LAPCOD
Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics
LAPCOD VII :: Venice 2019 :: 17-21 June 2019
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Use of Lagrangian dispersion models to estimate distributions of plastic marine litter, observations planning and environmental risks assessment.

Carlo Brandini, Bartolomeo Doronzo, Maria Fattorini, Massimo Perna, Stefano Taddei, Letizia Costanza, Chiara Lapucci
Consorzio LaMMA & Consiglio Nazionale delle Ricerche
(Abstract received 04/30/2019 for session D)
ABSTRACT

Lagrangian dispersion models are increasingly used to understand Plastic Marine Litter (PML) distribution from large to local scales, as well as to understand long period trends, or to find connections between sources and accumulation areas. Understanding PML distributions is crucial for the assessment of environmental risks, as plastic distribution significantly overlap with feeding areas of relevant marine species. The reliability of such distributions is strongly dependent on the quality of the hydrodynamic data used to force dispersion models, and this is particularly important in sea areas (eg in the Mediterranean) characterized by high seasonal and sub-seasonal variability as deduced by hydrodynamic observations and operational circulation models. Unfortunately PML observations shows a wide variety of values: although it is well known that such concentrations strongly depend on circulation, it is not clear which role have not permanent circulation features on such distributions. In this work we make a comparison, over the last few years (2015-2018), concerning the potential distributions of PML in North-Western Mediterranean (and in particular in the Pelagos Sanctuary area), deduced by lagrangian simulations. In this area marine debris observations are also available. Results are used to plan observation campaigns to characterize the presence of hot-spot and cold-spot areas for PML. Hydrodynamic models were also validated using available HF radars. Both the models and the observations show a significant degree of uncertainty and representativeness, that can be better represented in terms of probability distributions, in turn to be associated to environmental risk parameters.