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61,005 resultsShowing papers similar to Particle tracking algorithm and additional data for "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)"
ClearParticle tracking algorithm and additional data for "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)"
Researchers developed and published a particle tracking algorithm and supplementary datasets supporting validated settling velocity measurements for small microplastic particles in the 10-400 µm size range. The repository includes image processing routines, single-particle raw settling data, empirical model results for particle interaction effects, and supporting videos.
Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 μm)
This study developed and validated a precise laboratory method for measuring how fast small microplastic particles (10–400 µm) sink in water — a key parameter for predicting where microplastics accumulate in aquatic environments. The setup uses a temperature-controlled settling column with optical particle tracking and achieves high accuracy across a range of particle sizes and densities. Accurate settling velocity data for small microplastics is essential for modeling their transport and fate in rivers, lakes, and oceans, which informs risk assessments for aquatic organisms that live at different depths.
Additional data for "Settling Velocities of Small Microplastic Fragments and Fibers"
This data repository provides raw settling velocity measurements for small microplastic fragments and fibers, supporting a publication on their transport behavior in water. Settling velocity data is critical for modeling where microplastics deposit in rivers, lakes, and ocean sediments.
An Open-Source Computer Vision-Based Method for Microplastic Settling Velocity Calculation
Researchers developed an open-source computer vision method to measure microplastic settling velocities from video recordings, enabling low-cost quantification of how particles of different sizes and densities sink in water columns with implications for predicting MP fate in aquatic environments.
An Open-Source Computer-Vision-Based Method for Spherical Microplastic Settling Velocity Calculation
Researchers developed an open-source computer vision method to measure the settling velocity of spherical microplastics, replacing subjective manual methods with automated image analysis. The tool provides a standardized, accessible approach for predicting microplastic transport and fate in aquatic environments.
Identification and velocity measurement of microplastics based on machine learning
Researchers developed a machine learning framework to simultaneously track multiple microplastics in water and measure their terminal settling velocities, capturing particle interaction dynamics that conventional single-particle tracking methods miss.
Settling Velocities of Small Microplastic Fragments and Fibers
Researchers precisely measured the settling speeds of over 4,000 small microplastic particles in water and found that existing prediction models designed for larger microplastics do not work well for these tiny fragments and fibers. The settling speed depends on each particle's size, density, and shape, with the smallest particles sinking extremely slowly. Understanding how quickly microplastics settle in water is important because it determines how far they travel and how long they remain available to be consumed by aquatic organisms that humans may eventually eat.
Settling velocities of microplastics and tire and road wear particles
Researchers developed a high-precision optical imaging method to measure how fast small microplastics (10–400 micrometers) and tire-and-road wear particles sink through water, filling a critical data gap needed to predict where these pollutants accumulate in aquatic environments.
A new model for the terminal settling velocity of microplastics
A new empirical model for the terminal settling velocity of microplastics was developed and validated using 1,343 experimental measurements covering a range of particle shapes and materials. The model improves predictions of microplastic sedimentation rates, which are critical for understanding how plastic particles are transported and deposited in water bodies.
Coupled CFD-DEM modelling to assess settlement velocity and drag coefficient of microplastics
Researchers used computational fluid dynamics coupled with particle simulations to model how the size, shape, and density of microplastics affect their settling velocity and drag in water. Accurate physical models of microplastic behavior are essential for predicting where particles accumulate in rivers, lakes, and the ocean.
Towards better predicting the settling velocity of film-shaped microplastics based on experiment and simulation data
Researchers combined experimental and simulation data to better predict how film-shaped microplastics settle through water, since most existing models are based on spherical particles. They found that the particle definition approach was more suitable than equivalent spherical diameter for characterizing flat, irregular microplastics. The improved settling velocity predictions could help scientists better understand how film-shaped microplastics travel and accumulate in aquatic environments.
Particle Tracking Model
This is a numerical model dataset examining how microplastics absorbed into phytoplankton aggregates settle and cycle through ocean waters — not a standalone research article.
Settling behaviors of microplastic disks in acceleration fall
Researchers studied the settling behavior of disk-shaped microplastics during free-fall in water, using high-speed imaging to track the orientation and velocity of particles as they descended. Disk-shaped particles exhibited oscillating and tumbling motions that slowed settling compared to spheres of equivalent mass, with implications for predicting microplastic transport and deposition in aquatic environments.
Three-Dimensional Settling Dynamics of Environmental Microplastics
Researchers measured the three-dimensional settling dynamics of environmental microplastic particles in water, including lateral drift, settling paths, and horizontal velocities—dimensions poorly understood beyond simple vertical settling rates. The findings are essential for developing accurate models of how MPs distribute across river channels and water columns.
Predicted settling velocity of sampled MPFs
This is a dataset of predicted settling velocities for microplastic fibers using a newly proposed model — not a standalone research article.
A Simplified Experimental Method to Estimate the Transport of Non-Buoyant Plastic Particles Due to Waves by 2D Image Processing
Not a microplastics paper in the strict sense — this study develops and validates an image-processing method to track the movement of non-buoyant plastic debris particles under wave action in a laboratory wave tank, advancing the physical modeling tools used to predict where plastic pollution accumulates in coastal environments.
Machine learning-based prediction for settling velocity of microplastics with various shapes
Researchers developed machine learning models to predict the settling velocity of microplastics based on their size, density, and shape. They classified microplastic shapes into fiber, film, and fragment categories and identified the optimal shape parameter for each, achieving significantly better prediction accuracy than existing theoretical models. The study reveals that particle size has the greatest influence on settling velocity, which is important for understanding how microplastics move and distribute in water environments.
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
Researchers developed machine learning models to predict the settling velocity of microplastics in water, using particle shape, size, and density as inputs. The models outperformed traditional empirical equations, providing a more accurate tool for modeling microplastic transport and sedimentation.
Settling velocity of microplastic particles having regular and irregular shapes
Researchers measured how quickly microplastic particles of various shapes settle through water, testing 66 different particle types including spheres, cylinders, fibers, and irregular fragments. They found that particle shape significantly affects settling speed, with fibers and flat shapes sinking more slowly than spheres of the same size. The study provides new equations for predicting where microplastics end up in oceans and waterways based on their shape.
Dataset accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes"
This dataset accompanies a study on how flow velocity, particle size, and sediment grain size affect the transport and retention of polystyrene microplastics in riverbeds. The raw image files support research into how microplastics move through freshwater systems and accumulate in sediment.
Adding depth to microplastics for particle characterization and assessing settling behavior
This study developed methods to characterize microplastics in three dimensions rather than the conventional two-dimensional approach, obtaining volume and shape data that improves predictions of how particles settle and transport in water systems. Three-dimensional characterization significantly improved settling rate predictions compared to 2D image-based estimates.
Modeling Microplastic Transport in the Marine Environment: Testing Empirical Models of Particle Terminal Sinking Velocity for Irregularly Shaped Particles
Researchers tested multiple drag models for predicting the terminal settling velocity of irregularly shaped microplastic particles in seawater, identifying three high-precision models and demonstrating that settling velocity is largely stable across ocean depths and independent of initial particle velocity, improving the accuracy of marine microplastic transport simulations.
A Laboratory Dataset on Transport and Deposition of Spherical and Cylindrical Large Microplastics for Validation of Numerical Models
This paper presents a laboratory dataset on the transport and deposition of spherical microplastic particles under controlled flow conditions, providing empirical data on how particle size and flow velocity influence settling and lateral dispersion. The dataset is intended to support calibration of microplastic transport models.
Sinking velocity of sub-millimeter microplastic
Researchers measured the sinking velocities of irregularly shaped microplastic particles (polyamide, PMMA, and PET, 6–251 μm) and found they sink considerably slower than theoretical predictions for spheres of equivalent size, developing a predictive model based on particle size and excess density to better represent how real-world microplastics settle through the water column.