High nitrite concentration accelerates nitrite oxidising organism's death
Bibliographic record
Abstract
High nitrite is a known operation parameter to inhibit the biological oxidation of nitrite to nitrate. The phenomenon is traditionally expressed using a Monod-type equation with non-competitive inhibition, in which the reaction associated with the biomass growth is reduced when high nitrite is present. On the other hand, very high nitrite is also known to slay nitrifiers. To clarify the difference between the growth inhibition and the poisoning, cell counting for living microorganisms in the nitrite oxidiser-enriched activated sludge was conducted in batch conditions under various nitrite concentrations together with measurements of biomass chemical oxygen demand (COD) concentration and oxygen uptake rate. The experiments demonstrated that these measureable parameters were all decayed when nitrite concentration exceeded 100-500 mgN/L at pH 7.0 in the system, indicating that nitrite poisoning took place. Biomass growth was recognised in lower range of nitrite which was expressed with growth inhibition only. Based on the response, a kinetic model for the biological nitrite oxidation was developed with a modification of IWA ASM1. The model was further utilised to calculate a possibility to wash out nitrite oxidiser in the aeration tank where a part of the return activated sludge was exposed to high nitrite liquor in a side-stream partial nitritation reactor.
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".