Vertical profiling of Canadian wildfire smoke in the Baltimore-Washington Corridor – interactions with the planetary boundary layer and impact on surface air quality
Bibliographic record
Abstract
Abstract. The 2023 Canadian wildfires yielded record-breaking emissions that were transported long distances over large sections of the mid-Atlantic region, significantly impacting regional surface air quality. In this study, we analyzed the effect of long-distance transported wildfire smoke on the Baltimore-Washington Corridor (BWC), a highly populated and industrialized metropolitan region prone to air quality exceedances. Central to the analysis is the Vaisala CL61 ceilometer in Beltsville (suburban BWC), whose linear depolarization ratio (LDR) profiles provide a continuous, altitude-resolved fingerprint for distinguishing wildfire smoke from locally generated urban aerosols. By combining the LDR-derived with satellite imagery, surface air quality observations, and NOAA HYSPLIT trajectories, we analyzed four discrete smoke events to characterize smoke's vertical distribution and interaction with the planetary boundary layer (PBL). One of the cases showed that the timing of the smoke plume's apparent lowering over the study site in relation to synoptic frontal passage was decisive in determining its impact on air quality. In contrast, those events with well-mixed smoke in the PBL during the advection-driven conditions exhibited a clear deterioration in air quality near the surface, with particulate levels exceeding the regulation threshold. The results underscore the importance of accurately representing vertical mixing in smoke forecasts and illustrate the added value of routine ceilometer LDR measurements for real-time identification of lofted smoke plumes – information not attainable from column-integrated satellite products or surface monitors alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".