A non-targeted high-resolution mass spectrometry data analysis of dissolved organic matter in wastewater treatment
Bibliographic record
Abstract
The dissolved organic matter (DOM) in wastewater is typically described by a limited number of concentration measurements of select DOM fractions or micro-contaminants, which determine the removal efficiency in a wastewater treatment. Current methods do not necessarily reflect the true performance of the treatment with regard to environmental and public health risk. Herein we describe the development and application of a non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) data analysis for the evaluation of wastewater treatment processes. Our data analysis approach was applied to a real wastewater system with secondary biological treatment and tertiary treatment consisting of sand filtration, UV-treatment, and chlorination. We identified significant changes in DOM during wastewater treatment. The secondary treatment removed 1617 of 2409 (67%) detected molecular features (grouped isotopologues belonging to the same molecule) from the influent while 255 of 1047 (24%) new molecular features appeared in the secondary effluent. A reduction in the number of large molecules (>450 Da) and an increase in unsaturated molecular features of the effluent organic matter was observed. Van Krevelen plots revealed the distribution of unsaturation and heteroatoms and Kendrick mass defect plots uncovered CH 2 homologous series implying a removal of heavy constituents in that fraction. The demonstrated approach is a step towards a more comprehensive monitoring of DOM in wastewater and contributes to the understanding of current treatment technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".