Spatiotemporal Variation and Influencing Factors of Atmospheric CO_(2) Concentration in China

作     者：ZHU Weixin ZHANG Hong ZHANG Xiaoyu GUO Haohao LIU Yong 

作者机构：Institute of Loess PlateauShanxi UniversityTaiyuan 030006China Research Center for Scientific Development in Fenhe River ValleyTaiyuan Normal UniversityJinzhong 030619China College of Environmental&Resource SciencesShanxi UniversityTaiyuan 030006China 

出 版 物：《Chinese Geographical Science》 (中国地理科学(英文版))

年 卷 期：2025年第35卷第1期

页      面：149-160页

核心收录：

学科分类：07[理学] 0707[理学-海洋科学] 

基　　金：Under the auspices of National Natural Science Foundation of China(No.41871193,U1910207) Program for the Philosophy and Social Science of Shanxi Province(No.2023YJ107) 

主　　题：Greenhouse Gases Observing Satellite(GOSAT) CO_(2) concentration influencing factors pixel-based correlation Covari-ance Based Structural Equation Modeling(CB-SEM) China 

摘      要：Rapid increases in Carbon dioxide(CO_(2))levels could trigger unpredictable climate *** assessment of spatiotempor-al variation and influencing factors of CO_(2) concentration are helpful in understanding the source/sink balance and supporting the formu-lation of climate *** this study,Greenhouse Gases Observing Satellite(GOSAT)data were used to explore the variability of CO_(2) concentrations in China from 2009 to *** parameters,vegetation cover,and anthropogenic activities were combined to explain the increase in CO_(2) concentration,using pixel-based correlations and Covariance Based Structural Equation Modeling(CB-SEM)*** results showed that the influence of vertical CO_(2) transport diminished with altitude,with a distinct inter-annual in-crease in CO_(2) concentrations at 17 vertical ***,the highest values were observed in East China,whereas the lowest were observed in Northwest *** were significant seasonal variations in CO_(2) concentration,with maximum and minimum values in spring(April)and summer(August),*** to the pixel-based correlation analysis,the near-surface CO_(2) concentration was positively correlated with population(r=0.99,P0.05),soil water(r=0.29,P0.05),nightlight(r=0.28,P0.05);and negatively correlated with wind speed(r=−0.58,P0.05).CB-SEM analysis revealed that LAI was the most important con-trolling factor explaining CO_(2) concentration variation(total effect of 0.66),followed by emissions(0.58),temperature(0.45),precipita-tion(0.30),wind speed(−0.28),and soil water(−0.07).The model explained 93% of the increase in CO_(2) *** results provide crucial information on the patterns of CO_(2) concentrations and their driving mechanisms,which are particularly significant in the context of climate change.