Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols
Bibliographic record
Abstract
Abstract. From 2006 to 2007, the daily concentrations of major inorganic water-soluble constituents, mineral aerosol, organic carbon (OC) and elemental carbon (EC) in ambient PM10 samples were investigated from 16 urban, rural and remote sites in various regions of China, and were compared with global aerosol measurements. A large difference between urban and rural chemical species was found, normally with 1.5 to 2.5 factors higher in urban than in rural sites. Optically-scattering aerosols, such as sulfate (~16%), OC (~15%), nitrate (~7%), ammonium (~5%) and mineral aerosol (~35%) in most circumstance, are majorities of the total aerosols, indicating a dominant scattering feature of aerosols in China. Of the total OC, ~55%–60% can be attributed to the formation of the secondary organic carbon (SOC). The absorbing aerosol EC only accounts for ~3.5% of the total PM10. Seasonally, maximum concentrations of most aerosol species were found in winter while mineral aerosol peaks in spring. In addition to the regular seasonal maximum, secondary peaks were found for sulfate and ammonium in summer and for OC and EC in May and June. This can be considered as a typical seasonal pattern in various aerosol components in China. Aerosol acidity was normally neutral in most of urban areas, but becomes some acidic in rural areas. Based on the surface visibility observations from 681 meteorological stations in China between 1957 and 2005, four major haze areas are identified with similar visibility changes, namely, (1) Hua Bei Plain in N. China, and the Guanzhong Plain; (2) E. China with the main body in the Yangtze River Delta area; (3) S. China with most areas of Guangdong and the Pearl River Delta area; (4) The Si Chuan Basin in S.W. China. The degradation of visibility in these areas is linked with the emission changes and high PM concentrations. Such quantitative chemical characterization of aerosols is essential in assessing their role in atmospheric chemistry and weather-climate effects, and in validating atmospheric models.
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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".