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Papers
61,005 resultsShowing papers similar to Estimation of Pb and Cd Content in Soil Using Sentinel-2A Multispectral Images Based on Ensemble Learning
ClearSpatial 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.
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.
Strategies for Regulating the Bioavailability and Mobility of Se and Cd in Cd-Contaminated Seleniferous Soils: Coupling the Bioavailable Se:Cd Molar Ratio with Soil Properties
Not relevant to microplastics — this study examines how the ratio of bioavailable selenium to cadmium in naturally selenium-rich soils affects the mobility and availability of both elements, finding that soil type and land use drive these dynamics through changes in iron-aluminum oxides and organic matter.
Microplastic Pollution In Agricultural Lands And Its Environmental Impact Assessed Through Remote Sensing
Researchers combined field sampling and remote sensing to assess microplastic pollution in agricultural soils across three Indian locations, finding microplastics in both surface and subsurface layers and correlating pollution levels with land use patterns detectable by satellite imagery.
Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils.
Researchers combined hyperspectral imaging with machine learning algorithms to detect polyethylene and polyamide microplastics in soil samples. This rapid detection approach could support large-scale soil monitoring for microplastic contamination, which is important given that agricultural soils may accumulate plastics from mulch films, irrigation water, and sewage sludge.
Application of Remote Sensing for the Detection and Monitoring of Microplastics in the Coastal Zone of the Colombian Caribbean
Researchers explored using remote sensing technology, including Sentinel-2 satellite imagery and machine learning algorithms, to detect and monitor microplastic pollution along the Colombian Caribbean coast. The study found that combining multispectral satellite data with computational models shows promise for systematic, large-scale monitoring of coastal microplastic contamination in regions where ground-level surveillance remains limited.
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 deposits prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
Researchers integrated remote sensing, GNSS altimetric surveys, micro-Raman spectroscopy, and machine learning models to predict microplastic deposition patterns on urban sandy beaches along the central Sao Paulo coastline, finding MP concentrations ranging from 6 to 35 MPs/m2.
Evaluating Microplastic Pollution Along the Dubai Coast: An Empirical Model Combining On-Site Sampling and Sentinel-2 Remote Sensing Data
Researchers collected coastal water samples from Dubai and combined laboratory spectral measurements with Sentinel-2 satellite imagery to build a model that estimates microplastic concentrations from space. The model achieved an R² of 87% and was used to map microplastic pollution trends along the Dubai coast from 2018 to 2021. This remote-sensing approach demonstrates a scalable method for monitoring coastal microplastic pollution over large areas without intensive fieldwork.
Automatic Detection and Identification of Floating Marine Debris Using Multispectral Satellite Imagery
Researchers developed a machine learning approach using Sentinel-2 satellite imagery and extreme gradient boosting to automatically detect and distinguish floating plastic debris from other marine materials like driftwood and seaweed.
Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging
Researchers combined short-wave infrared hyperspectral imaging with machine learning algorithms to detect low concentrations of polyamide and polyethylene microplastics in soil samples, achieving accurate classification with implications for fast, non-destructive screening of agricultural land for plastic contamination.
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.
From mapping to modelling: the evolving multidimensional microplastic risks in China's farmlands
Researchers combined a national-scale soil survey with machine learning models to map and project microplastic risks across China's farmlands through 2050, finding that agricultural film use, population density, and GDP are key drivers, and that regional risk rankings will shift counter-intuitively depending on which socioeconomic development pathway is followed.
Hyperspectral estimation of mercury content of soil in Oasis city in arid zones of China
Researchers used hyperspectral remote sensing to develop a faster and cheaper method for estimating mercury contamination in soil near Urumqi, China. They tested multiple spectral transformation methods and found that certain approaches combined with machine learning models could accurately predict soil mercury levels. The technique offers a practical alternative to traditional laboratory-based soil testing for large-scale environmental monitoring.
New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data
Researchers used multi-temporal satellite imagery to map plastic-mulched farmland in China, providing a tool for monitoring the environmental risk of agricultural plastic use. Plastic mulch is a significant source of microplastic contamination in agricultural soils when film residues break down over time.
Towards Detecting Floating Objects on a Global Scale with Learned Spatial Features Using Sentinel 2
Researchers developed a machine learning approach using Sentinel-2 satellite imagery to detect floating plastic debris and marine litter on a global scale, demonstrating that learned spatial features can improve detection of large aggregations of floating objects on water surfaces.
The potentiality of GIS for assessing soil pollution – A review
Not relevant to microplastics — this review examines how Geographic Information Systems (GIS) can be applied to assess and map soil pollution from heavy metals, pesticides, and other contaminants, with no substantive microplastics content.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation data
Researchers used free Copernicus Earth observation satellite data and machine learning to detect waste plastic in marine and terrestrial environments at a large scale. The classifier was trained on Sentinel-1 and Sentinel-2 data and performed well for detecting larger plastic accumulations. Satellite-based detection could enable continuous, wide-area monitoring of plastic pollution at a fraction of the cost of ground surveys.
Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology
Researchers developed a method using hyperspectral imaging and machine learning to rapidly detect and classify different types of microplastics in farmland soil. The technology achieved high accuracy in identifying common plastic types like polyethylene and polypropylene in soil samples. Better detection tools like this are essential for monitoring microplastic contamination in agricultural land and understanding its potential impact on food safety.
Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
Researchers applied 3D laser scanning confocal microscopy coupled with a machine-learning algorithm for automated detection and quantification of microplastics from LDPE and PP mulch films in arable soil, addressing the lack of accurate quantification methods for agricultural MP contamination from plastic mulching and sewage sludge.
Remote sensing detection of plastic-mulched farmland using a temporal approach in machine learning: case study in tomato crops
Researchers tested machine learning classifiers on Sentinel-2 satellite time-series images to map plastic-mulched farmlands, achieving 99.7% accuracy using a multilayer perceptron model and demonstrating that a 3-image composite series reduces confusion with background vegetation — producing the first plastic mulch map for Latin America.
Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images
Researchers applied machine learning to aerial multispectral images for automated detection of plastic litter in natural areas, demonstrating that combining spectral data with classification algorithms can effectively identify and monitor plastic waste pollution.
Comparison of learning models to predict LDPE, PET, and ABS concentrations in beach sediment based on spectral reflectance
Researchers compared machine learning models to predict concentrations of LDPE, PET, and ABS microplastics in beach sediments using visible-near-infrared spectral reflectance, demonstrating that spectroscopic methods can efficiently estimate microplastic pollution in understudied terrestrial and coastal environments.
Efficient screening of microplastics in soils using hyperspectral imaging in the short-wave infrared range coupled with machine learning – A laboratory-based experiment
Researchers tested short-wave infrared hyperspectral imaging combined with machine learning to detect three types of microplastics in soil, finding it could identify elevated contamination but was not sensitive enough for typical environmental background levels. The technique shows most promise for screening heavily polluted sites like landfills and industrial areas.