Spatiotemporal evaluation of atmospheric CO(2) fluctuations in Shaanxi Province (2013-2022) utilizing multi-source satellite remote sensing data

利用多源卫星遥感数据对陕西省大气CO₂波动进行时空评价(2013-2022年)

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Abstract

Despite the rapid and well-documented surge in global atmospheric CO(2) levels, predominantly driven by fossil fuel combustion and industrialization, the characterization of CO(2) variations at regional scales remains notably sparse. This study integrates satellite remote sensing (RS) and ground-based measurements to examine the spatiotemporal distributions and drivers of CO(2) in China's Shaanxi Province from 2013 to 2022. Although Shaanxi has experienced rapid development, its CO(2) trends have remained unclear. By integrating CO(2) observations from satellite sources, specifically the Orbiting Carbon Observatory-2 (OCO-2) and Fourier Transform Spectrometer (FTS), with data from the World Data Centre for Greenhouse Gases (WDCGG) Hong Kong ground station, we have synthesized a uniquely comprehensive dataset that enables enhanced resolution in exploring intra-annual, interannual, and spatial CO(2) variations across the province. The results reveal pronounced seasonal CO(2) cycles and a consistent upward trend over the past decade. The monthly concentrations exhibited a sinusoidal pattern, oscillating between a minimum of 399.68 ± 6.58 ppm in August and peaking at 407.48 ± 6.58 ppm in April. High CO(2) regions within Shaanxi are predominantly found in its southern subtropical and temperate areas, reaching 418.4 ppm in 2022. From 2013 to 2022, the annual average CO(2) increased by 4.12% from 396 to 412.34 ppm, with a higher growth rate in southern compared to northern Shaanxi. This study elucidates the distinct spatiotemporal variations in CO(2) levels across Shaanxi Province, revealing prominent seasonal cycles and a discernible upward trend over the past decade. The results offer new insights into CO(2) characteristics and dynamics in this rapidly developing region of China, and further investigation into the factors underlying the observed variations is warranted.

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