Characterization and Source Apportionment of PM2.5 in an Urban Environment in Beijing
Why this work is in the frame
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Bibliographic record
Abstract
Daily 24-hour PM2.5 samples were collected continuously from January 1 to December 31, 2010. Elemental concentrations from Al to Pb were obtained using particle induced X-ray emission (PIXE) method. This was the first full year continuous daily PM2.5 elemental composition dataset in Beijing. Source apportionment analysis was conducted on this dataset using the positive matrix factorization method. Seven sources and their contributions to the total PM2.5 mass were identified and quantified. These include secondary sulphur– 13.8 μg/m3, 26.5%; vehicle exhaust– 8.9 μg/m3, 17.1%; fossil fuel combustion– 8.3 μg/m3, 16%; road dust– 6.6 μg/m3, 12.7%; biomass burning– 5.8 μg/m3, 11.2%; soil dust– 5.4 μg/m3, 10.4%; and metal processing– 3.1 μg/m3, 6.0%. Fugitive dusts (including soil dust and road dust) showed the highest contribution of 20.7 μg/m3 in the spring, doubling those in other seasons. On the contrary, contributions of the combustion source types (including biomass burning and fossil fuel combustion) were significantly higher in the fall (14.2 μg/m3) and in the winter (24.5 μg/m3) compared to those in the spring and summer (9.6 and 8.0 μg/m3, respectively). Secondary sulphur contributed the most in the summer while vehicle exhaust and metal processing sources did not show any clear seasonal pattern. The different seasonal highs and lows from different sources compensated each other. This explains the very small seasonal variations (< 20%) in the total PM2.5.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it