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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".