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Papers
61,005 resultsShowing papers similar to An Effective Machine Learning Scheme to Analyze and Predict the Concentration of Persistent Pollutants in the Great Lakes
ClearPredicting 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.
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 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.
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.
Enhancing water quality prediction: a machine learning approach across diverse water environments
Researchers compared seven machine learning models for predicting water quality parameters using six years of wastewater treatment plant data. The gradient boosting model performed best overall, accurately predicting parameters related to water contamination. While the study focuses on general water quality rather than microplastics specifically, these predictive tools could be applied to monitoring microplastic-relevant conditions in treatment systems.
Microplastic Loads in Freshwater Lakes: Prioritized Regions and Management Strategies
Researchers compiled trawl-net survey data from freshwater lakes globally, applied redundancy analysis and structural equation modeling to identify key drivers of microplastic concentrations, and used machine learning to estimate loads in under-sampled regions, producing the first global prioritization framework for lake microplastic management.
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.
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.
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.
Spatiotemporal graph neural networks for analyzing the influence mechanisms of river hydrodynamics on microplastic transport processes
Spatiotemporal graph neural networks were applied to model how microplastic contamination spreads across connected water bodies over time. This AI-driven modeling approach can improve real-time prediction and management of microplastic pollution in river and lake networks.
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.
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.
Geographical features and management strategies for microplastic loads in freshwater lakes
Researchers used machine learning to predict microplastic concentrations in lakes worldwide, estimating that the top 20 meters of global lake water holds roughly 10,167 tons of microplastics — equivalent to 508 million plastic bottles. Agricultural land use and urban development near waterways were the strongest predictors of contamination, with North America, Africa, and Asia showing the heaviest loads.
Enhanced spatiotemporal mapping of urban wetland microplastics: An interpretable CNN-GRU approach using satellite imagery and limited samples
Researchers built an interpretable CNN-GRU deep learning model combining satellite remote sensing with limited in-situ measurements to map microplastic distribution in urban wetlands with enhanced spatiotemporal resolution, enabling more comprehensive monitoring with less field sampling.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
Detection of Plastic Waste in Ocean Using Machine Learning Based Bi- LSTM With Triplet Attention Mechanism
Researchers developed a machine learning model using a bidirectional LSTM architecture with triplet attention mechanism to detect plastic waste in ocean environments, addressing the challenge of tracking plastic flow from rivers into marine ecosystems.
Decoding the PlasticPatch: Exploring the Global MicroplasticDistribution in the Surface Layers of Marine Regions with InterpretableMachine Learning
Researchers applied four interpretable machine learning algorithms to a calibrated global marine microplastic dataset to construct a predictive model of surface-layer microplastic distribution, finding that biogeochemical and anthropogenic factors are the dominant drivers of global marine microplastic pollution patterns.
Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
Researchers used machine learning to predict microplastic levels in rivers across seven coastal regions of China, identifying population density, urbanization, and industrial activity as the strongest predictors of contamination. The models successfully captured how microplastics accumulate and move through river systems using 19 different environmental and human factors. This approach could reduce the need for costly field sampling while helping target pollution management efforts where they are needed most.
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.
Machine learning models for forecasting microplastic dynamics in China’s coastal waters
Researchers used machine learning to analyze microplastic pollution patterns across China's four major coastal seas, drawing on over 1,100 data points from peer-reviewed studies. They found that urban centers and industrial activities are key drivers of contamination, with pollution levels varying significantly between marine, coastal, and estuary environments. The models project that economic development and education could reduce microplastic concentrations, while industrial expansion may increase them.
New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting
Researchers developed new deep learning models using graph neural networks and transformer architectures to predict dissolved oxygen levels in rivers, a key indicator of water quality. Their models outperformed traditional forecasting methods by better capturing complex patterns in environmental data over time. While focused on water quality monitoring, this type of predictive tool could help detect environmental changes linked to pollution, including from microplastics.
Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning
Researchers used machine learning models to investigate how different characteristics of microplastics affect bacterial communities in lake ecosystems. They found that the color, shape, and polymer type of microplastics all influenced bacterial functions related to carbon and nitrogen cycling and human health. The study suggests that specific microplastic features, such as yellow coloring and PET polymer type, have distinct impacts on microbial communities in freshwater environments.
Predicting microplastic quantities in Indonesian provincial rivers using machine learning models
This study used machine learning models to predict microplastic levels in rivers across 24 Indonesian provinces based on environmental and economic data. Temperature, economic output, and population density were the strongest predictors of microplastic pollution. The approach could help environmental agencies monitor and manage microplastic contamination in freshwater systems more efficiently.
Mapping the Plastic Legacy: Geospatial Predictions of a Microplastic Inventory in a Complex Estuarine System Using Machine Learning
Researchers applied machine learning geospatial modelling to predict microplastic distribution across a complex estuarine system, using sediment samples as a training dataset to generate spatial inventory maps of microplastic accumulation. The model leveraged the estuary's role as a land-sea interface and plastic accumulation bottleneck to produce high-resolution predictions of microplastic hotspots for monitoring and management purposes.