Machine Learning Assisted Inference of the Particle Charge Fraction and the Ion-induced Nucleation Rates during New Particle Formation Events

Abstract. The charge state of atmospheric new particles is controlled by both their initial charge state upon formation and subsequent interaction with atmospheric ions. By measuring the charge state of growing particles, the fraction of ion-induced nucleation (F_IIN) within total new particle formation (NPF) can be inferred, which is critical for understanding NPF mechanisms. However, existing theoretical approaches for predicting particle charge states suffer from inaccuracies due to simplifying assumptions, hence their ability to infer F_IIN is sometimes limited. Here we develop a numerical model to explicitly simulate the charging dynamics of new particles. Our simulations demonstrate that both particle growth rate and ion concentration substantially influence the particle charge state, while ion-ion recombination becomes important when the charged particle concentrations are high. Leveraging a large set of simulations, we constructed two regression models using residual neural networks. The first model (ResFWD) predicts the charge state of growing particles with known F_IIN values, while the second model (ResBWD) operates in reverse to estimate F_IIN based on the charge fraction of particles at prescribed sizes. Good agreement between the regression models and benchmark simulations demonstrates the potential of our approach for analysing ion-induced nucleation events. Sensitivity analysis further reveals that ResFWD and the benchmark simulations exhibit similar sensitivity to input noises, but the robustness of ResBWD requires that the information of initial particle charge state is retained at the prescribed sizes. Our study provides insights on charging dynamics of atmospheric new particles and introduces a new method for assessing ion-induced nucleation rates.