Bibliographic record
Abstract
Abstract. Biomass burning (BB) aerosol significantly affects climate by altering the radiation budget and atmospheric chemistry. Accurate source estimation is vital for climate modeling, yet global observations remain scarce. This study introduces a novel framework for assessing the contribution of transported BB aerosol to smoke-associated aerosol optical depth (BB AOD) at selected locations. The approach integrates satellite fire data (MODIS Active Fire Product) with air parcel trajectory models (HYSPLIT), aerosol transport models (NAAPS), BB emissions (FLAMBE), and plume rise (CAMS GFAS). Tested in Warsaw (Poland, Central Europe) over 2006–2022, the methodology reveals a prominent influence of long- range BB aerosol transport from North America. Analysis indicates that Canada (33.2 % ± 2.4 %) and the USA (32.8 % ± 7.6 %) together contribute approximately 66 % of BB AOD during the BB season in the Northern Hemisphere, surpassing nearer European sources. Among European regions, Eastern Europe accounts for 16.5 % ± 3.2 % of BB AOD, followed by the Iberian Peninsula (11.4 % ± 2.8 %) and Southern Europe (6.1 % ± 1.0 %). Incorporating vertical plume dynamics is crucial: a fixed plume-top threshold of 2250 m underestimates elevated Canadian plumes while overestimating lower European sources, whereas removing altitude constraints overestimates Canadian influence. These findings underscore the importance of transatlantic transport, plume-rise processes, and vertical aerosol distribution in regional climatology. The presented framework for assessing BB AOD contributions is universal and can be applied at any location. Future work should incorporate the specific aerosol types emitted during BB events and their aging processes.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.197 | 0.150 |
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".