Fate of N-nitrosodimethylamine and its precursors during a wastewater reuse trial in the Llobregat River (Spain)
Bibliographic record
Abstract
In summer 2019, a full-scale trial was carried out to investigate the effects in drinking water quality when tertiary treated wastewater was discharged into the Llobregat River upstream of the intake of one of the major drinking water treatment plants of Barcelona and its metropolitan area. Two scenarios were investigated, i.e. discharging the reclaimed water with and without chemical disinfection with chlorine. This study investigates the concentration of N-nitrosodimethylamine (NDMA) as the specific disinfection conditions employed in this trial may favor its formation. To this aim, both NDMA and NDMA formation potential, were measured. The river contained NDMA at very low concentrations, but the concentration of NDMA precursors was already high. The NDMA concentration was reduced from discharge to the river to drinking water intake probably due to a combined effect of dilution and photolysis. The formation potential was also reduced probably due to dilution and biodegradation. The concentration of NDMA in the drinking water was always low (<7.3 ng/L), although the formation potential was above 10 ng/L in one sample. Dissolved organic matter characterization by high resolution mass spectrometry revealed differences between the nature of the organic matter in the river before and after reclaimed water discharge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".