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Microwave-assisted pretreatments and analytical pyrolysis for the quantification of microplastics and correlated pollutants

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Researchers combined microwave-assisted extraction and digestion with analytical pyrolysis coupled to gas chromatography-mass spectrometry (Py-GC-MS) to characterize and quantify microplastics and associated pollutants including additives, persistent organic pollutants, and degradation products in environmental samples, using lyophilized mussel flour as a reference matrix. They found that microwave-assisted pretreatments significantly reduced sample preparation time while achieving reliable MP quantification alongside co-occurring chemical contaminants.

Microplastic pollution has been acknowledged as a global threat and one of the most relevant environmental issues. Different analytical techniques have been applied and optimized for microplastic analysis along with non-polymeric chemical species associated with them, such as additives, persistent organic pollutants, and polymers degradation products. Nevertheless, there is still a major lack of understanding of the interaction mechanisms of these classes of pollutants with the ecosystems. Depending on the environmental matrix, sample pretreatment before analysis can require numerous and time-intensive steps. In this work, we combined microwave-assisted extraction and digestion, together with analytical pyrolysis coupled with gas chromatography-mass spectrometry (Py-GC-MS), to characterize and quantify MPs and different classes of pollutants in environmental samples using flour deriving from lyophilized mussels as reference matrix. Microwave-assisted extraction under mild conditions was used to isolate phthalate plasticizers, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and contaminants of emerging concerns (CECs). Microwave-assisted digestion under harsh conditions was instead employed to isolate microplastics. This method allowed the quantification of more than 40 different contaminants in a single chromatographic run, with recoveries higher that 87 Also see: https://micro2024.sciencesconf.org/558636/document

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