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Nordic countries suffer from periodic worsening of the air quality during spring with high peak PM10 concentrations (airborne particulate matter with diameter less than 10 µm or 0.01 mm). Characteristic for the high springtime PM10 concentrations are high shares of coarse particles (with diameters between 2.5 and 10µm), a signature of non-exhaust traffic dust formed via abrasion and wear of pavement, traction control materials, vehicle brakes and tyres. This Policy Brief summarizes the current understanding of the road dust system and presents the mitigation measures and policies currently in place in the Nordic countries. It has been compiled as part of the NORTRIP project funded by the Climate and air pollution working group of the Nordic Council of Ministers by researchers from 11 Nordic institutes studying different aspects of traffic non-exhaust emissions and road dust.

De-icing of road surfaces is necessary in many countries during winter to improve vehicle traction. Large amounts of salt, most often sodium chloride, are applied every year. Most of this salt is removed through drainage or traffic spray processes but a certain amount may be suspended, after drying of the road surface, into the air and will contribute to the concentration of particulate matter. Though some measurements of salt concentrations are available near roads, the link between road maintenance salting activities and observed concentrations of salt in ambient air is yet to be quantified. In this study the NORTRIP road dust emission model, which estimates the emissions of both dust and salt from the road surface, is applied at five sites in four Nordic countries for ten separate winter periods where daily mean ambient air measurements of salt concentrations are available. The model is capable of reproducing many of the salt emission episodes, both in time and intensity, but also fails on other occasions. The observed mean concentration of salt in PM10, over all ten datasets, is 4.2 mu g/m(3) and the modelled mean is 2.8 mu g/m(3), giving a fractional bias of -0.38. The RMSE of the mean concentrations, over all 10 datasets, is 2.9 mu g/m(3) with an average R-2 of 0.28. The mean concentration of salt is similar to the mean exhaust contribution during the winter periods of 2.6 mu g/m(3). The contribution of salt to the kerbside winter mean PM10 concentration is estimated to increase by 4.1 +/- 3.4 mu g/m(3) for every kg/m(2) of salt applied on the road surface during the winter season. Additional sensitivity studies showed that the accurate logging of salt applications is a prerequisite for predicting salt emissions, as well as good quality data on precipitation. It also highlights the need for more simultaneous measurements of salt loading together with ambient air concentrations to help improve model parameterisations of salt and moisture removal processes.

Non-exhaust traffic induced emissions are a major source of airborne particulate matter in most European countries. This is particularly important in Nordic and Alpine countries where winter time road traction maintenance occurs, e.g. salting and sanding, and where studded tyres are used. Though the total mass generated by wear sources is a key factor in non-exhaust emissions, these emissions are also strongly controlled by surface moisture conditions. In this paper, Part 2, the road surface moisture sub-model of a coupled road dust and surface moisture model (NORTRIP) is described. We present a description of the road surface moisture part of the model and apply the coupled model to seven sites in Stockholm, Oslo, Helsinki and Copenhagen over 18 separate periods, ranging from 3.5 to 24 months. At two sites surface moisture measurements are available and the moisture sub-model is compared directly to these observations. The model predicts the frequency of wet roads well at both sites, with an average fractional bias of −2.6%. The model is found to correctly predict the hourly surface state, wet or dry, 85% of the time. From the 18 periods modelled using the coupled model an average absolute fractional bias of 15% for PM10 concentrations was found. Similarly the model predicts the 90'th daily mean percentiles of PM10 with an average absolute bias of 19% and an average correlation (R2) of 0.49. When surface moisture is not included in the modelling then this average correlation is reduced to 0.16, demonstrating the importance of the surface moisture conditions. Tests have been carried out to assess the sensitivity of the model to model parameters and input data. The model provides a useful tool for air quality management and for improving our understanding of non-exhaust traffic emissions., AuthorCount: 11Funding agencies:Nordic Council of Ministers Project NORTRIP BLS-306-00064; Norwegian Climate and Pollution Agency KLIF 4011009; Danish Environmental Protection Agency

Non-exhaust traffic induced emissions are a major source of particle mass in most European countries. This is particularly important in Nordic and Alpine countries where winter time road traction maintenance occurs, e.g. salting and sanding, and where studded tyres are used. In this paper, Part 1, the road dust sub-model of a coupled road dust and surface moisture model (NORTRIP) is described. The model provides a generalised process based formulation of the non-exhaust emissions, with emphasis on the contribution of road wear, suspension, surface dust loading and the effect of road surface moisture (retention of wear particles and suspended emissions). The model is intended for use as a tool for air quality managers to help study the impact of mitigation measures and policies. We present a description of the road dust sub-model and apply the model to two sites in Stockholm and Copenhagen where seven years of data with surface moisture measurements are available. For the site in Stockholm, where studded tyres are in use, the model predicts the PM10 concentrations very well with correlations (R-2) in the range of R-2 = 0.76-0.91 for daily mean PM10. The model also reproduces well the impact of a reduction in studded tyres at this site. For the site in Copenhagen the correlation is lower, in the range 0.44-0.51. The addition of salt is described in the model and at both sites this leads to improved correlations due to additional salt emissions. For future use of the model a number of model parameters, e.g. wear factors and suspension rates, still need to be refined. The effect of sanding on PM10 emissions is also presented but more information will be required before this can be confidently applied for management applications., AuthorCount:10