Analysis of spatio-temporal variations and influencing factors of atmospheric CO2 concentrations in energy resources development areas

doi:10.13745/j.esf.sf.2024.5.14

Abstract

Abstract:

Analyzing the spatio-temporal variations of atmospheric carbon concentrations in energy resources development areas and identifying influencing factors are crucial for exploring a high-quality development pathway in the context of “Dual Carbon”. Xinjiang Uygur Autonomous Region serves as a vital base for energy and strategic resources in China. This study oriented to the current status of energy resource development in Xinjiang Uygur Autonomous Region, we collected and preprocessed Orbiting Carbon Observatory-2 (OCO-2) carbon dioxide Level 3 data products from 2015 to 2021. We analyzed the temporal trends and spatial distribution patterns of atmospheric carbon concentration in the study area and structured a deep forest regression model to analyze the driving factors of the spatio-temporal variations in carbon concentration. The results indicate that: (1) Xinjiang Uygur Autonomous Region, Junggar Basin, Turpan-Hami Basin and Tarim Basin’s XCO₂ concentration exhibited a cyclic upward trend from 2015 to 2021, with a “decrease-first then increase” growth rate, showing a distinct “high in spring, low in winter” seasonal variation trend; (2) in spring, autumn, and winter, the spatial pattern of XCO₂ concentration in Xinjiang shows a “high in the north, low in the south” trend, with high XCO₂ concentrations accumulating in basin and energy resource development areas. Conversely, a trend of “low in the north, high in the south” observed in summer; (3) topographic relief, wind velocity, NDVI, land surface temperature, precipitation, 10-meter V wind, 10-meter U wind, and energy development intensity significantly influence the spatio-temporal distribution of regional XCO₂ concentration, showing notable spatial heterogeneity and significant differences. These findings contribute to understanding the mechanism of carbon concentration evolution in energy resource extraction areas and hold profound implications for achieving national carbon reduction targets, guiding carbon neutrality strategies, and monitoring carbon emission reduction effects.

Key words: energy resources development, spatio-temporal variations of XCO₂, influencing factor, deep forest regression model

CLC Number:

X821

YANG Hui, FAN Huaiwei, XU Xiao, ZHANG Yunhui, WANG Wenfeng, YAN Zhaojin, WANG Cheng, WANG Junhui, LIU Lei, WANG Ran, CI Hui. Analysis of spatio-temporal variations and influencing factors of atmospheric CO₂ concentrations in energy resources development areas[J]. Earth Science Frontiers, 2024, 31(4): 147-164.

Figures/Tables 16

Fig.1 Overview of the study area

Fig.2 Technical framework for analyzing the spatio-temporal variation and influencing factors of atmospheric carbon concentration in energy resources development areas

Fig.3 Spatial distribution of each influencing factor in 2021

Fig.4 Frame diagram of deep forest regression DF21

Fig.5 Variation trend of monthly atmospheric XCO2 concentration in Xinjiang Uygur Autonomous Region, Tuha Basin, Junggar Basin and Tarim Basin from 2015 to 2021

Fig.6 Annual mean monthly XCO2 concentration in Xinjiang Uygur Autonomous Region, Tuha Basin, Junggar Basin and Tarim Basin from 2015 to 2021

Fig.7 Spatial distribution of average XCO2 concentration in Xinjiang Uygur Autonomous Region from 2015 to 2021

Fig.8 Spatial distribution of seasonal mean XCO2 concentration in Xinjiang Uygur Autonomous Region from 2015 to 2021

Table 1 Variance inflation factor of each influencing factor

指标名称	VIF	相关系数
风场流速	1.469	0.175^**
地形起伏度	1.968	-0.578^**
NDVI	1.562	-0.147^**
能源开发强度	1.218	0.181^**
降水量	2.406	-0.740^**
地表温度	2.403	0.890^**
10 mV风	1.457	-0.360^**
10 mU风	1.506	-0.456^**
平均	1.896	—

Fig.9 Prediction and spatial distribution of carbon concentration observed by each model in Xinjiang Uygur Autonomous Region in 2021

Fig.10 Accuracy verification of each model

Fig.11 Score the importance of each influencing factor

Fig.12 Partial dependence plots of each influence factor

Fig.13 Analysis of basin scale differences

Table 2 Statistical indicators of key influencing factors in each region

区域	地表温度/K			能源开发强度
区域	Avg	Var	Avg	Var	Avg	Var	Avg	Var
准噶尔盆地	262.43	184.05	0.410	0.485	0.165	0.113	0.047	0.006 4
塔里木盆地	286.66	0.35	0.065	0.057	0.117	0.007	0.084	0.005 6
吐哈盆地	255.87	91.39	0.341	0.112	0.023	0.001	0.006	0.000 6

Fig.14 XCO2 concentration in energy resources development areas of Xinjiang Uygur Autonomous Region

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Analysis of spatio-temporal variations and influencing factors of atmospheric CO₂ concentrations in energy resources development areas

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