Simultaneous TOC and Ammonia Removal in Drinking-Water Biofilters: Influence of pH and Alkalinity
Bibliographic record
Abstract
A bench-scale biofiltration study was conducted to investigate the potential benefits of adjusting water pH and alkalinity as a simple water quality control on biofilter efficacy in terms of organic carbon removal, ammonia removal, and head loss development. Two biofilter columns were tested at pH values between 6.0 and 10.0 with low and high alkalinity levels of 25–50 and 180–220 mg CaCO3/L, respectively. Total organic carbon (TOC) removal was 67% at the lower pH range tested (6.0–7.5), and then decreased as the pH increased to an observed low of 31% removal at pH 10. Ammonia removal demonstrated the opposite trend, with a low of 13% removal at pH 6.0, 48% at pH 7.5, and greater than 90% at pH 9–10. An assessment of the available dissolved oxygen (DO) indicated it may have been a limiting factor in complete ammonia removal. Changes in alkalinity demonstrated a modest impact on biofilter activity, i.e., TOC, ammonia removals, and adenosine triphosphate (ATP) levels. Overall, pH 7.5 demonstrated an optimum condition for water quality and head loss control with 67% and 48% removal in terms of TOC and ammonia, respectively, and with the lowest head loss 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 | 0.000 |
| 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 | 0.001 | 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".