# From Elements to Isotopes: Fingerprinting Consumer Plastics

Manon Le Corre, Anne-Catherine Pierson-Wickmann, Bleuenn Gueguen, Maxime Pattier, Murielle Rabiller-Baudry, Aline Dia

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Manon Le Corre, Anne-Catherine Pierson-Wickmann, Bleuenn Gueguen, Maxime Pattier, Murielle Rabiller-Baudry, et al.. From Elements to Isotopes: Fingerprinting Consumer Plastics. Talanta, 2026, 302, pp.129404. 10.1016/j.talanta.2026.129404. insu-05457318v2

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# From elements to isotopes: Fingerprinting consumer plastics

Manon Le Corre<sup>a,*</sup>, Anne-Catherine Pierson-Wickmann<sup>a</sup>, Bleuenn Gueguen<sup>a ID</sup>, Maxime Pattier<sup>a</sup>, Murielle Rabiller-Baudry<sup>b</sup>, Aline Dia<sup>a</sup>

## ARTICLE INFO

### Keywords:

- Plastics

- Microplastics

- Elemental fingerprinting

- Isotopic tracers

- Copper

- Lead

- Strontium

## ABSTRACT

Polymer identification alone rarely informs on additive provenance or metal interactions. We tested whether multi-element profiles and selected metal isotopes provide complementary and reproducible fingerprints of consumer plastics. We analyzed 119 items spanning major polymers and colors after microwave digestion. Forty-one elements were quantified by ICP-MS; Cu, Sr, and Pb isotope ratios were measured by MC-ICP-MS on selected purified subsets. Elemental concentrations span over eight orders of magnitude (from Rare Earth Elements (REE) at µg kg⁻¹ to Ca at >10⁸ µg kg⁻¹). Non-parametric statistics reveal significant polymer and color effects; polypropylene (PP) shows higher Mg, Si, Cr, and Ni concentrations than polyethylene (PE), while color modulates Cu and some REE. Strontium isotope ratios vary widely (⁸⁷Sr/⁸⁶Sr = 0.70765–0.71320), consistent with contributions from disparate mineral fillers and pigments. Copper isotopes are systematically enriched in ⁶⁵Cu with variable values (δ⁶⁵Cu = +0.18‰ to +1.35‰), supporting the influence of distinct Cu-based pigment formulations. Lead isotope ratios further discriminate legacy Pb-bearing additives observed in a subset of items, consistent with distinct manufacturing and/or recycled streams. Overall, combined elemental-isotopic fingerprints thus provide complementary, integrated and transferable markers of additive provenance beyond spectroscopic polymer identification. These signatures open new avenues for source apportionment and understanding the fate of environmental plastics.

## 1. Introduction

Plastics are ubiquitous and pervasive in every terrestrial and aquatic environment due to their high production volumes, widespread use, durability, and resistance to degradation [1]. Global plastic production started in the 1950s, and exceeded 400 Mt annually in recent years (Plastics - the fast Facts 2024), with packaging and construction materials representing the largest applications. Despite efforts to mitigate plastic pollution, recycling rates remain modest due to challenges associated with polymer separation, contamination, and the economic viability of secondary plastic markets [2]. As a result, only a small fraction is effectively recycled, with the majority accumulating in landfills, soils, and natural waters [2,3], generating pollution in the atmosphere, glaciers, biosphere, and hydrosphere, and more recently documented in soils as well [4–9]. Once released in soils and aquatic environments, plastics fragment into micro- and nanoplastics that pose risks to biodiversity and biogeochemical cycles [10]. Plastic pollution can also affect the soil quality and impair the ecosystem services

provided by soils [4,11]. Their presence in all environmental compartments raises persistent questions about exposure, fate, and source attribution.

Beyond the base polymer, manufactured items carry complex additive packages (colorants, fillers, stabilizers, flame retardants, catalyst residues) [12] that can leach or be released as the polymer fragments during use and ageing, with potential implications for human and ecosystem health [13]. Additive loadings vary widely: they can be below 1% for pigments, stabilizers, antistatic agents, etc., but can reach up to 70% for plasticizers in PVC [12]. The European Chemicals Agency (ECHA) has listed more than 400 additives which more than 100 tons are annually used in plastics formulation in the EU. These include inorganic pigments such as chromium (III) oxides, copper compounds (e.g., copper sulfate), lead sulfachromate yellow, and titanium dioxide, as well as metals present as flame retardants and heat stabilizers (echa. europa.eu). Mainly composed of minerals such as mica, kaolin, clay, calcium carbonate, or barium sulfate, fillers are incorporated to reduce cost and tailor material properties [12]. The elemental composition of

<footnote><sup>a</sup> Univ Rennes, CNRS, Géosciences Rennes, UMR 6118, 35000, Rennes, France</footnote>

<footnote><sup>b</sup> Univ Rennes, CNRS, Institut des Sciences Chimiques de Rennes, UMR6226, 35000, Rennes, France</footnote>

<footnote>* Corresponding author. Univ Rennes, CNRS, Géosciences Rennes, UMR 6118, 35000, Rennes, France.
E-mail address: manon.le-corre@univ-rennes.fr (M. Le Corre).</footnote>
plastics, particularly their trace element content, has been increasingly studied [14–20]. Analyses of environmental, household, and electrical plastics revealed a broad range of metal contents [14]. Tire tread rubber showed a particular composition in metallic elements (concentrations of Cd, Cr, Ni, Pb, Zn), unlike the other plastic types studied (PE, PP, EPS, PS, PVC) [14]. Other studies showed a large diversity of elements present in variable concentrations in environmental and pristine samples [13,15–17,21]. Distinctive fingerprints have been linked to specific additives such as lead sulfochromate yellow, titanium dioxide, or iron manganese trioxide [14,22]. However, most studies focus on elemental concentrations, while little is known about the isotopic composition of these elements in plastics.

Most routine characterizations end at the polymer type (e.g., FTIR/
Raman) [9,23]. At best, they provide bulk elemental profiles by ICP-MS
[14,15,17,24], while targeted GC-MS reveals selected organic additives.
However, these tools rarely constrain the provenance of inorganic fillers
and metallic pigments, nor do they capture the geochemical information
needed to trace manufacturing streams or subsequent environmental
interactions. Moreover, the biological, physical, and chemical processes
driving plastic degradation in soils remain incompletely constrained.
Understanding the behavior of plastics in soils is crucial, because beyond
contamination, plastics can also be sites of sorption processes for pol-
lutants [25,26]. Understanding these mechanisms is crucial for
improving waste management and pollution mitigation strategies. As a
result, the analytical community lacks robust and comprehensive
markers that move beyond “what polymer is it?” towards “where did it
come from and how might it behave?”.

While multipoint elemental fingerprints can already discriminate product categories or manufacturing batches, they do not capture the geological or industrial provenance encoded in isotope ratios, nor do they resolve how ageing and weathering alter those signals. In principle, stable and radiogenic isotope systems (e.g., C, Cu, Sr, Pb, selected transition metals) add a second, largely independent axis of information: elemental concentrations track “how much” of each inorganic constituent is present; isotope ratios track “where it came from” and “how it was processed”. This complementarity has been exploited in geochemistry for decades but remains underused for plastics. Stable isotope techniques, particularly carbon isotopic signatures (δ¹³C), have been applied to distinguish bio-based from petroleum-derived plastics, as well as to trace their degradation in different environments [27–29]. Copper isotopes (δ⁶⁵Cu) have the potential to fingerprint Cu-based pigments and stabilizers; strontium isotopes (⁸⁷Sr/⁸⁶Sr) are sensitive to carbonate- or sulfate-derived fillers with distinct geological origins; and lead isotopes, though phased out in many jurisdictions, remain valuable tracers of legacy additives and manufacturing streams. Together, elements and isotopes can yield reproducible, transferable “fingerprints” that complement polymer identification and support source attribution, recycling forensics, and environmental tracing.

Despite this promise, integrated element-isotope datasets measured directly on plastics are scarce, and methodological guardrails are unevenly reported. Quantitative progress requires: (i) a large and diverse cohort of consumer items; (ii) validated digestion and purification workflows with low blanks. Applying them to plastics could open new opportunities for tracing plastics origin, ageing, and environmental fate. Here, we explore the potential of elemental and isotopic fingerprints as tracers of plastics. We analyze 119 consumer plastics spanning major polymers and colors. After microwave digestion, we quantify 41 elements by ICP-MS and measure Cu, Sr, and Pb isotopes on purified subsets. We verify accuracy and reproducibility with certified materials and bracketing strategies, and deploy non-parametric statistics. Here, we evaluate a combined elemental-isotopic strategy for consumer and packaging plastics. Using high-throughput ICP-MS for multi-element quantification and MC-ICP-MS for selected isotope ratios, we ask whether isotopic variables provide non-redundant information relative to elemental data for (i) discriminating polymer families and colors, (ii) tracing potential manufacturing inputs (pigments, fillers) and ageing,

and (iii) improving classification performance in realistic mixtures. We
evaluate the potential of these isotopes to trace plastic pollution in a soil
environment by comparing isotopic ratios in soils and plastics.

Altogether, the study provides a practical, quality-assured pathway to pair elemental “what/how much” with isotopic “where/how” in plastics analysis, with direct applications to environmental tracing, extended producer responsibility, and the authentication of recycled streams.

## 2. Materials and methods

2.1. Reagents, labware cleaning, and blank control

All chemicals used were of analytical grade. Solutions were prepared with ultrapure water (18.2 MΩ·cm, Milli-Q system, Millipore). Teflon (PTFE) labware was decontaminated in three sequential steps: (i) immersion in a 50% (v/v) nitric acid (HNO₃) bath for at least 12 h at 80°C, (ii) internal cleaning with 14.6 M HNO₃ for at least 12 h, and (iii) drying at 50°C. Between each step, the labware was rinsed three times with ultrapure water. Plastic labware was decontaminated following a similar protocol: (i) immersion in 10% (v/v) HNO₃ for 24 h at 45°C, (ii) triple rinsing with ultrapure water, and (iii) drying at 50°C.

For sample digestion and chromatographic separation, analytical grade HNO₃ was purified by double sub-boiling distillation in PFA lab-ware, while hydrochloric acid (HCl) was purified on a PFA DST-1000 system (Savillex). Blanks accompanied every batch and were carried through digestion, purification, and analysis.

## 2.2. Items and polymer identification

A total of 119 consumer plastic items were purchased in supermarkets or collected post-consumer. Items covered a range of colors: 1 grey, 2 beige, 5 transparent, 10 white, 18 blue, 7 yellow, 7 brown, 9 black, 13 orange, 8 pink, 12 red, 18 green, and 9 purple. Polymer type was assigned based on EU 97/129/EC labels when available; otherwise, polymers were identified by ATR-FTIR (Jasco FT/IR-4100, ATR-diamond).

Before ATR-FTIR analyses, all items were cleaned with ethanol and dried in a desiccator for at least 48 h. Spectra were acquired with 128 scans at a resolution of 2 cm⁻¹ over the range 4000-600 cm⁻¹, with air as background. The resulting spectra were processed using the Spectra Manager software and compared with a laboratory database of known polymer spectra for identification. The presence of characteristic peaks for each polymer was confirmed and a matching score of at least 90% was required to ensure reliable identification. Unidentified polymers were classified as unknown and grouped under the category “others”. Polymers identified in only 1 or 2 samples were also grouped under the category “others”, including acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV), and MaterBi®.

Each item received a unique identifier and metadata (polymer, color, nominal use, purchase year, country of origin) were recorded. Samples were handled with powder-free nitrile gloves in a clean bench; metal tools were avoided.

2.3. Microwave-assisted digestion

Small fragments (less than 0.5 cm) were cut using ceramic scissors, pre-cleaned with 0.37 M HNO₃, followed by ultrapure water. Between 50 mg and 100 mg of plastic fragments were digested in closed PTFE vessels with 5 mL of ultrapure 14.6 M HNO₃ (Multiwave 7000, Anton-Paar, Graz, Austria). A representative program ramped to 250°C at a rate of 6.6°C min⁻¹ over 35 min at up to 80 bar, held 25 min, then cooled to room temperature [22]. Digested samples were recovered with 0.37 M HNO₃. Blanks were systematically performed to monitor potential contamination. The ERM-EC680 polymer (CRM, JRC) was processed in
parallel to validate digestion and quantification (Table 1). Microwave digestion with HNO₃ was compared with other digestion methods by Alam et al. [30] for the determination of toxic heavy metals in PE and PVC plastics. High digestion efficiencies (>90%) were obtained by microwave digestion with HNO₃ [30].

## 2.4. Elemental analysis

After acid digestion, elemental concentrations (41 elements) were measured on an Agilent 7700x ICP-MS (GeOHeLiS, University of Rennes, France), using He collision mode for selected elements to reduce interferences [22]. Digests were diluted 10-fold prior to analysis. External calibration solutions were prepared from certified multi-element standard solutions (Inorganic Ventures) in matched matrices. Rhodium was used as an internal standard, and the SLRS-6 water standard was used for independent verification. The ERM-EC680 polymer reference material validated the full workflow (digestion and ICP-MS analysis). Instrumental limits of detection (LOD) and quantification (LOQ) for the 41 elements are reported in Table S1.

## 2.5. Isotopic analysis

Isotopic analyses of Cu, Sr, and Pb were performed on a limited number of samples due to the minimum concentrations required for isotopic measurements.

### 2.5.1. Strontium isotope analysis

Samples containing a minimum of 200 ng of strontium in the solution after microwave digestion were selected for the isotopic analysis. Strontium isotope ratios ($^{87}$Sr/$^{86}$Sr) were measured on a subset of 12 samples across three plastic types (PP (5), PE (6) and PS (1)) and six plastic colors (red (4), blue (3), white (2), brown (1), pink (1) and orange (1)).

Sr separation was performed using Sr-Spec resin (Triskem International®). The resin was precleaned several times with ultrapure water before 0.3 mL was added to each column. Additional cleaning steps included 3 washes with 3 mL of H₂O. Columns were conditioned with 3 M HNO₃.

Samples were aliquoted in order to load between 200 and 600 ng of Sr on the columns. Aliquots were evaporated to dryness and re-dissolved in 3 mL of 3 M HNO₃ before loading onto columns. The matrix was eluted by loading four sequential 1 mL additions of 3 M HNO₃. Then, Sr was recovered by sequentially adding ultrapure water (0.5 mL, 1 mL, and 2 x 1.5 mL). The final concentration was adjusted to 50 µg kg⁻¹ before measurement.

Strontium isotopic ratios ($^{87}$Sr/$^{86}$Sr) were measured at the GeOHeLiS analytical platform (University of Rennes, France) on a Nu Sapphire MC-ICP-MS. Instrumental mass bias was corrected using the standard-sample bracketing method with NIST SRM 987 (formerly NBS 987). Mass fractionation was monitored and corrected using the value $^{88}$Sr/$^{86}$Sr = 8.3752. Precision of the measurements is reported as 2 SD for replicate measurements of NIST SRM 987. The separation protocol was always performed with a NIST SRM 987, the post-column $^{87}$Sr/$^{86}$Sr mean was 0.710319 (SD = 0.00003) over 3 values. All blanks were <0.2 ng of Sr and considered negligible, therefore no blank correction was applied.

<table><caption>**Table 1**
ERM-EC680 standard concentrations of five elements: measured values from our study, certified values, and recoveries after microwave digestion.</caption><thead><tr><th></th><th>Measured values (µg kg<sup>-1</sup>)</th><th>Certified values (µg kg<sup>-1</sup>)</th><th>Recovery (%)</th></tr></thead><tbody><tr><td>Cr</td><td>8,241 ± 1,750</td><td>9,600 ± 500</td><td>86 ± 18 %</td></tr><tr><td>Zn</td><td>148,968 ± 31,346</td><td>194,000 ± 12,000</td><td>76 ± 16 %</td></tr><tr><td>As</td><td>4,052 ± 856</td><td>4,700 ± 400</td><td>86 ± 18 %</td></tr><tr><td>Cd</td><td>18,180 ± 3,850</td><td>20,800 ± 900</td><td>87 ± 18 %</td></tr><tr><td>Pb</td><td>10,294 ± 2,604</td><td>11,300 ± 400</td><td>91 ± 23 %</td></tr></tbody></table>

### 2.5.2. Copper isotopic analysis

Samples containing at least 400 ng of copper (Cu) were selected for Cu isotopic analysis. Eighteen samples were analyzed for copper isotopic composition across five plastic types (PP (5), PE (10), PS (1), PVC (1), and MaterBi (1)) and three colors (blue (10), green (7), and yellow (1)). Copper separation chemistry was performed on AG 1X8 Bio-Rad resin (200–400 mesh) following a protocol modified from Sossi et al. [31]. First, the resin was thoroughly cleaned a few times with ultrapure water in order to remove the fine particles. One mL of resin was loaded into each column and rinsed with ultrapure water. Resin cleaning was performed by adding 10 mL of 3 M HNO₃ and 10 mL of H₂O. Then, resin equilibration was achieved with 10 mL of 6 M HCl. Sample aliquots containing between 400 and 800 ng of copper were fully evaporated and re-dissolved in 1 mL of 6 M HCl before loading on the columns. The matrix was eluted with 1 mL of 6 M HCl twice. Copper fraction was then recovered in multiple steps with sequential additions of 6 M HCl (1 mL three times, 2 mL seven times, and 1 mL six times). After Cu elution, a cleaning step was performed by adding 10 mL H₂O, 10 mL 3 M HNO₃, and 10 mL H₂O. The Cu fraction was evaporated to dryness and re-dissolved in 0.37 M HNO₃. The final concentration was adjusted to 100 µg kg⁻¹ before measurement.

Copper isotopic compositions were measured at the GeOHeLiS analytical platform (University of Rennes, France) using a Nu Sapphire MC-ICP-MS. Instrumental mass bias was corrected by using the standard-sample bracketing with a NIST SRM 3114 isotopic reference material in between every sample. The $\delta^{65}$Cu was calculated relative to NIST SRM 3114 standard according to Eq. (1).

$$ \delta^{65}\text{Cu} = \left( \frac{\left( \frac{^{65}\text{Cu}}{^{63}\text{Cu}} \right)_{\text{sample}}}{\left( \frac{^{65}\text{Cu}}{^{63}\text{Cu}} \right)_{\text{NIST3114}}} - 1 \right) \times 1000 \quad (1) $$

All blanks registered were <2 ng and considered negligible. As a consequence, no blank correction was applied. For comparison with studies reporting $\delta^{65}$Cu relative to NIST SRM 976, $\delta^{65}$Cu values can be converted using: $\delta^{65}\text{Cu}_{\text{NIST976}} = \delta^{65}\text{Cu}_{\text{NIST3114}} - 0.07\%$ [32]. Precision of the measurements is reported as 2 SD for multiple measurements of NIST SRM 3114. The separation protocol was systematically checked with at least one NIST SRM 3114 standard; $\delta^{65}$Cu mean was −0.01‰ (SD = 0.06‰) over 11 values. To ensure reproducibility on plastic samples, at least one standard ERM-EC680 aliquot was analyzed in every sequence (prepared from independent microwave digests); $\delta^{65}$Cu mean was 0.37‰ (SD = 0.07‰) over 5 values.

### 2.5.3. Lead isotope analysis

Digested samples containing more than 200 ng of lead after ICP-MS analysis were selected. Aliquots were evaporated to dryness and re-dissolved in 2 mL of 0.5 M hydrobromic acid (HBr) before loading onto columns. Lead separation was performed in micro exchange columns filled with AG 1X8 (100–200 mesh) anion exchange resin. Firstly, the resin was cleaned by H₂O and 6 M HCl, then conditioned by 4 mL of 0.5 M HBr. Samples were loaded and the matrix was rinsed by a total of 3 mL of 0.5 M HBr. Then Pb was eluted by a total of 4 mL of 6 M HCl (1 mL, 2 mL, and 1 mL). At the end, the resin was cleaned by H₂O and 6 M HCl.

Five samples of PE were analyzed, all had different colors (green, yellow, orange, pink, and dark). Before analysis, the lead fraction was dissolved in 0.37 M HNO₃. Lead isotopic ratios were measured at the GeOHeLiS analytical platform (University of Rennes, France) on a Nu Sapphire MC-ICP-MS for 3 samples (green, orange, and yellow). ERM-EC680 (green) was analyzed in duplicate to evaluate the reproducibility of the method. Analysis was performed at a concentration of 50 µg kg⁻¹ before measurement.
µg kg⁻¹ of Pb and 10 µg kg⁻¹ of Tl. Mass fractionation was monitored and corrected using the value $205^{205}\text{Tl}/203^{203}\text{Tl} = 2.38714$. Standard-sample bracketing method with NIST SRM 981 every two samples was used to correct instrumental mass bias. Two samples were measured at the PARI platform (IPGP, France) on a Neptune Plus (ThermoScientific). Analysis was performed at a concentration of 100 µg kg⁻¹ of Pb and 20 µg kg⁻¹ of Tl. The standard-sample bracketing method with NIST SRM 981 isotopic reference material every four samples was used to correct instrumental mass bias. Mass fractionation was monitored and corrected with Tl. Precision of the measurements was expressed as 2 SD for NIST SRM 981 (multiple measurements).

Separation protocol was always performed with a NIST SRM 981 standard, mean $208^{208}\text{Pb}/204^{204}\text{Pb}$ was 36.6764 (SD = 0.00136), mean $207^{207}\text{Pb}/204^{204}\text{Pb}$ was 15.4871 (SD = 0.0004) and mean $206^{206}\text{Pb}/204^{204}\text{Pb}$ was 16.9454 (SD = 0.0002) over 4 values. Data were compared with values from White et al. [33]. All blanks registered were <0.1 ng of Pb and were considered as negligible. No blank correction was performed.

## 2.6. Statistical analysis

Data analysis was conducted in R-Studio (R version 4.3.2) with the ggplot2 package for visualization. Data were analyzed based on plastic types and colors to see whether they control elemental composition.

For color-based data, all 119 samples were considered. Some colors were grouped to obtain groups of comparable size and limit the number of color categories: black/grey/brown (15% of plastics), purple/pink (13% of plastics), white/transparent (12% of plastics), and beige/yellow (8% of plastics). Blue and green plastics were not grouped and represent respectively, 15% and 16% of the samples. Red and orange plastics were also processed separately and account for 11% and 10% of the samples, respectively.

For polymer-based analyses, only the main polymer types with at least four samples were selected. A subset composed of 108 samples was considered: PE (40 samples), PP (54 samples), PET (5 samples), PS (5 samples), PVC (4 samples). Eleven samples were excluded from this statistical analysis, since they were either not identified or their plastic type was only present in one or two samples.

Group differences (polymer, color) were evaluated using Kruskal-Wallis test, followed by Dunn's post hoc test ($p < 0.05$). Values below the quantification limit (LOQ) were not considered for statistical testing. The exclusion method was preferred to substitution methods (e. g. half-LOQ) to avoid introducing arbitrary assumptions about censored data and to preserve the integrity of rank-based comparisons that can be biased by substitution methods. Indeed, the non-parametric tests used in this study (Kruskal-Wallis and Dunn's post hoc), are sensitive to ranking.

# 3. Results and discussion

## 3.1. Cohort overview and polymer distribution

We analyzed 119 consumer and packaging plastics spanning PP, PE, PET, PS, PVC, and others (PLA/PHBV/MaterBi®/ABS and a few unknowns), with colors covering white/transparent to vivid pigments (blue, green, yellow, orange, red, purple, black, brown). Plastic types were characterized from product labels or by ATR-FTIR, and are reported in Fig. 1. The items are dominated by polypropylene (PP, 45.4%) and polyethylene (PE, 33.6%), with minor fractions of polyethylene terephthalate (PET, 4.2%), polystyrene (PS, 4.2%), and polyvinyl chloride (PVC, 3.4%). The remaining 9.2% were grouped as “others”.

## 3.2. Elemental composition of plastics

Element concentrations measured by ICP-MS are reported in Table S1. For each of the 41 elements, Table S1 provides the proportion of samples > LOQ, mean, median, maximum values, standard deviation, and instrumental limit of detection (LOD) and quantification (LOQ).

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Distribution of plastic items according to their type (n = 119). PP: polypropylene, PE: polyethylene, PET: polyethylene terephthalate, PS: poly-styrene, PVC: polyvinyl chloride, others include the remaining items (MaterBi®, PHBV, PLA, and ABS or items not confidently identified by ATR-FTIR).</figcaption></figure>

Across 119 consumer items, concentrations of major and trace elements span about eight orders of magnitude, from µg kg⁻¹ (e.g., REEs, some metalloids) to $>10^8$ µg kg⁻¹ (Ca). Elements detected in more than 70% of items include Mg, Si, Ca, Cr, Ni, Cu, Sr, Y, and Ba, while Li, B, As, and a few REEs are more sporadic (in less than 20% of items).

Major additives such as Ca and Ba were present at particularly high levels, consistent with their common use as fillers or flame retardants. High Ca, up to 217,121,614 µg kg⁻¹ in a polyethylene (PE) plastic bag, and frequent Sr, would be consistent with carbonate/sulfate fillers; Ba points to barite fillers/flame-retardant packages in a subset of samples, reaching up to 4,323,002 µg kg⁻¹. An illustrative outlier is a yellow PE item that shows concurrent Pb (15,109,693 µg kg⁻¹) and Cr (2,487,850 µg kg⁻¹) enrichment, a signature compatible with PbCrO₄ (chrome yellow) pigment [24]. These patterns indicate that elemental loads primarily encode additive packages rather than polymer chemistry *per se*. Few elements displaying a median concentration above 1000 µg kg⁻¹, included both major elements Na, Mg, Si, K, Ca, but also some metals Fe and Zn.

Median concentrations ranged from sub-µg kg⁻¹ levels to 143,692 µg kg⁻¹ for Si, highlighting the heterogeneity of plastic formulations. High median concentration was also noted for Ca ($94,691$ µg kg⁻¹) while rare earth elements (REE) such as Ho, Eu, Tb, and Lu exhibited the lowest medians. Compared to Klöckner et al. [14], our results are generally of the same order of magnitude, although Ba contents deviated significantly, reflecting variability in additive use. The median Ba concentration of 427 µg kg⁻¹ is lower than the concentration of 3400 µg kg⁻¹ from Klöckner et al. [14]. In general, Ca and Ba showed a wide dispersion, consistent with variable loads of $CaCO_3/BaSO_4$ fillers.

Fig. 2 presents boxplots for all analyzed elements. Few elements had a median concentration below $10$ µg kg⁻¹, Be, U, and a majority of REE (La, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu). These elements were also detected in a small number of samples. The other REE (Ce, Pr, Nd) exhibit median concentrations slightly higher than $10$ µg kg⁻¹. A lot of elements exhibited median concentrations between 10 and 1000 µg kg⁻¹, including the majority of trace metals (V, Cr, Mn, Co, Ni, Cu, Ga, Cd, Pb) and also some other elements such as Li, B, Sc, Rb, As, Sr, Y, Ba, Th. The boxplots of elements in plastics (Fig. 2) were compared to the same figure from Walsh et al. [15]. They follow a very similar pattern, with very low REE and Be concentrations. We can observe the same increasing trends in the concentrations of transition metals from Sc to Fe (except for Ti, which was not measured in our study), and from Co to Zn. They also measured relatively high concentrations in Na, Mg, K, and Ca
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Bulk elemental concentrations in 119 plastic items in log-scale, measured by ICP-MS, expressed in µg kg⁻¹. All REE were grouped under ΣREE.</figcaption></figure>

[15].

The concentrations of trace elements measured in this study gener-
ally fall within the range of values reported in the literature, although
some notable discrepancies exist [14–20]. For example, the Cu, Co, and
Cr concentrations (Table S1) are broadly consistent with previously re-
ported medians, for different polymer types (PE, PP, PET, PS, etc.) [14,
15,18,20], with the exception of two studies [16,19], which docu-
mented concentrations at least two orders of magnitude higher than
those reported here. A consistent pattern emerges in the works of Turner
and Solman [16] and Turner [19], where substantially higher concen-
trations of Cr, Fe, Ni, Cu and Cd were reported relative to our study,
whereas Zn concentrations were comparable. It is noteworthy that they
both employed X-ray fluorescence (XRF) analysis on marine litter sam-
ples, a methodological difference that may at least partly account for the
elevated concentrations observed in their work, as well as the origin of
the samples. On the other hand, our items present comparable mean
concentrations in Cu, Mn, Ni, and Zn, and much higher Cr and Pb mean
concentrations than those measured in environmental samples by
ICP-AES by Battulga et al. [18].

Another study [17] focused on metal and metalloid content in
different plastic types in pristine pellets, single-use plastics and recycled
pellets. Our items present comparable mean or median concentrations
for Be, Cu, K, Mg, Ni, Sc, U, Zn, and REE, while for the other elements,
Binda et al. [17] reported at least one order of magnitude lower values.
However, our data was not processed identically, as we excluded values
< LOQ to calculate the mean and median concentrations.

Elemental correlations also provide clues about additive use (Fig. 3). In this study, strong associations between Pb–Cr–∑REE, Sc–Y–Ni, Be–U–Th, and Ba–Ga were identified, consistent with the co-occurrence of these elements in certain additives or the similar chemical behavior of some elements (Ba and Ga, U and Th, Sc and Y). Correlation analysis (Fig. 3) reveals coherent element associations.

(i) Be–U–Th exhibiting mutual correlations. These elements are not specifically known to be additives and are present in very low concentrations (Fig. 2), but they might be incorporated with other additives or present in the petroleum products used for plastic formulation.

(ii) a strong correlation between Cr and Pb suggests the use of lead chromate (PbCrO₄) as a yellow pigment, in line with previous reports [14,24]. Such correlations reveal potential additive “packages” rather than independent element sources, however, identification of specific additives is not possible due to the diversity of additives used [14].

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Pearson correlation matrix on the dataset of elemental concentrations measured by ICP-MS in a total of 119 plastic items. All REE were grouped under ΣREE.</figcaption></figure>

(iii) unlike Klöckner et al. [14], correlations between Co–Mn–Fe and Ba–Cu–Ni were not observed, further highlighting variability between datasets and possibly reflecting differences in product sources, manufacturing practices, or sampling strategies.

Taken together, these findings underscore the complexity of metal
occurrence in plastics, where both methodological factors and product-
specific formulations contribute to variability. The detection of pig-
ments such as PbCrO₄, barite and carbonate fillers, and the variability in
additive-related correlations highlight not only the persistence of
potentially hazardous substances in consumer plastics but also their
relevance for environmental contamination and human exposure risk
assessments. EU regulations strongly restrict the use of Pb and Cr ad-
ditives in consumer products (e.g., Commission Regulation 2020/1245
on food contact plastics and European Parliament and Council Directive
94/62/EC on packaging and packaging waste); however, their presence
may persist in recycled plastics. In addition, weak or negative associa-
tions (Fig. 3) between pigment-linked elements (V, Co, Cu) support
distinct sources.

3.2.1. Elemental distribution amongst the plastic types

Non-parametric tests (Kruskal–Wallis) identify systematic differ-
ences between PP and PE (Fig. 4): PP displayed higher Mg, Si, Cr, and Ni
(p < 0.05), whereas PE showed relatively higher As. These trends are
consistent with the use of mineral fillers (e.g., SiO₂, MgCO₃) and other
additives in PP, while As enrichment in PE may reflect historical col-
orants. These contrasts likely reflect polyolefin-specific additives
choices rather than intrinsic polymer composition. Other polymers
(PET, PS, PVC) are represented by fewer items; trends are directionally
consistent but underpowered.

Beyond concentration levels, the present study provides further
insight into the distribution and associations of trace elements. In
agreement with Klöckner et al. [14], no clear metal-specific fingerprint
was identified. They emphasized the strong similarities in the metal
composition of PE and PP, which they attributed to the fact that both
polymers are polyolefins and often incorporate similar additives. How-
ever, our results reveal differences between these two polymers for three
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Element concentration (in µg kg⁻¹, in log scale unit) distributions are reported versus plastic types for: (a) Mg, (b) Si, (c) Cr, (d) Ni, and (e) As. Mg, Si, Cr, Ni, and As displayed concentrations above LOD in 76, 79, 88, 91, and 12 samples, respectively (detection for each plastic type n is presented in the x-axis).</figcaption></figure>

metals (Cr, Ni, and As), suggesting a more nuanced picture of polymer-specific metal content [14].

### 3.2.2. Elemental distribution versus the plastic colors

Color also exerted a strong control on Cu, Nd, and Dy concentrations (Fig. 5), suggesting a link to pigment formulations, whereas most elements showed no significant differences across color groups (p > 0.05).

The Cu concentrations ranged between 7.5 µg kg⁻¹ and 177,964 µg kg⁻¹, with elevated concentrations predominantly observed in blue and green items, which is consistent with the use of Cu-phthalocyanine

pigments and other Cu-based pigments (echa.europa.eu). Some of these items also present elevated Zn concentration, often used as a co-formulant or stabilizer (echa.europa.eu). Post hoc tests identified significant differences in Cu concentrations between blue/green plastics, and orange, white/transparent, pink/purple and red plastics (p = 1.75 x 10⁻⁷). In addition, Nd and Dy may track specific pigment formulation in colored plastics. Significant differences in Nd concentrations between blue and green plastics were identified by Dunn's post hoc test, whereas for Dy, significant differences between green and transparent/white plastics were identified.
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Element concentration (in µg kg⁻¹, log scale unit) distributions are reported versus plastic colors for: (a) Cu, (b) Nd, and (c) Dy. Cu, Nd, and Dy had concentrations above LOD in 96, 44, and 39 samples, respectively (the number of samples in which the element was detected for each color n is presented on the x-axis).</figcaption></figure>

Certain patterns, such as higher Sr concentrations in red plastics were observed, although not statistically confirmed. In addition, yellow, orange, or red items present sporadic high concentrations in Fe and Cr, as masterbatch carriers or niche pigments. Black items present a broader distribution in Fe, Mn, and Cr, consistent with mixed carbon blacks.

### 3.2.3. Elemental distribution versus material age

The dataset was divided into two groups based on purchase year: 19 samples were considered “old” as they were purchased more than 5 years ago and the other 93 samples were labelled as “new”. Items with unknown purchase year were excluded from this comparison. None of these samples was exposed to the environment. “Old” samples (>5 years) consistently displayed higher Mg, Si, V, Ni, Zn, and Cd (p < 0.05) than newer ones, indicating that additive composition has evolved over time due to regulations or new practices (Fig. 6). These differences are not explained by polymer or color alone underlining the temporal dimension in additive use.

### 3.3. Strontium isotope ($^{87}$Sr/$^{86}$Sr) data

Plastic items present a large range in $^{87}$Sr/$^{86}$Sr, from 0.707650 to 0.713200 (Table 2, Fig. 7). Three polymer types were analyzed: PE (n = 6), PS (n = 1), and PP (n = 5). The PE items present the extreme $^{87}$Sr/$^{86}$Sr of 0.713200, found in a pink PE sample. PE items ranged between 0.707650 and 0.713200 (n = 6), while the PP items present a narrower range between 0.707689 and 0.709996. The single PS sample

yielded 0.707879.

Fig. 8 presents the $^{87}$Sr/$^{86}$Sr ratios as a function of the inverse of Sr concentrations, no trend emerges from this representation. This figure suggests that the $^{87}$Sr/$^{86}$Sr ratios are not directly related to polymer type but rather to the geological origin of Sr-bearing additives, likely fillers or pigments. Specifically, three red plastics of different types presented high Sr concentrations and similar $^{87}$Sr/$^{86}$Sr ratios around 0.7077. One possible red pigment-bearing Sr is the strontium 4-[(5-chloro-4-methyl-2-sulphonatophenyl)azo]-3-hydroxy-2-naphthoate (1:1) ($C_{18}H_{11}ClN_2O_6S.Sr$), listed in the ECHA mapping exercise – Plastic additives initiative (echa.europa.eu). However, by contrast, one analyzed red sample displayed both contrasted concentration and Sr isotopic signature ($^{87}$Sr/$^{86}$Sr = 0.708722), suggesting that the red pigment ($C_{18}H_{11}ClN_2O_6S.Sr$) may involve diverse Sr sources and/or that Sr occurs in another additive.

A group of six samples showed close values (between 0.707650 and 0.707879), similar to those of phosphate rocks from Israel, Florida, or Syria [34]. Phosphate functions are present in several different additives such as flame retardants (Triethyl phosphate, Ammonium dihydrogenorthophosphate, etc.) and pigments (Iron orthophosphate, Trimagnesium bis(orthophosphate), etc.) (echa.europa.eu).

Such $^{87}$Sr/$^{86}$Sr values are common in the environment and are found in rocks, streams, soils, or seawater [35]. One way to use $^{87}$Sr/$^{86}$Sr to discriminate the contribution of plastics in water or soils would be an initial $^{87}$Sr/$^{86}$Sr strictly different from the environment. However, soils may also present comparable $^{87}$Sr/$^{86}$Sr as plastics, especially in the case
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Element concentration distributions (in µg kg⁻¹, in log scale) are reported versus plastic age for: (a) Mg, (b) Si, (c) V, (d) Ni, (e) Zn, and (f) Cd. Mg, Si, V, Ni, Zn, and Cd had concentrations above LOD in 84, 88, 70, 101, 71, and 24 samples, respectively (detection for each group n is presented on the x-axis for each element).</figcaption></figure>

of lime-treated lands [36]. $^{87}$Sr/$^{86}$Sr ratios in plastics overlap with those of phosphate rocks used in fertilizers [37], supporting the role of raw mineral sources in defining Sr isotopic variability.

While this diversity limits straightforward source attribution be-
tween the different anthropogenic Sr sources in soils (plastics, fertilizers,
etc.), Sr isotopes may still provide discrimination if the full history of soil
treatment is known or in controlled comparisons (e.g., between soils
with and without plastics). This approach has been attempted by Wahl
et al. [22], although the $^{87}$Sr/$^{86}$Sr could not be fully explained by a
three-member mixing equation. Strontium can also be used as a tracer of
calcium, as they exhibit the same behavior [38,39]. Ca is one of the
elements occurring in the highest concentrations in the plastics (median
concentration = 94,691 µg kg⁻¹).

As a perspective, it would be interesting to gather samples from all

around the world to see whether the area of recovery would influence
their respective isotopic signature. The wide range of $^{87}$Sr/$^{86}$Sr ratios in
plastics is likely to be linked to different geological origins of the addi-
tives used in plastic formulation.

3.4. Copper isotope ratio data

Copper isotope ratios (δ[^65]Cu, relative to NIST SRM 3114), presented in Table 3, range from +0.18 to +1.35‰ and are systematically enriched in the heavier isotope, determined on PE (n = 10), PP (n = 5), MaterBi® (n = 1), PVC (n = 1), and PS (n = 1). Most samples fell between +0.18‰ (±0.08‰) and +0.60‰ (±0.08‰), while two outliers reached +1.07‰ (±0.08‰) and +1.35‰ (±0.11‰) (Fig. 9). The variability according to the plastic type can only be studied for PP and PE because of the low
<caption>**Table 2**</caption>

$^{87}$Sr/$^{86}$Sr ratios (±2 SD) are reported with respect to plastic type, color, and Sr concentrations of the analyzed samples.

<table><thead><tr><th>Sample type</th><th>Plastic type</th><th>Color</th><th>[Sr] in µg kg<sup>-1</sup></th><th><sup>87</sup>Sr/<sup>86</sup>Sr</th><th>2 SD</th></tr></thead><tbody><tr><td>Bottle cap</td><td>PE</td><td>white</td><td>5,236</td><td>0.707650</td><td>0.000018</td></tr><tr><td>Bottle cap</td><td>PE</td><td>orange</td><td>23,524</td><td>0.710720</td><td>0.000018</td></tr><tr><td>Bottle cap</td><td>PE</td><td>blue</td><td>12,188</td><td>0.709574</td><td>0.000018</td></tr><tr><td>Transport wrap</td><td>PE</td><td>pink</td><td>4,456</td><td>0.713200</td><td>0.000018</td></tr><tr><td>Plastic bag</td><td>PE</td><td>white</td><td>10,439</td><td>0.708786</td><td>0.000018</td></tr><tr><td>Lid</td><td>PE</td><td>red</td><td>117,180</td><td>0.707735</td><td>0.000018</td></tr><tr><td>Packaging handle</td><td>PP</td><td>red</td><td>5,203</td><td>0.708722</td><td>0.000025</td></tr><tr><td>Face mask</td><td>PP</td><td>blue</td><td>5,701</td><td>0.707866</td><td>0.000018</td></tr><tr><td>Lid</td><td>PP</td><td>brown</td><td>4,473</td><td>0.707689</td><td>0.000018</td></tr><tr><td>Plastic envelope</td><td>PP</td><td>blue</td><td>6,817</td><td>0.709996</td><td>0.000018</td></tr><tr><td>Filing box</td><td>PP</td><td>red</td><td>263,791</td><td>0.707719</td><td>0.000018</td></tr><tr><td>Cup</td><td>PS</td><td>red</td><td>1,233,668</td><td>0.707879</td><td>0.000028</td></tr></tbody></table>

<figure><img src="image_11.png" /><figcaption>**Fig. 7.** $^{87}$Sr/$^{86}$Sr values are reported according to plastic type, the colors of the symbols correspond to the colors of the samples. Measurements were performed on a subset of 12 samples. Error bars are included within the symbols.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Evolution of $^{87}$Sr/$^{86}$Sr in 12 samples as a function of 1/[Sr] (expressed as 10⁻⁴) for plastics of different types and colors. [Sr] being the Sr concentrations in the plastics. Colors of the symbols correspond to the colors of the samples. Error bars are included within the symbols.</figcaption></figure>

representativeness of the other plastic types. PE samples showed the widest range (0.18‰–1.35‰), while PP samples ranged between 0.18‰ and 0.43‰. A narrower range of isotopic ratios in PP compared to PE

<caption>**Table 3**</caption>

$^{δ^{65}}Cu_{NIST3114}$ ratios (‰; ±2 SD) are reported according to the plastic type, color, and Cu concentrations of the analyzed samples.

<table><thead><tr><th>Sample type</th><th>Color</th><th>Plastic type</th><th>[Cu] in µg kg<sup>-1</sup></th><th>δ<sup>65</sup>Cu<sub>NIST3114</sub> (‰)</th><th>2 SD</th></tr></thead><tbody><tr><td>Bottle cap</td><td>Dark blue</td><td>PE</td><td>177,964</td><td><strong>0.18</strong></td><td>0.08</td></tr><tr><td>Lid</td><td>Light blue</td><td>PE</td><td>20,645</td><td><strong>0.47</strong></td><td>0.08</td></tr><tr><td>Bottle cap</td><td>Light blue</td><td>PE</td><td>13,675</td><td><strong>0.59</strong></td><td>0.08</td></tr><tr><td>Lid</td><td>Dark green</td><td>PE</td><td>62,571</td><td><strong>0.23</strong></td><td>0.08</td></tr><tr><td>Plastic reference material (ERM-EC680)</td><td>Dark green</td><td>PE</td><td>13,620</td><td><strong>0.45</strong></td><td>0.08</td></tr><tr><td>Bottle cap</td><td>Dark green</td><td>PE</td><td>24,312</td><td><strong>0.42</strong></td><td>0.08</td></tr><tr><td>Plastic reference material (ERM-EC681)</td><td>Dark green</td><td>PE</td><td>106,922</td><td><strong>0.49</strong></td><td>0.11</td></tr><tr><td>Bottle cap</td><td>Dark green</td><td>PE</td><td>11,632</td><td><strong>0.60</strong></td><td>0.08</td></tr><tr><td>Lid</td><td>Light green</td><td>PE</td><td>11,511</td><td><strong>1.07</strong></td><td>0.08</td></tr><tr><td>Bottle cap</td><td>Yellow</td><td>PE</td><td>45,961</td><td><strong>1.35</strong></td><td>0.11</td></tr><tr><td>Bottle cap</td><td>Light blue</td><td>PP</td><td>42,362</td><td><strong>0.18</strong></td><td>0.11</td></tr><tr><td>Intercalary</td><td>Light blue</td><td>PP</td><td>138,427</td><td><strong>0.23</strong></td><td>0.11</td></tr><tr><td>Plate</td><td>Dark blue</td><td>PP</td><td>88,974</td><td><strong>0.33</strong></td><td>0.11</td></tr><tr><td>Envelope</td><td>Light blue</td><td>PP</td><td>27,088</td><td><strong>0.35</strong></td><td>0.05</td></tr><tr><td>Pen cap</td><td>Light blue</td><td>PP</td><td>17,487</td><td><strong>0.43</strong></td><td>0.08</td></tr><tr><td>Document holder</td><td>Dark blue</td><td>PS</td><td>137,415</td><td><strong>0.46</strong></td><td>0.11</td></tr><tr><td>Pot</td><td>Light blue</td><td>PVC</td><td>85,956</td><td><strong>0.58</strong></td><td>0.11</td></tr><tr><td>Biodegradable bag</td><td>Light green</td><td>MaterBi</td><td>47,613</td><td><strong>0.37</strong></td><td>0.08</td></tr></tbody></table>

<figure><img src="image_13.png" /><figcaption>**Fig. 9.** $^{δ^{65}}Cu$ values (relative to NIST SRM 3114) expressed in ‰ reported according to the plastic types. Measurements were performed on a subset of 18 samples. Colors of the symbols correspond to the color of the samples.</figcaption></figure>

was also observed for Sr isotope analysis, suggesting a higher diversity of additives or additive sources for PE formulation. MaterBi®, PS, and PVC plastics were each represented by one sample with $^{δ^{65}}Cu$ values of 0.37‰ (±0.08‰), 0.46‰ (±0.11‰), and 0.58‰ (±0.11‰), respectively.

The $^{δ^{65}}Cu$ variability is broadly comparable between green and blue
samples. Blue items (n = 10) present δ⁶⁵Cu ranging between 0.18‰ and 0.59‰, whereas green items, all made of PE except one MaterBi® sample, present δ⁶⁵Cu ranging from 0.23‰ to 1.07‰ (Fig. 9). Both blue and green samples display the same increasing δ⁶⁵Cu linear trend with decreasing concentration (Fig. 10). However, a higher variability in δ⁶⁵Cu can be observed for the most concentrated samples.

The only yellow sample is distinct from all the other samples and presents the highest δ⁶⁵Cu (1.35‰ ± 0.11‰). Additional analyses of yellow samples are needed to determine whether this signature is typical of yellow samples, but the limiting factor is the needed Cu quantity for isotopic analyses. For blue and green samples, diverse copper-based pigments are likely to be used, causing variations between samples.

Cu isotope ratio is a known tracer in environmental studies, for example, it was used previously to trace some soil contamination sources [40] or processes in the case of acid mine drainage [41]. As plastics can be easily found in soils, we can compare their δ⁶⁵Cu signatures with those from topsoil. Most soils have a δ⁶⁵Cu value negative or slightly positive, in the literature, values range from -0.54‰ to +0.16‰ (relative to NIST SRM 976) [40,42–44]. These values differ from the values of most plastic samples analyzed here. Thus, Cu isotopes may provide a novel tracer to distinguish plastic-derived Cu inputs. However, the δ⁶⁵Cu ratios measured in soils can be influenced by the use of Cu-based fungicides. Babcsány et al. [43] measured δ⁶⁵Cu ratios in seven fungicides ranging from -0.21‰ to +0.91‰ relative to NIST SRM 976. El Azzi et al. [42] found a value of δ⁶⁵CuNIST976 of -0.34‰ for the Bordeaux mixture. To discriminate these Cu inputs from plastics, it would be important to characterize the fungicide application on the field, in terms of quantity and δ⁶⁵Cu values.

### 3.5. Lead isotopes

Lead isotopic ratio measurement could only be performed on a limited number of samples due to the low Pb concentration, particularly

<figure><img src="image_14.png" /><figcaption>**Fig. 10.** δ⁶⁵Cu in 18 samples is reported in ‰ as a function of the ratio 1/[Cu] (expressed as 10⁻⁵) with the related linear trend. [Cu] being the Cu concentration. Colors of the symbols correspond to the colors of the samples.</figcaption></figure>

<table><caption>**Table 4**

Lead isotopic compositions and respective uncertainties of PE items according to plastic color and Pb concentration.</caption><thead><tr><th></th><th>Plastic type</th><th>Color</th><th>[Pb] in µg kg<sup>-1</sup></th><th><sup>208</sup>Pb/<sup>204</sup>Pb</th><th>2 SD <sup>208</sup>Pb/<sup>204</sup>Pb</th><th><sup>207</sup>Pb/<sup>204</sup>Pb</th><th>2 SD <sup>207</sup>Pb/<sup>204</sup>Pb</th><th><sup>206</sup>Pb/<sup>204</sup>Pb</th><th>2 SD <sup>206</sup>Pb/<sup>204</sup>Pb</th></tr></thead><tbody><tr><td>ERM-EC680</td><td>PE</td><td>Green</td><td>8,834</td><td>37.6188</td><td>0.0121</td><td>15.5600</td><td>0.0047</td><td>17.7939</td><td>0.0039</td></tr><tr><td>ERM-EC680</td><td>PE</td><td>Green</td><td>10,493</td><td>37.6048</td><td>0.0121</td><td>15.5581</td><td>0.0047</td><td>17.7944</td><td>0.0039</td></tr><tr><td>Bin bag</td><td>PE</td><td>Black</td><td>4,227</td><td>38.4657</td><td>0.0024</td><td>15.7714</td><td>0.0010</td><td>19.7648</td><td>0.0009</td></tr><tr><td>Plastic bag</td><td>PE</td><td>Yellow</td><td>15,109,692</td><td>37.9859</td><td>0.0121</td><td>15.5891</td><td>0.0047</td><td>18.2054</td><td>0.0039</td></tr><tr><td>Plastic bag</td><td>PE</td><td>Pink</td><td>4,099</td><td>38.1432</td><td>0.0024</td><td>15.6604</td><td>0.0010</td><td>18.6457</td><td>0.0009</td></tr><tr><td>Buoy</td><td>PE</td><td>Orange</td><td>463,205</td><td>38.3701</td><td>0.0121</td><td>15.6160</td><td>0.0047</td><td>18.4665</td><td>0.0039</td></tr></tbody></table>

<figure><img src="image_15.png" /><figcaption>**Fig. 11.** <sup>207</sup>Pb/<sup>204</sup>Pb versus <sup>206</sup>Pb/<sup>204</sup>Pb of five PE samples from our study. The color of the symbols corresponds to the color of the samples. Error bars are included in the symbol.</figcaption></figure>

in new samples. The evolution of the legislation has indeed limited the use of Pb as an additive in plastics. However, Pb could still be a useful tracer of the sources of the plastics in soils for past contamination. Thus, lead isotope ratios were measured in a small subset of PE items, of different colors, reported in Table 4. Values of the <sup>208</sup>Pb/<sup>204</sup>Pb ratio are between 37.6048 and 38.4657. The <sup>207</sup>Pb/<sup>204</sup>Pb and <sup>206</sup>Pb/<sup>204</sup>Pb ratios were measured respectively from 15.5581 to 15.7714 and from 17.7939 to 19.7648. Good reproducibility was obtained from the microwave digestion to the isotopic analysis of two replicates of the plastic reference material ERM-EC680. Fig. 11 presents the <sup>207</sup>Pb/<sup>204</sup>Pb versus <sup>206</sup>Pb/<sup>204</sup>Pb ratios. For Pb isotopic ratios measurement, all samples exhibited different values and could all be distinguished. Among these samples, three are older (>5 years). Both maximal and minimal values of Pb isotopic ratios are from the two new samples. Thus, the sample age does not seem to influence the Pb isotopic ratios.

Li et al. [45] also analyzed two samples of PE and PVC, which presented Pb isotope ratios included in the range of our study. Such variability among samples is consistent with the use of diverse Pb-based pigments or stabilizers prior to regulatory restrictions. For instance, multiple Pb-based additives are used in plastic, as red or yellow pigments (i.e., Lead sulfochromate yellow, Lead chromate molybdate sulfate red) or heat stabilizers (i.e., Pentalead tetraoxide sulfate, Dioxobis (stearato)trilead) (echa.europa.eu). Our results are suggesting different sources of Pb in those additives. No correlation with color or polymer type could be observed due to the little number of samples.

### 3.6. Limitations

Isotopic analyses of Cu, Sr, and Pb were performed on a limited number of samples due to the analyte concentrations required. For Cu, mainly blue and green samples were analyzed because they displayed high Cu concentrations likely caused by Cu-based pigments. Analyses covering a wider range of colors will be necessary to assess the influence
of color - and therefore additives - on $δ^{65}$Cu. For Sr and Pb, only a small number of samples represented each color or polymer type. To draw robust conclusions on the influence of polymer types and colors, a larger dataset would be required. Nevertheless, two tendencies emerge from this study, (i) a narrower range of Cu and Sr isotopic ratios in PP compared to PE, (ii) a linear relationship between $δ^{65}$Cu and Cu concentrations.

A further limitation concerns changes induced by environmental ageing, including degradation, leaching of trace elements, and adsorption processes. Leaching during plastics degradation to micro- and nanoplastics has been reported [46,47], and micro- and nanoplastics can act as vectors for pollutants, such as trace elements. For example, Pb adsorption onto plastic particles has been reported [25]. Leaching and adsorption processes depend on environmental conditions but are likely to change the metal isotopic signatures of plastics. Further studies are therefore needed to investigate how Cu, Sr, and Pb isotopic signatures evolve during plastic degradation in environmental conditions.

Although the Sr isotopic signature of plastics may overlap with that of other anthropogenic sources, its application as a tracer of plastic pollution in soils remains feasible under specific conditions. Importantly, this study paves the way for a complementary multi-isotopic approach in which Sr isotopes are combined with additional tracers to enhance the discrimination of plastics from other anthropogenic inputs.

## 4. Conclusion

Our study demonstrated the analytical potential of elemental and isotopic fingerprints to characterize plastics. ICP-MS analyses revealed large variability in elemental composition across polymer types, colors, and ages, with distinctive enrichments in certain metals such as Cr, Ni, and Cu. Elemental concentrations alone showed high variability, reflecting the diversity of polymer formulations and limiting their specificity as stand-alone tracers.

Importantly, isotopic fingerprints were reproducible at low levels of analyte, confirming the feasibility of applying isotopic analyses to plastics. The Cu, Sr, and Pb isotopic signatures did not show a systematic relationship with polymer type. The investigated plastics showed wide ranges of Cu, Sr, and Pb isotope signatures consistent with the numerous additives used for plastic formulation. For Sr isotope ratio measurement, no clear correlations with colors or Sr concentrations were observed. This may, however, be due to the limited number of different colors investigated. Isotopic analyses provided additional discrimination: Sr isotopes reflected the contribution from mineral additives of diverse geological origins; Cu isotopes revealed systematic enrichment in heavy isotopes likely linked to pigments; and Pb isotopes distinguished Pb-bearing additive sources consistent with legacy pigments/stabilizers and/or recycled streams. However, the overlap of Sr isotopic signatures between plastics and other anthropogenic and natural sources limits its potential to be used alone as a tracer of plastic inputs in soils. Nevertheless, combining multiple isotopic systems and elemental profiles could enable reliable identification of plastics in complex environments.

The multi-element and isotopic tracers provided in this study give complementary information beyond polymer identification and may be applied to source attribution of environmental plastics, assessment of additive pathways, and monitoring of plastic ageing in soils and waters. Further validation on environmental samples is, however, required.

We deliver one of the first integrated element-isotope datasets for consumer plastics, demonstrating that Cu-Sr-Pb isotopes capture additive provenance beyond polymer identification. This coupled approach advances plastic source attribution and provides a quantitative and transferable basis for environmental tracing and recycling forensics. This novel approach could contribute to improving the understanding of plastic fate in the environment, in particular, the leaching of metallic additives and the adsorption of metals from soils onto plastics.

Furthermore, the combined elemental and isotopic approach could help screen potential high-risk additive packages by color and highlight

plastics consistent with recycled content. From an exposure perspective, the strongest drivers of metal content are pigments and fillers, which reflect design choices rather than the base polymer.

### CRediT authorship contribution statement

**Manon Le Corre:** Writing – original draft, Visualization, Formal analysis, Data curation. **Anne-Catherine Pierson-Wickmann:** Writing – review & editing, Supervision, Funding acquisition. **Bleuenn Gueguen:** Writing – review & editing, Validation, Formal analysis, Data curation. **Maxime Pattier:** Writing – review & editing, Resources, Formal analysis. **Murielle Rabiller-Baudry:** Writing – review & editing, Resources, Formal analysis. **Aline Dia:** Writing – review & editing, Supervision, Funding acquisition.

### Funding sources

Through the GeOHeLiS platform, this publication is also supported by the European Union through the European Regional Development Fund (FEDER), the French Ministry of Higher Education and Research, the French Region of Brittany, and Rennes Metropole. Financial support from Defi isotope MITI (Centre National de la Recherche Scientifique) is gratefully acknowledged.

### 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.

### Acknowledgements

The authors kindly acknowledge the support of GeOHeLiS platform (Université de Rennes), part of the RéGEF Research Infrastructure (fr-esr-fdr-ir-117), the PARI platform (Institut de Physique du Globe de Paris), and Institut des Sciences Chimiques de Rennes for the instrumental analysis. We particularly thank Mr. Dimitri Rigoussen for the MC-ICP-MS measurements in IPGP. We deeply thank Ms. Pauline Le Corre and Mr. Antoine Quennevat for their help in coding and statistics. We thank the two anonymous reviewers for their constructive comments, which contributed to improving this manuscript.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.talanta.2026.129404.

### Data availability

Data will be made available on request.

### References

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