the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Findings of the African Combustion Aerosol Collaborative Intercomparison Analysis (ACACIA) Pilot Project to Understand the Optical Properties of Biomass Burning Smoke
Abstract. Africa is a critical source of biomass burning (BB) aerosols, and its importance is increasing. The African Combustion Aerosol Collaborative Intercomparison Analysis (ACACIA) Pilot Project set to optically characterize BB aerosol generated from sub-Saharan African fuels. We used a photoacoustic spectrometer as a reference instrument to determine the multiple-scattering correction factor Cλ for an AE33 aethalometer at three wavelengths, which produced weighted mean values of C370 = 3.69, C470 = 5.65, and C520 = 6.39. Cλ increased with wavelength and C370 was statistically independent of the others, suggesting a single Cλ is insufficient, especially in BB scenarios. While a dependence of Cλ on burning state was not found, Cλ was shown to strongly relate to particle single scattering albedo (SSA, ω). When Cλ was plotted against SSA, values slowly rose at low SSA values, followed by a sharp rise around an SSA of ~0.9; indicating a larger correction needed for less absorbing aerosol. A number of functions operating on either SSA or Cλ were explored and the best function was -Cλ/(1‒Cλ) = Aω+B. This is an important parametrization of Cλ specifically geared towards BB aerosol from African fuels under different aging states, and is of particular importance for future field work in that continent. An Ångström matrix plot shows that African BB aerosol can have values more akin to dust, which demonstrates that these fuels are distinct in their wavelength dependence from more typical BB aerosol. Lastly, we examined the mass extinction and absorbance cross sections for BB aerosol generated for the same fuels with two different tube furnace setups. Not only is this combustion method flexible, it was found to be reproducible between labs.
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Status: open (until 22 Oct 2025)
- RC1: 'Comment on egusphere-2025-2720', Anonymous Referee #1, 20 Sep 2025 reply
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The study aims to optically characterize biomass burning aerosols from sub-Saharan African fuels, focusing on accurately determining the multiple-scattering correction factor for AE33 aethalometers and its relationship with particle single scattering albedo (SSA). The research develops a parametrization of the correction factor specific to African BB aerosols under different aging conditions, highlights their distinct wavelength dependence. I have following major questions for authors:
1.If emission data from different types of fuel combustion are fitted separately using your fit function, is there a large difference in the fitting quality? Is it possible that the fit works better for one or a few fuel types even if those types are not well-suited? Has the author considered this?
2.The paper mentions that PAM was used to simulate aging experiments. Specifically, what degree of aging equivalent 3days? or 7days? did the authors simulate? During the aging simulation, did the degree of aging vary? Moreover, the authors combined fresh and aged data in the linear relationship shown in Fig. 3, which makes it difficult to see the differences in SSA correlation between fresh and aged emissions. Therefore, it is unclear whether the authors’ statement that the results also apply to aged aerosols is justified. It is recommended that the authors present separate linear fit plots of the fit function for fresh and aged data.
3.First, Fig. 4 is difficult to interpret because the dashed lines are too cluttered. Second, what do the shaded areas represent? Does the pink shading indicate aged aerosols and the grey shading indicate fresh aerosols? From my understanding, there is still considerable discrepancy between the experimental data and the reference data. Since the comparison is made for similar sources, why do the authors’ experimental results differ so much from previous studies in Africa? If AAE and ASE differ substantially, could this affect the general applicability of the fit function to African fuel data?