Simple and rapid detection of microplastics in seawater using hyperspectral imaging technology

Hyperspectral imaging for identification of irregular-shaped microplastics in water

In this article, we demonstrate detection and identification of ten microplastic types directly in a water sample using an identification table derived from microplastic hyperspectral images. We selected a total of fourteen wavelengths which can be used to distinguish these ten microplastic types. We enhanced the visibility of these wavelengths by computationally removing water and baseline correcting with reflectance at 1550 nm. This method avoids, prevents, and eases most of the laborious sample preparation mandatory prior to analysis with robust techniques such as Raman spectroscopy and Fourier transform infrared spectroscopy. The ten different plastics were studied in water, first separately and then in a mixture. The microplastic concentrations varied depending on microplastic type and were kept <12 mg/ml per type. Finally, detection and identification were confirmed pixel-wise in a hyperspectral image of a realistic water matrix simulant including mixtures of only a few microplastic particles. All measurements have been performed with microplastics of different sizes and irregular shapes made in-house by milling commercial pellets and sheets. It enabled the establishment of a procedure for the identification of these vicious particles in real water samples. The present measurement setup of hyperspectral imaging and method of data analysis of a mixture of microplastics directly from a water-based sample may open a path towards fast, reliable, and on-site detection.

In-situ detection of microplastics in the aquatic environment: A systematic literature review

Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.

Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil

In this study, we critically evaluated the performance of an emerging technology, hyperspectral imaging (HSI), for detecting microplastics (MPs) in soil. We examined the technology's robustness against varying environmental conditions in five groups of experiments. Our findings show that near-infrared (NIR) hyperspectral imaging (HSI) effectively detects microplastics (MPs) in soil, though detection efficacy is influenced by factors such as MP concentration, color, and soil moisture. We found a generally linear relationship between the levels of MPs in various soils and their spectral responses in the NIR HSI imaging spectrum. However, effectiveness is reduced for certain MPs, like polyethylene, in kaolinite clay. Furthermore, we showed that soil moisture considerably influenced the detection of MPs, leading to nonlinearities in quantification and adding complexities to spectral analysis. The varied responses of MPs of different sizes and colors to NIR HSI present further challenges in detection and quantification. The research suggests pre-grouping of MPs based on size before analysis and proposes further investigation into the interaction between soil moisture and MP detectability to enhance HSI's application in MP monitoring and quantification. To our knowledge, this study is the first to comprehensively evaluate this technology for detecting and quantifying microplastics.

Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils

Soil, particularly in agricultural regions, has been recognized as one of the significant reservoirs for the emerging contaminant of MPs. Therefore, developing a rapid and efficient method is critical for their identification in soil. Here, we coupled HSI systems [i.e., VNIR (400-1000 nm), InGaAs (800-1600 nm), and MCT (1000-2500 nm)] with machine learning algorithms to distinguish soils spiked with white PE and PA (average size of 50 and 300 µm, respectively). The soil-normalized SWIR spectra unveiled significant spectral differences not only between control soil and pure MPs (i.e., PE 100% and PA 100%) but also among five soil-MPs mixtures (i.e., PE 1.6%, PE 6.9%, PA 5.0%, and PA 11.3%). This was primarily attributable to the 1st-3rd overtones and combination bands of C-H groups in MPs. Feature reductions visually demonstrated the separability of seven sample types by SWIR and the inseparability of five soil-MPs mixtures by VNIR. The detection models achieved higher accuracies using InGaAs (92-100%) and MCT (97-100%) compared to VNIR (44-87%), classifying 7 sample types. Our study indicated the feasibility of InGaAs and MCT HSI systems in detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soil. SYNOPSIS: One of two SWIR HSI systems (i.e., InGaAs and MCT) with a sample imaging surface area of 3.6 mm² per grid cell was sufficient for detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soils without the digestion and separation procedures.

Quantitative detecting low concentration polystyrene nanoplastics in aquatic environments via an Ag/Nb2CTx (MXene) SERS substrate

In this study, the plasmonic Ag nanoparticles (Ag NPs) were uniformly anchored on the high conductivity Nb2CTx (MXene) nanosheets to construct an Ag/Nb2CTx substrate for surface-enhanced Raman spectroscopy (SERS) detection of polystyrene (PS) nanoplastics. The KI addition (0.15 mol/L), the volume ratio between substrate colloid and nanoplastic suspension (2:1), and the mass ratio of Nb2CTx in substrate (14%) on SERS performance were optimized. The EM hot spots of Ag/Nb2CTx are significantly enlarged and enhanced, elucidated by FDFD simulation. Then, the linear relationship between the PS nanoplastics concentration with three different sizes (50, 300, and 500 nm) and the SERS intensity was obtained (R2 > 0.976), wherein, the detection limit was as low as 10-4 mg/mL for PS nanoplastic. Owing to the fingerprint feature, the Ag/Nb2CTx-14% substrate successfully discerns the mixtures from two-component nanoplastics. Meanwhile, it exhibits excellent stability of PS nanoplastics on different detection sites. The recovery rates of PS nanoplastics with different sizes in lake water ranged from 94.74% to 107.29%, with the relative standard deviation (RSD) ranging from 2.88% to 8.30%. Based on this method, the expanded polystyrene (EPS) decomposition behavior was evaluated, and the PS concentrations in four water environments were analyzed. This work will pave the way for the accurate quantitative detection of low concentration of nanoplastics in aquatic environments.

Use of an uncrewed surface vehicle and near infrared hyperspectral imaging for sampling and analysis of aquatic microplastics

Data on MP in aquatic environments have low resolution in space and time. Scaling up sampling and increasing analysis throughput are the main bottlenecks. We combined two approaches: an uncrewed surface vehicle (USV) and near infrared hyperspectral imaging (NIR-HSI) for sampling and analysis of MP > 300 μm. We collected 35 water samples over 4 d in a coastal area. Samples were analyzed using NIR-HSI and Fourier transform infrared spectroscopy (FTIR). Spiked samples were used to determine recovery. We conclude that using a USV can mitigate issues of traditional trawls like scalability, repeatability, and contamination. NIR-HSI detects more polyethylene but less polypropylene than FTIR analysis and reduces analysis time significantly. Highly variable concentrations were found at both sampling locations, with mean MP concentration of 0.28 and 0.01 MP m-3 for location A and B respectively. USV sampling in tandem with NIR-HSI is an effective analytical pipeline for MP monitoring.

Analysis of micro(nano)plastics based on automated data interpretation and modeling: A review

The widespread presence of micro(nano)plastics (MNPs) in the environment threatens ecosystem integrity, and thus, it is necessary to determine and assess the occurrence, characteristics, and transport of MNPs between ecological components. However, most analytical approaches are cost- and time-inefficient in providing quantitative information with sufficient detail, and interpreting results can be difficult. Alternative analyses integrating novel measurements by imaging or proximal sensing with signal processing and machine learning may supplement these approaches. In this review, we examined published research on methods used for the automated data interpretation of MNPs found in the environment or those artificially prepared by fragmenting bulk plastics. We critically reviewed the primary areas of the integrated analytical process, which include sampling, data acquisition, processing, and modeling, applied in identifying, classifying, and quantifying MNPs in soil, sediment, water, and biological samples. We also provide a comprehensive discussion regarding model uncertainties related to estimating MNPs in the environment. In the future, the development of routinely applicable and efficient methods is expected to significantly contribute to the successful establishment of automated MNP monitoring systems.

Unveiling microplastic spectral signatures under weathering and digestive environments through shortwave infrared hyperspectral sensing

Microplastic (MP) pollution presents a novel challenge for marine environmental protection, necessitating comprehensive and long-term monitoring and assessment approaches. Environmental MPs can undergo weathering and microorganism-related digestive processes, altering their original surface properties and chemical structure, thus complicating their quantification and identification. This study aims to establish a comprehensive hyperspectral database for weathered and digestion-degraded MPs, using a wide variety of polymer types collected as either virgin particles or commercial products (within a size range of approximately 3 mm), and to investigate the impact of these processes on their spectral characteristics. Polypropylene (PP) and polyethylene (PE) MPs exhibited significant responses to weathering treatment, as indicated by the formation of new characteristic peaks or slight peak shifts around 1679-1705 nm, which can be attributed to the formation of carbonyl and vinyl functional groups through Norrish reactions. Similarly, polyethylene terephthalate (PET), acrylonitrile butadiene styrene (ABS), and polystyrene (PS) MPs demonstrated notable degradation following digestive treatment, as evidenced by the emergence of new absorption peaks at approximately 1135-1165 nm, possibly associated with alterations involving carbonyl and vinyl functional groups. The results were further validated based on their comparable spectral characteristics of the resultant MPs to reference polymers and possible additives, considering a reasonably accurate match of approximately 80% for the studied MP samples. This study showcases the significant advantage of using shortwave infrared hyperspectral sensing for rapid identification of virgin and exposed MPs with a relatively large scan area after a simple sample preparation. This approach, combined with other complementary characterization techniques, shall provide highly throughput results for MPs identification. This research provides valuable insights into the features extracted from environmental MPs and establishes a foundation for improving their classification efficiency for environmental applications.

Recent advances on the methods developed for the identification and detection of emerging contaminant microplastics: a review

The widespread use of plastics, popular for their versatility and cost-efficiency in mass production, has led to their essential role in modern society. Their remarkable attributes, such as flexibility, mechanical strength, lightweight, and affordability, have further strengthened their importance. However, the emergence of microplastics (MPs), minute plastic particles, has raised environmental concerns. Over the last decade, numerous studies have uncovered MPs of varying sizes in diverse environments. They primarily originate from textile fibres and cosmetic products, with large plastic items undergoing degradation and contributing as secondary sources. The bioaccumulation of MPs, with potential ingestion by humans through the food chain, underscores their significance as environmental contaminants. Therefore, continuous monitoring of environmental and food samples is imperative. A range of spectroscopic techniques, including vibrational spectroscopy, Raman spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, hyperspectral imaging, and nuclear magnetic resonance (NMR) spectroscopy, facilitates the detection of MPs. This review offers a comprehensive overview of the analytical methods employed for sample collection, characterization, and analysis of MPs. It also emphasizes the crucial criteria for selecting practical and standardized techniques for the detection of MPs. Despite advancements, challenges persist in this field, and this review suggests potential strategies to address these limitations. The development of effective protocols for the accurate identification and quantification of MPs in real-world samples is of paramount importance. This review further highlights the accumulation of microplastics in various edible species, such as crabs, pelagic fish, finfish, shellfish, American oysters, and mussels, shedding light on the extreme implications of MPs on our food chain.

Satellite hyperspectral imaging technology as a potential rapid pollution assessment tool for urban landfill sites: case study of Ghazipur and Okhla landfill sites in Delhi, India

Hyperspectral imaging technology has been used for biochemical analysis of Earth's surface exploiting the spectral reflectance signatures of various materials. The new-generation Italian PRISMA (PRecursore IperSpettrale dellaMissione Applicativa) hyperspectral satellite launched by the Italian space agency (ASI) provides a unique opportunity to map various materials through spectral signature analysis for recourse management and sustainable development. In this study PRISMA hyperspectral satellite imagery-based multiple spectral indices were generated for rapid pollution assessment at Ghazipur and Okhla landfill sites in Delhi, India. It was found that the combined risk score for Okhla landfill site was higher than the Ghazipur landfill site. Various manmade materials identified, exploiting the hyperspectral imagery and spectral signature libraries, indicated presence of highly saline water, plastic (black, ABS, pipe, netting, etc.), asphalt tar, black tar paper, kerogen BK-Cornell, black paint and graphite, chalcocite minerals, etc. in large quantities in both the landfill sites. The methodology provides a rapid pollution assessment tool for municipal landfill sites.

A review on constructive classification framework of research trends in analytical instrumentation for secondary micro(nano)plastics: What is new and what needs next?

Secondary micro(nano)plastics generated from the degradation of plastics pose a major threat to environmental and human health. Amid the growing research on microplastics to date, the detection of secondary micro(nano)plastics is hampered by inadequate analytical instrumentation in terms of accuracy, validation, and repeatability. Given that, the current review provides a critical evaluation of the research trends in instrumental methods developed so far for the qualitative and quantitative determination of micro(nano)plastics with an emphasis on the evolution, new trends, missing links, and future directions. We conducted a meta-analysis of the growing literature surveying over 800 journal articles published from 2004 to 2022 based on the Web of Science database. The significance of this review is associated with the proposed novel classification framework to identify three main research trends, viz. (i) preliminary investigations, (ii) current progression, and (iii) novel advances in sampling, characterization, and quantification targeting both micro- and nano-sized plastics. Field Flow Fractionation (FFF) and Hydrodynamic Chromatography (HDC) were found to be the latest techniques for sampling and extraction of microplastics. Fluorescent Molecular Rotor (FMR) and Thermal Desorption-Proton Transfer Reaction-Mass Spectrometry (TD-PTR-MS) were recognized as the modern developments in the identification and quantification of polymer units in micro(nano)plastics. Powerful imaging techniques, viz. Digital Holographic Imaging (DHI) and Fluorescence Lifetime Imaging Microscopy (FLIM) offered nanoscale analysis of the surface topography of nanoplastics. Machine learning provided fast and less labor-intensive analytical protocols for accurate classification of plastic types in environmental samples. Although the existing analytical methods are justifiable merely for microplastics, they are not fully standardized for nanoplastics. Future research needs to be more inclined towards secondary nanoplastics for their effective and selective analysis targeting a broad range of environmental and biological matrices.

Eliminating the interference of water for direct sensing of submerged plastics using hyperspectral near-infrared imager

Interference from water in the reflectance spectra of plastics is a major obstacle to optical sensing of plastics in aquatic environments. Here we present evidence of the feasibility of sensing plastics in water using hyperspectral near-infrared to shortwave-infrared imaging techniques. We captured hyperspectral images of nine polymers submerged to four depths (2.5-15 mm) in water using a hyperspectral imaging system that utilizes near-infrared to shortwave-infrared light sources. We also developed algorithms to predict the reflectance spectra of each polymer in water using the spectra of the dry plastics and water as independent variables in a multiple linear regression model after a logarithmic transformation. A narrow 1100-1300 nm wavelength range was advantageous for detection of polyethylene, polystyrene, and polyvinyl chloride in water down to the 160-320 µm size range, while a wider 970-1670 nm wavelength range was beneficial for polypropylene reflectance spectrum prediction in water. Furthermore, we found that the spectra of the other five polymers, comprising polycarbonate, acrylonitrile butadiene styrene, phenol formaldehyde, polyacetal, and polymethyl methacrylate, could also be predicted within their respective optimized wavelength ranges. Our findings provide fundamental information for direct sensing of plastics in water on both benchtop and airborne platforms.

Developing and testing a workflow to identify microplastics using near infrared hyperspectral imaging

The analysis of microplastics (MP) is time-consuming which limits our capacity to monitor and mitigate plastic pollution. Here, near infrared (1000-2500 nm) hyperspectral imaging (NIR-HSI) offers an advantage over other spectroscopic techniques because it can rapidly image large areas relative to other systems. While NIR-HSI can successfully detect MP, accuracy and limitations of the method have not been fully explored. In addition, lack of open databases and analysis pipelines increases the barrier to use. In this work, we developed a spectral database containing preproduction pellets, consumer products and marine plastic debris, imaged using a Hyspex SWIR-320me imager. A SIMCA model identified four polymer types: polypropylene, polyethylene, polyethylene terephthalate and polystyrene (PP, PE, PET, PS) to identify MP in hyperspectral images. We determined the accuracy of size estimates for PS MP > 1000 μm using fluorescence microscopy and tested the impact of photooxidation on detection of plastics by NIR-HSI (PE, PP, PS, PET) and subsequent prediction by the SIMCA model. The model performed well across all polymers as shown by high specificity, sensitivity, and accuracy for internal cross validation (>88%), and sensitivity >80% for external validation. PS MP < 500 μm Feret diameter were not consistently detected by NIR-HSI when compared with fluorescence microscopy. However, estimates for Feret diameter were consistent for PS MP > 1000 μm. Analysis by NIR-HSI showed no spectral changes and SIMCA showed no decreased precision with increased photooxidation across polymer types. Recall varied across polymer type and photooxidation stage with no clear trends. This study shows that NIR-HSI is a rapid method which can accurately identify MP of the four most relevant polymer types, precluding the need to analyze particles one at a time. NIR-HSI can be a key technology for environmental monitoring of plastic debris where rapid analysis of multiple samples is required.

Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology

Microplastics (MPs) in farming soils can have a substantial impact on soil ecology and agricultural productivity, as well as affecting human health and the food chain cycle. As a result, it is vital to study MPs detection technologies that are rapid, efficient, and accurate in agriculture soils. This study investigated the classification and detection of MPs using hyperspectral imaging (HSI) technology and a machine learning methodology. To begin, the hyperspectral data was preprocessed using SG convolution smoothing and Z-score normalization. Second, the feature variables were extracted from the preprocessed spectral data using bootstrapping soft shrinkage, model adaptive space shrinkage, principal component analysis, isometric mapping (Isomap), genetic algorithm, successive projections algorithm (SPA), and uninformative variable elimination. Finally, three support vector machine (SVM), back propagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN) models were developed to classify and detect three microplastic polymers: polyethylene, polypropylene, and polyvinyl chloride, as well as their combinations. According to the experimental results, the best approaches based on three models were Isomap-SVM, Isomap-BPNN, and SPA-1D-CNN. Among them, the accuracy, precision, recall and F1_score of Isomap-SVM were 0.9385, 0.9433, 0.9385 and 0.9388, respectively. The accuracy, precision, recall and F1_score of Isomap-BPNN were 0.9414, 0.9427, 0.9414 and 0.9414, respectively, while the accuracy, precision, recall and F1_score of SPA-1D-CNN were 0.9500, 0.9515, 0.9500 and 0.9500, respectively. When their classification accuracy was compared, SPA-1D-CNN had the best classification performance, with a classification accuracy of 0.9500. The findings of this study shown that the SPA-1D-CNN based on HSI technology can efficiently and accurately identify MPs in farmland soils, providing theoretical backing as well as technical means for real-time detection of MPs in farmland soils.

Comparison of two rapid automated analysis tools for large FTIR microplastic datasets

One of the biggest issues in microplastic (MP, plastic items  <5 mm) research is the lack of standardisation and harmonisation in all fields, reaching from sampling methodology to sample purification, analytical methods and data analysis. This hampers comparability as well as reproducibility among studies. Concerning chemical analysis of MPs, Fourier-transform infrared (FTIR) spectroscocopy is one of the most powerful tools. Here, focal plane array (FPA) based micro-FTIR (µFTIR) imaging allows for rapid measurement and identification without manual preselection of putative MP and therefore enables large sample throughputs with high spatial resolution. The resulting huge datasets necessitate automated algorithms for data analysis in a reasonable time frame. Although solutions are available, little is known about the comparability or the level of reliability of their output. For the first time, within our study, we compare two well-established and frequently applied data analysis algorithms in regard to results in abundance, polymer composition and size distributions of MP (11-500 µm) derived from selected environmental water samples: (a) the siMPle analysis tool (systematic identification of MicroPlastics in the environment) in combination with MPAPP (MicroPlastic Automated Particle/fibre analysis Pipeline) and (b) the BPF (Bayreuth Particle Finder). The results of our comparison show an overall good accordance but also indicate discrepancies concerning certain polymer types/clusters as well as the smallest MP size classes. Our study further demonstrates that a detailed comparison of MP algorithms is an essential prerequisite for a better comparability of MP data.

Rapid shipboard measurement of net-collected marine microplastic polymer types using near-infrared hyperspectral imaging

Isolation and detection of microplastics (MP) in marine samples is extremely cost- and labor-intensive, limiting the speed and amount of data that can be collected. In the current work, we describe rapid measurement of net-collected MPs (net mesh size 300 µm) using a benchtop near-infrared hyperspectral imaging system during a research expedition to the subtropical North Atlantic gyre. Suspected plastic particles were identified microscopically and mounted on a black adhesive background. Particles were imaged with a Specim FX17 near-infrared linescan camera and a motorized stage. A particle mapping procedure was built on existing edge-finding algorithms and a polymer identification method developed using spectra from virgin polymer reference materials. This preliminary work focused on polyethylene, polypropylene, and polystyrene as they are less dense than seawater and therefore likely to be found floating in the open ocean. A total of 27 net tows sampled 2534 suspected MP particles that were imaged and analyzed at sea. Approximately 77.1% of particles were identified as polyethylene, followed by polypropylene (9.2%). A small fraction of polystyrene was detected only at one station. Approximately 13.6% of particles were either other plastic polymers or were natural materials visually misidentified as plastics. Particle size distributions for PE and PP particles with a length greater than 1 mm followed an approximate power law relationship with abundance. This method allowed at-sea, near real-time identification of MP polymer types and particle dimensions, and shows great promise for rapid field measurements of microplastics in net-collected samples.

Detection methods of micro and nanoplastics

Plastics and related contaminants (including microplastics; MPs and nanoplastics; NPs) have become a serious global safety issue due to their overuse in many products and applications and their inadequate management, leading to possible leakage into the environment and eventually to the food chain and humans. There is a growing literature reporting on the occurrence of plastics, (MPs and NPs) in both marine and terrestrial organisms, with many indications about the harmful impact of these contaminants on plants and animals, as well as potential human health risks. The presence of MPs and NPs in many foods and beverages including seafood (especially finfish, crustaceans, bivalves, and cephalopods), fruits, vegetables, milk, wine and beer, meat, and table salts, has become popular research areas in recent years. Detection, identification, and quantification of MPs and NPs have been widely investigated using a wide range of traditional methods, such as visual and optical methods, scanning electron microscopy, and gas chromatography-mass spectrometry, but these methods are burdened with a number of limitations. In contrast, spectroscopic techniques, especially Fourier-transform infrared spectroscopy and Raman spectroscopy, and other emerging techniques, such as hyperspectral imaging are increasingly being applied due to their potential to enable rapid, non-destructive, and high-throughput analysis. Despite huge research efforts, there is still an overarching need to develop reliable analytical techniques with low cost and high efficiency. Mitigation of plastic pollution requires establishing standard and harmonized methods, adopting holistic approaches, and raising awareness and engaging the public and policymakers. Therefore, this chapter focuses mainly on identification and quantification techniques of MPs and NPs in different food matrices (mostly seafood).

Visual characterization of microplastics in corn flour by near field molecular spectral imaging and data mining

As potential hazard to human health, microplastics have attracted increasing attention. Most current studies have addressed the characterization of microplastics from the environment. For microplastics in food, most detections focused on liquid systems such as alcohol, beverages, etc., while there has been quite rare research on microplastics in solid foods with complex matrices. Thus, this study attempted to use three molecular spectral imaging approaches, namely, Fourier transform infrared (FTIR), optical photothermal resonance infrared (O-PTIR), and confocal Raman spectral imaging, combined with chemometrics to characterize the presence of microplastics in corn flour. The results demonstrated that O-PTIR imaging can rapidly sense the presence of microplastics, but its data integrity and visualization were limited. By decomposing the image, FTIR and Raman acquired a more integral distribution. Wherein, microplastics were well depicted by Raman imaging coupled with independent component analysis. Moreover, O-PTIR imaging can quickly detect contaminants at low concentrations but with a low detection rate. Raman imaging underperformed in low-concentration samples but provided a better visualization in mid-concentration samples. Overall, the results confirmed that the visual detection of microplastics in powdered food can be realized by molecular spectral imaging coupled with data mining, which can provide a reference for the detection of microplastics in other foods.

Microplastics occurrence, detection and removal with emphasis on insect larvae gut microbiota

Microplastics have been identified in all living forms including human beings, the present need is to restrain its spread and devise measures to remediate microplastics from polluted ecosystems. In this regard, the present review emphasizes on the occurrence, sources detection and toxic effects of microplastics in various ecosystems. The removal of microplastics is prevalent by various physico-chemical and biological methods, although the removal efficiency by biological methods is low. It has been noted that the degradation of plastics by insect gut larvae is a well-known aspect, however, the underlying mechanism has not been completely identified. Studies conducted have shown the magnificent contribution of gut microbiota, which have been isolated and exploited for microplastic remediation. This review also focuses on this avenue, as it highlights the contribution of insect gut microbiota in microplastic degradation along with challenges faced and future prospects in this area.

Probing nanoplastics derived from polypropylene face masks with hyperspectral dark-field microscopy

The high worldwide consumption of cheap plastic goods has already resulted in a serious environmental plastic pollution, exacerbated by piling of disposed personal protective equipment because of the recent outbreak of COVID-19. The aim of this study was to assess the feasibility of dark-field hyperspectral microscopy in the 400-1000 wavelength range for detection of nanoplastics derived from weathered polypropylene masks. A surgical mask was separated to layers and exposed to UV radiation (254 nm) for 192 h. Oxidative degradation of the polypropylene was evidenced by ATR FT-IR analysis. UV treatment for 192 h resulted in generation of differently shaped micro- and nano-sized particles, visualized by dark-field microscopy. The presence of nanoparticles was confirmed by AFM studies. The hyperspectral profiles (400-1000 nm) were collected after every 48 h of the UV treatment. The distinct hyperspectral features faded after prolonged UV exposure, but the assignment of some particles to either blue or white layers of mask could still be made based on spectral characteristics.

Recent advances in the application of machine learning methods to improve identification of the microplastics in environment

Environmental pollution by microplastics (MPs) is a significant and complex global issue. Existing MPs identification methods have demonstrated significant limitations such as low resolution, long imaging time, and limited particle size analysis. New and improved methods for MPs identification are topical research areas, with machine learning (ML) algorithms proven highly useful in recent years. Critical literature reviews on the latest developments in this area are limited. This study closes this gap and summarizes the progress made over the last 10 years in using ML algorithms for identifying and quantifying MPs. We identified diverse combinations of ML methods and fundamental techniques for MPs identification, such as Fourier-transform infrared spectroscopy, Raman spectroscopy, and near-infrared spectroscopy. The most widely used ML model is the support vector machine, which effectively improves the conventional analysis method for spectral quality defects and improves detection accuracy. Artificial neural network models exhibit improved recognition effects, with shorter detection times and better MPs recognition efficiency. Our review demonstrates the potential of ML in improving the identification and quantification of MPs.

Development of multi-disturbance bagging Extreme Learning Machine method for cadmium content prediction of rape leaf using hyperspectral imaging technology

Exploring the cadmium (Cd) pollution in rape is of great significance to food safety and consumer health. In this study, a rapid, nondestructive and accurate method for the determination of Cd content in rape leaves based on hyperspectral imaging (HSI) technology was proposed. The spectral data of rape leaves under different Cd stress from 431 nm to 962 nm were collected by visible-near infrared HSI spectrometer. In order to improve the robustness and accuracy of the regression model, a machine learning algorithm was proposed, named multi-disturbance bagging Extreme Learning Machine (MdbaggingELM). The prediction models of Cd content in rape leaves based on MdbaggingELM and ELM-based method (ELM and baggingELM) were established and compared. The results showed that the model of the proposed MdbaggingELM method performed significantly in the prediction of Cd content, with Rp² of 0.9830 and RMSEP of 2.8963 mg/kg. The results confirmed that MdbaggingELM is an efficient regression algorithm, which played a positive role in enhancing the stability and the prediction effect of the model. The combination of MdbaggingELM and HSI technology has great potential in the detection of Cd content in rape leaves.

The micro-, submicron-, and nanoplastic hunt: A review of detection methods for plastic particles

Plastic particle pollution has been shown to be almost completely ubiquitous within our surrounding environment. This ubiquity in combination with a variety of unique properties (e.g. density, hydrophobicity, surface functionalization, particle shape and size, transition temperatures, and mechanical properties) and the ever-increasing levels of plastic production and use has begun to garner heightened levels of interest within the scientific community. However, as a result of these properties, plastic particles are often reported to be challenging to study in complex (i.e. real) environments. Therefore, this review aims to summarize research generated on multiple facets of the micro- and nanoplastics field; ranging from size and shape definitions to detection and characterization techniques to generating reference particles; in order to provide a more complete understanding of the current strategies for the analysis of plastic particles. This information is then used to provide generalized recommendations for researchers to consider as they attempt to study plastics in analytically complex environments; including method validation using reference particles obtained via the presented creation methods, encouraging efforts towards method standardization through the reporting of all technical details utilized in a study, and providing analytical pathway recommendations depending upon the exact knowledge desired and samples being studied.

Label-Free Live-Cell Imaging of Internalized Microplastics and Cytoplasmic Organelles with Multicolor CARS Microscopy

As the bioaccumulation of microplastics (MPs) is considered as a potential health risk, many efforts have been made to understand the cellular dynamics and cytotoxicity of MPs. Here, we demonstrate that label-free multicolor coherent anti-Stokes Raman scattering (CARS) microscopy enables separate vibrational imaging of internalized MPs and lipid droplets (LDs) with indistinguishable shapes and sizes in live cells. By simultaneously obtaining polystyrene (PS)- and lipid-specific CARS images at two very different frequencies, 1000 and 2850 cm^-1, respectively, we successfully identify the local distribution of ingested PS beads and native LDs in Caenorhabditis elegans. We further show that the movements of PS beads and LDs in live cells can be separately tracked in real time, which allows us to characterize their individual intracellular dynamics. We thus anticipate that our multicolor CARS imaging method could be of great use to investigate the cellular transport and cytotoxicity of MPs without additional efforts for pre-labeling to MPs.

Dynamics of airborne microplastics, appraisal and distributional behaviour in atmosphere; a review

The use of plastics is common across all aspects of human life owing to its durable and versatile nature. The generation and utilization of plastics are directly related to the anthropogenic activities. The extensive use of plastics and adoption of inappropriate waste-management frameworks has resulted in their release into the environment, where they may persist. Different environmental factors, such as, photochemical, thermo-oxidation, and biological degradation, can lead to the degradation of plastics into micro- (MPs) and nano-plastics (NPs). The behaviour and concentration of MPs in the terrestrial environment can depend on their size, density, and local atmospheric conditions. Microplastics and nanoplastics may enter the food web, carrying various organic pollutants, which bio-accumulate at different trophic levels, prompting organism health concerns. Microplastics being airborne identifies as new exposure route. Dietary and airborne exposure to MPs has led researchers to stress the importance of evaluating their toxicological potential. The primary goal of this paper is to explore the environmental fate of MPs from sources to sink in the terrestrial environment, as well as detail their potential impacts on human health. Additionally, this review article focuses on the presence of airborne microplastics, detailed sample pre-processing methods, and outlines analytical methods for their characterization.

Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning

The problem of automating the data analysis of microplastics following a spectroscopic measurement such as focal plane array (FPA)-based micro-Fourier transform infrared (FTIR), Raman, or QCL is gaining ever more attention. Ease of use of the analysis software, reduction of expert time, analysis speed, and accuracy of the result are key for making the overall process scalable and thus allowing nonresearch laboratories to offer microplastics analysis as a service. Over the recent years, the prevailing approach has been to use spectral library search to automatically identify spectra of the sample. Recent studies, however, showed that this approach is rather limited in certain contexts, which led to developments for making library searches more robust but on the other hand also paved the way for introducing more advanced machine learning approaches. This study describes a model-based machine learning approach based on random decision forests for the analysis of large FPA-μFTIR data sets of environmental samples. The model can distinguish between more than 20 different polymer types and is applicable to complex matrices. The performance of the model under these demanding circumstances is shown based on eight different data sets. Further, a Monte Carlo cross validation has been performed to compute error rates such as sensitivity, specificity, and precision.

Rapid detection of Pseudomonas aeruginosa based on lab-on-a-chip platform using immunomagnetic separation, light scattering, and machine learning

The rapid detection of the pathogenic bacteria in patient samples is crucial to expedient patient care. The proposed approach reports the development of a novel lab-on-a-chip device for the rapid detection of P. aeruginosa based on immunomagnetic separation, optical scattering, and machine learning. The immunomagnetic particles with a diameter of 5 μm were synthesized for isolating P. aeruginosa from the test sample. A microfluidic chip was fabricated, and three optical fibers were embedded for connecting a laser light and two photodetectors. The laser light was pointed towards the channel to pass light through the sample. A pair of photodetectors via optical fibers were arranged symmetrically at 45° to the channel. The photodetectors acquired scattered light from the flowing sample and converted the light to an electrical signal. The sample containing immunomagnetic beads linked with bacteria was injected into the microfluidic chip. The optimized conditions for performing the experiments were characterized for real-time detection of P. aeruginosa. The data acquisition system recorded the real-time light scattering from the test sample. After removing noise from the output waveform, five different time-domain statistical features were extracted from each waveform: standard mean, standard variance, skewness, kurtosis, and coefficient of variation. The pathogens classification was performed by training the discrimination model using extracted features based on machine learning algorithms. The support vector machines (SVM) with a sigmoid function kernel showed superior classification performance with 97.9% accuracy among other classifiers, including k-nearest neighbors (KNN), logistic regression (LR), and naïve Bayes (NB). The method can detect P. aeruginosa specifically and quantitatively with a limit of detection of 10² CFU/mL. The device can classify P. aeruginosa within 10 min with a total assay time of 25 min. The device was used to test its ability to detect pathogen from the serum and sputum specimens spiked with 10⁵ CFU/mL concentration of P. aeruginosa. The results indicate that light scattering combined with machine learning can be used to detect P. aeruginosa. The proposed technique is anticipated to be helpful as a rapid device for diagnosing P. aeruginosa related infections.

Extraction and detection methods of microplastics in food and marine systems: A critical review

The ubiquitous presence of microplastics as contaminants in the ecosystem has become a matter of environmental concern gaining considerable attention in the research community as well as public arena. Lack of efficient collection and improper management of plastic have resulted in the enormous amounts of plastic wastes landing into the marine systems with oceans being the ultimate sink. Due to non-biodegradability, these plastics break down into smaller fragments over a period of time leading to consumption by aquatic species, threatening marine life. In the recent years, a wide range of food products has also been contaminated with microplastics directly affecting human health. This review focuses on the separation and identification technologies for extraction and detection of microplastics in food and marine ecosystems. Efficient technologies like floatation, membrane separation, chemical treatment, enzymatic treatment, and other miscellaneous techniques have been discussed considering their merits and demerits. Additionally, identification technologies like optical detection, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, thermo-analytical methods, and hyperspectral imaging have been emphasized for the detection of microplastic particles. The emerging techniques like enzymatic digestion combined with hyperspectral imaging could be a possible way for obtaining higher separation efficiency and characterization with minimal harm to food sample. This article narrows the gap for choosing a standard separation technology for microplastic detection in food matrices keeping in mind the composition, particle size, shape, data visualization techniques and cost.

Chemical Analysis of Microplastics and Nanoplastics: Challenges, Advanced Methods, and Perspectives

Microplastics and nanoplastics have become emerging particulate anthropogenic pollutants and rapidly turned into a field of growing scientific and public interest. These tiny plastic particles are found in the environment all around the globe as well as in drinking water and food, raising concerns about their impacts on the environment and human health. To adequately address these issues, reliable information on the ambient concentrations of microplastics and nanoplastics is needed. However, micro- and nanoplastic particles are extremely complex and diverse in terms of their size, shape, density, polymer type, surface properties, etc. While the particle concentrations in different media can vary by up to 10 orders of magnitude, analysis of such complex samples may resemble searching for a needle in a haystack. This highlights the critical importance of appropriate methods for the chemical identification, quantification, and characterization of microplastics and nanoplastics. The present article reviews advanced methods for the representative mass-based and particle-based analysis of microplastics, with a focus on the sensitivity and lower-size limit for detection. The advantages and limitations of the methods, and their complementarity for the comprehensive characterization of microplastics are discussed. A special attention is paid to the approaches for reliable analysis of nanoplastics. Finally, an outlook for establishing harmonized and standardized methods to analyze these challenging contaminants is presented, and perspectives within and beyond this research field are discussed.

Hyperspectral Imaging as a Potential Online Detection Method of Microplastics

Microplastic pollution in aquatic environment has raised concern and as a result a number of studies have recently been published to find solutions for its rapid increase. Different methods have been proposed for microplastic identification. Spectral imaging shows a lot of promise for polymer identification; however, the identification time needs to be improved. Hyperspectral imaging (HSI) combined with chemometric analysis can reduce the identification times. In this study, we provide a review of recent studies related to polymer identification using HSI with a focus on the adopted classification algorithm and its factors for the online implementation of HSI. Furthermore, we review the limit of detection by HSI and the effect of particle size on classification accuracy. Additionally, performance of this method for various types of samples is also discussed. We conclude that HSI is possible to be a fast alternative for online microplastic detection.

The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging

Based on the micro-hyperspectral imaging technique, spherical engineered microplastic (polyethylene, 10-45 μm) and microalgae (Isochrysis galbana) (4-7 μm) were identified. In transmittance mode of MHSI, micro image cubes from 400 to 1000 nm were obtained from slides containing MP and MA in thin seawater. Classifiers like Support Vector Machine (SVM(Radial Basis Function (RBF))), Least Squares Support Vector Machine (LSSVM(RBF)), k-Nearest Neighbors, etc. were adopted and compared to classify MP and MA. In order to expand the imaging range of micro imaging, image stitching technology was adopted. In allusion to the stitched image cube, SVM(RBF) is suggested for the identification of MA and MP, with recall and precision > 0.86. The above results demonstrate that the MHSI is a promising technique, which can detect MPs with particle size Limit of Detection of 10-45 μm, and it is potential to further expand this LOD.

Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat

Stearic acid content is an important factor affecting mutton odor. To determine the distribution and content of stearic acid (C18:0) in lamb meat fast and nondestructively, a method integrating spectral and textural data of hyperspectral imaging (900-1700 nm) was proposed in this paper. Firstly, spectral information was obtained and preprocessed. Then, the spectral features were extracted by variable combination population analysis-genetic algorithm (VCPA-GA) and interval variable iterative space shrinking analysis (IVISSA). Subsequently, the prediction models of partial least squares regression (PLSR) and least-squares support vector machines (LSSVMs) were established and compared. The model constructed with SNVD-VCPA-GA-PLSR achieved better performance. To improve the prediction results of the models, the textural features were extracted using a gray-level co-occurrence matrix (GLCM) and fused with spectral features. The optimized model achieved good results, with R_c of 0.8716, RMSEC of 0.0793 g/100 g, RPD_c of 2.398, and R_p of 0.8121 with RMSEP of 0.1481 g/100 g and RPD_p of 1.756. Finally, the spatial distribution of the C18:0 content in lamb meat was visualized using an optimal model. The result indicated that it was feasible to predict and visualize the C18:0 content in lamb meat, providing a way for real-time detection of volatile fatty acid compounds in meat.

A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics

Microplastic pollution is a global concern theme, and there is still the need for less laborious and faster analytical methods aiming at microplastics detection. This article describes a high throughput screening method based on near-infrared hyperspectral imaging (HSI-NIR) to identify microplastics in beach sand automatically with minimum sample preparation. The method operates directly in the entire sample or on its retained fraction (150 μm-5 mm) after sieving. Small colorless microplastics (<600 μm) that would probably be imperceptible as a microplastic by visual inspection, or missed during manual pick up, can be easily detected. No spectroscopic subsampling was performed due to the high-speed analysis of line-scan instrumentation, allowing multiple microplastics to be assessed simultaneously (video available). This characteristic is an advantage over conventional infrared (IR) spectrometers. A 75 cm² scan area was probed in less than 1 min at a pixel size of 156 × 156 μm. An in-house comprehensive spectral dataset, including weathered microplastics, was used to build multivariate supervised soft independent modelling of class analogy (SIMCA) classification models. The chemometric models were validated for hundreds of microplastics (primary and secondary) collected in the environment. The effect of particle size, color and weathering are discussed. Models' sensitivity and specificity for polyethylene (PE), polypropylene (PP), polyamide-6 (PA), polyethylene terephthalate (PET) and polystyrene (PS) were over 99% at the defined statistical threshold. The method was applied to a sand sample, identifying 803 particles without prior visual sorting, showing automatic identification was robust and reliable even for weathered microplastics analyzed together with other matrix constituents. The HSI-NIR-SIMCA described is also applicable for microplastics extracted from other matrices after sample preparation. The HSI-NIR principals were compared to other common techniques used to microplastic chemical characterization. The results show the potential to use HSI-NIR combined with classification models as a comprehensive microplastic-type characterization screening.

Development of robust models for rapid classification of microplastic polymer types based on near infrared hyperspectral images

Hyperspectral data in the near infrared range were examined for nine common types of plastic particles of 1 mm and 100-500 μm sizes on dry and wet glass fiber filters. Weaker peak intensities were detected for small particles compared to large particles, and the reflectances were weaker at longer wavelengths when the particles were measured on a wet filter. These phenomena are explainable due to the effect of the correlation between the particle size and the absorption of infrared light by water. We constructed robust classification models that are capable of classifying polymer types, regardless of particle size or filter conditions (wet vs. dry), based on hyperspectral data for small particles measured on wet filters. Using the models, we also successfully classified the polymer type of polystyrene beads covered with microalgae, which simulates the natural conditions of microplastics in the ocean. This study suggests that hyperspectral imaging techniques with appropriate classification models allow the identification of microplastics without the time- and labor-consuming procedures of drying samples and removing biofilms, thus enabling more rapid analyses.

Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques

Microplastics (MPs) have been found in aqueous environments ranging from rural ponds and lakes to the deep ocean. Despite the ubiquity of MPs, our ability to characterize MPs in the environment is limited by the lack of technologies for rapidly and accurately identifying and quantifying MPs. Although standards exist for MP sample collection and preparation, methods of MP analysis vary considerably and produce data with a broad range of data content and quality. The need for extensive analysis-specific sample preparation in current technology approaches has hindered the emergence of a single technique which can operate on aqueous samples in the field, rather than on dried laboratory preparations. In this perspective, we consider MP measurement technologies with a focus on both their eventual field-deployability and their respective data products (e.g., MP particle count, size, and/or polymer type). We present preliminary demonstrations of several prospective MP measurement techniques, with an eye towards developing a solution or solutions that can transition from the laboratory to the field. Specifically, experimental results are presented from multiple prototype systems that measure various physical properties of MPs: pyrolysis-differential mobility spectroscopy, short-wave infrared imaging, aqueous Nile Red labeling and counting, acoustophoresis, ultrasound, impedance spectroscopy, and dielectrophoresis.

Towards the Development of Portable and In Situ Optical Devices for Detection of Micro and Nanoplastics in Water: A Review on the Current Status

The prevalent nature of micro and nanoplastics (MP/NPs) on environmental pollution and health-related issues has led to the development of various methods, usually based on Fourier-transform infrared (FTIR) and Raman spectroscopies, for their detection. Unfortunately, most of the developed techniques are laboratory-based with little focus on in situ detection of MPs. In this review, we aim to give an up-to-date report on the different optical measurement methods that have been exploited in the screening of MPs isolated from their natural environments, such as water. The progress and the potential of portable optical sensors for field studies of MPs are described, including remote sensing methods. We also propose other optical methods to be considered for the development of potential in situ integrated optical devices for continuous detection of MPs and NPs. Integrated optical solutions are especially necessary for the development of robust portable and in situ optical sensors for the quantitative detection and classification of water-based MPs.

Identification of black microplastics using long-wavelength infrared hyperspectral imaging with imaging-type two-dimensional Fourier spectroscopy

Despite recent progress in focal plane array Fourier transform infrared spectroscopy (FPA-FT-IR) for automatic microplastic (MP) discrimination, the analysis time is still too long (e.g., 9 h for a sample with a diameter of 47 mm) and the equipment is expensive. As a solution, a hyperspectral camera restricted to the near-infrared or short-wavelength infrared band could be applied. However, with these bands, the minimum discriminable size is only about 100 μm, and discrimination among darkly colored plastics is difficult. The long-wavelength infrared (LWIR) band is reportedly effective for discrimination among darkly colored plastics. In this study, we constructed a palm-sized LWIR hyperspectral camera (105 mm × 90 mm × 50 mm, 1.25 kg) for imaging-type two-dimensional Fourier spectroscopy. Our system used a general-purpose, inexpensive, and compact microbolometer for the LWIR band. This system could record the absorbance of black MPs (polystyrene, polyethylene, and polypropylene) in a 3.8 mm × 3.0 mm area in 36 s, which was less than 1/6th of the time required for FPA-FT-IR. Additionally, our system could obtain spectra for a 12 μm × 12 μm area. Because our device is cheaper and more compact than a FPA-FT-IR, it will be easier to take out in the field or on a research vessel.

Nanostructured Raman substrates for the sensitive detection of submicrometer-sized plastic pollutants in water

We prepared novel Raman substrates for the sensitive detection of submicron-sized plastic spheres in water. Anisotropic nanostar dimer-embedded nanopore substrates were prepared for the efficient identification of submicron-sized plastic spheres by providing internal hot spots of electromagnetic field enhancements at the tips of nanoparticles. Silver-coated gold nanostars (AuNSs@Ag) were inserted into anodized aluminum oxide (AAO) nanopores for enhanced microplastic (MP) detection. We found that surface-enhanced Raman scattering (SERS) substrates of AuNSs@Ag@AAO yielded stronger signals at the same weight percentages for polystyrene MP particles with diameters as small as 0.4 μm, whereas such behaviors could not be observed for larger MPs (diameters of 0.8 μm, 2.3 μm, and 4.8 μm). The detection limit of the submicrometer-sized 0.4 μm in our Raman measurements were estimated to be 0.005% (∼0.05 mg/g =50 ppm) along with a fast detection time of only a few min without any sample pretreatments. Our nano-sized dimensional matching substrates may provide a useful tool for the application of SERS substrates for submicrometer MP pollutants in water.

Microplastic's story

The problem of microplastic pollution is now the order of the day in front of everyone's eyes affecting the environment and the health of leaving creature. This work aims to retrace the history of microplastics in a critical way through a substantial bibliographic collection, defining the points still unresolved and those that can be resolved. Presence of marine litter in different environments is reviewed on a global scale, focusing in particular on micro and macro plastics definition, classification and characterization techniques.

Optimization of a hyperspectral imaging system for rapid detection of microplastics down to 100 µm

Plastic pollution has become one of the most emergent issues threating aquatic and terrestrial ecosystems. However, it is still challenging to rapidly detect small microplastics. Here, we present a method to rapidly detect microplastics using hyperspectral imaging in which we optimized a commercially available hyperspectral imaging system (Pika NIR-640, Resonon Inc., USA). The optimizations included: (1) changing the four-lamp assembly to a symmetrical set of converged-light near-infrared lamps that are placed sideways instead of above the sample stage; (2) adopting a macro-photography technique by applying an extension tube between the camera and the lens, and moving the lens of the hyperspectral camera to the imaging target (working distance of ~3 cm); (3) adjusting the exposure and aspect ratio by tuning the frame rate and scan speed of the imaging system. After optimization, the detection resolution of each pixel improved from 250 µm to 14.8 µm. With the optimized system, microplastics down to 100 µm in size were rapidly detected. This result is promising for the application of our new method in the accelerated detection of microplastics and will contribute to a better understanding of the microplastic pollution situation.

Critical Assessment of Analytical Methods for the Harmonized and Cost-Efficient Analysis of Microplastics

Microplastics are of major concerns for society and is currently in the focus of legislators and administrations. A small number of measures to reduce or remove primary sources of microplastics to the environment are currently coming into effect. At the moment, they have not yet tackled important topics such as food safety. However, recent developments such as the 2018 bill in California are requesting the analysis of microplastics in drinking water by standardized operational protocols. Administrations and analytical labs are facing an emerging field of methods for sampling, extraction, and analysis of microplastics, which complicate the establishment of standardized operational protocols. In this review, the state of the currently applied identification and quantification tools for microplastics are evaluated providing a harmonized guideline for future standardized operational protocols to cover these types of bills. The main focus is on the naked eye detection, general optical microscopy, the application of dye staining, flow cytometry, Fourier transform infrared spectroscopy (FT-Ir) and microscopy, Raman spectroscopy and microscopy, thermal degradation by pyrolysis-gas chromatography-mass spectrometry (py-GC-MS) as well as thermo-extraction and desorption gas chromatography-mass spectrometry (TED-GC-MS). Additional techniques are highlighted as well as the combined application of the analytical techniques suggested. An outlook is given on the emerging aspect of nanoplastic analysis. In all cases, the methods were screened for limitations, field work abilities and, if possible, estimated costs and summarized into a recommendation for a workflow covering the demands of society, legislation, and administration in cost efficient but still detailed manner.

On optical sensing of surface roughness of flat and curved microplastics in water

The growth of microplastic (MP) pollution is of increasing concern and represents a global challenge. In situ detection of these small particles is difficult because of their sizes, shapes, transparency or translucency, surface texture and ambient conditions. We propose and demonstrate the use of a prototype optical sensor to detect flat, nearly flat, curved and rough MPs prepared from commercial polyethylene terephthalate (PET) plastics and PET bottles in water. The prototype measures the specular reflection of a laser radiation incident on MPs, with a photodiode, and the transmitted laser speckle pattern, with a charge-coupled device (CCD) camera. The presence of the MPs as well as the optical surface roughness are determined from the specular reflection. Additionally, the so-called speckle contrast calculated from the speckle pattern, as a promising tool, is used to rank the rough MPs according to the different average surface roughness, to approximately twice the wavelength of the probing light. The novel application of laser speckle contrast and the optical roughness estimation allows the description of MP surface roughness in water. Moreover, in combination with earlier studies, these results, therefore, pave a way for the complete and a relatively easier description of MPs properties optical and also advances our step towards the development of simple and robust optical monitoring techniques for micro and nanoplastics in open and wastewater.

Characterization of microplastics on filter substrates based on hyperspectral imaging: Laboratory assessments

Microplastic pollution has become an urgent issue because it adversely affects ecosystems. However, efficient methods to detect and characterize microplastic particles are still in development. By conducting a series of laboratory assessments based on near-infrared hyperspectral imaging in the wavelength range of 900-1700 nm, we report the fundamental spectral features of (i) 11 authentic plastics and (ii) 11 filter substrate materials. We found that different plastic polymers showed distinct spectral features at 1150-1250 nm, 1350-1450 nm and 1600-1700 nm, enabling their automatic recognition and identification with spectral separation algorithms. Using an improved hyperspectral imaging system, we demonstrated the detection of three types of microplastic particles, polyethylene, polypropylene and polystyrene, down to 100 μm in diameter. As a filter substrate, a gold-coated polycarbonate filter (GPC0847-BA) showed constant reflectance over 900-1700 nm and a large radiative contrast against loaded plastic particles. Glass fiber filters (GF10 and GF/F) would also be suitable substrates due to their low cost and easy commercial availability. This study provides key parameters for applying hyperspectral imaging techniques for the detection of microplastics.

Separation, characterization and identification of microplastics and nanoplastics in the environment

Microplastics (MPs) have globally been detected in aquatic and marine environments, which has raised scientific interests and public health concerns during the past decade. MPs are those polymeric particles with at least one dimension <5 mm. MPs possess complex physicochemical properties that vary their mobility, bioavailability and toxicity toward organisms and interactions with their surrounding pollutants. Similar to nanomaterials and nanoparticles, accurate and reliable detection and measurement of MPs or nanoplastics and their characteristics are important to warrant a comprehensive understanding of their environmental and ecological impacts. This review elaborates the principles and applications of diverse analytical instruments or techniques for separation, characterization and quantification of MPs in the environment. The strength and weakness of different instrumental methods in separation, morphological, physical classification, chemical characterization and quantification for MPs are critically compared and analyzed. There is a demand for standardized experimental procedures and characterization analysis due to the complex transformation, cross-contamination and heterogeneous properties of MPs in size and chemical compositions. Moreover, this review highlights emerging and promising characterization techniques that may have been overlooked by research communities to study MPs. The future research efforts may need to develop and implement new analytical tools and combinations of hyphenated technologies to complement respective limitations of detection and yield reliable characterization information for MPs. The goal of this critical review is to facilitate the research of plastic particles and pollutants in the environment and understanding of their environmental and human health effects.

Optical transmission spectra study in visible and near-infrared spectral range for identification of rough transparent plastics in aquatic environments

Erosion of microplastics due to residence time in aquatic environments causes roughening of the microplastic. Unfortunately, currently used measurement methods do not provide information on the surface roughness of the microplastic embedded in water. In this study we propose a novel method by using transmittance to get information on the magnitude of the surface roughness of microplastics and to rank microplastics by thickness. For such a purpose, we studied optical properties such as dispersion, absorption of both plastics and water in the partial spectral range of visible light (Vis), transmission and scattering of light by plastic sheets, as well as, the calculated sample thickness in the Vis region. These were explored for the detection of both smooth and roughened plastic sheets immersed in water. Moreover, by using the transmission spectrum and refractive index of both plastic and water it is possible to estimate the average surface roughness of plastic samples. Our results suggest that the optical properties in the Vis region offer an interesting way for the detection of both rough and smooth plastic sheets and for ranking the type of plastics in an aquatic environment.

Quantification of floating riverine macro-debris transport using an image processing approach

A new algorithm has been developed to quantify floating macro-debris transport on river surfaces that consists of three fundamental techniques: (1) generating a difference image of the colour difference between the debris and surrounding water in the CIELuv colour space, (2) detecting the debris pixels from the difference image, and (3) calculating the debris area flux via the template matching method. Debris pixels were accurately detected from the images taken of the laboratory channel and river water surfaces and were consistent with those detected by visual observation. The area fluxes were statistically significantly correlated with the mass fluxes measured through debris collection. The mass fluxes calculated by multiplying the area fluxes with the debris mass per unit area (M/A) were significantly related to the flood rising stage flow rates and agreed with the mass fluxes measured through debris collection. In our algorithm, plastic mass fluxes can be estimated via calibration using the mass percentage of plastics to the total debris in target rivers. Quantifying riverine macro-plastic transport is essential to formulating countermeasures, mitigating adverse plastic pollution impacts and understanding global-scale riverine macro-plastic transport.