Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020
Bibliographic record
Abstract
Adapting to climate change as a consequence of increasing greenhouse gas (GHG) emissions is of paramount importance in the near future. Therefore, recognition of spatial and temporal variations of atmospheric carbon dioxide (CO2) concentration both globally and regionally is critical. The goal of this study was to analyze spatio-temporal patterns of atmospheric CO2 concentration (XCO2) for Iran over the period from 2003 to 2020 to shed light on the role of various biotic and abiotic controls. First, by using atmospheric XCO2 data obtained from the SCIAMACHY and GOSAT satellite instruments, a series of spatio-temporal XCO2 distribution maps were developed. Second, to understand of the potential causes underlying the spatio-temporal distributions in XCO2, the correlations between monthly XCO2 and vegetation abundance, air temperature, precipitation, and fossil fuel CO2 emissions were examined. The spatio-temporal patterns in XCO2 indicated an increasing gradient of XCO2 from north to south and from west to east in Iran, with the highest XCO2 in the central, southern and southeastern parts of the country. The findings revealed that XCO2 was negatively correlated with vegetation abundance and precipitation, and positively correlated with air temperature in different months from 2003 to 2020. Among the different explanatory variables, vegetation abundance explained most of the spatial variation in XCO2. Furthermore, in spring (April and May), which has the highest amount of vegetation abundance and precipitation, biotic controls had a substantial impact on the diffusion and absorption of XCO2 in the northern and northwestern parts of Iran. Our results suggest that CO2 is moved from the center of Iran to the outer parts of the country in summer (July–September) and vice-versa in winter (January–March). Our findings provide policy- and decision makers with crucial information regarding the spatio-temporal dynamics in XCO2 to reduce and, ultimately, halt its increase.
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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) | 0.000 | 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".