Fossil fuel combustion and biomass burning sources of global blackcarbon
Bibliographic record
Abstract
Abstract. We identify sources (fossil fuel versus biomass burning) of black carbon (BC) in the atmosphere and in deposition using a global 3D chemical transport model GEOS-Chem. We validate the simulated sources against carbon isotope measurements of BC around the globe and find that the model reproduces biomass burning contribution (fbb, %) in various regions within a factor of 2. GEOS-Chem shows that contribution from biomass burning in the Northern Hemisphere (fbb: 34 %) is much less than that in the Southern Hemisphere (52 %). Specifically, we find comparable contribution from biomass burning and fossil fuel in South Asia, S. America, S. Pacific, Australia and the Antarctic. fbb is the largest in Africa (64 %), followed by that in East Asia (40 %), Siberia (35 %), the Arctic (33 %), Canada (31 %), the US (25 %), and Europe (19 %). fbb is higher in summer (59–78 %) than in winter (28–32 %) in the Arctic, while it is higher in winter (42–58 %) and lower in summer (16–42 %) over the Himalayan–Tibetan plateau. The seasonal variations of fbb are relatively flat in North America, Europe, and Asia. We find that double biofuel emissions for domestic heating during cold seasons northern than 45° N increases fbb values in the Arctic and Europe in winter by ~ 30 %, resulting in a ~ 20 % reduction of discrepancies of fbb in the two regions. The remaining large negative discrepancies (Europe: 41 %; Arctic: 46 %) suggest that the biofuel emissions are probably still underestimated at high latitudes. Increasing fraction of thickly coated hydrophilic BC from 20 % to 70 % in fresh biomass burning plumes increases the fraction of hydrophilic BC in biomass burning plumes by 0–20 % (vary with seasons and regions), and thereby reduces atmospheric fbb by up to 11 %. Faster aging (4 hour e-folding time versus 1.15 days of e-folding time) of BC in biomass burning plumes reduces atmospheric fbb by 7 % (1–14 %), with the largest reduction in remote regions, such as the Arctic, the Antarctic and S. Pacific. Using size resolved scavenging accelerates scavenging of BC particles in both fossil fuel and biomass burning plumes, with a larger increase of the former. Thus, atmospheric fbb increases in most regions by 1–14 %. Overall, atmospheric fbb is determined by fbb in emissions mainly and by atmospheric processes, such as aging and scavenging, to a less extent.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".