Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements

Maria Cairoli; André van den Doel; Berber Postma; Tim Offermans; Henk Zemmelink; Gerard Stroomberg; Lutgarde Buydens; Geert van Kollenburg; Jeroen Jansen

doi:10.1038/s41545-023-00257-7

arXiv · 2207.04805

Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements

Maria Cairoli·André van den Doel·Berber Postma·Tim Offermans·Henk Zemmelink·Gerard Stroomberg·Lutgarde Buydens·Geert van Kollenburg·Jeroen Jansen

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Abstract

A comprehensive approach to protect river water quality is needed within the European Water Framework Directive. Non-target screening of a complete chemical fingerprint of the aquatic ecosystem is essential, to identify chemicals of emerging concern and to reveal their suspicious dynamic patterns in river water. This requires a new combination of two measurement paradigms: the path of potential pollution should be traced through the river network, while there may be many compounds that make up this chemical composition - both known and unknown. Dedicated data processing of ongoing GC-MS measurements at 9 sites along the Rhine using PARAFAC2 for non-target screening, combined with spatiotemporal modelling of these sites within the river network using path modelling (Process PLS), provided a new integrated approach to track chemicals through the Rhine catchment, and tentatively identify known and as-yet unknown potential pollutants based on non-target screening and spatiotemporal behaviour.

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BibTeX RIS

Maria Cairoli, André van den Doel, Berber Postma, Tim Offermans, Henk Zemmelink, Gerard Stroomberg, Lutgarde Buydens, Geert van Kollenburg, Jeroen Jansen. 2022-07-11. Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements. https://doi.org/10.1038/s41545-023-00257-7

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