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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