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61,005 resultsShowing papers similar to Projecting the sorption capacity of heavy metal ions onto microplastics in global aquatic environments using artificial neural networks
ClearMachine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
Machine Learning to Predict the Adsorption Capacity of Microplastics
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Machine Learning Prediction of Adsorption Behavior of Xenobiotics on Microplastics under Different Environmental Conditions
Researchers developed a machine learning model to predict how different xenobiotic chemicals adsorb onto microplastics under varying environmental conditions, providing a computational tool to assess microplastics as vectors for pollutant transport without requiring extensive laboratory experiments.
Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning
Researchers developed machine learning models using molecular descriptors to predict the adsorption capacity of microplastics for organic pollutants in aqueous environments, achieving high accuracy across multiple polymer types and enabling faster environmental risk assessment.
Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning
A machine learning-assisted meta-analysis of 3,340 records found that polyamide microplastics had the highest heavy metal adsorption capacity due to their C=O and N-H surface groups, while PVC showed the strongest adsorption strength from its halogen atoms. Lead was the most readily adsorbed metal, and random forest modeling identified heavy metal concentration, microplastic concentration, specific surface area, and pH as the dominant factors governing adsorption.
Machine Learning to Predict the Adsorption Capacity for Microplastics
Researchers developed and compared three machine learning models — random forest, support vector machine, and artificial neural network — to predict microplastic/water partition coefficients (log Kd) for chemical pollutant adsorption, addressing the limited experimental data available on microplastic adsorption capacity in aquatic environments.
Artificial Intelligence-Powered Prediction of Microplastic–Pollutant Adsorption Coefficients Enables Scalable Risk Prioritization in Aquatic Environments
Researchers developed an AI-powered neural network called MPAP to predict how microplastics adsorb hazardous pollutants in aquatic environments. Trained on over 1,100 adsorption records covering 403 compounds and six microplastic types, the model integrates molecular fingerprints, microplastic features, and water chemistry parameters to enable scalable risk prioritization for microplastic-pollutant interactions.
Fe(III) Adsorption onto Microplastics in Aquatic Environments: Interaction Mechanism, Influencing Factors, and Adsorption Capacity Prediction
This study investigated how iron (Fe III) attaches to different types of microplastics in both freshwater and saltwater, finding that aged and weathered microplastics absorb significantly more iron than new ones. Machine learning models were used to predict how much iron different microplastics can carry under various conditions. This is relevant to health because iron-loaded microplastics may be more toxic and more easily absorbed by organisms in the food chain.
Rapidly Predicting Aqueous Adsorption Constants of Organic Pollutants onto Polyethylene Microplastics by Combining Molecular Dynamics Simulations and Machine Learning
Researchers developed a computational method combining molecular simulations with machine learning to rapidly predict how organic pollutants adsorb onto polyethylene microplastics in water. The approach accurately predicted adsorption behavior across different conditions including particle size, water salinity, and pH without requiring time-consuming laboratory experiments. The tool could help environmental scientists more quickly assess how microplastics interact with and transport chemical contaminants in aquatic environments.
The adsorption and release mechanism of different aged microplastics toward Hg(II) via batch experiment and the deep learning method
This study examined how aging affects the adsorption and release of mercury (Hg(II)) by microplastics, combining batch experiments with deep learning models. Aged microplastics showed different mercury adsorption behavior compared to pristine particles, suggesting that environmental weathering alters the role of microplastics as vectors for heavy metals.
Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning
Researchers found that long-term aged microplastics adsorb 7-13 times more pharmaceuticals and personal care products than pristine microplastics, and developed machine learning models using infrared spectroscopy that predicted adsorption capacity with over 96% accuracy.
Study on the Adsorption Behavior and Mechanism of Heavy Metals in Aquatic Environment before and after the Aging of Typical Microplastics
Researchers investigated the adsorption behavior and mechanisms of heavy metals by typical microplastics before and after environmental aging, finding that aging significantly alters microplastics' surface properties and capacity to bind metals such as cadmium and lead in aquatic systems.
Microplastics as a vehicle of heavy metals in aquatic environments: A review of adsorption factors, mechanisms, and biological effects
This review summarizes how microplastics in water can absorb and carry toxic heavy metals like lead and cadmium, making them more dangerous to aquatic life than either pollutant alone. Environmental factors such as water acidity, salinity, and organic matter influence how much metal sticks to microplastic surfaces. Since contaminated seafood is a major source of human exposure, understanding these interactions is important for assessing health risks.
The use of artificial neural networks in modelling migration pollutants from the degradation of microplastics
Researchers used artificial neural networks to model the emission of additives from degrading microplastics, finding that machine learning could predict migration patterns from the vast range of polymer types, chemical structures, and environmental conditions involved. This approach could reduce the need for extensive laboratory testing by identifying high-risk scenarios for further investigation.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
Interactions of microplastics with heavy metals in the aquatic environment: Mechanisms and mitigation
This review synthesized mechanisms of heavy metal adsorption onto microplastics in aquatic environments and evaluated strategies for removing both contaminants simultaneously. The authors found that temperature, salinity, and plastic surface aging govern metal binding, and identified hybrid adsorbent materials as the most promising approach for co-removal of metals and microplastics from water.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
Metal adsorption by microplastics in aquatic environments under controlled conditions: exposure time, pH and salinity
Scientists systematically varied pH, salinity, and exposure time during metal adsorption experiments on different microplastic types, finding that pH had the greatest influence on metal uptake, with higher pH favoring adsorption of copper, lead, and cadmium onto most tested polymers.
Features of Heavy Metals Sorption by Microplastics in Environmentally Relevant Conditions
Experiments using aged PET microplastics in natural lake water showed that the particles sorb heavy metals (cobalt, nickel, copper, cadmium, lead) in environmentally relevant concentrations, but the sorption isotherms differed from those measured in synthetic laboratory solutions. This matters because microplastics acting as vectors for heavy metals in real freshwater conditions could increase metal bioavailability and toxicity to aquatic life and potentially to humans who drink the water.
Evaluating the role of microplastics as a vector in metal cycling within the River Thames
Researchers characterized how microplastics in River Thames water adsorb toxic heavy metals, comparing adsorption capacity across different plastic types and water chemistry conditions. Microplastics consistently adsorbed metals including lead, cadmium, and copper, providing the first data on metal-binding capacity for Thames microplastics and supporting their role as carriers of inorganic pollutants in urban rivers.
Adsorption mechanism of trace heavy metals on microplastics and simulating their effect on microalgae in river
Researchers investigated how three common types of microplastics adsorb trace heavy metals under varying temperature and salinity conditions in freshwater. They found that microplastics adsorb metals primarily through electrostatic forces in a single-layer pattern, with warmer temperatures and lower salinity increasing adsorption capacity. The study also showed that heavy metals carried by microplastics can inhibit the growth of freshwater microalgae, demonstrating how plastics act as vectors for metal contamination in rivers.
AI-Driven Framework Development for Predictive Classification of Microplastic Concentration of Aquatic Systems in the United States
Researchers compared four machine learning models—logistic regression, random forest, support vector machine, and a neural network—for predicting microplastic density in US coastal waters across three regions. The support vector machine performed best with 93.94% average accuracy, demonstrating the potential of AI-driven tools for microplastic monitoring.