Estimation of greenhouse gas emission from biological wastewater treatment plants
Bibliographic record
Abstract
Wastewater treatment plants, although they may have a small footprint, are known sources of substantial greenhouse gas (GHG) emissions. Two separate municipal wastewater treatment plants (Plant A and Plant B), both providing secondary level of treatment, were evaluated to quantify their GHG emissions and make a comparison. Plant A is a biological aerated filter (BAF) treatment system, while Plant B is an activated sludge (AS) system with two separate bioreactors; one is a plug-flow type and the other is completely mixed. BAF treatment system and the AS system, based on energy consumption, can release, on average, 0.02 and 0.03 kg of equivalent CO2 per m3 of treated wastewater, respectively. Plant B had significant higher off-site NO2 emission (0.005 kg of equivalent CO2 per m3) compared to onsite emission (0.0005 kg of equivalent CO2 per m3). In comparison, Plant A has overall higher NO2 emission (0.0075 kg of equivalent CO2 per m3) than plant B (0.0065 kg of equivalent CO2 per m3). However, Plant B has higher (overall) methane emissions than Plant A (0.024 and 0.011 kg of equivalent CO2 per m3, respectively). A regression analysis showed that temperature, SRT, and BOD5 loading rate strongly influence GHG emission.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".