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Author:

Qi, Hao-Yun (Qi, Hao-Yun.) | Wang, Xiao-Qi (Wang, Xiao-Qi.) | Cheng, Shui-Yuan (Cheng, Shui-Yuan.)

Indexed by:

EI

Abstract:

Meteorological and human factors during the specific epidemic are critical for effectively evaluating the causes of air quality changes in different areas. This study selected Xingtai City, Hebei Province as the research object, took 2020 epidemic situation as an experimental scenario of extreme emission reduction under the extreme control measures, and 2021 epidemic situation as an experimental analysis scenario of future normalized epidemic prevention and control. Compared with the period prior to the epidemic, the ozone concentration during the two epidemics increased, and the particle concentration during the 2021 epidemic also increased. The concentration of other pollutants during the 2020 epidemic decreased to varying degrees. Compared with the same period in 2019, the ozone concentration during the two epidemics also increased. In addition, the pollutant concentration during the 2021 epidemic declined more. Using LSTM algorithm and WRF-CMAQ model to quantify impacts of meteorological factors on the changes in pollutant concentration during the two epidemic periods. The human-induced changes in different pollutant concentrations were deduced as indicated by the results from the air quality simulation. The simulation of LSTM algorithm during the two outbreaks shows that human being had a negative impact on pollutants (reducing their concentration) and accounted for a high proportion in the total change, while the influence of meteorological factors simulated with CMAQ model was much higher than that with LSTM algorithm. Anthropogenic influences dominated during the 2020 epidemic period, while compared to that during the 2020 epidemic period, the impact of anthropogenic activities on pollutants (except NO2) was positive (promoting an increase in pollutant concentration) during the 2021 epidemic period. © 2022 Chinese Society for Environmental Sciences. All rights reserved.

Keyword:

Learning algorithms Ozone Disease control Air quality Long short-term memory COVID-19 Emission control

Author Community:

  • [ 1 ] [Qi, Hao-Yun]Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environmental and Life, Beijing University of Technology, Beijing; 100020, China
  • [ 2 ] [Wang, Xiao-Qi]Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environmental and Life, Beijing University of Technology, Beijing; 100020, China
  • [ 3 ] [Cheng, Shui-Yuan]Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environmental and Life, Beijing University of Technology, Beijing; 100020, China

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Source :

China Environmental Science

ISSN: 1000-6923

Year: 2022

Issue: 8

Volume: 42

Page: 3512-3521

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 16

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