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Mathematical modeling of microplastic abundance, distribution, and transport in water environments: A review

Chemosphere 2021 119 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 60 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Pelin Uzun, Sofi Farazande, Başak Güven

Summary

This review surveys mathematical models used to predict how microplastics move through and accumulate in rivers and oceans. Researchers categorized existing approaches by environment type and modeling method, identifying strengths and gaps in current simulation tools. The study highlights the need for better models that account for real-world complexity, including particle fragmentation and biofouling, to improve predictions of where microplastics end up.

Microplastic pollution in marine and riverine environments is a threat not only for the aquatic ecosystem itself but also for human activity and life. Although there are reviews regarding microplastic debris in environments, most of them focus on the studies on their type, occurrence, and distribution. Only a limited number of these studies focus on the modeling methods, usually concentrating on particular aspects, such as settling or bioaccumulation. In this paper, physically-based existing microplastics modeling studies are classified and reviewed according to the environment, modeling methodology, and input-output relationships. Considering the strengths and weaknesses of all modeling methodologies, it is deduced that more reliable results are obtained using hybrid methods, especially the coupling of hydrodynamic and process-based models, and hydrodynamics and statistical models. The significance of having much more consideration and knowledge on the microplastics' physical properties and the environmental processes affecting their fate and transport in the aquatic environments is revealed for future research. It has also been recommended that a standardized method for data calibration, validation, and verification is necessary to be able to compare the modeling results with field investigations more efficiently than it is currently.

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