The role of transport in New York's Air Quality impacts from the 2023 Canadian Wildfires
Bibliographic record
Abstract
Abstract In 2023, Canada experienced its most severe wildfire season on record, burning over 7.8 million hectares and releasing unprecedented carbon emissions surpassing the country’s previous record by several fold. These fires produced dense plumes that were transported into the Northeastern United States (US), including New York City (NYC), contributing to extreme air pollution in the area. This study investigates whether atmospheric transport from Canada to NYC during the 2023 wildfire season was unusual compared to previous years, considering the important role that transport consistently plays in carrying smoke over long distances, or whether the meteorological conditions were different than previous years, which led to the poor air quality in NYC. We use community atmospheric model (CAM) simulations driven by reanalysis winds for the period 1985–2023, along with daily PM 2.5 observations from the US environmental protection agency (EPA), to simulate long-range aerosol transport under base and high emission scenarios and analyze pollutant transport and concentrations over NYC. Results show that although long-range transport was necessary to bring smoke to NYC, the transport patterns in 2023 were not significantly different from those in previous years. Instead, the record-breaking PM 2.5 levels were mainly caused by the extraordinary magnitude of fire emissions. The model accurately captured the spatial distribution of smoke. These findings thus emphasize anomalously high fire emissions, rather than transport or meteorology, played a more crucial role in driving the air quality impacts of this event over the Northeastern US.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".