Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression
Mitigation measures and control strategies relating to the novel coronavirus disease 2019 (COVID-19) have been widely applied in many countries to reduce the transmission of this pandemic disease. China was the first country to implement a strong lockdown policy to control COVID-19 when countries wo...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/12/10/1338 |
| _version_ | 1797515276250513408 |
|---|---|
| author | Muhammad Aamir Zeyun Li Sibghatullah Bazai Raja Asif Wagan Uzair Aslam Bhatti Mir Muhammad Nizamani Shakeel Akram |
| author_facet | Muhammad Aamir Zeyun Li Sibghatullah Bazai Raja Asif Wagan Uzair Aslam Bhatti Mir Muhammad Nizamani Shakeel Akram |
| author_sort | Muhammad Aamir |
| collection | DOAJ |
| description | Mitigation measures and control strategies relating to the novel coronavirus disease 2019 (COVID-19) have been widely applied in many countries to reduce the transmission of this pandemic disease. China was the first country to implement a strong lockdown policy to control COVID-19 when countries worldwide were struggling to manage COVID-19 cases. However, lockdown causes numerous changes to air-quality patterns due to the low amount of traffic and the decreased human mobility it results in. To study the impact of the strict control measures of the new COVID-19 epidemic on the air quality of Hubei in early 2020, the air-quality monitoring data of Hubei’s four cities, namely Huangshi, Yichang, Jingzhou, and Wuhan, from 2019 to 2021, specifically 1 January to 30 August, was examined to analyze the characteristics of the temporal and spatial distribution. All air-quality pollutants decreased during the active-COVID-19 period, with a maximum decrease of 26% observed in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula>, followed by 23% of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>, and a minimum decrease of 5% observed in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi mathvariant="normal">O</mi><mn>3</mn></msub></mrow></semantics></math></inline-formula>. Changes in air pollutants from 2017 to 2021 were also compared, and a decrease in all pollutants through to 2020 was found. The air-quality index (AQI) recorded an increase of 2% post-COVID-19, which shows that air quality will worsen in future, but it decreased by 22% during the active-COVID-19 period. A path analysis model was developed to further understand the relationship between the AQI and air-quality patterns. This path analysis shows a strong correlation between the AQI and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>, however its correlation with other air pollutants is weak. Regression analysis shows a similar pattern of there being a strong relationship between AQI and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>r</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.97) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>r</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.93). Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, it is likely that the reduction in air pollution and the significant improvement in ambient air quality due to lockdowns provided substantial short-term health benefits. The government must implement policies to control the environmental issues which are causing poor air quality in post-COVID-19. |
| first_indexed | 2024-03-10T06:43:13Z |
| format | Article |
| id | doaj.art-356a4cfee5294729afcefb9cb105551c |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T06:43:13Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-356a4cfee5294729afcefb9cb105551c2023-11-22T17:26:03ZengMDPI AGAtmosphere2073-44332021-10-011210133810.3390/atmos12101338Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and RegressionMuhammad Aamir0Zeyun Li1Sibghatullah Bazai2Raja Asif Wagan3Uzair Aslam Bhatti4Mir Muhammad Nizamani5Shakeel Akram6Department of Computer Science, Huanggang Normal University, Huangzhou 438000, ChinaGeography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, MalaysiaDepartment of Computer Engineering, BUITEMS, Quetta 87300, PakistanFaculty of Information & Communication Technology, BUITEMS, Quetta 87300, PakistanSchool of Geography, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou 570228, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610000, ChinaMitigation measures and control strategies relating to the novel coronavirus disease 2019 (COVID-19) have been widely applied in many countries to reduce the transmission of this pandemic disease. China was the first country to implement a strong lockdown policy to control COVID-19 when countries worldwide were struggling to manage COVID-19 cases. However, lockdown causes numerous changes to air-quality patterns due to the low amount of traffic and the decreased human mobility it results in. To study the impact of the strict control measures of the new COVID-19 epidemic on the air quality of Hubei in early 2020, the air-quality monitoring data of Hubei’s four cities, namely Huangshi, Yichang, Jingzhou, and Wuhan, from 2019 to 2021, specifically 1 January to 30 August, was examined to analyze the characteristics of the temporal and spatial distribution. All air-quality pollutants decreased during the active-COVID-19 period, with a maximum decrease of 26% observed in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula>, followed by 23% of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>, and a minimum decrease of 5% observed in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi mathvariant="normal">O</mi><mn>3</mn></msub></mrow></semantics></math></inline-formula>. Changes in air pollutants from 2017 to 2021 were also compared, and a decrease in all pollutants through to 2020 was found. The air-quality index (AQI) recorded an increase of 2% post-COVID-19, which shows that air quality will worsen in future, but it decreased by 22% during the active-COVID-19 period. A path analysis model was developed to further understand the relationship between the AQI and air-quality patterns. This path analysis shows a strong correlation between the AQI and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>, however its correlation with other air pollutants is weak. Regression analysis shows a similar pattern of there being a strong relationship between AQI and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>r</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.97) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>PM</mi></mrow><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>r</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.93). Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, it is likely that the reduction in air pollution and the significant improvement in ambient air quality due to lockdowns provided substantial short-term health benefits. The government must implement policies to control the environmental issues which are causing poor air quality in post-COVID-19.https://www.mdpi.com/2073-4433/12/10/1338air pollutionCOVID-19particulate matterChina |
| spellingShingle | Muhammad Aamir Zeyun Li Sibghatullah Bazai Raja Asif Wagan Uzair Aslam Bhatti Mir Muhammad Nizamani Shakeel Akram Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression Atmosphere air pollution COVID-19 particulate matter China |
| title | Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression |
| title_full | Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression |
| title_fullStr | Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression |
| title_full_unstemmed | Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression |
| title_short | Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression |
| title_sort | spatiotemporal change of air quality patterns in hubei province a pre to post covid 19 analysis using path analysis and regression |
| topic | air pollution COVID-19 particulate matter China |
| url | https://www.mdpi.com/2073-4433/12/10/1338 |
| work_keys_str_mv | AT muhammadaamir spatiotemporalchangeofairqualitypatternsinhubeiprovinceapretopostcovid19analysisusingpathanalysisandregression AT zeyunli spatiotemporalchangeofairqualitypatternsinhubeiprovinceapretopostcovid19analysisusingpathanalysisandregression AT sibghatullahbazai spatiotemporalchangeofairqualitypatternsinhubeiprovinceapretopostcovid19analysisusingpathanalysisandregression AT rajaasifwagan spatiotemporalchangeofairqualitypatternsinhubeiprovinceapretopostcovid19analysisusingpathanalysisandregression AT uzairaslambhatti spatiotemporalchangeofairqualitypatternsinhubeiprovinceapretopostcovid19analysisusingpathanalysisandregression AT mirmuhammadnizamani spatiotemporalchangeofairqualitypatternsinhubeiprovinceapretopostcovid19analysisusingpathanalysisandregression AT shakeelakram spatiotemporalchangeofairqualitypatternsinhubeiprovinceapretopostcovid19analysisusingpathanalysisandregression |