Determination of the effect of vehicles emissions on air quality near roadways is important because vehicles are a major source of air pollution. A near roadway monitoring program was undertaken in Chicago between August 4 and October 30 2014 to measure ultrafine particles, carbon dioxide, carbon monoxide, traffic volume and speed, and wind direction and speed. The objective of this study was to develop a method to relate short term changes in traffic mode of operation to air quality near roadways using data averaged over 5 minute intervals to provide a better understanding of the processes controlling air pollution concentrations near roadways. Three different types of data analysis are provided to demonstrate the type of results that can be obtained from a near roadway sampling program based on 5 minute measurements: (1) development of vehicle emission factors (EFs) for ultrafine particles as a function of vehicle mode of operation, (2) comparison of measured and modeled CO2 concentrations, (3) application of dispersion models to determine concentrations near roadways. EFs for ultrafine particles are developed that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real world conditions. Two air quality models - CALINE4 and AERMOD -are used to predict the ultrafine particulate concentrations near roadway for comparison with measured concentrations. When using CALINE4 to predict air quality levels in the mixing cell, changes in surface roughness and stability class have no effect on the predicted concentrations. However, when using AERMOD to predict air quality in the mixing cell, changes in surface roughness have a significant impact on the predicted concentrations Implication The paper provides emission factors (EFs) that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real world conditions. The good agreement between monitoring and modeling results indicates that high resolution, simultaneous measurements of air quality, meteorological and traffic conditions can be used to determine real world, fleet wide vehicle EFs as a function of vehicle mode of operation under actual driving conditions.

作者：Dongqi, Wen；Wenjuan, Zhai；Sheng, Xiang；Zhice, Hu；Tongchuan, Wei；Kenneth E, Noll

来源：Journal of the Air & Waste Management Association (1995) 2017 年