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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. Plain Language Summary: This study evaluated the impact of COVID‐19 lockdown on the vertical distributions of NO2 and HCHO by developing the machine‐learning (ML) air pollution prediction models. The ML model allows us to quantify the timing and magnitude influencing real‐world air quality responses to surface pollution scenarios and meteorological factors. Compared with the simulations for vertical profiles in the normal scenarios, it can be found that NO2 maintained decreasing at all altitudes, while HCHO decreased at low altitudes and increased at high altitudes. In addition, the lockdown‐induced NO2 and HCHO were mainly driven by meteorological factors and surface pollutants above and below 0.5 km, respectively. Key Points: The machine learning model was applied to reproduce NO2 and HCHO profiles for the first timeQualifying the vertical distribution of reductions during COVID‐19 lockdown relative to the normal scenarioThe lockdown‐induced NO2 and HCHO were mainly driven by meteorological factors and surface pollutants above and below 0.5 km, respectively [ABSTRACT FROM AUTHOR]

The long-path differential optical absorption spectroscopy (LP-DOAS) technique was deployed in Shanghai to continuously monitor ozone (O3), formaldehyde (HCHO), nitrogen dioxide (NO2), nitrous acid (HONO), and nitrate radical (NO3) mixing ratios from September 2019 to August 2020. Through a clustering method, four typical clusters of the O3 diurnal pattern were identified: high during both the daytime and nighttime (cluster 1), high during the nighttime but low during the daytime (cluster 2), low during both the daytime and nighttime (cluster 3), and low during the nighttime but high during the daytime (cluster 4). The drivers of O3 variation for the four clusters were investigated for the day- and nighttime. Ambient NO caused the O3 gap after midnight between clusters 1 and 2 and clusters 3 and 4. During the daytime, vigorous O3 generation (clusters 1 and 4) was found to accompany higher temperature, lower humidity, lower wind speed, and higher radiation. Moreover, O3 concentration correlated with HCHO for all clusters except for the low O3 cluster 3, while O3 correlated with HCHO/NOx, but anti-correlated with NOx for all clusters. The lower boundary layer height before midnight hindered O3 diffusion and accordingly determined the final O3 accumulation over the daily cycle for clusters 1 and 4. The interactions between the O3 diel profile and other atmospheric reactive components established that higher HONO before sunrise significantly promoted daytime O3 generation, while higher daytime O3 led to a higher nighttime NO3 level. This paper summarizes the interplays between day- and nighttime oxidants and oxidation products, particularly the cause and effect for daytime O3 generation from the perspective of nighttime atmospheric components. [ABSTRACT FROM AUTHOR]

Recently, the occurrence of fog and haze over China has increased. The retrieval of trace gases from the multi-axis differential optical absorption spectroscopy (MAX-DOAS) is challenging under these conditions. In this study, various reported retrieval settings for formaldehyde (HCHO) and sulfur dioxide (SO2) are compared to evaluate the performance of these settings under different meteorological conditions (clear day, haze, and fog). The dataset from 1st December 2019 to 31st March 2020 over Nanjing, China, is used in this study. The results indicated that for HCHO, the optimal settings were in the 324.5–359 nm wavelength window with a polynomial order of five. At these settings, the fitting and root mean squared (RMS) errors for column density were considerably improved for haze and fog conditions, and the differential slant column densities (DSCDs) showed more accurate values compared to the DSCDs between 336.5 and 359 nm. For SO2, the optimal settings for retrieval were found to be at 307–328 nm with a polynomial order of five. Here, root mean square (RMS) and fitting errors were significantly lower under all conditions. The observed HCHO and SO2 vertical column densities were significantly lower on fog days compared to clear days, reflecting a decreased chemical production of HCHO and aqueous phase oxidation of SO2 in fog droplets. [ABSTRACT FROM AUTHOR]

Reduced mobility and less anthropogenic activity under special case circumstances over various parts of the world have pronounced effects on air quality. The objective of this study is to investigate the impact of reduced anthropogenic activity on air quality in the mega city of Shanghai, China. Observations from the highly sophisticated multi-axis differential optical absorption spectroscope (MAX-DOAS) instrument were used for nitrogen dioxide (NO2) and formaldehyde (HCHO) column densities. In situ measurements for NO2, ozone (O3), particulate matter (PM2.5) and the air quality index (AQI) were also used. The concentration of trace gases in the atmosphere reduces significantly during annual Spring Festival holidays, whereby mobility is reduced and anthropogenic activities come to a halt. The COVID-19 lockdown during 2020 resulted in a considerable drop in vertical column densities (VCDs) of HCHO and NO2 during lockdown Level-1, which refers to strict lockdown, i.e., strict measures taken to reduce mobility (43% for NO2; 24% for HCHO), and lockdown Level-2, which refers to relaxed lockdown, i.e., when the mobility restrictions were relaxed somehow (20% for NO2; 22% for HCHO), compared with pre-lockdown days, as measured by the MAX-DOAS instrument. However, for 2019, a reduction in VCDs was found only during Level-1 (24% for NO2; 6.62% for HCHO), when the Spring Festival happened. The weekly cycle for NO2 and HCHO depicts no significant effect of weekends on the lockdown. After the start of the Spring Festival, the VCDs of NO2 and HCHO showed a decline for 2019 as well as 2020. Backward trajectories calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicated more air masses coming from the sea after the Spring Festival for 2019 and 2020, implying that a low pollutant load was carried by them. No impact of anthropogenic activity was found on O3 concentration. The results indicate that the ratio of HCHO to NO2 (RFN) fell in the volatile organic compound (VOC)-limited regime. [ABSTRACT FROM AUTHOR]

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 2 ) 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 NO 2 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 2 and HCHO profiles during COVID-19 lockdown. The simulations illustrate that the NO 2 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, NO 2 maintained decreasing at all altitudes, while HCHO decreased at low altitudes and increased at high altitudes. During COVID-19 lockdown, the reduction of NO 2 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., (© 2022. American Geophysical Union. All Rights Reserved.)