Evaluation of the Pollution by PhACs in the Zala River (Spas vs. WWTPs)
Bibliographic record
Abstract
The spatio-temporal distribution of concentration and assessed environmental risk levels of the 3 most frequent pharmaceutically active compounds (PhACs) -caffeine (naturally occurring central nervous system stimulant), carbamazepine (antiepileptic), and diclofenac (nonsteroidal anti-inflammatory drug) -in water matrices globally [1] were investigated in Zala River (Hungary) in related to the local popular spas, wastewater treatment plants (WWTPs), and other pollution sources.The investigated area is famous for its healing thermal water baths/spas (4.1 million attendance at the baths of Zala county in the studied year), but the adequacy of treatment of used water from baths, and wastewater is questionable.While approximately 11.5 million m 3 of municipal wastewater is discharged into the Zala River annually, and 2.2 million m 3 of used water from bathing sites.Measurements of PhACs have already been carried out in this area, according to that results 66 active substances out of 134 were detected [2].In this study, sampling took place at 8 different sites 4 different times within a year.According to our results, the concentration of diclofenac in river waters in the last decade proved to be high in international comparison [2-5] (up to 2530 ng/L), and this compound presented high environmental risk level in the most sampled cases.Concentration changes depending on time of only diclofenac showed correlation with indicators of spa tourism (based on number of medical treatments in baths in Zala county), but only at sampling points near certain popular thermal baths.It can be explained by the fact that the spas with thermal water in the study area have a beneficial effect on joint inflammations and rheumatic pains, just like diclofenac [6].In other words, the visitors coming for medical treatment in these spas are most likely users of diclofenac, and this increases the level of local use and thereby environmental load by this PhAC.According to our further results, in the main tourist month (August -based on number of guest nights at commercial locations in Zala county) elevated concentrations of the 3 PhACs were not detected which can be explained by the strong and lasting UV radiation.Furthermore, the microbial activity and composition of activated sludge in WWTPs depend on temperature, and these all influence the degradation of the PhACs [7][8].Summarized, our data suggest that the local WWTPs should be reviewed, additionally necessity of treatment of used thermal water also in term of certain PhACs should be considered for sustainability and protection of aquatic ecosystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".