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61,005 resultsShowing papers similar to Predicting microplastic transport in open channels with different bed types and river regulation with machine learning techniques
ClearApplicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors
Researchers applied machine learning models to predict how microplastics move through open water channels under different flow conditions, vegetation patterns, and particle densities. They found that tree-based algorithms like Random Forest and Extreme Gradient Boost significantly outperformed traditional statistical models in prediction accuracy. The study demonstrates that machine learning can be a valuable tool for understanding and forecasting microplastic transport in waterways.
Dynamic prediction of large spherical and cylindrical microplastic deposition: a machine learning approach for transport and deposition
Researchers developed a machine learning model combined with dimensionless analysis to predict the deposition patterns of spherical and cylindrical microplastics in aquatic environments. The model accounts for varied flow conditions and particle shapes to improve predictions of where microplastics settle in water bodies. The study offers a practical tool for pollution monitoring efforts by helping predict microplastic accumulation hotspots in rivers and oceans.
Capture of plastic litter by sluice gate and trash racks
Researchers conducted hydraulic flume experiments to assess how well sluice gates and trash racks capture plastic litter of varying shapes and sizes, finding that each structure has a threshold particle size above which capture efficiency becomes reliable. The results suggest these water management devices can be optimised for plastic removal, offering a practical intervention point for reducing plastic transport through river systems.
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.
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.
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.
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
Researchers developed machine learning models to predict the settling velocity of microplastics in water, using particle shape, size, and density as inputs. The models outperformed traditional empirical equations, providing a more accurate tool for modeling microplastic transport and sedimentation.
Continuous Near-Bed Movements of Microplastics in Open Channel Flows: Statistical Analysis
Particle tracking velocimetry experiments in a laboratory flume showed that near-bed microplastic transport in open channels follows a normal streamwise velocity distribution, with transport behavior varying significantly by particle type and hydraulic conditions.
Mobility and retention of microplastic fibers and irregular plastic fragments in fluvial systems: an experimental flume study
Researchers conducted experimental flume studies to compare the mobility and retention of microplastic fibres and irregularly shaped plastic fragments in fluvial systems. The study found that particle shape strongly influences transport behaviour, with fibres exhibiting greater mobility and distinct retention patterns compared to irregular fragments, highlighting the need to move beyond spherical particle models in microplastic transport research.
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.
Plastic drift : Mapping the course of microplastic transport in turbulent riverine flows.
Researchers conducted laboratory experiments tracking the 3D trajectories of 24 negatively buoyant microplastic particles spanning a range of sizes, shapes, and densities in turbulent open channel flow, generating 720 trajectories to evaluate how well conventional sediment transport models apply to microplastics. Results revealed that the inherent variability in microplastic physical properties challenges direct application of sediment transport concepts to microplastic fate prediction in rivers.
The crucial factor for microplastics removal in large-scale subsurface-flow constructed wetlands
This study used field data from large-scale subsurface-flow constructed wetlands combined with machine learning to identify which design and operational parameters most critically determine microplastic removal efficiency. Hydraulic retention time and plant density were the dominant controlling factors, providing evidence-based design guidance for optimizing constructed wetlands for microplastic treatment.
Insights into suspended sediment and microplastic budget of a lowland river: integrating in-situ measurements, Sentinel-2 imagery, and machine learning
Researchers combined river measurements, satellite imagery, and machine learning to track how much microplastic the Tisza River in Hungary carries downstream each day. They found that flood events spike microplastic transport five-fold, meaning a small number of high-water days drive the majority of plastic particles reaching the sea.
A new modeling approach for microplastic drag and settling velocity
Researchers developed a novel machine learning-based modelling framework to predict drag coefficients and settling velocities for microplastics of varying shapes (1D, 2D, 3D, and mixed) in aquatic environments. The framework achieved coefficient of determination values of 0.86-0.95 for drag models, outperforming traditional theoretical and data-fitting approaches in both speed and accuracy.
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.
Microplastic distribution in a meandering river bed and its sedimentary predictors
Researchers investigated microplastic distribution patterns within a meandering riverbed and identified sedimentary predictors of microplastic accumulation, advancing understanding of within-channel spatial variability that affects large-scale pollution quantification. The study found that specific geomorphological features of meandering channels are strong predictors of local microplastic hotspots in riverbed sediments.
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.
Incipient Motion of Exposed Microplastics in an Open-Channel Flow
Researchers experimentally determined the conditions needed to initiate microplastic movement in open-channel water flows, finding that standard sediment transport thresholds do not apply to microplastics and proposing a new predictive formula that reduces error from 55.6% to 12.3%.
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.
Sedimentary abundance and major determinants of river microplastic contamination in the central arid part of Iran
A river in central Iran showed a sharp downstream gradient of microplastic contamination in sediments, with levels near a major wastewater treatment plant far exceeding upstream concentrations. Machine learning analysis identified human population density — the number of local residents and tourists — as the strongest predictor of microplastic levels, outperforming factors like sediment chemistry or river geometry. The results point to consumer plastic use and inadequate waste disposal as the dominant drivers of river microplastic pollution in arid urban regions, with practical implications for targeted management interventions.
Hydro-geomorphological features govern the distribution, storage, and transport processes of riverbed microplastics
This study examined how river channel shape, water flow, and sediment dynamics control where microplastics accumulate, travel, and are stored in riverbeds. Identifying these hydro-geomorphological drivers is important for predicting microplastic transport to downstream ecosystems and the ocean.
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.
Hydro-geomorphological features govern the distribution, storage, and transport processes of riverbed microplastics
This study examined how river channel shape, water flow, and sediment dynamics control where microplastics accumulate, travel, and are stored in riverbeds. Identifying these hydro-geomorphological drivers is important for predicting microplastic transport to downstream ecosystems and the ocean.
Microplastic deposit predictions on sandy beaches by geotechnologies and machine learning models
Researchers used geotechnologies and machine learning models to predict microplastic deposition hotspots on sandy beaches, identifying environmental and anthropogenic variables that drive spatial variation in beach microplastic accumulation.