Nitrate-Mediated Photooxidation of Steroid Estrogens: Efficacy and Prospects for Wastewater Treatment
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide UV light-based advanced oxidation processes have shown considerable promise for mitigation of trace organic contaminants (TrOCs). Recent work has garnered interest in ambient NO 3 – as a photooxidant within UV treatment processes. This work provides a systematic investigation on the efficacy of NO 3 – as a photooxidant for removal of aqueous 17β-estradiol (17β-E2) and its metabolites, estrone (E1) and estriol (E3). Results demonstrate that even low (1 mg L –1 ) concentrations of NO 3 – enhance degradation of 17β-E2 by >48% during medium-pressure UV (MP UV) treatment in comparison to control conditions, and NO 3 – concentrations ≥5 mg L –1 resulted in >90% removal of 17β-E2 at fluences ≥1000 mJ cm –2 . Three photoproducts consistent with known nitrogenous byproducts of 17β-E2 were also observed throughout treatment and found to persist even under high (2000 mJ cm –2 ) fluence conditions. In a municipal wastewater matrix, estrogen removal was improved under high (25 mg L –1 ) NO 3 – conditions as compared to ambient (∼3 mg L –1 ) levels. This work demonstrates the utility of NO 3 – as an in situ photooxidant for removal of TrOCs such as steroid estrogens in real waters and provides an impact to stakeholders interested in leveraging these processes in complex matrices such as municipal wastewater.
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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".