The lack of one standardized method to evaluate microplastic pollution in different aquatic environments worldwide represent a gap to fill for the scientist's community. To help overcome this challenge, we adapted an aquatic drone, named Jellyfishbot®, to sample microplastics. The aquatic drone has been compared with the actual most used method for sampling MPs in surface waters: the Manta net. In order to test the reliability of the aquatic drone in different environments, samples were collected in a river and coastal waters sites. The results obtained with the two methods were similar in term of MPs abundances, shapes and colors. It provides also a better reproducibility and more accurate sampling of MPs located in the surface waters mainly the lighter and smaller ones. This sampling method has the advantage of combining the benefits of Manta net sampling (i.e. a representative surface water sampling method that covers a large sampling area and volume (several tens m³) with those of pump filtration and grab sampling (easy access to confined and hard-to-reach areas). This new sampling method could be applied in different aquatic environments making it possible to compare the data and hence become a new standardized approach to evaluate microplastic pollution levels.

This paper presents a new Remote Hyperspectral Imaging System (RHIS) embedded on an Unmanned Aquatic Drone (UAD) for plastic detection and identification in coastal and freshwater environments. This original system, namely the Remotely Operated Vehicle of the University of Littoral Côte d’Opale (ROV-ULCO), works in a near-field of view, where the distance between the hyperspectral camera and the water surface is about 45 cm. In this paper, the new ROV-ULCO system with all its components is firstly presented. Then, a hyperspectral image database of plastic litter acquired with this system is described. This database contains hyperspectral data cubes of different plastic types and polymers corresponding to the most-common plastic litter items found in aquatic environments. An in situ spectral analysis was conducted from this benchmark database to characterize the hyperspectral reflectance of these items in order to identify the absorption feature wavelengths for each type of plastic. Finally, the ability of our original system RHIS to automatically recognize different types of plastic litter was assessed by applying different supervised machine learning methods on a set of representative image patches of marine litter. The obtained results highlighted the plastic litter classification capability with an overall accuracy close to 90%. This paper showed that the newly presented RHIS coupled with the UAD is a promising approach to identify plastic waste in aquatic environments.

Estuaries represent a transitional environment between continental and marine areas. Limited studies have evaluated how these complex systems contribute to plastic pollution dynamics at this interface. Here, an in situ experimental study was conducted in the Slack estuary, a small macrotidal estuarine system in northern France, to investigate the mobilization and deposition of plastic debris on an estuarine bank at a daily basis during six complete tidal cycles. To achieve this, plastics (macro, meso and microplastics with size ≥3 mm) of different composition and shape were manually deposited along an estuary bank on three different substrates: vegetation, gravel, and sand. The experimental design aimed to explore the complexity of the mobilization and deposition of plastic debris with regard to hydro-meteorological factors, types of substrates, size and shape of plastics. Results showed that tidal cycles played a significant role in plastic mobilization and deposition on the estuary bank. However, the nature of the substrate directly impacted the mobilization and deposition of plastics and the effect of wind may be particularly important for the deposition of allochthonous plastics. Most plastics (around 94 %, 37.9 ± 1.5 plastics/m²) were found to be mobilized after a complete tidal cycle while an average of 3.33 ± 1.8 plastics/m² was deposited during the same period. Results suggested that in small macrotidal estuaries, the daily net retention is very limited since most plastics were mobilized after a tidal cycle. However, in vegetated substrate, the daily net retention can be 2 to 3 times higher than in other types of substrates (gravel and sand) highlighting the potential of dense vegetation to serve as a retention area for plastic waste.