Machine Learning Reveals the Parameters Affecting the Gaseous Sulfuric Acid Distribution in a Coastal City: Model Construction and Interpretation

Although the fundamental mechanisms of atmos-pheric new particle formation events are largely associated with gaseous sulfuric acid monomer (SA), the parameters affecting SA generation and elimination remain unclear, especially in coastal areas where certain sulfur-containing precursors are abundant. In this study, we utilized machine learning (ML) in combination with field observations to map the link between SA and the influencing parameters. The developed random forest (RF) model performed well in creating simulations with an R2 of 0.90, and the significant factors were ultraviolet, methanesulfonic acid (MSA), SO2, condensation sink, and relative humidity in descending order. Among the five factors, MSA served as an indicator for sulfur-containing species from marine emissions. The black box of ML was broken to determine the marginal contribution of these five parameters to the model output using partial dependence plots and centered-individual conditional expectation plots. These results indicated that MSA had a positive impact on the performance of the RF model, and a co-occurring relationship was observed between MSA and SA during the nocturnal period. Our findings reveal that sulfur-containing species emitted from the marine environment have an impact on the formation of SA and should be considered in coastal areas.