Impact of model grid spacing on regional- and urban- scale air quality predictions of organic aerosol
Bibliographic record
Abstract
Abstract. Regional-scale chemical transport model predictions of urban organic aerosol to date tend to be biased low relative to observations, a limitation with important implications for applying such models to human exposure health studies. We used a nested version of Environment Canada's AURAMS model (42- to- 15- to- 2.5-km nested grid spacing) to predict organic aerosol concentrations for a temporal and spatial domain corresponding to the Border Air Quality and Meteorology Study (BAQS-Met), an air-quality field study that took place in the southern Great Lakes region in the summer of 2007. The use of three different horizontal grid spacings allowed the influence of this parameter to be examined. A domain-wide average for the 2.5-km domain and a matching 15-km subdomain yielded very similar organic aerosol averages (4.8 vs. 4.3 μg m−3, respectively). On regional scales, secondary organic aerosol dominated the organic aerosol composition and was adequately resolved by the 15-km model simulation. However, the shape of the organic aerosol concentration histogram for the Windsor urban station improved for the 2.5-km simulation relative to those from the 42- and 15-km simulations. The model histograms for the Bear Creek and Harrow rural stations were also improved in the high concentration "tail" region. As well the highest-resolution model results captured the midday 4 July organic-aerosol plume at Bear Creek with very good temporal correlation. These results suggest that accurate simulation of urban and large industrial plumes in the Great Lakes region requires the use of a high-resolution model in order to represent urban primary organic aerosol emissions, urban VOC emissions, and the secondary organic aerosol production rates properly. The positive feedback between the secondary organic aerosol production rate and existing organic mass concentration is also represented more accurately with the highest-resolution model. Not being able to capture these finer-scale features may partly explain the consistent negative bias reported in the literature when urban-scale organic aerosol evaluations are made using coarser-scale chemical transport models.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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".