Identifying Driving Factors of Atmospheric N₂O₅ with Machine Learning

Abstract

Dinitrogen pentoxide (N 2 O 5 ) 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 N 2 O 5 formation remains challenging using traditional statistical methods, impeding effective emission control measures to mitigate NO x -induced air pollution. Here, we adopted machine learning assisted by steady-state analysis to elucidate the driving factors of N 2 O 5 before and during the 2022 Winter Olympics (WO) in Beijing. Higher N 2 O 5 concentrations were observed during the WO period compared to the Pre-Winter-Olympics (Pre-WO) period. The machine learning model accurately reproduced ambient N 2 O 5 concentrations and showed that ozone (O 3 ), nitrogen dioxide (NO 2 ), and relative humidity (RH) were the most important driving factors of N 2 O 5 . Compared to the Pre-WO period, the variation in trace gases (i.e., NO 2 and O 3 ) along with the reduced N 2 O 5 uptake coefficient was the main reason for higher N 2 O 5 levels during the WO period. By predicting N 2 O 5 under various control scenarios of NO x and calculating the nitrate formation potential from N 2 O 5 uptake, we found that the progressive reduction of nitrogen oxides initially increases the nitrate formation potential before further decreasing it. The threshold of NO x was approximately 13 ppbv, below which NO x 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 x to mitigate haze pollution.

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