Postprint. This is the accepted version of a paper presented at Transportation Research Board annual meeting.

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1 Postprint This is the accepted version of a paper presented at Transportation Research Board annual meeting. Citation for the original published paper: Lundberg, J., Janhäll, S., Gustafsson, M., Erlingsson, S. (0) Calibration of the Swedish Studded Tyre Abrasion Wear Prediction Model and the Implication for the NORTRIP Road Dust Emission Model In: Transportation Research Board th annual meeting Washington, D.C. N.B. When citing this work, cite the original published paper. Permanent link to this version:

2 CALIBRATION OF THE SWEDISH STUDDED TYRE ABRASION WEAR PREDICTION MODEL AND THE IMPLICATION FOR THE NORTRIP ROAD DUST EMISSION MODEL Joacim Lundberg* Environment, Swedish National Road and Transport Research Institute, VTI Linköping, Sweden & Division of Building Materials, Royal Institute of Technology, KTH SE-00 Stockholm, Sweden Phone: + (0)-0 0, Fax: + (0)- joacim.lundberg@vti.se Sara Janhäll Environment, Swedish National Road and Transport Research Institute, VTI Box 0, Göteborg, Sweden Phone: + (0)- 0, Fax: + (0)- sara.janhall@vti.se Mats Gustafsson Environment, Swedish National Road and Transport Research Institute, VTI Linköping, Sweden Phone: + (0)-0, Fax: + (0)- mats.gustafsson@vti.se Sigurdur Erlingsson Pavement Technology, Swedish National Road and Transport Research Institute, VTI Linköping, Sweden & Faculty of Civil and Environmental Engineering, University of Iceland IS 0 Reykjavik, Iceland Phone: + (0)-0, Fax: + (0)- sigurdur.erlingsson@vti.se Submitted for presentation and publication at the th Annual Meeting of the Transportation Research Board (January -, 0) *Corresponding author Word count: 0 words text + tables and figures x 0 words (0 words) = words Submission Date: July, 0

3 Lundberg, Janhäll, Gustafsson, Erlingsson ABSTRACT An experimentally based prediction model of rut development due to studded tyres is available in Sweden and which had been found to work well. However, since it has not been validated since 00, during which traffic as well as road and tyre design have developed, the question has arisen regarding the model s current validity. Also, since the prediction model is used in the NORTRIP (NOn-exhaust Road Traffic Induced Particle emission) emission model, a natural question is how a change in the wear model will affect the emission model. In this paper, two versions of the abrasion model are compared to measurements at several recently constructed roads in Sweden to investigate the validity, while also proposing changes to allow for continued use. In addition, the impact on NORTRIP is briefly investigated. The paper first describes the abrasion models and their calibration, as well as the test sections for calibration. Both versions of the model, as expected, overestimated the wear and an update was suggested. It was also found that NORTRIP is indicatively affected by overestimating the contribution of pavement wear to the emissions. Keywords: PM0, Studded tyre, Abrasion, Rut, Wear, Modeling INTRODUCTION In cold regions pavement surface rutting is due to permanent deformation in the different layers of the structure as well as surface wear due to studded tyres. The permanent deformation in the unbound layers mainly takes place during the spring thaw and rainy periods, whilst the bituminous layers exhibit plastic deformations mainly when the asphalt layers is warm, i.e. during the summer period. Studded tyre wear, on the other hand, takes place during the winter months. Light traffic usage of studded tyres in Sweden began in the 0s (-), with the main purpose to improve vehicle maneuvering and thus improve traffic safety during winter conditions. Studded tyres are allowed (unrestricted or restricted use) in a number of countries, e.g. Finland, Norway, Canada, Russia and many of the states in the U.S.A. (). The main drawback of studded tyres is linked to the surface abrasion caused by the studs repeatedly hitting the pavement surface. This process also causes pollution in the form of non-exhaust PM0 (Particulate Matter with an aerodynamic diameter smaller than 0 µm) as well as increased noise pollution. The increased road noise pollution comes from both direct emissions from studded tyres and from the specific demands on the road surface that result in a rough texture that increases the noise due to difficulties in optimizing the texture (). Road noise contributes to such health issues as sleep disturbance, high blood pressure and indications of increased risk of ischaemic heart disease (). In urban environments, a large part of the measured mass concentration of particles (PM0) consists of non-exhaust particles (wear particles from road, tyre and brake sources) in areas where studded tyres are used (, ). PM0 is linked to different health issues, including increased cardiovascular and cerebrovascular mortality (e.g. ) but also respiratory diseases and daily mortality (e.g.,, 0). In a more recent study by van der Gon et al. (), a consensus statement was given stating that health risks due to non-exhaust PM0 from wear emissions are non-negligible. Furthermore, Amato et al. () have concluded in another review that the non-exhaust PM0 may be as hazardous as tail pipe PM. In recent years abrasion by studded tyres has decreased due to legislation, among other things, limiting the number of studs per tyre, improvement in stud types and their weight, and allowing only seasonal use of studs as well as the technological development of surface courses,

4 Lundberg, Janhäll, Gustafsson, Erlingsson including the development of more abrasion resistant wear surfacings. Currently, during the winter period, the studded tyre usage for light traffic is estimated to be -% in Sweden, depending on geographic region (). In Norway, studded tyre usage ranged between -% (). For Finland, the share is -% depending on region (). Over the last few decades, several regulations have been introduced in Sweden as an incitement to push development of studded tyres to decrease abrasion wear. Today the number of studs and the weight of studs, etc., are regulated in Sweden (, ), as well as in both Finland and Norway. Larger roads and streets are publicly owned in most countries and thus financed and maintained by taxes. Prediction models are required to optimize maintenance and construction of roads to allow efficient use of public funds. A pavement abrasion wear model (at first with the Mineral aggregate sub-model) was developed and implemented at the Swedish National Road and Transport Research Institute (VTI) in () with validations in 000, 00 and 00 (-) due stud related changes and new wearing course types. In 00 an alternative submodel (the Prall sub-model) was introduced. The Mineral aggregate model is currently partly used in the NORTRIP (NOn-exhaust Road Traffic Induced Particle emission) model used for modeling of non-exhaust PM0 for Nordic conditions (). NORTRIP is also used to model abatement strategies against non-exhaust PM0 emissions. Due to lack of up-to-date validations of the wear models, questions regarding current validity have been raised for the models, given the developments in regulations, road surfaces, studded tyre types (studs, material etc.) and traffic. Study Objectives The main purpose of this study was to determine the validity of the abrasion wear model for studded tyres. If found to be outdated, a secondary objective was to calibrate the model based on measurements performed during the last years. A third objective was to briefly investigate how the NORTRIP road dust emission model could be affected in case of an updated abrasion model. ABRASION MODEL DESCRIPTION The abrasion wear prediction model was developed based on a full-scale experimental indoor circular Road Simulator (RS), and was also further tested and validated with field performance. The first version of the model was released in (). The intention was to predict mean profile wear and maximum rut depth due to abrasion caused by studded tyre usage for bituminous bounded surface courses. The model consists of three parts:. Calculation of the magnitude of the studded tyre abrasion per vehicle with studded tyres.. Calculation of the abrasions distribution over one driving lane based on: a. The Mineral aggregate sub-model b. The Prall sub-model (added in 00 []).. Calculation of the yearly maintenance cost and lifespan. Required model input data The wear prediction model requires both traffic and pavement data to operate. The traffic data

5 Lundberg, Janhäll, Gustafsson, Erlingsson consists of the annual average daily traffic (AADT, only light vehicles for the selected lane) and the share of studded tyres as well as the wear period, defined as number of days where the studded tyre share exceeds %. Further inputs are the allowed speed, road type and whether deicing operations are used. The pavement data required consists of the largest aggregate size of the surface course, the Nordic abrasion value () and the amount of aggregates larger than mm when using the Mineral aggregate sub-model. To use the Prall sub-model the Prall abrasion value () is needed. For graphical presentation, the user can add main and sub-headings, the maximum allowed rut depth, the estimated rut depth due to other sources (e.g. deformation) and the number of years for graphical presentation. The Wear Model The Mineral aggregate sub-model was developed after extensive trials with laboratory manufactured slabs of two types of surface courses, Dense Asphalt Concrete (DAC) and Stone Mastic Asphalt (SMA), with the largest stone size between -0 mm and -0 mm, respectively. The pavement also had other variations in composition (e.g. mineral aggregate qualities, bituminous binders etc.) (). Slabs were tested in VTI s circular Road Simulator (RS), and the test was complemented with field testing where slabs were positioned in different test sections and exposed to real traffic and meteorological conditions. The RS have four axles, on which tyres are mounted, and in this case studded tyres were used for model development. The RS is located in a climate chamber allowing for climate control. The machine requires slabs to form a full pavement ring, making it possible to test different surfacings simultaneously ( slabs per surfacing). During model development, the standard abrasion test was performed at km/h with a wet surface and a temperature in the chamber around 0 C. Some variations in the test were also performed, e.g. tests at different speeds, with a dry road surface, etc. For all tests, the abrasion was measured with a laser profilometer at different intervals in the range of laps (about. million tyre passages). The field testing was performed by following up the rutting on the different test sections with the same laser profilometer, with measurements during spring and autumn to differentiate the rutting from studded tyre abrasion from deformation from heavy traffic. A good correlation between abrasion wear of slabs in the RS and slabs exposed during - years to real conditions was found during the model development (R = 0.-0.). All the measured wear data were put in relation to a reference pavement, i.e. DAC/B (Bitumen with penetration 0/00) with a specific hard Porphyry aggregate from Älvdalen, Sweden, which was given the reference relative wear of.0. By performing a multiple linear regression analysis for all observed data, a regression model was determined (). Material factor The first part of the model is calculation of a material factor, which is done by multiplying the relative wear with factors depending on the speed, on salting and on the studs, and calculated with one of two alternative sub-models. The relative wear RW [-] is calculated as: RW. 0. AN 0.0 D SMA () max 0. 0

6 Lundberg, Janhäll, Gustafsson, Erlingsson AN Nordic Abrasion Value [-], determined by () Dmax Largest Aggregate Size [mm] SMA Share Mineral Aggregate > mm [w.%] The regression model is valid for AN between -, Dmax in the range of -0 mm and SMA between 0- w.%. For the Prall sub-model, the corresponding equation is: RW A P AP Prall Abrasion Value [cm ], determined according to () The model is valid for AP between - cm. For both models, the material factor fm [-] is calculated by: f M RW f f f () v s st RW Relative Wear [-] fv Velocity factor [-], given in Table fs De-Icing factor [-], given in Table fst Stud factor [-], empirically set to 0. TABLE. Material factor for speed and de-icing Factor Velocity De-Icing Velocity [km/h] fv [-] Salt [-] fs [-] 0 0. Yes No Since the development of the model, more speed limits have been introduced in Sweden. An interpolation was thus performed for fv in Table, giving: f , v v R 0. () v Velocity [km/h] Wear distribution ()

7 Lundberg, Janhäll, Gustafsson, Erlingsson The second part of the model refers to the distribution of the abrasion wear across the traffic lane. The model is based on the traffic s lateral position (lateral wander), described by a normal distribution according to: x 0. f ( x) e ().0 f(x) σ x Normal Distribution Frequency Function Standard Deviation for selected road type section, given in Table [mm] Fixed position ranging from -00 < x < 00 in steps of 0 mm TABLE. Standard Deviation for different road types Type Section Road Type Lane Width [m] Standard Deviation [mm] m. m. 0 m. 0 Wide Lanes. 00 Highway 0 Tunnel + Road. 00 Extra Small 0 The amount of studded tyre traffic at each point per year in the tranverse profile is calculated as: SST T( x) f ( x) xd AADTL Wp () 00 T(x) Traffic in point x [vehicles/year] f(x) Normal distribution frequency function from equation xd 0 mm AADTL Average Annual Daily Traffic in user-selected Lane [vehicles/day] Wp Wear Period defined as days with studded tyre share > % SST Studded Tyre Share [%] Under current Swedish legislation (, ), Wp can under normal circumstances vary between and days. The days ( December March) is the period for which use of all types of winter tyres is mandatory and ( October April) days is the maximum number of days during which studded tyres are allowed. The wear per year at each point is calculated by:

8 Lundberg, Janhäll, Gustafsson, Erlingsson W T ( x) f M veh ( x) () x d W W(x) Abrasion wear in x [mm/year] T(x) Number of vehicles in x from equation [vehicles] fm Material Factor from equation [-] Wveh Abrasion wear per vehicle based on measured wear during validation [mm/vehicle] 0 mm xd The average profile wear (APW) is calculated as the sum of wear over the full width for two tracks, while the maximum track wear (MTW) is calculated as the maximum rut depth in one track. For the graphical presentation of the abrasion wear profile at a specific year set by the user, an initial abrasion wear is added to the profile at each point. This initial wear is an adjustment to the increased wear during the first winter season after the construction due to residual material present after construction. This adjustment is done by using a very high number of vehicles, currently set at 00,000. This calculation is similar to the calculation of studded tyre traffic and the abrasion wear in each point (equations -). For the prediction of rut development, an initial track wear of 0. mm is assumed for pavements where ASL > 0%, otherwise an initial track wear of.0 mm is assumed. For the Prall sub-model, an initial wear of 0. mm is assumed for SMA, while.0 mm is assumed for DAC. For both models, it is also calculated how many years this initial track wear takes place by calculating: Vi ti () SST AADTL W p 00 Wt, init di () t i d d d d (0) tot i k k ti Time with initial traffic [year] Vi Initial vehicles [#], set to AADTL Average Annual Daily Traffic in user-selected Lane [vehicles/day] Wp Wear Period defined as days with studded tyre share > % SST Share Studded Tyre [%] di Rut depth due to initial wear [mm] Wt,init Initial track wear [mm] dtot Total rut depth [mm] dk Current years rut depth [mm] Last year s rut depth [mm] dk- Model output

9 Rut Depth [mm] Rut by Abrasion [mm] Lundberg, Janhäll, Gustafsson, Erlingsson In extension to the above-mentioned outputs, the wear model also gives the user the Specific Abrasion (SA) [g/veh/km], defined as The amount of pavement, expressed in grams, which a vehicle with studded tyres wears on a stretch of one kilometre (). Other outputs are the worn amount of pavement per 00 m [tonnes] as well as per 00 m over the lifetime [tonnes] and the adjusted lifespan [years], which usually is limited to 0 years, as well as the wear rate [mm/year], the maximum rut depth [mm] at user specified year, calculated service life [year] and the yearly cost [SEK/m ]. The final outputs are given as graphical presentations, e.g. in Figure where the road E Uddevalla was used. Accumulated Rut Depth Allowed Rut Depth Year Year Year Year Year Year Year (a) Lane Width [mm] (b) 0 FIGURE. Presented results from the wear model for the road Mineral aggregate submodel, (a) rut depth development (red line) and allowed rut depth from user input, (b) the transverse rut profile over a fixed lane width. Similar graphs are produced for the Prall sub-model. FIELD DATA Data on the field road sections, used for model calibration, have been compiled from former research projects (, -). All relevant technical and traffic data are presented in Table. Field data were measured using the earlier described laser profilometer.

10 Lundberg, Janhäll, Gustafsson, Erlingsson TABLE. Field Data used for calibration. GAR = Gap Graded Rubber Asphalt, PMB = Polymer Modified Bitumen, SBS = Styrene- Butadiene-Styrene, EVA = Ethyl Vinyl Acetate. Wetfix is an adhesion promoter. Road and Location Wearing Course AN SMA Dmax AP Constr. Velocity AADTL Type De- Wp SST AAPW AMTW [-] [w.%] [mm] [cm ] Year [km/h] [veh./day] section Icing [days] [%] [mm/year] [mm/year] GAR 0.% Highway Yes 0 0. Wetfix E Uppsala GAR 0.% Highway Yes 0 0. Wetfix + % cement SMA 0/ Highway Yes SMA 0/0-0 0 Highway Yes SMA SBS% Highway Yes SMA Ref Highway Yes E Uddevalla SMA EVA Highway Yes SMA REF Highway Yes SMA SBS% Highway Yes GAR Highway Yes E Mölndal SMA PMB Highway Yes SMA 0/ Highway Yes E Södertälje SMA 0/00 > Highway Yes SMA 0/00 < Highway Yes 0.0. E Pershagen SMA 0/00 < Highway Yes Rv SMA 0/00 < 00 0 m Yes Rv SMA 0/00 < 00 0 m Yes 0.. SMA 0/00 < 00 0 m Yes

11 Lundberg, Janhäll, Gustafsson, Erlingsson EVALUATION METHODS Evaluation of model validity The evaluation of the model was done by comparing the measured abrasion wear rates and the calculated wear rates. This was done at the time of construction, meaning that the predicted wear rate is based on the data for construction year compared to the measured wear. The measured wear rates are both the mean profile wear rate (the mean abrasion over a full profile) as well as the mean track wear rate (the mean track abrasion for both tracks). These are compared to the mean profile wear rate and to the maximum track wear rate calculated in the models. Certain assumptions are made for the evaluation regarding the data, such as whether the data are available, and data for the construction year will be used for the model calculations. In cases of missing data, engineering judgement was used to make up for missing data. One such example is the regional studded tyre usage () since data at the time and for the specific roads were not available. Other data where such have been used give the amount of traffic and the distribution between lanes (0% in the investigated lane assumed, as used earlier in []). After input and calculation, the corresponding wear rates predicted from the model were plotted against the measured wear rates for the same period (see Figure ). Also, the measured wear rates for the different roads for each year were plotted against the predicted wear rates. These wear rates were then used to calculate a suitable reduction factor for calibration of the model. The wear rate is calculated as: WR AW k AW k () WR Wear Rate [mm/year] AW Accumulated Wear [mm] k current year Sensitivity analysis A sensitivity analysis was performed for the maximum and minimum values allowed for the different models. This evaluation was done by using one of the test roads and changing one parameter at a time to the maximum and minimum levels for which the model is valid or, in cases where no upper or lower limits existed, assumptions were made at reasonable levels. WEAR MODEL RESULTS AND DISCUSSION Evaluation of model validity

12 Lundberg, Janhäll, Gustafsson, Erlingsson 0 The results for the models are presented in Figure. For most objects both sub-models overestimate the calculated WR, both regarding MTW and APW. This indicates that a new correction is required to adjust for the decreased abrasion wear. This result is likely affected by the current lack of appropriate historical data, but the fact that the wear model was developed using old tyre types also gives reason to believe that newer regulations and the developments following them are at least partly responsible for the overestimation. Two test sections stand out from the results, most clearly seen for APW (test sections E Uppsala and ). These two road sections consist of GAR surfacings and this type of pavement was not included in the model development, which at a minimum indicates that the model is not functional for these types of surfacings and points to a need for a new regression model where such newer surfacings are included. FIGURE. Wear Rate for: (a) MTW Mineral aggregate sub-model, (b) APW Mineral aggregate sub-model, (c) MTW Prall sub-model, (d) APW Prall sub-model. A comparison of the two sub-models is presented in Figure.

13 Lundberg, Janhäll, Gustafsson, Erlingsson 0 FIGURE. Comparison between Mineral aggregate and Prall sub-models for: (a) MTW, (b) APW. As can be seen, based on the few data points available, the Prall sub-model gave a higher predicted wear for both MTW and APW, while also indicating that the models gave similar predictions at lower WR. Sensitivity analysis The sensitivity analysis of the different required data and their effect on the calculated wear rate are presented in Figure. In the sensitivity analysis, the highest and lowest limits were assumed to the highest and lowest values available in the dataset used for the model development. A lower limit of 0% of the measured traffic was assumed for the investigated lane and the higher limit was assumed to be 00% of the maximum measured traffic. It was also assumed that the wear period (Wp) ranged between the time span of - days stated earlier. For SST, the uppermost and lowest values assumed were based on data from (). For the Prall value (AP), the Prall sub-model was used.

14 Lundberg, Janhäll, Gustafsson, Erlingsson FIGURE. Single parameter sensitivity analysis of E Pershagen for: (a) Mineral aggregate sub-model, (b) Prall sub-model. For all cases, blue marks the lowest possible input data either deemed reasonable or the lowest value used when developing the model. Similarly, red shows the differences being the maximum values. For the type section, black marks the other type sections available. The type sections are defined in Table. The results show that different parameters have a large effect on the results. In common for both APW and MTW, the traffic parameters SST followed by the share of AADTL and the time parameter the wear period showed a high variation in the resulting wear rate. Regarding MTW, for both models the type section can have a large impact, although it is less likely to be based on a less reliable input since the lane width usually is easier to assume and control rather than, for example, the SST. The APW was, as expected, not influenced by the section type. The section type determines the lateral wander (see Table, standard deviation), which will affect the shape of the wear profile (e.g. Figure b). Given contant traffic, an increased lateral wander will increase the width of the track, causing a decreased maximum rut depth (MTW), while keeping the profile area constant, and thus also keeping the APW constant. For the Mineral aggregate sub-model, as seen, the pavement parameters AN followed by Dmax and SMA, resulting in a large impact on the wear rate, based on the maximum and minimum values used in the model. A similar result was seen for the Prall sub-model where the pavement parameter AP could have a high impact on the resulting wear rate. Calibration of the wear model After calculating the mean reduction required for each test road for both the MTW and APW and then calculating the total average between MTW and APW, a reduction factor of % for the Mineral aggregate model and % for the Prall model is suggested. This factor can be implemented either in itself, as shown in Equation, or as a reduction of any factor included in that equation, e.g. the reduction corresponds to a reduction of the stud factor (fst) from 0. to 0. and 0. for the Mineral aggregate sub-model and the Prall sub-model, respectively. Implementing these reduction factors gives the result presented in Figure. As can be seen, a better fit was achieved with the reduction factors, especially for the Prall sub-model. For

15 Lundberg, Janhäll, Gustafsson, Erlingsson the Minreal aggregate sub-model, a better fit was still achieved, although the same test section as before (E Uppsala -) stands out the most. The impact of this implementation was that, instead of overestimating most test sections, most sections were predicted more accurately compared to before (Figure ), although about half of the test sections will now be underestimated. 0 FIGURE. Illustration of Wear Rate after implementation of the calibration factors for: (a) MTW Mineral aggregate sub-model, (b) APW Mineral aggregate sub-model, (c) MTW Prall sub-model, (d) APW Prall sub-model. NORTRIP PARTICLE EMISSION MODEL The Mineral aggregate sub-model is part of the NORTRIP model (, ). Denby et al. () describes how most emission models deal with non-exhaust emissions by using emission factors and are therefore unable to assess mitigation strategies properly, since the process of road dust accumulation on road surfaces is not included. To help air quality managers in their work, the NORTRIP model was developed to allow studying the impact of policies and mitigation measures on the non-exhaust PM0 emissions. To study this phenomenon, the model was developed to lay emphasis on the road surface impact on non-exhaust PM0 from road traffic (e.g. contribution of abrasion wear, surface wetness impact on road dust retention, etc.). The contribution of pavement wear to PM0 is calculated using the RW of the Mineral aggregate sub-

16 Lundberg, Janhäll, Gustafsson, Erlingsson model (RW is known as hpave in NORTRIP). This model requires extensive background information regarding meteorological, traffic and dust abatement (e.g. road cleaning, dust binding) conditions. The output of road wear is then described for light vehicles with studded tyres by (, ): W pw W RW V a wear veh 0, pw () V ref, pw Wpw Road Wear per vehicle [g/km/veh] W0,pw Basic road wear parameter [g/km/veh], set to. g/km/veh Vveh Vehicle speed [km/h] Vref,pw Reference speed for road wear [km/h], set to 0 km/h awear Power law dependency [-], set to.0 Impact on NORTRIP To investigate the impact of a reduction of RW on NORTRIP, the impact on the emission factor for the following cases were run in NORTRIP, where current RW (Equation ) is decreased by:. 0%. 0%. 0%. 0% A dataset for Hornsgatan in Stockholm for the winter and dust season 00/00 was used, giving an R = 0. for PM0 data, using default settings for PM0. NORTRIP RESULTS AND DISCUSSION The results are presented in Figure. As can be seen, the mean concentration as well as the mean emission factor were, as expected, reduced when the abrasion wear was reduced by reducing RW. The difference between the observed and the modelled emission factors increased in all cases, implying that an update of the abrasion wear model would require other parts of the NORTRIP model to be adjusted to compensate for the reduced abrasion wear from studded tyres. These new findings would improve the understanding of road dust emissions.

17 Lundberg, Janhäll, Gustafsson, Erlingsson FIGURE. Changes in NORTRIP due to decreased abrasion wear, (a) changes in mean concentration for PM0, (b) changes in mean emission factor for PM0. CONCLUSION AND FINAL REMARKS Due to the new circumstances from changes in the studded tyre legislation (type, weight, number of studs etc.) it is important to adjust the model to the new circumstances trough calibration. Both prior and after calibration, the model was found to give valuable input, both as regards wear rates for maintenance planning and the understanding of emissions of wear particles to air. The results also showed that newer types of surfacings can give significantly different results. This significant difference was likely linked to the fact that the abrasion model was developed empirically for other types of surfacings than the more recently developed ones. It is thus not obvious that new types of surfacings would be correctly predicted with this abrasion model. The sensitivity analysis shows that the input data (e.g. pavement data) can have a large impact on the results. The proposed change in the model by using calibration factors (% and % for the Mineral aggregate model and the Prall model respectively) was seen to improve the wear predictions, while also resulting in a reduction for both the mean emission factors and the mean concentration of PM0 in NORTRIP, giving further insight in the understanding of wear particle emission. Further work should focus on gaining access to a larger quantity of measured data, including trying to access historical data for a more detailed evaluation of the model. The rapid development of tyre and pavement techniques and standards imply a need to perform a new regression analysis of road wear. Including assumed traffic increase and changes in the share of studded tyres (SST) in the model would improve usability. The connection between the particle emission model NORTRIP and this wear model should be further evaluated to improve both models. Running both the NORTRIP model and this road wear model in parallel at the same sites would give important insights into results for both areas. In continuation, given an updated regression analysis of the RW (Relative Wear, the regression in common for both models), the NORTRIP emission model should be updated regarding road wear contribution to PM0. ACKNOWLEDGEMENTS

18 Lundberg, Janhäll, Gustafsson, Erlingsson The authors wish to thank Håkan Carlsson (VTI) for the field abrasion wear data used as well as Leif Viman (VTI) and the Swedish Transport Administration for pavement data. The authors also wish to acknowledge Torbjörn Jacobsson (former VTI / currently Swedish Transport Administration) and Lars-Göran Wågberg (former VTI) for their original work with model development and previous validations. This work was performed within a PhD student program financed by the Swedish Transport Administration and VTI. REEFRENCES. Gustafsson, M., Berglund, C.M., Forsberg, B., Forsberg, I., Forward, S., Grudemo, S., Hammarström, U., Hjort, M., Jacobson, T., Johansson, C., Ljungman, A., Nordström, O., Sandberg, U., Wiklund, M., and Öberg, G., Effekter av vinterdäck: en kunskapsöversikt, VTI: Linköping. 00 (In Swedish). Doré, G. and Zubeck, H.K., Cold Regions Pavement Engineering. 00, Reston, VA, U.S.A.: American Society of Civil Engineers.. Zubeck, H., Alehire, L., Harvey, S., Porhola, S., and Larson, E., Socio-Economic Effects of Studded Tire Use in Alaska, University of Alaska Anchorage: Anchorage, Alaska, U.S.A. 00. WHO, Burden of disease from environmental noise: Quantification of healthy life years lost in Europe, World Health Organization: Copenhagen. 0. WHO, Health effects of transport-ralated air pollution, M. Krzyzanowski, B. Kuna-Dibbert, and J. Schneider, Editors, World Health Organization: Copenhagen. 00. Gustafsson, M., Blomqvist, G., and Jonsson, P., Damningsminimerad vinter- och barmarksdrift. Mått, medel och strategier, VTI Rapport 0, Väg- och transportforskningsinstitutet: Linköping. 0 (In Swedish). Stafoggia, M., Samoli, E., Alessandrini, E., Cadum, E., Ostro, B., Berti, G., Faustini, A., Jacquemin, B., Linares, C., Pascal, M., Randi, G., Ranzi, A., Stivanello, E., Forastiere, F., and Group, M.-P.S., Short-term associations between fine and coarse particulate matter and hospitalizations in Southern Europe: Results from the MED-PARTICLES project. Environ Health Perspect, 0. (): p Meister, K., Johansson, C., and Forsberg, B., Estimated short-term effects of coarse particles on daily mortality in Stockholm, Sweden. Environ Health Perspect, 0. 0(): p. -.. Brunekreef, B. and Forsberg, B., Epidemiological evidence of effects of coarse airborne particles on health. Eur Respir J, 00. (): p Forsberg, B., Hansson, H.C., Johansson, C., Areskoug, H., Persson, K., and Jarvholm, B., Comparative health impact assessment of local and regional particulate air pollutants in Scandinavia. Ambio, 00. (): p. -.. van der Gon, D., Hugo, A.C., Gerlofs-Nijland, M.E., Gehrig, R., Gustafsson, M., Janssen, N., Harrison, R.M., Hulskotte, J., Johansson, C., Jozwicka, M., Keuken, M., Krijgsheld, K., Ntziachristos, L., Riediker, M., and Cassee, F.R., The Policy Relevance of Wear Emissions from Road Transport, Now and in the Future An International Workshop Report and Consensus Statement. Journal of the Air & Waste Management Association, 0. (): p. -.. Amato, F., Cassee, F.R., Denier van der Gon, H.A., Gehrig, R., Gustafsson, M., Hafner, W., Harrison, R.M., Jozwicka, M., Kelly, F.J., Moreno, T., Prevot, A.S., Schaap, M., Sunyer, J., and Querol, X., Urban air quality: the challenge of traffic non-exhaust emissions. J Hazard Mater, 0. : p. -.. Trafikverket, Undersökning av däcktyp i Sverige. Vintern 0 (januari-mars), Trafikverket: Borlänge. 0 (In Swedish)

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