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61,005 resultsShowing papers similar to Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
ClearMachine 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.
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.
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.
Machine learning prediction and interpretation of the impact of microplastics on soil properties
Researchers applied machine learning models to predict how microplastics affect soil properties, finding that microplastic release into soils is 4 to 23 times higher than into oceans. The models identified key factors influencing soil changes, including microplastic type, size, and concentration, as well as soil texture. The study suggests that machine learning can help address the complexity of soil-microplastic interactions and improve predictions of environmental impacts.
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.
Source tracking, pollution load, and risk assessment of microplastics pollution in agricultural soils of Bangladesh using machine learning and multi-matrix approaches
One of the first comprehensive assessments of microplastic contamination in agricultural soils of Bangladesh found widespread MP occurrence across 64 samples from eight areas, with ecological risk assessment indicating potential harm to soil organisms. The study linked MP sources to irrigation water, plastic mulch, and sewage sludge application.
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.
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.
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.
Spatial prediction of physical and chemical properties of soil using optical satellite imagery: a state-of-the-art hybridization of deep learning algorithm
Not relevant to microplastics — this study uses deep learning models combining satellite imagery and topographic data to predict soil chemical properties (pH, organic carbon, phosphorus, potassium) across a region of Iran, with no connection to microplastic pollution.
Spatial Distribution and Geostatistical Prediction of Microplastic Abundance in a Micro-Watershed with Tropical Soils in Southeastern Brazil
Researchers used geostatistical methods to predict and spatially map microplastic abundance in agricultural soil across a micro-watershed with tropical soils in southeastern Brazil. The study found heterogeneous spatial distribution patterns influenced by land use and topography, demonstrating that kriging-based interpolation can produce reliable continuous maps for environmental risk assessment.
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.
A generative physics-informed machine learning model for soil microplastic accumulation dynamics
Researchers developed a physics-informed machine learning model to simulate and predict microplastic accumulation dynamics in soils, combining experimental data with mechanistic equations to overcome the limitations of heterogeneous field conditions. The integrated model outperformed purely data-driven approaches in predicting MP transport and retention in soil.
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.
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.
Impact of land-use patterns on soil microplastics: Distribution characteristics and driving factors in southern China’s Pearl River Delta
A study across different land-use types in China's Pearl River Delta found that agricultural land had higher soil microplastic concentrations than urban or forested areas, with land-use history and plastic mulch film use as the dominant factors controlling MP distribution and polymer composition.
Effects of soil properties and land use patterns on the distribution of microplastics: A case study in southwest China
Researchers surveyed microplastic contamination in soils across different land use types in Guizhou Province, southwest China. The study found that soil properties and land use patterns significantly influence microplastic abundance and distribution, with agricultural and urban soils generally showing higher contamination levels than less intensively managed areas.
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.
Microplastic diversity, risks and soil impacts: A multi-metric assessment across land-use systems
Researchers surveyed microplastic abundance, polymer diversity, and ecological risk across seven land-use types in India's Brahmaputra Valley, finding that built-up areas had the highest particle counts while forest soils paradoxically showed the greatest polymer hazard scores due to high-risk polymers, and that land-use type shapes both the quantity and composition of soil microplastic contamination.
Tall Trees and Small Plastics. Using Random Forest Classification to Identify Microplastic Pollution in Surface Soil Samples
Researchers used machine learning (random forest classification) to identify and distinguish twenty types of plastic particles in soil samples from agricultural land. Developing accurate, automated detection methods for microplastics in soil is essential for large-scale environmental monitoring.
Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models
Researchers used satellite imagery and machine learning to predict where microplastics accumulate on sandy beaches along Brazil's northern coast. They found that beach shape, slope, and proximity to urban areas were strong predictors of microplastic deposits. The study demonstrates that geotechnology tools can help identify pollution hotspots without costly field sampling at every location.
An introduction to machine learning tools for the analysis of microplastics in complex matrices
This paper introduces machine learning tools that can speed up the identification and counting of microplastics in complex samples like soil and water. While focused on analytical methods rather than health effects, faster and more accurate detection of microplastics is essential for understanding how much exposure humans actually face through food, water, and the environment.
Macro and microplastics in the soil: abundance, characterization, identification, and interactions under different land uses in an agricultural sub-basin
Researchers examined the abundance, characterization, identification, and interactions of macro- and microplastics in soils under different land uses within an agricultural sub-basin, assessing how land-use patterns influence plastic pollution distribution and potential interactions with the soil environment.