Effect of chemical oxygen demand load on the nitrification and microbial communities in activated sludge from an aerobic nitrifying reactor
Bibliographic record
Abstract
In this study, we explored the effect of chemical oxygen demand (COD) load on the nitrification and microbial communities in activated sludge isolated from an aerobic nitrifying tank. The activated sludge was cultured in three different COD groups: L-COD, 200 mg/L; M-COD, 1200 mg/L; H-COD, 4200 mg/L. The results indicated that the COD exerts a negligible effect on the nitrogen removal ability within the first 24 h. However, the nitrification rate decreased with culture time; the ammonium degradation rates were found to be 80.26%, 57.56%, and 43.43% at 72 h in the three COD groups, respectively. These values correspond to decreases of 19.40%, 41.83%, and 51.48%, respectively, in relation to those observed at 24 h. The activated sludge in the different COD groups exhibited similar community compositions after 24 h, as assessed by Illumina high-throughput sequencing, while a significant difference in the relative abundances of some organisms occurred after 48 and 72 h. Proteobacteria was the main phylum, with a relative abundance of >51.45%. The genera Aridibacter, Paracoccus, Nitrospira, and Nitrosomonas were suppressed by COD load over time. This study may contribute to our knowledge about the nitrification ability and microbial communities in activated sludge at different COD load levels.
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 | 0.001 | 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".