We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Transient Attracting Profiles in the Great Pacific Garbage Patch
AI summary Read the abstract
A 20-year dataset of Transient Attracting Profiles (TRAPs) in the North Pacific was computed from satellite-derived ocean velocities, revealing these flow features persist from days to seasons with an average lifespan of 6 days. Long-lived TRAPs at advanced lifecycle stages represent the best targets for debris cleanup operations in the Great Pacific Garbage Patch, as they most strongly induce large-scale confluence of drifting objects.
Abstract. A major challenge for cleanup operations in the Great Pacific Garbage Patch is the daily prediction of plastic concentrations that allows identifying hotspots of marine debris. Lagrangian simulations of large particle ensembles are the method in use and effectively reproduce observed particle distributions at synoptic scales 𝒪(1000 km). However, they lose accuracy at operational scales 𝒪(1–10 km), and operators regularly experience differences between predicted and encountered debris accumulations within the garbage patch. In Lagrangian methods, it would be common to ask this question: where do objects go as they follow the current? Here, we take a different approach and instead ask this question: which locations attract material? The recently developed concept of Transient Attracting Profiles (TRAPs) provides answers because TRAPs uncover the most attracting regions of the flow. TRAPs are the attracting form of hyperbolic Objective Eulerian Coherent Structures and are computable from the instantaneous strain field on the ocean surface. They describe flow features which attract drifting objects and could facilitate offshore cleanups that are currently taking place in the Great Pacific Garbage Patch. However, the concept remains unapplied since little is known about the persistence and attraction of these features, specifically within the Pacific. Therefore, we compute a 20-year dataset of daily TRAP detections from satellite-derived mesoscale velocities within the North Pacific subtropical gyre. We are the first to track these instantaneous flow features as they propagate through space and time. It allows us to study the life cycle of TRAPs, which can range from days to seasons and lasts an average of 6 d. We show how long-living TRAPs with lifetimes beyond 30 d intensify and weaken over their life cycle, and we demonstrate that the evolution stage of TRAPs affects the motion of nearby surface drifters. Our findings indicate that, at the mesoscale, operators in the Great Pacific Garbage Patch should search for long-living TRAPs that are at an advanced stage of their life cycle. These TRAPs are the most likely to induce a large-scale confluence of drifting objects and their streamlining into hyperbolic pathways. Such a streamlined bypass takes, on average, 5 d and creates an opportunity to filter the flow around TRAPs. But we also find TRAPs that retain material over multiple weeks where we suspect material clustering at the submesoscale. Prospective research could investigate this further by applying our algorithms to soon-available high-resolution observations of the flow. Our analysis contributes to a better understanding of TRAPs, which can even benefit other offshore operations besides ocean cleanups, such as optimal drifter deployment, oil spill containment, and humanitarian search and rescue.
More Papers Like This
Persistency and Surface Convergence Evidenced by Two Maker Buoys in the Great Pacific Garbage Patch
AI summary Read the abstract
Researchers tracked two drifting buoys released in the Great Pacific Garbage Patch in 2019 and compared their trajectories to circulation divergence fields, Lagrangian plastic dispersal models, and sea-level anomalies, demonstrating that persistent negative velocity divergence and elevated modelled plastic surface density coincide with surface convergence zones that concentrate floating debris.
On Clustering of Floating Tracers in Random Velocity Fields
AI summary Read the abstract
This mathematical modeling study explores how floating particles — including microplastics — cluster into dense patches on the ocean surface under turbulent currents, finding that realistic time-correlated ocean flows produce clusters far faster than simpler models predict. Understanding this clustering behavior is important for accurately assessing where microplastic pollution concentrates in the ocean and how organisms encounter it at ecologically meaningful densities.
Clustering of buoyant tracer in quasi-geostrophic coherent structures
AI summary Read the abstract
Using Lagrangian particle tracking in a turbulent quasi-geostrophic ocean model, researchers found that buoyant floating tracers cluster inside coherent vortex structures due to ageostrophic circulation effects, with implications for understanding how surface plastic debris concentrates in ocean eddies.
A theory for attractors of microplastic particles in the resonant structures of a 3D eddy
AI summary Read the abstract
Researchers developed a theoretical framework predicting the formation of attractors — closed-loop trajectories — for microplastic particles within the resonant structures of three-dimensional ocean eddies. The theory establishes criteria for when such attractors exist and provides a mechanism explaining observed accumulation of small rigid particles in recirculating oceanic flows.
The Role of the Unsteady Surface Wave‐Driven Ekman–Stokes Flow in the Accumulation of Floating Marine Litter
AI summary Read the abstract
Researchers modeled the role of wave-driven Ekman-Stokes flow in the accumulation of floating marine debris, finding that this near-surface current mechanism significantly influences where plastic litter concentrates at sea, with implications for predicting and targeting ocean cleanup efforts.
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.