Land use change and meteorology effect on atmospheric ammonia as seen by IASI
Bibliographic record
Abstract
Agriculture contributes to air pollution and is affected by atmospheric composition, meteorology and climate change. One of the important gases emitted from agricultural activities is ammonia (NH3), which makes up a large portion of the anthropogenic reactive nitrogen in the environment. Agricultural ammonia emissions contribute to the formation of inorganic fine particulate matter (PM2.5), causing multiple negative effects on human health and the overall air quality. Many studies proved the capability of the Infrared Atmospheric Sounding Interferometer (IASI) instrument aboard the Metop satellites in measuring ammonia from space. The series of 3 instruments provides a continuous view of the global atmosphere since 2006, allowing us to study NH3 and many other pollutants relevant to air quality. In this presentation, we explore the interaction between atmospheric ammonia on the one hand, and land and meteorological conditions on the other hand. To start, IASI NH3 total columns and ERA5 land surface temperatures are used to estimate the NH3 emission potential from the soil in agricultural fields. In addition to temperature, the emission potential is affected by the soil physical properties, fertilizer application practices and the concentrations of NH3 near the surface. The results are used to validate the emission potential of NH3 as derived from chemistry transport model (CTM) simulations. Then, we look at the spatio-temporal variability of ammonia, focusing on different source regions around the globe. The NH3 land-atmosphere exchanges depend on land cover and land use management, and on meteorology. We study this relationship in two test regions and periods: an agricultural region in Syria that was subject to land use change during the conflict, and over agricultural regions around safe megacities. In Syria we show that the detected changes in NH3 concentrations is driven by land use/cover rather than meteorology. Over megacities, in particular, Paris, Toronto and Mexico, the result is quite different. We show that the NH3 variability is mainly driven by meteorology, and interestingly, we can detect the fertilizers application period by looking at the NH3 – temperature relationship.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".