Articles | Volume 20, issue 6
https://doi.org/10.5194/acp-20-3273-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-3273-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Mar 2020
Research article |
| 19 Mar 2020
| 19 Mar 2020
Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Department of Atmospheric and Oceanic Science, Earth System Science
Interdisciplinary Center, University of Maryland, College Park, MD, USA
Department of Atmospheric and Oceanic Science, Earth System Science
Interdisciplinary Center, University of Maryland, College Park, MD, USA
Maureen Cribb
Department of Atmospheric and Oceanic Science, Earth System Science
Interdisciplinary Center, University of Maryland, College Park, MD, USA
Wei Huang
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Wenhao Xue
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Lin Sun
College of Geomatics, Shandong University of Science and Technology, Qingdao, China
Jianping Guo
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
Yiran Peng
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Alexei Lyapustin
Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Lei Liu
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Yimeng Song
Faculty of Architecture, The University of Hong Kong, Hong Kong SAR, China
Data sets
EARTHDATA NASA https://search.earthdata.nasa.gov/
ERA-Interim ECMWF https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim
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...
Altmetrics
Final-revised paper
Preprint