Dual‐Isotope Constraints on Seasonally Resolved Source Fingerprinting of Black Carbon Aerosols in Sites of the Four Emission Hot Spot Regions of China
Bibliographic record
Abstract
Abstract Despite much recent efforts, the emission sources of black carbon (BC) aerosols―central input to understanding and predicting environmental and climate impact―remain highly uncertain. Here we present observational δ13C/Δ14C‐based constraints on the sources of BC aerosols over the four seasons in each of the four key hot spot emission regions of China: Beijing‐Tianjin‐Hebei (BTH‐Wuqing; where Wuqing is the sampling location), Yangtze River Delta (YRD‐Haining), Pearl River Delta (PRD‐Zhongshan), and Sichuan Basin (SC‐Deyang). Overall, BC loadings were highest in winter, yet elevated loadings were also observed in other seasons, for example, spring at SC‐Deyang and fall at PRD‐Zhongshan. Annually, the dominant BC sources were coal (50 ± 20%) for BTH‐Wuqing, liquid fossil for YRD‐Haining (46 ± 8%) and PRD‐Zhongshan (48 ± 18%), whereas liquid fossil (42 ± 17%) and biomass burning (41 ± 14%) equally affected SC‐Deyang. There is also different but distinct seasonalities in BC sources for the different sites. As an example, for BTH‐Wuqing coal burning increased from summer to winter, while summer and spring BTH‐Wuqing were more influenced by liquid fossil. In contrast, for YRD‐Haining, the relative importance of emission sources was more constant over the year. These quantitative observational constraints on source‐seasonality of BC aerosols in receptor sites located in China's four key economic zones highlight that regulatory control on BC aerosol emissions from different fuels should consider both seasonal and regional variations. Our results also suggest that models on estimates of BC‐induced climate and air quality should consider variations over both regional and seasonal scales.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".