Characteristics, Formation Mechanisms and Potential Transport Pathways of PM2.5 at a Rural Background Site in Chongqing, Southwest China
Bibliographic record
Abstract
Daily PM2.5 samples were collected at a rural background station (JinYun) located in Chongqing across four consecutive seasons from October 2014 to July 2015. The major water-soluble inorganic ions (WSIIs), organic carbon (OC) and elemental carbon (EC) were analyzed, and their chemical characteristics, transport pathways and potential source regions were investigated. The average annual PM2.5 concentration was 56.2 ± 31.0 µg m–3, of which secondary inorganic aerosol (SNA) and carbonaceous aerosols composed 41.0% and 29.4%, respectively. Higher concentrations of and contributions from SO42–, which were likely caused by the secondary transformation of SO2 into SO42–, were observed in summer than in autumn and spring. Additionally, transportation from the urban area of Chongqing (Yubei) played an important role in elevating the SO42– during this season. Although the accumulation of PM2.5 during pollution episodes in winter was also due to aqueous-phase reactions, based on the entire year, NO3– formation may have been primarily driven by homogeneous gas-phase reactions. Furthermore, the aerosol environment was ammonium-rich, and NH4+ formation promoted the production of NO3– at lower temperatures. The carbonaceous component, which consisted of 81.0–84.6% OC, exhibited higher concentrations in winter than in the other seasons; 50.0–77.2% of the total OC, in turn, was contributed by primary organic carbon (POC). Potential source contribution function (PSCF) analysis suggests that the site was mainly affected by regional pollution originating in the southwestern and northern areas of Chongqing.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".