Novel insights on causes of disproportionate trends between particulate NO <sub>3</sub> <sup>−</sup> and NO <sub>x</sub> emissions in Canadian urban atmospheres
Bibliographic record
Abstract
Abstract. Particulate nitrate (NO3−) is a key target for controlling air pollution, yet its response to NOx abatement remains uncertain in cold climates. This study assesses trends of fine- and coarse-mode NO3− (f-NO3− and c-NO3−) during 1990–2019 in seven Canadian cities, making use of the long-term data collected by the National Air Pollution Surveillance (NAPS) network, and revealed disproportionate trends between NO3− and NOx emissions across Canada. In Edmonton, annual mean f-NO3− decreased by ~60 % from 2007–2019 while provincial NOx emissions declined by only 10–20 %; comparable patterns were also observed in five out of the six other cities in the most recent decade. Such disproportionate trends were diagnosed to be caused by reduced primary f-NO3− emissions, localized dispersion, and Arctic Oscillation–modulated wind anomalies. Conversely, all cities exhibited a transient f-NO3− increase during 1998–2007, coincident with early NOx controls and consistent with unintended enhancement of primary emissions of f-NO3− formed within stationary-combustion plumes. c-NO3− was largely insensitive to NOx reduction in most cities (except Edmonton), with its trends governed by neutralization reactions with alkaline aerosols rather than HNO3 availability. These findings can help interpret the weak or absent f-NO3− response to NOx reductions worldwide, especially in cold-climate 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".