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Discussion papers
https://doi.org/10.5194/acp-2019-815
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-815
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 23 Oct 2019

Submitted as: research article | 23 Oct 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

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

Jing Wei1, Zhanqing Li2, Wei Huang3, Wenhao Xue1, Lin Sun4, Jianping Guo5, Yiran Peng6, Jing Li7, Alexei Lyapustin8, Lei Liu9, Hao Wu1, and Yimeng Song10 Jing Wei et al.
  • 1State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
  • 2Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 3State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China
  • 4College of Geomatics, Shandong University of Science and Technology, Qingdao, China
  • 5State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
  • 6Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
  • 7Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
  • 8Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 9College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
  • 10Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Hong Kong

Abstract. Fine particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) shows adverse effects on human health and atmospheric environment. Satellite-derived aerosol products have been intensively adopted in estimating surface PM2.5 concentrations, but most previous studies failed to monitor air pollution over small-scale areas limited by coarse spatial-resolution (3–50 km) and low data-quality aerosol optical depth (AOD) products. Therefore, a new space-time extremely randomized trees (STET) model is developed that integrates spatiotemporal information to improve PM2.5 estimates at both spatial resolution and overall accuracy across China. To this end, the newly released MODIS MAIAC AOD product, meteorological and other auxiliary data are inputs to the STET model. Daily 1-km PM2.5 maps in 2018 across mainland China are produced. The STET model performs well with a high out-of-sample (out-of-station) cross-validation coefficient of 0.89 (0.88), a low root-mean-square error of 10.35 (10.97) μg/m3, a small mean absolute error of 6.71 (7.17) μg/m3, and a small mean relative error of 21.37 % (23.77 %), respectively. Particularly, it can well capture the PM2.5 concentrations at both regional and individual site scales. In addition, it posed a strong predictive power (e.g., monthly-R2 = 0.80) and can be used to predict the historical PM2.5 records. The North China Plain, the Sichuan Basin, and Xinjiang Province always are featured with high PM2.5 pollution, especially in winter. The STET model outperforms most models presented in previous related studies. More importantly, our study provides a new approach to obtain high-quality PM2.5 estimates, which is important for air pollution studies over urban areas.

Jing Wei et al.
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Status: open (until 18 Dec 2019)
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Short summary
This study introduced a new 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 a high accuracy and strong predictive power, and appears to outperform most models reported by pervious studies. Thus, it is of great importance for future air pollution studies at medium- or small-scale areas, and will be applied to produce the historical PM2.5 dataset across China.
This study introduced a new space-time extremely randomized trees (STET) approach to improve the...
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