Analyzing Regional Inluence of Particulate Matter on the City of Beijing, China
Bibliographic record
Abstract
The concentration levels of particulate matter (PM) in the city of Beijing (39.92°N, 116.46°E), China are dependant on the long-range transport of PM in addition to local stationary and mobile sources. To analyze the regional influences of PM10 on the city of Beijing for the year 2004, 366 back trajectories were generated using Hybrid Single-Particle Langragian Integrated Trajectory modeling. The trajectories were then characterized by regions traversed 24 hrs back in time. The comparative impact scores of the trajectories were calculated based on the emission rates of PM10 and its precursors in each region the trajectories traversed as well its distance to Beijing. For the purpose of the subject analysis, the 366 days in 2004 were categorized as good (< 50 µg/m3), moderate (50-150 µg/m3), and poor (> 150 µg/m3) air quality groups based on daily PM10 concentrations. Besides Hebei which surrounds Beijing in all directions, our results identified Inner Mongolia, Shanxi, and Mongolia as regions having greater influence on Beijing air quality due to the prevailing westerly and northwesterly winds. Overall, the higher the overall impact score of a particular trajectory cluster, the larger the probability of having poor air quality days and the smaller the probability of having good air quality days. The analysis also indicates that on an annual basis, when air masses travel from Shanxi, which is home to many coal-fired power plants, Beijing tends to have poor air quality due to high PM10 concentrations. In comparison, when air masses originating over Inner Mongolia, where anthropogenic emissions are low, Beijing tends to have good PM air quality. However, our case study showed that during the spring, air masses originating over Inner Mongolia and Mongolia tend to carry dust and sand to Beijing, leading to poor PM air quality.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".