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 (R2) in the range of R2 = 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.
•A coupled road dust and surface moisture model is presented and applied.•The surface moisture strongly determines the temporal variation of the road dust emissions.•The model predicts very well the temporal variation of suspended road dust emissions.•Time scales for suspension are significantly longer than previous estimates.•The model calculates the contribution of road salting to the emitted PM10 concentrations.
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.
•A coupled road dust and surface moisture non-exhaust emission model is applied to seven sites over 18 different periods.•Surface moisture is shown to be the dominant cause of variability of the road dust emissions.•The model explains half the variability seen in roadside PM10 measurements.•Salt is found to have an impact on the surface moisture and on the variability of emissions.
One of the most important parameters that controls the suspension of road dust particles in the air is road surface moisture. This is calculated every hour from a budget equation that takes into ...account precipitation, evaporation and runoff. During wet conditions a road dust layer is built up from road wear which strongly depends on the use of studded tyres and road sanding. The dust layer is reduced during dry road conditions by suspension of particles due to vehicle-induced turbulence. The dust layer is also reduced by wash-off due to precipitation. Direct non-tailpipe vehicle emissions due to the wear and tear of the road surface, brakes and tyres are accounted for in the traditional way as constant emission factors expressed as mass emitted per vehicle kilometre.
The model results are compared with measurements from both a narrow street canyon in the city centre of Stockholm and from an open highway outside the city. The model is able to account for the main features in the day-to-day mean PM
10 variability for the street canyon and for the highway. A peak in the PM
10 concentration is typically observed in late winter and early spring in the Nordic countries where studded tyres are used. This behaviour is due to a combination of factors: frequent conditions with dry roads, high number of cars with studded tyres and an accumulated road dust layer that increases suspension of particles. The study shows that using a constant emission factor for PM
10 or relating PM
10 emissions to NO
x
cannot be used for prediction of day-to-day variations in PM
10 concentrations in the traffic environments studied here. The model needs to describe variations in dust load, wetness of the road and how dust suspension interacts with these processes.
The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been ...reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish–Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The article illustrates the challenges in conducting road suspension measurements in densely trafficked urban conditions, and the numerous requirements for input data that are needed for accurately applying road suspension emission models.
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.
Almost all Swedish cities need to determine air pollution levels—especially PM10—close to major streets. SIMAIR is an internet tool that can be used by all Swedish municipalities to assess PM10, NO
...2, CO and benzene levels and how they compare to the EU directive. SIMAIR is delivered to the municipalities with all required input data pre-loaded and is meant to be used prior to decisions if and where, monitoring campaigns are required. The system includes a road and vehicle database with emission factors and a model to calculate non-tailpipe PM10 emissions. Regional and urban background contributions are pre-calculated and stored as hourly values on a 1×1
km
2 grid. The local contribution is calculated by the user, selecting either an open road or a street canyon environment.
A comparison between measured and simulated concentrations in four street locations shows that SIMAIR is able to calculate statistics of yearly mean values, 90-percentile and 98-percentile daily mean values and the number of days exceeding the limit value that are well within ±50% that EU requires for model estimates of yearly mean values. In comparison, all values except one are within ±25% which is the quality objective for fixed measurements according to the EU directive.
The SIMAIR model system is also able to separate the percentual contribution of the long-range transport from outside the city, the city contribution and the local contribution from the traffic of an individual street.
Based on the results from a 6-week monitoring campaign in an area close to a major highway north of Stockholm, Sweden, NOx emission factors representative for vehicle speeds of 100-120 km per h were ...determined to 0.61 g/veh,km for light duty and to 7.1 g/veh,km for heavy duty vehicles. The corresponding factors for particle number were 1.4 x 10(14) and 52 x 10(14) particles/veh,km, determined for an ambient temperature interval of +7 to +17 degrees C. The removal effects of coagulation and dry deposition on total number concentrations were assessed by numerical model simulations. Velocity and turbulence fields, including those produced by the vehicles, were simulated in a Computational Fluid Dynamics (CFD) model. Coagulation was found to be of little importance over the first 100 m downwind of the highway. The high friction velocities over the road surface created by vehicle movements enhanced deposition locally, contributing to the removal of approximately 10% of the particles originally emitted. Beyond a point 10 m downwind of the highway the removal rate was low and the ultrafine particles were almost inert while being advected over the next hundred meters. As a consequence, it seems reasonable to use monitored data from stations close to highways to estimate emission factors for particle number, assuming that the particles are inert. Those "effective" emission factors should be applicable for urban models with a larger spatial resolution.
Based on the results from a 6-week monitoring campaign in an area close to a major highway north of Stockholm, Sweden, NO x emission factors representative for vehicle speeds of 100−120 km per h were ...determined to 0.61 g/veh,km for light duty and to 7.1 g/veh,km for heavy duty vehicles. The corresponding factors for particle number were 1.4 × 1014 and 52 × 1014 particles/veh,km, determined for an ambient temperature interval of +7 to +17 °C. The removal effects of coagulation and dry deposition on total number concentrations were assessed by numerical model simulations. Velocity and turbulence fields, including those produced by the vehicles, were simulated in a Computational Fluid Dynamics (CFD) model. Coagulation was found to be of little importance over the first 100 m downwind of the highway. The high friction velocities over the road surface created by vehicle movements enhanced deposition locally, contributing to the removal of ap proximately 10% of the particles originally emitted. Beyond a point 10 m downwind of the highway the removal rate was low and the ultrafine particles were almost inert while being advected over the next hundred meters. As a consequence, it seems reasonable to use monitored data from stations close to highways to estimate emission factors for particle number, assuming that the particles are inert. Those “effective” emission factors should be applicable for urban models with a larger spatial resolution.
The concentrations of seventeen pollutants (particulate mass fractions PM2.5 and PM10, a range of metals, inorganic gases and organic compounds) are for the first time analyzed in a screening of the ...carcinogenic risk at a resolution of 1 × 1 km2 in ambient air in three Nordic countries. Modelled annual mean air concentrations in 2010 show no exceedances of the EU air quality limit, guideline or target values. The only modelled exceedance of US-EPA 1:100,000 cancer risk concentrations (0.12 ng/m3, US-EPA IRIS, 2015) occurs for B(a)P in Denmark, for approximately 80% of the Danish population. However, the EU target value threshold of 1 ng/m3 for B(a)P is not exceeded in the modelled values in any parts of Denmark. No emission data for B(a)P were available for the whole domain of the other two considered Nordic countries and important uncertainties are still related to the emissions. Long-range transport is significant for the concentrations of all of the considered pollutants, except for B(a)P that commonly originates mostly from local residential wood combustion. The ambient air concentrations of NOx, SO2, Cd, Cr and Pb also have significant contributions from national sources; 45–65% for NOx and SO2, and for the metals from 15 to 60% in urban areas and from 1 to 20% in rural areas, within the considered Nordic area. High national contributions occur especially in urban air, due to primarily road traffic, residential wood combustion, energy production and industrial point sources. It is recommended to monitor the influence from residential wood combustion more extensively, and to analyze longer time trends for long-term human exposure.
•A risk screening of 17 carcinogenics in air is performed at a resolution of 1 × 1 km2.•No exceedances of the EU air quality limit, guideline or target values are found.•B(a)P exceeds its US-EPA 1:100,000 cancer risk conc. for approx. 80% of the Danish population.•B(a)P commonly originates mostly from local residential wood combustion.•National and regional background contributions to air concentrations are estimated.
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