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Uptake/release of organic contaminants by microplastics: A critical review of influencing factors, mechanistic modeling, and thermodynamic prediction methods

Critical Reviews in Environmental Science and Technology 2020 51 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Domenica Mosca Angelucci, M. Concetta Tomei

Summary

This review critically examines the ability of microplastics to absorb and release organic chemical pollutants, evaluating the factors that influence this process and existing predictive models. Understanding whether microplastics act as significant vectors for pollutants into food chains requires better thermodynamic models that account for real-world complexity.

The role of microplastics (MPs) as a vector of chemicals is generally recognized: a wide range of micropollutants have been detected in MPs sampled in different environmental compartments, and, due to their ubiquitous distribution, they can be transferred through aquatic and terrestrial organisms into the food chain. Providing representative models is challenging due to the intrinsic dynamic evolution characterizing the natural phenomena, which cannot be adequately investigated in lab experiments. On the other side, simulation/prediction tools are strongly required because of the long time-scale characterizing the MPs’ persistence in the environment. This article provides a review of the updated literature on mechanistic models and predictive thermodynamic methods applied (or applicable), to describe the uptake/release processes of pollutants by MPs. Intrinsic and environmental factors influencing the process kinetics and the equilibrium distribution are discussed in detail. Mechanistic models and thermodynamic prediction methods are analyzed and their potentialities for application to MPs highlighted with specific examples. Finally, a critical analysis of the limitations related to the simplifying assumptions in modeling and to the representativeness of the lab-scale data is performed to evaluate their effects on the reliability of the predictions and to identify knowledge gaps and address future research needs.

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