Determination of pharmaceuticals from various therapeutic classes in dewatered sludge by pressurized liquid extraction and high performance liquid chromatography and tandem mass spectrometry (HPLC-MS/MS)
Bibliographic record
Abstract
Pharmaceuticals are released to the environment after human and animal consumption, which partly comes from accumulation in sewage sludge during wastewater treatment. This paper developed a method for the determination of N,N-diethyl-meta-toluamide (DEET) and 14 pharmaceuticals belonging to multiple therapeutic classes such as antibiotic, anti-inflammatory, antilipidemic, anti-hypertensive, and anticonvulsant in dewatered sludge. Pressurized liquid extraction using methanol/McIlvaine buffer (volume ratio, 1 : 1), associated with HPLC-MS/MS was proven to be effective for extraction and quantification of pharmaceuticals and DEET in dewatered sludge. Optimization procedures, including extraction method, extraction solvents and clean-up treatment, were carried out by simulating naturally aged samples to provide a more realistic extraction than previous methods, which were often done by spiking the standard solution before extraction without any aging procedures. The internal standard quantification method using six isotopically labelled compounds was applied to improve the poor absolute recoveries induced by severe matrix effects to obtain better relative recoveries (70–120%). Good relative standard deviations (lower than 19.0% for 7 repeats) and the limit of quantification (0.6–19.4 µg/kg) indicated that the developed method was reliable and sensitive to extract and quantify the studied pharmaceuticals in dewatered sludge. For samples collected from eight sewage treatment plants in Beijing, China, it was found that trimethoprim, caffeine, ketoprofen and ibuprofen were the most prominent contaminants, with the median concentrations reaching 97.6, 179.4, 268.0 and 153.0 µg/kg, respectively. The level of ketoprofen, ibuprofen, mefenamic acid, cabamazepine and diclofenac were similar to results reported from Spain, whilst the levels of other compounds were similar to those found in Germany, Canada and the US.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".