Multi-source SO <sub>2</sub> emission retrievals and consistency of satellite and surface measurements with reported emissions
Bibliographic record
Abstract
Abstract. Reported sulfur dioxide (SO2) emissions from US and Canadian sources have declined dramatically since the 1990s as a result of emission control measures. Observations from the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite and ground-based in situ measurements are examined to verify whether the observed changes from SO2 abundance measurements are quantitatively consistent with the reported changes in emissions. To make this connection, a new method to link SO2 emissions and satellite SO2 measurements was developed. The method is based on fitting satellite SO2 vertical column densities (VCDs) to a set of functions of OMI pixel coordinates and wind speeds, where each function represents a statistical model of a plume from a single point source. The concept is first demonstrated using sources in North America and then applied to Europe. The correlation coefficient between OMI-measured VCDs (with a local bias removed) and SO2 VCDs derived here using reported emissions for 1° by 1° gridded data is 0.91 and the best-fit line has a slope near unity, confirming a very good agreement between observed SO2 VCDs and reported emissions. Having demonstrated their consistency, seasonal and annual mean SO2 VCD distributions are calculated, based on reported point-source emissions for the period 1980–2015, as would have been seen by OMI. This consistency is further substantiated as the emission-derived VCDs also show a high correlation with annual mean SO2 surface concentrations at 50 regional monitoring stations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".