Enhancing anammox process at moderate temperature via employing anammox granular sludge reactor effluent addition
Bibliographic record
Abstract
The anaerobic ammonium oxidation (anammox) application on mainstream wastewater treatment is limited by the ambient environment and slow growth rate of anammox bacteria. Bulk liquid in established anammox granular sludge bioreactors naturally contains active factors that could potentially boost anammox process under suboptimal conditions. In this study, effluent of a granular sludge-based anammox up-flow anaerobic sludge blanket treating ammonium-rich wastewater was harvested (the effluent is named as UAE) and continuously added into a bioreactor treating low-strength wastewater at 25 °C to study the effects and mechanisms on enhancing the anammox process. The NH4+-N removal efficiencies increased from 6.5-32.5% to 89.7%-93.8%, and anammox genera accounted from 0.1% to 12.1% of microbial community in the receiving reactor with UAE addition. Coupled partial denitrification and dissimilatory nitrate reduction to ammonium (DNRA) processes with anammox were stimulated by UAE addition. Metagenomic analysis showed that the acyl-homoserine lactone-dependent quorum sensing molecules synthesis pathway was enhanced and an increased concentration of C8-HSL from 2.59 ng/L to 4.62 ng/L was observed in the UAE-receiving reactor. Selective amino acid transportation, amino acids biosynthesis and energy metabolic pathways of microbial community were upregulated with UAE addition. We demonstrate UAE as anammox biocatalyst and facilitate a deeper understanding for synergistic effect of active factors in UAE modified microhabitat for anammox metabolism in the UAE-receiving reactor.
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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 | 0.000 |
| 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 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".