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Discrete multivariate probability distributions of microplastic settling/rising velocity in the marine environment

2022 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea) 2022 3 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Paolo Bello, Agnese Pini, Simone Zazzini, Paolo Monti, Giovanni Leuzzi

Summary

This study derived discrete multivariate probability distributions to characterize the vertical settling and rising velocities of marine microplastics, accounting for the highly variable density, size, and shape properties of particles found in the ocean. Both standard and mass-weighted probability distributions were developed to capture the statistical behavior of microplastic vertical transport across realistic particle populations. The resulting parameterization provides a more physically realistic representation of microplastic vertical dynamics for incorporation into ocean transport models.

The settling/rising velocity is of key importance in the vertical distribution of microplastics in marine environment. It is generally parameterized with semi-empirical laws dependent on the density difference between particles and fluid, size and shape of the particle. However, these physicochemical properties are extremely variable, given the complex multitude of microplastics in the sea. Therefore, it is difficult to know the settling/rising velocity for all the particles in the sea. A multivariate probability distribution for the vertical settling/rising velocity of marine microplastics was thus derived. A discrete probability distribution and a discrete mass-weighted probability distribution of the settling/rising velocity was obtained. The results suggest that most microplastics, given the predominant contribution of sedimentation, may accumulate on the seabed or reside for long periods in the water column, confirming the hypotheses reported in literature. The velocity probability distribution is of essential use as an input in stochastic dispersion models to properly simulate the phenomenon and understand its influence and effects.

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