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A new model for the terminal settling velocity of microplastics

Marine Pollution Bulletin 2022 68 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 45 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Zijian Yu, Zijian Yu, Zijian Yu, Zijian Yu, Zijian Yu, Zijian Yu, Yang Ge, Yang Ge, Zijian Yu, Zijian Yu, Zijian Yu, Yang Ge, Zijian Yu, Zijian Yu, Yang Ge, Yang Ge, Yang Ge, Zijian Yu, Yang Ge, Yang Ge, Yang Ge, Zijian Yu, Zijian Yu, Wenming Zhang Wenming Zhang Wenming Zhang Zijian Yu, Zijian Yu, Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang Zijian Yu, Wenming Zhang Wenming Zhang Zijian Yu, Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang Wenming Zhang

Summary

A new empirical model for the terminal settling velocity of microplastics was developed and validated using 1,343 experimental measurements covering a range of particle shapes and materials. The model improves predictions of microplastic sedimentation rates, which are critical for understanding how plastic particles are transported and deposited in water bodies.

Microplastic (MP) settling process is important for the transport of microplastic particles (MPs, <5 mm) in water bodies. However, for the control parameter of the drag coefficient (C), no generalized formula has been proposed for MPs of different shapes and materials. In this study, a total of 1343 MP settling data were collected from the literature. It was found that the drag law for perfect spheres cannot reasonably predict C for MPs with particle Reynolds number of 1-10. A new formula for C was developed by introducing the dimensionless particle diameter (d) and two shape descriptors. The absolute error of the new formula is 15.2%, smaller than those (42.5-72.8%) of other existing formulas. Moreover, an explicit model was developed for MP settling velocity by correlating C, d, and shape descriptors, with lower absolute error (8.8%) than those (15.4-77.2%) of existing models.

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