使用累积 Pareto 分析中的富集因子，用于在车队中检测高排放车辆子集。使用了来自英国五个 EDAR（排放检测和报告系统）实地考察活动的大量数据集（超过 94000 次远程感应测量），这些数据是在 2016-17 年收集的。除了讨论高排放筛选标准外，还讨论了数据分析程序和未来实施的问题。结果表明，不同的污染物和车辆类型会产生不同的高排放趋势。例如，分析表明，在车队的这一子集中，分别有 23%和 51%的汽油车和柴油车对 80%的 NO 排放负责。总体而言，占车队总排放量 80%的车辆的贡献通常随着 EURO 等级的提高而降低，子集车队的排放变得更加均匀。与其他气态污染物相比，高排放物成分在污染物 PM 方面更为明显，与柴油车相比，在汽油车方面更为明显。
