Seasonal Variation of Mass Absorption Efficiency of Elemental Carbon in the Four Major Emission Areas in China
Bibliographic record
Abstract
As an important site-specific optical parameter widely used in climate models, the mass absorption efficiency (MAE) of elemental carbon (EC), varies dramatically with the source types and governs the direct radiative forcing (DRF) estimation. In this study, the MAE of EC for ambient samples collected from four major emission areas in China, i.e., Beijing-Tianjin-Hebei area (BTH), Yangtze River Delta area (YRD), Sichuan Basin area (SB), and Pearl River Delta area (PRD), as well as emissions from burning of residential honeycomb briquette, firewood and rice straw were investigated by using a filter-based method. The annual mean MAEEC over the four major emission areas is 7.51 m2/g.MAEEC in BTH and YRD during summer appears significantly higher than MAEEC in other seasons, while seasonal variations of MAEEC in SB and PRD suggest MAEEC in summer and autumn is higher than that in winter and spring. MAEEC for samples from fossil fuels burning and biomass open-burning is 2.10 times higher than that from residential biofuel burning, which could be one of the reasons for the higher MAEEC values during the seasons heavily affected by fossil fuels burning and biomass open-burning (i.e., summer and autumn) than winter for the four locations. Difference between the measured and AeroCom median value of MAEEC may cause underestimation of DRFEC over the studied area by a factor of 0.13. Spatial and temporal variations of MAEEC would also result in underestimations of DRFEC to different degrees varying with seasons and areas.
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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 | 0.000 | 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) | 0.000 | 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".