CHAP: High-resolution and High-quality Ambient Air Pollutants Dataset for China

There is a newer version of the record available.

Published November 12, 2019 | Version 1
Dataset Restricted

ChinaHighPM2.5 (Version 1)

Creators

  • 1. Beijing Normal University
  • 2. University of Maryland

Contributors

  • 1. Beijing Normal University
  • 2. Shandong University of Science and Technology
  • 3. Chinese Academy of Meteorological Sciences
  • 4. Tsinghua University
  • 5. NASA Goddard Space Flight Center

Description

The high-resolution (1 km) and high-quality PM2.5 data set in China (i.e., ChinaHighPM2.5 data set) from 2000 to 2018 are generated for the first time. This data set is generated using a newly developed space-time extremely randomized trees (STET) model based on the newly released MODIS Collection 6 MAIAC 1-km AOD products, meteorological variables, emissions, and auxiliary data. The ChinaHighPM2.5 data set can be greatly useful for air pollution studies in medium- or small-scale areas. 

Note that this dataset is closed since a new version is published at 10.5281/zenodo.3539349.

Notes

If you have any questions about the data set, please contact me (weijing_rs@163.com).

Files

Restricted

The record is publicly accessible, but files are restricted to users with access.

Additional details

Related works

Is referenced by
Journal article: 10.5194/acp-20-3273-2020 (DOI)
Journal article: 10.1016/j.rse.2019.111221 (DOI)

References

  • 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
  • Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., and Cribb, M. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach, Remote Sensing of Environment, 2019, 231, 111221. https://doi.org/10.1016/j.rse.2019.111221