Spatiotemporal Variation and Influencing Factors of Atmospheric CO₂ Concentration in China.

Zhu, Weixin; Zhang, Hong; Zhang, Xiaoyu; Guo, Haohao; Liu, Yong

Rapid increases in Carbon dioxide (CO2) levels could trigger unpredictable climate change. The assessment of spatiotemporal variation and influencing factors of CO2 concentration are helpful in understanding the source/sink balance and supporting the formulation of climate policy. In this study, Greenhouse Gases Observing Satellite (GOSAT) data were used to explore the variability of CO2 concentrations in China from 2009 to 2020. Meteorological parameters, vegetation cover, and anthropogenic activities were combined to explain the increase in CO2 concentration, using pixel-based correlations and Covariance Based Structural Equation Modeling (CB-SEM) analysis. The results showed that the influence of vertical CO2 transport diminished with altitude, with a distinct inter-annual increase in CO2 concentrations at 17 vertical levels. Spatially, the highest values were observed in East China, whereas the lowest were observed in Northwest China. There were significant seasonal variations in CO2 concentration, with maximum and minimum values in spring (April) and summer (August), respectively. According to the pixel-based correlation analysis, the near-surface CO2 concentration was positively correlated with population (r = 0.99, P < 0.001), Leaf Area Index (LAI, r = 0.95, P < 0.001), emissions (r = 0.91, P < 0.001), temperature (r = 0.60, P < 0.05), precipitation (r = 0.34, P > 0.05), soil water (r = 0.29, P > 0.05), nightlight (r = 0.28, P < 0.05); and negatively correlated with wind speed (r = −0.58, P < 0.05). CB-SEM analysis revealed that LAI was the most important controlling factor explaining CO2 concentration variation (total effect of 0.66), followed by emissions (0.58), temperature (0.45), precipitation (0.30), wind speed (−0.28), and soil water (−0.07). The model explained 93% of the increase in CO2 concentration. Our results provide crucial information on the patterns of CO2 concentrations and their driving mechanisms, which are particularly significant in the context of climate change.

LEAF area index; STRUCTURAL equation modeling; GREENHOUSE gases; WIND speed; CARBON dioxide

Chinese Geographical Science, 2025, Vol 35, Issue 1, p149

1002-0063

Academic Journal

10.1007/s11769-024-1484-z