Identifying sources and distribution of organic pollutants in a Moroccan river: Characterization of dissolved organic matter by absorption, excitation–emission fluorescence and chemometric analyses
Bibliographic record
Abstract
This study investigates surface water contamination of Ben-Kazza River in Morocco, fed by effluents from an adjacent lagoon-based wastewater treatment plant (WWTP) and seasonally by industrial effluents, and which occasionally serves to irrigate agricultural fields. This study has two purpose: i) to track the main sources of contamination through the evolution of dissolved organic matter (DOM) characteristics along the watercourse, and ii) to characterize the WWTP influents and effluents with a focus on the efficiency of the lagoon treatment. We characterized a total of 495 water samples across the watercourse and from the inlet and outlet of the WWTP, using UV–visible absorption and excitation–emission fluorescence coupled with chemometric analyses. Absorption indicators and fluorescence indices were calculated and compared across sampling points. Results highlight spatial shifts together with temporal changes in DOM. PARAFAC identified components that varied between protein-like, humic-like and anthropogenic-like fluorophores along the river, permitted to trace the anthropogenic components and their sources. The lagoon treatment appeared to better remove fresh organic material than humic material: fluorescence intensity decreased by 68 % for peak T1 and by 22 % for peak C. Maximum fluorescence intensities (Fmax) decreased across all PARAFAC components, leading to more than 55 % reduction of ΣFmax. • Characterization of DOM in water from groundwater, surface waters and WWTP effluents. • Exploring DOM by coupling UV-Vis absorption, EEM fluorescence, and chemometrics. • Assessment of wastewater treatment in lagoons using low-cost analytical techniques. • Tracking sources and gradient of organic pollutants along the river.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".