Evaluation of a road dust suspension model for predicting the concentrations of PM10 in a street canyon

We have slightly refined, evaluated and tested a mathematical model for predicting the vehicular suspension emissions of PM10. The model describes particulate matter generated by the wear of road pavement, traction sand, and the processes that control the suspension of road dust particles into the air. However, the model does not address the emissions from the wear of vehicle components. The performance of this suspension emission model has been evaluated in combination with the street canyon dispersion model OSPM. We used data from a measurement campaign that was conducted in the street canyon Runeberg Street in Helsinki from 8 January to 2 May, 2004. The model reproduced fairly well the seasonal variation of the PM10 concentrations, also during the time periods, when studded tyres and anti-skid treatments were commonly in use. For instance, the index of agreement (IA) was 0.83 for the time series of the hourly predicted and observed concentrations of PM10. The predictions of the model were found to be sensitive to precipitation and street traction sanding. The main uncertainties in the predictions are probably caused by (i) the cleaning processes of the streets, which are currently not included in the model, (ii) the uncertainties in the estimation of the sanding days, and (iii) the uncertainties in the evaluation of precipitation. This study provides more confidence that this model could potentially be a valuable tool of assessment to evaluate and forecast the suspension PM10 emissions worldwide. However, a further evaluation of the model is needed against other datasets in various vehicle fleet, speed and climatic conditions.

► We present a refined mathematical model for vehicular PM10 suspension emissions.
► The model reproduced fairly well the seasonal variation of the PM10.
► The predictions were found to be sensitive to precipitation and traction sanding.
► Uncertainties also caused by the street cleaning, not accounted in the model.
► Can be a valuable tool to evaluate and forecast the suspension emissions worldwide.