Observation of CO from space over megacities
Bibliographic record
Abstract
Increases of the global population combined with the economic growth in many of the developing countries are leading to an increase of urban surface area and thus globally the associated air quality issue. Human activities emit vast quantities of pollutants with carbon monoxide (CO) a prime example. Several nadir-viewing thermal infrared sounders monitor this gas from space but their limited sensitivity to the boundary layer is a well-known disadvantage of this technique. Our study investigates the performance of a new retrieval algorithm applied to Measurements of Pollution in the Troposphere (MOPITT) data (version 5) that combines the thermal infrared (TIR) with near-infrared (NIR) bands that are more sensitive to the boundary layer. This new data product is compared with the TIR-only product as well as measurements from the Infrared Atmospheric Sounding Interferometer (IASI), also a TIR sensor. The study focuses on eight megacities: Moscow, Paris, Mexico, Tehran, Baghdad, Los Angeles, Sao Paulo and Delhi. High-resolution maps of the CO distribution over these locations have been generated using a new pixel averaging technique that clearly demonstrates a CO hit-spot. Combining the satellite data with wind data from meteorological reanalysis a clear dependence of the CO distribution with near-surface wind speed direction is found. A clear reduction of CO emission over all sites between 2000-2005 and 2006-2011 is observed, reaching ~ 13% over Mexico and a megacity as Baghdad emitted the same amount of CO between 2006 and 2011 than Tehran or Mexico between 2000 and 2005.
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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 | 0.003 |
| 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".