# Tire road wear particles, microplastics and metals in sediment of stormwater detention basins: co-occurrence and ecological risk assessment

Zoé Iannuzzi, M. Carmen Casado-Martinez, Tiago De Oliveira, David Lebouil, Benoît J.D. Ferrari, Johnny Gasperi, Brice Mourier

► To cite this version:

Zoé Iannuzzi, M. Carmen Casado-Martinez, Tiago De Oliveira, David Lebouil, Benoît J.D. Ferrari, et al.. Tire road wear particles, microplastics and metals in sediment of stormwater detention basins: co-occurrence and ecological risk assessment. Environment International, 2025, 202, pp.109676. 10.1016/j.envint.2025.109676. hal-05171698

HAL Id: hal-05171698

https://univ-eiffel.hal.science/hal-05171698v1

Submitted on 25 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Full length article

# Tire road wear particles, microplastics and metals in sediment of stormwater detention basins: co-occurrence and ecological risk assessment

Zoé Iannuzzi<sup>a,b,*</sup>, M. Carmen Casado-Martinez<sup>c</sup>, Tiago De Oliveira<sup>d,e</sup>, David Lebouil<sup>b</sup>, Benoît J.D. Ferrari<sup>c</sup>, Johnny Gasperi<sup>d,e</sup>, Brice Mourier<sup>a,e</sup>

## ARTICLE INFO

Handling Editor: Adrian Covaci

### Keywords:

- Tire road wear particles

- Urban sediments

- Risk assessment

- Microplastics

- Metals

## ABSTRACT

Tire road wear particles (TRWP) and microplastics (MP) are increasingly present in the environment due to anthropogenic sources like industrial activities and road traffic. Their load is high in urban sediments and more specifically in stormwater management infrastructure such as detention basins. Eleven detention basins featured by contrasting land uses (industrial, urban, agricultural, and heterogeneous) were sampled in a French metropolitan city to examine how land use influences the presence of TRWP, MP, and metals in these basins. Results show that the concentration of TRWP is higher than MP in all samples, with the highest levels in industrial areas reaching 20,191 mg.kg⁻¹ and 3,266 mg.kg⁻¹, respectively. A positive and significant correlation is observed between urban related metals (Cd, Cu, Pb, Zn), TRWP and MP. The enrichment factor and pollution load index are used to classify the contamination of the basins and showed that all the basins are contaminated. Concerning the risk associated, a risk quotient is calculated for the three pollutants. A predicted non-effect concentration for sediments (PNEC<sub>sediment</sub>) is proposed for TRWP specifically developed in this study using available effect data from the literature, which is set at 1,000 mg.kg⁻¹. Two methods (polymeric risk index, risk score) are tested to consider the complexity of MP in the risk assessment, they are discussed in this study. The two methods show different results, highlighting the importance of considering the characteristics of MP in risk assessment.

## 1. Introduction

Most urban pollutants are conveyed by surface runoff from urban catchment areas (Yan et al., 2020), which contributes to the deterioration of water bodies (Müller et al., 2020) and urban sediments quality. These pollutants include metals, polycyclic aromatic hydrocarbons (PAHs), persistent organic pollutants such as polychlorinated biphenyls (PCBs), and pesticides (Gasperi et al., 2014; Sébastian et al., 2015; Wiest et al., 2018). According to their properties, they tend to accumulate in bottom sediments, where their ecotoxicological potential is well documented (Merchan et al., 2014; Kontchou et al., 2023). Urban sediments are also composed of pollutants such as microplastics (MP) and tire road wear particles (TRWP). However, the paucity of available data on these pollutants has impeded the establishment of a consensus on the associated risk (Baensch-Baltruschat et al., 2020; Li et al., 2024). MP are classified into two different groups: primary MP, which are plastics

particles produced in a small size such as microbeads or synthetic fibers; and secondary MP which correspond to the degradation and fragmentation of macroplastics into small particles (Cole et al., 2011). A third type of MP can be considered: synthetic elastomers, such as styrene-butadiene-rubber (SBR) used in tires, and those added to modified road bitumen compositions (Baensch-Baltruschat et al., 2020). Tires are composed of several components, including a base material, fillers, softeners, vulcanization agents, additives and metals (Sommer et al., 2018), which have similar physicochemical properties to those of MP. TRWP are formed by the aggregation of tire particles and abraded road materials. The aggregative process is complex and results from several influencing factors such as tire characteristics (size, tread depth, composition...), road surface characteristics (porosity, humidity, pavement construction...) and also vehicle operation (acceleration, braking...) and its characteristics (electronic braking systems, vehicle weight...) (Baensch-Baltruschat et al., 2020; De Oliveira et al. 2024a).

<footnote><sup>a</sup> ENTPE, Université Claude Bernard Lyon 1, CNRS, UMR 5023 LEHNA 69518, Vaulx-en-Velin Cedex, 3 Rue M. Audin France</footnote>

<footnote><sup>b</sup> INSA Lyon, DEEP, UR7429 69621 Villeurbanne, France</footnote>

<footnote><sup>c</sup> Swiss Centre for Applied Ecotoxicology, Lausanne, Switzerland</footnote>

<footnote><sup>d</sup> Gustave Eiffel University, GERS-LEE, F-44344 Bouguenais, France</footnote>

<footnote>* Corresponding author at: ENTPE, Université Claude Bernard Lyon 1, CNRS, UMR 5023 LEHNA 69518, Vaulx-en-Velin Cedex, 3 Rue M. Audin, France. E-mail address: zoe.iannuzzi@entpe.fr (Z. Iannuzzi).</footnote>
TRWP are considered a major source of MP to the environment (Sommer et al., 2018; Jarlskog et al., 2022) and also a major source of pollutants in stormwater (Mahjoub et al., 2023). Most often TRWP and MP are analyzed separately, even though the literature is increasingly showing the links between these emerging pollutants and the more traditional ones, metals in particular. Indeed, metals may have the same sources as tires and MP, as metals are often used in the manufacture of plastics or tires particularly in additives (Lithner et al., 2009; Unice et al., 2013; He et al., 2023). Studies also show that these contaminants have similar accumulation processes, for example near roads (Werkenthin et al., 2016). Furthermore, dissolved metals can also adsorb onto TRWP and MP, particularly when the degradation makes them more porous (He et al., 2023).

As the most traditional contaminants, TRWP and MP may also pose a risk for aquatic organisms and human health (Wagner et al., 2018; Baensch-Baltruschat et al., 2020). However, assessing these risks remains challenging due to the complexity of their interactions within the environment (Li et al., 2024). Several studies have proposed ecological risk assessment concepts for MP and TRWP emissions, taking into account factors such as polymeric composition, shape, size and degree of degradation (Bucci and Rochman, 2022; Yang et al., 2023; Cui et al., 2024). Additionally, several threshold values and predicted no effect concentrations (PNEC) have been specifically derived for both MP and TRWP (Van Cauwenbergh et al., 2015; Besseling et al., 2019).

This study aims to characterize TRWP, MP and metals in urban sediments from stormwater detention basins to better understand the co-occurrence of these pollutants according to urban watershed characteristics. The pollution load index (PLI) can be used to assess the pollutant load according to the catchment area, but also according to land use. Finally, the ecological risk associated with each pollutant is estimated for each basin. The limitations of this type of analysis are highlighted and allow for the consideration of several characteristics specific to MP for a more precise risk assessment.

## 2. Material and methods

### 2.1. Study area and sampling

The study focuses on the Lyon agglomeration, a European city crossed by the Rhône River, which ranks as the third most populous in France with 1,4 M inhabitants in 2020, spanning an area of 533.7 km². Most of the city's wastewater is collected in a combined sewage system; however, around 20 % is separated to collect only rainwater that is then conducted into detention basins that store rainwater, urban sediments and associated pollutants. Ten detention basins and one stilling basin downstream of a combined sewer overflow (CSO) outfall were studied to measure MP, TRWP and metals in sediments left in the basin by run-off water. These basins were selected according to their respective land use, using information on the proportions of industrial and commercial areas, urban areas, agricultural areas, forests, and natural areas. The land use data came from CORINE Land Cover 2018 and were categorized into QGIS (v.3.32.0). Each basin was attributed to one out of four groups according to the dominant land use of the urban catchment drained: urban, industrial, agricultural and heterogeneous (Table S1). Three detention basins are mostly urban: "Boulevard Urbain Est" (BUE), "Chemin de Feyzin" (Ch_Feyzin) and "ZAC". Three other basins are industrial: "Leopha", "Django" and, "Triangle". Another three are predominantly agricultural: "Pilon", "Buisson" and "Pierre-Blanche" (PB). Finally, the last basin, "Sathonay" (Sath_DB) and the CSO "Sathonay" (Sath_CSO), are considered to be predominantly heterogeneous in terms of land use. Sampling took place between April and October 2022. The detention basins are dry outside rainy weather. Sediments were collected randomly at five to eight sampling sites from each basin, depending on the size of the basin (Hebrard-Labit et al., 2006). Samples were collected using a stainless-steel shovel, then homogenized on site in one composite sample using a stainless-steel bucket and one kilogram

of sediment was recovered. The composite sample was separated in three analytical replicates. These replicates were used to measure organic matter content using the protocol of Heiri et al. (2001) and particle size distribution using a Mastersizer 3000 laser microgranulometer. Sediment samples were classified based on grain size: two basins were composed mainly of fine particles (<63 µm) ("Buisson", "Pilon"), four basins are composed of sands ("PB", "BUE", "Triangle", "ZAC") and four basins are at the limit between silt and sand ("Ch_Feyzin", "Django", "Leopha", "Sath_DB"). In terms of organic matter content, "Sath_CSO", "Sath_DB", "PB" and "Buisson" have the lowest content ranging from 1 % to 5 %. "BUE" and "Pilon" had moderate levels, ranging from 5 % to 15 % whereas the others had an organic matter content of over 15 % with the highest recorded being 25 % for "Ch_Feyzin" (Table S1). For more information on the urban catchment characteristics, sampling methods and physico-chemical parameters, please to refer to Iannuzzi et al. (2024).

### 2.2. Analysis of tire road wear particles (TRWP)

Pre-treatment consisted of drying the sample for 24 h in an oven at 105 °C and then sieving at 500 µm using a stainless-steel sieve. The Pyr-GC-MS analyses were then conducted using a multi-shot pyrolyzer EGA/ PY-3030D, equipped with an auto-shot sampler (AS-1020E) from FrontierLab (Fukushima, Japan). The pyrolyzer, set at 600 °C for 12 s, was coupled with the 8890 gas chromatography and the 5977B mass spectrometer from Agilent Technologies, Inc. (California, USA). More detailed information is provided in Table S2. For the quantification of SBR, a specific pyrolysis product, namely 4-phenylcyclohexene (4-PCH), was selected based on *m/z* = 104 Da. This marker was chosen due to its specificity for SBR, as opposed to the commonly used 4-vynilycyclohexene, which is also produced by other polymers such as styrene-butadiene-styrene, added to modified bitumen, and may ultimately lead to an overestimation of SBR (De Oliveira et al., 2024b). Additionally, 15 µl of a deuterated SBR, solubilized in chloroform at a concentration of 85 ng/µl, was used as the internal standard for the quantification. Approximately, 3 mg of sediment were weighed in pyrolysis cups using the Mettler Toledo AB265-S/FACT balance and a stainless-steel spatula. All samples were analysed in triplicates for a total of 33 samples. Three control samples (i.e., 1.10 µg SBR and 2.50 mg SiO₂) were analysed prior to the sediment sample sequence. The recovery calculation yielded 97 ± 4 %. Furthermore, the analysis of procedural blanks (i.e., no cup, cup only, cup + chloroform, cup + SiO₂) conducted prior to and during the sediment sample sequence showed no SBR contamination.

A conversion factor SBR-to-TRWP of 9.12 is applied to estimate the concentrations of TRWP (De Oliveira et al., 2024b). First, a conversion factor from SBR to TWP was calculated using data from Unice et al. (2012), Einsentraut et al. (2018), Rauert et al. (2021), and De Oliveira et al. (2024a), who reported a polymer content in tires to be 50 %, 11.3 %, 9.3 % and 38.9 %, respectively. Second, a TWP-to-TRWP conversion factor was applied to account for the mineral fraction present in TRWP. Based on the findings of Sommer et al. (2018), Klöckner et al. (2021), and Kreider et al. (2010) the estimated mineral content in TRWP ranges from 6–7 %, 25 %, and up to 53 %, respectively. The limit of quantification for SBR was determined to be 15 µg·g⁻¹ which is equivalent to 137 µg·g⁻¹ TRWP. Specific methodological details of the analytical protocol can be found in the referenced study (De Oliveira et al. 2024a).

### 2.3. Analysis of metals

Several metals were analyzed, including aluminum (Al), arsenic (As), cobalt (Co), chromium (Cr), copper (Cu), cadmium (Cd), mercury (Hg), iron (Fe), nickel (Ni), lead (Pb), antimony (Sb), tin (Sn) and zinc (Zn). Among these metals, two are considered priority hazardous substances under the European water framework directive (WFD), Hg and Cd and two as WFD priority substances, Ni and Pb. Cu, Zn and Pb are also
specific to an urban contamination (Chambers et al., 2016). In addition, directive 2006/11 for the protection of the aquatic environment against discharges of dangerous substances, which was repealed by the WFD as from the end of 2013, included 4 metals on list II, (pollutants that must be controlled and progressively reduced) As, Cr, Cu and Zn. Al and Fe are analyzed because they are terrogenous elements.

The quantification of metals was performed using the Agilent ICP-MS 7850 and ICP-OES 5800, following an adapted protocol of standard NF EN ISO 54321, which involves the digestion of elements soluble in aqua regia with a DigiPrep system Analytichem. An aliquot of 1 g of dry sample was mixed with 12 mL of hydrochloric acid (HCl) and 4 mL of nitric acid (HNO3) and the mixture, was subsequently heated to 105° for 2 h. The samples were filtered with Sartorius Minisart™ 0.45 µm filter before being analyzed with an ICP-OES or MS depending on the elements. Standards and blank underwent the same treatment. This extraction provides only partial, not total, quantification of metal concentrations.

## 2.4. Analysis of microplastics (MP)

For the analysis of MP, an initial step separated plastic particles by density using a sodium iodide solution. The second step consisted of a treatment to oxidize the organic matter using hydrogen peroxide. MP were identified using Fourier infrared micro-spectroscopy (PerkinElmer Spotlight 400 FT-IR Imaging System), then the SiMPle software was used to compare the spectra obtained with a reference database. This type of analysis provides the number of MP, polymer type and particle size. The software also identifies an estimated mass concentration by calculating the volume of each particle on the filter with a predefine density for each polymer. This method identifies only MP particles between 25 and 500 µm in size. More detailed information on the extraction protocol and quality control can be found in our previous referenced study (Iannuzzi et al., 2024).

## 2.5. Data analysis

### 2.5.1. Statistical analysis

Metals concentrations in the different urban catchments were compared using compositional statistic approach to correct the closure effect in the data matrix (Aitchison, 1986; Tolosana-Delgado, 2012). The principal component analysis (PCA) was carried out on centered log-ratio data, which enables compositional data to be processed.

The relationship between contaminants (TRWP, MP and metals) was explored as a function of land use. A non-parametric Spearman correlation (p-value < 0.05), was used to highlight relationships between TRWP, metals, MP, physico-chemical parameters and land uses. The statistical analyses were performed using triplicate measurements for each contaminant and percentages of each land use for each basin.

### 2.5.2. Enrichment factors

Enrichment factors (EF) were used to assess the degree of site contamination relative to the pre-industrial baseline concentrations (Reimann and De Caritat, 2005). The advantage of EF is the possibility to take into account the effects of grain size by normalizing to the Aluminum (Al) content, which is considered to be a proxy for fine grain-size sediment (Dendievel et al., 2020). In our study, Al concentrations were significantly correlated with fine sediment content supporting the use of this normalization approach.

Here, EF were calculated using the geochemical background of metals proposed by Dendievel et al. (2020) for the Upper Rhone (MeGb) (Table S3) as:

$$EF = \frac{\left( \frac{Me}{Al} \right)}{\left( \frac{Me_{Gb}}{Al_{Gb}} \right)} \quad (1)$$

Where Me is the concentration of the metal element in mg·kg⁻¹, Al is the concentration of Al, MeGb is the geochemical background for metal element Me and AlGb the geochemical background for Al (Eq. (1)).

The enrichment scale indicates that if the index is below 1.5 there is no or very low enrichment, between 1.5 and 5 the enrichment is moderate, between 5 and 20 the enrichment is considered significant and between 20 and 40 the enrichment is high. Above 40, the enrichment is considered extremely high.

### 2.5.3. Pollution indices per basin

The contamination factor (CF) (Eq. (2)) and the pollution load index (PLI) developed by Tomlinson, (1980) were calculated for each site as follows:

$$CF_i = C_i/C_0 \quad (2)$$

$$PLI_i = \sqrt[n]{CF_1 * CF_2 * \dots * CF_n} \quad (3)$$

The CF was calculated from Cᵢ which is the median concentration of the pollutant (calculated from triplicate values) at site i and C₀, which is the reference value of the pollutant at pristine or low impacted areas. For metals, the geochemical background measured close to the site (Dendievel et al., 2020) was chosen as reference value for the calculation of EF and CF (Table S3). For MP and TRWP, the natural background is theoretically 0 and there are no reference values in the literature on anthropogenic background concentrations for site similar detention basins. In our study, C₀ was set at the LOQ value for TRWP (137 mg·kg⁻¹) and the lowest concentration in our study for MP (0.22 mg·kg⁻¹). The PLI (Eq. (3)) was calculated for each basin (i) as the nᵗʰ root of the CF for pollutants present in the basin. When the PLI > 1, the basin is considered polluted.

A PLI by type of land-use was also calculated (Hoshyari et al., 2023) as:

$$PLI_{\text{Land-use}} = \sqrt[n]{PLI_1 * PLI_2 * \dots * PLI_n} \quad (4)$$

where n is, the number of sample sites for each land-use category. This measure allows to compare pollution levels between catchments with different types of dominant land use. According to the classification proposed by Pan et al. (2021), the PLI which is low when < 10, moderate between 10 and 20, high between 21 and 30 and extremely high if > 30.

### 2.5.4. Risk assessment

In the absence of a universally accepted methodology for assessing the risk posed by the presence of MP and, TRWP in sediments, several approaches were employed to facilitate a comprehensive comparison.

First, the traditional risk assessment paradigm of calculating risk quotients (ECB 2003) was used. The risk quotient for site i (RQi) was calculated as the ratio between the measured concentration of contaminant x (Cix) and the predicted non-effect concentration of this contaminant for sediments (PNECsediment,x). The PNEC is the concentration of a substance in the environment below which adverse effects are not expected to occur. Here, the PNECsediment,MP value of 539 MP·kg⁻¹ proposed by Yang et al. (2023) was used, which is intended to protect freshwater benthic organisms exposed to MP in sediments. This PNECsediment was preferred over others proposed in the literature for mass concentrations because our data are more robust in terms of number of MP than in terms of mass estimates. Concerning TRWP, an ad hoc PNECsediment,TRWP value of 1,000 mg·kg⁻¹ was derived for use in this study. Briefly, the PNECsediment was derived following the EC WFD Technical Guidance Document for EQS derivation (EC EQS TGD, EC 2018), using quality-checked effect data where benthic invertebrates were exposed directly to sediments spiked with TRWP available from the literature. The full derivation procedure is included in supplementary materials.

For metals, threshold effect concentration (TEC) values proposed by
MacDonald (2000) were used. These values define the level below which adverse effects on benthic organisms are unlikely to occur, thus aligned with the definition of PNECsediment (Table S3).

According to the classification proposed by Schuwirth (2020), a RQ < 0.1 indicates a high-quality status, with no significant risk because contaminants are well below levels of concern; 0.1 ≤ RQ < 1.0 indicates a good status, with low risk according to low-level contamination; 1.0 ≤ RQ < 2.0 indicates a moderate quality, with moderate risk because contaminants may start affecting benthic organisms; 2.0 ≤ RQ < 10 indicates a poor status, with a risk of significant adverse effects on benthic organisms, and RQ ≥ 10 indicates a bad status with a high risk of severe pollution with major ecological consequences.

For MP, new approaches have been proposed to account for the complexity of particles in risk assessment. In this work, two methods were tested. First, the polymeric risk index ($H_i$) and the pollution risk index (PRI) proposed by Kabir et al. (2021) were calculated as:

$$H_i = \sum_{j=1}^{m} (P_{ij}/C_i) * S_j \quad (5)$$

$$PRI = H_i * PLI_i \quad (6)$$

Where $H_i$ (Eq. (5)) is the hazard score and represents the summary polymeric risk at site i, with $P_{ij}$ the number of each type of polymer j identified at site i, $C_i$ is the MP abundance at site i, and $S_j$ the polymeric risk score proposed by Lithner et al. (2011) for each monomer. The PRI (Eq. (6)) is the result of multiplying the pollution load index (PLI, Eq. (3)) by the polymeric risk ($H_i$), specific to MP. A classification is proposed by Kabir et al. (2021) for $H_i$ and PRI. For $H_i < 10$, the mixture of MP at a given site falls in the lowest risk category I, risk category II is proposed for $H_i$ between 10 and 100, risk category III for $H_i$ between 101 and 1000, risk category IV for $H_i$ between 1001 and 10,000 and risk category V for $H_i > 10,000$. For PRI, the classification is low when <150, medium from 150 to 300, considerable from 300 to 600, high from 600 to 1200 and very high if PRI > 1200.

The second approach used for risk assessment of MP was the risk score (RS) proposed by Bucci and Rochman (2022). They propose the calculation of a hazard value for each specific type of MP particle that accounts for parameters likely to influence the risk. These parameters

include, the polymer, the size and the shape of the particles as well as the environmental chemistry. Here, the hazard value for each type of MP particle was calculated by summing up the ranking scores proposed by Bucci and Rochman (2022) for the corresponding polymer whereas the environmental chemistry score was attributed according to the predominant land use at each catchment area (Table S6). In our study, no shape analysis was carried out, which is why we did not add this score. The number of the same type of particle present in a given basin is then multiplied by its hazard value to obtain a particle-specific hazard value for each basin. Then, particle-specific hazard values for all particles present in a given basin, are summed to obtain a hazard score for each basin. Finally, the risk of the environmental sample is calculated by dividing the hazard score by the mass of sample. For PRI and RS, it is necessary to know the size of particles identified as well as the polymeric risk. This information is not known, for TRWP, thus these methods will only be tested on MP contamination identified by µ-FTIR.

## 3. Results and discussion

### 3.1. Co-occurrence of TRWP and MP

Fig. 1 shows the median mass concentrations of TRWP and MP in each basin, calculated from triplicate measurements (Table S7). To the best of our knowledge, this is the first comparison of the presence of tire wear particles and MP in sediments. The results reveal higher concentrations of TRWP compared to those estimated for MP in most of the studied sites, and also an average concentration two times higher for TRWP. It is important to mention that the mass of MP estimated using infrared spectroscopy is subject to uncertainties compared to the MP particles counts, and is not as reliable as the SBR measurement obtained through GC/MS pyrolysis in TRWP analysis. Moreover, our analytical method for MP does not account for particles smaller than 25 µm. The MP concentrations are therefore probably underestimated. However, estimated MP masses and abundances follow the same ranking between basins. The Fig. 1 shows that basins with high MP concentrations are also those with the highest TRWP concentrations.

For TRWP, five basins had concentrations below the limit of

<figure><img src="image_2.png" /><figcaption>Fig. 1. Median mass concentration of TRWP and MP (mg·kg⁻¹) for each site (n = 11) grouped into four categories according to land use (logarithm scale). Error bars (standard deviation) are calculated from the triplicate measurements for each sample. The dotted line shows the limit of quantification of TRWP (137 mg·kg⁻¹), five basins have a TRWP value below this limit.</figcaption></figure>
quantification, set at 137 mg·kg⁻¹ in our study, and only those with mainly industrial and urban land use had concentrations above the LOQ. The highest concentrations of both TRWP and MP were found in “Leopha” with 19,400 and 2,469 mg·kg⁻¹ respectively, followed by “Django” with 15,707 mg·kg⁻¹ for TRWP and 503,7 mg·kg⁻¹ MP. For the other basins the concentration ranged between 5,662 and 1,749 mg·kg⁻¹ for TRWP and from 189 to 0.3 mg·kg⁻¹ for MP. The high concentrations of TRWP in “Leopha” and “Django” were expected and are consistent with the high density of logistics platforms and road traffic within the urban catchment area. Conversely, catchment areas with a low traffic density and located on the outskirts of transit areas reported TRWP concentrations below the quantification limit. MP concentrations were also higher in urban and industrial basins but, unlike TRWP, MP were detected across in all basins. This broader presence is likely due to both the more diffuse nature of MP sources and the higher sensitivity of the analytical method. TRWP are most certainly present in agricultural and heterogeneous basins, but at low concentrations. These MP represent a wide range of polymers and are mainly polypropylene, followed by polyethylene and polystyrene (Iannuzzi et al., 2024).

Few studies have examined the presence of TRWP in street run-off or urban sediments. Järlskog et al. (2022) measured between 3,500 and 14,500 mg·kg⁻¹ of TRWP in sediment from a gully pot in Sweden. Rødland et al. (2022) found 35,800 mg·kg⁻¹ of TRWP in the sediments of a gully pot in Norway. The values from the various studies are broadly comparable, falling within the same order of magnitude. The differences observed can be mainly attributed to differences in the analytical techniques and quantification methods used in the different studies. In addition, some factors specific to the study site may also contribute to these differences (vehicle speed, intensity of road traffic, ...). A related study conducted in France investigated TRWP concentrations in an infiltration pond next to a roadway with a speed limit of 70 km·h⁻¹ and a traffic density of 100,000 vehicles per day. This study reported values of up to 6,521 mg·kg⁻¹ based on the quantification of both styrene-butadiene rubber and butadiene rubber (De Oliveira et al., 2024b). To better compare both studies, the values derived from De Oliveira et al.

(2024b) were re-evaluated using the same quantification method presented here. Hence, the concentration found in the infiltration pond can be as high as 3,412 mg·kg⁻¹.

Moreover, urban sediments accumulate in stormwater basins over several years until they are cleaned up. In our case, the cleaning date is not known, but it takes place around every five years. For “Django” basin, cleaning was carried out less than 2 months before sampling. De Oliveira et al. (2024b) indicate that the pond had a sediment accumulation during 8 months.

## 3.2. Metal contamination

The median metal concentrations (mg·kg⁻¹) for each of the study sites are presented in Table S8. A comparison of our results with previous studies on metal contamination in detention and infiltration basins from the Lyon agglomeration is presented in Table S9. The principal component analysis (PCA) biplot shows two groups of metal elements, the two dimensions explaining 78 % of the variability. On the one hand, stable and terrigenous metals, and on the other, metals resulting mainly from human activity (Fig. 2). Catchments with predominantly urban and industrial areas have higher concentrations of metals associated with urban anthropogenic sources such as Cu, Sn, Sb, Zn, Pb and Cd. Several studies showed that Cu, Zn and Pb tend to be present at higher concentrations in industrial areas (Müller et al., 2020). On the other hand, the predominantly agricultural basins are characterized by the concentrations of terrigenous metals such as Cr, Fe, As, Al, Ni and Co and, lower levels of contamination compared to urban and industrial basins.

To assess the anthropogenic contribution to metal contamination, EF were calculated for all metals (Table S10) by using pre-industrial reference concentrations (Dendievel et al., 2020). Metals that did not belong to the “urban metals” group in the PCA appear as moderately enriched in all basins (values between 1.5 and 5). For example, the EF for Co reaches 3.33 for “Django” and 2.78 for the “Sathonay” basin. A similar trend is observed for Cr, which is moderately enriched in “BUE”, “Django”, “Leopha” and “Sath_CSO”. The “urban metals” Cd, Cu, Pb and

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Principal component analysis on median metal concentrations with a centered log-ratio in biplot with dimension 1 and 2 for all sites classified by catchment areas groups.</figcaption></figure>
Zn showed the highest metal enrichments (Fig. 3), in particular at in-
dustrial basins. For example, “Leopha” sediments showed an extremely
high enrichment for Zn (EF = 42.3) and very high for Cu (EF = 28.9),
and “Django” showed a very high Zn and Cu enrichment (EF ranging
from 26.3 to 33.6). Overall, these basins had the highest EF, repre-
senting a characteristic signature of urban environments. Traffic-related
sources of metal emissions include: rechargeable bat teries, pigments
and coatings for Cd; brake pads, building siding for Cu; automobile
batteries and paints for Pb; and automobile exhaust, tires, galvanized
steel for Zn (Chambers et al., 2016). Regarding drainage surfaces,
several studies have suggested that the corrosion of structural metals,
particularly roofs and facades, is responsible for a large proportion of
Cu, Pb and Zn in stormwater. Pb is particularly prevalent in the facades
of older buildings, while Zn is found in gutters (Müller et al., 2020).
Agricultural basins (“Buisson”, “Pilon”, “PB”) showed the lowest EF for
all four metals, while sediments from the “Sath_CSO” had the highest Cu
and Pb enrichment, among all the studied basins. A study in Australia
about sediments in an urban river showed a moderate enrichment for
Mn, Cu and Zn, low for Fe and Ni and very high for Pb in an industrial
area with heavy traffic (He et al., 2023). Relatively similar observations
have been reported on sections of the Rhône close to our study sites.
Between 2000 and 2018, the Rhône River exhibited EF values of
approximately 1.2 for Cu and Pb, around 1 for Zn, and about 2 for Cd
(Dendievel et al., 2020), indicating no substantial metal enrichment
compared to our context. This discrepancy was expected, as contami-
nation is typically more concentrated in urban sediments than in
riverine sediments.

3.3. Pollution indices

To assess contamination levels, the pollution load index (PLI) was
calculated for TRWP, MP and urban metals in each basin. Seven basins
had high PLI between 15.39 and 56.83, indicating strong pollution,
while 5 basins had PLI between 0.97 and 2.63, which still indicates that
the sites are polluted (Table S11). The PLI calculated for type of land use
showed that a site's contamination is linked to its land use, as there was
a clear distinction between very high pollution in industrial catchments

(37.35), followed by urban catchments (18.53) and far behind agricul-
tural and heterogeneous catchments (1.49 and 1.77) (Table S12). To
date, only one study has evaluated MP contamination in topsoils in
urban context, reporting higher levels in urban soils compared to in-
dustrial and agricultural soils (Hoshyari et al., 2023). In aquatic envi-
ronments, studies assessing MP contamination using the PLI have shown
high levels in surface waters (Wang et al., 2021; Huang et al., 2023),
whereas sediments in the Yangtze River exhibited a low PLI level (Yang
et al., 2023).

3.4. Correlation between pollutants and influencing parameters

The correlation matrix among TRWP, MP, and metals concentra-
tions, sediment properties and land use revealed a positive and signifi-
cant correlation of contaminant concentrations with the proportion of
industrial area and a negative correlation with the proportions of agri-
cultural and natural areas (Fig. 4), confirming the preliminary insight
into pollutant sources explained above. This result is consistent with the
PCA biplot (Fig. S1), where dimension 1 clearly separates industrial/
urban basins from agricultural/heterogeneous basins. The correlation
matrix also reported significant positive correlations between TRWP,
MP and all metals variables, with values ranging from 0.7 to 0.9 (p-
value < 0.001). De Oliveira et al. (2024b) also observed a significant
correlation between metals and TRWP, which reinforces the idea that
ponds and detention basins are integrators of pollutants. Detention ba-
sins are integrating structures that retain water and promote the po-
tential sorption of dissolved elements on particles, as well as the settling
of particulate pollutants. De Oliveira et al. (2024b) explains that tire and
metals could aggregate with sediments, particularly when the presence
of OM is high, and would facilitate this process. In this study, a strong
positive correlation between pollutants and OM is also observed. How-
ever, these correlations are not consistently observed. For example,
Rødland et al. (2023) found no correlation between TRWP and metals in
roadside soils, likely due to differences in environmental settings and
pollutant dynamics.

The strong correlation found in our study between “urban” metals
and TRWP suggests that metals could be a suitable proxy for TRWP

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Median enrichment factors for Cd, Cu, Pb and Zn for detention basins with the land use classification.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Spearman's correlation matrix with all values (n = 33) for pollutants, the main physico-chemical parameters and the urban watersheds characteristics. Only significant Rho coefficients (p-value < 0.05) are presented.</figcaption></figure>

contamination in stormwater detention basins. However, the TRWP/ metal ratio (Table S13) ranged from 1.64 to 10.8 and despite the increased ratios for predominantly industrial basins, in particular “Django” and “Leopha”, this approach does not yet help in attributing metals to a precise source such as TRWP emissions at a given basin.

### 3.5. Risk assessment for urban sediments in detention basins

The Table 1 presented the RQ for each pollutant and each basin. Among the different contaminants targeted in this study, MP had the highest RQ falling within the poor and bad quality classes for all basins. The RQ for metals and TRWP showed higher variation across sites. Industrial basins, particularly “Leopha” and “Django”, exhibit the highest risk, with RQs for TRWP and Zn in the bad quality class (concentrations above then times the PNEC<sub>sediment</sub>). “Triangle” and the three urban basins were mostly of poor quality for TRWP and Zn. For both industrial and urban basins, Cd, Cu and Pb concentrations classified the sediments as of medium or poor quality. Sediments from heterogeneous and agricultural basins displayed substantially lower risk across all pollutants, falling in the high or good quality for TRWP and most metals (Table 1).

The outcome of the RQ calculation is highly dependent on the PNEC used and an inaccurate or unreliable PNEC can lead to a flawed or unreliable risk assessment outcome. Here we used for metals, the TEC

values proposed by MacDonald et al. (2000), which are widely used since their proposal several decades ago (Wenning et al., 2005) and the traditional risk assessment paradigm based on the RQ calculation is relatively well established for metals in sediments (ICMM 2016). This is not the case for MP and TRWP. For MP, the PNEC<sub>sediment</sub> value of 539 MP·kg<sup>-1</sup> from Yang et al. (2023) was used while a PNEC<sub>sediment</sub> value of 1,000 mg·kg<sup>-1</sup> for TRWP was derived in this study. The two PNEC<sub>sediment</sub> adhere to the recommended approach in chemical regulation for the derivation of PNEC values intended to protect benthic invertebrates from direct sediment exposure (ECHA 2017) (Table S5) but remaining uncertainties may influence the quality of RQ interpretations. Uncertainties are not related to the number of available effect studies, which is surprisingly high for MP and TRWP compared to other chemicals (Sobanska et al. 2014). The quality of the studies was carefully assessed for TRWP before use for the test set-up and conditions (Casado-Martinez et al. 2024). The main remaining uncertainty is due to the lack of standard methods for testing the ecotoxicity of particulate tire emissions. Thus, the effect studies retained for the derivation of the PNEC<sub>sediment</sub> included different test materials, age and leaching conditions, which may lead to different exposure conditions to an unknown mixture of particles and leached chemicals, which may lead to differences in the effects. The same uncertainty likely remains for the PNEC<sub>sediment</sub> for MP. Yang et al. (2023) does not explicitly mention whether effect data were quality checked against general criteria for aquatic toxicity studies (Moermond et al., 2015) or criteria specifically proposed for MP effect studies (De Ruijter et al. 2020). The possibility that Yang et al. (2023) retained the lowest effect datum for each species not meeting quality requirements for PNEC derivation may have biased the PNEC<sub>sediment</sub> towards lowest values leading to higher RQ for MP compared to that of metals and TRWP. A critical assessment of the reliability of the proposed PNEC<sub>sediment</sub> (goodness of fit of the species sensitivity distribution model and corresponding confidence limits of the estimated hazard concentration) could also not be performed. Finally, it is important to note that the RQ calculation considers only the account of a given contaminant, even though TRWP and MP are particulate contaminants, so the risk may depend on size, type of polymer or shape. It is therefore relevant to assess risk using a holistic approach (Li et al., 2024). Furthermore, MP, TRWP and metals are different types of substances and likely have different modes of toxic action, this their risk cannot be simply estimated by addition RQ calculation.

### 3.6. Assessing complex risk for MP

The risk associated to MP is difficult to evaluate due to the complexity and diversity of polymer types, sizes, shapes, product types, additives, etc., which can interact with the environment and therefore modify the associated risk (Rochman et al., 2019). To account for the

**Table 1**

Risk quotient calculated for each basin and each type of pollutant. The color code indicates the quality status per pollutant with blue: high, green: good, yellow: moderate, orange: poor and red: bad.

<table><thead><tr><th>Type</th><th>Site</th><th>TRWP</th><th>MP</th><th>Cd</th><th>Cu</th><th>Pb</th><th>Zn</th></tr></thead><tbody><tr><td rowspan="3">Industrial</td><td>Leopha</td><td>19.4</td><td>2'336</td><td>1.3</td><td>6.2</td><td>1.6</td><td>12.9</td></tr><tr><td>Django</td><td>15.7</td><td>604</td><td>1.5</td><td>7.4</td><td>2.9</td><td>13.2</td></tr><tr><td>Triangle</td><td>4.1</td><td>509</td><td>0.5</td><td>4.2</td><td>1.6</td><td>4.8</td></tr><tr><td rowspan="3">Urban</td><td>BUE</td><td>2.1</td><td>235</td><td>0.8</td><td>5.9</td><td>2.3</td><td>8.5</td></tr><tr><td>ZAC</td><td>1.7</td><td>122</td><td>0.7</td><td>3.6</td><td>2.6</td><td>5.5</td></tr><tr><td>Ch_Feyzin</td><td>5.7</td><td>721</td><td>0.7</td><td>3.6</td><td>1.4</td><td>4.9</td></tr><tr><td rowspan="2">Heterogeneous</td><td>Sath_DB</td><td>0.07</td><td>4.6</td><td>0.2</td><td>0.4</td><td>0.4</td><td>0.4</td></tr><tr><td>Sath_CSO</td><td>0.07</td><td>15.9</td><td>0.2</td><td>2.2</td><td>1.1</td><td>1.2</td></tr><tr><td rowspan="3">Agricultural</td><td>Buisson</td><td>0.07</td><td>3.9</td><td>0.2</td><td>0.4</td><td>0.4</td><td>0.4</td></tr><tr><td>Pilon</td><td>0.07</td><td>30.9</td><td>0.3</td><td>0.7</td><td>0.6</td><td>1.1</td></tr><tr><td>PB</td><td>0.07</td><td>56.8</td><td>0.1</td><td>0.3</td><td>0.4</td><td>0.2</td></tr></tbody></table>
diversity of MP found in environmental media, two methods have been proposed in risk assessment. The first method proposed by Kabir et al. (2021) is the derivation of a hazard score specific for each site (Hi) according to the abundance and hazard properties for the different types of polymers present. The calculated Hi values for the sediments in this study distinguished “BUE” as the basin with the most hazardous mixture of MP (Hi highest in category IV), followed by “Sath_CSO”, “ZAC”, “Django”, and “Pilon” with levels in category III (Table 2). Only “Buisson” and “Sath_DB” were classified in the least hazardous categories (I and II respectively). Indeed, the Hi value depends on the proportion and the type of polymer. For example, polypropylene was relatively abundant in all basins (proportion between 0.9 and 0.5) but has a low polymeric score. On the contrary, the proportion of polyvinyl chloride (PVC) for the industrial basins “Django” and “Leopha” (0.04 and 0.01, respectively) was much lower than at the urban basin “BUE” (0.07). With the polymeric risk score for PVC being 10'551, there was a remarkable increase in the Hi index for “BUE” compared to “Django” and “Leopha”. Similarly, polyurethane (PU) has a polymeric score of 7'384 and “Sath_CSO” and “BUE” basins had a PU proportion around 0.02, compared to 0.005 for “Django” and 0.006 for “Leopha”. These examples highlight the usefulness of identifying polymers when estimating the hazard of MP mixture for risk assessments. Basins that are predominantly agricultural and heterogeneous may contain polymers that are more hazardous than those found in industrial or urban basins, a difference that would not be captured if a single PNEC<sub>sediment</sub> is applied for the entire (unknow) MP mixture.

The calculated hazard score Hi can be further used in risk assessment through the calculation of the PRI index, which incorporates the PLI as an exposure metrics. The overall risk profile obtained from the calculated PRI was similar to that derived from the RQ, with sediments from all detention basins falling within the highest risk category. Only “Buisson” and “Sath_DB”, classified as poor quality according to the RQ, and “PB”, which fell into the bad quality category, were placed into the two lowest risk categories according to the PRI. Conversely, the PRI placed “BUE” together with “Django” and “Leopha” at the top of the MP risk scale, according to the highest hazardous MP mixture (Hi) in these basins. Like in our study, Kabir et al. (2021) used the PRI to assess MP pollution in surface waters from Japanese rivers and found high risks for stations dominated by agricultural, urban, and residential land uses, with risk being dependent on both higher MP abundance and the presence of highly toxic polymers. Hoshyari et al. (2023) also used this same approach to study MP pollution in the topsoil of Shiraz metropolis and identified the highest risks close to high density-population or high traffic zones, although the PRI values were lower than those found in this study.

The second method that takes into consideration the complexity of MP in risk assessment was developed by Bucci and Rochman (2022). They proposed a framework for calculating a risk value (RS) based on different scores assigned to polymer types, their size and shape, and environmental chemistry. The RS calculation shares similarities with the PRI, (i.e., the derivation of a hazard score for each particle type and its integration with an exposure indicator), but resulted in a different ranking of basins compared to the PRI. The RS rankings were more closely aligned with MP abundance in the samples and, therefore, with the RQ results (Table 2). For example, while “Leopha” ranked first with the RS and RQ and third with the PRI, the urban basin “BUE” ranked second with the PRI method, third with the RS and fifth with the RQ. As for the PRI, these changes can be attributed to the incorporation of toxicity as a function of particle type and size. For instance, “Leopha” contained 28 small particles (10–99 µm) of highly toxic polymers (such as polyvinyl chloride, polyurethane, polyamide, ...), while only 4 of these were present in “BUE”. MP abundance remains the main criterion in the RS calculation, and industrial and urban basins exhibited the highest MP concentrations. In addition, the chemical load score, which was higher for industrial and urban basins (0.9) compared to agricultural and heterogeneous ones (0.7), further contributed to the elevated RS for these basin types. This chemical score was attributed for each type of basin after Bucci and Rochman (2022) following the overall results of the metal contamination per type of basin. While it can be assumed that introducing this additional dimension improves the comparison among sites, as Bucci and Rochman (2022) already highlighted the “proposed framework is just that, and requires new research for application”. First, the scores used to rank the hazard of each type of particle requires further ecotoxicity testing and validation. Although based on existing data, they are not yet fully supported by experimental evidence. Second, the hazard values are calculated by summing the scores of each dimension considered (size, shape, ...), which assumes additivity of effects, a simplification that may not accurately reflect bio-chemical interactions.

Gouin et al. (2019), in collaboration with several stakeholders, proposed a framework for environmental risk assessment of MP. This study highlighted two key priorities: (i) identifying standard reference materials for MP particles to be used in toxicity studies, and (ii) using particles in toxicity tests that accurately reflect those found in the environment. Instead of testing each particle individually, Bucci and Rochman (2022) propose a testing strategy designed to improve our understanding of the relative toxicity associated with each dimension of MP. This approach would help to reduce uncertainties in MP risk assessment, as well as for TRWP, by enabling more accurate calculations of particle- and sample-specific hazards, while accounting for particle

<table><caption>**Table 2**
Polymeric risk index (Hi), pollution risk index (PRI) and risk score (RS) for MP.</caption><thead><tr><th>Type</th><th>Site</th><th>Hi MP</th><th>PRI MP</th><th>RS MP</th></tr></thead><tbody><tr><td rowspan="3">Industrial</td><td>Leopha</td><td>274</td><td>15'551</td><td>912</td></tr><tr><td>Django</td><td>554</td><td>27'302</td><td>307</td></tr><tr><td>Triangle</td><td>251</td><td>4'664</td><td>199</td></tr><tr><td rowspan="3">Urban</td><td>BUE</td><td>1'044</td><td>20'905</td><td>201</td></tr><tr><td>ZAC</td><td>775</td><td>11'928</td><td>111</td></tr><tr><td>Ch_Feyzin</td><td>379</td><td>7'825</td><td>189</td></tr><tr><td rowspan="2">Heterogeneous</td><td>Sath_DB</td><td>6.64</td><td>8.69</td><td>2.72</td></tr><tr><td>Sath_CSO</td><td>680</td><td>1'634</td><td>17.1</td></tr><tr><td rowspan="3">Agricultural</td><td>Buisson</td><td>17.6</td><td>17.0</td><td>4.89</td></tr><tr><td>Pilon</td><td>525</td><td>1'384</td><td>15.9</td></tr><tr><td>PB</td><td>124</td><td>161</td><td>22.3</td></tr></tbody></table>
complexity. However, the risk assessment is also influenced by the un-
certainty of exposure estimates. In our study, it is likely that our
analytical method (µFTIR) underestimates the smallest MP particles,
which are considered to pose the highest risk (Rochman et al., 2019).
Moreover, the effects of TRWP particle size are not considered in the risk
assessment, as the method used (Pyr-CG-MS) does not allow for size-
based characterization. The improvement of analytical methods for
more accurate characterization and quantification of MP and TRWP
remains a critical and challenging priority.

4. Conclusion

The concentrations and associated ecological risks of metals, TRWP, and MP were examined in urban sediments collected from detention basins in the Lyon metropolitan area, France. TRWP levels were found to be higher than MP concentrations in the sediments. Complementary metal analyses revealed positive correlation among the three pollutants, which were particularly strong in certain detention basins and closely linked to land use types. Industrial basins showed the highest concentrations for all pollutants followed by urban ones. The EF and PLI calculations confirmed significant anthropogenic enrichment of sediments across basins, although appropriate background concentrations for MP and TRWP remain to be defined. Several approaches were used to assess the ecological risks of sediments for each basin, each with inherent uncertainties and limitations. Traditional risk assessment method, such as the RQ based on the PNEC<sub>sediment</sub> limit values, identified a higher ecological risk associated with MP compared to TRWP and metals, in particular at urban and industrial sites. Novel approaches that account for particle complexity, an aspect not yet considered in current PNEC derivations, provided refined insights into risks associated with specific polymer types. However, these emerging frameworks still rely on assumptions that lack sufficient ecotoxicological validation. In the context of climate change, with increased rainfall intensity and prolonged dry periods, stormwater runoff is expected to be more enriched with harmful substances (Müller et al., 2020), potentially leading to even greater pollutant accumulation in detention basin sediments. This study raises important concerns regarding sediment management and the potential future reuse, given the high load of emerging pollutants and associated risks. Overall, this work underscores the need to re-evaluate the role of stormwater detention basins not only as water management infrastructure but also as critical environmental sinks for particulate contaminants such as MP and TRWP.

Ethical approval

This declaration is not applicable.

Declaration of generative IA and AI-assisted technologies in the
writing process

During the preparation of this work the authors used Deepl in order
to improve the readability and language of the manuscript. After using
this tool, the authors reviewed and edited the content as needed and take
full responsibility for the content of the published article.

CRediT authorship contribution statement

**Zoé Iannuzzi:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data cura- tion, Conceptualization. **M. Carmen Casado-Martinez:** Writing – re- view & editing, Writing – original draft, Validation, Methodology, Investigation, Formal analysis. **Tiago De Oliveira:** Writing – review & editing, Writing – original draft, Investigation, Data curation, Concep- tualization. **David Lebouil:** Software, Formal analysis, Data curation. **Benoît J.D. Ferrari:** Writing – review & editing, Validation, Method- ology. **Johnny Gasperi:** Writing – review & editing, Validation,

Supervision, Conceptualization. **Brice Mourier:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Funding

This work was carried out thanks to financial support for Zoé Ian-
nuzzi’s internship as carried out as part of the Sedi-Plast research pro-
gram funded by the French Research Agency (ANR-19-CE34-0012), and
finally the french MESR ministry for Zoé Iannuzzi’s thesis grant.

Acknowledgements

The authors would like to thank Daniel Donze, Thérèse Bastide and Soline Lelay (Univ. Lyon 1 and ENTPE, UMR CNRS 5023 LEHNA) for their help to visit the detention basins and to collect samples. We would like to thank the Grand Lyon for agreeing to let visit the site studied and also the OTHU more specifically Nicolas Walcker and Serge Naltchayan for their help. The authors also thanks Maxime Chaillou for the pyrolysis analysis concerning TRWP.

**Appendix A. Supplementary material**

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.envint.2025.109676.

Data availability

Data will be made available on request.

References

Aitchison, J., 1986. The Statistical Analysis of Compositional Data. Chapman and Hall, London – New York.

Baensch-Altruschat, B., Kocher, B., Stock, F., Reifferscheid, G., 2020. Tyre and road wear particles (TRWP) - a review of generation, properties, emissions, human health risk, ecotoxicity, and fate in the environment. Sci. Total Environ. 733, 137823. https://doi.org/10.1016/j.scitotenv.2020.137823.

Besseling, E., Redondo-Hasselerharm, P., Foekema, E.M., Koelmans, A.A., 2019. Quantifying ecological risks of aquatic micro- and nanoplastic. Crit. Rev. Environ. Sci. Technol. 49, 32–80. https://doi.org/10.1080/10643389.2018.1531688.

Bucci, K., Rochman, C.M., 2022. Microplastics: a multidimensional contaminant requires a multidimensional framework for assessing risk. Micropl. Nanol. 2, 7. https://doi.org/10.1186/s43591-022-00028-0.

Casado-Martinez, M.C., Dell'Ambrogio, G., Campiche, S., Kroll, A., Lauber, E., Marti-Roura, M., Mendez-Fernandez, L., Renaud, M., Tierbach, A., Wildi, M., Wong, J.W. Y., Werner, I., Junghans, M., Ferrari, B.J.D., 2024. Incorporation of sediment- and soil-specific aspects in the criteria for reporting and evaluating ecotoxicity data (CRED). Integr. Environ. Assess. Manag. 20, 2162–2174. https://doi.org/10.1002/ieam.4948.

Chambers, L.G., Chin, Y.-P., Filippelli, G.M., Gardner, C.B., Herndon, E.M., Long, D.T., Lyons, W.B., Macpherson, G.L., McElmurry, S.P., McLean, C.E., Moore, J., Moyer, R. P., Neumann, K., Nezat, C.A., Soderberg, K., Teutsch, N., Widom, E., 2016. Developing the scientific framework for urban geochemistry. Appl. Geochem. 67, 1–20. https://doi.org/10.1016/j.apgeochem.2016.01.005.

Cole, M., Lindeque, P., Halsband, C., Galloway, T.S., 2011. Microplastics as contaminants in the marine environment: a review. Mar. Pollut. Bull. 62, 2588–2597. https://doi.org/10.1016/j.marpolbul.2011.09.025.

Cui, X., Yang, T., Li, Z., Nowack, B., 2024. Meta-analysis of the hazards of microplastics in freshwaters using species sensitivity distributions. J. Hazard. Mater. 463, 132919. https://doi.org/10.1016/j.jhazmat.2023.132919.

De Oliveira, T., Muresan, B., Ricordel, S., Lumière, L., Truong, X.-T., Poirier, L., Gasperi, J., 2024a. Realistic assessment of tire and road wear particle emissions and their influencing factors on different types of roads. J. Hazard. Mater. 465, 133301. https://doi.org/10.1016/j.jhazmat.2023.133301.

De Oliveira, T., Dang, D.P.T., Chaillou, M., Roy, S., Caubrière, N., Guillon, M., Mabilais, D., Ricordel, S., Jean-Soro, L., Béchet, B., Paslaru, B.M., Poirier, L., Gasperi, J., 2024b. Tire and road wear particles in infiltration pond sediments:
occurrence, spatial distribution, size fractionation and correlation with metals. Sci. Total Environ. 955, 176855. https://doi.org/10.1016/j.scitotenv.2024.176855.

De Ruijter, V.N., Redondo-Hasselserharm, P.E., Gouin, T., Koelmans, A.A., 2020. Quality criteria for microplastic effect studies in the context of risk assessment: a critical review. Environ. Sci. Technol. 54, 11692-11705. https://doi.org/10.1021/acs.est.0c03057.

Dendievale, A.-M., Mourier, B., Dabrin, A., Delile, H., Coyneal, A., Gosset, A., Liber, Y., Berger, J.-F., Bedell, J.-P., 2020. Metal pollution trajectories and mixture risk assessed by combining dated cores and subsurface sediments along a major European river (Rhone River, France). Environ. Int. 144, 106032. https://doi.org/10.1016/j.envint.2020.106032.

Directive 2006/11/EC of the European Parliament and of the Council of 15 February 2006 on pollution caused by certain dangerous substances discharged into the aquatic environment of the Community. http://data.europa.eu/eli/dir/2006/11/oj.

ECB, 2003. Technical Guidance Document (TGD) for risk assessment in support of Commission Directive 93/67/EEC on Risk Assessment for new notified substances Commission Regulation (EC) No 1488/94 on Risk Assessment for existing substances Directive 98/8/EC of the European Parliament and of the Council concerning the placing of biocidal products on the market. European communities, Ispra (Italy).

Eisentraut, P., Dümichen, E., Ruhl, A.S., Jekel, M., Albrecht, M., Gehde, M., Braun, U., 2018. Two birds with one stone—fast and simultaneous analysis of microplastics: microparticles derived from thermoplastics and tire wear. Environ. Sci. Technol. Lett. 5, 608–613. https://doi.org/10.1021/acs.estlett.8b00446.

Gouin, T., Becker, R.A., Collot, A., Davis, J.W., Howard, B., Inawaka, K., Lampi, M., Ramon, B.S., Shi, J., Hopp, P.W., 2019. Toward the development and application of an environmental risk assessment framework for microplastic. Environ. Toxicol. Chem. 38, 2087–2100. https://doi.org/10.1002/etc.4529.

Gasperi, J., Sebastian, C., Ruban, V., Delamaiter, M., Percot, S., Wiest, L., Mirande, C., Cauros, E., Demare, D., Diallo Kessoo Kessoo, M., Saad, M., Schwartz, J.J., Dubois, P., Fratta, C., Wolff, H., Moilleron, R., Chebbo, G., Cren, C., Millet, M., Barraud, S., Gromaire, M.C., 2014. Micropollutants in urban stormwater: occurrence, concentrations, and atmospheric contributions for a wide range of contaminants in three French catchments. Environ. Sci. Pollut. Res. 21, 5267–5281. https://doi.org/10.1074/s11356-013-2396-0.

He, B., Liu, A., Duodu, G.O., Wijesiri, B., Ayoko, G.A., Goonetilleke, A., 2023. Distribution and variation of metals in urban river sediments in response to microplastics presence, catchment characteristics and sediment properties. Sci. Total Environ. 856, 159139. https://doi.org/10.1016/j.scitotenv.2022.159139.

Hébrard-Labit, C., 2006. Guide technique : Recommendations pratiques pour la gestion des produits de l'assainissement pluvial (Guide technique). Laboratoire Central des Ponts et Chaussées (LCPC).

Heiri, O., Lotter, A.F., Lemcke, G., 2001. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results.

Hoshyari, E., Hassanzadeh, N., Keshavarzi, B., Jaafarzadeh, N., Rezaei, M., 2023. Characterization of microplastic, metals associated and ecological risk assessment in the topsoil of shiraz metropolitan, south west of Iran. Chemosphere 335, 139060. https://doi.org/10.1016/j.chemosphere.2023.139060.

Huang, Q., Liu, M., Cao, X., Liu, Z., 2023. Occurrence of microplastics pollution in the Yangtze River: distinct characteristics of spatial distribution and basin-wide ecological risk assessment. Water Res. 229, 119431. https://doi.org/10.1016/j.watres.2022.119431.

Iannuzzi, Z., Mourier, B., Winiarski, T., Lipeme-Kouyi, G., Polomé, P., Bayard, R., 2024. Contribution of different land use catchments on the microplastic pollution in detention basin sediments. Environ. Pollut. 348, 123882. https://doi.org/10.1016/j.envpol.2024.123882.

ICMM, 2016. Metals environmental risk assessment guidance: Risk characterization. Fact sheet 01, International Council of Mining, May 2016. Available online: Microsoft Word - FS1 Risk Characterisation_Final_May 2016.docx.

Järlskog, I., Jaramillo-Vogel, D., Rausch, J., Gustafsson, M., Strömvall, A.-M., Andersson-Sköld, Y., 2022. Concentrations of tire wear microplastics and other traffic-derived non-exhaust particles in the road environment. Environ. Int. 170, 107618. https://doi.org/10.1016/j.envint.2022.107618.

Kabir, A.H.M.E., Sekine, M., Imai, T., Yamamoto, K., Kanno, A., Higuchi, T., 2021. Assessing small-scale freshwater microplastics pollution, land-use, source-to-sink conduits, and pollution risks: perspectives from Japanese rivers polluted with microplastics. Sci. Total Environ. 768, 144655. https://doi.org/10.1016/j.scitotenv.2020.144655.

Klöckner, P., Seiwert, B., Wagner, S., Reemtsma, T., 2021. Organic markers of tire and road wear particles in sediments and soils: transformation products of major antiozonants as promising candidates. Environ. Sci. Technol. 55, 11723–11732. https://doi.org/10.1021/acs.est.1c02723.

Kontchou, J.A., Baetz, N., Grabner, D., Nachev, M., Tuerk, J., Sures, B., 2023. Pollutant load and ecotoxicological effects of sediment from stormwater retention basins to receiving surface water on Lumbriculus variegatus. Sci. Total Environ. 859, 160185. https://doi.org/10.1016/j.scitotenv.2022.160185.

Kreider, M.L., Panko, J.M., McAtee, B.L., Sweet, L.I., Finley, B.L., 2010. Physical and chemical characterization of tire-related particles: comparison of particles generated using different methodologies. Sci. Total Environ. 408, 652–659. https://doi.org/10.1016/j.scitotenv.2009.10.016.

Li, C., Li, X., Bank, M.S., Dong, T., Fang, J.-K.-H., Leusch, F.D.L., Rillig, M.C., Wang, J., Wang, L., Xia, Y., Xu, E.G., Yang, Y., Zhang, C., Zhu, D., Liu, J., Jin, L., 2024. The "microplastome" — a holistic perspective to capture the real-world ecology of microplastics. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.3c08849. acs.est.3c08849.

Lithner, D., Damberg, J., Dave, G., Larsson, Å., 2009. Leachates from plastic consumer products - screening for toxicity with Daphnia magna. Chemosphere 74, 1195-1200. https://doi.org/10.1016/j.chemosphere.2008.11.022.

Lithner, D., Larsson, Å., Dave, G., 2011. Environmental and health hazard ranking and assessment of plastic polymers based on chemical composition. Sci. Total Environ. 409, 3309-3324. https://doi.org/10.1016/j.scitotenv.2011.04.038.

MacDonald, D.D., Ingersoll, C.G., Berger, T.A., 2000. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol. 39, 20–31. https://doi.org/10.1007/s002440010075.

Mahjoub, A., Hashemi, S.H., Petroody, S.S.A., 2023. The role of baseflow and stormwater in transport of tire and bitumen particles in Tehran city: a dense urban environment. J. Contam. Hydrol. 256, 104180. https://doi.org/10.1016/j iconhyd.2023.104180.

Merchan, C.G., Perrodin, Y., Barraud, S., Sébastian, C., Becouze-Lareure, C., Bazin, C., Kouyi, G.L., 2014. Spatial variability of sediment ecotoxicity in a large storm water detention basin. Environ. Sci. Pollut. Res. 21, 5357–5366. https://doi.org/10.1007/s11356-013-2325-2.

Moermond, C.T.A., Kase, R., Korkaric, M., Ågerström, M., 2015. CRED: criteria for reporting and evaluating ecotoxicity data. Environ. Toxicol. Chem. 35, 1297–1309. https://doi.org/10.1002/etc.3259.

Müller, A., Österlund, H., Marsalek, J., Viklander, M., 2020. The pollution conveyed by urban runoff: a review of sources. Sci. Total Environ. 709, 136125. https://doi.org/10.1016/j.scitotenv.2019.136125.

Pan, Z., Liu, Q., Jiang, R., Li, W., Sun, X., Lin, H., Jiang, S., Huang, H., 2021. Microplastic pollution and ecological risk assessment in an estuarine environment: the Dongshan Bay of China. Chemosphere 262, 127876. https://doi.org/10.1016/j.chemosphere.2020.127876.

Raert, C., Rodland, E.S., Okoffo, E.D., Reid, M.J., Meland, S., Thomas, K.V., 2021. Challenges with quantifying tire road wear particles: recognizing the need for further refinement of the ISO technical specification. Environ. Sci. Technol. Lett. 8, 231–236. https://doi.org/10.1021/acs.estlett.0c00949.

Reimann, C., De Caritat, P., 2005. Distinguishing between natural and anthropogenic sources for elements in the environment: regional geochemical surveys versus enrichment factors. Sci. Total Environ. 337, 91–107. https://doi.org/10.1016/j.scitotenv.2004.06.011.

Rochman, C.M., Brookson, C., Bikker, J., Djuric, N., Earn, A., Bucci, K., Athey, S., Huntington, A., McIlwraith, H., Munno, K., De Frond, H., Kolomijeca, A., Erdle, L., Grbic, J., Bayoumi, M., Borrelle, S.B., Wu, T., Santoro, S., Werbowski, L.M., Zhu, X., Giles, R.K., Hamilton, B.M., Thaysen, C., Kaura, A., Klasios, N., Ead, L., Kim, J., Sherlock, C., Ho, A., Hung, C., 2019. Rethinking microplastics as a diverse contaminant suite. Environ. Toxicol. Chem. 38, 703–711. https://doi.org/10.1002/etc.4371.

Rodland, E.S., Lind, O.C., Reid, M., Heier, L.S., Skogsberg, E., Snilsberg, B., Gryteselv, D., Meland, S., 2022. Characterization of tire and road wear microplastic particle contamination in a road tunnel: from surface to release. J. Hazard. Mater. 435, 129032. https://doi.org/10.1016/j.jhazmat.2022.129032.

Rodland, E.S., Heier, L.S., Lind, O.C., Meland, S., 2023. High levels of tire wear particles in soils along low traffic roads. Sci. Total Environ. 903, 166470. https://doi.org/10.1016/j.scitotenv.2023.166470.

Schuwirth, N., 2020. Towards an integrated surface water quality assessment: aggregation over multiple pollutants and time. Water Res. 186, 116330. https://doi.org/10.1016/j.watres.2020.116330.

Sébastian, C., Becouze-Lareure, C., Lipeme Kouyi, G., Barraud, S., 2015. Event-based quantification of emerging pollutant removal for an open stormwater retention basin - loads, efficiency and importance of uncertainties. Water Res. 72, 239–250. https://doi.org/10.1016/j.watres.2014.11.014.

Sobanska, M.A., Cesnaitis, R., Sobanski, T., Versonnen, B., Bonnomet, V., Tarazona, J.V., Coen, W.D., 2014. Analysis of the ecotoxicity data submitted within the framework of the REACH regulation. Part 1. General overview and data availability for the first registration deadline. Sci. Total Environ. 470–471, 1225–1232. https://doi.org/10.1016/j.scitotenv.2013.10.074.

Sommer, F., Dietze, V., Baum, A., Sauer, J., Gilge, S., Maschowski, C., Gieré, R., 2018. Tire abrasion as a major source of microplastics in the environment. Aerosol Air Qual. Res. 18, 2014–2028. https://doi.org/10.4209/aaqr.2018.03.0099.

Tolosana-Delgado, R., 2012. Uses and misuses of compositional data in sedimentology. Sed. Geol. 280, 60–79. https://doi.org/10.1016/j.sedgeo.2012.05.005.

Tomlinson, D.L., Wilson, J.G., Harris, C.R., Jeffrey, D.W., 1980. Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgolander Meeresunters 33, 566–575. https://doi.org/10.1007/BF02414780.

Unice, K.M., Kreider, M.L., Panko, J.M., 2013. Comparison of tire and road wear particle concentrations in sediment for watersheds in France, Japan, and the United States by quantitative pyrolysis GC/MS analysis. Environ. Sci. Technol. https://doi.org/10.1021/es400871j, 130710100101002.

Unice, K.M., Kreider, M.L., Panko, J.M., 2012. Use of a deuterated internal standard with pyrolysis-GC/MS dimeric marker analysis to quantify tire tread particles in the environment. IERPH 9, 4033–4055. https://doi.org/10.3390/ijerph9114033.

Van Cauwenberge L., 2015. Occurrence, effects and risks of marine microplastics. Thesis submitted in fulfilment of the requirements for the degree of Doctor (PhD) in Applied Biological Sciences.

Wagner, S., Hüffer, T., Klöckner, P., Wehrhahn, M., Hofmann, T., Reemtsma, T., 2018. Tire wear particles in the aquatic environment - a review on generation, analysis, occurrence, fate and effects. Water Res. 139, 83–100. https://doi.org/10.1016/j.watres.2018.03.051.

Wang, G., Lu, J., Li, W., Ning, J., Zhou, L., Tong, Y., Liu, Z., Zhou, H., Xiaiyazi, N., 2021. Seasonal variation and risk assessment of microplastics in surface water of the Manas River Basin China. Ecotoxicol. Environ. Saf. 208, 111477. https://doi.org/10.1016/j.ecoenv.2020.111477.
Wenning RJ, Batley GE, Ingersoll CG, Moore DW, editors. 2005. Use of sediment quality guidelines and related tools for the assessment of contaminated sediments. Pensacola (FL): society of environmental toxicology and chemistry (SETAC). 815 p.

Werkenthin, M., Kluge, B., Wessolek, G., 2016. Assessment of metal retention in newly constructed highway embankments. Environ. Sci. Pollut. Res. 23, 23619–23629.
https://doi.org/10.1007/s11356-016-7526-z.

Wiest, L., Baudot, R., Lafay, F., Bonjour, E., Becouze-Lareure, C., Aubin, J.-B., Jame, P., Barraud, S., Kouyi, G.L., Sébastian, C., Vulliet, E., 2018. Priority substances in accumulated sediments in a stormwater detention basin from an industrial area. Environ. Pollut. 243, 1669–1678. https://doi.org/10.1016/j.envpol.2018.09.138.

Yan, H., Vosswinkel, N., Ebbert, S., Lipeme Kouyi, G., Mohn, R., Uhl, M., Bertrand-Krajewski, J.-L., 2020. Numerical investigation of particles’ transport, deposition and resuspension under unsteady conditions in constructed stormwater ponds. Environ. Sci. Eur. 32, 76. https://doi.org/10.1186/s12302-020-00349-y.

Yang, H., Sun, F., Liao, H., Guo, Y., Pan, T., Wu, F., 2023. The pollution of microplastics in sediments of the Yangtze River Basin: occurrence, distribution characteristics, and basin-scale multilevel ecological risk assessment. Water Res. 243, 120322. https://doi.org/10.1016/j.watres.2023.120322.