Spatiotemporal high-resolution (daily, 1km) ground-level ozone (O3) dataset across China from 2000 to present

We developed a full-coverage, high-resolution estimation model using the XGBoost algorithm to generate daily ground-level ozone concentration data at a 1-km spatial resolution, with satellite LST serving as the primary predictor. Tested on 2019 samples from China, our model demonstrated strong predictive accuracy, achieving an R² of 0.91 and an RMSE of 13.51 μg/m³ through 10-fold cross-validation. It effectively captured short-term local variations in ozone levels. We then applied this method to estimate long-term spatiotemporal high-resolution ozone concentrations from 2000 to 2020. The 21-yr monthly data and 2019 daily are archieved in NetCDF format. If you want to use this dataset, please cite the following publication. 

--He, Q., Cao, J., Saide, P. E., Ye, T., & Wang, W. (2024). Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning. Environmental Science & Technology. [url]

We are going to archive the daily dataset between 2000 to 2020 and it will be published here! If you want to use the daily data now, please contact us via [e-mail].

We also share other atmopsheric reconstruction datasets:

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.

For full-covereage, 1-km, PM2.5 data in China, please go to 10.5281/zenodo.8437234 or 10.5281/zenodo.8347128.

If you have any questions or comments, please feel free to contact us via [e-mail]