School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China.
School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China.
Sci Total Environ. 2023 Feb 1;858(Pt 2):159588. doi: 10.1016/j.scitotenv.2022.159588. Epub 2022 Nov 2.
As China is the world's largest CO emitter, it is important to understand the spatio-temporal variation of atmospheric CO to reduce carbon emissions. Satellite remote sensing for carbon monitoring has been widely used and studied because of its long-term and large-scale characteristics. However, the satellite data results are very sparse with significant gaps due to narrow swath and other factors on CO retrieval. The simple interpolation methods ignore the influential factors of CO and loss the spatial resolution, which leads to the inability to quantify the spatio-temporal variation well. This study developed a machine learning method that considers carbon emissions, vegetation, and meteorology. Using the column-averaged dry-air mole fraction of CO (XCO) data of SCIAMACHY, GOSAT, and OCO-2, we derived monthly-scale contiguous XCO data across China from 2003 to 2019 with 0.25° resolution. The results showed a good agreement with the satellite measurements, with the bias and standard deviation of 0.11 and 1.38 ppmv for the validation dataset, respectively. Moreover, the results were consistent with the model simulation and in-situ sites, indicating the ability to reflect long-term spatio-temporal variation with a finer texture. We analyzed the spatial distribution, seasonal variation, and long-term trends of XCO in China, revealing that the machine learning method has comparable performance to model simulations. The results showed that XCO is dominated by anthropogenic emissions spatially and has a clear seasonal cycle, with a larger amplitude the further north. The long-term trend shows the XCO increased by an average rate of 2.17 ppmv per year from 2003 to 2019 in China, which is consistent with the global. The method and data can further study the carbon cycle and climate change.
作为世界上最大的二氧化碳排放国，了解大气二氧化碳的时空变化以减少碳排放非常重要。卫星遥感在碳监测方面得到了广泛的应用和研究，因为它具有长期和大规模的特点。然而，由于 CO 反演中狭窄的幅宽和其他因素，卫星数据的结果非常稀疏，存在很大的差距。简单的插值方法忽略了 CO 的影响因素，损失了空间分辨率，导致无法很好地量化时空变化。本研究开发了一种考虑碳排放、植被和气象的机器学习方法。利用 SCIAMACHY、GOSAT 和 OCO-2 的柱平均干空气 CO 摩尔分数（XCO）数据，我们从 2003 年到 2019 年以 0.25°的分辨率导出了中国各地逐月连续的 XCO 数据。结果与卫星测量结果吻合较好，验证数据集的偏差和标准偏差分别为 0.11 和 1.38 ppmv。此外，结果与模型模拟和现场站点一致，表明具有以更精细的纹理反映长期时空变化的能力。我们分析了中国 XCO 的空间分布、季节变化和长期趋势，表明机器学习方法具有与模型模拟相当的性能。结果表明，XCO 在空间上主要由人为排放控制，具有明显的季节性周期，越往北幅度越大。长期趋势表明，2003 年至 2019 年中国 XCO 以平均每年 2.17 ppmv 的速度增加，与全球趋势一致。该方法和数据可以进一步研究碳循环和气候变化。
