| Summary: | Mass absorption cross-section of black carbon (MAC<sub>BC</sub>) describes the absorptive cross-section per unit mass of black carbon, and is, thus, an essential parameter to estimate the radiative forcing of black carbon. Many studies have sought to estimate MAC<sub>BC</sub> from a theoretical perspective, but these studies require the knowledge of a set of aerosol properties, which are difficult and/or labor-intensive to measure. We therefore investigate the ability of seven data analytical approaches (including different multivariate regressions, support vector machine, and neural networks) in predicting MAC<sub>BC</sub> for both ambient and biomass burning measurements. Our model utilizes multi-wavelength light absorption and scattering as well as the aerosol size distributions as input variables to predict MAC<sub>BC</sub> across different wavelengths. We assessed the applicability of the proposed approaches in estimating MAC<sub>BC</sub> using different statistical metrics (such as coefficient of determination (R<sup>2</sup>), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MAC<sub>BC</sub> appropriately, but the prediction performance varies across approaches and atmospheric environments. Based on an uncertainty evaluation of our models and the empirical and theoretical approaches to predict MAC<sub>BC</sub>, we preliminarily put forth support vector machine (SVM) as a recommended data analytical technique for use. We provide an operational tool built with the approaches presented in this paper to facilitate this procedure for future users.
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