Towards an improved understanding of nitrogen dioxide emissions from forest fires 
Bibliographic record
Abstract
Smoke from wildfires are a significant source of air pollution, which can adversely impact ecosystems and the air quality in downwind populated areas. With increasing severity of wildfires over the years, these are a significant threat to air quality in densely populated areas. Emissions from wildfires are most commonly estimated by a bottom-up approach, using proxies such fuel type, burn area, and emission factors. Emissions are also commonly derived with a top-down approach, using satellite observed Fire Radiative Power. Furthermore, wildfire emissions can also be estimated directly from satellite-borne measurements. Here, we present advancements and improvements of direct emission estimates of forest fire NOx emissions by using TROPOMI (Tropospheric Monitoring Instrument) high-resolution satellite datasets, including NO2 vertical column densities (VCDs) and information on plume height and aerosol scattering. The effect of smoke aerosols on the sensitivity of TROPOMI to NO2 (via air mass factors) is estimated with recalculated VCDs, and validated with aircraft observations. Different top-down emission estimation methods are tested on synthetic data to determine the accuracy, and the sensitivity to parameters, such as wind fields, satellite sampling, instrument noise, NO2:NOx conversion ratio, species atmosphere lifetime and plume spread. Lastly, the top-down, bottom-up and direct emission estimates of fire emissions are quantitatively compared.
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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 | 0.002 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".