Removal of dissolved organic carbon (DOC) from high DOC and hardness water by chemical coagulation – relative importance of monomeric, polymeric and colloidal aluminum species
Bibliographic record
Abstract
This study investigates the mechanism of Dissolved Organic Carbon (DOC) removal from water with high alkalinity and DOC, typical in the Canadian Prairie, by three aluminum based coagulants: aluminum sulphate (alum), polyaluminum chloride (PACl), and aluminum chlorohydrate (ACH). Our focus is to discern the role of aluminum species: Ala, Alb, and Alc to explain the performance of these coagulants in the removal of DOC. Removal of organic compounds is quantified by measurement of DOC, DOC fractions, and UV254.Results show that coagulation with alum at pH of 6.0 achieves highest DOC removal attributed to the highest content of in situ formed polymeric species (Alb). At pH adjusted to 7 and 8 ACH shows the highest content of Alb and consequently better removal of DOC compared to alum and PACl. When no pH adjustment is applied, coagulation with ACH achieves the highest DOC and UV removal, because of the highest concentration of Alb and Alc species in the solution.Trihalomethane Formation Potential (THMFP) of the water after the application of coagulation has also been studied. Water coagulated with alum shows the lowest trihalomethane formation potential (94.7 μg L−1 T) in comparison to the raw water (202.4 μg L−1) followed by ACH and PACl. This can be related to the coagulant effectiveness in reduction of hydrophobic acid (HPOA) as the main precursor for THMs formation.
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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".