Evaluation of fluorescence excitation–emission and LC-OCD as methods of detecting removal of NOM and DBP precursors by enhanced coagulation
Bibliographic record
Abstract
Bench-scale tests were conducted to evaluate enhanced coagulation as a method for removing natural organic matter (NOM) from a surface water to reduce the formation of disinfection by-products (DBPs). Aluminium sulphate (alum) and two polyaluminium chloride (PACl) coagulants were used, as well as alum with pH depression. Using a PACl coagulant alone or alum with pH depression was shown to attain 35% removal of TOC at lower dosages (31 and 29 mg/L, respectively) when compared to the use of alum alone (43 mg/L). In addition to TOC and UV254, a fluorescence excitation–emission matrix (FEEM) approach and liquid chromatography–organic carbon detection (LC-OCD) were used to further characterize the removal of NOM in both untreated and filtered waters. Principal component analysis of FEEM was able to identify the presence of humic-like substances (HS), protein-like substances (PS), and colloidal/particulate matter (CPM); HS were found to have a close correlation with TOC and UV254. LC-OCD enabled the quantitative detection of hydrophobic and hydrophilic DOC; the latter was further separated into five components, the largest of which was HS. Strong linear correlations were calculated between TOC, UV254, HS, and hydrophilic DOC (r2 > 0.96); these parameters were also found to be closely correlated with the formation of trihalomethanes (THMs, r2 > 0.78) and haloacetic acids (HAAs, r2 > 0.92). Linear correlations with THMs and HAAs indicated that FEEM and LC-OCD provide good measures of DBP precursors when compared with TOC and UV254.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".