Estimating the impact of the 2004 Alaskan forest fires on episodic particulate matter pollution over the eastern United States through assimilation of satellite‐derived aerosol optical depths in a regional air quality model
Bibliographic record
Abstract
During the summer of 2004, extensive wildfires burned in Alaska and western Canada; the fires were the largest on record for Alaska. Smoke from these fires was observed over the continental United States in satellite images, and a variety of chemical tracers associated with the fires were sampled by aircrafts deployed during the International Consortium for Atmospheric Research on Transport and Transformation field experiment. Several recent studies have quantified the impacts of the long‐range transport of pollution associated with these fires on tropospheric CO and O3 levels over the eastern United States. This study quantifies the episodic impact of this pollution transport event on surface‐level fine particulate matter (PM2.5) concentrations over the eastern United States during mid‐July 2004, through the complementary use of remotely sensed, aloft, and surface measurements, in conjunction with a comprehensive regional atmospheric chemistry‐transport model. A methodology is developed to assimilate MODIS aerosol optical depths in the model to represent the impacts of the fires. The resultant model predictions of CO and PM2.5 distributions are compared extensively with corresponding surface and aloft measurements. On the basis of the model calculations, a 0.12Tg enhancement in tropospheric PM2.5 mass loading over the eastern United States is estimated on 19 July 2004 due to the fires. This amount is significantly larger (approximately a factor of 8) than the total daily anthropogenic fine particulate matter emissions for the continental United States. Analysis of measured and modeled PM2.5 surface‐level concentrations suggests that the transport of particulate matter pollution associated with the fires resulted in a 24–42 % enhancement in median surface‐level PM2.5 concentrations across the eastern United States during 19–23 July 2004.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".