分析能源资源开发区域大气碳浓度的时空变化和影响因素,对于探索“碳达峰”“碳中和”背景下能源资源开发高质量发展路径至关重要。新疆维吾尔自治区是我国重要的能源和战略资源基地,本文面向新疆维吾尔自治区的能源资源开发现状,采集并预处理了2015—2021年轨道碳观测卫星-2(Orbiting Carbon Observatory-2,OCO-2)二氧化碳L3数据产品,分析研究区大气碳浓度的时间变化趋势和空间分布格局,构建深度森林回归模型,并分析各影响因素对碳浓度时空变化的驱动作用。结果表明:(1)新疆维吾尔自治区、准噶尔盆地、吐哈盆地和塔里木盆地XCO₂浓度在2015—2021年均呈周期性上升趋势,增长率呈“先减后增”,且季节变化趋势呈现明显的“春季高冬季低”;(2)在春、秋和冬季,新疆XCO₂浓度空间格局呈现“北高南低”的趋势,在盆地区域及能源资源开发区域出现XCO₂高浓度积聚现象,夏季则呈现“北低南高”趋势;(3)地形起伏、风场流速、NDVI、地表温度、降水量、10 mV风、10 mU风和能源开发强度对区域XCO₂浓度时空分布有显著影响,各因素呈现明显的空间异质性和显著差异。研究结果有助于理解能源资源开采区域的大气碳浓度时空演变机制,在国家碳减排目标的实现、指导碳中和策略、追踪碳减排效果等方面具有深远意义。

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.