Effects of Ozone and Biological Degradation on the Removal and Transformation of Highly Hydrophilic DOC in a Conventional Water Treatment Process
Bibliographic record
Abstract
This study examined the removal and transformation of total dissolved organic carbon (tDOC) and its hydrophilic fraction (HPI) in a full-scale water treatment plant utilizing coagulation/softening, ozonation, and biologically active filters (BAFs). The plant is supplied by high-DOC riverine water (9.9 ± 0.1 mg/L), with primarily hydrophilic DOC (HPI = 83%). The coagulation/softening units reduced HPI DOC by ~42%, leaving the hydrophobic DOC concentration unchanged. The treatment altered the characteristics of HPI fractions; HPI DOC displayed lower SUVA and higher percentages of low molecular weight compounds (LMWs < 1 kDa) and specific trihalomethane formation potential (STHMFP) than the raw water HPI. The plant’s 0.4 mg O3/mg-C ozone dose decreased SUVA but did not alter DOC biodegradability. Increasing the ozone dose to 1 mg O3/mg-C converted all DOC to HPI, enhanced biodegradability from 6.3% to 26%, and lowered STHMFP from 73.4 to 11 µg/mg-C. A 28-day biodegradation increased the percentage of LMWs while decreasing the proportion of compounds with MW > 1 kDa, so we could not confirm that LMWs were more biodegradable than larger compounds. The STHMFP of the Coag/Soft water treated by 1 mgO3/mg-C increased to 60 ± 7 µg/mg-C after biodegradation due to the formation of LMWs.
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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) | 0.000 | 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".