Seasonal characteristics, formation mechanisms and source origins of PM <sub>2.5</sub> in two megacities in Sichuan Basin, China
Bibliographic record
Abstract
Abstract. To investigate the characteristics of PM2.5 and its major chemical components, formation mechanisms, and geographical origins in the two biggest cities, Chengdu (CD) and Chongqing (CQ) in Sichuan Basin, the most densely populated basin in China, daily PM2.5 samples were collected simultaneously at one urban site in each city from October 2014 to July 2015. Annual mean concentrations of PM2.5 were 67.0 ± 43.4 and 70.9 ± 41.4 μg m−3 at CD and CQ, respectively. Secondary inorganic aerosols (SNA) and organic matter (OM) accounted for 41.1 % and 26.1 %, respectively, of PM2.5 mass at CD, and 37.4 % and 29.6 % at CQ. Seasonal variations of PM2.5 and its major chemical components were significant, usually with the highest values in winter and the lowest in summer. SNA and OM were 1.7–3.4 times higher on polluted days than on clean days at both sites, whereas their percentage contributions to PM2.5 varied differently among the components and between the two sites. Gas-phase oxidation probably played an important role on the formation of secondary aerosols when PM2.5 mass varied in the range of 75–150 μg m−3, while heterogeneous transformation was likely the major mechanism on the heavy polluted days. Geographical regions causing high PM2.5 were identified to mainly distribute within the basin at both sites based on potential source contribution function (PSCF) analysis.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".