Microplastics In Environmental Samples Research Articles

Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.

Oil-based extraction as an efficient method for the quantification of microplastics in environmental samples

BackgroundWastewater treatment plant outlets are a major source of microplastics, with more than 90% retained in sewage sludge. No standardised method for the extraction, quantification, and characterisation of microplastics in sewage sludge or soil exists, and direct comparison of studies is often impossible. Our aim was to validate oil extraction efficiency with and without pre-treatment with Fenton’s reagent of selected microplastics in various types of environmental samples (sewage sludge and organic-rich substrates).ResultsOxidation with Fenton’s reagent removed up to 90% of organic material, which improves the recovery rate and made quantification and characterisation easier and more reliable, regardless of type, shape, size, or density of the selected microplastic particles used in this study. Pre-treatment, as a pre-step of the oil extraction method, was shown to be important in reducing organic matter in all environmental samples, including sewage sludge and organic-rich substrates. It also improved the reliability of the selected method, shortened its duration, and, by reducing organic matter, made extracted microplastics more visible. The recovery rate was better for particles 1–5 mm and lower for particles 0.1 < 1 mm.ConclusionsBy achieving up to a 100% recovery rate for certain types of microplastics (polypropylene and polystyrene), the selected method proved to be a promising extraction method. It was also shown to be efficient in the organic-rich substrates, for which the characterisation of microplastic particles was done by Fourier transform infrared spectroscopy. The most commonly detected types of microplastics in organic-rich substrates were polyethylene, polypropylene, polystyrene and polyester.

Pyr-GC-Orbitrap-MS method for the target/untargeted analysis of microplastics in air

Pyrolysis-gas chromatography coupled to Orbitrap-mass spectrometry is a novel technique that allows the low level and precise determination of microplastics in environmental samples. In this paper, we develop and assess the target and untargeted performance of Pyr-GC-Orbitrap-MS. The method was optimized for 10 plastic polymers: polymethyl methacrylate, nylon-6,6, polypropylene, nitrile butadiene rubber, polyvinyl chloride, polyethylene terephthalate, acrylonitrile butadiene styrene, polyethylene, polycarbonate, and polystyrene. Standards were home-made using a diamond driller to attain mean sizes within the range of 45–382 µm. A step-by-step optimization of the analytical procedure was carried out. First, accurate mass measurement of each polymer at 60,000 resolution was studied to select the 3 most intense and selective quantification and confirmation ions. Second, internal standard quantification was optimized, and good linearity, repeatability, and reproducibility were obtained. Blank contribution and instrumental detection limit were evaluated for each polymer. Finally, the combined and expanded uncertainty of the Pyr-GC-Orbitrap-MS method was calculated to determine the sources of variation, considering that home-made standards were used. To evaluate method performance, targeted and non-targeted analysis of indoor air samples collected from gyms and department stores were carried out. The Pyr-GC-Orbitrap-MS methodology herein described can be applied for the quantitative assessment of MPs and other substances in different matrices.

Fast Forward: Optimized Sample Preparation and Fluorescent Staining for Microplastic Detection

The fast, affordable, and standardized detection of microplastics (MP) remains one of the biggest challenges in MP research. Comparable data are essential for appropriate risk assessments and the implementation of laws and limit values. The fluorescent staining of MP in environmental samples is a possible solution to this problem. This study investigates the optimization of a sample preparation process (hydrogen peroxide digestion) and the staining process (temperature, concentration, time, surfactants as staining aids) for using a specifically developed fluorescent dye for MP detection. The optimization is performed by comparing the sample preparation process and staining of MP from different polymers and natural particles. Further, the suitability of the optimized process for the detection of fluoropolymers and tire abrasion was tested. The results show that the optimized method (increased temperature and optimized stain concentration) can detect microplastics reliably with a total sample preparation and measurement time of 2.5–3 h per sample, reaching recovery rates of 93.3% (polypropylene) to 101.7% (polyester). Moreover, two of the three tested fluoropolymers could be detected reliably. Tire abrasion could not be detected with the here presented method, as the black color leads to strong quenching. A long-term study measuring the MP pollution in the effluent of a municipal wastewater treatment plant compared the optimized and original processes and confirmed the stability of the improved method for routine measurements and contamination control.

Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging

The fast and accurate identification of MPs in environmental samples is essential for the understanding of the fate and transport of MPs in ecosystems. The recognition of MPs in environmental samples by spectral classification using conventional library search routines can be challenging due to the presence of additives, surface modification, and adsorbed contaminants. Further, the thickness of MPs also impacts the shape of spectra when FTIR spectra are collected in transmission mode. To overcome these challenges, PlasticNet, a deep learning convolutional neural network architecture, was developed for enhanced MP recognition. Once trained with 8000 + spectra of virgin plastic, PlasticNet successfully classified 11 types of common plastic with accuracy higher than 95%. The errors in identification as indicated by a confusion matrix were found to be caused by edge effects, molecular similarity of plastics, and the contamination of standards. When PlasticNet was trained with spectra of virgin plastic it showed good performance (92%+) in recognizing spectra that had increased complexity due to the presence of additives and weathering. The re-training of PlasticNet with more complex spectra further enhanced the model's capability to recognize complex spectra. PlasticNet was also able to successfully identify MPs despite variations in spectra caused by variations in MP thickness. When compared with the performance of the library search in identifying MPs in the same complex dataset collected from an environmental sample, PlasticNet achieved comparable performance in identifying PP MPs, but a 17.3% improvement. PlasticNet has the potential to become a standard approach for rapid and accurate automatic recognition of MPs in environmental samples analyzed by FPA FT-IR imaging.

Sequential quantification of number and mass of microplastics in municipal wastewater using Fourier-transform infrared spectroscopy and pyrolysis gas chromatography-mass spectrometry

Plastic pollution is a significant environmental concern because microplastics (MPs) accumulate in various ecosystems; therefore, the accurate identification and quantification of MPs in environmental samples is crucial. This study presents a new sequential analytical method that combines Fourier-transform infrared spectroscopy (FTIR) and pyrolysis-gas chromatography/mass spectrometry (Pyr-GC/MS) to characterize and quantify MPs. FTIR with a microscope allows the identification of the polymer type and physical dimensions of MPs, whereas Pyr-GC/MS enables determining the chemical composition of MPs with plastic additives. Pretreated wastewater influent samples spiked with reference MPs were filtered through an Al2O3 disk for FTIR analysis, and the surface contents were collected and subjected to Pyr-GC/MS analysis. The mass of the reference MPs estimated using FTIR were in good agreement but were slightly lower than those obtained using Pyr-GC/MS. This finding supports the notion that the proposed sequential method can be used to determine both the number and the mass of MPs in environmental samples.

Influence of intrinsic plastics characteristics on Nile Red staining and fluorescence

Nile Red is a lipophilic staining dye often used in the detection of microplastics in environmental samples. This work evaluated the influence of characteristics of plastic particles collected from sandy beaches (n = 60) on fluorescent emissions after Nile Red staining. Most particles presented medium to high fluorescence (81.6%). Polymer type (polyethylene, polypropylene, polystyrene), weathering (i.e., carbonyl index, methyl index), and crystallinity did not affect fluorescence intensity (grey value) or color (RGB values). Conversely, particle color significantly influenced fluorescence, with lower fluorescence seen for some blue, green, and red particles, and higher in some white, yellow, and orange particles, suggesting an influence of pigments used in plastics on Nile Red staining. Wide variations in fluorescence were observed for the same particle color, possibly resulting from the use of different pigments. Therefore, future studies should tackle the issue of the interference of plastic pigments in the detection and characterization of plastic particles.

Effect of ultrasonication conditions on polyethylene microplastics sourced from landfills: A precursor study to establish guidelines for their extraction from environmental matrices

Establishing concentration of microplastics (MPs), designated as CMP, in aqueous, semi-solid and solid samples originating from unscientifically created landfills/dumpsites (UCLDs) and engineered landfills (ELFs) is of utmost importance to assess their impact on the geoenvironment. However, the accuracy of CMP will be guided by the extraction efficiency of MPs from these samples. The extraction of MPs from semi-solid and solid samples of UCLDs/ELFs would be cumbersome, mainly due to their trapping in solid aggregates (including organic matter). Such aggregates need to be dispersed to release the MPs, which can be achieved through the assistance of ultrasonication (US) in the presence of an appropriate dispersing agent. However, mere dispersion of solid aggregates during the US might not result in the complete release of MPs adhered (AMPs) to MPs native (NMPs) to these samples. This is because MPs would adhere to the surface of the adjacent ones due to various physical-mechanical-thermal-chemical processes that prevail in landfills. Hence, guidelines for US-assisted extraction of MPs should be developed by considering an approach that would ensure (i) cleaning of NMPs’ surface and (ii) release of AMPs without damaging the former. This necessitates understanding the influence of US parameters such as energy applied (Eus), time (tus) and direct/indirect exposure of NMPs from landfills that would control CMP. In this context, the influence of above mentioned US parameters on the (i) surface cleaning of polyethylene NMPs and (ii) release of AMPs and their concentrations (CAMP) was investigated. It was observed that Eus equal to 500 kJ/L during the indirect method of US would result in surface cleaning of NMPs and complete release of AMPs without damaging the farmer’s structure. The present work acts as a precursor study to establish the guidelines for the US-assisted extraction of MPs in environmental samples. Also, a generalized relationship between Eus and CAMP, which can be employed to study the impact of landfill type on the release of MPs during the US was developed.

Microplastics are overestimated due to poor quality control of reagents

Microplastics are widely distributed in the environment and pose potential ecological risks, increasing to be one of the most important environmental pollutants. However, when assessing the characteristics of microplastic contamination in environmental samples, inadequate quality control measures for the working solutions may introduce additional microplastic contamination and lead to an overestimation of microplastic abundance in the samples. In this study, we evaluated the microplastic contamination characteristics in commonly used flotation and digestion reagents to assess errors caused by microplastics in the reagents. The results showed that the abundance of microplastics in the reagents ranged from 0.8 to 43.4 items/g, with the abundance of microplastics in flotation reagents being lower than that in digestion reagents. The shapes of the detected microplastics included particles, fibers, and fragments, and their size and outline were generally small, with most being below 100 µm. The most common types of polymers detected were polyethylene and polypropylene. In order to improve the universality and readability of the results, the detected microplastic abundances were converted into the actual application concentration of the working fluid. It was found that the potential contamination of microplastics in untreated flotation solutions ranged from 1.5 to 30.8 items/mL, while in digestion solutions ranged from 0.1 to 2.3 items/mL. Our study emphasizes the need for quality control measures, such as suction filtration, when evaluating microplastics in environmental samples or conducting chemical and biological tests related to microplastics.

Tissue Clearing To Localize Microplastics via Three-Dimensional Imaging of Whole Organisms.

Understanding the biological impacts of plastic pollution requires an effective methodology to detect unlabeled microplastics in environmental samples. Detecting unlabeled microplastics in an organism generally requires a digestion protocol, which results in the loss of spatial information on the distribution of microplastic within the organism and could lead to the disappearance of the smaller plastics. Fluorescence microscopy allows visualization of ingested microplastics but many labeling strategies are nonspecific and label biomass, thus limiting our ability to distinguish internalized plastics. While prelabeled plastics can be used to avoid nonspecific labeling, this approach precludes the detection of environmental microplastics in organisms. Also, using prelabeled microplastics can affect the viability of the organism and impact plastic uptake. Thus, a method was developed that employs nonspecific labeling with a tissue-clearing technique. Briefly, unlabeled microplastics are stained with a fluorescent dye after ingestion by the organism. The tissue-clearing technique then removes tissue-bound dye while rendering the structurally intact organism transparent. The internalized plastics remain stained and can be visualized in the cleared tissue with fluorescence microscopy. The technique is demonstrated using polystyrene beads in living aquatic organismsTigriopus californicusandDaphnia magnaand by spiking a model vertebrate (Cephalochordata) with different microplastics.

Assessment of filter subsampling and extrapolation for quantifying microplastics in environmental samples using Raman spectroscopy

A common method for characterizing microplastics (MPs) involves capturing the plastic particles on a filter after extraction and isolation from the sediment particles. Microplastics captured on the filter are then scanned with Raman spectroscopy for polymer identification and quantification. However, scanning the whole filter manually using Raman analysis is a labor-intensive and time-consuming process. This study investigates a subsampling method for Raman spectroscopic analysis of microplastics (operationally defined here as 45–1000 μm in size) present in sediments and isolated onto laboratory filters. The method was evaluated using spiked MPs in deionized water and two environmentally contaminated sediments. Based on statistical analyses, we found quantification of a sub-fraction of 12.5 % of the filter in a wedge form was optimal, efficient, and accurate for estimating the entire filter count. The extrapolation method was then used to assess microplastic contamination in sediments from different marine regions of the United States.

Improving the Detectability of Microplastics in River Waters by Single Particle Inductively Coupled Plasma Mass Spectrometry.

Detection of microplastics in environmental samples requires fast, sensitive and selective analytical techniques, both in terms of the size of the microparticles and their concentration. Single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) allows the detection of plastic particles down to ca. 1 µm and down to concentrations of 100 particles per mL. In SP-ICP-MS, detection of carbon-containing particles is hampered by the presence of other forms of carbon (carbonates, organic matter, microorganisms…). An acidic pre-treatment of river water samples with 10% (v/v) nitric acid for 24 h allowed the reduction of the presence of dissolved carbon to ultrapure water levels and the digestion of potential microorganisms in the samples, recovering polystyrene microparticles up to 80%. Carbon-containing particles were detected in most of the samples analysed from Spanish and French Pyrenean rivers. The presence of microplastics in these samples was confirmed by Raman microscopy and their morphology was defined by electron microscopy combined with energy-dispersive X-ray spectroscopy. The developed SP-ICP-MS method is suitable for the rapid screening of river waters for the presence of microplastics, which can then be analysed by inherently slower but more selective techniques (e.g., Raman microscopy).

Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy

This research project investigates the potential of machine learning for the analysis of microplastic Raman spectra in environmental samples. Based on a data set of > 64,000 Raman spectra (10.7% polymer spectra) from 47 environmental or waste water samples, two methods of deep learning (one single model and one model per class) with the Rectified Linear Unit function (ReLU) (hidden layer) as the activation function and the sigmoid function as the output layer were evaluated and compared to human-only annotation. Based on the one-model-per-class algorithm, an approach for human–machine teaming was developed. This method makes it possible to analyze microplastic (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) spectra with high recall (≥ 99.4%) and precision (≥ 97.1%). Compared to human-only spectra annotation, the human–machine teaming reduces the researchers’ time required per sample from several hours to less than one hour.

Previous successes and untapped potential of pyrolysis-GC/MS for the analysis of plastic pollution.

There is growing concern from scientists, policy makers, and the public about the contamination of natural and indoor environments with plastics, particularly micro/nanoplastics. Typically, characterizing microplastics in environmental samples requires extensive sample processing to isolate particles, followed by spectroscopic methodologies to identify particle polymer composition. Spectroscopic techniques are limited in their ability to provide polymer mass or advanced chemical composition (e.g., chemical additive content), which are important for toxicological assessments. To achieve mass fraction quantification and chemical characterization of plastics in environmental samples, many researchers have turned to thermoanalytical spectrometric approaches, particularly pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Sample preparation for Py-GC/MS may be approached similarly to techniques needed for spectroscopic approaches (e.g., isolate particles on a filter), employ pressurized solvent extraction, or use ultrafiltration techniques to concentrate nanoplastics. Great strides have been made in using calibration curves to quantify plastics in complex matrices. However, the approaches to the pyrolysis thermal program, as well as calibrant and sample preparation, are inconsistent, requiring refinement and harmonization. This review provides a critical synthesis of previous Py-GC/MS work and highlights opportunities for novel and improved Py-GC/MS analysis of plastics in the future.

Distribution of microplastics in freshwater systems in an urbanized region: A case study in Flanders (Belgium)

Microplastics (MPs) are an emerging pollutant of concern in all known aquatic ecosystems. However, studies at a regional scale on MP pollution in freshwater systems and the necessary risk assessments are limited. Therefore, in this study, we examined microplastic concentrations, size distributions, and polymer types in surface waters and sediments in the geographic region Flanders (Belgium), as a case study for a densely populated region and one of the most developed parts of Europe. Samples have been taken on nine different locations, of which five were repeated in a different weather condition. In total 43 aqueous and nine sediment samples have been collected. The quantity and identity of the microplastics in the samples were determined with μFTIR spectroscopy in the range of 25–1000 μm. The MPs' abundances in surface waters and sediments ranged from 0 to 4.8 MP L−1 (average = 0.48 MP L−1) and from 0 to 9558 MP kg−1 dry weight (average = 2774.57 ± 2317.93 MP kg−1 DW), respectively. Polystyrene and polypropylene were the most common polymer compositions found. No correlations were observed between microplastic concentrations in the sediment/the surface water samples and the measured environmental variables rainfall, conductivity, pH, dissolved oxygen content, waterway flow rate and width, and surrounding land use. Risk assessment results for the measured surface water concentrations through the risk quotient (RQ) method and the probabilistic risk assessment framework suggest that most of the sampled sites in Flanders posed negligible risks to freshwater biota, while this was not the case for some of the sediment concentrations. Our results illustrate the need to urgently develop analytical methods that can routinely measure the full size range of MP in environmental samples to adequately assess risks for the environment.

Microplastics in road dust: A practical guide for identification and characterisation

The contamination of the environment by microplastics (MPs) in road dust poses a serious ecological and health concern. MPs have been detected in road dust worldwide and their presence has been mainly attributed to plastic litter fragmentation and vehicle tyre abrasion. Although current technologies such as Raman and Fourier Transform InfraRed spectroscopy as well as Scanning Electron Microscopy are capable of detecting MPs in road dust, the analysis of MPs shape and MPs smaller than 20 μm is limited and often labour demanding. More accurate, cost-effective and rapid techniques have now become necessary to analyse MPs in road dust, particularly since the development of large infrastructure projects that incorporate recycled plastic into road assets and roadside furniture. Nile red (NR) staining is a promising technique to identify MPs in environmental samples; however, it has not yet been applied to road dust. This study investigates the use of NR fluorescence microscopy to detect MPs in road dust and provides information about MP amount, shape and size distribution. The staining duration and temperature, solvent selection and NR concentration were optimised considering 33 different road dust materials, including 13 types of plastic. The NR staining procedure developed in this work is capable of successfully differentiating between MPs down to 1 μm and other non-plastic road dust materials. Future applications include assessing the contribution of plastic-modified roads to MP pollution, comparing the level of MP pollution in urban and rural areas and providing a rapid, simple, inexpensive and reliable monitoring approach for further studies to compare MP using a singular optimised methodology.

Analysis of microplastics in the environment: Identification and quantification of trace levels of common types of plastic polymers using pyrolysis-GC/MS

This work describes the development of analytical workflows based on pyrolysis coupled with gas chromatography-mass spectrometry (Pyr-GC/MS) for the qualitative and quantitative analysis of 12 of the most common plastic polymers in environmental samples. The most suitable characteristic pyrolyzate compounds and respective indicator ions were selected for each polymer in order to obtain the most appropriate response for analytical purposes. Additionally, commercial pyrolyzates and polymers libraries were used to confirm the identity of the detected microplastics. The method was validated, showing a good linearity for all the plastic polymers (R2 > 0.97) and limits of detection between 0.1 (polyurethane) to 9.1 µg (polyethylene). The developed methodology was successfully applied for the analysis of plastic polymers in environmental microplastic samples collected in three Mediterranean beaches (NE Spain).•Fast and reproducible Pyr-GC/MS method for the analysis of the 12 most common plastic polymers in a single GC/MS run•Straightforward analytical workflows using pyrolyzates and polymers libraries enable a fast identification and quantification of microplastics in environmental samples

Feasibility of maximum probable number method for the quantification of microplastics in environmental samples

This study introduces the maximum probable number (MPN) method for the quantification of microplastic items (MPI) using samples with dilute MPI concentrations. The MPN method does not rely on counting individual MPI in the sample; instead, it determines the maximum probable numbers of MPI based on the no MPI capture probability (NMCP) among replicated sampling from a dilute solution. The key parameters of MPN include the MPI concentration, the sampling volume (SV), the SV level (SVL) (the number of SV), and the number of replicates at each SV. The comparison of the results obtained from visual counting and the MPN test demonstrated that MPN is a reliable method for MPI quantification. The optimal NMCP range was determined to be between 0.7 to 0.95, which can be used as a control factor for the determination of the appropriate MPI concentration and SVs for the MPN quantification of MPI. The MPN method uses dilute solutions for MPN quantification, it accounts for random variation of sampling, and avoids direct number counting. Thus, it is a more reliable, simpler, and less laborious MPI quantification method compared to the direct visual number counting method.

A novel approach to extract, purify, and fractionate microplastics from environmental matrices by isopycnic ultracentrifugation

The increasing accumulation of microplastics (MP) in the environment is considered one of the most important environmental challenges of our times. Reliable extraction and detection methods for MP in environmental samples are essential for determining the extent of pollution and assessing ecological risks. However, extraction of MP from complex environmental matrices such as soil remains technically challenging. Today, density-based extractions with saturated salt solutions are widely applied. Nevertheless, current methods do not allow for the fractionation of different MP particle types according to their specific polymer densities. Here, we present a novel isopycnic ultracentrifugation approach for the simultaneous extraction and fractionation of MP mixtures based on the particle-specific buoyant densities. In this proof-of-concept study, diffusion-based density gradients were prepared using caesium chloride media, covering a density range between 1.1 and 1.5 g mL−1, sufficient to resolve many common polymer densities. We selected MP particles with a low (polyamide; PA66), medium (polybutylene adipate terephthalate; PBAT), and high (polyethylene terephthalate; PET) density to validate separation performance. Both pristine and soil-incubated MP mixtures showed clear banding patterns at expected buoyant densities after isopycnic separation. μFTIR imaging of subsamples collected from resolved MP fractions showed a polymer-specific separation of ≥87.6 %. In addition, the quantitative recovery of MP particles from soil was between 86 and 99 %. The potential of isopycnic ultracentrifugation to preserve MP-associated biofilms was also assessed. Soil-incubated MP particles were inspected by confocal laser scanning microscopy before and after isopycnic separation, indicating a preservation of bioorganic structures. Hence, isopycnic ultracentrifugation offers a powerful novel approach for a polymer-specific extraction and resolution of MP particles with a wide potential for applications in MP research.

An In Situ Depolymerization and Liquid Chromatography-Tandem Mass Spectrometry Method for Quantifying Polylactic Acid Microplastics in Environmental Samples.

Polylactic acid (PLA) is the most commonly used biodegradable plastic with rapid growth in recent years. This leads to predictable increased pollution of PLA microplastics (MPs) in the environment. However, quantification methods for the PLA MPs are still lacking. In this study, a method based on alkali-assisted thermal depolymerization and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was developed to quantify PLA MPs. After the background monomer compound was removed, PLA MPs were efficiently depolymerized to lactic acid and detected by LC-MS/MS with a limit of quantification of 18.7 ng/g. The ideal recovery of spiked PLA MPs of 93% was obtained, and the PLA MPs did not need to be separated or extracted in advance from the environmental samples. Using this method, PLA MPs were detected in all the sediment samples of a reservoir at a range of 53.5-491 ng/g dw, and the concentrations decreased with the sediment depth. In addition, after soaking in water at 95 °C for 30 min, approximately 12 μg of PLA MPs was released from a single teabag.