Identification Of Microplastics Research Articles

Polydopamine-encapsulated carbon dots to boost analytical performance for microplastics detection in fluorescence mode.

Akind of sulfur-doped carbon dots was preparedwhich were encapsulated with polydopamine (S-CDs@PDA) that has fluorescence response on polyethylene (PE) microplastics (MPs). Modified membranes were constructed using S-CDs@PDA for MP detection. Through heating and vacuum filtration process, yellow emission from the modified membrane appeared because of the combination between S-CDs@PDA and PE MPs. Notably, the fluorescence signal value of PE MPs detected by S-CDs@PDA-modified membrane was 21.3% higher than that of unmodifiedS-CDs membrane, and the detection efficiency was 8% higher than that of S-CDs membrane. The minimum detection limit for modified membranes was 4mg. Due to the good adhesion of polydopamine (PDA), S-CDs@PDA-modified membrane was more easily adhered to PE MPs, showing its excellent detection ability. The rapid quantitative detection of PE MPs in 10min was realized with a linear equation of y = 3081x + 3686.1 in a linear range of 4-14mg. Such modified membrane exhibited excellent anti-photobleaching using continuous 365-nm excitation and its sensing performance was further confirmed in sea and river water samples. S-CDs@PDA could detect solid MP powders as well as MPs dispersed in liquids. The detection method constructed with a modified glass fiber filter membrane enables rapid identification and quantitative detection of PE MPs without relying on large-scale instrumentation. This study provided research ideas for fluorescent tracing of PE MPs and paved the way for quantitative detection of micron-sized plastics with smaller particle sizes.

Integrating C-H Information to Improve Machine Learning Classification Models for Microplastic Identification from Raman Spectra.

Research has shown microplastic particles to be pervasive pollutants in the natural environment, but labor-intensive sample preparation, data acquisition, and analysis protocols continue to be necessary to navigate their diverse chemistry. Machine learning (ML) classification models have shown promise for identifying microplastics from their Raman spectra, but all attempts to date have focused on the lower energy "fingerprint" region of the spectrum. We explore strategies to improve ML classification models based on the k-nearest-neighbor algorithm by including other regions of the Raman spectra. The information content inherent in C-H bonds, which occur in the higher frequency region of 2500-3600 cm-1, is found to be particularly powerful in improving classification model performance. Variations in the relative intensity of peaks arising from C-H vibrations improve identification capabilities for plastics that the fingerprint region alone struggles with, such as resolving acrylonitrile butadiene styrene from polystyrene and identifying poly(vinyl chloride), polyurethane, and polyoxymethylene. Testing of strategies to both acquire and analyze data across the two regions is explored for their efficacy and their compatibility with real-world sampling restrictions. We find that localized normalization of spectra, independently acquired in the two regions, provides the most direct and effective route to improving the ML classification performance.

Identification and detection of label-free polystyrene microplastics in maize seedlings by Raman spectroscopy.

Microplastics are a new type of pollutants that have attracted attention recently. However, there is limited research on the uptake of environmental microplastics by plants. In this study, scanning electron microscopy (SEM), micro-Raman spectroscopy, and Raman mapping were employed to identify and detect label-free micron-sized polystyrene (PS) microplastics accumulated in the roots and stems of maize (Zea mays L.) seedlings. The results demonstrated that the Raman spectra of PS microplastics were predominantly concentrated in the xylem and ducts of seedlings, confirming the transfer behavior of microplastics in the plants. The Raman spectra of PS microplastics in seedlings exhibited distinctive peaks at 621, 1002, 1030, and 1604cm-1, and the matching scores of these spectra with the standard PS Raman spectrum ranged from 40.61% to 86.93%. Additionally, the Raman mapping facilitated the precise identification and visualization of microplastics within the roots and stems of seedlings. The smallest size of the detected PS microplastics was ∼2μm. This study provides new insights into the use of Raman spectroscopy for the detection of microplastics in plants.

Identification and characterization of microplastics released during actual use of disposable cups using laser direct infrared imaging

Disposable plastic cups are commonly used as beverage containers. This study investigated the characteristics of microplastics (MPs) discharged from plastic cups in everyday situations and assessed the emission of MPs...

Microplastics in Landfill Environments: Distribution, Characteristics, and Risks from Gampong Jawa, Indonesia

Landfills are generally considered the ultimate solution for waste management. However, the degradation process of plastic waste in landfills causes the release of microplastic particles into the surrounding environment and threatens human health. The distribution and properties of microplastics in four environment matrices, soil, leachate, river water, and well water surrounding the landfill, are examined in this study. Sampling was conducted at the inflow and outflow areas of the leachate ponds., The soil at the top (0–5 cm) and bottom (5–20 cm), upstream and downstream surface water adjacent to the landfill, and community wells within a radius of fewer than 700 meters from the landfill. Microplastic analysis used a gradual extraction method with saturated NaCl for density separation, 30% hydrogen peroxide for organic matter degradation, and 0.05 M FeSO4 as a catalyst. Physical character identification of microplastics using a microscope showed microplastic contamination at all study sites. The results showed an abundance of microplastics was found in well water samples (808 to 979 items/L), leachate (209 to 757 items/L), surface water (6.29 to 7.2 items/L), and soil (23,340 to 23,420 items/kg). Types of microplastics found consist of fragments, fibers, films, pellets, foam, and rods. The size of microplastics found ranged from 1.897 µm to 1,642.79 µm. Fourier Transform Infrared spectroscopy examination identified polyethylene terephthalate (PET) plastic compounds in soil and leachate materials. The high concentration of microplastics in well water indicates potential groundwater contamination from landfill activities that may impact the surrounding community. This study provides preliminary insights into how landfills may contribute to environmental microplastic contamination. It paves the way for further research to develop mitigation strategies.

Atomically Layered Bronze Nanoalloy Featuring Perpetual Aureolin Fluorescence Adept Microplastic Identification Less than 1 μm in Water

Atomically Layered Bronze Nanoalloy Featuring Perpetual Aureolin Fluorescence Adept Microplastic Identification Less than 1 μm in Water

Presence of Microplastic in Bottom Sediments of the Bach Hac River in Phu Tho Province, Vietnam

Microplastic pollution has been one of the alarming environmental problems globally due to its widespread distribution and potential health risks. Rivers are considered one of the main pathways for microplastics receiving and transporting from land to the oceans. Furthermore, sedimentary environments are considered the main reservoirs of microplastics in the aquatic environments. This study was conducted to evaluate the distribution of microplastics in bottom sediment samples collected at Bach Hac River, Phu Tho, Vietnam. Visual verification and chemical identification of microplastics were performed using a μFT-IR spectrometer using a Nicolet iN10 MX Infrared Imaging Microscope. The results found microplastics present in all sediment samples. The concentrations of microplastics detected were in the range of 798.08 – 1454.3 microplastics/kg of dry sediment. The most detected major polymer type was Polyethylene Terephthalate (PET), accounting for 60% of the total polymers found. The most common shape of detected microplastic was fragments (72.02%). Most of the detected microplastics were small in size (20- 150 µm), accounting for 81.80%. The findings from this research could contribute to the database about microplastic pollution in Vietnam and toward the assessment of the potential ecological impacts of microplastics. Keywords: Microplastics, Polymers, Sediment, μFT-IR spectroscopy.

Reducing the Uncertainty of Microplastic Identification and the Preferred Use of the Varnish clam (Nuttallia obscurata) as Compared to Other Bivalves as a Biomonitor of Plastic Pollution

Reducing the Uncertainty of Microplastic Identification and the Preferred Use of the Varnish clam (<i>Nuttallia obscurata</i>) as Compared to Other Bivalves as a Biomonitor of Plastic Pollution

Machine learning based workflow for (micro)plastic spectral reconstruction and classification

With the advancement of artificial intelligence, it is foreseeable that computer-assisted identification of microplastics (MPs) will become increasingly widespread. Therefore, exploring a machine learning-based workflow to facilitate the identification of MPs is both meaningful and practically significant. However, interferences present in MPs spectra often compromise identification accuracy, making the improvement of spectral quality a critical prerequisite for precise identification. This study developed a fully machine learning-based workflow that combines spectral reconstruction and identification of MPs. To enhance the quality of MPs spectra, two reconstruction models named autoencoders (AE) and V-like convolutional neural networks (VCNN) were employed. Then, four classification models including decision tree, random forest, linear support vector machines (LSVM) and 1D convolutional neural networks were developed to accurately identify MPs. In terms of reconstruction, VCNN outperformed AE with a higher R2 value of 0.965, while both models outperformed conventional widely used Savitzky-Golay algorithm. For classification, LSVM exhibited the best performance with an overall accuracy of 91.35% on the original dataset and 98.00% on the VCNN-reconstructed dataset. When applied to real environmental datasets, a slight decrease in performance was observed, but a maximum top-1 accuracy of 71.43% and top-3 accuracy of >90% was still practically significant, indicating that the combined workflow has great potential for spectral reconstruction and identification of MPs.

Moving toward automated µFTIR spectra matching for microplastic identification: addressing false identifications and improving accuracy

Infrared spectroscopy is a widely used tool for studying microplastics and identifying microparticles. Researchers rely on spectral libraries to differentiate between synthetic and natural materials. Unfortunately, spectral library matching is not perfect, and best practices require researchers to use time consuming, manual peak matching to assess spectral matches. Moving toward automated matching requires increased confidence in the matching process. Using spectra matching software may increase the efficiency of particle identification, however some matching strategies may confuse natural materials such as cotton, silk, and plant matter with common classes of synthetics such as polyesters and polyamides. In this experiment, we prepared 22 pristine sample materials from natural and synthetic sources and measured micro-Fourier transform infrared (µFTIR) spectra in transmission mode for each sample using a Thermo Nicolet iN10 MX instrument. The collected spectra were then input into two spectral library matching systems (Omnic Picta and Open Specy), using a total of five identification routines. Next, we placed a subset of four pristine microplastic materials in a biologically active river system for two weeks to simulate environmental samples. These simulated environmental samples were processed using 10% hydrogen peroxide for 24 h to remove organic contamination and then identified using the strongest performing library. We found that libraries with fewer sample spectra produced lower correlation matches and that using derivative correction greatly reduced the number of inaccuracies in identifying materials as either natural or synthetic. We also found that environmental fouling reduced the correlation value of library matches when compared to pristine particles, however the effect was not consistent across the four materials tested. Overall, we found that the accuracy of automated library matching in the tested systems and processing routines varied from 64.1 to 98.0% for distinguishing between natural and synthetic materials, and that a high Hit Quality Index (HQI) did not always correlate with accuracy. These results are important for the microplastic field, demonstrating a need to rigorously test spectral libraries and processing routines with known materials to ensure identification accuracy.

Potential source contribution function coupled with mass spectrometry detection to identify source of atmospheric polyethylene terephthalate

Source identification of atmospheric microplastics (MPs) is crucial for the development of mitigation policies. Compared with wind directions or backward trajectories of air masses, the potential source contribution function (PSCF) analysis identifies more comprehensive sources of atmospheric particles. However, conducting PSCF analysis requires hourly pollutant concentration data, which cannot be met by the atmospheric MPs abundance obtained through commonly used methods. In this study, total suspended particles (TSP) samples were collected hourly and the concentrations of atmospheric polyethylene terephthalate (PET) were detected using a liquid chromatography-tandem mass spectrometry. Atmospheric concentrations of PET MPs were 112.9 ± 39.04 ng/m³ (average ± SD). Based on the hourly backward trajectories of air masses and the varied PET concentrations at the sampling site, potential sources of atmospheric PET were identified by PSCF analysis. The backward trajectory-based method indicates that atmospheric PET of the target site in this study primarily originates from dry farmlands. In comparison, both the residential areas and the dry farmlands were identified by PSCF as major sources of atmospheric PET at the receptor site. In contrast, both the backward-trajectory based method and PSCF analysis indicate that TSP mainly originates from the dry farmlands near the sampling site. This indicates that atmospheric PET in urban areas may have different sources from those of TSP, and PSCF is a suitable method for identifying sources of atmospheric PET.

Challenges in Raman spectroscopy of (micro)Plastics: The interfering role of colourants

Rising plastic consumption leads to widespread microplastic (MP) contamination. Raman spectroscopy is widely used for MP identification due to its ability to analyse particles as small as 1 μm. However, it faces challenges such as interference from pigments and additives. In this study, we aim to assess the accuracy of Raman micro-spectroscopy in identifying coloured plastic samples by applying various oxidative treatments to eliminate the possible interference effect caused by colourants associated with the sample. Standard and coloured microplastics were analysed using a Raman imaging microscope. Coloured plastics were treated with H2O2 30%, Sodium hypochlorite 5%, and Fenton reagent (H2O2 30% and Ferrous sulphate 0.2 M) for 24, 48, and 72 h. The Raman spectra were acquired after treatment to assess the impact of the treatment procedure on the polymer identification. Our results revealed that colourants significantly impact Raman spectra by peak broadening and/or fluorescence effects, which reduces identification accuracy and match scores Red pigments particularly obscure polymer identification. Treatments like oxidation and Fenton's reagent showed limited effectiveness. Additives in plastic samples can affect the accuracy of polymer identification by the Raman spectroscopy technique. Common treatment procedures do not improve the accuracy of identification. In order to improve the reliability of Raman analysis, essential factors such as utilizing multiple excitation lasers and appropriate CCD detectors, establishing a comprehensive reference library of colourants and additives, and employing advanced techniques like time-gated Raman spectroscopy or Surface-Enhanced Raman Spectroscopy (SERS) should be considered.

Identification and degradation potential of microplastics by indigenous bacteria isolated from Putri Cempo Landfill, Surakarta, Indonesia

Plastic waste on agricultural land can break down into microplastics (&lt; 5 mm), which plants can absorb through their roots, potentially inhibiting plant growth. Utilizing microplastic-degrading bacteria isolated from landfills offers a potential solution to microplastic contamination in agriculture. This study aimed to isolate and identify bacteria from the Putri Cempo Landfill and evaluate their ability to degrade different types of plastic contaminants found in agricultural environments. Microorganisms were isolated from soil samples using Soil Extract Media (SEM), and pure cultures were established. Bacterial isolates were tested for their microplastic-degrading potential using polyethylene terephthalate (PET) plastic fragments. Molecular analysis was conducted to determine the taxonomy of the bacteria. Further degradation tests were performed on different types of microplastic contaminants (mulch, polybags, and sacks) to identify the most degradable material. Six bacterial isolates were obtained, with isolates CP1 and CP2 demonstrating microplastic degradation rates of 2.43% and 1.15%, respectively, over a 20-day incubation period. Molecular analysis identified CP1 as Bacillus anthracis str. and CP2 as Bacillus cereus ATCC 14579. Subsequent degradation tests on various agricultural microplastic contaminants revealed that sack materials treated with Bacillus cereus showed the highest degradation rate, with an 8.8% weight reduction, while polybag materials showed the lowest degradation rate, with a weight loss of only 0.59%.

From microplastics to pixels: testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification.

Despite the urgent need for accurate and robust observations of microplastics in the marine environment to assess current and future environmental risks, existing procedures remain labour-intensive, especially for smaller-sized microplastics. In addition to this, microplastic analysis faces challenges due to environmental weathering, impacting the reliability of research relying on pristine plastics. This study addresses these knowledge gaps by testing the robustness of two automated analysis techniques which combine machine learning algorithms with fluorescent colouration of Nile red (NR)-stained particles. Heterogeneously shaped uncoloured microplastics of various polymers-polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC)-ranging from 100 to 1000µm in size and weathered under semi-controlled surface and deep-sea conditions, were stained with NR and imaged using fluorescence stereomicroscopy. This study assessed and compared the accuracy of decision tree (DT) and random forest (RF) models in detecting and identifying these weathered plastics. Additionally, their analysis time and model complexity were evaluated, as well as the lower size limit (2-4µm) and the interoperability of the approach. Decision tree and RF models were comparably accurate in detecting and identifying pristine plastic polymers (both > 90%). For the detection of weathered microplastics, both yielded sufficiently high accuracies (> 77%), although only RF models were reliable for polymer identification (> 70%), except for PET particles. The RF models showed an accuracy > 90% for particle predictions based on 12-30 pixels, which translated to microplastics sized < 10µm. Although the RF classifier did not produce consistent results across different labs, the inherent flexibility of the method allows for its swift adaptation and optimisation, ensuring the possibility to fine-tune the method to specific research goals through customised datasets, thereby strengthening its robustness. The developed method is particularly relevant due to its ability to accurately analyse microplasticsweathered under various marine conditions, as well as ecotoxicologically relevant microplastic sizes, making it highly applicable to real-world environmental samples.

Microplastics: Features of appearance, identification methods (subject review)

Polymer packaging materials have become firmly embedded in our way of life. They are used in the manufacture of household items, as well as in the pharmaceutical, chemical, and automotive industries. Production and application of polymer packaging are expanding rapidly encompassing various spheres of the industry. According to Plastics Europe Market Research Group (PEMRG), global plastics production reached 348 million tons in 2017 and is likely to reach 33 billion tons by 2050. At the same time, about 26 million tons of plastic waste are generated annually on the territory of the European Union (EU), of which only 30% is collected for recycling. Despite this, in many countries of the world, including Russia, more than 50% of polymer materials are disposed of at landfills, where under the influence of external environmental factors (temperature, humidity), their destruction occurs with the formation of huge quantities of micro- and nanoplastics. Most people do not consider the environmental problems associated with microplastics to be serious. However, many studies aimed at in-depth study of this problem have proved that micro- and nanoplastics have significant negative effects on terrestrial and marine animals, as well as on human health, whether directly or indirectly. The identification of microplastics in various model environments and living systems is usually based on the use of infrared spectroscopy and Raman spectrophotometry. Each of the methods has its advantages and disadvantages, mainly related to sample preparation to improve the accuracy of identification. This review is devoted to the problem of formation and identification of microplastics in various natural objects.

From the highway to receiving water bodies: identification and simultaneous quantification of small microplastics (< 100µm) in highway stormwater runoff.

Highway stormwater (HSW) runoff is among the environment's most important sources of microplastics. This study aimed to characterize via vibrational spectroscopy and quantify SMPs (small microplastics < 100µm) in HSW runoff from a trafficked highway entering a facility equipped with a filtration system and in those flowing out to the receiving water body near agricultural activities. Samples of the inlet runoff (from the highway) and outlet runoff (the discharge into the environment) were collected in different periods to investigate potential seasonal and spatial differences. The sampling, methodology, and analysis were thoroughly carried out to quantify and simultaneously identify SMPs via Micro-FTIR to obtain a specific novel dataset to assess the environmental quality of highway pollution. A significant difference between inlet and outlet samples was reported; the highest abundance in inlet samples was 39813 ± 277 SMPs L.1 (SW10 IN; average length of 77µm), while the highest one in outlet samples was 15173 ± 171 SMPs L-1 (SW10 OUT; SMPs' average length of 63µm). Polyamide 6 (PA 6) and High-Density Polyethylene (HDPE) were predominant. Our results show that these HSW treatment plants, designed for managing regulated pollutants, can intercept SMPs, improving the quality of HSW runoff discharged into the environment.

Investigating multiple vegetable oils and recycled variant for microplastics extraction from water, integrated with Raman spectroscopy

The global production and disposal of plastics have led to pervasive contamination of natural environments, representing considerable risks to human health and ecosystems. This study introduces a novel oil-based method for extracting microplastics (MPs) from water samples, with a focus on optimizing extraction conditions and improving the quality of MPs identification using Raman spectroscopy. Various parameters including the type of oil, salinity, temperature, air incorporation, and washing solvent were investigated to enhance extraction efficiency and spectroscopic identification accuracy. Sunflower oil emerged as the preferred extraction medium due to its compatibility with Raman spectroscopy, offering high recovery efficiencies for polypropylene (PP) and polystyrene (PS). Additionally, ethanol was identified as a better washing solvent compared to hexane, improving MPs identification. The optimised method was then applied to environmental water samples, revealing matrix effects and challenges with digestion step. Despite these challenges, the proposed method represents a significant advancement in microplastic analysis, offering reliable detection and quantification in aquatic environments. Further optimisation is needed to address matrix effects and improve recovery efficiency, especially for smaller microplastics.

Microplastics in different nasal irrigation options.

We aimed to assess the presence of microplastics in nasal irrigation methods commonly used in the treatment of sinusitis and rhinitis, and to evaluate human exposure. A total of 150 samples were included in the study, consisting of nasal wash bottles containing nasal irrigation solution, seawater spray, syringes for nasal irrigation with isotonic solution. The amount of microplastics per millilitre in the samples and patient exposure during single use were assessed separately for each method and product. All samples were filtered using a stainless steel vacuum filter on filter paper with a pore size of 1.2μm, washed at least three times with distilled water and incubated at 45°C for 24h to prevent mould growth. Identification and counting of microplastics was performed using a Leica Flexacam C1 camera connected to an M80 stereomicroscope. The presence of microplastics was confirmed by the hot needle method and Nile red staining. An average of 6.49 ± 13.08 microplastics/product was detected in all filtered samples. The lowest microplastic count was 0 microplastics/product in syringes and the highest was 92 microplastics/product in nasal wash bottles. Significant differences in the amount of microplastics individuals were exposed to during a single use were found between nasal wash bottles and seawater brands, while no significant differences were found between syringe brands. When nasal wash kits, seawater sprays and isotonic nasal rinses were evaluated separately, significant differences were found in the number of microplastics, the microplastics/ml ratio and the number of microplastics exposed during a single use. The highest microplastic exposure was found in nasal irrigation bottles. The exposure of individuals to microplastics increases with medical support treatments, regardless of intranasal or intravenous administration. Due to the inflammation, oxidative stress and proliferation caused by microplastics, new regulations and inspections of production conditions should be implemented worldwide to reduce exposure.

Identification of microplastics in cooked sausages

Identification of microplastics in cooked sausages

Efficient microplastic identification by hyperspectral imaging: A comparative study of spatial resolutions, spectral ranges and classification models to define an optimal analytical protocol

Microplastics (MPs) pollution is a global and challenging issue, necessitating the development of efficient analytical strategies for their detection to monitor their environmental impact.This study aims to define an optimal analytical protocol for characterizing MPs by hyperspectral imaging (HSI), comparing different setups based on spatial resolution, spectral range and classification models. The investigated MPs include polymers commonly found in the environment, such as polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE), subdivided in three size classes (1000–2000 μm, 500–1000 μm, 250–500 μm). Furthermore, MP particles with diameters ranging from 30 to 250 μm were assessed to determine the limit of detection (LOD) in the different configurations. Hyperspectral images were acquired with two spatial resolutions, 150 and 30 μm/pixel, and two spectral ranges, 1000–1700 nm (NIR) and 1000–2500 nm (SWIR). Three classification models, Partial Least Square-Discriminant Analysis (PLS-DA), Error Correction Output Coding-Support Vector Machine (ECOC-SVM) and Neural Network Pattern Recognition (NNPR) were tested on the acquired images. The correctness of these models was evaluated by prediction maps and statistical parameters (Recall, Specificity and Accuracy). The results demonstrated that for MP particles larger than 250 μm, the optimal setup is a spatial resolution of 150 μm/pixel and a spectral range of 1000–1700 nm, utilizing a linear classification model like PLS-DA. This approach offers accurate predictions while being time- and cost-efficient. For MPs smaller than 250 μm, a higher spatial resolution of 30 μm/pixel with a spectral range of 1000–2500 nm and a non-linear classification method like ECOC-SVM is preferable. The LOD is 250 μm for the 150 μm/pixel resolution and ranges from 100 to 200 μm for the 30 μm/pixel resolution. These findings provide a valuable guide for selecting the appropriate HSI acquisition conditions and data processing methods to optimally characterize MPs of different sizes.