Global dry deposition of nitrogen dioxide and sulfur dioxide inferred from space‐based measurements
Bibliographic record
Abstract
Abstract A method is developed to estimate global NO2 and SO2 dry deposition fluxes at high spatial resolution (0.1°×0.1°) using satellite measurements from the Ozone Monitoring Instrument (OMI) on the Aura satellite, in combination with simulations from the Goddard Earth Observing System chemical transport model (GEOS‐Chem). These global maps for 2005–2007 provide a data set for use in examining global and regional budgets of deposition. In order to properly assess SO2 on a global scale, a method is developed to account for the geospatial character of background offsets in retrieved satellite columns. Globally, annual dry deposition to land estimated from OMI as NO2 contributes 1.5 ± 0.5 Tg of nitrogen and as SO2 contributes 13.7 ± 4.0 Tg of sulfur. Differences between OMI‐inferred NO2 dry deposition fluxes and those of other models and observations vary from excellent agreement to an order of magnitude difference, with OMI typically on the low end of estimates. SO2 dry deposition fluxes compare well with in situ Clear Air Status and Trends Network‐inferred flux over North America (slope = 0.98, r = 0.71). The most significant NO2 dry deposition flux to land per area occurs in the Pearl River Delta, China, at 13.9 kg N ha−1 yr−1, while SO2 dry deposition has a global maximum rate of 72.0 kg S ha−1 yr−1 to the east of Jinan in China's Shandong province. Dry deposition fluxes are explored in several urban areas, where NO2 contributes on average 9–36% and as much as 85% of total NOy dry deposition.
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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) | 0.001 | 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".