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?

This manuscript by Fiddler et al. presents the findings of the ACACIA pilot project, which aimed to characterize the optical properties of BB aerosols from African fuels and to intercompare measurement techniques (AE33 aethalometer vs. photoacoustic reference). The study is significant and timely: Africa is a major source of BB aerosol with growing impact on climate and air quality, yet its aerosol optical properties are less studied. The work fits well within the scope of Atmospheric Measurement Techniques, as it deals with instrument calibration, intercomparison, and methodological advances in aerosol optical measurements. Most of my criticisms are relatively major and should be straightforward to address.   I believe the paper will be a valuable contribution to AMT after the authors revise the manuscript in response to the specific comments below.

1. The use of a three-wavelength photoacoustic spectrometer (PASS-3) as the reference for AE33 absorption measurements is appropriate. However, since the PASS-3 provided data at 405 nm and 532 nm (with the 781 nm channel not used) while the AE33 has channels at 370, 470, 520 nm, the authors extrapolate or interpolate the absorption to those wavelengths (likely assuming a power-law wavelength dependence via an Ångström exponent). Please clarify in the methods how this extrapolation was done and discuss the associated uncertainty. Figure S1 (burn 17 example) is mentioned for extrapolating using AAE – the manuscript should ensure the procedure is described in Section 2.4. How sensitive are the derived Cλ values to the assumed power-law? For example, if the aerosol absorption does not follow a strict power-law between 405 and 532 nm, could that bias the extrapolated α_abs at 370 or 470 nm? Since the PASS measurement uncertainty is noted as ~40%, it would also be useful to comment on how this uncertainty propagates to the reported Cλ. Right now the paper reports variability (standard deviations) of Cλ across runs, but a statement on the absolute accuracy or uncertainty of the Cλ values (accounting for instrument calibration uncertainties) would strengthen confidence in the results.
2. Tables 3 and 4 contain many functional forms, which makes the findings hard to follow. The manuscript should down-select and focus on the best-performing function (-Cλ/(1–Cλ) = A·ω + B), highlights its advantages, and discuss its potential physical meaning. Otherwise, it may appear overly data-driven without mechanistic insight.
3. The reported AAE values above 10 are unusual and exceed most prior studies. The manuscript should provide a more careful discussion of the possible causes. Without this, readers may question the reliability of these extreme values. On the other hand, negative or very low ASE might indicate presence of super-micron particles or ash that scatter more efficiently at longer wavelengths – perhaps related to soil/dust or inorganic residue in the fuel (especially dung might contain soil, and savannah grass fires could entrain dust). A short discussion linking these observations to possible physical causes would strengthen the impact. Currently the manuscript states the observation (dust-like optical values) but does not delve into why. Even if a detailed chemical analysis is outside the scope, a sentence like “These extreme Ångström exponent values may result from the high fraction of smoldering organic carbon (leading to very strong wavelength-dependent absorption) and/or the presence of coarse ash particles (leading to anomalous scattering spectra), distinguishing African BB aerosol from typical forest-fire smoke” would be helpful. Additionally, please clarify in the figure/caption how the reader should interpret negative ASE values – some readers might be unfamiliar with the idea that a negative exponent is possible. It might be worth noting that this occurs when larger particle modes cause scattering to increase with wavelength in the measured range (or note if it is within experimental uncertainty). This clarification can prevent confusion.
4. Table 5 shows MAC discrepancies as large as a factor of 9, which cannot be fully explained by the SMPS upper size cut alone. A dedicated subsection on measurement uncertainties and potential systematic bias would improve transparency and address concerns regarding reproducibility.

Minor coments:

1. In figures 2 and 3, pls add 95% confidence bands around fit lines.
2. Use color coding to distinguish fuels, which aid readability.
3. The NCAT combustion chamber description is too brief and only references McRee et al.(2024). Pls summarize key features, such as chamber volume, RH control, oxidant usage.
4. When referring to figures in the text, use consistent style (e.g., “Fig. 4” vs “Figure 4”). It looks like the manuscript mostly uses “Figure” spelled out, which is fine. Just ensure each figure is called out in order. I noticed Figure 1 is not explicitly referenced in the portions I read (it might be referenced in Section 2.2 or 2.3 when describing the setups – if not, please include a reference to Fig. 1 in the text so readers know to look at the schematic/configuration).
5. In Equation formatting, make sure all variables are defined. Equation (3) defines MAC_λ = α_abs,λ / M – earlier in the text, define “M” as the particulate mass concentration (µg m⁻³) if not already done. Likewise, if “MEC” (mass extinction cross-section) is used as a term, define it clearly on first use (I believe it is defined around Eq. 2, but just to be certain).