Investigating the impact of Glyoxal retrieval from MAX-DOAS observations during haze and non-haze conditions in Beijing.

This study presents the Multi Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements for Glyoxal (CHOCHO) in Beijing, China (39.95°N, 116.32°E). CHOCHO is the smallest compound of di-carbonyl group. As a primary sink of CHOCHO, its photolysis with NOx (oxides of nitrogen) results in the production of tropospheric ozone. Therefore, the focus of CHOCHO DOAS measurements is increasing in trend. We did the measurements from 09 May 2017 to 09 September 2017. The study was conducted to compare different retrieval settings in order to reveal best DOAS fit settings for CHOCHO; furthermore, effect of haze and non-haze days on CHOCHO concentration was examined. The root mean square of residual and Differential Slant Column density (dSCD) error was reduced when measurements were done with lower wavelength limit around 432-438 nm and upper intervals around 455-460 nm. Thus, lower wavelength intervals around 432-438 nm and upper intervals around 457-460 nm were best for the retrieval of dSCDs for CHOCHO. Meteorological conditions like haze or non-haze days did not have significant effect on DOAS fit parameters. The CHOCHO vertical column densities range from 1.33E+14 to 9.77E+14 molecules/cm2 during the study period with average of 6.16E+14 molecules/cm2. The results indicated that during haze days CHOCHO concentration was higher because of lower rate of photolysis and atmospheric oxidation potential. Our results did not show any significant weekend effect on CHOCHO atmospheric concentration.

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

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.