Spatial-temporal variations of atmospheric NH <sub>3</sub> concentration and its dry deposition across China based on one decade of satellite and ground-based observations
Bibliographic record
Abstract
Abstract. Ammonia (NH3), a key alkaline gas in the atmosphere, significantly influences ecosystem nitrogen cycling and the formation of fine particulate matter (PM2.5). However, limited ground-based monitoring hinders understanding of NH3’s spatial and temporal dynamics and its dry deposition across China, which is ranked as one of global largest NH3 emission hotspots. This study integrated 2013–2023 satellite-derived NH3 column concentrations from the Cross-track Infrared Sounder (CrIS) with ground in-situ observations. We used the GEOS-Chem transport model and a random forest algorithm to simulate NH3 dry deposition fluxes and explore the driving forces behind observed trends. Our results show that NH3 concentrations were the highest in the North China Plain (>10 ppb), with notable annual and seasonal increases. NH3 concentration in 2023 were 14–31 % higher than in 2013. CrIS retrievals aligned well with in-situ data, though were generally about twice as high. Dry deposition fluxes exhibited a clear east-west gradient, with maxima in the North China Plain and Sichuan Basin. Increases in NH3 concentrations and deposition were most pronounced in urban, cropland, and forest regions, with urban areas experiencing the fastest growth and grasslands the highest total deposition. The national mean NH3 concentration and dry deposition flux were 4.98 ppb and 0.51 g m⁻2 yr⁻1, respectively. Anthropogenic emissions explained 77 % of the variability in NH3 concentration trend, while meteorological factors accounted for the remainder. 70 %–80 % of deposition trend was governed by atmospheric NH3 concentration changes. This study highlights growing ammonia pollution and informs nitrogen management strategies in China.
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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".