Natural influent bioaugmentation of activated sludge water resource recovery systems: implications for low temperature nitrification and heterotrophic population structures
Bibliographic record
Abstract
Abstract This work aimed at demonstrating the natural bioaugmentation of biological activated sludge systems with nitrifying biomass from influent wastewater in lab-scale reactors. Three sequencing batch reactors (SBR) were fed with sterile synthetic wastewater. While nitrification was complete at a temperature of 8 °C and a SRT of 20 days, it failed when the temperature was lowered to 5 °C, and the SRT decreased to 7 days. In the test period, the sterile synthetic wastewater fed to the Test Reactor was supplemented by influent solids harvested at a full-scale treatment facility at a total suspended solids concentration of 100 mg/L, which corresponded to approximately 5 mg-COD/L of nitrifying biomass. Upon this addition, nitrification was restores. Subsequent halting the supply of influent solids to the Test Reactor led a rapid failure of nitrification and washout of nitrifiers from the SBR. Reproducibility was demonstrated by switching the feed composition between the Test and Negative control reactors. PCR-based amplicon sequencing analyses targeting the amoA , and nxrB genes of the Nitrospira genus have shown that the influent wastewater governed the structure and composition of the activated sludge nitrifying populations. The most abundant ammonia-oxidizing bacteria (AOB) and Nitrospira -related nitrite-oxidizing bacteria (NOB) in the influent seeds occurred as the most dominant ones in the activated sludge. This pattern was observed even when the influent seeds varied over time. The heterotrophic populations were less affected by the influent seeds with the activated sludge and raw sewage showing distinct microbial populations based on principal coordinate analysis (PCoA). However, the immigrant populations appeared to modulate the structure of the activated sludge heterotrophic communities to some extent. These findings provide concrete evidence of the presence of active nitrifiers in raw wastewater capable of supporting nitrification in an otherwise non-conducive environment. This may have important implications on process design, operation and optimization of wastewater treatment systems. Highlights Lab-scale reactors fed sterile synthetic wastewater at low temperatures and SRTs. Nitrification failed when conditions were adjusted to 5 °C and a SRT of 7 days. Nitrification restored by addition of real wastewater influent solids. Nitrifiers in solids from sewers naturally bioaugment activated sludge systems. Activated sludge models should consider the immigration of nitrifiers with influent. Graphical abstract
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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".