Improving the predictions of black carbon (BC) optical properties at various aging stages using a machine-learning-based approach

It is necessary to accurately determine the optical properties of highly absorbing black carbon (BC) aerosols to estimate their climate impact. In the past, there has been hesitation about using realistic fractal morphologies when simulating BC optical properties due to the complexity involved in the simulations and the cost of the computations. In this work, we demonstrate that, by using a benchmark machine learning (ML) algorithm, it is possible to make fast and highly accurate predictions of the optical properties for BC fractal aggregates. The mean absolute errors (MAEs) for the optical efficiencies ranged between 0.002 and 0.004, whereas they ranged between 0.003 and 0.004 for the asymmetry parameter. Unlike the computationally intensive simulations of complex scattering models, the ML-based approach accurately predicts optical properties in a fraction of a second. Physiochemical properties of BC, such as total particle size (number of primary particles (Npp), outer volume equivalent radius (ro), mobility diameter (Dm), outer primary particle size (ao), fractal dimension (Df), wavelength (λ), and fraction of coating (fcoating), were used as input parameters for the developed ML algorithm. An extensive evaluation procedure was carried out in this study while training the ML algorithms. The ML-based algorithm compared well with observations from laboratory-generated soot, demonstrating how realistic morphologies of BC can improve their optical properties. Predictions of optical properties like single-scattering albedo (ω) and mass absorption cross-section (MAC) were improved compared to the conventional Mie-based predictions. The results indicate that it is possible to generate optical properties in the visible spectrum using BC fractal aggregates with any desired physicochemical properties within the range of the training dataset, such as size, morphology, or organic coating. Based on these findings, climate models can improve their radiative forcing estimates using such comprehensive parameterizations for the optical properties of BC based on their aging stages.