Bridging the Gap Between Single-Particle Measurements and Aerosol Models With SPIN-Sim

KYUHAENG LEE, Maria Zawadowicz, Xiaoli Shen, Daniel Cziczo, Arthur J. Sedlacek, Matthew West, Nicole Riemer, University of Illinois at Urbana-Champaign

     Abstract Number: 398
     Working Group: Instrumentation and Methods

Abstract
Aerosol mixing state, the distribution of chemical species across individual aerosol particles, is crucial for quantifying aerosol optical, chemical, and microphysical properties. For example, a particle’s absorptivity depends on whether light-absorbing components such as black carbon are externally or internally mixed with other species. Accurately representing this compositional complexity in aerosol models is essential, but direct comparisons between particle-resolved simulations and composition-resolved measurements (e.g., single-particle mass spectrometer) remain challenging. While particle-resolved Monte Carlo aerosol models such as PartMC track the mass of individual species per particle, single-particle mass spectrometers report ion signals as a function of mass-to-charge ratios. These signals do not scale linearly with species mass, making it challenging to infer per-particle composition directly. To bridge this gap, we introduce SPIN-sim (Single Particle Instrument Simulator), a framework that converts mass spectrometer measurements into model-comparable outputs. Using Non-negative Matrix Factorization (NMF), SPIN-sim decomposes measured mass spectra to estimate the fractional contribution of individual species within mixed particles. Tests on synthetic mixtures demonstrate that SPIN-sim can reconstruct species fractions with errors below 5% for most particles. Ongoing work focuses on extending this approach to laboratory data, including calibration for instrument-specific response. By enabling a quantitative mapping between single-particle measurements and particle-resolved model outputs, SPIN-sim provides a new pathway for validating mixing state representation in aerosol models and advancing model-measurement integration in aerosol science.