Microplastic (MP) debris is recognized to be one of the most serious threats to marine environments. They are found in all seas and oceanic basins worldwide, even in the most remote areas. This is further proof that the transport of MPs is very efficient. In the present study, we focus our attention on MPs’ transport owing to the Stokes drift generated by sea waves. Recent studies have shown that the interaction between heavy particles and Stokes drift leads to unexpected phenomena mostly related to inertial effects. We perform a series of laboratory experiments with the aim to directly measure MPs’ trajectories under different wave conditions. The main objective is to quantify the inertial effect and, ultimately, suggest a new analytical formulation for the net settling velocity. The latter formula might be implemented in a larger scale transport model in order to account for inertial effects in a simplified approach.

The occurrence and the effects of hydraulic jump instabilities on a natural river confluence in a small river basin in Liguria (Italy) is here investigated. Hydraulic jump instability has been extensively studied in controlled and simplified laboratory rectangular flumes. In the present study, a scaled physical model of the Chiaravagna River and Ruscarolo Creek confluence has been used, retaining the realistic geometry of the reaches. This reach has been subject to frequent floods in the last twenty years and the entire area of the confluence has been redesigned to decrease the flood risk. A series of experiments has been performed varying the discharge on the two reaches and the geometrical configurations. Free surface levels and two dimensional horizontal velocities have been measured in several positions along the physical model. The analysis of the water levels and velocities reveals that oscillations characterised by large amplitude and low frequency occur under particular hydraulic conditions. These oscillations have been found to be triggered by the hydraulic jump toe instability of the smallest reach of the confluence. Aiming at reducing the amplitude of the oscillations, which can be of the order of the flow depth, possible constructive solutions have been tested to control or damp the oscillations. Indeed, the insertion of a longitudinal dyke at the confluence has proven to be an effective solution to limit the amplitude of the transversal oscillations.

We report the results of a series of numerical simulations performed with the aim to describe the dispersion of heavy particles transported by sea waves. Recent studies investigated the interplay between the wave Stokes drift and the inertial character of negatively buoyant particles that, ultimately, yields an augmented settling velocity. Our interest is to investigate the possible occurrence of a Brownian regime that would allow for the definition of a diffusion coefficient. The velocity and acceleration auto-correlation functions and the corresponding integral time scales show that already at a very low Stokes number the particles behave very differently from the fluid. The main consequence is that an asymptotic diffusive regime is rarely observed, except as a transient regime or when the background random noise is comparable with the wave field velocities.

Dispersion processes in environmental flows have been traditionally studied under the strong assumption of homogeneous, isotropic and stationary turbulence. To overcome this limitation, we propose a new approach that combines autocorrelation analysis of simulated Lagrangian trajectories together with unsupervised clustering. To test the approach, we consider several dynamic scenarios around a coastal gulf, subject to different forcing, in order to compare our method with other approaches. Lagrangian trajectories forced by the varying coastal circulation exhibited different behaviors, looping and non‐looping paths, and produced a variety of Lagrangian autocorrelation functions. Our approach proves to be able to reveal spatio‐temporal variations in ocean dispersion processes without any a priori knowledge of the character of the trajectories. Clusters based on the autocorrelation functions are associated to different inhomogeneous dispersion processes. Finally, we propose a new stochastic model capable of predicting the different forms of autocorrelations.