Toronto area ozone: Long‐term measurements and modeled sources of poor air quality events
Bibliographic record
Abstract
Abstract The University of Toronto Atmospheric Observatory and Environment Canada's Centre for Atmospheric Research Experiments each has over a decade of ground‐based Fourier transform infrared (FTIR) spectroscopy measurements in southern Ontario. We present the Toronto area FTIR time series from 2002 to 2013 of two tropospheric trace gases—ozone and carbon monoxide—along with surface in situ measurements taken by government monitoring programs. We interpret their variability with the GEOS‐Chem chemical transport model and determine the atmospheric conditions that cause pollution events in the time series. Our analysis includes a regionally tagged O3 model of the 2004–2007 time period, which quantifies the geographical contributions to Toronto area O3. The important emission types for 15 pollution events are then determined with a high‐resolution adjoint model. Toronto O3, during pollution events, is most sensitive to southern Ontario and U.S. fossil fuel NOx emissions and natural isoprene emissions. The sources of Toronto pollution events are found to be highly variable, and this is demonstrated in four case studies representing local, short‐, middle‐, and long‐range transport scenarios. This suggests that continental‐scale emission reductions could improve air quality in the Toronto region. We also find that abnormally high temperatures and high‐pressure systems are common to all pollution events studied, suggesting that climate change may impact Toronto O3. Finally, we quantitatively compare the sensitivity of the surface and column measurements to anthropogenic NOx emissions and show that they are remarkably similar. This work thus demonstrates the usefulness of FTIR measurements in an urban area to assess air quality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".