Removal of pharmaceuticals and personal care products in a membrane bioreactor wastewater treatment plant
Bibliographic record
Abstract
Ninety-nine pharmaceuticals and personal care products (PPCPs) were analyzed in influent, final effluent, and biosolids samples from a wastewater treatment plant employing a membrane bioreactor (MBR). High concentrations in influent were found for acetaminophen, caffeine, metformin, 2-hydroxy-ibuprofen, paraxanthine, ibuprofen, and naproxen (10(4)-10(5) ng/L). Final effluents contained clarithromycin, metformin, atenolol, carbamazepine, and trimethoprim (>500 ng/L) at the highest concentrations, while triclosan, ciprofloxacin, norfloxacin, triclocarban, metformin, caffeine, ofloxacin, and paraxanthine were found at high concentrations in biosolids (>10(3) ng/g dry weight). PPCP removals varied from -34% to >99% and 23 PPCPs had ≥90% removal. Of the studied PPCPs, 26 compounds have been rarely or never studied in previous membrane bioreactor (MBR) investigations. The removal pathway showed that acetaminophen, 2-hydroxy-ibuprofen, naproxen, ibuprofen, codeine, metformin, enalapril, atorvastatin, caffeine, paraxanthine, and cotinine exhibited high degradation/transformation. PPCPs showing strong sorption to solids included triclocarban, triclosan, miconazole, tetracycline, 4-epitetracycline, norfloxacin, ciprofloxacin, doxycycline, paroxetine, and ofloxacin. Trimethoprim, oxycodone, clarithromycin, thiabendazole, hydrochlorothiazide, erythromycin-H2O, carbamazepine, meprobamate, and propranolol were not removed during treatment, and clarithromycin was even formed during treatment. This investigation extended our understanding of the occurrence and fate of PPCPs in an MBR process through the analysis of the largest number of compounds in an MBR study to date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".