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
Viewed
Total article views: 6,588 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
5,041
1,423
124
6,588
454
134
190
HTML: 5,041
PDF: 1,423
XML: 124
Total: 6,588
Supplement: 454
BibTeX: 134
EndNote: 190
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads
(calculated since 15 Sep 2021)
Total article views: 5,257 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
4,279
882
96
5,257
266
116
170
HTML: 4,279
PDF: 882
XML: 96
Total: 5,257
Supplement: 266
BibTeX: 116
EndNote: 170
Views and downloads (calculated since 25 Feb 2022)
Cumulative views and downloads
(calculated since 25 Feb 2022)
Total article views: 1,331 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
762
541
28
1,331
188
18
20
HTML: 762
PDF: 541
XML: 28
Total: 1,331
Supplement: 188
BibTeX: 18
EndNote: 20
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads
(calculated since 15 Sep 2021)
Viewed (geographical distribution)
Total article views: 6,588 (including HTML, PDF, and XML)
Thereof 4,472 with geography defined
and 2,116 with unknown origin.
Total article views: 5,257 (including HTML, PDF, and XML)
Thereof 3,223 with geography defined
and 2,034 with unknown origin.
Total article views: 1,331 (including HTML, PDF, and XML)
Thereof 1,249 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...