Identifying Driving Factors of Atmospheric NO with Machine Learning.

Dinitrogen pentoxide (NO) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly promoting nitrate formations. However, identifying key environmental drivers of NO formation remains challenging using traditional statistical methods, impeding effective emission control measures to mitigate NO-induced air pollution. Here, we adopted machine learning assisted by steady-state analysis to elucidate the driving factors of NO before and during the 2022 Winter Olympics (WO) in Beijing. Higher NO concentrations were observed during the WO period compared to the Pre-Winter-Olympics (Pre-WO) period. The machine learning model accurately reproduced ambient NO concentrations and showed that ozone (O), nitrogen dioxide (NO), and relative humidity (RH) were the most important driving factors of NO. Compared to the Pre-WO period, the variation in trace gases (i.e., NO and O) along with the reduced NO uptake coefficient was the main reason for higher NO levels during the WO period. By predicting NO under various control scenarios of NO and calculating the nitrate formation potential from NO uptake, we found that the progressive reduction of nitrogen oxides initially increases the nitrate formation potential before further decreasing it. The threshold of NO was approximately 13 ppbv, below which NO reduction effectively reduced the level of night-time nitrate formations. These results demonstrate the capacity of machine learning to provide insights into understanding atmospheric nitrogen chemistry and highlight the necessity of more stringent emission control of NO to mitigate haze pollution.