Classification Of Microplastics Research Articles

Remote 3D Imaging and Classification of Pelagic Microorganisms with A Short‐Range Multispectral Confocal LiDAR

AbstractPlankton is essential to maintain healthy aquatic ecosystems since it influences the biological carbon pump globally. However, climate change‐induced alterations to oceanic properties threaten planktonic communities. It is therefore crucial to monitor their abundance to assess the health status of marine ecosystems. In situ optical tools unlock high‐resolution measurements of sub‐millimeter specimens, but state‐of‐the‐art underwater imaging techniques are limited to fixed and small close‐range volumes, requiring the instruments to be vertically dived. Here, a novel scanning multispectral confocal light detection and ranging (LiDAR) system for short‐range volumetric sensing in aquatic media is introduced. The system expands the inelastic confocal principle to multiple wavelength channels, allowing the acquisition of 4D point clouds combining near‐diffraction limited morphological and spectroscopic data that is used to train artificial intelligence (AI) models. Volumetric mapping and classification of microplastics is demonstrated to sort them by color and size. Furthermore, in vivo autofluorescence is resolved from a community of free‐swimming zooplankton and microalgae, and accurate spectral identification of different genera is accomplished. The deployment of this photonic platform alongside AI models overcomes the complex and subjective task of manual plankton identification and enables non‐intrusive sensing from fixed vantage points, thus constituting a unique tool for underwater environmental monitoring.

Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy.

In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.

Feasibility Study for the Development of a Low-Cost, Compact, and Fast Sensor for the Detection and Classification of Microplastics in the Marine Environment.

The detection and classification of microplastics in the marine environment is a complex task that implies the use of delicate and expensive instrumentation. In this paper, we present a preliminary feasibility study for the development of a low-cost, compact microplastics sensor that could be mounted, in principle, on a float of drifters, for the monitoring of large marine surfaces. The preliminary results of the study indicate that a simple sensor equipped with three infrared-sensitive photodiodes can reach classification accuracies around 90% for the most-diffused floating microplastics in the marine environment (polyethylene and polypropylene).

Adsorption of Different Pollutants by Using Microplastic with Different Influencing Factors and Mechanisms in Wastewater: A Review.

The studies on microplastics are significant in the world. According to the literature, microplastics have greatly specific surface areas, indicating high adsorption capacities for highly toxic pollutants in aquatic and soil environments, and these could be used as adsorbents. The influencing factors of microplastic adsorption, classification of microplastics, and adsorption mechanisms using microplastics for adsorbing organic, inorganic, and mixed pollutants are summarized in the paper. Furthermore, the influence of pH, temperature, functional groups, aging, and other factors related to the adsorption performances of plastics are discussed in detail. We found that microplastics have greater advantages in efficient adsorption performance and cost-effectiveness. In this paper, the adsorptions of pollutants by microplastics and their performance is proposed, which provides significant guidance for future research in this field.

Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning

Microplastics quantification and classification are demanding jobs to monitor microplastic pollution and evaluate the potential health risks. In this paper, microplastics from daily supplies in diverse chemical compositions and shapes are imaged by scanning electron microscopy. It offers a greater depth and finer details of microplastics at a wider range of magnification than visible light microscopy or a digital camera, and permits further chemical composition analysis. However, it is labour-intensive to manually extract microplastics from micrographs, especially for small particles and thin fibres. A deep learning approach facilitates microplastics quantification and classification with a manually annotated dataset including 237 micrographs of microplastic particles (fragments or beads) in the range of 50 μm–1 mm and fibres with diameters around 10 μm. For microplastics quantification, two deep learning models (U-Net and MultiResUNet) were implemented for semantic segmentation. Both significantly outmatched conventional computer vision techniques and achieved a high average Jaccard index over 0.75. Especially, U-Net was combined with object-aware pixel embedding to perform instance segmentation on densely packed and tangled fibres for further quantification. For shape classification, a fine-tuned VGG16 neural network classifies microplastics based on their shapes with high accuracy of 98.33%. With trained models, it takes only seconds to segment and classify a new micrograph in high accuracy, which is remarkably cheaper and faster than manual labour. The growing datasets may benefit the identification and quantification of microplastics in environmental samples in future work.

A method to remove cellulose from rich organic samples to analyse microplastics

Knowing the amount of microplastics that currently reach wastewater is extremely important today. Furthermore, carrying out a good quantification and detection of the type of plastic provides valuable information. However, the wastewater is loaded, in addition to a high concentration of organic matter, with a high concentration of cellulose at the treatment plant influent, which seriously hinders detection, quantification and classification of microplastics. The abundance of cellulose materials makes them possible to become false positives for microplastics. Numerous studies on the analysis of microplastics in different matrices show how to remove organic matter from samples, but there are very few studies on the removal of cellulose, which is also found in the samples and hinders their analysis. This study offers a method that combines, for the analysis of microplastics, the already known advanced oxidation treatments for the elimination of organic matter with the novel cellulose removal treatment of the samples with the aim of reducing the amount of cellulose in the influent samples of conventional wastewater treatment plants. To remove the cellulose, 40 mL of a solution of urea 8%, sodium hydroxide 8% and thiourea 6.5% (by weight) were added for every 100 mg of dry sample. The beakers were placed in the freezer at minus 20 °C for 40 min and were then placed in agitation until they reached room temperature. After that, the samples were passed through a 53 μm mesh sieve. They were washed 15 times with 30 mL of ultra-pure water. The method is called UTS because of the acronym of its reagents (Urea/Thiourea/Sodium Hydroxide). By using the UTS method it is possible to reduce almost completely the cellulose residues from the influent sewage, and sludge samples by 97.6% in a second UTS treatment and 98.2% in a third UTS treatment. In all cases analyzed, the microplastics were identified as high density polyethylene (HDPE) with correlation indices higher than 0.97, which shows that the treatment is harmless for this type of plastic material. The UTS method in combination with the WPO is an efficient and effective method for the analysis of microplastics in different matrices where cellulose and organic matter may cause possible interferences.

Digital holographic imaging and classification of microplastics using deep transfer learning.

We devise an inline digital holographic imaging system equipped with a lightweight deep learning network, termed CompNet, and develop the transfer learning for classification and analysis. It has a compression block consisting of a concatenated rectified linear unit (CReLU) activation to reduce the channels, and a class-balanced cross-entropy loss for training. The method is particularly suitable for small and imbalanced datasets, and we apply it to the detection and classification of microplastics. Our results show good improvements both in feature extraction, and generalization and classification accuracy, effectively overcoming the problem of overfitting. This method could be attractive for future in situ microplastic particle detection and classification applications.

METHOD FOR QUANTIFYING AND CHARACTERIZATION OF MICROPLASTICS IN SAND BEACHES

In recent years microplastics have become an issue of global concern due to the negative effects that they cause in the environment, such as their ingestion by marine species and their capacity to adsorb pollutants from water. To face this problem research has been performed around the world to study the presence of microplastics in marine environments. However, the use of different on-field sampling and laboratory characterization techniques, sometimes difficult to replicate, hinders the possibility to compare results. This paper proposes a methodology for sampling and characterization of microplastics on the 1 – 5 mm range present on sand beaches. First, a comparison is made between the different techniques used for sampling, extraction, classification and identification of microplastics on studies performed on the last five years. Then the proposed methodology, which involves sampling in the high tide zone, at 5 cm depth, is explained in detail. The method has a low cost, as it uses simple equipment available in most of the labs. It can be easily replicable and allows presenting results in different units. The decision criteria, materials and steps in each of its seven stages (identification of sampling zone, selection of sampling points, sampling, drying, extraction, quantification and classification of microplastics) are described. Also, it explains a process to eliminate false positives caused by organic matter or shells. This work aims to improve the efficiency, replicability and homogeneity in the study of microplastics.

A case study of identification and classification of microplastics using aluminium sulfate in wastewater treatment facility on the university campus

This study is to identify the Microplastics in the effluent of the treatment plant on the University campus. The plastic with less than 5 (five) mm size is universally considered as Microplastics. Volume reduced sampling method is used and for sample concentration, Aluminium sulfate as the coagulant is used. A sample was concentrated as the liquid and solid powder. One micron filter paper is used. Visual identification was done using the dissecting microscope, For particles smaller than 40X magnification scanning electron microscope (SEM) we used. For Identification of polymers Fourier Transform Infrared Spectroscopy (FT-IR) was used. In FT-IR test results all the liquid samples were negative in a result, and all the solid samples were positive that, it showed the presence of polymer in the sample, the plastics identified are Polyethylene, polystyrene, Polyvinyl chloride, Polyamide (nylon) in the solid sample.