Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach
Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change durin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/5/902 |
| _version_ | 1797601121115570176 |
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| author | Mughair Aslam Bhatti Zhiyao Song Uzair Aslam Bhatti Naushad Ahmad |
| author_facet | Mughair Aslam Bhatti Zhiyao Song Uzair Aslam Bhatti Naushad Ahmad |
| author_sort | Mughair Aslam Bhatti |
| collection | DOAJ |
| description | Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R<sup>2</sup> value of more than 72% average in each year and decreased MAE value, which was better than other studies’ deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM<sub>2.5</sub> by 14%, PM<sub>10</sub> by 18%, NO<sub>2</sub> by 14%, and SO<sub>2</sub> by 16% during the COVID-B period. This study found an increase in O<sub>3</sub> by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM<sub>10</sub> by 42%, PM<sub>2.5</sub> by 97%, NO<sub>2</sub> by 89%, SO<sub>2</sub> by 36%, CO by 58%, O<sub>3</sub> by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality. |
| first_indexed | 2024-03-11T03:56:58Z |
| format | Article |
| id | doaj.art-b0819e7e0e564f3fbf981d6626e80c0b |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T03:56:58Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-b0819e7e0e564f3fbf981d6626e80c0b2023-11-18T00:26:36ZengMDPI AGAtmosphere2073-44332023-05-0114590210.3390/atmos14050902Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning ApproachMughair Aslam Bhatti0Zhiyao Song1Uzair Aslam Bhatti2Naushad Ahmad3School of Geography, Nanjing Normal University, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Nanjing 210023, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570100, ChinaDepartment of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi ArabiaSeveral countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R<sup>2</sup> value of more than 72% average in each year and decreased MAE value, which was better than other studies’ deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM<sub>2.5</sub> by 14%, PM<sub>10</sub> by 18%, NO<sub>2</sub> by 14%, and SO<sub>2</sub> by 16% during the COVID-B period. This study found an increase in O<sub>3</sub> by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM<sub>10</sub> by 42%, PM<sub>2.5</sub> by 97%, NO<sub>2</sub> by 89%, SO<sub>2</sub> by 36%, CO by 58%, O<sub>3</sub> by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.https://www.mdpi.com/2073-4433/14/5/902air quality patternBi-LSTMair pollutionCOVID-19 |
| spellingShingle | Mughair Aslam Bhatti Zhiyao Song Uzair Aslam Bhatti Naushad Ahmad Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach Atmosphere air quality pattern Bi-LSTM air pollution COVID-19 |
| title | Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach |
| title_full | Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach |
| title_fullStr | Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach |
| title_full_unstemmed | Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach |
| title_short | Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach |
| title_sort | predicting the impact of change in air quality patterns due to covid 19 lockdown policies in multiple urban cities of henan a deep learning approach |
| topic | air quality pattern Bi-LSTM air pollution COVID-19 |
| url | https://www.mdpi.com/2073-4433/14/5/902 |
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