Characterization of Chemical Composition in PM2.5 in Beijing before, during, and after a Large-Scale International Event
Bibliographic record
Abstract
To commemorate the 70th anniversary of the victory of the Chinese people's Anti-Japanese War and the World Anti-Fascist War, an international parade was held in Beijing in September 2015. In order to ensure satisfactory air quality during this event, a phased emission control measures were taken in Beijing and its surrounding provinces. The 24-h PM2.5 samples were collected in Beijing from August 1 to September 15, 2015 covering the period before, during and after this large-scale event. The observed PM2.5 data, meteorological data, emission reduction measures, and air mass trajectory simulation results were systematically analyzed to understand the pollution characteristics and chemical compositions of PM2.5 in Beijing. The results indicated that PM2.5 concentration during the two emission control phases was reduced by 61.7% comparing to the non-control period, but the regional transport of pollutants and meteorological conditions had a more prominent impact on PM2.5 than emission reduction during phase 2. The secondary water-soluble ions including SO42–, NO3–, and NH4+ were found as the main ions present in PM2.5. During the entire emission control period, organic carbon (OC) and elemental carbon (EC) mass concentrations were decreased by 53.1% and 57.9%. A PM2.5 mass balance was analyzed, and it was found that the organic matter accounted for 29.3, 37.6 and 28.5% of the PM2.5 mass before, during and after the emission control, while the contribution of mobile sources to PM2.5 was relatively outstanding after a series of emission control measures.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".