Origin of smoke in the record-breaking air-pollution event in New York, June 2023
Bibliographic record
Abstract
On June 6 2023, New York City (NY) was covered in heavy smoke and the skies were colored an orange hue, air quality indexes in the city reached hazardous levels. The smoke was attributed to the Canadian wildfires that were ongoing for several months before the event, however, this link, as well as the synoptic and large-scale mechanisms governing the elevated smoke concentrations have not been verified. In this work, we aim to trace the atmospheric pathways of the smoke and identify the role of large- and synoptic-scale systems effecting the smoke movement and accumulation in NY.We used Lagrangian analyses of CAMS reanalysis data to trace the concentration of CO along airmass trajectories both backward from NY and forward from the largest fires. Our results show that the smoke originated from fires in Ontario, and not from the larger, and more distant, fires in Alberta. During the event, there were two peaks of increased pollution in NY itself. After the smoke reached NY for the first time, it then entered a large and stationary cyclone off the coast causing the smoke to recirculate and cause a second peak of extreme smoke pollution in NY. We also find that, most of the smoke from the extensive fires in Alberta was transported at tropopause level towards Greenland and Europe.The case that occurred in NY in June 2023 illustrates the key role of a cyclone for the downstream advection of smoke plumes from large wildfires and for elevating smoke concentrations to hazardous levels.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".