Staggering reductions in atmospheric nitrogen dioxide across Canada in response to legislated transportation emissions reductions
Bibliographic record
Abstract
It is well established that atmospheric nitrogen dioxide (NO 2 ), associated mainly with emissions from transportation and industry, can have adverse effects on both human and ecosystem health. Specifically, atmospheric NO 2 plays a role in the formation of ozone, and in acidic and nutrient deposition. As such, international agreements and national legislation, such as the On-Road Vehicle and Engine Emission Regulations (SOR/2003-2) , and the Federal Agenda on Cleaner Vehicles, Engines and Fuel have been put into place to regulate and limit oxidized nitrogen emissions. The objective of this study was to assess the response of ambient air concentrations of NO 2 across Canada to emissions regulations. Current NO 2 levels across Canada were examined at 137 monitoring sites, and long-term annual and quarterly trends were evaluated for 63 continuous monitoring stations that had at least 10 years of data during the period 1988–2013. A non-parametric Mann-Kendall test (Z values) and Sen's slope estimate were used to determine monotonic trends; further changepoint analysis was used to determine periods with significant changes in NO 2 air concentration and emissions time-series data. Current annual average NO 2 levels in Canada range between 1.16 and 14.96 ppb, with the national average being 8.43 ppb. Provincially, average NO 2 ranges between 3.77 and 9.25 ppb, with Ontario and British Columbia having the highest ambient levels of NO 2 . Long-term tend analysis indicated that the annual average NO 2 air concentration decreased significantly at 87% of the stations (55 of 63), and decreased non-significantly at 10% (5 of 63) during the period 1998–2013. Concentrations increased (non-significantly) at only 3% (2 of 63) of the sites. Quarterly long-term trends showed similar results; significant decreases occurred at 84% (January–March), 88% (April–June), 83% (July–September), and 81% (October–December) of the sites. Declines in transportation emissions had the most influence on NO 2 air concentrations, and changepoint analysis identified three significant changepoints for the air concentration of NO 2 and transportation emissions data. The air concentration changepoints occurred immediately following changepoints in transportation emissions. The introduction of emissions limiting legislation, primarily from transportation sources, has lead to dramatic decreases of 32% in NO × emissions (42% from transportation sources [road, rail, air, marine]) and 47% in ambient NO 2 concentrations across Canada. With respect to human health, legislated changes in transportation emissions have the greatest impact on ambient concentration in urban areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".