Broadband optical properties of biomass‐burning aerosol and identification of brown carbon chromophores
Bibliographic record
Abstract
Abstract The radiative effects of biomass‐burning aerosols on regional and global scales can be substantial. Accurate modeling of the radiative effects of smoke aerosols requires wavelength‐dependent measurements and parameterizations of their optical properties in the UV and visible spectral ranges along with improved description of their chemical composition. To address this issue, we used a recently developed approach to retrieve the time‐ and spectral‐dependent optical properties of ambient biomass‐burning aerosols from 300 to 650 nm wavelengths during a regional nighttime bonfire festival in Israel. During the biomass burning event, the overall absorption at 400 nm increased by about 2 orders of magnitude, changing the single scattering albedo from a background level of 0.95 to 0.7. Based on the new retrieval method, we provide parameterizations of the wavelength‐dependent effective complex refractive index from 350 to 650 nm for freshly emitted and slightly aged biomass‐burning aerosols. In addition, PM 2.5 filter samples were collected for detailed offline chemical analysis of the water‐soluble organics that contribute to light absorption. Nitroaromatics were identified as major organic species responsible for the increased absorption at 400 to 500 nm. Typical chromophores include 4‐nitrocatechol, 4‐nitrophenol, nitrosyringol, and nitroguaiacol; oxidation‐nitration products of methoxyphenols; and known products of lignin pyrolysis. Our findings emphasize the importance of both primary and secondary organic aerosols from biomass burning in absorption of solar radiation and in effective radiative forcing.
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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".