This paper presents a methodology for estimating fleet emission rates from measured roadside concentrations. By filtering measurements based on meteorological conditions, including effective wind speeds above $2\text{m}\cdot {\text{s}}^{-1}$ and periods where the receptor is downwind, we find our simplified approach can compare well with the more sophisticated Research LINE source (RLINE) model. We applied our method to two years of roadside air pollution and traffic measurements at a Toronto, Canada, highway site to estimate minutely emission rates (ER, mass∙m⁻¹∙s⁻¹) and emission factors (EF, mass∙vehicle⁻¹∙ km⁻¹) for carbon dioxide (CO₂), nitrogen oxides (NO_X), carbon monoxide (CO), black carbon (BC), particulate matter mass less than 2.5 μm in diameter (PM_2.5), particle number (PN), and ozone (O₃) over a two-year period. Re-entering these emission rates to a multi-lane RLINE model showed favorable agreement between predicted and measured concentrations for all pollutants with 85-87% of predicted concentrations falling within a factor of two of measured. A multiple-input linear regression was used to determine light-duty vehicle (LDV) and medium/heavy-duty vehicle (MDV + HDV)-specific emission factors, which fell in or near ranges previously reported for all pollutants. More generally, the method proposed here can allow researchers to easily measure emission rates and factors from roadways using near-road concentration measurements and simple analysis methods, and can exclude some or all micrometeorological inputs, allowing researchers to perform inverse dispersion modeling in regions where such inputs are unavailable. The results also provide updated data on Canadian vehicle emissions and refine the relationships between emissions and traffic composition and speed.

本文提出了一种根据测得的路边浓度估算车队排放率的方法。通过过滤基于气象条件的测量值，包括以上的有效风速$2\text{米}\cdot {\text{s}}^{-\mathrm{1个}}$在接收器处于顺风的时期，我们发现我们的简化方法可以与更复杂的Research LINE Source（RLINE）模型进行比较。我们将方法应用于加拿大多伦多高速公路站点的两年路边空气污染和交通测量，以估算分钟排放率（ER，mass∙m ^-1 ss ^-1）和排放因子（EF，mass∙vehicle ^{− 1} ∙km ^-1）用于二氧化碳（CO ₂），氮氧化物（NO _X），一氧化碳（CO），黑碳（BC），直径小于2.5μm的颗粒物质量（PM _2.5），颗粒数（ PN）和臭氧（O ₃），为期两年。将这些排放率重新输入多车道RLINE模型显示，所有污染物的预测浓度与实测浓度之间具有良好的一致性，其中85-87％的预测浓度落在实测值的两倍之内。使用多输入线性回归来确定轻型车辆（LDV）和中/重型车辆（MDV + HDV）的特定排放因子，这些排放因子均落入或接近先前针对所有污染物报告的范围。更笼统地说，这里提出的方法可以使研究人员可以使用近路浓度测量和简单的分析方法轻松地测量道路的排放速率和因子，并且可以排除一些或全部微气象输入，从而使研究人员可以在这样的区域进行逆扩散建模。输入不可用。