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61,005 resultsShowing papers similar to Microplastic deposit predictions on sandy beaches by geotechnologies and machine learning models
ClearMicroplastic 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.
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.
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.
Microplastic Deposits Prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
Researchers used remote sensing, GNSS positioning, Raman spectroscopy, and machine learning to predict microplastic deposition on urban beaches along the Sao Paulo coastline in Brazil. Microplastic concentrations ranged from 6 to 35 particles per square meter, with the highest densities near the Port of Santos linked to industrial activities. The predominant types were foams, fragments, and pellets, and machine learning models showed high predictive accuracy for mapping their 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.
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.
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.
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.
Predicting microplastic accumulation zones and shoreline changes along the Kelantan coast, Malaysia, using integrated GIS and ANN models
Researchers combined GIS with an artificial neural network to predict microplastic accumulation zones along Malaysia's Kelantan coast, achieving R=0.972 predictive accuracy and identifying shoreline erosion-prone areas as the primary deposition hotspots for microplastic 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.
A novel polymer-sensitive index coupled with multivariate and machine learning modeling for microplastic risk assessment in coastal sediments of the bay of Bengal
Scientists found that popular tourist beaches in Bangladesh have much higher levels of tiny plastic particles (called microplastics) in the sand compared to less-visited areas, with some of the most dangerous types of plastics concentrated where people spend the most time. The researchers discovered that simply counting plastic particles isn't enough—the type of plastic matters more for health risks, since some plastics are more toxic than others. This research shows that heavily-used beaches need better waste management to protect both tourists and local communities from potentially harmful plastic pollution.
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.
The influence of depositional environment on the abundance of microplastic pollution on beaches in the Bristol Channel, UK
Researchers assessed the extent and variability of microplastic pollution across multiple beaches in the Bristol Channel, UK, finding that depositional environment characteristics significantly influenced the abundance and distribution of microplastic contamination in beach sand.
Machine learning approach for automated beach waste prediction and management system: A case study of Mumbai
Researchers developed a machine learning system to predict beach waste generation patterns in Mumbai, aiming to enable more effective and automated waste management for one of the world's most polluted coastal cities.
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.
Environmental assessment of urban beaches using geotechnologies in a case study from the Vesuvian coast, Italy
An integrated drone, bathymetry, and sediment survey of an urban beach near Naples, Italy found consistent coastal erosion, coarse sand composition, and high levels of anthropogenic debris including microplastics. The study demonstrates a reproducible geotechnology-based method for simultaneously monitoring beach morphology and plastic pollution in degraded coastal environments.
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 beaching dependence on sediment grain size
Researchers sampled microplastics across a Mediterranean protected beach and found that accumulation is strongly influenced by sediment grain size — fine-grained sands trap more surface microplastics due to lower infiltration capacity — while fiber shape promotes entanglement in sediment pores and proximity to tourism and port activities drives spatial pollution hotspots.
Decoding the PlasticPatch: Exploring the Global MicroplasticDistribution in the Surface Layers of Marine Regions with InterpretableMachine Learning
Researchers applied four interpretable machine learning algorithms to a calibrated global marine microplastic dataset to construct a predictive model of surface-layer microplastic distribution, finding that biogeochemical and anthropogenic factors are the dominant drivers of global marine microplastic pollution patterns.
Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches
Hyperspectral imaging combined with machine learning was applied to identify and classify microplastics on sandy beach surfaces, offering a faster and more scalable alternative to conventional spectroscopic analysis for large-area environmental monitoring.
Recognizing microplastic deposits on sandy beaches by altimetric positioning, μ-Raman spectroscopy and multivariate statistical models
Researchers combined satellite positioning, Raman spectroscopy, and statistical modeling to map and characterize microplastic deposits on sandy beaches along the Sao Paulo coast in Brazil. They found that microplastic distribution was linked to beach elevation, tidal patterns, and proximity to industrial and port activities. The study introduces a replicable methodology for systematically monitoring plastic pollution on coastlines.
Beach morphodynamics and its relationship with the deposition of plastic particles: A preliminary study in southeastern Brazil
Researchers found that beach morphodynamic characteristics influence the deposition of plastic particles on beaches in São Paulo, Brazil, with 745 particles recovered — mostly styrofoam — and accumulation patterns correlating with beach profile dynamics.
Testing the factors controlling the numbers of microplastics on beaches along the western Gulf of Thailand
Researchers measured microplastic concentrations on beaches along the western Gulf of Thailand and applied statistical models to link abundance patterns to ocean surface currents and land-based pollution sources, finding that current direction and proximity to riverine inputs were the strongest predictors of beach MP levels.
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.