Abstract. Among the various measurement approaches to quantify the light absorption coefficient (Babs), filter-based absorption photometers are dominant in monitoring networks around the globe. Numerous correction algorithms have been introduced to minimize the artifacts due to the presence of the filter in these instruments. However, from our recent studies conducted during the Fire Influence on Regional and Global Environments Experiment (FIREX) laboratory campaign, corrected filter-based Babs remains biased high by roughly a factor of 2.5 when compared to a reference value using a photoacoustic instrument for biomass burning emissions. Similar overestimations of Babs from filter-based instruments exist when implementing the algorithms on 6 months of ambient data from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) user facility from 2013 (factor of roughly 3). In both datasets, we observed an apparent dependency on single-scattering albedo (SSA) and the absorption Ångström exponent (AAE) in the agreement between Babs based on existing correction factors and the reference Babs. Consequently, we developed a new correction approach that is applicable to any filter-based absorption photometer that includes light transmission from the filter-based instrument as well as the derived AAE and SSA. For the FIREX and SGP datasets, our algorithm results in good agreement between all corrected filter-based Babs values from different filter-based instruments and the reference (slopes ≈1 and R2≈0.98 for biomass burning aerosols and slopes ≈1.05 and R2≈0.65 for ambient aerosols). Moreover, for both the corrected Babs and the derived optical properties (SSA and AAE), our new algorithms work better or at least as well as the two common correction algorithms applied to a particle soot absorption photometer (PSAP). The uncertainty of the new correction algorithm is estimated to be ∼10 %, considering the measurement uncertainties of the operated instruments. Therefore, our correction algorithm is applicable to any filter-based absorption photometer and has the potential to “standardize” reported results across any filter-based instrument.

Mass absorption cross-section of black carbon (MACBC) 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 MACBC 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 MACBC 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 MACBC across different wavelengths. We assessed the applicability of the proposed approaches in estimating MACBC using different statistical metrics (such as coefficient of determination (R2), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MACBC 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 MACBC, 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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