Ammonia emissions and depositions over the contiguous United States derived from IASI and CrIS using the directional derivative approach
Bibliographic record
Abstract
Abstract. Atmosphere ammonia (NH3), primarily emitted from agriculture, poses significant threats to ecosystems, climate, and human health through nitrogen deposition and secondary aerosol formation. NH3 flux estimates remain highly uncertain due to limited direct observations and complex emission–deposition processes. Here, we estimated NH3 fluxes over the contiguous United States using satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI, 2008–2022) and Cross-track Infrared Sounder (CrIS, 2012–2022). By applying a directional derivative approach, we minimized the impact of offsets in satellite-derived vertical column densities. Our results highlight major agricultural emission hotspots, including the San Joaquin Valley in California, the Snake River Valley in Idaho, the Texas panhandle, the Great Plains, Southeastern Pennsylvania, and Eastern North Carolina. NH3 removal was predominantly driven by deposition near sources rather than chemical transformation, with strong sinks in vegetation-dense regions such as forests, grasslands, shrublands, and wetlands. Seasonal flux variations showed peaks in warm months and lower values in winter, driven by temperature-dependent volatilization from livestock production and fertilizer application. Satellite-based estimates aligned well with bottom-up inventories, effectively capturing spatial and seasonal patterns while revealing additional insights into key flux hotspots and peak seasons. CrIS consistently reported higher fluxes than IASI, especially in spring, reflecting differences in their overpass times. Combining IASI (morning overpass) and CrIS (midday overpass) observations enables a better understanding of diurnal NH3 flux dynamics. These findings provide critical insights into NH3 spatiotemporal variabilities, complementing inventory-based approaches and informing nitrogen management and environmental policy, particularly in regions with limited ground-based monitoring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".