Modeling the Impact of Traffic Conditions on the Variability of Mid-Block Roadside PM2.5 on an Urban Arterial

摘要

Modeling Impact of Traffic Conditions on Variability of Midblock Roadside Fine Particulate Matter Concentrations on an Urban Arterial: This paper presents an innovative modeling of fine particulate matter (PM2.5) concentrations as a function of very high resolution meteorological and traffic data. Peak period measurements were taken at a mid-block roadside location on an urban arterial commuter roadway. To capture the impact of dynamic traffic conditions, data were analyzed at 10-second intervals, with substantially higher resolution than typical roadside air quality study designs. Particular attention was paid to changes in traffic conditions, including fleet mix, queuing and vehicle platooning over the course of the study period, and the effect of these changes on PM2.5. Significant correlations were observed between vehicle platoons and increases in PM2.5 concentrations. Traffic state analysis was employed to determine median PM2.5 levels before and after the onset of congestion. A multivariate regression model was estimated to determine significant PM2.5 predictors while controlling for autocorrelation. Significance was found not only in the simultaneous traffic variables but also in lagged traffic variables; additionally, the effects of vehicle types and wind direction were quantified. Modeling results indicate that traffic conditions and vehicle type do have a significant impact on roadside PM2.5 concentrations. For instance, the addition of one heavy vehicle was shown to increase PM2.5 concentrations by 2.45% when wind blew across the roadway before reaching the monitoring location. This study serves as a demonstration of the abilities of very high resolution data to identify the effects of relatively minute changes in traffic conditions on air pollutant concentrations.