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20 resultsShowing papers similar to Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
ClearMachine 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.
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.
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.
Microplastics in China’s surface water systems: Distribution, driving forces and ecological risk
Researchers compiled over 14,000 samples from across China to map microplastic pollution in surface water systems using machine learning models. They found that microplastic abundance varied enormously across regions, driven by a complex mix of human activities and natural conditions. The ecological risk assessment revealed that watersheds in nearly all Chinese provinces face high to extremely high contamination levels, underscoring the urgency of nationwide management efforts.
Are we underestimating the driving factors and potential risks of freshwater microplastics from in situ and in silico perspective?
Researchers combined field sampling with machine learning predictions to assess microplastic contamination in rivers of China's Yangtze River Delta, incorporating land use, hydrology, and particle properties. The study found that conventional assessments may underestimate risk by overlooking smaller particle sizes and high-density polymers, and that textile manufacturing effluents are a major underrecognized source.
Microplastic pollution in the Yangtze River: Characterization, influencing factors, and scenario-based predictions using machine learning method
Microplastic pollution in the Yangtze River was characterized across multiple sampling sites, documenting spatial patterns in particle abundance, polymer types, and size distributions. As one of the world's largest rivers, the Yangtze's microplastic burden has major implications for plastic delivery to the Pacific Ocean.
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.
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.
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.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
A Predictive Framework for Marine Microplastic Pollution using Machine Learning and Spatial Analysis
Researchers developed a machine learning framework integrated with geospatial analysis to predict microplastic pollution density across ocean regions. The Gradient Boosting model achieved the highest accuracy with 97% predictive performance, and spatial visualizations revealed pollution hotspots concentrated near industrial coastlines and major ocean current pathways.
Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China
Researchers applied machine learning models — including random forest and support vector regression — to predict the spatial distribution of soil microplastic pollution in China's Taihu Lake region, finding that soil texture, population density, and proximity to known plastic sources were the dominant drivers, with nearly half of urban soils showing serious contamination.
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.
Microplastic pollution in sophisticated urban river systems: Combined influence of land-use types and physicochemical characteristics
This study assessed microplastic pollution across an urban river network in China, finding that land-use type and water physicochemical properties jointly influence microplastic distribution, with industrial and residential areas contributing highest loads.
Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning
Using machine learning on data from 107 urban areas worldwide, researchers identified the key factors driving microplastic levels in stormwater runoff, including weather patterns, land use, and waste management practices. The study found that inconsistent definitions of what counts as a "microplastic" across different studies is a major barrier to comparing contamination levels between cities.
Prediction of Microplastic Emissions in River Basins Based on Mathematical Models
Researchers developed a mathematical model integrating multivariate linear regression and stepwise regression to predict microplastic emissions in the Guangzhou section of the Pearl River Basin, using historical abundance data to forecast 2025 annual average concentrations in this urban river system.
Using machine learning to reveal drivers of soil microplastics and assess their stock: A national-scale study
Using machine learning on data from 621 sites across China, researchers identified nine key factors driving microplastic distribution in soil, including population density, plastic production, and agricultural practices. The study estimated that Chinese topsoil contains a substantial stock of microplastics, with concentrations varying widely by region. This large-scale analysis helps predict where microplastic contamination is worst, which is important for understanding human exposure through food grown in contaminated soil.
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.
Abundance, Distribution and Drivers of Microplastic Contaminant in Urban River Environments
Researchers surveyed microplastic distribution in urban river environments and identified key drivers of accumulation hotspots, finding that land use, hydrology, and infrastructure factors concentrated microplastics at predictable locations that could inform targeted management interventions.