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20 resultsShowing papers similar to Machine learning models for forecasting microplastic dynamics in China’s coastal waters
ClearExploring 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.
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.
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.
Multi-scenario simulation of future marine microplastic distribution under data scarcity: A deep learning approach
Predicting where microplastics will be in the ocean in the future is difficult because monitoring data is sparse and ocean dynamics are complex. This study developed a deep learning model that uses limited data from the Taiwan Strait and Norwegian coast to forecast microplastic distribution under multiple future scenarios, projecting that concentrations in the Taiwan Strait could reach 312–376 particles per cubic meter by around 2030 — a sharp increase — while Norwegian coastal waters would rise more slowly. The research demonstrates that AI approaches can help fill the data gaps in microplastic monitoring and improve our ability to anticipate where pollution hotspots will emerge.
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.
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.
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.
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.
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.
Nationwide meta-analysis of microplastic distribution and risk assessment in China's aquatic ecosystems, soils, and sediments
A nationwide meta-analysis of 7,766 sampling sites across China found that microplastic distribution is influenced by economic development, population density, and geography, with generally higher concentrations in prosperous areas. The pollution varies significantly across water, soil, and sediment compartments, highlighting the need for AI-based regulatory frameworks to manage standardized risk assessment.
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.
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.
Global distribution of marine microplastics and potential for biodegradation
Researchers created a global map predicting marine microplastic pollution using machine learning based on over 9,400 samples and assessed the potential for biodegradation using marine metagenome data. The study found that microplastics converge in subtropical gyres and polar seas, and identified marine microbial communities with genetic potential for plastic biodegradation, suggesting nature may offer partial solutions to this pollution problem.
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.
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.
Microplastics in Chinese coastal waters: A mini-review of occurrence characteristics, sources and driving mechanisms
This mini-review compiles data on microplastic pollution across four Chinese coastal water regions, examining both natural and social factors that drive contamination levels. Researchers found that microplastic abundance varied significantly depending on sampling methods, and identified key sources including fishing activities, river discharge, and coastal development. The study highlights the need for standardized monitoring approaches to better track microplastic pollution trends in Chinese waters.
Microplastic pollution in Chinese bays: Sampling method comparisons, key drivers, and economic influence
Researchers compiled microplastic data from over 300 sampling stations across 13 bays in China and compared three different water sampling methods. They found that microplastic distribution was heterogeneous across bays and that sampling method significantly affected measured abundance, though not the types of polymers detected. The study also found a positive correlation between regional economic development and microplastic pollution levels, suggesting that human activity intensity is a key driver of coastal contamination.
Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy
Researchers developed an interpretable deep learning model integrating historical microplastic sampling data, seasonal patterns, and large-scale ocean-atmosphere climate indices to forecast spatiotemporal marine microplastic distribution, identifying climate drivers and offering a policy-relevant tool for ocean pollution management.
Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning
Researchers used interpretable machine learning algorithms to predict global marine microplastic distribution patterns based on calibrated field data. The study found that biogeochemical and human activity factors had the greatest influence on microplastic concentrations, which ranged from about 0.2 to 27 particles per cubic meter across the world's oceans, providing a framework for pollution management and decision-making.