Trends of CO and NO<sub>2</sub> Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine
The first case of COVID-19 in Iran was reported on 19 February 2020, 1 month before the Nowruz holidays coincided with the global pandemic, leading to quarantine and lockdown. Many studies have shown that environmental pollutants were drastically reduced with the spread of this disease and the decli...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Pollutants |
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| Online Access: | https://www.mdpi.com/2673-4672/2/2/12 |
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| author | Siavash Shami Babak Ranjgar Jinhu Bian Mahdi Khoshlahjeh Azar Armin Moghimi Meisam Amani Amin Naboureh |
| author_facet | Siavash Shami Babak Ranjgar Jinhu Bian Mahdi Khoshlahjeh Azar Armin Moghimi Meisam Amani Amin Naboureh |
| author_sort | Siavash Shami |
| collection | DOAJ |
| description | The first case of COVID-19 in Iran was reported on 19 February 2020, 1 month before the Nowruz holidays coincided with the global pandemic, leading to quarantine and lockdown. Many studies have shown that environmental pollutants were drastically reduced with the spread of this disease and the decline in industrial activities. Among these pollutants, nitrogen dioxide (NO<sub>2</sub>) and carbon monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes in these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three periods from 11 March to 8 April 2019, 2020, and 2021 were investigated. To this end, timeseries of the Sentinel-5P TROPOMI and <i>in situ</i> data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results of the NO<sub>2</sub> derived from Sentinel-5P were in agreement with the <i>in situ</i> data acquired from ground-based stations (average correlation coefficient = 0.7). Moreover, the results showed that the concentration of NO<sub>2</sub> and CO pollutants in 2020 (the first year of the COVID-19 pandemic) was 5% lower than in 2019, indicating the observance of quarantine rules, as well as people’s initial fear of the coronavirus. Contrarily, these pollutants in 2021 (the second year of the COVID-19 pandemic) were higher than those in 2020 by 5%, which could have been due to high vehicle traffic and a lack of serious policy- and law-making by the government to ban urban and interurban traffic. These findings are essential criteria that might be used to guide future manufacturing logistics, traffic planning and management, and environmental sustainability policies and plans. Furthermore, using the COVID-19 scenario and free satellite-derived data, it is now possible to investigate how harmful gas emissions influence air quality. These findings may also be helpful in making future strategic decisions on how to cope with the virus spread and lessen its negative social and economic consequences. |
| first_indexed | 2024-03-09T22:42:22Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2673-4672 |
| language | English |
| last_indexed | 2024-03-09T22:42:22Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pollutants |
| spelling | doaj.art-2f4271b0b7b946c4b273356c29e7ede22023-11-23T18:35:40ZengMDPI AGPollutants2673-46722022-04-012215617110.3390/pollutants2020012Trends of CO and NO<sub>2</sub> Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth EngineSiavash Shami0Babak Ranjgar1Jinhu Bian2Mahdi Khoshlahjeh Azar3Armin Moghimi4Meisam Amani5Amin Naboureh6Department of Geodesy, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1969764499, IranDepartment of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1969764499, IranResearch Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, ChinaDepartment of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1969764499, IranDepartment of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1969764499, IranWood Environment and Infrastructure Solutions, Ottawa, ON K2E 7L5, CanadaResearch Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, ChinaThe first case of COVID-19 in Iran was reported on 19 February 2020, 1 month before the Nowruz holidays coincided with the global pandemic, leading to quarantine and lockdown. Many studies have shown that environmental pollutants were drastically reduced with the spread of this disease and the decline in industrial activities. Among these pollutants, nitrogen dioxide (NO<sub>2</sub>) and carbon monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes in these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three periods from 11 March to 8 April 2019, 2020, and 2021 were investigated. To this end, timeseries of the Sentinel-5P TROPOMI and <i>in situ</i> data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results of the NO<sub>2</sub> derived from Sentinel-5P were in agreement with the <i>in situ</i> data acquired from ground-based stations (average correlation coefficient = 0.7). Moreover, the results showed that the concentration of NO<sub>2</sub> and CO pollutants in 2020 (the first year of the COVID-19 pandemic) was 5% lower than in 2019, indicating the observance of quarantine rules, as well as people’s initial fear of the coronavirus. Contrarily, these pollutants in 2021 (the second year of the COVID-19 pandemic) were higher than those in 2020 by 5%, which could have been due to high vehicle traffic and a lack of serious policy- and law-making by the government to ban urban and interurban traffic. These findings are essential criteria that might be used to guide future manufacturing logistics, traffic planning and management, and environmental sustainability policies and plans. Furthermore, using the COVID-19 scenario and free satellite-derived data, it is now possible to investigate how harmful gas emissions influence air quality. These findings may also be helpful in making future strategic decisions on how to cope with the virus spread and lessen its negative social and economic consequences.https://www.mdpi.com/2673-4672/2/2/12Sentinel-5PTROPOMIair pollutionGoogle Earth EngineCOVID-19Iran |
| spellingShingle | Siavash Shami Babak Ranjgar Jinhu Bian Mahdi Khoshlahjeh Azar Armin Moghimi Meisam Amani Amin Naboureh Trends of CO and NO<sub>2</sub> Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine Pollutants Sentinel-5P TROPOMI air pollution Google Earth Engine COVID-19 Iran |
| title | Trends of CO and NO<sub>2</sub> Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine |
| title_full | Trends of CO and NO<sub>2</sub> Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine |
| title_fullStr | Trends of CO and NO<sub>2</sub> Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine |
| title_full_unstemmed | Trends of CO and NO<sub>2</sub> Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine |
| title_short | Trends of CO and NO<sub>2</sub> Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine |
| title_sort | trends of co and no sub 2 sub pollutants in iran during covid 19 pandemic using timeseries sentinel 5 images in google earth engine |
| topic | Sentinel-5P TROPOMI air pollution Google Earth Engine COVID-19 Iran |
| url | https://www.mdpi.com/2673-4672/2/2/12 |
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