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61,005 resultsShowing papers similar to In Silico Analysis of Contaminant Persistence: From QSARs to Machine Learning Models
ClearVersatile 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.
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 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.
Current Status of Emerging Contaminant Models and Their Applications Concerning the Aquatic Environment: A Review
This review categorizes the various computer models used to predict how emerging contaminants, including microplastics and pharmaceuticals, behave in aquatic environments. Researchers compared conventional water quality models, multimedia fugacity models, and machine learning approaches, finding that machine learning models offer the most versatility for tasks like contaminant identification and risk assessment. The study highlights that while modeling capabilities have advanced rapidly, gaps remain in applying these tools to real-world water pollution scenarios.
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.
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.
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.
Environmental fate and exposure models: advances and challenges in 21st century chemical risk assessment
This review examines how computational models are used to predict where chemicals — including nanomaterials and microplastics — end up in the environment, tracing 25 years of progress and identifying key remaining gaps. It highlights the ongoing need for better bioavailability estimates and calls for expert groups to help translate these scientific advances into regulatory policy.
Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles
Researchers used machine learning to predict how toxic different mixtures of metal nanoparticles are to bacteria. Their models outperformed traditional methods at predicting combined toxicity effects. While focused on engineered nanoparticles rather than microplastics, the computational approach could be adapted to predict health risks from the complex mixtures of nano-sized pollutants people encounter.
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.
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.
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-driven QSAR models for predicting the cytotoxicity of five common microplastics
Researchers used machine learning to predict the toxicity of five common microplastic types on human lung cells, finding that particle size, plastic type, and exposure concentration were the most important factors determining harm. This computational approach could help assess the health risks of different microplastics more efficiently than traditional lab testing alone.
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.
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.
Prediction of organic compounds adsorbed by polyethylene and chlorinated polyethylene microplastics in freshwater using QSAR
Researchers used QSAR modeling to predict the adsorption behavior of 13 organic compounds onto polyethylene and chlorinated polyethylene microplastics under freshwater conditions, finding that most chemicals exhibited higher adsorption to chlorinated polyethylene than to standard polyethylene.
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.
A QSAR prediction model for adsorption of organic contaminants on microplastics: Dubinin-Astakhov plus linear solvation energy relationships
Researchers combined the Dubinin-Astakhov isotherm model with linear solvation energy relationships to build a QSAR model predicting the adsorption of pharmaceuticals and personal care products onto various microplastic polymer types.
A New Method for Environmental Risk Assessment of Pollutants Based on Multi-dimensional Risk Factors
Researchers proposed a new synthetic risk factor (SRF) method for environmental risk assessment that integrates toxicity endpoint values, environmental exposure levels, persistence properties, and compartment-specific features across multiple environmental media into a single multi-dimensional evaluation framework. The approach addresses a key limitation of traditional chemical risk assessment methods that assess toxicity and exposure without accounting for persistent pollutant behavior across different environmental compartments.
Machine Learning-Driven QSAR Modeling for Predicting Short-Term Exposure Limits of Hydrocarbons and Their Derivatives
Researchers developed machine learning-based QSAR models to predict short-term exposure limits (STELs) for hydrocarbons and their derivatives, addressing the critical gap in occupational health data for many chemicals. The models showed strong predictive performance and provide a faster alternative to experimentally determining STELs for new compounds.
In Silico Models for Predicting Adsorption of Organic Pollutants on Atmospheric Nanoplastics by Combining Grand Canonical Monte Carlo/Density Functional Theory and Quantitative Structure Activity Relationship Approach
This computational study used molecular simulations and machine learning to build predictive models for how 48 different organic pollutants — including flame retardants — adsorb onto 12 types of atmospheric nanoplastics. The resulting models revealed that van der Waals and electrostatic forces dominate these interactions, and a single multi-dimensional model can rapidly screen thousands of pollutant-nanoplastic combinations at once. Accurate adsorption predictions are crucial for assessing how nanoplastics carry toxic chemicals through the atmosphere and into ecosystems or human lungs.
Moving persistence assessments into the 21st century: A role for weight-of-evidence and overall persistence
This review argues for modernizing chemical persistence assessments by moving beyond simplified laboratory biodegradation tests toward weight-of-evidence approaches that incorporate multiple fate processes and environmental conditions to better predict real-world persistence of chemicals including plastic additives.
Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges
This review examines different computer modeling approaches for predicting how nanomaterials, including nanoplastics, accumulate in living organisms. Traditional models developed for dissolved chemicals often give inaccurate results for nanoparticles because they behave differently in biological systems. Newer machine learning approaches show promise for better predictions, which could help scientists estimate how much nanoplastic actually builds up in the body without needing extensive animal testing.