Lumière

A Beji

K Deboudt

S Khardi

B Muresan

L Lumière

road and test track experiments

Keywords: Particles, tire and road wear, mobile measurements, emission determinants, highemission situations. Highlights

The purpose of this study is to identify the parameters governing particle emissions at the rear of the front wheel of an instrumented light-duty vehicle. The information obtained from the road is complemented by data acquired on a test track. By comparing the signals recorded at various locations around the vehicle, a distinction can be drawn between rear of the wheel particles (RoWP) and particles resulting from tire and road wear (TRWP). Both types of emissions are highly dependent on driving speed. TRWP mainly fall in the size ranges of < 0.03 µm and 0.05-0.30 µm by their number, and 0.1 -0.6 µm and 1.0 -15 µm by mass when using a mean density of 2.0 g/cm 3 . Within these ranges, TRWP account on average for 40% and 25% of RoWP emissions, respectively. We also evaluate herein the influence of: i) pavement contamination by particulate matter, ii) road geometry, iii) driving area, and iv) structural elements affecting road traffic. Test track experiments finally provide information on the significance of accelerations, decelerations, pavement texture and age. This knowledge compilation may be implemented in future non-exhaust particulate emissions standards; moreover, it establishes a basis for realistically assessing emission dynamics and identifying affected areas.

Introduction

Since exhaust particulate emissions are subject to increasingly stringent standards, the share of non-exhaust particulate (NEP) emissions is bound to increase. In nearly thirty years, European standard thresholds for exhaust particulate emissions have been reduced by a factor of over 30 (from 140 mg/km to below 4.5 mg/km between the Euro 1 and Euro 6d standards) (EEA, 2016). This reduction has been implemented despite strong differences between measurements carried out on the road and on chassis dynamometers (ICCT, 2017;[START_REF] Eca | The EU's response to the "dieselgate" scandal[END_REF]. Over the past few decades, attention has been shifted to NEP emissions, especially particles emitted by the braking system or the tire-road interface. The increasing complexity of materials used in brake pads and tires, coupled with the limited amount of updated information provided by manufacturers, would suggest that a vast field of study will emerge in the coming years [START_REF] Blau | Compositions, functions, and testing of friction brake materials and their additives[END_REF][START_REF] Camatini | Microcharacterization and identification of tire debris in heterogeneous laboratory and environmental specimens[END_REF][START_REF] Lindenmuth | Chapter 1 in The Pneumatic Tire[END_REF]. In addition, the toxicity of chemical compounds used in materials' manufacturing (heavy metals, persistent organic compounds, flame retardants, endocrine disruptors, nanopowders, etc.) underscores the potential of NEP to exert adverse effects on the health of ecosystems adjacent to roads, as well as on the human populations inhabiting these ecosystems [START_REF] Kreider | Physical and chemical characterization of tire-related particles: Comparison of particles generated using different methodologies[END_REF][START_REF] Amato | Non-exhaust emissions: An urban air quality problem for public health; impact and mitigation measures[END_REF][START_REF] Charron | Identification and quantification of particulate tracers of exhaust and non-exhaust vehicle emissions[END_REF].

It is widely accepted that the atmospheric loading of NEP can be comparable to that of exhaust particles [START_REF] Querol | PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain[END_REF][START_REF] Lenschow | Some ideas about the sources of PM10[END_REF] and will ultimately dominate due to the transition toward hybrid and electric vehicles. In urban areas, NEP emissions are estimated to account for a few tens of percent up to > 70% of PM10 (i.e. a class of particles with a mean diameter of 10 µm) [START_REF] Lenschow | Some ideas about the sources of PM10[END_REF][START_REF] Querol | Speciation and origin of PM10 and PM2.5 in selected European cities[END_REF][START_REF] Thorpe | Estimation of particle resuspension source strength on a major London Road[END_REF][START_REF] Harrison | Estimation of the contributions of brake dust, tire wear, and resuspension to non-exhaust traffic particles derived from atmospheric measurements[END_REF]. In a city street environment, tire fragments produced by wear can alone constitute between 1 and 10 μg/m 3 of air or a few percent of PM10 (nearly 460,000 tons of rubber were dispersed along European roads in 2006: [START_REF] Wik | Occurrence and effects of tire wear particles in the environment -A critical review and an initial risk assessment[END_REF]. NEP emission factors identified in databases (Copert 4, EMEP and PIARC) and the literature [START_REF] Abu-Allaban | Tailpipe, resuspended road dust, and brake-wear emission factors from on-road vehicles[END_REF][START_REF] Ketzel | Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emission factors for practical street pollution modeling[END_REF][START_REF] Bukowiecki | PM10 emission factors for non-exhaust particles generated by road traffic in an urban street canyon and along a freeway in Switzerland[END_REF] range from less than 5 mg/km/vehicle to as high as 50 mg/km/vehicle for passenger cars and from 100 mg/km/vehicle to over 1,000 mg/km/vehicle for heavy trucks. The relative contributions of resuspension and wear (tires, brakes, road surface) to NEP emissions lie on the order of 50/50 for passenger cars and 75/25 for heavy-duty vehicles. However, these ratios should only be considered as a guide since they depend on many variables, including vehicle dynamics, tire and road surface properties, and weather conditions.

Studies devoted to characterizing tire wear particles (TWP) have been mostly based on roadside and laboratory measurements using tire simulators (such as inner and outer drum test benches); moreover, they more rarely comprise experiments carried out on the road [START_REF] Kwak | Characterization of non-exhaust coarse and fine particles from on-road driving and laboratory measurements[END_REF][START_REF] Kwak | On-road and laboratory investigations on non-exhaust ultrafine particles from the interaction between the tire and road pavement under braking conditions[END_REF][START_REF] Pirjola | Road dust emissions from paved roads measured using different mobile systems[END_REF]. Simulations demonstrate that the particles collected at the rear of instrumented wheels (RoWP) can be smaller than expected [START_REF] Thorpe | Sources and properties of non-exhaust particulate matter from road traffic: A review[END_REF]. Depending on the simulated driving conditions, RoWP would fall in the ultrafine (peaking at 20-30 nm), fine (approx. 50% by mass of emitted PM10) and coarse size classes [START_REF] Grigoratos | Experimental investigation of tread wear and particle emission from tyres with different treadwear marking[END_REF]. The RoWP generated by means of drum test benches originate mainly from tire wear, while bearing in mind that for any given vehicle, only 15% of the RoWP > 0.3 µm collected on the road actually come from its own tires [START_REF] Kreider | Physical and chemical characterization of tire-related particles: Comparison of particles generated using different methodologies[END_REF].

RoWP emissions measured in the laboratory and on the road may however differ, as a result of: the impossibility of completely reproducing the stresses that develop at the tire-road interface; the resuspension induced by the vehicle body or nearby traffic; weathering and variations in asphalt concrete roughness, including contamination by dust from the nearby environment and multiple road traffic emissions.

In fact, measurements conducted on the road lend insight into the complexity of the emission mechanisms of both RoWP and abraded particles (i.e. from tires and the road surface: TRWP). This complexity can in part be resolved by coupling on-road measurements with data obtained under semi-controlled conditions, which are reflective of actual driving conditions. This study investigates the influence of driving conditions and road texture on the emission dynamics and size distribution of RoWP and TRWP. The data acquired seek to answer the following question: "What are the determinants for RoWP emissions on the road, and to what extent do these determinants also affect the dynamics of TRWP emissions?" Our search for an answer led to employing a two-step strategy involving a simultaneous on-road assessment of vehicle parameters and particulate emissions followed by a test track investigation of the relevant parameters previously identified. The results obtained have made it possible to propose mechanisms that explain the dynamics of RoWP and TRWP emissions on the road, as well as strategies to limit emissions depending on the vehicle dynamics, road surface properties or the type of driving area.

Materials and methods

Vehicle instrumentation

This research was carried out by using specially equipped vehicles operated by the Gustave Eiffel University. In all, 8 vehicles were instrumented, composed of 7 passenger cars and a van. The results proposed herein stem from measurement series representative of emission trends under actual traffic conditions recorded by a fully-instrumented vehicle, i.e. a Renault Clio 3,1.6i 16V,111 hp (one of the most common models found in France). This vehicle is gasoline-powered, subject to the Euro 4 emission standard and fitted with commercial summer tires (Michelin TM Energy Saver, 185/60 R 15 88H), which feature asymmetrical structures with three straight grooves running around the center of the rubber tread. The tire surface is mostly amorphous, with Si fiber and Fe-enriched inclusions. The vehicle enables accessing several key parameters related to engine operations and vehicle dynamics. These parameters include: engine speed and acceleration, fuel consumption, wheel and ground speeds, brake pressure, and steering angle. All parameters were monitored at a frequency of 10 Hz and continuously recorded by means of on-board computers.

In addition to the vehicle dynamics parameters, particle size distributions were recorded in real-time using four devices: two Electrical Low Pressure Impactors (ELPI Dekati™), and two Optical Particle Counters (OPC Grimm™ EDM 1.108). The ELPI devices measured the number size distribution of particles with an aerodynamic diameter lying between 0.007 and 10 µm, at a time step of 1 s. This device operates at a flow rate of 10 L/min, with 12 size channels. The OPCs were introduced in order to probe, on an ancillary basis, the fine and coarse mode particles. The method employed in an attempt to splice ELPI and OPC signals is provided in Appendix 1. These devices output real-time measurements of particles with an optical diameter lying between 0.35 and 22.5 µm; they feature 15 size channels, a flow rate of 1.2 L/min and a time step of 6 s. All apparatuses used are calibrated by their manufacturers, with calibrations being verified on a daily basis against similar devices operated by partners (ensuring a signal discrepancy less than 20%). According to the manufacturers, the analytical uncertainties on ELPI and OPC measurements are 5% and 3% respectively. However, these percentages are only indicative as laboratory controls show that they are in fact closer to 7% and 15%, in this order. Taking into account the sampling and particle transfer biases, the overall accuracy of the measurements will be slightly higher than 80%. The mass size distributions were assessed from the number size distribution in considering the particles as spheres. Furthermore, it is worth noting that despite the differences in absolute RoWP densities depending on their sources (pavement and tire wear, resuspension of recent and aged dust from total vehicular emissions and the surrounding environment, salting, etc.), a reference density of 2.0 g/cm 3 was used in the calculations. This value is reflective of a compromise between the densities of the asphalt concrete, particulate contaminants deposited on the pavement and TRWPs obtained in the laboratory (i.e. 1.5-2.7 g/cm 3 ; Klöckner et al., 2019).

A trailer containing the scientific instrumentation, along with two power generators, was hitched to the test vehicle. The trailer and analytical equipment made up nearly 30% of the weight of the vehicle with three passengers on board. It resulted in a median fuel consumption surplus of 25% (from 5.5 l/100 km to 6.8 l/100 km), which translated into a median increase in longitudinal forces and torques at the tire-road interface of less than 10% (respectively from 205 N to 220 N and 30 Nm to 35 Nm). This increase was much smaller than that observed in most acceleration or braking situations, i.e. the longitudinal forces and torques then exceed 500 N and 200 Nm, respectively.

We instrumented the front wheels because they are the drive wheels: that is, where the stresses at the tire-road interface are the highest. They are also the wheels for which forces and torques are recorded by the vehicle's on-board computer. In fact, if the measurements had been taken at the rear wheels, it would have been more difficult to distinguish local TRWP emissions. They would have been mixed with emissions generated at the front wheels or road dust resuspended by turbulence under the vehicle floor (Appendix 2). Consequently, the air intakes for particle collection were placed at the rear of the front tire (driver's side) and in the backspace of the wheel near the braking system (Fig. 1). The former collection point pertains to RoWP, whereas the latter is focused on particles emanating from resuspension and the braking system during strong decelerations. The proportion of TRWP was approximated by subtracting the normalized concentrations measured in the backspace of the wheel from those of RoWP (see Section 3.2). The air intake for RoWP was set 15 cm behind the tire and 7 cm above ground. It was oriented so as to form a 15-degree angle with the vertical, thus constituting an intermediate position intended to capture particles originating from both the tire-road interface and the resuspension induced by tire action [START_REF] Thivolle-Cazat | Flow analysis around a rotating wheel[END_REF][START_REF] Rajaratnam | Investigation of wheelhouse flow interaction and the influence of lateral wheel displacement[END_REF]. Under such conditions, the airflow velocity was nearly 80% less than the vehicle speed [START_REF] Kwak | Characterization of non-exhaust coarse and fine particles from on-road driving and laboratory measurements[END_REF] and barely influenced by the ambient wind direction [START_REF] Etyemezian | Vehicle-based road dust emission measurement (III): Effect of speed, traffic volume, location, and season on PM10 road dust emissions in the Treasure Valley[END_REF]. According to preliminary numerical simulations carried out using the Ansys Fluent code line, the angle between the sample intake and these streamlines stemming from the tire-road interface varied from 40° at 30 km/h to 60° at 110 km/h.

Calculation of emissions and correction factors

Preliminary measurements and fluid dynamics simulations on the positioning and orientation of the air intake served to assess particulate emissions toward the rear of the wheel (E) as well as the aspiration efficiency of the instruments (η). The emission E (expressed in particles or mg per sec or, upon division by driving speed, in particles or mg per km) was calculated as follows:

E = (Equation 1)
where "µ" is a correction factor for the change in streamline orientation (Fig. 1) based on driving speed and steering angle (from < 0.05 to 1.2, with an average of 0.85 for on-road measurements). " U0 " is the mean free flow velocity at the rear of the wheel (a linear function of driving speed, in m/s) and " C " the related mean concentration of particles (in particles/m 3 or mg/m 3 ). Based on the results of preliminary numerical simulations, this calculation assumed a parabolic shape of both airflow velocity and concentration profiles in the horizontal plane. Their optima were the ones measured at the sampling point. "A" is the area of a surface beneath the vehicle containing the sampling point; it averages 0.03 m² and is determined by the intersection of streamlines from the tire-road interface and a plane perpendicular to the driving direction. Like with the positioning and orientation of the air intake, the change in A with increasing driving speed and steering angle (i.e. decrease and increase, respectively) was determined beforehand and expressed by correction factor µ. The values of η were estimated using an empirical approach developed by [START_REF] Belyaev | Techniques for collection of representative aerosol samples[END_REF] and then applied in [START_REF] Kwak | Characterization of non-exhaust coarse and fine particles from on-road driving and laboratory measurements[END_REF], i.e.:

η=1+ U 0 U cos θ -1 1- 1 1+ 2+0.62 U 0 U Stk ! (Equation 2)
where U is the intake velocity and Stk the Stokes number. The total uncertainty (including analytical and procedural steps) on emissions was estimated according to the error propagation technique and lies between 10% and 60% (typically 25%). The influence of the efficiencies of aspiration, tubing and the isokinetic probe will be detailed further below. The calculated efficiencies were all close to 100% for particles smaller than 10 µm, i.e. for the vast majority of relevant particles by number. The values were not as high for coarse particles measured by OPC devices at speeds exceeding 70 km/h, thus leading to semi-quantitative estimations in the 10-22.5 µm range (0.7 < η < 1.7). However, since particles larger than 10 µm account for less than 1% of accessible particles, the number-weighted emissions were not corrected for this deviation.

The influence of tubing on measured concentrations was evaluated prior to sampling. The tubing used to convey particles to the ELPI devices was 5-m long antistatic hoses with an inner diameter of 8 mm. 3-m long antistatic hoses with a 4-mm inner diameter were used to transport particles to the OPC device. Data show that the particle loss was insignificant (i.e. less than 2%/m) and homogeneously distributed between fine and coarse particles: between < 1% and 3% variation in the ratio between fine (< 1 µm) and micrometric (> 1 µm) particle number concentrations. Consequently, emissions were not corrected for this loss. It should be noted that throughout the manuscript, statistical analyzes focused on correlation coefficients, p-values and comparison of means using either Bonferroni or Holm-Šídák parametric tests (α =0.05). These tests were performed after examining the independence, normality and homoscedasticity of the data with XLSTAT and/or SIGMA PLOT applications. When normality and homoscedasticity criteria were not met, analyzes were performed using medians with the Kruskal-Wallis non-parametric test on ranks. The proposed results are generally statistically significant at a 95% level.

Lastly, the influence of an isokinetic nozzle at the sample intake was examined by driving the vehicle over the same roads in the presence or absence of nozzles (from TRC Tecora).

Concentrations were typically, but not systematically, lower in the absence of an isokinetic nozzle. The change in the number-weighted concentration was nevertheless limited (ranging from 3% to 9%) and increased with vehicle speed. The variation in the fine-to-micrometric concentration ratio was lowest at 50 km/h, i.e. less than 3%, and did not exceed 15% (5% on average) at speeds over 30 km/h. This finding likely reflects the decreased airflow velocity observed at the rear of the tire and therefore the limited change in aspiration efficiency. The variation in the fine-to-micrometric concentration ratio was greater at speeds below 30 km/h, as illustrated by a decrease of approx. 15-40%, which can be attributed to the lower emission rates of the instrumented vehicle and the significant influence of ambient road traffic.

The reference test track

A 2.3-km long test track with a maximum speed of 140 km/h was deployed in order to evaluate RoWP and TRWP emission dynamics in a semi-controlled environment (Fig. S1).

This test track was equipped with 15 pavements composed of distinct wearing courses representative of French and European roads or more specific road surfaces. This set-up made it possible to test different qualities of tire-road contact, ranging from highly gritty textures to the absence of gravel and therefore of macrotextures. Test track gravel originated from local quarries: Le Cellier, Vrignaie, and Les Pontreaux. The coarsest gravel fraction (6-10 mm) was rhyolite, while the 0-2 mm, 0-4 mm, 2-6 mm and, on rare occasion, the 4-10 mm fractions were gneiss. As a mix design example, the proportions of the various fractions used to construct the conventional test track pavement were: 0-4 mm (45%), and 4-10 mm (55%).

The relative percentage of bitumen 60/70 was 6.2% by mass.

The road surface texture can be divided into three major categories: microtexture, macrotexture and megatexture [START_REF] Piarc | International experiment to compare and harmonize skid resistance and texture measurements[END_REF]. Megatexture reflects the broad unevenness of the road surface (i.e. on the scale of a few tens to hundreds of millimeters) and has therefore been excluded from this research. Macrotexture displays characteristic dimensions, i.e. deviation of the pavement surface from the local planar surface of the road, in the 0.5-50 mm range; it is determined by means of volumetric sand patch tests carried out at various spots along the tire tracks of the pavement (HK/HD, 1989). The characteristic microtexture dimensions, i.e. defined as the wavelength and amplitude of the cavities and edges on gravel particle surfaces, are less than 0.5 mm [START_REF] Lowne | The effect of road surface texture on tyre wear[END_REF][START_REF] Dahl | Traffic-generated emissions of ultrafine particles from pavement-tire interface[END_REF][START_REF] Gustafsson | Factors influencing PM10 emissions from road pavement wear[END_REF]. Microtexture relates to the microscopic morphology, crystallographic characteristics and chemical composition of both the asphalt concrete and the deposited dust; it is assessed in arbitrary friction units (i.e. a.u.) using a standard skid resistance tester (HK/HD, 1989). Both the micro and macrotexture of the test track pavements are evaluated annually; their relevant values are listed in Table 1.

Six pavement types were used to assess the influence of texture on RoWP emissions; this assessment entailed driving the instrumented vehicle on the test track at a speed of 50 km/h. Of the pavements tested, three were also used to determine TRWP emissions. To improve data representativeness, the track was not previously cleaned from ambient dust deposition.

Each pavement tested was selected according to its representativeness or due to its distinct micro and macrotexture properties; these pavements consisted of either asphalt concrete (AC), stone mastic asphalt (SMA) or surface dressing (SD) (Table 1). The SMA was a gap-graded mineral mixture with a coarse deformation-resistant aggregate matrix. The matrix was filled with bitumen, mastic and several stabilizing additives. Surface dressings were prepared by spreading a hot bituminous binder onto an existing pavement, then uniformly distributing and sealing the aggregates. This technique usually resulted in increasing both noise emissions and road surface macrotexture. In addition to experiments designed to evaluate the role played by texture, the influence of pavement aging was also examined by driving at constant speeds within the 50-110 km/h interval on two semi-coarse asphalt concrete pavements, 11 (AC2006) and 37 (AC1980) years old respectively at the time of the experiments.

All tests were conducted by the same pilot and co-pilot. Beyond the key parameters recorded by on-board computers, the co-pilot logged for each test the vehicle activity and driving conditions. Tests were carried out under dry weather conditions (T = 20-25°C with 50%-80% humidity) and in the absence of any notable wind (less than 5 m/s). Background particles were measured when the vehicle was stationary and facing the wind or running at speeds below 10 km/h (i.e. with emissions from the instrumented vehicle becoming negligible). The background was then manually subtracted for each particle fraction before any type of data processing. To do this, we used a moving average (taken over 20-40 min). This allowed us to adjust the baseline of the signal as well as its variations without significantly altering the superimposed structures. The quality of the adjustment was checked by making sure that the average values correspond to the punctual measurements. Potential contamination by "fresh" brake wear particle (BWP) emissions was also assessed by simultaneously monitoring emissions at the rear and in the backspace (i.e. 15 cm below and 12 cm downstream of the braking system) of the instrumented wheel. Because of the differences among vehicles in the positioning of the braking system and the configuration of the wheel backspace, these distances could vary in a sensible way (from 5 cm to 10 cm). Nevertheless, care has been taken to ensure that the overall configuration and aerodynamics remain relatively consistent (including the position of the air intake in relation to that of the braking system and other internal parts such as the steel beams). The resultant signals and associated size distributions showed that contamination with BWP cannot be excluded during situations where the vehicle comes to a stop after heavy braking (between 8% and 27% of RoWP). In other cases, the majority of BWP emissions were incorporated into the resuspended dust cloud and then discharged downstream of the vehicle without quantitatively reaching the air intake for RoWP.

On-road RoWP measurements

The on-road measurement campaign was intended to identify and evaluate the determinants of RoWP and TRWP emission dynamics under various driving conditions. The route driven was a 25-km long loop in the Nantes metropolitan area (northwestern France), which encompasses a population of nearly 950,000. The area's vehicle count reached 350,000 in 2016, with traffic being busiest in remote suburban locations (over 100,000 vehicles per day on certain ring road sections) or else in the city core [START_REF] Auran | Territorial dynamics in the Nantes metropolis[END_REF][START_REF] Dreal | The Nantes ring road stakes & projects[END_REF]. The tested route passed through different environments, from rural sectors to suburban and urban districts, and covered several road types (i.e. country roads, ring roads, motorways, city streets, speedlimited streets), thus involving a range of traffic situations (e.g. stops at traffic lights, navigating roundabouts, passing junctions, etc.). The atmospheric blanks were determined for each road type by a applying the same procedure as described for the measurements on the test track.

In order to both maintain a low contribution of exhaust particles from ambient road traffic and increase the consistency of traffic conditions, on-road measurements were carried out between 11 am and 5 pm while taking care to avoid the noon rush hour. Traffic conditions were recorded manually by the co-pilot as well as using an on-board camera. The camera was used to search for the causes of variations in emission signals in relation to the zone traveled. The information obtained was qualified according to the operator's interpretation when the vehicle was returned to the laboratory. Although heavy-traffic situations cannot always be avoided in urban zones, these measurements allowed the RoWP and TRWP emission dynamics to be examined over a wide range of speeds: from 0 km/h (i.e. the vehicle was stopped) to over 90 km/h. Depending on traffic conditions and analyzer response, one or two successive runs may have been required to compile complete datasets. As with measurements conducted on the test track, most trips took place under the same weather conditions: clear skies with light wind (wind speed below 7 m/s) and no rain during the entire previous week. The air temperature and relative humidity ranged from 10°C to 20°C and from 50% to 90%, respectively. These conditions should prevent hygroscopic particle growth and potential droplet formation that could affect the response of OPCs at humidity percentages close to 100% [START_REF] Hänel | Computation of the extinction of visible radiation by atmospheric aerosol particles as a function of the relative humidity, based upon measured properties[END_REF][START_REF] Hegg | A theoretical study of the effect of relative humidity on light scattering by tropospheric aerosols[END_REF]. In addition to weather and traffic conditions, the co-pilot also manually recorded the apparent characteristics of the road surface (i.e. macrotexture, dryness or contamination of dust).

Results and discussion

Determinants of RoWP emission dynamics on the road

It was generally acknowledged that the driving speed factor can decisively alter the dynamics of RoWP emissions. The data obtained from on-road measurements with different vehicles confirmed this assertion (Appendix 3) while nuancing it at the same time. As was the case in the study by [START_REF] Mathissen | Investigation on the potential generation of ultrafine particles from the tire-road interface[END_REF], the slope-to-intercept ratios for the curves plotting total concentration measured in number or mass by the ELPI device (namely CELPI(RoWP)) vs. driving speed were low (i.e. less than 0.01, Fig. S2). In contrast, in considering the average CELPI(RoWP) values of sections traveled at constant speed, exponential increases (r² > 0.90) were found above 70 km/h. Such correlations were identified despite a significant increase in the dilution rate, thus highlighting that ELPI RoWP emissions (hereinafter referred to as EELPI(RoWP)) per unit of time intensify with driving speed (Fig. 2). An estimation indicated that RoWP emissions for the instrumented wheel were close to 10 9 particles/s (or 0.05 mg/s) at 90 km/h, and their magnitude was approximated using the least-square regression model with the following power trends: EELPI(RoWP) (particles/s) = 4.0 10 6 v 1.22 ; r 2 = 0.70, p < 0.01 (Equation 3a) EELPI(RoWP) (mg/s) = 5.8 10 -4 v 1.04 ; r 2 = 0.60, p < 0.01 (Equation 3b)

where "v" denotes the driving speed in km/h. These relationships incorporate the changes in both the airflow velocity and orientation of streamlines arising from the tire-road interface with changes in driving speed. However, they should only be considered as indicative as most RoWP were induced by resuspension (see Section 3.2), and a significant percentage of emissions was potentially aged RoWP deposited on the pavement and then re-emitted a variable number of times. In addition, turbulence and particulate emissions from road traffic were more likely to affect the concentration and size distribution of RoWP recorded at low speeds. Most RoWP were in the < 0.1 µm range by number and 0.05-0.40 µm or 1.0-10 µm by mass, i.e. roughly 90% and 80%, respectively. It should also be noted that relatively constant evolutions (r 2 < 0.10) were found when expressing EELPI(RoWP) values per kilometer and plotting them vs. driving speed. On the one hand, the limited overall variation in emissions per kilometer could be explained by a quasi-linear increase of temporal emissions with speed (Eqs. 3a and b), while on the other hand, the dispersion of individual values stems from local variations in speed-independent factors, e.g. pavement texture, road surface contamination with dust, road traffic intensity.

The squared correlation coefficient in Equations 3(a,b) underscores that driving speed could be used to capture a significant part of variations in RoWP emissions. As a consequence, several other speed-dependent parameters (i.e. fuel consumption, engine speed, throttle opening) were positively correlated with the RoWP emission rate per unit of time. However, an analysis of residuals indicates that Equations 3 and 4 (see Section 3.2) should be used with caution. This is mainly due to the fact that the residuals are not normally distributed, but most likely reflect the superposition of two or more Gaussian distributions (Appendix 4). Further analysis shows that due to the diversity of generation and emission processes, the proposed equations would be: i) better suited to estimate continuous (i.e. non-peaked) emissions and ii) less efficient at speeds exceeding 50-70 km/h. The estimates obtained at higher speeds should then be considered as indicative of an order-of-magnitude. Remarkably, no overall correlation could be found with tire temperature, except at low speeds: a significant increase in EELPI(RoWP) accompanied the rise in tire temperature or drive shaft torque at speeds slower than 5 km/h (0.07 < r² < 0.16, p < 0.05, Fig. S3). This finding most likely reflects a mechanical heating of the tread and particle detachment from the tire and road surface that would have occurred during the first few moments of driving or acceleration (e.g. when starting up the vehicle from a standstill). Particle detachment can be thought of as the removal of weakly bound solids, i.e. including tire parts, metal inclusions and dust fragments; it becomes more prevalent with an increase in: stresses exerted at the interface between tire and road, centrifugal forces on the tire tread and the flow of air around the wheel. Detachment was also evidenced at the initiation of high-speed accelerations by sudden changes in the RoWP emission dynamics in the form of concentration peaks (Fig. 3), despite the absence of a trivial relationship between peak height and acceleration intensity. Most peaks correspond to an increase in the torque exerted on the wheel, in tire temperature as well as in the number concentration of coarse particles within the 0.05-0.25 µm size range and, more markedly, within the 1-10 µm interval (multiplicative factors of 1.1-1.3 and 1.2-2.5, respectively). This interval was consistent with the size of particulate contaminants observed on the tire tread (Fig. 4).

Like in the acceleration case study, decelerations were accompanied by sudden increases in EELPI(RoWP) values. Let's note however that EELPI(RoWP) is a simple function of neither the pressure exerted on brake pads nor the deceleration rate. For instance, mild braking at 80 km/h can generate twice as many particles as heavy braking at 30 km/h. The particulate concentrations recorded during decelerations were among the highest, i.e. close to 1.5 10 10 particles/m 3 or 1,000 µg/m 3 , yielding emission peaks approaching 10 9 particles/s and 3.5 mg/s, respectively. At the same time, the proportion of coarse particles in the emissions was increasing considerably by number: within the size range of 0.05-0.25 µm and, especially, within the 1-15 µm interval (multiplicative factors of 1.1-1.2 and 1.3-7.0, respectively). These ranges are consistent with the BWP sizes reported in the literature [START_REF] Beji | Nonexhaust particle emissions under various driving conditions: Implications for sustainable mobility[END_REF], though the data acquired do indicate a higher proportion of particles larger than a micron. It is therefore likely that emitted particles or particles deposited on the pavement will agglomerate at the tire-road interface during braking. These may grow to a size of several micrometers or fragment in order to form new agglomerates. Braking strength and wheel speed would then partly control the agglomeration dynamics and the emission of the particles formed.

The coincidence of braking situations and RoWP emission peaks, as well as the proximity between observed particle sizes during accelerations and decelerations, further suggests that:

i) the RoWP measured during decelerations accounted for an excess of BWP; and ii) BWP deposited on the pavement and tire surface were potentially re-emitted during accelerations.

According to the Kruskal-Wallis test, the excess is most significant when comparing to particles whose diameter lies close to but outside the BWP size ranges (i.e. up to almost +30%).. Re-emission would involve resuspension, detachment and wear processes. The presence, on both the tire tread and surface of the coarsest RoWP, of metal fragments enriched with elements found in brake pads and BWP (i.e. Fe, Ba, Al and Zn) reinforces this hypothesis (Fig. 4). Based on our observations, the existence of composite RoWP, comprising BWP, TWP, pavement wear particles (PWP) and/or other mineral inclusions, was definitely likely; this inquiry would require further research in order to assess environmental stability and toxicity.

Determinants of TRWP emission dynamics on the road

Strong and meaningful correlations were derived between the number concentrations measured at the rear and in the backspace of the instrumented wheel (0.40 < r² < 0.86, p < 0.01). A good level of agreement was also found between size distribution in the intervals typically associated with resuspension particles, i.e. in the nanometric and coarse size ranges.

For these reasons and under specified conditions, the particles aspirated in the backspace of the wheel would be representative of the resuspension particles in RoWP emissions. In other words, concentrations recorded at the rear and in the backspace of the instrumented wheel can be associated with the presence and absence of TRWP, respectively. The proportion of TRWP can thus be estimated by subtracting the normalized concentration measured in the backspace of the wheel from that measured at the rear of the tire (Appendix 5). This calculation assumes that residual particles present on the tire tracks of the pavement were refractory and therefore relatively unaffected by the crossing of the instrumented wheel (see below). It was moreover postulated that the influence of the particle detachment process was limited, except in acceleration situations or when driving on heavily dust-contaminated roads. A similar postulate was formulated for BWP contamination during heavy braking situations (see Section 2.3).

The TRWP size distribution was mostly bimodal (Fig. 5); emissions mainly fell in the size ranges of < 0.03 µm and 0.05-0.30 µm by number and 0.1-0.6 µm and 1.0-15 µm by mass.

When visible, the mass-weighted sub-micron fraction of TRWP was much less prominent than that of RoWP. In the size ranges of the maxima, these two modes made up 5-60% (40% on average) and < 5-50% (25% on average) of RoWP emissions, respectively. The PM10 TRWP were close to 20% of the PM10 RoWP, resulting in individual PM10 PWP and PM10 TWP emissions of < 0.1-150 (average of 2.5) mg/km/vehicle. This calculation assumes that the TWP/PWP ratio is close to unity, i.e. a PM10 PWP of 3-8 mg/km/vehicle compared to 2.4-9.0 mg/km/vehicle for TWP (Denier van der [START_REF] Denier Van Der Gon | Emissies door wegdekslijtage ten gevolge van het wegverkeer[END_REF]EMEP, 2019). A similar approach has indicated that PM2.5 TRWP would average 18% of the measured PM2.5 RoWP concentrations, thus leading to individual PM2.5 PWP and PM2.5 TWP emissions of < 0.1-75 (average of 1.2) mg/km/vehicle. Our data lie within the lower limit of the emission factor values available in the literature, which reflects the inability of on-board measurement devices to monitor particles larger than a few tens of micrometers whose contribution by mass is probably quite significant. These data however partly confirmed the results obtained using road simulators, which highlighted that the size distribution of TRWP was most often dominated by nanoparticles (0.02-0.09 µm) [START_REF] Panko | Physio-chemical analysis of airborne tire wear particles[END_REF][START_REF] Jrc | Non-exhaust traffic related emissions_Brake and tyre wear PM[END_REF][START_REF] Grigoratos | Experimental investigation of tread wear and particle emission from tyres with different treadwear marking[END_REF]. Our measurements further show that the nanoparticles dominated in number for 45-50% of the driving time. Their contribution to TRWP emissions increased with time, while the ratio of < 0.03 µm to 0.05-0.30 µm size categories decreased with tire temperature (0.14 < r² < 0.25, p < 0.01). This finding suggests that the categories TRWP<0.03µm and TRWP0.05-0.30µm would originate from distinct sources or mechanisms, e.g. the repeated wear of contaminants that continuously accumulate on the tire tread or the primary emission of abraded particles at the tire-road interface.

To refine these results, the influence of driving conditions on TRWP emissions was examined for road sections where |dv/dt| was less than 0.2 m/s 2 and speed remained in the 30-90 km/h interval. Assuming that the TWP/PWP ratio lies close to unity, then EELPI(TWP) by mass and per kilometer would barely increase with speed or exceed the mass loss specified by the tire manufacturer: 20 mg/km/vehicle. In fact, the EELPI(TWP) averages ranged from 2 to 3 mg per kilometer per vehicle, yet individual values varied widely within a range of 0.08 to 20 mg/km/vehicle. This limited increase nevertheless indicates a moderate intensification of tire and/or pavement wear mechanisms with speed, thus making high-speed road sections preferred sites for atmospheric air mass contamination with TRWP. In contrast with the emissions per mass and per kilometer, EELPI(TRWP) values per second increased significantly with driving speed (Fig. 2). The values calculated for the instrumented wheel were fitted by the following relationships: EELPI(TRWP) (particles/s) = 9.1 10 5 v 1.26 ; r 2 = 0.84, p < 0.01 (Equation 4a) EELPI(TRWP) (mg/s) = 2.1 10 -4 v 1.18 ; r 2 = 0.72, p < 0.01 (Equation 4b)

It logically follows that under specified conditions, the proportion of TRWP in numberweighted RoWP emissions is either relatively constant or gradually increases with speed.

These proportions were calculated at 26%-27% by number and 57%, 61%, 64% and 66% by mass at 30 km/h, 50 km/h, 70 km/h and 90 km/h, in this order. The correlation coefficient indicates that driving speed could be the main factor responsible for the recorded variations in EELPI(TRWP) values. At high speeds, the smaller contribution of particles resulting from resuspension can be explained by both the intensification of wear mechanisms (most likely of the tire tread) and a lower degree of pavement dust contamination. While the former process would seem to prevail [START_REF] Wong | Theory of ground vehicles[END_REF][START_REF] Manas | Wear of tyre treads[END_REF], the effect of the latter cannot be minimized [START_REF] Thorpe | Estimation of particle resuspension source strength on a major London Road[END_REF]. Compared to the hard shoulder of the motorway, the gravel making up the tire tracks actually revealed fewer particle deposits and smaller particle sizes (i.e. Fig. S4; [START_REF] Changarnier | Observations of dry particles behaviour at the tyre/road interface[END_REF]. These less numerous particle deposits were often optically opaque, which suggests a greater content of high atomic number elements. The coarser particles initially present on the gravel would have been caught at high speed on the vehicle's tire tread, mixed with adjacent materials or else broken into smaller pieces. The fragments would have been carried away by the induced heterogeneous turbulence, thus enhancing the apparent contribution of wear particles. The importance of this observation lies in the fact that the remaining particulate contaminants can act as a third body (i.e. wear particles whose mechanical behavior and chemical composition contrast significantly with those of the materials initially brought into contact) altering the TRWP production mechanism. In contrast to tire sidewalls or grooves, a multitude of metal fragments and mineral inclusions along the tread were found as sources of local punctures or tears (Fig. 4), which implies that reducing TRWP emissions requires measures adapted to the driving speed as well as to the degree and composition of road contamination. Such measures may include more frequent sweeping or vacuuming of city streets and suburban roads, plus the implementation of wear-resistant materials for tires and high-speed road pavements. In addition, above 70 km/h, a limited decrease in both maximum driving speed and the magnitude of speed variations can significantly lower RoWP and TRWP emission rates.

Among other factors capable of affecting TRWP emission rates, the influence of tire temperature and road slope was specifically evaluated. As was the case for RoWP, EELPI(TRWP) per second increased with tire temperature and drive shaft torque at speeds below 5 km/h (0.12 < r² < 0.14, p < 0.05, Fig. S3) and, more episodically, at the start of the highest accelerations (Fig. 3). This finding suggests that the detachment of particulate contaminants from the pavement and tire tread (see the previous paragraph and Section 3.1) could also affect TRWP emission through generation of a third body promoting pavement abrasion and/or point tears in the tire tread. With regard to the influence of road slope, all measurements were carried out at speeds between 55 km/h and 65 km/h. Data show that TRWP emissions exhibit higher percentages of particles smaller than 0.03 µm when climbing or descending 5-8% slopes. These increases (between 60% and 90% by number) were recorded to the detriment of 0.05-0.30 µm particles (between -15% and -25%), thus indicating that high-speed driving on steep roads can alter the TRWP size distribution in a way that favors the emission of nanometric particles. Moreover, the percentage of micrometric TRWP above 3 µm also increased when climbing or braking during descents.

Recorded changes in the size distribution suggest: i) development of the abrasion mechanisms more toward the depth of the tire tread and/or road surface; ii) establishment of particle agglomeration mechanisms (see Section 3.1) or ii) contamination by coarse particles (including BWP) from the vehicle or surrounding road traffic.

Spatial distribution of emissions in relation to the areas being crossed

Previous studies have pointed out that urban driving is often associated with higher wear per kilometer. Accordingly, more tire tread and asphalt are being lost during acceleration, braking and cornering, and the amount of material lost would thus tend to be greater near heavilytrafficked junctions and on road bends. The ELPI data locally contradicted this assertion because both number-and mass-weighted RoWP and TRWP emissions could be significantly lower in urban areas than in the remainder of the crossed areas (p < 0.05, Fig. 5), except for remote suburban roads (i.e. close to the metropolitan fringe). This finding primarily reflects the lower driving speeds and numerous idling periods associated with urban driving. The limited emissions for remote suburban roads can be explained by a combination of moderate speeds (around 50 km/h) and frequent sweeping or vacuuming of the pavement. This reasoning equally applies to more centrally located suburban roads. However, due to the increase in road traffic, both exhaust and locally re-suspended particles were more likely to be superimposed on RoWP and TRWP vehicle emissions. In fact, compared to data from remote suburban locations, RoWP and TRWP emissions in more central suburban areas exhibit higher proportions of particles smaller than 0.03 µm or larger than 1.0 µm.

As could be expected, the highest emissions were recorded on the motorway or country roads.

Their ELPI-averaged emissions exceeded the values for city streets, suburban and ring roads by factors of 3-12 and 2-21, 2-18 and 6-10, and 2-4 and 2-5 by number and mass for RoWP and 4-20 and 2-24, 3-14 and 2-8, and 3-6 and 2-6 by number and mass for TRWP, respectively. The overall disparity increases not only with the difference in driving speed but also with the degree of apparent road surface dust contamination. Country roads and motorways were more prone to increased particulate deposition from adjacent industrial facilities or agricultural activities. The influence of pavement dust contamination was evaluated by comparing the emissions from uncontaminated and contaminated lanes of the same motorway section located near a stone quarry exit (items numbered 1 and 3 in the upper panels of Fig. 5). This evaluation was carried out after correcting for the speed difference, i.e. less than 10 km/h. On the one hand, at 80-90 km/h, EELPI(RoWP) and EELPI(TRWP) values increased by factors of 0.5-2.4 (average: 1.2) and 0.7-2.6 (average: 1.7), respectively. Yet on the other hand, OPC data show a sharp increase in coarse RoWP and TRWP emissions, i.e. by factors of 2.4-5.6 (average: 3.9) and 1.2-4.5 (average: 2.6), respectively. Increases were most pronounced for particles larger than one micron, particularly in the 1-5 µm size range: multiplicative factors of 1.9-7.2 (average: 4.9). The data overall suggest that the mobilizable fraction of particulate contaminants less than one micrometer in diameter was likely small.

Put otherwise, although micrometric dust particles were readily driven off, the persistence of a third body (probably made up of refractory, i.e. hardly mobilizable, submicron particles) considerably increased the wear of both pavement and tire tread. The presence of refractory contaminants in the form of submicron particles was corroborated by microscopic observations showing the smaller diameter of dust aggregates deposited on the gravel constituting the tire track surface (see Section 3.2). It is worth noting that regular pavement cleaning would have the benefit of limiting emissions of both RoWP and TRWP. However, since a notable share of the submicron particles deposited on the pavement was due to wear processes, it is likely that TRWP emissions will progressively intensify after cleaning until a new equilibrium state has been reached.

Extending this line of investigation further, the magnitude of RoWP and TRWP emissions was probed for various structural elements and equipment affecting road traffic (bar charts in Fig. 5). The data show that total emissions were highest for high-grade bridges, followed by heavily-trafficked junctions or large roundabouts (items numbered 1, 2 and 4 in the bar charts, lower panels of Fig. 5). Depending on the data weighting, the difference in relative emissions for the last two structures and the entire route was not always significant at the 0.05 confidence level. This observation suggests that the dynamics of RoWP and TRWP emissions would be more sensitive to road slope than to steering angle. However, the role of the steering angle should not be overlooked, as emissions increase significantly when cornering at 25-35 km/h. As regards RoWP, the multiplicative factors for the widest (i.e. steering angle below 40 degrees) and tightest (steering angle exceeding 40 degrees) corners were in the 1.4-3.1

(average: 2.1) and 1.7-3.7 (average: 2.8) ranges, respectively. The corresponding TRWP values were 1.5-2.1 (average: 1.8) and 1. 4-3.5 (average: 2.6). The data have been corrected

for the difference in driving speed with respect to steering angle. The increase was fairly consistent across RoWP size classes, which means that cornering did not substantially modify the shape of their size distribution curves. This reasoning is also partly true for TRWP emissions, except for particles < 0.03 µm, which exhibited a stronger increase of their number-weighted contribution: multiplicative factors for the widest and tightest corners of 2.6±0.7 and 7±2, respectively. Moreover, the lowest emissions were typically found for structures with driving either at significantly reduced speed or stopped, i.e. small roundabouts or traffic lights. The latter case however was depicted by increased contamination in the exhaust particle size range during startup. Once again, the data underscore the central role played by speed in controlling RoWP and TRWP emissions and, consequently, the potential benefits of structural elements that reduce speed without disrupting traffic flow.

Assessment of RoWP emission dynamics on the test track

Results on the influence of speed and its variation on RoWP emissions have been published in another paper [START_REF] Beji | Nonexhaust particle emissions under various driving conditions: Implications for sustainable mobility[END_REF]; they confirmed that RoWP emissions on the test track were mostly in the < 0.1 µm range by number, with an accumulation mode centered at 0.2-0.3 µm and a major coarse mode centered at 2-4 µm by mass. The data show a strong increase in emissions at higher speeds and during the most intense accelerations. Regarding decelerations, RoWP emissions increased with both braking intensity and speed at the start of braking. The strongest accelerations (> 2.50 m/s²) favored the production of particles smaller than 0.1 µm, while higher speeds and heavy high-speed decelerations increased the rate of coarse particles. All these observations were consistent with the data obtained from on-road measurements. However, on-road measurements were unable to give access to the RoWP emissions associated with the most extreme accelerations and decelerations and, therefore, to nano-and micrometric particles associated with more intense road dust fragmentation or pavement and tire wear at higher temperature.

Another advantage of test track measurements is the possibility of assessing the influence of pavement texture (Table 1) on the RoWP emission rate (Fig. 6). At a speed of 50 km/h, the relative ELPI RoWP concentration (i.e. divided by the average concentration measured on AC2 pavement and after background subtraction) decreased linearly with increasing microtexture (Eq. 5).

RRoWP = -1.3 RMicTex + 2.1; r 2 = 0.90, p < 0.05 (Equation 5)

where RRoWP denotes the ratio of the average RoWP concentration measured on the pavement to that of the AC2 pavement, and RMicTex (resp. RMacTex) the ratio of the corresponding micro/macrotexture values. Since the microtexture was assessed using a standard skid resistance tester, Equation 5indicates that high microtexture values would improve driving safety while potentially limiting RoWP emissions. The ratio of the highest relative concentration (AC3 data) to the lowest (SD1 data) exceeds 7, thus further suggesting that the decrease in emissions may be large. Alternatively, it implies that traffic-induced pavement polishing might increase RoWP levels. Whereas the ultrafine particulate concentration decreases with increasing microtexture, both accumulation and coarse mode RoWP levels exhibit a bulge-shaped evolution, with maxima observed for AC1 or SMA pavements. These nonlinear trends demonstrate that additional factors, including macrotexture, deposited dust quantities and induced air turbulence, play significant roles in RoWP emissions. Except for the extreme macrotexture values (i.e. for AC1 and/or SD2 pavements), the accumulation and coarse mode RoWP concentrations do decrease with increasing macrotexture. The decreases were meaningful, large and closely approximated by the following equations: RRoWP_ACC = -1.3 RMacTex + 2.5; r 2 = 0.90, p < 0.05 (Equation 6a) RRoWP_CRS = 7.3 exp(-2.0 RMacTex); r 2 = 0.89, p < 0.05 (Equation 6b)

where the RoWP_ACC and RoWP_CRS subscripts stand for the accumulation and coarse mode RoWP, respectively. Hence, pavements with a high level of macrotexture would contribute to both an increase in skid resistance and a decrease in fine and micrometric RoWP levels. This result is consistent with data provided by China and James (2012); nonetheless, it proves to be counterintuitive since deep textures would favor tire wear and collection of emissions, then the partial re-suspension of dust deposited into surface cavities [START_REF] Lundberg | Texture influence on road dust load[END_REF][START_REF] Blomqvist | Road surface dust load is dependent on road surface macro texture[END_REF]. The data obtained therefore need to be supplemented by microscopic and macroscopic measurements on a wider range of pavements. It is however important to stress that road pavement may be optimized to maintain a high standard of driving safety while improving air quality. This optimization can be achieved by using pavements whose formulations and textures have been adapted to local road traffic and surrounding sources of particulate matter.

Lastly, the influence of road pavement aging has been investigated by means of the AC1980 and AC2006 pavements. Macrotexture and microtexture values indicate that the AC1980 pavement has not yet reached the late stage associated with polished aggregate (Table 1).

Instead, it represents an intermediate stage of increased skid resistance. Concentration measurements have indeed shown that this 37-year-old pavement accounts for lower RoWP levels: relative decrease of 5-40% by number. The decrease was less pronounced at low speed for the ultrafine mode, while the decreases in accumulation and coarse mode RoWP concentration were minimal at 70 km/h. This finding highlights that the texture evolution does influences the variation amplitude of fine and micrometric RoWP levels with speed. As a result, the aging effect would be more apparent at speeds above 90 km/h.

Assessment of TRWP emission dynamics on the test track

The shape of the size distribution curves for TRWP emissions on the test track is similar to that of on-road emissions. Nevertheless, in spite of the fact that all vehicle parts, weather conditions and instrument settings remain unchanged, the mode mean diameters shift to higher values (see item numbered 5, upper panels of Fig. 5). At speeds from 10 to 90 km/h, the number size distribution after background removal was bimodal, with an ultrafine mode centered around 0.03-0.06 µm and a more minor accumulation mode between 0.15 µm and 0.5 µm. The mass size distribution was also bimodal, with an accumulation mode centered at 1.0 µm and a coarse mode between 4 µm and 15 µm. For purposes of comparison, a unimodal mass size distribution (centered at approx. 4 µm) was observed when using a tire simulator [START_REF] Gibaek | Characteristics of tire wear particles generated by a tire simulator under various driving conditions[END_REF]. The (coefficient;exponent) vectors associated with Equations 4a and 4b were (4.1 10 5 ;1.00) and (9.8 10 -5 ;1.18) for number and mass-weighted TRWP emissions (r² > 0.85, p < 0.05), respectively. Accordingly, the emissions of fine and ultra-fine TRWP on the test track underestimated those determined on the road, especially at high speeds. This result was not surprising since the traffic was limited and the pavement frequently cleaned (by sweeping and vacuuming), hence EELPI(RoWP) estimates were 7-15% by number and 50-60% by mass of the road measurements. These percentages became 30-70% and 50-60% when considering the OPC data. Test track measurements would therefore better reflect TRWP emissions on roads with low submicron particulate contamination.

Like with on-road measurements, concentration peaks were recorded during the first moments of accelerations and at the end of decelerations (Fig. S5). The height of these peaks increases with speed variation in the case of accelerations: by factors of 1.0, 1.2-2.3, 1.9-4.8 and 2.9-7.5 at 0.80 m/s² (set as the reference), 0.85 m/s², 1.05 m/s² and 1.35 m/s², respectively (Fig. 7).

Only minor changes in the average mode size were detected. Compared to the size distributions obtained at constant speeds in the 85-90 km/h range however, higher levels of ultrafine and micrometric TRWP were observed during accelerations, e.g. at 1.35 m/s², multiplicative factors of 6.1-7.8 for ultrafine TRWP and 3.6-7.0 for micrometric TRWP. This finding probably reflected the detachment of particles from the tire and road surface as opposed to any continuous wear processes. As for deceleration, the height of the concentration peaks increases with vehicle speed recorded at the start of braking.

Consequently, mild braking at 90 km/h can generate more than twice as much TRWP as heavy braking at 50 km/h. The key conclusion drawn here is that the emission factors for braking situations might be proportional to the kinetic energy of the vehicle (Fig. 7) and therefore to its stopping distance. This conclusion is also consistent with data available in the literature showing that a reduction in vehicle kinetic energy (i.e. the amount of energy to be dissipated) yields less tire wear during braking situations [START_REF] Janićijević | Tyre wear during braking[END_REF][START_REF] Ma | Evaluating the tire wear quantity and differences based on vehicle and road coupling method[END_REF].

However, the importance of braking force should not be minimized, given that heavier braking at the same speed ([×1.3-×6.3] power range) always leads to a higher total TRWP concentration. The resulting multipliers were in the ranges of: 1.3-3.4 and 1.1-2.7 in number and mass, respectively. With respect to vehicle speed or kinetic energy, the maximum increase in peak height is observed for an increase in braking power by a factor of 3-4. This means that wear mechanisms would be less effective during the initial and terminal stages of heavy braking, i.e. when the variation in braking power most pronounced. Moreover, the size distribution was similar to that of acceleration, i.e. higher percentages by number of nanometric and ultrafine particles than those determined at stabilized speeds. The percentage of particles smaller than 0.1 µm increased on the whole with the height of concentration peaks, probably due to the combined emissions of ultrafine particles and readily condensing volatile organic compounds. An added contribution of coarse particles (centered at 6 µm) was also observed during the heaviest decelerations, possibly reflecting the plume of particles mobilized in the wake of the tire or vehicle body.

The influence of road pavement aging on TRWP emissions has been examined by comparing data for AC1980 and AC2006 pavements. At constant speeds of 50 km/h or 90 km/h, no significant differences were identified between the emission factors or size distributions for both pavements, suggesting that under these specified conditions, the TRWP emission mechanism was less sensitive to changes in texture properties than that of RoWP. The significance of pavement type was nevertheless considerable. For instance, the numberweighted emission of ultrafine TRWP on a SMA pavement was 50-70% lower than that of AC pavements, which may be reflective of the original purpose during the mid-1960's of designing SMA pavements as a wearing course with particularly high resistance to studded tires [START_REF] Drüschner | Stone mastic asphalt. Asphalt guide[END_REF]. Because of the limited amount of data, it is unclear however whether this lower emission resulted from the mixture composition, pavement texture or a combination of both.

Conclusion

For a given combination of pavement, tire and vehicle type, RoWP and TRWP emissions were shown to be highly dependent on driving speed. This result, although intuitive, paves the way for developing calculation tools to estimate the emission of these two particle categories during a vehicle trip. However, an accurate assessment (i.e. on the order of 10%) is not easily achievable due to the variety of factors altering the relationship with speed, including pavement and tire composition and texture, road surface contamination with mineral dust and wear particles, road traffic characteristics, and driver behavior. In addition, low-cost dust monitors could be mounted in vehicles to detect high emission situations and alert the driver to reduce driving speed.

A key result was the predominant share of nanoparticles and ultrafine particles in numberweighted RoWP emissions. A significant share of the ultrafine RoWP emissions stemmed from abrasion processes, which participated in the constitution of a third body that amplified TRWP emissions. It would therefore seem that TRWP emission dynamics partly reflect the state of equilibrium between the accumulation of this third body and its discharge. This distinctly dynamic process would involve the agglomeration and successive recycling of particulate deposits, present on tires and the road surface, applied to a variety of particles, including metal particles released during braking, and involving several mechanisms, e.g. the detachment of particles trapped on the tire during acceleration. This result prefigures the emerging problem of composite particles, whose components stem from braking, tire or road wear processes; it also prompts us to further examine their toxicity and determine whether they can collect and convey exhaust emissions.

The actual location and extent of health and environmental problems caused by RoWP and TRWP emissions remain largely unknown. This statement is especially true given that our results challenge the commonly accepted position of the most highly-emitting situations.

Urban cores would not be the most conducive to these emissions, but instead suburban areas where road surface particle contamination, traffic speed and its variations were all more pronounced. Nevertheless, let's point out that a decrease in speed below 70 km/h, smoother traffic flow and frequent pavement cleaning would contribute to a significant reduction in both types of emissions. Optimized tires and/or adapted pavements, milder slopes, structural elements and road equipment affecting traffic can supplement this reduction as well.

In closing, despite the obvious contrast in emission rates of both RoWP and TRWP, test track measurements do appear to be a valuable source of information. The limitations encountered were due to differences in traffic flow and pavement cleaning frequency. However, in the future, these limitations will also make it possible to study in detail the role of road surface contaminants and the third body in TRWP generation and dust re-emission. Those will equally yield access to a controlled evaluation of how pavement texture and aging affect the wear mechanisms developing at the tire-road interface. The data obtained so far indicates that higher micro and macrotexture values would result in lower RoWP emissions while enhancing driving safety. This result would not readily apply to TRWP emissions since the nature of the mix materials used in the pavement would also play a specific role. Further research is needed in order to objectify this hypothesis and, as a long-term objective, determine the potential link between noise and particulate emissions.

Figure 1 :

 1 Figure 1: Illustrations (upper and lower panels) and schematic view (middle panel) of the instrumental set-up implemented to monitor particulate emissions from the tire-road interface.The orientation and velocity of streamlines were calculated at stabilized driving speeds using Computational Fluid Dynamics (CFD) modeling with the Ansys Fluent application. The red coloring represents the highest velocities.

Figure 2 :

 2 Figure 2: Evolution of RoWP and TRWP emissions, in number (upper panels) and mass (lower panels), measured on the road vs. driving speed. The empty and filled circles stand for emissions per unit of distance and time, respectively. The square symbols refer to situations where TRWP contamination by exhaust and brake particles was limited. While the fitting curves account for the entire dataset, the square symbol data were used to formulate Equations 4a and b.

Figure 3 :

 3 Figure 3: Changes in two key vehicle variables (driving speed and torque on the instrumented wheel) and particulate emissions per kilometer as a function of the area traversed.

Figure 4 :

 4 Figure 4: Photomicrographs and qualitative elemental compositions of brake wear particles (BWP), RoWP and various surface areas of tires used on the road and in test track experiments. BWP were collected on filters or impaction plates during braking. The sample intake was set in the backspace of the wheel (mobile measurements) or lateral to the wheel (measurements carried out using a chassis dynamometer). The proposed qualitative compositions provide an overview of several dozen observations.

Figure 5 :

 5 Figure 5: Standardized size distributions and relative contributions (in the form of bar charts +1 S.E.) of RoWP and TRWP for various types of roads (upper panels) and driving situations (lower panels). The proposed size distributions involve ELPI and OPC data. The data

Figure 6 :

 6 Figure 6: Upper panel, relative contributions (+1 S.E.) of ultrafine, accumulation and coarse RoWP modes for various types of pavements of specified macro-and microtextures. They have been obtained by dividing the corresponding number concentrations by those of the conventional AC2 pavement. Lower panels, contributions of ultrafine, accumulation and coarse modes to total RoWP and TRWP concentrations (in number and mass) for one SMA and two AC (aged 11 and 37 years) pavements.

Figure 7 :

 7 Figure 7: Standardized TRWP concentrations plotted as a function of speed variation (panel A: acceleration / panels B and C: deceleration) or kinetic energy recorded at the start of braking (panel D) (error bars: ±1 S.E.). A distinction was drawn between mild and heavy decelerations as well as between data from different analytical instruments. Standardization was performed by dividing with the values obtained during the lowest accelerations or decelerations.

Table captions Table 1 :

 captions1 Characteristics of the various types of pavements used in the test track experiments.

Acknowledgments 25 This study was funded by ADEME (French Environment and Energy Management Agency) through the "Physical Characterization of the Non-Exhaust Particle Emissions by Road Vehicles" project (or Captatus) (grant number: 1566C0016). In particular, the authors gratefully acknowledge L. Gagnepain with ADEME for fruitful scientific exchanges and would like to thank L. Suard, S. Louis, A. Guilloux and S. Buisson for their assistance in setting up the test track experiments and ensuring vehicle availability. Authors finally acknowledge R. Sachs who edited the transcript and the constructive comments by three anonymous reviewers.

The particles of interest


Sarah Perez

Francisco J Valdés-Parada

Didier Lasseux

Philippe Poncet

Second

Second order deviation of permeability due to unresolved morphological features at the pore scale

InterPore2022

Abstract ID : 27 Second order deviation of permeability due to unresolved morphological features at the pore scale Contenu Pore-scale dynamics of one phase flow commonly involves adherence (no-slip) boundary conditions at the fluid/solid interface. However, improved modeling such as flows at moderate Knudsen numbers (i.e. for values below 0.1), or homogenization of rock matrix roughness, may require slip conditions [START_REF] Lasseux | A macroscopic model for slightly compressible gas slip flow in homogeneous porous media[END_REF][START_REF] Lasseux | An improved macroscale model for gas slip flow in porous media[END_REF]. It turns out that a lack of knowledge on the rock matrix wall, built by X-Ray micro tomography, leads to the same type of slip conditions

v = β 2 (I -nn) • (∇v + ∇v T ) • n
where v is the pore-scale velocity, n is the unit normal vector oriented towards the fluid and β is twice the slip-length.

The macroscopic model corresponding to steady one-phase flow in the creeping regime (Stokes equations) at the pore-scale with the slip condition was derived in [START_REF] Lasseux | A macroscopic model for slightly compressible gas slip flow in homogeneous porous media[END_REF][START_REF] Lasseux | An improved macroscale model for gas slip flow in porous media[END_REF]. The macroscopic momentum equation corresponds to Darcy's law in which the permeability tensor is slip-dependent. This leads to a computation of an apparent permeability that depends on β and that we denote K β . This apparent permeability can be expanded in a power series of a Knudsen number, the zeroth-order term identifying to the intrinsic permeability with no slip and the higher order terms to slip-correction tensors, the first one generalizing, for an ideal gas, the classical Klinkenberg correction. All the tensors are given by the solution of coupled closure problems at the successive orders [START_REF] Lasseux | An improved macroscale model for gas slip flow in porous media[END_REF]. While this expansion has been addressed in [START_REF] Lasseux | A macroscopic model for slightly compressible gas slip flow in homogeneous porous media[END_REF] and [START_REF] Lasseux | An improved macroscale model for gas slip flow in porous media[END_REF] and used in the case of synthetic geometries, it has been used in large three-dimensional real geometries in [START_REF] Perez | On the Deviation of Computed Permeability Induced by Unresolved Morphological Features of the Pore Space[END_REF] in the context of isotropic permeability, that is to say K β = κ β I where κ β is a scalar. In this last case, the expansion

κ β = κ0 + βλ1 + β 2 2 λ2 + O(β 3 )
is shown to describe the uncertainty on the permeability values that results from the gray scale uncertainty generated by micro-tomography. The coefficients λ k , capturing this uncertainty estimation, are given by the solutions of the Stokes-like closure problems reported in [START_REF] Lasseux | An improved macroscale model for gas slip flow in porous media[END_REF] by formal expansion (from averaged equations) and re-established in [START_REF] Perez | On the Deviation of Computed Permeability Induced by Unresolved Morphological Features of the Pore Space[END_REF] by asymptotic analysis (two-scale homogenization). They satisfy a non-homogeneous Dirichlet boundary condition (prescribed velocity) at the pore walls, whose value involves the immediate lower order slip momentum.

While the first order of this expansion has been detailed in [START_REF] Perez | On the Deviation of Computed Permeability Induced by Unresolved Morphological Features of the Pore Space[END_REF], its second order estimation is provided for the first time in this presentation for operational 3D geometries: we apply our results to the high resolution rock sample studied in [START_REF] Hume | A velocity-vorticity method for highly viscous 3D flows with application to digital rock physics[END_REF] and show that the second order brings a significant improvement of the uncertainty estimation on the absolute permeability. These results in a real geometry confirm what was anticipated in [START_REF] Lasseux | An improved macroscale model for gas slip flow in porous media[END_REF] for simplified 2D geometries and are relevant as they show the importance of slip at the macroscale.

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In person

Country

France

Student Poster Award

No, do not submit my presenation for the student posters award.

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Time Block B (14:00-17:00 CET)

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