Summertime Spatial Variations in Atmospheric Particulate Matter and Its Chemical Components in Different Functional Areas of Xiamen, China
Bibliographic record
Abstract
Due to the highly heterogeneous and dynamic nature of urban areas in Chinese cities, air pollution exhibits well-defined spatial variations. Rapid urbanization in China has heightened the importance of understanding and characterizing atmospheric particulate matter (PM) concentrations and their spatiotemporal variations. To investigate the small-scale spatial variations in PM in Xiamen, total suspended particulate (TSP), PM10, PM5 and PM2.5 measurements were collected between August and September in 2012. Their average mass concentrations were 102.50 μg∙m−3, 82.79 μg∙m−3, 55.67 μg∙m−3 and 43.70 μg∙m−3, respectively. Organic carbon (OC) and elemental carbon (EC) in PM2.5 were measured using thermal optical transmission. Based on the PM concentrations for all size categories, the following order for the different functional areas studied was identified: hospital > park > commercial area > residential area > industrial area. OC contributed approximately 5%–23% to the PM2.5 mass, whereas EC accounted for 0.8%–6.95%. Secondary organic carbon constituted most of the carbonaceous particles found in the park, commercial, industrial and residential areas, with the exception of hospitals. The high PM and EC concentrations in hospitals were primarily caused by vehicle emissions. Thus, the results suggest that long-term plans should be to limit the number of vehicles entering hospital campuses, construct large-capacity underground parking structures, and choose hospital locations far from major roads.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".