Source Sector and Region Contributions to Black Carbon and PM <sub>2.5</sub> in the Arctic
Bibliographic record
Abstract
Abstract. The impacts of BC and PM2.5 emissions from different source sectors (e.g. transportation, power, industry, residential, and biomass burning) and source regions (e.g. Europe, North America, China, Russia, Central Asia, South Asia, and the Middle East) to Arctic BC and PM2.5 concentrations are investigated using a series of sensitivity runs with WRF-STEM modeling framework. The simulations are validated using aircraft observations over the Arctic during spring and summer 2008. Emissions from power, industrial, and biomass burning sectors are found to be the main contributors to the Arctic PM2.5 with contributions of ~ 30 %, ~ 25 %, and ~ 20 % respectively. In contrast, the residential and transportation sectors are identified as the major contributors to Arctic BC with contributions of ~ 38 % and ~ 30 %. Anthropogenic emissions are the most dominant contributors (~ 88 %) to the BC surface concentration over the Arctic; however, the contribution from biomass burning is significant over the summer (up to ~ 50 %). Among all geographical regions, Europe and China have the highest contributions to the BC surface concentrations with contributions of ~ 46 % and ~ 25 % respectively. Further sensitivity runs show that among various economic sectors of all geographic regions, European and Chinese residential sector contribute up to ~ 25 % and ~ 14 % to the Arctic average surface BC concentration. For Arctic PM2.5, the anthropogenic emissions contribute >~ 75 % at the surface annually, with contributions of ~ 25 % from Europe and ~ 20 % from China; however, the contributions of biomass burning emissions are significant in particular during spring and summer. The contributions of each geographical region to the Arctic PM2.5 and BC vary significantly with altitude. The simulations show that the BC from China is transported to the Arctic in the mid-troposphere, while, BC from European emission sources are transported near the surface under 5 km, especially during winter.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".