Satellite‐Observed Impacts of Wildfires on Regional Atmosphere Composition and the Shortwave Radiative Forcing: A Multiple Case Study
Bibliographic record
Abstract
Abstract Emissions of aerosols and trace gases from wildfires and their direct shortwave radiative forcing (DSRF) at the top of atmosphere were studied using satellite observations from Moderate‐Resolution Imaging Spectroradiometer, Atmospheric Infrared Sounder, Clouds and Earth Radiant Energy System on Aqua, and Ozone Monitoring Instrument on Aura. The dominant fuel types of the selected fire cases in the northeast of China (NEC), Siberia (Russia), and California (USA) are cropland, mixed forest, and needle‐leaf forest, respectively. For the cropland fire case in NEC, the fire radiative power‐based emission coefficients (Ce) of aerosol is 20.51 ± 2.55 g/MJ, half that of the forest fire cases in Siberia (40.01 ± 9.21 g/MJ) and California (45.23 ± 8.81 g/MJ), and the carbon monoxide (CO) Ce (23.94 ± 11.83 g/MJ) was about one third and half that of the forest fire cases in Siberia and California, respectively. However, the NOx (NO2 + NO) Ce (2.76 ± 0.25g MJ−1) of the cropland fire in NEC was nearly 3 times that of those forest fire cases. Ratios of NOx to aerosol, HCHO, and CO in the cropland case in NEC show much higher values than those in the forest fire cases. Despite the differences of the Ce and the composition ratios, the DSRF efficiency of smoke aerosol at the top of atmosphere showed similar values among those fire cases. Our results highlight the large variability of emission rate and relative chemical composition but similar DSRF efficiencies among wildfires, which would provide valuable information for understanding the impact of fire on air quality and climate.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".