基于卫星遥感的大气CO<sub>2</sub>浓度时空特征及驱动因素分析——以河北省为例

赵兵杰; 王春博; 高鹏远; 左伟昆; 智子薇; 薛丙欣

doi:10.13205/j.hjgc.202310005

基于卫星遥感的大气CO₂浓度时空特征及驱动因素分析——以河北省为例

doi: 10.13205/j.hjgc.202310005

河北省煤田地质局物测地质队(河北省煤炭地下气化研究中心), 河北邢台 054000

基金项目:

河北省一般公共预算财政资金项目（13000022P00B04410020E）

详细信息

作者简介:
赵兵杰(1993-),女,硕士,工程师,主要从事地理信息与遥感应用工作。bingjie_zhao@163.com

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出版历程
- 收稿日期: 2023-07-14
- 网络出版日期: 2023-12-26

ANALYSIS OF SPATIOTEMPORAL CHARACTERISTICS AND DRIVING FACTORS OF ATMOSPHERIC CO₂ CONCENTRATION BASED ON SATELLITE REMOTE SENSING: A CASE STUDY OF HEBEI PROVINCE

Geophysical Survey Team of Hebei Province Coal Geological Bureau (Hebei Coal Underground Gasification Research Center), Xingtai 054000, China

摘要

摘要: 二氧化碳（CO₂）是引起温室效应的主要气体，卫星遥感为监测CO₂提供了新的技术手段。基于二氧化碳柱平均干空气混合比（XCO₂）浓度数据，结合自回归模型、空间自相关、冷热点分析等数理统计方法，分析了河北省XCO₂浓度时空演变特征，并采用皮尔逊相关系数、地理加权回归分析了自然和人类活动对XCO₂浓度的驱动作用。结果表明：1）河北省XCO₂浓度呈周期变化，4月达到峰值，8月达到谷值。在年内具有冬春季高、夏秋季低的明显季节特征。2）河北省XCO₂浓度时间序列具有自相关性，最佳滞后时长为2个月。3）河北省XCO₂浓度在空间上显著正相关。整体呈西北浓度低、东南浓度高的空间分布格局，在冀南、冀中、冀北分别形成了聚集性的高值区域。4）地理、植被、气象对XCO₂浓度具有显著的负影响作用，数字高程模型（DEM）、归一化植被指数（NDVI）、降水三变量组合和降水、DEM双变量组合可以较好地解释XCO₂浓度。通过增加植被覆盖、降水可抑制大气CO₂浓度。研究结果可为河北省制定碳减排措施提供科学参考。
- XCO₂ /
- 自回归模型 /
- 冷热点分析 /
- 地理加权回归 /
- 驱动因素
Abstract: Carbon dioxide (CO₂) is the main greenhouse gas that causes the greenhouse effect, and satellite remote sensing provides a new technique for CO₂ monitoring. Based on the carbon dioxide column average dry air mixing ratio (XCO₂) data and mathematical statistical methods, such as autoregressive model, spatial autocorrelation, and hot and cold spot analysis, we analyzed the spatiotemporal evolution characteristics of XCO₂ concentration in Hebei Province. Pearson correlation coefficient and geographical weighted regression were used to analyze the driving effects of natural and human activities on XCO₂ concentration. The results were as follows:1) the average monthly XCO₂ concentration in Hebei province varies periodically, reaching a peak in April and a trough in August. In addition, it is higher in winter and spring, lower in summer and autumn. 2) the time series of XCO₂ concentration in Hebei province has autocorrelation, and the best lag time is 2 months. 3) the concentration of XCO₂ in Hebei province is significantly positively correlated in space. The overall distribution pattern of XCO₂ concentration was lower in the northwest and higher in the southeast, and clustered high-value regions were formed in southern, central, and northern Hebei. 4) geography, vegetation and meteorology have significant negative effects on XCO₂ concentration. The tri-variate combination of digital elevation model (DEM), normalized difference vegetation index (NDVI) and precipitation and the bivariate combination of precipitation and DEM can better explain XCO₂ concentration. The study showed that CO₂ concentration can be inhibited by increasing vegetation cover and precipitation, and it can provide a scientific reference for carbon emission reduction measures in Hebei Province.
- XCO₂ /
- AR model /
- hot and cold spot analysis (Getis-Ord Gi^*) /
- geographical weighted regression /
- driving factor

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参考文献(28)

[1]	张强, 韩永翔, 宋连春.全球气候变化及其影响因素研究进展综述[J].地球科学进展, 2005(9):990-998.
[2]	吴兑.温室气体与温室效应[M].北京:气象出版社, 2003.
[3]	SCHUUR E A, MCGUIRE A D, SCHADEL C, et al.Climate change and the permafrost carbon feedback[J].Nature, 2015, 520(7546):171-179.
[4]	杨晓钰, 王中挺, 潘光, 等.卫星遥感温室气体的大气观测技术进展[J].大气与环境光学学报, 2022, 17(6):581-597.
[5]	WANG Z T, MA P F, ZHANG L J, et al.Systematics of atmospheric environment monitoring in China via satellite remote sensing[J].Air Quality, Atmosphere & Health, 2021, 14(2):157-169.
[6]	曹良中, 叶辉, 张力, 等.中亚近地面CO₂浓度时空分布及驱动因子分析[J].遥感信息, 2021, 36(6):51-59.
[7]	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.
[8]	何月, 绳梦雅, 雷莉萍, 等.长三角地区大气NO₂和CO₂浓度的时空变化及驱动因子分析[J].中国环境科学, 2022, 42(8):3544-3553.
[9]	莫祝坤, 李明泽, 王斌.黑龙江省森林大气CO₂施肥效应的时空变化趋势[J].东北林业大学学报, 2022, 50(9):71-77. DOI: 10.13759/j.cnki.dlxb.2022.09.007.
[10]	KUMAR K R, REVADEKAR J V, TIWARI Y K.AIRS retrieved CO₂ and its association with climatic parameters over India during 2004-2011[J].Science of the Total Environment, 2014, 476-477(1):79-89.
[11]	NOEL S, REUTER M, BUCHWITZ M, et al.Retrieval of greenhouse gases from GOSAT and GOSAT-2 using the FOCAL algorithm[J].Atmospheric Measurement Techniques, 2022(11):15.
[12]	PEIRO H, CROWELL S, SCHUH A, et al.Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2(OCO-2) version 9 and in situ data and comparison to OCO-2 version 7[J].Atmospheric Chemistry and Physics, 2022, 22(2):1097-1130.
[13]	肖钟湧, 陈颖锋, 林晓凤, 等.基于多源卫星遥感数据的中国2003-2018年CO₂时空变化研究[J].遥感学报, 2022, 26(12):2486-2496.
[14]	武红, 谷树忠, 周洪, 等.河北省能源消费、碳排放与经济增长的关系[J].资源科学, 2011, 33(10):9.
[15]	JIN C L, XUE Y, JIANG X X, et al.A long-term global XCO₂ dataset:ensemble of satellite products[J/OL].Atmospheric Research, 2022, 279:106385.https://doi.org/10.1016/j.atmosres.2022.106385.
[16]	TAKAKU, JUNICHI, FUTAMURA, et al.High resolution DEM generation from ALOS PRISM data-simulation and evaluation[J].2004(1):405-407.
[17]	MCNICHOLL B, LEE Y H, CAMPBELL A G, et al.Evaluating the reliability of air temperature from ERA5 reanalysis data[J].IEEE Geoscience and Remote Sensing Letters, 2022, 19.
[18]	LI Y Q, LI X J, LIAN J, et al.Temporal and spatial variation of vegetation coverage in Beijing-Tianjin-Hebei Metropolis Circle with MODIS vegetation indices data product[J].Key Engineering Materials, 2012, 500:123-129.
[19]	张树京.时间序列分析简明教程[M].北京:清华大学出版社, 2003.
[20]	YIN C, HE Q, LIU Y, et al.Inequality of public health and its role in spatial accessibility to medical facilities in China[J].Applied Geography, 2018, 92:50-62.
[21]	YE Weifeng, MA Zhongyu, HA Xiuzhen.Spatial-temporal patterns of PM_2.5 concentrations for 338 Chinese Cities[J].Science of the Total Environment, 2018, 631/632:524-533.
[22]	GETIS A, ORD J K.The analysis of spatial association by use of distance statistics[J].Geographical Analysis, 1992.
[23]	ORD J K, GETIS A.Local spatial autocorrelation statistics:distributional issues and an application[J].Geographical Analysis, 2010, 27(4):286-306.
[24]	张世勍.基于地面监测和卫星遥感的中国大气甲烷浓度时空分布及其对人为排放的响应[D].上海:华东师范大学, 2022.
[25]	陶洋, 祝小钧, 杨柳.基于皮尔逊相关系数和信息熵的多传感器数据融合[J].小型微型计算机系统, 2023, 44(5):1075-1080.
[26]	BRUNSDON C, FOTHERINGHAM A S, CHARLTON M.Geographically weighted regression:a method for exploring spatial nonstationarity[J].Geographical Analysis, 1996, 28(4):281-298.
[27]	任杨千千, 连懿, 李海笑, 等.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.
[28]	加亦瑱, 陶明辉, 丁思佳, 等.基于卫星遥感的中国地区XCO₂和XCH₄时空分布研究[J].大气与环境光学学报, 2022, 17(6):679-692.