Application of satellite-based sulfur dioxide observations to support the cleantech sector: Detecting emission reduction from copper smelters
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we present the result of the application of space-based sulfur dioxide (SO2) observations to evaluate the efficacy of cleantech solutions in reducing air polluting emissions from metal smelting. We analyze the Ozone Monitoring Instrument (OMI) satellite-based SO2 observations over Tsumeb (Namibia) and Bor (Serbia) copper smelters, where two sulfur-capture plants, designed to transform gaseous SO2 emissions into sulfuric acid, were implemented in 2015. We observe a reduction in the annual SO2 emissions by up to 90% after 2015 at both smelters, as a result of the implementation of the sulfuric acid plants. The OMI-based emission estimates are mostly in line with those reported at facility-level and reproduce the same year-to-year variability. This variability is driven by the changes in the copper production, the sulfur-to-copper ratio and by the technology employed to reduce the SO2 emissions. OMI observations are directly used by the company operating the sulfuric acid plants to confirm the efficacy of the employed technology using independent satellite-based observations. The results demonstrate how satellite-based observations are able to detect relative changes in SO2 emissions and can be used to verify and complete existing emission informations. The approach presented here can be applied to other sources on global scale to support cleantech companies as well as decision-makers involved in environmental policies and sustainable development.
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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 | 0.001 |
| 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 it