Identification of microplastics in a large water volume by integrated holography and Raman spectroscopy

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.

Development of automated microplastic identification workflow for Raman micro-imaging and evaluation of the uncertainties during micro-imaging

In this study, an automated identification workflow for Raman micro-imaging (RMI) was developed, and the performance was evaluated by artificial samples of microplastic (MP) microsphere with different sizes and types. Theoretical detection rate and estimated particle size were derived and compared with experimental data. Results show that the proposed workflow can identify plastic types and estimate the size of the MP microspheres under different conditions for most cases. However, size of laser spot and discrepancy between sample surface and focal plane can influence RMI results in two ways. Firstly, small particles are more likely to be detected. Secondly, estimated sizes of particles are more likely to be overestimated. The derived uncertainties can serve as a reference for future experimental design and further investigation of more complex situations. The workflow is accessible online, and interested researchers can adjust the parameter values as necessary to suit their specific circumstances.

Multimodal image and spectral feature learning for efficient analysis of water-suspended particles

We have developed a method to combine morphological and chemical information for the accurate identification of different particle types using optical measurement techniques that require no sample preparation. A combined holographic imaging and Raman spectroscopy setup is used to gather data from six different types of marine particles suspended in a large volume of seawater. Unsupervised feature learning is performed on the images and the spectral data using convolutional and single-layer autoencoders. The learned features are combined, where we demonstrate that non-linear dimensional reduction of the combined multimodal features can achieve a high clustering macro F1 score of 0.88, compared to a maximum of 0.61 when only image or spectral features are used. The method can be applied to long-term monitoring of particles in the ocean without the need for sample collection. In addition, it can be applied to data from different types of sensor measurements without significant modifications.

Development of an inter-confirmatory plastic characterization system using spectroscopic techniques for waste management

Ever-accumulating amounts of plastic waste raises alarming concern over environmental and public health. A practical solution for addressing this threat is recycling, and the success of an industry-oriented plastic recycling system relies greatly on the accuracy of the waste sorting technique adapted. We propose a multi-modal spectroscopic sensor which combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy in a single optical platform for characterizing plastics based on elemental and molecular information to assist the plastic identification-sorting process in recycling industries. The unique geometry of the system makes it compact and cost-effective for dual spectroscopy. The performance of the system in classifying industrially important plastic classes counting PP, PC, PLA, Nylon-1 1, and PMMA is evaluated, followed by the application of the same in real-world plastics comprising PET, HDPE, and PP in different chemical-physical conditions where the system consumes less than 30 ms for acquiring LIBS-Raman signals. The evaluation of the system in characterizing commuting samples shows promising results to be applied in industrial conditions in future. The study on effect of physical-chemical conditions of plastic wastes in characterizing them using the system shows the necessity for combining multiple techniques together. The proposal is not to determine the paramount methodology to characterize and sort plastics, but to demonstrate the advantages of dual-spectroscopy sensors in such applications. The outcomes of the study suggest that the system developed herein has the potential of emerging as an industrial-level plastic waste sorting sensor.

Evaluation of cytogenotoxic potential and embryotoxicity of KRS-Cauvery River water in zebrafish (Danio rerio)

In the Cauvery River (CR), indiscriminate discharge of waste causes unexplained skeletal deformity in some fish species present in the water. To investigate this phenomenon, we analyzed the biological, physical, and chemical parameters present in the water and then evaluated the toxicity effects on the zebrafish (Danio rerio) model. The zebrafish were treated with KRS-CR water samples collected from three stations (fast-flowing water [X], slow-flowing [Y], and stagnant [Z] water), before and after filtration. Firstly, we detected microscopic organisms (MO) such as Cyclops, Daphnia, Spirogyra, Spirochaeta, and total coliform (Escherichia coli), which are bioindicators of water pollution present in the samples. All physicochemical parameters analyzed, including heavy metals before and after filtration of the water with Millipore filter paper (0.45 µm), were within the acceptable limits set by standard organizations, except for decreased dissolved oxygen (DO), and increased biochemical oxygen demand (BOD), and chemical oxygen demand (COD), which are indicators of hypoxic water conditions, as well as the presence of microplastics (polybutene (< 15 µm), polyisobutene (≤ 20 µm), and polymethylpentene (≤3 mm)) and cyclohexyl in CR water samples. Zebrafish embryos treated with the water samples, both before and after filtration exerts the same cytogenotoxic effects by inducing increased reactive oxygen species (ROS) production, which triggers subcellular organelle dysfunctions, DNA damage, apoptosis, pericardial edema, skeletal deformities, and increased mortality. As a result, we observed that both water samples and zebrafish larvae had significantly less oxygen using SEM and EDS. Our findings show that KRS-CR water can induce cytogenotoxic and embryotoxic defects in zebrafish due to hypoxic water conditions triggered by the microplastics influx. The present study would provide valuable insights for health hazards evaluation and future river water treatment strategies.

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.

Raman imaging combined with an improved PCA/algebra-based algorithm to capture microplastics and nanoplastics

Raman imaging has advanced recently to be able to directly visualise microplastics and even nanoplastics.

Inclusion of shape parameters increases the accuracy of 3D models for microplastics mass quantification

As microplastics may bring about adverse effects on living organisms, it is important to establish more precise quantification approaches to better understand their dynamics. One method to determine the concentration of microplastics is to estimate their mass using three-dimensional (3D) models, but its accuracy is not well known. In this study, we evaluated the shape of the particles and verified the accuracy of a 3D model-based mass estimation using samples from a tidal flat facing Tokyo Bay. The particle shape evaluation suggested that the microplastics were flat and irregular in shape; based on these data, we created two types of models to estimate their mass. As a result, an accuracy of mass estimation by our model was higher than other models that consider the slenderness and flatness of particles. The optimization of mass estimation methods based on 3D models may improve the reliability of microplastic evaluation in monitoring studies.

Design of a confocal micro-Raman spectroscopy system and research on microplastics detection

Traditional micro-Raman spectroscopy technology has the disadvantages of a weak signal and low signal-to-noise ratio. To fix these issues, a cost-effective and rigorous design method is proposed in this paper, whereby a confocal micro-Raman spectroscopy system is designed and built, and a low-cost reflector and high-pass filter are introduced into the Raman signal-receiving module. The Raman light incident is fully perpendicular to the coupling lens by adjusting the reflection angle of the mirror, making the focus of the coupling lens highly conjugate with the focus of the microscope objective, to enhance the intensity of the Raman signal and improve the signal-to-noise ratio. In order to better apply this technology to the detection and study of microplastics in offshore sediments, a reflective illumination light path is used to avoid the visual interference caused by the capillary structure and opacity of the glass cellulose filter membrane. The detection and analysis of the microplastics on the glass cellulose filter membrane have been carried out by the confocal micro-Raman system designed, which is low cost and capable of obtaining good detection results and meeting the requirements of microplastics detection. The system designed in this paper is expected to be applied to the research and development of Raman detection equipment for microplastics in marine sediments, which is beneficial to promote the development of marine microplastic monitoring technology in the world.

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.

Selective Imaging of Microplastic and Organic Particles in Flow by Multimodal Coherent Anti-Stokes Raman Scattering and Two-Photon Excited Autofluorescence Analysis

Microplastic pollution is an urgent global issue. While spectroscopic techniques have been widely used for the identification of plastics collected from aquatic environments, these techniques are often labor-intensive and time-consuming due to sample collection, preparation, and long measurement times. In this study, a method for the two-dimensional detection and classification of flowing microplastic and organic biotic particles with high spatial and temporal resolutions has been proposed based on the simultaneous detection of coherent anti-Stokes Raman scattering (CARS) and two-photon excited autofluorescence (TPEAF) signals. Poly(methyl methacrylate) (PMMA), polystyrene (PS), and low-density polyethylene (LDPE) particles with sizes ranging from several tens to hundreds of micrometers were selectively detected in flow with an average velocity of 4.17 mm/s by CARS line scanning. With the same flow velocity, flowing PMMA and alga particles were measured using a multimodal system of CARS and TPEAF signals. The average intensities of both PMMA and alga particles in the CARS signals at a frequency of 2940 cm^-1 were higher than the background level, while only algae emitted TPEAF signals. This allowed the classification of PMMA and alga particles to be successfully performed in flow by the simultaneous detection of CARS and TPEAF signals. With the proposed method, the monitoring of microplastics in a continuous water flow without collection or extraction is possible, which is game-changing for the current sampling-based microplastic analysis.

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.

Flow-Through Quantification of Microplastics Using Impedance Spectroscopy

Understanding the sources, impacts, and fate of microplastics in the environment is critical for assessing the potential risks of these anthropogenic particles. However, our ability to quantify and identify microplastics in aquatic ecosystems is limited by the lack of rapid techniques that do not require visual sorting or preprocessing. Here, we demonstrate the use of impedance spectroscopy for high-throughput flow-through microplastic quantification, with the goal of rapid measurement of microplastic concentration and size. Impedance spectroscopy characterizes the electrical properties of individual particles directly in the flow of water, allowing for simultaneous sizing and material identification. To demonstrate the technique, spike and recovery experiments were conducted in tap water with 212-1000 μm polyethylene beads in six size ranges and a variety of similarly sized biological materials. Microplastics were reliably detected, sized, and differentiated from biological materials via their electrical properties at an average flow rate of 103 ± 8 mL/min. The recovery rate was ≥90% for microplastics in the 300-1000 μm size range, and the false positive rate for the misidentification of the biological material as plastic was 1%. Impedance spectroscopy allowed for the identification of microplastics directly in water without visual sorting or filtration, demonstrating its use for flow-through sensing.

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.

Identification of Plastic Type and Surface Roughness of Film-Type Plastics in Water Using Kramers–Kronig Analysis

The knowledge of the plastic type, thickness, and the nature of the surface is important towards the monitoring of microplastic pollution in water bodies, especially when vis-NIR spectroscopy is utilized. Factors such as complex environment and surface roughness induced-light scattering of the probing light limit the optical detection of these parameters in in-situ measurements, however. In this paper, a novel application of Kramers–Kronig analysis was exploited to identify both smooth and rough film-type macroplastics with unknown thickness. This method is particularly useful in the in-situ identification of unknown film-like macroplastics; although the sample is large, the ratio function is detected from an area that corresponds to the size of a MP. Therefore, it can be applied for the case of large size MPs. The validity of the method was demonstrated using transmittance data for smooth and roughened plastics given in Kanyathare et al., 2020.