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

TL;DR

This paper presents an integrated method combining non-target GC-MS screening and spatiotemporal path modelling to trace and identify pollutants in river water, enhancing pollution monitoring within the European Water Framework Directive.

Contribution

It introduces a novel combined approach using PARAFAC2 and Process PLS for tracking and identifying chemicals in river networks, including unknown pollutants.

Findings

Successfully tracked chemical pathways in the Rhine river network.

Identified potential emerging pollutants through non-target screening.

Demonstrated the effectiveness of integrated spatiotemporal modelling.

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.