We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Recent trends in marine microplastic modeling and machine learning tools: Potential for long-term microplastic monitoring
AI summary Read the abstract
This review examines how numerical models and machine learning tools can improve long-term monitoring of microplastic transport in marine environments. The authors argue that combining physics-based transport models with image-based machine learning can better predict microplastic pathways and distribution, addressing current data and accuracy gaps.
The increase in the global demand for plastics, and more recently during the pandemic, is a major concern for the future of plastic waste pollution and microplastics. Efficient microplastic monitoring is imperative to understanding the long-term effects and progression of microplastic effects in the environment. Numerical models are valuable in studying microplastic transport as they can be used to examine the effects of different parameters systematically to help elucidate the fate and transport processes of microplastics, thus providing a holistic view of microplastics in the ocean environment. By incorporating physical parameters (such as size, shape, density, and identity of microplastics), numerical models have gained better understanding of the physics of microplastic transport, predicted sinking velocities more accurately, and estimated microplastic pathways in marine environments. However, availability of large amounts of information about microplastic physical and chemical parameters is sparse. Machine learning and computer-vision tools can aid in acquiring environmental information and provide input to develop more accurate models and verify their predictions. More accurate models can further the understanding of microplastic transport, facilitate monitoring efforts, and thus optimize where more data collection can take place to ultimately improve machine learning tools. This review offers a perspective on how image-based machine learning can be exploited to help uncover the physics of microplastic transport behaviors. Additionally, the authors hope the review inspires studies that can bridge the gap between numerical modeling and machine learning for microplastic analysis to exploit their joined potential.
More Papers Like This
Recent trends in marine microplastic modeling and machine learning tools: Potential for long-term microplastic monitoring
AI summary Read the abstract
This review examines recent advances in numerical modeling and machine learning tools for tracking marine microplastic transport, highlighting their potential for improving long-term microplastic monitoring and understanding environmental fate.
Combining numerical modeling and machine learning for microplastic monitoring
AI summary Read the abstract
This perspective piece argues that combining numerical ocean transport models with machine learning could improve microplastic monitoring by filling gaps in observational data. Interdisciplinary approaches that integrate modeling and AI are increasingly seen as necessary tools for tracking plastic pollution across vast ocean areas.
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
AI summary Read the abstract
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.
A Simplified Experimental Method to Estimate the Transport of Non-Buoyant Plastic Particles Due to Waves by 2D Image Processing
AI summary Read the abstract
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.
Dynamic prediction of large spherical and cylindrical microplastic deposition: a machine learning approach for transport and deposition
AI summary Read the abstract
Researchers developed a machine learning model combined with dimensionless analysis to predict the deposition patterns of spherical and cylindrical microplastics in aquatic environments. The model accounts for varied flow conditions and particle shapes to improve predictions of where microplastics settle in water bodies. The study offers a practical tool for pollution monitoring efforts by helping predict microplastic accumulation hotspots in rivers and oceans.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.