Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model
With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we establ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/13/2525 |
| _version_ | 1797411467706761216 |
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| author | Zigeng Song Yan Bai Difeng Wang Teng Li Xianqiang He |
| author_facet | Zigeng Song Yan Bai Difeng Wang Teng Li Xianqiang He |
| author_sort | Zigeng Song |
| collection | DOAJ |
| description | With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we established a machine learning (ML) model to analyze annual and seasonal changes in primary air pollutants in 2020 compared to 2018 and 2019 over central and eastern China. The root mean squared errors (RMSE) for the PM<sub>2.5</sub>, PM<sub>10</sub>, O<sub>3</sub>, and CO validation dataset were 9.027 μg/m<sup>3</sup>, 20.312 μg/m<sup>3</sup>, 10.436 μg/m<sup>3</sup>, and 0.097 mg/m<sup>3</sup>, respectively. The geographical random forest (RF) model demonstrated good performance for four main air pollutants. Notably, PM<sub>2.5</sub>, PM<sub>10</sub>, and CO decreased by 44.1%, 43.2%, and 35.9% in February 2020, which was likely influenced by the COVID-19 lockdown and primarily lasted until May 2020. Furthermore, PM<sub>2.5,</sub> PM<sub>10</sub>, O<sub>3</sub>, and CO decreased by 16.4%, 24.2%, 2.7%, and 19.8% in 2020 relative to the average values in 2018 and 2019. Moreover, the reduction in O<sub>3</sub> emissions was not universal, with a significant increase (~20–40%) observed in uncontaminated areas. |
| first_indexed | 2024-03-09T04:46:30Z |
| format | Article |
| id | doaj.art-003775f7373f4ad7979c4a161ccf7155 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T04:46:30Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-003775f7373f4ad7979c4a161ccf71552023-12-03T13:15:00ZengMDPI AGRemote Sensing2072-42922021-06-011313252510.3390/rs13132525Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning ModelZigeng Song0Yan Bai1Difeng Wang2Teng Li3Xianqiang He4College of Oceanography, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaWith the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we established a machine learning (ML) model to analyze annual and seasonal changes in primary air pollutants in 2020 compared to 2018 and 2019 over central and eastern China. The root mean squared errors (RMSE) for the PM<sub>2.5</sub>, PM<sub>10</sub>, O<sub>3</sub>, and CO validation dataset were 9.027 μg/m<sup>3</sup>, 20.312 μg/m<sup>3</sup>, 10.436 μg/m<sup>3</sup>, and 0.097 mg/m<sup>3</sup>, respectively. The geographical random forest (RF) model demonstrated good performance for four main air pollutants. Notably, PM<sub>2.5</sub>, PM<sub>10</sub>, and CO decreased by 44.1%, 43.2%, and 35.9% in February 2020, which was likely influenced by the COVID-19 lockdown and primarily lasted until May 2020. Furthermore, PM<sub>2.5,</sub> PM<sub>10</sub>, O<sub>3</sub>, and CO decreased by 16.4%, 24.2%, 2.7%, and 19.8% in 2020 relative to the average values in 2018 and 2019. Moreover, the reduction in O<sub>3</sub> emissions was not universal, with a significant increase (~20–40%) observed in uncontaminated areas.https://www.mdpi.com/2072-4292/13/13/2525air pollutants concentrationCOVID-19 lockdownmachine learningsatellite remote sensing |
| spellingShingle | Zigeng Song Yan Bai Difeng Wang Teng Li Xianqiang He Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model Remote Sensing air pollutants concentration COVID-19 lockdown machine learning satellite remote sensing |
| title | Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model |
| title_full | Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model |
| title_fullStr | Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model |
| title_full_unstemmed | Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model |
| title_short | Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model |
| title_sort | satellite retrieval of air pollution changes in central and eastern china during covid 19 lockdown based on a machine learning model |
| topic | air pollutants concentration COVID-19 lockdown machine learning satellite remote sensing |
| url | https://www.mdpi.com/2072-4292/13/13/2525 |
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