Impact of CrIS-Derived NH <sub>3</sub> Emission Updates on Simulated Nitrate and Ammonium Aerosols over East Asia
Bibliographic record
Abstract
Fine particulate matter (PM 1 diameter <1 μm) strongly affects air quality and health, with sulfate, nitrate, and ammonium (SNA) as major components across East Asia. Because NH 3 is a key precursor of SNA formation, accurate NH 3 emission data are essential for reliable SNA simulations. This study improves NH 3 emission inventories in East Asia by integrating the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Cross-Track Infrared Sounder (CrIS), with extensive evaluation against ground-based and aircraft observations from the Korea-United States Air Quality (KORUS-AQ) campaign. Iterative linear inversion of NH 3 emissions using WRF-Chem markedly enhances simulations of nitrate and ammonium, in agreement with both the aircraft and surface measurements. The absolute biases of nitrate and ammonium were reduced from 89.7 and 50.1% to 13.6 and 0.6%, respectively, compared to the aircraft observations over Seoul. However, uncertainties in nocturnal NH 3 emissions remain potential sources of nighttime biases in nitrate and ammonium. Overall, the results indicate that nitrate aerosol in most of East Asia is sensitive to NH 3 emission changes. To advance our understanding of SNA formation and support effective aerosol mitigation policies, improved characterization of the diurnal cycle of NH 3 emissions through ground-based monitoring and high-resolution geostationary satellite observations is urgently needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".