Emission Factors of Key Air Pollutants Arising from Biomass Burning
Bibliographic record
Abstract
Biomass burning from wildfires, agricultural activities, and domestic combustion is a significant global source of particulate matter (PM) and trace gases. In Canada, wildfires are a natural occurrence, but their frequency and intensity have increased dramatically in recent decades, with the 2023 fire season surpassing all previous records. Wildfires emit a complex mixture of gases and PM, including greenhouse gases such as CO2 and CH4, which comprise over 90% of total carbon emissions. Additionally, biomass combustion releases a plethora of organic compounds that can be either released into the gas phase or be part of PM, depending on their volatilities. The particle-bound compounds include various sugar compounds, including anhydro sugars such as levoglucosan, a key molecular tracer in atmospheric aerosol. Despite the significant impact of biomass burning on air quality, human health, and climate, emission factors (EFs)—which quantify pollutant release per unit mass of burned fuel—remain limited, particularly for Canadian fuels. Current EF estimates rely heavily on global averages derived from airborne measurements, which mix fresh and aged smoke plumes with non-biomass burning sources, introducing uncertainties. Additionally, the quantification of levoglucosan is traditionally performed using highly specialized chromatography and mass spectrometry, which require costly instrumentation. To address these challenges, this research aims to: (1) determine EFs of key air pollutants for relevant Canadian fuels under controlled laboratory conditions, and (2) develop a novel method to determine levoglucosan using a glucometer. The first objective was achieved through combustion experiments with surface fuels, including grass, mulch, ponderosa pine, and peat, evaluating emissions across different combustion stages—flaming and smoldering. The second objective involved developing a hydrolysis method to convert levoglucosan into glucose for detection using a glucometer sensor. Optimization of hydrolysis conditions, extraction from biomass burning filters, and sensor validation were performed. The findings of this study have significant implications for atmospheric modeling and emissions inventories. The EF data for regionally relevant Canadian fuels can contribute to global databases and improve models, enhancing simulations of pollutant transport and air quality. Furthermore, the hydrolysis-sensor method presents a cost-effective alternative to conventional levoglucosan quantification techniques, reducing analytical complexity while maintaining reliability. By refining EF estimates and introducing an accessible analytical method, this research advances our understanding of biomass burning emissions and their environmental impacts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".