Monitoring seasonal variations of haloacetic acids (HAAS) in low-TOC and low-chlorine networks and assessing risk to public health: Muş, Türkiye case
Bibliographic record
Abstract
Chlorine not only removes parasitic pathogenic microorganisms in water, but also causes the formation of disinfection by-products (DBPs) that can be carcinogenic to humans, due to reacting with natural organic matter (NOMs) in raw water sources. Haloacetic acids (HAAs) are one of the most well-known and common disinfection by-product groups (DBPs) in the literature. In the risk definitions of the EPA, some of its components have been identified as carcinogenic. Therefore, determination of HAA concentration in water and execution of a risk analysis are very important in terms of determining the possible effects on public health. This study aimed to monitor the seasonal and spatial variations of haloacetic acids (HAAs) in 2 different water supplies (surface and groundwater) serving the city center of Muş Province, Türkiye, and to demonstrate their public health implications. In this context, an analytical study was conducted covering 4 seasons. According to the results, although the amount of NOM in water bodies was less than 1 mg‧L−1, the HAA5 content may occasionally exceed the USEPA limits of 60 µg‧L−1, but did not exceed the Canadian 80 µg‧L−1 limit. When the WHO limit values were examined on a component basis, it was determined that the MCAA concentrations in both water sources sometimes exceeded the limit of 20 µg‧L−1 in the July and October sampling periods. The risk level related to maximum DCAA level in the main network by means of ingestion pathway was found to be 18.7 times higher for women and 16.5 times higher for men when compared with USEPA risk criteria. Also, in the Muratpaşa water network, risk from DCAA exceeds the USEPA risk level 15.2-fold in women and 13.4-fold in men. However, since it was also found that the level of free chlorine in the network does not meet the required level, it should be noted that there may be an increase in the risk level if there is adequate chlorination in the supply.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".