Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 14, issue 2
Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-943-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-943-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 2
Data description paper
 | 
25 Feb 2022
Data description paper |  | 25 Feb 2022

Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005–2017 based on a multi-variable random forest model

Runmei Ma, Jie Ban, Qing Wang, Yayi Zhang, Yang Yang, Shenshen Li, Wenjiao Shi, Zhen Zhou, Jiawei Zang, and Tiantian Li

Supplement

https://doi.org/10.5194/essd-14-943-2022-supplement

Data sets

Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model Runmei Ma, Jie Ban, Qing Wang, Yayi Zhang, and Tiantian Li https://doi.org/10.5281/zenodo.4009308

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Short summary
We constructed multi-variable random forest models based on 10-fold cross-validation and estimated daily PM2.5 and O3 concentration of China in 2005–2017 at a resolution of 1 km. The daily R2 values of PM2.5 and O3 were 0.85 and 0.77. The meteorological variables can significantly affect both PM2.5 and O3 modeling. During 2005–2017, PM2.5 exhibited an overall downward trend, while O3 experienced the opposite. The temporal trend of PM2.5 and O3 had spatial characteristics during the study period.
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