Quantitative Decomposition of Influencing Factors to Aerosol pH Variation over the Coasts of the South China Sea, East China Sea, and Bohai Sea
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aerosol acidity acts as a crucial parameter in regulating atmospheric chemistry; however, quantifying its major influencing factors is rare, especially at coastal regions which represent complex interfaces from both terrestrial and marine emissions and land breeze–sea breeze interactions. Three field campaigns conducted at coastal sites of the South China Sea, East China Sea, and Bohai Sea all revealed high aerosol acidity. By using a decomposition method based on the NHx phase-partitioning equilibrium, NH3 and relative humidity (RH) were identified as the two most important driving factors to hourly aerosol pH variation. In addition, the contributions of driving factors to the diel aerosol pH variation were first revealed. RH and temperature tended to increase (decrease) the aerosol pH during nighttime (daytime), while NH3 exhibited a reverse diurnal pattern. The diel cycles of aerosol liquid water and gas-particle partitioning of ammonium were responsible for the behavior of the driving factors of diel aerosol pH variation. Moreover, this study also highlighted that nonvolatile cations accounted for 8%–17% of the hourly aerosol pH variation, demonstrating that the role of sea salts in regulating coastal aerosol acidity cannot be ignored.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it