Mass absorption cross- of black carbon (MAC(BC)) describes the absorptive cross- 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(BC) 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(BC) 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(BC) across different wavelengths. We assessed the applicability of the proposed approaches in estimating MAC(BC) using different statistical metrics (such as coefficient of determination (R-2), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MAC(BC) 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(BC), 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.

In this study, real-time absorption coefficients of carbonaceous species in PM2.5 was observed using a dual-spot 7-wavelength Aethalometer between November 1, 2016 and December 31, 2017 at an urban site of Gwangju. In addition, 24-hr integrated PM2.5 samples were simultaneously collected at the same site and analyzed for organic carbon and elemental carbon (OC and EC) using the thermal-optical transmittance protocol. A main objective of this study was to estimate mass absorption cross (MAC) values of black carbon (BC) particles at the study site using the linear regression between aethalometer-based absorption coefficient and filter-based EC concentration. BC particles observed at 880 nm is mainly emitted from combustion of fossil fuels, and their concentration is typically reported as equivalent BC concentration (eBC). eBC concentration calculated using MAC value of 7.77 m(2)/g at wavelength of 880 nm, which was proposed by a manufacturer, ranged from 0.3 to 7.4 mu g/m(3) with an average value of 1.9 +/- 1.2 mu g/m(3), accounting for 7.3% (1.5 similar to 20.9%) of PM2.5. The relationship between aerosol absorption coefficients at 880 nm and EC concentrations provided BC MAC value of 15.2 m(2)/g, ranging from 11.4 to 16.2 m(2)/g. The eBC concentrations calculated using the estimated MAC of 15.2 m(2)/g were significantly lower than those reported originally from aethalometer, and ranged from 0.2 to 3.8 mu g/m(3), with an average of 1.0 +/- 0.6 mu g/m(3), accounting for 3.7% of PM2.5 (0.8 similar to 10.7%). Result from this study suggests that if the MAC value recommended by the manufacturer is applied to calculate the equivalent BC concentration and radiative forcing due to BC absorption, they would result in significant errors, implying investigation of an unique MAC value of BC particles at a study site.