Laboratory transformations of black carbon from fresh biomass burning: changes in coating and mass absorption efficiency
Bibliographic record
Abstract
Wildfires and open biomass burning emit climate-warming gases and particles into the atmosphere (IPCC, 2021). Light absorbing carbonaceous aerosols (LACs) emitted by such biomass burning events are a mixture of black carbon (BC) particles and other absorbing species, such as soluble light-absorbing organic molecules and tarballs (Corbin et al., 2019). In addition to the relative concentration of each particle type, the role of the mixing-state of these species among themselves and with non-absorbing species is also a crucial factor driving the particles light absorption (Cappa et al., 2019).To constrain the role of aerosol particles released into the atmosphere by biomass burning, a laboratory experiment was carried out during summer 2024 at the Northern Forestry Center in Edmonton, Canada. Fuel types characteristic of Canadian wildfires and domestic heating were burned, including, grass, ponderosa pine, peat, mulch and mixtures mulch withTo be able to provide an accurate description of the mixing-state of the particles and its role on the absorption properties, highly-detailed measurements are essential. We have chosen mass-resolved measurements as they are related to the absorption through the mass absorption cross-section, a less complicated paradigm than using size and morphology. To this end, we have followed the method described by Naseri et al. (2024) where a centrifugal particle mass analyzer (CPMA, Cambustion Ltd.; Olfert and Collings, 2005) is used in tandem with a single-soot photometer (SP2-XR, Droplet Inc.). The aerosol light absorption is measured with a traceably calibrated dual-wavelength photo-thermal interferometer (PTAAM-2λ, Haze Instruments d.o.o.; Drinovec et al., 2022). To detect the relevance of tarballs, sampling on Transmission Electron Microscopy (TEM) grids was performed for every fuel We measured over 40 samples, of which we were able to perform 18 mass-segregated absorption measurements. We found a large variation of the coating in fresh smoke from different fuel types, with some samples, such as grass, containing highly coated particles. Figure 1 shows that for grass samples, there was a high level of coating, with a group of particles with BC mass mrBC of around 10-1 fg and particle mass mp of around 4 fg, and another one with mrBCof 2 fg and mp of 7 fg. The coating of the BC particles with these aerosols can result in an enhancement of the absorption by a factor of 3 at the UV and a factor of 2 at the infrared, with an increasing enhancement as the fraction of coating vs BC increases (Zhang et al., 2018).Figure 1. Particles counts of refractory BC mass (mrBC) in fg measured by the SP2-XR vs the mass selected (mp) by the CPMA for a grass-burning sample.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".