The effect of biogas ebullition on ammonia emissions from animal manure–processing lagoons
Bibliographic record
Abstract
Abstract Various models have been developed to determine ammonia (NH3) emissions from animal manure–processing lagoons to enable relatively simple estimations of emissions. These models allow estimation of actual emissions without intensive field measurements or “one‐size‐fits‐all” emission factors. Two mechanisms for lagoon NH3 emissions exist: (a) diffusive gas exchange from the water surface and (b) mass‐flow (bubble transport) from NH3 contained within the ebullition gas bubble (as it rises to the surface) produced from anaerobic decomposition of organic matter. The purpose of this research is to determine whether gas ebullition appreciably affects NH3 emissions and therefore should be considered in emissions models. Specifically, NH3 mass‐flow emissions were calculated and compared with calculated diffusive NH3 emissions. Mass‐flow NH3 emissions were evaluated based on a two‐film model, in connection with the acid dissociation constant of ammonium, to predict the degree of NH3 gas saturation within the bubbles. Average daily ammoniacal nitrogen concentration, pH, and measured biological gas production (ebullition) in conjunction with literature values for Henry's law constant were used to calculate emissions from NH3 saturation of ebullition gases. Ebullition enhancement of NH3 surface emissions due to increased turbulence was estimated from average lagoon ebullition rates and literature values of turbulence enhancement. Ebullition enhancement of NH3 surface emissions and ebullition mass‐flow NH3 emissions was determined to be <10% and <0.052%, respectively, of total NH3 emissions. Therefore, because ebullition effects are small, they may be neglected when developing process models to estimate NH3 emissions from water surfaces of swine manure processing lagoons.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".