The impacts of precursor reduction and meteorology on ground-level ozone in the Greater Toronto Area
Bibliographic record
Abstract
Abstract. Tropospheric ozone (O3) is a major component of photochemical smog and is a known human health hazard as well as a damaging factor for vegetation. Its precursor compounds, nitrogen oxides (NOx) and volatile organic compounds (VOCs), have a variety of anthropogenic and biogenic sources and exhibit non-linear effects on ozone production. As an update to previous studies on ground-level ozone in the GTA, we present an analysis of NO2, VOC and O3 data from federal and provincial governmental monitoring sites in the GTA from 2000–2012. We show that over the study period, summertime 24 h VOC reactivity and NO2 midday (11:00–15:00) concentrations at all sites decreased significantly; since 2000, all sites experienced a decrease in NO2 of 28–62% and in measured VOC reactivity of at least 53–71%. Comparing 2002/2003 to 2011/2012, the summed reactivity of OH towards NO2 and a suite of measured VOCs decreased from 8.6 to 4.6 s−1. Ratios of reactive VOC pairs indicate that the effective OH concentration experienced by primary pollutants in the GTA has increased significantly over the study period. Despite the continuous decrease in precursor levels, ozone concentrations are not following the same pattern at all stations; it was found that the Canada-Wide Standard for ozone continues to be exceeded at all monitoring stations. Additionally, while the years 2008–2011 had consistently lower ozone levels than previous years, 2012 experienced one of the highest recorded summertime ozone concentrations and a large number of smog episodes. We demonstrate that these high ozone observations in 2012 may be a result of the number of days with high solar radiation, the number of stagnant periods and the transport of high ozone levels from upwind regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".