Gap-free 1km PM2.5 dataset in China (2000-present)

<p>This is the monthly PM2.5 estimates across China from 2000 to 2022.&nbsp;If you want daily dataset, please go to&nbsp;<a href="https://zenodo.org/record/7229348">10.5281/zenodo.7229348</a>. More datasets can be found on the right&nbsp;navigation panel of&nbsp;<a href="https://zenodo.org/record/4569557">10.5281/zenodo.4569557</a>.&nbsp;</p> <p><strong>We also estimate other atmospheric data:</strong></p> <p>For full-coverage, 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>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method. <strong>Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance.&nbsp;</strong>In our proposed method, observed predictor&nbsp;information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The monthly average data during 2000-2022 are released here in CSV&nbsp;format (if you want GEOTIFF files, please go to&nbsp;<a href="https://zenodo.org/record/8347128">10.5281/zenodo.8347128</a>)&nbsp;and free for non-commercial use.&nbsp;<em><strong>If you want use our dataset, please cite the following publication.&nbsp;</strong></em></p> <p>The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively.&nbsp;</p> <p><strong>-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., &amp; Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020.&nbsp;<em>Journal of Environmental Management</em>,&nbsp;<em>342</em>, 118145.[<a href="https://doi.org/10.1016/j.jenvman.2023.118145">url</a>]</strong></p> <p><strong>-He, Q., Wang, W., Song, Y., Zhang, M., &amp; Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020.&nbsp;<em>Atmospheric Research</em>,&nbsp;<em>281</em>, 106481.[<a href="https://doi.org/10.1016/j.atmosres.2022.106481">url</a>]</strong></p> <p>&nbsp;</p> <p>&nbsp;</p>