Acidic Pharmaceuticals in Sewage-Methodology, Stability Test, Occurrence, and Removal from Ontario Samples
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A gas chromatography/mass spectrometry (GC/MS) method for the determination of 11 selected acidic pharmaceuticals in sewage influent and effluent at trace levels has been developed. The drugs studied were salicylic acid, clofibric acid, ibuprofen, acetaminophen, gemfibrozil, fenoprofen, naproxen, ketoprofen, diclofenac, fenofibrate, and indomethacin, which are commonly used as analgesic/anti-inflammatory agents or lipid regulators. The antibacterial agent triclosan was also included in this study. A solid-phase extraction procedure using the Waters Oasis HLB (hydrophilic-lipophilic balance) cartridge was optimized for the extraction and elution of these compounds. The acids were then converted into their trimethylsilyl (TMS) derivatives. Final analysis was performed with a Mass Selective Detector in the EI/SIM (electron impact/selected ion monitoring) mode. Recoveries of the drugs in spiked distilled water samples at 1 and 0.1 μ/L levels were better than 80%. Similar recoveries of the drugs were obtained from fortified final effluent samples except for acetaminophen, which could not be recovered even if the extraction was performed immediately. The detection limits for the drugs, based on a concentration factor of 1000, were between 10 and 20 ng/L. A stability study indicated that, except for salicylic acid and acetaminophen, the other acidic pharmaceuticals were better than 75% recovered after the samples had been stored at 4°C in the dark for up to seven days. This method has been applied to quantify acidic drugs in wastewater samples collected from several sewage treatment plants in Ontario. While clofibric acid, acetaminophen, fenoprofen, and fenofibrate have never been detected, the other eight compounds were found in nearly all the influent and effluent samples, from low μ/L to low ng/L levels. Eight sewage treatment plants removed from 0 to 98% of these drugs from the influent.
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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 | 0.015 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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 it