Removal of DOC and its fractions from surface waters of the Canadian Prairie containing high levels of DOC and hardness
Bibliographic record
Abstract
In this paper removal of dissolved organic carbon (DOC) and its fractions by chemical coagulation was studied. Raw water was collected from the Red River (Manitoba, Canada). This source water has a DOC concentration ranging from 8 to 12 mg L−1 and total hardness of about 400 mg L−1 CaCO3, which represents a typical surface water quality of the Canadian Prairie. Four coagulants were tested at different pH levels: alum, ferric sulfate, ferric chloride and titanium sulfate. Coagulation effectiveness was evaluated by removal of DOC, DOC fractions, specific UV absorbance (SUVA), and trihalomethane formation potential (THMFP) of the coagulated water. The water DOC was separated into six fractions based on hydrophobicity and acid base functionality: hydrophobic acid (HPOA), hydrophobic base (HPOB), hydrophobic neutral (HPON), hydrophilic acid (HPIA), hydrophilic base (HPIB), and hydrophilic neutral (HPIN). Results showed that ferric sulfate had the highest total DOC removal of 66% while ferric chloride had the lowest DOC reduction of 54%. Although the THMFP was found to be lowered significantly with all four coagulants the ferric chloride showed the greatest THMFP reduction. Fractionation results found a significant reduction in the HPOA fraction for all coagulants with 91% for ferric chloride as the highest removal value. Poor removal of hydrophilic fractions was found for all coagulants. The result of this study showed that total DOC reduction cannot guarantee THMFP reduction and coagulation should be optimized to remove DOC fractions which form most THMs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".