Dataset Information

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX-DOAS Observations and Machine Learning Models.

ABSTRACT: Responses to the COVID-19 pandemic led to major reductions on air pollutant emissions in modern history. To date, there has been no comprehensive assessment for the impact of lockdowns on the vertical distributions of nitrogen dioxide (NO₂) and formaldehyde (HCHO). Based on profiles from 0 to 2 km retrieved by Multi-AXis-Differential Optical Absorption Spectroscopy observation and a large volume of real-time 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 NO₂ 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 NO₂ and HCHO profiles during COVID-19 lockdown. The simulations illustrate that the NO₂ decreased in 2020 by 43.8%, 45.5%, and 44.6%, relative to 2017, 2018, and 2019, respectively. For HCHO, the lockdown-induced situation presented the declines of 28.6%, 32.1%, and 10.9%, respectively. In the comparisons of vertical distributions, NO₂ maintained decreasing at all altitudes, while HCHO decreased at low altitudes and increased at high altitudes. During COVID-19 lockdown, the reduction of NO₂ and HCHO from the variation of surface pollutants was dominated below 0.5 km, while the relevant meteorological factors played a more significant role above 0.5 km.

SUBMITTER: Zhang S

PROVIDER: S-EPMC9538289 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO<sub>2</sub> and HCHO From MAX-DOAS Observations and Machine Learning Models.

Zhang Sanbao S Wang Shanshan S Xue Ruibin R Zhu Jian J Tanvir Aimon A Li Danran D Zhou Bin B

Journal of geophysical research. Atmospheres : JGR 20220809 15

Responses to the COVID-19 pandemic led to major reductions on air pollutant emissions in modern history. To date, there has been no comprehensive assessment for the impact of lockdowns on the vertical distributions of nitrogen dioxide (NO<sub>2</sub>) and formaldehyde (HCHO). Based on profiles from 0 to 2 km retrieved by Multi-AXis-Differential Optical Absorption Spectroscopy observation and a large volume of real-time data at a suburb site in Shanghai, China, four types of machine learning mode ...[more]

PMID: 36245640

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Project description:NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011-2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea-United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated datasets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a factor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori profiles) but mostly to OMI's large footprint (> 312 km2).

Dataset Information

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX-DOAS Observations and Machine Learning Models.

Publications

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO<sub>2</sub> and HCHO From MAX-DOAS Observations and Machine Learning Models.

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Dataset Information

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO2 and HCHO From MAX-DOAS Observations and Machine Learning Models.

Publications

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO<sub>2</sub> and HCHO From MAX-DOAS Observations and Machine Learning Models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX-DOAS Observations and Machine Learning Models.