Satellite measurements oversee China’s sulfur dioxide emission reductions from coal-fired power plants
Bibliographic record
Abstract
To evaluate the real reductions in sulfur dioxide (SO 2 ) emissions from coal-fired power plants in China, Ozone Monitoring Instrument (OMI) remote sensing SO 2 columns were used to inversely model the SO 2 emission burdens surrounding 26 isolated power plants before and after the effective operation of their flue gas desulfurization (FGD) facilities. An improved two-dimensional Gaussian fitting method was developed to estimate SO 2 burdens under complex background conditions, by using the accurate local background columns and the customized fitting domains for each target source. The OMI-derived SO 2 burdens before effective FGD operation were correlated well with the bottom-up emission estimates ( R = 0.92), showing the reliability of the OMI-derived SO 2 burdens as a linear indicator of the associated source strength. OMI observations indicated that the average lag time period between installation and effective operation of FGD facilities at these 26 power plants was around 2 years, and no FGD facilities have actually operated before the year 2008. The OMI estimated average SO 2 removal equivalence (56.0%) was substantially lower than the official report (74.6%) for these 26 power plants. Therefore, it has been concluded that the real reductions of SO 2 emissions in China associated with the FGD facilities at coal-fired power plants were considerably diminished in the context of the current weak supervision measures.
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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 | 0.000 | 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) | 0.000 | 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".