Molecular Properties of Brown Carbon Aerosol from Biomass Burning of Wildland Fuels at the Fire Sciences Laboratory
Bibliographic record
Abstract
Wildfires are a large and increasing source of absorbing organic aerosol (brown carbon) in North America, with a direct impact on the global radiative budget. Brown carbon from wildfires is a complex and poorly characterized mixture of compounds with varying composition, volatility, and reactivity. We conducted controlled burns of western United States fuels at the Missoula Fire Sciences Laboratory during the FIREX FireLab 2016 study. We measured water-soluble brown carbon absorption, total aerosol absorption, and aerosol composition with a shared thermally denuded inlet at temperatures between ambient and 250 °C. We simultaneously collected filter samples at ambient temperature and 250 °C for detailed analysis of molecular size, hydrophobicity, and octanol–water partitioning coefficient log( K OW ) using chromatographic separation techniques with wavelength-resolved absorption detection. For the controlled burns, ∼92% of the water-soluble brown carbon at 365 nm consisted of low-volatility organic compounds with log( C sat ) values between (−5.1 ± 2) to (0.4 ± 2) and oxygen-to-carbon ratios between 0.0–1.1. Species with molecular mass <500 Da contributed 82 ± 13% of the absorption at 365 nm, while species >500 Da contributed only 6.2 ± 3.7%. Thermodenuder temperatures of 250 °C were equivalent to log( C sat ) < −9 with observed oxygen-to-carbon ratios of 1.2 ± 0.3. We found that while only ∼6% of water-soluble brown carbon at 365 nm persisted at these temperatures, approximately 50% of the total absorption in the offline samples remained, with an increased contribution by molecules >500 Da of 15 ± 12%. HPLC analysis showed that the compounds removed at 250 °C had log( K OW ) values between 2.9 ± 0.7 and 3.5 ± 0.7 and contained aliphatic, aromatic, hydroxyl, and carbonyl functional groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".