Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

Wei, Jing; Li, Zhanqing; Pinker, Rachel T.; Wang, Jun; Sun, Lin; Xue, Wenhao; Li, Runze; Cribb, Maureen

doi:https://doi.org/10.5194/acp-21-7863-2021

Articles | Volume 21, issue 10

https://doi.org/10.5194/acp-21-7863-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Satellite and ground-based remote sensing of aerosol optical,...

https://doi.org/10.5194/acp-21-7863-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 10

Research article

|

25 May 2021

Research article |

| 25 May 2021

Himawari-8-derived diurnal variations in ground-level PM_2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

Jing Wei, Zhanqing Li, Rachel T. Pinker, Jun Wang, Lin Sun, Wenhao Xue, Runze Li, and Maureen Cribb

Data sets

ChinaHighAirPollutants (CHAP) J. Wei https://weijing-rs.github.io/product.html

Short summary

This study developed a space-time Light Gradient Boosting Machine (STLG) model to derive the high-temporal-resolution (1 h) and high-quality PM_2.5 dataset in China (i.e., ChinaHighPM_2.5) at a 5 km spatial resolution from the Himawari-8 Advanced Himawari Imager aerosol products. Our model outperforms most previous related studies with a much lower computation burden in terms of speed and memory, making it most suitable for real-time air pollution monitoring in China.

Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

Data sets

Himawari-8-derived diurnal variations in ground-level PM_2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)