Opportunities to improve polyethylene microparticle analysis by pyrolysis-gas chromatography/mass spectrometry

Pyrolysis Gas Chromatography/Mass Spectrometry (Py-GC/MS) has become the method of choice for determining microplastics in various matrices, largely because of its ability to analyze small sample quantities. Identification and subsequent determination of polyethylene (PE) microparticles in complex matrices by Py-GC/MS requires significant technical expertise and experimental care to avoid false positives. For example, many biological matrices contain lipids that produce some of the same pyrolysis products (pyrolyzates) as PE during pyrolysis. Other matrices (e.g., air, sediment, and wastewater) may present similar challenges. If these lipidic components are not or cannot be removed during sample preparation, or via the double shot pyrolysis approach, false positive identification of PE is likely without appropriate supplementary verification protocols. Saturated very-long-chain fatty acids and wax, analyzed by Py-GC/MS in this study, were shown to produce some of the same pyrolyzates as PE. These materials were falsely identified as PE when a software algorithm relying on a few non-unique qualifier ions was used alone for data interpretation. In this study, we also analyzed known standards to define and emphasize the necessary chromatographic and spectral features one should observe for positive identification of PE from Py-GC/MS analysis. Suggestions for improving data interpretation strategies for PE and an evaluation of the double shot approach along with a common KOH digestion procedure are also discussed. Notably, when KOH base digestion was used without salt removal, the double shot approach lost its effectiveness for removing fatty acids. Furthermore, pyrolysates of the KOH-neutralized acids were more abundant and resembled those of PE more than the pure acids. All learnings suggest that a dedicated study is required to evaluate matrix effects and validate matrix removal procedures for any meaningful Py-GC/MS microparticle analysis in biological samples.