The SMOKE Emission Processor and Community Multi-Scale Air Quality Model (CMAQ) applied to Southern Ontario
Bibliographic record
Abstract
As part of an ongoing effort to develop regional-scale air quality modelling capabilities for photo-oxidants and atmospheric aerosols in Canada, the 1995 emission inventories for the US and eastern Canada were processed through the Sparse Matrix Operator Kernel Emissions Model (SMOKE). This was done to prepare gridded, temporalized and speciated emissions for use in the Community Multi-Scale Air Quality Model (CMAQ). SMOKE is a state-of-the-art emission inventory processing system recently developed by the MCNC Supercomputing Center in North Carolina, and CMAQ is an atmospheric chemistry/transport model developed by the US EPA and currently incorporated into their MODELS-3 modelling framework. This is the first time that the 1995 emission inventory for Canada has been processed with SMOKE. The emissions were processed to permit simulation of several scenarios so that issues of trans-boundary pollutant transport and the impact of coal-fired power plants on regional air quality in Southern Ontario could be explored. A July 1999 smog episode that produced elevated levels of ozone and PM2.5 in Southern Ontario has been used as the test case for this study. This paper describes the many challenges that were encountered in gridding, temporalizing and speciating the Canadian emission inventory and presents some of the results of both the emission processing and the air quality modelling.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".