Fire weather risk analysis over Portugal in the last decades and their impacts over the atmosphere  - The Monchique study case
Bibliographic record
Abstract
More intense fire seasons have been favoured by climate changes worldwide, like Russia, Brazil, the USA, Canada and Portugal. Portugal experienced numerous severe fire seasons with catastrophic wildfires that caused enormous impacts in the last years. This study aimed to investigate the fire risk evolution in Portugal over the last 40 years and the extreme wildfire emission impacts derived from remote sensing data. First, the Fire Weather Index (FWI) from 1979 to 2020, at 0.25º spatial resolution, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis version 4 based on meteorological variables, was used. Then, FWI monthly mean values and trends were analysed for four districts of Southern Portugal (Beja, Evora, Faro and Portalegre). The results indicate that the Faro district presented extreme fire risk values, which peaked on August 2, 2018, one day before the Monchique (a mountain in Faro) wildfire began and lasted between August 3 and 10. The Monchique wildfire was the most destructive in Portugal during 2018, with almost 27.000 ha burned. Second, based on the previous results, atmospheric products derived from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite, the first Copernicus mission dedicated to atmospheric composition monitoring, were collected. These datasets were obtained from Google Earth Engine (GEE), the online platform that combines multiple imageries and datasets with cloud processing to perform analyses. The Carbon monoxide (CO) and Nitrogen dioxide (NO2) concentrations, as well as Absorbing Aerosol Index (AAI) products were analysed during the fire event. The concentrations released by the wildfire reached values 3 and 5 times higher than usual for CO and NO2, respectively. Therefore, the work confirms that extreme wildfire events can release huge pollutant concentrations into the atmosphere. Also, the Sentinel-5 products are useful to evaluate the fire emission evolution in extreme wildfires events and may constitute additional valuable information to combine with ground-based information to map air quality related to wildfire occurrences. This research was funded by the European Union through the European Regional Development Fund in the framework of the Interreg V A Spain - Portugal program (POCTEP) through the CILIFO project (Ref.: 0753-CILIFO-5-E), FIREPOCTEP project (0756-FIREPOCTEP-6-E), and also by national funds through FCT - Foundation for Science and Technology, I.P. under the PyroC.pt project (Refs. PCIF/MPG/0175/2019), ICT project (Refs. UIDB/04683/2020 and UIDP/04683/2020), and TOMAQAPA (PTDC/CTAMET/ 29678/2017).
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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 | 0.002 |
| 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) | 0.001 | 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".