Spatiotemporal high-resolution (daily, 1-km) atmospheric CO2 reconstruction data 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>The daily XCO2 data are archieved in NetCDF format. If you want to use this dataset, please cite the following publication. If you want annual or monthly data, please go to <a href="../records/10022905">10.5281/zenodo.10022905</a>.</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.&nbsp;<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>We also share other reconstruction datasets of atmopsheric parameters:</p> <p>For full-coverage, daily, 1-km, AOD data in China, please go to&nbsp;<a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p> <p>For full-covereage, daily, 1-km, PM2.5 data in China, please go to&nbsp;<a href="../doi/10.5281/zenodo.8437234">10.5281/zenodo.8437234 or&nbsp;</a><a href="../record/8347128">10.5281/zenodo.8347128.</a></p> <p>For full-coverage, daily, 1-km ozone data in China, please go to <a href="13623698">10.5281/zenodo.13623698</a>.</p>