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Issue 16, 2019
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Automated identification and quantification of microfibres and microplastics

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Abstract

The ubiquitous presence of microlitter (ML), precisely microplastics (MP) and microfibres (MF) in the global environment is of growing concern for science, and society in general. Reliable methods are urgently needed for the identification and quantification of these emerging environmental pollutants. Recently a rapid Fourier transform infrared (FTIR) imaging pipeline was developed for automated identification and quantification of MP. However, although the usefulness for the quantification of MP could already be shown in several studies, microfibres could not be targeted so far by the developed analysis pipeline. In this study we present a novel approach for the simultaneous identification and quantification of MP and MF. By concentrating the sample on membrane filters and applying a BaF2 window on top of the filter, all objects – including MF – are fixed in the focal plane of the FTIR microscope. Furthermore, the analysis pipeline was augmented with algorithms which take into consideration the filamentous structure of MF. The novel analysis pipeline now allows to separate MP and MF via a preselection of fibres from the dataset by object size and shape. MP and MF are subsequently further investigated for specific polymer types and lengths/sizes. After parameter optimization the newly developed analysis approach was applied to archived samples from previous studies on treated waste water. The results were compared with respect to the original detected polymer types and numbers, but also considered MF detection.

Graphical abstract: Automated identification and quantification of microfibres and microplastics

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Publication details

The article was received on 16 Jan 2019, accepted on 21 Mar 2019 and first published on 21 Mar 2019


Article type: Paper
DOI: 10.1039/C9AY00126C
Citation: Anal. Methods, 2019,11, 2138-2147

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    Automated identification and quantification of microfibres and microplastics

    S. Primpke, P. A. Dias and G. Gerdts, Anal. Methods, 2019, 11, 2138
    DOI: 10.1039/C9AY00126C

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