Concentração de fármacos na água tratada: revisão sistemática e metanálise
Bibliographic record
Abstract
A wide range of pharmaceutical compounds can be detected in potable water in concentrations ranging from ng/L to μg/L. Such compounds comprise the micropollutants of emerging interest, and indiscriminate use can cause harmful effects human health. Moreover, they are persistent in the aquatic environment, where they can cause a change in the microbial community. However, the regulatory framework aimed at controlling the pharmaceuticals occurrence in potable water is still incipient. This study aims to investigate the occurrence of pharmaceutical compounds in potable water through a systematic review of this topic in the scientific literature. For this purpose, a bibliographic search was conducted in published literature between 2000 and 2021. Published data from each paper (concentration, city, country, and analytical detection method) was analyzed statistically. The information comprised 468 concentration data, distributed in 143 compounds in 17 countries worldwide. Maximum concentrations of Prednisone (6.323 ng/L), Caffeine (5.845 ng/L), Betamethasone (2.620 ng/L), Iopamidol (2.400 ng/L), Triclocarban (2.055 ng/L) and Lincomicina (1.413 ng/L) were detected in Belo Horizonte, Campinas, Ganges river (India) and Ontario (Canada), respectively. Furthermore, the concentrations of Iopamidol, Ibuprofen, Diclofenac, Triclosan and Carbamazepine exceeded the Australian guidelines for potable water by 2.4×, 1.4×, 4×, 2.1× and 2×, respectively. Despite the aforementioned data, the results indicate that there is still little systematic monitoring or comprehensive studies on the occurrence of pharmaceuticals in treated water. More studies should be developed in order to track the sources of contamination and understand the risk to which the population is subjected by the water 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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.038 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".