Tropospheric NO <sub>2</sub> Patterns in Eastern Canada Using the First Year of TEMPO Observations
Bibliographic record
Abstract
Abstract The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument enables an unprecedented assessment of diurnal and community‐scale variations in tropospheric nitrogen dioxide (NO 2 ) across North America. This study presents the first exploratory analysis of NO 2 patterns in eastern Canada, including Ontario, Quebec, and the Atlantic provinces, using TEMPO observations. We analyzed TEMPO data (V03) gridded at 0.02° × 0.02° from September 2023 to August 2024 and compared it with the Tropospheric Monitoring Instrument (TROPOMI) and surface‐level measurements from Canadian national regulatory monitors. With the hourly resolution of TEMPO, we observed diurnal trends and hotspots that were not recognized by once‐per‐day TROPOMI measurements and pinpointed undermonitored areas. NO 2 in eastern Canada's eight major metropolitan areas, ports, and industrial cities similarly peaked in early morning and declined in later hours. Still, TEMPO detected variations in their hours of peaks and spikes, seasonal, and weekday‐weekend distributions. In Atlantic Canada, correlations between TEMPO and TROPOMI column densities, as well as column‐surface alignments, were lower (Spearman's ρ = 0.41–0.53) compared to the Quebec City‐Windsor Corridor (Spearman's ρ = 0.81–0.90), primarily due to a wider dynamic range of pollution in the latter region. The two regions' TEMPO‐TROPOMI mean absolute differences were 19.3% and 17.1%, respectively. Temporal variations (e.g., a later weekday morning peak in Ontario cities) and TEMPO's identification of additional undermonitored hotspots provide insights into air quality control planning. Our findings motivate future research using multiyear TEMPO data to investigate atmospheric NO 2 sources, transport, exposure, and associated population health impacts in Canada.
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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 | 0.002 |
| 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) | 0.001 | 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".