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: 3,743 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,212
1,404
127
3,743
481
118
189
HTML: 2,212
PDF: 1,404
XML: 127
Total: 3,743
Supplement: 481
BibTeX: 118
EndNote: 189
Views and downloads (calculated since 25 Nov 2016)
Cumulative views and downloads
(calculated since 25 Nov 2016)
Total article views: 2,559 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,609
866
84
2,559
258
95
135
HTML: 1,609
PDF: 866
XML: 84
Total: 2,559
Supplement: 258
BibTeX: 95
EndNote: 135
Views and downloads (calculated since 03 May 2018)
Cumulative views and downloads
(calculated since 03 May 2018)
Total article views: 1,184 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
603
538
43
1,184
223
23
54
HTML: 603
PDF: 538
XML: 43
Total: 1,184
Supplement: 223
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: 3,743 (including HTML, PDF, and XML)
Thereof 3,687 with geography defined
and 56 with unknown origin.
Total article views: 2,559 (including HTML, PDF, and XML)
Thereof 2,523 with geography defined
and 36 with unknown origin.
Total article views: 1,184 (including HTML, PDF, and XML)
Thereof 1,164 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...
