Investigation of endocrine disrupting compounds in membrane bioreactor and UV processes
Bibliographic record
Abstract
Endocrine disrupting compounds (EDCs) in the environment have recently emerged as a major issue in Canada and around the globe. The primary objective of this thesis was to investigate the fate of EDCs in two wastewater treatment processes, membrane bioreactors (MBRs) and ultraviolet (UV) disinfection. Two submerged MBR systems using hollow fiber membranes from two membrane manufacturers were tested. The results from a bench-scale and a pilot scale MBR for the treatment of swine wastewater with high concentration of EDCs showed that over 94% of the estrogenic activity (EA) in the influent was reduced through the MBR process. Biological degradation was the dominant removal mechanism for the removal of EDCs in MBRs. Over 85% of the influent EA was reduced by biodegradation through the MBR process. The other MBR system was built to study the removal mechanisms of two estrogens in a hybrid MBR with the addition of powdered activated carbon (PAC). The effects of PAC dosing on MBR overall performance was studied as well. It was found that PAC dosing could increase the removal rates of 17β-estradiol (E2) and 17α-ethinylestradiol (EE2) by 3.4% and 15.8%, respectively and result in a slower rate of trans-membrane pressure (TMP) increase during MBR operation, which could significantly reduce the operating cost for membrane cleaning and/or replacement. The operating cost for PAC dosing could be offset by the benefit achieved from reducing the cost for membrane maintenance. The slower rate of TMP increase in the PAC-MBR was associated with the lower concentrations of soluble extracellular polymeric substances and colloidal organic compounds in the PAC-MBR sludge. The degradation kinetics of three estrogens, estrone (E1), E2, and EE2 in de-ionized water by UV irradiation was studied. The experimental results showed both the apparent concentrations and overall EA of all three investigated estrogens in water decreased with direct UV irradiation. To further study the impact of UV on the overall EA of wastewater, the EA of pre-UV and post-UV samples from five wastewater treatment plants were measured in both liquid and solid phase by Yeast Estrogen Screen assay. It was found that the EA of wastewater decreased after UV disinfection in three of the investigated plants whereas it increased in the other two plants. This observation needs to be further studied because it might have significant impacts on the application of UV systems for wastewater disinfection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".