Anthropogenic Activities Increase the Proportion of Soot in Black Carbon Particles and Thereby Intensify Atmospheric Radiative Forcing
Bibliographic record
Abstract
Black carbon (BC) is a continuum of combustion products, encompassing char-BC and soot-BC, exhibits variations in particle size and radiative forcing (RF), which critically influence its role in the atmospheric radiative balance. However, the understanding of its long-term compositional changes and responses to natural and anthropogenic factors remains limited, and few atmospheric radiative modeling studies have specifically examined the distinct contributions of char-BC and soot-BC components. In this study, we trace the compositional changes of BC over the past ∼500 years using sediments from Huguangyan Maar Lake, China and separately quantify the impacts of char-BC and soot-BC on the RF of atmospheric BC. Our findings reveal that the proportion of soot-BC in BC has increased by 2.5 times since 1950 CE. In earlier periods, wildfires driven by the East Asian summer monsoon were the primary contributors to the dominance of char-BC in BC. However, the contribution of human activities to the rise in soot-BC has progressively increased from approximately 10% around 1950 CE to 80% by around 2010 CE, significantly altering BC composition and leading to a 5-fold increase in atmospheric BC RF. These results suggest that ongoing human activities will likely continue to alter BC composition and the atmospheric radiative balance, emphasizing the importance of distinguishing BC components in atmospheric models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".