Using multi-satellite observations to constrain ammonia emissions and unlock their potential over open water
Bibliographic record
Abstract
Reducing uncertainty in ammonia ( $$\:\text{N}{\text{H}}_{3}$$ ) emissions, particularly those over open water, which have largely been unexplored, remains a key challenge. This study refines 2019 $$\:\text{N}{\text{H}}_{3}$$ emissions over the south-central United States (SCUS) using inverse modeling technique with Cross-track Infrared Sounder (CrIS) data and assesses its impact on inorganic $$\:\text{P}{\text{M}}_{2.5}$$ . We also present a novel assessment of $$\:\text{N}{\text{H}}_{3}$$ emissions constrained by Infrared Atmospheric Sounding Interferometer (IASI) and CrIS datasets both individually and combined. For the first time, we demonstrate the potential of refining $$\:\text{N}{\text{H}}_{3}$$ emissions over open water using satellite data, specifically over the northwestern Gulf of Mexico (NWGOM). Annual posterior NH₃ emissions exceeded prior estimates over SCUS by 1.43 GgNa−1 (2.5-fold), raising average concentrations by 2.9 ppb (3.4-fold), particularly in Texas, New Mexico, and Oklahoma, and increasing levels of particulate ammonium (1.26-fold), sulfate (1.01-fold), and nitrate (2-fold). Combined IASI/CrIS outperformed individual datasets when compared with surface measurements. Over NWGOM, average $$\:\text{N}{\text{H}}_{3}$$ concentrations increased significantly by 1.4 ppb, predominantly driven by biological nitrogen fixation. This study highlights the potential of satellite data to refine $$\:\text{N}{\text{H}}_{3}$$ emissions over open water and emphasizes the role of multi-satellite datasets and high-resolution regional inverse modeling in improving air quality forecasts and global emission estimates.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".