Global monitoring of air pollution over land from the Earth Observing System‐Terra Moderate Resolution Imaging Spectroradiometer (MODIS)
Bibliographic record
Abstract
Moderate Resolution Imaging Spectroradiometer (MODIS) measurements (7 channels: 0.47–2.1 μm, 250–500 m resolutions) provide us with new insights into the characteristics of global aerosols. MODIS retrieves not only aerosol loading but also the fraction of fine mode particle. In this paper we demonstrate MODIS capability for use in monitoring global, regional, and local air pollution. Three case studies in northern Italy, Los Angeles, and Beijing showed the conclusive results of applying MODIS‐derived aerosol optical depths (τ a ) to regional and local air pollution in terms of accuracy (Δτ a = ±0.05 ± 0.2τ a ) and spatial sensitivity of the retrievals. Under stagnant condition, accumulated aerosol abundance can reach τ a > 1 (at 0.55 μm) before being removed by wind or precipitation. The correlation found between Aerosol Robotic Network (AERONET) daily averaged τ a and 24‐hour PM 10 (particulate matter with diameter <10 μm) concentration (μg/m 3 ) in northern Italy is encouraging with correlation coefficient ∼0.82. The derivation of PM concentration from satellite measurements may be possible once we know the detailed aerosol vertical distribution. To compare aerosol loading in different regions of the globe, we choose the two most populated regions (eastern China and India) and the two most industrialized regions (the eastern United States/Canada and western Europe). The time series of MODIS monthly mean τ a from July 2000 to May 2001 depicts a strong seasonal variation with maxima in the spring/summer and minima in the winter. The clear separation between (1) the eastern United States/Canada and western Europe and (2) eastern China and India shows that the τ a values in (2) are 50% to 2–3 times higher compared to those in (1). The enhancements of aerosol loading were due to smoke as originated from Montana/Idaho forest fires transported to the eastern United States in late August 2000 and dust outbreaks from Taklimakan and Gobi Deserts to eastern China as well as smoke from Southeast Asia to southern China in February–April 2001.
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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 | 0.000 | 0.000 |
| 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".