近日，山西大学张红团队的最新研究揭示了中国大气CO₂浓度的时空变化及其影响因素。这一研究成果发表在2025年1月14日出版的国际学术期刊《中国地理科学》上。

本研究以温室气体观测卫星（GOSAT）数据为研究对象，探讨2009-2020年中国CO₂浓度的变化特征。基于像素的相关性和基于协方差的结构方程模型（CB-SEM）分析，研究人员将气象参数、植被覆盖和人为活动结合起来，以解释CO₂浓度的增加。结果表明，CO₂垂直输送的影响随海拔高度的增加而减弱，在17个垂直水平上CO₂浓度年际增加明显。从空间上看，东部最高，西北最低。CO₂浓度在春季（4月）和夏季（8月）分别达到最大值和最小值。

基于像素的相关分析显示，近地表CO₂浓度与种群呈正相关(r=0.99, P<0.001)，叶面积指数(LAI, r= 0.95, P<0.001)，排放量(r=0.91, P <0.001)，温度(r =0.60, P<0.05)，降水量(r=0.34, P >0.05)，土壤水分(r=0.29, P>0.05)，夜光(r=0.28, P <0.05)；与风速呈负相关(r=0.58, P<0.05)。CB-SEM分析显示，LAI是影响CO₂浓度变化的最重要控制因子（总效应为0.66），其次是排放（0.58）、温度（0.45）、降水（0.30）、风速（-0.28）和土壤水分（-0.07）。研究结果强调，该模型解释了CO₂增加的93％浓度，同时提供了关于CO₂浓度模式及其驱动机制的重要信息，这在气候变化的背景下尤为重要。

据悉，二氧化碳（CO₂）水平的快速增长可能引起不可预测的气候变化。CO₂浓度的时空变化特征及其影响因素的评估有助于理解碳汇平衡，为气候政策的制定提供依据。

附：英文原文

Title: Spatiotemporal Variation and Influencing Factors of Atmospheric CO₂ Concentration in China

Author: Zhu, Weixin, Zhang, Hong, Zhang, Xiaoyu, Guo, Haohao, Liu, Yong

Issue&Volume: 2025-01-14

Abstract: 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.

DOI: 10.1007/s11769-024-1484-z

Source: https://link.springer.com/article/10.1007/s11769-024-1484-z