Tire Wear Monitoring Approach for Hotspot Identification in Road Deposited...

[Full title: Tire Wear Monitoring Approach for Hotspot Identification in Road Deposited Sediments from a Metropolitan City in Germany]

Citation: Venghaus, D.; Neupert, J.W.; Barjenbruch, M. Tire Wear Monitoring Approach for Hotspot Identification in Road Deposited Sediments from a Metropolitan City in Germany Sustainability 2023 , 15 , 12029. https://doi.org/10.3390/ su 151512029 Academic Editor: Anish Kumar Warrier Received: 27 June 2023 Revised: 1 August 2023 Accepted: 2 August 2023 Published: 5 August 2023 Copyright: © 2023 by the authors Licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) sustainability Article Tire Wear Monitoring Approach for Hotspot Identification in Road Deposited Sediments from a Metropolitan City in Germany Daniel Venghaus *, Johannes Wolfgang Neupert and Matthias Barjenbruch Department of Urban Water Management, Technical University of Berlin, Gustav-Meyer-Allee 25, 13355 Berlin, Germany; neupert@tu-berlin.de (J.W.N.) * Correspondence: daniel.venghaus@tu-berlin.de Abstract: Plastic in the environment poses an increasing challenge. Microplastics, which include tire wear, enter the aquatic environment via different pathways, and increasing vehicle traffic leads to increased tire wear. This paper describes an approach for how inner-city tire wear hotspots can systematically be identified by sampling road-deposited sediments (RDS) by sweeping. Within the investigations herein described, six inner-city monitoring sites were sampled. The total masses of solids as well as the amount of styrene-butadiene rubber (SBR) representing Tire and Road Wear Particles (TRWP) were determined. It was shown that the sites differ significantly from each other with regard to SBR parts. The amount of SBR in the curve was on average eight times higher than in the slope, and in the area of the traffic lights, it was on average three times higher than in the slope. The RDS mass results also differ but with a factor of 2 for the curve and of 1.5 for the traffic light. The investigations and the corresponding results in this paper are unique, and the monitoring approach can be used in the future to derive and optimize sustainable measures in order to reduce the discharge of TRWP into the environment by road runoff Keywords: tire wear; microplastics; road sediments; sweeping; road runoff; hot spots; SBR 1. Introduction Plastic and its resulting microplastics, which include tire wear, are an increasing environmental challenge. Tire abrasion particles enter the aquatic environment through various pathways, and increasing vehicle traffic inevitably leads to increased tire abrasion [ 1 ]. Abrasion occurs as a product of friction between the tire and the road surface during normal vehicle use [ 2 ]. Both friction surfaces rub off, ultimately creating an agglomerate of the tire tread material and the road surface. Studies therefore also speak of tire-road wear particles or TRWP (Tire and Road Wear Particles) [ 2 , 3 ]. For the production of the tire, more than 200 raw materials are used [ 4 ]. The tread of the tire consists of rubber (e.g., styrene–butadiene rubber (SBR)) and filler (e.g., soot) as well as other ingredients [ 5 , 6 ]. Additives are used for the manufacturing process and performance [ 7 ]. Leaching can dissolve the substances from the particle, and represents an additional risk for aquatic ecosystems [ 8 – 10 ]. A link has already been established between salmon mortality and road runoff water due to the substance N-(1,3-dimethylbutyl)-N’-phenyl-1,4-benzenediamine (6 PPD) and its quinone, and 6 PPD is used in tires as an antioxidant [ 8 ]. In 2018, 360 million t of plastic were produced worldwide, with 61 million t in Europe [ 11 ]. Parts of this mass can enter the environment as microplastics. In addition, 15 million t of synthetic rubber and 29 million t of rubber were produced [ 12 ], and 5.1 million t of tires were produced in Europe [ 13 ]. In Germany, there are currently 47.7 million registered passenger cars [ 14 ]. The emission rates vary due to different influencing factors, which are discussed in Section 2 . For example, for passenger cars and for lorries, respectively, 132 mg/km and 850 mg/km of emitted tire wear can be assumed in Sustainability 2023 , 15 , 12029. https://doi.org/10.3390/su 151512029 https://www.mdpi.com/journal/sustainability

Sustainability 2023 , 15 , 12029 2 of 14 urban areas under normal conditions [ 15 ]. The total abrasion quantities in Germany can be calculated based on the annual mileage of the Kraftfahrt–Bundesamt (breakdown by vehicle type [ 16 ]) and the specific abrasion quantity per km [ 17 ]. This results in an estimated annual amount of 95,546–98,370 t for Germany, which corresponds to a specific population equivalent (PE) of 1148.9–1183.7 g/(PE*a) [ 18 ]. The released TRWPs are about 4–350 µ m in size (100 µ m in average), with a density range of 1.2–1.8 g/cm 3 [ 2 , 19 ]. For the entire EU, 450,000 t–1,300,000 t of tire abrasion can be assumed [ 20 , 21 ]. For Germany, the quantities generated can be divided into similar proportions between urban areas (29%), rural roads (33%), and highways (38%) [ 17 ]. In relation to the EU, it can be assumed that the larger share is related to both urban areas (40%) and rural roads (40%) rather than to highways (20%) [ 21 ]. The input of tire abrasion into the environment is balanced for Germany in Baensch-Baltruschat et al., and, according to this, 66–76% end up in the soil and 12–20% end up in surface water [ 17 ]. The airborne rate is specified as 5% [ 17 , 22 , 23 ]. Due to different drainage systems for road runoff, the majority of the input of tire wear to surface waters originates from urban areas [ 17 ]. From urban water management, microplastics and tire abrasion can generally enter limnic systems and oceans via three ways: • Input of treated wastewater from wastewater treatment plants • Combined sewer overflows • Discharge of storm water from separate sewer systems In Germany, between 8120 and 16,820 t of tire abrasion are estimated to enter surface waters via separate sewer systems (46%), combined sewer overflows (11%), and via wastewater treatment plants (2%) as shown in Figure 1 [ 17 ]. Sustainability 2023 , 15 , 12029 2 of 15 respectively, 132 mg/km and 850 mg/km of emi tt ed tire wear can be assumed in urban areas under normal conditions [15]. The total abrasion quantities in Germany can be calculated based on the annual mileage of the Kraftfahrt–Bundesamt (breakdown by vehicle type [16]) and the speci fi c abrasion quantity per km [17]. This results in an estimated annual amount of 95,546–98,370 t for Germany, which corresponds to a speci fi c population equivalent (PE) of 1148.9–1183.7 g/(PE*a) [18]. The released TRWPs are about 4–350 µm in size (100 µm in average), with a density range of 1.2–1.8 g/cm 3 [2,19]. For the entire EU, 450,000 t–1,300,000 t of tire abrasion can be assumed [20,21]. For Germany, the quantities generated can be divided into similar proportions between urban areas (29%), rural roads (33%), and highways (38%) [17]. In relation to the EU, it can be assumed that the larger share is related to both urban areas (40%) and rural roads (40%) rather than to highways (20%) [21]. The input of tire abrasion into the environment is balanced for Germany in Baensch-Baltruschat et al., and, according to this, 66– 76% end up in the soil and 12–20% end up in surface water [17]. The airborne rate is speci fi ed as 5% [17,22,23]. Due to di ff erent drainage systems for road runo ff , the majority of the input of tire wear to surface waters originates from urban areas [17]. From urban water management, microplastics and tire abrasion can generally enter limnic systems and oceans via three ways: • Input of treated wastewater from wastewater treatment plants. • Combined sewer over fl ows. • Discharge of storm water from separate sewer systems. In Germany, between 8120 and 16,820 t of tire abrasion are estimated to enter surface waters via separate sewer systems (46%), combined sewer over fl ows (11%), and via wastewater treatment plants (2%) as shown in Figure 1 [17]. Figure 1. Tire wear pathways into surface waters. Calculation for Germany [17]. The estimated amounts of tire abrasion for the di ff erent rates into the water body have not yet been proved completely y the analytical data so far. The fi rst investigations show for combined sewage the highest concentrations of microplastics for polyethylene (PE), but polystyrene (PS), polypropylene (PP), and styrene–butadiene rubber (SBR) could also be identi fi ed [24,25]. In Scheid et al. [24], SBR concentrations averaging 9–89 µg/L were detected in the storm sewer. In Unice et al. [26], extensive model calculations that Figure 1. Tire wear pathways into surface waters. Calculation for Germany [ 17 ]. The estimated amounts of tire abrasion for the different rates into the water body have not yet been proved completely y the analytical data so far. The first investigations show for combined sewage the highest concentrations of microplastics for polyethylene (PE), but polystyrene (PS), polypropylene (PP), and styrene–butadiene rubber (SBR) could also be identified [ 24 , 25 ]. In Scheid et al. [ 24 ], SBR concentrations averaging 9–89 µ g/L were detected in the storm sewer. In Unice et al. [ 26 ], extensive model calculations that consider transport phenomena and river hydrology are used. Extensive sensitivity analyses and probabilistic studies provide information on the reliability of the results. According to these, between 2–5% of the tire abrasion that is generated in the catchment area of the Seine river reaches the estuary [ 26 ]. Such estimates are hard to balance by field tests on this scale.

Sustainability 2023 , 15 , 12029 3 of 14 In Menguistu et al. [ 27 ], SBR concentrations in gullypots sediments were investigated, and a difference between traffic volumes was shown. Up to 150 mg tire wear/g sediment were measured [ 27 ]. In Kwak et al. [ 28 ], the impacts of the driving style and the topology were shown. A significant increase in emitted airborne particle mass concentration was measured, which was caused by higher lateral acceleration rates with higher velocities [ 28 ]. In general, data are lacking, and environmental measurements are needed to make reliable statements [ 29 ]. In order to be able to derive effective and sustainable measures on site and for all relevant stakeholders, road-related investigations to identify possible high emission locations (hotspots) are necessary. Since the input of tire wear into surface waters in urban areas is mainly from the area of the separate sewer system, hotspots should be identified by environmental samples, for which appropriate measures can be derived Therefore, the objective of this study is to describe an approach to monitor tire wear hotspots by analyzing RDS samples, which will be taken over a defined period of time on a relevant part of the road 2. Materials and Methods For the identification of potential hotspots, an understanding of the main influencing factors of tire-wear generation is essential. From the experience of an external panel of experts, the abrasion factors for a passenger car tire have been estimated, as shown in Figure 2 . The analysis shows that the topology and the driving behavior of motorists are the greatest influencing factors with regards to tire abrasion [ 30 ]. Similar results can be found in the TyreWearMapping report. When modelling the physical factors influencing the tire in connection with the German road network, the model calculated about 80% of the tire wear at intersections and curves [ 31 ]. Sustainability 2023 , 15 , 12029 3 of 15 consider transport phenomena and river hydrology are used. Extensive sensitivity analyses and probabilistic studies provide information on the reliability of the results. According to these, between 2–5% of the tire abrasion that is generated in the catchment area of the Seine river reaches the estuary [26]. Such estimates are hard to balance by fi eld tests on this scale. In Menguistu et al. [27], SBR concentrations in gullypots sediments were investigated, and a di ff erence between tra ffi c volumes was shown. Up to 150 mg tire wear/g sediment were measured [27]. In Kwak et al. [28], the impacts of the driving style and the topology were shown. A signi fi cant increase in emi tt ed airborne particle mass concentration was measured, which was caused by higher lateral acceleration rates with higher velocities [28]. In general, data are lacking, and environmental measurements are needed to make reliable statements [29]. In order to be able to derive e ff ective and sustainable measures on site and for all relevant stakeholders, road-related investigations to identify possible high emission locations (hotspots) are necessary. Since the input of tire wear into surface waters in urban areas is mainly from the area of the separate sewer system, hotspots should be identi fi ed by environmental samples, for which appropriate measures can be derived. Therefore, the objective of this study is to describe an approach to monitor tire wear hotspots by analyzing RDS samples, which will be taken over a de fi ned period of time on a relevant part of the road. 2. Materials and Methods For the identi fi cation of potential hotspots, an understanding of the main in fl uencing factors of tire-wear generation is essential. From the experience of an external panel of experts, the abrasion factors for a passenger car tire have been estimated, as shown in Figure 2. The analysis shows that the topology and the driving behavior of motorists are the greatest in fl uencing factors with regards to tire abrasion [30]. Similar results can be found in the TyreWearMapping report. When modelling the physical factors in fl uencing the tire in connection with the German road network, the model calculated about 80% of the tire wear at intersections and curves [31]. Figure 2. In fl uencing factors on tire wear [30]. On the part of the road surface, it can be assumed that the microstructure has an e ff ect on abrasion. The contact surfaces and the corresponding tangential stresses are relevant Figure 2. Influencing factors on tire wear [ 30 ]. On the part of the road surface, it can be assumed that the microstructure has an effect on abrasion. The contact surfaces and the corresponding tangential stresses are relevant here [ 32 ]. The vehicle and tire design take into account the targeted tire load [ 30 ]. In tire wear tests, driving stability, driving comfort, and steering behavior, as well as driving safety, durability, and economy, are all factors that are considered, among others [ 32 ]. The influencing factors of temperature and wet/dry define the ambient temperature and the proportion of the wet road [ 30 ]. Since driving behavior is also often linked to the topology of the road, it is difficult to consider it separately. It has been assumed that acceleration behavior is most likely to occur at intersections with traffic lights. Further increased stress

Sustainability 2023 , 15 , 12029 4 of 14 situations for the friction process between tire and road are expected at curves, roundabouts, and slopes. As a reference to increased stress situations, a straight road section with an expected constant speed has been selected in order to consider the tire-wear emission with a “neutral” driving style. The measuring locations were chosen as close to each other as possible in order to ensure a similar traffic volume. To verify the methodology, further measurements were made at a paved public location in a park where no tire wear from vehicles is expected because access is only possible via stairs. In the context of this work, the results for the systematic 24 h sweeping samples of the measuring points shown below are presented. All of the selected sampling sites are located near the Humboldthain Park in the Wedding district of Berlin The area in which the sites are located is an urban residential area with various services of general interest and parks. The buildings usually have five floors, and there are tree grates along most of the streets. A distinctive feature of the sampling spots, labelled “straight”, “traffic light”, and “curve”, is the rail line that runs along the eastern side below the street, but the impact of this could not be investigated in this piece of research. The streets where the measurement sites are located have a roof profile, and they thus drain to both sides of the street, which is common for the Berlin metropole region. The samples were taken on one side of the road. The roundabout, as shown in Figure 3 , connects four roads, and it has a section where the road is straight rather than curved. The details of the sampling locations are shown in Table 1 . Sustainability 2023 , 15 , 12029 4 of 15 here [32]. The vehicle and tire design take into account the targeted tire load [30]. In tire wear tests, driving stability, driving comfort, and steering behavior, as well as driving safety, durability, and economy, are all factors that are considered, among others [32]. The in fl uencing factors of temperature and wet/dry de fi ne the ambient temperature and the proportion of the wet road [30]. Since driving behavior is also often linked to the topology of the road, it is di ffi cult to consider it separately. It has been assumed that acceleration behavior is most likely to occur at intersections with tra ffi c lights. Further increased stress situations for the friction process between tire and road are expected at curves, roundabouts, and slopes. As a reference to increased stress situations, a straight road section with an expected constant speed has been selected in order to consider the tire-wear emission with a “neutral” driving style. The measuring locations were chosen as close to each other as possible in order to ensure a similar tra ffi c volume. To verify the methodology, further measurements were made at a paved public location in a park where no tire wear from vehicles is expected because access is only possible via stairs. In the context of this work, the results for the systematic 24 h sweeping samples of the measuring points shown below are presented. All of the selected sampling sites are located near the Humboldthain Park in the Wedding district of Berlin. The area in which the sites are located is an urban residential area with various services of general interest and parks. The buildings usually have fi ve fl oors, and there are tree grates along most of the streets. A distinctive feature of the sampling spots, labelled “straight”, “tra ffi c light”, and “curve”, is the rail line that runs along the eastern side below the street, but the impact of this could not be investigated in this piece of research. The streets where the measurement sites are located have a roof pro fi le, and they thus drain to both sides of the street, which is common for the Berlin metropole region. The samples were taken on one side of the road. The roundabout, as shown in Figure 3, connects four roads, and it has a section where the road is straight rather than curved. The details of the sampling locations are shown in Table 1. Figure 3. Selected sampling and reference (*) locations ( © OpenStreetMap [33]). Figure 3. Selected sampling and reference (*) locations ( © OpenStreetMap [ 33 ]).

Sustainability 2023 , 15 , 12029 5 of 14 Table 1. Sampling locations details Sampling Location Road Width [m] Curb Height [m] Walkway Width [m] Slope in Driving Direction Overlay by Tree Boundary to Tree Slice Sustainability 2023 , 15 , 12029 5 of 15 Table 1. Sampling locations details. Sampling Location Road Width [m] Curb Height [m] Walkway Width [m] Slope in Driving Direction Overlay by Tree Boundary to Tree Slice Straight 10.5 0.12 7.7 0.9% 10% Yes Tra ffi c lights 8.8 0.13 3.9 1.0% 70% Yes Curve (r = 65 m) 11.3 0.04 3.8 0.8% 0% No Slope 11.4 0.03 7.7 3.2% 50% Yes Roundabout 8.4 0.13 6.4 2.2% 0% No 2.1. Road-Deposited Sediment Sampling by Sweeping The road-deposited sediment (RDS) sampling by sweeping was preferred over sampling with a vacuum cleaner for reasons of practicality in the fi eld. For sweep sampling, a hall broom made of horsehair with a width of 80 cm (Figure 4) was used. The RDS was swept steadily from both ends of the sampling area into its center. Based on the experience of a street cleaning company, a corresponding operating technique was derived: the broom has to be operated with short powerful strokes instead of long soft strokes. Thus, it was expected that we would also be able to extract material from the interstices of the rock matrix. The movements of the “sweeping strokes” were carried out according to the principle “three steps forward, two steps back”. To collect the RDS, a hand brush made of horsehair (Figure 4) and a metal dustpan was used and placed in a sample jar. The entire surface was then cleaned again with the hand sweeper, and the dustpan was permanently guided in front of the broom in such a way that material mobilized by the RDS reached the dustpan directly. The material obtained was then transferred from the sweeping plate into a glass sample container. (During the transferring process, care must be taken to ensure that no sample material is lost from the sweeper plate due to wind from passing vehicles.) Figure 4. Hall broom with horsehair ( left ) and hand brush with dustpan ( center ) and examples of the RDS samples (( right ): ( a ) 20–50 µm, ( b ) 50–100 µm and ( c ) 100–500 µm). Recovery rates for the described RDS sweep sampling of 90% for the fraction 20–500 µm could be determined, and they were evaluated at the edge of the pavement with a de fi ned test material for the asphalt layer that was sampled. The analysis of RDS from the roadway o ff ers the possibility of determining the TRWP generated under real conditions over a de fi ned period of time for a de fi ned distance considering the actual number of motor vehicles driven. These values for the fraction 20– Straight 10.5 0.12 7.7 0.9% 10% Yes Sustainability 2023 , 15 , 12029 5 of 15 Table 1. Sampling locations details. Sampling Location Road Width [m] Curb Height [m] Walkway Width [m] Slope in Driving Direction Overlay by Tree Boundary to Tree Slice Straight 10.5 0.12 7.7 0.9% 10% Yes Tra ffi c lights 8.8 0.13 3.9 1.0% 70% Yes Curve (r = 65 m) 11.3 0.04 3.8 0.8% 0% No Slope 11.4 0.03 7.7 3.2% 50% Yes Roundabout 8.4 0.13 6.4 2.2% 0% No 2.1. Road-Deposited Sediment Sampling by Sweeping The road-deposited sediment (RDS) sampling by sweeping was preferred over sampling with a vacuum cleaner for reasons of practicality in the fi eld. For sweep sampling, a hall broom made of horsehair with a width of 80 cm (Figure 4) was used. The RDS was swept steadily from both ends of the sampling area into its center. Based on the experience of a street cleaning company, a corresponding operating technique was derived: the broom has to be operated with short powerful strokes instead of long soft strokes. Thus, it was expected that we would also be able to extract material from the interstices of the rock matrix. The movements of the “sweeping strokes” were carried out according to the principle “three steps forward, two steps back”. To collect the RDS, a hand brush made of horsehair (Figure 4) and a metal dustpan was used and placed in a sample jar. The entire surface was then cleaned again with the hand sweeper, and the dustpan was permanently guided in front of the broom in such a way that material mobilized by the RDS reached the dustpan directly. The material obtained was then transferred from the sweeping plate into a glass sample container. (During the transferring process, care must be taken to ensure that no sample material is lost from the sweeper plate due to wind from passing vehicles.) Figure 4. Hall broom with horsehair ( left ) and hand brush with dustpan ( center ) and examples of the RDS samples (( right ): ( a ) 20–50 µm, ( b ) 50–100 µm and ( c ) 100–500 µm). Recovery rates for the described RDS sweep sampling of 90% for the fraction 20–500 µm could be determined, and they were evaluated at the edge of the pavement with a de fi ned test material for the asphalt layer that was sampled. The analysis of RDS from the roadway o ff ers the possibility of determining the TRWP generated under real conditions over a de fi ned period of time for a de fi ned distance considering the actual number of motor vehicles driven. These values for the fraction 20– Traffic lights 8.8 0.13 3.9 1.0% 70% Yes Sustainability 2023 , 15 , 12029 5 of 15 Table 1. Sampling locations details. Sampling Location Road Width [m] Curb Height [m] Walkway Width [m] Slope in Driving Direction Overlay by Tree Boundary to Tree Slice Straight 10.5 0.12 7.7 0.9% 10% Yes Tra ffi c lights 8.8 0.13 3.9 1.0% 70% Yes Curve (r = 65 m) 11.3 0.04 3.8 0.8% 0% No Slope 11.4 0.03 7.7 3.2% 50% Yes Roundabout 8.4 0.13 6.4 2.2% 0% No 2.1. Road-Deposited Sediment Sampling by Sweeping The road-deposited sediment (RDS) sampling by sweeping was preferred over sampling with a vacuum cleaner for reasons of practicality in the fi eld. For sweep sampling, a hall broom made of horsehair with a width of 80 cm (Figure 4) was used. The RDS was swept steadily from both ends of the sampling area into its center. Based on the experience of a street cleaning company, a corresponding operating technique was derived: the broom has to be operated with short powerful strokes instead of long soft strokes. Thus, it was expected that we would also be able to extract material from the interstices of the rock matrix. The movements of the “sweeping strokes” were carried out according to the principle “three steps forward, two steps back”. To collect the RDS, a hand brush made of horsehair (Figure 4) and a metal dustpan was used and placed in a sample jar. The entire surface was then cleaned again with the hand sweeper, and the dustpan was permanently guided in front of the broom in such a way that material mobilized by the RDS reached the dustpan directly. The material obtained was then transferred from the sweeping plate into a glass sample container. (During the transferring process, care must be taken to ensure that no sample material is lost from the sweeper plate due to wind from passing vehicles.) Figure 4. Hall broom with horsehair ( left ) and hand brush with dustpan ( center ) and examples of the RDS samples (( right ): ( a ) 20–50 µm, ( b ) 50–100 µm and ( c ) 100–500 µm). Recovery rates for the described RDS sweep sampling of 90% for the fraction 20–500 µm could be determined, and they were evaluated at the edge of the pavement with a de fi ned test material for the asphalt layer that was sampled. The analysis of RDS from the roadway o ff ers the possibility of determining the TRWP generated under real conditions over a de fi ned period of time for a de fi ned distance considering the actual number of motor vehicles driven. These values for the fraction 20– Curve (r = 65 m) 11.3 0.04 3.8 0.8% 0% No Sustainability 2023 , 15 , 12029 5 of 15 Table 1. Sampling locations details. Sampling Location Road Width [m] Curb Height [m] Walkway Width [m] Slope in Driving Direction Overlay by Tree Boundary to Tree Slice Straight 10.5 0.12 7.7 0.9% 10% Yes Tra ffi c lights 8.8 0.13 3.9 1.0% 70% Yes Curve (r = 65 m) 11.3 0.04 3.8 0.8% 0% No Slope 11.4 0.03 7.7 3.2% 50% Yes Roundabout 8.4 0.13 6.4 2.2% 0% No 2.1. Road-Deposited Sediment Sampling by Sweeping The road-deposited sediment (RDS) sampling by sweeping was preferred over sampling with a vacuum cleaner for reasons of practicality in the fi eld. For sweep sampling, a hall broom made of horsehair with a width of 80 cm (Figure 4) was used. The RDS was swept steadily from both ends of the sampling area into its center. Based on the experience of a street cleaning company, a corresponding operating technique was derived: the broom has to be operated with short powerful strokes instead of long soft strokes. Thus, it was expected that we would also be able to extract material from the interstices of the rock matrix. The movements of the “sweeping strokes” were carried out according to the principle “three steps forward, two steps back”. To collect the RDS, a hand brush made of horsehair (Figure 4) and a metal dustpan was used and placed in a sample jar. The entire surface was then cleaned again with the hand sweeper, and the dustpan was permanently guided in front of the broom in such a way that material mobilized by the RDS reached the dustpan directly. The material obtained was then transferred from the sweeping plate into a glass sample container. (During the transferring process, care must be taken to ensure that no sample material is lost from the sweeper plate due to wind from passing vehicles.) Figure 4. Hall broom with horsehair ( left ) and hand brush with dustpan ( center ) and examples of the RDS samples (( right ): ( a ) 20–50 µm, ( b ) 50–100 µm and ( c ) 100–500 µm). Recovery rates for the described RDS sweep sampling of 90% for the fraction 20–500 µm could be determined, and they were evaluated at the edge of the pavement with a de fi ned test material for the asphalt layer that was sampled. The analysis of RDS from the roadway o ff ers the possibility of determining the TRWP generated under real conditions over a de fi ned period of time for a de fi ned distance considering the actual number of motor vehicles driven. These values for the fraction 20– Slope 11.4 0.03 7.7 3.2% 50% Yes Sustainability 2023 , 15 , 12029 5 of 15 Table 1. Sampling locations details. Sampling Location Road Width [m] Curb Height [m] Walkway Width [m] Slope in Driving Direction Overlay by Tree Boundary to Tree Slice Straight 10.5 0.12 7.7 0.9% 10% Yes Tra ffi c lights 8.8 0.13 3.9 1.0% 70% Yes Curve (r = 65 m) 11.3 0.04 3.8 0.8% 0% No Slope 11.4 0.03 7.7 3.2% 50% Yes Roundabout 8.4 0.13 6.4 2.2% 0% No 2.1. Road-Deposited Sediment Sampling by Sweeping The road-deposited sediment (RDS) sampling by sweeping was preferred over sampling with a vacuum cleaner for reasons of practicality in the fi eld. For sweep sampling, a hall broom made of horsehair with a width of 80 cm (Figure 4) was used. The RDS was swept steadily from both ends of the sampling area into its center. Based on the experience of a street cleaning company, a corresponding operating technique was derived: the broom has to be operated with short powerful strokes instead of long soft strokes. Thus, it was expected that we would also be able to extract material from the interstices of the rock matrix. The movements of the “sweeping strokes” were carried out according to the principle “three steps forward, two steps back”. To collect the RDS, a hand brush made of horsehair (Figure 4) and a metal dustpan was used and placed in a sample jar. The entire surface was then cleaned again with the hand sweeper, and the dustpan was permanently guided in front of the broom in such a way that material mobilized by the RDS reached the dustpan directly. The material obtained was then transferred from the sweeping plate into a glass sample container. (During the transferring process, care must be taken to ensure that no sample material is lost from the sweeper plate due to wind from passing vehicles.) Figure 4. Hall broom with horsehair ( left ) and hand brush with dustpan ( center ) and examples of the RDS samples (( right ): ( a ) 20–50 µm, ( b ) 50–100 µm and ( c ) 100–500 µm). Recovery rates for the described RDS sweep sampling of 90% for the fraction 20–500 µm could be determined, and they were evaluated at the edge of the pavement with a de fi ned test material for the asphalt layer that was sampled. The analysis of RDS from the roadway o ff ers the possibility of determining the TRWP generated under real conditions over a de fi ned period of time for a de fi ned distance considering the actual number of motor vehicles driven. These values for the fraction 20– Roundabout 8.4 0.13 6.4 2.2% 0% No 2.1. Road-Deposited Sediment Sampling by Sweeping The road-deposited sediment (RDS) sampling by sweeping was preferred over sampling with a vacuum cleaner for reasons of practicality in the field. For sweep sampling, a hall broom made of horsehair with a width of 80 cm (Figure 4 ) was used. The RDS was swept steadily from both ends of the sampling area into its center. Based on the experience of a street cleaning company, a corresponding operating technique was derived: the broom has to be operated with short powerful strokes instead of long soft strokes. Thus, it was expected that we would also be able to extract material from the interstices of the rock matrix. The movements of the “sweeping strokes” were carried out according to the principle “three steps forward, two steps back”. To collect the RDS, a hand brush made of horsehair (Figure 4 ) and a metal dustpan was used and placed in a sample jar. The entire surface was then cleaned again with the hand sweeper, and the dustpan was permanently guided in front of the broom in such a way that material mobilized by the RDS reached the dustpan directly. The material obtained was then transferred from the sweeping plate into a glass sample container. (During the transferring process, care must be taken to ensure that no sample material is lost from the sweeper plate due to wind from passing vehicles.) Sustainability 2023 , 15 , 12029 5 of 15 Table 1. Sampling locations details. Sampling Location Road Width [m] Curb Height [m] Walkway Width [m] Slope in Driving Direction Overlay by Tree Boundary to Tree Slice Straight 10.5 0.12 7.7 0.9% 10% Yes Tra ffi c lights 8.8 0.13 3.9 1.0% 70% Yes Curve (r = 65 m) 11.3 0.04 3.8 0.8% 0% No Slope 11.4 0.03 7.7 3.2% 50% Yes Roundabout 8.4 0.13 6.4 2.2% 0% No 2.1. Road-Deposited Sediment Sampling by Sweeping The road-deposited sediment (RDS) sampling by sweeping was preferred over sampling with a vacuum cleaner for reasons of practicality in the fi eld. For sweep sampling, a hall broom made of horsehair with a width of 80 cm (Figure 4) was used. The RDS was swept steadily from both ends of the sampling area into its center. Based on the experience of a street cleaning company, a corresponding operating technique was derived: the broom has to be operated with short powerful strokes instead of long soft strokes. Thus, it was expected that we would also be able to extract material from the interstices of the rock matrix. The movements of the “sweeping strokes” were carried out according to the principle “three steps forward, two steps back”. To collect the RDS, a hand brush made of horsehair (Figure 4) and a metal dustpan was used and placed in a sample jar. The entire surface was then cleaned again with the hand sweeper, and the dustpan was permanently guided in front of the broom in such a way that material mobilized by the RDS reached the dustpan directly. The material obtained was then transferred from the sweeping plate into a glass sample container. (During the transferring process, care must be taken to ensure that no sample material is lost from the sweeper plate due to wind from passing vehicles.) Figure 4. Hall broom with horsehair ( left ) and hand brush with dustpan ( center ) and examples of the RDS samples (( right ): ( a ) 20–50 µm, ( b ) 50–100 µm and ( c ) 100–500 µm). Recovery rates for the described RDS sweep sampling of 90% for the fraction 20–500 µm could be determined, and they were evaluated at the edge of the pavement with a de fi ned test material for the asphalt layer that was sampled. The analysis of RDS from the roadway o ff ers the possibility of determining the TRWP generated under real conditions over a de fi ned period of time for a de fi ned distance considering the actual number of motor vehicles driven. These values for the fraction 20– Figure 4. Hall broom with horsehair ( left ) and hand brush with dustpan ( center ) and examples of the RDS samples (( right ): (a) 20–50 µ m, (b) 50–100 µ m and (c) 100–500 µ m) Recovery rates for the described RDS sweep sampling of 90% for the fraction 20–500 µ m could be determined, and they were evaluated at the edge of the pavement with a defined test material for the asphalt layer that was sampled The analysis of RDS from the roadway offers the possibility of determining the TRWP generated under real conditions over a defined period of time for a defined distance considering the actual number of motor vehicles driven. These values for the fraction 20– 500 µ m describe the main corresponding input potential for TRWP, which can be washed off the roadway during a rain event.

Sustainability 2023 , 15 , 12029 6 of 14 2.2. Investigation of the Particles Distribution across the Road It has already been shown in previous investigations that the largest proportion of RDS is deposited in the edge area of the road. Pitt et al. [ 34 ] found 90% of the solids in the 0.3 m wide edge area of the road, and by investigating over half of a 5 m wide road cross section, Grottker [ 35 ] found 96% of the solids within 0.5 m to the curb These findings are vital for the study of TRWP on urban road surfaces. It is assumed that TRWPs are part of the RDS, and that they accumulate mainly in the peripheral area of the road. For the RDS distribution as well as for the corresponding SBR accumulation, cross sweeps were carried out over the road cross-section in the study area at the “straight” location. The investigations were carried out in order to verify whether the distribution of TRWP follows the same analogy as the RDS does. The results were important in order to define the relevant tracks of the lane to sample TRWP In order to be able to take samples on the entire lane, the lane must be closed for the time of sample taking. By identifying and confirming the relevant tracks for TRWP, the most practical way of sampling could be defined For investigations, the 4.1 m wide roadway, including the bike lane, was divided into five sections, each 0.8 m wide, at the “straight” location. Figure 5 shows the test setup Samples were obtained during the day on lanes 1 to 5 over a length of 15 m after 24 h each on two consecutive working days in May and then on one working day in July. The length of the sample areas of 15.0 m was chosen in order to ensure that sufficiently large masses of solids were picked up. The surface was cleaned 24 h before by a corresponding basic cleaning according to the same sweeping pattern, so that the samples were exposed to the same external influences Sustainability 2023 , 15 , 12029 6 of 15 500 µm describe the main corresponding input potential for TRWP, which can be washed o ff the roadway during a rain event. 2.2. Investigation of the Particles Distribution across the Road It has already been shown in previous investigations that the largest proportion of RDS is deposited in the edge area of the road. Pi tt et al. [34] found 90% of the solids in the 0.3 m wide edge area of the road, and by investigating over half of a 5 m wide road cross section, Gro tt ker [35] found 96% of the solids within 0.5 m to the curb. These fi ndings are vital for the study of TRWP on urban road surfaces. It is assumed that TRWPs are part of the RDS, and that they accumulate mainly in the peripheral area of the road. For the RDS distribution as well as for the corresponding SBR accumulation, cross sweeps were carried out over the road cross-section in the study area at the “straight” location. The investigations were carried out in order to verify whether the distribution of TRWP follows the same analogy as the RDS does. The results were important in order to de fi ne the relevant tracks of the lane to sample TRWP. In order to be able to take samples on the entire lane, the lane must be closed for the time of sample taking. By identifying and con fi rming the relevant tracks for TRWP, the most practical way of sampling could be de fi ned. For investigations, the 4.1 m wide roadway, including the bike lane, was divided into fi ve sections, each 0.8 m wide, at the “straight” location. Figure 5 shows the test setup. Samples were obtained during the day on lanes 1 to 5 over a length of 15 m after 24 h each on two consecutive working days in May and then on one working day in July. The length of the sample areas of 15.0 m was chosen in order to ensure that su ffi ciently large masses of solids were picked up. The surface was cleaned 24 h before by a corresponding basic cleaning according to the same sweeping pa tt ern, so that the samples were exposed to the same external in fl uences. Figure 5. Subdivision of the sampling location “Straight”. 2.3. 24 h Sampling With the fi ndings of the RDS particle distribution investigations across the road, the dimensions of the detailed studies of the hotspots were adjusted. The sampling time interval should be as short as possible or as long as necessary in order to minimize external in fl uences and, at the same time, to represent the tra ffi c pa tt ern. Accordingly, a time interval of 24 h was chosen. The areas sampled for over 24 h have a length of 6.0 m and a width of 1.6 m parallel to the driving direction. The width of the surface is divided into two areas of 0.8 m each––track 1, which is adjacent to the outer road wheel, and track 2, which is adjacent to the outer road in the direction of the center of the road (Figure 6). The 24 h sampling is preceded by a corresponding basic cleaning according to the same sweeping pa tt ern. This ensures that the sample of the 24 h sampling consists only of Figure 5. Subdivision of the sampling location “Straight” 2.3. 24 h Sampling With the findings of the RDS particle distribution investigations across the road, the dimensions of the detailed studies of the hotspots were adjusted. The sampling time interval should be as short as possible or as long as necessary in order to minimize external influences and, at the same time, to represent the traffic pattern. Accordingly, a time interval of 24 h was chosen. The areas sampled for over 24 h have a length of 6.0 m and a width of 1.6 m parallel to the driving direction. The width of the surface is divided into two areas of 0.8 m each—track 1, which is adjacent to the outer road wheel, and track 2, which is adjacent to the outer road in the direction of the center of the road (Figure 6 ). The 24 h sampling is preceded by a corresponding basic cleaning according to the same sweeping pattern. This ensures that the sample of the 24 h sampling consists only of particles that have arisen in the 24 h since the previous cleaning. The samples can be taken all over the year within a dry period.

Sustainability 2023 , 15 , 12029 7 of 14 Sustainability 2023 , 15 , 12029 7 of 15 particles that have arisen in the 24 h since the previous cleaning. The samples can be taken all over the year within a dry period. Figure 6. Sampling area: sweeping surface in the road space with characteristic dimensions. Tra ffi c Counting The tra ffi c volume is an important parameter for the investigation and comparability of the volume of solid ma tt er and the amount of TRWP on road surfaces. By selecting the measurement sites, it was considered that the annual average daily tra ffi c (AADT) is comparable. In order to determine the real tra ffi c (average actual daily tra ffi c) within a 24 h sampling campaign, radar measuring device “tra ffi c counters” were used from Wavetec (Figure 7). Figure 6. Sampling area: sweeping surface in the road space with characteristic dimensions Traffic Counting The traffic volume is an important parameter for the investigation and comparability of the volume of solid matter and the amount of TRWP on road surfaces. By selecting the measurement sites, it was considered that the annual average daily traffic (AADT) is comparable. In order to determine the real traffic (average actual daily traffic) within a 24 h sampling campaign, radar measuring device “traffic counters” were used from Wavetec (Figure 7 ). Sustainability 2023 , 15 , 12029 8 of 15 Figure 7. Tra ffi c counting with radar meter—Wavetec. The radar meter makes it possible to record the time, the vehicle length, and the speed of passing vehicles on a speci fi c road cross-section. With the parameter vehicle length, di ff erent vehicle types can be identi fi ed: heavy-duty vehicle (HDV), low-duty vehicle (LDV), and bicycles. For the analysis of the data, a separation limit of 2.3 m and a speed of 26 km/h were applied in order to distinguish motor vehicles from cyclists. Vehicles less than 7.0 m in length were evaluated as LDV. The remaining vehicles were evaluated as HDV. The measuring devices were set up at the sweeping areas in order to record the tra ffi c on the respective side of the road. In order to detect the vehicles with the radar meter, they must pass the meter on a straight section of the road. 2.4. Analysitcs and Evaluation The determination of the tire abrasion content from all of the described environmental samples was carried out externally according to ISO/TS 21396 [36] using pyrolysis/GC- MS [32]. For chemical analysis and the detection of tire wear, the marker SBR-BR was chosen. For fractionating the sample, a sieve cascade of four sieves with mesh sizes of 500 µm, 100 µm, 50 µm, and 20 µm was installed on an analytical sieve shaker twice for 5 min with a 2 min break in between [37]. Due to standard regulations for sieving of a dry sample with standardized sieves, the lower cut o ff limit was 20 µm [38]. With the size spectrum of 20–500 µm, more than 90% of the expected TRWP could be described [2]. The di ff erences of the SBR and the RDS masses among the sampling locations were tested using one-way analysis of variance (ANOVA) followed by a Tukey pairwise comparison test in order to identify the relevant sampling tracks (Figure 5) and the inner-city hotspots (Figure 3). 3. Results and Discussion This section describes in four steps the distribution of RDS and SBR across the road. For the six regularly sampled sites, both the RDS and the SBR values are presented and hotspots are identi fi ed. The representation of the AADT serves the comparability of the measuring points. 3.1. Investigation of the Particles Distribution across the Road The results of the cross sweeps for the relative mass distribution of all solid particles within the RDS of the fraction sieved 20–500 µm are shown in Figure 8. On average, a total Figure 7. Traffic counting with radar meter—Wavetec The radar meter makes it possible to record the time, the vehicle length, and the speed of passing vehicles on a specific road cross-section. With the parameter vehicle length, different vehicle types can be identified: heavy-duty vehicle (HDV), low-duty vehicle (LDV), and bicycles. For the analysis of the data, a separation limit of 2.3 m and a speed

Sustainability 2023 , 15 , 12029 8 of 14 of 26 km/h were applied in order to distinguish motor vehicles from cyclists. Vehicles less than 7.0 m in length were evaluated as LDV. The remaining vehicles were evaluated as HDV The measuring devices were set up at the sweeping areas in order to record the traffic on the respective side of the road. In order to detect the vehicles with the radar meter, they must pass the meter on a straight section of the road 2.4. Analysitcs and Evaluation The determination of the tire abrasion content from all of the described environmental samples was carried out externally according to ISO/TS 21396 [ 36 ] using pyrolysis/GC- MS [ 32 ]. For chemical analysis and the detection of tire wear, the marker SBR-BR was chosen. For fractionating the sample, a sieve cascade of four sieves with mesh sizes of 500 µ m, 100 µ m, 50 µ m, and 20 µ m was installed on an analytical sieve shaker twice for 5 min with a 2 min break in between [ 37 ]. Due to standard regulations for sieving of a dry sample with standardized sieves, the lower cut off limit was 20 µ m [ 38 ]. With the size spectrum of 20–500 µ m, more than 90% of the expected TRWP could be described [ 2 ]. The differences of the SBR and the RDS masses among the sampling locations were tested using one-way analysis of variance (ANOVA) followed by a Tukey pairwise comparison test in order to identify the relevant sampling tracks (Figure 5 ) and the inner-city hotspots (Figure 3 ). 3. Results and Discussion This section describes in four steps the distribution of RDS and SBR across the road For the six regularly sampled sites, both the RDS and the SBR values are presented and hotspots are identified. The representation of the AADT serves the comparability of the measuring points 3.1. Investigation of the Particles Distribution across the Road The results of the cross sweeps for the relative mass distribution of all solid particles within the RDS of the fraction sieved 20–500 µ m are shown in Figure 8 . On average, a total mass of 68.9 g RDS was determined for track 1–5 for the fraction 20–500 µ m. To find out whether the TRWPs are distributed analogously to the RDS across the pavement cross-section, the rubber content was measured in the respective fraction. For this purpose, styrene-butadiene rubber (SBR) was chosen as the marker. On average, an SBR quantity of 147.5 mg was detected for tracks 1–5 for the fraction 20–500 µ m. This investigation was repeated on different days, and was conducted three times in total ( n = 3) Sustainability 2023 , 15 , 12029 9 of 15 mass of 68.9 g RDS was determined for track 1–5 for the fraction 20–500 µm. To fi nd out whether the TRWPs are distributed analogously to the RDS across the pavement crosssection, the rubber content was measured in the respective fraction. For this purpose, styrene-butadiene rubber (SBR) was chosen as the marker. On average, an SBR quantity of 147.5 mg was detected for tracks 1–5 for the fraction 20–500 µm. This investigation was repeated on di ff erent days, and was conducted three times in total ( n = 3). Figure 8. Mass distributions of RDS and SBR for the roadway cross-section. The RDS results show a signi fi cant di ff erence of track 1 to tracks 2–5 (pairwise t -test ( p < 0.0001) (Table S 1)). The results show that most of the RDS are located on track 1 (95.9 m%) and track 2 (3.0 m%). The low standard deviations of the RDS mass distribution on all tracks shows the homogeneity of the results. The SBR results show a signi fi cant di ff erence of track 1 compared to tracks 2–5 (pairwise t -test ( p < 0.05) (Table S 2)). The mass distribution of SBR is less constant within the three measurement days, as evidenced by the high standard deviations of track 1 (26.5 m%) and track 2 (23.5 m%). Nevertheless, most of the SBR is deposited in track 1 (79.3 m%) and track 2 (17.4 m%). The measurements clearly show that in terms of RDS masses and in order to determine SBR contents in the inner-city area, it is su ffi ciently representative to sample 1.6 m (track 1 and 2) from the curb to the center of the roadway. The RDS results can also be con fi rmed with reference to the literature [39]. The distribution of the SBR across the lane width is described here for the fi rst time. For the sampling of RDS, the sampling of track 1 would be su ffi cient, but for the consideration of SBR, sampling of tracks 1 and 2 is recommended. 3.2. 24 h Sampling RDS The total masses by fraction from all 24 h samplings ( n ) per investigation site are shown in Figure 9. The RDS masses per track have been determined as described in the methodology section, and they are speci fi cally shown in meters along the edge (Figure 5). The sampling locations were determined with the ANOVA and Tukey test. No signi fi cant di ff erences are found here (Table S 3). The values shown can be converted to g/m 2 using a factor of 1.2, but for reasons of consistency with the sampling procedure, the value m*edge is deliberately chosen. In this way, the distribution along the lane is underlined. The values of RDS vary from 5.11 g/m*edge at the straight to 10.00 g/m*edge at the curve. The di ff erences between “straight”, “tra ffi c lights”, and “slope” can be explained by the di ff erent boundary conditions, as mentioned in Table 1, at the sites. Leaves from trees and solids from the tree slice can potentially end up in the sampling area. There is no direct overlay by trees at the curve and the roundabout, admi tt edly, but the surrounding area does have many trees and sandy beds, which leads to comparable boundary conditions. The sampling spot in the park was not in fl uenced by vehicle tra ffi c at all. Approximately 10% of the area is covered by trees, similar to the measurement site “straight”, and the immediate surroundings are green areas. Figure 8. Mass distributions of RDS and SBR for the roadway cross-section The RDS results show a significant difference of track 1 to tracks 2–5 (pairwise t -test ( p < 0.0001) (Table S 1)). The results show that most of the RDS are located on track 1 (95.9 m%) and track 2 (3.0 m%). The low standard deviations of the RDS mass distribution on all tracks shows the homogeneity of the results The SBR results show a significant difference of track 1 compared to tracks 2–5 (pairwise t -test ( p < 0.05) (Table S 2)). The mass distribution of SBR is less constant within the three measurement days, as evidenced by the high standard deviations of track 1 (26.5 m%)

Sustainability 2023 , 15 , 12029 9 of 14 and track 2 (23.5 m%). Nevertheless, most of the SBR is deposited in track 1 (79.3 m%) and track 2 (17.4 m%). The measurements clearly show that in terms of RDS masses and in order to determine SBR contents in the inner-city area, it is sufficiently representative to sample 1.6 m (track 1 and 2) from the curb to the center of the roadway. The RDS results can also be confirmed with reference to the literature [ 39 ]. The distribution of the SBR across the lane width is described here for the first time. For the sampling of RDS, the sampling of track 1 would be sufficient, but for the consideration of SBR, sampling of tracks 1 and 2 is recommended 3.2. 24 h Sampling RDS The total masses by fraction from all 24 h samplings ( n ) per investigation site are shown in Figure 9 . The RDS masses per track have been determined as described in the methodology section, and they are specifically shown in meters along the edge (Figure 5 ). The sampling locations were determined with the ANOVA and Tukey test. No significant differences are found here (Table S 3) Sustainability 2023 , 15 , 12029 10 of 15 Figure 9. RDS results for the sampling locations for track 1 (t 1) and track 2 (t 2). The total masses for all sampling locations consist mainly of the coarse fraction of 100 ‒ 500 µm. This conclusion corresponds to the expectation from the literature [40,41]. The roadside sampling spots are in similar ranges. For the locations “roundabout” and “curve”, the values are increased compared to the locations “straight” and “slope”. The in fl uencing parameters on tire wear are not re fl ected in the RDS results of the sampled sites, and they do not appear to have any signi fi cant quantitative or qualitative in fl uence on the RDS characteristics. For this reason, additional analysis of the SBR content is essential for monitoring purposes. 3.3. 24 h Sampling SBR As described in Section 2.4, the mass of SBR was determined for all of the sampling sites with repeated sampling ( n ). The masses of SBR/m*edge determined in Figure 10 show clear di ff erences within the sampling spots. The SBR results are signi fi cantly higher at the curve site in pair-wise comparisons to the sites “straight”, “slope”, and “roundabout” (one-way ANOVA Tukey Test, 95% con fi dence level (Table S 4)). As expected, the mean SBR value of the curve is the highest at 34.43 ± 36.06 mg/m*edge. It is assumed that the higher lateral forces lead to increased emergence. The high standard deviation results from a single value (67.3 mg/m*edge) in the fraction 100–500 µm of track 2. The in fl uence of a single vehicle is suspected, since there are no abnormalities in tra ffi c load, vehicle types, or the sampling protocol. The sampling site of the tra ffi c lights provides the second highest SBR value of 16.03 ± 8.12 mg/m*edge, which is probably also due to acceleration when starting as well as negative acceleration when braking. The “straight” road section 5.46 ± 4.21 mg/m*edge and the “slope” 4.50 ± 3.89 mg/m*edge show lower values but are in the same size range. The values do not correspond to any particular stress, and the driving situation was predominantly fl uent. The roundabout 0.49 ± 0.45 mg/m*edge has, contrary to expectations, the lowest SBR results values within the fi ve road sites. In order to be able to carry out the tra ffi c count, the straight section of the roundabout had to be selected (see Section 2.3 above). It is assumed that the road layout at the sampling point and the relatively low speed led to low lateral forces, which a ff ect the measured values. For a detailed description of a roundabout, several measuring points within the roundabout should be considered for future measurements. In the fraction 100–500 µm, most SBR was measured at all tra ffi c locations. As expected, no SBR was found at the park sampling location because no vehicles operate there. Figure 9. RDS results for the sampling locations for track 1 (t 1) and track 2 (t 2) The values shown can be converted to g/m 2 using a factor of 1.2, but for reasons of consistency with the sampling procedure, the value m*edge is deliberately chosen. In this way, the distribution along the lane is underlined. The values of RDS vary from 5.11 g/m*edge at the straight to 10.00 g/m*edge at the curve. The differences between “straight”, “traffic lights”, and “slope” can be explained by the different boundary conditions, as mentioned in Table 1 , at the sites. Leaves from trees and solids from the tree slice can potentially end up in the sampling area. There is no direct overlay by trees at the curve and the roundabout, admittedly, but the surrounding area does have many trees and sandy beds, which leads to comparable boundary conditions. The sampling spot in the park was not influenced by vehicle traffic at all. Approximately 10% of the area is covered by trees, similar to the measurement site “straight”, and the immediate surroundings are green areas The total masses for all sampling locations consist mainly of the coarse fraction of 100–500 µ m. This conclusion corresponds to the expectation from the literature [ 40 , 41 ]. The roadside sampling spots are in similar ranges. For the locations “roundabout” and “curve”, the values are increased compared to the locations “straight” and “slope”. The influencing parameters on tire wear are not reflected in the RDS results of the sampled sites, and they do not appear to have any significant quantitative or qualitative influence on the RDS characteristics. For this reason, additional analysis of the SBR content is essential for monitoring purposes 3.3. 24 h Sampling SBR As described in Section 2.4 , the mass of SBR was determined for all of the sampling sites with repeated sampling ( n ).

Sustainability 2023 , 15 , 12029 10 of 14 The masses of SBR/m*edge determined in Figure 10 show clear differences within the sampling spots. The SBR results are significantly higher at the curve site in pair-wise comparisons to the sites “straight”, “slope”, and “roundabout” (one-way ANOVA Tukey Test, 95% confidence level (Table S 4)). As expected, the mean SBR value of the curve is the highest at 34.43 ± 36.06 mg/m*edge. It is assumed that the higher lateral forces lead to increased emergence. The high standard deviation results from a single value (67.3 mg/m*edge) in the fraction 100–500 µ m of track 2. The influence of a single vehicle is suspected, since there are no abnormalities in traffic load, vehicle types, or the sampling protocol. The sampling site of the traffic lights provides the second highest SBR value of 16.03 ± 8.12 mg/m*edge, which is probably also due to acceleration when starting as well as negative acceleration when braking. The “straight” road section 5.46 ± 4.21 mg/m*edge and the “slope” 4.50 ± 3.89 mg/m*edge show lower values but are in the same size range. The values do not correspond to any particular stress, and the driving situation was predominantly fluent. The roundabout 0.49 ± 0.45 mg/m*edge has, contrary to expectations, the lowest SBR results values within the five road sites. In order to be able to carry out the traffic count, the straight section of the roundabout had to be selected (see Section 2.3 above). It is assumed that the road layout at the sampling point and the relatively low speed led to low lateral forces, which affect the measured values. For a detailed description of a roundabout, several measuring points within the roundabout should be considered for future measurements. In the fraction 100–500 µ m, most SBR was measured at all traffic locations. As expected, no SBR was found at the park sampling location because no vehicles operate there Sustainability 2023 , 15 , 12029 11 of 15 Figure 10. SBR results for the sampling locations for track 1 (t 1) and track 2 (t 2). Despite the strongly fl uctuating measured values, it can be seen that the locations “curve” and “tra ffi c lights” with a higher load resulting from the driving situation show signi fi cantly more SBR than the locations “slope” and “straight”, and they can therefore be classi fi ed as hotspots. The “Tyre Wear Mapping” report, which identi fi es curves and intersections as the largest emission location by modeling the physical parameters, came to a similar conclusion [31]. The results shown here are generally in line with expectations. The in fl uencing factors mentioned in Figure 2 are re fl ected in the results. For the fi rst time, it is possible to identify the magnitudes of di ff erent emission hotspots. These fi ndings are an important element in the targeted and e ffi cient selection of sustainable measures to reduce microplastics from tires entering into the environment. These have to be selected speci fi cally for the location. For the “curve”, for example, this could mean that the maximum permi tt ed speeds should be reduced. Additionally, when planning new roads, a curve radius as large as possible can help. For tra ffi c lights, a reduction of the acceleration events by “stop and go” would be an additional solution. Furthermore, treating the road runo ff in the gully using retro fi t fi lter systems is another way to reduce the input into the environment as quickly as possible. The fi lter systems can be supplemented very well by targeted road cleaning. 3.4. Tra ffi c Counting In order to exclude the in fl uence of tra ffi c volume and vehicle type and thereby ensure the comparability of the sites, the tra ffi c was counted. Regarding the main results, the average actual daily tra ffi c for passenger cars (LDV) and their average travel speeds are shown in Table 2. For the measuring points, comparable tra ffi c amounts of 5836–6308 and average speeds of 40–41 km/h can be observed. Only for the roundabout was a lesser speed of 25 km/h determined. The tra ffi c amount of 6262 for the tra ffi c lights was detected for the approaching vehicles 50 m beforehand in order to be able to reliably count the passing vehicles. The average speed cannot be transferred to the sampling point at the tra ffi c lights because vehicles drive through, brake, or stop there. Figure 10. SBR results for the sampling locations for track 1 (t 1) and track 2 (t 2) Despite the strongly fluctuating measured values, it can be seen that the locations “curve” and “traffic lights” with a higher load resulting from the driving situation show significantly more SBR than the locations “slope” and “straight”, and they can therefore be classified as hotspots. The “Tyre Wear Mapping” report, which identifies curves and intersections as the largest emission location by modeling the physical parameters, came to a similar conclusion [ 31 ]. The results shown here are generally in line with expectations. The influencing factors mentioned in Figure 2 are reflected in the results. For the first time, it is possible to identify the magnitudes of different emission hotspots. These findings are an important element in the targeted and efficient selection of sustainable measures to reduce microplastics from tires entering into the environment These have to be selected specifically for the location. For the “curve”, for example, this could mean that the maximum permitted speeds should be reduced. Additionally, when planning new roads, a curve radius as large as possible can help. For traffic lights, a reduction of the acceleration events by “stop and go” would be an additional solution.

Sustainability 2023 , 15 , 12029 11 of 14 Furthermore, treating the road runoff in the gully using retrofit filter systems is another way to reduce the input into the environment as quickly as possible. The filter systems can be supplemented very well by targeted road cleaning 3.4. Traffic Counting In order to exclude the influence of traffic volume and vehicle type and thereby ensure the comparability of the sites, the traffic was counted Regarding the main results, the average actual daily traffic for passenger cars (LDV) and their average travel speeds are shown in Table 2 . For the measuring points, comparable traffic amounts of 5836–6308 and average speeds of 40–41 km/h can be observed. Only for the roundabout was a lesser speed of 25 km/h determined. The traffic amount of 6262 for the traffic lights was detected for the approaching vehicles 50 m beforehand in order to be able to reliably count the passing vehicles. The average speed cannot be transferred to the sampling point at the traffic lights because vehicles drive through, brake, or stop there Table 2. Average actual daily traffic/vehicle type: LDV (<7 m) HDV (>7 m) (SD = standard deviation) Sampling Location Vehicle Type Num. [vehicle/day] SD [vehicle/day] Share Ø-Speed [km/h] SD [km/h] Sustainability 2023 , 15 , 12029 12 of 15 Table 2. Average actual daily tra ffi c/vehicle type: LDV (<7 m) HDV (>7 m) (SD = standard deviation). Sampling Location Vehicle Type Num. [vehicle/day] SD [vehicle/day] Share Ø-Speed [km/h] SD [km/h] Straight Bicycle 886 113 13% 15 4 LDV 5932 320 84% 40 6 HDV 246 41 3% 40 6 Traffic lights Bicycle 1086 52 14% 16 4 LDV 6262 221 82% 40 6 HDV 272 35 4% 40 6 Curve Bicycle 1012 52 15% 15 3 LDV 5510 139 81% 24 3 HDV 275 41 4% 24 3 Slope Bicycle 987 53 14% 13 4 LDV 5836 110 83% 41 6 HDV 212 9 3% 41 6 Roundabout Bicycle 1216 45 15% 16 2 LDV 6308 211 78% 25 5 HDV 596 5 7% 25 5 According to Bäumer et al. [42], the share of LDVs in urban motorised tra ffi c in Germany is 95%. The results of the following table con fi rm the range (95–96%) for motorised tra ffi c. Therefore, the sampling locations are representative for a German metropolitan region. The share of trucks and bicycles is very low. It is assumed that the relevant quantities result from cars. Although the emission rate of the HDV is higher, the dominant share is due to the tra ffi c volume of the LDV. 4. Conclusions Tire wear is one of the largest sources of microplastics. The tra ffi c and transport processes on the roads play an important role in terms of society’s needs. Even by increasing e-mobility currently and in the future, the emission of tire abrasion cannot be properly solved. In order to derive sustainable solutions over the entire use phase of a tire, intensive interdisciplinary cooperation between all of the relevant stakeholders is required. Through a systematic hotspot approach, both preventive and acute sustainable solution concepts can be implemented and tested for e ff ectiveness. Within the monitoring approach, dry environmental samples of RDS with a de fi ned sampling protocol and systematic 24 h samples with automatic tra ffi c counting could be successfully implemented. A basic cleaning of the sampling areas according to the same pa tt ern has to be conducted before this can take place, though. It can be assumed that with the sampling of the edge area, the total mass of the RDS with up to 98.9%, as well as the accumulating amount of TRWP with up to 96.7%, can be sampled representatively. Emission di ff erences for TRWP could be determined by comparing di ff erent stress situation points, such as the straight, the slope, the curve, and the tra ffi c lights, and it could be shown that the areas of the curve and the tra ffi c lights describe inner-city TRWP hotspots. The parameters in fl uencing tire wear, which were selected on the basis of the various sampling sites, correlate with the SBR amount. A correlation of RDS and SBR for the hotspots is not possible. Therefore, SBR determinations must be made speci fi cally for monitoring in the future. Straight Bicycle 886 113 13% 15 4 LDV 5932 320 84% 40 6 HDV 246 41 3% 40 6 Sustainability 2023 , 15 , 12029 12 of 15 Table 2. Average actual daily tra ffi c/vehicle type: LDV (<7 m) HDV (>7 m) (SD = standard deviation). Sampling Location Vehicle Type Num. [vehicle/day] SD [vehicle/day] Share Ø-Speed [km/h] SD [km/h] Straight Bicycle 886 113 13% 15 4 LDV 5932 320 84% 40 6 HDV 246 41 3% 40 6 Traffic lights Bicycle 1086 52 14% 16 4 LDV 6262 221 82% 40 6 HDV 272 35 4% 40 6 Curve Bicycle 1012 52 15% 15 3 LDV 5510 139 81% 24 3 HDV 275 41 4% 24 3 Slope Bicycle 987 53 14% 13 4 LDV 5836 110 83% 41 6 HDV 212 9 3% 41 6 Roundabout Bicycle 1216 45 15% 16 2 LDV 6308 211 78% 25 5 HDV 596 5 7% 25 5 According to Bäumer et al. [42], the share of LDVs in urban motorised tra ffi c in Germany is 95%. The results of the following table con fi rm the range (95–96%) for motorised tra ffi c. Therefore, the sampling locations are representative for a German metropolitan region. The share of trucks and bicycles is very low. It is assumed that the relevant quantities result from cars. Although the emission rate of the HDV is higher, the dominant share is due to the tra ffi c volume of the LDV. 4. Conclusions Tire wear is one of the largest sources of microplastics. The tra ffi c and transport processes on the roads play an important role in terms of society’s needs. Even by increasing e-mobility currently and in the future, the emission of tire abrasion cannot be properly solved. In order to derive sustainable solutions over the entire use phase of a tire, intensive interdisciplinary cooperation between all of the relevant stakeholders is required. Through a systematic hotspot approach, both preventive and acute sustainable solution concepts can be implemented and tested for e ff ectiveness. Within the monitoring approach, dry environmental samples of RDS with a de fi ned sampling protocol and systematic 24 h samples with automatic tra ffi c counting could be successfully implemented. A basic cleaning of the sampling areas according to the same pa tt ern has to be conducted before this can take place, though. It can be assumed that with the sampling of the edge area, the total mass of the RDS with up to 98.9%, as well as the accumulating amount of TRWP with up to 96.7%, can be sampled representatively. Emission di ff erences for TRWP could be determined by comparing di ff erent stress situation points, such as the straight, the slope, the curve, and the tra ffi c lights, and it could be shown that the areas of the curve and the tra ffi c lights describe inner-city TRWP hotspots. The parameters in fl uencing tire wear, which were selected on the basis of the various sampling sites, correlate with the SBR amount. A correlation of RDS and SBR for the hotspots is not possible. Therefore, SBR determinations must be made speci fi cally for monitoring in the future. Traffic lights Bicycle 1086 52 14% 16 4 LDV 6262 221 82% 40 6 HDV 272 35 4% 40 6 Sustainability 2023 , 15 , 12029 12 of 15 Table 2. Average actual daily tra ffi c/vehicle type: LDV (<7 m) HDV (>7 m) (SD = standard deviation). Sampling Location Vehicle Type Num. [vehicle/day] SD [vehicle/day] Share Ø-Speed [km/h] SD [km/h] Straight Bicycle 886 113 13% 15 4 LDV 5932 320 84% 40 6 HDV 246 41 3% 40 6 Traffic lights Bicycle 1086 52 14% 16 4 LDV 6262 221 82% 40 6 HDV 272 35 4% 40 6 Curve Bicycle 1012 52 15% 15 3 LDV 5510 139 81% 24 3 HDV 275 41 4% 24 3 Slope Bicycle 987 53 14% 13 4 LDV 5836 110 83% 41 6 HDV 212 9 3% 41 6 Roundabout Bicycle 1216 45 15% 16 2 LDV 6308 211 78% 25 5 HDV 596 5 7% 25 5 According to Bäumer et al. [42], the share of LDVs in urban motorised tra ffi c in Germany is 95%. The results of the following table con fi rm the range (95–96%) for motorised tra ffi c. Therefore, the sampling locations are representative for a German metropolitan region. The share of trucks and bicycles is very low. It is assumed that the relevant quantities result from cars. Although the emission rate of the HDV is higher, the dominant share is due to the tra ffi c volume of the LDV. 4. Conclusions Tire wear is one of the largest sources of microplastics. The tra ffi c and transport processes on the roads play an important role in terms of society’s needs. Even by increasing e-mobility currently and in the future, the emission of tire abrasion cannot be properly solved. In order to derive sustainable solutions over the entire use phase of a tire, intensive interdisciplinary cooperation between all of the relevant stakeholders is required. Through a systematic hotspot approach, both preventive and acute sustainable solution concepts can be implemented and tested for e ff ectiveness. Within the monitoring approach, dry environmental samples of RDS with a de fi ned sampling protocol and systematic 24 h samples with automatic tra ffi c counting could be successfully implemented. A basic cleaning of the sampling areas according to the same pa tt ern has to be conducted before this can take place, though. It can be assumed that with the sampling of the edge area, the total mass of the RDS with up to 98.9%, as well as the accumulating amount of TRWP with up to 96.7%, can be sampled representatively. Emission di ff erences for TRWP could be determined by comparing di ff erent stress situation points, such as the straight, the slope, the curve, and the tra ffi c lights, and it could be shown that the areas of the curve and the tra ffi c lights describe inner-city TRWP hotspots. The parameters in fl uencing tire wear, which were selected on the basis of the various sampling sites, correlate with the SBR amount. A correlation of RDS and SBR for the hotspots is not possible. Therefore, SBR determinations must be made speci fi cally for monitoring in the future. Curve Bicycle 1012 52 15% 15 3 LDV 5510 139 81% 24 3 HDV 275 41 4% 24 3 Sustainability 2023 , 15 , 12029 12 of 15 Table 2. Average actual daily tra ffi c/vehicle type: LDV (<7 m) HDV (>7 m) (SD = standard deviation). Sampling Location Vehicle Type Num. [vehicle/day] SD [vehicle/day] Share Ø-Speed [km/h] SD [km/h] Straight Bicycle 886 113 13% 15 4 LDV 5932 320 84% 40 6 HDV 246 41 3% 40 6 Traffic lights Bicycle 1086 52 14% 16 4 LDV 6262 221 82% 40 6 HDV 272 35 4% 40 6 Curve Bicycle 1012 52 15% 15 3 LDV 5510 139 81% 24 3 HDV 275 41 4% 24 3 Slope Bicycle 987 53 14% 13 4 LDV 5836 110 83% 41 6 HDV 212 9 3% 41 6 Roundabout Bicycle 1216 45 15% 16 2 LDV 6308 211 78% 25 5 HDV 596 5 7% 25 5 According to Bäumer et al. [42], the share of LDVs in urban motorised tra ffi c in Germany is 95%. The results of the following table con fi rm the range (95–96%) for motorised tra ffi c. Therefore, the sampling locations are representative for a German metropolitan region. The share of trucks and bicycles is very low. It is assumed that the relevant quantities result from cars. Although the emission rate of the HDV is higher, the dominant share is due to the tra ffi c volume of the LDV. 4. Conclusions Tire wear is one of the largest sources of microplastics. The tra ffi c and transport processes on the roads play an important role in terms of society’s needs. Even by increasing e-mobility currently and in the future, the emission of tire abrasion cannot be properly solved. In order to derive sustainable solutions over the entire use phase of a tire, intensive interdisciplinary cooperation between all of the relevant stakeholders is required. Through a systematic hotspot approach, both preventive and acute sustainable solution concepts can be implemented and tested for e ff ectiveness. Within the monitoring approach, dry environmental samples of RDS with a de fi ned sampling protocol and systematic 24 h samples with automatic tra ffi c counting could be successfully implemented. A basic cleaning of the sampling areas according to the same pa tt ern has to be conducted before this can take place, though. It can be assumed that with the sampling of the edge area, the total mass of the RDS with up to 98.9%, as well as the accumulating amount of TRWP with up to 96.7%, can be sampled representatively. Emission di ff erences for TRWP could be determined by comparing di ff erent stress situation points, such as the straight, the slope, the curve, and the tra ffi c lights, and it could be shown that the areas of the curve and the tra ffi c lights describe inner-city TRWP hotspots. The parameters in fl uencing tire wear, which were selected on the basis of the various sampling sites, correlate with the SBR amount. A correlation of RDS and SBR for the hotspots is not possible. Therefore, SBR determinations must be made speci fi cally for monitoring in the future. Slope Bicycle 987 53 14% 13 4 LDV 5836 110 83% 41 6 HDV 212 9 3% 41 6 Sustainability 2023 , 15 , 12029 12 of 15 Table 2. Average actual daily tra ffi c/vehicle type: LDV (<7 m) HDV (>7 m) (SD = standard deviation). Sampling Location Vehicle Type Num. [vehicle/day] SD [vehicle/day] Share Ø-Speed [km/h] SD [km/h] Straight Bicycle 886 113 13% 15 4 LDV 5932 320 84% 40 6 HDV 246 41 3% 40 6 Traffic lights Bicycle 1086 52 14% 16 4 LDV 6262 221 82% 40 6 HDV 272 35 4% 40 6 Curve Bicycle 1012 52 15% 15 3 LDV 5510 139 81% 24 3 HDV 275 41 4% 24 3 Slope Bicycle 987 53 14% 13 4 LDV 5836 110 83% 41 6 HDV 212 9 3% 41 6 Roundabout Bicycle 1216 45 15% 16 2 LDV 6308 211 78% 25 5 HDV 596 5 7% 25 5 According to Bäumer et al. [42], the share of LDVs in urban motorised tra ffi c in Germany is 95%. The results of the following table con fi rm the range (95–96%) for motorised tra ffi c. Therefore, the sampling locations are representative for a German metropolitan region. The share of trucks and bicycles is very low. It is assumed that the relevant quantities result from cars. Although the emission rate of the HDV is higher, the dominant share is due to the tra ffi c volume of the LDV. 4. Conclusions Tire wear is one of the largest sources of microplastics. The tra ffi c and transport processes on the roads play an important role in terms of society’s needs. Even by increasing e-mobility currently and in the future, the emission of tire abrasion cannot be properly solved. In order to derive sustainable solutions over the entire use phase of a tire, intensive interdisciplinary cooperation between all of the relevant stakeholders is required. Through a systematic hotspot approach, both preventive and acute sustainable solution concepts can be implemented and tested for e ff ectiveness. Within the monitoring approach, dry environmental samples of RDS with a de fi ned sampling protocol and systematic 24 h samples with automatic tra ffi c counting could be successfully implemented. A basic cleaning of the sampling areas according to the same pa tt ern has to be conducted before this can take place, though. It can be assumed that with the sampling of the edge area, the total mass of the RDS with up to 98.9%, as well as the accumulating amount of TRWP with up to 96.7%, can be sampled representatively. Emission di ff erences for TRWP could be determined by comparing di ff erent stress situation points, such as the straight, the slope, the curve, and the tra ffi c lights, and it could be shown that the areas of the curve and the tra ffi c lights describe inner-city TRWP hotspots. The parameters in fl uencing tire wear, which were selected on the basis of the various sampling sites, correlate with the SBR amount. A correlation of RDS and SBR for the hotspots is not possible. Therefore, SBR determinations must be made speci fi cally for monitoring in the future. Roundabout Bicycle 1216 45 15% 16 2 LDV 6308 211 78% 25 5 HDV 596 5 7% 25 5 According to Bäumer et al. [ 42 ], the share of LDVs in urban motorised traffic in Germany is 95%. The results of the following table confirm the range (95–96%) for motorised traffic. Therefore, the sampling locations are representative for a German metropolitan region The share of trucks and bicycles is very low. It is assumed that the relevant quantities result from cars. Although the emission rate of the HDV is higher, the dominant share is due to the traffic volume of the LDV 4. Conclusions Tire wear is one of the largest sources of microplastics. The traffic and transport processes on the roads play an important role in terms of society’s needs. Even by increasing e-mobility currently and in the future, the emission of tire abrasion cannot be properly solved. In order to derive sustainable solutions over the entire use phase of a tire, intensive interdisciplinary cooperation between all of the relevant stakeholders is required. Through a systematic hotspot approach, both preventive and acute sustainable solution concepts

Sustainability 2023 , 15 , 12029 12 of 14 can be implemented and tested for effectiveness. Within the monitoring approach, dry environmental samples of RDS with a defined sampling protocol and systematic 24 h samples with automatic traffic counting could be successfully implemented. A basic cleaning of the sampling areas according to the same pattern has to be conducted before this can take place, though. It can be assumed that with the sampling of the edge area, the total mass of the RDS with up to 98.9%, as well as the accumulating amount of TRWP with up to 96.7%, can be sampled representatively Emission differences for TRWP could be determined by comparing different stress situation points, such as the straight, the slope, the curve, and the traffic lights, and it could be shown that the areas of the curve and the traffic lights describe inner-city TRWP hotspots The parameters influencing tire wear, which were selected on the basis of the various sampling sites, correlate with the SBR amount. A correlation of RDS and SBR for the hotspots is not possible. Therefore, SBR determinations must be made specifically for monitoring in the future The particles that are potentially washed off by the road runoff and that are often directly flushed into the corresponding surface waters can be sampled in the future by defined 24 h road sediment sampling. With the described procedure, inner-city hotspots for tire abrasion can successively be identified. Through further investigations and an increasingly growing database, it should also be possible to reduce the large standard deviation of the SBR values in the future. Possible reasons for the deviations could be the drifting of tire abrasion particles from the study area. This could be caused by individual special vehicles that lead to particularly high amounts of turbulence for a short time, such as agricultural machinery or construction vehicles. Unusually aggressive driving by individual drivers could also lead to particularly high readings. Here, unnecessarily strong acceleration probably plays just as much of a role as hazard braking. These correlations cannot be derived from the measured values collected in this study. To describe individual load scenarios, individual tests should be carried out on defined test tracks. These results could be used in the future to derive appropriate correction factors Frequencies of emitted tire abrasion could be determined for different curve radii and intersection situations based on the described sweep sampling method. The findings can be used, for example, to control the targeted deployment of street sweepers at the relevant hotspots shortly before a rain event or to enable the selection of suitable decentralized filters for cleaning street runoff water. In addition, data obtained in the described manner can be stored in corresponding digital planning tools in order to take emissions into account in urban planning Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su 151512029/s 1 . Author Contributions: Conceptualization, D.V. and J.W.N.; methodology, D.V.; validation, D.V. and J.W.N.; formal analysis, D.V.; investigation, D.V. and J.W.N.; resources, M.B.; data curation, D.V. and J.W.N.; writing—original draft preparation, D.V.; writing—review and editing, D.V. and J.W.N.; visualization, D.V. and J.W.N.; supervision, M.B.; project administration, D.V.; funding acquisition, D.V. All authors have read and agreed to the published version of the manuscript Funding: The research results were generated within the framework of the joint project “RAU— Reifenabrieb in der Umwelt”, which was funded by the German Federal Ministry of Education and Research as part of the measure “Plastik in der Umwelt” (grant number: 13 NKE 011 A) Institutional Review Board Statement: Not applicable Informed Consent Statement: Not applicable Data Availability Statement: Data are contained within the article or Supplementary Material Acknowledgments: We acknowledge support by the German Research Foundation and the Open Access Publication Fund of TU Berlin.

Sustainability 2023 , 15 , 12029 13 of 14 Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results References 1 Alonso Raposo, M.; Ciuffo, B.; Alves Dias, P.; Ardente, F.; Aurambout, J.; Baldini, G.; Baranzelli, C.; Blagoeva, D.; Bobba, S.; Braun, R.; et al The Future of Road Transport: Implications of Automated, Connected, Low-Carbon and Shared Mobility ; Publications Office of the European Union: Luxembourg, 2019; ISBN 978-92-76-14318-5. [ CrossRef ] 2 Kreider, M.L.; Panko, J.M.; McAtee, B.L.; Sweet, L.I.; Finley, B.L. Physical and chemical characterization of tire-related particles: Comparison of particles generated using different methodologies Sci. Total Environ 2010 , 408 , 652–659. [ CrossRef ] [ PubMed ] 3 Son, C.E.; Choi, S.-S. Preparation and Characterization of Model Tire–Road Wear Particles Polymers 2022 , 14 , 1512. [ CrossRef ] [ PubMed ] 4 ETRMA 2020—The European Tyre Industry Fatcs and Figures “Did You Know That?”. 2020. Available online: https://www. etrma.org/wp-content/uploads/2019/12/Figures-leaflet-updated-front-2019-larger-NEW-LABEL.pdf (accessed on 4 June 2023) 5 Eisentraut, P.; Dümichen, E.; Ruhl, A.S.; Jekel, M.; Albrecht, M.; Gehde, M.; Braun, U. Two Birds with One Stone—Fast and Simultaneous Analysis of Microplastics: Microparticles Derived from Thermoplastics and Tire Wear Environ. Sci. Technol. Lett 2018 , 5 , 608–613. [ CrossRef ] 6 Sommer, F.; Dietze, V.; Baum, A.; Sauer, J.; Gilge, S.; Maschowski, C.; Gier é , R. Tire Abrasion as a Major Source of Microplastics in the Environment Aerosol Air Qual. Res 2018 , 18 , 2014–2028. [ CrossRef ] 7 Ciullo, P.A.; Hewitt, N. Compounding Materials Rubber Formul 1999 , 4–49 8 Tian, Z.; Zhao, H.; Peter, K.T.; Gonzalez, M.; Wetzel, J.; Wu, C.; Hu, X.; Prat, J.; Mudrock, E.; Hettinger, R. A ubiquitous tire rubber-derived chemical induces acute mortality in coho salmon Science 2021 , 371 , 185–189. [ CrossRef ] [ PubMed ] 9 Seiwert, B.; Nihemaiti, M.; Troussier, M.; Weyrauch, S.; Reemtsma, T. Abiotic oxidative transformation of 6-PPD and 6-PPD quinone from tires and occurrence of their products in snow from urban roads and in municipal wastewater Water Res 2022 , 212 , 118122. [ CrossRef ] [ PubMed ] 10 Cao, G.; Wang, W.; Zhang, J.; Wu, P.; Zhao, X.; Yang, Z.; Hu, D.; Cai, Z. New Evidence of Rubber-Derived Quinones in Water, Air, and Soil Environ. Sci. Technol 2022 , 56 , 4142–4150. [ CrossRef ] [ PubMed ] 11 Statista: Kunststoffindustrie. Statista. 2019. Available online: https://de.statista.com/statistik/studie/id/7275/dokument/ kunststoffindustrie-statista-dossier/ (accessed on 7 August 2020) 12 Hohmann, M. Statistiken zu Kautschuk. Statista. 2019. Available online: https://de.statista.com/statistik/studie/id/7288 /dokument/kautschuk-statista-dossier/ (accessed on 7 August 2020) 13 Ahlswede, A. Statistiken zum Reifenmarkt. Statista. 2019. Available online: https://de.statista.com/themen/2668/reifenmarkt/ (accessed on 7 August 2020) 14 Statista. Weltweite Automobilindustrie. Statista. 2019. Available online: https://de.statista.com/statistik/studie/id/31199/ dokument/weltweite-automobilindustrie-statista-dossier/ (accessed on 7 August 2020) 15 Deltares and TNO 2016 Emissieschattingen Diffuse bronnen Emissieregistratie—Bandenslijtage Wegverkeer. Available online: https://docplayer.nl/28992900-Emissieschattingen-diffuse-bronnen-emissieregistratie-remslijtage-versie-mei-2016.html (accessed on 4 June 2023) 16 Kraftfahrtbundesamt: Verkehr in Kilometern—Inländerfahrleistung. Entwicklungen der Fahrleistungen nach Fahrzeugarten Seit 2015. KBA. 2019. Available online: www.kba.de/DE/Statistik/Kraftverkehr/VerkehrKilometer/vk_inlaenderfahrleistung/vk_ inlaenderfahrleistung_inhalt.htm (accessed on 7 August 2020) 17 Baensch-Baltruschat, B.; Kocher, B.; Kochleus, C.; Stock, F.; Reifferscheid, G. Tyre and road wear particles—A calculation of generation, transport and release to water and soil with special regard to German roads Sci. Total Environ 2021 , 752 , 141939 [ CrossRef ] [ PubMed ] 18 Statistisches Bundesamt: Bevölkerungsstand—Bevölkerung nach Bundesländern Destatis 2019 Available online: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsstand/Tabellen/bevoelkerungnichtdeutsch-laender.html (accessed on 24 August 2020) 19 Jung, U.; Choi, S.-S. Classification and Characterization of Tire-Road Wear Particles in Road Dust by Density Polymers 2022 , 14 , 1005. [ CrossRef ] [ PubMed ] 20 Wagner, S.; Hüffer, T.; Klöckner, P.; Wehrhahn, M.; Hofmann, T.; Reemtsma, T. Tire wear particles in the aquatic environment—A review on generation, analysis, occurrence, fate and effects Water Res 2018 , 139 , 83–100. [ CrossRef ] [ PubMed ] 21 Hann, S.; Sherrington, C.; Jamieson, O.; Hickman, M.; Bapasola, A Investigating Options for Reducing Releases in the Aquatic Environment of Microplastics Emitted by Products ; Eunomia Research & Consulting Ltd.: Bristol, UK, 2018 22 Baensch-Baltruschat, B.; Kocher, B.; Stock, F.; Reifferscheid, G. Tyre and road wear particles (TRWP)—A review of generation, properties, emissions, human health risk, ecotoxicity, and fate in the environment Sci. Total Environ 2020 , 733 , 137823. [ CrossRef ] [ PubMed ] 23 Ten Broeke, H.; Hulskotte, J.; Denier van der Gon, H.A.C Road Traffic Tire Wear. Emission Estimates for Diffuse Sources, Netherlands Emission Inventory ; TNO Built Environment and Geosciences: Utrecht, The Netherlands, 2008.

Sustainability 2023 , 15 , 12029 14 of 14 24 Scheid, C.; Abusafia, A.; Steinmetz, H. Mikroplastik in kommunalen entwässerungssystemen—Aufkommen und relevanzen. In Proceedings of the Repräsentative Untersuchungsstrategien für ein Integratives Systemverständnis von Spezifischen Einträgen von Kunststoffen in die Umwelt (RUSEKU) DWA Seminar—Mikroplastik im Abwasser—Einordnung und Handlungsempfehlungen, Hannover, Germany, 20 October 2022 25 Scheer, H.; Fuhrmann, T.; Emscher Wassertechnik GmbH REPLAWA: Reduktion des Eintrags von Plastik über das Abwasser in die aquatische Umwelt ; Final Report; Bundesministerium für Bildung und Forschung: Berlin, Germany, 2022 26 Unice, K.M.; Weeber, M.P.; Abramson, M.M.; Reid, R.C.D.; van Gils, J.A.G.; Markus, A.A.; Vethaak, A.D.; Panko, J.M. Characterizing export of land-based microplastics to the estuary—Part I: Application of integrated geospatial microplastic transport models to assess tire and road wear particles in the Seine watershed Sci. Total Environ 2019 , 646 , S 1639–S 1649. [ CrossRef ] [ PubMed ] 27 Mengistu, D.; Heistad, A.; Coutris, C. Tire wear particles concentrations in gully pot sediments Sci. Total Environ 2021 , 769 , 144785. [ CrossRef ] [ PubMed ] 28 Kwak, J.-H.; Kim, H.; Lee, J.; Lee, S. Characterization of non-exhaust coarse and fine particles from on-road driving and laboratory measurements Sci. Total Environ 2013 , 458–460 , 273–282. [ CrossRef ] [ PubMed ] 29 Mennekes, D.; Nowack, B. Tire wear particle emissions: Measurement data where are you? Sci. Total Environ 2022 , 830 , 154655 [ CrossRef ] [ PubMed ] 30 Cettour-Janet, D. European Tyre and Rim Technical Organisation—ETRTO: Tyre Abrasion How to develop a Method for quantitative assessment. 2021. Available online: https://www.tyreandroadwear.com/wp-content/uploads/2021/12/20210614 -ETRTO-Abrasion-Test-for-TireTech 2021.pdf (accessed on 7 June 2023) 31 TyrewearMapping 2021, final report: “Reifenabrieb—Ein unterschätztes Umweltproblem?” Digitales Planungsund Entscheidungsinstrument zur Verteilung, Ausbreitung und Quantifizierung von Reifenabrieb in Deutschland. Available online: https://www.umsicht.fraunhofer.de/content/dam/umsicht/de/dokumente/kompetenz/prozesse/tyrewearmappingschlussbericht.pdf (accessed on 7 June 2023) 32 Venghaus, D.; Frank Schmerwitz, F.; Reiber, J.; Sommer, H.; Lindow, F.; Herper, D.; Pohrt, R.; Barjenbruch, M Reifenabrieb in der Umwelt—RAU ; Final Report; Plastik in der Umwelt: Berlin, Germany, 2021 33 © OpenStreetMap. Open Database License opendatacommons.org. Available online: https://www.openstreetmap.org/ copyright/en (accessed on 4 June 2023) 34 Pitt, R.; Amy, G Toxic Materials Analysis of Street Surface Constituents ; U.S. Environmental Protection Agency: Washington, DC, USA, 1973 35 Grottker, M. Runoff quality from a street with medium traffic loading Sci. Total Environ 1987 , 59 , 457–466. [ CrossRef ] 36 ISO/TS 21396:2017. Rubber—Determination of Mass Concentration of Tire and Road Wear Particles (TRWP) in Soil and Sediments —Pyrolysis-GC/MS Method. ISO: Geneva, Switzerland, 2017 37 DIN 66165-1:2016-08; Particle Size Analysis—Sieving analysis—Part 1: Fundamentals Procedure. 2016. Available online: https://www.beuth.de/en/standard/din-66165-1/255316600 (accessed on 4 June 2023) 38 DIN 66165-2:2016-08; Particle Size Analysis—Sieving analysis—Part 2: Procedure. 2016. Available online: https://www.beuth. de/en/standard/din-66165-2/255334374 (accessed on 4 June 2023) 39 Sieker, F.; Grottker, M Beschaffenheit von Straßenoberflächenwasser bei Mittler Verkehrsbelastung ; Forschung Straßenbau und Straßenverkehrstechnik; Bundesminister für Verkehr, Abt.Straßenbau: Berlin, Germany, 1988; Volume 530 40 Schmitt, T.G.; Welker, A.; Dierschke, M.; Uhl, M.; Maus, C.; Remmler, F. Entwicklung von Prüfverfahren für Anlagen zur dezentralen Niederschlagswasserbehandlung im Trennverfahren. In Final Report to the German Federal Foundation for the Environment ; Deutsche Vereinigung für Wasserwirtschaft, Abwasser und Abfall: Hennef, Germany, 2010 41 Gelhardt, L.; Huber, M.; Welker, A. Development of a Laboratory Method for the Comparison of Settling Processes of RoadDeposited Sediments with Artificial Test Material Water Air Soil Pollut 2017 , 228 , 467. [ CrossRef ] 42 Bäumer, M.; Hautzinger, H.; Pfeiffer, M.; Stock, W.; Lenz, B.; Kuhnimhof, T.; Köhler, K. Fahrleistungserhebung 2014— Inländerfahrleistung. In Berichte der Bundesanstalt für Straßenwesen (BASt) ; Verkehrstechnik V 290. IVT Research GmbH (Mannheim); Institut für Verkehrsforschung DLR: Berlin, Germany; Bergisch-Gladbach, Germany, 2017 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.