Key Laboratory for Aerosol-Cloud-Precipitation of China
Meteorological Administration, Collaborative Innovation Center on Forecast
and Evaluation of Meteorological Disasters, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese
Academy of Meteorological Sciences, Beijing 100081, China
Key Laboratory for Aerosol-Cloud-Precipitation of China
Meteorological Administration, Collaborative Innovation Center on Forecast
and Evaluation of Meteorological Disasters, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Institute of Urban Meteorology, China Meteorological
Administration, Beijing 100089, China
Wei Wang
China National Environmental
Monitoring Center, Beijing, 100012, China
Qing Hou
Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese
Academy of Meteorological Sciences, Beijing 100081, China
Zengyuan Guo
Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese
Academy of Meteorological Sciences, Beijing 100081, China
Chao Wang
Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese
Academy of Meteorological Sciences, Beijing 100081, China
Viewed
Total article views: 3,798 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,245
1,425
128
3,798
496
123
193
HTML: 2,245
PDF: 1,425
XML: 128
Total: 3,798
Supplement: 496
BibTeX: 123
EndNote: 193
Views and downloads (calculated since 25 Nov 2016)
Cumulative views and downloads
(calculated since 25 Nov 2016)
Total article views: 2,605 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,642
878
85
2,605
266
100
139
HTML: 1,642
PDF: 878
XML: 85
Total: 2,605
Supplement: 266
BibTeX: 100
EndNote: 139
Views and downloads (calculated since 03 May 2018)
Cumulative views and downloads
(calculated since 03 May 2018)
Total article views: 1,193 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
603
547
43
1,193
230
23
54
HTML: 603
PDF: 547
XML: 43
Total: 1,193
Supplement: 230
BibTeX: 23
EndNote: 54
Views and downloads (calculated since 25 Nov 2016)
Cumulative views and downloads
(calculated since 25 Nov 2016)
Viewed (geographical distribution)
Total article views: 3,798 (including HTML, PDF, and XML)
Thereof 3,740 with geography defined
and 58 with unknown origin.
Total article views: 2,605 (including HTML, PDF, and XML)
Thereof 2,567 with geography defined
and 38 with unknown origin.
Total article views: 1,193 (including HTML, PDF, and XML)
Thereof 1,173 with geography defined
and 20 with unknown origin.
The GRAPES–CUACE aerosol adjoint model was developed and applied in detecting PM2.5 sources for haze events in eastern China (EC). The response time of Beijing PM2.5 pollution peaks to local and surrounding emissions is quantized for regional transport of air pollution over the EC. The adjoint results agreed well with the Models-3/CMAQ assessments. The adjoint method is powerful in simulating the receptor–source relationship and can be utilized in dynamic air quality control scheme design.
The GRAPES–CUACE aerosol adjoint model was developed and applied in detecting PM2.5 sources for...
