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A novel method for comparing passenger car fleets and identifying high-chance gross emitting vehicles using kerbside remote sensing data

机译:一种小说乘客车队和识别高机会遥感数据的高机会综合发射车辆的新方法

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摘要

The quantification and comparison of NO_x 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 13th percentile for Euro 3 petrol to the 2nd percentile for Euro 6 petrol, with Euro 6 diesel having a 5th 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.
机译:原位汽车船队NO_X排放的量化和比较,以及最高排放者的识别是一个持续的挑战。随着新的和越来越复杂的排放系统渗透到市场,这一挑战将变得更加重要。我们将真实数据与新的现场统计方法相结合,以描述舰队规模的排放行为,并识别候选总发射器车辆。在2015年使用遥感装置观察了19,605辆乘用车。其中,736年是6欧元6艘乘用车。对于大多数燃料类型和欧元标准,每单位燃料燃烧比的观察污染物的分布遵循了牙龈分布的不对称形状。由于存在高发差异的子集,Gumbel分布方法无法完全复制汽油或欧元6欧元汽车的测量分布,从欧元3欧元的第13个百分位到欧元6级的第13百分位数汽油,欧元6柴油,具有5个百分位的价值。对于欧元6欧元的柴油机没有观察到异常值分数。非模型分数类似于Gumbel分布式数据,并且在某些情况下可以被建模为单独的分布,其中船队表现为它们的叠加。结果表明,VSP与此行为没有直接相关,并且假设它是由发射控制系统造成的次优先。次优操作的原因超出了本文的范围,但可能与空气燃料混合物传感器相关联,冷启动运行和催化转化器的劣化。较大的数据套装有更多欧元6辆乘用车需要完全测试这一点。该方法将该方法应用于来自更广泛部署的遥感装置的较大数据集将允许观察者识别潜在的有问题车辆,以进一步调查它们的排放控制系统。

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