A satellite‐based assessment of transpacific transport of pollution aerosol
Bibliographic record
Abstract
It has been well documented that pollution and dust from east Asia can be transported across the North Pacific basin, reaching North America and beyond. In this study, we assess the transpacific transport of “pollution aerosol” (defined as a mixture of aerosols from urban/industrial pollution and biomass burning) by taking advantage of the much improved measurement accuracy and enhanced new capabilities of satellite sensors in recent years. A 4‐year (2002 to 2005) climatology of optical depth for pollution aerosol was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) observations of fine‐ and coarse‐mode aerosol optical depths. The pollution aerosol mass loading and fluxes were then calculated using measurements of the dependence of aerosol mass extinction efficiency on relative humidity and of aerosol vertical distributions from field campaigns and available satellite observations in the region. We estimated that about 18 Tg/a pollution aerosol is exported from east Asia to the northwestern Pacific Ocean, of which about 25% reaches the west coast of North America. The imported flux of 4.4 Tg/a to North America is equivalent to about 15% of local emissions from the United States and Canada. The pollution fluxes are largest in spring and smallest in summer. For the period we have examined the strongest export and import of pollution particulates occurred in 2003, largely because of record intense Eurasia boreal forest fires in spring and summer. The overall uncertainty of pollution fluxes is estimated at a factor of 2. Simulations by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) and Global Modeling Initiative (GMI) models agree quite well with the satellite‐based estimates of annual and latitude‐integrated fluxes, with larger model‐satellite differences in latitudinal and seasonal variations of fluxes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".