# 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 Gherras, Olivier Favez, Jean-Eudes Petit, Jordi Rovira, Gang Chen, Marta Via, Stephen Platt, Minna Aurela, Benjamin Chazeau, et al.

<figure><img src="image_1.png" /></figure>

► To cite this version:

Marjan Savadkoohi, Mohamed Gherras, Olivier Favez, Jean-Eudes Petit, Jordi Rovira, et al. Addressing the advantages and limitations of using Aethalometer data to determine the optimal Absorption Ångström Exponents (AAEs) values for eBC source apportionment. Atmospheric Environment, 2025, 349, pp.121121. 10.1016/j.atmosenv.2025.121121. hal-04970292

HAL Id: hal-04970292

https://hal.science/hal-04970292v1

Submitted on 28 Feb 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Journal Pre-proof

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 Gherras, Olivier Favez, Jean-Eudes Petit, Jordi Rovira, Gang I. Chen, Marta Via, Stephen 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, Iasons Stavroulas, Krista Luoma, Nicolas Marchand, Nikos Mihalopoulos, Tuukka Petäjä, Andre S.H. Prevot, 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

PII: S1352-2310(25)00096-2

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

Reference: AEA 121121

To appear in: *Atmospheric Environment*

Received Date: 3 December 2024

Revised Date: 12 February 2025

Accepted Date: 20 February 2025

Please cite this article as: Savadkoohi, M., Gherras, M., Favez, O., Petit, J.-E., Rovira, J., Chen, G.I., Via, M., Platt, S., Aurela, M., Chazeau, B., de Brito, J.F., Riffault, V., Eleftheriadis, K., Flentje, H., Gysel-Beer, M., Hueglin, C., Rigler, M., Gregorič, A., Ivančič, M., Keernik, H., Maasikmets, M., Liakakou, E., Stavroulas, I., Luoma, K., Marchand, N., Mihalopoulos, N., Petäjä, T., Prevot, A.S.H., Daellenbach, K.R., Vodička, P., Timonen, H., Tobler, A., Vasilescu, J., Dandocsi, A., Mbengue, S., Vratolis, S., Zografou, O., Chauvigné, A., Hopke, P.K., Querol, X., Alastuey, A., Pandolfi, M., Addressing the advantages and limitations of using Aethalometer data to determine the optimal Absorption Ångström Exponents (AAEs) values for eBC source apportionment, *Atmospheric Environment*, https://doi.org/10.1016/j.atmosenv.2025.121121.

<figure><img src="image_3.png" /></figure>
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2025 Published by Elsevier Ltd.
Addressing the advantages and limitations of using Aethalometer data to determine
the optimal Absorption Ångström Exponents (AAEs) values for eBC source
apportionment

Marjan Savadkoohi,ᵃᵇ*
Mohamed Gherras,ᶜ
Olivier Favez,ᶜ
Jean-Eudes Petit,ᵈ
Jordi Rovira,ᵃᵉ
Gang I. Chen,ᵉ
Marta Via,ᵉ
Stephen Platt,ʰ
Minna Aurela,ᵢ
Benjamin Chazeau,ⱼ
Joel F. de Brito,⁵
Véronique Riffault,⁵
Kostas Eleftheriadis,¹
Harald Flentje,⁵
Martin Gysel-Beer,ⁿ
Christoph Hueglin,⁰
Martin Rigler,ᵇ
Asta Gregorič,ᵇ
Matic Ivančič,ᵇ
Hannes Keernik,⁹,¹
Marek Maasikmets,⁹
Eleni Liakakou, r
Iasons Stavroulas, r
Krista Luoma, i,s
Nicolas Marchand, j
Nikos Mihalopoulos, r
Tuukka Petäjä, s
Andre S. H. Prevot, n
Kaspar R. Daellenbach, n
Petr Vodička, t
Hilkka Timonen, i
Anna Tobler, n,u
Jeni Vasilescu, v
Andrei Dandocsi, w
Saliou Mbengue, x
Stergios Vratolis,¹
Olga Zografou,¹
Aurélien Chauvigné, y
Philip K. Hopke, z, aa
Xavier Querol,ª
Andrés Alastuey,ª and Marco Pandolfi a,*

ª Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain

ᵇDepartment of Mining, Industrial and ICT Engineering (EMIT), Manresa School of Engineering (EPSEM), Universitat Politècnica de Catalunya (UPC), Manresa 08242, Spain

ᶜInstitut national de l'environnement Industriel et des risques (INERIS), Verneuil-en-Halatte, France

ᵈLaboratoire des Sciences du Climat et de l'Environnement, CEA/Orme des Merisiers, Gif-sur-Yvette, France

Department of Applied Physics-Meteorology, Universitat de Barcelona, Barcelona, 08028, Spain

f Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London W12 0BZ, UK

g Centre for Atmospheric Research, University of Nova Gorica, Vipavska 11c, SI-5270 Ajdovščina, Slovenia

h NILU, 2027 Kjeller, Norway

Atmospheric Composition Research, Finnish Meteorological Institute, 00560, Helsinki, Finland

j Aix Marseille Univ., CNRS, LCE, Marseille, France

k IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, Centre for Energy and Environment, Lille, France

¹ENRACT, Institute of Nuclear and Radiological Science & Technology, Energy & Safety, NCSR Demokritos, 15310 Ag. Paraskevi, Athens, Greece

m German Meteorological Service (DWD), Observatory Hohenpeissenberg, Germany

ⁿPSI Center for Energy and Environmental Sciences, 5232 Villigen PSI, Switzerland

º Laboratory for Air Pollution and Environmental Technology, Swiss Federal Laboratories for Materials Science and Technology (Empa), Duebendorf, Switzerland

p Aerosol d.o.o., Kamniška 39A, 1000 Ljubljana, Slovenia

q Estonian Environmental Research Centre, Air Quality Management Department, Tallinn, Estonia

r Institute for Environmental Research & Sustainable Development, National Observatory of Athens, Athens, Greece

s Institute for Atmospheric and Earth System Research/Physics (INAR), Faculty of Science, University of Helsinki, Helsinki, Finland
¹Institute of Chemical Process Fundamentals, Czech Academy of Sciences, Rozvojová 1/135, 165 00, Prague 6, Czech Republic

²Datalystica Ltd., Parkstrasse 1, 5234 Villigen, Switzerland

³National Institute of Research and Development for Optoelectronics INOE 2000, Magurele, Romania

⁴National University of Science and Technology POLITEHNICA Bucharest, Bulevardul Iuliu Maniu 1-3, Bucharest, 061071, Bucharest, Romania

⁵Global Change Research Institute, Czech Academy of Sciences, 603 00 Brno, Czech Republic

⁶AERIS/ICARE Data and Services Center, Univ. Lille, CNRS, CNES, Villeneuve-d'Ascq, France

⁷Department of Public Health Sciences, University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA

⁸Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, USA

¹Now at the University of Tartu, Institute of Physics, W. Ostwaldi 1, 50411 Tartu, Estonia

**\* Corresponding authors.**

**E-mail addresses:** marjan.savadkoohi@idaea.csic.es (M. Savadkoohi); marco.pandolfi@idaea.csic.es (M. Pandolfi).

**Abstract**

The apportionment of equivalent black carbon (eBC) to combustion sources from liquid fuels (mainly fossil; eBC<sub>LF</sub>) and solid fuels (mainly non-fossil; eBC<sub>SF</sub>) 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<sub>LF</sub>) and solid fuels (AAE<sub>SF</sub>), which are essential parameters for the AE approach. AAEs were calculated from Aethalometer data as the fit in a log-log space of the six absorption coefficients (470-950 nm) versus the corresponding wavelengths. Our results demonstrate that AAE<sub>LF</sub> can be robustly determined as the 1st percentile (PC1) of AAE values from fits with R<sup>2</sup>>0.99. This R<sup>2</sup>-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<sub>SF</sub> 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, while summer data should be used to calculate PC1 to maximize the signal from liquid fuel sources. The derived PC1 (AAE<sub>LF</sub>) and PC99 (AAE<sub>SF</sub>) values ranged from 0.79 to 1.08, and 1.45 to 1.84, respectively. The AAE<sub>SF</sub> values were further compared with those constrained using the signal at mass-to-charge 60 (m/z 60), a marker for fresh biomass combustion, measured by aerosol chemical speciation monitor (ACSM) and aerosol mass spectrometry (AMS) instruments deployed at 16 sites. Overall, the AAE<sub>SF</sub> values derived from the two methods showed strong agreement, with a coefficient of determination of 0.78. However, the uncertainties in both approaches can vary depending on site-
specific sources, and in certain environments, such as at traffic-dominated sites, neither approach
may be fully applicable.

**Keywords:** Aethalometer approach; Source apportionment; Absorption Ångström exponent; Equivalent black carbon; Solid fuels; Liquid fuels

# 1. Introduction

The relative proportion of black carbon (BC) and brown carbon (BrC) in carbonaceous aerosol (CA) particles partly determines the spectral dependence of light absorption ($b_{abs}$) described by the absorption Ångström exponent (AAE) that can be used for source apportionment (SA) of BC (e.g., Sandradewi et al., 2008). The underlying hypothesis in BC SA is that BC originating from fossil fuel (mostly liquid) combustion sources dominates the measured $b_{abs}$, whereas both BC and BrC from non-fossil (mostly solid) combustion sources contribute to $b_{abs}$. BC particles absorb efficiently over a wide spectral range, from Ultra Violet (UV) to Infra-Red (IR) and exhibit low wavelength dependence (i.e., AAE close to 1). Conversely, BrC can efficiently absorb radiation more strongly in the UV region than in the visible, thus determining a stronger wavelength dependence of the measured $b_{abs}$ (i.e., AAE>1).

Consequently, the AAE calculated from $b_{abs}$ measurements progressively increases with increasing contribution from solid fuel sources to CA (e.g., Helin et al., 2021; Minderytė et al., 2022; Titos et al., 2017). BC SA is commonly performed using the Aethalometer (AE) approach, which relies on two source-specific AAEs. This AE approach was originally developed by Sandradewi et al. (2008) to separate biomass burning ($BC_{BB}$) and traffic ($BC_{TR}$) contributions to BC in environments where these two combustion sources predominate. In this study, we define the AAE of BC emitted by fossil (or liquid) fuel combustion as $AAE_{LF}$ and the AAE linked to solid fuel combustion as $AAE_{SF}$. The AAE values originally proposed by Sandradewi et al. (2008) were 1 when BC is emitted by traffic ($AAE_{LF}$) and 2 when BC is emitted by biomass burning ($AAE_{SF}$). AAE estimations by Zotter et al. (2017) (0.9 and 1.68 for $AAE_{LF}$ and $AAE_{SF}$, respectively) were obtained by constraining the AAEs using independent radiocarbon ($^{14}$C) analysis to quantify fossil and non-fossil contribution to elemental carbon (EC). However, it is important to note that these values were obtained using previous Aethalometer devices (AE31), which employed different filter tapes and cut-off sizes, potentially affecting the AAE estimates.

Other methodologies have employed multi-tracer or macro-tracer approaches, incorporating auxiliary measurements such as levoglucosan (LG), organic carbon (OC), EC, $^{14}$C, or a combination of different SA techniques to improve the accuracy of the AE approach in attributing CA to their specific sources (e.g., Bibi et al., 2021; Briggs and Long, 2016; Fourtziou et al., 2017; Martinsson et al., 2017, Mousavi et al., 2019). Nevertheless, such collocated measurements are
not consistently available across air quality (AQ) monitoring networks (Ravi Kiran et al., 2019),
posing challenges for BC source attribution, thus limiting the effective implementation of a
harmonized methodology for BC source apportionment at a European scale.

As previously mentioned, we define an AAE pair as AAE<sub>LF</sub> and AAE<sub>SF</sub>. However, the terminology used in BC source apportionment has evolved over time. Initially, BC sources were categorized into traffic and biomass burning (Sandradewi et al., 2008; Zotter et al., 2017). More recently, the terms "liquid fuels" (e.g., gasoline or diesel) and "solid fuels" (i.e., biomass or coal) have been adopted to provide a more comprehensive classification, and this terminology will be used in the present study. This distinction is important, as the spectral dependence of b<sub>abs</sub> from solid fuel combustion is not limited to BB but can also include coal combustion, which exhibits similarly high spectral dependence (e.g., Ni et al., 2021). Moreover, in addition to the type of fuel burned, the burning conditions (efficiency) are also important factors in determining AAE values.

By way of example, Helin et al. (2021) studied the AAE variation from different fuel combustion
sources, including emissions from ships, buses, coal-fired power plants, and residential wood
burning (WB). They reported a broad range of AAE values (470-950 nm), spanning from -0.2 to
2.0. Their findings revealed that emissions from ship engines using high sulfur heavy fuel oil
(HFO) exhibited high AAE values, while wood combustion emissions demonstrated lower yet
highly variable AAEs, generally higher than those associated with traffic emissions. Shipping
emissions, in particular, can exhibit optical characteristics similar to those of solid fuel sources
(Corbin et al., 2018; Helin et al., 2021). However, their associated AAE values remain
insufficiently characterized. This variability arises from differences in marine fuel compositions
and engine loads, which influence particulate matter (PM) emissions (Nunes et al., 2017;
Schlaerth et al., 2021), including OC and BC, thereby leading to a variable AAE ranging from 1
to 2 (Aakko-Saksa et al., 2018). The relatively high content of polycyclic aromatic hydrocarbons
(PAHs) and metals in HFO (Lima et al., 2005) has been implicated in the elevated AAE values
observed in experimental studies (Aakko-Saksa et al., 2018; Helin et al., 2021). Similarly, high
AAE values have been associated with primary and photochemically aged coal combustion
emissions that produce highly absorbing OA particles (e.g., Ni et al., 2021).

Despite the complexity in the source dependent spectral dependence of the b<sub>abs</sub>, the majority of
BC SA studies performed so far employing the AE approach have used the AAE pairs provided
by Sandradewi et al. (2008) or Zotter et al. (2017) (e.g., Cuesta-Mosquera et al., 2024; Liakakou
et al., 2020; Tian et al., 2023; Velazquez-Garcia et al., 2024). Recently, Savadkoohi et al. (2023)
compared the relative contributions of eBC<sub>SF</sub> and eBC<sub>LF</sub> using the two aforementioned AAE pairs
and demonstrated that the relative contribution of eBC<sub>SF</sub> sources increased by 25-45% in urban
Europe when using the values from Zotter et al. (2017) compared to those from Sandradewi et al.
(2008). Although there is a consensus that AAE<sub>LF</sub> values fall within the range of 0.8–1.2, the AAE<sub>SF</sub> values can vary significantly due to factors such as type of fuel burnt, combustion conditions, and the resulting CA properties, such as morphology, size distribution, and aging (Becerril-Valle et al., 2017; Bond et al., 2013; Fuller et al., 2014; Garg et al., 2016; Gyawali et al., 2009; Harrison et al., 2013; Helin et al., 2021; Herich et al., 2011; Kirchstetter et al., 2004; Lack and Cappa, 2010; Lack and Langridge, 2013; Li et al., 2016; Liu et al., 2014; Martinsson et al., 2017; Saleh et al., 2013; Sandradewi et al., 2008; Titos et al., 2017; Wang et al., 2021; Zhang et al., 2019; Zotter et al., 2017). Therefore, the AE approach is highly sensitive to the selection of AAE pairs for BC SA, introducing considerable uncertainty. Despite its limitations including semi-quantitative nature and high associated uncertainty, AE approach remains a valuable tool for analyzing temporal variations in eBC sources in response to AQ policies. For instance, Savadkoohi et al. (2023) observed consistent trends in both eBC<sub>LF</sub> and eBC<sub>SF</sub>, demonstrating the impact of AQ directives on eBC mass concentrations across Europe.

This study investigates the feasibility of using experimental AAE values derived from AE33 $b_{\text{abs}}$ data to determine the AAE$_{LF}$ and AAE$_{SF}$. The methodology can also be extended to other multi-wavelength absorption measurements such as the AE36. The proposed method builds upon the approach introduced by Tobler et al. (2021), employing the AAE probability density function to visually identify AAE$_{LF}$ and AAE$_{SF}$ from the left and right tails of the AAE distribution, respectively. Applying this approach, Tobler et al. (2021) reported values of 0.85 for AAE$_{LF}$ and 1.90 for AAE$_{SF}$. This approach was also recently applied by Glojek et al. (2024) to determine AAE pairs in an Alpine valley influenced by emissions from cement plants, obtaining AAE$_{LF}$ of 1.0 and AAE$_{SF}$ of 2.1. Likewise, Rovira et al. (2024) effectively used the PC1 of the AAE distribution to estimate AAE$_{LF}$ at 44 European sites, obtaining values ranging from approximately 0.9 to 1.1.

In this study, we examined the application of this approach by proposing the use of the 1st and 99th percentiles (PC1 and PC99) to mathematically derive site-specific AAE<sub>LF</sub> and AAE<sub>SF</sub> pairs. We hypothesize that PC1 represents conditions in which $b_{\text{abs}}$ is dominated by BC from liquid fuel sources, while PC99 corresponds to conditions where BC is dominated by solid fuel sources. Additionally, we assessed the performance of the proposed approach based on PCs by incorporating external methods, such as the signal at mass-to-charge 60 (m/z 60; $C_2H_4O_2^+$) from aerosol chemical speciation monitor (ACSM) measurements, as an independent indicator of BB emissions (Chen et al., 2022; Fourtziou et al., 2017; Fröhlich et al., 2015; Srivastava et al., 2019). Moreover, the proposed approach can be implemented as an additional tool to enhance the accuracy and comparability of eBC mass concentrations dataset, particularly those compiled in 2023 pilot phase and SA conducted within the framework of the RI-URBANS project. The findings are directly applicable to monitoring stations using AE33 measurements. Specifically,
our objectives were (i) to refine the results of the AE SA approach; (ii) to evaluate the model's performance using site-dependent AAE<sub>LF</sub> and AAE<sub>SF</sub>; and (iii) to assess the applicability of this site-dependent approach for eBC SA within the pilot eBC dataset.

## 2. Methodology

### 2.1 Measurements sites and data processing

Multi-wavelength absorption data from AE33 instruments were collected from 16 monitoring stations across Europe in the framework of the RI-URBANS project. These included 6 urban (UB), 1 traffic (TR), 5 suburban (SUB), and 4 regional background (RB) sites. Some of these sites were previously analyzed in the study by Chen et al. (2022), who provided a detailed description of their characteristics. For the purposes of this study, raw AE33 data were obtained directly from data providers. The raw AE33 data collected from ambient measurements at 15 sites were treated as historical datasets. Additionally, AE33 data as well as the signal at mass-to-charge ratio 60 (m/z 60) from ACSM or AMS measurements, were collected during the RI-URBANS 2023 pilot phase, and were used for an independent estimation of the AAE<sub>SF</sub> at pilot sites. AE33 and ACSM data were then aggregated to a 30-minute resolution to match the temporal resolution of the ACSM measurements, except for the BIR-RB and HYY-RB sites, where data were averaged to 4-hour and 3-hour intervals, respectively. Table 1 details each monitoring site, including their settings, measurement periods, and data sources. Fig. S1 shows the distribution of these sites across Europe, supplying AE33 and ACSM data, covering diverse regions.

<table><caption>**Table 1.** Detailed characteristics of the 16 measurement sites supplying data. UB, urban background; SUB, suburban background; RB, regional background; and TR, Traffic.</caption><thead><tr><th>Site</th><th>Country</th><th>Acronym</th><th>Type</th><th>Latitude</th><th>Longitude</th><th>Data coverage</th><th>Type of data</th></tr></thead><tbody><tr><td>Barcelona</td><td>Spain</td><td>BCN</td><td>UB</td><td>41.3872</td><td>2.1157</td><td>2017-2018</td><td>Historical</td></tr><tr><td>Athens-NOA</td><td>Greece</td><td>ATHN</td><td>UB</td><td>37.9747</td><td>23.7164</td><td>2016-2017 &amp; 2023</td><td>Historical &amp; pilot</td></tr><tr><td>Zürich-Kaserne</td><td>Switzerland</td><td>ZUR</td><td>UB</td><td>47.3667</td><td>8.5500</td><td>2016-2017 &amp; 2023</td><td>Historical &amp; pilot</td></tr><tr><td>Paris-Chatelet</td><td>France</td><td>PAR</td><td>UB</td><td>48.8621</td><td>2.3446</td><td>2023</td><td>Pilot</td></tr><tr><td>Tartu</td><td>Estonia</td><td>TAR</td><td>UB</td><td>58.3707</td><td>26.7358</td><td>2016-2017</td><td>Historical</td></tr><tr><td>Marseille-Longchamp</td><td>France</td><td>MAR</td><td>UB</td><td>43.3053</td><td>5.3947</td><td>2017-2018</td><td>Historical</td></tr><tr><td>Sirta</td><td>France</td><td>SIR</td><td>SUB</td><td>48.7086</td><td>2.1490</td><td>2016-2017 &amp; 2023</td><td>Historical &amp; pilot</td></tr><tr><td>Athens-Demokritos</td><td>Greece</td><td>ATHD</td><td>SUB</td><td>37.9953</td><td>23.8158</td><td>2017-2018</td><td>Historical</td></tr><tr><td>Lille</td><td>France</td><td>LIL</td><td>SUB</td><td>50.6110</td><td>3.1404</td><td>2016-2017</td><td>Historical</td></tr><tr><td>RADO-Bucharest</td><td>Romania</td><td>BUC</td><td>SUB</td><td>44.3440</td><td>26.0120</td><td>2016-2017 &amp; 2023</td><td>Historical &amp; pilot</td></tr><tr><td>Kraków</td><td>Poland</td><td>KRA</td><td>SUB</td><td>50.0656</td><td>19.9156</td><td>2018-2019</td><td>Historical</td></tr></tbody></table>
<table><tr><td>Helsinki-<br>Mäkelänkatu</td><td>Finland</td><td>HEL</td><td>TR</td><td>60.1964</td><td>24.9522</td><td>2017-2018 &amp;<br>2023</td><td>Historical &amp; pilot</td></tr><tr><td>Birkenes</td><td>Norway</td><td>BIR</td><td>RB</td><td>58.3833</td><td>8.2500</td><td>2016-2018</td><td>Historical</td></tr><tr><td>Hyytiälä</td><td>Finland</td><td>HYY</td><td>RB</td><td>61.8500</td><td>24.2833</td><td>2017-2018</td><td>Historical</td></tr><tr><td>Hohenpeissenberg</td><td>Germany</td><td>HOH</td><td>RB</td><td>47.8014</td><td>11.0097</td><td>2017-2019</td><td>Historical</td></tr><tr><td>Košetice</td><td>Czech<br>Republic</td><td>KOS</td><td>RB</td><td>49.5733</td><td>15.0810</td><td>2019</td><td>Historical</td></tr></table>

The raw historical AE33 data were processed using a harmonized methodology to ensure consistency with the pilot data following the procedure described by Savadkoohi et al. (2023, 2024) to obtain the absorption coefficient ($b_{abs}$). In brief, the seven-wavelength $b_{abs}$ were recalculated from equivalent black carbon (eBC) mass concentrations using harmonization factors (H*) (Müller and Fiebig, 2018). Applying H* varies depending on the filter tape used and accounts for differences in the multiple-scattering correction factor ($C_0$) compared to those implemented by the manufacturer in Aethalometer firmware. Specifically, for AE33 instruments with filter tapes M8020/M8050, H* is 2.21 and $C_0$ is 1.57, while for the new filter tape, M8060, H* is 1.76 and $C_0$ is 1.39 (Yus-Díez et al., 2021; Müller and Fiebig, 2018). Further details on this data processing methodology can be found in the study by Savadkoohi et al. (2023, 2024). In addition, the periods of data used and the potential bias caused by older AE filter tapes should be considered for the experimental AAE determination. In cases, where AAE calculations were performed using data collected from 2018 onwards, the recommended M8060 filter tape was introduced. However, historical data (2016-2017) involved older filter tapes. Thus, we recommend to use the proposed approach based on PCs with the new filter tape, as this is the approach applied for the pilot sites SA.

## 2.2 Determining AAE pairs from AE33 data: An experimental approach

AAE<sub>LF</sub> and AAE<sub>SF</sub> were estimated as PC1 and PC99, respectively, based on the experimental AAE values. The AAE was calculated by applying a log-log fit to the $b_{abs}$ measurements across the 470–950 nm wavelength range at a 30-minute time resolution. This range was selected based on the goodness-of-fit metric ($R^2$) for PC1, ensuring minimal noise from high measurement uncertainties in low absorption conditions under a clean atmosphere. The PC1 approach, recently applied in different European studies by Navarro-Barboza et al. (2024) and Rovira et al. (2024). As shown in Fig. S2, $R^2$-filtering refines AAE distribution by filtering out poorly fitted values ($R^2 > 0.99$), thereby constraining the left and right tails of AAE to source-specific values for liquid and solid fuel emissions, respectively. To minimize solid fuel contributions, PC1 was derived exclusively from summer observations, while PC99 was calculated from unfiltered AAE using only winter observations to capture the maximum signal of solid fuel combustion. Alternatively, AAE<sub>LF</sub> and AAE<sub>SF</sub> can be estimated using summer morning and winter afternoon rush hours data to refine source identification. However, calculating AAE at different times of the day does not
change the diurnal pattern of eBC fractions. A result also reported by Zheng et al. (2021), who observed no significant differences in AAE values throughout the day. Our results were consistent across sites with no statistically significant differences between full dataset and rush hours estimates (p = 0.27 for PC1 and p = 0.31 for PC99).

Following the R² filtering criterion (Tobler et al., 2021), PC1 effectively represents emissions from liquid fuels, while PC99, derived from unfiltered AAE, corresponds to solid fuel combustion sources, particularly BB, which exhibits higher AAE and $b_{abs}$ values due to the presence of BrC-rich organic aerosols (Font et al., 2022; Jiang et al., 2024). This approach was validated using both historical and pilot datasets. Finally, the experimentally derived AAE pairs were applied to quantify the relative contributions of eBC$_{LF}$ and eBC$_{SF}$ using the AE approach (Sandradewi et al., 2008), enabling cross-site comparisons.

## 2.3 eBC source apportionment: Aethalometer approach

eBC source apportionment was performed using the AE approach (Sandradewi et al., 2008) for the wavelengths of 470 nm to 950 nm. This method uses high-temporal-resolution $b_{abs}$ measurements from the AE33 to quantify the relative contributions of liquid and solid fuel combustion sources to eBC. The approach is based on the assumption that BC from fossil fuel (mostly liquid) combustion dominates the measured $b_{abs}$, while solid fuel sources emit both BC and BrC, which absorb light in the UV-VIS spectral range, thereby increasing the AAE. The AE approach was initially developed in a region characterized by the presence of two primary sources of BC: biomass burning and fossil fuels. Thus, the AAE pairs in this approach consist of the AAE of BC emitted only by liquid fuel sources and the AAE of BC (+BrC) emitted only by solid sources. However, the use of percentile-based thresholds (PC1 and PC99) in this study, makes the methodology independent without being constrained by assumptions of the dominant fuel type in predefined liquid and solid source categorizations.

## 2.4 Determination of AAE$_{SF}$ constrained by *m/z* 60

The AE approach was constrained by integrating external tracer measurements such as *m/z* 60 (Blanco-Alegre et al., 2022; Harrison et al., 2013; Isolabella et al., 2024; Kaskaoutis et al., 2021a; Massabò et al., 2015) to determine the best fit in terms of AAE$_{SF}$. In this alternative approach, AAE$_{SF}$ was derived by maximizing the R² correlation between *m/z* 60 and the eBC$_{SF}$ estimates derived from the AE approach, using a constant AAE$_{LF}$ value set as PC1. The AAE$_{SF}$ parameter was systematically varied within a range of 1.1 to 2.6 to identify the value that maximizes the correlation between eBC$_{SF}$ and *m/z* 60, while intentionally minimizing its correlation with eBC$_{LF}$. This iterative optimization aimed to refine the accuracy of AE approach and was tested using both AE33 pilot phase data and historical datasets. However, it is important to note that organic
compounds contributing to *m/z* 60, such as LG, are not chemically stable and may undergo photo-degradation (photo-bleaching) during long-range transport (Liu et al., 2020). This effect could lead to an underestimation of BB contributions when using *m/z* 60 as a constraint for the AE approach (Sandradewi et al., 2008; Zheng et al., 2021, Yttri et al., 2011).

## 2.5 eBC pilot phase dataset and source apportionment software

The “pilot phase” dataset (2023) was developed based on the procedure described in Fig. S3. In summary, raw AE33 data were initially stored as text/ASCII-format files on a computer at the monitoring stations. These raw data files can be subsequently transformed into ACTRIS level 0 data files using a Python-based procedure developed in collaboration with ECAC-WCCAP (Topical center unit coordinating in-situ measurements of aerosol microphysical properties within ACTRIS). These Level 0 data files contain measurements’ metadata, including instrument information, measurement conditions, and AE33 operational parameters, as well as results from real-time measurements (typically covering the most recent hour). The Level 0 data files were then gathered through EBAS (https://ebasnilu.no/) on a centralized server located at AERIS-ICARE (CNRS), as part of ECAC-ACMCC (Topical center unit coordinating in-situ online measurements of aerosol chemical speciation), where they could be processed to 15-minute time resolution averages. For near real-time applications during the pilot phase, automatic data filtering protocol was implemented based on the consistency of the measurements at the 7 wavelengths: i) defining an upper and a lower value (of 0.7 and 3, respectively) for ‘acceptable’ range of ambient AAE, and ii) discarding data points where the spectral dependence exponential fit exhibited a correlation coefficient below the user-defined threshold (typically R² < 0.9). A detailed description of this data processing, along with a simplified version of the Python code, is provided in the supplementary materials and is available online via Zenodo (https://doi.org/10.5281/zenodo.14066407) for implementation on local computers.

## 2.6 Methodological limitations

The AE approach is subject to significant uncertainties stemming from a range of contributing factors related to both the model's assumptions and external variables. These include wavelength selection, fixed versus variable AAE values, measurement noise, non-BC absorbing components, source-specific variability, and temporal resolution, among others. Several studies have examined these uncertainties from different perspectives. For instance, Harrison et al. (2013) identified maximum random uncertainties of up to 10% in AAE values, and reported that the AE approach requires enhancement in regions where coal emissions represent a significant source. Similarly, Martinsson et al. (2017) reported that AAE values for wood burning and fossil fuels are associated with uncertainties of 30% and 10%, respectively.
Uncertainties in the mass absorption cross-section (MAC), a critical parameter in the AE approach, have also been widely discussed. Healy et al. (2017) noted that MAC related uncertainties could reach 25% in extreme cases due to variability in BC coating thickness, at each measurement site. They estimated an error of 20% when independent measurements relating to traffic or BB emissions are unavailable. As highlighted by Alfoldy et al. (2022), a systematic error of 25% in BC measurements, results in total uncertainties of 31% for liquid fuels and 33% for BB sources. As discussed by Alfoldy et al. (2022), while the carbon content of diesel fuel is well-characterized, wood carbon content can vary significantly, introducing an additional 7% uncertainty when the specific type of burned wood is unknown. Consequently, the total uncertainties for liquid fuel emissions remains at 31%, while it increases to about 40% for BB emissions. Saarikoski et al. (2021) highlighted the challenges in interpreting the AE approach due to the presence of multiple eBC sources beyond the conventional categories of wood combustion and vehicle emissions, as well as the uncertainty in identifying a unique set of AAE values applicable to each site. Similarly, Virkkula et al. (2021) noted that using any constant value may impose significant limitations on the AE approach.

Zheng et al. (2021) estimated propagation of errors, indicating uncertainties of approximately 28% for liquid fuel and around 56% for wood burning, consistent with earlier reports by Favez et al. (2010). Overall, the literature suggests that uncertainties in AAE values are site-specific and are subject to modification in future studies. It is important to note that the AE approach provides only indicative information and the PC approach applied to the AE model is a way to refine its results. However, it does not fully compensate for the model's inherent limitations. Therefore, although methodological adjustments can enhance the interpretation of AE model results, they cannot fully resolve the uncertainties in cases where the AE model underperforms. Nevertheless, despite these methodological uncertainties, BC SA remains a valuable tool for investigating variations in the chemical composition of CA, particularly at sites with long-term datasets to study eBC trends (e.g., Savadkoohi et al., 2023).

# 3. Results and discussion

## 3.1 Variation of AAE

Table 2 presents the statistical distribution of experimental AAE (470-950 nm) for datasets unfiltered by the $R^2$ threshold. The observed variability in AAE across sites reflects differences in source characteristics. The Median AAE values at UB sites, such as ATHN-UB (1.42) and TAR-UB (1.33), showed higher averages compared to other UB sites like MAR-UB (1.12) and BCN-UB (1.13). This indicates a higher contribution of solid fuel sources, mostly residential BB, which tends to elevate AAE values. The relatively low standard deviations (0.10 to 0.15) indicate higher consistency in aerosol compositions within UB sites. The lower PCs (PC5 and PC25),
approaching or below 1.00, indicate a stronger influence from liquid fuel sources in the left tail of the AAE distribution. Conversely, higher PC values (PC75, PC95) show more variable AAE values, likely due to heterogeneous combustion conditions, diverse local and regional source contributions, including secondary organic aerosol (SOA) formation. SUB sites, such as KRA-SUB (1.34) and BUC-SUB (1.34), showed slightly higher median AAE values and variability compared to UB sites. This is likely attributed to the complex composition of coal used for residential heating, which is highly dependent on the specific coal type (Tobler et al., 2021), as further reflected in the higher standard deviations (e.g., BUC-SUB = 0.21). For instance, the increase in AAE at higher PCs at both BUC-SUB and KRA-SUB may point to BB contributions from local and long-range transport sources, as well as coal combustion. The lower variability at the lower PCs for SUB sites suggests a more consistent impact of liquid fuel sources. RB sites showed the narrowest range of AAE values, with the lowest median recorded at BIR-RB (1.20) and the highest at KOS-RB (1.38). The variability in standard deviations and PCs indicates considerable heterogeneity in source composition at these locations, influenced by regional and long-range transport (e.g., KOS-RB), particularly from regional BB or aged aerosols.

<table><caption>**Table 2.** Statistical measures of unfiltered AAE for urban, traffic, sub-urban, and regional background sites using historical data. Standard deviation (SD) indicating the spread of data around the mean.</caption><thead><tr><th>Sites</th><th>Median</th><th>Mean</th><th>SD</th><th>PC5</th><th>PC25</th><th>PC75</th><th>PC95</th></tr></thead><tbody><tr><td>ATHN-UB</td><td>1.42</td><td>1.42</td><td>0.15</td><td>1.20</td><td>1.31</td><td>1.52</td><td>1.64</td></tr><tr><td>BCN-UB</td><td>1.13</td><td>1.13</td><td>0.11</td><td>0.97</td><td>1.05</td><td>1.20</td><td>1.31</td></tr><tr><td>MAR-UB</td><td>1.12</td><td>1.14</td><td>0.11</td><td>1.02</td><td>1.07</td><td>1.19</td><td>1.35</td></tr><tr><td>TAR-UB</td><td>1.33</td><td>1.35</td><td>0.15</td><td>1.14</td><td>1.25</td><td>1.42</td><td>1.63</td></tr><tr><td>ZUR-UB</td><td>1.23</td><td>1.24</td><td>0.10</td><td>1.10</td><td>1.17</td><td>1.30</td><td>1.41</td></tr><tr><td>HEL-TR</td><td>1.20</td><td>1.20</td><td>0.10</td><td>1.04</td><td>1.12</td><td>1.28</td><td>1.37</td></tr><tr><td>ATHD-SUB</td><td>1.22</td><td>1.24</td><td>0.11</td><td>1.09</td><td>1.16</td><td>1.30</td><td>1.45</td></tr><tr><td>BUC-SUB</td><td>1.34</td><td>1.33</td><td>0.21</td><td>1.02</td><td>1.18</td><td>1.48</td><td>1.64</td></tr><tr><td>KRA-SUB</td><td>1.34</td><td>1.34</td><td>0.15</td><td>1.15</td><td>1.23</td><td>1.43</td><td>1.54</td></tr><tr><td>LIL-SUB</td><td>1.21</td><td>1.26</td><td>0.16</td><td>1.09</td><td>1.15</td><td>1.33</td><td>1.56</td></tr><tr><td>SIR-SUB</td><td>1.18</td><td>1.21</td><td>0.16</td><td>1.00</td><td>1.09</td><td>1.31</td><td>1.51</td></tr><tr><td>BIR-RB</td><td>1.20</td><td>1.21</td><td>0.14</td><td>1.00</td><td>1.13</td><td>1.29</td><td>1.45</td></tr><tr><td>HOH-RB</td><td>1.25</td><td>1.25</td><td>0.14</td><td>1.06</td><td>1.16</td><td>1.34</td><td>1.45</td></tr><tr><td>HYY-RB</td><td>1.25</td><td>1.23</td><td>0.18</td><td>0.92</td><td>1.12</td><td>1.34</td><td>1.48</td></tr><tr><td>KOS-RB</td><td>1.38</td><td>1.36</td><td>0.20</td><td>0.99</td><td>1.27</td><td>1.49</td><td>1.62</td></tr></tbody></table>

Fig. 1 shows the distribution of experimental AAE (470-950 nm) as a function of (f60), the ratio of the integrated signal at m/z 60 to the total signal in the organic component mass spectrum (i.e., the fraction of m/z 60 to the total organic mass) using historical data. Here we used a threshold for the f60 = 0.3 % of OA, which was deemed appropriate based on ambient observations at several locations. This value of f60 ~0.3% has been reported as a background level in air masses not impacted by active open BB (Cubison et al., 2011). The f60 marker was often associated with
elevated AAE values due to the influence of BB at most sites. However, sites like HYY-RB, and BIR-RB showed different patterns, where the influence of *f60* was less pronounced at the right tail of the AAE distribution. In cases where high AAE values coincide with high *f60* levels (e.g., at SIR-SUB, BUC-SUB, and KRA-SUB), it can be inferred that BB was the dominant source driving high AAE values.

Across Europe, BB emissions constitute a major source of BC in non-UB areas, such as rural or RB sites (Becerril-Valle et al., 2017; Kristensson et al., 2020; Mbengue et al., 2020; Savadkoohi et al., 2023). However, the presence of other solid fuel sources may also influence the observed AAE variability at certain RB sites (e.g., HYY-RB, HOH-RB, and BIR-RB). At these locations, high AAE values were not consistently associated with correspondingly high *f60*, indicating potential contributions from aged BB plumes. This reduces the effectiveness of *f60* as an indicator, particularly when compared to sites exposed to primary BB sources (Cubison et al., 2011). This issue is particularly relevant at remote sites in Northern Europe, where short-range transport of aged aerosol particles or natural sources, particularly biogenic secondary organic aerosol (BSOA) in summer contributes to CA (Chen et al., 2022; Yttri et al., 2020, 2011). In previous studies, BB organic aerosol (BBOA) was not detected at HYY-RB. Instead, aged primary organic aerosol (POA) was predominant in winter, while high BSOA was observed in summer at this site (Chen et al., 2022). At HOH-RB, BBOA has been reported for only 2% of the total PM₁, reflecting the site's mountain-top location, which is frequently isolated from near-surface flows of local and regional BB plumes, particularly during late-autumn and winter seasons (Flenje et al., 2015).

At BIR-RB, reductions in OC/EC from traffic and industrial sources have been more pronounced than those from BB emissions (Yttri et al., 2020). The observed pattern in the BIR-RB data may be attributed to the low signal-to-noise ratios (SNR) in the ACSM measurements, considering that Yttri et al. (2021) demonstrated residential wood burning emissions (quantified using LG) were significantly higher in winter than in summer at this site. Distinct patterns were also observed at TAR-UB and ZUR-UB sites. At TAR-UB, *f60* values were distributed over a wider AAE range, indicating a significant contribution from BBOA throughout the year (Chen et al., 2022). At ZUR-UB, previous studies have identified sources of BrC linked to primary emissions from urban activities, with liquid fuel combustion playing a significant role during summer (Zotter et al., 2017). Overall, while *f60* is generally associated with primary BB emissions, the presence of other solid fuel sources and aged organic particles with different chemical or physical properties may also contribute to the observed variability in AAE. This complexity limits the effectiveness of *f60* in differentiating non-BB solid fuel sources, particularly in regions where OA and non-biomass BrC contributions may confound results. To address these challenges more effectively, we
employed experimental AE33 data to define PC1 and PC99 thresholds, providing a more refined approach for distinguishing eBC sources.

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Frequency distribution of experimental AAE (470-950 nm) for historical AE33 observations, as a function of f60.</figcaption></figure>
Fig. 2 shows the distribution of both filtered and unfiltered experimental AAE values. The PC1 values were filtered using an $R^2$ threshold > 0.99 from summer data, while the PC99 values remained unfiltered and were derived from winter data. It is also important to note that even in summer, minor contributions from solid fuel sources may persist, with traffic emissions also contributing to BrC, as can be observed in the diurnal pattern of eBC fractions in Fig. S4. Consequently, the derived $AAE_{LF}$ confirms that the $eBC_{LF}$ component resolved by AE approach, includes contributions from both liquid and solid fuels associated with traffic emissions. As a result, the PC99 threshold becomes less effective at traffic-dominated sites, where vehicle exhaust emissions consistently outweighs solid fuel contributions year-round (Tobler et al., 2021). For instance, at the BCN-UB, where traffic emissions remain a dominant source, the PC99 may underestimate the actual AAE of solid fuels due to the significant influence of liquid fuels. Therefore, the PC99 is likely to provide only a lower-bound estimate of $AAE_{SF}$, as the true AAE for solid fuels is expected to be higher, especially at sites heavily influenced by traffic.

The filtered $AAE_{LF}$ values ranging from 0.79 to 1.08 were consistent and comparable with those reported in the literature for BC from liquid fuel combustion (e.g., Blanco-Alegre et al., 2020; Bond and Bergstrom, 2006; Favez et al., 2010; Kaskaoutis et al., 2021a; Mousavi et al., 2019; Sandradewi et al., 2008; Titos et al., 2017; Zheng et al., 2021; Zotter et al., 2017). However, PC1 values below 0.90 were observed at three SUB sites (i.e., SIR, BUC, ATHD), which may be attributed to variations in sampling periods or different filter tape usage. In addition, variability in PC1 likely reflects differences in combustion conditions, including variations in vehicle fleets (Zotter et al., 2017). This highlights the complexity of environments with multiple emission sources, where combustion efficiency and fuel content vary considerably. As a result, applying a harmonized $AAE_{LF}$ value of 1.0 cannot be extrapolated to all types of traffic plumes, as also demonstrated by Garg et al. (2016) and Wang et al. (2021).

For instance, in regions characterized by significant shipping emissions or a variety of fuel types, incorporating external tracers from reference methods becomes crucial when more than two well-defined BC sources are present. This ensures the robust application of the AE approach (Ivančič et al., 2022; Velazquez-Garcia et al., 2024; Zotter et al., 2017). This scenario applies to MAR-UB, which recorded a PC1 value of 0.87, relatively low compared to other UB sites. Recent source apportionment studies at MAR-UB indicate that, liquid fuel sources were associated with traffic emissions and harbor/industrial activities. These sources account for the organic fraction linked to shipping emissions and may also include SOA formed through photochemical aging during the transport of plumes toward the monitoring station (Chazeau et al., 2022).

The unfiltered $AAE_{SF}$ values observed in this study ranged from 1.45 at HEL-TR and BCN-UB to 1.78 and 1.84 at BUC-SUB and LIL-SUB, respectively. The lowest PC99 value of 1.45, was
consistent with the findings of Zotter et al. (2017) based on winter observations at the 470–950 nm and serves as a reasonable threshold for AAE<sub>SF</sub>. Zotter et al. reported AAE<sub>SF</sub> values between 1.23 and 2.17, derived using three different AAE<sub>LF</sub> values of 0.9, 1.0, and 1.1. Notably, all AAE<sub>SF</sub> values reported here were below 2.0, with the lowest values observed at sites with high traffic emissions (BCN-UB, HEL-TR). Although values below the commonly reported 1.8–2.2 range are less typical, Zotter et al. (2017) reported that under certain conditions, lower AAE<sub>SF</sub> values can still be valid for the AE approach. Consequently, the determination of AAE<sub>SF</sub> remains subject to considerable uncertainty due to the variability in light-absorbing properties of OA, which are influenced by factors such as fuel type, combustion conditions, combustion stage, and whether the BC is fresh or aged (Kumar et al., 2018; Liu et al., 2018). These factors complicate the interpretation of AAE<sub>SF</sub> values. For instance, at the MAR-UB, BB emissions were attributed to open green waste burning and residential heating from hardwood combustion during fall and winter, respectively, (Salameh et al., 2018) with the effect of shipping emissions in the same city.

To evaluate the uncertainty associated with PC1 and PC99 values across different sites within the same city, a sensitivity test was performed using simultaneous AE33 data from BCN-TR, BCN-UB, the Port of BCN, and Manlleu, a small town in northeastern Spain influenced by both traffic and high BB emissions. As shown in Fig. S5, in Manlleu, PC99 (AAE<sub>SF</sub>= 1.72) was comparable to values reported by Zotter et al. (2017). A recent study by Canals-Angerri et al. (2024), using data from the 2016-2017 campaign, identified domestic and commercial BB as the main source of PM at this site, contributing 48% to OA. In contrast, the Port of BCN showed the highest PC99 values (AAE<sub>SF</sub>= 2.49), while BCN-TR recorded the lowest (AAE<sub>SF</sub>= 1.54). The BCN-UB site (AAE<sub>SF</sub>= 1.64), impacted by road traffic and nearby industrial activities, was less influenced by BB emissions compared to Manlleu. However, the UB site also reflected the impact of emissions from BCN port, where shipping emissions often classified as liquid fuels can replicate those of solid fuel combustion, particularly when high-sulfur content HFO are used (e.g., Helin et al., 2021). This suggests that shipping activities can contribute significantly to light-absorbing OAs, especially in port areas. Furthermore, results indicate that PC1 values showed relatively limited variation (ranging from 0.91 to 1.05) compared to PC99, thereby reinforcing the reliability of PC1 in estimating AAE<sub>LF</sub>. Nevertheless, these results also emphasize the effectiveness of PC99 in locations where BB sources substantially contribute to eBC concentrations.
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Experimental determination of AAE<sub>LF</sub> and AAE<sub>SF</sub> derived from historical summer and winter data based on percentile approach. The PC1 is obtained after filtering out data points with poor signal-to-noise ratios using an R²>0.99 criterion, while PC99 represents the unfiltered data.</figcaption></figure>

At the LIL-SUB site, AAE<sub>SF</sub> values of up to 1.60 (370–880 nm) have previously been reported, particularly during winter when residential heating and agricultural waste burning were the dominant sources (Velazquez-Garcia et al., 2024). This finding is consistent with our reported value of 1.84 at this site. The AAE<sub>SF</sub> value of 1.78 observed at BUC-SUB can be attributed to the city's increased reliance on solid fuels, specifically wood and coal for heating during colder months (Ilie et al., 2023). Additionally, the relatively high AAE<sub>SF</sub> of 1.76 at KOS-RB can be explained by aged CA resulting from long-range transport, as well as BB and coal combustion from local sources used for domestic heating in winter (Pokorná et al., 2022). The impact of aged OAs from BB also influenced this rural site (Lhotka et al., 2025). Moreover, at SIR-SUB, the AAE<sub>SF</sub> value of 1.67 correlates with an observed increase in BBOA emissions during colder months over an extended period (Zhang et al., 2020, 2019).
Table 3 summarizes the experimentally derived AAE pairs, comparing both filtered and unfiltered AAE values from PC1 and PC99 using summer and winter observations, respectively. Recent estimates of AAE pairs by Zotter et al. (2017) were primarily based on winter data. Therefore, incorporating high-time resolution eBC concentrations from summer observations provides further evaluation of the AE approach. The impact of filtering on PC1 was more pronounced at SUB and RB sites. For instance, at KOS-RB, the unfiltered PC1 value of 0.53 increased to 0.93 after filtering, yielding a more representative value for fresh traffic emissions. The unfiltered PC99 values during winter were consistently higher or comparable to filtered values across all sites (e.g., at ATHN-UB= 1.72 and HYY-RB= 1.53). Overall, biases in AAE estimates can range from +20% to -40% when considering the broad range of AAE values reported in the literature. For more experimentally constrained AAE ranges, the uncertainty in short-wavelength $b_{abs}$ has been reported to range from +7% to -22% (Lack and Langridge, 2013).

The AE approach was applied using AAE pairs of PC1 (B) and PC99 (A) to determine eBC source fractions. Notably, the use of low $AAE_{SF}$ values can lead to an overestimation of the eBC from solid fuel sources due to the mathematical characteristics of the bilinear AE approach. Zotter et al. (2017) similarly demonstrated this at ZUR-UB, highlighting the variability introduced by different AAE pair selections. Likewise, Savadkoohi et al. (2023) reported different eBC source fractions when using AAE pairs from Sandradewi et al. (2008), with even more discrepancies observed when comparing their results to the AAE pairs recommended by Zotter et al. (2017) in the same study. Consequently, a degree of uncertainty persists regarding the accurate attribution of solid and liquid fuel sources at the studied sites (Zheng et al., 2021).

<table><caption>**Table 3.** Filtered PC1 and unfiltered PC99 of the AAE and corresponding $eBC_{LF}$ and $eBC_{SF}$ relative percent contribution for the entire historical dataset. A) Unfiltered, B) Filtered, \*Summer, \*\*Winter. eBC relative % source fractions were obtained from the PC1\*(B) and the PC99\*(A).</caption><thead><tr><th>Sites</th><th>PC1* (a)</th><th>PC99** (b)</th><th>PC1* (b)</th><th>PC99** (a)</th><th>eBC<sub>LF</sub>%</th><th>eBC<sub>SF</sub>%</th></tr></thead><tbody><tr><td>ATHN-UB</td><td>1.11</td><td>1.52</td><td>1.08</td><td>1.72</td><td>41.7</td><td>58.3</td></tr><tr><td>BCN-UB</td><td>1.02</td><td>1.45</td><td>1.03</td><td>1.46</td><td>75.5</td><td>24.5</td></tr><tr><td>MAR-UB</td><td>0.87</td><td>1.49</td><td>0.87</td><td>1.52</td><td>57.8</td><td>42.2</td></tr><tr><td>TAR-UB</td><td>1.06</td><td>1.61</td><td>1.06</td><td>1.61</td><td>48.0</td><td>52.0</td></tr><tr><td>ZUR-UB</td><td>0.94</td><td>1.50</td><td>0.94</td><td>1.58</td><td>52.9</td><td>47.1</td></tr><tr><td>HEL-TR</td><td>0.98</td><td>1.45</td><td>0.98</td><td>1.45</td><td>50.9</td><td>49.1</td></tr><tr><td>ATHD-SUB</td><td>0.80</td><td>1.62</td><td>0.80</td><td>1.63</td><td>49.4</td><td>50.6</td></tr><tr><td>BUC-SUB</td><td>0.76</td><td>1.77</td><td>0.79</td><td>1.78</td><td>44.3</td><td>55.7</td></tr><tr><td>KRA-SUB</td><td>1.07</td><td>1.61</td><td>1.07</td><td>1.62</td><td>45.5</td><td>54.5</td></tr><tr><td>LIL-SUB</td><td>1.00</td><td>1.82</td><td>1.00</td><td>1.84</td><td>67.2</td><td>32.8</td></tr><tr><td>SIR-SUB</td><td>0.82</td><td>1.62</td><td>0.82</td><td>1.67</td><td>54.5</td><td>45.5</td></tr><tr><td>HOH-RB</td><td>0.96</td><td>1.58</td><td>0.98</td><td>1.61</td><td>57.7</td><td>42.3</td></tr><tr><td>HYY-RB</td><td>0.84</td><td>1.24</td><td>0.91</td><td>1.53</td><td>46.4</td><td>53.6</td></tr><tr><td>KOS-RB</td><td>0.53</td><td>1.72</td><td>0.93</td><td>1.76</td><td>46.0</td><td>54.0</td></tr></tbody></table>
## 3.2 Correlation between AAE<sub>SF</sub> and m/z 60

In order to constrain the AE approach using *m/z* 60, a fixed PC1 derived from summer-filtered AAE was employed. The differentiation between the two eBC fractions based on *m/z* 60 requires a strong correlation between *m/z* 60 and eBC<sub>SF</sub>. However, selecting the optimal AAE<sub>SF</sub> value involves a degree of subjectivity, as it requires balancing multiple criteria. As shown in Fig. 3, at low AAE<sub>SF</sub> values, the correlation between *m/z* 60 and eBC<sub>SF</sub> initially mirrors that of total eBC where eBC<sub>SF</sub> closely approximates total eBC. The correlation then gradually increases until reaching a peak, after which it stabilizes. This stabilization occurs because the remaining particles primarily represent the eBC<sub>SF</sub> component. Conversely, the eBC<sub>LF</sub> correlation curve increases, approaching total eBC correlation due to the inclusion of eBC<sub>SF</sub> within eBC<sub>LF</sub>. Within an acceptable AAE<sub>SF</sub> range, the correlation of eBC<sub>SF</sub> should not fall below the total eBC correlation with *m/z* 60. Similarly, the correlation of eBC<sub>LF</sub> should not exceed the total eBC correlation with *m/z* 60. This is because as eBC<sub>LF</sub> tends to reduce the correlation, whereas eBC<sub>SF</sub> increases it.

As shown in Fig. S6, at ATHN-UB, a site influenced by both traffic and residential heating emissions, a strong correlation was observed between eBC<sub>SF</sub> and *m/z* 60 at an AAE<sub>SF</sub> value around 1.72. This aligns with long-term optical characterization indicating significant contributions from residential wood burning during winter at this site (Kaskaoutis et al., 2021b). Previous studies have also reported a strong correlation between *m/z* 60 and BB at this site, with eBC concentrations being strongly associated with BB tracers such as LG and *m/z* 60 during winter, especially at night when residential wood combustion intensifies (Liakakou et al., 2020).

At BUC-SUB, the optimal AAE<sub>SF</sub> was approximately 1.80. The *m/z* 60 correlation indicates significant contributions from both liquid and solid fuel sources. However, this site is known for significant BB emissions in winter and long-range transport BB mostly in summer (Chen et al., 2022), which correlated well with the *m/z* 60. At SIR-SUB, a small fraction of eBC<sub>SF</sub> resulted in R<sup>2</sup> values steadily increasing with increasing AAE<sub>SF</sub>, stabilizing at R<sup>2</sup> ≈ 0.8. This indicates an improving correlation with *m/z* 60 as AAE<sub>SF</sub> rises. Similarly, at ZUR-UB, R<sup>2</sup> values for eBC<sub>SF</sub> increased with AAE<sub>SF</sub>, peaking near 1.60, showing a marked increase in correlation with *m/z* 60 as AAE<sub>SF</sub>. This value was consistent with increased residential WB emissions in winter (Grange et al., 2020). The method generally performed consistently across sites, with minor variations (see Fig. 3).

Typically, when the correlation with eBC<sub>SF</sub> reached its maximum, the correlation for eBC<sub>LF</sub> tended to be minimum. However, this was not always the case. At some sites such as HOH-RB, HYY-RB, KOS-RB, and TAR-UB as shown in Fig. S7, the *m/z* 60 constraint was less effective
for different reasons. The observed decline in the correlation of eBC<sub>SF</sub> with increasing AAE<sub>SF</sub>, and in some cases, the correlation falling below that of total eBC with *m/z* 60, suggests that the model may not be effectively differentiating between the two eBC fractions. This trend complicates the assumption that eBC<sub>SF</sub> should result in a stronger correlation with *m/z* 60 than eBC<sub>LF</sub>, thereby raising uncertainties regarding the validity of attributing only two distinct eBC sources at these sites. Such behavior was specifically observed at RB sites, when the correlation of eBC<sub>LF</sub> or total eBC with *m/z* 60 exceeds that of eBC<sub>SF</sub>, contradicting the expected results. At KOS-RB, the correlation was particularly weak (R² < 0.1). This can be due to the fact that coal combustion was not captured by the *m/z* 60 signal, despite its correlation with eBC<sub>SF</sub> (Lhotka et al., 2025). At BIR-RB, the dataset lacked sufficient winter data points for ACSM analysis. Overall, the weak correlation at some sites may be attributed to multiple sources with similar optical properties, secondary aerosol formation, the presence of non-BC components among other potential issues that yet many of its properties and potential impacts remain unknown or fall beyond the scope of our current analysis.

The optimal AAE<sub>SF</sub> values derived using *m/z* 60 constraint were highly consistent with those obtained from unfiltered winter AAE data (PC99). At BCN-UB and HEL-TR sites, dominated by traffic-related emissions, neither PC99 nor *m/z* 60 produced reasonable AAE<sub>SF</sub> values. Yet, the *m/z* 60 approach was effective at UB and SUB sites, where *b*<sub>abs</sub> are dominated by POA. However, the *m/z* 60 constraint was less effective where SOA fraction is dominant and highly variable, particularly at RB sites. This can be attributed to the degradation of LG, as indicated by the *f*60 marker, which typically degrades rapidly under ambient conditions. Velazquez-Garcia et al. (2024) reported that significant depletion of BrC components can occur within 24 hours of atmospheric aging, even under the relatively low photochemical activity during winter, particularly in emissions from residential heating sources.
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Determination of AAE<sub>SF</sub> from winter observations constrained with *m/z* 60 using historical data. PC1 is fixed and filtered (R<sup>2</sup> > 0.99) from summer observation.</figcaption></figure>

Figure 4 compares the *m/z* 60-derived AAE<sub>SF</sub> values with PC99 from winter data. The linear regression analysis demonstrated a strong correlation (R<sup>2</sup> = 0.78) for unfiltered data, while filtering reduced the correlation (R<sup>2</sup> = 0.40), likely due to the exclusion of solid fuel signals. Given the impact of mixed aerosols on chemical and optical properties, we recommend avoiding filtering PC99 when constraining AAE<sub>SF</sub>. Since, the AAE values are highly dependent on the chemical composition of CA particles, with higher AAE values correlating with increased b<sub>abs</sub> of BrC.

The *m/z* 60 constraint was particularly useful for pilot sites to derive the optimal AAE<sub>SF</sub> value, as shown in Fig. S6. The AAE<sub>SF</sub> values were consistent with those derived from historical data, with minor differences likely attributed to the use of winter 2023 data for the correlation analysis. Overall, the use of *m/z* 60 for determining AAE<sub>SF</sub> was found to be highly relevant; however, the uncertainties associated with this approach can vary considerably depending on the site specific sources. The correlation between the relative contributions of eBC<sub>SF</sub> obtained from the complete dataset using unfiltered PC99 and *m/z* 60 was assessed using quantitative statistical metrics, as shown in Fig. S8 for winter data. The metrics used include Normalized Mean Error (NME), Root Mean Square Error (RMSE), the slope and intercept of the regression line, and the coefficient of
determination (R²). Among all monitoring sites, SIR-SUB, LIL-SUB, and KRA-SUB demonstrated the highest R² values between eBCSF and m/z 60 during winter observations.

<figure><img src="image_7.png" /><figcaption>**Figure 4.** Correlation between the PC99 of winter AAE data and optimal values from the *m/z* 60 method for unfiltered (red) and filtered (blue) datasets. Linear regression lines for both datasets are shown, indicating a strong positive correlation for unfiltered data (R² = 0.78) compared to a weaker correlation for filtered data (R² = 0.39), highlighting the influence of filtering on the strength of the correlation.</figcaption></figure>

Figure S9 compares eBC fraction estimates from two methods: PC-based approach and the *m/z* 60 constraint, both applied to the entire dataset. The two approaches produced comparable results, especially at UB sites where primary sources dominate. However, minor differences can be attributed to the *m/z* 60 method's incorporating BB tracer to capture secondary eBC sources and aged aerosols, especially in SUB areas such as KRA-SUB and SIR-SUB, where primary sources are less dominant. The limited number of data points where eBCSF > eBCLF, makes it challenging to constrain AAEsF only based on PCs. As a result, optimizing AAEsF can be performed using auxiliary data or adopting from sites where solid fuel sources far outweigh those from liquid fuels. However, applying mathematically defined AAE pairs verifies reasonable results to use in the AE approach compared to constant AAE values that can introduce significant uncertainties, particularly when tracer measurements, such as *m/z* 60, are not available at monitoring sites. Thus, by using site-specific AAE pairs, the accuracy of eBC SA can be improved.

**4. Conclusions**

This study assessed the performance of the Aethalometer approach for eBC source apportionment using site-specific Absorption Ångström Exponent (AAE<sub>LF</sub> and AAE<sub>SF</sub>) values (470-950 nm) across 16 European sites. The analysis included both historical and 2023 pilot phase AE33
datasets, requiring harmonization of eBC mass concentrations and absorption coefficients, based
on the latest methodological recommendations. The study tested the application of percentile-
based method to determine site-specific AAE<sub>LF</sub> and AAE<sub>SF</sub>. The results emphasize the need for
site-specific calibration, as fixed, AAE values fail to account for regional variability in eBC
sources. This study highlights several findings and recommendations:

* Adapting AAE<sub>SF</sub> and AAE<sub>LF</sub> values to site-specific characteristics is crucial for AE33-based source apportionment. In the absence of ancillary data, the percentile-based AAE approach provides a practical alternative for refining eBC source apportionment.

* AAE values derived from fits with R²> 0.99 using summer data (0.79-1.08) are recommended for estimating AAE<sub>LF</sub> defined as the 1st percentile of ambient AAE distributions to minimize brown carbon (BrC) sources.

* For AAE<sub>SF</sub>, the 99th percentile should be calculated from unfiltered winter data to maximize BrC signal.

* The correlation method using *m/z* 60 demonstrated robust performance using both historical and pilot AE33 data, with a strong correlation (R² = 0.78) between *m/z* 60 derived AAE<sub>SF</sub> values and unfiltered 99th percentile AAE.

* Unfiltered AAE values are then suitable for calculating the 99th percentile.

* The proposed methodology enhances consistency in eBC source apportionment across European sites by effectively integrating historical and pilot-phase AE33 data.

Despite its advantages, the AE approach has its limitations:

* It effectively captures general trends but struggles with accurate source apportionment in high-traffic areas, where eBC from liquid fuel combustion predominates, limiting the use of 99th percentile.

* The correlation method requires ancillary collocated measurements (e.g., *m/z* 60, levoglucosan, or ¹⁴C), which may not be available at all sites, limiting its applicability.

* Considering "ideal" extreme cases, neither PC1 (for isolated areas) nor PC99 (for traffic sites) is effective. This method achieves optimal results at urban and suburban sites, though at sites influenced by shipping emissions (e.g., BCN and MAR), and low-efficiency coal combustion, BrC contributions must be carefully considered when apportioning eBC from solid fuels.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
**Data availability**

Any requests for data and materials should be addressed to the corresponding author, M. Savadkoohi.

**Code availability.** The source code is available at https://doi.org/10.5281/zenodo.14066407.

Any updates will also be published on Zenodo.

**Supplementary material**

Supplementary data to this article can be found online.

**Acknowledgments**

This work was funded and supported within the framework of the Research Infrastructures Services Reinforcing AQ Monitoring Capacities in European Urban & Industrial AreaS (RI-URBANS) project. The RI-URBANS project (https://riurbans.eu/, contract 101036245), is a European H2020-Green Deal initiative that aims to provide comprehensive tools for the measurement and analysis of advanced AQ parameters (RI-URBANS, 2020). RI-URBANS is implementing the ACTRIS (https://www.actris.eu/) strategy for the development of services for improving air quality in Europe. The authors would like to also thank the support from "Agencia Estatal de Investigacion" from the Spanish Ministry of Science and Innovation under the project CAIAC (PID2019-108990RB-I00), AIR-PHONEMA (PID2022-142160OB-I00), the Generalitat de Catalunya (AGAUR, SGR-447), and the FOCI Project (G.A. 101056783).

M. Savadkoohi would like to thank the Spanish Ministry of Science and Innovation for her FPI grant (PRE-2020-095498). We also thank the COLOSSAL Team for providing ACSM and AE33 data. The authors also thank the Helsinki Region Environmental Services, HSY project Urbaani ilmanlaatu 2.0, French national center for Atmospheric data and services, AERIS for providing support to data access and software development. Measurements at BUC-SUB site were supported by the Core Program within the Romanian National Research Development and Innovation Plan 2022-2027, carried out with the support of MCID, project no. PN 23 05. The LIL-SUB site has been supported by the Labex CaPPA project, which is funded by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir) under contract ANR-11-LABX-0005-01; the CLIMIBIO and ECRIN projects, all financed by the Regional Council "Hauts-de-France" and the European Regional Development Fund (ERDF); the COST COLOSSAL Action (CA16109); and the French Ministry of Environment. The KOS-RB measurements has been supported by the MEYS of the Czech Republic, within the project for support of the national research infrastructure ACTRIS-CZ (LM2023030).
References

Aakko-Saksa, P., Koponen, P., Aurela, M., Vesala, H., Piimäkorpi, P., Murtonen, T., Sippula, O., Koponen, H., Karjalainen, P., Kuittinen, N., Panteliadis, P., Rönkkö, T., Timonen, H., 2018. Considerations in analysing elemental carbon from marine engine exhaust using residual, distillate and biofuels. J. Aerosol Sci. 126, 191–204. https://doi.org/10.1016/j.jaerosci.2018.09.005

Alfoldy, B., Gregorič, A., Ivančič, M., Ježek, I., Rigler, M., 2022. Source apportionment of black carbon and combustion-related CO2 for the determination of source-specific emission factors. Atmos. Meas. Tech. 135–152. https://doi.org/10.5194/amt-16-135-2023

Becerril-Valle, M., Coz, E., Prévôt, A.S.H., Močnik, G., Pandis, S.N., Sánchez de la Campa, A.M., Alastuey, A., Díaz, E., Pérez, R.M., Artíñano, B., 2017. Characterization of atmospheric black carbon and co-pollutants in urban and rural areas of Spain. Atmos. Environ. 169, 36–53. https://doi.org/10.1016/j.atmosenv.2017.09.014

Bibi, Z., Coe, H., Brooks, J., Williams, P.I., Reyes-Villegas, E., Priestley, M., Percival, C.J., Allan, J.D., 2021. Technical note: A new approach to discriminate different black carbon sources by utilising fullerene and metals in positive matrix factorisation analysis of high-resolution soot particle aerosol mass spectrometer data. Atmos. Chem. Phys. 21, 10763–10777. https://doi.org/10.5194/acp-21-10763-2021

Blanco-Alegre, C., Calvo, A.I., Alves, C., Fialho, P., Nunes, T., Gomes, J., Castro, A., Oduber, F., Coz, E., Fraile, R., 2020. Aethalometer measurements in a road tunnel: A step forward in the characterization of black carbon emissions from traffic. Sci. Total Environ. 703, 135483. https://doi.org/10.1016/j.scitotenv.2019.135483

Blanco-Alegre, C., Fialho, P., Calvo, A.I., Castro, A., Coz, E., Oduber, F., Prévôt, A.S.H., Močnik, G., Alves, C., Giardi, F., Pazzi, G., Fraile, R., 2022. Contribution of coal combustion to black carbon: Coupling tracers with the aethalometer model. Atmos. Res. 267. https://doi.org/10.1016/j.atmosres.2021.105980

Bond, T.C., Bergstrom, R.W., 2006. Light absorption by carbonaceous particles: An investigative review. Aerosol Sci. Technol. 40, 27–67. https://doi.org/10.1080/02786820500421521

Bond, T.C., Doherty, S.J., Fahey, D.W., Forster, P.M., Berntsen, T., Deangelo, B.J., Flanner, M.G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P.K., Sarofim, M.C., Schultz, M.G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S.K., Hopke, P.K., Jacobson, M.Z., Kaiser, J.W., Klimont, Z., Lohmann, U., Schwarz, J.P., Shindell, D., Storelvmo, T., Warren, S.G., Zender, C.S., 2013. Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos. 118, 5380–5552. https://doi.org/10.1002/jgrd.50171

Briggs, N.L., Long, C.M., 2016. Critical review of black carbon and elemental carbon source apportionment in Europe and the United States. Atmos. Environ. 144, 409–427. https://doi.org/10.1016/j.atmosenv.2016.09.002

Canals-Angerri, A., Via, M., Lara, R., Alastuey, A., Minguillón, M.C., Pandolfi, M., van Drooge, B.L., Querol, X., 2024. Causes of the unremitting high ambient levels of PM10 in a suburban background site in NE Spain. Environ. Pollut. 363. https://doi.org/10.1016/j.envpol.2024.125113

Chazeau, B., El Haddad, I., Canonaco, F., Temime-Roussel, B., D'Anna, B., Gille, G., Mesbah, B., Prévôt, A.S.H., Wortham, H., Marchand, N., 2022. Organic aerosol source apportionment by using rolling positive matrix factorization: Application to a Mediterranean coastal city. Atmos. Environ. X 14. https://doi.org/10.1016/j.aeaoa.2022.100176

Chen, G., Canonaco, F., Tobler, A., Aas, W., Alastuey, A., Allan, J., Atabakhsh, S., Aurela, M., Baltensperger, U., Bougiatioti, A., De Brito, J.F., Ceburnis, D., Chazeau, B., Chebaicheb, H., Daellenbach, K.R., Ehn, M., El Haddad, I., Eleftheriadis, K., Favez, O., Flentje, H., Font, A., Fossum, K., Freney, E., Gini, M., Green, D.C., Heikkinen, L., Herrmann, H., Kalogridis, A.C., Keernik, H., Lhotka, R., Lin, C., Lunder, C., Maasikmets, M., Manousakas, M.I., Marchand, N., Marin, C., Marmureanu, L., Mihalopoulos, N., Močnik, G., Nčcki, J., O'Dowd, C., Ovadnevaite, J., Peter, T., Petit, J.E., Pikridas, M., Matthew Platt, S., Pokorná, P., Poulain, L., Priestman, M., Riffault, V., Rinaldi, M., Różański, K., Schwarz, J., Sciare, J., Simon, L., Skiba, A., Slowik, J.G., Sosedova, Y., Stavroulas, I., Styszko, K., Teinemaa, E., Timonen, H., Tremper, A., Vasilescu, J., Via, M., Vodička, P., Wiedensohler, A., Zografou, O., Cruz Minguillón, M., Prévôt, A.S.H., 2022. European aerosol phenomenology – 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets. Environ. Int. 166. https://doi.org/10.1016/j.envint.2022.107325

Corbin, J.C., Pieber, S.M., Czech, H., Zanatta, M., Jakobi, G., Massabò, D., Orasche, J., El Haddad, I., Mensah, A.A., Stengel, B., Drinovec, L., Mocnik, G., Zimmermann, R., Prévôt, A.S.H., Gysel, M., 2018. Brown and Black Carbon Emitted by a Marine Engine Operated on Heavy Fuel Oil and Distillate Fuels: Optical Properties, Size Distributions, and Emission Factors. J. Geophys. Res.
Atmos. 123, 6175–6195. https://doi.org/10.1029/2017JD027818

Cubison, M.J., Ortega, A.M., Hayes, P.L., Farmer, D.K., Day, D., Lechner, M.J., Brune, W.H., Apel, E., Diskin, G.S., Fisher, J.A., Fuelberg, H.E., Hecobian, A., Knapp, D.J., Mikoviny, T., Riemer, D., Sachse, G.W., Sessions, W., Weber, R.J., Weinheimer, A.J., Wisthaler, A., Jimenez, J.L., 2011. Effects of aging on organic aerosol from open biomass burning smoke in aircraft and laboratory studies. Atmos. Chem. Phys. 11, 12049–12064. https://doi.org/10.5194/acp-11-12049-2011

Cuesta-Mosquera, A., Glojek, K., Močnik, G., Drinovec, L., Gregorič, A., Rigler, M., Ogrin, M., Romshoo, B., Weinhold, K., Merkel, M., Van Pinxteren, D., Herrmann, H., Wiedensohler, A., Pöhlker, M., Müller, T., 2024. Optical properties and simple forcing efficiency of the organic aerosols and black carbon emitted by residential wood burning in rural central Europe. Atmos. Chem. Phys. 24, 2583–2605. https://doi.org/10.5194/acp-24-2583-2024

Favez, O., El Haddad, I., Piot, C., Boréave, A., Abidi, E., Marchand, N., Jaffrezo, J.L., Besombes, J.L., Personnaz, M.B., Sciare, J., Wortham, H., George, C., D’Anna, B., 2010. Inter-comparison of source apportionment models for the estimation of wood burning aerosols during wintertime in an Alpine city (Grenoble, France). Atmos. Chem. Phys. 10, 5295–5314. https://doi.org/10.5194/acp-10-5295-2010

Flentje, H., Briel, B., Beck, C., Collaud Coen, M., Fricke, M., Cyrys, J., Gu, J., Pitz, M., Thomas, W., 2015. Identification and monitoring of Saharan dust: An inventory representative for south Germany since 1997. Atmos. Environ. 109, 87–96. https://doi.org/10.1016/j.atmosenv.2015.02.023

Font, A., Ciupek, K., Butterfield, D., Fuller, G.W., 2022. Long-term trends in particulate matter from wood burning in the United Kingdom: Dependence on weather and social factors. Environ. Pollut. 314, 120105. https://doi.org/10.1016/j.envpol.2022.120105

Fourtziou, L., Liakakou, E., Stavroulas, I., Theodosi, C., Zarmpas, P., Psiloglou, B., Sciare, J., Maggos, T., Bairachtari, K., Bougiatioti, A., Gerasopoulos, E., Sarda-Estève, R., Bonnaire, N., Mihalopoulos, N., 2017. Multi-tracer approach to characterize domestic wood burning in Athens (Greece) during wintertime. Atmos. Environ. 148, 89–101. https://doi.org/10.1016/j.atmosenv.2016.10.011

Fröhlich, R., Crenn, V., Setyan, A., Belis, C.A., Canonaco, F., Favez, O., Riffault, V., Slowik, J.G., Aas, W., Aijälä, M., Alastuey, A., Artiñano, B., Bonnaire, N., Bozzetti, C., Bressi, M., Carbone, C., Coz, E., Croteau, P.L., Cubison, M.J., Esser-Gietl, J.K., Green, D.C., Gros, V., Heikkinen, L., Herrmann, H., Jayne, J.T., Lunder, C.R., Minguillón, M.C., Moňnik, G., O’Dowd, C.D., Ovadnevaite, J., Petralia, E., Poulain, L., Priestman, M., Ripoll, A., Sarda-Estève, R., Wiedensohler, A., Baltensperger, U., Sciare, J., Prévôt, A.S.H., 2015. ACTRIS ACSM intercomparison - Part 2: Intercomparison of ME-2 organic source apportionment results from 15 individual, co-located aerosol mass spectrometers. Atmos. Meas. Tech. 8, 2555–2576. https://doi.org/10.5194/amt-8-2555-2015

Fuller, G.W., Tremper, A.H., Baker, T.D., Yttri, K.E., Butterfield, D., 2014. Contribution of wood burning to PM10 in London. Atmos. Environ. 87, 87–94. https://doi.org/10.1016/j.atmosenv.2013.12.037

Garg, S., Chandra, B.P., Sinha, V., Sarda-Esteve, R., Gros, V., Sinha, B., 2016. Limitation of the Use of the Absorption Angstrom Exponent for Source Apportionment of Equivalent Black Carbon: A Case Study from the North West Indo-Gangetic Plain. Environ. Sci. Technol. 50, 814–824. https://doi.org/10.1021/acs.est.5b03868

Glojek, K., Dinh Ngoc Thuy, V., Weber, S., Uzu, G., Manousakas, M., Elazzouzi, R., Džepina, K., Darfeuil, S., Ginot, P., Jaffrezo, J.L., Žabkar, R., Turšič, J., Podkoritnik, A., Močnik, G., 2024. Annual variation of source contributions to PM10 and oxidative potential in a mountainous area with traffic, biomass burning, cement-plant and biogenic influences. Environ. Int. 189, 108787. https://doi.org/10.1016/j.envint.2024.108787

Grange, S., Lötscher, H., Fischer, A., Emmenegger, L., Hueglin, C., 2020. Evaluation of equivalent black carbon source apportionment using observations from Switzerland between 2008 and 2018. Atmos. Meas. Tech. 13, 1867–1885. https://doi.org/10.5194/amt-13-1867-2020

Gyawali, M., Arnott, W.P., Lewis, K., Moosmüller, H., 2009. In situ aerosol optics in Reno, NV, USA during and after the summer 2008 California wildfires and the influence of absorbing and non-absorbing organic coatings on spectral light absorption. Atmos. Chem. Phys. 9, 8007–8015. https://doi.org/10.5194/acp-9-8007-2009

Harrison, R.M., Beddows, D.C.S., Jones, A.M., Calvo, A., Alves, C., Pio, C., 2013. An evaluation of some issues regarding the use of aethalometers to measure woodsmoke concentrations. Atmos. Environ. 80, 540–548. https://doi.org/10.1016/j.atmosenv.2013.08.026

Healy, R.M., Sofowote, U., Su, Y., Debosz, J., Noble, M., Jeong, C.H., Wang, J.M., Hilker, N., Evans, G.J., Doerksen, G., Jones, K., Munoz, A., 2017. Ambient measurements and source apportionment of fossil fuel and biomass burning black carbon in Ontario. Atmos. Environ. 161, 34–47. https://doi.org/10.1016/j.atmosenv.2017.04.034
Helin, A., Virkkula, A., Backman, J., Pirjola, L., Sippula, O., Aakko-Saksa, P., Väätäinen, S., Mylläri, F., Järvinen, A., Bloss, M., Aurela, M., Jakobi, G., Karjalainen, P., Zimmermann, R., Jokiniemi, J., Saarikoski, S., Tissari, J., Rönkkö, T., Niemi, J. V., Timonen, H., 2021. Variation of Absorption Ångström Exponent in Aerosols From Different Emission Sources. J. Geophys. Res. Atmos. 126, 1–21. https://doi.org/10.1029/2020JD034094

Herich, H., Hueglin, C., Buchmann, B., 2011. A 2.5 year's source apportionment study of black carbon from wood burning and fossil fuel combustion at urban and rural sites in Switzerland. Atmos. Meas. Tech. 4, 1409–1420. https://doi.org/10.5194/amt-4-1409-2011

Ilie, A., Vasilescu, J., Talianu, C., Iojă, C., Nemuc, A., 2023. Spatiotemporal Variability of Urban Air Pollution in Bucharest City. Atmosphere (Basel). 14, 1–16. https://doi.org/10.3390/atmos14121759

Isolabella, T., Bernardoni, V., Bigi, A., Brunoldi, M., Mazzei, F., Parodi, F., Prati, P., Vernocchi, V., Massabò, D., 2024. A new software toolkit for optical apportionment of carbonaceous aerosol 1363–1373. https://doi.org/10.5194/amt-17-1363-2024

Ivančič, M., Gregorič, A., Lavrič, G., Alföldy, B., Ježek, I., Hasheminassab, S., Pakbin, P., Ahangar, F., Sowlat, M., Boddeker, S., Rigler, M., 2022. Two-year-long high-time-resolution apportionment of primary and secondary carbonaceous aerosols in the Los Angeles Basin using an advanced total carbon–black carbon (TC-BC(λ)) method. Sci. Total Environ. 848. https://doi.org/10.1016/j.scitotenv.2022.157606

Jiang, K., Xing, R., Luo, Z., Huang, W., Yi, F., Men, Y., Zhao, N., Chang, Z., Zhao, J., Pan, B., Shen, G., 2024. Pollutant emissions from biomass burning: A review on emission characteristics, environmental impacts, and research perspectives. Particuology 85, 296–309. https://doi.org/10.1016/j.partic.2023.07.012

Kaskaoutis, D.G., Grivas, G., Stavroulas, I., Bougiatioti, A., Liakakou, E., Dumka, U.C., Gerasopoulos, E., Mihalopoulos, N., 2021a. Apportionment of black and brown carbon spectral absorption sources in the urban environment of Athens, Greece, during winter. Sci. Total Environ. 801, 149739. https://doi.org/10.1016/j.scitotenv.2021.149739

Kaskaoutis, D.G., Grivas, G., Stavroulas, I., Liakakou, E., Dumka, U.C., Dimitriou, K., Gerasopoulos, E., Mihalopoulos, N., 2021b. In situ identification of aerosol types in Athens, Greece, based on long-term optical and on online chemical characterization. Atmos. Environ. 246, 118070. https://doi.org/10.1016/j.atmosenv.2020.118070

Kirchstetter, T.W., Novakov, T., Hobbs, P. V., 2004. Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon. J. Geophys. Res. D Atmos. 109, 1–12. https://doi.org/10.1029/2004JD004999

Kristensson, A., Ausmeel, S., Pauraite, J., Eriksson, A., Ahlberg, E., Bycenkiene, S., Degórska, A., 2020. Source contributions to rural carbonaceous winter aerosol in North-Eastern Poland. Atmosphere (Basel). 11, 1–19. https://doi.org/10.3390/atmos11030263

Kumar, N.K., Corbin, J.C., Bruns, E.A., Massabó, D., Slowik, J.G., Drinovec, L., Močnik, G., Prati, P., Vlachou, A., Baltensperger, U., Gysel, M., El-Haddad, I., Prévôt, A.S.H., 2018. Production of particulate brown carbon during atmospheric aging of residential wood-burning emissions. Atmos. Chem. Phys. 18, 17843–17861. https://doi.org/10.5194/acp-18-17843-2018

Lack, D.A., Cappa, C.D., 2010. Impact of brown and clear carbon on light absorption enhancement, single scatter albedo and absorption wavelength dependence of black carbon. Atmos. Chem. Phys. 10, 4207–4220. https://doi.org/10.5194/acp-10-4207-2010

Lack, D.A., Langridge, J.M., 2013. On the attribution of black and brown carbon light absorption using the Ångström exponent. Atmos. Chem. Phys. 13, 10535–10543. https://doi.org/10.5194/acp-13-10535-2013

Li, J., Liu, C., Yin, Y., Kumar, K.R., 2016. Numerical investigation on the Ångström exponent of black carbon aerosol. J. Geophys. Res. Atmos. 121, 3506–3518. https://doi.org/10.1002/2015jd024718

Liakakou, E., Stavroulas, I., Kaskaoutis, D.G., Grivas, G., Paraskevopoulou, D., Dumka, U.C., Tsagkaraki, M., Bougiatioti, A., Oikonomou, K., Sciare, J., Gerasopoulos, E., Mihalopoulos, N., 2020. Long-term variability, source apportionment and spectral properties of black carbon at an urban background site in Athens, Greece. Atmos. Environ. 222, 117137. https://doi.org/10.1016/j.atmosenv.2019.117137

Lima, A.L.C., Farrington, J.W., Reddy, C.M., 2005. Combustion-derived polycyclic aromatic hydrocarbons in the environment - A review. Environ. Forensics 6, 109–131. https://doi.org/10.1080/15275920590952739

Liu, C., Chung, C.E., Yin, Y., Schnaiter, M., 2018. The absorption Ångström exponent of black carbon: From numerical aspects. Atmos. Chem. Phys. 18, 6259–6273. https://doi.org/10.5194/acp-18-6259-2018

Liu, D., Allan, J.D., Young, D.E., Coe, H., Beddows, D., Fleming, Z.L., Flynn, M.J., Gallagher, M.W., Harrison, R.M., Lee, J., Prevot, A.S.H., Taylor, J.W., Yin, J., Williams, P.I., Zotter, P., 2014. Size
distribution, mixing state and source apportionment of black carbon aerosol in London during winter time. Atmos. Chem. Phys. 14, 10061–10084. https://doi.org/10.5194/acp-14-10061-2014

Liu, D., He, C., Schwarz, J.P., Wang, X., 2020. Lifecycle of light-absorbing carbonaceous aerosols in the atmosphere. npj Clim. Atmos. Sci. 3. https://doi.org/10.1038/s41612-020-00145-8

Lhotka, R., Pokorná, P., Vodička, P., Žíková, N., Chen, G.I., Prévôt, A.S.H., Mbengue, S., Schwarz, J., Ždímal, V., 2025. Influence of meteorological conditions and seasonality on PM1 and organic aerosol sources at a rural background site. Atmos. Environ. 344. https://doi.org/10.1016/j.atmosenv.2025.121028

Martinsson, J., Abdul Azeem, H., Sporre, M.K., Bergström, R., Ahlberg, E., Öström, E., Kristensson, A., Swietlicki, E., Eriksson Stenström, K., 2017. Carbonaceous aerosol source apportionment using the Aethalometer model-evaluation by radiocarbon and levoglucosan analysis at a rural background site in southern Sweden. Atmos. Chem. Phys. 17, 4265–4281. https://doi.org/10.5194/acp-17-4265-2017

Massabò, D., Caponi, L., Bernardoni, V., Bove, M.C., Brotto, P., Calzolai, G., Cassola, F., Chiari, M., Fedi, M.E., Fermo, P., Giannoni, M., Lucarelli, F., Nava, S., Piazzalunga, A., Valli, G., Vecchi, R., Prati, P., 2015. Multi-wavelength optical determination of black and brown carbon in atmospheric aerosols. Atmos. Environ. 108, 1–12. https://doi.org/10.1016/j.atmosenv.2015.02.058

Mbengue, S., Serfozo, N., Schwarz, J., Holoubek, I., Žíková, N., Šmejkalová, A.H., Holoubek, I., 2020. Characterization of Equivalent Black Carbon at a regional background site in Central Europe: Variability and source apportionment. Environ. Pollut. 260. https://doi.org/10.1016/j.envpol.2019.113771

Minderytė, A., Pauraite, J., Dudoitis, V., Plauškaitė, K., Kilikevičius, A., Matijošius, J., Rimkus, A., Kilikevičienė, K., Vainorius, D., Byčenkienė, S., 2022. Carbonaceous aerosol source apportionment and assessment of transport-related pollution. Atmos. Environ. 279. https://doi.org/10.1016/j.atmosenv.2022.119043

Mousavi, A., Sowlat, M.H., Lovett, C., Rauber, M., Szidat, S., Bo, R., Borgini, A., Marco, C. De, Ruprecht, A.A., Sioutas, C., Boffi, R., Borgini, A., De Marco, C., Ruprecht, A.A., Sioutas, C., Bo, R., Borgini, A., Marco, C. De, Ruprecht, A.A., Sioutas, C., 2019. Source apportionment of black carbon (BC) from fossil fuel and biomass burning in metropolitan Milan, Italy. Atmos. Environ. 203, 252–261. https://doi.org/10.1016/j.atmosenv.2019.02.009

Müller, T., Fiebig, M., 2018. ACTRIS In Situ Aerosol: Guidelines for Manual QC of AE33 absorption photometer data. https://www.actris-ecac.eu/measurement-guidelines.html

Navarro-Barboza, H., Rovira, J., Obiso, V., Pozzer, A., Via, M., Alastuey, A., Querol, X., Perez, N., Savadkoohi, M., Chen, G., Yus-Díez, J., Ivancic, M., Rigler, M., Eleftheriadis, K., Vratolis, S., Zografou, O., Gini, M., Chazeau, B., Marchand, N., Prevot, A., Dallenbach, K., Ehn, M., Luoma, K., Petäjä, T., Tobler, A., Necki, J., Aurela, M., Timonen, H., Niemi, J., Favez, O., Petit, J.-E., Putaud, J.-P., Hueglin, C., Pascal, N., Chauvigné, A., Conil, S., Pandolfi, M., and Jorba, O.: Characterization of Brown Carbon absorption in different European environments through source contribution analysis, EGUSphere, https://doi.org/10.5194/egusphere-2024-2086

Ni, H., Huang, R.J., Pieber, S.M., Corbin, J.C., Stefenelli, G., Pospisilova, V., Klein, F., Gysel-Beer, M., Yang, L., Baltensperger, U., Haddad, I. El, Slowik, J.G., Cao, J., Prévôt, A.S.H., Dusek, U., 2021. Brown Carbon in Primary and Aged Coal Combustion Emission. Environ. Sci. Technol. 55, 5701–5710. https://doi.org/10.1021/acs.est.0c08084

Nunes, R.A.O., Alvim-Ferraz, M.C.M., Martins, F.G., Sousa, S.I.V., 2017. The activity-based methodology to assess ship emissions - A review. Environ. Pollut. 231, 87–103. https://doi.org/10.1016/j.envpol.2017.07.099

Pokorná, P., Žíková, N., Vodička, P., Lhotka, R., Mbengue, S., Holubová Šmejkalová, A., Riffault, V., Ondráček, J., Schwarz, J., Ždímalmal, V., 2022. Chemically speciated mass size distribution, particle density, shape and origin of non-refractory PM1 measured at a rural background site in central Europe. Atmos. Chem. Phys. 22, 5829–5858. https://doi.org/10.5194/acp-22-5829-2022

Ravi Kiran, V., Venkat Ratnam, M., Krishna Murthy, B. V., Kant, Y., Prasad, P., Roja Raman, M., Rao, S.V.B.B., Lakshmi Kumar, T. V., Maitra, A., Kiran, V.R., Ratnam, M.V., Murthy, B.V.K., Kant, Y., Prasad, P., Raman, M.R., Rao, S.V.B.B., Kumar, T.V.L., Maitra, A., 2019. An empirical method for source apportionment of black carbon aerosol: Results from Aethalometer observations at five different locations in India. Environ. Pollut. 254, 112932. https://doi.org/10.1016/j.envpol.2019.07.100

Rovira, J., Savadkoohi, M., Chen, G.I., Močnik, G., Aas, W., Alados-Arboledas, L., Artiñano, B., Aurela, M., Backman, J., Banerji, S., Beddow, D., Brem, B., Chazeau, B., Coen, M.C., Colombi, C., Conil, S., Costabile, F., Coz, E., de Brito, J.F., Eleftheriadis, K., Favez, O., Flentje, H., Freney, E., Gregorič, A., Gysel-Beer, M., Harrison, R., Hueglin, C., Hyvärinen, A., Ivančič, M., Kalogridis, A.-C., Keernik, H., Konstantinos, G., Laj, P., Liakakou, E., Lin, C., Listrani, S., Luoma, K., Maasikmets, M.,
Manninen, H.E., Marchand, N., dos Santos, S.M., Mbengue, S., Mihalopoulos, N., Nicolae, D., Niemi, J. V., Norman, M., Ovadnevaite, J., Petit, J.-E., Platt, S., Prévôt, A.S.H., Pujadas, M., Putaud, J.-P., Riffault, V., Rigler, M., Rinaldi, M., Schwarz, J., Silvergren, S., Teinemaa, E., Teinilä, K., Timonen, H., Titos, G., Tobler, A., Vasilescu, J., Vratolis, S., Yttri, K.E., Yubero, E., Zíková, N., Alastuey, A., Petäjä, T., Querol, X., Yus-Díez, J., Pandolfi, M., 2025. A European aerosol phenomenology – 9: Light absorption properties of carbonaceous aerosol particles across surface Europe. Environ. Int. 195, 109185. https://doi.org/10.1016/J.ENVINT.2024.109185Saarikoski, S., Niemi, J. V., Aurela, M., Pirjola, L., Kousa, A., Rönkö, T., Timonen, H., 2021. Sources of black carbon at residential and traffic environments obtained by two source apportionment methods. Atmos. Chem. Phys. 21, 14851–14869. https://doi.org/10.5194/acp-21-14851-2021

Salameh, D., Pey, J., Bozzetti, C., El Haddad, I., Detournay, A., Sylvestre, A., Canonaco, F., Armengaud A., Piga, D., Robin, D., Prevot, A.S.H., Jaffrezo, J.L., Wortham, H., Marchand, N., 2018. Sources of PM2.5 at an urban-industrial Mediterranean city, Marseille (France): Application of the ME-2 solver to inorganic and organic markers. Atmos. Res. 214, 263–274. https://doi.org/10.1016/j.atmosres.2018.08.005

Saleh, R., Hennigan, C.J., McMeeking, G.R., Chuang, W.K., Robinson, E.S., Coe, H., Donahue, N.M., Robinson, A.L., 2013. Absorptivity of brown carbon in fresh and photo-chemically aged biomass-burning emissions. Atmos. Chem. Phys. 13, 7683–7693. https://doi.org/10.5194/acp-13-7683-2013

Sandradewi, J., Prévôt, A.S.H., Szidat, S., Perron, N., Alfarra, M.R., Lanz, V.A., Weingartner, E., Baltensperger, U.R.S., 2008. Using aerosol light absorption measurements for the quantitative determination of wood burning and traffic emission contribution to particulate matter. Environ. Sci. Technol. 42, 3316–3323. https://doi.org/10.1021/es702253m

Savadkoohi, M., Pandolfi, M., Favez, O., Putaud, J., Eleftheriadis, K., Fiebig, M., Hopke, P.K., Laj, P., Wiedensohler, A., Alados-arboledas, L., Bastian, S., Chazeau, B., María, C., Colombi, C., Costabile, F., Green, D.C., Hueglin, C., Liakakou, E., Luoma, K., Listrani, S., Mihalopoulos, N., Petit, J., Rattigan, O. V, Reche, C., Timonen, H., Titos, G., Tremper, A.H., Vratolis, S., Vodi, P., Yubero, E., Harrison, R.M., Pet, T., Querol, X., 2024. Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations. Environ. Int. 185. https://doi.org/10.1016/j.envint.2024.108553

Savadkoohi, M., Pandolfi, M., Reche, C., Niemi, J. V, Mooibroek, D., Titos, G., Green, D.C., Tremper, A.H., Hueglin, C., Coz, E., Liakakou, E., Mihalopoulos, N., Stavroulas, I., Alados-arboledas, L., Beddows, D., Brito, J.F. De, Bastian, S., Baudic, A., Colombi, C., Costabile, F., Estell, V., Matos, V., Gaag, E. Van Der, Norman, M., Silvergren, S., Petit, J., Putaud, J., Rattigan, O. V, Timonen, H., Tuch, T., Merkel, M., Weinhold, K., Vratolis, S., Vasilescu, J., Favez, O., Harrison, R.M., Laj, P., Wiedensohler, A., Hopke, P.K., Pet, T., Querol, X., 2023. The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe. Environ. Int. J. 178. https://doi.org/10.1016/j.envint.2023.108081

Schlaerth, H., Ko, J., Sugrue, R., Preble, C., Ban-Weiss, G., 2021. Determining black carbon emissions and activity from in-use harbor craft in Southern California. Atmos. Environ. 256, 118382. https://doi.org/10.1016/j.atmosenv.2021.118382

Srivastava, D., Favez, O., Petit, J.E., Zhang, Y., Sofowote, U.M., Hopke, P.K., Bonnaire, N., Perraudin, E., Gros, V., Villenave, E., Albinet, A., 2019. Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements. Sci. Total Environ. 690, 944–955. https://doi.org/10.1016/j.scitotenv.2019.06.378

Tian, J., Wang, Q., Ma, Y., Wang, J., Han, Y., Cao, J., 2023. Impacts of biomass burning and photochemical processing on the light absorption of brown carbon in the southeastern Tibetan Plateau. Atmos. Chem. Phys. 23, 1879–1892. https://doi.org/10.5194/acp-23-1879-2023

Titos, G., del Águila, A., Cazorla, A., Lyamani, H., Casquero-Vera, J.A., Colombi, C., Cuccia, E., Gianelle, V., Močnik, G., Alastuey, A., Olmo, F.J., Alados-Arboledas, L., 2017. Spatial and temporal variability of carbonaceous aerosols: Assessing the impact of biomass burning in the urban environment. Sci. Total Environ. 578, 613–625. https://doi.org/10.1016/j.scitotenv.2016.11.007

Tobler, A.K., Skiba, A., Canonaco, F., Močnik, G., Rai, P., Chen, G., Bartyzel, J., Zimnoch, M., Styszko, K., Nęcki, J., Furger, M., Rózański, K., Baltensperger, U., Slowik, J.G., Prevot, A.S.H., 2021. Characterization of non-refractory (NR) PM1 and source apportionment of organic aerosol in Kraków, Poland. Atmos. Chem. Phys. 21, 14893–14906. https://doi.org/10.5194/acp-21-14893-2021

Velazquez-Garcia, A., De Brito, J.F., Crumeyrolle, S., Chiapello, I., Riffault, V., 2024. Assessment of light-absorbing carbonaceous aerosol origins and properties at the ATOLL site in Northern France 107–122. https://doi.org/10.5194/ar-2-107-2024

Virkkula, A., 2021. Modeled source apportionment of black carbon particles coated with a light-scattering shell. Atmos. Meas. Tech. 14, 3707–3719. https://doi.org/10.5194/amt-14-3707-2021
Wang, Q., Liu, H., Ye, J., Tian, J., Zhang, T., Zhang, Y., Liu, S., Cao, J., 2021. Estimating Absorption Ångström Exponent of Black Carbon Aerosol by Coupling Multiwavelength Absorption with Chemical Composition. Environ. Sci. Technol. Lett. 8, 121–127. https://doi.org/10.1021/acs.estlett.0c00829

Yttri, K.E., Canonaco, F., Eckhardt, S., Evangeliou, N., Fiebig, M., Gundersen, H., Hjellbrekke, A.-G., Lund Myhre, C., Platt, S.M., Prévôt, A., Simpson, D., Solberg, S., Surratt, J., Tørseth, K., Uggerud, H., Vadset, M., Wan, X., Aas, W., 2020. Trends, composition, and sources of carbonaceous aerosol at the Birkenes Observatory, northern Europe, 2001–2018. Atmos. Chem. Phys. Discuss. 10, 1–45. https://doi.org/10.5194/acp-21-7149-2021

Yttri, K.E., Simpson, D., Nojgaard, J.K., Kristensen, K., Genberg, J., Stenström, K., Swietlicki, E., Hillamo, R., Aurela, M., Bauer, H., Offenberg, J.H., Jaoui, M., Dye, C., Eckhardt, S., Burkhart, J.F., Stohl, A., Glasius, M., 2011. Source apportionment of the summer time carbonaceous aerosol at Nordic rural background sites. Atmos. Chem. Phys. 11, 13339–13357. https://doi.org/10.5194/acp-11-13339-2011

Yus-Díez, J., Bernardoni, V., Močnik, G., Alastuey, A., Ciniglia, D., Ivančič, M., Querol, X., Perez, N., Reche, C., Rigler, M., Vecchi, R., Valentini, S., Pandolfi, M., 2021. Determination of the multiple-scattering correction factor and its cross-sensitivity to scattering and wavelength dependence for different AE33 Aethalometer filter tapes: a multi-instrumental approach. Atmos. Meas. Tech. Discuss. 14, 6335–6355. https://doi.org/10.5194/amt-2021-46

Zhang, G., Peng, L., Lian, X., Lin, Q., Bi, X., Chen, D., Li, M., Li, L., Wang, X., Sheng, G., 2019. An improved absorption Ångström exponent (AAE)-based method for evaluating the contribution of light absorption from brown carbon with a high-time resolution. Aerosol Air Qual. Res. 19, 15–24. https://doi.org/10.4209/aaqr.2017.12.0566

Zhang, Y., Albinet, A., Petit, J.E., Jacob, V., Chevrier, F., Gille, G., Pontet, S., Chrétien, E., Dominik-Sègue, M., Levigoureux, G., Močnik, G., Gros, V., Jaffrezo, J.L., Favez, O., 2020. Substantial brown carbon emissions from wintertime residential wood burning over France. Sci. Total Environ. 743. https://doi.org/10.1016/j.scitotenv.2020.140752

Zhang, Y., Favez, O., Petit, J.E., Canonaco, F., Truong, F., Bonnaire, N., Crenn, V., Amodeo, T., Prévôt, A.S.H., Sciare, J., Gros, V., Albinet, A., 2019. Six-year source apportionment of submicron organic aerosols from near-continuous highly time-resolved measurements at SIRTA (Paris area, France). Atmos. Chem. Phys. 19, 14755–14776. https://doi.org/10.5194/acp-19-14755-2019

Zheng, H., Kong, S., Chen, N., Fan, Z., Zhang, Y., Yao, L., Cheng, Y., Zheng, S., Yan, Y., Liu, D., Zhao, D., Liu, C., Zhao, T., Guo, J., Qi, S., 2021. A method to dynamically constrain black carbon aerosol sources with online monitored potassium. npj Clim. Atmos. Sci. 4. https://doi.org/10.1038/s41612-021-00200-y

Zotter, P., Herich, H., Gysel, M., El-Haddad, I., Zhang, Y., Mocnik, G., Hüglin, C., Baltensperger, U., Szidat, S., Prévôt, A.S.H., 2017. Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol. Atmos. Chem. Phys. 17, 4229–4249. https://doi.org/10.5194/acp-17-4229-2017
* AAE<sub>LF</sub> was robustly identified as the 1st percentile of R<sup>2</sup>-filtered AAE values.

* AAE<sub>SF</sub> was determined using the 99th percentile of unfiltered AAE values.

* Seasonal data optimized liquid fuel (summer) and solid fuel (winter) signals.

* AAE<sub>SF</sub> values agreed with AMS/ACSM data, with R<sup>2</sup> = 0.78 across 16 sites.
**Declaration of interests**

[x] The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

[ ] The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

<figure><img src="image_8.png" /></figure>