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

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Published November 12, 2019 | Version 3
Dataset Restricted

ChinaHighPM2.5 (Version 3)

Creators

  • 1. 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 MODIS/Terra+Aqua MAIAC AOD products together with other auxiliary 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 Level 3 (L3) yearly 0.01 degree (≈ 1 km) gridded ground-level PM2.5 products in Eastern China (ECHAP_PM2.5_Y1K) from 2000 to 2020, which are averaged from the Level 2 daily products. Note that the data for the year 2000 is averaged from March 2000 to December 2000. The annual PM2.5 estimates are highly related to ground-based measurements (R2 = 0.94) with an average root-mean-square error (RMSE) of 5.07 µg m-3.

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

Notes

CHAP website: https://weijing-rs.github.io/product.html

Files

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Additional details

Related works

Is referenced by
Journal article: 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