Dynamic Coupling Between Atmospheric CO2 Concentration and Land Surface Temperature in Major Urban Agglomerations in China: Insights for Sustainable Urban Development DOI Open Access

Qiwen Sun,

Xuesheng Zhao,

Yiying Hua

и другие.

Sustainability, Год журнала: 2024, Номер 16(21), С. 9484 - 9484

Опубликована: Окт. 31, 2024

To provide new insights into the integrated management of carbon and heat for sustainable urban development, this study systematically investigates complex relationship between atmospheric CO2 concentrations land surface temperature (LST). Utilizing OCO-2 OCO-3 satellite observations, combined with meteorological conditions, air pollutants, spatial characteristics, a high-resolution (0.1° × 0.1°) monthly column concentration (XCO2) dataset China spanning 2015 to 2022 was generated using Random Forest algorithm. The focuses on agglomerations, conducting centroid migration coupling analyses XCO2 LST elucidate their spatiotemporal distribution patterns evolution. Results reveal significant seasonal variations in XCO2, which has exhibited gradual increase over years. distributions agglomerations show high degree consistency, centroids either converging or following similar movement trajectories. Additionally, coordination improved annually, indicating closer interrelationship. These findings enhance our understanding climate system dynamics essential scientific evidence decision-making support addressing change. By clarifying connection LST, contributes development more effective strategies reduction island mitigation, thereby advancing cities towards greener, lower-carbon, pathways.

Язык: Английский

Dynamic Coupling Between Atmospheric CO2 Concentration and Land Surface Temperature in Major Urban Agglomerations in China: Insights for Sustainable Urban Development DOI Open Access

Qiwen Sun,

Xuesheng Zhao,

Yiying Hua

и другие.

Sustainability, Год журнала: 2024, Номер 16(21), С. 9484 - 9484

Опубликована: Окт. 31, 2024

To provide new insights into the integrated management of carbon and heat for sustainable urban development, this study systematically investigates complex relationship between atmospheric CO2 concentrations land surface temperature (LST). Utilizing OCO-2 OCO-3 satellite observations, combined with meteorological conditions, air pollutants, spatial characteristics, a high-resolution (0.1° × 0.1°) monthly column concentration (XCO2) dataset China spanning 2015 to 2022 was generated using Random Forest algorithm. The focuses on agglomerations, conducting centroid migration coupling analyses XCO2 LST elucidate their spatiotemporal distribution patterns evolution. Results reveal significant seasonal variations in XCO2, which has exhibited gradual increase over years. distributions agglomerations show high degree consistency, centroids either converging or following similar movement trajectories. Additionally, coordination improved annually, indicating closer interrelationship. These findings enhance our understanding climate system dynamics essential scientific evidence decision-making support addressing change. By clarifying connection LST, contributes development more effective strategies reduction island mitigation, thereby advancing cities towards greener, lower-carbon, pathways.

Язык: Английский

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