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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. 

Abstract. Observations of the organic components of the natural aerosol are scarce in Antarctica, which limits our understanding of natural aerosols and their connection to seasonal and spatial patterns of cloud albedo in the region. From November 2015 to December 2016, the ARM West Antarctic Radiation Experiment (AWARE) measured submicron aerosol properties near McMurdo Station at the southern tip of Ross Island. Submicron organic mass (OM), particle number, and cloud condensation nuclei concentrations were higher in summer than other seasons. The measurements included a range of compositions and concentrations that likely reflected both local anthropogenic emissions and natural background sources. We isolated the natural organic components by separating a natural factor and a local combustion factor. The natural OM was 150 times higher in summer than in winter. The local anthropogenic emissions were not hygroscopic and had little contribution to the CCN concentrations. Natural sources that included marine sea spray and seabird emissions contributed 56% OM in summer but only 3% in winter. The natural OM had high hydroxyl group fraction (55%), 6% alkane, and 6% amine group mass, consistent with marine organic composition. In addition, the Fourier transform infrared (FTIR) spectra showed the natural sources of organic aerosol were characterized by amide group absorption, which may be from seabird populations. Carboxylic acid group contributions were high in summer and associated with natural sources, likely forming by secondary reactions. 

Wildfires emit mixtures of light‐absorbing aerosols (including black and brown carbon, BC and BrC, respectively) and more purely scattering organic aerosol (OA). BC, BrC, and OA interactions are complex and dynamic and evolve with aging in the atmosphere resulting in large uncertainties in their radiative forcing. We report microphysical, optical, and chemical measurements of multiple plumes from the Woodbury Fire (AZ, USA) observed at Los Alamos, NM, after 11–18 hr of atmospheric transit. This includes periods where the plumes exhibited little entrainment as well as periods that had become more dilute after mixing with background aerosol. Aerosol mass absorption cross sections (MAC) were enhanced by a factor of 1.5–2.2 greater than bare BC at 870 nm, suggesting lensing by nonabsorbing coatings following a core‐shell morphology. Larger MAC enhancement factors of 1.9–5.1 at 450 nm are greater than core‐shell morphology can explain and are attributed to BrC. MAC of OA (MAC_Org) at 450 nm was largest in intact portions of the plumes (peak value bounded between 0.6 and 0.9 m²/g [Org]) and decreased with plume dilution. We report a strong correlation between MAC_Org(450 nm) with the f_C2H4O2(a tracer for levoglucosan‐like species) of coatings and of bulk OA indicating that BrC in the Woodbury Fire was coemitted with levoglucosan, a primary aerosol. f_C2H4O2and MAC_Org(450 nm) are shown to vary between the edge and the core of plumes, demonstrating enhanced oxidation of OA and BrC bleaching near plume edges. Our process‐level finding can inform parameterizations of mixed BC, BrC, and OA properties for wildfire plumes in climate models.

 

Soot particles form during combustion of carbonaceous materials and impact climate and air quality. When freshly emitted, they are typically fractal-like aggregates. After atmospheric aging, they can act as cloud condensation nuclei, and water condensation or evaporation restructure them to more compact aggregates, affecting their optical, aerodynamic, and surface properties. Here we survey the morphology of ambient soot particles from various locations and different environmental and aging conditions. We used electron microscopy and show extensive soot compaction after cloud processing. We further performed laboratory experiments to simulate atmospheric cloud processing under controlled conditions. We find that soot particles sampled after evaporating the cloud droplets, are significantly more compact than freshly emitted and interstitial soot, confirming that cloud processing, not just exposure to high humidity, compacts soot. Our findings have implications for how the radiative, surface, and aerodynamic properties, and the fate of soot particles are represented in numerical models.