{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T02:38:49Z","timestamp":1782959929349,"version":"3.54.5"},"reference-count":119,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shortly after the outbreak of the novel infectious disease (COVID-19) started at the end of 2019, it turned into a global pandemic, which caused the lockdown of many countries across the world. Various strict measures were adopted to reduce anthropogenic activities in order to prevent further spread and infection of the disease. In this study, we utilized continental scale remotely sensed data along with city scale in situ air quality observations for 2020 as well as data from the baseline period (2015\u20132019) to provide an early insight on air pollution changes in response to the COVID-19 pandemic lockdown, by combining both continental and city scales. For the continental scale analysis, data of NO2, SO2, and O3 were acquired from the ozone monitoring instrument (OMI) and data of aerosol optical depth (AOD) were collected from the moderate resolution imaging spectroradiometer (MODIS). For city scale analysis, data of NO2, CO, PM2.5, O3, and SO2 were derived from ground-based air quality observations. Results from satellite observations at the continental scale showed that concentrations of NO2, SO2, and AOD substantially dropped in 2020 during the lockdown period compared to their averages for the baseline period over all continents, with a maximum reduction of ~33% for NO2 in East Asia, ~41% for SO2 in East Asia, and ~37% for AOD in South Asia. In the case of O3, the maximum overall reduction was observed as ~11% in Europe, followed by ~10% in North America, while a slight increase was found in other study regions. These findings align with ground-based air quality observations, which showed that pollutants such as NO2, CO, PM2.5, and SO2 during the 2020 lockdown period decreased significantly except that O3 had varying patterns in different cities. Specifically, a maximum reduction of ~49% in NO2 was found in London, ~43% in CO in Wuhan, ~38% in PM2.5 in Chennai, and ~48% in SO2 in Beijing. In the case of urban O3, a maximum reduction of ~43% was found in Wuhan, but a significant increase of ~47% was observed in Chennai. It is obvious that restricted human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality, especially across the metropolitan cities.<\/jats:p>","DOI":"10.3390\/rs12152420","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T10:16:49Z","timestamp":1595931409000},"page":"2420","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Unprecedented Temporary Reduction in Global Air Pollution Associated with COVID-19 Forced Confinement: A Continental and City Scale Analysis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7276-5649","authenticated-orcid":false,"given":"Zhijie","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Geography, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0726-1807","authenticated-orcid":false,"given":"Arfan","family":"Arshad","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9165-5584","authenticated-orcid":false,"given":"Chuanrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7706-6778","authenticated-orcid":false,"given":"Saddam","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Irrigation and Drainage, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4558-3292","authenticated-orcid":false,"given":"Weidong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"ref_1","unstructured":"Worldometer (2020, June 02). 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