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.

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Publication Details

Journal
Atmospheric Environment
Published
2025-02-22
DOI
https://doi.org/10.1016/j.atmosenv.2025.121121
Citations
8
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
10.41

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article

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

Hannes Keernik, Stergios Vratolis, Petr Vodička, Andrê S. H. Prévôt et al.
8 citations
Atmospheric Environment
Atmospheric chemistry and aerosols
10.41
article

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

Hannes Keernik, Stergios Vratolis, Petr Vodička, Andrê S. H. Prévôt, Olivier Favez, Saliou Mbengue, Benjamin Chazeau, Joël Brito, Stephen M. Platt, Christoph Hueglin, N. Mihalopoulos, Minna Aurela, Aurélien Chauvigné, Andrés Alástuey, Asta Gregorič, Gang Chen, Krista Luoma, Marco Pandolfi, Véronique Riffault, Marta Via, H. Flentje, Jean‐Eudes Petit, Iasonas Stavroulas, Hilkka Timonen, Xavier Querol, Marek Maasikmets, Matic Ivančič, Marjan Savadkoohi, Nicolas Marchand, Konstantinos Eleftheriadis, Martin Gysel‐Beer, Tuukka Petäjä, Anna Tobler, Olga Zografou, Eleni Liakakou, Philip Karl Hopke, Martin Rigler, Jeni Vasilescu, Jordi Rovira, Kaspar R. Daellenbach, Andrei Valentin Dandocsi, Mohamed Gherras
article en
8 citations

Abstract

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.

Atmospheric EnvironmentVol. 349
Finnish Meteorological Institute (FI), Centre National de la Recherche Scientifique (FR), Deutscher Wetterdienst (DE), University of Nova Gorica (SI), National Centre of Scientific Research "Demokritos" (GR), Institut Mines-Télécom (FR), Aix-Marseille Université (FR), Université de Lille (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Paul Scherrer Institute (CH), Kjeller Innovasjon (Norway) (NO), Institute of Materials Science (VN), Estonian Environmental Research Center (Estonia) (EE), Laboratoire des Sciences du Climat et de l'Environnement (FR), Institut national de l'environnement industriel et des risques (FR), Institute of Nuclear and Particle Physics (GR), Informatizacija Energetika Avtomatizacija (Slovenia) (SI), Institute of Environmental Assessment and Water Research (ES), MRC Centre for Environment and Health, Imperial College London (GB), Swiss Federal Laboratories for Materials Science and Technology (CH), Universitat de Barcelona (ES), Universitat Politècnica de Catalunya (ES)
Ministerul Cercetării, Inovării şi Digitalizării, Agence Nationale de la Recherche, Ministerstvo Školství, Mládeže a Tělovýchovy, Generalitat de Catalunya, Ministry of Environment, Ministerio de Ciencia e Innovación, Horizon 2020 Framework Programme, Agència de Gestió d'Ajuts Universitaris i de Recerca, European Regional Development Fund
Openalex Percentile: Top 1%
Atmospheric chemistry and aerosols
10.41
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