Search for: All records

Abstract. Measurement of light absorption of solar radiation byaerosols is vital for assessing direct aerosol radiative forcing, whichaffects local and global climate. Low-cost and easy-to-operate filter-basedinstruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through thefilter are widely used to infer aerosol light absorption. However,filter-based absorption measurements are subject to artifacts that aredifficult to quantify. These artifacts are associated with the presence ofthe filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural andurban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collectedsynchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inletat the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two mostcommonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean squareerror of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-locatednephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revisedform of the Virkkula (2010) algorithm suitable for the SGP site has beenproposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicabilityof our proposed RFR algorithm, we trained and tested it on a dataset oflaboratory measurements of combustion aerosols. Input variables to thealgorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-basedTricolor Absorption Photometer, and scattering coefficients from amultiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learningapproaches offer a promising path to correct for biases in long-termfilter-based absorption datasets and accurately quantify their variabilityand trends needed for robust radiative forcing determination.