Comparative efficiency of oxidation processes to remove acesulfame in water treatment plants supplied by surface water sources
Bibliographic record
Abstract
Abstract The effects of oxidizing agents during water treatment on the concentration of an artificial sweetener were evaluated in full-scale conditions. Five drinking water treatment plants (DWTPs) located in a northern environment with high seasonal variations which use different raw water sources and different combinations of oxidants (ozone, chlorine, UV radiation) were investigated through the removal of the artificial sweetener acesulfame (ACE) along their treatment chains. In total, 98 sampling campaigns were conducted at these DWTPs. Raw water (impacted by variable tidal and hydrodynamic conditions), partially treated water within the DWTPs, and fully treated drinking water were sampled during eight months over the period of higher variability of source water quality. Results showed ACE concentrations in raw waters vary on a seasonal basis: higher in winter and summer (when rivers have low water discharges) and lower during spring and fall. Multi-barrier treatment systems under study were effective for the removal of acesulfame due specifically to the effect of ozone and chlorine during oxidation steps, while no removal was observed using physico-chemical (coagulation flocculation, filtration) and UV treatments. Depending on the number of treatment steps that involved ozonation or chlorination and the position of these oxidative processes in the treatment chain, removal of ACE varied from 24% to 90% in the plants under study. The results indicate that increasing oxidant doses would result in better removal of ACE and other contaminants, but these strategies must consider unknown transformation products, potentially with greater toxicological effects than their precursors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".