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To date, there has been no comprehensive assessment for the impact of lockdowns on the vertical distributions of nitrogen dioxide (NO\n                    <jats:sub>2<\/jats:sub>\n                    ) and formaldehyde (HCHO). Based on profiles from 0 to 2\u00a0km retrieved by Multi\u2010AXis\u2010Differential Optical Absorption Spectroscopy observation and a large volume of real\u2010time data at a suburb site in Shanghai, China, four types of machine learning models were developed and compared, including multiple linear regression, support vector machine, bagged trees (BT), and artificial neural network. Ultimately BT model was employed to reproduce NO\n                    <jats:sub>2<\/jats:sub>\n                    and HCHO profiles with the best performance. Predictions with different meteorological and surface pollution scenarios were conducted from 2017 to 2019, for assessing the corresponding impacts on the changes of NO\n                    <jats:sub>2<\/jats:sub>\n                    and HCHO profiles during COVID\u201019 lockdown. The simulations illustrate that the NO\n                    <jats:sub>2<\/jats:sub>\n                    decreased in 2020 by 43.8%, 45.5%, and 44.6%, relative to 2017, 2018, and 2019, respectively. For HCHO, the lockdown\u2010induced situation presented the declines of 28.6%, 32.1%, and 10.9%, respectively. In the comparisons of vertical distributions, NO\n                    <jats:sub>2<\/jats:sub>\n                    maintained decreasing at all altitudes, while HCHO decreased at low altitudes and increased at high altitudes. During COVID\u201019 lockdown, the reduction of NO\n                    <jats:sub>2<\/jats:sub>\n                    and HCHO from the variation of surface pollutants was dominated below 0.5\u00a0km, while the relevant meteorological factors played a more significant role above 0.5\u00a0km.\n                  <\/jats:p>","DOI":"10.1029\/2021jd036377","type":"journal-article","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T04:39:02Z","timestamp":1659760742000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Impact Assessment of COVID\u201019 Lockdown on Vertical Distributions of NO\n                    <sub>2<\/sub>\n                    and HCHO From MAX\u2010DOAS Observations and Machine Learning Models"],"prefix":"10.1029","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0761-6687","authenticated-orcid":false,"given":"Sanbao","family":"Zhang","sequence":"first","affiliation":[{"name":"Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP<sup>3<\/sup>) Department of Environmental Science and Engineering Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2590-9563","authenticated-orcid":false,"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP<sup>3<\/sup>) Department of Environmental Science and Engineering Fudan University  Shanghai China"},{"name":"Institute of Eco\u2010Chongming (IEC)  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7153-1939","authenticated-orcid":false,"given":"Ruibin","family":"Xue","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP<sup>3<\/sup>) Department of Environmental Science and Engineering Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1926-3364","authenticated-orcid":false,"given":"Jian","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP<sup>3<\/sup>) Department of Environmental Science and Engineering Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aimon","family":"Tanvir","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP<sup>3<\/sup>) Department of Environmental Science and Engineering Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danran","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP<sup>3<\/sup>) Department of Environmental Science and Engineering Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1781-138X","authenticated-orcid":false,"given":"Bin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP<sup>3<\/sup>) Department of Environmental Science and Engineering Fudan University  Shanghai China"},{"name":"Institute of Eco\u2010Chongming (IEC)  Shanghai China"},{"name":"Institute of Atmospheric Sciences Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"13","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"e_1_2_7_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/bf00058655"},{"key":"e_1_2_7_1_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.282.5392.1305"},{"key":"e_1_2_7_1_4_1","unstructured":"CDS (Climate Data Store). 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Retrieved fromhttps:\/\/quotsoft.net\/air\/"},{"key":"e_1_2_7_1_8_1","doi-asserted-by":"publisher","DOI":"10.1023\/a:1009793806086"},{"key":"e_1_2_7_1_9_1","doi-asserted-by":"publisher","DOI":"10.5194\/acp-11-10565-2011"},{"key":"e_1_2_7_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2010.03.010"},{"key":"e_1_2_7_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2020.09.011"},{"key":"e_1_2_7_1_12_1","doi-asserted-by":"publisher","DOI":"10.1029\/2005jd006618"},{"key":"e_1_2_7_1_13_1","doi-asserted-by":"publisher","DOI":"10.1029\/2011jd015938"},{"key":"e_1_2_7_1_14_1","unstructured":"GES DISC (Goddard Earth Sciences Data and Information Services Center). (2021).TROPOMI NO2and HCHO[Dataset]. 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