# Investigations on vehicle non-exhaust particle emissions: real-time measurements

Ines Belkacem, Ali Helali, Salah Khardi, Khalifa Slimi

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Ines Belkacem, Ali Helali, Salah Khardi, Khalifa Slimi. Investigations on vehicle non-exhaust particle emissions: real-time measurements. International Journal of Environmental Science and Technology, 2022, 19 (12), pp.11749-11762. 10.1007/s13762-022-03955-w. hal-03659795

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# Investigations on vehicle non-exhaust particle emissions: real-time measurements

I. Belkacem<fnref n="1" /> · A. Helali<fnref n="2" />,<fnref n="3" /> · S. Khardi<fnref n="4" /> · K. Slimi<fnref n="1" />

## Abstract

Despite the potentially harmful effects on health and environmental impacts correlated with non-exhaust emissions (e.g., brake, tire and road wear and resuspension), but very limited environmental research studies have measured the contribution of vehicle non-exhaust emissions in real-world measurements and at different road segment types. Therefore, to fill this gap in academic literature, the main purpose of this study is to collect the knowledge base required to orient towards more sustainable mobility. During July 2019, continuous real-world measurements of non-exhaust particles (NEPs) were recorded over three different road segment types (urban, rural and motorway) in the city of Bron, near Lyon (France). Data were obtained using a native algorithm based on the GRIMM analyzer, series 1.108 Portable Aerosol Spectrometer. Results have confirmed abundant fine/coarse non-exhaust particle emissions which increase with the average vehicle speed and during sharp acceleration/deceleration maneuvers. Motorway area has recorded the highest particle number concentrations as compared to urban and rural areas, which reaches $1.85 \times 10^7$ cm⁻<fnref n="3" /> due to increased vehicle speed, pavement materials and abrupt vehicle acceleration and deceleration maneuvers. In addition, the highest PM ratio was registered on motorway area and the lowest PM ratio appeared on urban road due to vehicle speed. It has been concluded that NEPs depended on the changes in driving conditions and road pavement type.

**Keywords** Air quality · Non-exhaust emissions · Real driving conditions · Particle number concentration · Road traffic

## Introduction

Road traffic is the main source in the degradation of the ambient air quality (Belkacem et al. 2020, 2022; Silva et al. 2020). The total non-exhaust PM (PM2.5 and PM10) induced by light-duty vehicles worldwide will increase by

$53.5\%$ along with transport demand in 2030 as compared to 2017 (OECD 2020). Road, tire and brake wear emissions have developed from 39% of UK PM10 road traffic emissions in 2000 to 73% in 2018 and from 26% in 2000 to 67% in 2018 for PM2.5 (COMEAP 2020). Diesel particle filters DPFs are considerably effective tools for assessing diesel particulate emissions. This is accounted for by the fact that vehicle exhaust PM emissions in developed countries have decreased abruptly; on the other side, vehicle non-exhaust emissions now strongly exceed vehicle exhaust emissions (AQEG 2019). Emissions from road traffic can be classified into three different classes: (1) primary and secondary particles induced from vehicle exhaust; (2) primary and secondary particles produced from the tire, brake and road wear; (3) primary particles engendered from resuspension due to vehicle-wheel turbulence (Schauer et al. 2006; Thorpe and Harrison 2008; Mehel and Murzyn 2015). Resuspension and wear parts can be considered as non-exhaust emissions. Atmospheric Particulate Matter PM2.5 or fine PM are considered to cause 412, 000 premature deaths per year, including 33, 200 in France (European Environment Agency

<footnote>I. Belkacem
ines.belkacem2014@gmail.com</footnote>

<footnote n="1">¹ Laboratory of Thermal and Energy Systems Studies (LESTE), National Engineering School, University of Monastir, Monastir, Tunisia</footnote>

<footnote n="2">² Mechanical Laboratory of Sousse, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia</footnote>

<footnote n="3">³ Department of Transport Technology and Engineering, Higher Institute of Transport and Logistics, University of Sousse, Sousse, Tunisia</footnote>

<footnote n="4">⁴ Contact and Structure Mechanics Laboratory LaMCoS, Campus LyonTech La Doua - INSA Lyon.27bis, Avenue Jean Capelle, 69621 Villeurbanne Cedex, France</footnote>
2015). The particles induced from road dust, tire, road sur-
face and brake wear are emitted with a diameter <10µm,
that induces a significant health risk for the human body.
Despite the important burden of non-exhaust particle emis-
sions on public health and the environment, few regulations
target them explicitly (OECD 2020). This is due to the lack
of uniform technique/protocol of measurements. Accord-
ing to the UK National Atmospheric Emissions Inventory
(NAEI), the NEPs are strongly higher than exhaust emissions
for both PM2.5 and PM10. The impact on NEP emissions
remains unclear, as the evolution of the advanced propa-
gation of regenerative braking systems (e.g., the energy
recovery mechanism employed in electric vehicles that
makers resistance braking through the vehicle's engine act-
ing as a generator to transform kinetic energy into electrical
energy) will probably mitigate brake wear emissions. This
is because these vehicles require conventional friction brak-
ing for abrupt deceleration when stationary and at very lim-
ited speeds. Then again, ICE vehicles tend to be lower than
electric vehicles; this is leading to decrease in resuspension
and tire wear emissions (Timmers and Achten 2016; AQEG
2019; Eddows and Harrison 2021). Coarse and fine particles
from non-exhaust sources have been related to short-term
morbidity and mortality (Ostro et al. 2011; Meister et al.
2012). Small particles less than 10µm can easily penetrate
into the bloodstream, lungs and hearts (Environmental Pro-
tection Agency 2017). However, in order to contribute to
a productive and healthy indoor air environment, vehicle
ventilation systems should be equipped with filters that are
able to remove PM1 particles, since the smallest particles are
the most harmful to human health like pulmonary inflam-
mation and asthma exacerbation (Shao et al. 2017; Niu et al.
2020). It has been demonstrated that regularly exposure to
nanoparticles may harmfully affect human health (Feng et al.
2020). A comparative study by Zwozdziak et al. (2016) dem-
onstrated that PM1 has a greater effect on the lung function
parameters compared to PM2.5 for school children aged
between 13 and 14 years.

In order to realize a sustainable environment and mobil-
ity, it is important to quantify not only particle mass con-
centration but also particle number concentration. Given the
pronounced negative impacts of particulate matter on human
public health, it is incumbent on authorities on to mitigate
these emissions.

Disc brakes are the most frequent type of braking system for the recent technological vehicle generation, including an abradable brake pad, which works on the brake disc (Grigoratos and Martini 2015a). Particles are engendered from the frictional wear during the braking action, but the fraction of this PM (less than 10µm) is not yet fully mastered (Grigoratos and Martini 2014a). In fact, the composition of brake friction material is frequently proprietary/confidential and can be influenced by the manufacturer and the

tire of brake system (e.g., ceramic, non-asbestos organic,
semi-metallic, etc.) (Grigoratos and Martini 2014b, 2015b;
Thiyagarajan et al. 2015). There is a diversity of commer-
cial brake friction materials which exceeds 200 ingredients.
Tire and road wear particle emissions are induced when
tires associate with the road surface through abrasion and
friction (Grigoratos et al. 2018a; Singh et al. 2020). These
particles are internally comprising and mix both the embed-
ded road materials, depending on the type of road surface
(e.g., concrete/asphalt) and the tread rubber (Grigoratos and
Martini 2014b). The tire compositions are multiple, and their
proprietary data make it complicated to estimate the tire
tread composition. They contained a mixture of synthetic
and natural rubbers (40–60%), vulcanization agents (2–5%),
fillers (20–35%), textile and metal reinforcements (5–10%),
process), process oils (12–15%) and additives (5–10%)
(Panko et al. 2019; Baensch-Baltruschat et al. 2020). Road
vehicle dust resuspension emissions are engendered by
different mechanisms: turbulent micro-eddies, subsequent
and attachment of material from the tire and road surface
due to high-speed airflows, and movement of the vehicle as
air is expelled from the tire (AQEG 2019; Harrison 2020).
Various sources participate in the composition of road dust
resuspension, which makes quantifying and separating the
different emission sources quite difficult (Harrison 2020;
Furger et al. 2020). Nevertheless, particle size distribution
of resuspension is over 10µm, of which crustal and soil mate-
rial reveal the dominant components in road dust (Kasimov
et al. 2019; Huang et al. 2020).

Even though highly developed vehicle technologies
have been made for mitigating PM emissions from motor
exhausts, no actions were taken to reduce the non-exhaust
proportion of emissions, inducing a substantial increase of
the total emissions. The relative contribution of road and tire
wear particles is anticipated to gradually multiply at short-
medium-long term (Vanherle et al. 2021). Tire wear particles
can also be constituted as evaporative emissions, resulting
in the tire followed by coagulation and condensation (Jekel
2019). The emissions of tire road wear particles (TRWPs)
constitute a major non-exhaust particles source (Kwak et al.
2013; Grigoratos and Martini 2015a), including the road sur-
face wear and tires as well as the resuspension of road dust.
Practically, in road transport, the most primary fine particles
are produced from the exhaust, whereas coarse particles are
mainly induced from non-exhaust sources (Grigoratos and
Martini 2014a). The change in particle emissions is due to
many factors, such as traffic conditions, meteorological,
ambient particulate pollution, variation in tire composition
and texture, driving style and road surface features (Grig-
oratos et al. 2018a; Park et al. 2018).

In order to take complete and accurate information about
particle measurements, both laboratory and real-time meas-
urements can be considered as complementary tools. In fact,
in real-time measurements, all driving and traffic condi-
tions (driver behavior, road dust, road signs, etc.) will be
taken into account. However, in laboratory measurements,
it could be less difficult to distinguish between the four
non-exhaust sources (tire, road, brake wear and resuspen-
sion). For this reason, laboratory measurements have been
enhanced through on-road-real driving emissions (RDE) in
order to surpass the limitation of each technique (Belkacem
et al. 2021). In addition, laboratory studies are not suita-
ble to represent the entire vehicle fleet, as tires and brakes
that can change significantly between the performance and
manufacturers specifications. Meanwhile, there may be dis-
similarities between real-world and simulations conditions,
including emissions driving styles.

A number of studies have been conducted towards char-
acterizing the non-exhaust particles (NEPs) on road (Har-
rison et al. 2012; Kwak et al. 2013) or in the laboratory
measurements (Grigoratos et al. 2018b; Foitzik et al. 2018).
In addition, some studies have focused on evaluating the
effect of acceleration and deceleration maneuvers, without
taking into account the real traffic conditions (real driving
conditions) (e.g., Kwak et al. 2014) or with a predefined test
cycle (e.g., Pelkmans and Debal 2006). However, these latter
mentioned conditions may not reflect real particle number
concentrations.

Nowadays, data related to vehicle non-exhaust emissions are still insufficient. In particular, few research studies accurately measured the contribution of vehicle non-exhaust emissions (brake, tire and road surface wear and resuspension) via real-world measurements taking into consideration the real traffic conditions and driver behavior (e.g., speed, acceleration, etc.) in different road types including urban, rural, national roads and motorway areas. These deficiencies engender the evaluation of climate and health impacts, accurate vehicular emission inventories and the establishment of efficient regulatory measures to reduce air pollution in different road types. In addition, real-world measurements allow to determine several parameters under a wide range of conditions, which is not the case in laboratory measurements. They provide continuous time-series data at the unit time scale.

Fundamentally, this study tends to measure real NEPs
in different road segment areas of Lyon—France; urban,
rural, and motorway, during July 2019. In fact, implement-
ing similar measurements and sampling methodologies are
needed to provide comparative results. The parameters that
were changed from one to another sampling day were; road
type, vehicle speed and acceleration/deceleration maneu-
vers, while keeping the same driver during all measurement
days. Thus, instantaneous particle emissions are applied
in individual level analysis because they provide accurate
spatial and temporal analyses. Finally, this research meas-
ured the effect of real vehicle speed profile and acceleration/

deceleration maneuvers on NEP concentrations in urban,
rural and motorway areas. In this study, results have con-
firmed abundant fine/coarse non-exhaust particle emissions
in all sampling days. Motorway area has recorded the highest
PNCs compared to urban and rural areas, this is due to high
vehicle speed and pavement materials.

**Materials and methods**

Study areas

Measurements took place during July 2019 in the vicinity
of Lyon (France). Lyon is France's second largest conurba-
tion and third largest city; it is located in central-eastern
of France between Paris and Marseille, in the Auvergne-
Rhône-Alpes Region. The National Institute of Statistics and
Economic Studies (INSEE) has estimated the population at
515,695 as of 2016, with location coordinates of 45° 45'
35" North, 4° 50' 32" East. The total mileage traveled for
all sampling day measurements was approximately 172 km,
including roundtrip literary. This large population number
underscores the importance of this research through expos-
ing the risk to the human body to different types of road
traffic air pollution (specifically for urban and rural roads).
Non-exhaust particle concentrations including resuspen-
sion, brake, tire and road surface wear were collected during
the morning or evening, depending on climate conditions
(Table 1). Emission thresholds are identified for compounds
regulated in the year that a vehicle category is introduced
on European roads: pre-1992 (Euro 0), 1992–95 (Euro 1),
1996–99 (Euro 2), 2000–04 (Euro 4), 2005 – 08 (Euro
4), 2009/2011–13 (Euro 5a/b) and 2014-present (Euro 6).
Measurements have mainly been obtained in urban (Bron-
Décines-Meyzieu), rural (Corbas-Saint-Just Chaleyssin) and
motorway (A 46) (Fig. 1). The mileage traveled was not the
same on all sampling days, it depended upon the road type
(urban, rural or motorway).

Figure 1a depicts the first day's itinerary of mobile sam-
pling measurements on an urban road. The operating mode
was adjusted according to the speed, idle acceleration and
time via the global positioning system (GPS) under real-
world monitoring with a 1 – *s* resolution. The itinerary was
extended from Bron to Meyzieu. Figure 1b illustrates the
rural itinerary (Corbas-Saint-Just Chaleyssin). Figure 1c
represents the third day's mobile sampling itinerary along
the A46 motorway. According to the official website of
France's Ministry for the Ecological and Inclusive Transi-
tion (http://www.dir.centre-est.developpement-durable.gouv.
fr/donnees197de-trafic-r87.html), the average traffic volume
traveling on the A46 amounted to 85,760 vehicles per day,
with 19% composed of Heavy Duty Vehicles (HDVs). In
all sampled measurements, the current study has taken into
<table><caption>**Table 1** Related data for all sampling measurement days</caption><thead><tr><th>Measurement day</th><th>Direction</th><th>Road type</th><th>Distance (km)</th><th>Min. temp. (°C)</th><th>Max. temp (°C)</th><th>Mean temp. (°C)</th><th>Av. wind speed (km/h)</th><th>Precipitation (mm)</th><th>Av. vehicle speed (km/h)</th></tr></thead><tbody><tr><td rowspan="2">D1</td><td>Forward</td><td rowspan="2">Urban</td><td>33</td><td rowspan="2">17</td><td rowspan="2">30</td><td rowspan="2">20</td><td rowspan="2">14</td><td rowspan="2">No</td><td rowspan="2">34</td></tr><tr><td>Backward</td><td>32</td></tr><tr><td rowspan="2">D2</td><td>Forward</td><td rowspan="2">Rural</td><td>15</td><td rowspan="2">17</td><td rowspan="2">31</td><td rowspan="2">21</td><td rowspan="2">8</td><td rowspan="2">No</td><td rowspan="2">40.00</td></tr><tr><td>Backward</td><td>16</td></tr><tr><td rowspan="2">D3</td><td>Forward</td><td rowspan="2">Motorway</td><td>35</td><td rowspan="2">18</td><td rowspan="2">32</td><td rowspan="2">24</td><td rowspan="2">3</td><td rowspan="2">No</td><td rowspan="2">89.82</td></tr><tr><td>Backward</td><td>39.6</td></tr></tbody></table>

account roundtrip itinerary. Table 1 lists detailed information on all the mobile measures, including time, road type, mileage traveled and direction. Three days of measurements were processed in order to evaluate non-exhaust particle concentrations for the three tested road types, again in both directions. Trip duration varied according to road type. One sample had been gathered for each direction route (two-samples a day). In fact, climate conditions (ambient temperature, humidity, wind direction and wind speed, etc.) could be changed, which might influence particle number concentration and particle mass concentration.

## Instrumentations

Non-exhaust PM concentrations (PM1, PM2, PM10 and PM2.5/PM10) are measured using a light-scattering method because a real-world technique is always more suitable to assess rapid changes in PM levels. An optical particle counter (OPC, Grimm™, ref. EDM 108) was applied to measure PM concentrations at a flow rate of 1.2 L min⁻¹ and time resolution of 6 s. A series of real-world measurements of particles were then taken with an optical diameter ranging from 0.35 to 22.5 µm size channels). This device had been calibrated by its manufacturer using dolomite dust. The instantaneous vehicle speed profiles have been recorded using the global positioning system (MOUCHARD GPS747 Pro). Before starting sampling measurements, GPS time and OPC time were synchronized.

## Sampling and measurement strategies

Non-exhaust particle emissions (resuspension, tire, brake and road surface wear) were investigated under real-time driving conditions (continuous speed profiles, with different mileages varying between 32 up to around 75 km). The same tires have been used during all sampling measurement days. The registration year of the light utility vehicle used for sampling days was 2001, corresponding to Euro 3 class. It is a diesel-fueled utility vehicle.

The summer tire (dimension 185/75R16C-Michelin.) was chosen to study variation in wear and particle emission between different tires and roads under real-time conditions. Sampling measurements have been taken under dry weather conditions (T = 17–32 °C with 23–67% humidity) and moderate wind speed (<30 km h⁻¹).

The iso-kinetic sampling has been considered during all measurement days. The same driver was kept and maintained for all the experimentation in order to maintain the same driving style and conditions throughout all sampling days. Instantaneous speed profiles and instantaneous acceleration profiles were recorded at 1 Hz frequency regardless of driving conditions. The scientific instrument was installed behind the utility car. The GRIMM was chosen due to its small size and light weight compared to other atmospheric particle instruments (for example, PEMS, FMPS). In fact, heavy instruments may provide inaccurate data due to their weight on vehicle engine efficiency. A GRIMM did not require to power generators due to the built-in battery that lasts between six- to eight-hours continuous operations. Furthermore, the highest TRWPs are generally recorded in accumulation and coarse modes (Kim and Lee 2018; Beji et al. 2020). In addition, sampling measurements were registered under real-time driving characterized by high acceleration and deceleration maneuvers.

The inlet sampling probe was fixed in the axis of the wheel 5 cm from the ground, 10 cm behind the tire and with a diameter of 1 cm. PM could be divided into PM1.0, PM2.5 and PM10 based on the optical diameter smaller than 1 µm, 2.5 µm and 10 µm, respectively. The GRIMM efficiency for radial symmetric sampling is influenced by wind speed. INRS (2012) conducted a comparative study of GRIMM1.108 response against 6 other devices, namely TEOM1400, spectrometer TSI APS 3321, GRIMM1.109, LIGHTHOUSE HH3016IAQ, GLA 5000PVC and GLA 5000PVC. High suitability between GRIMM results and the six tested devices had been obtained.

Measurements were taken on three different road segments in the vicinity of Lyon (France). Thirty minutes were required before starting the trip in order to load all necessary
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Road map of the various study areas</figcaption></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>
tools inside the light utility vehicle. All staff members exer-
cised caution not to exceed five hours of charging, which
would have caused the battery to discharge. Moreover,
before initiating each sampling measurement, zero cali-
brations were regulated on both the GRIMM1.108 aerosol
spectrometers and the GPS. Next, a blank test was run for a
10-min duration using a filter in order to eliminate any stale
lingering particle in the GRIMM pipe. During this process,
one team member ensured that the standby button was acti-
vated before taking measurements.

**Results and discussion**

Particle number distributions under various driving conditions

Studies have reported the generation of ultrafine particles
with diameters of 30–60 nm and particle with diameter
sizer lower than 30 nm for studded and non-studded tires,
respectively (Park et al. 2017; Foitzik et al. 2018), equally
with the emission of a coarse fraction (Kwak et al. 2013;
Kim and Lee 2018). The vehicle of the actual study was
fitted with summer tires. Figure 2 presents the PNCs on
various road segments (urban, rural and motorway). The
particle number was characterized by a high concentra-
tion level with a diameter less than 1 m, which reaches up
to 1.85 × 10⁷ cm⁻³ on motorway. Whenever the particle

diameter exceeded 1.3 m, the PNs decreased rapidly. It is
worth pointing out that this trend stems from large parti-
cles being eliminated faster. This is because non-exhaust
particles are essentially characterized by fine/coarse parti-
cles. We shall point out that during all measurement days,
PNCs were characterized by a bimodal distribution with
0.35 and 1.3 m, respectively. It was found that cold start
emissions (about the first 2 km of the trip) were composed
of fine. In fact, we expect that cold tires can cause differ-
ent brake and road wear than 2 km. The high temperature
in the brake/rotor interface favors high fine particles. As
demonstrated by Niemann et al. (2020), the high tempera-
ture can engender high brake particles. In addition, they
outlined that the number of particles emitted showed a
clear increase with brake temperature. In fact, in the begin-
ning the brakes/tires are generally in a cold state, and then
the temperature gradually increases due to the friction and
the rise of acceleration/deceleration maneuvers.

However, in other available published studies when deal-
ing with measurement of PNCs with an optical diameter
ranging from 6 nm to 22.5 m, it was shown that bimodal
distribution centered at approximately 200 nm and a coarse
peak at 2 m (Beji et al. 2020). This slight difference in modal
size could be explained by several factors including the dif-
ference in sampling methods, traffic conditions and instru-
mental properties. In fact, the current study has taken into
account all real driving and traffic conditions (engendered

<figure><img src="image_5.png" /><figcaption>Fig. 2 corresponding road traffic maps</figcaption></figure>
traffic (Fig. 2), road geometry, driver behavior (acceleration/deceleration maneuvers)).

Figure 3a reveals that on the urban road, the majority of sampled particles have diameters smaller than 0.35 m

and extend to $1.9×10^6$ cm$^{-3}$, followed by a second light peak at 1.3 m that reaches $1.49×10^5$ cm$^{-3}$. The same trends are shown in Fig. 3b. The highest particle concentrations appeared at 0.35 µm with $3.4×10^6$ cm$^{-3}$ in rural area. The

<figure><img src="image_6.png" /><figcaption>**Fig. 3** Non-exhaust particle size distributions under real driving conditions in urban (a), rural (b) and motorway (c) areas</figcaption></figure>
high concentration on rural road as compared to urban road can be explained by the high vehicle speed. Figure 3c illustrates particle size distributions on motorway. The maximum motorway PNCs were higher than both obtained in urban and rural zones, which reached as high as $1.8 \times 10^7$ cm$^{-3}$ due to high speed, pavement materials and abrupt vehicle acceleration and deceleration maneuvers.

Generally, concrete roads (motorway) are more pollutant than asphalt roads (urban-rural), as verified by Rogula-Kozłowska et al. (2014). They demonstrated that concrete surfaces cause more tire abrasion than asphalt surfaces; PM emissions are 1.4–2 times higher for concrete roads than for asphalt roads. However, this result remains relative and depends upon the tire type, drive behavior and climate conditions. In addition, a study conducted by Penkala et al. (2018) demonstrated that asphalt surface causes less tire abrasion than concrete. Moreover, the motorway area is characterized by high speed which favors the change of resuspension emissions, especially in dry road surface conditions, which favor the increase of vehicle induced turbulence (road dust resuspension).

## Effect of average vehicle speed on PM concentrations

In real-time measurements, different instantaneous speed profiles depending on road type (urban, rural and motorway) were taken into account to investigate the effect of vehicle speed on PM mass. Figure 4 shows the average PM10 and PM2.5 concentrations. It is worth noting that the increase of PM concentrations was related to the average driving speed conditions. In fact, the highest PM ratio appeared on the motorway due to high vehicle speed ($V_{m1}^5 = 89.82$ km h$^{-1}$). In contrast, the lowest PM ratio was found on urban road

($V_{m2}^6 = 34.92$ km h$^{-1}$) as validated by several studies (i.e., Grigoratos et al. 2018; Kim and Lee 2018; Beji et al. 2020). The relative contribution of PM2.5 to PM10 (the ratio PM2.5/PM10) varied from 0.31 to 0.435. However, Kim and Lee (2018) reported lower values compared to our obtained results, where they reported 0.24 up to 0.32 as the contribution of PM2.5 to PM10. This is because particulate matter from road traffic is also induced from resuspension in addition to tire, brake and road wear, which is not the case in laboratory measurements. Indeed, particulate matter resuspension could be defined as freshly produced particles from abrasion and stress as well as older located road dust refreshed and transported into the atmosphere.

The results show that PM concentrations were positively correlated with the average vehicle speed. Particles occur through volatilization and by shearing Kreider et al. (2010) and friction forces. In fact, the shearing forces mechanism practically induces coarse particles, whereas the volatile mechanism produces smaller fine particles via evaporation (Kim and Lee 2018).

Under real-time measurements, the shear stress acting on tire tread was wide, mainly on the motorway due to high speed and the state of the tire. Indeed, the used tire in this experience is not put into circulation for the first time. Contrary to what was found by Kim and Lee (2018), limited shear stress was acting on tire tread with low PM10 particles. This can be explained by the difference in experimentation as in this current study, we took into account all real driving conditions including road dust emissions induced by mechanical vibration sieve Wang et al. (2012), which is not the case for laboratory measurements. In this study, non-exhaust PM10 concentration presents 64.58 µm cm$^{-3}$. The pronounced volatilization process related to high speed was produced on the motorway as well as on rural roads

<figure><img src="image_7.png" /><figcaption>**Fig. 4** Average particulate PM10 and PM2.5 concentrations and standard deviations</figcaption></figure>
(ring). In fact, the selected rural road geometry features a
curvature that requires more power from the engine and
higher speed in order to ensure equilibrium in the middle
of the roadway. Moreover, it is characterized by high traffic
density, especially by the heavy vehicle category. Here, we
notice that the increase in non-exhaust PM concentration is
strongly related to road pavement material (concrete/asphalt)
and to the increase of the average vehicle speed vice versa
as validated by several studies (i.e., Grigoratos et al. 2018b;
Kim and Lee 2018; Beji et al. 2020). This might be due to
turbulence air streams, suspension, increasing road wear of
accumulated particles and a high proportion of heavy-duty
vehicles (HDVs). In fact, Wagner et al. (2018) conducted
a review paper where they outlined that HDVs produced
approximately ten times more TWP than passenger cars and
light duty vehicles (LDVs).

Figure 5 shows that the particulate mass concentration
is significantly correlated with the average vehicle speed,
as demonstrated in OECD (2020). The coefficients of
determinations were 0.83 and 0.85 for PM10 and PM2.5,
respectively.

Vehicle speed effect on particle number concentrations

In order to investigate the effect of vehicle speed on the
evolution of particles (in terms of number concentration)
induced from non-exhaust in the range of 0.35–22.5 m,
simultaneous assessment is taken out applying the GPS for
vehicle speed on different road surface types (urban, rural
and motorway). The variation in the total number of non-
exhaust concentrations versus vehicle speed in urban, rural
and motorways is provided in Fig. 6a–c, respectively. The
maximum PNCs were measured on motorway 1.8×10⁷ cm⁻³,
while le minimum PNCs were gathered on urban area

1.9×10⁶ cm⁻³. These findings may be explained by the high speed on motorway resulting in a substantial increase of PNCs as outlined by Béji et al. (2020). Obviously, this result highlights an increase in the coarse particles at high speed. This is because more shear stresses and turbulent air streams developing at the road tire pavement interface induced an increase in particle concentrations. Consequently, a remarkable change took place in the super-micron fraction of the wheel rear ambient aerosol. This current work and other available published studies have confirmed that vehicle speed is an important parameter to be considered to accomplish strategies to mitigate non-exhaust particle emissions from road traffic (i.e., Foitzik et al. 2018).

Positive and negative vehicle acceleration effects
on non-exhaust particle concentrations

In order to evaluate the influence of vehicle speed on
non-exhaust particle concentrations, it is important to
include acceleration and deceleration (A/D) maneuvers
in the analysis. In specific road geometry (curvature with
sharp radius, speed bumps, etc.), traffic conditions (con-
gested traffic, accident, etc.) and driver behavior (aggres-
sive driving) (Luhana et al. 2004), both acceleration and
deceleration maneuvers can engender a pronounced effect
on non-exhaust emissions. Significant non-exhaust parti-
cle concentrations were registered in coarse mode during
A/D cycles (Fig. 7). Figure 7a shows NEP concentrations
versus positive and negative acceleration in an urban area.
It demonstrates several negative acceleration maneuvers
due to speed bumps, pedestrian crossing and traffic light.
Abrupt positive vehicle acceleration [10–38 ms⁻²] induced
abundant NEP concentrations. This significant increase in
particles rate that varies from 1.79×10⁴ to 1.07×10⁵ cm⁻³
may be explained by differing generation mechanisms like

<figure><img src="image_8.png" /><figcaption>Fig. 5 PM concentration versus
average vehicle speed</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6** Vehicle speed versus total number of concentrations recorded in urban, rural and motorway areas</figcaption></figure>

tire wear under elevated temperature or related with a more
abundant road dust fragmentation. Figure 7b illustrates NEP
concentrations versus positive and negative acceleration in
rural area. The geometry of the rural road is generally char-
acterized by a high curvature change radius, thus requiring

more A/D maneuvers. The rural NEP concentrations were 3
times higher when comparing the positive vehicle accelera-
tion passing from (-24) to (+57.13) ms⁻². In contrast, with
stable speed where the positive and negative vehicle accel-
eration were around zero, it showed a significant decrease
<figure><img src="image_10.png" /><figcaption>**Fig. 7** Evolution of non-exhaust particle concentrations versus positive and negative accelerations in urban (a), rural (b) and motorway (c) areas</figcaption></figure>

in NEP concentrations. Rural road is characterized by sharp rate of positive and negative vehicle due to road geometric (curvatures) and road maintenance during measurement days. Figure 7c indicates motorway NEP concentrations versus positive and negative acceleration maneuvers. The NEP concentrations were 11 times higher when comparing the

negative vehicle acceleration passing from (+52) to (-12) ms⁻². The significant increase in NEP concentrations with sharp positive and negative vehicle acceleration maneuvers suggests that aggressive driving produces more abundantly tire road wear particles than with an economical driving style (Boulter 2005; Kwak et al. 2013). Despite that, the
urban area is distinguished by a high rate of vehicle A/D maneuvers, while not admitting the highest particle emissions. This is because in the urban area, generally the speed does not exceed 50 km/h⁻¹, which cannot engender a sharp vehicle A/D maneuvers. Unlike to motorway and rural roads, with a sudden vehicle A/D maneuvers can engender extreme A/D rates.

## Conclusion

Three different road segment types have been controlled in real-time measurements, using the same equipment and sampling protocol, which allow to obtain complementary knowledge of vehicle speed effect on non-exhaust particle emissions (resuspension, tire, brake and road wear). On motorway real-time driving conditions, the NEP concentrations are higher in comparison with particles induced in rural and urban areas due to high speed, pavement materials and abrupt vehicle acceleration and deceleration maneuvers. NEP concentrations were characterized by a bimodal distribution at coarse modes with 0.35 and 1.3 µm ranges, respectively, within the three selected measurement days. Large coarse NEP concentrations were formed from the road dust resuspension, under stress of the tire with the pavement. Information gathered on the key parameters assessing non-exhaust particles in real-time measurements can be employed to issue recommendations considered to mitigate non-exhaust particle emissions and ensure a sustainable mobility. The speed, sharp positive and negative vehicle acceleration maneuvers have a pronounced effect on NEP concentrations. The relative contribution of PM2.5 to PM10 (the ratio PM2.5/PM10) varied from 0.31 to 0.435. The abundant increase in NEPs with abrupt positive and negative vehicle acceleration maneuvers propounds that irrational driving generates more significantly non-exhaust particles than with rational (economic) driving style. Therefore, road users must be made aware of the importance of Eco-Driving (Training and Education). In addition, a green infrastructure is applied, which tends to mitigate pollution with suspended particles by filtration and absorption. Moreover, NEP concentrations can vary with driving style and conditions. In addition, it has been demonstrated that tire road wear can engender substantial particles with respect to mass and/or number concentration. Furthermore, NEPs could be significant contributors to particles in motorway areas. Further researches on real-time measurements should be conducted to definitively define the effects of NEPs on human health, climate change and environment impact. In fact, this study presents some limitations related to the ability to differentiate between the four sources (tire, road, brake wear and resuspension) of non-exhaust emissions.

Measuring non-exhaust PNC and PMC in real-time measurements, taking into account real traffic conditions, makes it possible to distinguish which variable has the greatest impact on non-exhaust particles. Other parameters could be taken into consideration in future studies such as weight and climate conditions, etc. This gives more reflections in research in order to improve the ambient air and human health, ensuring a sustainable environment and mobility.

**Acknowledgements** The authors gratefully acknowledge the University of Monastir and the National Engineering School of Monastir for having sponsored periods of residence in France to conduct this research. Thanks are also extended to the University of Gustave Eiffel (Ex- IFSTTAR) for its technical and logistical supports.

**Authors' contribution** IB was involved in the conceptualization, methodology, data curation, visualization and writing—original draft. AH contributed to the supervision, visualization, investigation and validation. SK helped in the supervision, investigation, writing—review and editing, validation. KS was involved in the supervision, investigation, validation, writing—review and editing.

## Declarations

**Conflict of interest** The authors hereby declare that they have no known competing financial interests or personal relationships that in any way could have influenced the work reported in this paper.

## References

*   Air Quality Expert Group (AQEG) (2019) Non-exhaust emissions from road traffic, report of the air quality expert group to the UK department for environment, food and rural affairs; Scottish government; Welsh government and department of the environment in northern Ireland. https://uk-air.defra.gov.uk/assets/documents/reports/cat09/1907101151_20190709_Non_Exhaust_Emissions_typeset_Final.pdf

*   Baensch-Baltruschat B, Kocher B, Stock F, Reifferscheid G (2020) Tyre and road wear particles (TRWP)—a review of generation, properties, emissions, human health risk, ecotoxicity, and fate in the environment. Sci Total Environ 733:137823

*   Beji A, Deboudt K, Khardi S, Muresan B, Flament P, Fourmentin M, Lumière L (2020) Non-exhaust particle emissions under various driving conditions: implications for sustainable mobility. Transp Res D Transp Environ 81:102290

*   Belkacem I, Khardi S, Helali A, Slimi K, Serindat S (2020) The influence of urban road traffic on nanoparticles: roadside measurements. Atmos Environ 242:117786

*   Belkacem I, Helali A, Khardi S, Chrouda A, Slimi K (2021) Road traffic nanoparticles characteristics: sustainable environment and mobility. Geosci Front. https://doi.org/10.1016/j.gsf.2021.101196

*   Belkacem I, Helali A, Khardi S, Slimi K (2022) Predicting of particle exhaust-emissions from urban road traffic using artificial neural networks (ANNs). Adv Mater Mech Manuf. https://doi.org/10.1007/978-3-030-84958-0_39

*   Boulter PG (2005) A review of emission factors and models for road vehicle non-exhaust particulate matter. TRL report PPR065. TRL Limited, Wokingham

*   Committee on the Medical Effects of Air Pollutants (COMEAP) Statement on the evidence for health effects associated with exposure to non-exhaust particulate matter from road transport;
Committee on the Medical Effects of Air Pollutants: Chilton, UK, 2020. Eionet. Projected Emissions (March 2020 Submission). Updated 2020. http://cdr.eionet.europa.eu/gb/un/clrtap/projected/envxmo40w/overview. Accessed on 12 Oct 2020

Eddows DC, Harrison RM (2021) PM10 and PM2.5 emission factors for non-exhaust particles from road vehicles: dependence upon vehicle mass and implications for battery electric vehicles. Atmos Environ 244:117886

Environmental Protection Agency (2017) Particulate matter (PM) pollution: health and environmental effects of particulate matter (PM) health effects

European Environment Agency (2015) The European environment—state and outlook 2019 [WWW Document]. European Environment Agency

Feng XL, Shao LY, Xi CX, Jones TP, Zhang DZ, BéruBé KA (2020) Particle-induced oxidative damage by indoor size-segregated particulate matter from coal-burning homes in the Xuanwei lung cancer epidemic area, Yunnan Province, China. Chemosphere 256:127058

Foitzik MJ, Unrau HJ, Gauterin F, Dörnhöfer J, Koch T (2018) Investigation of ultrafine particulate matter emission of rubber tires. Wear 394–395:87–95

Furger M, Rai P, Slowik JG, Cao J, Visser S, Baltensperger U, Prévôt AS (2020) Automated alternating sampling of PM10 and PM2.5 with an online XRF spectrometer. Atmos Environ 5:100065

Grigoratos T, Martini G (2014a) Non-exhaust traffic related emissions. brake and tyre wear PM. https://publications.jrc.ec.europa.eu/repository/bitstream/JRC89231/jrc89231-online%20final%20version%202.pdf. Accessed on 5 Nov 2020

Grigoratos T, Martini G (2014b) Non-exhaust traffic related emissions. brake and tyre wear PM. Rep EUR 26648:7–9

Grigoratos T, Martini G (2015a) Brake wear particle emissions: a review. Environ Sci Pollut Res 22:2491–2504

Grigoratos T, Martini G (2015b) Brake wear particle emissions: a review. Environ Sci Pollut Res Int 22:2491–2504

Grigoratos T, Gustafsson M, Eriksson O, Martini G (2018a) Experimental investigation of tread wear and particle emission from tyres with different tread wear marking. Atmos Environ 182:200–212

Grigoratos T, Gustafsson M, Eriksson O, Martini G (2018b) Experimental investigation of tread wear and particle emission from tyres with different tread wear marking. Atmos Environ 182:200–212

Harrison RM (2020) Airborne particulate matter. Philos Trans R Soc A Math Phys Eng Sci 378:20190319

Harrison RM, Jones AM, Gietl J, Yin J, Green DC (2012) Estimation of the contributions of brake dust, tire wear, and resuspension to non-exhaust traffic particles derived from atmospheric measurements. Environ Sci Technol 46:6523–6529

Huang S, Taddei P, Lawrence J, Martins MA, Li J, Koutrakis P (2020) Trace element mass fractions in road dust as a function of distance from road. J Air Waste Manag Assoc 70:34001

INRS (2012) Mesure de concentrations dans les conduits de transport de pollutions particulières. HST ND 2363: 228–312

Jekel M (2019) Scientific Report on Tyre and Road Wear Particles, TRWP, in the aquatic environment. European TRWP Platform

Kasimov NS, Kosheleva NE, Vlasov DV, Nabelkina KS, Ryzhov AV (2019) Physicochemical properties of road dust in Moscow. Geogr Environ Sustain 12(4):96–113

Kim G, Lee S (2018) Characteristics of tire wear particles generated by a tire simulator under various driving conditions. Environ Sci Technol 52(21):12153–12161

Kreider ML, Panko JM, McAtee BL, Sweet LI, Finley BL (2010) Physical and chemical characterization of tire-related particles: comparison of particles generated using different methodologies. Sci Total Environ 408(3):652–659

Kwak J, Kim H, Lee J, Lee S (2013) Characterization of non-exhaust coarse and fine particles from on-road driving and laboratory measurements. Sci Total Environ 458–460:273–282

Kwak J, Lee S, Lee S (2014) On-road and laboratory investigations on non-exhaust ultrafine particles from the interaction between the tire and road pavement under braking conditions. Atmos Environ 97:195–205

Luhana L, Sokhi R, Warner L, Mao H, Boulter P, McCrae I, Wright J, Reeves N, Osborn D (2004) Non-exhaust particulate measurements: results. In: Deliverable 8 of the European Commission DG TREN 5th Framework Particulates Project

Mehel A, Murzyn F (2015) Effect of air velocity on nanoparticles dispersion in the wake of a vehicle model: wind tunnel experiments. Atmos Pollut Res 6:612–617

Meister K, Johansson C, Forsberg B (2012) Estimated short-term effects of coarse particles on daily mortality in Stockholm, Sweden. Environ Health Perspect 120:3431–3436

Niu X, Chuang HC, Wang X, Ho SSH, Li L, Qu L, Chow JC, Watson JG, Sun J, Lee S, Cao J, Ho KF (2020) Cytotoxicity of PM2.5 vehicular emissions in the ShingMun tunnel. Hong Kong Environ Pollut 263:114386

Ostro B, Tobias A, Querol X, Alastuey A, Amato F, Pey J, Perez N, Sunyer J (2011) The effects of particulate matter sources on daily mortality: a case-crossover study of Barcelona, Spain. Environ Health Perspect 119:21781–21787

Panko JM, Hitchcock KM, Fuller GW, Green D (2019) Evaluation of tire wear contribution to PM2.5 in urban environments. Atmosphere 10:99

Park I, Lee J, Lee S (2017) Laboratory study of the generation of nanoparticles from tire tread. Aerosol Sci Technol 51(2):188–197

Park I, Kim H, Lee S (2018) Characteristics of tire wear particles generated in a laboratory simulation of tire/road contact conditions. J Aerosol Sci 124:30–40

Organisation for Economic Co-operation and Development (OECD) (2020) Working party on integrating environmental and economic policies non-exhaust emissions from road transport causes, consequences and policy responses. JT03463487. www.oecd.org

Pelkmans L, Debal P (2006) Comparison of on-road emissions with emissions measured on chassis dynamometer test cycles. Transp Res D Transp Environ 11(4):233–241

Penkała M, Ogrodnik P, Rogula-Kozłowska W (2018) Particulate matter from the road surface abrasion as a problem of non-exhaust emission control. Environments 5(1):9

Rogula-Kozłowska W (2014) Traffic-generated changes in the chemical characteristics of size-segregated urban aerosols. Bull Environ Contam Toxicol 93:493–502

Schauer JJ, Lough GC, Shafer MM, Christensen WF, Arndt MF, DeMinter JT, Park JS (2006) Characterization of metals emitted from motor vehicles. Health Effects Inst 133:1–76

Shao L, Hu Y, Shen R, Schäfer K, Wang J, Wang J, Schnelle- Kreis J, Zimmermann R, BéruBé K, Suppan P (2017) Seasonal variation of particle-induced oxidative potential of airborne particulate matter in Beijing. Sci Total Environ 579:1152–1160

Silva LF, Schneider IL, Artaxo P, Núñez-Blanco Y, Pinto D, Flores ÉM, Gómez-Plata L, Ramirez O, Dotto GL (2020) Particulate matter geochemistry of a highly industrialized region in the Caribbean: basis for future toxicological studies. Geosci Front 13:101115

Singh V, Biswal A, Kesarkar AP, Mor S, Ravindra K (2020) High resolution vehicular PM10 emissions over megacity Delhi: relative contributions of exhaust and non-exhaust sources. Sci Total Environ 699:134273

Thiyagarajan V, Kalaichelvan K, Vijay R, Singaravelu DL (2015) Influence of thermal conductivity and thermal stability on the fade and recovery characteristics of non-asbestos semi-metallic disc brake pad. J Braz Soc Mech Sci Eng 38:1207–1219
Thorpe AJ, Harrison RM (2008) Sources and properties of non-exhaust particulate matter from road traffic: a review. Sci Total Environ 400:270-282

Timmers VRJH, Achten PAJ (2016) Non-exhaust PM emissions from electric vehicles. Atmos Environ 134:10-17

Zwozdziak A, Sówka I, Willak-Janc E, Zwozdziak J, Kwiecińska K, Balińska-Miśkiewicz W (2016) Influence of PM1 and PM2.5 on lung function parameters in healthy schoolchildren—a panel study. Environ Sci Pollut Res Int 23:23892-23901