Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements
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.
Cite
@article{arxiv.2207.04805,
title = {Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements},
author = {Maria Cairoli and André van den Doel and Berber Postma and Tim Offermans and Henk Zemmelink and Gerard Stroomberg and Lutgarde Buydens and Geert van Kollenburg and Jeroen Jansen},
journal= {arXiv preprint arXiv:2207.04805},
year = {2024}
}
Comments
19 pages, 7 figures, 2 tables; added references; modified order of Authors; edited and extended Introduction, Methods, Results and discussion sections