Combined influences of sources and atmospheric bleaching on light absorption of water-soluble brown carbon aerosols
Bibliographic record
Abstract
Abstract Light-absorbing Brown Carbon (BrC) aerosols partially offset the overall climate-cooling of aerosols. However, the evolution of BrC light-absorption during atmospheric transport is poorly constrained. Here, we utilize optical properties, ageing-diagnostic δ 13 C-BrC and transport time to deduce that the mass absorption cross-section (MAC WS-BrC ) is decreasing by ~50% during long-range oversea transport, resulting in a first-order bleaching rate of 0.24 day ‒1 during the 3-day transit from continental East Asia to a south-east Yellow Sea receptor. A modern 14 C signal points to a strong inverse correlation between BrC light-absorption and age of the source material. Combining this with results for South Asia reveals a striking agreement between these two major-emission regions of rapid photobleaching of BrC with a higher intrinsic absorptivity for BrC stemming from biomass burning. The consistency of bleaching parameters constrained independently for the outflows of both East and South Asia indicates that the weakening of BrC light absorption, thus primarily related to photochemical processes rather than sources, is likely a ubiquitous phenomenon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".