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 megacities, Chengdu (CD) and Chongqing (CQ), in Sichuan Basin of southwest China, daily PM2.5 samples were collected simultaneously at one urban site in each city for four consecutive seasons from autumn 2014 to summer 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 aerosol (SNA) and organic matter (OM) accounted for 41.1 and 26.1 % of PM2.5 mass at CD, and 37.4 and 29.6 % at CQ, respectively. Seasonal variations of PM2.5 and major chemical components were significant, usually with the highest mass concentration in winter and the lowest in summer. Daily PM2.5 concentration exceeded the national air quality standard on 30 % of the sampling days at both sites, and most of the pollution events were at the regional scale within the basin formed under stagnant meteorological conditions. The concentrations of carbonaceous components were higher at CQ than CD, likely partially caused by emissions from the large number of motorcycles and the spraying processes used during automobile production in CQ. Heterogeneous reactions probably played an important role in the formation of SO42−, while both homogeneous and heterogeneous reactions contributed to the formation of NO3−. Geographical origins of emissions sources contributing to high PM2.5 masses at both sites were identified to be mainly distributed within the basin 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) | 0.001 | 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".