Unveiling the drivers of atmospheric methane variability in Iran: A 20-year exploration using spatiotemporal modeling and machine learning DOI Creative Commons
Seyed Mohsen Mousavi, Naghmeh Mobarghaee Dinan, Saeed Ansarifard

et al.

Environmental Challenges, Journal Year: 2024, Volume and Issue: 15, P. 100946 - 100946

Published: April 1, 2024

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH4) is crucial for mitigating its impacts implementing emission reduction strategies. This study comprehensively investigates XCH4 driving (environmental, meteorological, anthropogenic activity) across Iran over 20 years, from 2003 to 2022. It combines multi-source satellite observations, advanced spatiotemporal modeling techniques, correlation analysis, machine learning algorithms. The analysis showed notable variation, with high levels in central, southern, eastern lower northwest north. Moreover, distinct seasonal cycles emerged, maximum occurring during summer (August-September) minimum spring (April-May). Correlation variable importance assessment were developed elucidate key drivers governing dynamics. revealed that vegetation cover, precipitation, soil moisture negatively correlated XCH4, while temperature indices a positive correlation, exhibiting highest time dispersion quantity among studied variables. Permutation Importance technique, used Random Forest classifier, learning-based approach considers role all variables together, land surface temperature, wind speed, moisture, cover are dominant controls, their ranked respectively. Surprisingly, emissions played relatively minor shaping distributions at regional scale. These findings highlight significant influence meteorological ecosystem processes on modulation, revealing intricate Earth system feedbacks inform targeted mitigation strategies predictive models curbing greenhouse gas climate change impacts.

Language: Английский

Unveiling the drivers of atmospheric methane variability in Iran: A 20-year exploration using spatiotemporal modeling and machine learning DOI Creative Commons
Seyed Mohsen Mousavi, Naghmeh Mobarghaee Dinan, Saeed Ansarifard

et al.

Environmental Challenges, Journal Year: 2024, Volume and Issue: 15, P. 100946 - 100946

Published: April 1, 2024

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH4) is crucial for mitigating its impacts implementing emission reduction strategies. This study comprehensively investigates XCH4 driving (environmental, meteorological, anthropogenic activity) across Iran over 20 years, from 2003 to 2022. It combines multi-source satellite observations, advanced spatiotemporal modeling techniques, correlation analysis, machine learning algorithms. The analysis showed notable variation, with high levels in central, southern, eastern lower northwest north. Moreover, distinct seasonal cycles emerged, maximum occurring during summer (August-September) minimum spring (April-May). Correlation variable importance assessment were developed elucidate key drivers governing dynamics. revealed that vegetation cover, precipitation, soil moisture negatively correlated XCH4, while temperature indices a positive correlation, exhibiting highest time dispersion quantity among studied variables. Permutation Importance technique, used Random Forest classifier, learning-based approach considers role all variables together, land surface temperature, wind speed, moisture, cover are dominant controls, their ranked respectively. Surprisingly, emissions played relatively minor shaping distributions at regional scale. These findings highlight significant influence meteorological ecosystem processes on modulation, revealing intricate Earth system feedbacks inform targeted mitigation strategies predictive models curbing greenhouse gas climate change impacts.

Language: Английский

Citations

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