The variation of nitrifying bacterial population sizes in a sequencing batch reactor (SBR) treating low, mid, high concentrated synthetic wastewater
Bibliographic record
Abstract
The purpose of this study was to correlate the population size of ammonia-oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) with nitrification performance under various operational conditions (chemical oxygen demand (COD) concentration, dissolved oxygen (DO), and hydraulic retention time (HRT)) and influent allylthiourea (ATU) shock. The AOB (genera Nitrosomonas and Nitrosospira ) and NOB (genera Nitrobacter and Nitrospira ) communities were analyzed using fluorescent in situ hybridization (FISH). Ammonia-oxidizing bacteria and NOB accounted for 6.2 ± 0.9% and 2.5 ± 0.3% in total biomass, respectively. The population sizes of AOB and NOB varied with different levels of COD, DO, and HRT. Nitrosomonas and Nitrospira were dominant under conditions favorable for nitrification, while Nitrosospira outcompeted Nitrosomonas under adverse conditions (low [NH4+], low DO, short HRT, and ATU shock), and Nitrobacter outcompeted Nitrospira at high substrate concentrations (COD and [NH4+]). Under ATU shock that inhibited the oxidation of NH4+ to NO2–, AOB population was substantially reduced with the stepwise increase of ATU dosage, and led to a corresponding decrease of NOB population. There was a discrepancy between nitrifying bacterial populations and their functions. Although AOB outnumbered NOB in all tests and became more dominant at low DO and short HRT, NH4+ oxidation, instead of NO2– oxidation, was the rate-limiting reaction for nitrification and susceptible to the adverse conditions. The study demonstrated the importance of elucidating the shifts of nitrifying bacterial population to optimize process design and operation at different influent characteristics, aeration intensity, retention time, and potential influent toxic shock.
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 | 0.001 |
| 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".