Predicting Nitrate Radical Reaction Rate Constants of Organic Compounds in the Atmospheric Aqueous Phase Affected by Inorganic Ions

The
oxidation of organic compounds by nitrate radicals (NO3) in the atmospheric aqueous phase makes significant contributions
to the production of secondary organic aerosols (SOA) and brown carbon
(BrC). However, relevant kinetic parameters remain scarce, particularly
in aerosol liquid water (ALW), where high concentrations of inorganic
ions coexist. In this study, we developed a predictive model to estimate
the aqueous-phase reaction rate constants between NO3 and
organic compounds (kNO3) using a novel
machine learning (ML)-based approach. By simultaneously considering
the chemical properties of the reactants and experimental conditions,
this model enables an accurate prediction of kNO3 across ionic strength (I) ranges of 0–6
M, while also accounting for the influence of different ionic species.
The model’s predictive accuracy, generalization ability, and
applicability domain (AD) are evaluated, followed by a mechanistic
interpretation via Shapley additive explanation (SHAP) analysis. In
summary, this study provides a valuable supplementary tool for estimating
kinetic parameters in both cloud droplets and ALW. As an application,
the model is employed to predict kNO3 values
for phenolic compounds emitted from biomass burning (BB), extending
the currently available data set for atmospheric modeling.