The influence of nitrite and pH (nitrous acid) on aerobic-phase, autotrophic N<sub>2</sub>O generation in a wastewater treatment bioreactor
Bibliographic record
Abstract
Autotrophic ammonia oxidizing bacteria (AOB) are capable of generating nitrous oxide (N 2 O), via nitrite reduction, in oxygen-limited environments. The recognition of the environmental fate and effects of N 2 O, as a "greenhouse gas" has prompted researchers to study N 2 O generation and emission control in wastewater treatment systems. Oxygen, often expressed in terms of the bioreactor liquid dissolved oxygen concentration, is generally viewed as the most important variable with respect to influencing N 2 O generation. However, some literature data suggest that the nitrite concentration may also influence AOB N 2 O generation under oxygen-limited conditions, although there are contradictions in the reported information. This paper presents the findings of an investigation that specifically examined the sensitivity of aerobic-phase biomass N 2 O generation to changes in bioreactor nitrite concentration, via supply of exogenous nitrite, as well as changes in nitrous acid concentration through mixed liquor pH manipulation, in a bench-scale wastewater treatment bioreactor. The data demonstrate the significant influence of nitrite availability on biomass N 2 O generation in the studied system. Most of the collected data suggest that nitrous acid, as opposed to nitrite proper, was the actual AOB nitrite reductase (NiR) electron acceptor, based on observed N 2 O generation. In this case, the bioreactor mixed liquor pH, as well as nitrite concentration, would be important with respect to AOB N 2 O generation and greenhouse gas emissions. Key words: ammonia oxidation, autotrophic denitrification, nitrite reduction, nitrous oxide.
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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 | 0.000 | 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 | 0.000 | 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 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".