Quantitative Determination of Poly(methyl Methacrylate) Micro/Nanoplastics by Cooling-Assisted Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry: Theoretical and Experimental Insights.

Abstract

Determinations of micro/nanoplastics (MNPs) in environmental samples are essential to assess the extent of their presence in the environment and their potential impact on ecosystems and human health. With the aim to provide a sensitive method with simplified pretreatment steps, cooling-assisted solid-phase microextraction (CA-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) is proposed as a new approach to quantify mass concentrations of MNPs in water and soil samples. The herein proposed CA-SPME method offers the unique advantage of integrating the thermal decomposition of MNPs and enrichment of signature compounds into one step. Poly(methyl methacrylate) (PMMA) was used as a model substance to verify the method performance in this work. Theoretical insights demonstrated that pyrolysis is the rate-determining step during the extraction process and that PMMA is effectively decomposed at 350 °C with an estimated incubation time of 13 min. Eight compounds were identified in the pyrolysis products by CA-SPME-GC-MS with the use of a DVB/CAR/PDMS coating, wherein methyl methacrylate was considered as the best indicator and dimethyl 2-methylenesuccinate was selected as the confirmation compound. Under the optimized conditions, the proposed method exhibited wide linearity (0.5-2000 μg for water and 5-1000 μg for soil) and high sensitivity, with limits of detection of 0.014 and 0.28 μg for water and soil, respectively. Finally, the proposed method was successfully applied for determinations of PMMA MNPs in real water and soil samples with satisfactory recoveries attained. The method only required the employment of a filter membrane for water analysis, while soil samples were analyzed directly without any pretreatment. The solvent-free approach, straightforward operation, and high sensitivity of the proposed method show great potential for the analysis of MNPs in different environmental samples.