Published June 15, 2021 | Version 3
Dataset Restricted

ChinaHighPM2.5: MODIS/Terra+Aqua 1 km Ground-level PM2.5 Dataset for China (Closed)

Authors/Creators

  • 1. University of Iowa
  • 2. University of Maryland

Description

ChinaHighPM2.5 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.

This is the MODIS/Terra+Aqua daily 1 km (D1K) ground-level PM2.5 dataset in Eastern China from 2013 to 2020, and this dataset yields a high quality with average cross-validation coefficient of determination (CV-R2) values ranging from 0.86 to 0.90, and RMSE values ranging from 10.0 to 18.4 μg/m3 on a daily basis.

Note that this dataset is closed access since a longer-term (2000-2021), seamless, high-resolution (1 km), and higher quality ChinaHighPM2.5 dataset (Version 4) is available at:  http://doi.org/10.5281/zenodo.3539349

More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

Notes

Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).

Files

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Additional details

Related works

Is referenced by
10.1016/j.rse.2020.112136 (DOI)

References

  • Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136
  • 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. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees, Atmospheric Chemistry and Physics, 2020, 20(6), 3273-3289. https://doi.org/10.5194/acp-20-3273-2020