The effect of meteorological and chemical factors on the agreement between observations and predictions of fine aerosol composition in southwestern Ontario during BAQS-Met
Bibliographic record
Abstract
Abstract. The Border Air Quality and Meteorology Study (BAQS-Met) was an intensive, collaborative field campaign during the summer of 2007 that investigated the effects of transboundary pollution, local pollution, and local meteorology on air quality in southwestern Ontario. This analysis focuses on the measurements of the inorganic constituents of particulate matter with diameter of less than 1 μm (PM1), with a specific emphasis on nitrate. We evaluate the ability of AURAMS, Environment Canada's chemical transport model, to represent regional air pollution in SW Ontario by comparing modelled aerosol inorganic chemical composition with measurements from Aerosol Mass Spectrometers (AMS) onboard the National Research Council (NRC) of Canada Twin Otter aircraft and at a ground site in Harrow, ON. The agreement between modelled and measured pNO3− at the ground site (observed mean (Mobs) = 0.50 μg m−3; modelled mean (Mmod) = 0.58 μg m−3; root mean square error (RSME) = 1.27 μg m−3) was better than aloft (Mobs = 0.32 μg m−3; Mmod = 0.09 μg m−3; RSME = 0.48 μg m−3). Possible reasons for discrepancies include errors in (i) emission inventories, (ii) atmospheric chemistry, (iii) predicted meteorological parameters, or (iv) gas/particle thermodynamics in the model framework. Using the inorganic thermodynamics model, ISORROPIA, in an offline mode, we find that the assumption of thermodynamic equilibrium is consistent with observations of gas and particle composition at Harrow. We develop a framework to assess the sensitivity of PM1 nitrate to meteorological and chemical parameters and find that errors in both the predictions of relative humidity and free ammonia (FA ≡ NH3(g) + pNH4+ − 2 · pSO42-) are responsible for the poor agreement between modelled and measured values.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".