Institute for Transport Studies, University of Leeds, 34-40 University Rd, Leeds LS2 9JT, United Kingdom of Great Britain and Northern Ireland.
Institute for Transport Studies, University of Leeds, 34-40 University Rd, Leeds LS2 9JT, United Kingdom of Great Britain and Northern Ireland.
Sci Total Environ. 2021 Jan 1;750:142088. doi: 10.1016/j.scitotenv.2020.142088. Epub 2020 Sep 6.
The quantification and comparison of NO emission from in-situ car fleets, and identification of the highest emitters is an ongoing challenge. This challenge will become more important as new and increasingly complex emissions removal systems penetrate the market. We combine real-world data with new-to-the-field statistical methods to describe fleet-scale emissions behaviours and identify candidate gross-emitter vehicles. 19,605 passenger cars were observed using a Remote Sensing Device across Aberdeen in 2015. Of these, 736 were Euro 6 Passenger Cars. The distribution of observed pollutant per unit of fuel burnt ratios for most fuel type and Euro standards followed an asymmetrical shape best characterised by the Gumbel distribution. The Gumbel distribution approach was not able to fully replicate the distribution of measurements of petrol or Euro 6 diesel cars due to the presence of a subset of high-emitting outliers, ranging from the 13 percentile for Euro 3 petrol to the 2 percentile for Euro 6 petrol, with Euro 6 diesel having a 5 percentile outlier value. No outlier fraction was observed for pre-Euro 6 diesels. The off-model fractions resembled Gumbel distributed data and in some cases could be modelled as a separate distribution with the fleet behaving as a superposition of them. It is shown that VSP was not directly linked to this behaviour and it is hypothesised that it is caused by the emissions control systems operating sub-optimally. The reasons for sub-optimal operation are beyond the scope of this paper but may be linked to air-fuel mixture sensors, cold-start running and deterioration of the catalytic converter. Larger data-sets with more Euro 6 passenger cars are required to fully test this. Application of this methodology to larger data sets from more widely deployed remote sensing devices will allow observers to identify potentially problematic vehicles for further investigation into their emission control systems.
原位车排的 NO 排放量化和比较，以及识别最高排放者，是一个持续存在的挑战。随着新的、越来越复杂的排放去除系统进入市场，这一挑战将变得更加重要。我们将实际数据与新的领域统计方法相结合，以描述车队规模的排放行为，并识别候选总排放车辆。2015 年，在阿伯丁使用遥测设备观察了 19605 辆乘用车，其中 736 辆为欧 6 乘用车。大多数燃料类型和欧洲标准的观察到的污染物与单位燃料燃烧比的分布呈不对称形状，最好用 Gumbel 分布来描述。由于存在一小部分高排放异常值，从欧 3 汽油的 13 百分位到欧 6 汽油的 2 百分位，欧 6 柴油的 5 百分位异常值，Gumbel 分布方法无法完全复制汽油或欧 6 柴油车测量值的分布。对于前欧 6 柴油车，没有观察到异常值分数。离群分数类似于 Gumbel 分布数据，在某些情况下，它们可以作为一个单独的分布进行建模，车队的行为类似于它们的叠加。结果表明，VSP 与这种行为没有直接联系，据推测，这是由于排放控制系统运行不佳造成的。运行不佳的原因不在本文的讨论范围之内，但可能与空燃比传感器、冷启动运行和催化转化器的劣化有关。需要更大的数据集和更多的欧 6 乘用车来充分验证这一点。将这种方法应用于更广泛部署的遥测设备的更大数据集，将使观测者能够识别出潜在的有问题的车辆，以便对其排放控制系统进行进一步调查。
