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Micro-and nanoplastics in biological samples following a drinking water intervention in Barcelona, Spain

  • Emma Calikanzaros,

    Roles Formal analysis, Visualization, Writing – original draft, Writing – review & editing

    Affiliations ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain

  • Carolina Donat-Vargas,

    Roles Supervision, Writing – review & editing

    Affiliations ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain, Polyphenol Research Group, Department of Nutrition, Food Sciences and Gastronomy, Faculty of Pharmacy and Food Sciences, INSA-University of Barcelona, Barcelona, Spain, Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden

  • Guillaume Chevance,

    Roles Conceptualization, Supervision, Writing – review & editing

    Affiliations ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain

  • Cathryn Tonne,

    Roles Conceptualization, Writing – review & editing

    Affiliations ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

  • Maria Rosa Boleda,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Aigües de Barcelona, Empresa Metropolitana de Gestió del Cicle Integral de l’Aigua, Barcelona, Spain

  • Joan Dalmau,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Aigües de Barcelona, Empresa Metropolitana de Gestió del Cicle Integral de l’Aigua, Barcelona, Spain

  • Xavier Borrell,

    Roles Investigation, Resources

    Affiliation Institute of Environmental Assessment and Water Research – Consejo Superior de Investigaciones Científicas, Barcelona, Spain

  • Rachida Mazigh,

    Roles Investigation, Resources

    Affiliation Institute of Environmental Assessment and Water Research – Consejo Superior de Investigaciones Científicas, Barcelona, Spain

  • Marta Llorca,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Institute of Environmental Assessment and Water Research – Consejo Superior de Investigaciones Científicas, Barcelona, Spain

  • Marinella Farré,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Institute of Environmental Assessment and Water Research – Consejo Superior de Investigaciones Científicas, Barcelona, Spain

  • Cristina M. Villanueva

    Roles Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing

    cristina.villanueva@isglobal.org

    Affiliations ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain, Hospital del Mar Medical Research Institute, Barcelona, Spain

Abstract

Micro- and nanoplastics (MNPs) are emerging contaminants of concern, and drinking water may represent an important exposure pathway. Evidence on internal exposure and contribution of drinking water remains limited. Under the hypothesis that bottled water consumption may lead to higher body burden of MNPs compared to tap water, this exploratory study investigated changes in MNPs and plastic-related additives in human biological samples following sequential changes in drinking water consumption. A randomized crossover intervention was conducted in volunteers (N = 3) sequentially switching between tap and bottled water consumption in randomized phases during 7 weeks (May-July 2022) in Barcelona, Spain. Stool (N = 48), urine (N = 48), tap water (N = 24), and bottled water (N = 2) samples were collected. MNP polymers 0.7-20 µm and chemicals used as additives in plastic production were quantified in biological samples through double suspect screening using high-performance liquid chromatography-high resolution mass spectrometry. Microplastics >1 µm in water were analyzed by pyrolysis-gas chromatography-mass spectrometry. MNP polymers were detected in N = 40 (83%) stool samples and were above quantification limits (QL) in N = 30 (63%). Polyethylene was > QL in N = 20 (42%), polyamide in N = 8 (17%), polypropylene in N = 8 (17%), and polyoxymethylene in N = 2 (4%) samples. Detection rates were lower in urine samples. Plastic-related additives were identified in all urine samples, N = 27 different additives, with N = 15 > QL (median concentration range: 0.03-2196 µg/L). Microplastic polymers including polypropylene, polyethylene, polyethylene terephthalate, and polyvinyl chloride were found in 62% of tap water samples (median total concentration: 0.18 µg/L), and polyethylene was detected in bottled water. Within the scope of this exploratory study, no significant differences between phases were found in MNP polymers and plastic additives concentrations in biological samples. Low detection rates of MNP polymers in stool and urine limited the statistical power. Larger, controlled studies are needed to further evaluate the contribution of drinking water on human MNP exposure.

1. Introduction

Plastic is omnipresent in our lives. Estimated 22 million tons of plastic waste enter ecosystems annually, contributing to a cumulative legacy of over 6 gigatons [1], following the exponential increase of plastic use since the 1950s [2]. Weathering of plastic debris lead to fragmentation into smaller pieces, with micro- and nanoplastics (MNPs) representing a diverse array of petroleum-derived polymers measuring less than 5 mm for microplastics and <1 µm for nanoplastics. Estimated 75,000–300,000 tons of microplastics are released into the environment every year in Europe [3], leading to widespread contamination of water, food, and air [4].

Evidence on the presence of MNPs in the close human environment has raised public health concerns [5]. MNPs can cross biological barriers in the gut and lungs, enter bloodstream and be delivered to tissues including brain, kidneys and reproductive organs [6, 7]. Experimental studies have shown that MNPs can induce a spectrum of adverse biological responses, ranging from DNA damage, to inflammation and gut microbiota dysbiosis [1]. However, there is limited human evidence on exposure and health effects in the population [8].

Occurrence of MNPs in drinking water has been reported in multiple studies [911], and it has been suggested that drinking water could be a major source of human exposure [1214]. In particular, higher concentration of MNPs have been reported in bottled compared to public supply [9, 15, 16]. Under the hypothesis that consuming bottled water may lead to higher body burden of MNPs compared to tap water, we aimed to explore changes in MNPs measured in biological samples following changes in drinking water source. We conducted an intervention study to compare concentrations of MNPs and plastic additives in stool and urine samples following randomized sequential changes between bottled and tap water consumption among volunteers in Barcelona, Spain.

2. Materials and methods

2.1. Ethics statement

The study protocol, questionnaires, consent form, and instructions for sample collection were evaluated and approved by the CEIm Parc de Salut Mar ethics committee (2021/10168/I). Study participants were enrolled after signing an informed consent form.

2.2. Study design and study participants

We followed a single-case experimental design (SCEDs), that allows to evaluate individual response to an intervention. SCEDs are particularly well-suited to test innovative hypotheses and establish robust causal inferences at the individual level. This approach is beneficial in contexts where data collection is costly or when group-level data fail to effectively capture individual-level variability [17]. Recognized as relevant for assessing interventions, SCEDs are classified as “level 1” evidence in the Oxford Centre for Evidence-Based Medicine 2011 levels of evidence [18]. We recruited 3 volunteers to conduct 3 single-case ABAB trials, where N = 3 is the minimum number of replications to ensure confidence of tested associations [19].

Participants switched between tap (phase A, baseline) and bottled (phase B, intervention) water consumption in randomly assigned phase duration (i.e., number of observations/phase) using the shiny app “SCDA” (https://tamalkd.shinyapps.io/scda/). The resulting sequence by participant is shown in Table 1. A washout period of at least seven days was implemented between phase transitions to clear the digestive tract of any MNPs remaining from the previous water consumption phase. This approach ensured that MNPs analyzed originated exclusively from the water consumed in that specific phase. Assuming at least one bowel movement per day, we estimated that a seven-day period would be sufficient to eliminate residual MNPs from the intestinal tract. The study spanned approximately 7 weeks between the 16th of May 2022 and the 4th of July 2022, and each participant provided a total of 16 observations (biological samples).

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Table 1. Sequence of tap (A) and bottled (B) water phases in the three participants of the single-case crossover ABAB trial with randomly generated length. One week was left for wash-out between phases.

https://doi.org/10.1371/journal.pwat.0000469.t001

For the bottled water phase we selected a local bottled mineral water brand based on prior evidence from the study area [11]. We selected one of the most popular brands in Barcelona and provided volunteers 1.5-liter bottles to drink and cook during the bottled water phase (B). For consistency between phases, we supplied a portable metal bottle to drink water outside home.

2.3. Recruitment of participants

We designed flyers to advertise the study through our main institution, ISGlobal, and other public buildings as well as social media. In addition, an existing volunteer database in ISGlobal was used as a source of potential participants. Advertisements included a web link to an online questionnaire to ascertain eligibility: residential address, age, sex, dietary habits, type of drinking water consumed, smoking, motivation to participate, usual defecation frequency, gastrointestinal diseases, and usual working location. We identified and selected healthy participants living in Barcelona who usually consumed unfiltered tap water, worked from home, were non-smokers, adhered to a healthy diet with minimal use of packaged food, reported a usual defecation frequency of at least once a day, and exhibited high motivation towards the research topic. Three participants who met the eligibility criteria were enrolled, and received a financial reward of 200 euros at the end of sample collection to encourage good practices.

2.4. Data collection

Participants completed an online questionnaire to self-report data on socio-demographics, lifestyle habits, weight, height, health status, and medication use. Dietary habits were collected through a validated semi-quantitative food frequency questionnaire. Additionally, participants undertook daily ecological momentary assessments (EMA) throughout the study fieldwork, facilitated via the GDPR-compliant smartphone application “Ethica Data” (https://ethicadata.com/). Three notifications per day (morning, noon, evening), prompted participants to provide information regarding the plastic packaging and storage of food items consumed during breakfast, lunch, and dinner. An optional image capture of meal ingredients was also included. The EMA also included an evening report of the total amount of water consumed over the day.

2.5. Sample collection

2.5.1. Biological samples.

Stool and urine samples were self-collected by participants following instructions and materials provided. For stool, a big garbage bag was used to completely cover the toilet, and a big piece of aluminum foil was placed on top of it in order to intercept the stool and avoid contact with the plastic garbage bag. A disposable wooden spoon was used to collect a stool sample (50 grams), that was placed in another aluminum foil, used to completely wrap it, that in turn was placed in a 120 mL container for storage and transportation. Urine samples were collected in 150 mL polyethylene terephthalate (PET) containers (bisphenol A free) with screw aluminum cap in parallel to the stool sample collection phase, including a sample late at night and one first in the morning, leading to a variable number of urine samples. Samples were labeled with the volunteer’s code, time and date, and stored in hermetic seal bags in the refrigerator until shipment to the research institute on the same day. Participants telephoned the study personnel after each stool sample collection in order to arrange a courier to collect and transport samples to the research institute, where samples were promptly stored at -20ºC. Samples were centrally shipped to the laboratory for analysis after fieldwork completion. Prior than that, urine samples where thawed to mix night and morning samples into a unique homogenized sample.

2.5.2. Water samples.

During tap water consumption phase, a daily water sample was collected by study personnel following previously validated methods [20]. To facilitate sampling, which involved filtering 26–40 liters over approximately 30 minutes, water was collected from the nearest public access point in the distribution system prior to the household. Comparability of MNP concentrations relative to the tap water at participants residence was confirmed by two sampling campaigns where water samples were simultaneously collected in both locations (Table A in S1 File). Prior to tap water sampling, all material was cleaned to minimize contamination. We used a mix of analysis grade ethanol from Merck (Darmstadt, Germany) and MilliQ water supplied by MilliQ system from Millipore (Bedford, MA, USA) in a proportion of 70:30 [20]. Water was filtered on site using a PTFE holder (Savillex, Eden Prairie, MN, USA) containing a 13 mm glass fiber filter with 1 μm pore size (Millipore, Burlington, MA, USA). The filtration system was designed to prevent air exposure, with water flowing directly from the cabinet to the filter, thereby minimizing the risk of external contamination. Additionally, no loss of microplastics occurred during the preconcentration stage. Immediately after filtration, filters were placed in glass petri dishes for further analysis. To prevent no microplastic contamination from the sampling apparatus, several 1L blanks of HPLC-grade water were processed using a peristaltic pump, particularly to verify the absence of contamination. A total of 24 drinking water samples from the water distribution network were collected from each participant, according to the predetermined scheme outlined in Table 1 (7 samples for participant 1 and 3, and 10 samples for participant 2). Two bottled water samples of the commercial brand provided to participants were analyzed for microplastic concentrations using the same procedure. This limited bottled water sampling effort was deemed sufficient as the quality of natural mineral water is generally stable, and prior evidence indicated that MNP contamination primarily originates from packaging rather than raw water sources [11].

2.6. Experimental analysis of water samples

2.6.1. Py-GC/MS analytical methodology.

Water samples were analyzed using a previously developed analytical methodology [21]. Briefly, samples were analyzed utilizing pyrolysis coupled to gas chromatography/mass spectrometry (Py-GC/MS) using a 6890N gas chromatograph connected to a 5977B MSD single quadrupole mass spectrometer (Agilent, CA, USA), equipped with a multi-shot pyrolyzer EGA/PY-3030D (Frontier Laboratories, Fukushima, Japan). Pyrolysis was conducted at a temperature of 600ºC in single shot mode. The interface temperature was set at 300ºC. The injection was performed with a split ratio of 10:1 at a temperature of 300ºC. Chromatographic separation was performed using a DB-5MS fused-silica capillary column (5% phenyl- 95% methylpolysiloxane) measuring 30 m x 0.25 mm I.D. x 0.25 µm film thickness (Agilent, CA, USA). The initial oven temperature was set at 40ºC (1 minute), then ramped to 320ºC at a rate of 20ºC/min and held for 5 minutes. The total runtime was 20 minutes. Helium (99.9999%) was used as the carrier gas, at a constant flow rate of 1 mL/min. The mass spectrometer operated in electron ionization mode at 70 eV. The temperatures of the MS Source, MS Quad, and transfer line were set at 230ºC, 150ºC, and 280ºC, respectively. The acquisition was conducted in full scan mode (50–550 m/z). Specific pyrolysis ions for each polymer type were used to identify and quantify MPs. Additionally, the obtained results were verified using the F-Search software (Frontier Laboratories).

Quantification was performed through internal standard calibration of microplastic standards. In addition, 4 procedural blanks were performed along the sample batch and successfully analyzed. Only the samples with an area greater than three times the blank were considered as positives. Moreover, to minimize interferences, all the used materials (glass fiber filters, pyrolysis cups, and tweezers) were burned at 450ºC for 3 hours.

2.6.2. Validation parameters for the MNPs from water samples.

Quality controls and recoveries applied in this study are those published in the previous study by Dalmau-Soler, 2024 [21]. Methods limit of detection (MLOD) and quantification (MLOQ) were calculated by using the response of each MNPs in spiked 1L of HPLC-grade water (between 7 and 30 µg) based on a signal-to-noise (S/N) ratio of 3 and 10 respectively.

2.7. Experimental analysis of biological samples

2.7.1. Extraction procedures.

Plastic particles were extracted according to the Fenton digestion [22, 23]. Very briefly, 100 mL of 30% H₂O₂ and 40 mL of an iron catalyst solution were mixed with 0.5 g of lyophilized sample in 400 mL glass vials. The mixture was incubated at 40°C for 5 hours for digestion. Subsequently, the sample was centrifuged at 3,500 rpm for 15 minutes. The centrifuged sample was filtered using 0.7 µm glass fiber filters. 50 mL of 65% HNO₃ was added to the filters for digestion at 50°C for 30 minutes, followed by 10 minutes at 70°C. After digestion, the mixture was eluted from the filters, and the filters were washed with 50 mL of HPLC-grade water twice. A final wash with 50 mL of ethanol was applied. The filters were dried overnight in an oven at 40°C, then extracted with 10 mL of toluene in an ultrasonic bath for 15 minutes. This extraction step was repeated twice, and the resulting toluene extracts were combined and evaporated under a nitrogen stream until approximately 1 mL remained. The concentrated extract was transferred to a liquid chromatography (LC) vial and evaporated to a final volume of 1 mL.

0.5 mL of urine samples were extracted with 0.5 mL of toluene in an ultrasonic bath for 10 minutes. The supernatant was collected and transferred to an LC vial. This extraction process was repeated twice, and the resulting supernatants were combined and evaporated under a nitrogen stream until the sample volume was reduced to 0.5 mL. For the analysis of plastic additives, the same procedure was performed, but methanol was used as the extraction solvent instead of toluene.

2.7.2. Determination of MNPs and plastic additives.

MNPs quantitative analyses in stool and urine samples was carried out by size exclusion chromatography and with high-resolution mass spectrometry (LC(SEC)-HRMS) using the previously validated method for complex samples using a QExactive-hybrid quadrupole−Orbitrap mass analyzer [10, 24, 25]. The separation was done in an Acquity HPLC system from Waters Corp. (Milford, MA, United States) equipped with an advanced polymer column (Acquity APC XT45 1.7 µm particle size), using toluene as an isocratic mobile phase and at a flow rate of 0.5 mL/min for 10 µL of sample injected. The chromatographic system was coupled to an HRMS Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer from Thermo Fisher Scientific (San José, CA, United States), equipped with an atmospheric pressure photo ionization (APPI) source which was operating in negative and positive ionization conditions. Acquisition data was in full scan mode from 500 to 3,000 m/z and at a resolution of 30,000 full widths at half maximum (FWHM). Data acquisition and processing was carried out by Xcalibur v4.2 software (Thermo Fisher Scientific). The suspect screening approach for MNP polymers identification was according to the procedure previously developed and applied by the IDAEA-CSIC research center [10, 11]. In summary, the identification (level 2) of polymers was by means of confirmation by Kendrick Mass Defect (KMD) analysis followed by confirmation (level 1). For the final confirmation and quantification of the polymers an external calibration curve of polymer standards was performed based on equivalent concentrations.

Plastic additives were analyzed in the same instrumentation as described above but the chromatographic separation was achieved by means of Purospher STAR RP-18 endcapped column (5 µm, 2 x 125 mm) from Merck. The mobile phase consisted of acetonitrile 100% (A) and water (B) in negative conditions and water acidified at 0.1% (v/v) with formic acid (B) for positive ionization mode as described in previous work [10, 11]. Data acquisition in HRMS equipped with an electrospray ionization source was in full scan-data dependent MS2 (FS-ddMS2) mode (from 100 to 1,500 m/z) for the 5 most intense ions. Data processing was performed using the Xcalibur Qual Browser software (Thermo Fisher Scientific, San Jose, CA, USA), and Compound Discoverer software version 3.3 SP1 (Thermo Fisher Scientific, San Jose, CA, USA), respectively. For the suspect screening approach, the information contained in an internal database and Chemspider for structural information and MzCloud, as mass spectra database was used. The tentative identification criteria were based on those described by previous studies [26, 27]. Other filters applied were mass tolerance of 5 ppm, the retention time (tolerance ± 2.5% min), isotopic fit (>90%), fragmentation and the previously mentioned parameters in addition to the mass peak resolution and response. The suspect screening was performed as follows; the FS-ddMS2 spectra were processed with Compound Discoverer to obtain a list of candidates between the ones contained in the inclusion list. Plastic additives tentatively identified at level 2 were the suspected structures matching with the experimental fragmentation spectra comparing them with the internal DBs or theoretical patterns. Finally, confirmation (identification level 1) was achieved by standard comparison and then quantification was carried out by external standard curves.

2.7.3. Validation parameters for the MNP extraction from stool samples.

The extraction method is widely applied to eliminate organic matter in the study of plastic particles from faces, and the performance of the analytical method was already validated for different complex matrices. Here, we have tested the approach for the quantitative analysis of NPs polymers in faces. Due to the lack of reference materials for faces free of microplastics, a wastewater sludge was selected for validation purposes and it is an accepted simulant material in terms of composition and complexity [28, 29]. The ILOD was estimated from the injection of the lowest calibration curve point (20 pg). The sludge was previously extracted to prepare the plastic-free matrix. Two grams of dry sludge were extracted by ultrasonic-assisted extraction with 10 ml of toluene for 10 min, the supernatant was collected, and the process was repeated four more times (quintuplicate samples). The MLOD, MLOQ, recoveries, and matrix effects for faces were detected by spiking experiments in sludge. The residual sludge was dried overnight at 40 °C. After this time, the quintuplicates were homogenized, and 0.5 g of the matrix was spiked with a mixture of selected polymers PE, PBD, PI, PP and PS in a range between 5 and 50 µg/kg each one at three spiking concentrations (5, 10 and 50 µg/kg) from a standard prepared in toluene from 1,200 Da pure polymer, and β-caprolactam at the same concentration for the measure of polyamide (PA) polymer type. In parallel, two samples of 0.5 g without spike were used as control blanks to monitor any remaining polymer the sludge might have. Then, the samples were extracted according to the protocol described in section 2.6.1. The main quality parameters for pure polymer standards at 1,200 Da and instrumental level showed good limits of quantification, from 0.05 µg/kg fg for polypropylene (PP) to 0.25 fg for PA. The method limits of quantification, calculated experimentally as 9 times the S/N ratio from the spiked samples, ranged from 0.10 (for polystyrene or PS) to 0.77 (PBD) µg/Kg. Furthermore, the recovery rates calculated by the external calibration curve in toluene solvent were between 40% (PE) and 65% (PBD), with an intraday precision below 30% in all the cases.

2.7.4. Validation parameters for the MNP extraction from urine samples.

0.5 mL of simulated urine composed by ultra-pure water with 2% of NaCl and 3% of urea was spiked with a mixture of selected polymers PE, PBD, PI, PP and PS at 10 µg/L each one (in matrix) from a standard prepared in toluene from c.a. 1,200 Da pure polymer, and β-caprolactam at the same concentration for the measure of PA. This was done also in quintuplicate, and the samples were extracted according to the protocol described in the section 2.5.1. The main quality parameters showed good limits of quantification ranging from 0.05 µg/L (PS) to 0.5 µg/L (PBD). The recovery rates were calculated by external calibration curve, also in toluene, and ranged from 60% (PE) to 70% (PS) with an intraday precision below 16%.

2.7.5. Blanks analysis of stool and urine samples.

Several measures were implemented to avoid background contamination and all sample pre-treatment and sample manipulation were performed in a laminar airflow bench in a plastic-free laboratory using cotton lab coats. All the equipment used for sampling manipulation was glass whenever possible. Moreover, 2 procedural blanks consisting of ultra-pure HPLC-water were carried out with each extraction batch to assess the eventual presence of MNPs that may come from external sources, and caps from LC vials were changed for aluminum foil to prevent contamination during the LC-HRMS analyses. Results showed that no MNP were detected during the procedural blanks analysis. In addition, the possible cross contamination coming from the instrumental analysis was monitored by injecting solvent blanks of toluene (polymers) and water-methanol 9:1 (plastic additives) between each sample injection.

2.8. Data analysis of stool and urine samples

Visual and quantitative analyses were employed in a complementary manner, as recommended for single-case trials [30]. Concentration of plastic polymers and additives with the highest detection rates in stool and urine samples were plotted to visually identify changes in concentration between phases and eventual trends. We calculated the Tau-U A vs. B coefficient and 95% confidence interval (CI) to estimate the effect size of the treatment (bottled water) on the concentration of MNPs and plastic additives in biological samples. Tau-U A vs. B is a non-parametric correlation statistic, used in single-case experimental designs [31]. An effect size below 0.2 is considered a small effect; 0.2–0.6 a moderate effect; 0.6–0.8 a large effect; and 0.8 or above a very large effect [32]. When Tau-U A vs. B is null, it can be assumed that the measured outcome is independent of the intervention. For the Tau-U A vs. B analysis, we included MNPs and plastic additives quantified in at least 60% of samples. Concentrations below the MLOQ were assigned the mean between the MLOD and the MLOQ, and concentrations below the MLOD were assigned ½ MLOD. All analyses were performed using R studio version 4.3.0. The analyses have been conducted with the R package “scan” at version 0.60.0.9999 [33] and “scplot” at version 0.3.9 [34].

3. Results

3.1. Study population

Characteristics of the study participants are outlined in Table 2. Over the course of approximately 7 weeks, we conducted three single-case trials with female participants with a mean (±SD) age at enrollment of 52 (±9.4) years. All the expected samples were collected. The response rate to the daily EMA varied among participants (58% for participant 1, 75% for participant 2 and 90% for participant 3). The majority of meals of the participants included food items packaged in plastic (54% for participant 1, 75% for participant 2 and 73% for participant 3).

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Table 2. Description of the study participants and follow-up information. p25 and p75 correspond to the percentile 25 and 75.

https://doi.org/10.1371/journal.pwat.0000469.t002

3.2. MNPs and plastic additives quantification – aggregated analysis

Table 3 outlines the presence of MNPs in stool, urine and drinking water samples. MNPs were detected in 83% of stool samples: 88% in tap water phases (phase A), and 79% in bottle water phases (phase B). Polyethylene (PE), polypropylene (PP), polyamide (PA) and polyoxymethylene (POM) were found in stool samples from both phases. Proportion of samples with detected MNPs in phase A and B, respectively, was 33% and 58% for PE, 46% and 21% for PP, 42% and 38% for PA, 13% and 4% for POM.

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Table 3. Micro- and nanoplastic polymers. Number (%) of samples above the methods limit of detection (MLOD) and quantification (MLOQ) and concentrations measured in stool (µg/Kg), urine (µg/L), and drinking water (µg/L) samples by phase.

https://doi.org/10.1371/journal.pwat.0000469.t003

Urine samples showed MNP polymers detection rates of 56% (58% in phase A, 54% in phase B, Table 3), with PE, PP, and PA detected in both phases (Table 3). In addition, polybutene-1 (PB1) was identified in phase A and polymethyl methacrylate (PMMA) was detected in one phase B sample. Concentrations exhibited considerable variability between polymers, spanning from a median value of 20.7 µg/L (interquartile range - IQR: 18.8-22.5) for PA and 655.1 µg/L (IQR: 416.6-655.3) for PB1 during phase A to 28.5 µg/L (IQR: 22.6-35.8) for PA and 119.0 µg/L (IQR: 26.0-154.5) for PE during phase B. PB1, that occurred at the highest concentrations, was only detected in 3 consecutive samples from participant 3.

Four distinct plastic polymers were detected in 63% of tap water samples: PE, PP, polyethylene terephthalate (PET) and polyvinyl chloride (PVC) (Table 3). PP and PE were the most frequently detected, with detection rates of 38% (median: 0.18 µg/L, IQR: 0.15-0.21 µg/L) and 25% (median: 0.06 µg/L, IQR: 0.04-0.08 µg/L), respectively. PET was present in 8% of samples (median: 0.30 µg/L, IQR: 0.29-0.30 µg/L) and PVC was present in 4% of samples (median: 0.29 µg/L). In bottled water, only PE was identified at an estimated concentration of 0.08 µg/L.

Table 4 summarizes the detection and quantification rates, along with the concentrations, of chemicals screened for their known use in plastic production. These substances are collectively referred to as “plastic additives,” acknowledging that some may have alternative uses or origins. Of the 38 chemicals screened, 27 were detected, with 15 quantified. The different plastic additives found included various plasticizers (e.g., phthalates), flame retardants and other processing aids, but also fatty acids, such as nonanoic acid and stearic acid, naturally occurring in the human diet and metabolism. The function and sources of each screened chemical are shown in Table B in S1 File. Chemicals from the most to the least frequently detected were nonanoic acid, stearic acid, 2,6-di-tert-butylphenol, hexamethylcyclotrisiloxane, oleic acid, palmitic acid, diethyl phthalate, bisphenol A, tributyl phosphate, myristic acid, azelaic acid, linoleic acid, triphenyl phosphate, dibutyl phthalate. Azaleic acid occurred at the highest concentrations but was only detected in one of the participants with median concentrations of 1200.5 µg/L (IQR: 1006.6-1575.45) in phase A and 2195.4 µg/L (IQR: 590.3-3676.3) in phase B. The second highest concentrations were observed for linoleic acid with a median of 7.8 µg/L (IQR: 6.5-18.9) in phase A, 8.2 µg/L (IQR: 5.7-13.3) in phase B. No clear difference was observed between plastic additives levels from phase A and levels from phase B.

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Table 4. Chemicals used as additives in plastic production detected in urine samples. Number (%) of samples above the methods limit of detection (MLOD) and quantification (MLOQ), and concentrations (µg/L) of plastic additives in urine samples (N = 48). Only values above the MLOQ were taken into account to calculate the median, percentile 25 and 75. The sum was calculated for each sample as the addition of all plastic additives’ concentrations above MLOQ and values < MLOD-MLOQ were considered as 0.

https://doi.org/10.1371/journal.pwat.0000469.t004

3.3. Intervention results – participant-level analysis

Fig 1 depicts the concentrations of PE in stool samples and 2,6-di-tert-butylphenol in urine samples throughout the different study phases. While only two of the most relevant chemicals are displayed, comprehensive data on all other chemicals can be accessed in the supplementary material Fig A to Fig U in S1 File. In general, measurements were below the methods limit of quantification (MLOQ) during most of the study period, interspersed with occasional peaks. No consistent visual patterns nor discernible differences between measurements taken during phase A (tap water) and phase B (bottled water) were observed.

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Fig 1. Concentration of polyethylene (PE) in stool samples(A), and 2,6-di-tert-butylphenol (B) in urine samples over study phases (A = tap water; B = bottled water) among the study participants.

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Fig 2 presents a forest plot summarizing Tau-U effect sizes and their 95% confidence intervals (CIs) for each participant, focusing on polyethylene (PE) in stool and selected plastic additives detected in urine. These plastic additives were selected for analysis due to their high detection rates, potential toxicity, and specificity as plastic-related substances. The analysis revealed that none of the participants exhibited conclusive effect size coefficients. Most CIs were wide and crossed zero, indicating no significant effects attributable to the intervention.

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Fig 2. Forest plot of Tau-U [95% confidence interval] of polyethylene (PE) in stool and plastic additives in urine samples.

N = 16 per participant. Concentrations below the method limit of quantification (MLOQ) have been assigned the mean between the method limit of detection (MLOD) and the MLOQ. Concentrations below the MLOD have been assigned ½ MLOD.

https://doi.org/10.1371/journal.pwat.0000469.g002

3.4. Other exposure routes

Fig 3 illustrates the daily consumption of food items packaged in plastic per participant. The distribution of these consumptions appears relatively consistent over time, with some random fluctuations unique to each participant seemingly unrelated to the intervention. Participant 1 reported consuming between 3 and 7 plastic-packaged food items daily, with a median of 3 (IQR: 3–4). Participant 2’s consumption ranged from 2 to 10 items per day, with a median of 6 (IQR: 4.5-7), and participant 3’s consumption ranged from 2 to 10 items per day, with a median of 5 (IQR: 4–6).

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Fig 3. Daily consumption of plastic-packaged food items by participant according to responses from the Ethica App questionnaire.

Interquartile range = IQR.

https://doi.org/10.1371/journal.pwat.0000469.g003

The number of samples with MNPs and plastic additives above the MLOQ in urine and stool samples are depicted in Fig 4. All urine samples contained at least 2 plastic additives. The majority of samples (42, 87.5%) had 6 or more distinct plastic additives, while only a small fraction (6, 12.5%) contained fewer (Fig 4A). MNPs had a lower quantification rate in both urine and stool samples, and most samples had 0 or 1 quantifiable polymer. Quantification rate was higher in stool compared to urine samples.

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Fig 4. Number of samples above the methods limit of quantification (MLOQ) by number of analytes for plastic additives in urine sample(A), plastic polymers in urine (B) and stool (C) samples (N = 48).

https://doi.org/10.1371/journal.pwat.0000469.g004

4. Discussion

Our exploratory study revealed the presence of various MNP polymers in 83% and 56% of stool and urine samples, respectively (PE, PP, PA, POM, PMMA, and PB1). However, the number of samples with MNP polymers above quantification limits was 63% in stool and 31% of urine samples for the sum of polymers, while for specific polymers range was 4–42% in stool samples and 4–13% in urine samples. Chemicals known to be used as additives in plastic production were detected in 100% of urine samples, with a total of 27 different chemicals detected. We did not observe changes in MNP polymers or plastic additives concentrations in biological samples that could be attributed to shifts in drinking water source.

In contrast with previous studies showing the presence of MNPs in stool [23, 3541] and urine [4244] samples, our findings display lower detection rates of polymers in biological samples, with concentrations close to the quantification limits. Comparison with previous studies is hampered by differences in methodologies, since most prior studies have focused on counting plastic particles. Among the few studies quantifying polymer concentrations, Zhang et al. [45] analyzed microplastics down to 2 mm in 1-year old infants (N = 6) and adults (N = 10) from New York City (USA), with median PET concentrations in stool samples of 36 µg/g in children and 2.6 µg/g in adults. Song et al. [44] examined MNPs in urine samples from 12 individuals in Chongqing, China, detecting particles as small as 13 µm with an average concentration of 1.50 mg/kg using Py-GC/MS and LDIR spectroscopy. Another group reported a median microplastic concentration of 5.06 μg/g in urine and 97.36 μg/g in feces among 26 college students in China using Py-GC/MS [41]. A study conducted in three universities in China [45] including 78 participants found a median microplastic concentration of 54.7 μg/g in feces among students [40]. These discrepancies highlight the challenging comparison of results across studies, given the lack of standardized methodologies, particularly regarding detection limits and analytical quality.

Our study was conceived to measure MNPs in biological samples under real-life conditions through a drinking water intervention. We did not control for other potential MNPs sources (e.g., cosmetics, food, ambient air), assuming that these would remain roughly unchanged over the study period. However, given the ubiquity of MNPs exposure sources, we acknowledge this may partly explain the inability to identify changes in biological samples attributable to the drinking water intervention. On the other hand, detection rate of MNPs in biological samples was unexpectedly low considering previous studies, which has additionally hindered our ability to identify changes that are probably small in magnitude. The local context is also relevant. Most of previous studies measuring MNPs in stool samples have been conducted in China, where exposure levels may differ to Barcelona, Spain. Previous studies in Barcelona have shown comparable MNPs concentrations in tap and bottled water, aligning with our findings that bottled and tap water lead comparable ingested exposures as measured in stool and urine samples among study participants [11].

Previous studies exploring MNPs levels in urine and stool samples relative to drinking water sources report mixed findings. Song et al. [44] found no significant differences in urinary MNPs concentrations among individuals consuming tap water or water from reservoirs with different piping materials including plastic. Conversely, Yan et al. [23], Zhang et al. [38] and Song et al. [40] report higher fecal MNPs levels in individuals consuming bottled water. Studies in different settings and populations are needed to increase our understanding about exposure to microplastics.

Plastic-related additives in urine were observed at higher detection frequencies compared to MNPs. Still, we did not observe different concentrations by drinking water phase, which may indicate that drinking water is not a main source. To our knowledge, no previous studies have examined plastic-related additives in urine in relation to drinking water exposure, making this a novel contribution that highlights the need for further research to confirm these patterns.

The polymer types present in biological samples offer valuable insights into their origin and therefore the potential exposure pathways. The presence of PE and PP, common polymers found in surface water, across both water sources and in biological samples aligns with their widespread use, notably in drinking water systems and bottle caps. However, the unexpected presence of POM in stool and PB1 in urine suggest other exposure sources. PB1 is commonly used in seal-peel food packaging, and POM, polymers with high resistance can be found in cars’ interiors, zippers, locks and household items such as grinders.

Previous studies have reported elevated levels of fecal MNPs in individuals consuming plastic-packaged food [23, 37, 38] and the leaching of MNPs into food and beverages from plastic packaging [46, 47]. Although our study design focused on drinking water, an exploratory analysis of self-reported plastic food packaging revealed minimal variation across phases, and did not show a consistent link with MNPs peaks in biological samples. Further studies are warranted to elucidate the contribution of different exposure pathways to MNPs and plastic additives.

Including repeated samples over 7 weeks provides a valuable longitudinal assessment of MNPs and plastic additives in biological and water samples over a significant time period to inform about temporal variation. The use of advanced analytical chemistry techniques including HPLC-HRMS allowed us to quantify MNP polymers in human feces and urine down to 0.7 µm, providing a robust and highly sensitive methodology for detecting plastic particles in the nano-range, which is the biologically relevant fraction, as smaller particles are more abundant and more likely to cross biological barriers [4850].

From a policy and water governance perspective, the absence of measurable changes following the drinking water intervention suggests that, under the conditions assessed, drinking water likely represents a relatively minor exposure pathway compared to other sources such as food, air, or personal care products. While these findings do not indicate a dominant exposure route via drinking water, uncertainties related to analytical sensitivity, temporal variability, and the detection of nanoplastics support the application of the precautionary principle in risk management. The precautionary principle is a core normative value of European Union environmental and public health policy, which holds that lack of full scientific certainty should not delay measures to prevent potential harm. Given the existing and emerging evidence linking plastic exposure to adverse health effects across life stages, and the ubiquitous nature of exposure, even small individual-level effects may translate into meaningful population-level impacts. At present, there are no internationally agreed regulatory thresholds for MNPs in drinking water. In this context, the present results provide baseline human biomonitoring evidence that can inform ongoing risk assessment discussions. Findings further support targeted monitoring strategies focused on high-risk drinking water sources or treatment stages, alongside upstream source control measures, which may be particularly relevant for vulnerable populations or settings with limited treatment infrastructure.

A main limitation of this study lies in the low detection frequency of MNPs in biological samples leading to reduced signal to detect differences attributable to the intervention. Although this could not be anticipated given the novelty of the present study, this unexpected outcome provides valuable information to design future studies. Recommendations to improve sensitivity and accuracy in the measurement of MNPs in biological samples include to collect a larger stool sample volume, immediately freeze at -80ºC and lyophilize the sample as soon as possible in order to increase sample homogeneity. Although N = 3 is the minimum number of participants in single-case studies for reliable conclusions, it is a limited number of participants to generalize findings. The reduced number of bottled water samples analyzed could be considered a limitation. However, prior research in the study area indicates minimal variability between bottled water brands [39], suggesting minimal variability in bottled water samples in the study area. The analysis of tap water samples nearby home could have led to exposure measurement error through tap water. However, minimal differences were observed in the two sampling campaigns conducted to compare residential vs. street measurements. In addition, these results did not affect the main objective, i.e., compare bottled vs. tap water phases.

5. Conclusions

Polyethylene, polyamide, polypropylene, and polyoxymethylene were detected and quantified in a reduced percentage of stool samples at concentrations close to the quantification limit. While no clear trends in MNP polymer concentrations or plastic-related additives in biological samples were observed in relation to the drinking water intervention, findings should be interpreted with caution given the exploratory design and limited sample size. Taken together, results suggest that, under the study conditions, drinking water is unlikely to represent a dominant pathway of human exposure to MNPs However, given the ubiquity of MNPs, methodological uncertainties, and emerging evidence of potential health effects, findings support precautionary action. The present study provides baseline evidence to inform ongoing risk assessment and policy discussions. Future research should prioritise larger, well-controlled studies capable of disentangling source contributions across multiple exposure pathways, alongside methodological harmonization to support proportionate, science-based decision-making in drinking water management.

Supporting information

S1 File.

Table A: Comparison of micro and nanoplastics concentrations (μg/L) between contemporaneous samples collected at the household tap and in the nearest point in the street. PMMA: Polymethyl methacrylate, PP: Polypropylene, PVC: Polyvinyl chloride, PC: Polycarbonates, PE: Polyethylene, PET: Polyethylene terephthalate, PS: Polystyrene. Table B Main uses of the plastic additives detected in urine samples. The different uses of the screened plastic additives were retrieved from the PlastChem Project database. Figure A: Concentration (µg/Kg) of Polyoxymethylene (POM) in stool samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure B: Concentration (µg/Kg) of Polyamide (PA) in stool samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure C: Concentration (µg/Kg) of Polypropylene (PP) in stool samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure D: Concentration (µg/L) of Polyethylene (PE) in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure E: Concentration (µg/L) of Polypropylene (PP) in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure F: Concentration (µg/L) of Polyamide (PA) in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure G: Concentration (µg/L) of Polybutene-1 (PB1) in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure H: Concentration (µg/L) of stearic acid in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure I: Concentration (µg/L) of nonanoic acid in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure J: Concentration (µg/L) of linoleic acid in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure K: Concentration (µg/L) of oleic acid in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure L: Concentration (µg/L) of stearic acid in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure M: Concentration (µg/L) of palmitic acid in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure N: Concentration (µg/L) of azelaic acid in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure O: Concentration (µg/L) of myristic acid in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure P: Concentration (µg/L) of tributyl phosphate in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure Q: Concentration (µg/L) of triphenyl phosphate in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure R: Concentration (µg/L) of hexamethylcyclotrisiloxane in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure S: Concentration (µg/L) of diethyl phthalate urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure T: Concentration (µg/L) of bisphenol A in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant. Figure U: Concentration (µg/L) of dibutyl phthalate in urine samples during baseline (A = tap water) and treatment phases (B = bottled water) for each participant.

https://doi.org/10.1371/journal.pwat.0000469.s001

(DOCX)

Acknowledgments

We acknowledge the contribution of Olga Pérez and Lourdes Arjona (Barcelona Institute for Global Health), and Susana González and Armand Alseda (CETAQUA) in the fieldwork.

References

  1. 1. Landrigan PJ, Raps H, Cropper M, Bald C, Brunner M, Canonizado EM, et al. The minderoo-monaco commission on plastics and human health. Ann Glob Health. 2023;89(1):23.
  2. 2. Geyer R, Jambeck JR, Law KL. Production, use, and fate of all plastics ever made. Sci Adv. 2017;3(7):e1700782. pmid:28776036
  3. 3. Yuan Z, Nag R, Cummins E. Human health concerns regarding microplastics in the aquatic environment - from marine to food systems. Sci Total Environ. 2022;823:153730.
  4. 4. Zuri G, Karanasiou A, Lacorte S. Microplastics: Human exposure assessment through air, water, and food. Environ Int. 2023;179:108150.
  5. 5. Campanale C, Massarelli C, Savino I, Locaputo V, Uricchio VF. A detailed review study on potential effects of microplastics and additives of concern on human health. Int J Environ Res Public Health. 2020;17(4):1212.
  6. 6. Wu P, Lin S, Cao G, Wu J, Jin H, Wang C. Absorption, distribution, metabolism, excretion and toxicity of microplastics in the human body and health implications. J Hazard Mater. 2022;437:129361.
  7. 7. Ali N, Katsouli J, Marczylo EL, Gant TW, Wright S, Bernardino de la Serna J. The potential impacts of micro-and-nano plastics on various organ systems in humans. EBioMedicine. 2024;99:104901. pmid:38061242
  8. 8. Microplastics are everywhere — we need to understand how they affect human health. Nat Med. 2024 Apr 1;30(4):913–913.
  9. 9. Danopoulos E, Twiddy M, Rotchell JM. Microplastic contamination of drinking water: A systematic review. PLoS One. 2020;15(7):e0236838. pmid:32735575
  10. 10. Vega-Herrera A, Llorca M, Borrell-Diaz X, Redondo-Hasselerharm PE, Abad E, Villanueva CM. Polymers of micro(nano) plastic in household tap water of the Barcelona Metropolitan Area. Water Research. 2022;220:118645.
  11. 11. Vega-Herrera A, Garcia-Torné M, Borrell-Diaz X, Abad E, Llorca M, Villanueva CM. Exposure to micro(nano)plastics polymers in water stored in single-use plastic bottles. Chemosphere. 2023;343:140106.
  12. 12. Heo SJ, Moon N, Kim JH. A systematic review and quality assessment of estimated daily intake of microplastics through food. Rev Environ Health. 2024.
  13. 13. Cox KD, Covernton GA, Davies HL, Dower JF, Juanes F, Dudas SE. Human consumption of microplastics. Environ Sci Technol. 2019;53(12):7068–74.
  14. 14. Senathirajah K, Attwood S, Bhagwat G, Carbery M, Wilson S, Palanisami T. Estimation of the mass of microplastics ingested - A pivotal first step towards human health risk assessment. J Hazard Mater. 2021;404:124004.
  15. 15. Schymanski D, Goldbeck C, Humpf H-U, Fürst P. Analysis of microplastics in water by micro-Raman spectroscopy: Release of plastic particles from different packaging into mineral water. Water Res. 2018;129:154–62. pmid:29145085
  16. 16. Gambino I, Bagordo F, Grassi T, Panico A, De Donno A. Occurrence of microplastics in tap and bottled water: Current knowledge. Int J Environ Res Public Health. 2022;19(9):5283. pmid:35564678
  17. 17. Tanious R, Onghena P. Randomized single-case experimental designs in healthcare research: What, why, and how?. Healthc Basel Switz. 2019;7(4):143.
  18. 18. Guyatt GH, Haynes RB, Jaeschke RZ, Cook DJ, Green L, Naylor CD, et al. Users’ Guides to the Medical Literature: XXV. Evidence-based medicine: Principles for applying the Users’ Guides to patient care. JAMA. 2000;284(10):1290–6.
  19. 19. Kratochwill TR, Levin JR. Enhancing the scientific credibility of single-case intervention research: randomization to the rescue. Psychol Methods. 2010;15(2):124–44. pmid:20515235
  20. 20. Dalmau-Soler J, Ballesteros-Cano R, Ferrer N, Boleda MR, Lacorte S. Microplastics throughout a tap water supply network. Water Environ J. 2021;:1–7.
  21. 21. Dalmau-Soler J, Boleda MR, Lacorte S. Routine method for the analysis of microplastics in natural and drinking water by pyrolysis coupled to gas chromatography-mass spectrometry. J Chromatogr A. 2024;1730:465153.
  22. 22. Yan Z, Zhao H, Zhao Y, Zhu Q, Qiao R, Ren H. An efficient method for extracting microplastics from feces of different species. J Hazard Mater. 2020;384:121489.
  23. 23. Yan Z, Liu Y, Zhang T, Zhang F, Ren H, Zhang Y. Analysis of microplastics in human feces reveals a correlation between fecal microplastics and inflammatory bowel disease status. Environ Sci Technol. 2022;56(1):414–21.
  24. 24. Llorca M, Vega-Herrera A, Schirinzi G, Savva K, Abad E, Farré M. Screening of suspected micro(nano)plastics in the Ebro Delta (Mediterranean Sea). J Hazard Mater. 2021;404:124022.
  25. 25. Vega-Herrera A, Savva K, Lacoma P, Santos LHMLM, Hernández A, Marmelo I. Bioaccumulation and dietary bioaccessibility of microplastics composition and cocontaminants in Mediterranean mussels. Chemosphere. 2024;363:142934.
  26. 26. Schymanski EL, Singer HP, Longrée P, Loos M, Ruff M, Stravs MA, et al. Strategies to characterize polar organic contamination in wastewater: Exploring the capability of high resolution mass spectrometry. Environ Sci Technol. 2014;48(3):1811–8. pmid:24417318
  27. 27. Krauss M, Singer H, Hollender J. LC–high resolution MS in environmental analysis: From target screening to the identification of unknowns. Anal Bioanal Chem. 2010;397(3):943–51.
  28. 28. Penn R, Ward BJ, Strande L, Maurer M. Review of synthetic human faeces and faecal sludge for sanitation and wastewater research. Water Res. 2018;132:222–40.
  29. 29. Velkushanova K, Brdjanovic D, Koottatep T, Strande L, Buckley C, Ronteltap M. Methods for Faecal Sludge Analysis. IWA Publishing; 2021 [cited 2025 Mar 20. ]. https://library.oapen.org/handle/20.500.12657/48780
  30. 30. Tate RL, Perdices M, Rosenkoetter U, Shadish W, Vohra S, Barlow DH. The single-case reporting guideline in behavioural interventions (SCRIBE) 2016 statement. Aphasiology. 2016;30(7):862–76.
  31. 31. Brossart DF, Laird VC, Armstrong TW. Interpreting Kendall’s Tau and Tau-U for single-case experimental designs. Cogent Psychol. 2018;5(1):1518687.
  32. 32. Vannest KJ, Ninci J. Evaluating intervention effects in single-case research designs. J Couns Dev. 2015;93(4):403–11.
  33. 33. Wilbert J, Lüke T. Scan: Single-Case Data Analyses for Single and Multiple Baseline Designs. [English]. CRAN. 2023. https://CRAN.R-project.org/package=scan
  34. 34. Wilbert, J. Scplot: Plot function for single-case data frames. 2023.
  35. 35. Schwabl P, Köppel S, Königshofer P, Bucsics T, Trauner M, Reiberger T, et al. Detection of various microplastics in human stool: A prospective case series. Ann Intern Med. 2019;171(7):453–7.
  36. 36. Ho Y-W, Lim JY, Yeoh YK, Chiou J-C, Zhu Y, Lai KP, et al. Preliminary findings of the high quantity of microplastics in faeces of hong kong residents. Toxics. 2022;10(8):414. pmid:35893847
  37. 37. Zhang X, He X, Pan D, Shi L, Wu Y, Yang Y. Effects of thermal exposure to disposable plastic tableware on human gut microbiota and metabolites: A quasi-experimental study. J Hazard Mater. 2024;462:132800.
  38. 38. Zhang N, Li YB, He HR, Zhang JF, Ma GS. You are what you eat: Microplastics in the feces of young men living in Beijing. Sci Total Environ. 2021;767:144345.
  39. 39. Wibowo AT, Nugrahapraja H, Wahyuono RA, Islami I, Haekal MH, Fardiansyah Y, et al. Microplastic contamination in the human gastrointestinal tract and daily consumables associated with an indonesian farming community. Sustainability. 2021;13(22):12840.
  40. 40. Song Y, Zhang J, Shen X, Yang L, Jia Y, Song F. Microplastics in stools and their influencing factors among young adults from three cities in China: A multicenter cross-sectional study. Environ Pollut. 2025;364(Pt 2):125168.
  41. 41. Song Y, Zhang J, Yang L, Huang Y, Zhang N, Ma G. Internal and external microplastic exposure in young adults: A pilot study involving 26 college students in Changsha, China. Environ Res. 2024;263:120250.
  42. 42. Massardo S, Verzola D, Alberti S, Caboni C, Santostefano M, Eugenio Verrina E, et al. MicroRaman spectroscopy detects the presence of microplastics in human urine and kidney tissue. Environ Int. 2024;184:108444. pmid:38281449
  43. 43. Pironti C, Notarstefano V, Ricciardi M, Motta O, Giorgini E, Montano L. First evidence of microplastics in human urine, a preliminary study of intake in the human body. Toxics. 2022;11(1):40. pmid:36668766
  44. 44. Song X, Chen T, Chen Z, Du L, Qiu X, Zhang Y. Micro(nano)plastics in human urine: A surprising contrast between Chongqing’s urban and rural regions. Sci Total Environ. 2024;917:170455.
  45. 45. Zhang J, Wang L, Trasande L, Kannan K. Occurrence of polyethylene terephthalate and polycarbonate microplastics in infant and adult feces. Environ Sci Technol Lett. 2021;8(11):989–94.
  46. 46. Hernandez LM, Xu EG, Larsson HCE, Tahara R, Maisuria VB, Tufenkji N. Plastic teabags release billions of microparticles and nanoparticles into tea. Environ Sci Technol. 2019;53(21):12300–10. pmid:31552738
  47. 47. Joseph A, Parveen N, Ranjan VP, Goel S. Drinking hot beverages from paper cups: Lifetime intake of microplastics. Chemosphere. 2023;317:137844.
  48. 48. Qian N, Gao X, Lang X, Deng H, Bratu TM, Chen Q, et al. Rapid single-particle chemical imaging of nanoplastics by SRS microscopy. Proc Natl Acad Sci. 2024;121(3):e2300582121. pmid:38190543
  49. 49. Bostan N, Ilyas N, Akhtar N, Mehmood S, Saman RU, Sayyed RZ. Toxicity assessment of microplastic (MPs); a threat to the ecosystem. Environ Res. 2023;234:116523.
  50. 50. Amorim MJB, Scott-Fordsmand JJ. Plastic pollution - A case study with enchytraeus crypticus - from micro-to nanoplastics. Environ Pollut. 2021;271:116363.