Combined effects of application-specific plastic
composition and environmental conditions on algal
pigments and microbial activity of freshwater biofilms

Camille M Touchet, Florian Mermillod-Blondin, Sylvain Dolédec, Zoraida J

Quiñones-Rivera, Ludovic Guillard, Laurence Volatier, Félix Vallier,

Marjolaine Rey, Stefan Krause, Laurent Simon

► To cite this version:

Camille M Touchet, Florian Mermillod-Blondin, Sylvain Dolédec, Zoraida J Quiñones-Rivera, Ludovic Guillard, et al.. Combined effects of application-specific plastic composition and environmental conditions on algal pigments and microbial activity of freshwater biofilms. Environmental Pollution, 2025, 387, pp.127323. 10.1016/j.envpol.2025.127323. hal-05338470

HAL Id: hal-05338470

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# Combined effects of application-specific plastic composition and environmental conditions on algal pigments and microbial activity of freshwater biofilms☆

Camille M. Touchet<sup>a,1 Ⓓ</sup>, Florian Mermillod-Blondin<sup>a</sup>, Sylvain Dolédec<sup>a</sup>,

Zoraida J. Quiñones-Rivera<sup>a</sup>, Ludovic Guillard<sup>a</sup>, Laurence Volatier<sup>a,b</sup>, Félix Vallier<sup>a</sup>,

Marjolaine Rey<sup>d</sup>, Stefan Krause<sup>a,c</sup>, Laurent Simon<sup>a,* Ⓓ</sup>

## ARTICLE INFO

### Keywords:

- Plastic materials

- Plastosphere

- Microbial biomasses

- Microbial activities

- Algal pigments

- Freshwater

## ABSTRACT

Plastic pollution is extremely complex due to the variety of materials resulting from the mixing of synthetic polymers and chemical additives to match their intended applications. Such application-dependent chemical compositions complicate the assessment of plastics' ecotoxicological effects and impacts on biological communities and ecosystem functioning. Our study investigates how plastics affect freshwater microbial community composition and metabolic activity. To that end, we selected eight plastic materials from four polymer types and grouped them by their regulatory classification application ("medical or food grade" vs. "non-medical or food grade") to distinguish material effects from application-dependent chemical composition. After six weeks of biofilm development in three freshwater basins, we analyzed microbial biomass, metabolic functions (photo-synthesis, respiration, esterase activity), and algal pigment composition on plastic surfaces and on a chemically inert control. We found that microbial biomass and metabolic activity were more controlled by environmental factors (e.g. suspended solids and light exposure) than by the polymer material and its application, except for esterase activity which was significantly higher in biofilms found on plastics than those found on the control. Algal pigment composition was controlled by both application-dependent plastic composition and environmental conditions. Our study emphasizes the need to consider material properties (e.g. polymer type), intended application (e.g. specific chemical composition) and environmental conditions (e.g. light, suspended solids) to better understand plastic-microbe interactions, which would allow for improved guidance and consequently effective future legislation on polymers and chemical additives.

## 1. Introduction

Despite nearly two decades of research on plastic waste in diverse ecosystems, its potential toxicity and ecological impact remain poorly understood. Plastics are a diverse group of materials that result from the mixing of polymer resins with chemical additives. Such additives are used to optimize the plastic's properties for its specific application.

Many societal benefits of plastics come from their design to be persistent, long-lasting, and low cost (Thompson et al., 2009). However, the resulting wide variety of chemical compositions may have potential ecological effects (Geyer et al., 2017; Marturano et al., 2017; Thompson et al., 2009). Additionally, plastic accumulation raises serious environmental and public health concerns, making them emerging pollutants with lasting effects (Barnes et al., 2009; Krause et al., 2021).

<footnote><sup>a</sup> Université Claude Bernard Lyon 1, LEHNA UMR 5023, CNRS, ENTPE, F-69622, Villeurbanne, France</footnote>

<footnote><sup>b</sup> Université Claude Bernard Lyon 1, LEHNA UMR 5023, CNRS, ENTPE, F-69518, Vaulx-en-Velin, France</footnote>

<footnote><sup>c</sup> School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK</footnote>

<footnote><sup>d</sup> Univ Lyon, Univ Claude Bernard Lyon 1, CNRS UMR 5557, INRAE UMR 1418, VetAgroSup, Ecologie Microbienne, Villeurbanne, 69622, France</footnote>

<footnote>* This paper has been recommended for acceptance by Maria Cristina Fossi.</footnote>

<footnote>* Corresponding author. Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 - LEHNA, Laboratoire d'Ecologie des Hydrosystèmes Naturels et Anthropisés, 6, rue Raphaël Dubois - Bât. Forel, Cedex, Villeurbanne, F-69622, France.</footnote>

<footnote>E-mail address: laurent.simon@univ-lyon1.fr (L. Simon).</footnote>

<footnote n="1">¹ present adress: MIVEGEC, Univ. Montpellier, CNRS, IRD, Montpellier, France.</footnote>
In freshwater ecosystems, microbial communities developing on surfaces, like rocks, wood, or leaves, perform key ecosystem functions (e.g., nutrient cycling, Mulholland et al., 1994), and are important food sources for a wide diversity of invertebrates (Cummins, 1974). These communities also colonize plastic surfaces, forming the 'plastisphere': microbial assemblages specifically associated with plastics (Amaral-Zettler et al., 2020, 2020; Bairoliya et al., 2024; Barros and Seena, 2021; Li et al., 2024; Zettler et al., 2013). Although interest in plastisphere characteristics and functioning is growing (Gong et al., 2019; Miao et al., 2019; Parrish and Fahrenfeld, 2019; Wu et al., 2019), significant knowledge gaps remain about the plastic-biofilm interactions, especially in freshwater environments (Barros and Seena, 2021; Bocci et al., 2024). For example, high-throughput sequencing studies have shown that plastic-associated bacterial communities differ from those on other materials or in the water column (Barros and Seena, 2021; Bocci et al., 2024), often with a reduced diversity (Hoellein et al., 2014; McCormick et al., 2014, 2016; Miao et al., 2019) but not always (e.g. Tang, 2024). Other studies have reported functional differences among plastic-associated microbial communities (e.g. Debroas et al., 2017; Delacuvellerie et al., 2022; Xu et al., 2024). For example, Miao et al. (2019) and Wang et al. (2021) showed increased amino-acid metabolism in biofilms that developed on microplastics (polyethylene and polypropylene) compared to biofilms that developed on natural materials and macroplastics. These findings raise questions about the influence of different plastics on biofilm composition and activity, considering the diversity of polymers produced globally and the chemical treatments associated with them.

Plastic-biofilm interactions are influenced by microscale factors (e.g. surface roughness, hydrophobicity, additives leaching), environmental variables (e.g. temperature, light, nutrients, suspended solids), and experimental conditions (e.g. colonization time, lab or in situ experiments). However, the consequences of specific factors are difficult to disentangle as they are rarely studied as a whole, leading to inconsistent trends (Yu et al., 2023). For instance, most comparative studies of biofilms developed on plastics and other materials do not control the surface roughness of tested materials despite this property strongly influencing cell adhesion and biofilm growth (Al-Amshawee et al., 2021; Freixa et al., 2023; Gong et al., 2019; Hunt and Parry, 1998; Krsmanovic et al., 2021). Moreover, studies focusing on plastic-biofilm interactions have only considered few polymers (mostly less than 3, Barros and Seena, 2021) or tested the effects of polymer types and additives separately (e.g. Fawcett et al., 2021; Fernández-Juárez et al., 2021). In addition to the surface and composition of plastics, environmental parameters such as temperature, nutrients, turbidity and exposure to light influence the development of biofilms (Tang, 2024; Vincent et al., 2022; Yu et al., 2023). It is therefore necessary to study the interactions between environmental factors and the nature of plastic materials (Binda et al., 2024; Nava et al., 2025).

Therefore, our study aimed to fill this knowledge gap by analyzing plastic-biofilm interactions across a variety of polymer materials, under well-characterized environmental conditions. These polymers had comparable surface roughness but differed in potential toxicity due to regulations linked to their intended applications (medical or food-grade vs. non-medical or food-grade). Inert material was included as a control. Our objective was to evaluate the potential influence of environmental factors and plastic additives on biofilms, as medical and food grade plastics are more regulated in Europe to limit potentially harmful constituents that could endanger environmental and human health (e.g., European Parliament, 2004), under well-characterized environmental conditions. Accordingly, we tested the following hypotheses:

**H1.** Since materials have contrasting chemical properties that can influence microbial adhesion (Dang and Lovell, 2015; Hossain et al., 2019) and organic matter sources for biofilms (Chen et al., 2021; Rummel et al., 2017), we expected differences in biofilm biomass, elemental and pigment composition between control and plastic materials.

**H2.** In parallel with increased biofilm growth on plastic materials compared with control materials, we expected biofilm metabolic activity to be higher on plastic materials as observed in previous studies (Debroas et al., 2017; Miao et al., 2019; Wang et al., 2021).

**H3.** Since medical and food-grade plastics are subject to content regulations for safety requirements it was expected that there would be lower leaching of dissolved organic compounds from medical and food grade plastic materials compared to non-medical and food grade plastic materials. Consequently, significant differences in microbial community composition and activity were expected between biofilms on plastic materials with different applications.

## 2. Materials and methods

### 2.1. Experimental sites

Three comparable settling basins (B1, B2, and B3) filled through the banks with inflow from the Rhône River were selected as experimental sites for the biofilm colonization of plastics and controls (see Fig. S1A in supplementary material). Basins, located on the Crépieux-Charmy protected wellfield (Vaulx-en-Velin and Rillieux-la-Pape, France), are reservoirs used for managing aquifer recharge. During the experiment (six weeks incubation from September 26th to November 7th, 2022), each basin was equipped with underwater sensors to monitor light intensity and water temperature at the sampling sites (Pendant® MX Temp/Light, HOBO®, Prosensor, Amanvillers, France). Water samples were collected weekly, and physicochemical variables (electric conductivity, water temperature, pH, dissolved oxygen) were measured with a multiparameter probe (Odeon Open X, Ponsel Mesure company). At each basin, 30 mL of water was filtered (Whatman GF/F filter, porosity: 0.7 µm) and analyzed for $NH_4^+$, $NO_3^-$ and $PO_4^{3-}$ using a sequential colorimetric analyzer (SMARTCHEM 200, AMS Alliance, France). One litre of water was also filtered on a pre-weighted GF/C filter (porosity: 1.2 µm) to measure total suspended solids (TSS).

### 2.2. Tested materials

Four of the most used plastic polymers were considered, i.e., polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), and polystyrene (PS) (Geyer et al., 2017). For each polymer, two plastic materials were selected based on their intended applications, to represent a variety of chemical compositions with regard to additives. Each plastic material was purchased to fit into an application group “medical or food grade” (MF) or “non-medical or food grade” (NMF). The MF group included orthopedic polyethylene and polypropylene as well as food grade polystyrene, while the NMF group included industrial plastic materials (Richardson Matières plastiques, Marseille, France) (see manufacturer information in Tables S1 and S2 and elemental composition of plastics in Table 3 in supplementary material). Although we did not have information on the additive content of these specific materials, MF plastics in general are subject to numerous European regulations (e. g. Directive 1999/45/EC, Directives 90/385/EEC, 93/42/EEC or 98/79/EC for plastics for medical use and Regulation (EC) No 1935/2004 for plastics for food use) which limit their chemical composition, and certain substances – mainly carcinogens, mutagens, substances toxic to reproduction or endocrine disruptors – are already subject to bans (e.g. bisphenol A or undecafluorohexanoic acid (PFHxA) in plastics intended for food applications, or DEHP (bis(2-ethylhexyl) phthalate) in medical plastics). No MF plastic was available for PVC, therefore, two NMF plastics were selected based on their contrasted technical properties, assuming it will reflect their chemical composition (see Tables S2 and S3 in supplementary material and elemental composition of the plastics in Touchet et al. (2025)). An inert material - frosted glass - was used as a control (noted CO). All materials were used in the form of 70×30×3 mm plates.
Prior to the incubation, plastic materials were sanded with P500-grit (~30 µm grain) sandpaper to obtain a roughness comparable to frosted glass. The comparability of all materials was checked using the Wetted Layer Technique (Bergey, 2006; Harrod and Hall, 1962; Voisin et al., 2016; supplementary methodology M1 in supplementary material).

The potential of each material to release organic compounds was evaluated by the leaching of dissolved organic carbon (DOC). After incubating each material in triplicate in ultrapure water during six weeks on an orbital shaker, DOC analyses were performed on water samples with a total carbon analyzer (multi-N/C 3100, Analytik Jena, Jena, Germany) following Lebon et al. (2023).

## 2.3. Incubation, sample collection and transport

For each material, four plates were placed in an aluminum support designed at the laboratory (Fig. S1B in supplementary material). The plates were held vertically (as in Briand et al., 2012), at a depth of 30 cm below the water surface (Shen et al., 2021), using a polyethylene foam float (Fig. S1C in supplementary material). Nine aluminum supports (one for each material) were randomly anchored to a central float positioned in the center of the basin to avoid being shaded by shoreline vegetation. The float was attached to a rope secured to metal rods on either side of the basin (Fig. S1C in supplementary material). These incubation systems were replicated three times per basin.

After 6-week incubation, all materials were completely colonized by biofilms. They were randomly dismantled from the aluminum supports and transported to the laboratory in glass jars filled with basin water. Once at the laboratory, all plates were maintained in the dark at 20 °C for 1 h to acclimatize before microbial activity was measured.

## 2.4. Biofilm and algal biomass

### 2.4.1. Sample preparation

In the laboratory, all plates were rinsed with filtered basin water (Whatman GF/F filters, porosity: 0.7 µm) to remove sediment particles and fauna. Half of the plates were used to measure biofilm biomass and algal content; biofilms were scraped with sterile razor blades from plates and suspended in 2 mL of filtered basin water. Suspensions were preserved at -20 °C and then freeze-dried for 48 h. The other half of the plates were kept intact in basin water to measure microbial activity (see below).

### 2.4.2. Biofilm biomass

The mass of organic carbon was used as a proxy for biofilm biomass. Freeze-dried biofilm samples were weighed with a precision balance (Sartorius CPA225D) to obtain the total dry mass of biofilm per surface for each plate. Afterwards, 5 mg of freeze-dried biofilm was weighed in silver capsules to the nearest 0.01 mg (balance Mettler Toledo XP6). Samples were acidified with HCl 2M and dried in an oven at 55 °C for 24 h. Capsules were wrapped in tin foil before analysis, following the 'capsule method' (Brodie et al., 2011). The method uses an elemental analyzer (Thermo FlashEA 1112) to determine organic carbon and nitrogen content, expressed as percentages of sample dry mass, and C/N ratio. Aspartic acid was used as a calibration standard, and birch leaf reference material (Elemental Microanalysis) as quality control. The percentage of organic carbon obtained by elemental analysis was then converted into total quantity of organic carbon per surface of plate using the total dry mass of biofilm (µg of organic C.cm⁻²).

### 2.4.3. Determination of algal biomass

To assess algal biomass, we performed chlorophyll-α analysis. For this, 20 mg of freeze-dried biofilm was weighed (Mettler Toledo XP6) in a glass vial covered with aluminum foil. Then, 5 mL of extraction solvent (90:10 mix of acetone and ultrapure water) was added to the samples, after which samples were left in the dark at 4 °C for 4 h. After extraction, samples were centrifuged at 2862 g for 10 min, and the supernatant was

then transferred into a quartz spectrophotometer cell. Absorbances were read at 750, 663, 645, and 630 nm following the SCOR-UNESCO protocol (Jeffrey et al., 2005; SCOR-UNESCO, 1966). Chlorophyll-α concentration (µg/g biofilm) was calculated with the SCOR-UNESCO equation (SCOR-UNESCO, 1966):

$$ \text{Chl } a = (v / (M \times L)) \times [11.64 (A_{663}-A_{750}) - 2.16 (A_{645}-A_{750}) + 0.10 (A_{630}-A_{750})] $$

With A$_{750}$, A$_{663}$, A$_{645}$ and A$_{630}$ the absorbances measured, v the volume of extraction solvent (mL), M the dry mass of the sample (g), L the cell length (cm). Chlorophyll-α content was converted using the dry mass of the sample and expressed in µg of chlorophyll-α.cm⁻².

## 2.5. Microbial activities

### 2.5.1. Respiration and photosynthetic rates

After 30 min of acclimatization in the dark, colonized plates were placed in individual hermetically sealed 125-mL glass vials filled with filtered basin water (Whatman GF/F filters, porosity: 0.7 µm) and saturated in dissolved oxygen using an aquarium bubbler. Glass vials were equipped with magnetic stirrers to prevent dissolved oxygen gradients and kept in the dark at 20 °C to monitor respiration rates. Dissolved oxygen was measured optically (FireStingG02, PyroScience®, Aachen, Germany) every 30 min for 3 h using luminescent sensors (OXSP5-SUB, Pyroscience®, Aachen, Germany) previously placed in the bottles, allowing monitoring inside sealed vials. The measuring device emits red light that excites the luminescent sensor, and the intensity of its excitation reflects the oxygen concentration around the sensor. The linear decrease in oxygen concentration was used to calculate the biofilm respiration rate, expressed in mg.h⁻¹. (µg of organic carbon)⁻¹. Subsequently, photosynthesis was monitored every 15 min for 1.5 h with the same respirometer system under light exposure. The linear increase in oxygen concentration was used to calculate the photosynthesis rate expressed in mg.h⁻¹. (µg of chlorophyll-α)⁻¹.

### 2.5.2. Esterase activity

Esterase activity was quantified by using the fluorescein diacetate (FDA) hydrolysis method (Jørgensen et al., 1992; Mermillod-Blondin et al., 2005). The colonized plates were placed in glass petri dishes with 50 mL of phosphate buffer solution (pH = 7.6) and 1.7 mL of FDA solution (concentration = 2 mg. mL⁻¹). The incubation was maintained in the dark at 20 °C until a green coloration appeared (between 1 and 1.5 h). The supernatant was then filtered (Whatman GF/F filter), and the FDA concentration was determined by its absorbance measured at 490 nm with a spectrophotometer (Shimadzu). Esterase activity was expressed in µmol of FDA. h⁻¹. (g of organic C)⁻¹.

### 2.5.3. Algal physiological state

Chlorophyll fluorescence was measured on the day of sampling on colonized plates after a 30-min acclimatization at 20 °C in the dark, using a pulse amplitude modulation fluorometer (model PAM 210, Hansatech Instrument). The minimum fluorescence (Fo) was obtained upon excitation with a weak measuring beam from a pulse light emitting diode. The maximum fluorescence (Fm) was determined after exposure to a saturating pulse of white light to close all reaction centers. The theoretical maximum quantum efficiency at the photosystem II (PSII), Fv/Fm (= (Fm - Fo)/Fm), was used as an indicator of photosynthetic efficiency. This ratio is between 0 and a maximum value of 0.84 (<0.85) and provides a quick and useful estimate of light reaction activity and photosynthetic rate, which decreases when algae are stressed or damaged (Courcoul et al., 2022; Roháček and Barták, 1999).

### 2.5.4. Algal composition and diversity using pigment analysis

Algal composition was determined using High Performance Liquid Chromatography (HPLC), following Leavitt and Hodgson (2001). For
each sample, 20 mg of freeze-dried biofilm was weighed and placed in a glass vial. Five mL of extraction solvent (80:15:5 acetone, methanol, and ultrapure water, respectively) was added to the sample. Samples were tightly capped and stored for 24 h at -20 °C in the dark. After the 24-hr extraction, samples were filtered using PTFE syringe filters (0.22 µm, Clearline), stored in clear glass vials (Sigma Aldrich), covered with aluminum foil, and nitrogen-dried for 2 h. Dried samples were stored at -20 °C in the dark until analysis. 500 µL of injection solution with Sudan II as internal standard (5:12.5:25:57.5 mix of methanol, Sudan II solution, iron pairing reagent and acetone as described in Smol et al. (2001), see the detailed description of the reagents in supplementary material) was added to each sample. An aliquot of 100 µL was then transferred to 1.5 mL amber vials fitted with glass inserts.

Samples were analyzed with an Agilent 1100 HPLC (Agilent Technologies) coupled to a G4212B DAD detector (Agilent Technologies). Chemstation B.04.03 software (Agilent Technologies) managed the system. Separation was performed using a LC Microsorb 100-3 C18 column (100 mm × 4.6 mm; 3 µm particle size, Agilent Technologies) at 25 °C. For each, 5 µL of sample was injected (25 µL for the standards). The mobile phase was a linear gradient of a 10:90 ratio of Ion Pairing Reagent (IPR, as described in Smol et al., 2001, see Table S4 in supplementary material) and methanol (solvent A), and a 27:73 ratio of acetone and methanol (solvent B). The linear gradient at a flow rate of 1 mL min⁻¹ was: 0-1.5 min 0 % solvent B, 1.5 min to 12.5 min 0-100 % solvent B, 12.5 min to 23 min 100 % solvent B, 23 min to 26 min 100-0 % solvent B, 26 min to 31 min 0 % solvent B. All solvents used were HPLC grade.

Chromatograms were integrated at 435 nm using the MassHunter Qualitative Analysis software (version B.07.00). Peaks were identified by their retention time and absorption spectra. Commercial standards of pigments were used as an external calibration to confirm the identification and quantify pigment concentrations (DHI Group, Hørsholm, Denmark, see full standard list and concentrations in Supplementary Material Tables S5 and S6). On the 56 peaks detected per sample, 10 were identified as pigments corresponding to: chlorophyll-a, b, c2, chlorophyllide-a, fucoxanthin, lutein, violaxanthin, alpha- and beta-carotene and pheophytin-a. To allow for sample comparisons, concentrations were normalized using a coefficient based on the sample mass (mass/20).

## 2.6. Data analysis

Physico-chemical parameters were compared among basins using Fisher-Pitman K-sample asymptotic permutation tests for discrete data (N-NO³, N-NH₄⁺, P-PO₄³⁻, Total Suspended Solids, temperature) and Kruskal-Wallis's chi-squared test for continuous data (light monitoring). To test the effects of the basins, the nature of the materials, and their applications on all variables related to biofilm biomass, composition, and microbial activities, two-way Analyses of Variance were performed, one to test the effects of the materials and basins, and another grouping the materials by applications. When assumptions were met, two-way parametric ANOVAs were used to test the effects of material (or application) and basin, as well as their interaction. Otherwise, Scheirer-Ray-Hare non-parametric tests were performed with the rcompanion package (version 2.4.30, Mangiafico, 2023). When statistical tests were significant (p < 0.05) and to determine which experimental conditions differed, self-corrected post-hoc Tukey's tests or Games-Howell non-parametric tests were performed using the rstatix package (version 0.7.2, Kassambara, 2023). Outliers were removed based on the Inter Quartile Range (IQR) method, excluding values outside the range [Quartile 1-1.5 × IQR, Quartile 3 + 1.5 × IQR] (Tukey et al., 1977). The difference in each algal pigment between each material or application and the control was tested using t-tests (for material) or Wilcoxon tests (for application). In addition, to reduce the dimensionality of the algal pigment composition data set, we performed principal component analysis (PCA) for each basin separately, using the ade4 (version 1.7-22,

Thioulouse et al., 2018) and *adegraphics* (version 1.0-21, Siberchicot et al., 2017) packages. We did not use the usual centering at work in PCA on covariance or correlation or matrix (see Doledec and Chessel, 1991; Jolliffe and Cadima, 2016; Noy-Meir, 1973; Thioulouse et al., 2018), which consists in calculating the deviation between each raw value and the average of each variable and potentially standardized. Here, we centered variables by calculating the deviation between each raw value and the average control value for a given variable, and we standardized values by variable. This operation relies on a simple translation of the origin from the factorial map. Resulting PCA factorial maps thus placed the control material at the origin of the multivariate space and the magnitude of sample coordinates obtained from their pigment composition materialized the magnitude of the deviation of each sample of any combination of material and application to the control. The advantage of this procedure was that, for each basin, the effect of each plastic material or application (MF and NMF) on the pigment composition could be displayed in reduced dimensions. To assess the influence of materials and applications on biofilm pigment composition, we performed a between-groups Monte-Carlo test. The resulting between-groups variance characterizing the degree of separation between applications was tested against simulated values obtained after 999 permutations of the rows of the biofilm pigment composition table. For each basin, Wilcoxon tests were further used on the factorial coordinates of the first two PCA axes (i.e. delivering most of the variability in of biofilm pigment composition) to identify groups of pigments specifically influenced by materials or applications. All statistical analyses were performed using R, version 4.1.2 (R Core Team, 2024). Data and code are available at https://doi.org/10.5281/zenodo.16275025.

## 3. Results

### 3.1. Released rates of dissolved organic carbon among application groups

Before the incubation of materials in basins, we measured the ability of each material to release organic compounds in water. Based on this experiment, a significant difference in released DOC was measured between materials depending on their application (one-way ANOVA, F = 27.05, *p* < 0.010, see Fig. S2 in supplementary material). Higher DOC-release rates were measured in the NMF group (1.3 ± 0.7 µg cm⁻², week⁻¹) compared to the MF group (0.6 ± 0.4 µg cm⁻², week⁻¹; Tukey's post-hoc tests, *p* < 0.05). The control group displayed negligible DOC leaching (leached DOC: 0.2 ± 0.05 µg cm⁻², week⁻¹).

### 3.2. Physico-chemistry of basin waters

Comparable oligotrophic conditions and water temperatures were observed in the three basins during the incubation period (N-NO₃⁻, N-NH₄⁺, P-PO₄³⁻ and temperature in Table 1).

Contrastingly, the concentration of total suspended solids varied significantly among basins (chi-squared = 6.1, df = 2, *p* < 0.050), with a decreasing gradient from B1 and B3 to B2. The mean light received also differed significantly among basins (Kruskal-Wallis's chi-squared = 55.28, df = 2, *p* < 0.0001) and was inversely related to the total

<caption>**Table 1**</caption>

- Overall water quality of the three basins (X̄ ± SD; N-NO₃⁻ = nitrates, N-NH₄⁺ = ammonium, P-PO₄³⁻ = phosphate, TSS = Total Suspended Solids).

<table><thead><tr><th></th><th>N-NO<sub>3</sub><br>(mg. L<sup>-1</sup>)</th><th>N-NH<sub>4</sub><sup>+</sup><br>(µg. L<sup>-1</sup>)</th><th>P-PO<sub>4</sub><sup>3-</sup><br>(µg. L<sup>-1</sup>)</th><th>TSS<br>(mg. L<sup>-1</sup>)</th><th>Light<br>(lux)</th><th>Temperature<br>(°C)</th></tr></thead><tbody><tr><td>B1</td><td>1.11<br>(±0.47)</td><td>20.9<br>(±12.8)</td><td>11.2<br>(±12.0)</td><td>2.46<br>(±1.81)</td><td>1253<br>(±2573)</td><td>17.1 (±1.6)</td></tr><tr><td>B2</td><td>1.23<br>(±0.60)</td><td>17.3<br>(±13.8)</td><td>9.1<br>(±9.1)</td><td>1.28<br>(±0.67)</td><td>1815<br>(±4274)</td><td>17.2 (±1.5)</td></tr><tr><td>B3</td><td>1.08<br>(±0.37)</td><td>15.8<br>(±10.6)</td><td>4.0<br>(±5.5)</td><td>2.06<br>(±1.18)</td><td>1566<br>(±3216)</td><td>17.4 (±1.4)</td></tr></tbody></table>
suspended solid concentration.

### 3.3. Biofilm and algal biomasses

The biofilm organic carbon content and chlorophyll-α concentration were not significantly influenced by materials or applications (Fig. 1A–C, D, and F), but significant differences were detected between basins (Fig. 1B and E). On the one hand, the highest biofilm and algal biomass were measured in the basin with the highest suspended solids concentration (B1 in Fig. 1B and E). On the other hand, biofilms which developed in the basin with the lowest concentration of suspended solids (B2) contained higher percentages of organic carbon and total nitrogen than biofilms from the two other basins (Fig. S3 in supplementary material). The C/N ratio was significantly lower in B3 than in the other two basins (Fig. S4 in supplementary material).

### 3.4. Microbial activity

No statistical differences were detected between materials or applications for the rates of respiration (Fig. S5; supplementary material) and photosynthesis in biofilms (Fig. 2A–C). In contrast, significant differences in biofilm photosynthetic rates occurred between basins (Fig. 2B, Scheirer-Ray-Hare post-hoc test, $H = -22.55, p < 0.0001$). More precisely, biofilms in B3 and B1 were characterized by lower rates than biofilms in B2 (Fig. 2B).

Materials, applications or basins did not influence the algal physiological state (PSII) within the biofilms. Values ranged from 0.63 to 0.80 ($\bar{x} = 0.72$), indicating no physiological damage to the algal biofilm (see Fig. S5 in supplementary material).

In comparison to biofilms on the control, higher esterase activity (one-way ANOVA, $F = 4.93, p < 0.001$) was measured for biofilms on 4 plastic materials (PE, PP, PVC1, and PVC2, Fig. 2D). In addition, esterase activity within the biofilm on plastic materials from the MF and NMF applications was significantly higher (1.9 times more FDA) than in biofilms found on the control (Scheirer-Ray-Hare post-hoc test, $H = 11.85, p < 0.010$; Fig. 2F).

### 3.5. Algal composition and diversity

No significant difference was observed between different materials and applications for the 10 measured pigments, but the concentrations of 6 pigments differed significantly between basins; chlorophyll-c2, fucoxanthin, chlorophyll-b, pheophytin-α, alpha-carotene had higher median values in B2 than in B3, and violaxanthin had higher median value in B2 than in B3 and B1. Basin B3 had the lowest pigment concentrations compared to the others (Fig. S6–S10, supplementary material).

Separate decentered PCA allowed us to display the differences in pigment composition between plastic applications and control for each basin (Fig. 3). Overall, the between application permutation test demonstrated no overall effect with observed between-group variance ratios of 0.321 ($p = 0.911$), 0.151 ($p = 0.178$), and 0.238 ($p = 0.092$) for B1, B2, and B3 respectively. The first two axes of the decentered PCA extracted 68 %, 65 % and 78 % of the variability in the datasets for B1, B2, and B3 respectively. The same analysis was carried out on materials but showed no overall effect (Fig. S11 in supplementary material).

However, when we only consider the first PCA axis, an effect of the materials on biofilm pigment composition was detected for B1 (t-test on the factorial coordinates of the PCA's first axis, $t = -7.814, df = 23, p < 0.0001$), but not for B2 (t-test, $t = 0.0358, df = 23, p > 0.050$) and B3 (t-test, $t = -1.6363, df = 23, p > 0.050$). A significant effect of applications on biofilm algal pigment composition was revealed in B1 along the first PCA axis coordinates for both MF and NMF groups compared with the control (Wilcoxon test, MF, $p < 0.01$, NMF, $p < 0.001$) (Fig. 3). This effect was not detected in basins B3 and B2, which had lower suspended solids levels than B1. When considering plastic materials individually, results were less significant due to a loss of statistical power, and only the biofilm developed on PE-MF in B1 exhibited a pigment composition statistically different from those of the control (Fig. S11, supplementary material).

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Biofilm biomass showing the distribution of organic carbon (A to C) and chlorophyll-α (D to F) content measured for each material (A vs. D respectively), basin (B vs. E) and application (C vs. F). Applications are represented by the colors as follows: white = control (CO), gray = medical or food grade plastics (MF), black = non-medical or food grade plastics (NMF). Materials are named as follows: CO = control, PE = polyethylene, PP = polypropylene, PS = polystyrene, PVC = polyvinyl chloride. The statistical significance of the results is as follows: $p < 0.0001$ ‘***’, $p < 0.001$ ‘**’, $p < 0.05$ ‘*’.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Photosynthesis rate and FDA consumption measured for each material (A vs. D respectively), basin (B vs. E) and application (C vs. F). Applications are represented by the colors as follows: white = control, gray = medical or food grade plastics (MF), black = non-medical or food grade plastics (NMF). Materials are named as follows: CO = control, PE = polyethylene, PP = polypropylene, PS = polystyrene, PVC = polyvinyl chloride. The significance of the results is indicated as follows: $p < 0.001$ ‘***’, $p < 0.01$ ‘**’, $p < 0.05$ ‘*’.</figcaption></figure>

# 4. Discussion

## 4.1. Influence of materials on biofilm biomass and algal composition (H1)

The first hypothesis (H1) concerning significant differences in total algal biomass and biomass composition among materials was not validated by our experiment. Both organic carbon and chlorophyll-α concentrations were comparable among materials, but a significant influence of the environmental conditions in all basins was detected (Fig. 1B and E). Indeed, the unexpected gradient of suspended solids and light exposure between the three basins probably played a key role in the development of biofilms on the materials. Specifically, the highest algal and total biofilm biomasses were measured in the basin with the highest concentration of suspended solids (B1). In addition, the concentrations of chlorophyll-c2, fucoxanthin, chlorophyll-b, pheophytin-a, alpha-carotene, and violaxanthin in biofilms significantly differed among the three basins, whereas only the biofilm on PE-MF in B1 exhibited a pigment composition statistically different from the control.

These observations are consistent with previous studies highlighting interactions between environmental and material effects on biofilm biomass. For example, Oberbeckmann et al. (2018) reported that, along a salinity and nutrient gradient, the environment had a prominent influence on the biofilm assemblages even if there were plastic-specific assemblage factors. This example underlined that differences in biofilms between plastic materials were the highest when nutrient concentrations were the lowest. In the present study, nutrient conditions between basins were comparable, highlighting that suspended solids and light exposure could also mask the potential effects of materials on biofilm growth. Indeed, biofilm chlorophyll-α content was the highest in the basin with the lowest light exposure (B1) and the lowest in the basin with the highest light exposure (B2).

In contrast to our findings, several studies have shown differences in biofilm biomass and composition among plastic materials (Guo et al., 2022; Laffet et al., 2023; Miao et al., 2020; Nava et al., 2022). For example, Miao et al. (2020) measured a higher total biofilm biomass on macroplastics than on natural materials. Nava et al. (2022) showed that

algal biomass could depend on the plastic polymer with more biomass on polyethylene terephthalate than high-density polyethylene plastic. However, these studies have sometimes compared different materials such as Guo et al. (2022), which compared a PE plastic bag with a PP lunch box and a PET water bottle which may present different surface roughness. This property was mostly not measured (except in Miao et al. (2020) that found a significant effect of surface roughness) and indeed not controlled among materials in these studies, although this property is recognized as a major factor in the development of biofilms on surfaces (Hunt and Parry, 1998; Krsmanovic et al., 2021). Therefore, these studies make it difficult to compare biofilm development between different substrates.

Our study demonstrates that environmental factors, such as light and suspended solids, can have a stronger influence on biofilms than the type of plastic material, even in nearby basins filled with the same water. This highlights the need to monitor environmental parameters alongside characterizing plastic materials to better compare studies and generalize findings, which is a current challenge in the plastisphere research.

## 4.2. Influence of materials on biofilm activities (H2)

Contrary to hypothesis H2, we did not observe a positive effect of plastic materials compared to glass on biofilm activities linked to oxygen fluxes (see photosynthesis rates in Fig. 2, but also respiration rates in Supplementary Material Fig. S5). As observed for biofilm biomass, the local environmental conditions had greater control than materials over these variables. In fact, the photosynthetic activity of biofilms was the highest on materials incubated in the basin with the greatest exposure to sunlight and the lowest concentrations of suspended solids (B2). Conversely, biofilms developed in basin B1, which had the lowest exposure to light, exhibited the lowest photosynthesis rate. These results are consistent with previous studies showing that the photosynthesis rate of biofilms is generally affected by light intensity, nutrient availability (Litchman et al., 2003), and the presence of pollutants (Petsas et al., 2017). Here, we observed no physiological damage of algal biofilms with relatively high values of Fv/Fm (Fig. S5, supplementary
Suspended solids

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Separate PCA performed on each basin to illustrate the differences in pigment composition among plastic applications (medical or food grade (MF) in gray or non-medical or food grade (NMF) in black) and the control (displayed at the origin of each factorial map). The correlation circles are presented per basin with the concentrations of the 10 measured algal pigments with chlorophyll-a (Chl_a), chlorophyll-b (Chl_b), chlorophyllide-a (Chld_a), chlorophyll-c2 (Chl_c2), fucoxanthin (Fuco), lutein (Lut), violaxanthin (Viola), pheophytin a (Pheo), beta-carotene (B-caro), and alpha-carotene (A-caro). The black arrow (suspended solids) indicates the TSS gradient among basins, from low to high.</figcaption></figure>
material), suggesting that microalgal communities were not stressed by environmental conditions and material properties. We can therefore assume that only a light attenuation effect due to the concentrations of suspended solids in basins in the basins explained the biofilm photosynthesis rates.

Unlike respiration and photosynthetic rates, hypothesis H2 was validated for esterase activity which was higher in plastic-associated biofilms than in biofilms developed on controls. Half of the plastic materials studied led to significantly higher esterase activity of the biofilms, in comparison with the control: PE-MF and PP-MF, as well as PVC-NMF-1 and PVC-NMF-2 (Fig. 2D).

As esterase activity is an enzymatic activity involved in the degradation of complex organic molecules (Adam and Duncan, 2001; Battin, 1997), this response could have been due to biofilm stimulation by organic compounds associated with plastic materials. Our measurements of DOC leaching supported this hypothesis, as higher release rates of DOC were obtained from plastic materials than from glass. DOC leached from plastic has been shown to be highly biodegradable in seawater (Egea et al., 2024; Romera-Castillo et al., 2018) and lakes (Sheridan et al., 2022) and the stimulation of biofilm heterotrophic activity by plastic leachates has been also demonstrated in controlled experiments with polyethylene (Egea et al., 2024; Sudhakar et al., 2008) and polyamide (Yamano et al., 2008). However, this remains a hypothesis to be confirmed, as the results of this experiment do not allow us to assert that the observed stimulation comes from DOC leached from plastics. Microorganisms in the plastisphere could consume carbon from plastic directly from its biodegradation (e.g. Sudhakar et al., 2008; Wilkes and Aristilde, 2017; Yamano et al., 2008) or by modifying the surface of the material through processes like oxidation, hydrolysis, depolymerization, or mineralization (e.g. Klein et al., 2018; Wilkes and Aristilde, 2017; Yuan et al., 2020), or the plastic could simply be a surface facilitating their access to the DOC already present in the water column.

Anyway, the observed increase in esterase activity in the plastisphere suggests that plastic surfaces may enhance microbial activity, which could increase the degradation of organic matter compared with other materials. Extending this research to additional enzymatic functions could provide insights into how plastic pollution can affect the ability of freshwater ecosystems to break down complex organic compounds, especially as previous studies indicate that plastisphere communities have the potential to degrade and metabolize xenobiotics, like hydrocarbons (Bryant et al., 2016; Chomiak et al., 2024; Debros et al., 2017).

### 4.3. Influence of plastic applications on biofilm characteristics (H3)

Hypothesis H3 about the differences in DOC leaching and biofilm characteristics between application groups was partially validated by our results. Although no significant effects were measured for biomass and biofilm activities, DOC leaching from the materials was higher for NMF plastics than for MF, or the control, which was the lowest (Fig. S2, supplementary material). The algal pigment compositions of the biofilms showed differences depending on the application group (Fig. 3).

More specifically, the algal pigment composition of the biofilms was different in both MF and NMF application groups compared to the control in basin B1, which had the highest suspended solids level. However, this pattern was not observed in the two other basins, underlining that biofilm pigment compositions are strongly influenced by environmental conditions (concentration of suspended solids and light exposure), as observed for biofilm biomass. Indeed, differences in biofilm pigment concentrations were measured between basins, regardless of the material used: the highest concentrations of chlorophyll-c2, chlorophyll-b, fucoxanthin, pheophytin-a, alpha-carotene, violaxanthin and fucoxanthin were produced in biofilms developed in the basin with the highest light exposure. Thus, differences in light exposure among the basins can explain the observed inconsistency of interactions between plastic application groups and biofilms.

Recent studies and reviews (e.g. Binda et al., 2024; Nava et al., 2025; Nava and Leoni, 2021) noted that the factors which have the greatest influence on the plastisphere are still unclear. There is still debate as to whether specific material properties (e.g. Cai et al., 2019; Guo et al., 2022; Hossain et al., 2019) or environmental factors (e.g. Delacuvellerie et al., 2022; Vincent et al., 2022) play a greater role in the formation of microorganism assemblages on plastics. The present study supports the view that plastic materials, when they have comparable roughness, do not exert a strong influence on algal pigment composition. The different chemical compositions linked to plastic applications have an influence but that is not strong enough to overcome environmental factors.

## 5. Conclusion

This study pioneers the quantitative analysis of the impact of multiple plastic materials with different applications on freshwater microbial communities, with a focus on microalgae, a component of biofilms that has rarely been studied until now. Our findings reveal that microbial biomass and metabolic activities were predominantly shaped by environmental factors (e.g., suspended solids, light exposure) rather than materials or their applications, except for the esterase activity that was strongly higher on all plastic-associated biofilms compared to the control. Such observations could result from the greater leaching of dissolved organic carbon from plastics, which could stimulate heterotrophic processes. Algal pigments, in contrast, were influenced by interactions between plastic applications and environmental conditions, suggesting that selective pressures, such as light and suspended solids, play a significant role in shaping microbial assemblages and modulating their activity in these systems. This study emphasizes the importance of monitoring environmental parameters, as well as characterizing plastic materials, to better understand the mechanisms influencing microbial dynamics on plastics. Such an approach is essential to address current challenges in plastisphere research and to improve study comparability and generalization of findings. Further research should broaden the scope of microbial exposure to a diverse range of plastic materials and consider their chemical compositions across varied freshwater ecosystems. Such data are crucial to inform international policy, particularly considering the ongoing global treaty on plastic pollution, which must identify polymers and chemicals of environmental concern.

### CRediT authorship contribution statement

**Camille M. Touchet:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Florian Mermillod-Blondin:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization. **Sylvain Dolédec:** Writing – review & editing, Visualization, Methodology, Formal analysis. **Zoraida J. Quinones-Rivera:** Methodology. **Ludovic Guillard:** Methodology, Investigation. **Laurence Volatier:** Writing – review & editing, Validation, Methodology, Investigation, Conceptualization. **Félix Vallier:** Methodology, Investigation. **Marjolaine Rey:** Writing – review & editing, Validation, Methodology, Investigation, Formal analysis. **Stefan Krause:** Writing – review & editing, Writing – original draft, Validation, Resources, Methodology, Investigation, Funding acquisition, Conceptualization. **Laurent Simon:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.

### Funding sources

This work has been funded by the IDEXLYON of Université de Lyon (UdL) within the program "Investissements d'Avenir" (ANR-16-IDEX-0005) and by the EU Horizon Europe (GA N°101072777 - PlasticiUnderground HEUR-MSCA-2021-DN-01). It was performed within the
framework of the EUR H2O'Lyon (ANR-17-EURE-0018) and the Rhone Basin Long Term Environment Research (LTSER ZABR). SK was funded by the Royal Society (INF\R\2\212060), NERC (SmartWater), and the Leverhulme Trust (RPG-2021-030; RPG-2017-377).

## 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.

## Acknowledgments

We would like to express our gratitude to "Eau Publique du Grand Lyon" for providing access to "Crépieuch-Charmy" wellfield through the research platform, to the Microbial Ecology laboratory platform, CESN ('Centre d'Etude des Substances Naturelles') for the HPLC analyses, and to the SedAqua + platform (Research Federation FR3278 BioEEnViS, Université Claude-Bernard Lyon 1) for the experimental systems used to measure oxygen fluxes. We also thank Louise Gauthier for her technical assistance on plates and incubation systems preparation.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2025.127323.

## Data availability

Data and code are available at https://doi.org/10.5281/zenodo.16275025.

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