Biofiltration field study for cold Fe(II)- and Mn(II)-rich groundwater: accelerated Mn(II) removal kinetics and cold-adapted Mn(II)-oxidizing microbial populations
Bibliographic record
Abstract
Abstract Removal of Mn(II) from Fe(II)- and Mn(II)-rich groundwater in cold regions is challenging, due to slow Mn(II) removal kinetics below 15 °C. This study demonstrated onset, acclimation, and acceleration of Mn(II) removal in a two-stage pilot-scale biofilter (Fe and Mn filters) at varying low on-site temperatures (8–14.8 °C). Mn(II) removal commenced at 8 °C in the Mn filter after Fe(II) removal. A shift in redox-pH conditions favored biological Mn(II) removal and Mn(II)-oxidizing bacteria increased. The Mn filter reached steady-state functioning after 97 days, exhibiting high removal efficiencies (97 ± 0.9%). Yet, first-order rate constants (k) for Mn(II) removal were low (10−6–10−5 min−1; t1/2 = ∼40 d). After consecutive backwashes and filter inoculation with backwashed sludge, k remarkably accelerated to 0.21 min−1 (t1/2 = 3.31 min at 11 ± 0.6 °C). The cold-adapted microbial consortium (51 genera), including Pseudomonas, Leptothrix, Flavobacterium, and Zoogloea, cultured from the field-aged biofilter rapidly produced biogenic Mn oxides at 8 °C, confirmed by electron paramagnetic resonance spectroscopy. Birnessite and pyrolusite detected by synchrotron-based powder X-ray diffraction, and a repetitive birnessite-like surface morphology on ripened filter materials, reflected autocatalytic oxidation. Shifting in k indicated the vertical progress of biofilter ripening, which was not limited by low temperature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".