Advances and challenges of anammox-based PN/A and PD/A coupled processes in treating diverse wastewater qualities: A review
Bibliographic record
Abstract
The anammox process is critical for sustainable nitrogen removal, yet widespread use faces operational, environmental and microbial challenges. This review evaluates recent advances in anammox-based coupled processes, particularly PN/A and PD/A, highlighting their adaptation to varied wastewater types. PN/A has been extensively validated at full scale for high-ammonia wastewaters, including sludge digestion liquor, landfill leachate, and industrial effluents, and is now being extended to mainstream municipal applications. However, persistent barriers such as NOB suppression, sensitivity to low temperature, and heterotrophic competition under high C/N conditions continue to limit its performance. In contrast, successful PD/A deployment in mainstream wastewater depends on innovative solutions to temperature-related constraints, including biofilm buffering, metabolic adaptation, and kinetic optimization. The performance of both PN/A and PD/A systems is closely tied to wastewater composition (e.g. such as salinity, organic load, and the presence of toxic compounds) and its influence on microbial kinetics. Emerging innovations, including EPS-enriched biofilms, granular sludge, quorum sensing microbial regulation, and AI-driven controls have enhanced system resilience. Furthermore, integrated approaches enabling simultaneous nitrogen and phosphorus removal and novel reactor configurations are expanding the practical applicability of anammox processes, supporting resource recovery goals. This review synthesizes mechanistic insights, highlights full-scale implementation cases, and outlines emerging frontiers such as nanotechnology-enhanced biofilms and digital twin modelling for process optimization. By bridging microbial ecology with advanced process engineering, this work provides strategic direction for scaling up anammox-based systems in pursuit of energy-neutral and sustainable wastewater treatment under tightening environmental regulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".