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Understanding the Microplastic ‘model’: Applications of Modelling to Track Microplastics in Aquatic Ecosystems
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Researchers reviewed mathematical modelling techniques used to track how microplastics move and spread through rivers, oceans, and other aquatic environments. Understanding these models is critical for predicting where plastic pollution accumulates and which ecosystems and communities face the greatest exposure risk.
The omnipresence of microplastics (MPs) in aquatic environments poses a significant threat to ecosystems and human health. Previous literature has predominantly focused on the abundance, distribution, impacts, and detection methodologies of MP, with only limited studies addressing the classification and modelling approaches in MP research. Various modelling techniques have been developed to simulate the movement and dispersion of MPs; each has unique applications and methodologies. The review provides an overview of the various modelling approaches, highlighting their methodologies, strengths, and limitations. We attempt to study the existing categories of models: Lagrangian, Eulerian, Lagrangian-Eulerian (hybrid), mass-balance, statistical, and machine-learning models. Each category and its respective models are analysed thoroughly. In addition, this review highlights the significant challenges in current modelling practices, including data scarcity, unrealistic assumptions in biological processes, and the need for integrated modelling frameworks. For future research, we suggest incorporating biofouling, degradation rate, turbulence, and vertical mixing and conducting more in-situ experiments to obtain accurate input data and standardised sampling methods. This will enhance the reliability of models and their applicability in real-world conditions.
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Numerical models can simulate how microplastics move through coastal environments by integrating hydrodynamic, biological, and atmospheric processes, helping predict accumulation hotspots and exposure zones. These modeling tools are vital for guiding cleanup efforts and assessing risks to coastal communities and marine food sources.
A Machine-Learning Model for Investigating Microplastics Source–Receptor Relationships in Aquatic Environments
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Researchers developed a machine-learning model to trace where microplastics in aquatic environments come from and where they end up, potentially giving scientists and regulators a more powerful tool to identify pollution sources and prioritize cleanup efforts.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.