New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model
Bibliographic record
Abstract
Abstract Ammonia surface flux is bidirectional; that is, net flux can be either upward or downward. In fertilized agricultural croplands and grasslands there is usually more emission than deposition especially in midday during warmer seasons. In North America, most of the ammonia emissions are from agriculture with a significant fraction of that coming from fertilizer. A new bidirectional ammonia flux modeling system has been developed in the Community Multiscale Air Quality (CMAQ) model, which has close linkages with the Environmental Policy Integrated Climate (EPIC) agricultural ecosystem model. Daily inputs from EPIC are used to calculate soil ammonia concentrations that are combined with air concentrations in CMAQ to calculate bidirectional surface flux. The model is evaluated against surface measurements of NH3 concentrations, NH4+ and SO42− aerosol concentrations, NH4+ wet deposition measurements, and satellite retrievals of NH3 concentrations. The evaluation shows significant improvement over the base model without bidirectional ammonia flux. Comparisons to monthly average satellite retrievals show similar spatial distribution with the highest ammonia concentrations in the Central Valley of California (CA), the Snake River valley in Idaho, and the western High Plains. In most areas the model underestimates, but in the Central Valley of CA, it generally overestimates ammonia concentration. Case study analyses indicate that modeled high fluxes of ammonia in CA are often caused by anomalous high soil ammonia loading from EPIC for particular crop types. While further improvements to parameterizations in EPIC and CMAQ are recommended, this system is a significant advance over previous ammonia bidirectional surface flux models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".