Evaluating the Effect of Variability in Biomass Burning Emissions Inventories on Modeled Smoke Concentrations: Insights from the 2021 Canadian Wildfire Season
Bibliographic record
Abstract
Wildfire smoke is increasingly recognized as a significant source of air pollution that leads to public health issues. Over the past few decades, air pollution in Canada has been reduced due to effective regulations. However, fine particulate emissions (i.e., particles with an aerodynamic diameter of less than 2.5 μm (PM2.5)) from wildfires have shown upward trends as climate change exacerbates the frequency and likelihood of wildfires. According to the Canadian Interagency Forest Fire Centre (CIFFC) in 2021, there were 18% more fire starts and nearly a 61% increase in the total area burned compared to the past 10-year average in Canada. The emissions inventories used for modeling the impact of fires on air quality and climate exhibit several discrepancies in emissions estimates, primarily due to the different types of satellite products used for identifying fires and measuring burned area, as well as differences in emission factors describing the vegetative fuels burned. This variability of fire emission inventories leads to uncertainties in predicting air quality. Using the GEOS-Chem chemical transport model, we studied how differences in emissions estimates among three commonly used global biomass burning inventories—the Global Fire Emissions Database 4 (GFED4), the Global Fire Assimilation System (GFAS), and the Quick-Fire Emissions Database 2 (QFED2)—and a newly developed regional biomass burning emission inventory, the Canadian Forest Fire Emissions Prediction System (CFFEPS), affect modeled concentrations of PM2.5 during the 2021 wildfire season in Canada. To examine the sensitivity of simulated PM2.5 to different biomass burning emission datasets, we compared them with ground based PM2.5 data from 70 NAPS (National Air Pollution Surveillance) stations across Canada, from east to west. The simulated PM2.5 concentrations showed significant variation in model performance based on the geographic location of the monitoring stations, particularly between the western and eastern regions of Canada. These findings indicate the importance of considering the strengths and weaknesses of each fire inventory, as some inventories may more accurately represent fire emissions in certain regions than others.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".