Evaluating manganese removal in groundwater using pilot scale biofilters: The role of filter media characteristics during start-up
Bibliographic record
Abstract
• The startup period of biofilters is influenced by the characteristics of the filter media. • Mn removal mechanisms differ between GAC and anthracite/sand biofilters during startup. • ATP levels were inversely proportional to biofilter effluent Mn concentrations during startup. • Biofilters successfully reduced Mn concentrations to below the aesthetic objective threshold of 20 μg/L. This study investigated the influence of filter media characteristics on manganese (Mn) removal in groundwater biofilters during the start-up phase. Six pilot scale biofilters containing three different granular activated carbons (GAC), two anthracite, and one sand media were run for 133 days to examine their Mn removal performance at a drinking water utility, while also monitoring ATP and bacterial growth as indicators of biological activity. Key findings demonstrate the critical role of media characteristics, especially for GAC media. Initial Mn adsorption on GAC, with its higher surface area, higher macropore volume, and surface charge, promoted physicochemical and biological oxidation, thus contributing to the early onset of Mn removal during the start-up of the GAC biofilters. In contrast, biological processes dominated Mn removal on anthracite and sand biofilters during start-up. As expected, the presence of Mn-oxidizing bacteria was detected in biofilters, and ATP levels were correlated to Mn removal until the biofilters were acclimated, showing the potential of ATP as an acclimation monitoring metric. Once acclimated, all biofilters consistently reduced Mn levels from 60.9 ± 4.5 µg/L to below 5 µg/L (>90 % removal), while concurrently removing iron, thereby highlighting the biofilter's effectiveness at low water temperatures (<15 °C). This study demonstrates the advantage of GAC media for Mn removal in biofilters during start-up in regions with lower water temperatures.
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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) | 0.001 | 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".