Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset in China (2000-present) - 2023

Full-coverage daily estimates spanning the years 2019-2022 are archived here. These records, organized by month, are available for download in GEOTIFF format. 

We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2022 in China using a random forest-based hindcast modeling method. 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. In our proposed method, observed predictor 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 daily average data during 2000-2022 are released here and free for non-commercial use. If you want use our dataset, please cite the following publication. 

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. 

-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. Journal of Environmental Management, 342, 118145.[url]

-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. Atmospheric Research, 281, 106481.[url]

If you want more data (e.g.daily estimates before 2015), have any question, or further collaborate with us, please contact us via [e-mail].

If you want to use monthly estimates from 2000 to 2022, please go to 10.5281/zenodo.8084388.

if you want to use daily estimates from 2015 to 2018, please go to 10.5281/zenodo.8437234.

if you want to use daily estimates from 2019 to 2022, please go to Version 1 (10.5281/zenodo.10048172).

We also estimate other atmospheric data:

For full-coverage, 1-km, AOD data in China, please go to harvard dataverse. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.

For full-coverage, 1-km, CO2 data in China, please go to 10.5281/zenodo.10022904. This dataset was reconstructed based on OCO-2 XCO2 retrievals and machine learning algorithm.