Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 21, issue 17
Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13149-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-13149-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 17
Research article
 | 
06 Sep 2021
Research article |  | 06 Sep 2021

Forecasting and identifying the meteorological and hydrological conditions favoring the occurrence of severe hazes in Beijing and Shanghai using deep learning

Chien Wang

Viewed

Total article views: 1,908 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,415 464 29 1,908 136 18 17
  • HTML: 1,415
  • PDF: 464
  • XML: 29
  • Total: 1,908
  • Supplement: 136
  • BibTeX: 18
  • EndNote: 17
Views and downloads (calculated since 19 Apr 2021)
Cumulative views and downloads (calculated since 19 Apr 2021)

Viewed (geographical distribution)

Total article views: 1,908 (including HTML, PDF, and XML) Thereof 1,921 with geography defined and -13 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 01 Jun 2023
Download
Short summary
Haze caused by abundant atmospheric aerosols has become a serious environmental issue in many countries. An innovative deep-learning machine has been developed to forecast the occurrence of hazes in two Asian megacities (Beijing and Shanghai) and has achieved good overall accuracy. Using this machine, typical regional meteorological and hydrological regimes associated with haze and non-haze events in the two cities have also been, arguably for the first time, successfully categorized.
Read more
Altmetrics
Final-revised paper
Preprint