Pith. Sign in

RECORD

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

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Maria Cairoli (1), André van den Doel (1), Berber Postma (1), Tim Offermans (1), Henk Zemmelink (2), Gerard Stroomberg (1, 3), Lutgarde Buydens (1)
show 13 more authors
Geert van Kollenburg (1, 4), Jeroen Jansen (1) ((1) Radboud University, Department of Analytical Chemistry & Chemometrics, Nijmegen, The Netherlands (2) Rijkswaterstaat, Lelystad, The Netherlands (3) RIWA Rijn, Nieuwegein, The Netherlands (4) Eindhoven University of Technology, Interconnected Resource-aware Intelligent Systems, Eindhoven, The Netherlands)

Applications · Submitted 11 Jul 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

how it is stated · asserted 0.92 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2207.04805 (version 3)
Pith IDOZHQ3NEN, Pith's permanent record of this paper and its review
Citation3), 4), Andr\'e van den Doel (1), Berber Postma (1), Department of Analytical Chemistry & Chemometrics, Eindhoven, Geert van Kollenburg (1, Gerard Stroomberg (1, Henk Zemmelink (2), Interconnected Resource-aware Intelligent Systems, Jeroen Jansen (1) ((1) Radboud University, Lelystad, Lutgarde Buydens (1), Maria Cairoli (1), Nieuwegein, Nijmegen, the Netherlands), The Netherlands (2) Rijkswaterstaat, The Netherlands (3) RIWA Rijn, The Netherlands (4) Eindhoven University of Technology, Tim Offermans (1). "Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements." arXiv:2207.04805 (2022). https://pith.science/paper/OZHQ3NEN
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,720 input tokens.

What the abstract itself states, as probabilities: claims first 0.16 · states limitation 0.51 · human participants 0.03 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.50 · compares with prior results 0.06 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 A comprehensive approach to protect river water quality is needed within the European Water Framework Directive. background 1.00 0.04 0.03 asserted 0.98
s1 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. background 0.86 0.09 0.05 asserted 1.00
s2 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. gap 0.53 0.09 0.05 asserted 0.66
s3 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. contribution 0.64 0.96 0.76 asserted 0.92

What kind of paper the classifier reports this is: method or tool 0.97 · evidence from observation or experiment 0.87.

What was examined, and what was not

sources read
  • arXiv listing abstract v3
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.