中国大陆CO<sub>2</sub>浓度时空分布特征及驱动因素

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摘要以中国大陆地区为研究区域,基于全球变化大数据云共享平台的CO₂浓度时空连续数据集、欧洲中期天气预报中心(ECMWF)、MODIS以及中国年鉴筛选的驱动因子数据集(人口总数、GDP、能源消费数据),借助相关性分析方法及地理加权回归分析方法对2015~2019年CO₂浓度分布时空特征与驱动因子之间的关系进行分析.结果表明:CO₂浓度按年周期持续上升,其增长率呈明显的空间聚集性,同时重新标度极差法分析结果表明CO₂浓度仍呈增长趋势;驱动因子敏感性分析显示人类活动是导致CO₂浓度增长的主要因素;自然和人类活动对区域CO₂浓度的影响呈现出明显的空间异质性,区域位置不同,自然和人类活动等驱动因子对区域的影响作用有差异.

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	任杨千千
	连懿
	李海笑
	高晖春
	董建康
	贺梦璇

关键词 ： CO₂浓度, 驱动因子, 地理加权回归分析

Abstract：This paper analyzes the spatiotemporal characteristics of CO₂ concentration distribution in China mainland during 2015~2019 and the driving factors by integrating CO₂ data from the European Centre for Medium-Range Weather Forecast (ECMWF), MODIS data, and population, GDP, energy consumption data from China Yearbook. The results of geographically weighted regression analysis and correlation analysis show that the atmospheric CO₂ in China rose periodically. Human activity is the principal factor affecting CO₂ concentration. The effects of natural and human activities on atmospheric present high heterogeneity in different regions.

Key words： CO₂ concentration influencing factors geographically weighted regression analysis

收稿日期: 2022-08-29

PACS:

X24

基金资助:天津市科学技术普及项目(22KPHDRC00140);国家自然科学基金资助项目(41971306);国家基础调查项目(2019FY202501-03);天津师范大学基金资助项目(52XB1502,2022KYCX073Y)

通讯作者: 连懿,讲师,fishlice@163.com E-mail: fishlice@163.com

作者简介: 任杨千千(1998-),女,湖南邵阳人,天津师范大学硕士研究生,主要从事大气CO₂监测变化研究.renyangqianqian@163.com.

引用本文:

任杨千千, 连懿, 李海笑, 高晖春, 董建康, 贺梦璇. 中国大陆CO₂浓度时空分布特征及驱动因素[J]. 中国环境科学, 2023, 43(4): 1919-1929. RENYANG Qian-qian, LIAN Yi, LI Hai-xiao, GAO Hui-chun, DONG Jian-kang, HE Meng-xuan. Spatial and temporal distribution of CO₂ concentration in mainland China and its influencing factors. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(4): 1919-1929.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2023/V43/I4/1919

[1]	邓安健,郭海波,胡洁,等.GOSAT卫星数据监测中国大陆上空CO2浓度时空变化特征[J]. 遥感学报, 2020,24(3):319-325. Deng A J, Guo H B, Hu J, et al. Temporal and distribution characteristic of CO2 concentration over China based on GOSAT satellite data[J]. Journal of Remote Sensing(Chinese), 2020,24(3):319-325.
[2]	Zhu X H, Lu K F, Peng Z R, et al. Spatiotemporal variations of carbon dioxide (CO2) at Urban neighborhood scale:Characterization of distribution patterns and contributions of emission sources[J]. Sustainable Cities and Society, 2022,78:103646.
[3]	Li C, Li H, Qin X. Spatial heterogeneity of carbon emissions and its influencing factors in China:Evidence from 286 prefecture-level cities[J]. International Journal of Environmental Research and Public Health, 2022,19(3):1226.
[4]	Wang Q, Chiu Y H, Chiu C R. Driving factors behind carbon dioxide emissions in China:A modified production-theoretical decomposition analysis[J]. Energy Economics, 2015,51:252-260.
[5]	Jung M, Reichstein M, Schwalm C R, et al. Compensatory water effects link yearly global land CO2 sink changes to temperature[J]. Nature, 2017,541(7638):516-520.
[6]	Humphrey V, Zscheischler J, Ciais P, et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage[J]. Nature, 2018,560(7720):628-631.
[7]	Zhang M N, Trevor F, Keenan, Luo X Z, et al. Elevated CO2 moderates the impact of climate change on future bamboo distribution in Madagascar[J]. Science of the Total Environment, 2022,810, 152235.
[8]	Nicolas Delbart, Spring Phenology of the Boreal Ecosystems, Arctic Hydrology, Permafrost and Ecosystems, 2021:559-581.
[9]	庄亚辉.全球生物地球化学循环研究的进展[J]. 地学前缘, 1997,(Z1):167-172. Zhang Y H. Advances in global biogeochemical cycling studies[J]. Earth Science Frontiers, 1997,(Z1):167-172.
[10]	Piao S, Liu Q, Chen A, et al. Plant phenology and global climate change:Current progresses and challenges[J]. Global Change Biology, 2019,25(6):1922-1940.
[11]	周慧敏.陆地生态系统净CO2交换的空间格局及其模型关键参数优化[D]. 上海:华东师范大学, 2021. Zhou H M. Spatial pattern of net CO2 exchange in terrestrial ecosystems and optimization of its model parameters[D]. Shanghai:East China Normal University, 2021.
[12]	王立猛,何康林.基于STIRPAT模型的环境压力空间差异分析——以能源消费为例[J]. 环境科学学报, 2008,(5):1032-1037. Wang L M, He K L. Analysis of spatial variations in environmental impact based on the STIRPAT model——A case study of energy consumption[J]. Acta Scientiae Circumstantiae, 2008,(5):1032-1037.
[13]	卢娜,曲福田,冯淑怡,等.基于STIRPAT模型的能源消费碳足迹变化及影响因素——以江苏省苏锡常地区为例[J]. 自然资源学报, 2011,26(5):814-824. Lu N, Qu F T, Feng S Y, et al. Trends and determining factors of energy consumption carbon footprint——An analysis for Suzhou-Wuxi-Changzhou region based on STIRPATM model[J]. Journal of Natural Resources, 2011,26(5):814-824.
[14]	Hurst H E. Long-term storage capacity of reservoirs[J]. Transactions of the American Society of Civil Engineers, 1951,116:770-808.
[15]	赵晶,王乃昂.近50年来兰州城市气候变化的R/S分析[J]. 干旱区地理, 2002,(1):90-95. Zhao J, Wang N A. analysis of urbanization effect on climate in Lanzhou[J]. Arid Land Geography, 2002,(1):90-95.
[16]	杨强,王婷婷,陈昊,等.基于MODIS EVI数据的锡林郭勒盟植被覆盖度变化特征[J]. 农业工程学报, 2015,31(22):191-198,315. Yang Q, Wang T T, Chen H, et al. Characteristics of vegetation cover change in Xilin Gol League based on MODIS EVI data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(22):191-198,315.
[17]	龙家勇,吴承祯,洪伟,等.中国省域二氧化碳排放量的空间自相关分析[J]. 生态经济, 2011,(10):49-53. Long J Y, Wu C Z, Hong W, et al. Spatial autocorrelation analysis of Chinese provincial carbon dioxide emissions[J]. Ecological Economy, 2011,(10):49-53.
[18]	卢宾宾,葛咏,秦昆,等.地理加权回归分析技术综述[J]. 武汉大学学报(信息科学版), 2020,45(9):1356-1366. Lu B B, Ge Y, Qin K, et al. A review on geographically weighted regression[J]. Geomatics and Information Science of Wuhan University, 2020,45(9):1356-1366.
[19]	Huang N, Wang L, Song X P, et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover[J]. Science Advances, 2020,6(41):eabb8508.
[20]	Pierre F, Matthew W J, Michael O, et al. Global Carbon Budget 2021, Earth Syst. Sci. Data, 14,1917-2005, https://doi.org/10.5194/essd-14-1917-2022,2022.
[21]	张绍会,谢冰,张华,等.基于AIRS卫星的全球和东亚地区CO2时空特征分析[J]. 大气与环境光学学报, 2019,14(6):442-454. Zhang S H, Xie B, Zhang H, et al. Spatial-Temporal distribution characteristics of CO2 over globe and east asia by AIRS satellite[J]. Journal of atmospheric and environmental optics, 2019,14(6):442-454.
[22]	张扬.干旱绿洲区葡萄农田碳收支过程研究[D]. 兰州:兰州大学, 2021. Zhang Y. Study on carbon budget process of grape farmland in Arid Oasis region[D]. Lanzhou:Lanzhou University, 2021.
[23]	Bacastow R B. Atmospheric carbon dioxide concentration and the observed airborne fraction[J]. Carbon Cycle Modelling, 1981:103-112.
[24]	Lintner B R. Characterizing global CO2 interannual variability with empirical orthogonal function/principal component (EOF/PC) analysis[J]. Geophysical Research Letters, 2002,29(19):27-1-27-4.
[25]	Jones C D, Cox P M. Constraints on the temperature sensitivity of global soil respiration from the observed interannual variability in atmospheric CO2[J]. Atmospheric Science Letters, 2001,2(1-4):166-172.
[26]	Gérard J C, Nemry B, Francois L M, et al. The interannual change of atmospheric CO2:Contribution of subtropical ecosystems?[J]. Geophysical Research Letters, 1999,26(2):243-246.