Development and application of a sustainable approach for the determination of 95 pharmaceutical substances and metabolites in urban wastewater by means of ultra-high-performance liquid-chromatography-tandem mass spectrometry
Bibliographic record
Abstract
In the last few years, the interest in pharmaceutical drugs and their metabolites as environmental pollutants is gaining growing importance. Regular monitoring and timely actions are decisive to implement appropriate water resource management strategies and to evaluate the efficiency of traditional or innovative wastewater treatment plants (WWTPs). This study describes the development and validation of an analytical procedure using 10 mL sample volume followed by direct-injection in ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) for the simultaneous determination of 95 pharmaceutical drugs and 10 of their main metabolites in wastewater. Adequate sensitivity was recorded for all target analytes, with limits of detection below 5 ng/L for 60 out of 95 analytes and recoveries exceeding 80% for all the analytes under study. A total of 42 target analytes were detected in almost all sites, and limited differences were observed among several pharmaceutical drug arrays found at different sampling sites. In addition, an abatement yield higher than 50% was observed for only 12 of the 42 detected substances. The procedure, which combined direct injection and small sample volume collection, showed great potential and efficiency for the high-performance determination of pharmaceutical drugs in wastewater.
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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 | 0.000 | 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 | 0.001 |
| 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".