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 (N2O), via nitrite reduction, in oxygen-limited environments. The recognition of the environmental fate and effects of N2O, as a "greenhouse gas" has prompted researchers to study N2O 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 N2O generation. However, some literature data suggest that the nitrite concentration may also influence AOB N2O 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 N2O 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 N2O 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 N2O generation. In this case, the bioreactor mixed liquor pH, as well as nitrite concentration, would be important with respect to AOB N2O 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 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 | 0.001 |
| 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 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".