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61,005 resultsShowing papers similar to Microplastic deposits prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
ClearMicroplastic 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.
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.
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.
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.
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.
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.
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.
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.
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.
Developing Beach Litter Monitoring System Based on Reflectance Characteristics and its Abundance
Researchers developed a beach litter monitoring system using optical reflectance characteristics of plastic debris, training a remote sensing model to detect and classify litter items on sandy beach surfaces. The system demonstrated accurate detection of common plastic litter types and offers a scalable, automated alternative to manual beach surveys.
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.
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.
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.
Coastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery
Researchers used high-resolution satellite imagery combined with machine learning to detect and map coastal marine debris density in southern Japan, finding that satellite-based methods can estimate debris amounts and types on beaches with reasonable accuracy.
Enhanced spatiotemporal mapping of urban wetland microplastics: An interpretable CNN-GRU approach using satellite imagery and limited samples
Researchers built an interpretable CNN-GRU deep learning model combining satellite remote sensing with limited in-situ measurements to map microplastic distribution in urban wetlands with enhanced spatiotemporal resolution, enabling more comprehensive monitoring with less field sampling.
Coastal Dynamics Analysis Based on Orbital Remote Sensing Big Data and Multivariate Statistical Models
Not relevant to microplastics — this remote sensing study uses satellite data and statistical models to analyze 36 years of shoreline change along the São Paulo, Brazil coastline, focusing on erosion and accretion rates.
Microplastics on Santos Beach: Sources of Pollution, Waste Characteristics and Possible Collection Solutions
Researchers characterized microplastics collected from three zones of Santos beach in Brazil, finding contamination dominated by fragments and films near sewage outfalls. The study highlights inadequate waste management as the primary driver of beach microplastic accumulation and assessed feasibility of mechanical collection interventions.
Microplastics on Santos Beach: Sources of Pollution, Waste Characteristics and Possible Collection Solutions
This Brazilian study mapped and characterized microplastic contamination on Santos beach near submarine sewage outfalls and storm drains, finding plastic pollution hotspots linked to coastal discharge infrastructure. The authors estimated that ~60 tons of solid waste enter the sea daily in the region and identified possible collection solutions.
Microplastics distribution on the beach sediment based on satellite remote sensing: A case study in Bali, Indonesia
Researchers examined how seasonal ocean currents and tourism activity influence microplastic distribution across three beaches in Bali, Indonesia, between January and July 2024. The study integrated polymer-level characterization with site-specific hydrodynamic data and satellite remote sensing to map microplastic accumulation patterns in beach sediments.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
Spatial and Temporal Distribution of Chemically Characterized Microplastics within the Protected Area of Pelagos Sanctuary (NW Mediterranean Sea): Focus on Natural and Urban Beaches
Researchers sampled large microplastics on three Mediterranean beaches within the Pelagos Sanctuary over one year, finding that more urbanized beaches had higher contamination and that abundance varied seasonally. Portable Raman spectroscopy was successfully used for on-site polymer identification, offering a faster field method.
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.
Toward in Situ Identification of Microplastics in Water Using Raman Spectroscopy and Machine Learning
This study developed an early-stage system combining Raman spectroscopy and machine learning to identify microplastics directly in ocean water in real time, without needing to collect and process samples in a lab. A support vector machine classifier trained on spectral libraries correctly identified all pristine microplastic samples and most environmental ones, demonstrating that field-deployable automated detection is feasible. Accurate real-time monitoring tools are urgently needed to understand where microplastics concentrate in the ocean and to track pollution trends.
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.