Numerical modeling of the beach process of marine plastics: A probabilistic and diagnostic approach with a particle tracking method. (March 2020)

• Record Type: Journal Article
• Authors: Hinata, Hirofumi; Sagawa, Nao; Kataoka, Tomoya; Takeoka, Hidetaka
• Is Part Of: Marine pollution bulletin. Volume 152(2020)
• Publication Date: 2020-03
• Subjects: Beach process -- Residence time -- Partitioning coefficient -- Beaching and backwashing probabilities -- Particle tracking method -- Diagnostic approach; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2020.110910
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: A model of the beach process of marine plastics was proposed based on the assumption of the beaching and backwashing flux balance, and its applicability was examined by means of time-invariant linear system analysis and particle tracking experiments with respect to the ratio between the residence time of plastics on a beach ( τ r ) and the period of nearshore current variability ( T 0 ). Based on the theory, the balance was expected to hold when τ r / T 0 was much smaller than 1; however, good agreement was obtained between the theory and the particle tracking method for much larger values of τ r / T 0 . The parameters, which are diagnostically given in the model, will be prognostically decided by the coastal dynamics in the future to develop robust beach process models. Nevertheless, we believe that a diagnostic approach would be another pillar in the strategy for estimating the amounts and distributions of marine plastics in the coming years. Highlights: A beach process model of marine plastics for micro- to macroplastics was proposed. The model was based on the assumption of the beaching and backwashing flux balance. The model was validated by a linear system theory and particle tracking experiments. The model allows us to examine the seasonal to interannual variabilities of plastics.