Assessment of the Integrated ARPS–CMAQ Modeling System through Simulating PM <sub>10</sub> Concentration in Beijing, China
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Bibliographic record
Abstract
In this study, the Advanced Regional Prediction System (ARPS) model was introduced to replace the default weather module in Models-3 Community Multiscale Air Quality (CMAQ) modeling system, to provide a complete meteorological dataset required for urban air quality simulations. The ARPS model was chosen because of its nonhydrostatic dynamics, the generalized terrain-following height vertical coordinate, and the Arakawa-C horizontal grids, which are identical to the ones used in CMAQ, as well as its capabilities of simulating assorted-scale meteorological circulations within a large spectrum (especially at small scales). To assess its performance, the integrated ARPS–CMAQ modeling system was applied to Beijing, China, for simulating its PM10 concentration. The study region was delineated into a two-level nested-grid domain with spatial resolutions of 12 and 4 km, respectively. Three monitoring stations within the urban area of Beijing representing different categories of city function zones were selected to provide measurement data. The months of January 2002, August 2002, January 2004, and September 2004 were used as target months for model performance evaluation through scatter plots and correlation coefficient analysis between the simulated concentrations and observed data. The results show that a satisfactory agreement between modeled and observed data has been reached. The integrated ARPS–CMAQ modeling system was then employed to investigate two PM10 pollution episodes that occurred during the periods of July 5–16 and August 12–19 of 2002, respectively. It was found that the transboundary PM10 contributions from Beijing's surrounding provinces played a leading role in the occurrences of both PM10 pollution episodes. This indicates that, while Beijing needs to take positive steps to reduce its own pollution emissions, much effort should also be placed on demanding more pollution reduction and better environmental performance from its surrounding provinces. This study represents a continuous effort in combining meteorological models with air quality models for providing an integrated scientific modeling tool to city councils for addressing the crucial air quality issues.
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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 | 0.000 | 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