Nitrous oxide production by lithotrophic ammonia-oxidizing bacteria and implications for engineered nitrogen-removal systems
Bibliographic record
Abstract
Chemolithoautotrophic AOB (ammonia-oxidizing bacteria) form a crucial component in microbial nitrogen cycling in both natural and engineered systems. Under specific conditions, including transitions from anoxic to oxic conditions and/or excessive ammonia loading, and the presence of high nitrite (NO₂⁻) concentrations, these bacteria are also documented to produce nitric oxide (NO) and nitrous oxide (N₂O) gases. Essentially, ammonia oxidation in the presence of non-limiting substrate concentrations (ammonia and O₂) is associated with N₂O production. An exceptional scenario that leads to such conditions is the periodical switch between anoxic and oxic conditions, which is rather common in engineered nitrogen-removal systems. In particular, the recovery from, rather than imposition of, anoxic conditions has been demonstrated to result in N₂O production. However, applied engineering perspectives, so far, have largely ignored the contribution of nitrification to N₂O emissions in greenhouse gas inventories from wastewater-treatment plants. Recent field-scale measurements have revealed that nitrification-related N₂O emissions are generally far higher than emissions assigned to heterotrophic denitrification. In the present paper, the metabolic pathways, which could potentially contribute to NO and N₂O production by AOB have been conceptually reconstructed under conditions especially relevant to engineered nitrogen-removal systems. Taken together, the reconstructed pathways, field- and laboratory-scale results suggest that engineering designs that achieve low effluent aqueous nitrogen concentrations also minimize gaseous nitrogen emissions.
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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".