Nitrous oxide production by lithotrophic ammonia-oxidizing bacteria and implications for engineered nitrogen-removal systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it