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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.010 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 teacher head, 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".