Full-coverage, 1-km atmospheric carbon dioxide (CO2) dataset across China

<p>We employed an enhanced regression-based machine learning model to reconstruct full-coverage daily atmospheric CO2 concentrations in China from 2015 to 2020 at a 0.01&deg; spatial resolution. Utilizing spatiotemporal high-resolution column-averaged dry-air mole fraction of CO2 (XCO2) data from the Orbiting Carbon Observatory 2 (OCO-2) as the dependent variable and multi-source environmental factors as independent variables, we achieved overall, spatial, and temporal cross-validation R2 [RMSE] results of 0.98 [0.74 ppm], 0.95 [1.15 ppm], and 0.93 [1.44 ppm], respectively.&nbsp;</p> <p>&nbsp;</p> <p>The annual mean and monthly mean data are archieved in Geotiff format. If you want to use this dataset, please cite the following publication. If you want to more data (e.g., &nbsp;daily XCO2 estimates), please contact us via qqhe@whut.edu.cn.</p> <p>--He, Q., Ye, T., Chen, X., Dong, H., Wang, W., Liang, Y., &amp; Li, Y. (2023). Full-coverage mapping high-resolution atmospheric CO2 concentrations in China from 2015 to 2020: Spatiotemporal variations and coupled trends with particulate pollution. <em>Journal of Cleaner Production</em>, 139290. [<a href="https://doi.org/10.1016/j.jclepro.2023.139290">url</a>]</p> <p>&nbsp;</p> <p>If you want daily data, please go to <a href="13623590">10.5281/zenodo.13623590</a>. If you have any questions or suggestions, please contact us via qqhe@whut.edu.cn.</p> <p>&nbsp;</p> <p>If you want more atmospheric-related datasets, e.g., full-coverage, 1-km AOD and PM2.5 datasets over China, please go to <a href="https://doi.org/10.5281/zenodo.7229348">10.5281/zenodo.7229348.</a></p>