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20 resultsShowing papers similar to Predicting microplastic quantities in Indonesian provincial rivers using machine learning models
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.
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.
Machine learning approaches for predicting microplastic pollution in peatland areas
Researchers used machine learning models to predict microplastic quantities in peatland sediments in Vietnam from easily measurable environmental parameters. The study found that pH, total organic carbon, and salinity were the most influential factors, and that Least-Square Support Vector Machines and Random Forest models could effectively predict microplastic contamination levels.
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.
Microplastic Contamination in Yogyakarta's Rivers: Spatial Analysis and Factor Assessment to Identify Key Pollutants
This study used multiple linear regression and water quality monitoring data from eight rivers in Yogyakarta, Indonesia to identify which environmental factors — including microplastic content, pH, COD, and turbidity — significantly predict river water quality.
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.
Modeling spatiotemporal patterns of microplastic pollution in the lupit river using multilinear regression
Researchers modelled spatiotemporal patterns of microplastic pollution in the Lupit River using multiple linear regression with four predictors — population, seasonality, macroplastic frequency, and volumetric flow rate — finding widespread contamination across rural, residential, informal settlement, and commercial zones.
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.
Evaluation of microplastic pollution in urban lentic ecosystem using remote sensing, GIS, and Support Vector Machine (SVM): relevance for environmental and ecological risk
Researchers assessed microplastic pollution in 24 urban ponds and lakes in Kolkata, India, finding significantly higher concentrations during the post-monsoon season, with fibers making up about 59% of all particles. They developed machine learning and remote sensing models that achieved up to 98% accuracy in identifying water bodies and predicting microplastic levels from satellite imagery. The study demonstrates that combining field sampling with remote sensing technology can enable large-scale monitoring of urban microplastic pollution.
Machine learning-driven analysis of soil microplastic distribution in the Bang Pakong Watershed, Thailand
Researchers used machine learning techniques to analyze the distribution and influencing factors of soil microplastic contamination in the Bang Pakong Watershed in Thailand. The study identified key environmental and land-use variables that predict microplastic occurrence, providing a data-driven approach for understanding how microplastics distribute across agricultural and urban landscapes.
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.
A Regional Difference Analysis of Microplastic Pollution in Global Freshwater Bodies Based on a Regression Model
Analysis of microplastic data from 37 freshwater locations worldwide found pollution is highest in Asia, that developing countries have more contamination than developed ones, and that urban areas exceed rural areas. Population density and GDP both correlated with microplastic concentrations, confirming human activity as the primary driver.
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.
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.
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.
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.
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.
Occurrence of microplastic pollution in rivers globally: Driving factors of distribution and ecological risk assessment
Researchers constructed a global dataset of microplastic pollution across 862 river water and 445 sediment samples, identifying population density, GDP, and plastic waste generation as key driving factors of riverine microplastic distribution and ecological risk.
Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy
Researchers developed satellite-based models using neural network algorithms to estimate riverine microplastic concentrations, using suspended sediment concentration as a proxy, offering a cost-effective approach for broad-scale freshwater microplastic monitoring.