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61,005 resultsShowing papers similar to Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
ClearMachine 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.
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.
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.
Machine 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.
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.
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.
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.
Projecting the sorption capacity of heavy metal ions onto microplastics in global aquatic environments using artificial neural networks
Machine learning models accurately predicted how much heavy metals like cadmium, lead, and zinc would adsorb onto microplastics in rivers, lakes, and oceans, based on factors like metal concentration and water salinity. Aged microplastics showed higher metal sorption capacity than virgin plastics, and predicted values matched real-world field measurements.
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.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
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.
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.
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.
Elucidating microplastic adsorption mechanisms in biomass composite materials through interpretable machine learning
Researchers used interpretable machine learning to study how biomass composite materials adsorb microplastics from water. They found that initial microplastic concentration and surface electrical potential were the most important factors determining adsorption effectiveness. The study demonstrates that data-driven approaches can help design more efficient and sustainable materials for removing microplastics from contaminated water.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
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.
Developing Predictive Models for Carrying Ability of Micro-Plastics towards Organic Pollutants
Researchers developed predictive models for microplastic adsorption of organic pollutants, using quantitative structure-activity relationships to estimate how different polymer types and pollutant properties influence sorption capacity.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
Machine learning modeling of microplastics removal by coagulation in water and wastewater treatment
Researchers developed machine learning models to predict how effectively coagulation, a common water treatment process, can remove microplastics under different conditions. The best model achieved 96% accuracy and found that water temperature had the biggest negative effect on removal, while adding coagulant aids had the most positive effect. These tools could help water treatment plants optimize their processes to better remove microplastics from drinking water.
Hybrid Ensemble Machine Learning Models with SHAP Explainability for Robust Prediction of Suspended Particle Attachment Efficiency in Complex Environmental Systems
Scientists developed a new computer model that can better predict how tiny particles—including microplastics—clump together and move through the environment. The model found that salt levels in water are the main factor controlling how single particles stick together, while electrical charge differences matter most when different types of particles interact. This research could help us better understand how microplastics and other harmful particles spread through water systems and potentially affect human health.
Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.
Researchers developed an ensemble machine learning algorithm to predict the ecotoxicity of micro(nano)plastics loaded with environmental pollutants, addressing a key knowledge gap where most studies examine plastic particles alone. The model revealed that co-pollutant loading substantially amplifies toxicity and that particle characteristics govern outcomes.
Prediction of the joint toxicity of microplastics and organic pollutants on algae based on machine learning
Researchers used machine learning models to predict the combined toxicity of microplastics and organic pollutants on algae, achieving high accuracy with gradient-boosted decision tree models. They found that microplastic concentration, particle size, and the hydrophobicity of organic pollutants were the most important factors influencing toxic effects. The study provides a computational framework that could help assess environmental risks from microplastic-pollutant mixtures more efficiently than traditional laboratory testing.
Investigating the adsorption of organic compounds onto microplastics via experimental, simulation, and prediction methods
This review systematically examined experimental, simulation, and predictive modeling approaches for studying the adsorption of organic compounds onto microplastics, synthesizing findings on how molecular interactions, environmental conditions, and plastic aging affect microplastic vector behavior for organic pollutants.