N-Nitrosamines and their precursors in wastewater effluents from selected industries in Spain
Bibliographic record
Abstract
N-nitrosamines (NAs), and N-nitrosodimethylamine (NDMA) in particular, are hazardous disinfection byproducts (DBPs) relevant when wastewater impacts drinking water sources and, in water reuse practices. Our study investigates the concentrations of NDMA and five additional NAs and their precursors in industrial wastewater effluents. Aiming to identify potential differences between industrial typologies, wastewaters from 38 industries belonging to 11 types of the UN International Standard Industrial Classification of All Economic Activities system (ISIC) were analysed. Results show that the presence of most NAs and their precursors cannot be linked to a specific industry type as these were in general very different within the classes. Nevertheless, N-nitrosomethylethylamine (NMEA) and N-nitrosopiperidine (NPIP) as well as precursors for N-nitrosodiethylamine (NDEA), NPIP and N-nitrosodibuthylamine (NDBA) could be rank with different concentrations between ISIC classes (p-value < 0.05). Specific industrial wastewater with notable high concentrations of NAs and their precursors were identified too. The effluents with the highest concentration of NDMA belong to the ISIC C2011 class (Manufacture of basic chemical), while the effluents with the highest concentration of NDMA precursors were from the ISIC C1511 class (Tanning and dressing of leather; dressing and dyeing of fur). Other relevant NAs found were NDEA in ISIC class B0810 (Quarrying of stone, sand, and clay) and ISIC class C2029 (Manufacture of other chemical products).
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".