China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
Jie Ban
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
Qing Wang
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
Yayi Zhang
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
Yang Yang
Institute of Urban Meteorology, China Meteorological Administration,
Beijing, 100089, China
Shenshen Li
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Wenjiao Shi
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy
of Sciences, Beijing 100049, China
Zhen Zhou
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
School of Marine Technology and Geomatics, Jiangsu Ocean University,
Lianyungang 222000, China
Jiawei Zang
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
School of Marine Technology and Geomatics, Jiangsu Ocean University,
Lianyungang 222000, China
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
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5,003
1,416
118
6,537
453
127
183
HTML: 5,003
PDF: 1,416
XML: 118
Total: 6,537
Supplement: 453
BibTeX: 127
EndNote: 183
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads
(calculated since 15 Sep 2021)
Total article views: 5,218 (including HTML, PDF, and XML)
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Total
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EndNote
4,246
879
93
5,218
266
112
166
HTML: 4,246
PDF: 879
XML: 93
Total: 5,218
Supplement: 266
BibTeX: 112
EndNote: 166
Views and downloads (calculated since 25 Feb 2022)
Cumulative views and downloads
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Total article views: 1,319 (including HTML, PDF, and XML)
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757
537
25
1,319
187
15
17
HTML: 757
PDF: 537
XML: 25
Total: 1,319
Supplement: 187
BibTeX: 15
EndNote: 17
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads
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Total article views: 6,537 (including HTML, PDF, and XML)
Thereof 4,416 with geography defined
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Total article views: 5,218 (including HTML, PDF, and XML)
Thereof 3,179 with geography defined
and 2,039 with unknown origin.
Total article views: 1,319 (including HTML, PDF, and XML)
Thereof 1,237 with geography defined
and 82 with unknown origin.
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.
We constructed multi-variable random forest models based on 10-fold cross-validation and...