The objective of this paper is to study mid-block roadside fine particulate matter (PM2.5) concentrationvariation as a function of very high resolution meteorological and traffic data. Morning peak periodmeasurements were taken at a mid-block roadside location on an urban arterial commuter roadway. Tocapture the impact of dynamic traffic conditions, data were analyzed at 10-second intervals, asubstantially higher resolution than typical roadside air quality study designs. Particular attention waspaid to changes in traffic conditions, including fleet mix, queuing and vehicle platooning over the courseof the study period, and the effect of these changes on PM2.5. Significant correlations were observedbetween vehicle platoons and increases in PM2.5 concentrations. Traffic state analysis was employed todetermine median PM2.5 levels before and after the onset of congestion. A multivariate regression modelwas estimated to determine significant PM2.5 predictors while controlling for autocorrelation. Significancewas 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 state(e.g. congestion) and vehicle type have a significant impact on roadside PM2.5 concentrations. This studyserves as a demonstration of the abilities of very high resolution data to identify the effects of relativelyminute changes in traffic conditions on air pollutant concentrations.
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