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Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations

Marjan Savadkoohi, Marco Pandolfi, Olivier Favez, Jean-Philippe Putaud, Konstantinos Eleftheriadis, Markus Fiebig, Philip K. Hopke, Paolo Laj, Alfred Wiedensohler, Lucas Alados-Arboledas, Susanne Bastian, Benjamin Chazeau, Álvaro Clemente María, Cristina Colombi, Francesca Costabile, David C. Green, Christoph Hueglin, Eleni Liakakou, Krista Luoma, Stefano Listrani, Nikos Mihalopoulos, Nicolas Marchand, Griša Močnik, Jarkko V. Niemi, Jakub Ondráček, Jean-Eudes Petit, Oliver V. Rattigan, Cristina Reche, Hilkka Timonen, Gloria Titos, Anja H. Tremper, Stergios Vratolis, Petr Vodička, Eduardo Yubero Funes, Naděžda Zíková, Roy M. Harrison, Tuukka Petäjä, Andrés Alastuey, Xavier Querol

PII: S0160-4120(24)00139-9

DOI: https://doi.org/10.1016/j.envint.2024.108553

Reference: EI 108553

To appear in: *Environment International*

Received Date: 20 December 2023

Revised Date: 1 March 2024

Accepted Date: 1 March 2024

Please cite this article as: M. Savadkoohi, M. Pandolfi, O. Favez, J-P. Putaud, K. Eleftheriadis, M. Fiebig, P.K. Hopke, P. Laj, A. Wiedensohler, L. Alados-Arboledas, S. Bastian, B. Chazeau, A.C. María, C. Colombi, F. Costabile, D.C. Green, C. Hueglin, E. Liakakou, K. Luoma, S. Listrani, N. Mihalopoulos, N. Marchand, G. Močnik, J.V. Niemi, J. Ondráček, J-E. Petit, O.V. Rattigan, C. Reche, H. Timonen, G. Titos, A.H. Tremper, S. Vratolis, P. Vodička, E.Y. Funes, N. Zíková, R.M. Harrison, T. Petäjä, A. Alastuey, X. Querol, Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations, *Environment International* (2024), doi: https://doi.org/10.1016/j.envint.2024.108553

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© 2024 Published by Elsevier Ltd.
Recommendations for reporting equivalent Black Carbon (eBC) mass concentrations based on long-term pan-European in-situ observations

Marjan Savadkoohi,ᵃᵇ* Marco Pandolfi,ª* Olivier Favez,ᶜ Jean-Philippe Putaud,ᵈ Konstantinos Eleftheriadis,ᵉ Markus Fiebig,ᵉ Philip K. Hopke,ᵉ Paolo Laj,ʰ Alfred Wiedensohler,⁴ Lucas Alados-Arboledas,⁴ Susanne Bastian,⁴ Benjamin Chazeau,¹,ᵇ Álvaro Clemente María,ⁿ Cristina Colombi,º Francesca Costabile,ᵉ David C. Green,⁹,ᵉ Christoph Hueglin,⁵ Eleni Liakakou,¹ Krista Luoma,⁵ Stefano Listrani,⁵ Nikos Mihalopoulos,¹ Nicolas Marchand,¹ Griša Močnik,⁹,ₓ Jarkko V. Niemi,⁹ Jakub Ondráček,⁹ Jean-Eudes Petit,aa Oliver V. Rattigan,ᵇᵇ Cristina Reche,ª Hilkka Timonen,⁵ Gloria Titos,⁴ Anja H. Tremper,⁹ Stergios Vratolis,ᵉ Petr Vodička,⁹ Eduardo Yubero Funes,ⁿ Naděžda Zíková,⁹ Roy M. Harrison,cc, dd Tuukka Petäjä,ee Andrés Alastueyª, Xavier Querolª

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

b. Department of Natural Resources & Environment, Industrial & TIC Engineering (EMIT-UPC), Manresa, Spain

c. Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France

d. European Commission, Joint Research Centre (JRC), Ispra, Italy

e. Environmental Radioactivity & Aerosol Technology for Atmospheric & Climate Impact Lab, INRaSTES, NCSR "Demokritos", Athens, Greece

f. Dept. Atmospheric and Climate Research, NILU-Norwegian Institute for Air Research, Kjeller, Norway

g. Department of Public Health Sciences, University of Rochester School of Medicine & Dentistry, Rochester, NY, USA

h. Univ. Grenoble, CNRS, IRD, IGE, 38000 Grenoble, France

i. Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany

j. Andalusian Institute for Earth System Research (IISTA-CEAMA), University of Granada, Granada, Spain

k. Saxon State Office for Environment, Agriculture and Geology/Saxon State Department for Agricultural and Environmental Operations, Dresden, Germany

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

m. Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen, Switzerland

n. Atmospheric Pollution Laboratory (LCA), Department of Applied Physics, Miguel Hernández University, Avenida de la Universidad S/N, 03202, Elche, Spain

o. Arpa Lombardia, Settore Monitoraggi Ambientali, Unità Operativa Qualità dell'Aria, Milano, Italy

p. Institute of Atmospheric Sciences and Climate-National Research Council, Rome, Italy

q. MRC Centre for Environment and Health, Environmental Research Group, Imperial College London, UK

r. HPRU in Environmental Exposures and Health, Imperial College London, UK

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

t. Institute for Environmental Research & Sustainable Development, National Observatory of Athens, Athens, Greece
u. Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, Finland

v. ARPA Lazio, Regional Environmental Protection Agency, Rome, Italy

w. Center for Atmospheric Research, University of Nova Gorica, Nova Gorica, 5270, Slovenia

x. Jozef Stefan Institute, Ljubljana, 1000, Slovenia

y. Helsinki Region Environmental Services Authority (HSY), Helsinki, Finland

z. Laboratory of Aerosols Chemistry and Physics, Institute of Chemical Process Fundamentals, Academy of Sciences of the Czech Republic, Rozvojova, Prague, Czech Republic

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

bb. Division of Air Resources, New York State Dept of Environmental Conservation, NY, USA

cc. Division of Environmental Health & Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom

dd. Department of Environmental Sciences, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah, Saudi Arabia

ee. Institute for Atmospheric and Earth System Research/Physics (INAR), Faculty of Science, University of Helsinki, Helsinki, Finland

**Highlights:**

* Studying MAC variability impact on eBC estimation.

* Presenting spatial, seasonal and temporal MAC variability in Europe.

* Proposing the site-specific MAC as the most effective approach to estimate eBC.

* Long-term trend analysis links decreasing EC trends with method-dependent corrected eBC trends.

E-mail address: marjan.savadkoohi@idaea.csic.es (M. Savadkoohi), marco.pandolfi@idaea.csic.es (M. Pandolfi)

**Abstract**

A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient ($b_{abs}$) to eBC. This study investigates the spatial-temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and $b_{abs}$ measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling regression MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50%) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m² g⁻¹ from 12 aethalometers

<footnote>* Corresponding author.</footnote>
at 880 nm, and 10.6 ± 4.7 m² g⁻¹ from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasizes the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.

**Keywords:** eBC, MAC; FAPs; EC; Absorption; Site specific MAC; Rolling MAC

# 1. Introduction

Black Carbon (BC) is one of the key targets for current research on air quality (AQ). The definition of BC is highly reliant on the measurement techniques employed, and one of the main challenges in this regard is the lack of a systematic approach to harmonize BC concentrations obtained from various measurement instruments, thus causing difficulties when comparing data across different monitoring sites (Ciupek et al., 2021). To address this concern, efforts to establish more standardized methodologies for reporting atmospheric BC are required. Commonly, BC mass concentrations are determined indirectly by optical instruments (e.g., Bond et al., 2013). Filter absorption photometers (FAPs) such as aethalometer (AE) and multi-angle absorption photometers (MAAP) provide continuous BC data by measuring light attenuation from particles collected on a filter at specific wavelengths. The measured attenuation is converted to absorption coefficient ($b_{abs}$) and then into equivalent black carbon (eBC) mass concentrations using pre-defined mass absorption cross-sections (MAC). In this case, according to Petzold et al. (2013), the term eBC should be used instead of BC as a proxy of $b_{abs}$ when FAPs are employed.

To estimate eBC mass concentrations, harmonized measurements of $b_{abs}$ from FAPs and reliable determination of MAC values are essential. The prevalent approach for converting the $b_{abs}$ to eBC mass concentration involves applying the instrument manufacturers fixed MAC values that were obtained through optical methods (Bond and Bergstrom, 2006; Snyder and Schauer, 2007). MAAP uses the constant MAC value of 6.6 m² g⁻¹ at a nominal wavelength of 637 nm to convert the optically measured $b_{abs}$ to eBC mass concentration. The MAC value for the MAAP at $\lambda$= 637 nm were derived through the comparison of $b_{abs}$ (MAAP) and BC mass concentrations ($M_{BC}$) measurements conducted at four sites in Germany (Petzold and Schönlinner, 2004) using the German reference method VDI2465 Part 1 (GRM, Schmid et al., 2001). The multi-wavelength dual-spot AE, (AE33, Magee Scientific) applies a nominal MAC value of 7.77 m² g⁻¹ at 880 nm which is the reference wavelength for providing eBC from AE instruments. The value of 7.77 m² g⁻¹ at $\lambda$= 880 nm was initially determined through the comparative analysis of optical and thermal measurements on filters loaded with refractory carbonaceous material (Gundel et al., 1984) and traced to new optical measurements (Drinovec et al., 2015). Older AE models, 7-wavelengths AE31 and 2-wavelengths AE21 and AE22 (operating at 370 and 880 nm), employ a mass attenuation cross-section of 16.62 m² g⁻¹ at 880 nm (Gundel et al., 1984). The ratio between the mass attenuation and absorption cross sections is the multiple-scattering enhancement factor ($C_0$) of 2.14 that accounts for artifacts associated with the presence of the filter tape (Weingartner et al., 2003).

While nominal MAC values are used by FAPs for converting $b_{abs}$ to eBC mass concentration, empirical studies revealed a wide range of experimental MAC values across different sites. Indeed, MAC is considerably variable and it is influenced by factors such as location e.g., urban, rural, and high-altitude (Salako et al., 2012; Wang et al., 2013) BC particle size (Zhao et al., 2021), internal mixing (Cao et al., 2015; Cho et al., 2019; Knox et al., 2009; Yuan et al., 2021; Zanatta et al., 2016), combustion sources (Olson et al., 2015; Wu et al., 2021), selected $b_{abs}$ wavelengths (Mbengue et al., 2021; Srivastava et al., 2022), monitoring periods (Ciupek et al., 2021; Ram and Sarin, 2009), and instrument types (Kondo et al., 2009; Nordmann et al., 2013). For instance, freshly emitted uncoated eBC has been reported to have a MAC of approximately 7.5 ± 1.2 m² g⁻¹ at 550 nm (Bond and Bergstrom, 2006), confirmed by a recent study where an average MAC of about 8.0 ± 0.7 m² g⁻¹ at 550 nm was reported (Liu et al., 2020). However, atmospheric eBC particles can have a wide range of MAC values. For example, Presler-Jur et al. (2017) reported values ranging from 6.9 to 9.4 m² g⁻¹ in different U.S. regions and seasons. Similarly, Nordmann
et al. (2013) found MAC values ranging from 3.9 to 7.4 m² g⁻¹ at 550 nm across Central Europe, while Mbengue et al. (2021) reported a range of 7.84 ± 2.79 m² g⁻¹ at 660 nm to 13.90 ± 4.97 m² g⁻¹ at 370 nm. Wu et al. (2021) observed MAC values ranging from 2.71 to 5.91 m² g⁻¹ at 870 nm for different combustion sources, including biofuel stoves and diesel trucks. Ciupek et al. (2021) reported varied MAC values at different London sites, ranging from 7.4 to 12.6 m² g⁻¹ at 880 nm. Additionally, studies by Srivastava et al. (2022), Grange et al. (2020), Pandolfi et al. (2014), and Zanatta et al. (2016) have provided further insights into the range of MAC values across different measurement sites and regions.

The variation of these experimental MAC values causes difficulties in determining accurate eBC mass concentration and, consequently, in properly representing eBC in AQ models (Grange et al., 2020; Ciupek et al., 2021). These problems arise because using the nominal MAC from FAPs without considering the observed MAC spatial-temporal variability can result in misleading eBC estimations (e.g., Ciupek et al., 2021; Moschos et al., 2021; Srivastava et al., 2022). When employing a nominal MAC, although the babs data might be comparable, the resulting eBC concentrations may not be directly comparable across different sites. For this reason, the determination of in situ MAC holds significant importance for constraining uncertainty in eBC derived from FAPs measurements (Kondo et al., 2009; Zhao et al., 2021). As shown here and reported elsewhere (e.g., Ciupek et al., 2021; Mbengue et al., 2021; Pileci et al., 2021), having simultaneous FAPs and EC measurements helps providing more reliable and accurate estimation of high time resolution eBC from FAPs (Grange et al., 2020; Helin et al., 2018; Ivančič et al., 2022; Srivastava et al., 2022).

Furthermore, the incorporation of eBC in the new European Union (EU) directive (Council of the European Union, 2023) evidences a growing awareness of the need to address the eBC adverse effects on both human health and the environment (Janssen et al., 2011, Janssen et al., 2012; Karamfilova, 2022; Kuula et al., 2022). In fact, the current revision of the EU ambient AQ directive and World Health Organization AQ Guideline (WHO-AQG) also emphasize the importance of monitoring eBC concentrations to gain new insights into its impact on AQ and climate (WHO, 2021). To incorporate eBC as a new variable in AQ guidelines and to develop effective mitigation strategies, it is crucial to estimate its mass concentration in a consistent way throughout the air quality monitoring networks (AQMNs) with minimal uncertainties. In response to these growing demands for standardized and reliable estimation of eBC, various initiatives have been undertaken by research infrastructures such as ACTRIS (Laj et al., 2023, under review), EURAMET (STANBC, 2023) and the Working Group WG35 of the European Committee for Standardization (CEN). These entities have highlighted the significance of harmonizing eBC measurement procedures to ensure consistency and comparability of data across different monitoring networks and within research studies. Through their efforts, comprehensive recommendations and guidelines are being developed to establish robust protocols, including various sampling techniques, instruments, and data processing methodologies (Measurement guidelines, 2022). Here we present a robust, sensitivity analysis of MAC values and eBC mass concentration to support these guidelines.

## 2. Methodology

In this study, we quantified the mass concentrations of eBC through in situ MAC assessment at 22 sites to: i) normalize eBC observations across the sites and ii) propose more advanced methods to determine site-specific eBC mass concentrations. These objectives align with the aim of measurement consistency and minimizing instrument-to-instrument variability observed in the intercomparison of FAPs (Asmi et al., 2021; Cuesta-Mosquera et al., 2021; Helin et al., 2018; Müller et al., 2011; Pileci et al., 2021). Furthermore, our approach uses different levels of correction schemes for eBC estimation including the determination of median MAC value for the 22 sites and site-specific and rolling regression MAC values. The rolling approach has been recently suggested by Grange et al. (2020) and consists of continuous EC measurements over an extended period. It was proposed to be an efficient way to refine eBC mass concentrations. Within this context, the primary focus of this study was to perform a comparative analysis between various methods to estimate eBC mass concentrations. Following this, site-specific MAC values can be determined for each of the 22 sites where EC concentration data were available. Additionally, this study explored potential long-term variations in experimental MAC across the sites.

### 2.1 Measurement sites

Out of the 50 European monitoring sites supplying FAPs data to the RI-URBANS project (Savadkoohi et al., 2023), 22 sites (21 EU, 1 US) also provided EC mass concentration measurements. Table 1 presents an overview of the sites and instruments used for in situ measurements of eBC and babs
from FAPs and EC mass concentrations. The 22 measurement sites from 18 cities used here include urban background (UB, 13 sites), traffic (TR, 4 sites), sub-urban (SUB, 2 sites) and regional background (RB, 3 sites) sites (Figure 1).

## 2.2 Instrumentation

In this study, the employed FAPs were the AE33 (12 out of 22 sites) and the MAAP (10 out of 22 sites). Additionally, three sites (LND_UB, LND_TR, and ROC_UB) provided data using the AE22 or AE21 models. The EC mass concentrations were measured using thermal-optical organic/elemental carbon, OC-EC Sunset analyzers either offline or online (Karanasiou et al., 2020). MAAP instruments account for compensation of filter-loading-induced artifacts, that impact the $b_{abs}$ calculation (Petzold and Schönlinner, 2004), simultaneously measuring the intensity of light transmitted through the aerosol-loaded filter tape and scattered back from the filter spot, and subsequently converts these data to particle light absorption. The MAAP specifies a measurement wavelength of 670 nm, although its actual wavelength is 637 nm (Müller et al., 2011). The default setting for the MAC is $6.6 \text{ m}^2 \text{ g}^{-1}$, enabling the conversion of optically measured $b_{abs}$ data into eBC mass concentrations. The AE33 dual-spot Aethalometer (Magee Scientific) measures light attenuation ($\sigma_{ATN}$) through aerosol-loaded filter tape and determines $b_{abs}$ at seven distinct wavelengths (370, 470, 520, 590, 660, 880, and 950 nm) using an assumed wavelength-dependent MAC and a wavelength independent filter multiple-scattering parameter ($C_0$). The AE33 converts the optical absorption coefficients into eBC mass concentrations using a nominal MAC value of $7.77 \text{ m}^2 \text{ g}^{-1}$ at 880 nm and default $C_0$ values that depend on the filter tape used (e.g. Savadkoohi et al., 2023). Filter loading effects are measured and corrected online using algorithms implemented in AE33 (Drinovec et al., 2015). Older AE models such as the AE22, and AE21 were used at three sites (i.e., LND_UB, LND_TR, and ROC_UB) to measure $\sigma_{ATN}$ through the filter at two (AE22, and AE21; 370 and 880 nm) wavelengths using an instrument-derived mass attenuation cross section value of $16.62 \text{ m}^2 \text{ g}^{-1}$ at 880 nm. However, these models required a manual post-processing correction for loading effects, and algorithms for correcting this artifact have been developed and tested by different researchers (Collaud Coen et al., 2010; Virkkula et al., 2007; Weingartner et al., 2003). More detailed information on these techniques can be found in previous reports (e.g., Drinovec et al., 2015; Müller et al., 2011; Petzold et al., 2005; Petzold and Schönlinner, 2004; Savadkoohi et al., 2023).

## 2.3 Data processing

Over the long-time span of the compiled datasets, the minimum data coverage was one year to the maximum twelve years. Regarding FAPs measurements, Level 2 quality assured/quality checked (QA/QC) data from the EBAS database (EBAS, http://ebas nilu.no/) were used for three sites (LEJ_TR1, GRA_UB and IPR_RB). Figure S1 visualizes the distribution of eBC dataset, including data coverage, data capture, and missing data from FAPs at 22 sites. For the other sites, data providers directly supplied hourly averaged data as either NeBC mass concentrations, or $b_{abs}$. To ensure the comparability of measurements across the sites, data were processed using harmonized measurement protocols according to the ACTRIS recommendations for reporting $b_{abs}$ from FAPs (Müller and Fiebig, 2018a, 2018b). More details of the methodology employed to derive $b_{abs}$ and subsequently estimate eBC mass concentrations are available in detail in Figure 2 of Savadkoohi et al. (2023). For most monitoring sites, NeBC concentrations were directly supplied by data providers and used for defining the $b_{abs}$. In cases where $b_{abs}$ data were provided (BER_TR, ZUR_UB, PAY_RB, ATH_SUB), eBC mass concentrations were determined by employing nominal MAC values and harmonization factors (H*). This harmonization process involves the application of instrument-specific H* factor to convert the light attenuation captured by FAPs into $b_{abs}$. ACTRIS H* factors have been established to ensure consistency and comparability of $b_{abs}$ data for different instruments and measurement sites (Müller and Fiebig, 2018a; Laj et al., 2020; Müller et al., 2011; Zanatta et al., 2016). Further details regarding the appropriate selection of the $C_0$ and the standardized H* factors can be found in Savadkoohi et al. (2023). As previously mentioned, AE33 instruments were already equipped with online factor loading corrections (Drinovec et al., 2015), while data obtained from older AE models (AE21 and AE22) were corrected offline by the data providers. Specifically, the correction scheme proposed by Turner et al. (2007), was applied to the AE21 data from ROC_UB (Rattigan et al., 2013), whereas the scheme introduced by Virkkula et al. (2007) was applied to the AE22 data from LND_UB/LND_TR. The AE31 data from ZUR_UB, BER_TR and PAY_RB has been compensated for the filter-loading using schemes introduced by Drinovec et al. (2015) and Weingartner et al. (2003).
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Location of the cities and type of 22 monitoring sites with eBC and EC mass concentration measurement data being supplied to this study, UB, urban background; SUB, suburban background; TR, traffic; RB, regional background.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Figure 2.** The overall scheme by which to process corrected and normalized eBC mass concentrations in order to obtain site-specific MAC values. More details are provided in sections 2.4, 2.5 and 2.6. (a): 637 nm is the reference wavelength of the MAAP to derive MAAP-equivalent absorption coefficient; (b): 1.05 is the correction factor to the true wavelength (637 nm); (c) Efficient Rolling/Windowed Operations using “zoo” and “RcppRoll” R packages.</figcaption></figure>
<table><caption>**Table 1.** Characteristics and instrumental details of the 22 measurement sites supplying eBC and EC mass concentrations data. Table contains detailed information such as the location of each site, the type of site (UB, Urban Background; TR, Traffic; SUB, Suburban Background; RB, Regional Background), the FAPs used for measurements, and specific measurement details. Finland (FI), United Kingdom (UK), United States (US), France (FR), Spain (ES), Greece (GR), Germany (DE), Switzerland (CH), and Italy (IT), Czech Republic (CZ).</caption><thead><tr><th>Site</th><th>City-<br>Country</th><th>Station<br>Type</th><th>Acronym</th><th>Coordinates</th><th>Data provider</th><th>cut-off<br>eBC</th><th>FAPS</th><th>Wavelengths<br>(λ)</th><th>Nominal<br>MAC (m²<br>g⁻¹)</th><th>EC<br>measurement<br>method:<br>Online/offline</th><th>Thermal-optical<br>protocol</th><th>Cut-off EC</th></tr></thead><tbody><tr><td>Palau Reial</td><td>Barcelona-<br>ES</td><td>UB</td><td>BCN_UB</td><td>41.387,<br>2.115</td><td>IDAEA-CSIC</td><td>PM10</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Offline</td><td>EUSAAR-2</td><td>PM₁₀</td></tr><tr><td>UGR</td><td>Granada-<br>ES</td><td>UB</td><td>GRA_UB</td><td>37.18, -3.58</td><td>Universidad de<br>Granada</td><td>Total</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Offline</td><td>EUSAAR-2</td><td>PM₁₀</td></tr><tr><td>Longchamp</td><td>Marseille-<br>FR</td><td>UB</td><td>MAR_UB</td><td>43.305,<br>5.394</td><td>AtmoSud-LCE</td><td>PM2.5</td><td>AE33</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM₁</td></tr><tr><td>Dresden-<br>Winckelmannstrasse</td><td>Dresden-<br>DE</td><td>UB</td><td>DDW_UB</td><td>51.036,<br>13.730</td><td>LfULG</td><td>PM1</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Offline</td><td>EUSAAR-2</td><td>PM₁</td></tr><tr><td>North Kensington</td><td>London-<br>UK</td><td>UB</td><td>LND_UB</td><td>51.521, -<br>0.213</td><td>Imperial<br>College</td><td>PM2.5</td><td>AE22</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>QUARTZ until<br>01/Feb/2016,<br>EUSAAR2 from<br>03/Feb/2016</td><td>PM₁₀ until<br>27/Feb/2019,<br>PM₂.₅ from<br>01/Mar/2019</td></tr><tr><td>Rochester</td><td>Rochester-<br>US</td><td>UB</td><td>ROC_UB</td><td>43.146, -<br>77.542</td><td>NYS Dept of<br>Environment</td><td>PM2.5</td><td>AE21</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>IMPROVE-A</td><td>PM₂.₅</td></tr></tbody></table>
<table><tr><td>Bollwerk</td><td>Bern-CH</td><td>TR</td><td>BER_TR</td><td>46.951, 7.441</td><td>EMPA</td><td>PM2.5</td><td>AE31-AE3</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>Kaserne</td><td>Zurich-CH</td><td>UB</td><td>ZUR_UB</td><td>47.378, 8.530</td><td>EMPA</td><td>PM2.5</td><td>AE31-AE33</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>NOA</td><td>Athens-GR</td><td>UB</td><td>ATH_UB</td><td>37.973, 23.718</td><td>NOA</td><td>PM10-PM2.5</td><td>AE33</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>Villa Ada</td><td>Rome-IT</td><td>UB</td><td>ROM_UB</td><td>41.932, 12.507</td><td>CNR-ISAC</td><td>PM1</td><td>AE33</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>Pascal</td><td>Milan-IT</td><td>UB</td><td>MLN_UB</td><td>45.464, 9.188</td><td>Arpa Lombardia</td><td>PM10</td><td>MAAP</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>10</sub></td></tr><tr><td>Dresden-Nord</td><td>Dresden-DE</td><td>TR</td><td>DRD_TR</td><td>51.064, 13.7413</td><td>LfULG</td><td>PM1</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>10</sub></td></tr><tr><td>Mäkelänkatu</td><td>Helsinki-FI</td><td>TR</td><td>HEL_TR2</td><td>60.196, 24.952</td><td>HSY</td><td>PM1</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>1</sub></td></tr><tr><td>Mitte</td><td>Leipzig-DE</td><td>TR</td><td>LEJ_TR1</td><td>51.344, 12.377</td><td>TROPOS</td><td>PM10</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>10</sub></td></tr><tr><td>Marylebone Road</td><td>London-UK</td><td>TR</td><td>LND_TR</td><td>51.522, -0.1546</td><td>Imperial College</td><td>PM2.5</td><td>AE22</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>QUARTZ until 01/Feb/2016, EUSAAR2 from 03/Feb/2016</td><td>PM<sub>10</sub> until 08/Oct/2019, PM<sub>2.5</sub> from 10/Oct/2019</td></tr></table>
<table><tr><td>Demokritos</td><td>Athens-GR</td><td>SUB</td><td>ATH_SUB</td><td>37.99, 23.82</td><td>NCSR</td><td>PM10</td><td>AE33</td><td>880 nm</td><td>7.77</td><td>Online</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>Sirta</td><td>Paris-FR</td><td>SUB</td><td>PAR_SUB</td><td>48.7086, 2.1588</td><td>LSCE / INERIS</td><td>PM1</td><td>AE33</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>Ispra</td><td>IPR_IT</td><td>RB</td><td>IPR RB</td><td>45.8, 8.633</td><td>EC-JRC-IES</td><td>PM10</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Online</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>SMEAR II</td><td>Hyytiälä-FI</td><td>RB</td><td>SMR RB</td><td>61.847, 24.295</td><td>University of Helsinki</td><td>PM10</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Online</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>Payerne</td><td>PAY-CH</td><td>RB</td><td>PAY RB</td><td>46.813, 6.944</td><td>EMPA</td><td>PM10</td><td>AE31-AE33</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>2.5</sub></td></tr><tr><td>Prague Suchdol</td><td>Prague-CZ</td><td>UB</td><td>PRG_UB</td><td>50.1268, 14.384</td><td>ICPF</td><td>PM10</td><td>MAAP</td><td>637 nm</td><td>6.6</td><td>Online</td><td>EUSAAR-2</td><td>PM<sub>1</sub></td></tr><tr><td>UMH</td><td>ELX-ES</td><td>UB</td><td>UMH_UB</td><td>38.2785, 0.6878</td><td>Universitat Miguel Hernández de Elche</td><td>PM10</td><td>AE33</td><td>880 nm</td><td>7.77</td><td>Offline</td><td>EUSAAR-2</td><td>PM<sub>10</sub></td></tr></table>
## 2.4 Mass absorption cross-section (MAC) of equivalent black carbon

The procedure used to obtain in situ MAC from the collocated measurements of EC and $b_{abs}$ at each site is summarized in Figure 2. MAC values obtained using this method are hereafter referred to as experimental or site-specific MAC. To construct the necessary dataset for analysis, the AE and MAAP measurements were aggregated to match the EC time resolutions. Unlike the continuous measurements of eBC obtained from FAPs, the EC observations were available with varying sampling intervals spanning from 1 to 12 days and different time resolutions typically ranging from 1, 3 to 12, or 24 hours. Considering this, the dates corresponding to both EC and eBC observations were matched, resulting in creation of a self-consistent database (see step 1, Figure 2). No efforts were made to impute or interpolate missing observations, and incomplete data were excluded from the datasets to mitigate potential errors. Data processing and statistical analysis were performed using R statistical computing software. Specifically, the following packages were used: openair (Carslaw and Ropkins, 2012), zoo: S3 infrastructure for regular and irregular time series (Zeileis and Grothendieck, 2005), the 'RcppRoll' package (Ushey, 2022) and other R data analysis packages that enabled efficient rolling computations.

Given that eBC and EC particles are mostly submicron, variations in the cut-off inlets for their measurements would introduce minimal uncertainty and the overall accuracy of eBC and EC measurements should remain relatively unaffected. Moreover, the reported uncertainty for the $b_{abs}$ of MAAP at 637 nm is 15%, as reported by Zanatta et al. (2016) and Müller et al. (2011), while Petzold and Schönlinner (2004) have reported uncertainties of 12% for $b_{abs}$ at 637 nm. Additionally, Valentini et al. (2021) and Yus-Díez et al. (2021) and references herein, reported a possible overestimation of the $b_{abs}$ from MAAP compared to other non-reference techniques for measuring absorption. Regarding the AE33, uncertainties of around 15% and 25% have been reported for $b_{abs}$ and eBC, respectively (Rigler et al., 2020). The uncertainty of the estimated MAC for MAAP, according to Zanatta et al. (2016), is around 29% when EC is measured with EUSAAR-2 Protocol. In our study, the uncertainty associated with the MAC at 880 nm is estimated to be around 30-35% considering the use of filter types M8020 and M8060 and the reference C values as discussed by Yus-Díez et al. (2021). Despite the additional uncertainty in the absorption measurements provided by AE33 due to variations of the C factor, we use the term MAC in this work irrespective of the FAP used for its calculation to be consistent with the terminology widely used in literature for reporting mass absorption cross section. Taking this into account, the additional aforementioned uncertainties in the measurements of $b_{abs}$ can be minimized by continuously correcting the calculated eBC with simultaneous EC measurements following the CEN standards.

Additionally, the uncertainties associated with EC measurements, arising from both offline and online methods, cannot be disregarded in the determination of experimental MAC. As previously estimated, the deviation in EC average concentrations between Semi-Continuous and offline analyzers was quantified at 18%, following the procedures outlined in the EN 16909:2017 standard (Karanasiou et al., 2020). In addition a laboratory-to-laboratory variability of 10% in EC data has been reported by Rigler et al. (2020). This is associated with uncertainties related to the OC-EC split point, influenced by aerosol type and chemical composition and charring of organic material in thermal optical methods (Karanasiou et al., 2020; Rigler et al., 2020). Earlier studies have indicated a range of uncertainties in EC determination, spanning from 2 to 7%, in accordance with the EUSAAR-2 protocol in Europe (Cavalli and Putaud, 2010; Karanasiou et al., 2015).

To obtain the experimental MAC values at each site, the $b_{abs}$ wavelengths from the MAAP (637 nm), AE33, AE21, and AE22 instruments (880 nm), were used. The calculations were performed using the equations outlined in steps 3 and 4 of Figure 2. The determination of experimental MAC was obtained as the ratio of the $b_{abs}$ to EC mass concentration "MAC$^{λ} = \frac{Abs^{λ}}{[EC]}". This calculation was performed using the averaged $b_{abs}$ over a 24-hour period (Grange et al., 2020; Laborde et al., 2013; Pileci et al., 2021). An alternative approach to account for temporal variations in MAC is the implementation of a rolling simple least-squares linear regression model, which relates daily aggregated $b_{abs}$ values to corresponding EC concentrations, resulting in rolling mean MAC values. Grange et al. (2020) previously applied this method and demonstrated its potential for achieving consistent results. Applying this approach to the compiled datasets allowed for comparisons between NeBC and ReBC mass concentrations obtained through the rolling method (see step 5; Figure 2). For the rolling mean regression technique (Zeileis and Grothendieck, 2005), a 14-day right-aligned time window was selected appropriate, differing from a 180-day center-aligned window used by Grange et al. (2020). The choice of window size depended on the data's sample size and periodicity, with shorter windows suitable for shorter intervals and larger windows for longer ones.
More detail on the rolling method is provided in the supplementary information, where a sensitivity study is performed (Figure S2).

The numbers of observations used to calculate the site-specific MAC were not consistent across the sites. For example, the EC filter samples collected at the HEL_TR2 site consisted of only 38 daily observations, which may not be representative of the area (Helin et al., 2018). Conversely, the sites PAR_SUB and IPR RB had the highest number of compiled EC observations which can provide more robust statistical confidence. To study the statistical significance of variations in the mean of the experimental MAC in different seasons, we initially performed a normality test to examine the homogeneity of variances among various sites and seasons. This step aimed to determine the appropriateness of selecting either the parametric analysis of variance (ANOVA) or the non-parametric Kruskal-Wallis test, followed by the Dunn's test. The selection of an appropriate statistical approach was contingent upon the fulfillment of assumptions regarding the data distribution and homogeneity of variances.

## 2.5 Trend analysis

The method employed for the trend analysis (step 6; Figure 2) was a Theil-Sen slope regression estimator, a robust, non-parametric estimator provided by the openair R package (Carslaw and Ropkins, 2012). The aim of this analysis was to assess the statistical significance and slopes of long-term trends in NeBC, ReBC and EC mass concentrations, as well as the experimental MAC at 637 or 880 nm. Theil-Sen method accurately calculates 95% confidence intervals, even when dealing with non-normal data and non-constant error variance, and it is also resilient to outliers. Only monitoring sites that had at least 9 years of continuous eBC and EC mass concentration data, with a minimum of 75% valid measurements during that period, were considered for the trend analysis (i.e., BCN_UB, ROC_UB, IPR Rede, PAR_SUB, and ZUR_UB).

# 3. Results and discussion

## 3.1 Spatial variability of experimental mass absorption cross-section (MAC)

Figure 3 shows box plots of the experimentally determined MAC for MAAPs and AEs, normalized through concurrent EC measurements by site typology. The internal black solid line within the boxplots indicates the median value, distinguished by instrument type and measuring wavelengths (637 nm and 880 nm). The distribution and spread of these box plots provide the range of EC-derived MAC values and their variability across the different sites. The MAC variability over the sampled sites was not geographically dependent and no consistent pattern was observed in the deviation of the experimental MAC values across different site types. The median experimental MAC value of $10.6 \pm 4.7 \text{ m}^2 \text{ g}^{-1}$ (range: $7-14 \text{ m}^2 \text{ g}^{-1}$; Figure 3) was obtained for MAAP measurements at 637 nm across 10 sites. This experimental median MAC value is close to the value of $10.0 \text{ m}^2 \text{ g}^{-1}$ previously proposed by Zanatta et al. (2016) for MAC, using data from European RB sites measured from FAPs at 637 nm. The median MAC value of $7.8 \pm 3.4 \text{ m}^2 \text{ g}^{-1}$ (range: $5-12 \text{ m}^2 \text{ g}^{-1}$; Figure 3) was determined at 880 nm for AE measurements at 12 sites (see Figure 3). The relative percent deviation from the median, obtained as 16% for MAAP, whereas AEs deviated by 37%. Despite the large variability of the reported MAC, it is important to note that the median AE33 MAC reported here ($7.8 \text{ m}^2 \text{ g}^{-1}$) is very close to the nominal AE33 MAC of $7.77 \text{ m}^2 \text{ g}^{-1}$ at 880 nm. However, despite this similarity, the nominal eBC reported by the AE33 is overestimated by around 50% due to the filter multiple-scattering parameter ($C_0$) as the main source of uncertainty (e.g., Savadkoohi et al., 2023). In the case of the MAAP, the eBC reported by the MAAP is mostly affected by the nominal MAC used ($6.6 \text{ m}^2 \text{ g}^{-1}$) which is much lower compared to the MAC value of $10.6 \text{ m}^2 \text{ g}^{-1}$ reported here.
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Variability in experimental MAC at instrument-specific wavelengths (MAAP: 637 nm; AEs: 880 nm) across 22 sites categorized by site type (UB, urban background; SUB, suburban background; TR, traffic; RB, regional background). The upper panel corresponds to AEs at 880 nm, while the lower panel corresponds to MAAP instruments at 637 nm. The solid black line represents the total experimental median MAC for 12 AEs at 880 nm (7.8 ± 3.4 m² g⁻¹) and 10 MAAPs at 637 nm (10.6 ± 4.7 m² g⁻¹). Box-and-whisker plots depict the 25th and 75th quartiles, with the median indicated by a notched inner line. The mean is represented by the red points within the box plots.</figcaption></figure>
## 3.2 Site-specific mass absorption cross-section (MAC)

Table 2 contains the site-specific MAC values presented in Figure 3. According to the data presented in Table 2, among the urban sites, the highest site-specific MAC values were observed at PRG_UB, MLN_UB, DDW_UB, and GRA_UB. To consider the potential contributors, MLN_UB, has been previously recognized to be influenced by the emissions from biomass burning (BB) specifically during winter (Mousavi et al., 2019). DDW_UB is located 1.7 km away from the city center, is grouped into the strongly traffic influenced site and is usually influenced by the plumes of BC originating from domestic heating in Eastern Europe (Sun et al., 2020, 2019). In GRA_UB, there is also a large influence of open BB for agricultural waste removal at the agricultural lands surrounding the city (Casquero-Vera et al., 2021; Titos et al., 2017). This additional source together with low wind dispersion and frequent temperature inversions during the cold season facilitates the accumulation of aged secondary aerosols (van Drooge et al., 2022). In PRG_UB, the sampling site is located in a residential area prone to domestic heating at the north-west suburb of the city, about 250 m from the nearest road, and 8 km from the international Václav Havel airport (Zíková et al., 2016). Different origins of carbonaceous aerosols have also been observed at this site mainly from combustion (biomass, coal for heating) in winter and biogenic aerosols in summer (Vodička et al., 2023, 2022).

Among traffic sites, higher site-specific MAC values were observed at the HEL_TR2 and LEJ_TR1 sites. LEJ_TR1 is in a central city area along a roadside. This site was primarily characterized by elevated particle number concentrations (PNCs), which can be attributed to the freshly emitted and quickly nucleated particles from traffic exhaust emissions (Sun et al., 2020; Trechera et al., 2023). HEL_TR2 is located on the curbside of a busy traffic street referred to as street canyon (SC, Mäkelänkatu) in Northern Europe. Air quality at this site has been reported to be influenced by high traffic density (Barreira et al., 2021; Helin et al., 2018). Between the two SUB sites, ATH_SUB showed an elevated MAC in contrast to PAR_SUB that can be attributed to both traffic-related and BB sources (Diapouli et al., 2017). This station is exposed to transported pollutants originating from the urban area of Athens under most atmospheric conditions (Gini et al., 2022). Among the RB sites, the observed high MAC value at IPR RB, can be attributed to local emissions, local meteorology, long-ranged transport, and its topography (Pandolfi et al., 2018; Putaud et al., 2014). PAY RB showed a high MAC value. In previous studies, a significant increase in MAC values at PAY RB was observed, possibly due to instrument artifacts. This increase coincided with instrument replacement in June 2016, after which MAC values showed reduced variability (Grange et al., 2020). Alternatively, sites like ZUR_UB, BER_TR, MAR_UB, and PAR_SUB exhibited low MAC values, indicative of potential influence from diverse sources of eBC, and other specific characteristics of the sampling site, such as local emissions, meteorological conditions, and geographical features.

<table><caption>**Table 2.** Site-specific experimental MAC values for 22 monitoring stations obtained as the ratio of the $b_{abs}$ to EC mass concentration at the instrument specific wavelengths AE 880 nm; MAAP 637 nm.</caption><thead><tr><td>Sites</td><td>FAPs</td><td>Site-specific MAC [m<sup>2</sup>g<sup>-1</sup>], AE 880 nm; MAAP 637 nm</td><td>Number of observations</td></tr></thead><tbody><tr><td>BCN_UB</td><td>MAAP</td><td>9.6 ± 2.6</td><td>1035</td></tr><tr><td>GRA_UB</td><td>MAAP</td><td>10.4 ± 3.3</td><td>391</td></tr><tr><td>ROC_UB</td><td>AE21</td><td>7.2 ± 2.8</td><td>1390</td></tr><tr><td>ATH_UB</td><td>AE33</td><td>6.1 ± 4.0</td><td>1244</td></tr><tr><td>MAR_UB</td><td>AE33</td><td>5.1 ± 1.7</td><td>120</td></tr></tbody></table>
<table><tr><td>DDW_UB</td><td>MAAP</td><td>10.4 ± 4.1</td><td>179</td></tr><tr><td>LND_UB</td><td>AE22</td><td>8.8 ± 2.6</td><td>1565</td></tr><tr><td>BER_TR</td><td>AE31-AE33</td><td>5.7 ± 3.5</td><td>350</td></tr><tr><td>ZUR_UB</td><td>AE33</td><td>4.9 ± 2.3</td><td>338</td></tr><tr><td>ROM_UB</td><td>AE33</td><td>8.8 ± 3.1</td><td>43</td></tr><tr><td>MLN_UB</td><td>MAAP</td><td>11.2 ± 6.5</td><td>1142</td></tr><tr><td>PRG_UB</td><td>MAAP</td><td>14.0 ± 3.7</td><td>296</td></tr><tr><td>UMH_UB</td><td>AE33</td><td>7.3 ± 2.1</td><td>136</td></tr><tr><td>HEL_TR2</td><td>MAAP</td><td>12.4 ± 4.7</td><td>38</td></tr><tr><td>LEJ_TR1</td><td>MAAP</td><td>13.8 ± 4.6</td><td>164</td></tr><tr><td>DDN_TR</td><td>MAAP</td><td>9.6 ± 2.6</td><td>181</td></tr><tr><td>LND_TR</td><td>AE22</td><td>9.7 ± 1.8</td><td>1856</td></tr><tr><td>ATH_SUB</td><td>AE33</td><td>10.1 ± 2.3</td><td>513</td></tr><tr><td>PAR_SUB</td><td>AE31-AE33</td><td>5.5 ± 3.0</td><td>2190</td></tr><tr><td>SMR_RB</td><td>MAAP</td><td>7.3 ± 4.7</td><td>432</td></tr><tr><td>IPR_RB</td><td>MAAP</td><td>10.8 ± 4.4</td><td>3945</td></tr><tr><td>PAY_RB</td><td>AE31-AE33</td><td>12.4 ± 4.1</td><td>494</td></tr></table>

Figure 4 presents the frequency distribution of the experimental MAC values at 637 and 880 nm based on instrument type across different sites throughout the entire measurement period with a skewness bias at ZUR_UB, BER_TR, MAR_UB, and PAR_SUB. Notably, both MLN_UB and IPR_RB exhibited a greater degree of dispersion in the distribution pattern. The underlying factors that contribute to the bimodality in some sites as PAY_RB, BER_TR and ROM_UB may indicate the presence of distinct sources contributing to the observed MAC values representing different physical or chemical processes affecting b<sub>abs</sub>. This might be linked to seasonal or temporal variations or arise from instrument-related factors or
measurement artifacts. The variation in the standard deviations of MAC values, indicates changes in average aerosol characteristics on a regular basis, which are generally influenced by varying sources that may differ between summer and winter seasons.

Understanding the temporal and spatial variability in MAC requires considering studies using similar measurement techniques, e.g., MAAP and AEs, at consistent wavelengths. Direct comparisons might be difficult due to site differences, seasonal variations, and measurement methodologies. Experimental MAC values in the literature were mainly reported from short-term campaigns, limiting direct site-to-site comparisons. In addition, seasonal changes affect eBC emissions and therefore, MAC, emphasizing the need for long-term measurements to derive site-specific MAC values. These variations relate to aerosol composition, instrument responses, and time-dependent factors (Ram and Sarin, 2009; Yuan et al., 2021). Heterogeneous carbonaceous species from different sources impact MAC, especially wood burning and other biomass sources (Laborde et al., 2013; Wu et al., 2021; Yuan et al., 2021; Zhang et al., 2020). Meteorological conditions, such as temperature, influence MAC (Srivastava et al., 2022), but this correlation varies by region (Pandolfi et al., 2014). Coating materials, more prevalent in summer, and BC mixing states can significantly affect MAC values (Mbengue et al., 2021, 2020; Pandolfi et al., 2011, 2014; Zanatta et al., 2016; Zhao et al., 2021). Size-dependent MAC variations were also found to be source-specific (Bond and Bergstrom, 2006; Conrad and Johnson, 2019; Knox et al., 2009; Kondo et al., 2009).
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Frequency distribution of experimental MAC for the whole available period. The color shading represents the type of site (UB, urban background; SUB, suburban background; TR, traffic; RB, regional background). The corresponding median MAC is shown in black sticks.</figcaption></figure>
3.3 Seasonality of the experimental mass absorption cross-section (MAC)

Figure 5 presents the seasonal variation of MAC across different sites. As already noted, the MAC and its variation strongly depends on source-dependent eBC, physical properties as morphology, size, internal mixing and coating, among others. Other factors as the cross-sensitivity to scattering (Yus-Díez et al., 2021) could add additional uncertainty on the measured babs and consequently on the MAC. Yus-Díez et al. (2021) observed that high single scattering albedo (SSA) of the particles collected on the filter tape can cause an increase of C0 (cross-sensitivity to scattering). However, according to Yus-Díez et al. (2021), no cross-sensitivity to scattering was observed in BCN_UB when contrasted with a regional background station (Montseny, MSY) and a mountaintop station (Montsec, MSA). This divergence was attributed to the consistently low SSA in BCN_UB. Such a pattern may potentially represent a general characteristic observable across our UB and TR-influenced sites (e.g. Laj et al., 2020). Thus, we consider that the cross-sensitivity to scattering could make marginal contribution and, in any event, falls within the overall uncertainty. A detailed description of the aforementioned physical factors causing the observed MAC seasonal cycles falls beyond the scope of this paper, primarily due to the necessity for additional detailed observations. These include, but are not limited to, comprehensive PM chemical speciation, single eBC particle characterization, scattering measurements, and concurrent monitoring of MAAP and AE measurements, for instance, as studied in Yus-Díez et al. (2021, 2022). Consequently, where feasible, we reference previously published works to interpret the observed cycles.
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**Figure 5.** Monthly variations in experimental MAC at 880 and 637 nm for 22 sites. The color shading represents the type of site (UB, urban background; SUB, suburban background; TR, traffic; RB, regional background). Box plots depict the 10th and 90th quantiles.

In addition, there has been reported different degrees of influence attributed to dust interactions on seasonal pattern of MAC in different geographical regions, such as Spain, Greece, Italy and Germany spanning from March to August (Athanasopoulou et al., 2016; Diapouli et al., 2016; Flentje et al., 2015; Gini et al., 2022; Gobbi et al., 2019; Pandolfi et al., 2014; Querol et al., 2019; Varga et al., 2014). Indeed, intense desert dust episodes (DDE) can affect the measured $b_{abs}$ and consequently the calculated eBC concentrations. For example, Ripoll et al. (2015) have shown the effects of intense DDE on MAAP measurements performed at a high-altitude remote site in NE Spain (Montsec; MSA) where the concentrations of eBC are usually low. In fact, the mass absorption efficiency of dust particles is much lower compared to that of eBC (and especially in the visible range) and, consequently, an effect of dust on $b_{abs}$ measurements during DDE can be significant where eBC concentrations are low. Moreover, the imaginary refractive index (indicating the absorptive properties of a particle) of dust particles is the highest in the UV spectral region and strongly decreases toward the visible/near-IR spectral range. The wavelengths used in this study for eBC determination, 637 nm (MAAP; visible) and 880 nm (AE; near-IR), are consequently less affected by dust absorption compared to the UV spectral region. Thus, at urban sites, where eBC is expected to dominate the $b_{abs}$, a significant effect of DDE on eBC concentrations is not expected. However, despite a potential minor impact of dust at 880 nm, it cannot be entirely ruled out, particularly when the cut-off for $b_{abs}$ is PM₁₀. For this reason, we examined the influence of dust events on the calculated local MAC in Eastern Mediterranean Basin with frequent influences of dust (Sahara) such as ATH_SUB site using nephelometer data available in EBAS for Demokritos to detect dust days (using scattering Ångström exponent SAE<1). Based on Figure S3, the presence of 190 days of dust event observations did not apply a dominant influence on our dataset from 2017 to 2020. This comparison is evidenced by the calculated r² values when contrasting datasets that include all days, those excluding dust events, and those comprising only dust event observations. The exclusion of data related to dust events did not produce substantially different MAC values compared to those derived from the complete dataset. Moreover, as recently stated by Savadkoohi et al. (2023), particularly at UB sites such as BCN_UB, where eBC is expected to dominate the $b_{abs}$, the influence of sporadic desert dust on eBC concentrations is comparatively minimal over long timescales as evidenced by the correlation between eBC and NO₂ concentrations at BCN_UB.

In general, Figure 5 shows that the seasonal dependency at the UB sites followed different patterns, with elevated MAC values during the spring and summer or winter seasons depending on the site. ROC_UB exhibits significantly higher MAC values during the summertime compared to winter that might be attributed to local recreational wood burning (Masiol et al., 2019; Wang et al., 2012). At the ATH_UB site, monthly variations appear to be linked to the impact of BB, particularly during December to February in contrast to June to August. However, this variation does not attain statistical significance. GRA_UB showed significantly higher MAC values in winter that can be associated with open BB emissions of agricultural residues and domestic heating fueled by biomass sources in the rural area of Granada during the colder months (Lyamani et al., 2011; Titos et al., 2017). ZUR_UB showed low seasonality (i.e., not significant) compared to other Swiss sites that has been previously reported by Grange et al. (2020). Typically, summertime $b_{abs}$ measures in Swiss sites have been reported to be higher than those observed during wintertime. However, this seasonal variation has been noted to coincide with intra-instrumental variations, suggesting a plausible instrument artifact, possibly indicative of a slow degradation in sensitivity (Grange et al., 2020).

The BCN_UB site exhibits a seasonal fluctuation in MAC, characterized by comparatively elevated values in the summer. The results of statistical tests indicate a significant difference between summer and both spring and autumn. In contrast, the summer period has been previously characterized by a higher prevalence of available material for coating, such as seconday organic aerosol (SOA), as well as an increase in shipping emissions in BCN_UB (Pandolfi et al., 2020; Via et al., 2021). A more pronounced seasonal variation during winter and spring was observed at the MLN_UB site with statistically significant differences noted in winter vs. spring and summer vs. spring. However, Mousavi et al. (2019) has reported only slight seasonal changes in the MAC at 880 nm for the same site during three distinct seasons over 2017–2018. Their observation aligns with the outcomes of earlier studies by Zotter et al. (2017), indicating that MAC values are more influenced by the measurement location and site characteristics than by the season. At the PRG_UB site, the deviation observed in the fifth month is attributed to the annual biomass burning event that takes place during first May night in Czechia (Vodička et al., 2022). The increase in MAC during the tenth month is linked to the start of the heating season, typically occurring in mid-October.
Divergent seasonal patterns at the TR sites indicated data dispersion without uniform similarities across these sites. In BER_TR, as well as the ZUR_UB site, seasonal fluctuations were apparent in the MAC values. In Bern, a significant difference in the mean MAC was observed between the winter and autumn, as shown in Figure 5. However, this difference was not found to be statistically significant in the winter compared to the summer. The observed high temporal pattern of MAC in summer, as previously documented by Grange et al., (2020), can be attributed to higher b<sub>abs</sub> measurements during the summer compared to winter. However, it is important to note that this pattern may not exclusively be linked to an increase in MAC but may also be pertinent to EC measurements at this particular site. Considering the four TR sites in this study, the seasonal pattern was found to be more pronounced at LND_TR as shown in Figure 5. The statistical analysis revealed significant differences between winter and the other seasons at this site. This pattern aligns with the observations of Ciupek et al. (2021) who has documented a clear seasonal variation with a reduction in MAC values during the winter. This winter decrease in MAC has been reported to be associated with the changes to the SOA concentration and a combination of other changes in the physical properties of atmospheric aerosols (Ciupek et al., 2021; Zhang et al., 2018). The DRD_TR and LEJ_TR1 sites showed a more pronounced seasonal pattern of MAC during summer compared to winter. These sites have previously been categorized as sites strongly influenced by traffic, sharing a similar source influence (Sun et al., 2019). Monthly variation of experimental MAC observed in IPRlys exhibited a similarity to the seasonal pattern of b<sub>abs</sub> and EC mass concentration, as observed by Zanatta et al. (2016) at this site. IPRlys, which was reported to be strongly affected by anthropogenic emissions, generally had higher EC concentrations compared to other RB sites (Sandrini et al., 2014). Our results found a statistically significant difference in the mean of experimental MAC between winter and summer with high values observed during the summer. A comparable and statistically significant monthly variation in summer is also evident at SMRlys site.

In contrast, the ATH_SUB and PAR_SUB sites showed distinct monthly variations. PAR_SUB exhibited the lowest MAC values during the spring months, while reaching peak levels during autumn and winter, a trend that can be linked to increased residential wood burning emissions during colder periods (Zhang et al., 2019). At ATH_SUB, MAC exhibited a pronounced seasonal dependency, which was statistically significant between summer vs. winter with higher MAC in summer. This may be attributed to the formation of secondary sulfate and organic aerosols during the summer which results in coatings of the MAC, commonly referred to as lensing effect (Pandolfi et al., 2018). In other sites (i.e., ROM_UB, MAR_UB, and HEL_TR2), the number of observations was insufficient to characterize the temporal variability of the corresponding region. It limits the interpretation of monthly variations and the identification of consistent seasonal patterns. However, MAR_UB demonstrated a statistically significant difference in experimental MAC between winter and summer, considering the available number of observations. Overall, the results of the mean analysis methods employed to assess the significance of differences in experimental MAC for various seasons, using both parametric (ANOVA) and non-parametric (Kruskal-Wallis) tests, revealed distinct patterns of p-values across 22 sites. Detailed p-values for all sites are provided in Table S1, while Figure S4 visually depicts the difference in the mean experimental MAC in different seasons, presented with a 95% confidence interval.

### 3.4 Impact of chosen MAC value on estimation of eBC mass concentration

Considering the observed variations in MAC values among the monitoring sites and the presence of strong seasonal dependency, NeBC mass concentrations were recalculated from b<sub>abs</sub> at 637 and 880 nm. This correction method was performed using median MAC from 22 sites, site-specific MAC, and site-specific rolling MAC (regression). Subsequently, the corrected eBC mass concentrations obtained through these methods were named: MeBC (using median MAC), LeBC (local MAC), and ReBC (Rolling MAC) respectively. Figure S5 shows the monthly variations of NeBC, MeBC, LeBC, and ReBC mass concentrations. The difference is seen to be quasi constant throughout the months. This pattern was consistently observed across all other sites, showing variations and relative percentage differences (RPD%) in both MAAP and AEs measurements. RPD% were computed to quantify the degree of variability between NeBC and ReBC, as well as between NeBC and MeBC. The sites PRG_UB, ROM_UB, UMH_UB, MLN_UB, DRD_UB, and LND_TR showed the highest RPD% in descending order.

This variation, estimated to reach up to 70% when comparing NeBC to ReBC. Moreover, it is important to note that employing MeBC derived from median MAC at sites without EC measurements may introduce certain errors. The RPD of NeBC and MeBC varied within the range of 26-67% (see Figure S5). In addition, the mean absolute error (MAE) values of MeBC and ReBC are presented in Table S2. As shown in Figure S5, the corrected eBC mass concentrations were highly sensitive to the uncertainty and variability
associated with the chosen MAC value. Especially, LeBC aligned more closely with the ReBC. Nevertheless, MeBC exhibited a substitution that occasionally coincides with the rolling and site-specific approaches; conversely, in certain cases, it aligns either below or above the results of the other two methods, reflecting the relatively high uncertainty in use of constant MAC value. However, these corrected eBC values consistently remained lower than the NeBC mass concentrations. Consequently, it can be inferred that employing a constant MAC value for different sites within the AQMNs can yield misleading results. This highlights the critical importance of employing site-specific MAC to mitigate the potential inaccuracies produced by NeBC mass concentration.

Among the various approaches employed here for MAC estimation, the site-specific and rolling approaches were found to be more robust. The rolling and site-specific methods could consider the physical-chemical attributes of each site over a period of time to estimate the eBC mass concentration. Furthermore, as depicted in Figure S5, median values occasionally resulted in an overestimation or underestimation of the eBC mass concentration when compared to the values derived from the site-specific and rolling approaches. It is worth noting that not all monitoring sites are able to adopt these methods due to the lack of EC measurements. However, it is of prime interest to refine the estimation of eBC mass concentration within research studies, as it may contribute to significantly-improved assessments of the climate and AQ implications associated with BC particles.

## 3.5 eBC mass concentrations and MAC trend analysis

MAC values were not only dependent on the site and/or season but can also have long-term changes. Studying long-term trends of experimental MAC from prolonged monitoring measurements are beneficial for robust MAC determination. Therefore, long-time series of NeBC and EC mass concentration data from monitoring sites (specifically BCN_UB, IPRlys, ROC_UB, PAR_SUB, and ZUR_UB) were analyzed with Theil-Sen slope regression for trend analysis (Carslaw and Ropkins, 2012). There are very few published studies on the long-term trends of experimental MAC at sites measuring EC, such as Ciupek et al. (2021). Figure S6 shows statistically significant decreasing trends in eBC mass concentration using nominal MAC across all studied sites over time. These decreasing eBC trends were consistent with previous studies documenting long-term eBC trends in Europe and the US (Collaud Coen et al., 2020; Jafar and Harrison, 2021; Luoma et al., 2021; Masiol et al., 2018; Savadkoohi et al., 2023). Similar decreasing trends in EC concentrations were observed aligned with previous findings in other European regions. However, there are studies reporting an increasing trend in EC concentration such as a rural site in the UK (Ciupek et al., 2021).

PAR_SUB (-7.64%/year) showed the most pronounced and statistically significant decreasing trends in NeBC mass concentration. This declining pattern corresponded consistently with the statistically significant downward trend observed in EC mass concentration at this site. However, a modestly increasing trend (1.47%/year) was observed in the experimental MAC. This observation could potentially be attributed to the relatively stable trend in eBC wood burning concentrations that can vary depending on factors like the source of the wood, burning conditions, and a significant decreasing trend of eBC traffic at the PAR_SUB site, as reported by Zhang et al. (2019). Upon applying the rolling MAC approach, a statistically significant decreasing trend (-6.28%/year) was observed for ReBC, although it was generally lower than the trend observed for NeBC concentration.

At the ZUR_UB site, a modest decline in NeBC mass concentrations (-1.9%/year) was detected. Conversely, EC mass concentration exhibited a statistically significant downward trend (-5.73%/year) at this site. The experimental MAC displayed a slight increase with a slope of 1.57%/year. As for PAR_SUB, this could potentially be linked to the wood burning emissions decreasing at a lower rate in comparison to the decline observed in traffic emissions at this site, a phenomenon previously discussed by Grange et al. (2020) for Zürich. These findings underscore the significance of considering specific emission sources and their respective trends as important factors in understanding the long-term variations in MAC values. By applying the rolling MAC, a pronounced descending trend of ReBC was observed with annual decrease of -4.57% over the years 2012 to 2022.

Among all sites, the ROC_UB site showed a statistically significant decreasing trend in NeBC concentrations at a rate of -3.76% per year. The estimated experimental MAC demonstrated a declining trend over time with a statistically significant slope of -3.76%/year. In contrast, the EC concentration exhibited a slight increase with a slope of 1.2%/year although not statistically significant. However, when employing the rolling MAC, almost no statistically significant trend was observed for ReBC at ROC_UB
(0.56 %/year). This difference might be the result of changes in emissions, evolving regulatory measures, and the potential decline in wood-burning emissions over the years 2009-2022, suggesting a possible change in EC sources as a plausible explanation for the declining trend in experimental MAC.

At the IPRRB site, a significant declining trend was observed in both NeBC (-3.58 %/year) and EC concentrations (-3.53 %/year). These trends contradicted the FAPs measurement trend for years prior to 2010, which was reported by Putaud et al. (2014) over the 2004–2010 period. Interestingly, MAC displayed a distinct behavior at this site, exhibiting a slight increasing but not significant trend with a slope of 0.59 %/year. This finding suggests that coatings might contribute to the elevation of MAC to some degree at this site (Kalbermatter et al., 2022), influenced by pollution from the Po Valley (Zanatta et al., 2016). Furthermore, ReBC mass concentration also demonstrated a statistically significant trend (-3.25 %/year) at IPR RB.

At the BCN_UB site, both NeBC (-4.95 %/year) and EC mass concentrations (-3.69 %/year) displayed statistically significant decreasing trends. This trend in NeBC concentration aligns with a recent study by Savadkoohi et al. (2023) who reported a decrease of -4.7% yr⁻¹ between 2010 and 2020 at the same site. Additionally, the observed trend is consistent with the other studies that reported a significant NeBC decrease (-18%) from 2014 to 2018 at the same site (Via et al., 2021), suggesting the influence of traffic emission reduction policies as a possible cause (Carnerero et al., 2021). Interestingly, the decreasing trend of experimental MAC at BCN_UB was not statistically significant, indicating a slight decrease over the measurement period. Furthermore, our result identified a significant decreasing trend in the ReBC mass concentration, with a slightly different slope of -4.62 %/year between 2010 and 2020.

4. Conclusions and recommendations

The primary aims of this study were i) to establish a consistent estimation of normalized equivalent black carbon mass concentration (eBC) from different FAPs in different environments and ii) to examine the variability of mass absorption cross-section (MAC) and iii) to refine methodologies for the calculation of site-specific eBC mass concentration. This investigation was performed by analyzing the long-term changes in experimental MAC across 22 sites, using co-located observations of absorption coefficients (babs) from FAPs and elemental carbon (EC). The resulting median MAC values were as follows: 7.8 ± 3.4 m² g⁻¹ at 880 nm from 12 AEs and 10.6 ± 4.7 m² g⁻¹ at 637 nm from 10 MAAPs. The median MAC from MAAP reported here closely aligns with the default MAC value of 10.0 m² g⁻¹ recommended by ACTRIS based on previous study from Zanatta et al. (2016).

The difference between the nominal MAC employed in FAPs and the median MAC values reported in this study, was very pronounced in the case of MAAP that uses a MAC of 6.6 m² g⁻¹ at 637 nm for reporting eBC concentrations. Consequently, nominal eBC (NeBC) concentrations derived from MAAP are overestimated approximately 40% when compared to the median MAC of 10.6 m² g⁻¹ for eBC calculation. Contrastingly, for AE33 instruments, the nominal MAC value at 880 nm (7.77 m² g⁻¹) closely aligns with the median MAC reported in this study (7.8 m² g⁻¹). Nonetheless, the primary source of uncertainty in the reported nominal eBC values from AE33 instruments arises from the utilization of a filter multiple-scattering parameter (C₀) with a value of 1.39 in the AE33 software. This value is approximately 45% lower than the value of 2.44 selected for harmonizing absorption measurements from AE33 instruments, as noted in previous studies (e.g., Savadkoohi et al., 2023). We performed a comparison between the nominal eBC mass concentrations provided by FAPs and the concentrations of eBC obtained using various experimental MAC values, including median MAC calculated for the 22 sites, site-specific MAC, and rolling MAC. NeBC consistently exhibited an overestimation ranging from 40% to 80%.

Given the observed strong temporal and spatial variability of the experimental MAC values reported here, we conclude that having a local estimation of the MAC from simultaneous babs and EC measurements is the best approach to reduce the eBC uncertainty. Moreover, considering the temporal variability of the MAC, the rolling MAC can be considered as the most appropriate conversion factor to estimate eBC concentrations from optical measurements. In fact, in this case the eBC concentrations are continuously normalized to the concentrations of EC for which a reference method exists (EN16909: 2017). This long-term continuous normalization is also useful when the trends of eBC are analyzed. Indeed, the long-term trend analysis reported here revealed that the trend of eBC obtained using any constant MAC value (nominal, local or median MAC) might lead to misleading interpretation of the eBC trend if any trend in MAC is also observed. Thus, the long-term trends of eBC explicitly mirror the EC trends only when the rolling MAC is used.
Therefore, investigating a site-specific MAC is recommended, when and where possible, instead of relying on the nominal MAC values provided by instrument manufacturers. To strengthen the applicability of site-specific MAC values for improved estimation of eBC mass concentrations, this study recommends the use of co-located measurements of $b_{abs}$ and EC mass concentrations by expanding monitoring networks to include regular EC sampling and periodically using EC measurements to obtain rolling MAC. Other researchers also recommended this approach to effectively employ $b_{abs}$ data for improved analysis and interpretation (Ciupek et al., 2021; Contini et al., 2018; Helin et al., 2018; Grange et al., 2020; Salako et al., 2012). This approach addresses two significant aspects. Firstly, it enables a more accurate estimation of the mass concentration of absorbing particles for AQ assessment. Secondly, it facilitates the experimental determination of MAC for climate-related studies including aerosol direct radiative forcing calculations. However, whenever EC observations are unavailable, we recommend applying the median MAC value of 10.6 m² g⁻¹ obtained in this work when $b_{abs}$ is provided by MAAP at 637 nm and the MAC value of 7.8 m² g⁻¹ when harmonized $b_{abs}$ is provided by aethalometers at 880 nm.

## Data availability

The time series of eBC mass concentrations and absorption coefficients of 22 studied sites are available online at (DOI: https://doi.org/10.5281/zenodo.7982201). Any requests for materials should be addressed to the corresponding author, M. Savadkoohi.

## 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). These parameters include eBC, ultrafine particles, and oxidative potential in urban environments, which will consequently allow for the enhanced assessment of AQ policies. 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 Investigación" from the Spanish Ministry of Science and Innovation under the project CAIAC (PID2019-108990RB-I00), AIRPHONEMA (PID2022-142160OB-I00), and the Generalitat de Catalunya (AGAUR, SGR-447). M. Savadkoohi would like to thank the Spanish Ministry of Science and Innovation for her FPI grant (PRE-2020-095498). This study is also partly funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Environmental Exposures and Health, a partnership between UK Health Security Agency (UKHSA) and Imperial College London, and the UK Natural Environment Research Council. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA or the Department of Health and Social Care. The work in Helsinki and Hyytiälä is supported by Academy of Finland flagship "Atmosphere and Climate Competence Center (ACCC), project numbers 337549, 337552, 334792, 328616, 345510 and the Technology Industries of Finland Centennial Foundation Urban Air Quality 2.0 project and European Commission via FOCI-project (grant number 101056783). The work performed in Rome (IT) was supported by ARPA Lazio, the regional Environmental Protection Agency. Measurements at Granada urban station were possible thanks to MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR under the projects PID2020-120015RB-I00 and PID2021-128757OB-I00, ACTRIS-España (CGL2017-90884REDT), and University of Granada Plan Propio through Excellence Research Unit Earth Science and Singular Laboratory AGORA (LS2022-1). Partial support of this work by the project "PANhellenic infrastructure for Atmospheric Composition and climatE change" (MIS 5021516) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). Partial funding was obtained by Slovenian ARRS/ARIS program P1-0385. Partial support for operating stations in France is received by ACTRIS-FR supported by French Ministry for research and Education and by France2030 initiative under project ANR-21-ESRE-0013. Elche data were possible thanks to the support of European Union NextGenerationEU/PRTR" (CAMBIO project, ref. TED2021-131336B-I00) and by the
Valencian Regional Government (Generalitat Valenciana, CIAICO/2021/280 research project). This research was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the Large Research Infrastructure Support Project - ACTRIS Participation of the Czech Republic (ACTRIS-CZ LM2023030).

**Declaration of Competing Interests**

Griša Močnik is employed by Haze Instruments d.o.o., the manufacturer of the aerosol instrumentation.

**CRediT authorship contribution statement**

**Marjan Savadkoohi:** Data curation, Formal analysis, Investigation, Visualization, Writing - Original Draft, Writing - Review & Editing. **Marco Pandolfi:** Conceptualization, Methodology, Validation, Resources, Writing - Review & Editing, Supervision, Funding acquisition. **Olivier Favez:** Validation, Review & Editing. **Jean-Philippe Putaud:** Data curation, Validation, Review & Editing, **Konstantinos Eleftheriadis:** Data curation, Review & Editing. **Markus Fiebig:** Review. **Philip K. Hopke:** Data curation, Validation, Review & Editing. **Paolo Laj:** Validation, Review & Editing. **Alfred Wiedensohler:** Validation, Review & Editing. **Lucas Alados-Arboledas, Susanne Bastian, Benjamin Chazeau, Álvaro Clemente María Cristina, Cristina Colombi, Francesca Costabile, David C. Green:** Data curation. **Christoph Hueglin:** Data curation, Review & Editing. **Eleni Liakakou, Krista Luoma, Stefano Listrani, Nikos Mihalopoulos, Nicolas Marchand:** Data curation. **Griša Močnik:** Validation, Review & Editing. **Jarkko V. Niemi, Jakub Ondráček, Jean-Eudes Petit, Oliver V. Rattigan, Cristina Reche:** Data curation. **Hilkka Timonen:** Data curation, Review. **Gloria Titos:** Data curation, Review. **Anja H. Tremper, Stergios Vratolis, Petr Vodička, Eduardo Yubero Funes, Naděžda Žíková:** Data curation. **Roy M. Harrison:** Validation & Review. **Tuukka Petäjä:** Validation, Resources, Review, Funding acquisition. **Andrés Alastuey:** Validation, Resources, Writing - Review & Editing, Supervision, Funding acquisition. **Xavier Querol:** Validation, Resources, Writing - Review & Editing, Supervision, Funding acquisition.

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CRediT authorship contribution statement

**Marjan Savadkoohi:** Data curation, Formal analysis, Investigation, Visualization, Writing - Original Draft, Writing - Review & Editing. **Marco Pandolfi:** Conceptualization, Methodology, Validation, Resources, Writing - Review & Editing, Supervision, Funding acquisition. **Olivier Favez:** Validation, Review & Editing. **Jean-Philippe Putaud:** Data curation, Validation, Review & Editing, **Konstantinos Eleftheriadis:** Data curation, Review & Editing. **Markus Fiebig:** Review. **Philip K. Hopke:** Validation, Review & Editing. **Paolo Laj:** Validation, Review & Editing. **Alfred Wiedensohler:** Validation, Review & Editing. **Lucas Alados-Arboledas:** Data curation. **Susanne Bastian:** Data curation. **Benjamin Chazeau:** Data curation. **Álvaro Clemente María Cristina:** Data curation. **Cristina Colombi:** Data curation. **Francesca Costabile:** Data curation. **David C. Green:** Data curation. **Christoph Hueglin:** Data curation, Validation, Review & Editing. **Eleni Liakakou:** Data curation. **Krista Luoma:** Data curation. **Stefano Listrani:** Data curation. **Nikos Mihalopoulos:** Data curation. **Nicolas Marchand:** Data curation. **Griša Močnik:** Validation, Review & Editing. **Jarkko V. Niemi:** Data curation. **Jakub Ondráček:** Data curation. **Jean-Eudes Petit:** Data curation. **Oliver V. Rattigan:** Data curation. **Cristina Reche:** Data curation. **Hilkka Timonen:** Data curation, Review. **Gloria Titos:** Data curation, Review. **Anja H. Tremper:** Data curation. **Stergios Vratolis:** Data curation. **Petr Vodička:** Data curation. **Eduardo Yubero Funes:** Data curation. **Naděžda Zíková:** Data curation. **Roy M. Harrison:** Validation & Review. **Tuukka Petäjä:** Validation, Resources, Review, Funding acquisition. **Andrés Alastuey:** Validation, Resources, Writing - Review & Editing, Supervision, Funding acquisition. **Xavier Querol:** Validation, Resources, Writing - Review & Editing, Supervision, Funding acquisition.

Declaration of interests

☐ 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:

Griša Močnik is employed by Haze Instruments d.o.o., the manufacturer of the aerosol instrumentation.