Deep learning approach for automatic microplastics counting and classification

Towards quality-assured measurements of microplastics in soil using fluorescence microscopy

Fluorescence microscopy is increasingly seen as a fast, user-friendly, and high-throughput method for detecting microplastics (MPs) in soil; however, its effectiveness across diverse MP types and soil properties remains underexplored. This study tested a fluorescence microscopy-Nile red (NR) staining approach on eight MP types, covering both biodegradable and non-biodegradable plastics, in three size ranges (≤ 150 µm, 100-250 µm, 500-1000 µm) across loamy, clayey, and sandy soils. Each sample, processed in triplicate, underwent a relatively quick and straightforward extraction procedure involving density separation, organic digestion, and NR staining, followed by fluorescence and bright-field microscopy. A new digital image analysis pipeline using Image J was developed to expedite and (semi)automate MP quantification. Recoveries ranged from 80% to 90% for MPs with a Feret diameter of 500-1000 µm, regardless of soil type. In contrast, the recovery of smaller MPs (Feret dia. ≤ 250 µm) varied depending on the soils and plastic types: recoveries for low-density polyethylene (LDPE) reached 85% in sandy soil and 90% in loamy soil, whereas those for biodegradable polybutylene adipate terephthalate/polylactic acid (PBAT/PLA) were only 60% and 10%, respectively. The lowest recovery rate was observed in clayey soil and for biodegradable plastics. The method was tested on non-agricultural soil samples, yielding a MP mean number concentration of 20.7 ± 9.0 MPs/g for MPs sized from dia. ≥ 25 µm, comparable to Fourier transform infrared (FPA-µ-FTIR) results of 13.1 ± 7.3 MPs/g (p > 0.05). We conclude that fluorescence microscopy with NR staining and automated particle quantification offers a time-efficient, reproducible, and accurate method for MP detection in light-textured soils, whereas limitations remain for reliable MP analysis in clay-dominated soils.

Automatic pre-screening of outdoor airborne microplastics in micrographs using deep learning

Airborne microplastics (AMPs) are prevalent in both indoor and outdoor environments, posing potential health risks to humans. Automating the process of identifying potential particles in micrographs can significantly enhance the research and monitoring of AMPs. Although deep learning has shown substantial promise in microplastics analysis, existing studies have primarily focused on high-resolution images of samples collected from marine and freshwater environments. In contrast, this work introduces a novel approach by employing enhanced U-Net models (Attention U-Net and Dynamic RU-NEXT) along with the Mask Region Convolutional Neural Network (Mask R-CNN) to identify and classify outdoor AMPs in low-resolution micrographs (256 × 256 pixels). A key innovation involves integrating classification directly within the U-Net-based segmentation frameworks, thereby streamlining the workflow and improving computational efficiency. This marks an advancement over previous work where segmentation and classification were performed separately. The enhanced U-Net models attained average classification F1-scores exceeding 85% and segmentation accuracy above 77% on test images. Additionally, the Mask R-CNN model achieved an average bounding box precision of 73.32%, a classification F1-score of 84.29%, and a mask precision of 71.31%. The proposed method provides a faster and more accurate means of identifying AMPs compared to thresholding techniques. It also functions effectively as a pre-screening tool, substantially reducing the number of particles requiring labour-intensive chemical analysis. By integrating advanced deep learning strategies into AMPs research, this study paves the way for more efficient monitoring and characterisation of microplastics.

Machine learning-based analysis of microplastic-induced changes in anaerobic digestion parameters influencing methane yield

Microplastics (MPs) present significant challenges for anaerobic digestion (AD) processes used in energy recovery from contaminated organic waste. Given that optimal AD conditions vary widely across studies when MPs are present, a robust predictive model is essential to accurately assess these complex effects. This study applied four machine learning algorithms to predict methane yield using two datasets-one with and one without MPs. Among these, gradient boosting regression demonstrated the highest prediction accuracy, with testing R2 values of 0.996 for systems without MP pollution and 0.998 with MP pollution. This model was then further optimized by removing redundant and low-importance features, refining its predictive power. Feature importance analysis revealed that digestion time and substrate organic matter content were key parameters positively correlated with methane production. In the presence of MPs, substrate pH and inoculum total solids emerged as critical factors, with partial dependence plots offering deeper insights into their optimal conditions. This research offers new perspectives on the intricate effects of MPs on methane production, which could inform the optimization of AD processes in environments contaminated by MPs.

Rapid detection of microfibres in environmental samples using open-source visual recognition models

Microplastics, particularly microfibres (< 5 mm), are a significant environmental pollutant. Detecting and quantifying them in complex matrices is challenging and time-consuming. This study presents two open-source visual recognition models, YOLOv7 and Mask R-CNN, trained on extensive datasets for efficient microfibre identification in environmental samples. The YOLOv7 model is a new introduction to the microplastic quantification research, while Mask R-CNN has been previously used in similar studies. YOLOv7, with 71.4 % accuracy, and Mask R-CNN, with 49.9 % accuracy, demonstrate effective detection capabilities. Tested on aquatic samples from Seyðisfjörður, Iceland, YOLOv7 rapidly identifies microfibres, outperforming manual methods in speed. These models are user-friendly and widely accessible, making them valuable tools for microplastic contamination assessment. Their rapid processing offers results in seconds, enhancing research efficiency in microplastic pollution studies. By providing these models openly, we aim to support and advance microplastic quantification research. The integration of these advanced technologies with environmental science represents a significant step forward in addressing the global issue of microplastic pollution and its ecological and health impacts.

Microplastics monitoring in freshwater systems: A review of global efforts, knowledge gaps, and research priorities

The escalating production of synthetic plastics and inadequate waste management have led to pervasive microplastic (MP) contamination in aquatic ecosystems. MPs, typically defined as particles smaller than 5 mm, have become an emerging pollutant in freshwater environments. While significant concern about MPs has risen since 2014, research has predominantly concentrated on marine settings, there is an urgent need for a more in-depth critical review to systematically summarize the current global efforts, knowledge gaps, and research priorities for MP monitoring in freshwater systems. This review evaluates the current understanding of MP monitoring in freshwater environments by examining the distribution, characteristics, and sources of MPs, alongside the progression of analytical methods with quantitative evidence. Our findings suggest that MPs are widely distributed in global freshwater systems, with higher abundances found in areas with intense human economic activities, such as the United States, Europe, and China. MP abundance distributions vary across different water bodies (e.g., rivers, lakes, estuaries, and wetlands), with sampling methods and size range selections significantly influencing reported MP abundances. Despite great global efforts, there is still a lack of harmonized analyzing framework and understanding of MP pollution in specific regions and facilities. Future research should prioritize the development of standardized analysis protocols and open-source MP datasets to facilitate data comparison. Additionally, exploring the potential of state-of-the-art artificial intelligence for rapid, accurate, and large-scale modeling and characterization of MPs is crucial to inform effective strategies for managing MP pollution in freshwater ecosystems.

Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors

With the massive release of microplastics (MPs) into the environment, research related to MPs is advancing rapidly. Effective research methods are necessary to identify the chemical composition, shape, distribution, and environmental impacts of MPs. In recent years, artificial intelligence (AI)-driven machine learning methods have demonstrated excellent performance in analyzing MPs in soil and water. This review provides a comprehensive overview of machine learning methods for the prediction of MPs for various tasks, and discusses in detail the data source, data preprocessing, algorithm principle, and algorithm limitation of applied machine learning. In addition, this review discusses the limitation of current machine learning methods for various task analysis in MPs along with future prospect. Finally, this review finds research potential in future work in building large generalized MPs datasets, designing high-performance but low-computational-complexity algorithms, and evaluating model interpretability.

Artificial intelligence-empowered collection and characterization of microplastics: A review

Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.

Data driven AI (artificial intelligence) detection furnish economic pathways for microplastics

Microplastics pollution is killing human life, contaminating our oceans, and lasting for longer in the environment than it is used. Microplastics have contaminated the geochemistry and turned the water system into trash barrel. Its detection in water is easy in comparison to soil and air so the attention of researchers is focused on it for now. Being very small in size, microplastics can easily cross the water filtration system and end up in the ocean or lakes and become the prospective challenge to aquatic life. This review piece provides the hot research theme and current advances in the field of microplastics and their eradication through the virtual world of artificial intelligence (AI) because Microplastics have confrontation with clean water tactics.

Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data

Plastic products' widespread applications and their non-biodegradable nature have resulted in the continuous accumulation of microplastic waste, emerging as a significant component of ecological environmental issues. In the field of microplastic detection, the intricate morphology poses challenges in achieving rapid visual characterization of microplastics. In this study, photoacoustic imaging technology is initially employed to capture high-resolution images of diverse microplastic samples. To address the limited dataset issue, an automated data processing pipeline is designed to obtain sample masks while effectively expanding the dataset size. Additionally, we propose Vqdp2, a generative deep learning model with multiple proxy tasks, for predicting six forms of microplastics data. By simultaneously constraining model parameters through two training modes, outstanding morphological category representations are achieved. The results demonstrate Vqdp2's excellent performance in classification accuracy and feature extraction by leveraging the advantages of multi-task training. This research is expected to be attractive for the detection classification and visual characterization of microplastics.

Intelligent classification and pollution characteristics analysis of microplastics in urban surface waters using YNet

Microplastics (MPs, ≤ 5 mm in size) are hazardous contaminants that pose threats to ecosystems and human health. YNet was developed to analyze MPs abundance and shape to gain insights into MPs pollution characteristics in urban surface waters. The study found that YNet achieved an accurate identification and intelligent classification performance, with a dice similarity coefficient (DSC) of 90.78%, precision of 94.17%, and recall of 89.14%. Analysis of initial MPs levels in wetlands and reservoirs revealed 127.3 items/L and 56.0 items/L. Additionally, the MPs in effluents were 27.0 items/L and 26.3 items/L, indicating the ability of wetlands and reservoirs to retain MPs. The concentration of MPs in the lower reaches of the river was higher (45.6 items/L) compared to the upper reaches (22.0 items/L). The majority of MPs detected in this study were fragments, accounting for 51.63%, 54.94%, and 74.74% in the river, wetland, and reservoir. Conversely, granules accounted for the smallest proportion of MPs in the river, wetland, and reservoir, representing only 11.43%, 10.38%, and 6.5%. The study proves that the trained YNet accurately identify and intelligently classify MPs. This tool is essential in comprehending the distribution of MPs in urban surface waters and researching their sources and fate.

Cory's shearwater as a key bioindicator for monitoring floating plastics

The potential of using organisms as bioindicators of marine litter has been an area of general interest in multiple scientific and monitoring programs across the globe. Procellariiformes seabirds are particularly vulnerable to plastic contamination, which makes them a research focus group. This study investigated plastic ingestion in deceased fledglings and adults Cory's shearwaters (Calonectris borealis) collected over eight years (2015 to 2022) at two Atlantic archipelagos: the Azores and the Canaries. Necropsies were carried out in a total of 1,238 individuals showing a high prevalence of plastic ingestion (90%), with approximately 80% of items recovered from the gizzard. Fledglings carried greater plastic loads compared to adults, yet plastic morphologies were similar between both age classes. The temporal analyses conducted with generalised additive mixed-effect models revealed a distinct temporal trend in plastic numbers, but not in terms of plastic mass. In addition, the spatial analyses showed that Cory's shearwaters from the Canary Islands ingest a higher quantity of plastic and a greater proportion of threadlike items than the Azorean birds. These results suggest higher contamination at the NW Africa foraging grounds next to the Canaries and highlight fisheries as a potential source of marine litter in that region. On the other hand, the information gathered from the Azorean birds suggests they would be able to monitor changes in the composition of the plastic items floating in the North Atlantic Subtropical Gyre. Overall, our outcomes support the use of Cory's shearwater fledglings that are victims of light pollution as a key bioindicator of plastic contamination in the North Atlantic. For its policy application, the presented threshold value in combination with the assessment method will enable effective tracking of floating plastic litter in the framework of the MSFD and OSPAR.

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.

The "Microplastome" - A Holistic Perspective to Capture the Real-World Ecology of Microplastics

Microplastic pollution, an emerging pollution issue, has become a significant environmental concern globally due to its ubiquitous, persistent, complex, toxic, and ever-increasing nature. As a multifaceted and diverse suite of small plastic particles with different physicochemical properties and associated matters such as absorbed chemicals and microbes, future research on microplastics will need to comprehensively consider their multidimensional attributes. Here, we introduce a novel, conceptual framework of the "microplastome", defined as the entirety of various plastic particles (<5 mm), and their associated matters such as chemicals and microbes, found within a sample and its overall environmental and toxicological impacts. As a novel concept, this paper aims to emphasize and call for a collective quantification and characterization of microplastics and for a more holistic understanding regarding the differences, connections, and effects of microplastics in different biotic and abiotic ecosystem compartments. Deriving from this lens, we present our insights and prospective trajectories for characterization, risk assessment, and source apportionment of microplastics. We hope this new paradigm can guide and propel microplastic research toward a more holistic era and contribute to an informed strategy for combating this globally important environmental pollution issue.

A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light

Hazardous compounds from microplastics in coastal and marine environments are adsorbed by live organisms, affecting human and marine life. It takes time, money and effort to study the distribution and type of microplastics in the environment, using appropriate expensive equipment in a laboratory. However, deep learning can assist in identifying and quantifying microplastics from an image. This paper presents a novel microplastic classification method that combines the benefits of UV light with deep learning. The Faster-RCNN model with a ResNet-50-FPN backbone was implemented to detect and identify microplastics. Microplastic images from the field taken under UV light were used to train and validate the model. This classification model achieved a high precision of 85.5-87.8%, and the mAP scores were 33.9% on an internal test set and 35.7% on an external test set. This classification approach provides a high-accuracy, low-cost, and time-effective automated identification and counting of microplastics.

Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks

Microplastics (MPs), measuring less than 5 mm, pose threats to ecological security and human health in urban waters. Additionally, they act as carriers, transporting pollutants from terrestrial systems into oceanic circulation, contributing to global pollution. Recognizing the significance of identifying MPs in urban waters, one potential solution to the time-consuming and labor-intensive manual identification process is the application of a convolutional neural network (CNN). Therefore, having a reliable CNN model that efficiently and accurately identifies MPs is essential for extensive research on MPs pollution in urban waters. In this work, an MPs dataset with complex background was acquired from urban waters in southern China. The dataset was used to train and validate CNN models, including UNet, UNet2plus, and UNet3plus. Subsequently, the computational and inference performance of the three models was evaluated using a newly collected MPs dataset. The results showed that UNet, UNet2plus, UNet3plus, after being trained for 120 epochs, provided efficient inferences within less than 1 s, 2 s, and 3 s for 100 MPs images, respectively. Accurate segmentation with mIoU of 91.45 ± 5.93 % and 91.08 ± 6.18 % was achieved using UNet and UNet2plus, respectively, while UNet3plus exhibited a lower performance with only 82.21 ± 10.33 % mIoU. This work demonstrated that UNet and UNet2plus deliver efficient and accurate identification of MPs in urban waters. Developing CNN models that efficiently and accurately identify MPs is crucial for reducing manual time, especially in large-scale investigations of MPs pollution in urban waters.

High-throughput microplastic assessment using polarization holographic imaging

Microplastic (MP) pollution has emerged as a global environmental concern due to its ubiquity and harmful impacts on ecosystems and human health. MP assessment has therefore become increasingly necessary and common in environmental and experimental samples. Microscopy and spectroscopy are widely employed for the physical and chemical characterization of MPs. However, these analytical methods often require time-consuming pretreatments of samples or expensive instrumentation. In this work, we develop a portable and cost-effective polarization holographic imaging system that prominently incorporates deep learning techniques, enabling efficient, high-throughput detection and dynamic analysis of MPs in aqueous environments. The integration enhances the identification and classification of MPs, eliminating the need for extensive sample preparation. The system simultaneously captures holographic interference patterns and polarization states, allowing for multimodal information acquisition to facilitate rapid MP detection. The characteristics of light waves are registered, and birefringence features are leveraged to classify the material composition and structures of MPs. Furthermore, the system automates real-time counting and morphological measurements of various materials, including MP sheets and additional natural substances. This innovative approach significantly improves the dynamic monitoring of MPs and provides valuable information for their effective filtration and management.

Microplastics in Ecuador: A review of environmental and health-risk assessment challenges

Pollution from plastic debris and microplastics (MPs) is a worldwide issue. Classified as emerging contaminants, MPs have become widespread and have been found not only in terrestrial and aquatic ecosystems but also within the food chain, which affects both the environment and human health. Since the outbreak of COVID-19, the consumption of single-use plastics has drastically increased, intensifying mismanaged plastic waste in countries such as Ecuador. Therefore, the aim of this review is to 1) summarize the state of MP-related knowledge, focusing on studies conducted with environmental matrices, biota, and food, and 2) analyze the efforts by different national authorities and entities in Ecuador to control MP contamination. Results showed a limited number of studies have been done in Ecuador, which have mainly focused on the surface water of coastal areas, followed by studies on sediment and food. MPs were identified in all samples, indicating the lack of wastewater management policies, deficient management of solid wastes, and the contribution of anthropogenic activities such as artisanal fishing and aquaculture to water ecosystem pollution, which affects food webs. Moreover, studies have shown that food contamination can occur through atmospheric deposition of MPs; however, ingredients and inputs from food production, processing, and packaging, as well as food containers, contribute to MP occurrence in food. Further research is needed to develop more sensitive, precise, and reliable detection methods and assess MPs' impact on terrestrial and aquatic ecosystems, biota, and human health. In Ecuador specifically, implementing wastewater treatment plants in major cities, continuously monitoring MP coastal contamination, and establishing environmental and food safety regulations are crucial. Additionally, national authorities need to develop programs to raise public awareness of plastic use and its environmental effects, as well as MP exposure's effects on human health.

Micro (nano) plastics uptake, toxicity and detoxification in plants: Challenges and prospects

Plastic pollution has emerged as a global challenge affecting ecosystem health and biodiversity conservation. Terrestrial environments exhibit significantly higher plastic concentrations compared to aquatic systems. Micro/nano plastics (MNPs) have the potential to disrupt soil biology, alter soil properties, and influence soil-borne pathogens and roundworms. However, limited research has explored the presence and impact of MNPs on aquaculture systems. MNPs have been found to inhibit plant and seedling growth and affect gene expression, leading to cytogenotoxicity through increased oxygen radical production. The article discusses the potential phytotoxicity process caused by large-scale microplastics, particularly those unable to penetrate cell pores. It also examines the available data, albeit limited, to assess the potential risks to human health through plant uptake.

Automatic classification of microplastics and natural organic matter mixtures using a deep learning model

Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators. Therefore, in this study, deep learning (DL) was applied to improve the classification accuracies for mixtures of microplastic and natural organic matter (MP-NOM). A convolutional neural network (CNN)-based DL model with a spatial attention mechanism was adopted to classify substances from their Raman spectra. Subsequently, the classification results were compared with those obtained using conventional Raman spectral library software to evaluate the applicability of the model. Additionally, the crucial spectral band for training the DL model was investigated by applying gradient-weighted class activation mapping (Grad-CAM) as a post-processing technique. The model achieved an accuracy of 99.54%, which is much higher than the 31.44% achieved by the Raman spectral library. The Grad-CAM approach confirmed that the DL model can effectively identify MPs based on their visually prominent peaks in the Raman spectra. Furthermore, by tracking distinctive spectra without relying solely on visually prominent peaks, we can accurately classify MPs with less prominent peaks, which are characterized by a high standard deviation of intensity. These findings demonstrate the potential for automated and objective classification of MPs without the need for NOM preprocessing, indicating a promising direction for future research in microplastic classification.

Identification and detection of microplastic particles in marine environment by using improved faster R-CNN model

Microplastics refer to plastic particles measuring less than 5 mm, which has led to serious environmental problem and the detection of these tiny particles is crucial for understanding the corresponding distribution and impact on the marine environment. In this paper, an improved faster region-based convolutional neural network (R-CNN) model was developed for the identification and detection of microplastic particles. In the proposed model, the residual network-50 (ResNet-50) is employed as the backbone with the replacement of the traditional one to enhance the feature extraction capability and the feature pyramid networks (FPN) module is introduced together for solving the multi-scale target detection. By using the improved Faster R-CNN model, the network model performance is enhanced where the average confidence of detecting unique microplastic particles in the marine environment reaches as high as 99%. Moreover, the microparticles mixture was bounded precisely via the predicted bounding boxes without missing detection and wrong detection. In this way, the successful identification of polystyrene microplastic particles from the particles suspension with similar shapes but various conditions of backgrounds, brightness, distributions and object sizes, was achieved by employing the proposed improved Faster R-CNN model, enabling the accurate detection of microplastic particles in marine environment.

Machine learning: Next promising trend for microplastics study

Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.

Quantifying microplastic ingestion, degradation and excretion in insects using fluorescent plastics

Plastic pollution is a growing threat to our natural environment. Plastic waste/pollution results from high emissions of both macro (>5 mm) and microplastics (MPs; <5 mm) as well as environmental fractioning of macroplastics into MPs. MPs have been shown to have a range of negative impacts on biota. Harmonized methods to accurately measure and count MPs from animal samples are limited, but what methods exist are not ideal for a controlled laboratory environment where plastic ingestion, degradation and elimination can be quantified and related to molecular, physiological and organismal traits. Here, we propose a complete method for isolating and quantifying fluorescent MPs by combining several previously reported approaches into one comprehensive workflow. We combine tissue dissection, organic material digestion, sample filtering and automated imaging techniques to show how fluorescently labelled MPs provided to insects (e.g. in their diet) in a laboratory setting can be isolated, identified and quantified. As a proof of concept, we fed crickets (Gryllodes sigillatus) a diet of 2.5% (w/w) fluorescently labelled plastics and isolated and quantified plastic particles within the gut and frass.

Adverse health effects and mechanisms of microplastics on female reproductive system: a descriptive review

Microplastics (MPs), with a diameter of less than 5 mm, include polymers such as polystyrene, polypropylene, and polyethylene. The MPs occur in different morphologies including fragments, beads, fibers, and films that are swallowed by fresh water and land-based animals and enter their food chain, where they produce hazardous effects such as uterine toxicity, infertility, and neurotoxicity. The aim of this review is to explore the effects of polystyrene MPs (PS-MPs) on the female reproductive system and understand the mechanisms by which they produce reproductive toxicity. Several studies suggested that the exposure to PS-MPs increased the probability of larger ovaries with fewer follicles, decreased the number of embryos produced, and decreased the number of pregnancies in female mice. It also changed sex hormone levels and caused oxidative stress, which could have an impact on fertility and reproduction. Exposure to PS-MPs caused the death of granulosa cells through apoptosis and pyroptosis via activation of the NLRP3/caspase pathway and disruption of the Wnt-signaling pathway. Activation of TL4/NOX2 caused the uterine fibrosis resulting in endometrium thinning. The PS-MPs had a negative impact on ovarian capacity, oocyte maturation, and oocyte quality. Furthermore, the PS-MPs disrupted the hypothalamus-pituitary-gonadal axis in marine animals, resulting in a decrease in hatching rate and offspring body size, causing trans-generational effects. It also reduced fecundity and produced germ-line apoptosis. The main focus of this review was to explore the different mechanisms and pathways through which PS-MPs adversely impact the female reproductive system.

High microplastics concentration in liver is negatively associated with condition factor in the Benguela hake Merluccius polli

Microplastics (MPs) affect both marine and terrestrial biota worldwide for their harmful effects, which range from physical cell damage to physiological deterioration. In this research, microplastics were quantified from gills, liver and muscle of demersal Benguela hakes Merluccius polli (n = 94), caught by commercial trawling from northwest African waters. Plastic polymers were identified using Fourier Transformed-infraRed spectroscopy (FT-iR). Fulton's k condition factor and the degree of DNA degradation in liver were measured. None of the individuals were free of MPs, whose concentration ranged from 0.18 particles/g in muscle to 0.6 in liver. Four hazardous polymers were identified: 2-ethoxyethylmethacrylate, polyester, polyethylene terephthalate, and poly-acrylics. MP concentration in liver was correlated negatively with the condition factor, suggesting physiological damage. Positive association of MP concentration and liver DNA degradation was explained from cell breakage during trawl hauls during decompression, suggesting an additional way of MPs harm in organisms inhabiting at great depth. This is the first report of potential MPs-driven damage in this species; more studies are recommended to understand the impact of MP pollution on demersal species.

Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning

Microplastics (MPs) are currently recognized as emerging pollutants; their identification and classification are therefore essential during their monitoring and management. In contrast to most studies based on small datasets and library searches, this study developed and compared four machine learning-based classifiers and two large-scale blended plastic datasets, where a 1D convolutional neural network (CNN), decision tree, and random forest (RF) were fed with raw spectral data from Fourier transform infrared spectroscopy, while a 2D CNN used the corresponding spectral images as the input. With an overall accuracy of 96.43% on a small dataset and 97.44% on a large dataset, the 1D CNN outperformed other models. The 1D CNN was the best at predicting environment samples, while the RF was the most robust with less spectral data. Overall, RF and 2D CNNs might be evaluated for plastic identification with fewer spectral data; however, 1D CNNs were thought to be the most effective with sufficient spectral data. Accordingly, an open-source MP spectroscopic analysis tool was developed to facilitate a quick and accurate analysis of existing MP samples.

A fit-for-purpose categorization scheme for microplastic morphologies

Microplastic categorization schemes are diverse, thereby posing challenges for cross-study comparisons. Further, categorization schemes are not necessarily aligned with and, thus, useful for applications such as source reduction initiatives. To address these challenges, we propose a hierarchical categorization approach that is "fit for purpose" to enable the use of a scheme that is tailored to the study's purpose and contains categories, which, if adopted, would facilitate interstudy comparison. The hierarchical categorization scheme is flexible to support various study purposes (e.g., to support regulation and toxicity assessment) and it aims to improve the consistency and comparability of microplastics categorization. Categorization is primarily based on morphology, supplemented by other identification methods as needed (e.g., spectroscopy). The use of the scheme was illustrated through a literature review aimed at critically evaluating the categories used for reporting microplastic morphologies in North American freshwater environments. Categorization and grouping schemes for microplastic particles were highly variable, with up to 19 different categories used across 68 studies, and nomenclature was inconsistent across particle morphologies. Our review demonstrates the necessity for a "fit for purpose" categorization scheme to guide the information needs of scientists and decision-makers for various research and regulatory objectives across global, regional, and local scales. Integr Environ Assess Manag 2023;19:422-435. © 2022 SETAC.

Automated method for routine microplastic detection and quantification

Microplastics (MPs) are a heterogeneous group of solid polymers with dimensions <5 mm, which are a widespread contaminant of the environment. Their ubiquitous presence grabbed researchers' attention in the last decade, and the problem of MPs detection and quantification is currently a topic of utmost importance. Most identification and quantification protocols are still based on the visual count, which is an extremely time-consuming and error-prone task due to operator subjectivity. To address such an issue, different software analysis procedures are available, but they mainly rely either on the use of optical microscopy, covering a minimal area for each sample (mm² size), or they allow only the identification of the largest particles (>1 mm). Here, a semi-automatic innovative image processing method for quantifying and measuring microplastics on filter membrane substrates is presented and validated, comparing results with data obtained using visual counting performed by an experienced operator. The algorithm was tested with artificially generated microplastic images and samples taken from natural environments. Samples of Borgio Verezzi show cave sediment and Po River water were filtered on a glass filter membrane, and photographs were taken under 365 nm illumination, both without and with Nile Red staining. The proposed image analysis method, implemented in an easy-to-use Python script, was quite accurate and fast (about 10 s/image average processing time), showing an average deviation below 10 %, which is further reduced to about 8 % if the samples are stained with Nile Red.

A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges

Due to the rapid artificial intelligence technology progress and innovation in various fields, this research aims to use science mapping tools to comprehensively and objectively analyze recent advances, hot-spots, and challenges in artificial intelligence-based microplastic-imaging field from the Web of Science (2019-2022). By text mining and visualization in the scientific literature we emphasized some opportunities to bring forward further explication and analysis by (i) exploring efficient and low-cost automatic quantification methods in the appearance properties of microplastics, such as shape, size, volume, and topology, (ii) investigating microplastics water-soluble synthetic polymers and interaction with other soil and water ecology environments via artificial intelligence technologies, (iii) advancing efficient artificial intelligence algorithms and models, even including intelligent robot technology, (iv) seeking to create and share robust data sets, such as spectral libraries and toxicity database and co-operation mechanism, (v) optimizing the existing deep learning models based on the readily available data set to balance the related algorithm performance and interpretability, (vi) facilitating Unmanned Aerial Vehicle technology coupled with artificial intelligence technologies and data sets in the mass quantities of microplastics. Our major findings were that the research of artificial intelligence methods to revolutionize environmental science was progressing toward multiple cross-cutting areas, dramatically increasing aspects of the ecology of plastisphere, microplastics toxicity, rapid identification, and volume assessment of microplastics. The above findings can not only determine the characteristics and track of scientific development, but also help to find suitable research opportunities to carry out more in-depth research with many problems remaining.

Deep learning based approach for automated characterization of large marine microplastic particles

The rapidly growing concern of marine microplastic pollution has drawn attentions globally. Microplastic particles are normally subjected to visual characterization prior to more sophisticated chemical analyses. However, the misidentification rate of current visual inspection approaches remains high. This study proposed a state-of-the-art deep learning-based approach, Mask R-CNN, to locate, classify, and segment large marine microplastic particles with various shapes (fiber, fragment, pellet, and rod). A microplastic dataset including 3000 images was established to train and validate this Mask R-CNN algorithm, which was backboned by a Resnet 101 architecture and could be tuned in less than 8 h. The fully trained Mask R-CNN algorithm was compared with U-Net in characterizing microplastics against various backgrounds. The results showed that the algorithm could achieve Precision = 93.30%, Recall = 95.40%, F1 score = 94.34%, AP^bb (Average precision of bounding box) = 92.7%, and AP^m (Average precision of mask) = 82.6% in a 250 images test dataset. The algorithm could also achieve a processing speed of 12.5 FPS. The results obtained in this study implied that the Mask R-CNN algorithm is a promising microplastic characterization method that can be potentially used in the future for large-scale surveys.

A temporal assessment of microplastics distribution on the beaches of three remote islands of the Yasawa archipelago, Fiji

This is the first study that investigated the presence, distribution, and composition of microplastics, MPs (1-5 mm) on beaches in the Yasawa Islands, Fiji. A temporal assessment over three years on six beaches was undertaken to investigate different beach traits on MP abundance. Average MP concentration was 4.5 ± 11.1 MPs·m^-2 with significantly higher concentrations were found on east-facing beaches than west (p < 0.001), and higher on the storm line compared to the high tide line (p < 0.001). No difference was found between tourist and local beaches (p = 0.21). These results demonstrate the role of current-driven ocean transport of plastic pollution in this part of The South Pacific. ATR FT-IR analysis showed that across all sites 34 % of MPs were polypropylene (PP), 33 % polystyrene (PS), 25 % polyethylene (PE), and 8 % other polymer types. Further studies are needed to assess the potential impacts of MPs on Fiji's coral reefs and marine life.

Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion

Microplastics as emerging pollutants have been heavily accumulated in the waste activated sludge (WAS) during biological wastewater treatment, which showed significantly diverse impacts on the subsequent anaerobic sludge digestion for methane production. However, a robust modeling approach for predicting and unveiling the complex effects of accumulated microplastics within WAS on methane production is still missing. In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable analysis was explored to reveal the relationships between key variables (e.g., concentration, type, and size of microplastics) and methane production. The results showed that the gradient boosting machine had better prediction performance (mean squared error (MSE) = 17.0) than common neural networks models (MSE = 58.0), demonstrating that the AutoML algorithms succeeded in predicting the methane production and could select the best machine learning model without human intervention. Explainable analysis results indicated that the variable of microplastic types was more important than the variable of microplastic diameter and concentration. The existence of polystyrene was associated with higher methane production, whereas increasing microplastic diameter and concentration both inhibited methane production. This work also provided a novel modeling approach for comprehensively understanding the complex effects of microplastics on methane production, which revealed the dependence relationships between methane production and key variables and may be served as a reference for optimizing operational adjustments in anaerobic digestion processes.

MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams

Environmental monitoring of microplastics (MP) contamination has become an area of great research interest, given potential hazards associated with human ingestion of MP. In this context, determination of MP concentration is essential. However, cheap, rapid, and accurate quantification of MP remains a challenge to this date. This study proposes a deep learning-based image segmentation method that properly distinguishes fluorescent MP from other elements in a given microscopy image. A total of nine different deep learning models, six of which are based on U-Net, were investigated. These models were trained using at least 20,000 patches sampled from 99 fluorescence microscopy images of MP and their corresponding binary masks. MP-Net, which is derived from U-Net, was found to be the best performing model, exhibiting the highest mean F1-score (0.736) and mean IoU value (0.617). Test-time augmentation (using brightness, contrast, and HSV) was applied to MP-Net for robust learning. However, compared to the results obtained without augmentation, no clear improvement in predictive performance could be observed. Recovery assessment for both spiked and real images showed that, compared to already existing tools for MP quantification, the MP quantities predicted by MP-Net are those closest to the ground truth. This observation suggests that MP-Net allows creating masks that more accurately reflect the quantitative presence of fluorescent MP in microscopy images. Finally, MAP (Microplastics Annotation Package) is introduced, an integrated software environment for automated MP quantification, offering support for MP-Net, already existing MP analysis tools like MP-VAT, manual annotation, and model fine-tuning.

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.

Lagrangian Modeling of Marine Microplastics Fate and Transport: The State of the Science

Microplastics pollution has led to irreversible environmental consequences and has triggered global concerns. It has been shown that water resources and marine food consumers are adversely affected by microplastics due to their physico-chemical characteristics. This study attempts to comprehensively review the structure of four well-known Lagrangian particle-tracking models, i.e., Delft3D—Water Quality Particle tracking module (D-WAQ PART), Ichthyoplankton (Ichthyop), Track Marine Plastic Debris (TrackMPD), and Canadian Microplastic Simulation (CaMPSim-3D) in simulating the fate and transport of microplastics. Accordingly, the structure of each model is investigated with respect to addressing the involved physical transport processes (including advection, diffusion, windage, beaching, and washing-off) and transformation processes (particularly biofouling and degradation) that play key roles in microplastics’ behavior in the marine environment. In addition, the effects of the physical properties (mainly size, diameter, and shape) of microplastics on their fate and trajectories are reviewed. The models’ capabilities and shortcomings in the simulation of microplastics are also discussed. The present review sheds light on some aspects of microplastics’ behavior in water that were not properly addressed in particle-tracking models, such as homo- and hetero-aggregation, agglomeration, photodegradation, and chemical and biological degradation as well as additional advection due to wave-induced drift. This study can be regarded as a reliable steppingstone for the future modification of the reviewed models.

Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil

Microplastics (MPs) are emerging environmental pollutants and their accumulation in the soil can adversely affect the soil biota. This study aims to employ hyperspectral imaging technology for the rapid screening and classification of MPs in farmland soil. In this study, a total of 600 hyperspectral data are collected from 180 sets of farmland soil samples with a hyperspectral imager in the wavelength range of 369- 988 nm. To begin, the hyperspectral data are preprocessed by the Savitzky-Golay (S-G) smoothing filter and mean normalization. Second, principal component analysis (PCA) is used to minimize the dimensions of the hyperspectral data and hence the amount of data, making the subsequent model easier to construct. The cumulative contribution rate of the first three principal components is reached 98.37%, including the main information of the original spectral data. Finally, three models including decision tree (DT), support vector machine (SVM), and convolutional neural network (CNN) are established, all of which can achieve well classification effects on three MP polymers including polyethylene (PE), polypropylene (PP), and polyvinyl chloride (PVC) in farmland soil. By comparing the recognition accuracy of the three models, the classification accuracy of DT and SVM is 87.9% and 85.6%, respectively. The CNN model based on the S-G smoothing filter obtains the best prediction effect, the classification accuracy reaches 92.6%, exhibiting obvious advantages in classification effect. Altogether, these results show that the proposed hyperspectral imaging technique identifies the soil MPs rapidly and nondestructively, and provides an effective automated method for the detection of polymers, requiring only rapid and simple sample preparation.

Neural Network Analysis for Microplastic Segmentation

It is necessary to locate microplastic particles mixed with beach sand to be able to separate them. This paper illustrates a kernel weight histogram-based analytical process to determine an appropriate neural network to perform tiny object segmentation on photos of sand with a few microplastic particles. U-net and MultiResUNet are explored as target networks. However, based on our observation of kernel weight histograms, visualized using TensorBoard, the initial encoder stages of U-net and MultiResUNet are useful for capturing small features, whereas the later encoder stages are not useful for capturing small features. Therefore, we derived reduced versions of U-net and MultiResUNet, such as Half U-net, Half MultiResUNet, and Quarter MultiResUNet. From the experiment, we observed that Half MultiResUNet displayed the best average recall-weighted F1 score (40%) and recall-weighted mIoU (26%) and Quarter MultiResUNet the second best average recall-weighted F1 score and recall-weighted mIoU for our microplastic dataset. They also require 1/5 or less floating point operations and 1/50 or a smaller number of parameters over U-net and MultiResUNet.

Extraction and identification methods of microplastics and nanoplastics in agricultural soil: A review

As the abundance of microplastics and nanoplastics (MPs/NPs) increases in the environment, their presence in agricultural soil has become of interest. MPs/NPs can affect soil physical and chemical properties and be absorbed by plants and soil animals, causing physical and chemical damage. Soil MPs exceeding a certain concentration cause significant harm. Therefore, the extraction and identification of MPs in soil are vital for determining soil pollution. However, soils contain many other particles of similar size to MPs/NPs, making it more difficult to distinguish them than in water bodies. No standardized extraction and identification method is available to quantify MPs/NPs in soil. Various methods have been described in the literature, but they involve many different procedures for sampling, purification, digestion, and identification. This paper reviews extraction and identification methods for MPs/NPs in soil, sediment, and water and summarizes agricultural soil sampling and preservation, MPs/NPs separation, organic matter removal, and MPs/NPs identification. We also compare the advantages and disadvantages of existing methods and propose future research topics.