Advanced Oxidation for the Removal of Pharmaceuticals from Municipal Wastewater: Connecting Laboratory to Application Scale
Bibliographic record
Abstract
The presence of pharmaceuticals in water is an emerging issue in water research, the major source of which is the discharge of treated municipal wastewater. While technologies have been proposed to remove pharmaceuticals from municipal wastewater, a gap between laboratory studies and application scale remains. This thesis connects laboratory investigations and analysis to application scale treatment for the removal of pharmaceuticals from municipal wastewater. Focused on advanced oxidation processes, studies were conducted with controlled laboratory investigations to gain detailed mechanistic understanding to support application scale studies. Venlafaxine was used as a candidate pharmaceutical, with subsequent laboratory studies also investigating carbamazepine, fluoxetine and sulfamethoxazole. Tests were conducted in both pure water and municipal wastewater matrix. Results demonstrated that UV photolysis, UV/H2O2 and ozonation were all effective means of degrading pharmaceuticals in municipal wastewater. UV with peracetic acid was tested and found to provide little advantage over UV/H2O2. Kinetics were assessed and found to follow pseudo first-order degradation in all cases. Total UV fluence was determined to be an effective means of modelling degradation kinetics. Mineralization pathways were found to be congruent with sequential hydroxylation for all compounds investigated at a laboratory scale. Laboratory results informed application scale testing at the Advancing Canadian Wastewater Assets (ACWA) Research Wastewater Treatment Plant. The facility is embedded in a functioning treatment plant and runs as a unit process in an active treatment train, making the results comparable to full implementation. 13 neutral pharmaceuticals were studied, with concentrations in the ng/L-µg/L range. Treatment processes tested at an application scale were UV photolysis, UV/H2O2, ozonation and reverse osmosis. Reverse osmosis showed high removal of 12 of the 13 compounds, though it has the drawback of producing a concentrated waste stream. Ozone was found to be highly effective for degrading most compounds and presents as the most viable process for full implementation. UV photolysis showed modest removal of all compounds. It was noted that laboratory results for UV photolysis were transferable to application scale with total volumetric UV fluence used for comparison. UV/H2O2 showed similar results to UV photolysis at application scale.
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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 | 0.000 |
| 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".