ANALYSIS OF SPATIOTEMPORAL CHARACTERISTICS AND DRIVING FACTORS OF ATMOSPHERIC CO<sub>2</sub> CONCENTRATION BASED ON SATELLITE REMOTE SENSING: A CASE STUDY OF HEBEI PROVINCE

ZHAO Bingjie; WANG Chunbo; GAO Pengyuan; ZUO Weikun; ZHI Ziwei; XUE Bingxin

doi:10.13205/j.hjgc.202310005

Volume 41 Issue 10

Oct. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(10): 30-36

ZHAO Bingjie, WANG Chunbo, GAO Pengyuan, ZUO Weikun, ZHI Ziwei, XUE Bingxin. ANALYSIS OF SPATIOTEMPORAL CHARACTERISTICS AND DRIVING FACTORS OF ATMOSPHERIC CO2 CONCENTRATION BASED ON SATELLITE REMOTE SENSING: A CASE STUDY OF HEBEI PROVINCE[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 30-36. doi: 10.13205/j.hjgc.202310005

Citation:

ZHAO Bingjie, WANG Chunbo, GAO Pengyuan, ZUO Weikun, ZHI Ziwei, XUE Bingxin. ANALYSIS OF SPATIOTEMPORAL CHARACTERISTICS AND DRIVING FACTORS OF ATMOSPHERIC CO₂ CONCENTRATION BASED ON SATELLITE REMOTE SENSING: A CASE STUDY OF HEBEI PROVINCE[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 30-36. doi: 10.13205/j.hjgc.202310005

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

doi: 10.13205/j.hjgc.202310005

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

Received Date: 2023-07-14
Available Online: 2023-12-26

Abstract

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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References(28)

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