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ACP | Articles | Volume 20, issue 6
Atmos. Chem. Phys., 20, 3273–3289, 2020
https://doi.org/10.5194/acp-20-3273-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 20, 3273–3289, 2020
https://doi.org/10.5194/acp-20-3273-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Mar 2020

Research article | 19 Mar 2020

Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees

Jing Wei et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jing Wei on behalf of the Authors (25 Jan 2020)  Author's response    Manuscript
ED: Publish as is (14 Feb 2020) by Hang Su
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Short summary
This study introduced an enhanced space–time extremely randomized trees (STET) approach to improve the 1 km resolution ground-level PM2.5 estimates across China using the remote sensing technology. The STET model shows high accuracy and strong predictive power and appears to outperform most models reported by previous studies. Thus, it is of great importance for future air pollution studies at medium- or small-scale areas and will be applied to generate the historical PM2.5 dataset across China.
This study introduced an enhanced space–time extremely randomized trees (STET) approach to...
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