Addressing the advantages and limitations of using Aethalometer data to determine the optimal absorption Ångström exponents (AAEs) values for eBC source apportionment

The apportionment of equivalent black carbon (eBC) to combustion sources from liquid fuels (mainly fossil; eBC LF ) and solid fuels (mainly non-fossil; eBC SF ) is commonly performed using data from Aethalometer instruments (AE approach). This study evaluates the feasibility of using AE data to determine the absorption Ångström exponents (AAEs) for liquid fuels (AAE LF ) and solid fuels (AAE SF ), which are fundamental parameters in the AE approach. AAEs were derived from Aethalometer data as the fit in a logarithmic space of the six absorption coefficients (470–950 nm) versus the corresponding wavelengths. The findings indicate that AAE LF can be robustly determined as the 1st percentile (PC1) of AAE values from fits with R 2 > 0.99. This R 2 -filtering was necessary to remove extremely low and noisy-driven AAE values commonly observed under clean atmospheric conditions (i.e., low absorption coefficients). Conversely, AAE SF can be obtained from the 99th percentile (PC99) of unfiltered AAE values. To optimize the signal from solid fuel sources, winter data should be used to calculate PC99, whereas summer data should be employed for calculating PC1 to maximize the signal from liquid fuel sources. The derived PC1 (AAE LF ) and PC99 (AAE SF ) values ranged from 0.79 to 1.08, and 1.45 to 1.84, respectively. The AAE SF values were further compared with those constrained using the signal at mass-to-charge 60 ( m/z 60), a tracer for fresh biomass combustion, measured using aerosol chemical speciation monitor (ACSM) and aerosol mass spectrometry (AMS) instruments deployed at 16 sites. Overall, the AAE SF values obtained from the two methods showed strong agreement, with a coefficient of determination (R 2 ) of 0.78. However, uncertainties in both approaches may vary due to site-specific sources, and in certain environments, such as traffic-dominated sites, neither approach may be fully applicable. • AAE LF was robustly identified as the 1st percentile of R 2 -filtered AAE values. • AAE SF was determined using the 99th percentile of unfiltered AAE values. • Seasonal data optimized liquid fuel (summer) and solid fuel (winter) signals. • AAE SF values agreed with AMS/ACSM data, with R 2 = 0.78 across 16 sites.