Published August 16, 2020 | Version 1

ChinaHighPM2.5: MODIS/Terra+Aqua 1 km Ground-level PM2.5 Dataset for the Beijing-Tianjin-Hebei Region

Authors/Creators

  • 1. Beijing Normal University

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). This dataset 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 the linear mixed effect (LME) model. 

This is the MODIS/Terra+Aqua monthly 1 km ground-level PM2.5 dataset in the Beijing-Tianjin-Hebei region from 2000 to 2018, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) reaching 0.85 and a root-mean-square error (RMSE) of 21.49 µg m-3 on a daily basis.

If you use this dataset for related scientific research, please cite the corresponding reference (Xue et al., 2021, JCP):

Xue, W., Zhang, J.,  Zhong, C., Li, X., and Wei, J. Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region, Journal of Cleaner Production, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742

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

Xue_et_al-JCLP-2021.pdf

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Additional details

Related works

References
Journal article: 10.1016/j.jclepro.2020.123742 (DOI)

References

  • Xue, W., Zhang, J., Zhong, C., Li, X., and Wei, J. Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region, Journal of Cleaner Production, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742