# Analysis of source regions and transport pathways of sub-micron aerosol components in Europe

Michelle Y Schneider, Jianhui Jiang, Ying Chen, Wenche Aas, Samira Atabakhsh, Minna Aurela, Claudio Belis, Aikaterini Bougiatioti, Michael Bressi, Francesco Canonaco, et al.

► To cite this version:

Michelle Y Schneider, Jianhui Jiang, Ying Chen, Wenche Aas, Samira Atabakhsh, et al. Analysis of source regions and transport pathways of sub-micron aerosol components in Europe. Environmental Pollution, 2025, 385, pp.127110. 10.1016/j.envpol.2025.127110. hal-05266598v2

HAL Id: hal-05266598

https://hal.science/hal-05266598v2

Submitted on 30 Sep 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_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Analysis of source regions and transport pathways of sub-micron aerosol
components in Europe ⭐

Michelle Y. Schneider<sup>a,b</sup>, Jianhui Jiang<sup>c</sup>, Ying Chen<sup>d</sup>, Wenche Aas<sup>e</sup>,
Samira Atabakhsh<sup>f</sup>, Minna Aurela<sup>g</sup>, Claudio Belis<sup>h</sup>, Aikaterini Bougiatioti<sup>i</sup>,
Michael Bressi<sup>h</sup>, Francesco Canonaco<sup>j</sup>, Benjamin Chazeau<sup>a,k</sup>, Hasna Chebaicheb<sup>l,m,n</sup>,
Mikael Ehn<sup>o</sup>, Konstantinos Eleftheriadis<sup>p</sup>, Olivier Favez<sup>m,n</sup>, Harald Flentje<sup>q</sup>, Anna Font<sup>b,1</sup>,
Evelyn Freney<sup>r</sup>, Stefania Gilardoni<sup>s</sup>, Maria I. Gini<sup>p</sup>, David C. Green<sup>b</sup>, Liine Heikkinen<sup>o,t,u</sup>,
Hannes Keernik<sup>v,2</sup>, Radek Lhotka<sup>w</sup>, Chunshui Lin<sup>x</sup>, Marek Maasikmets<sup>v</sup>,
Nicolas Marchand<sup>k</sup>, María Cruz Minguillón<sup>y</sup>, Jaroslaw Necki<sup>z</sup>, Jurgita Ovadnevaite<sup>x</sup>,
Marco Paglione<sup>aa</sup>, Julija Pauraite<sup>ab</sup>, Jean-Eudes Petit<sup>ac</sup>, Michael Pikridas<sup>ad</sup>, Stephen Platt<sup>e</sup>,
Petra Pokorná<sup>w</sup>, Vanes Poluzzi<sup>ae</sup>, Laurent Poulain<sup>f</sup>, Véronique Riffault<sup>l,n</sup>,
Matteo Rinaldi<sup>aa</sup>, Jean Sciare<sup>ad</sup>, Yulia Sosedova<sup>i</sup>, Iasonas Stavroulas<sup>i</sup>, Hilkka Timonen<sup>g</sup>,
Anna Tobler<sup>j</sup>, Jeni Vasilescu<sup>af</sup>, Marta Via<sup>y,3</sup>, Petr Vodička<sup>w</sup>, Yunjiang Zhang<sup>ag</sup>,
Olga Zografou<sup>p</sup>, Kaspar Rudolf Daellenbach<sup>a</sup>, Abhishek Upadhyay<sup>a</sup>, Gang I. Chen<sup>b,*</sup>,
Manousos-Ioannis Manousakas<sup>a,4,**</sup>, Imad El Haddad<sup>a</sup>, André S.H. Prévôt<sup>a</sup>

<footnote>a PSI Center for Energy and Environmental Sciences, Paul Scherrer Institute, 5232, Villigen, Switzerland</footnote>

<footnote>b MRC Centre for Environment and Health, Environmental Research Group, Imperial College London, W12 OBZ, London, United Kingdom</footnote>

<footnote>c Global Institute for Urban and Regional Sustainability, School of Ecological and Environmental Sciences, East China Normal University, 200241, Shanghai, China</footnote>

<footnote>d College of Life and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, United Kingdom</footnote>

<footnote>e NILU, Kjeller, 2007, Norway</footnote>

<footnote>f Leibniz Institute for Tropospheric Research (TROPOS), 04 318, Leipzig, Germany</footnote>

<footnote>g Atmospheric Composition Research, Finnish Meteorological Institute, 00101, Helsinki, Finland</footnote>

<footnote>h European Commission - Joint Research Centre, Unit Clean Air and Climate, 21027, Ispra, Italy</footnote>

<footnote>i Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236, Athens, Greece</footnote>

<footnote>j Datalystica Ltd., Parkstrasse 1, 5234, Villigen, Switzerland</footnote>

<footnote>k Aix Marseille Univ, LCE, Marseille, France</footnote>

<footnote>l IMT Nord Europe, Institut Mines-Télécom, Université de Lille, Centre for Energy and Environment, 59000, Lille, France</footnote>

<footnote>m Institut National de l'Environnement Industriel et des Risques (INERIS), 60550, Verneuil-en-Halatte, France</footnote>

<footnote>n Laboratoire Central de Surveillance de la Qualité de l'Air (LCSQA), 60550, Verneuil-en-Halatte, France</footnote>

<footnote>o Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014, Helsinki, Finland</footnote>

<footnote>p ENRACT, Institute of Nuclear and Radiological Science & Technology, Energy & Safety, NCSR Demokritos, 15310, Athens, Greece</footnote>

<footnote>q German Meteorological Service (DWD), 82383, Observatory Hohenpeissenberg, Germany</footnote>

<footnote>r Laboratoire de Météorologie Physique, UMR6016, Université Clermont Auvergne-CNRS, 63170, Aubière, France</footnote>

<footnote>s National Research Council, Institute of Polar Sciences (CNR-ISP), 40129, Bologna, Italy</footnote>

<footnote>t Department of Environmental Science, Stockholm University, 114 18, Stockholm, Sweden</footnote>

<footnote>u Bolin Centre for Climate Research, 114 18, Stockholm, Sweden</footnote>

<footnote>v Air Quality and Climate Department, Estonian Environmental Research Centre (EERC), Marja 4D, Tallinn, Estonia</footnote>

<footnote>w Institute of Chemical Process Fundamentals, Czech Academy of Sciences, Rozvojová 1, Prague 6, 16500, Czech Republic</footnote>

<footnote>x School of Natural Sciences, Physics, University of Galway, Centre for Climate and Air Pollution Studies, Ryan Institute, H91 CF50, Galway, Ireland</footnote>

<footnote>y Institute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034, Barcelona, Spain</footnote>

<footnote>z AGH University of Krakow, Faculty of Physics and Applied Computer Science, 30-059, Krakow, Poland</footnote>

<footnote>aa Italian National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), 40129, Bologna, Italy</footnote>

<footnote>ab SRI Center for Physical Sciences and Technology (FTMC), 10257, Vilnius, Lithuania</footnote>

<footnote>ac Laboratoire des Sciences du Climat et de l'Environnement (LSCE), 91190, Gif-sur-Yvette, France</footnote>

<footnote>* This paper has been recommended for acceptance by Ying-Hsuan Lin.</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: gang.chen@imperial.ac.uk (G.I. Chen), m.manousakas@ipta.demokritos.gr (M.-I. Manousakas).</footnote>
A B S T R A C T

It is important to study aerosols and their origins, as they pose various negative health and environmental impacts. In this study, we combined year-long datasets from 15 different countries with Trajectory Statistical Methods (TSMs) for the first time at this comprehensive scale. We found possible source regions and seasonal variations of various particulate matter (PM) components in Europe, including total organic aerosol (OA), biomass burning OA (BBOA), oxygenated OA (OOA), ammonium (NH₄), nitrate (NO₃), and sulphate (SO₄). We found that for all of the studied components, Eastern Europe was among the highest contributors. For NO₃, other important source regions were Northern France and the Benelux, while for SO₄ there were significant contributions from the Mediterranean region. We also compared our measurement-based model with simulated concentrations of an atmospheric chemistry transport model (CAMx). We observed a satisfactory agreement in regions where we had sufficient coverage with air pollution monitoring stations. The main deviations for OA were found around the Po Valley, where CAMx consistently estimated higher concentrations, while the TSM analysis did not highlight it as a hotspot because long-term monitoring datasets in this region are lacking. CAMx also underestimated the concentrations around Poland, mainly from residential burning. Our results provide opportunities to refine European emission inventories and deliver valuable information on long-range transported air pollutants. This work suggests that policies mitigating air pollution in Eastern Europe and the Benelux could help improve overall air quality in entire Europe more efficiently.

1. Introduction

Particulate matter (PM) remains a persistent environmental chal-
lenge, with adverse effects on human health, ecological systems, and
global climate (Harrison & Yin, 2000). PM varies in size and composi-
tion and is emitted from a wide range of sources, encompassing both
natural and anthropogenic emissions. Among the primary sources of PM
are transportation, biomass burning, dust resuspension, industrial pro-
cesses, and agricultural activities. The impacts of PM on human health
are profound and diverse. Inhalation of PM can lead to respiratory
problems such as asthma, bronchitis, and other respiratory infections
(Samoli et al., 2016). Fine particulate matter, known as PM2.5 (particles
with aerodynamic diameters of 2.5 µm or smaller), can penetrate deep
into the lungs and even enter the bloodstream, increasing the risk of
cardiovascular diseases, strokes, cancers, and other systemic health is-
sues (Mitsakou et al., 2007).

Particulate matter consists of both organic and inorganic compo-
nents. Examples of the latter are sulphate, nitrate, chloride, ammonium,
and black carbon. The sources and formation processes of these inor-
ganic components are relatively well understood, while the other part of
PM, organic aerosols (OA), consists of thousands of compounds; there-
fore, their formation processes and sources remain challenging to
deconvolute. Previous measurements across the world suggested that
OA can dominate the overall PM mass, making up 20–90% (Chen et al.,
2022; Crippa et al., 2014; Jimenez et al., 2009). Recent studies have
reported an overview of OA sources in Europe by employing advanced
source apportionment techniques (Chen et al., 2022).

While local sources undoubtedly play a significant role in aerosol
pollution, it is crucial not to overlook the substantial impact of trans-
boundary transport. PM can travel vast distances, extending far beyond
its original emission point and affecting regions remote from its sources.
To better grasp the complex interactions between aerosols and long-
range transport, researchers have increasingly turned to trajectory sta-
tistical methods (TSMs) (Salvador et al., 2008). These analytical tech-
niques leverage air mass backward trajectories, which trace the path of

air masses from receptor locations back to their source regions, and PM
concentration data collected at various receptors (Salvador et al., 2010).
TSMs do not account for factors like atmospheric dispersion, chemical
conversion, or dry and wet deposition through the Hybrid
Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT;
NOAA's Air Resources Laboratory, accessed 11.1.25). However, they
offer simplicity and effectiveness in application. TSMs can assist in
pinpointing relevant source regions and distinguishing the airflow pat-
terns associated with the transport of regional and large-scale air
pollution. By examining the relationship between air mass trajectories
and PM concentrations, TSMs enable the identification of potential
emission hotspots, the characterisation of pollutant transport pathways,
and the assessment of the efficacy of regional air quality management
strategies. Utilising TSMs with a large number of trajectories can notably
diminish trajectory uncertainty, which arises from interpolation and
truncation processes, low temporal or spatial resolution of wind data, or
inadequate selection of starting heights (Lupu & Maenhaut, 2002).

Previous studies have highlighted the potential of incorporating
multiple receptors in TSMs to enhance accuracy (Hsu et al., 2003; Lee &
Ashbaugh, 2007). In this study, we leverage comprehensive datasets
utilised in harmonised OA source apportionment across Europe to
identify potential source hotspots in the region using TSMs. This has not
previously been done on this scale and with such high-quality source
apportionment data from high-time resolution measurements (30
min–4 h). By integrating all datasets, trajectories converge at various
receptor sites, facilitating a more robust analysis (Stohl, 1996). This
convergence, together with variations in concentrations at each site,
amplifies the redistribution process, thereby improving efficacy. The
findings from TSM-based investigations are invaluable for policymakers,
environmental agencies, and public health authorities aiming to devise
targeted interventions to mitigate the impacts of transboundary aerosol
pollution.

2. Methodology

In this study, we used the chemical composition data and the source
apportionment results of 22 air quality observational sites from 14
different countries over Europe, published in Chen et al. (2022). The
source apportionment results were retrieved through Positive Matrix
Factorization (PMF) analysis by following the harmonised protocol in
Chen et al. (2022). In addition, five sites in Italy were included, some of
whose observational results have been published previously in Paglione
et al. (2020). An overview of the countries and the measurement periods
can be found in Fig. 1. Measurement devices include the Aerodyne

<footnote>ad Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, 2121, Nicosia, Cyprus</footnote>

<footnote>ae Arpae Emilia-Romagna, Centro Tematico Regionale Qualità dell'Aria, 40139, Bologna, Italy</footnote>

<footnote>af National Institute of Research and Development for Optoelectronics, 077125, Magurele, Romania</footnote>

<footnote>ag Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Joint International Research Laboratory of Climate and Environment Change, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, 210044, Nanjing, China</footnote>

<footnote n="1">¹ Now at: IMT Nord Europe, Institut Mines-Télécom, Université de Lille, Centre for Energy and Environment 59000, Lille, France.</footnote>

<footnote n="2">² Now at Institute of Physics, University of Tartu, 50090, Tartu, Estonia</footnote>

<footnote n="3">³ Now at: Center for Atmospheric Research, University of Nova Gorica, 5270 Ajdovščina, Slovenia</footnote>

<footnote n="4">⁴ Now at: ENRACT, Institute of Nuclear and Radiological Science & Technology, Energy & Safety, NCSR Demokritos, 15310, Athens, Greece</footnote>
Aerosol Mass Spectrometer (AMS) and the Aerosol Chemical Speciation Monitor (ACSM), which both deliver real-time mass concentrations of aerosol species (i.e., organic, nitrate, sulphate, ammonium, and chloride). In particular, data from 18 Q-ACSM (quadrupole ACSM (Ng et al., 2011)), 3 ToF-ACSM (time-of-flight ACSM (Fröhlich et al., 2013)), 1 C-ToF-AMS (compact time-of-flight AMS (Drewnick et al., 2005)), and 2 HR-ToF-AMS (High-Resolution ToF-AMS (Paglione et al., 2020)) were used in this study. All ACSM data used here comply with the Standard Operating Procedures (SOP) (COST COLOSSAL Q-ACSM SOP, 2021; ToF-ACSM SOP, 2022) of the instruments as best as possible. Intercomparisons of the ACSM instruments have been performed regularly (Crenn et al., 2015; Freney et al., 2019; Fröhlich et al., 2015; Märmureanu et al., 2025), thus minimising systematic uncertainties of the instruments. In addition, the procedures to calibrate the different versions of ACSM and AMS are essentially the same to ensure consistency in accuracy across measurement sites.

The measurements of the concentrations were averaged every 12 h (at 12 a.m. and 12 p.m.). These averaged values were used in a TSM analysis by combining them with trajectory endpoints using an R script. The script can be found in the supplementary material. A statistical analysis (Concentration-Weighted Trajectory, CWT) was performed using the *openair* R package (Carslaw, 2019), an open-source tool for analysing air pollution data. The R script for plotting can be found in the supplementary material, and the results are shown in section 3.

We chose to focus on specific PM components based on their universality, i.e., their presence in most of the stations, and their relative importance based on the results from Chen et al. (2022). We studied two organic PM factors: biomass burning OA (BBOA) and oxygenated OA (OOA), as well as total organics, i.e., the sum of all OA factors. BBOA originates mainly from residential heating activities; therefore it is more regional and tends to be more prevalent in rural areas. OOA is an important factor, as it makes up around 70% of the total OA. Rapid oxidation of primary emissions (e.g., BBOA, HOA, COA, biogenic) can be a considerable pathway of OOA formation. Consequently, biogenic OOA is more prevalent in warmer seasons, while anthropogenic OOA has larger contributions in colder seasons. In addition, OOA is expected to be transported over long distances. Other organic components resolved by Chen et al. (2022), such as traffic OA or cooking OA, were not considered, as they only exist in specific urban environments. Even more site-specific factors, such as cigarette smoke and ship industry OA, were also not considered for the same reasoning. Additionally, we also considered three inorganic PM components: ammonium ($NH_4$), nitrate ($NO_3$), and sulphate ($SO_4$). $NH_4$ contributes on average 10% of overall sub-micron aerosol. In Europe, ammonium emissions are dominated by agriculture, with some contribution from traffic exhaust (Liu et al.,

2024). $NO_3$ has a similar overall PM contribution but shows a higher variability than $NH_4$, as it can make up to 30% of total aerosol. $NO_3$ comes from the oxidation of $NO_x$, which in Europe is mostly emitted from traffic and industry. $SO_4$ is the highest inorganic sub-micron aerosol contributor, averaging 16%. Sulphate originates from the oxidation of $SO_2$, mainly emitted from heavy fuel oil and coal combustion (Bressi et al., 2021).

## 2.1. Back-trajectory analysis

The backward trajectories were modelled using HYSPLIT, which has been widely used in many previous studies to successfully investigate the impacts of atmospheric transport and dispersion globally (Chen et al., 2021; Escudero et al., 2011; Stein et al., 2015). For the input meteorological data, we used the Global Data Assimilation System (GDAS) global archive from 2006 to the present with 1° resolution, provided by the National Weather Service's National Centres for Environmental Prediction (NCEP) (GDAS, accessed 11.1.25). We used each of the 27 measurement stations as endpoints and modelled one 72-h back-trajectory every 12 h (at 12 a.m. and 12 p.m. UTC) for the periods where the ACSM/AMS data were available, with consideration of the computational capacity. The height of the trajectories arriving at the endpoints was set to 200 m above ground, and points in the trajectories reaching over 1500 m above ground were filtered out.

Generally, trajectory positional uncertainties range from ±15% to ±30% of the path length, depending on atmospheric conditions and data resolution (Draxler, 1999; Stohl, 1998). For HYSPLIT, the error of individual trajectories is typically around 1–3° of latitude/longitude at mid-latitudes. By aggregating a large number of trajectories in TSMs, the influence of the individual errors decreases, as they are typically normally distributed and tend to cancel each other out. TSMs can accurately predict broad areas of pollution sources, even if the endpoints of the trajectories are subject to errors; repeatedly elevated pollution levels associated with a specific region will still be captured in the statistical output. While interpreting the results, it is important to note that TSMs are useful to determine broad source regions, rather than point sources (Stohl, 1996).

## 2.2. Concentration-weighted trajectory

The *openair* package provides two kinds of statistical methods to identify possible source locations by using back-trajectories: the Potential Source Contribution Function (PSCF) and the Concentration-Weighted Trajectory (CWT). Both methods divide the map into grid cells (i, j) and are easily applicable to multiple receptors. The PSCF method

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** The contributing countries and the measurement periods considered in this experiment. 24 ACSM measurements and 3 AMS measurements were used in this study (Chen et al., 2022; Paglione et al., 2020).</figcaption></figure>
takes the number of times a source concentration was high when the trajectories passed through the cell (i,j), and divides it by the number of times a trajectory passed through the cell (i,j). The CWT, on the other hand, calculates the mean concentration of species (i.e., their logarithm) for each grid cell as follows:

$$ \ln(\bar{C}_{ij}) = \frac{1}{\sum_{k=1}^{N} \tau_{ijk}} \sum_{k=1}^{N} \ln(c_k) \tau_{ijk} $$

where i and j are the indices of the grid, k the index of the trajectory, N the total number of trajectories used in the analysis, c_k the pollutant concentration measured upon arrival of trajectory k, and τ_{ijk} the residence time of trajectory k in grid cell (i,j). With this CWT method, air parcels that stay longer in a cell are weighted higher, which is not the case for PSCF. Additionally, the PSCF method requires a weighting function for multi-site analysis, which remains a challenge. Furthermore, as previously mentioned, PSCF relies on an arbitrary threshold, causing the model to emphasise high source contributions. This can result in an imbalanced representation of moderate and relatively high source contributions. For these reasons, we have chosen to use CWT for the purpose of this study. A high value of $\bar{C}_{ij}$ means that air parcels passing through cell (i,j) would, on average, cause high concentrations at the receptor site (Carslaw, 2019; Wilks, 2019).

## 2.3. Air quality modelling

An atmospheric chemistry transport model, the Comprehensive Air Quality Model with Extensions (CAMx) (Yarwood et al., 2024), was

deployed to simulate the distribution and transport of air pollutants for the year 2017. As shown in Fig. S8, the spatial variability and pollution hotspots (including Po Valley) of the CAMx results for 2013 and 2017 are broadly consistent. We selected 2017 because it had the largest overlap of the ACSM/AMS data (see Fig. 1), making it more suitable for comparison with the TSM analysis. In addition, one year of modelling results should capture most relevant scenarios of meteorological conditions. The model domain covers nearly all of Europe (Fig. 2), with a horizontal resolution of 0.25 × 0.125° (longitude x latitude) and 14 vertical layers from the near surface to 7 km above sea level. The high-resolution (0.1 × 0.1°) European emission inventory, Copernicus Atmosphere Monitoring Service-Regional Inventory-Version 4, developed by the Netherlands TNO (Kuenen et al., 2022), is deployed in the simulation. The gas phase chemistry is simulated using the Carbon Bond 6 Revision 2 mechanism (Hildebrandt Ruiz & Yarwood, 2013). The ISORROPIA model (Nenes et al., 1998) is used to simulate the thermodynamic and gas-particle partitioning of inorganic aerosols. Future studies should consider using a more advanced mechanism (e.g., ISORROPIA II (Fountoukis & Nenes, 2007)) to improve the model performance on secondary inorganic aerosols. Simulation of secondary organic aerosols is carried out using a 1.5-dimensional volatility basis set module (Koo et al., 2014). The CAMx is driven by the hourly meteorology from the regional Weather Research and Forecasting model (WRF, version 3.7), which is driven by and nudged to a 6-hourly reanalysis of global meteorology from the European Centre for Medium-range Weather Forecasts (ECMWF). The chemistry initial and boundary conditions for CAMx are provided by the MOZART4 global model (Horowitz et al., 2003). The CAMx and this configuration have been validated in many previous studies and shown to reproduce the air

<figure><img src="image_3.png" /><figcaption>Fig. 2. Frequency plot of the trajectories without the concentrations. The colours indicate the percentages of the total amount of trajectories passing through each grid cell. The pink dots indicate the locations of the measurement sites. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
pollution over Europe well (Daellenbach et al., 2020, 2023; Jiang et al., 2019a, 2019b). More details of the model configuration and validation are given in our previous research (Daellenbach et al., 2020; Jiang et al., 2019a, 2019b).

### 3. Results and discussion

The frequencies of the trajectories, without the concentrations, are presented in Fig. 2. Fig. 3 shows the yearly averaged results for the different species (BBOA, OOA, total organics, NH₄, NO₃, SO₄). For each species, the plot on the left-hand side displays the result of the TSM analysis, i.e., the specific source areas. On the right-hand side, respectively, we have plotted the specific local concentrations as simulated by CAMx (Jiang et al., 2019b). Additionally, emission inventories of a few selected species can be found in Fig. S1. Here we focus on the comparison with the simulated concentrations in order to obtain a comprehensive picture of both the source regions and the regions most affected by air pollution. Note that the scales for the TSM and CAMx results are different in Fig. 3 to better visualise some of the patterns. Fig. S2 shows the side-by-side comparisons with the same scaling. Fig. 4 contains the seasonal plots for June, July, August (JJA) and December, January, February (DJF) of BBOA, NO₃, and SO₄, all of which we expected to have strong seasonality.

To begin with, looking at the frequency plot (Fig. 2), we notice that the highest percentage of trajectories are very close to the measurement

stations, which is expected. A significant percentage of trajectories are located over continental Europe, and there are also some over the Atlantic. Overall, Fig. 2 indicates adequate coverage of the air masses over the European domain of interest.

It is important to note that in our analysis, all of the available ACSM/ AMS data were included using the CWT method in a single run, not accounting for different campaign durations and times. As can be seen in Fig. 1, some sites have much shorter campaigns and fewer data points overall. Even though the CWT method weighs by concentration, not the number of measurement points, this could still potentially lead to an underestimation of shorter campaigns. To study this limitation, we have conducted different analyses, which are included in the supplementary material. First, Fig. S3 shows the year-round TSM analysis for only the stations in the Po Valley, which had shorter campaigns compared to other regions. Fig. S4 shows the results of the TSM analysis using only January data, the month with the highest number of overlapping measurements across the Po Valley stations, averaged over all years. Fig. S5 again shows only the January average, excluding the stations in Krakow and Bucharest. Comparing Fig. S4 with the original DJF plots for BBOA, NO₃, and SO₄, we see that the overall picture does not change significantly. Even excluding Krakow and Bucharest from the analysis (Fig. S5) only changes the scaling, not the source regions that are highlighted. Comparing measurements from different times and of different durations undoubtedly poses a challenge, and we acknowledge this limitation, which is generally difficult to quantify in our analysis. But seeing

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Comparison of year-round TSM and CAMx results for BBOA, OOA, total OA, NH₄, NO₃, and SO₄. Color scale is in µg·m⁻³. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 4. Seasonal plots showing TSM results for BBOA, NO₃, and SO₄, with summer results on top and winter results on the bottom. Color scale is in µg·m⁻³. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

that our results are sufficiently robust under different circumstances gives us confidence in our approach and its ability to identify aerosol source regions, while also allowing for a straightforward integration of datasets in the future. Nonetheless, we highlight the need for long-term measurement datasets and a widespread network of stations.

## 3.1. BBOA

The results of the TSM analysis show that most of the contributions are from continental Europe. This is expected since BBOA is only emitted over land. Most notably, the highest contributions come from Eastern Europe, stretching from Southern Poland to Northern Greece. There is limited BBOA from Western Europe.

In agreement with the TSM results, high BBOA concentrations were simulated in Eastern Europe by CAMx, in particular in Hungary, Romania, and the Balkans. This would suggest that most of the BBOA emitted in those regions stays local as it is highly reactive in the atmosphere (Hennigan et al., 2010) and oxidises quickly. Concentration hotspots are also observed around major cities. The most significant difference between CAMx and TSM predictions can be observed in the Po Valley, where CAMx estimates a high BBOA concentration while it is not identified as a source region by the TSM analysis. When including the PMF results for Po Valley in the analysis we find a marginal increase in concentrations in those regions (Fig. S3), compared to the analysis without those five sites. However, this region is still not at the same level of concentration as Eastern Europe. In the Po Valley, CAMx might underestimate oxidation processes leading to the formation of OOA from BBOA, an important source of OOA as described in Paglione et al. (2020). Also, CAMx utilises meteorological data from the ECMWF, while the TSM implements NOAA's GDAS dataset, potentially leading to different air mass transport into and out of the Po Valley. The strongest hypothesis for the overestimation of BBOA in the Po Valley by CAMx is, however, that the model did not capture fog scavenging processes (Gilardoni et al., 2014), which leads to a general overestimation of total organics (Jiang et al., 2019b). As we include multiple different sites in the TSM analysis, we conclude that the Po Valley is, comparatively, a

less important source region of BBOA than Eastern Europe.

Fig. 4 shows high BBOA concentrations in winter, indicating the importance of domestic heating sources. A high contribution is also observed from Finland in the summer.

## 3.2. OOA

Our analysis indicates that OOA mainly originates from continental Europe, with the highest concentrations observed in Eastern Europe (from Southern Poland to Romania). Although Chen et al. (2022) reported exceptionally high concentrations measured by the Krakow station, excluding this station from our analysis did not significantly alter the results, as illustrated in Fig. S6. Therefore, we can confidently conclude that these regions indeed serve as hotspots for OOA.

CAMx also indicates high concentrations in Eastern Europe, particularly in the Czech Republic, suggesting a more local influence of OOA sources. This can also be seen in Hungary and Southern Romania, which are elevated in both the CAMx and TSM approaches. While CAMx predicts high concentrations in the Po Valley, the TSM results show comparatively low contributions from this region. The general overestimation of primary OA sources by CAMx, combined with its exclusion of fog scavenging processes (Gilardoni et al., 2014; Jiang et al., 2019b), may lead to artificially high simulated production of OOA in the Po Valley. This further suggests that the Po Valley might not contribute substantially to the overall air pollution burden in Europe. Despite the presence of surrounding stations, the TSM did not predict significant contributions from Turkey, even though CAMx simulated high concentrations there. This could possibly be due to limited trajectories passing through this region, as depicted in Fig. 2.

## 3.3. Total organics

A distinct east-west contrast in OA concentrations is evident in both the CAMx and TSM approaches, indicating that OA is not transported over the entire continent. OA contributions primarily originate from continental Europe, notably Southern Poland, extending to Serbia and
Northern Bulgaria. CAMx also predicts high OA concentrations in that region, in addition to the Benelux, parts of Germany, the Po Valley, and Turkey. It also depicts localised high OA concentrations around major cities, which are beyond the resolution of the TSM analysis. The TSM results highlight concentrations exceeding 16 µg m⁻³ over Poland, compared to the simulations reaching only up to 4 µg m⁻³. This disparity could be attributed to exceptionally high concentrations around Krakow (Tobler et al., 2021), which might disproportionately affect the TSM analysis. However, performing the TSM analysis without the Krakow station (Fig. S6) still highlights Eastern Europe as the main source region. This could indicate inconsistencies in long-range transport of air masses between CAMx and TSM due to the different meteorological datasets used. Alternatively, it might suggest a tendency for CAMx to underestimate actual OA levels in Southern Poland. Notably, the emission inventory used in CAMx does not include coal combustion, which is a significant emission source particularly in Eastern Europe. Yet, multi-site observations from this region would be needed to confirm this tendency. In addition, the emission inventory we used as model inputs (TNO-CAMS) was 102% lower than EDGAR emissions in non-EU countries (Kuenen et al., 2022), which may also lead to underestimation of OA, for example in Serbia.

### 3.4. $NH_4$

Our TSM analysis shows the highest $NH_4$ contributions from the region in and around Krakow. Overall, Eastern Europe is rather elevated, as are some regions in Western Europe, such as the Benelux and Germany. The highest CAMx concentrations are located over Belgium, Germany, Czechia, the Po Valley, and Poland, with some slightly elevated concentrations over the southeast of the UK, Northern France, Slovakia, the Balkan region, Ukraine, the south of Romania, and Turkey. Overall, both approaches show a consistent spatial distribution except for the Po Valley, suggesting that the emission inventory used in CAMx is generally consistent with the measurements. For the Po Valley, the differences in meteorology, including the omission of fog scavenging (Gilardoni et al., 2014; Jiang et al., 2019b) as mentioned above, most likely lead to the discrepancies between CAMx and the TSM analysis. The latter also generally showed pronounced contributions of PM components in Southern Poland, which leads to higher $NH_4$ concentrations than simulated by CAMx (Fig. S2). From Fig. 3, we see that $SO_4$ shows higher concentrations for the TSM results than for CAMx, due to the absence of coal combustion sources in CAMx. $NH_4$ and $SO_4$ were found to be correlated, because they both depend on neutralisation processes (Shon et al., 2020). $NH_4$ concentrations in CAMx might also be directly affected by the underestimation of coal combustion emissions (Z. Chen et al., 2022).

### 3.5. $NO_3$

In the TSM analysis, we observe significant contributions from Northern France, the Benelux region, and Eastern Europe, which align with the expected patterns given the influence of high $NO_x$ and $NH_3$ emissions from urban areas, particularly those with heavy traffic. Surprisingly, we also detected relatively elevated contributions over the Atlantic, which prompted further investigation. Upon conducting our analysis without data from selected stations in the UK, Ireland, Paris, and Lille, we found that this oceanic influence disappeared, as illustrated in Fig. S7. This suggests that these unexpected findings are likely artefacts of the TSM process. Additionally, Fig. 2 shows a notable proportion of trajectories passing over the Atlantic, corroborating our assessment. As expected, $NO_3$ exhibits a significant seasonal trend, with higher concentrations in winter due to enhanced partitioning to particle phase at lower temperatures (Fig. 4) (Bressi et al., 2021; Poulain et al., 2011).

We observe that CAMx estimated concentrations of $NO_3$ that are considerably higher than the TSM results across the UK, Northern France, Germany, and Czechia, potentially caused by a scarcity of

measurements in those regions, leading to an underestimation in the TSM analysis. There are some agreements between CAMx and the TSM results in the Benelux region, Eastern and Southern Europe (Fig. S2). The discrepancy between CAMx and the TSM results in the Po Valley, with significantly higher concentrations from the CAMx simulations, could again be caused by the different meteorological datasets and the lack of fog scavenging.

### 3.6. $SO_4$

In winter, the TSM analysis predicts the highest concentrations over Eastern Europe, particularly in Poland, which is likely due to industrial emissions and residential coal burning for heating (Figs. 3 and 4). Significant contributions are also observed from the Mediterranean region, attributed to high ship emissions and enhanced summertime photochemistry in this region (Figs. 3 and 4). There is an overlap in hotspots between the TSM and CAMx simulations over Poland, the Balkans, the Mediterranean, and Turkey. However, the TSM analysis showed much higher concentrations than CAMx in Eastern Europe, likely due to the underestimation by CAMx of emissions from industrial and coal combustion sources. In addition, CAMx might slightly underestimate ship exhaust near the Aegean Sea and southwest of the Turkish coastline, resulting in relatively higher concentrations in these regions for the TSM analysis. Nevertheless, $SO_4$ could still be underestimated in the Balkans, where coal power plants are a common source due to both the scarcity of measurements in this region and limitations of the emission inventories. Indeed, the model underestimation of $SO_4$ is a known issue for many air quality models (Im et al., 2015). A primary explanation is the lack of accurate heterogeneous formation mechanisms in the model (Miao et al., 2020). Meanwhile, uncertainties in emissions, meteorological fields, and deposition processes can all contribute to model bias. From Fig. S1 we see that Eastern Europe and the Balkans are exactly the regions with intensive $SO_2$ emissions. Those regions reportedly have a larger uncertainty in $SO_2$ emissions than, for example, Western European countries (Vestreng et al., 2007). In addition, other species like $NH_3$ could also influence the model performance in $SO_4$ by mediating the chemical reactions. The emission inventory we used as model inputs (TNO-CAMS) was 102% lower than EDGAR emissions in non-EU countries (Kuenen et al., 2022), which may also lead to underestimation of $SO_4$. The TSM results for $SO_4$ in the UK show less pronounced contributions compared to the high concentrations predicted by CAMx, indicating a potential overestimation of $SO_2$ emissions in these regions.

## 4. Conclusion

In this study, we investigated various PM components, both organic (BBOA, OOA, and total OA) and inorganic ($NH_4$, $NO_3$, $SO_4$), and their long-range transport in Europe. Our aim was to identify their geographic origins by combining source apportionment results and measurements from 27 air quality observational sites with TSMs. Using numerous trajectories and receptors reduced uncertainties, providing a comprehensive analysis of source origins. We then compared these results to concentration estimates from the CAMx air quality model in Europe. However, we acknowledge the caveat that this analysis combines several receptors with different time periods, and the fact that back-trajectory analysis does not reproduce all atmospheric physicochemical processes.

Starting with the organic components, most contributions come from continental Europe. BBOA hotspots are located around Southern Poland to Northern Greece. The main source regions predicted by our analysis align well with regions of high concentrations as simulated by CAMx, except for the Po Valley. Seasonally, concentrations are low in summer and high in winter, likely due to increased usage of biomass for residential heating purposes. For OOA, the highest contributions are also from Eastern Europe, in particular Southern Poland to Romania. CAMx also highlights this region, but predicts overall lower absolute
concentrations, which could indicate transport processes. Excluding the Poland station did not change the results, suggesting that this region is a major contributor, possibly also underestimated by CAMx. The TSM analysis also shows discrepancies with the CAMx results in the Po Valley, potentially indicating an overestimation by CAMx, or transport processes. For total organics, the highest contributions are from Southern Poland to Bulgaria, with a clear east-west contrast well captured by our TSM analysis. Overall, Eastern Europe seems to be the most important contributor.

For inorganic components, NH₄ shows high contributions from Poland, and the TSM results generally match predictions of regions with high concentrations from CAMx except for the Po Valley. The NO₃ contribution is highest from Northern France, Benelux, and Eastern Europe due to NOₓ emissions from traffic and agricultural emissions in the Benelux region. The CAMx results show higher concentrations than the TSM analysis in Northern Europe and the Po Valley, but good consistency in Eastern Europe overall. The TSM analysis shows lower NO₃ concentrations in summer, likely due to higher temperatures, photochemistry keeping NO₃ in the gas phase, and higher vertical dilution. SO₄ concentrations are high in Poland, likely from industrial and heating emissions, and over the Mediterranean, due to photochemistry and ship exhaust. This matches the highlights from CAMx, though we do not find elevated concentrations over the Benelux and Germany. Seasonally, SO₄ is higher in winter over Poland and in summer over the Mediterranean, supporting heating-related emissions and the photochemical transformation of SO₂ to SO₄, respectively.

In conclusion, we have developed a novel approach to leverage large available datasets to have a comprehensive picture of the geographical origins of different PM components, such as BBOA, OOA, total OA, NH₄, NO₃, and SO₄ across Europe. This approach demonstrated great consistency with the concentrations simulated by CAMx. With the help of more data available in the future, the results of this analysis could be used to validate and improve the emission inventories of air quality chemical transport models. By understanding the intricate interplay between local emissions and long-range transport, stakeholders can implement informed strategies to improve air quality on a larger scale, protect human health, and safeguard ecosystems in both source and receptor regions.

### CRediT authorship contribution statement

**Michelle Y. Schneider:** Writing – review & editing, Writing – original draft, Visualization, Validation, Investigation, Formal analysis, Conceptualization.
**Jianhui Jiang:** Writing – review & editing, Visualization, Methodology, Formal analysis.
**Ying Chen:** Writing – review & editing, Visualization, Methodology, Formal analysis.
**Wenche Aas:** Writing – review & editing, Formal analysis, Data curation.
**Samira Atabakhsh:** Formal analysis, Data curation.
**Minna Aurela:** Writing – review & editing, Formal analysis, Data curation.
**Claudio Belis:** Writing – review & editing, Formal analysis, Data curation.
**Aikaterini Bougiatioti:** Formal analysis, Data curation.
**Michael Bressi:** Writing – review & editing, Formal analysis, Data curation.
**Francesco Canonaco:** Formal analysis, Data curation.
**Benjamin Chazeau:** Writing – review & editing, Formal analysis, Data curation.
**Hasna Chebaicheb:** Formal analysis, Data curation.
**Mikael Ehn:** Writing – review & editing, Funding acquisition, Data curation.
**Konstantinos Eleftheriadis:** Funding acquisition, Data curation.
**Olivier Favez:** Funding acquisition, Data curation.
**Harald Flentje:** Writing – review & editing, Formal analysis, Data curation.
**Anna Font:** Writing – review & editing, Formal analysis, Data curation.
**Evelyn Freney:** Funding acquisition, Formal analysis, Data curation.
**Stefania Gilardoni:** Funding acquisition, Data curation.
**Maria I. Gini:** Formal analysis, Data curation.
**David C. Green:** Writing – review & editing, Funding acquisition, Formal analysis, Data curation.
**Liine Heikkinen:** Formal analysis, Data curation.
**Hannes Keernik:** Formal analysis, Data curation.
**Radek Lhotka:** Formal analysis, Data curation.
**Chunshui Lin:** Formal analysis, Data curation.

**Marek Maasikmets:** Formal analysis, Data curation.
**Nicolas Marchand:** Funding acquisition, Data curation.
**María Cruz Minguillón:** Funding acquisition, Data curation.
**Jaroslaw Necki:** Funding acquisition, Data curation.
**Jurgita Ovadnevaite:** Writing – review & editing, Funding acquisition, Formal analysis, Data curation.
**Marco Paglione:** Writing – review & editing, Funding acquisition, Data curation.
**Julija Pauraite:** Formal analysis, Data curation.
**Jean-Eudes Petit:** Writing – review & editing, Funding acquisition, Formal analysis, Data curation.
**Michael Pikridas:** Writing – review & editing, Formal analysis, Data curation.
**Stephen Platt:** Formal analysis, Data curation.
**Petra Pokorná:** Formal analysis, Data curation.
**Vanes Poluzzi:** Formal analysis, Data curation.
**Laurent Poulain:** Writing – review & editing, Formal analysis, Data curation.
**Véronique Riffault:** Funding acquisition, Formal analysis, Data curation.
**Matteo Rinaldi:** Formal analysis, Data curation.
**Jean Sciare:** Data curation.
**Yulia Sosedova:** Formal analysis, Data curation.
**Iasonas Stavroulas:** Writing – review & editing, Formal analysis, Data curation.
**Hilkka Timonen:** Funding acquisition, Data curation.
**Anna Tobler:** Formal analysis, Data curation.
**Jeni Vasilescu:** Writing – review & editing, Formal analysis, Data curation.
**Marta Via:** Formal analysis, Data curation.
**Petr Vodička:** Formal analysis, Data curation.
**Yunjiang Zhang:** Formal analysis, Data curation.
**Olga Zografou:** Formal analysis, Data curation.
**Kaspar Rudolf Daellenbach:** Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Abhishek Upadhyay:** Formal analysis.
**Gang I. Chen:** Writing – review & editing, Visualization, Validation, Supervision, Investigation, Formal analysis, Data curation, Methodology, Conceptualization.
**Manousos-Ioannis Manousakas:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization.
**Imad El Haddad:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Data curation, Conceptualization.
**André S.H. Prévôt:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Data curation, Conceptualization.

### 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.

### Acknowledgements

All co-authors participated in or were supported by the COST COLOSSAL Action CA16109. The authors gratefully acknowledge the NOAA Air Resources Laboratory for providing the HYPLIT transport and dispersion model. Furthermore, we want to acknowledge the Swiss data science centre (grant C08-20, Aurora), the Swiss federal office of environment (FOEN) and the Joint Research Program of the Swiss National Science Foundation (SNSF grant no. 189883) for their financial support. We want to thank CNRS-INSU for supporting measurements performed at the SI-COPDD, and those within the long-term monitoring aerosol program SNO-CLAP, both of which are components of the ACTRIS French Research Infrastructure, and whose data is hosted at the AERIS data centre (https://www.aeris-data.fr/). We further acknowledge EPA Ireland and the Department of Environment, Climate and Communications, Horizon Europe (grant no. 101081430 - PARIS), as well as the National Key Research and Development Program of China (2023YFC3710400), and the National Natural Science Foundation of China (42207122). Additionally, this work is 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). Measurements from the Czech site were supported by the Ministry of Education, Youth and Sports of the Czech Republic, within the project for support of the national research infrastructure ACTRIS–CZ (LM2023030). ATOLL measurements have been supported by the Labex CaPPA project (ANR-11-LABX-0005-01), which is funded
by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir); the CDP AREA (R-CDP-24-003-AREA), funded by the French State under the France-2030 programme and the Initiative of Excellence of the University of Lille; the CLIMIBIO and ECRIN projects, both financed by the Regional Council "Hauts-de-France" and the European Regional Development Fund (ERDF); CNRS-INSU through ACTRIS-FR and SNO-CLAP; and the French Ministry of Environment through the CARA program of the Laboratoire Central de Surveillance de la Qualité de l'Air (LCSQA). Hasna Chebaicheb's PhD grant was supported by the LCSQA funded by the French Ministry of Environment. Mikael Ehn, Liine Heikkinen, Minna Aurela, and Hilkka Timonen are funded by Research Council of Finland (grants 317380, 345982, 337549, 3570902 and 357905).

Lastly, we want to mention the invaluable work by the following people: Andres Alastuey (andres.alastuey@idaea.csic.es) from CSIC, Gregory Gille (gregory.gille@atmosud.org) from Atmosud, Erik Teinemaa (erik.teinemaa@klab.ee) from the Estonian Environmental Research Centre (EERC), Anja Tremper (anja.tremper@imperial.ac.uk) and Max Priestman (m.priestman@imperial.ac.uk) from the MRC Centre for Environment and Health at Imperial College London.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2025.127110.

### Data availability

The code to execute the TSM analysis can be found in the supporting material. Raw data of the TSM data is publicly available through Zenodo: https://zenodo.org/records/6672710. The raw data of CAMx results are available through a request to the corresponding authors.

### References

Bressi, M., Cavalli, F., Putaud, J.P., Fröhlich, R., Petit, J.E., Aas, W., Aijälä, M., Alastuey, A., Allan, J.D., Aurela, M., Berico, M., Bougiatioti, A., Bukowiecki, N., Canonaco, F., Crenn, V., Dusanter, S., Ehn, M., Elsasser, M., Flentje, H., et al., 2021. A European aerosol phenomenology - 7: high-time resolution chemical characteristics of submicron particulate matter across Europe. Atmos. Environ. X 10, 100108. https://doi.org/10.1016/j.aeaoa.2021.100108.

Carslaw, D., 2019. The openair manual open-source tools for analysing air pollutiondata. https://github.com/davidcarslaw/openair.

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., 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, 107325. https://doi.org/10.1016/j.envint.2022.107325.

Chen, Y., Beig, G., Archer-Nicholls, S., Drysdale, W., Acton, W.J.F., Lowe, D., Nelson, B., Lee, J., Ran, L., Wang, Y., Wu, Z., Sahu, S.K., Sokhi, R.S., Singh, V., Gadi, R., Nicholas Hewitt, C., Nemitz, E., Archibald, A., McFiggans, G., Wild, O., 2021. Avoiding high ozone pollution in Delhi, India. Faraday Discuss. 226, 502–514. https://doi.org/10.1039/D0FD00079E.

Chen, Z.-L., Song, W., Hu, C.-C., Liu, X.-J., Chen, G.-Y., Walters, W.W., Michalski, G., Liu, C.-Q., Fowler, D., Liu, X.-Y., 2022. Significant contributions of combustion-related sources to ammonia emissions. Nat. Commun. 13 (1), 7710. https://doi.org/10.1038/s41467-022-35381-4.

COST Action CA16109 COLOSSAL Chemical On-Line cComposition and Source Apportionment of fine aerosol, W. G. 1., 2021. Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM) - Standard Operating Procedure. https://www.actris-ecac.eu/.

COST Action CA16109 COLOSSAL Chemical On-Line cComposition and Source Apportionment of fine aerosol, W. G. 1, 2022. Time of Flight Aerosol Chemical Speciation Monitors (TOF-ACSM) - Standard Operating Procedure. https://www.actris-ecac.eu/.

Crenn, V., Sciare, J., Croteau, P.L., Verlhac, S., Fröhlich, R., Belis, C.A., Aas, W., Aijälä, M., Alastuey, A., Artinano, B., Baisnée, D., Bonnaire, N., Bressi, M., Canagaratna, M., Canonaco, F., Carbone, C., Cavalli, F., Coz, E., Cubison, M.J., et al., 2015. ACTRIS ACSM intercomparison – Part 1: reproducibility of concentration and fragment results from 13 individual Quadrupole Aerosol Chemical Speciation Monitors (Q-ACSM) and consistency with co-located instruments. Atmos. Meas. Tech. 8 (12), 5063–5087. https://doi.org/10.5194/amt-8-5063-2015.

Crippa, M., Canonaco, F., Lanz, V.A., Aijälä, M., Allan, J.D., Carbone, S., Capes, G., Ceburnis, D., Dall, apos, Osto, M., Day, D.A., DeCarlo, P.F., Ehn, M., Eriksson, A., Freaney, E., Hildebrandt Ruiz, L., Hillamo, R., Jimenez, J.L., Juninnen, H., Kiendler-Scharr, A., et al., 2014. Organic aerosol components derived from 25 AMS data sets across Europe using a consistent ME-2 based source apportionment approach. Atmos. Chem. Phys. 14 (12), 6159–6176. https://doi.org/10.5194/acp-14-6159-2014.

Daellenbach, K.R., Manousakas, M., Jiang, J., Cui, T., Chen, Y., El Haddad, I., Fermo, P., Colombi, C., Prévôt, A.S.H., 2023. Organic aerosol sources in the Milan metropolitan area – receptor modelling based on field observations and air quality modelling. Atmos. Environ. 307, 119799. https://doi.org/10.1016/j.atmosenv.2023.119799.

Daellenbach, K.R., Uzu, G., Jiang, J., Cassagnes, L.-E., Leni, Z., Vlachou, A., Stefenelli, G., Canonaco, F., Weber, S., Segers, A., Kuenen, J.J.P., Schaap, M., Favez, O., Albinet, A., Aksoyoglu, S., Dommen, J., Baltensperger, U., Geiser, M., El Haddad, I., et al., 2020. Sources of particulate-matter air pollution and its oxidative potential in Europe. Nature 587 (7834), 414–419. https://doi.org/10.1038/s41586-020-2902-8.

Draxler, R.R., 1999. HYSPLIT4 user's guide. https://www.arl.noaa.gov/documents/reports/hysplit_user_guide.pdf.

Drewnick, F., Hings, S.S., DeCarlo, P., Jayne, J.T., Gonin, M., Fuhrer, K., Weimer, S., Jimenez, J.L., Demerjian, K.L., Borrmann, S., Worsnop, D.R., 2005. A new time-of-flight aerosol mass spectrometer (TOF-AMS)—instrument description and first field deployment. Aerosol. Sci. Technol. 39 (7), 637–658. https://doi.org/10.1080/02786820500182040.

Escudero, M., Stein, A.F., Draxler, R.R., Querol, X., Alastuey, A., Castillo, S., Avila, A., 2011. Source apportionment for African dust outbreaks over the Western Mediterranean using the HYSPLIT model. Atmos. Res. 99 (3–4), 518–527. https://doi.org/10.1016/j.atmosres.2010.12.002.

Fountoukis, C., Nenes, A., 2007. Isorropia II: a computationally efficient thermodynamic equilibrium model for $K^{+}-Ca^{2+}-Mg^{2+}-NH_{4}^{2+}-Na^{+}-SO_{4}^{2-}-NO_{3}^{-}-Cl^{-}-H_{2}O$ aerosols. Atmos. Chem. Phys. 7 (17), 4639–4659. https://doi.org/10.5194/acp-7-4639-2007.

Freney, E., Zhang, Y., Croteau, P., Amodeo, T., Williams, L., Truong, F., Petit, J.-E., Sciare, J., Sarda-Esteve, R., Bonnaire, N., Arumae, T., Aurela, M., Bougiatioti, A., Mihalopoulos, N., Coz, E., Artinano, B., Crenn, V., Elste, T., Heikkinen, L., et al., 2019. The second ACTRIS inter-comparison (2016) for Aerosol Chemical Speciation Monitors (ACSM): calibration protocols and instrument performance evaluations. Aerosol. Sci. Technol. 53 (7), 830–842. https://doi.org/10.1080/02786826.2019.1608901.

Fröhlich, R., Crenn, V., Setyan, A., Belis, C.A., Canonaco, F., Favez, O., Riffault, V., Slowik, J.G., Aas, W., Aijälä, M., Alastuey, A., Artinano, B., Bonnaire, N., Bozzetti, C., Bressi, M., Carbone, C., Coz, E., Croteau, P.L., Cubison, M.J., et al., 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 (6), 2555–2576. https://doi.org/10.5194/amt-8-2555-2015.

Fröhlich, R., Cubison, M.J., Slowik, J.G., Bukowiecki, N., Prévôt, A.S.H., Baltensperger, U., Schneider, J., Kimmel, J.R., Gonin, M., Rohner, U., Worsnop, D.R., Jayne, J.T., 2013. The TOF-ACSM: a portable aerosol chemical speciation monitor with TOFMS detection. Atmos. Meas. Tech. 6 (11), 3225–3241. https://doi.org/10.5194/amt-6-3225-2013.

GDAS (Global Data Assimilation System). National Centers for Environmental Information (NCEI). Retrieved January 11, 2025, from https://www.ncei.noaa.gov/products/weather-climate-models/global-data-assimilation.

Gilardoni, S., Massoli, P., Giulianelli, L., Rinaldi, M., Paglione, M., Pollini, F., Lanconelli, C., Poluzzi, V., Carbone, S., Hillamo, R., Russell, L.M., Facchini, M.C., Fuzzi, S., 2014. Fog scavenging of organic and inorganic aerosol in the Po Valley. Atmos. Chem. Phys. 14 (13), 6967–6981. https://doi.org/10.5194/acp-14-6967-2014.

Harrison, R.M., Yin, J., 2000. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Sci. Total Environ. 249 (1–3), 85–101. https://doi.org/10.1016/S0048-9697(99)00513-6.

Hennigan, C.J., Sullivan, A.P., Collett, J.L., Robinson, A.L., 2010. Levoglucosan stability in biomass burning particles exposed to hydroxyl radicals. Geophys. Res. Lett. 37 (9), 2–5. https://doi.org/10.1029/2010GL043088.

Hildebrandt Ruiz, L., Yarwood, G., 2013. Interactions between Organic Aerosol and NOy: Influence on Oxidant Production. Final Report Prepared For The Texas Air Quality Research Program (Project 12-012). the University of Texas at Austin and ENVIRON International Corporation.

Horowitz, L.W., Walters, S., Mauzerall, D.L., Emmons, L.K., Rasch, P.J., Granier, C., Tie, X., Lamarque, J., Schultz, M.G., Tyndall, G.S., Orlando, J.J., Brasseur, G.P., 2003. A global simulation of tropospheric ozone and related tracers: description and evaluation of MOZART, version 2. J. Geophys. Res. Atmos. 108 (D24). https://doi.org/10.1029/2002JD002853.

Hsu, Y.-K., Holsen, T.M., Hopke, P.K., 2003. Comparison of hybrid receptor models to locate PCB sources in Chicago. Atmos. Environ. 37 (4), 545–562. https://doi.org/10.1016/S1352-2310(02)00886-5.

Im, U., Bianconi, R., Solazzo, E., Kioutsiouikis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., Denier van der Gon, H., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., et al., 2015. Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMII phase 2. Part II: particulate matter. Atmos. Environ. 115, 421–441. https://doi.org/10.1016/j.atmosenv.2014.08.072.

Jiang, J., Aksoyoglu, S., Ciarelli, G., Oikonomakis, E., El-Haddad, I., Canonaco, F., O'Dowd, C., Ovadnevaite, J., Minguillón, M.C., Baltensperger, U., Prévôt, A.S.H., 2019a. Effects of two different biogenic emission models on modelled ozone and
aerosol concentrations in Europe. Atmos. Chem. Phys. 19 (6), 3747–3768. https://doi.org/10.5194/acp-19-3747-2019.

Jiang, J., Aksoyoglu, S., El-Haddad, I., Ciarelli, G., Denier van der Gon, H.A.C., Canonaco, F., Gilardoni, S., Paglione, M., Minguillón, M.C., Favez, O., Zhang, Y., Marchand, N., Hao, L., Virtanen, A., Florou, K., O'Dowd, C., Ovadnevaite, J., Baltensperger, U., Prévot, A.S.H., 2019b. Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme. Atmos. Chem. Phys. 19 (24), 15247–15270. https://doi.org/10.5194/acp-19-15247-2019.

Jimenez, J.L., Canagaratna, M.R., Donahue, N.M., Prevot, A.S.H.H., Zhang, Q., Kroll, J. H., DeCarlo, P.F., Allan, J.D., Coe, H., Ng, N.L., Aiken, A.C., Docherty, K.S., Ulbrich, I.M., Grieshop, A.P., Robinson, A.L., Duplissy, J., Smith, J.D., Wilson, K.R., Lanz, V.A., et al., 2009. Evolution of organic aerosols in the atmosphere. Science 326 (5959), 1525–1529. https://doi.org/10.1126/science.1180353.

Koo, B., Knipping, E., Yarwood, G., 2014. 1.5-Dimensional volatility basis set approach for modeling organic aerosol in CAMx and CMAQ. Atmos. Environ. 95, 158–164. https://doi.org/10.1016/j.atmosenv.2014.06.031.

Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., Denier van der Gon, H., 2022. CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling. Earth Syst. Sci. Data 14 (2), 491–515. https://doi.org/10.5194/essd-14-491-2022.

Lee, S., Ashbaugh, L., 2007. Comparison of multi-receptor and single-receptor trajectory source apportionment (TSA) methods using artificial sources. Atmos. Environ. 41 (6), 1119–1127. https://doi.org/10.1016/j.atmosenv.2006.10.019.

Liu, X., Lara, R., Dufresne, M., Wu, L., Zhang, X., Wang, T., Monge, M., Reche, C., Di Leo, A., Lanzani, G., Colombi, C., Font, A., Sheehan, A., Green, D.C., Makkonen, U., Sauvage, S., Salameh, T., Petit, J.-E., Chatain, M., et al., 2024. Variability of ambient air ammonia in urban Europe (Finland, France, Italy, Spain, and the UK). Environ. Int. 185, 108519. https://doi.org/10.1016/j.envint.2024.108519.

Lupu, A., Maenhaut, W., 2002. Application and comparison of two statistical trajectory techniques for identification of source regions of atmospheric aerosol species. Atmos. Environ. 36 (36–37), 5607–5618. https://doi.org/10.1016/S1352-2310(02)00697-0.

Märmureanu, L., Marin, C.A., Vasilescu, J., Petit, J.-E., Amodeo, T., Truong, F., Antonescu, B., Minguillon, M.C., Green, D.C., Zainab, B., Ovadnevaite, J., Elste, T., Coz, E., Allan, J., Croteau, P.L., Jayne, J., Canagaratna, M.R., Williams, L., Gros, V., et al., 2025. Evaluation of aerosol chemical speciation monitor response to different mixtures of organic and inorganic aerosols. Aerosol. Sci. Technol. 59 (1), 16–33. https://doi.org/10.1080/02786826.2024.2412999.

Miao, R., Chen, Q., Zheng, Y., Cheng, X., Sun, Y., Palmer, P.I., Shrivastava, M., Guo, J., Zhang, Q., Liu, Y., Tan, Z., Ma, X., Chen, S., Zeng, L., Lu, K., Zhang, Y., 2020. Model bias in simulating major chemical components of PM<sub>2.5</sub> in China. Atmos. Chem. Phys. 20 (20), 12265–12284. https://doi.org/10.5194/acp-20-12265-2020.

Mitsakou, C., Housiadas, C., Eleftheriadis, K., Vratolis, S., Helmis, C., Asimakopoulos, D., 2007. Lung deposition of fine and ultrafine particles outdoors and indoors during a cooking event and a no activity period. Indoor Air 17 (2), 143–152. https://doi.org/10.1111/j.1600-0668.2006.00464.x.

Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: a new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquat. Geochem. 4 (1), 123–152. https://doi.org/10.1023/A:1009604003981.

Ng, N.L., Herndon, S.C., Trimborn, A., Canagaratna, M.R., Croteau, P.L., Onasch, T.B., Sueper, D., Worsnop, D.R., Zhang, Q., Sun, Y.L., Jayne, J.T., 2011. An aerosol chemical speciation monitor (ACSM) for routine monitoring of the composition and mass concentrations of ambient aerosol. Aerosol. Sci. Technol. 45 (7), 780–794. https://doi.org/10.1080/02786826.2011.560211.

NOAA (National Oceanic and Atmospheric Administration). Air Resources Laboratory (ARL) – Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model. Retrieved January 11, 2025, from https://www.arl.noaa.gov/hysplit/.

Paglione, M., Gilardoni, S., Rinaldi, M., Decesari, S., Zanca, N., Sandrini, S., Giulianelli, L., Bacco, D., Ferrari, S., Poluzzi, V., Scotto, F., Trentini, A., Poulain, L., Herrmann, H., Wiedensohler, A., Canonaco, F., Prévot, A.S.H., Massoli, P., Carbone, C., et al., 2020. The impact of biomass burning and aqueous-phase processing on air quality: a multi-year source apportionment study in the Po Valley, Italy. Atmos. Chem. Phys. 20 (3), 1233–1254. https://doi.org/10.5194/acp-20-1233-2020.

Poulain, L., Spindler, G., Birmili, W., Plass-Dülmer, C., Wiedensohler, A., Herrmann, H., 2011. Seasonal and diurnal variations of particulate nitrate and organic matter at the IFT research station Melpitz. Atmospheric Chemistry and Physics 11 (24), 12579–12599. https://doi.org/10.5194/acp-11-12579-2011.

Salvador, P., Artínano, B., Pio, C., Afonso, J., Legrand, M., Puxbaum, H., Hammer, S., 2010. Evaluation of aerosol sources at European high altitude background sites with trajectory statistical methods. Atmos. Environ. 44 (19), 2316–2329. https://doi.org/10.1016/j.atmosenv.2010.03.042.

Salvador, P., Artínano, B., Querol, X., Alastuey, A., 2008. A combined analysis of backward trajectories and aerosol chemistry to characterise long-range transport episodes of particulate matter: the Madrid air basin, a case study. Sci. Total Environ. 390 (2–3), 495–506. https://doi.org/10.1016/j.scitotenv.2007.10.052.

Samoli, E., Atkinson, R.W., Analitis, A., Fuller, G.W., Green, D.C., Mudway, I., Anderson, H.R., Kelly, F.J., 2016. Associations of short-term exposure to traffic-related air pollution with cardiovascular and respiratory hospital admissions in London, UK. Occup. Environ. Med. 73 (5), 300–307. https://doi.org/10.1136/oemed-2015-103136.

Shon, Z.H., Kang, M., Park, G., Bae, M., 2020. Impact of temporary emission reduction from a large-scale coal-fired power plant on air quality. Atmos. Environ. X 5, 100056. https://doi.org/10.1016/J.AEAOA.2019.100056.

Stein, A.F., Draxler, R.R., Ralph, G.D., Stunder, B.J.B., Cohen, M.D., Ngan, F., 2015. NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96 (12), 2059–2077. https://doi.org/10.1175/BAMS-D-14-00110.1.

Stohl, A., 1996. Trajectory statistics-A new method to establish source-receptor relationships of air pollutants and its application to the transport of particulate sulfate in Europe. Atmos. Environ. 30 (4), 579–587. https://doi.org/10.1016/S1352-2310(95)00314-2.

Stohl, A., 1998. Computation, accuracy and applications of trajectories—a review and bibliography. Atmos. Environ. 32 (6), 947–966. https://doi.org/10.1016/S1352-2310(97)00457-3.

Tobler, A.K., Skiba, A., Canonaco, F., Moćnik, G., Rai, P., Chen, G., Bartyzel, J., Zimnoch, M., Styszko, K., Nęcki, J., Furger, M., Różański, 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 (19). https://doi.org/10.5194/acp-21-14893-2021.

Vestreng, V., Myhre, G., Fagerli, H., Reis, S., Tarrasón, L., 2007. Twenty-five years of continuous sulphur dioxide emission reduction in Europe. Atmos. Chem. Phys. 7 (13), 3663–3681. https://doi.org/10.5194/acp-7-3663-2007.

Wilks, D.S., 2019. Statistical methods in the atmospheric sciences. In: Statistical Methods in the Atmospheric Sciences, fourth ed. Elsevier. https://doi.org/10.1016/C2017-0-03921-6. Fourth Edition.

Yarwood, G., Wilson, G., Emery, C., Morris, R., 2024. User's guide COMPREHENSIVE AIR QUALITY MODEL WITH EXTENSIONS. Ramboll Environment and Health, Version 7.30. www.ramboll.comwww.camx.comwww.camx.comwww.camx.comACKNOWLEDGMENTS.