Application of zeta potential measurements for coagulation control: pilot-plant experiences from UK and US waters with elevated organics
Bibliographic record
Abstract
At present little is known about the relationship between raw water characteristics, such as natural organic matter (NOM) content, and the universal applicability of coagulation optimisation through surface charge measurement. This research aims to investigate this issue by comparing case study sites in the US (Poudre River, Fort Collins, Colorado) and the UK (Albert Reservoir, Halifax) across periods of elevated organic levels. During the period of April to June 2004 in raw Poudre River water dissolved organic carbon (DOC) levels increased rapidly from 3.5 to 7.4 mg L−1 as a direct result of the spring snowmelt runoff, whereas at Albert reservoir, which is a moorland peat catchment, DOC concentrations varied between 7.8 and 10.1 mg L−1 during the period of January to March 2004. NOM is a highly heterogeneous mixture of organic compounds that vary with regards to acidity, molecular weight, hydrophobicity and charge density. XAD resin adsorption techniques were employed to fractionate the water into its hydrophobic and hydrophilic components. Results revealed that NOM composition and characteristics can vary both temporally and spatially, with increased DOC concentrations associated with both an increase in hydrophobic content and charge density. Optimising coagulation based on a zeta potential range (−10 to +5 mV) produced stable average DOC residuals for both locations, although the exact value is also dependent on the hydrophilic composition of the water and the coagulant used, with alum removing approximately 0.5 mg L−1 less DOC compared with ferric.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".