Dispersion Modeling of SO2 Emissions from a Lignite Fired Thermal Power Plant using CALPUFF
Bibliographic record
Abstract
In this work, dispersion of sulfur dioxide (SO2) in the vicinity of Mae Moh power plant, the largest fossil fuel power plant in northern Thailand, was investigated using well known air dispersion model. The area of 2,500 km2 around the plant was studied, with spatial resolution of 200 x 200 m2. Publicly available MM5 and CALMET software were used to provide meteorological conditions within the study domain, while CALPUFF was used to simulate the patterns of SO2 dispersion, based on actual plant operations in winter, summer and rainy seasons of the year 2009. Comparison against measurements from monitoring stations was made. Simulated results were found to agree qualitatively and quantitatively well with measured data. Root mean squared errors were found in the range between 2.19 to 8.32 µg/m3. The CALPUFF model can be used for SO2 dispersion prediction with satisfactory accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".