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Distribution and Aggregation of Microparticles in Upper-Ocean Turbulence Dataset

posted on 2024-04-09, 19:47 authored by Federico PizziFederico Pizzi, Lluis JofreLluis Jofre, Francesco Capuano, Joan Grau, Mona Rahmani

Microplastic pollution poses a significant environmental threat to marine ecosystems globally. Understanding the distribution and aggregation of microplastics in ocean turbulence is crucial for developing effective mitigation strategies. This dataset offers comprehensive access to nine high-fidelity direct numerical simulations cases characterized by turbulent flow field and Lagrangian point particles, representing conditions observed in the upper ocean layer. The dataset comprises a range of physical parameters facilitating in-depth exploration and modeling of the flow dynamics linked to microplastics and biogenic particles. This includes quantifying particle collisions and coalescence, analyzing aggregate properties such as motion and composition, and tracking the trajectories of microparticles. The present openly accessible repository aims to support research efforts in modelling and predicting the impact of microplastics on marine environments.

The simulations, are systematically organized into individual cases, each assigned to dedicated folders for easy access and data processing. Within each case folder (9 in total), subfolders are allocated for the flow field, particles, ) and aggregates data. A README.txt file in each case folder provides essential information, including main parameters, dimensionless numbers, file organization, and instructions for running the solver with those parameters. Fundamental outputs, such as flow field mesh and velocity and particle characteristics, are present in Flow_folder and Prts_folder for each instantaneous file spaced uniformly in time. Derived quantities, including aggregate properties, are stored in Agg_folder in csv format. The Coll_coal_folder contains simulation status files detailing the temporal progression of collision and coalescence events.


This work is funded by the TRITON project (TED2021-132623A-I00) of the Agencia Estatal de Investigación (Spain).


Research Institution(s)

Universitat Politècnica de Catalunya · BarcelonaTech (UPC); University of British Columbia

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Competing Interest Statement

The authors declare no competing interests.