Global Estimates of Average Ground-Level Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth
Bibliographic record
Abstract
Exposure to airborne particles can cause acute or chronic respiratory disease and can exacerbate heart disease, some cancers, and other conditions in susceptible populations. Ground stations that monitor fine particulate matter in the air (smaller than 2.5 microns, called PM2.5) are positioned primarily to observe severe pollution events in areas of high population density; coverage is very limited, even in developed countries, and is not well designed to capture long-term, lower-level exposure that is increasingly linked to chronic health effects. In many parts of the developing world, air quality observation is absent entirely. Instruments aboard NASA Earth Observing System satellites, such as the MODerate resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR), monitor aerosols from space, providing once daily and about once-weekly coverage, respectively. However, these data are only rarely used for health applications, in part because the can retrieve the amount of aerosols only summed over the entire atmospheric column, rather than focusing just on the near-surface component, in the airspace humans actually breathe. In addition, air quality monitoring often includes detailed analysis of particle chemical composition, impossible from space. In this paper, near-surface aerosol concentrations are derived globally from the total-column aerosol amounts retrieved by MODIS and MISR. Here a computer aerosol simulation is used to determine how much of the satellite-retrieved total column aerosol amount is near the surface. The five-year average (2001-2006) global near-surface aerosol concentration shows that World Health Organization Air Quality standards are exceeded over parts of central and eastern Asia for nearly half the year.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".