Machine learning revealing key factors influencing HONO chemistry in Beijing during heating and non-heating periods.

Nitrous acid (HONO) is of great interest due to its contribution to hydroxyl (OH) radicals by self-photolysis. Nowadays, machine learning (ML) algorithms are good at capturing complicated non-linear relationships between predictors and dependent variables. Here, using the whole year of 2018 of observed HONO and related pollutant data at an urban site in Beijing, an ML-RF (random forest) model is carried out to predict HONO concentrations and explore the main factors influencing HONO formation mechanisms. ML-RF models show satisfactory performance during the heating, non-heating and whole year periods with R values of 0.95, 0.96 and 0.95, respectively. Primary emissions and diffusion have an obvious influence on ambient HONO during the heating period, while chemical formation processes such as NO 2 heterogeneous reaction and photolysis of nitrate are important for HONO during the non-heating period with higher RH and stronger solar intensity. O 3 and NH 3 are the most important variables for HONO in both periods, indicating the close relationship of HONO with atmospheric oxidation and the important role of NH 3 in HONO formation processes. Although there are deviations due to some variability in HONO formation mechanisms between years, ML-RF models based on 2018 data are able to roughly predict HONO for three periods in 2017 and 2021. Overall, machine learning with limited meteorological and pollutant parameters offers great advantages in HONO prediction, and it can also provide some clues to improve the chemical mechanisms of HONO by finding related variables of ambient HONO. [Display omitted] • Machine learning predicted HONO shows satisfactory performance in 2018. • O 3 and NH 3 are the most important HONO predictors. • Main HONO sources are different between heating and non-heating periods. • Machine learning model is feasible to predict HONO across years. [ABSTRACT FROM AUTHOR]