Analyzing Aerosol Properties of Air Parcels Above Boone, NC, During the 2023 Summer Canadian Wildfire Season
Bibliographic record
Abstract
Air mass source regions and meteorological factors significantly influenced aerosol loading along air mass trajectories over Boone, North Carolina, between June 1, 2023, to August 31, 2023. This study examines the impact of northeast Canadian wildfires on aerosol loading, quantified by the particle light scattering coefficients at 550 nm measured at the NOAA Federated Aerosol Monitoring site at Appalachian State University (APP). Using NOAA’s HYSPLIT trajectory model, hourly back trajectories originating at 500 meters above ground level at APP were analyzed over a 96-hour timespan and categorized into four aerosol loading classifications based on the aerosol light scattering coefficient at 550 nm measured at APP. All air parcel trajectories originating in eastern Canada were associated with the high and very high aerosol load classifications. Statistical analysis shows that wildfire-sourced parcels exhibit elevated temperatures and variability in solar flux. The findings establish a link between Canadian wildfire activity and increased aerosol loading in Boone, NC, emphasizing the relationships between source region, transport dynamics, and atmospheric conditions. These results provide a framework for further exploration of aerosol source regions and their broader environmental impacts. KEYWORDS: Back-trajectory Analysis; HYSPLIT; Canadian Wildfires; Aerosols; Particle Light Scattering; Meteorology; Wildfire Impact; Air Mass Trajectories
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".