Modeling of sulfur dioxide emissions in Ahvaz City, southwest of Iran during 2013
Bibliographic record
Abstract
Sulfur dioxide has two important sources in the atmosphere and this is why most of scientists believe in a geographic split in the globe. Power plants, major emitter of SO2, are located in north hemisphere such as in Russia, China, Canada and the USA. In south hemisphere, phytoplankton produces a massive amount of dimethyl sulfide (DMS) and dimethyl disulfide (DMDS). Then these types of reduced products dissociate in the atmosphere and convert into SO2. It is a colorless gas which is released from burning coal, high sulfur coal and diesel fuel. The sulfur dioxide emissions from transportation systems, steel, oil and other industries are major concerns of air pollution in Ahvaz city, Iran. The main objective of this study was to determine the behavior of data over the time in a specific statistical model framework and compare through intended one to implement the Box-Jenkins method to make time series models in Ahvaz (located in Southwestern Iran), during 2013. Data of sulfur dioxide from four monitoring stations were collected at the first step and processed by Excel software; finally, the model of sulfur dioxide dispersion were evaluated. Time series analysis showed that air pollutants were associated with one step delay of sulfur dioxide and two steps delay of moving average. The finding of this study showed that the average concentration of sulfur dioxide in winter was higher than in summer. According to the results of this study,distribution of sulfur dioxide data has a correlated structure over the time; therefore the time series model is an appropriate model to explain the behavior of sulfur dioxide over the time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".