School of Geosciences and Info-Physics, Central South University, Changsha 410017, China.
Int J Environ Res Public Health. 2022 Aug 31;19(17):10853. doi: 10.3390/ijerph191710853.
The extreme climate caused by global warming has had a great impact on the earth's ecology. As the main greenhouse gas, atmospheric CO concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO and various factors affecting the spatial distribution of CO, this study used multisource satellite-based data and a random forest model to reconstruct the daily CO column concentration (XCO) with full spatial coverage in the Beijing-Tianjin-Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO concentrations from 2015 to 2019 in the Beijing-Tianjin-Hebei region was conducted using the established model. The study of the spatial distribution of XCO concentration in the Beijing-Tianjin-Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO map has the potential to monitor regional carbon emissions and evaluate emission reduction.
全球变暖导致的极端气候对地球生态系统产生了巨大影响。作为主要的温室气体，大气 CO 浓度变化及其空间分布是气候变化评估的主要不确定因素之一。遥感卫星可以获取全球大气中 CO 浓度的变化。然而，卫星观测模式和云/气溶胶等因素仍存在一些问题（例如时间分辨率低、覆盖不完全）。本研究通过分析大气 CO 的源和影响 CO 空间分布的各种因素，利用多源卫星数据和随机森林模型，重建了京津冀地区具有完全空间覆盖的日 CO 柱浓度（XCO）。基于 2015 年 1 月 1 日至 2019 年 12 月 31 日的匹配数据集，模型的性能通过决定系数（R）=0.96、均方根误差（RMSE）=1.09ppm 和平均绝对误差（MAE）=0.56ppm 来证明。同时，基于样本的十折交叉验证（10-CV）结果显示 R=0.91、RMSE=1.68ppm 和 MAE=0.88ppm，基于空间位置的 10-CV 结果显示 R=0.91、RMSE=1.68ppm 和 MAE=0.88ppm。最后，利用建立的模型对京津冀地区 2015-2019 年的日 XCO 浓度进行了无缝空间映射。京津冀地区 XCO 浓度的空间分布研究表明其具有空间分异和季节性变化特征。此外，每日 XCO 图具有监测区域碳排放和评估减排的潜力。
