SO2 Emission Monitoring with Remote Sensing for Turkey
Bibliographic record
Abstract
Extended Abstract In recent years, greenhouse gas and pollutant emission inventory studies have become more important issues after the ratification of international clean air acts, such as Kyoto Protocol. Emission inventories based on satellite based measurements of atmospheric trace gases are powerful improvement tools as compared to measurements from conventional platforms. They allow the use of data from global monitoring with uniform instrumental features in long time series as well as the identification and quantification of different pollution sources like biomass burning [1], ship emissions [2], volcanic activities [3], lightning [4] and anthropogenic emissions [5]. Typically, there are three sensor types used for remote sensing, they are ground based, airborne, and space borne, respectively. In literature, there are many studies used as separately or in combinations of these three types of sensor data. For the region where Turkey is located, many emission inventory studies are prepared on the basis of bottom-up approach in general or in regional scales [6]. Some of these studies have carried out on sectoral basis [7], however some are prepared by taking total emissions into account. Yet, there are not any emission inventory studies that are prepared with the use of remote sensing on the basis of top-down approach for Turkey. Therefore, it is aimed to prepare an emission inventory on the basis of top-down approach with remote sensing. In this study, Polar Orbiting Meteorological Satellites (MetOp-A), The Global Ozone Monitoring Experiment-2 (GOME-2), Satellite Application Facility for Atmospheric Composition and UV Radiation (O3M-SAF) offline data products are used for the aim of SO2 emission mapping for Turkey. O3M-SAF offline data products are gathered from European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) [8], within the membership of Turkish State Meteorological Service. Hierarchical Data Format (HDF) files of this data products are transformed to usable data formats for Geographic Information System (GIS) applications with MATLAB software. SO2 vertical column data are retrieved from this data collection between January to May of 2014. Statistically quality check performed to prepare this data set for GIS applications which are performed via MapInfo software. This five month data is in the range of 0.0014-4.8642 DU with average of 1.003 DU, and the median of 0.821 DU. In highly populated cities and specific areas at where fossil fuelled power plants or industrial parks are located, pollution intensity can be easily observed from this SO2 emission map of Turkey.
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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".