Quantitative Decomposition of Influencing Factors to Aerosol pH Variation over the Coasts of the South China Sea, East China Sea, and Bohai Sea
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 NH x phase-partitioning equilibrium, NH 3 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 NH 3 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".