# Sequential combination of micro-FTIR imaging spectroscopy and pyrolysis-GC/MS for microplastic quantification. Application to river sediments

Nadia Bouzid, Bruno Tassin, Johnny Gasperi, Rachid Dris

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Nadia Bouzid, Bruno Tassin, Johnny Gasperi, Rachid Dris. Sequential combination of micro-FTIR imaging spectroscopy and pyrolysis-GC/MS for microplastic quantification. Application to river sediments. Analytical Methods, 2025, 10.1039/D5AY00237K. hal-05040743

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Submitted on 25 Apr 2025

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# Sequential combination of micro-FTIR imaging spectroscopy and pyrolysis-GC/MS for microplastic quantification. Application to river sediments

Nadia Bouzid*<sup>a</sup>, Bruno Tassin<sup>a</sup> and Johnny Gasperi<sup>b</sup>, Rachid Dris<sup>a</sup>

Several studies have focused on quantifying microplastics (MP) in the environment using µ-FTIR and Py-GC/MS, the most common analytical methods. However, their application to complex matrices like sediments is affected by interferences specific to each method. In this study, we developed a protocol combining µ-FTIR and Py-GC/MS for sequential analysis of MP (10-500 µm) in 16 river sediment samples, targeting PE, PP, and PS polymers. Mass concentrations were estimated from the particle volume in µ-FTIR and measured directly by Py-GC/MS using internal calibration. Results show consistency between the two methods across different sites, with variability of two orders of magnitude in concentrations: from 0.3 to 50 items g⁻¹ and 0.2 to 17 µg g⁻¹ for µ-FTIR, and 0.8 to 21 µg g⁻¹ for Py-GC/MS. Replicate analyses (2 to 6 per site) revealed that intra-site variability was mainly influenced by sample preparation and, to a lesser extent, by the measurement technique. While estimated and measured concentrations were similar, discrepancies were observed in polymer proportions: PP predominated in µ-FTIR, while PS was more prevalent in Py-GC/MS. These differences are explained by the specific limitations of each method, especially the limited detection of synthetic fibres and tyre or road abrasion particles by µ-FTIR, which are detected as MP by Py-GC/MS. This comparative study provides recommendations for evaluating compatibility between studies using either technique and offers guidelines for selecting the most appropriate method based on research interests

## 1. Introduction

The use of plastics has become a significant environmental concern, particularly due to the release, formation, and accumulation of microplastics (MP) in the environment. Plastics are made of polymers, which confer valuable properties, including exceptional durability. However, plastics are extensively used to produce single-use items, with the most common polymers for packaging - polyethylene (PE), polypropylene (PP) and polystyrene (PS) - also being the most found in the environment.¹ Due to their small size, ranging from 1 µm to 5 mm, and low density, MP can be transported through different environmental and urban compartments. Many studies have focused on understanding these complex transfer mechanisms and documenting the presence of these particles even in regions remote from sources of contamination.²,³ Toxic additives used in the formulation of plastics represent a major vector of chemical contamination, with MP posing a risk to organisms that ingest these particles.⁴

To address this growing concern, various analytical techniques have been employed to analyse MP, study their spatial distribution and assess their impacts on ecosystems. Fourier-transform infrared spectroscopy (FTIR) was the first technique used to analyse MP in sea water⁵ and remains one of the most common techniques to confirm the plastic or non-plastic nature of particles in environmental samples.⁶ This technique has the advantage of being non-destructive and provides information on the number, size, and morphological characteristics of MP. This information is crucial for the identification of the sources of contamination and to assess the potential toxicity of MP.⁷,⁸

The reliability of FTIR detection method has been considerably enhanced by the coupling with microscope equipped with focal-plane array (FPA). FPA detector allows

several pixels to be captured simultaneously. The detector is used with a motorized stage to scan areas several millimeters square, thus avoiding any pre-selection of particles based on visual criteria. This approach generates two-dimensional spectral images of particles concentrated on a filter, producing large datasets that can be processed by advanced software for accurate MP identification.⁹ However, there are methodological constraints, particularly the size of the particles analysed, which cannot be smaller than 10 µm. Furthermore, the results obtained as a number of particles can be restrictive for comparisons of contamination levels between different environmental and urban compartments due to the heterogeneity of particle sizes.¹⁰,¹¹ To provide mass data, the number of MP can be converted into polymer mass using models based on an approximation of MP volume and the density of polymers.¹²,¹³ However, only a few studies have demonstrated the relevance of these approximations, and many questions remain regarding the reliability of their application to environmental samples.⁸,¹⁴

Pyrolysis coupled with gas chromatography and mass spectrometry (Py-GC/MS) is an increasingly used technique for the identification and mass quantification of MP.¹⁵,¹⁶ It is a destructive technique, based on the analysis of molecules formed by the thermal degradation of polymers for their identification. The underlying hypothesis is that the volatile fragments formed are specific to polymers and thus allow their identification.¹⁷ Calibration curves are used to quantify polymer mass, and it is recommended to use an internal standard for matrix bias correction.¹⁸,¹⁹ In recent years, Py-GC/MS has been applied for the simultaneous quantification of MP in several matrices: marine organisms²⁰, atmospheric deposition²¹,²², drinking water²³, freshwater²⁴, wastewater effluents and sludges²⁵ and soils and sediments²⁶⁻²⁸.

In most studies, regardless of the technique used, samples undergo several preparation steps to concentrate the MP and
eliminate interfering substances. The removal of organic matter is generally carried out by enzymatic digestion²⁹, chemical oxidation³⁰, or acid-base reactions³¹. Additional treatments such as density separation³² or polymer dissolution²⁶,³³ are used to isolate MP from mineral particles. However, in the residue obtained after applying these treatment steps, undesirable substances remain, and MP represent only a small fraction of the particles.³⁴ For spectroscopic techniques, these particles can overlap with MP and prevent their detection. For Py-GC/MS, residual organic substances that remain after the treatment steps can produce pyrolysis fragments identical to those of the target synthetic polymers, potentially leading to biases.¹⁷

Given the varied and distinct analytical interferences depending on the method applied, it is necessary to assess their comparability on complex matrices. Comparison of µ-FTIR imaging and Py-GC/MS is also useful for assessing the reliability of each method by verifying the consistency of the results. Few studies have focused on a quantitative comparison, either by measuring MP on parallel sub-samples¹¹,³⁵ or by applying both methods sequentially to the same sample⁸,³⁶. These comparative studies demonstrated consistency between the methods regarding MP contamination levels. However, differences were observed in the proportion of each type of polymer quantified. Several hypotheses have been proposed to explain these discrepancies, but further investigations are needed to determine whether they stem from the analytical technique itself, or can be attributed to variability due to sub-sampling or protocol repeatability. To specifically assess the variability associated with the type of analysis (µ-FTIR or Py-GC/MS) and limit the impact of the preparation steps, it is essential to apply these techniques sequentially and in the same MP size range. Since the preliminary study by Primpke et al. in 2020⁸, only one study has been carried out to extend methodological comparisons based on sequential analyses.¹⁴

In this study, a sequential approach was applied to compare the µ-FTIR and Py-GC/MS methods for analysing MP in river sediments. The main objective was to assess the sub-sampling reproducibility and reliability of each technique for quantifying MP. Sediment samples were prepared to isolate MP in the 10-500 µm size range to ensure compatibility with both methods. We compared the estimated masses of PE, PP and PS derived from µ-FTIR data with the masses measured by Py-GC/MS, using an internal calibration. These polymers were chosen because of their prevalence in the environment and because the Py-GC/MS quantification method showed to be robust for these polymers. This study aims to provide a better understanding of the consistency and complementarity of µ-FTIR and Py-GC/MS in the analysis of complex sedimentary matrices, and to offer insight into how method differences can affect intercomparisons of studies.

# 2. Materials and methods

## 2.1. Sediment collection and sub-sampling

Sediment samples were taken from the banks and quays of the Seine River (6 samples: SSM, SPT, SMO, STE, SBG, STS) and two of its tributaries, the Yonne (1 sample: YCE) and Essonne Rivers

(1 sample: EAR), in the Greater Paris area during the February 2021 flood (ESI, Table S1). Approximately 200 to 500 g of sediment were collected just after the peak flow. Samples were taken from each site using a metal shovel and stored in glass containers. Then, they were homogenized manually using a spatula. Water content was determined, and approximately 10 g sub-samples (dry weight) were obtained by quartering. To assess intra-sample variability, duplicate sub-samples were prepared for four samples (SSM, SPT, SMO, STS). All the sub-samples labelled "01" were prepared in the same first batch and their duplicates in a second preparation batch labelled "02". The same preparation methods were applied to both batches except for the initial homogenisation: in the second batch, longer and more thorough mixing was carried out using a spatula. For the sub-sample STE, five replicate sub-samples (STE-01 to 05) were prepared to estimate variability more precisely (ESI, Table S2). Sub-samples STE-02 to STE-05 were prepared using the same homogenisation process as for the second batch. A total of 16 sub-samples were prepared for MP extraction before a sequential analysis using µ-FTIR followed by Py-GC/MS.

## 2.2. Laboratory contamination prevention

To limit contamination by MP in the laboratory, sub-sampling and other pretreatment steps were performed avoiding the use of plastic materials. In the absence of suitable plastic substitutes, PTFE wash bottles were used as this polymer was not targeted in the study. To prevent atmospheric deposition, samples were covered with aluminum foil, and most manipulations were conducted under a laminar flow hood. Glassware was calcined at 500°C for 3 hours to eliminate any potentially plastic contaminants. In addition, all solutions were filtered through a glass fiber filter 0.7 µm pore size.

## 2.3. 10-500 µm microplastic extraction

To isolate MP from the sediment matrix, density separation was performed on the sub-samples using a sodium iodide solution (d = 1.60-1.70 g cm⁻³). A JAMSS (JAMSTEC Microplastic Sediment Separator) device was used for separation, custom-built according to drawings provided by Nakajima et al.³⁷ The supernatant was recovered after 24 hours and sieved to 500 µm. Particles larger than 500 µm were excluded because their thickness is not compatible with transmission µ-FTIR spectroscopy analysis. Although such large particles are very rare, their high mass significantly exceeds the calibration ranges chosen for Py-GC/MS analysis. The fraction below 500 µm was then filtered through a 10 µm mesh, 47 mm diameter stainless steel filter using a vacuum filtration unit. The residue on the filter was transferred to a beaker with 100 ml of 30% H₂O₂ to oxidize organic matter. After heating at 40°C for 24 hours with stirring at 300 rpm, the sample was filtered through aluminium oxide filters (Whatman® alumina Anodisc filter discs 0.2 µm pore size). Depending on the quantity of particles present in the residue of each sub-sample, 1 to 8 aluminium oxide filters were required for analysis by µ-FTIR spectroscopy (ESI, Table S2).

## 2.4. Microplastic µ-FTIR analysis
The *Anodisc* filters were analysed using µ-FTIR spectroscopic imaging (Nicolet iN10 MX, Thermo Fisher Scientific) with a pixel resolution of 25 µm, which is considered the observable size limit in the results. Morever, although the entire 10-500 µm fraction was analyzed without prior size fractionation, the MP were categorized into five defined size classes for the data analysis (25–50 µm, 51–100 µm, 101–150 µm, 151–300 µm, 301–500 µm). The µ-FTIR detectors have a spectral range of 4000-720 cm⁻¹, but the *Anodisc* filters are not transparent to IR below 1200 cm⁻¹; therefore, a spectral range of 4000-1200 cm⁻¹ is considered in this study. The spectra were acquired in transmission mode with a resolution of 8 cm⁻¹ and 1 scan per pixel. The spectral data were analyzed with the open access software *siMPle* (https://simple-plastics.eu/, version v.1.1.β) using the *AAU pipeline* mode, to generate an image of the particles detected as plastic in the samples. A variety of polymers and particles were detected in the samples, but the focus for this study was placed on PE, PP and PS, as the quantification method in Py-GC/MS was developed for these polymers.

The maximum Feret diameter obtained by the software was used to define particle size. The surface area of the particles was calculated from the number of pixels in the image, and an estimation of the mass was calculated automatically as described in Simon et al., 2018.¹³ The particle shape was approximated to a two-dimensional ellipse, with the largest diameter equivalent to maximum Feret diameter on the 2D projection and an area corresponding to that calculated from the projection. Assuming that the particle adopts an ellipsoidal shape in three dimensions, the smallest diameter is estimated as 60% of the second diameter of this ellipse. Then the total mass of PE, PP and PS is calculated using the density of polymers registered in the software library.

## 2.5. Sample preparation for transfer and analysis in Py-GC/MS

To transfer the residue from the *Anodisc* filters to the pyrolysis cups, a preparation protocol was developed, inspired by Primpke et al., 2020.⁸ The residue on the *Anodisc* filters was first resuspended in a beaker with ultrapure water and subjected to sonication for 30 seconds. The filters were then removed and rinsed over the beaker with ultrapure water. Next, the contents of the beaker were filtered onto a 21 mm diameter quartz fiber filter (Whatman® QM-A quartz filters, 2.2 µm pore size). After drying, the filter was weighed and cryo-mill to obtain a homogeneous powder (three cycles of 2 minutes at 8 cps in liquid nitrogen, Freezer Mill 6775, Cole-Parmer®). Before applying this protocol to all the samples, the remobilisation procedure was validated as effective by a second µ-FTIR analysis of *Anodisc* filters. While the µ-FTIR analysis confirmed the efficiency of the remobilization step, the other steps in the transfer process, such as cryo-milling and transfer to the pyrolyzer cup, were not independently verified for performance in this study.

For each sample, 2 to 8 pyrolysis cups were filled with approximately 2-5 mg of cryo-milled powder, corresponding to 25-50% of the total filter mass with sample residue. The mass of

milled filters to be distributed in each cup were calculated based on µ-FTIR mass estimations, to ensure that polymer quantities fit with calibration curves. In addition, STS-01 sample was analyzed for almost 80% (10 pyrolysis cups) to assess the homogeneity of the cryo-milling procedure.

## 2.6. Py-GC/MS quantification method

2.6.1. PS-d₅ as internal standard. Polystyrene-d5 (PS-d₅) was chosen as internal standard since it is expected to have physicochemical properties like the targeted polymers. In addition, PS-d₅ is a polymer which can be purchased from chemical suppliers and has been used in several studies.²⁴,²⁶,³⁸,³⁹ Prior to use this deuterated polymer in the calibration method, the commercial reference (P42518-D5PS, PolymerSource) was analyzed in the presence of silica to verify its pyrogram and confirm its purity. Indeed, some H-D transfer can appear over time, or during pyrolysis in the presence of mineral matrix, as demonstrated by Lauschke et al., 2021.¹⁹ In addition, analyses of 5 µg of PS-d₅ mixed with 2 µg of PS were carried out to check the resolution of the signals from the deuterated and non-deuterated styrene dimer on the pyrograms.

2.6.2. PE, PP and PS calibration curves. In consideration of the existing literature, it was chosen to prepare a mix of plastic particles dispersed in silica for PE, PP and PS calibration curves.²⁶ The multi-polymers mix was obtained by cryo-milling plastic particles of PE, PP and PS (PE: 427772-250G; PP: 428116-250G; PS: 331651-25G, MilliporeSigma) in silica (cryo-milling: 3 cycles of 2 minute at 8 cps, Freezer Mill 6775, Cole-Parmer). A primary mix was prepared, containing 100 µg of PP, 100 µg of PS and 300 µg of PE per mg silica. This mix was then diluted with silica by a factor of 100 to produce a secondary mix (ESI, Table S3). Appropriate quantities of these two mixes were weighed with a microbalance (XP26, Mettler Toledo, readability 1 µg) directly into pyrolysis cups to establish calibration curves of about 0.04-4 µg for PS and PP, and 0.12-12 µg for PE with 5 µg of PS-d₅ as internal standard (ESI, Table S4 and Fig. S2).

The analyses were carried out with a pyrolyzer (Pyroprobe 6250, CDS Analytical) coupled to a gas chromatograph (TRACE 1300, Thermo Fisher Scientific) and a mass spectrometer (ISQ 7000, Thermo Fisher Scientific). The samples were pyrolyzed at 650°C for 30 seconds. Injections were performed with a split ratio of 1:50, and the molecules were eluted through an apolar column (Rxi-5Sil MS 60 m x 0.25 mm x 0.5 µm). The oven temperature was initially set to 50°C and held for 1 min, then increased at 10°C/min to 300°C (held 10 min). The mass spectrometer operates with an electron impact ionization source (70 eV) and spectrum acquisition was performed in full scan mode from 50 to 500 m/z.
**2.6.3. Polymer markers selection.** Indicator compounds for polymer identification and quantification were selected based on the literature.<sup>17,40</sup> For PP, 2,4-dimethyl-1-heptene (DM-C7) and 2,4,6,8-tetramethyl-1-undecene were selected as indicator compounds. Although the m/z 126 ion is sometimes suggested as offering a more reproducible option for quantification<sup>41,42</sup>, the m/z 70 ion of DM-C7 was used in this study as it is more abundant. For PS, 2,4-diphenylbut-1-ene (styrene dimer) and 2,4,6-triphenylhex-1-ene (styrene trimer) were chosen as indicator compounds and the m/z 208 ion of styrene dimer was selected to establish the calibration curves. Styrene trimer was not selected due to the risk of cross-contamination, as evidenced by the presence of a carry-over signal of this compound identified in the analysis blanks after sample injections. Given the lack of consensus on PE markers among the C<sub>15-20</sub> chain-length triplets - dienes *m/z 82*, alkenes *m/z 83* and alkanes *m/z 85* - all these compounds were initially considered (ESI, Fig. S1). The ratios of diene/alkene and alkene/alkane for the same chain-length were calculated in the

standards and compared with those in the sediment samples to identify the compound with the most similar ratio in each sequence. The dienes, alkenes and alkanes derived from natural organic substances in the samples are not produced in the same proportions as those derived from PE pyrolysis.<sup>43,44</sup> This difference enables reliable identification and quantification of PE in the samples. The C<sub>19</sub> diene/alkene ratio shows the strongest correlation between standards and samples, suggesting these pyrolysis products are less affected by interferences (ESI, Table S5). Therefore, fragment *m/z 83* alkenes were chosen to establish the calibration curves as they present a most intense signal in comparison to dienes (ESI, Fig. S2).

**2.6.4. Signal-to-Noise ratio for LOD and LOQ estimation.**
Procedure blanks were carried out over each sample

preparation batch (n = 3), using the same reagents and equipment as those used for samples. The blanks were analyzed in eight pyrolysis cups containing 8% of the total blank filter mass with the residue (approx. 5 mg). The limits of detection (LOD) and quantification (LOQ) were calculated with a signal-to-noise (S/N) approach. The signal areas in the blanks were integrated at the expected retention times for the target compounds. Then, the LOD was calculated by taking the signal median of the eight blank measurements and adding three times the standard deviation. Similarly, the LOQ was determined by taking the median signal plus ten times the standard deviation.

# 3. Results and discussion

## 3.1. Internal standard corrections

<figure><img src="image_2.png" /></figure>

Individual analyses of PS and PS-d<sub>5</sub> confirmed the purity of the commercial reference and the distinct retention times for styrene dimer and deuterated styrene dimer with the GC/MS method used (Fig. 1A and C). However, when PS and PS-d<sub>5</sub> were pyrolyzed together, a co-elution of three peaks was observed on the pyrograms (Fig. 1B). The mass spectrum associated with the central peak at 20.56 min indicates the presence of fragments at *m/z 91* and *96* and a molecular ion at *m/z 213*. This molecular ion probably corresponds to a hybrid pyrolysis product resulting from the combination of a styrene unit and styrene-d<sub>5</sub>. Until now, the formation of hybrid compounds had been reported in the case of copolymer pyrolysis, but not in the case of separate polymers pyrolyzed simultaneously.<sup>45</sup> Nevertheless, the formation of hybrid dimers by reactions between radicals originating from PS and PS-d<sub>5</sub> seems very probable, as oligomer formation follows an intermolecular and

<caption>**Fig. 1** Pyrograms (TIC: Total Ion Chromatogram) of 5 µg of PS and PS-d<sub>5</sub> pyrolyzed individually (A and C) or mixed (B) with corresponding mass spectra and molecular structures for styrene dimer.</caption>
not an intramolecular reaction. Given the observations on the
formation of hybrid molecules, PS quantification was carried
out by addition of the signal from the hybrid styrene dimer
(m/z 213) and from the styrene dimer (m/z 208). Correction of
the PE, PP and PS indicator ion areas was carried out by
calculating the ratio with the area of deuterated styrene dimer
(m/z 218).

**3.2. µ-FTIR measurements: abundance, polymer type and size of MP**

The µ-FTIR analysis revealed a total of 2,239 MP across all samples, with PP being the most prevalent polymer, representing 76% of the total, followed by PE at 10%. These two polymers were the most abundant in all samples, while PS (4.4%) was identified in approximately three-quarters of the samples. These findings are consistent with those of other studies, where the combined amount of PE, PP, and PS generally accounts for 70 to 80% of the MP detected in river sediments.46,47 Other polymer types were found in smaller quantities (between 0.1% and 3.7%), including polyvinyl chloride (PVC), polyurethanes (PU), polyesters (PET), polyamides (PA), as well as various other polymers such as alkyds, acrylics, polyvinyl acetate, and copolymers like acrylonitrile-butadiene-styrene (ABS) and cellulose acetate.

Fig. 2 shows the data set obtained by µ-FTIR analysis for each sediment sub-samples, including particle number concentrations as well as mass estimations. Concentrations show a difference of over two orders of magnitude a wide, from 0.3 to 50 items g⁻¹ (sediment dry weight), (Fig. 2A). Variability between sub-sample replicates will be examined in the following sections. The MP concentrations observed in this study are consistent with those reported in other river sediment research, particularly in studies by Rolf et al. in 2024¹¹ and 2022⁴⁸, which focused on similar particle size ranges (between 11 and 1000 µm). Rolf et al. 2024 reported concentrations ranging from 8 to 70 items g⁻¹ for, while Rolf et al., 2022 found 25 to 50 items g⁻¹. Similarly, Dhivert et al., 2022⁴⁹ observed concentrations between 0.3 and 30 items g⁻¹ for MP sized 25 to 5000 µm. These results indicate a comparable distribution of MP concentrations across studies. However, they are considerably higher than those reported in studies that have targeted large MP (> 100 µm).⁵⁰ Several studies have shown that the detection threshold, related to the methodology used to prepare and analyze MP, has a considerable impact on the concentrations measured.⁶,¹⁰

In this study, a homogeneous size distribution was observed
among the different sub-samples, with a majority of particles
smaller than 100 µm (Fig. 3A). The proportion of large MP (300-
500 µm) in the samples is less than 2%. The number of particles
increases as the size class decreases, except for the 25-50 µm
class. This observation occurs despite the particle size
distribution in aquatic environments being assumed to show a
power-law distribution.⁵¹ A comparable size distribution has
been documented in studies employing the same methodology
and imaging µ-FTIR spectroscopy equipment to analyze water
and river sediment samples¹¹,⁴⁸,⁵², sediments from wastewater
decantation⁵³, or atmospheric deposits⁵⁴,⁵⁵. As the distribution
observed in the sediment samples does not correspond to the
expected distribution and does not vary according to matrix

type, the under-representation of particles in the 25-50 µm
range can be attributed to a methodological artefact. These size
classes are close to the detection limit of the equipment,
making them more difficult to detect.⁶ On the other hand, the
underrepresentation of particles below 50 µm could be
attributed to the sediment preparation protocol. If the final
analyzed filter still contains a lot of plastic or non-plastic
particles, the small MP can be masked. These analytical
constraints can be overcome by reducing the initial sample
quantity or by analyzing only a fraction of the residue after
purification.⁵⁶ However, the analysis must be carried out on a
sufficient number of aliquots (3 to 10) to demonstrate the
representativity of the sub-sampling.⁵⁷,⁵⁸

The under-representation of smaller MP may also be caused
by losses during the preparation protocol, especially during
density separation. Stile et al., 2021⁵⁹ calculated recovery rates
with MP spiked into sediments and showed a better recovery
rate for the 150-500 µm size class than for the 50-150 µm class.

<caption>Fig. 2 Concentration of PE, PP and PS in sediment sub-samples in (A) items g⁻¹ and (B) estimated masses in µg g⁻¹ based on siMPle software.</caption>

Moreover, this recovery rate decreases in samples rich in
organic matter.⁶⁰ To allow more reliable comparison between
<figure><img src="image_3.png" /><figcaption>Fig. 3 (A) MP size distribution in sediment sub-samples and (B) size distribution by polymer type (PE, PP, PS).</figcaption></figure>

studies, extraction protocols need to be better evaluated for each MP size class, with recovery tests harmonized and adapted to complex environmental matrices.⁶¹

A noteworthy difference is observed in the particle size distribution according to the type of polymer for PE, PP, and PS (Fig. 3B). The particle size distribution of PE is slightly dominated by particles larger than 100 µm, while PP and PS exhibit a comparable size distribution. This has a direct impact on the mass estimation of these three polymers by the *siMPle* software. Indeed, the estimated mass is proportional to the volume of an ellipsoid, thus the mass distributions accord greater weight to larger particles. Consequently, a higher proportion of PE is observed when the results are converted to mass (Fig. 2B). These observations are consistent with those presented by Primpke et al., 2020⁸, with a strong dependence between the estimated mass and the amount of MP larger than 100 µm in the samples.

### 3.3. Homogeneity between pyrolysis-injected aliquots

To assess the homogeneity of cryo-mill fractions, variability was calculated between pyrolysis-injected aliquots of the same sub-sample. Most of the injected aliquots presented polymer masses above the S/N based LOD estimations, respectively 1.2, 0.17, and 0.31 µg for PE, PP, and PS, and 3.0, 0.28, and 1.1 µg for LOQ estimations. Some aliquots showed concentrations above the highest point of the calibration curves for PE and PS. Fig. 4 shows the measured masses of PE, PP and PS in the aliquots, with the respective percentage relative standard deviation (RSD) for each sub-sample. The RSD is calculated by dividing the standard deviation (SD) by the mean values of the aliquots. The established protocol demonstrated effective homogenization, with median RSD of 13%, 28%, and 17% for PE, PP, and PS, respectively. In the case of sample STS-01, for which almost the entire sample (79%) was analyzed in 10 aliquots, an RSD of 32% was calculated. This relatively high RSD is due to only one aliquot presenting a PS quantity that was twice as high

as the other nine. This discrepancy could be attributed to a particle that was not adequately ground during the cryo-mill procedure. If the highest value of the 10 aliquots were not taken into account, the RSD would be 17%. The same applies to sample SMO-02, which would have a variability of 7% instead of 36% if the aliquot with a higher concentration than the other three were excluded. The results obtained for homogenization by cryo-milling are in the same range to those reported by Albignac et al., 2023⁴³, who also worked on solid "dilutions". They calculated an RSD of 6 to 25% depending on the polymer (PMMA, PP, PE, PET, PS and PC) for 6 injections of plastic standards cryo-mill with glass fiber filters. The results are also comparable to those obtained with pressurised liquid extraction methods, as presented by Dierkes et al., 2019²⁶. They reported RSD of 10% or less on PE, PP and PS extracted from environmental samples (soil and sediment) with tetrahydrofuran and then precipitated and mixed with silica.

In the context of environmental samples, the representativeness of sub-sampling for Py-GC/MS injection is rarely evaluated, as the entire sample is commonly injected in a single analysis.²⁷,⁶² However, for future studies, it is essential to evaluate the fraction to be injected according to the level of contamination expected from the matrix analyzed and its sampling context. For highly contaminated matrices, such as river sediments collected in urban areas or sewage sludge, a small fraction of the sample after treatment steps may be sufficient to quantify the targeted polymers. However, to ensure representative measurements, we recommend injecting several independent aliquots of sub-samples. For low contamination matrices such as drinking water or seawater, it is necessary to maximize the amount of sample concentrated into the pyrolysis cup. In these cases, the repeatability of the measurement must be verified by taking multiple samples.

Optimizing the quantities injected involves conflicting methodological constraints. On the one hand, it is crucial to maximize the quantity injected to improve the representativeness of the results. However, it is imperative to
<figure><img src="image_4.png" /><figcaption>Fig. 4 PE, PP and PS masses measured in each pyrolysis-injected aliquots of sub-samples.</figcaption></figure>

limit the quantity injected to avoid saturation of the signal of the targeted polymers on chromatograms, thus compromising their quantification. In this study, the analysis of variable fractions and variable numbers of aliquots revealed that insufficient sub-sampling can lead to quantification biased by an outlier. Consequently, in the case of residues homogenized by cryo-milling, it appears necessary to inject at least 25% of the total sample to obtain robust and representative results.

### 3.4. Homogeneity between sediment sub-samples

Notable differences were observed between the duplicate samples. Regardless of the analytical technique applied, the results consistently showed lower MP concentrations in the "01" sub-samples compared to the corresponding "02" duplicates (Fig. 2 and 4). For the "01" group, concentrations ranged from 0.3 to 11 items g⁻¹ and 0.8 to 1.3 µg g⁻¹ in Py-GC/MS, while for the "02" group, concentrations ranged from 5.3 to 18 items g⁻¹ and 4.5 to 20 µg g⁻¹. These observations appear to be the result of insufficient sample homogenization prior to sub-sampling, since during preparation of batch "01" the samples were homogenized for less time than during batch "02". Although recovery rates were not assessed in this study, these observations highlight the reproducibility limits of the protocol for extracting MP from sediments. As some studies point out<sup>63,64</sup>, it is essential to assess these variabilities and progress towards harmonizing extraction protocols for solid matrices. These protocols are strongly influenced by factors such as sediment granulometry, the presence of plant debris or other natural organic matter. In addition, it is essential to consider the smallest size classes of MP, as these recovery tests are often carried out with particle sizes well above those present in the samples studied, compromising the reliability of the calculated rates.<sup>61,65</sup>

To obtain a more accurate assessment of the variability inherent in the sample, four additional sub-samples (STE-02 to 05) were prepared in the same batch. Fig. 5 illustrates the intra-sample variability, expressed by the RSD of sub-samples for Py-GC/MS measurements (22%) and those obtained by µ-FTIR in

mass estimation (30%) and particle number (41%). The variability becomes more marked when analyzing results polymer by polymer. For Py-GC/MS, RSDs are 21% for PE, 31% for PP and 27% for PS. By contrast, with µ-FTIR, RSDs reach 57%, 39% and 82% respectively for particle number, and 47% for PE, 20% for PP and 176% for PS for mass estimation. Conversion to mass reduced intra-sample variability for PP, where a lot of small particles (42 to 116 items, 25 to 100 µm) were detected. On the other hand, for PS, with only 1 to 9 particles detected in the 10 g sub-samples, this conversion amplified intra-sample variability. For sample comparison, it is therefore important to consider mass conversion only for polymers detected in high number, in order to avoid interpretation errors due to mass extrapolation.

Table 1 shows the coefficients of variation (RSD) of STE sub-samples according to size classes, based on µ-FTIR results. Classes 101-150 µm and 151-300 µm show lower intra-sample variability (RSDs of 24% and 32%), while classes 25-50 µm and 301-500 µm show greater dispersion (RSDs of 61% and 67%). As previously mentioned, the detection of particles below 100 µm may be limited by methodological constraints. Moreover, the insufficient number of particles detected in the size classes

<figure><img src="image_5.png" /><figcaption>Fig. 5 Number of MP, estimated and measured masses of PE, PP and PS in the replicates of the STE sub-samples prepared in the same batch</figcaption></figure>
<table><caption>**Table 1** Intra-sample variability of the number of particles per size class in STE sub-samples</caption><thead><tr><th rowspan="2">Size class (µm)</th><th colspan="7">Number of particles in STE sub-samples</th></tr><tr><th>STE-02</th><th>STE-03</th><th>STE-04</th><th>STE-05</th><th>Mean</th><th>STD</th><th>RSD</th></tr></thead><tbody><tr><td>[25-50]</td><td>14</td><td>29</td><td>14</td><td>48</td><td>26</td><td>16</td><td>61%</td></tr><tr><td>[51-100]</td><td>46</td><td>52</td><td>20</td><td>79</td><td>49</td><td>24</td><td>49%</td></tr><tr><td>[101-150]</td><td>15</td><td>19</td><td>11</td><td>13</td><td>15</td><td>3</td><td>24%</td></tr><tr><td>[151-300]</td><td>7</td><td>12</td><td>8</td><td>6</td><td>8</td><td>3</td><td>32%</td></tr><tr><td>[301-500]</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>67%</td></tr></tbody></table>

above 300 µm prevents meaningful comparison between samples. This aligns with Rolf et al., 2024¹¹, who observed low proportions of large MP, even though they analyzed relatively high quantities of sediment (250 g dry weight). These observations highlight the need to better assess intra-sample variability, for both small and large size classes of MP, to better determine how contamination levels can be compared between studies.

### 3.5. Comparison between µ-FTIR and Py-GC/MS

**3.5.1. Consistencies and discrepancies.** Fig. 6 shows the average particle number and polymer mass concentrations for each sampling site. The total concentrations obtained by µ-FTIR and Py-GC/MS for the three analyzed polymers are of the same order of magnitude, although variations are observed between sites. The highest mass concentration is observed at the EAR site, with a value between 15 and 20 µg g⁻¹, while the highest particle number is recorded at the SBG site, with approximately 50 items g⁻¹. These differences are related to the proportion of large particles: at the EAR site, about 42% of MP are larger than 100 µm, compared to only 17% at the SBG site. Since mass is extrapolated from particle size, a higher proportion of large MP leads to higher mass concentrations. These trends are also corroborated by the Py-GC/MS analyses.

Despite this overall consistency between mass estimation and mass measurements, substantial discrepancies are observed in the proportions of PE, PP and PS between the two methods. In µ-FTIR, PP is the dominant polymer, whereas it is the least abundant in Py-GC/MS results. Conversely, PS, which is detected in low amounts by µ-FTIR, is more prevalent than PP in Py-GC/MS analyses (Fig. 6). Several factors explain these discrepancies. First, the detection limit of µ-FTIR plays a key role. The formation of MP aggregates and filter clogging caused by organic matter, minerals and microorganisms in samples reduce the filter threshold cut-off.⁶⁶ Therefore, even if filtration in the range of 10-500 µm was applied, smaller particles, including nanoplastics, may still be retained but not detected by µ-FTIR. In contrast, these smaller particles can be quantified by Py-GC/MS if they are present in high numbers.⁶⁷ Additionally, µ-FTIR is limited in detecting fine-diameter fibers, whose polymer composition can still be quantified by Py-GC/MS. Analytical techniques such as Py-GC/MS need to be improved for their application to the quantification of finer fractions of micro- and nano-plastics in environmental samples.

Discrepancies in results also arise from approximations in the *siMPle* software. Particle masses are calculated based on a 2D projection, which can underestimate the mass of films compared to fragments. Furthermore, the polymer densities in the *siMPle* database, do not always reflect the actual variability caused by manufacturing techniques and added fillers. Additives are generally added in small quantities (<5% by weight), but some compounds, particularly plasticisers, can represent from 10% to 70% of the weight of the plastic.⁶⁸ Py-GC/MS enables the quantification of only synthetic polymers, which constitute only a fraction of the total composition of the particle. In contrast, spectroscopic methods analyze the entire

<figure><img src="image_6.png" /></figure>

plastic particle, which may exhibit a substantial difference in

<caption>**Fig. 6** Number of MP, estimated and measured masses of PE, PP and PS for each sample site. Uncertainties for the µ-FTIR data were estimated based on the intra-sample variance of STE replicates and applied uniformly to all samples. For Py-GC/MS data, uncertainties combine the intra-sample variance of STE replicates and the variance from injected pyrolysis aliquots.</caption>

volume and mass compared to the pure polymer.
Finally, plastic aging effects can also influence the results. Studies have shown that UV-induced aging reduces the quantity of specific pyrolytic products used for polymer quantification. Ainali et al., 2021⁶⁹ observed that pyrolytic products characteristic of PP, such as DM-C7, are generated in lower amounts when the polymer undergoes UV-induced alterations, leading to an underestimation of its mass. Similar results were reported by Toapanta et al., 2021⁷⁰ for PP particles subjected to photo-oxidation. Further research is needed to better understand the impact of MP aging on their pyrolytic signature and to extend these observations to other polymers.

**3.5.2. Patterns between PE, PP and PS in Py-GC/MS.** Fig. 7C shows the ratios between the masses of PE, PP and PS measured by Py-GC/MS. Positive proportional relationships were observed between these three polymers, both in samples with low concentrations, close to or below the LOQ, and in highly contaminated samples. However, the mass estimates obtained by µ-FTIR do not show a correlation Fig. 7A and 7B. Given the specificity of the markers chosen to quantify these

were analyzed. Hermabessiere et al., 2018⁷¹ showed that some plastic particles, identified as PE or PP by µ-Raman spectroscopy, could show a signal in Py-GC/MS corresponding to a mixture of PE and PP. Moreover, the correlation between PE, PP and PS could be attributed to the presence of particles resulting from tyre and road abrasion. Indeed, polymer-modified bitumen and synthetic rubbers contain styrenic polymers combined with ethylene copolymers, such as ethylene vinyl acetate (EVA), to improve the thermal stability or strength of the material.⁷²,⁷³ EVA exhibits a pyrolytic fingerprint very similar to that of PE, characterized by triplets of dienes, alkenes, and alkanes.⁷ The PS signal detected with the styrene dimer would result from the pyrolysis of particles made of styrenic polymers such as styrene butadiene rubber (SBR), ABS, or styrene-acrylonitrile, which are also used in the formulation of synthetic rubbers. SBR is a polymer that is abundant in roadside sediments due to tyre wear abrasion.³⁸,⁷⁴ In this study, the main SBR indicator compounds, 4-ethenylcyclohexene and 4-phenylcyclohexene, were detected in the majority of the samples. However, these black particles are not detectable by

three polymers, these observations do not appear to result from measurement artifacts or interferences. Indeed, it seems unlikely that a residue of organic matter would proportionally

transmission spectroscopy methods, and explain why higher levels of PS were measured by Py-GC/MS. Thus, given the prevalence of tire-derived particles in the environment, we

<figure><img src="image_7.png" /><figcaption>Fig. 7 Ratio of PP/PE, PS/PE and PS/PP for both methods.</figcaption></figure>

affect the signal of all three polymers.

Considering the similar trend between PE, PP and PS measured in Py-GC/MS, it can be assumed that copolymers

recommend that tire markers also be considered to estimate their contribution in PS quantification.
## 4. Conclusions

The sample preparation protocol for the sequential analysis of MP in sediments by µ-FTIR and Py-GC/MS demonstrated the overall consistency between these two methods, while highlighting their respective limitations. We confirmed the relevance of the *siMPle* estimate of MP mass in the 10-500 µm size range, although differences were observed in the proportions of PE, PP and PS. These variations highlight the software approximations and analytical limitations of both approaches. For example, µ-FTIR spectroscopy cannot discriminate between copolymers or black particles from tyre and road abrasion, whereas these materials are detected and quantified as MP in Py-GC/MS. In addition, µ-FTIR tends to underestimate MP in the form of fibers, which could generate inconsistencies, particularly in matrices rich in synthetic fibers, such as wastewater treatment plant effluents or atmospheric deposits.

These inconsistencies may also result from changes in the pyrolytic fingerprint of polymers due to their weathering in the environment, or from matrix interference. It is therefore essential to continue research into the impact of plastics aging, particularly for the fine fraction of MP, in order to improve the accuracy of the Py-GC/MS analyses.

Analysis of the sediment samples in replicates also revealed inter-sample variability. Py-GC/MS appeared to be more reproducible, but differences were observed between replicates, mainly due to insufficient homogenization of the sediments prior to the treatment steps. These results highlight the need to improve and standardize the sample preparation steps in order to ensure better representativeness of the sub-samples.

For robust characterization of MP in the environment, a combination of analytical techniques is the ideal option. However, the high cost of equipment and the specific expertise required often limit this approach. These methods can nevertheless be used independently, depending on the characteristics of the samples and the objectives of the study. µ-FTIR, for example, enables the size distribution of MP to be studied, which is essential for assessing environmental risks, while also offering great relevance for exploring the diversity of polymers and identifying specific sources of pollution. Provided that the polymers are included in the spectral database, this technique allows their identification and a reliable estimate of their mass.

On the other hand, Py-GC/MS, although theoretically not limited by particle size, requires specific standards for each polymer to be quantified, which makes the analyses more complex. Furthermore, as we have shown, the use of PS-d₅ as an internal standard may pose certain limitations and it will be necessary to investigate other options. Nevertheless, it remains a validated method for monitoring major polymers such as PE, PP and PS, which account for a significant proportion of plastics produced and released. In addition, GC/MS methods allow to analyze some of the organic additives present in plastic formulations (such as UV-stabilizers, flame retardants and antioxidants)⁷⁵ which would offer a more complete understanding of environmental impacts.

In combination, these two methods, supported by standardized protocols, give a solid basis for exploring the diversity of MP in various environmental matrices. This approach is crucial to extend our understanding of the impacts of plastic pollution and to provide more comprehensive assessments of the risks associated with this growing issue.

## Author contributions

**Nadia Bouzid:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Johnny Gasperi:** Writing – review & editing, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Bruno Tassin:** Writing – review & editing, Methodology, Investigation, Funding acquisition, Conceptualization. **Rachid Dris:** Writing – review & editing, Methodology, Investigation, Conceptualization.

## Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work presented in this article.

## Data availability

The data supporting this article have been included as part of the Supplementary Information.

## Acknowledgements

The authors would like to thank the PRAMMICS analytical platform for providing µ-FTIR spectroscopy and Py-GC/MS equipment.

This work was carried out as part of the Sedi-Plast research program (2020-2024) funded by the French National Research Agency (ANR-19-CE34-001).

## Notes and references

1. G. Erni-Cassola, V. Zadjelovic, M. I. Gibson and J. A. Christie-Oleza, *J. Hazard. Mater.*, 2019, **369**, 691–698.

2. R. W. Obbard, *Curr. Opin. Environ. Sci. Health*, 2018, **1**, 24–29.

3. S. Padha, R. Kumar, A. Dhar and P. Sharma, *Environ. Res.*, 2022, **207**, 112232.

4. M. Wagner, L. Monclús, H. P. H. Arp, K. J. Groh, M. E. Løseth, J. Muncke, Z. Wang, R. Wolf and L. Zimmermann, *State of the science on plastic chemicals - Identifying and addressing chemicals and polymers of concern*, Zenodo, 2024.

5. R. C. Thompson, Y. Olsen, R. P. Mitchell, A. Davis, S. J. Rowland, A. W. G. John, D. McGonigle and A. E. Russell, *Science*, 2004, **304**, 838–838.

6. R. Dris, M. Beaurepaire, N. Bouzid, C. Stratmann, M. T. Nguyen, F. Bordignon, J. Gasperi and B. Tassin, in *Microplastic Contamination in Aquatic Environments (Second Edition)*, ed. E. Y. Zeng, Elsevier, 2024, pp. 65–91.

7. A. Käppler, M. Fischer, B. M. Scholz-Böttcher, S. Oberbeckmann, M. Labrenz, D. Fischer, K.-J. Eichhorn and B. Voit, *Anal. Bioanal. Chem.*, 2018, **410**, 5313–5327.
8 S. Primpke, M. Fischer, C. Lorenz, G. Gerdts and B. M. Scholz-Böttcher, *Anal. Bioanal. Chem.*, 2020, **412**, 8283-8298.

9 S. Primpke, R. K. Cross, S. M. Mintenig, M. Simon, A. Vianello, G. Gerdts and J. Vollertsen, *Appl. Spectrosc.*, 2020, **74**, 1127-1138.

10 D. Range, C. Scherer, F. Stock, T. A. Ternes and T. O. Hoffmann, *Water Res.*, 2023, **245**, 120567.

11 M. Rolf, H. Laermanns, J. Horn, L. Kienzler, C. Pohl, G. Dierkes, S. Kernchen, C. Laforsch, M. G. J. Löder and C. Bogner, *Sci. Total Environ.*, 2024, **927**, 171927.

12 M. Barchiesi, M. Kooi and A. A. Koelmans, *Environ. Sci. Technol.*, 2023, **57**, 14015-14023.

13 M. Simon, N. van Alst and J. Vollertsen, *Water Res.*, 2018, **142**, 1-9.

14 S.-Y. Lee, J. An and J.-H. Kwon, *Environ. Pollut.*, 2023, **336**, 122452.

15 J. La Nasa, G. Biale, D. Fabbri and F. Modugno, *J. Anal. Appl. Pyrolysis*, 2020, **149**, 104841.

16 N. Yakovenko, A. Carvalho and A. ter Halle, *TrAC Trends Anal. Chem.*, 2020, **131**, 115979.

17 M. Fischer and B. M. Scholz-Böttcher, *Environ. Sci. Technol.*, 2017, **51**, 5052-5060.

18 N. Bouzid, C. Anquetil, R. Dris, J. Gasperi, B. Tassin and S. Derenne, *Microplastics*, 2022, **1**, 229-239.

19 T. Lauschke, G. Dierkes, P. Schweyen and T. A. Ternes, *J. Anal. Appl. Pyrolysis*, 2021, **159**, 105310.

20 F. Ribeiro, E. D. Okoffo, J. W. O'Brien, S. Fraissinet-Tachet, S. O'Brien, M. Gallen, S. Samanipour, S. Kaserzon, J. F. Mueller, T. Galloway and K. V. Thomas, *Environ. Sci. Technol.*, 2020, **54**, 9408-9417.

21 P. Luo, M. Bai, Q. He, Z. Peng, L. Wang, C. Dong, Z. Qi, W. Zhang, Y. Zhang and Z. Cai, *Anal. Chem.*, 2023, **95**, 3556-3562.

22 H. Mizuguchi, H. Takeda, K. Kinoshita, M. Takeuchi, T. Takayanagi, N. Teramae, W. Pipkin, K. Matsui, A. Watanabe and C. Watanabe, *J. Anal. Appl. Pyrolysis*, 2023, **171**, 105946.

23 I. V. Kirstein, F. Hensel, A. Gomiero, L. Iordachescu, A. Vianello, H. B. Wittgren and J. Vollertsen, *Water Res.*, 2021, **188**, 116519.

24 H. Laermanns, G. Reifferscheid, J. Kruse, C. Földi, G. Dierkes, D. Schaefer, C. Scherer, C. Bogner and F. Stock, *Front. Environ. Sci.*, DOI:10.3389/fenvs.2021.794895.

25 E. D. Okoffo, C. Rauert and K. V. Thomas, *Sci. Total Environ.*, 2023, **856**, 159251.

26 G. Dierkes, T. Lauschke, S. Becher, H. Schumacher, C. Földi and T. Ternes, *Anal. Bioanal. Chem.*, 2019, **411**, 6959-6968.

27 M. Fischer and B. M. Scholz-Böttcher, *Anal. Methods*, 2019, **11**, 2489-2497.

28 Z. Steinmetz, A. Kintzi, K. Muñoz and G. E. Schaumann, *J. Anal. Appl. Pyrolysis*, 2020, **147**, 104803.

29 M. G. J. Löder, H. K. Imhof, M. Ladehoff, L. A. Löschel, C. Lorenz, S. Mintenig, S. Piehl, S. Primpke, I. Schrank, C. Laforsch and G. Gerdts, *Environ. Sci. Technol.*, 2017, **51**, 14283-14292.

30 R. R. Hurley, A. L. Lusher, M. Olsen and L. Nizzetto, *Environ. Sci. Technol.*, 2018, **52**, 7409-7417.

31 A. Dehaut, A.-L. Cassone, L. Frère, L. Hermabessiere, C. Himber, E. Rinnert, G. Rivière, C. Lambert, P. Soudant, A. Huvet, G. Duflos and I. Paul-Pont, *Environ. Pollut.*, 2016, **215**, 223-233.

32 V. Hidalgo-Ruz, L. Gutow, R. C. Thompson and M. Thiel, *Environ. Sci. Technol.*, 2012, **46**, 3060-3075.

33 S. Fuller and A. Gautam, *Environ. Sci. Technol.*, 2016, **50**, 5774-5780.

34 N. P. Ivleva, *Chem. Rev.*, 2021, **121**, 11886-11936.

35 F. Ö. Sefiloglu, C. N. Stratmann, M. Brits, M. J. M. Van Velzen, Q. Groenewoud, A. D. Vethaak, R. Dris, J. Gasperi and M. H. Lamoree, *Environ. Pollut.*, 2024, **351**, 124088.

36 I. V. Kirstein, F. Hensel, A. Gomiero, L. Iordachescu, A. Vianello, H. B. Wittgren and J. Vollertsen, *Water Res.*, 2021, **188**, 116519.

37 R. Nakajima, M. Tsuchiya, D. J. Lindsay, T. Kitahashi, K. Fujikura and T. Fukushima, *PeerJ*, 2019, **7**, e7915.

38 I. Goßmann, D. Herzke, A. Held, J. Schulz, V. Nikiforov, C. Georgi, N. Evangeliou, S. Eckhardt, G. Gerdts, O. Wurl and B. M. Scholz-Böttcher, *Nat. Commun.*, 2023, **14**, 3707.

39 K. M. Unice, M. L. Kreider and J. M. Panko, *Int. J. Environ. Res. Public Health*, 2012, **9**, 4033-4055.

40 S. Tsuge, H. Ohtani and C. Watanabe, *Pyrolysis - GC/MS Data Book of Synthetic Polymers: Pyrograms, Thermograms and MS of Pyrolyzates*, Elsevier, 2011.

41 S. T. Anuar, R. S. Altarawnah, A. A. Mohd Ali, B. Q. Lee, W. M. A. W. M. Khalik, K. M. K. K. Yusof and Y. S. Ibrahim, *Polymers*, 2022, **14**, 3054.

42 E. Duemichen, P. Eisentraut, M. Celina and U. Braun, *J. Chromatogr. A*, 2019, **1592**, 133-142.

43 M. Albignac, T. de Oliveira, L. Landebrit, S. Miquel, B. Auguin, E. Leroy, E. Maria, A. F. Mingotaud and A. ter Halle, *J. Anal. Appl. Pyrolysis*, 2023, **172**, 105993.

44 C. Rauert, Y. Pan, E. D. Okoffo, J. W. O'Brien and K. V. Thomas, *J. Env. Expo Assess*, 2022, **1**, 13.

45 H. Ohtani, S. Tsuge, Y. Matsushita and M. Nagasawa, *Polym. J.*, 1982, **14**, 495-499.

46 S. Gao, N. Orlowski, F. K. Bopf and L. Breuer, *WIREs Water*, 2024, **11**, e1713.

47 L. Landebrit, R. Sanchez, L. Soccalingame, M. Palazot, M. Kedzierski, S. Bruzeau, M. Albignac, W. Ludwig, J. F. Ghiglione and A. ter Halle, *Environ. Sci. Pollut. Res.*, DOI:10.1007/s11356-024-34486-1.

48 M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J. N. Möller, C. Laforsch, M. G. J. Löder and C. Bogner, *Sci. Total Environ.*, 2022, **836**, 155141.

49 E. Dhivert, N. N. Phuong, B. Mourier, C. Grosbois and J. Gasperi, *Water Res.*, 2022, **225**, 119187.

50 A. A. Koelmans, N. H. M. Nor, E. Hermsen, M. Kooi, S. M. Mintenig and J. De France, *Water Res.*, 2019, **155**, 410-422.

51 M. Kooi and A. A. Koelmans, *Environ. Sci. Technol. Lett.*, 2019, **6**, 551-557.

52 E. Dhivert, N. N. Phuong, B. Mourier, C. Grosbois and J. Gasperi, *Water Res.*, 2022, **225**, 119187.

53 M. T. Nguyen, N. N. Phuong, M. Saad, B. Tassin, T. Gillet, S. Guérin-Rechdaoui, S. Azimi, V. Rocher, J. Gasperi and R. Dris, *Environ. Sci. Pollut. Res.*, 2024, **31**, 10501-10507.

54 M. Beaurepaire, J. Gasperi, B. Tassin and R. Dris, *Environ. Pollut.*, 2024, **344**, 123354.

55 S. Kernchen, M. G. J. Löder, F. Fischer, D. Fischer, S. R. Moses, C. Georgi, A. C. Nölscher, A. Held and C. Laforsch, *Sci. Total Environ.*, 2022, **818**, 151812.

56 P. M. Anger, E. von der Esch, T. Baumann, M. Elsner, R. Niessner and N. P. Ivleva, *TrAC Trends Anal. Chem.*, 2018, **109**, 214-226.

57 H. De Frond, A. M. O'Brien and C. M. Rochman, *Chemosphere*, 2023, **310**, 136772.

58 J. Yang, M. Monnot, Y. Sun, L. Asia, P. Wong-Wah-Chung, P. Doumenq and P. Moulin, *Water Res.*, 2023, **232**, 119711.

59 N. Stile, C. Raguso, A. Pedruzzi, E. Cetojevic, M. Lasagni, A. Sanchez-Vidal and F. Saliu, *Mar. Pollut. Bull.*, 2021, **168**, 112436.

60 F. Radford, L. M. Zapata-Restrepo, A. A. Horton, M. D. Hudson, P. J. Shaw and I. D. Williams, *Anal. Methods*, 2021, **13**, 1695-1705.

61 C. Way, M. D. Hudson, I. D. Williams and G. J. Langley, *Sci. Total Environ.*, 2022, **805**, 150227.

62 A. Gomiero, K. B. Øysæd, T. Agustsson, N. van Hoytema, T. van Thiel and F. Grati, *Chemosphere*, 2019, **227**, 705-714.
63 A. L. Lusher, K. Munno, L. Hermabessiere and S. Carr, *Appl. Spectrosc.*, 2020, **74**, 1049–1065.

64 J. N. Möller, M. G. J. Löder and C. Laforsch, *Environ. Sci. Technol.*, 2020, **54**, 2078–2090.

65 F. Ruggero, R. Gori and C. Lubello, *J. Polym. Environ.*, 2020, **28**, 739–748.

66 J. Sun, X. Dai, Q. Wang, M. C. M. van Loosdrecht and B.-J. Ni, *Water Res.*, 2019, **152**, 21–37.

67 E. D. Okoffo and K. V. Thomas, *Water Res.*, 2024, **254**, 121397.

68 Hansen et al., Hazardous Substances in PlasticMaterials, COWI in cooperation with Danish Technological Institute, 2013. E. Hansen, N. Nillson, D. Lithner, C. Lassen, https://scholar.google.com/scholar_lookup?title=Hazardous%20Substances%20in%20Plastic%20Materials&author=E.%20Hansen&publication_year=2013, (accessed 2 February 2024).

69 N. M. Ainali, D. N. Bikiaris and D. A. Lambropoulou, *J. Anal. Appl. Pyrolysis*, 2021, **158**, 105207.

70 T. Toapanta, E. D. Okoffo, S. Ede, S. O’Brien, S. D. Burrows, F. Ribeiro, M. Gallen, J. Colwell, A. K. Whittaker, S. Kaserzon and K. V. Thomas, *Sci. Total Environ.*, 2021, **796**, 148835.

71 L. Hermabessiere, C. Himber, B. Boricaud, M. Kazour, R. Amara, A.-L. Cassone, M. Laurentie, I. Paul-Pont, P. Soudant, A. Dehaut and G. Duflos, *Anal. Bioanal. Chem.*, 2018, **410**, 6663–6676.

72 R. Mujal-Rosas, M. Marin-Genesca and J. Ballart-Prunell, *Sci. Eng. Compos. Mater.*, 2015, **22**, 231–243.

73 G. Urtekin, M. S. Ullah, R. Yildirim, G. Ozkoc and M. Kodal, *Polym. Compos.*, 2023, **44**, 8303–8329.

74 T. De Oliveira, B. Muresan, S. Ricordel, L. Lumière, X.-T. Truong, L. Poirier and J. Gasperi, *J. Hazard. Mater.*, 2024, **465**, 133301.

75 F. Akoueson, C. Chbib, S. Monchy, I. Paul-Pont, P. Doyen, A. Dehaut and G. Duflos, *Sci. Total Environ.*, 2021, **773**, 145073.