Impact of Interactive Aerosol Feedbacks on Photolysis Rates and Air Quality for Urban and Industrial Areas in Canada using the GEM-MACH Air Quality Model
Bibliographic record
Abstract
The photolysis module in Environment and Climate Change Canada’s on-line chemical transport model GEM-MACH (GEM: Global Environmental Multi-scale – MACH: Modelling Air quality and Chemistry) was improved by using the on-line chemical composition and size-resolved representation of atmospheric aerosols in GEM-MACH to calculate the attenuation of radiation in the photolysis module. We coupled both the GEM-MACH aerosol module and the MESSy-JVAL (Modular Earth Sub-Model System) photolysis routine through the use of the on-line aerosol modeled data and a new Mie lookup table for the model-generated extinction efficiency, absorption and scattering cross sections of each aerosol. The new algorithm applies a lensing correction factor to the black carbon absorption efficiency (core-shell parametrization) and calculates the scattering and absorption optical depth and asymmetry factor of black carbon, sea-salt, dust and other internally mixed components. In order to evaluate the effects of these modifications on the performance of the GEM-MACH model, a series of simulations with the updated version of MESSy-JVAL and wildfire emission inputs from the Canadian Forest Fire Emissions Prediction System (CFFEPS) were carried out, and the model aerosol optical depth (AOD) output was compared to the previous version of MESSy-JVAL, satellite data, ground-based measurements, and re-analysis products. The comparison of the updated version of MESSy-JVAL with the previous version showed significant improvements in the model performance with the implementation of the new photolysis module and adopting the online interactive aerosol concentrations in GEM-MACH.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".