We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Modelling Microplastic Dynamics in Estuaries: A Comprehensive Review, Challenges and Recommendations
Summary
This comprehensive review examines how process-based computer models have been used to simulate microplastic transport and fate in estuaries — the complex, tidal zones where rivers meet the sea. It evaluates different modeling approaches for capturing hydrodynamics, particle behavior, and interactions with sediment, identifying key gaps and inconsistencies in how microplastic properties are represented. Better estuarine models are needed to predict where plastics accumulate, how long they persist, and what risks they pose to coastal ecosystems and the communities that depend on them.
Abstract. The study of microplastic transport and fate in estuaries poses significant challenges due to the complex, dynamic nature of these ecosystems and the diverse characteristics of microplastics. Process-based numerical models have become indispensable for studying microplastics, complementing observational data by offering insights into transport processes and dispersion trends that are difficult to capture through in-situ measurements alone. Effective model implementations require an accurate representation of the hydrodynamic conditions, relevant transport processes, particle properties, and their dynamic behaviour and interactions with other environmental components. In this paper, we provide a comprehensive review of the different process-based modelling approaches used to study the transport of microplastics in estuaries, including Eulerian Idealized 2DV models, Eulerian Realistic Models, Lagrangian Particle Tracking Models, and Population Balance Equation Models. We detail each approach and analyze previous applications, examining key aspects such as parameterizations, input data, model setups, and validation methods. We assess the strengths and limitations of each approach and provide recommendations, good practices, and future directions to address challenges, improve the accuracy of predictions, and advance modelling strategies, ultimately benefiting the research field.
Sign in to start a discussion.