Spatioseasonal Variations of Atmospheric Ammonia Concentrations Over the United States: Comprehensive Model‐Observation Comparison
Bibliographic record
Abstract
Abstract Atmospheric ammonia plays an important role in a number of environmental issues, including new particle formation and aerosol indirect radiative forcing. Over the United States, atmospheric ammonia has seen an increasing trend due in most part to the declining SO2 and NOx emissions. We conduct the first comprehensive assessment of multiyear Goddard Earth Observing System (GEOS)‐Chem simulated ammonia concentration ([NH3]) over conterminous United States along with surface observations from all 90 National Atmospheric Deposition Program Ammonia Monitoring Network (AMoN) sites that have at least 2 years of continuous measurements. Model‐simulated [NH3] is along empirically expected lines with regard to temporal trends, seasonal variations, and spatial distribution. GEOS‐Chem‐simulated [NH3], compared to AMoN observed values, has weighted average correlation (τ) of 0.50 ± 0.15 and mean fractional bias (MFB) of −8.8 ± 56%. Most sites (63 out of 90) have −60% < MFB < +60%. The deviations from observed values vary spatially and seasonally, and there is significant wintertime underestimation (−44 ± 58%) across most of conterminous United States (except the Pacific states). The largest positive deviations occur in the Pacific states (101 ± 46%) and the largest negative deviations in the Southern Plain states (−73 ± 39%) and the Mountain states (−73 ± 84%), both in the winter months. Over the Great Plains region, GEOS‐Chem simulated [NH3] shows a much stronger dependence to emissions than AMoN observed [NH3], indicating scope for improved representation of emissions for the region. Over Southeast United States, there appears to be the strong effect of the changing emissions of SO2 and NOx in both modeled and observed [NH3].
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".