Title :

Microplastic inputs to the Mediterranean Sea during wet and dry seasons: The case of two Lebanese coastal outlets

Author(s) :

Sawan, Rosa [Auteur correspondant]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
National Center for Marine Sciences [Lebanon]
Doyen, Périne [Auteur]
BioEcoAgro - Equipe 8 - Food and Digestive Microbial Ecosystems: Interactions - Dynamics - Application(s)
BioEcoAgro - UMR transfrontalière INRAe - UMRT1158
Viudes, Florence [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Amara, Rachid [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Université du Littoral Côte d'Opale [ULCO]
Mahfouz, Céline [Auteur]
National Center for Marine Sciences [Lebanon]

Journal title :

Marine Pollution Bulletin

Pages :

115781

Publisher :

Elsevier

Publication date :

2024-01

ISSN :

0025-326X

English keyword(s) :

Microplastics
Precipitation
Rivers
Urbanization Plastic flows
Surface water

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Laboratoire d'Océanologie et de Géosciences (LOG) - UMR 8187

Source :

Harvested from HAL