Detection Of Microplastics Research Articles

Detection and characterisation of microplastics in tap water from Gauteng, South Africa

This study reports the presence, concentration, and characteristics of microplastics (MPs) in tap water in three suburbs in Gauteng Province in South Africa. Physical characterisation was conducted using stereomicroscopy and scanning electron microscopy following staining of MPs with the Rose Bengal dye. The concentrations of MPs in all samples ranged from 4.7 to 31 particles/L, with a mean of 14 ± 5.6 particles/L. Small-sized (<1 mm) and fibrous-shaped MPs were most abundant in all samples. Fibers accounted for 83.1% of MPs in samples from all the three areas, followed by fragments (12.4%), pellets/beads (3.1%), and films (1.5%), with a minor variation in the distribution of shapes and sizes in samples from each area. Raman microspectroscopy was used for chemical analysis, and five polymers were identified, namely: high-density polyethylene, polyurethane, polyethylene terephthalate, poly(hexamethylene terephtalamide), and poly(acrylamide-co-acrylic acid). C.I Pigment Red 1, C.I. Solvent Yellow 4, Potassium indigotetrasulphonate, and C.I Pigment Black 7 were the colourants detected. These colourants are carcinogenic and mutagenic and are potentially toxic to humans. The prevalence of MPs in tap water implies their inadequate removal during water treatment. For instance, the presence of poly(AM-co-AA) suggests that drinking water treatment plants may be a potential source of MPs in tap water. Other polymers, e.g., high-density polyethylene may be released from pipes during the transportation of drinking water. The estimated daily consumption of MPs from tap water was 1.2, 0.71, and 0.50 particles/kg.day for children, men, and women, respectively. The findings of this study provide evidence of the presence of MPs in drinking water in South Africa, thus giving some insights into the performance of treatment plants in removing these contaminants and a benchmark for the formulation of standard limits for the amount of MPs in drinking water.

Impact of Microplastics on Human Health: A Comprehensive Review

Abstract This comprehensive review explores microplastic (MP) contamination in food and Incorporating nanotechnology into food packaging. It covers origins, pathways, health consequences, and analytical challenges. Microplastics (MPs), which are generation due to the breaking down of plastic garbage, presents significant dangers to both the environment and human health. These small particles, sometimes unseen to the unaided eye, can accumulation in ecosystems, harm wildlife, and even entering food chain, potentially cause health issues for humans who consuming contaminated food or water. Exposure occurs through ingesting MPs in food, notably in processed foods, canned goods, and infant milk powder, raising concerns about breast cancer. Nanotechnology in food packaging enhances characteristics but raises migration risks, introducing substances such as phthalates (PAEs), non-PAEs plasticizers (NPPs), bisphenols (BPs), and metal nanoparticles, posing health risks. Microwavable plastic food containers release both intentionally included substances and inadvertently included substances. Consumer attitudes toward nanotechnology are positive, driven by perceived benefits. Analytical techniques for MP detection, including micro-Raman spectroscopy and hyperspectral imaging, face challenges like sample preparation. Multidisciplinary approaches and collaboration are crucial for complex MP analysis in diverse food matrices. The paper discusses MP accumulation in the human body, noting its presence in placentas, bodily fluids, and lower limb joints, raising health questions. Despite challenges in understanding the full health impact, standardized methods and collaboration are urgently needed. This holistic view of MP contamination and nanotechnology in food packaging underscores the necessity of safeguarding food safety and public health against pervasive environmental issues.

Detection of Microplastics and Heavy Metals in Famous Edible Molluscs in Can Gio Biosphere Reserve of Vietnam

Can Gio, designated in 2000, is the first Biosphere Reserve of Vietnam. In recent years, local agriculture, tourism, households, nearby industrial zones, and maritime transport have released substances into rivers flowing through Can Gio Mangrove to the East Sea and transported into creatures. This study aims to investigate microplastics and heavy metals in the famous seafood of Can Gio. A Raman microscope was used to identify microplastics from the tissue of oysters, grease snails, and clams after enzymatic treatment with trypsin. Heavy metals were quantified with ICP-MS after acidic treatment. As a result, microplastics were found in almost all samples, even up to 10 in a random individual of bivalve. Fibers dominated, especially from 30 to 150 µm long. The metal with the highest content was zinc, followed by copper, especially in oysters. Non-essential metals were also present in the soft tissue in the order As &gt; Pb &gt; Cd. The accumulation of metals in Can Gio’s molluscs was generally lower than in other regions and still in the allowed range for consumption of Vietnamese Standard. Although there have not been any Vietnamese regulations on the permissible level of microplastics for consumption, regular seafood intake will increase the risk of chronic poisoning and unknown disorders caused by the accumulation of microplastics and heavy metals.

Comparative Evaluation of Analytical Techniques for Quantifying and Characterizing Polyethylene Microplastics in Farmland Soil Samples

The presence of microplastics in soil has become a significant global concern, posing a threat to environmental, agricultural, and human health. However, the use of various detection methods has led to significant discrepancies in the statistics reported for the abundance of soil microplastics across the available literature, resulting in poor comparability across the available literature. Studies have shown that plastic film residue is among the main primary sources of microplastics in farmland soils. Therefore, we manufactured and selected polyethylene microplastics (PE-MPs) from plastic film and combined them with soil samples after density separation treatment, forming a spiked test soil sample, which was subjected to density separation and extraction using a ZnCl2 solution. The experiment used visual inspection by stereo microscopy, micro-Fourier transform infrared spectroscopy, micro-Raman spectrometry, laser direct infrared imaging, and pyrolysis-gas chromatography–mass spectroscopy for the quantitative analysis of spiked soil. This study systematically assessed and compared these five common soil microplastic detection methods. The results revealed different quantitative detection accuracies across various particle size ranges. Micro-Fourier transform infrared spectroscopy showed a broader applicable particle size range (20–5000 μm) and higher detection rate (91%), while the detection range (&gt;50 μm) and rate (70%) of visual inspection through stereo microscopy were lower. For the identification of microplastic morphology, laser direct infrared imaging performed better, whereas micro-Fourier transform infrared spectroscopy had the advantage in determining the types of polymers present over micro-Raman spectrometry. The advantages, disadvantages, and detection rates of the above five detection methods were clarified during the detection process. Considering these findings and similar studies, we suggest three combinations of the evaluated methods to optimize the detection of PE-MPs.

A new underwater in-situ microplastics detection system based on micro-Raman spectroscopy: Development and sea trials

In-situ underwater observation for marine microplastics (MPs) study has long been desired but challenged by the difficulty in achieving continuous MPs enrichment and MPs analysis in underwater environments. Herein, a novel underwater system for in-situ detection of MPs was introduced. An innovative design with a filter mesh flipped 180° driven by a stepper motor was adopted to ensure the self-cleaning of the filter mesh and continuous MPs enrichment, reducing the frequency of manual maintenance required for the system. A special-designed optical detection chamber based on micro-Raman spectroscopy featured a 13 × 10 mm imaging field of view and a 50 µm imaging resolution was developed to gather morphology and composition information of MPs. During sea trials, the system successfully detected polypropylene (PP) microplastic and other suspended substances in seawater including microalgae, organic substances, and sands. This study lays the groundwork for future research into underwater in-situ investigation of marine MPs pollution.

Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network

With the increasing interest in microplastics (MPs) pollutants, quantitative analysis of MPs in water environment is an important issue. Vibrational spectroscopy, represented by Raman spectroscopy, is widely used in MP detection because they can provide unique fingerprint characteristics of chemical components of MPs, but it is difficult to provide quantitative information. In this paper, an ingenious method for quantitative analysis of MPs in water environment by combining Raman spectroscopy and convolutional neural network (CNN) is proposed. It is innovatively proposed to collect the average mapping spectra (AMS) of the samples to improve the uniformity of Raman spectroscopy detection, and to increase the effective detection range of concentration by filtering different volumes of the same MP solutions. In order to verify the universality and effectiveness of the proposed method, 6 different sizes of Polyethylene (PE) MPs were used as detection objects and mixed into 5 different actual water environments. The R2 and RMSE of CNN for identifying the concentration of PE solutions could reach 0.9972 and 0.033, respectively. Meanwhile, by comparing machine learning models such as Random Forest (RF) and Support Vector Machine (SVM) were compared, and CNN combined with Raman spectroscopy has significant advantages in identifying the concentration of MPs.

Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics

The accumulation of microplastics (MPs) resulting from disposal of plastic waste into water sources, poses a significant threat to aquatic organisms. These are readily ingested by organisms, leading to the accumulation of harmful substances, disrupting their biological processes. Current methods for identifying microplastics have notable drawbacks, including low resolution, extended imaging time, and restricted particle size analysis. Integrating Raman spectroscopy with machine learning (ML) proves to be an effective approach for identifying and classifying MPs, especially in scenarios where they are found in environmental media or mixed with various types. Machine learning (ML) can be vital tool in assisting Raman analysis, owing to its robust feature extraction capabilities. This comprehensive review outlined the utilization of various machine learning techniques in conjunction with Raman spectral features for diverse investigations related to microplastics. The methodologies discussed encompass Principal Component Analysis, K-Nearest Neighbour, Random Forest, Support Vector Machine, and various deep learning algorithms.

Preparation of synthetic micro- and nano plastics for method validation studies

Microplastic (MP) pollution is a persisting global problem. Accurate analysis is essential in quantifying the effects of microplastic pollution and develop novel technologies that reliably and reproducibly measure microplastic content in various samples. The most common methods for this are FTIR and Raman spectroscopy. Coloured, standardized beads are often used for method validation tests, which limits the conclusions to a very specific case rarely observed in the natural environment. This study focuses on the preparation of reference micro- and nanoplastics via cryogenic milling and shows their use for FTIR and Raman method validation studies. MPs can now be reproducibly milled from various plastics, offering the advantages of a better representation of MPs in real environment. Moreover, this study highlights issues with the current detection methods, up to now considered as the most reliable ones for MP detection and identification. Such issues, e.g. misidentification, will need to be addressed in the future. Additionally, milled MPs were used in experiments with commercial high-resolution imaging device, enabling a possible in-situ optical detection of microplastics. These experiments represent a step forward in understanding MPs in a water sample and provide a basis for a more accurate detection and identification directly from water, which would considerably reduce the time of analysis.

Peptide-Decorated Microneedles for the Detection of Microplastics.

The escalating utilization of plastics in daily life has resulted in pervasive environmental pollution and consequent health hazards. The challenge of detecting and capturing microplastics, which are imperceptible to the naked eye, is exacerbated by their diminutive size, hydrophobic surface properties, and capacity to absorb organic compounds. This study focuses on the application of peptides, constituted of specific amino acid sequences, and microneedles for the rapid and selective identification of microplastics. Peptides, due to their smaller size and greater environmental stability compared with antibodies, emerge as a potent solution to overcome the limitations inherent in existing detection methodologies. To immobilize peptides onto microneedles, this study employed microneedles embedded with gold nanorods, augmenting them with sulfhydryl (SH) groups at the peptides' termini. The sensor developed through this methodology exhibited efficient peptide binding to the microneedle tips, thereby facilitating the capture of microplastics. Raman spectroscopy was employed for the detection of microplastics, with the results demonstrating successful attachment to the microneedles. This novel approach not only facilitates localized analysis but also presents a viable strategy for the detection of microplastics across diverse environmental settings.

Advanced analytical techniques for assessing and detecting microplastic pollution in water and wastewater systems

AbstractThe current study focuses on the sources and formations of microplastics (MPs) in receiving environments, addressing various analytical methods used for the detection and characterization of MPs in water and wastewater, including particle size distribution analysis, FTIR, Raman, SEM‐EDS, thermo analytical methods (Pyro GC‐MS, TGA‐MS, TED‐GC‐MS, TGA‐SPE/TDS‐GC‐MS) and combine techniques (Tagging method, Liquid chromatography, SEM dispersive X‐ray spectrometer). The study fills a gap in the literature by evaluating the advantages and limitations of advanced analytical methods, in addition to commonly used methods like FTIR, Raman, and SEM, providing a perspective on other significant analytical approaches. By examining and comparing different analytical methods used to identify and detect MPs in water and wastewater environments, the research guides scientists in making informed choices when selecting a specific method. A better understanding of these methods will inform future research and facilitate more effective management of potential environmental impacts and human health risks. This study also contributes significantly to preserving water resources, human health, and establishing a sustainable environment by encouraging the generation of actionable information to assess the potential risks of MPs in water and wastewater sources and to develop strategies for reducing their presence.

Microfluidic Detection and Analysis of Microplastics Using Surface Nanodroplets.

Detection of microplastics from water is crucial for various reasons, such as food safety monitoring, monitoring of the fate and transport of microplastics, and development of preventive measures for their occurrence. Currently, microplastics are detected by isolating them using filtration, separation by centrifugation, or membrane filtration, subsequently followed by analysis using well-established analytical methods, such as Raman spectroscopy. However, due to their variability in shape, color, size, and density, isolation using the conventional methods mentioned above is cumbersome and time-consuming. In this work, we show a surface-nanodroplet-decorated microfluidic device for isolation and analysis of small microplastics (diameter of 10 μm) from water. Surface nanodroplets are able to capture nearby microplastics as water flows through the microfluidic device. Using a model microplastic solution, we show that microplastics of various sizes and types can be captured and visualized by using optical and fluorescence microscopy. More importantly, as the surface nanodroplets are pinned on the microfluidic channel, the captured microplastics can also be analyzed using a Raman spectroscope, which enables both physical (i.e., size and shape) and chemical (i.e., type) characterization of microplastics at a single-particle level. The technique shown here can be used as a simple, fast, and economical detection method for small microplastics.

Baking releases microplastics from polyethylene terephthalate bakeware as detected by optical photothermal infrared and quantum cascade laser infrared

The use of plastic bakeware is a potential source of human exposure to microplastics (MPs). However, characterizing MPs remains a challenge. This study aims to employ optical photothermal infrared (O-PTIR) and quantum cascade laser infrared (QCL-IR) technology to characterise polyethylene terephthalate (PET) MPs shed from PET bakeware during the baking process. The bakeware, filled with ultrapure water, underwent baking cycles at 220 °C for 20 min, 60 min, and three consecutive cycles of 60 min each. Subsequently, particles present in the ultrapure water were collected using an Al2O3 filter. O-PTIR and QCL-IR were used to characterise PET MPs collected from the filtration. Analysis revealed that QCL-IR spectra exhibited broader absorption peaks, compared to O-PTIR. Notably, MP spectra obtained from both techniques displayed common absorption peaks around 1119, 1623, 1341 and 1725 cm−1. The dominant size of PET MPs detected by O-PTIR and QCL-IR was 1–3 μm and 5–20 μm, respectively. The quantity of identified PET MPs using O-PTIR was 18 times greater than that with QCL-IR, which was attributed to variations in spatial resolution, sampling methods for spectra collection, and data analysis employed by the two methods. Importantly, findings from both techniques highlighted a notably large quantity of MPs released from PET bakeware, particularly evident after 3 cycles of 60 min of baking, suggesting a substantial increase in the potential ingestion of MPs, especially in scenarios involving extended baking durations. The research outcomes will guide consumers on minimizing the intake of microplastics by using PET bakeware for shorter baking time. Additionally, the study will yield valuable insights into the application of O-PTIR and QCL-IR for MPs detection, potentially inspiring advancements in MPs detection methodologies through cutting-edge technologies.

Detection and Identification of Various Microplastics in Different Orthodontic Adhesives.

Background Microplastics are acknowledged as significant environmental contaminants. The clinical use of dental materials, particularly adhesives containing plastic polymers, can give rise to the production of plastic micro- and nanoparticles, which subsequently find their way into the environment. The aim of the study was to detect different microplastics and identify them in various orthodontic adhesives. Materials and methods Four different light cure orthodontic adhesives, including Transbond XT (3M Unitek, Monrovia, CA), Ormco Enlight (Ormco, Orange, CA), Orthofix SPA (Orthofix, Verona, Italy), and Aqualine LC (Tomy International Inc, Tokyo, Japan), were collected and placed in separate Eppendorf tubes. Microplastics present in each adhesive were identified using scanning electron microscopy. Subsequently, each specimen was suspended in hydrogen peroxide, placed within a shaking incubator, and analyzed using Fourier transform infrared spectroscopy (FTIR) to identify the type of polymer. Results The scanning electron microscope shows the surface morphologyand the most predominant types of microplastics identified were fibers, fragments, and pellets. FTIRresultsshowed the presence of several major functional groups, including hydroxyl, amine, ester, fluoro, and halo groups. Conclusion When contrasted with the quantity of microplastic waste generated by other sectors like the textile, cosmetic, and fishing industries, the microparticulate waste stemming from dental adhesives has a minimal effect on environmental deterioration. Strategies for addressing this concern should give precedence to reducing the use of these materials and adopting effective recovery methods, which could potentially involve recycling processes.

An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-

An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-

The effect of the significant acid and alkaline environment and oxidation agent on the microplastic integrity

The research examined the ability of each treatment to recover a known quantity of particles and the impact on identifying polymer type using a microscope. By conducting extensive research on the changes induced in the chemical structures and morphologies of microplastics (MPs) there is potential for enhancing the detection and analysis of MPs by incorporating pre-treatment methods in the future. When treating environmental matrices with high organic content, chemical digestion treatment is required to get rid of microplastics and release particles. This type of treatment involves using a range of chemical agents, including acids, bases, and oxidizing agents. Unfortunately, there has been limited research into the chemical resistance of various types of microplastics to these substances. To address this issue, a study was conducted to examine the chemical resistance of four species of microplastics high-density polyethylene (HD-PE), low-density polyethylene (LD-PE), polypropylene (PP) and polyethylene terephthalate (PET) to hydrogen peroxide (H2O2), potassium hydroxide (KOH), nitric acid (HNO3) and acetic acid (CH3COOH). The results showed that acidic and alkaline substances were the most destructive to microplastics, while oxidative reagents resulted in fewer changes to plastic properties. These findings provide valuable insights into the properties of MPs and their response to strong acids, bases and oxidizing agents, which can serve as a reference for future studies on MP pre-treatment. In addition, used as a guideline to update current protocols and ensure that microplastics can be treated without causing damage.

ELM combined with differential Raman spectroscopy for the detection of microplastics in organisms

Aiming at the problems of low extraction efficiency, high false detection rate, weak Raman signal and serious interference by fluorescence signal in the detection of microplastics in marine organisms, this paper establishes a set of rapid detection methods for microplastics in organisms, including confocal Raman spectroscopy, fluorescence imaging, differential Raman spectroscopy, and rapid identification of microplastics based on the ELM modeling assistance. Firstly, to address the problem of low extraction efficiency of microplastics, we explored and optimized the digestion method of tissues, which effectively improved the digestion effect of fish tissues and excluded the influence of tissues on microplastics detection. Aiming at the problems of high misdetection rate and low pre-screening efficiency of microplastics, fluorescence imaging technology is adopted to realize the visualization and detection of microplastics, which effectively improves the detection efficiency and precision of microplastics. Based on the confocal microscopy Raman spectroscopy detection system built independently in the laboratory, using 784/785 nm as the excitation light, the differential Raman spectroscopy technique effectively excludes the interference of fluorescence signals in the Raman spectra, and improves the signal-to-noise ratio of the Raman spectra, and the recovery rate of the Raman characteristic peaks in the differential Raman spectroscopy reaches 100 % compared to the traditional baseline correction method, which is 33.3 % higher than that of the baseline correction method. Finally, a microplastic identification model is constructed based on ELM to assist in realizing the rapid and accurate identification of microplastics. The more complete detection method of microplastics in marine organisms proposed in this paper can realize the rapid and nondestructive, efficient and accurate detection of microplastics in fish, which can help to further promote the development of marine microplastics monitoring technology.

Machine learning driven methodology for enhanced nylon microplastic detection and characterization

In recent years, the field of microplastic (MP) research has evolved significantly; however, the lack of a standardized detection methodology has led to incomparability across studies. Addressing this gap, our current study innovates a reliable MP detection system that synergizes sample processing, machine learning, and optical photothermal infrared (O-PTIR) spectroscopy. This approach includes examining high-temperature filtration and alcohol treatment for reducing non-MP particles and utilizing a support vector machine (SVM) classifier focused on key wavenumbers that could discriminate between nylon MPs and non-nylon MPs (1077, 1541, 1635, 1711 cm−1 were selected based on the feature importance of SVM-Full wavenumber model) for enhanced MP identification. The SVM model built from key wavenumbers demonstrates a high accuracy rate of 91.33%. Results show that alcohol treatment is effective in minimizing non-MP particles, while filtration at 70 °C has limited impact. Additionally, this method was applied to assess MPs released from commercial nylon teabags, revealing an average release of 106 particles per teabag. This research integrates machine learning with O-PTIR spectroscopy, paving the way for potential standardization in MP detection methodologies and providing vital insights into their environmental and health implications.

Limits of the detection of microplastics in fish tissue using stimulated Raman scattering microscopy.

We demonstrate the detection sensitivity of microplastic beads within fish tissue using stimulated Raman scattering (SRS) microscopy. The intrinsically provided chemical contrast distinguishes different types of plastic compounds within fish tissue. We study the size-dependent signal-to-noise ratio of the microplastic beads and determine a lower boundary for the detectable size. Our findings demonstrate how SRS microscopy can serve as a complementary modality to conventional Raman scattering imaging in order to detect and identify microplastic particles in fish tissue.

Biotechnological interventions for monitoring and mitigating microplastic pollution and development of alternatives to single‐use plastics

AbstractThe dawn of mass plastic production in the early 20th century has accelerated the penetration of microplastics into the environment, making them known to be an insidious threat to diverse ecosystems. The current scenario for microplastics is dire, as they pervade living organisms and disrupt vital functions while also affecting the environment by altering soil quality. They pose an inherent risk to human health, making their elimination a multifaceted challenge. Due to factors such as small size, low biodegradability, and ubiquity, microplastics are particularly challenging to detect, and methods for their elimination from terrestrial and marine environments are an ever‐evolving field of research. The detection of such microplastics has necessitated the use of various biotechnological techniques to monitor microplastic pollution. The control of microplastic pollution in natural ecosystems can be met by replacing mass‐produced single‐use plastics with viable bio‐based alternatives. The main objectives of this review are to discuss how microplastic pollution is currently monitored, assessed, and controlled using satellite‐generated imagery complemented with a range of biotechnological and bioremedial techniques. This review also traces the development of a range of bio‐based plastic alternatives that are observed to potentially replace single‐use plastics. Further discussion on the various challenges and prospects for the mitigation of microplastics will also be conducted, stressing the importance of future research.

The pyrolysis mechanism analysis of polystyrene and phenol–formaldehyde resin microplastics using mass spectrometry

As a new pollutant, microplastics (MPs) have characteristics such as environmental persistence, biological toxicity, bioaccumulation and so on, even at low concentrations in the nature. At present, the detection methods for MPs are still in the development stage, and mass spectrometry (MS) is one of the most effective methods for microplastic detection and analysis. This paper utilizes a self-developed temperature programmed system pyrolysis (Py) device and a time of flight mass spectrometer (TOFMS) to establish a Py-PI-TOFMS using a single photon ionization (PI) source, and to construct an analysis technology for microplastics. The pyrolysis of thermoplastic MPs mainly follows the free radical pyrolysis mechanism, where the energy obtained through heating reaches the bond energy connected between atoms, and the molecular chains undergo fracture to generate various small molecule compounds. We used Py-PI-TOFMS technology to analyze the pyrolysis products of microplastics of polystyrene (PS) and phenol–formaldehyde resin (PF), explore the rules of free radical reactions, composition of pyrolysis products, bond dissociation characteristics, and combine theoretical calculations to study the thermodynamic and kinetic mechanisms in the pyrolysis process.