Antibiotic residues in final effluents of European wastewater treatment plants and their impact on the aquatic environment
Bibliographic record
Abstract
A comprehensive monitoring of a broad set of antibiotics in the final effluent of wastewater treatment plants (WWTPs) of 7 European countries (Portugal, Spain, Ireland, Cyprus, Germany, Finland, and Norway) was carried out in two consecutive years (2015 and 2016). This is the first study of this kind performed at an international level. Within the 53 antibiotics monitored 17 were detected at least once in the final effluent of the WWTPs, i.e.: ciprofloxacin, ofloxacin, enrofloxacin, orbifloxacin, azithromycin, clarithromycin, sulfapyridine, sulfamethoxazole, trimethoprim, nalidixic acid, pipemidic acid, oxolinic acid, cefalexin, clindamycin, metronidazole, ampicillin, and tetracycline. The countries exhibiting the highest effluent average concentrations of antibiotics were Ireland and the southern countries Portugal and Spain, whereas the northern countries (Norway, Finland and Germany) and Cyprus exhibited lower total concentration. The antibiotic occurrence data in the final effluents were used for the assessment of their impact on the aquatic environment. Both, environmental predicted no effect concentration (PNEC-ENVs) and the PNECs based on minimal inhibitory concentrations (PNEC-MICs) were considered for the evaluation of the impact on microbial communities in aquatic systems and on the evolution of antibiotic resistance, respectively. Based on this analysis, three compounds, ciprofloxacin, azithromycin and cefalexin are proposed as markers of antibiotic pollution, as they could occasionally pose a risk to the environment. Integrated studies like this are crucial to map the impact of antibiotic pollution and to provide the basis for designing water quality and environmental risk in regular water monitoring programs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".