Speaker
Description
Light-absorbing aerosols, particularly black carbon (BC) and mineral dust (MD), remain among the most uncertain light-absorbing components in our understanding of Earth's radiative balance. The mass absorption cross-section (MAC) serves as the critical link between aerosol mass and light absorption, yet literature reported values vary dramatically –by a factor of four for BC alone (Wang et al., 2023). This variability stems not only from natural differences in particle sources and morphology but also from fundamental limitations in existing measurement approaches. In this study, we propose integrating two complementary techniques: photothermal aerosol absorption monitor (PTAAM) (Drinovec et al., 2022); and the single particle soot photometer (SP2). PTAAM measures aerosol light absorption with high sensitivity, while SP2 provides single-particle resolution of the scattering and incandescence signal of refractory particle, from which the rBC mass is derived (Tian et al., 2025). By coupling these instruments with advanced machine learning (ML) algorithms, particularly graph neural networks (GNNs), we aim to extract more physically meaningful MAC values from complex atmospheric datasets (Lamb et al., 2023). The methodology herein proposed involves several innovations. First, we will extend SP2 calibration beyond its traditional rBC focus to accurately quantify other refractory absorbing particles, such as iron oxide content in mineral dust. Second, multi-wavelength PTAAM measurements will be coupled with classifying instrumentation to achieve mass-resolved absorption data. Third, data fusion algorithms (i.e., GNN, Random Forest, XGBoost) will leverage the complementary strengths of both techniques. Transfer learning approaches, such as pretraining GNNs on laboratory or numerically generated aerosol populations and fine tuning them on field PTAAM–SP2 measurements, may help bridge the gap between controlled laboratory conditions and field observations, though the generalization of such models requires careful validation. Ultimately, this work will provide climate modelers with more constrained aerosol light-absorption parameters, potentially reducing uncertainties in radiative forcing estimates.