Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020
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 th...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | Atmospheric Environment: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259016212200017X |
| _version_ | 1811245318172311552 |
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| author | Seyed Mohsen Mousavi Naghmeh Mobarghaee Dinan Saeed Ansarifard Oliver Sonnentag |
| author_facet | Seyed Mohsen Mousavi Naghmeh Mobarghaee Dinan Saeed Ansarifard Oliver Sonnentag |
| author_sort | Seyed Mohsen Mousavi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T14:37:52Z |
| format | Article |
| id | doaj.art-225b1b0d592c497e8550972ce3f20410 |
| institution | Directory Open Access Journal |
| issn | 2590-1621 |
| language | English |
| last_indexed | 2024-04-12T14:37:52Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Atmospheric Environment: X |
| spelling | doaj.art-225b1b0d592c497e8550972ce3f204102022-12-22T03:29:00ZengElsevierAtmospheric Environment: X2590-16212022-04-0114100163Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020Seyed Mohsen Mousavi0Naghmeh Mobarghaee Dinan1Saeed Ansarifard2Oliver Sonnentag3Department of Environmental Planning and Design, Shahid Beheshti University, 1983969411, Tehran, Iran; Corresponding authors.Department of Environmental Planning and Design, Shahid Beheshti University, 1983969411, Tehran, Iran; Corresponding authors.Department of Physics, Shahid Beheshti University, G.C., Evin, 19839, Tehran, Iran; School of Physics, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, IranDépartement de Géographie, Université de Montréal, Montréal, QC, CanadaAdapting 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.http://www.sciencedirect.com/science/article/pii/S259016212200017XCO2Spatio-temporalGeostatisticsIranODIACSatellite |
| spellingShingle | Seyed Mohsen Mousavi Naghmeh Mobarghaee Dinan Saeed Ansarifard Oliver Sonnentag Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020 Atmospheric Environment: X CO2 Spatio-temporal Geostatistics Iran ODIAC Satellite |
| title | Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020 |
| title_full | Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020 |
| title_fullStr | Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020 |
| title_full_unstemmed | Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020 |
| title_short | Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020 |
| title_sort | analyzing spatio temporal patterns in atmospheric carbon dioxide concentration across iran from 2003 to 2020 |
| topic | CO2 Spatio-temporal Geostatistics Iran ODIAC Satellite |
| url | http://www.sciencedirect.com/science/article/pii/S259016212200017X |
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