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Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters

机译:基于羽状分析的车队空气污染物排放以及高排放者的贡献

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An automated identification and integration method has been developed for in-use vehicle emissions under real-world conditions. This technique was applied to high-time-resolution air pollutant measurements of in-use vehicle emissions performed under real-world conditions at a near-road monitoring station in Toronto, Canada, during four seasons, through month-long campaigns in 2013-2014. Based on carbon dioxide measurements, over 100 000 vehicle-related plumes were automatically identified and fuel-based emission factors for nitrogen oxides; carbon monoxide; particle number; black carbon; benzene, toluene, ethylbenzene, and xylenes (BTEX); and methanol were determined for each plume. Thus the automated identification enabled the measurement of an unprecedented number of plumes and pollutants over an extended duration. Emission factors for volatile organic compounds were also measured roadside for the first time using a proton transfer reaction time-of-flight mass spectrometer; this instrument provided the time resolution required for the plume capture technique. Mean emission factors were characteristic of the light-duty gasoline-dominated vehicle fleet present at the measurement site, with mean black carbon and particle number emission factors of 35 mg kg fuel(-1) and 7.5 x 10(14) # kg fuel(-1), respectively. The use of the plume-by-plume analysis enabled isolation of vehicle emissions, and the elucidation of co-emitted pollutants from similar vehicle types, variability of emissions across the fleet, and the relative contribution from heavy emitters. It was found that a small proportion of the fleet (<25 %) contributed significantly to total fleet emissions: 100, 100, 81, and 77% for black carbon, carbon monoxide, BTEX, and particle number, respectively. Emission factors of a single pollutant may help classify a vehicle as a high emitter; however, regulatory strategies to more efficiently target multi-pollutant mixtures may be better developed by considering the co-emitted pollutants as well.
机译:已经开发了一种自动识别和集成方法,用于在现实条件下使用中的车辆排放。通过在2013-2014年进行的为期一个月的活动,该技术已在加拿大多伦多的一个近路监测站的四个季度中,用于在现实条件下进行的高分辨率高分辨率空气中正在使用的车辆排放的空气污染物测量。根据二氧化碳的测量,自动识别了超过10万辆与车辆相关的羽流,并基于燃料的氮氧化物排放因子;一氧化碳;粒子数黑炭苯,甲苯,乙苯和二甲苯(BTEX);确定每个羽状物的甲醇和甲醇。因此,自动识别功能可以在延长的时间内测量出前所未有数量的羽状物和污染物。还使用质子转移反应飞行时间质谱仪首次在路边测量了挥发性有机化合物的排放因子;该仪器提供了羽流捕获技术所需的时间分辨率。平均排放因子是存在于测量现场的轻型汽油车队的特征,平均黑碳和颗粒数排放因子为35 mg kg燃料(-1)和7.5 x 10(14)#kg燃料( -1)。通过逐羽分析可以隔离车辆排放,并阐明相似车辆类型的共同排放污染物,整个车队的排放变化以及重型排放源的相对贡献。发现小部分车队(<25%)对车队总排放量有显着贡献:黑碳,一氧化碳,BTEX和颗粒数量分别为100%,100%,81%和77%。单一污染物的排放因子可能有助于将车辆归类为高排放者;但是,通过考虑共同排放的污染物,可以更好地制定出更有效地针对多种污染物混合物的监管策略。

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