Systematic calibration of N2O emissions from a full-scale WWTP including a tracer test and a global sensitivity approach
Bibliographic record
Abstract
Nitrous oxide (N2O) is a greenhouse gas (GHG) emitted during biological nitrogen removal from wastewater treatment plants (WWTPs). Some modelling tools have been proposed to predict N2O emissions during the design and operation of WWTPs. In this study, the novel ASM2d-N2O model, which accounts for the production of N2O in nutrient removal WWTPs, was used to study the associated emissions from a full-scale WWTP with two independent lines. Firstly, the hydraulics of the WWTP was characterized by a residence time distribution test, showing the flow was equally divided into the two treatment lines (49.3 vs. 50.7%), that each reactor worked as an ideal continuous stirred tank reactor and the secondary settler model flux was similar to a plug-flow reactor. The ASM2d-N2O model was then calibrated using experimental data obtained under dynamic conditions. A global sensitivity analysis was used to select, among 59 model parameters, five candidates that resulted to be related to nitrifying organisms. Different parameter subsets up to four parameters were evaluated, being the subset [µNOB, qAOB_AMO, KO2_NOB, KNO2_NOB] the best, achieving 53.3% reduction of the calibration cost function. The model fit obtained provided a reasonably description of nutrients and N2O emission trends, considering the inherent operational variability suffered in full-scale WWTPs. Finally, a simulation-based study showed that, for the given WWTP and operational conditions, an unbalanced distribution of flow-rate between the two treatment lines did not result in a significant increase on N2O emissions. The results obtained show that this model can be a suitable tool for predicting N2O emissions in full-scale WWTPs, and can therefore be used to find operational conditions that help to minimise these emissions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".