Application of MM5-CAMx-PSAT Modeling Approach for Investigating Emission Source Contribution to Atmospheric<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mtext>SO</mml:mtext></mml:mrow><mml:mn mathvariant="bold">2</mml:mn></mml:msub></mml:mrow></mml:math>Pollution in Tangshan, Northern China
Why this work is in the frame
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Bibliographic record
Abstract
The MM5-CMAx-PSAT modeling approach was presented to identify the variation of emission contribution from each modeling grid to regional and urban air quality per unit emission rate change. The method was applied to a case study in Tangshan Municipality, a typical industrial region in northern China. The variation of emission contribution to the monthly atmospheric SO 2 concentrations in Tangshan from each modeling grid of 9 × 9 km per 1000 t/yr of emission rate change was simulated for four representative months in 2006. It was found that the northwestern part of Tangshan region had the maximum contribution variation ratio (i.e., greater than 0.36%) to regional air quality, while the lowest contribution variation ratio (i.e., less than 0.3%) occurred in the coastal areas. Principal component analysis (PCA), canonical correlation analysis (CCA), and Pearson correlation analysis indicated that there was an obvious negative correlation between the grid-based variation of emission contribution to regional air quality and planetary boundary layer height (PBLH) as well as wind speed, while terrain data presented insignificant impacts on emission contribution variation. The proposed method was also applied to analyze the variation of emission contribution to the urban air quality of Tangshan (i.e., a smaller scale).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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