The fate of natural organic matter during UV/H2O2 advanced oxidation of drinking water
Bibliographic record
Abstract
At conditions within the range of those typically applied for commercial drinking water applications, the impact of ultraviolet and hydrogen peroxide (UV/H2O2) advanced oxidation (AO) on the concentration, spectral characteristics, hydrophobicity, and biodegradability of natural organic matter (NOM) in a raw surface water and ultrafiltered surface water was studied. At an initial H2O2 dose of 20 mg L−1, UV/H2O2 mineralized less than 15% of the NOM in raw surface water at a fluence of 1500 mJ cm−2. Natural organic matter in ultrafiltered surface water was mineralized by at least 27% after about 1500 mJ cm−2. Partial oxidation of NOM led to ring opening of aromatic structures, cleavage of conjugated double bonded carbon structures, and reduction in the degree of aromatic substitution. The UV/H2O2 AO preferentially reacted with hydrophobic fractions of NOM leading to the formation of hydrophilic products. The treatment oxidized recalcitrant NOM into more readily biodegradable compounds with significant increases in formaldehyde and acetaldehyde concentrations. Depending on NOM properties for a given water, UV/H2O2 AO may cause partial oxidation of NOM leading to the formation of biodegradable compounds. The presence of these species may need to be addressed with a downstream process capable of improving biological stability.
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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 | 0.000 |
| 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) | 0.001 | 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".