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Molecular-level insights into derivation dynamics of microplastic-derived dissolved organic matter
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Researchers used molecular-level analysis to investigate the formation dynamics of dissolved organic matter derived from microplastics (MPs-DOM) in natural surface waters, finding that this ubiquitous contaminant affects not only aquatic organisms but also undergoes complex chemical transformations that influence its environmental fate and toxicological relevance.
Currently, microplastic-derived dissolved organic matter (MPs-DOM) is ubiquitous in natural surface waters, which not only contributes to dissolved organic carbon (DOC), but also becomes a specific component with significant environmental implications that differ from those of natural organic matter (NOM). Considering the continual disposal of MPs and plastic wastes into aquatic environments, MPs-DOM is constantly derived, and the molecular composition may be distinct over the derivation processes. Thus, in the current study, the kinetics of derivation and the variation of molecular features of MPs-DOM were investigated in comparison with those of NOM. The results showed that the zero-order model well fitted the experimental data and that film diffusion was the rate-limiting step for the UV derivation processes of MPs-DOM. The DOCUV/DOCdark ratio was generally correlated with derivation time, following the order of kPBAT (0.526) > kPLA (0.472) > kPET (0.147) > kPE (0.056) > kNOM (0.005), indicating that UV irradiation governed the derivation of MPs-DOM. Fluorescence spectra and HPLC-MS/MS indicated that the additives, together with monomers and oligomers of the polymers and their oxygenated products, contributed to MPs-DOM, while the components of MPs-DOM were largely different from those of NOM. FT-ICR-MS further demonstrated that the molecular composition of MPs-DOM derived from distinct polymer types varied separately, demonstrating that MPs-DOM at different derivation stages may show diverse environmental implications. In the future, the dynamic derivation of MPs-DOM can be further studied using artificial intelligence techniques, such as machine learning, to support a comprehensive assessment of its environmental implications.
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