Machine learning: Next promising trend for microplastics study

An efficient and precise (micro)plastic identification method: feature infrared spectra extraction based on EIS-VIP-CARS and ANN modeling

Understanding microplastics' (MPs) ecological impact necessitates their precise identification. To address the issue of the competitive adaptive reweighted sampling (CARS) algorithm extracting numerous feature wavenumber points (FWPs) that often miss transmittance peaks (TPs), resulting in high computational load and low accuracy in artificial neural network (ANN) models, this study introduces a novel approach. Initially, the equal interval sampling (EIS) method is employed to capture the main information of the full spectra. Subsequently, the variable importance in projection (VIP) is innovatively integrated into the CARS to formulate the EIS-VIP-CARS method for extracting feature spectra (FS). Using 20 typical MPs as the subjects, this study compares the identification performance of ANN models using full-spectra, EIS, CARS, EIS-CARS, VIP-CARS, and EIS-VIP-CARS. The results show that VIP-CARS extracts 128 FWPs, a reduction of 49.41% compared to CARS. Moreover, the distribution of these FWPs is more concentrated around the TPs and their vicinity. The accuracy of MPs by the ANN model based on VIP-CARS is generally higher than that of CARS. EIS-VIP-CARS extracts 55 FWPs, representing a reduction of 58.65% and 57.03% compared to EIS and VIP-CARS, respectively. The overall distribution of these points closely aligns with the distribution of functional groups. The ANN model based on EIS-VIP-CARS can achieve a similar accuracy for MPs as the model based on EIS, both greater than 99%, demonstrating good generalization ability. The accuracies of the MNN and convolutional neural network (CNN) models are higher than those of the SNN model, but the modeling time is longer. The ANN model established using the EIS-VIP-CARS is an efficient and precise approach for the identification of MPs in infrared spectroscopy. This study provides technical references for the research on the environmental behavior of MPs and is also of significant importance for the classification and management of plastic waste.

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.

Investigating the adsorption of organic compounds onto microplastics via experimental, simulation, and prediction methods

Exploring the adsorption of organic compounds onto microplastics (MPs) is of great significance for understanding their environmental fate and evaluating their ecological risks. To date, various techniques, e.g., experiments, simulations, and prediction models, have been utilized for exploring the adsorption of different organic compounds onto MPs. In this review, we systematically introduce the sources of MPs, the interactions between MPs and organic compounds, the factors influencing the adsorption of organic compounds onto MPs, and research advances in investigating the adsorption of organic compounds by microplastics with different techniques. We also point out that the structures of MPs and environmental factors can have distinct effects on the adsorption mechanisms, and the adsorption mechanisms for numerous organic compounds onto MPs are still unclear. Besides, there is a paucity of multi-dimensional models for predicting the adsorption of organic compounds by MPs under different environmental conditions with a single click. We hope that our review can provide insights into the environmental behavior and fate of organic compounds and microplastics, as well as also guiding future research on the adsorption of organic compounds onto microplastics.

Deep-learning enabled rapid and low-cost detection of microplastics in consumer products following on-site extraction and image processing

Microplastic (MP) contamination has become a major concern in recent times, posing a significant threat to the environment and human health. Existing techniques for MP detection require access to expensive and specialized microscopy setups and often demand long turnaround time and extensive labor. Herein, we propose a simple platform for MP detection, where MPs are extracted from salt, sugar, teabag, toothpaste and toothpowder samples, and imaged using a low-cost mobile phone-based microscopy setup. The extraction process involves the isolation of MPs from their matrices using the well-established density separation technique with ZnCl2 solution (1.7 g cm-3) and hydrogen peroxide (H2O2) to oxidize organic matter. A commercially available miniaturized microscopy attachment (TinyScope, $10) is fixed on top of an ordinary cell phone camera and is used to capture about 2490 images of MPs obtained from five different product categories. The YOLOv5 deep learning model was used to detect microplastics in images. It was trained on a dataset of 1990 images, validated with 250 images, and tested on a separate set of 250 images. The presence of plastic content in the detected samples was confirmed by performing attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy and the morphologies of the MPs were determined using the field-emission scanning electron microscopy (FE-SEM). Results show that the deep-learning enabled image processing approach can identify MPs with an accuracy of 98%. Overall, the fast, accurate, and affordable detection of MPs in low-resource settings can lead to the monitoring of MP content in consumer products on a more frequent basis.

Enhancing meteorological data reliability: An explainable deep learning method for anomaly detection

Accurate meteorological observation data is of great importance to human production activities. Meteorological observation systems have been advancing toward automation, intelligence, and informatization. Yet, instrumental malfunctions and unstable sensor node resources could cause significant deviations of data from the actual characteristics it should reflect. To achieve greater data accuracy, early detections of data anomalies, continuous collections and timely transmissions of data are essential. While obvious anomalies can be readily identified, the detection of systematic and gradually emerging anomalies requires further analyses. This study develops an interpretable deep learning method based on an autoencoder (AE), SHapley Additive exPlanations (SHAP) and Bayesian optimization (BO), in order to facilitate prompt and accurate anomaly detections of meteorological observational data. The proposed method can be unfolded into four parts. Firstly, the AE performs anomaly detections based on multidimensional meteorological datasets by marking the data that shows significant reconstruction errors. Secondly, the model evaluates the importance of each meteorological element of the flagged data via SHapley Additive exPlanation (SHAP). Thirdly, a K-sigma based threshold automatic delineation method is employed to obtain reasonable anomaly thresholds that are subject to the data characteristics of different observation sites. Finally, the BO algorithm is adopted to fine-tune difficult hyperparameters, enhancing the model's structure and thus the accuracy of anomaly detection. The practical implication of the proposed model is to inform agricultural production, climate observation, and disaster prevention.

Predicting microplastic quantities in Indonesian provincial rivers using machine learning models

Microplastic pollution has surfaced as a critical environmental concern, affecting ecosystems and human health globally. This study explored the application of several machine learning models, including the Tree algorithm, k-Nearest Neighbors (kNN), Random Forest (RF), Linear Regression (LR), Support Vector Machine (SVM), and Neural Networks (NN), to predict microplastic concentrations in the rivers of Indonesia's 24 provinces. By utilizing both environmental and anthropogenic data, the Tree algorithm exhibited the best performance, achieving a coefficient of determination (R2) of 0.838 and a mean absolute percentage error (MAPE) of 0.242 on unseen testing data, thereby highlighting strong predictive capability. Key variables influencing microplastic abundance included annual average temperature, gross domestic product (GDP) per capita and population density. The results underscored the necessity of utilizing comprehensive datasets for effective modeling and highlighted the potential of machine learning to enhance environmental monitoring efforts. This research provides critical insights for policymakers and stakeholders aiming to address the growing issue of microplastic pollution in freshwater systems, providing a foundation for the development of more effective environmental management strategies.

Unraveling Microplastic Effects on Gut Microbiota across Various Animals Using Machine Learning

Microplastics, rapidly expanding and durable pollutant, have been shown to significantly impact gut microbiota across a spectrum of animal species. However, comprehensive analyses comparing microplastic effects on gut microbiota among these species are still limited, and the critical factors driving these effects remain to be clarified. To address these issues, we compiled 1352 gut microbiota samples from six animal categories, employing machine learning to conduct an in-depth meta-analysis. Our study revealed that mice, compared with other animals, not only exhibit a heightened susceptibility to the toxic effects of microplastics─evidenced by decreased gut microbiota diversity, increased Firmicutes/Bacteroidetes ratios, destabilized microbial networks, and disruption in the equilibrium of beneficial and harmful bacteria─but also possess limited potential to degrade microplastics, unlike earthworms and insects. Furthermore, machine learning models confirmed that exposure duration is the key factor driving changes induced by microplastics in gut microbiota. We also identified Lactobacillus, Helicobacter, and Pseudomonas as potential biomarkers for detecting microplastic toxicity in the animal gut. Overall, these findings provide valuable insights into the health risks and driving factors associated with microplastic exposure across multiple animal species.

Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics

Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especially to microalgae. The aim of this study is to use machine learning models to predict the effects of MPs on algal growth and to evaluate the relative importance of different features (MP properties, algal characteristics, and experimental conditions) through model interpretability analysis. Based on literature search, 408 samples were collected as inputs for the models. Three integrated machine learning algorithms, Random Forest (RF), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM), were used to construct classification prediction models for algal growth. Our results show that the LightGBM model yields the best performance, with a total accuracy rate of 0.8305 and a Kappa value of 0.7165. The model interpretability analysis indicates that "Exposure time", "MP concentrations", and "MP size" are the most influential features affecting algal growth. For "Exposure time", which belongs to experimental conditions, 72-216 h of MP exposure was found to exert the greatest effects on algal growth. The impact of MPs on algal growth increases with increasing MP concentrations over the range of 0 to 300 mg/L. Smaller MPs exert more effects on algal growth, and MPs are more likely to inhibit algal growth when the ratio of algal cell size to MP size is higher. Our study successfully established prediction models for evaluating the effects of various MP properties on algal growth. This study also provides insights into the prediction of MP toxicity in organisms.

Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction

Surface waters, particularly the river systems, constitute a vital freshwater resource for human beings and aquatic life on Earth. In economically developed and densely populated coastal regions, river water is facing severe microplastic pollution, posing a threat to public health and ecological safety. Reliable prediction of microplastic abundance (MPA) can significantly reduce the costs associated with microplastic field sampling and analysis. This study employed spatial correlation, geographical detector, principal component analysis and five mainstream machine learning models to analyze 79 datasets of MPAs in seven coastal areas of China and performed correlation, regression and attribution analyses based on 19 terrestrial influencing factors that potentially affect the MPA life cycle processes (generation, aging, and migration). The results showed that the Neural Network (NN) and the Gaussian Process Regression (GPR) models achieved the best prediction performance, with the predicted R2 close to 1. Principal component analysis and Shapley additive explanations concluded that meteorological factors, in particular the annual geotemperature, surface solar radiation, and annual relative humidity, had a key influence on the aging of microplastics. The second key factor in improving the MPA prediction ability was the dynamic description of microplastic migration, which was primarily governed by hydrological factors such as annual precipitation and average terrain slope. Unexpectedly, the effects of land use and level of urbanization were relatively small in describing the generation of microplastics. Only the percentage of built areas was strongly correlated with the MPA levels. Note that the MPA prediction and its contribution factors may vary across different basins. Nevertheless, the findings of this study are applicable to predicting and analyzing the distribution of microplastics in other coastal rivers, and for indicating the main contributing factors, ultimately serving as a basis for guiding microplastic pollution control strategies in different river basins.

Modeling the temporal evolution of plastic film microplastics in soil using a backpropagation neural network

Plastic films are a crucial aspect of agricultural production in China, as well as a key source of microplastics in farmland. However, research into the environmental behavior of microplastics derived from polyethylene (PE) and biodegradable plastic films such as polybutylene adipate-co-terephthalate (PBAT) is limited by inadequate knowledge of their evolution and fate in soil. Therefore, we conducted controlled soil incubation experiments using new and aged microplastics derived from prepared PE and PBAT plastic films to determine their temporal evolution characteristics in soil. The results indicated that PBAT microplastics exhibited more pronounced changes in abundance, size, and shape over time than PE microplastics. Notably, the magnitude and timing of changes in newly introduced PBAT microplastics were consistently delayed relative to those of aged microplastics. Specifically, the abundance of aged PBAT microplastics initially increased then decreased, whereas their size continuously decreased, ultimately reaching 21.9 % and 47.5 % of the initial values, respectively. Furthermore, we constructed a novel backpropagation neural network model based on our morphological and spectral data, which effectively identified the incubation duration of PE and PBAT microplastics, with recognition accuracies of 98.1 % and 84.6 %, respectively. These findings offer a novel perspective for assessing the environmental persistence and fate of plastic film microplastics.

Conversion of hazardous waste into thermal conductive polymer: A prediction and guidance from machine learning

The preparation methods and thermal conductivity (TC) of the reported thermal conductive polymers vary significantly. A method to clarify the relationship between TC and influencing factors and to reach consistent conclusions is needed. In this study, we compiled 403 sets of data from the literature. Six typical features and three machine learning (ML) algorithms were selected and optimized. XGBoost algorithm achieved the best prediction of TC of thermal conductive polymer (correlation coefficient with 0.855). To further investigate the influence of the 6 features on the TC of thermal conductive polymer, we conducted the SHapley Additive exPlanations (SHAP) analysis. Based on the above results, pyrrhotite tailings were determined as the filler and the corresponding process parameters were also determined. However, the above model built based on literature was still unsatisfactory. We further optimized XGBoost and built XGBoost-Exp through data from the real experiment. Finally, a small percentage (23%) of real experimental data can significantly improve the prediction power of XGBoost-Exp for unseen data (correlation coefficient with 0.815). To summarize, XGBoost-Exp exhibits exceptional predictive performance for the TC of the unseen data, offering valuable insights into the influence of various features. Meanwhile, this method provides a new perspective for the utilization of hazardous sulfide minerals.

Characterizing sector-oriented roadside exposure to ultrafine particles (PM0.1) via machine learning models: Implications of covariates influences on sectors variability

Ultrafine particles (UFPs; PM0.1) possess intensified health risk due to their smaller size and unique spatial variability. One of major emission sources for UFPs is vehicle exhaust, which varies based on the traffic composition in each type of roadside sector. The current challenge of epidemiological UFPs study is limited characterization ability due to expensive instruments. This study assessed the UFPs particle number concentrations (UFPs PNC) exposure dose for typical healthy adults and children at three different roadside sectors, including industrial roadside (IN), residential roadside (RS), and urban background (UB). Furthermore, this study also developed and utilized machine learning (ML) algorithms that could accurately characterize the UFPs exposure dose and explain the covariates effects on the model outputs, representing the intra-urban variability of UFPs between sectors. It was found that the average inhaled UFPs dose for healthy adults and children during off-peak season (warm period) were 1.71 ± 0.19 × 1010; 1.28 ± 0.22 × 1010; 1.09 ± 0.18 × 1010 #/hour and 1.33 ± 0.15 × 1010; 0.99 ± 0.17 × 1010; 0.86 ± 0.14 × 1010 #/hour at IN, RS, UB. Inhaled UFPs were mainly deposited in tracheobronchial (TB) respiratory fraction for adults (67.7%) and in alveoli (ALV) fraction for children (67.5%). Among three ML algorithms implemented in this study, XGBoost possessed the highest UFPs PNC exposure dose estimation performances with R2 = 0.965; 0.959; 0.929 & RMSE = 0.79 × 108; 0.54 × 108; 0.15 × 105 #/hour at IN, RS, and UB which then followed by multiple linear regression (MLR), and random forest (RF). Furthermore, SHAP analysis from the XGBoost model has successfully pointed out the spatial variability of each roadside sector by quantifying the approximated contributions of covariates to the model's output. Findings in this study highlighted the potential use of ML models as an alternative for preliminary particle exposure source apportionment.

Segmentation of beach plastic fragments' contours based on self-organizing map and multi-shape descriptors: A rapid indication of fragmentation and wearing types

Environmental plastic fragments have been verified as byproducts of large plastic and its secondary pollutants including micro and nanoplastics. There are few quantitative studies available, but their contours have values for the weathering mechanisms. We used geometric descriptors, fractal dimensions, and Fourier descriptors to characterize field and artificial polyethylene and polypropylene samples as a means of investigating the contour characteristics. It provides a methodological framework for contour classification. Unsupervised classification was performed using self-organizing neural networks with size-invariance parameters. We revealed the isometric phenomenon of plastic fragments during fragmentation, i.e., that the degree of contour rounding and complexity increase and decrease, respectively, with decreasing fragment size. With an average error rate of 8.9 %, we can distinguish artificial samples from field samples. It was also validated by the difference in Carbonyl Index between groups. We propose a two-stage process for plastic fragmentation and give three types of contour features which were key in the description of fragmented contours, i.e., size, complexity, and rounding. Our work will improve the accuracy of characterizations regarding the weathering and fragmentation processes of certain kinds of plastic fragments. The contour parameters also have the potential to be applied in more realistic scenarios and varied polymers.

Nationwide meta-analysis of microplastic distribution and risk assessment in China's aquatic ecosystems, soils, and sediments

Microplastic (MP) accumulation has recently become a pressing global environmental challenge. As a major producer and consumer of plastic products, China's MP pollution has garnered significant attention from researchers. However, accurate and comprehensive investigations of national-level MP pollution are still lacking. In this study, we systematically collated a national MP pollution dataset consisting of 7766 water, soil, and sediment sampling sites from 544 publicly published studies, revealing the spatiotemporal distribution and potential risks of MP pollution in China. The results indicate that MP distribution is influenced by various regional factors, including economic development level, population distribution, and geographical environment, exhibiting considerable range and complexity. MP concentrations are generally higher in economically prosperous areas, but the degree of pollution varies significantly across different environmental media. Given the uncertainty and lack of standardized data in traditional microplastic risk assessment methods, this article highlights the urgency of developing a comprehensive big data and artificial intelligence (AI)-based regulatory framework. This work provides a substantial amount of accurate MP pollution data and offers a fresh perspective on leveraging AI for microplastic pollution regulation.

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.

A highly accurate and semi-automated method for quantifying spherical microplastics based on digital slide scanners and image processing

Microplastics (MPs), the emerging pollutants appeared in water environment, have grabbed significant attention from researchers. The quantitative method of spherical MPs is the premise and key for the study of MPs in laboratory researches. However, the manual counting is time-consuming, and the existing semi-automated analysis lacked of robustness. In this study, a highly accurate quantification method for spherical MPs, called VS120-MC was proposed. VS120-MC consisted of the digital slide scanner VS120 and the MPs image processing software, MPs-Counter. The full-area scanning photography was employed to fundamentally avoid the error caused by random or partition sampling modes. To accomplish high-performance batch recognition, the Weak-Circle Elimination Algorithm (WEA) and the Variable Coefficient Threshold (VCT) was developed. Finally, lower than 0.6% recognition error rate of simulated images with different aggregated indices was achieved by MPs-Counter with fast processing speed (about 2 s/image). The smallest size for VS120-MC to detect was 1 μm. And the applicability of VS120-MC in real water body was investigated. The measured value of 1 μm spherical MPs in ultra-pure water and two kinds of polluted water after digestion showed a good linear relationship with the Manual measurements (R2 = 0.982,0.987 and 0.978, respectively). For 10 μm spherical MPs, R2 reached 0.988 for ultra-pure water and 0.984 for both of the polluted water. MPs-Counter also showed robustness when using the same set of parameters processing the images with different conditions. Overall, VS120-MC eliminated the error caused by traditional photography and realized an accurate, efficient, stable image processing tool, providing a reliable alternative for the quantification of spherical MPs.

Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques

Microplastic contamination presents a significant global environmental threat, yet scientific understanding of its morphological distribution within ecosystems remains limited. This study introduces a pioneering method for comprehensive microplastic assessment and environmental monitoring, integrating photoacoustic imaging and advanced deep learning techniques. Rigorous curation of diverse microplastic datasets enhances model training, yielding a high-resolution imaging dataset focused on shape-based discrimination. The introduction of the Vector-Quantized Variational Auto Encoder (VQVAE2) deep learning model signifies a substantial advancement, demonstrating exceptional proficiency in image dimensionality reduction and clustering. Furthermore, the utilization of Vector Quantization Microplastic Photoacoustic imaging (VQMPA) with a proxy task before decoding enhances feature extraction, enabling simultaneous microplastic analysis and discrimination. Despite inherent limitations, this study lays a robust foundation for future research, suggesting avenues for enhancing microplastic identification precision through expanded sample sizes and complementary methodologies like spectroscopy. In conclusion, this innovative approach not only advances microplastic monitoring but also provides valuable insights for future environmental investigations, highlighting the potential of photoacoustic imaging and deep learning in bolstering sustainable environmental monitoring efforts.

Advancements in Raman imaging for nanoplastic analysis: Challenges, algorithms and future Perspectives

BACKGROUND

While the concept of microplastic (<5 mm) is well-established, emergence of nanoplastics (<1000 nm) as a new contaminant presents a recent and evolving challenge. The field of nanoplastic research remains in its early stages, and its progress is contingent upon the development of reliable and practical analytical methods, which are currently lacking. This review aims to address the intricacies of nanoplastic analysis by providing a comprehensive overview on the application of advanced imaging techniques, with a particular focus on Raman imaging, for nanoplastic identification and simultaneous visualisation towards quantification.

RESULTS

Although Raman imaging via hyper spectrum is a potentially powerful tool to analyse nanoplastics, several challenges should be overcome. The first challenge lies in the weak Raman signal of nanoplastics. To address this, effective sample preparation and signal enhancement techniques can be implemented, such as by analysing the hyper spectrum that contains hundred-to-thousand spectra, rather than a single spectrum. Second challenge is the complexity of Raman hyperspectral matrix with dataset size at megabyte (MB) or even bigger, which can be adopted using different algorithms ranging from image merging to multivariate analysis of chemometrics. Third challenge is the laser size that hinders the visualisation of small nanoplastics due to the laser diffraction (λ/2NA, ∼300 nm), which can be solved with involving the use of super-resolution. Signal processing, such as colour off-setting, Gaussian fitting (via deconvolution), and re-focus or image re-construction, are reviewed herein, which show a great promise for breaking through the diffraction limit.

SIGNIFICANCE

Overall, current studies along with further validation are imperative to refine these approaches and enhance the reliability, not only for nanoplastics research but also for broader investigations in the realm of nanomaterials.

The adsorption and release mechanism of different aged microplastics toward Hg(II) via batch experiment and the deep learning method

Aged microplastics are ubiquitous in the aquatic environment, which inevitably accumulate metals, and then alter their migration. Whereas, the synergistic behavior and effect of microplastics and Hg(II) were rarely reported. In this context, the adsorptive behavior of Hg(II) by pristine/aged microplastics involving polystyrene, polyethylene, polylactic acid, and tire microplastics were investigated via kinetic (pseudo-first and second-order dynamics, the internal diffusion model), Langmuir, and Freundlich isothermal models; the adsorption and desorption behavior was also explored under different conditions. Microplastics aged by ozone exhibited a rougher surface attached with abundant oxygen-containing groups to enhance hydrophilicity and negative surface charge, those promoted adsorption capacity of 4-20 times increment compared with the pristine microplastics. The process (except for aged tire microplastics) was dominated by a monolayer chemical reaction, which was significantly impacted by pH, salinity, fulvic acid, and co-existing ions. Furthermore, the adsorbed Hg(II) could be effectively eluted in 0.04% HCl, simulated gastric liquids, and seawater with a maximum desorption amount of 23.26 mg/g. An artificial neural network model was used to predict the performance of microplastics in complex media and accurately capture the main influencing factors and their contributions. This finding revealed that aged microplastics had the affinity to trap Hg(II) from freshwater, whereafter it released the Hg(II) once transported into the acidic medium, the organism's gastrointestinal system, or the estuary area. These indicated that aged microplastics could be the sink or the source of Hg(II) depending on the surrounding environment, meaning that aged microplastics could be the vital carrier to Hg(II).