Impact of UV irradiation on disinfection by-product formation and speciation from post-chlorination of dissolved organic matter
Bibliographic record
Abstract
Abstract The objective of this study was to investigate the effects of combined low-pressure ultraviolet (UV)irradiation and chlorination on the formation of disinfection by-products (DBPs) from different dissolved organic matter (DOM) as DBP precursors. Commercially available humic acid (HA), extracellular organic matter (EOM) from green algae, cyanobacteria, and diatom, namely Scenedesmus quadricauda (SQ), Merismopedia sp. (Msp), and Phaedactylum tricornutum (PT), were used as the sources of DOM. The DBP formation increased with increasing total residual chlorine; EOM from PT presented the highest formation potential followed by HA, Msp, and SQ. The low dosage of 40 mJ/cm2 UV irradiation is insignificant to change the DBP formation from HA and SQ; however, it decreased the DBP formation from bromide-containing EOM of PT and promoted the DBP formation from EOM of Msp at various total residual chlorines. The DBP formation of each DOM correlated well with total residual chlorine. The maximum DBP formation potential (DBPFP) reduction of 42.25 and 13.75% for haloacetic acid formation potential (HAAFP) and trihalomethane formation potential (THMFP) was obtained at the UV irradiation dosage of 300 mJ/cm2 for EOM of PT. However, for the EOM derived from Msp, a maximum increase of 58.1 and 51.1% for HAAFP and THMFP was observed after UV-chlorination.
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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".