Computational Quantum Chemistry-Based Atmospheric Isotope Fractionation Prediction Framework: Application in Sulfate Aerosol Formation Mechanisms

Atmospheric isotope serves as an effective source tracer for tracking the pollutants fate. Its precise source apportionment capability is constrained by the comprehensive assessment of isotope fractionation. This study aimed to propose a Computational Quantum Chemistry (CQC)-based Atmospheric Isotope Fractionation Prediction (AIFP) framework. It can quantify the isotope fractionation mechanisms. This framework utilizes CQC to perform a pipeline for reaction mechanism determination, atmospheric species geometry calculation, and parameter post-processing. It integrates the isotope-substitution method with the fractionation model to establish isotope fractionation standard equations in complex atmospheric chemical systems, providing a theoretical basis for atmospheric isotope tracing. This framework was applied to investigate sulfur (34S) fractionation and sulfate (pSO42–) formation. This application developed a localized 34S fractionation parameter scheme, revealing that pSO42– pollution is driven by NO2 and TMI oxidation. These oxidants account for 58.3 ± 1.0% of its pollution, highlighting the critical role of controlling the transformation conditions of key oxidants in haze pollution mitigation. Our framework extends the atmospheric isotope application, providing a rapid and efficient isotope fractionation prediction framework.