Ozonation of natural organic matter and aquatic humic substances: the effects of ozone on the structural characteristics and subsequent trihalomethane formation potential
Bibliographic record
Abstract
Abstract The effect of ozonation on the structural and chemical characteristics of natural organic matter (NOM) and its isolated humic fractions, humic acid (HA) and fulvic acid, were studied using Fourier transform infrared coupled to attenuated total reflectance (FTIR-ATR), ultraviolet/visible (UV/Vis) spectroscopy, and synchronous scanning fluorescence (SSF) spectroscopy. The results were linked to the effect of ozonation on trihalomethane formation potential (THMfp) reduction for water standards with high THM precursors. Results showed that ozonation at a dose of 1 mg ozone/mg dissolved organic carbon (DOC) was capable of reducing DOC, UV absorbance at 254 nm (UV254), and THMfp by up to 42%, 95%, and 89% for the HA water standard, respectively. The study of UV/Vis, FTIR-ATR, and SSF revealed trends showing that ozone can alter the composition of DOC in the water standards, causing a significant reduction in aromaticity. The reduction of UV254 for each ozonated sample also affirms that ozone mainly targets aromatic moieties contained in NOM. FTIR-ATR results showed that the reduction of unsaturated functional groups, including aromatic rings and C = C bonds in the water standards tested, were the main components impacted by ozone application. SSF results also revealed that ozonation decreases the fluorescence intensity of the maximum peak – as well as the whole spectra.
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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".