Determination of the Occurrence of Trihalomethanes in the Drinking Water Supply of the City of Cuenca, Ecuador
Bibliographic record
Abstract
Water chlorination, fundamental for its microbiological safety, generates by-products, such as trihalomethanes (THMs), potentially associated with carcinogenic and reproductive risks. This study determined the levels of chloroform (CHCl3) in drinking water in Cuenca, Ecuador, a topic that has been little explored in the region. During five months, water samples were collected from three water treatment systems (Cebollar, Tixan, and Sustag), and in situ measurements of physicochemical parameters such as free chlorine, pH, temperature, electrical conductivity, and turbidity were performed in the storage and distribution area. The determination of CHCl3 was performed following the Hach protocol. For data analysis, the Kruskal–Wallis test was employed, followed by Dunn’s post hoc method and Spearman’s correlation coefficient. The results revealed a progressive decrease in free residual chlorine throughout the distribution systems. CHCl3 concentrations ranged from 11.75 µg/L to 21.88 µg/L, remaining below the Ecuadorian regulatory limit of 300 µg/L. There was no consistent correlation between CHCl3 and physicochemical parameters. These findings align with previous research, suggesting that the variability in CHCl3 formation is associated with different water treatment conditions and environmental variables. This study highlights the importance of monitoring disinfection processes to minimize THMs and other DBPs, ensure public health, and contribute to sustainable drinking water management in Ecuador.
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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 | 0.001 |
| 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) | 0.001 | 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".