Determining the temporal and spatial variations of pollutants in Toronto
Bibliographic record
Abstract
This thesis aims to assess the spatial and temporal variations of pollutants in Toronto. Annual, seasonal, and monthly pollutant trends were computed from as early as 1974 to as late as the end of 2022. Decreasing NO, NO2, NOx and CO trends were found, perhaps associated with the elimination of coal-fired power plants, and vehicular technology improvements, leading to emissions reductions. Applying two different techniques to trace pollutant measurements from air quality stations upwind, NO, NO2, NOx , and CO concentrations were found to be relatively high around Downsview and Pearson Airport, as well as the York University power plant. Additionally, relatively high NO2 and NOx levels were denoted around Highway 401, suggesting vehicular and aircraft emissions sources. The greatest concentrations of PM2.5, PM10, O3, and SO2 were concluded to originate from outside the city. Income information was obtained by neighborhood and related to the spatial distribution of pollutants in Toronto. A negative relationship was found between NO and NOx levels and income. This is because the lowest and highest income neighborhoods (generally located in north and south Toronto, respectively) mostly coincided with relatively high and low NO, NOx concentrations, respectively. TROPOMI satellite measurements over a gridded plane in Toronto also display the highest and lowest NO2 and HCHO levels over Pearson Airport and Lake Ontario.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".