Identification and description of fire emission plumes from Sentinel-5p observations   
Bibliographic record
Abstract
Understanding the dynamics and characteristics of emission plumes from wildfires is of paramount importance for environmental monitoring and policy decisions. These plumes, composed of various greenhouse gases and pollutants, can have far-reaching consequences on global climate, air quality and health. In this study, a data-driven approach to detect and characterise emission plumes from wildfires utilising TROPOMI (Tropospheric Monitoring Instrument) of the Sentinel-5p satellite observations of nitrogen oxides (NOx), carbon monoxide (CO) and aerosols. The analysis leverages VIIRS active fire data to identify locations of fire occurrence, laying the foundation for plume detection. The primary hypothesis states that a plume image consists of three components: a plume body or core, a transitional zone from plume to clear sky, and the clear sky itself. To realise this hypothesis, a data-driven unsupervised algorithm to identify and map plumes is developed, which is based on kernel functions to pre-process the Sentinel-5p images. These kernels effectively highlight plume-related features, allowing for more precise delineation. Subsequently, Gaussian Mixture Models (GMM) are utilised to classify the images into three components of the plume according to the main hypothesis. In instances where multiple plume candidates exist, a Gaussian distance weighting function to identify the likeliest plume is employed. Furthermore, the mapping of the plume-clean air transition zones is further evaluated by employing Monte Carlo simulations to validate and refine the transition zone assessments. To verify the detections, plumes of methane (CH4), carbon monoxide (CO), formaldehyde (HCHO), nitrogen dioxide (NO2) and aerosols for several plumes over the Amazon and Alberta are extracted and the plume properties are related to different landcover types. The findings of this study provide valuable insights into the development of an advanced methodology for plume detection, which has broad implications for the understanding and monitoring of fire emissions and atmospheric research.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".