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Physisorption and Chemisorption Mechanisms Influencing Micro (Nano) Plastics-Organic Chemical Contaminants Interactions: A Review

微塑料 吸附 环境化学 吸附 污染 化学 污染物 环境科学 有机化学 生态学 生物
作者
Omowumi D. Agboola,Nsikak U. Benson
出处
期刊:Frontiers in Environmental Science [Frontiers Media]
卷期号:9 被引量:310
标识
DOI:10.3389/fenvs.2021.678574
摘要

Microplastics, which serve as sources and vector transport of organic contaminants in both terrestrial and marine environments, are emerging micropollutants of increasing concerns due to their potential harmful impacts on the environment, biota and human health. Microplastic particles have a higher affinity for hydrophobic organic contaminants due to their high surface area-to-volume ratio, particularly in aqueous conditions. However, recent findings have shown that the concentrations of organic contaminants adsorbed on microplastic surfaces, as well as their fate through vector distribution and ecological risks, are largely influenced by prevailing environmental factors and physicochemical properties in the aquatic environment. Therefore, this review article draws on scientific literature to discuss inherent polymers typically used in plastics and their affinity for different organic contaminants, as well as the compositions, environmental factors, and polymeric properties that influence their variability in sorption capacities. Some of the specific points discussed are (a) an appraisal of microplastic types, composition and their fate and vector transport in the environment; (b) a critical assessment of sorption mechanisms and major polymeric factors influencing organic contaminants-micro (nano) plastics (MNPs) interactions; (c) an evaluation of the sorption capacities of organic chemical contaminants to MNPs in terms of polymeric sorption characteristics including hydrophobicity, Van der Waals forces, π–π bond, electrostatic, and hydrogen bond interactions; and (d) an overview of the sorption mechanisms and dynamics behind microplastics-organic contaminants interactions using kinetic and isothermal models. Furthermore, insights into future areas of research gaps have been highlighted.
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