ChinaHighPM₂.₅ (2022–Present)

<p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM_2.5&nbsp;dataset for China&nbsp;<strong>from 2022 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R²) of 0.92, a root-mean-square error (RMSE) of 10.76 &micro;g m^-3, and a mean absolute error (MAE) of 6.32 &micro;g m^-3&nbsp;on a daily basis.</p> <p>If you use the ChinaHighPM_2.5&nbsp;dataset in your scientific research, please cite the following references (Wei et al., RSE, 2021; Wei et al., ACP, 2020):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M.&nbsp;<a href="https://weijing-rs.github.io/publications/Wei_et_al-RSE-2021.pdf">Reconstructing 1-km-resolution high-quality PM_2.5&nbsp;data records from 2000 to 2018 in China: spatiotemporal variations and policy implications</a>.&nbsp;<em>Remote Sensing of Environment</em>, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136</p> </li> <li> <p>Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y.&nbsp;<a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2020.pdf">Improved 1 km resolution PM_2.5&nbsp;estimates across China using enhanced space-time extremely randomized trees</a>.&nbsp;<em>Atmospheric Chemistry and Physics</em>, 2020, 20(6), 3273&ndash;3289. https://doi.org/10.5194/acp-20-3273-2020</p> </li> </ul> <p><strong>The data for the period 2000&ndash;2021 are accessible at: <a href="https://doi.org/10.5281/zenodo.3539349">https://doi.org/10.5281/zenodo.3539349</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>