Assessment of PM <sub>10</sub> Emission Sources for Priority Regulation in Urban Air Quality Management Using a New Coupled MM5-CAMx-PSAT Modeling Approach
Bibliographic record
Abstract
In this study, a new method was proposed to systematically identify the local PM10 emission sources for priority regulation in urban air quality management through a coupled MM5-CAMx-PSAT modeling system. Parameters of emission source contribution ratio (ESCR) and normalized local ESCR were introduced to reflect the source contribution in terms of its total emission amount and per unit emission amount, and were used for identifying the emission sources for priority regulation. The proposed method was then applied to a case study in Beijing, China. Three scenarios were examined, including (a) analysis of only seven PM10 emission source categories for the entire Beijing; (b) analysis of just 13 emission districts in Beijing; and (c) comprehensive analysis of seven emission source categories in each emission district. The following emissions were identified for priority regulation: (a) stationary emissions from the urban center of Beijing and Fengtai districts; (b) industrial fugitive emissions from Chaoyang, Fengtai, and Shijingshan districts; (c) road dust emissions from the urban center of Beijing, Chaoyang, Fengtai, Shijingshan, and Haidian districts; (d) construction site dust emissions from the urban center of Beijing, Chaoyang, Fengtai, and Haidian districts; (e) bare land emissions from Chaoyang district; and (f) vehicle exhaust emissions from the urban center of Beijing, Chaoyang, Fengtai, and Haidian districts. Results indicated that the proposed method could be successfully implemented at different levels for different air quality management purposes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".