2023
DOI: 10.1038/s41545-023-00257-7
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: To safeguard the quality of river water, a comprehensive approach is required within the European Water Framework Directive. It is vital to conduct non-target screening of the complete chemical fingerprint of the aquatic ecosystem, as this will help to identify chemicals of emerging concern and uncover their unusual dynamic patterns in river water. Achieving this goal calls for an advanced combination of two measurement paradigms: tracing the potential pollution path through the river network and detecting the… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
3
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 6 publications
(4 citation statements)
references
References 48 publications
0
3
0
Order By: Relevance
“…A more in‐depth analysis of the possible sources and drivers of chloride‐discharge hysteresis at Lobith is therefore necessary to draw sensible conclusions and suggest mitigation measures for chloride export based on hysteresis patterns. More comprehensive statistical models, for example, path models (Cairoli et al., 2023; Fernandes et al., 2018), could help relate the spatio‐temporal influence of different sources to these patterns. However, this goes beyond the scope of the current study.…”
Section: Discussionmentioning
confidence: 99%
“…A more in‐depth analysis of the possible sources and drivers of chloride‐discharge hysteresis at Lobith is therefore necessary to draw sensible conclusions and suggest mitigation measures for chloride export based on hysteresis patterns. More comprehensive statistical models, for example, path models (Cairoli et al., 2023; Fernandes et al., 2018), could help relate the spatio‐temporal influence of different sources to these patterns. However, this goes beyond the scope of the current study.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, data dimensionality can be reduced, decreasing the risk of incomplete componentization and missing compounds that cannot be detected by feature-based peak detection [21]. Data processing protocols based on multiple samples align well with real-world aquatic NTS advancements and perspectives, such as monitoring pollution pathways in river water, wastewater samples undergoing chemical or biological treatment, or water samples measured under a variety of extraction/instrumental conditions [20,22,23].…”
Section: Data Processing: Feature Extractionmentioning
confidence: 98%
“…The recently used supervised classification and multivariate statistical tools in NTS of water samples are partial least squares-discriminant analysis (PLS-DA), its orthogonal directed model (OPLS-DA), support vector machines with linear kernels (SVM), PLS-path modeling, and multivariate ANOVA models [22,23,27,28]. Due to their multivariate advantage, these methods complement univariate statistics (e.g., volcano plots, Mann-Whitney U test, ANOVA) because they consider all features simultaneously and can consider pollutant compound interrelations associated with the outcome (Fig.…”
Section: Data Analysis: Pattern Recognitionmentioning
confidence: 99%
“…Its game-changing potential is directly linked to the passive transport of eDNA in streamwater, providing a spatial integration of biodiversity information. This approach is paralleling the revolution in environmental chemistry, where local samples can be used to spatial infer sources and contamination levels (Abbott et al, 2018; Cairoli et al, 2023; Fairbairn et al, 2016). In analogy, the occurrence of single organisms, whole communities and derived ecological indices can be attributed and spatially assigned in any riverine networks.…”
Section: Introductionmentioning
confidence: 99%