The effect of pH and alkalinity on drinking water biofiltration performance
Bibliographic record
Abstract
Two bench-scale biofiltration columns were monitored to examine the influence of water-quality parameters, including pH and alkalinity, as a cost-effective approach to enhance drinking water biofiltration efficiency in terms of organic carbon removal, ammonia removal, and headloss buildup reduction.The biofilters were operated at pH values 6.0, 7.5, 9.0, and 10.0 with low and high alkalinity levels (25-50) and (180-220) mg CaCO3/L.Applying a higher pH level of 7.5 compared to 6.0 led to similar total organic carbon (TOC) removal efficiency (65% and 67%).Raising the pH to 10.0 resulted in a significantly lower TOC removal efficiency (31%).Increasing pH was also observed to influence ammonia removal significantly such that ammonia removal efficiency improved from 13% at pH 6.0 to 93% at pH 10.0; however, the higher pH was no longer attributed to biological removal but ammonia stripping.The assessment of theoretical oxygen demand revealed that dissolved oxygen (DO) availability was an influential factor in nitrification efficiency.The higher alkalinity levels at each pH level resulted in higher adenosine triphosphate (ATP) concentrations, but no direct correlation was observed between ATP and TOC removal.Overall, pH 7.5 demonstrated optimal biofilter conditions in terms of water quality and operational considerations with average TOC and ammonia removal at 68% and 48% efficiency, respectively, with the lowest headloss development.Overall, pH 7.5 demonstrated an optimum condition for water quality and headloss control with 68% and 48% removal in terms of TOC and ammonia removal, respectively, and with the lowest headloss development.
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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 | 0.001 |
| 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 | 0.001 | 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".