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

Marjan Savadkoohi, Mohamed Gerras, Olivier Favez, Jean-Eudes Petit, Jordi Rovira, Gang I. Chen, Marta Via, Stephen Matthew Platt, Minna Aurela, Benjamin Chazeau, Joel F. De Brito, Véronique Riffault, Kostas Eleftheriadis, Harald Flentje, Martin Gysel-Beer, Christoph Hueglin, Martin Rigler, Asta Gregorič, Matic Ivančič, Hannes Keernik, Marek Maasikmets, Eleni Liakakou, Iasonas Stavroulas, Krista Luoma, Nicolas Marchand, Nikos Mihalopoulos, Tuukka Petäjä, André S.H. Prévôt, Kaspar R. Daellenbach, Petr Vodička, Hilkka Timonen, Anna Tobler, Jeni Vasilescu, Andrei Dandocsi, Saliou Mbengue, Stergios Vratolis, Olga Zografou, Aurélien Chauvigné, Philip K. Hopke, Xavier Querol, Andrés Alastuey, Marco Pandolfi

Publikasjonsdetaljer

Publikasjonstype: Vitenskapelig artikkel

Tidsskrift: Atmospheric Environment

Volum: 349

Artikkelnummer: 121121

Original versjon/DOI: https://doi.org/10.1016/j.atmosenv.2025.121121

NVA: https://hdl.handle.net/11250/3182815

Sammendrag:

The apportionment of equivalent black carbon (eBC) to combustion sources from liquid fuels (mainly fossil; eBCLF) and solid fuels (mainly non-fossil; eBCSF) 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 (AAELF) and solid fuels (AAESF), 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 AAELF can be robustly determined as the 1st percentile (PC1) of AAE values from fits with R2 > 0.99. This R2-filtering was necessary to remove extremely low and noisy-driven AAE values commonly observed under clean atmospheric conditions (i.e., low absorption coefficients). Conversely, AAESF 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 (AAELF) and PC99 (AAESF) values ranged from 0.79 to 1.08, and 1.45 to 1.84, respectively. The AAESF 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 AAESF values obtained from the two methods showed strong agreement, with a coefficient of determination (R2) 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.