Issue 18, 2019

Microplastics analysis in environmental samples – recent pyrolysis-gas chromatography-mass spectrometry method improvements to increase the reliability of mass-related data

Abstract

Thermal methods are of increased relevance in the field of microplastics (MP) analysis. The presented method improvements emphasize the potential of pyrolysis gas-chromatography mass-spectrometry (Py-GCMS) methods for mass-related MP quantification in environmental samples. A previously established Curie-Point (CP)-pyrolyzer is compared to a micro furnace (MF) pyrolyzer of higher sample capacity. The two Py-GCMS systems are examined in terms of calibration aspects like dynamic range, linearity, process standard deviation and overall sensitivity. Here, MF-PyGCMS provided advantages. Depending on the samples and their residual organic matrix content, the related pyrolysis products may interact with relevant indicator ions of interesting polymers. This can hamper or even impede any calibration and quantification of MP in the given sample. An internal standard mixture added just before the pyrolysis process (ISTDpy) mimics these interactions to a certain extent. Based on selected peak ratios, ISTDpy offers a possible quantification option in those cases. The application in selected environmental samples (sea salt, surface water and muddy sediment) after adequate preconcentration illustrates the capability and sensitivity of MF-Py-GCMS for MP-quantification regarding the encountered concentrations (ppt–ppm).

Graphical abstract: Microplastics analysis in environmental samples – recent pyrolysis-gas chromatography-mass spectrometry method improvements to increase the reliability of mass-related data

Supplementary files

Article information

Article type
Technical Note
Submitted
22 Mar 2019
Accepted
09 Apr 2019
First published
10 Apr 2019

Anal. Methods, 2019,11, 2489-2497

Microplastics analysis in environmental samples – recent pyrolysis-gas chromatography-mass spectrometry method improvements to increase the reliability of mass-related data

M. Fischer and B. M. Scholz-Böttcher, Anal. Methods, 2019, 11, 2489 DOI: 10.1039/C9AY00600A

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