Influence of Fuel Properties on the Light Absorption of Fresh and Laboratory-Aged Atmospheric Brown Carbon Produced from Realistic Combustion of Boreal Peat and Spruce Foliage
Bibliographic record
Abstract
Climate change has exacerbated fire activity in the boreal region. Consequently, smoldering boreal peatland fires are an increasingly important source of light-absorbing atmospheric organic carbon (“brown carbon”; BrC). To date, however, BrC from this source remains largely unstudied, which limits our ability to predict its climate impact. Here, we use size-exclusion chromatography coupled with diode array UV–vis detection to examine the molecular-size-dependent light absorption properties of fresh and photoaged aqueous BrC extracts collected during laboratory combustion of boreal peat and live spruce foliage. The atmospheric stability of BrC extracts varies with chromophore molecular size and fuel type: in particular, the high-molecular-weight fractions of both peat- and spruce-BrC are more resistant to photobleaching than their corresponding low-molecular-weight fractions, and total light absorption by peat-BrC persists over longer illumination timescales than that of spruce-BrC. Importantly, the BrC molecular size distribution itself varies with fuel properties ( e.g., moisture content) and to an even greater extent with fuel type. Overall, our findings suggest that the accurate estimation of BrC radiative forcing, and the overall climate impact of wildfires, will require atmospheric models to consider the impact of regional diversity in vegetation/fuel types.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".