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Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants

Authors :
Ma Yunfeng
Zhao Huijie
Feng Lei
Jin Siyu
Zhao Di
Wang Shuai
Liu Qiyao
Gao Kunyu
Xu Zhengqing
Source :
Open Geosciences, Vol 15, Iss 1, Pp 4755-63 (2023)
Publication Year :
2023
Publisher :
De Gruyter, 2023.

Abstract

In the present research, a statistical analysis of all pollution incidents occurring from 2015 to 2019 in the cities of the urban agglomeration centered on Shenyang was performed. The results indicated that heavy pollution mainly occurred during the heating season, and the main pollutants were (particulate matter) PM2.5 and PM10. It was also determined that the heavy pollution that occurred during the heating season in Shenyang was of the soot type. The weather research forecast-chemistry (WRF-Chem) was used to simulate the meteorological elements and particle concentration during the two heavy pollution periods in 2019 and compared the simulation data with the monitoring data to verify the simulation performance of the model. Results demonstrated that the model had a better simulation effect on temperature and pressure than on wind speed and wind direction. By comparing the hourly particle concentration data, it was found that the simulation results for pollutants obtained with the WRF-Chem model were lower than the measured values. The simulation effect on PM2.5 was better than that on PM10, and the simulation results were basically consistent in the high- and low-value areas, and the time of peak and valley was basically synchronous. It was proven that the selected parameterization scheme properly simulated the weather situation and changes in pollutants during heavy pollution events in the Shenyang area. These results verified the application value of the WRF-Chem model during the investigation of heavy pollution events.

Details

Language :
English
ISSN :
23915447
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Open Geosciences
Publication Type :
Academic Journal
Accession number :
edsdoj.35dd3a8ba9c49e19b4f15625dd8d211
Document Type :
article
Full Text :
https://doi.org/10.1515/geo-2022-0415