Toward More Efficient and Sustainable Autotrophic Ammonia Removal From Wastewater
Bibliographic record
Abstract
There is significant need to adapt biological nitrogen removal (BNR) systems to meet the evolving needs of future generations, such that the deleterious impacts of reactive nitrogen pollution can be mitigated in a cost-effective and sustainable manner. The overall goal of this research was therefore to explore autotrophic ammonia removal from a future-facing perspective, in order to generate insights that could inform potential solutions for more stable and sustainable BNR. Three chapters of this dissertation are manuscripts that focus on a particular challenge with respect to the operation of BNR systems, including: i. influent streams with very high ammonia concentrations; ii. greenhouse gas emissions during autotrophic ammonia oxidation; and iii. incomplete ammonia removal caused by alkalinity depletion. Chapter2incorporatedalargeextantofrecentresearchtoassessthelimitationsofconventionalpractices for treating high strength ammonia wastewater and provides a synthesis of emerging approaches for more cost-effective and sustainable treatment. The advantages of exploiting biofilm and aerobic granular sludge technologies were specifically investigated, as was the use of anammox-based processes as beneficial alternatives to conventional nitrification and denitrification. Chapter 3 used novel growth systems to investigate CO2 emissions during autotrophic ammonia oxidation and demonstrated a linear relationship between ammonia removal and gaseous CO2 production. The results provided evidence to suggest that the current approach of excluding bicarbonate-derived CO2 emissions from GHG accounting of BNR processes may lead to an underestimation of the climate change impact of conventional wastewater treatment systems. Chapter 4 investigated the nitrification performance of fixed-film bioreactors with different biomass retention characteristics, to determine the effect of enhanced biomass retention towards improved nitrification during periods of alkalinity induced stress. Although a higher biomass concentration offered the capacity to achieve faster ammonia removal rates in batch mode, the bioreactors exhibited similar performance in continuous-flow mode and the retention of additional nitrifying biomass did not provide any biomass concentration-dependent mechanisms for improved resilience to acidic conditions. Overall, the work described herein represents progress towards a more complete understanding of autotrophic ammonia oxidation as it applies to the stable and sustainable operation of BNR systems.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".