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Abstract. In recent years, satellite remote sensing has been increasingly used in the long-term observation of ozone (O3) precursors and its formation regime. In this work, formaldehyde (HCHO) data from Ozone Monitoring Instrument (OMI) were used to analyze the temporal and spatial distribution of HCHO vertical column densities (VCDs) in Shanghai from 2010 to 2019. HCHO VCDs exhibited the highest value in summer and the lowest in winter, the high VCD being concentrated in western Shanghai. Temperature largely influences HCHO by affecting the biogenic emissions and photochemical reactions, and industry was the major anthropogenic source. The satellite-observed formaldehyde-to-nitrogen dioxide ratio (FNRSAT) reflects that the O3 formation regime had significant seasonal characteristics and gradually manifested as a transitional ozone formation regime dominating in Shanghai. The uneven distribution in space was mainly reflected in the higher FNRSAT and surface O3 concentration in suburban areas. To compensate for the shortcoming of FNRSAT that it can only characterize O3 formation around satellite overpass time, correction of FNRSAT was implemented with hourly surface FNR and O3 data. After correction, the O3 formation regime showed the trend moving towards being VOC-limited in both time and space, and the regime indicated by FNRSAT can better reflect O3 formation for a day. This study can help us better understand HCHO characteristics and O3 formation regimes in Shanghai and also provide a method to improve FNRSAT for characterizing O3 formation in a day, which will be significant for developing O3 prevention and control strategies.

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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.

The highly enantioselective copper-catalyzed propargylic amination of propargylic esters with amine hydrochloride salts has been realized for the first time using copper salts with chiral N,N,P-ligands. This method features a...