{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T16:42:16Z","timestamp":1768408936590,"version":"3.49.0"},"reference-count":73,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["108-2111-M-008-036-MY2"],"award-info":[{"award-number":["108-2111-M-008-036-MY2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["110-2111-M-008-008"],"award-info":[{"award-number":["110-2111-M-008-008"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["110-2634-F-008-008"],"award-info":[{"award-number":["110-2634-F-008-008"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Many regions worldwide suffer from heavy air pollution caused by particulate matter (PM2.5) and nitrogen dioxide (NO2), resulting in a huge annual disease burden and significant welfare costs. Following the outbreak of the COVID-19 global pandemic, enforced curfews and restrictions on human mobility (so-called periods of \u2018lockdown\u2019) have become important measures to control the spread of the virus. This study aims to investigate the improvement in air quality following COVID-19 lockdown measures and the projected benefits for environmental health. China was chosen as a case study. The work projects annual premature deaths and welfare costs by integrating PM2.5 and NO2 pollutant measurements derived from satellite imagery (MODIS instruments on Terra and Aqua, and TROPOMI on Sentinel-5P) with census data archived by the Organization for Economic Co-operation and Development (OECD). A 91-day timeframe centred on the initial lockdown date of 23 January 2020 was investigated. To perform the projections, OECD data on five variables from 1990 to 2019 (mean population exposure to ambient PM2.5, premature deaths, welfare costs, gross domestic product and population) were used as training data to run the Autoregressive Integrated Moving Average (ARIMA) and multiple regression models. The analysis of the satellite imagery revealed that across the regions of Beijing, Hebei, Shandong, Henan, Xi\u2019an, Shanghai and Hubei, the average concentrations of PM2.5 decreased by 6.2, 30.7, 14.1, 20.7, 29.3, 5.5 and 17.3%, while the NO2 decreased by 45.5, 54.7, 60.5, 58.7, 63.6, 50.5 and 66.5%, respectively, during the period of lockdown restrictions in 2020, as compared with the equivalent period in 2019. Such improvements in air quality were found to be beneficial, reducing in 2020 both the number of premature deaths by approximately 97,390 and welfare costs by over USD 74 billion.<\/jats:p>","DOI":"10.3390\/rs15020530","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T06:52:10Z","timestamp":1673851930000},"page":"530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8100-5529","authenticated-orcid":false,"given":"Yuei-An","family":"Liou","sequence":"first","affiliation":[{"name":"Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Road, Jhongli District, Taoyuan City 320317, Taiwan"},{"name":"Taiwan Group on Earth Observations, Zhubei City 32001, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0652-5951","authenticated-orcid":false,"given":"Trong-Hoang","family":"Vo","sequence":"additional","affiliation":[{"name":"Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Road, Jhongli District, Taoyuan City 320317, Taiwan"},{"name":"Taiwan Group on Earth Observations, Zhubei City 32001, Taiwan"},{"name":"Institute of Geography, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet Road, Cau Giay District, Hanoi 100000, Vietnam"}]},{"given":"Kim-Anh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Road, Jhongli District, Taoyuan City 320317, Taiwan"},{"name":"Taiwan Group on Earth Observations, Zhubei City 32001, Taiwan"},{"name":"Institute of Geography, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet Road, Cau Giay District, Hanoi 100000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2686-1362","authenticated-orcid":false,"given":"James P.","family":"Terry","sequence":"additional","affiliation":[{"name":"College of Natural and Health Sciences, Zayed University, Dubai 19282, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"ref_1","unstructured":"WHO (2016). 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