Michelle H. DiBenedetto

Abstract Particle settling and rise velocities play a central role in modeling microplastic (MP) transport, residence times, and distributions in aquatic systems. Recent studies have focused on deriving accurate models for MP settling velocities, but the detailed particle-shape information required is usually unavailable in real-world data. Here, we investigate the relative importance of settling velocity model choice versus particle size and shape information using a previously published laboratory dataset of measured settling velocities for irregular MP fragments. We find that uncertainty in particle geometry dominates model error, while differences between commonly used settling velocity models are comparatively small. Settling and rise velocities vary widely across realistic MP sizes and shapes, which leads to orders-of-magnitude differences in residence times. We show how this differential settling can alter observed particle size distributions: for example, even when the underlying fragmentation spectrum follows a power law, we find separate power-law exponents for large and small particles due to differential vertical transport. Our results imply that improving geometric characterization of particles is more critical for modeling vertical transport than further refinement of settling formulas, and that commonly used power-law extrapolations across size classes may be physically inconsistent. These findings have direct implications for interpreting MP observations and for the harmonization and risk-assessment frameworks that rely on size-distribution corrections.

This upload contains experimental data taken of spherical and nonspherical particles in the Washington Air Sea Interaction Research Facility (WASIRF). Each mat file corresponds to one experimental run with a given windspeed and particle type. The windspeeds and particle types for each run are contained in the spreadsheet run_parameters.xlsx. Each mat file contains particle position and velocity data in the array smtracks, and particle orientation and angular velocity data in the array smangles. Rows of each array correspond to particle observations. The columns contain the following data for each observation: Columns of smtracks: 1. x [m] 2. z [m] 3. u [m/s] 4. w [m/s] 5. track ID 6. time counter for each track 7. frame number 8. a_x [m/s^2] 9. a_z [m/s^2] Columns of smangles: 1. px 2. pz 3. py 4. px_dot [s^-1] 5. pz_dot [s^-1] 6. py_dot [s^-1] 7. px_ddot [s^-2] 8. pz_ddot [s^-2] 9. py_ddot [s^-2]

This upload contains experimental data taken of spherical and nonspherical particles in the Washington Air Sea Interaction Research Facility (WASIRF). Each mat file corresponds to one experimental run with a given windspeed and particle type. The windspeeds and particle types for each run are contained in the spreadsheet run_parameters.xlsx. Each mat file contains particle position and velocity data in the array smtracks, and particle orientation and angular velocity data in the array smangles. Rows of each array correspond to particle observations. The columns contain the following data for each observation: Columns of smtracks: 1. x [m] 2. z [m] 3. u [m/s] 4. w [m/s] 5. track ID 6. time counter for each track 7. frame number 8. a_x [m/s^2] 9. a_z [m/s^2] Columns of smangles: 1. px 2. pz 3. py 4. px_dot [s^-1] 5. pz_dot [s^-1] 6. py_dot [s^-1] 7. px_ddot [s^-2] 8. pz_ddot [s^-2] 9. py_ddot [s^-2]

While millions of metric tons of plastic enter the ocean each year, substantial amounts also continuously wash ashore. In this study we use observations from NOAA's Global Drifter Program to study the pathways by which surface-floating material leaves the open ocean and beaches along coastlines. We analyze trajectories of undrogued drifters as a proxy for buoyant plastic debris and find that approximately 20% of undrogued drifters beach within one year of losing their drogue. Beaching is highly heterogeneous, occurring in distinct coastal hotspots that are connected to large regions of the open ocean. By identifying coherent beaching watersheds, we show that a substantial portion of the ocean surface can produce beaching drifters. We further show that beaching probability depends strongly on coastal winds. For example, drifters are far more likely to beach under onshore winds, and beaching probability increases with the magnitude of the onshore wind component. These results provide insight into global beaching pathways and can be used to improve how beaching is parameterized in large-scale models of plastic debris.

Although plankton typically have slow swim speeds relative to ocean flows, they can potentially enhance their transport by exploiting certain flow features. For example, a theorized 'surfing' strategy describes how plankton can preferentially sample upwelling areas of the flow by simply sensing and actively reorienting in response to local velocity gradients. In this study, we present the first experimental evidence that real plankton may be able to surf turbulence. We studied the bottom-heavy, planktonic larval snail Crepidula fornicata as our model organism. By observing these plankton in a jet-stirred turbulence tank, we show that they indeed have complex responses to velocity gradients. In particular, we found that they actively rotate to oppose the local vorticity, which contrasts with the typical passive, gyrotactic response. We compared our observations with those of simulated surfing plankton to demonstrate the applicability of the surfing theory to our data, where we found good agreement. Finally, we observed that the real plankton can preferentially sample upwelling areas of the flow in some cases, enhancing their transport relative to their swimming speed alone, similar to the proposed surfing theory.

Microplastic pollution is now ubiquitous in marine environments, posing risks to ecosystem and human health. In order to assess and mitigate this threat, we require accurate prediction of microplastic fate and transport in the ocean. While progress has been made studying global-scale transport pathways, our models often fall short at smaller scales; processes such as vertical transport, horizontal dispersion, particle transformation, and boundary fluxes (e.g., at beaches and the air–sea interface) remain poorly understood. The difficulty lies in the physical features of plastic particles: namely, near-neutral buoyancy in seawater, finite size, and irregular shape. These complexities are compounded by the multiscale forcing from waves and turbulence near the ocean surface where microplastics tend to reside. This review synthesizes recent advances in the fluid dynamics of marine plastic transport, emphasizing the role of fluid–particle interactions in ocean flows and highlighting outstanding challenges.

Quantifying kinetic energy (KE) and enstrophy transfer, mixing, and dissipation in the Arctic Ocean is key to understanding polar ocean dynamics, which are critical components of the global climate system. However, in ice-covered regions, limited eddy-resolving observations challenge characterizing KE and enstrophy transfer across scales. Here, we use satellite-derived sea ice floe rotation rates to infer the surface ocean enstrophy spectra in the marginal ice zone. Employing a coarse-graining approach, we treat each floe as a local spatial filter. The method is validated with idealized sea ice-ocean simulations and applied to floe observations in the Beaufort Gyre. Our results reveal steepened spectral slopes at low sea ice concentrations, indicating enhanced mesoscale activity during the spring-to-summer transition. High-resolution simulations support these findings but overestimate enstrophy, highlighting a denser array of observations. Our two-dimensional spectral estimates are the first of their kind, providing a scalable approach for mapping Arctic Ocean characteristics.

Abstract The High‐Pressure Plankton Observatory (HiPPO) is designed to quantify motions of zooplankton for behavioral study, including swimming and metabolic responses to environmental perturbations. It builds on prior chamber designs while filling gaps in capability for resolving orientation of small (< 1 mm) plankton, tracking their movements over ecologically relevant spatial scales, and recording in flow‐through conditions on a vessel at sea. The HiPPO chamber has a direct light path for silhouette imaging of zooplankton as they move vertically and horizontally across a 3.56 cm diameter viewing area. Seawater forced by a high‐performance liquid chromatography pump is exchanged continuously through the chamber, but flushing of zooplankton is prevented by fine mesh at the ports. A high‐resolution camera/computer setup enables sustained imaging of plankton motions for quantitative analysis. Application of HiPPO to an investigation of larval behavior of deep‐sea hydrothermal vent species revealed swimming behaviors similar to those of shallow‐water species, including upward and downward helices, meandering, and short hovers. In conditions with microbial biofilm (a potential settlement cue) on a 2024 expedition, vent larvae unexpectedly swam rapidly upward in tight helices at velocities (0.15 cm s −1 ) higher than those observed in prior experiments with no biofilm (0.03 cm s −1 ). Many factors varied between the 2024 and earlier trials, so the difference cannot be attributed with certainty to a cue response. This study describes key new features of HiPPO and demonstrates the system's ability to document novel zooplankton behavior.

Abstract Separating the effects of waves and turbulence in oceanographic time series is an ongoing challenge because surface wave motion and turbulence fluctuations can occur at overlapping frequencies. Therefore, simple bandpass filters cannot effectively separate their dynamics. While more advanced decomposition techniques have been developed, they often entail restrictive assumptions about the wave and turbulence interactions, require synchronized measurements, and/or only decompose the signal spectrally without a time series reconstruction. We present our new wave–turbulence decomposition technique which uses dynamic mode decomposition (DMD). The technique is signal agnostic so it can be applied to any time series, and our only assumptions are that the waves and turbulence can be separated and that the waves are the most coherent features in the signal. Our approach requires minimal tuning, where the main user input is the wave frequency range of interest. To demonstrate the method, we apply it to synthetic, field, and laboratory data and compare the results to other modal decomposition methods. A sensitivity analysis on the synthetic data shows that the most sensitive parameter to the accuracy is the rank truncation in the DMD, and that the decomposition performs the best when the wave energy in the signal is of equal or greater magnitude than that of the turbulence. Given the accuracy of our decomposition, we are able to analyze the velocity autocorrelation of the separated turbulence time series with minimal wave contamination. Overall, our decomposition method outperforms the other decomposition methods and provides for robust separation of the waves and turbulence, demonstrating wide applicability to ocean signal processing. Significance Statement When measuring physical, chemical, and biological quantities in the ocean, the measurements are often influenced by both waves and turbulence. Isolating the individual effects of waves and turbulence on those variables is important to a wide range of analyses, such as estimating how momentum, heat, and nutrients are mixed throughout the water column. In this work, we propose a new method to separate the wave and turbulence components in ocean-data time series. When tested on laboratory, field, and synthetic data, our method was able to separate the wave and turbulence components of a signal more effectively than previously proposed algorithms.

The extent to which particles such as larvae, seagrass pollen, and microplastics are dispersed by waves and currents has many ecological impacts. Here, we systematically examine the effect of a comprehensive set of parameters on the dispersion of ellipsoidal particles in a wave-current flow using a numerical computation approach. Our results show that all of the parameters considered have some effect on the particle dispersion, but that the settling-wave timescale ratio has the greatest effect.

Separating the effects of waves and turbulence in oceanographic time series is an ongoing challenge because surface wave motion and turbulence fluctuations can occur at overlapping frequencies. Therefore, simple bandpass filters cannot effectively separate their dynamics. While more advanced decomposition techniques have been developed, they often entail restrictive assumptions about the wave and turbulence interactions, require synchronized measurements, and/or only decompose the signal spectrally without a time-series reconstruction. We present our new wave-turbulence decomposition technique which uses dynamic mode decomposition (DMD). The technique is signal-agnostic so it can be applied to any time series, and our only assumptions are that the waves and turbulence can be separated and that the waves are the most coherent features in the signal. Our approach requires minimal tuning, where the main user input is the wave frequency range of interest. To demonstrate the method, we apply it to synthetic, field, and laboratory data, and compare the results to other mode-based decomposition methods. A sensitivity analysis on the synthetic data shows that the most sensitive parameter to the accuracy is the rank truncation in the DMD, and that the decomposition performs the best when the wave energy in the signal is of equal or greater magnitude than that of the turbulence. Given the accuracy of our decomposition, we are able to analyze the velocity autocorrelation of the separated turbulence time series with minimal wave contamination. Overall, our decomposition method outperforms the other decomposition methods and provides for robust separation of the waves and turbulence, demonstrating wide applicability to ocean signal processing.

The problem of predicting microplastic transport in oceans and estuaries has spurred new research into fluid-particle interactions involving theory, simulations, and laboratory experiments. We discuss wide-ranging challenges including the modeling of inertial particles in waves and turbulence, particle transformation, the influence of submesoscale ocean processes, and predicting global transport.

Lagrangian particle tracking experiments are a key tool to understanding particle transport in fluid flows. However, tracking particles over long distances is expensive and limited by both the intensity of light and number of cameras. In order to increase the length of measured particle trajectories in a large fluid volume with minimal cost, we developed a large-scale particle-shadow-tracking method. This technique is able to accurately track millimeter-scale particles and their orientations in meter-scale laboratory fluid flows. By tracking the particles’ shadows cast by a wide beam of collimated light from a high-power LED, 2D particle position and velocity can be obtained, as well as their 3D orientation. Compared with traditional volumetric particle tracking techniques, this method is able to measure particle kinematics over a larger area using much simpler imaging and tracking techniques. We demonstrate the method on sphere, disk, and rod particles in a wavy wind-driven flow, where we successfully track particles and reconstruct their orientations.

Abstract Predicting the vertical distribution of microplastics in the ocean surface mixed layer is necessary for extrapolating surface measurements and comparing observations across conditions. The competing mechanisms that control the vertical distribution are particle buoyancy, which is primarily a function of particle properties and drives microplastics to accumulate at the ocean surface, and turbulent mixing, which disperses microplastics throughout the mixed layer and depends on local hydrodynamics. In this study, we focused on the physical properties of microplastics collected within one vertical profile in the North Pacific. We measured the size, shape, and rise velocity of all microplastics collected, finding that average size and rise velocity decay with depth. In addition, we demonstrate how the vertical distribution of the microplastics depends on the rise velocity of the microplastics by segregating the data into three regimes based on a ratio of microplastic rise velocity and a characteristic turbulence velocity scale. Using an individual model for each regime, we can extrapolate the vertical distribution of microplastics to the bottom of the mixed layer and find the total concentration of microplastics. The total extrapolated concentration using the combined model results in 10× the concentration of the surface net alone and 47% more than a model which does not consider the different microplastic regimes. Finally, we discuss how the vertical distribution also depends on microplastic form, finding that lines are approximately well-mixed whereas the concentration of fragments decays with depth. These observations indicate the importance of considering the appropriate rise velocity regime when predicting the vertical distribution of microplastics.