High-resolution modelling of organic aerosol over Europe: exploring spatial and temporal variability and drivers

Daniel Trejo Banos; Abhishek Upadhyay; Yun Cheng; Jianhui Jiang; Petros Vasilakos; Andrea Nava; Pavol Ševera; Benjamin Flueckiger; Aikaterini Bougiatioti; Ana Maria Sanchez De La Campa Verdona; Andrea Schemmel; Andrés Alastuey; Anikó Vasanits; Anna Font; Anna Tobler; Aude Bourin; Attila Machon; Benjamin Chazeau; Benjamin Bergmans; Célia A. Alves; Céline Voiron; Christoph Hueglin; Chunshui Lin; Claudio A. Belis; Cristina Colombi; Cristina Reche; Daniel Alejandro Sanchezrodas Navarro; Dario Massabò; David C. Green; Eleonora Cuccia; Evelyn Freney; Fabio Giardi; Francesco Canonaco; Gaëlle Uzu; Gang I. Chen; Hannes Keernik; Harald Flentje; Hartmut Herrmann; Hasna Chebaicheb; Hilkka Timonen; Hugo Denier van der Gon; Iasonas Stavroulas; Imre Salma; Jaroslav Schwarz; Jaroslaw Necki; Jean Sciare; Jean Eudes Petit; Jean Luc Jaffrezo; Jeni Vasilescu; Jesús D. De La Rosa; Julija Pauraite; Jurgita Ovadnevaite; Karl Espen Yttri; Konstantinos Eleftheriadis; Laurent Poulain; Livio Belegante; Lucas Alados-Arboledas; Manousos Ioannis Manousakas; Marco Paglione; Marek Maasikmets; María Cruz Minguillón; Maria I. Gini; Matteo Rinaldi; Michael Pikridas; Minna Aurela; Nicolas Marchand; Olga Zografou; Olivier Favez; Petr Vodička; Petra Pokorná; Radek Lhotka; Samira Atabakhsh; Sébastien Conil; Sonia Castillo; Stefania Gilardoni; Stephen M. Platt; Stuart K. Grange; Vanes Poluzzi; Varun Kumar; Véronique Riffault; Wenche Aas; Xavier Querol; Yulia Sosedova; Nicole Probst-Hensch; Danielle Vienneau; André S.H. Prévôt; Kees de Hoogh; Kaspar R. Daellenbach; Ekaterina Krymova; Imad El Haddad

doi:10.1016/j.envint.2026.110143

High-resolution modelling of organic aerosol over Europe: exploring spatial and temporal variability and drivers

Daniel Trejo Banos
, Abhishek Upadhyay
, Yun Cheng
, Jianhui Jiang
, Petros Vasilakos
, Andrea Nava
, Pavol Ševera
, Benjamin Flueckiger
, Aikaterini Bougiatioti
, Ana Maria Sanchez De La Campa Verdona
, Andrea Schemmel
, Andrés Alastuey
, Anikó Vasanits
, Anna Font
, Anna Tobler
, Aude Bourin
, Attila Machon
, Benjamin Chazeau
, Benjamin Bergmans
, Célia A. Alves

Céline Voiron, Christoph Hueglin, Chunshui Lin, Claudio A. Belis, Cristina Colombi, Cristina Reche, Daniel Alejandro Sanchezrodas Navarro, Dario Massabò, David C. Green, Eleonora Cuccia, Evelyn Freney, Fabio Giardi, Francesco Canonaco, Gaëlle Uzu, Gang I. Chen, Hannes Keernik, Harald Flentje, Hartmut Herrmann, Hasna Chebaicheb, Hilkka Timonen, Hugo Denier van der Gon, Iasonas Stavroulas, Imre Salma, Jaroslav Schwarz, Jaroslaw Necki, Jean Sciare, Jean Eudes Petit, Jean Luc Jaffrezo, Jeni Vasilescu, Jesús D. De La Rosa, Julija Pauraite, Jurgita Ovadnevaite, Karl Espen Yttri, Konstantinos Eleftheriadis, Laurent Poulain, Livio Belegante, Lucas Alados-Arboledas, Manousos Ioannis Manousakas, Marco Paglione, Marek Maasikmets, María Cruz Minguillón, Maria I. Gini, Matteo Rinaldi, Michael Pikridas, Minna Aurela, Nicolas Marchand, Olga Zografou, Olivier Favez, Petr Vodička, Petra Pokorná, Radek Lhotka, Samira Atabakhsh, Sébastien Conil, Sonia Castillo, Stefania Gilardoni, Stephen M. Platt, Stuart K. Grange, Vanes Poluzzi, Varun Kumar, Véronique Riffault, Wenche Aas, Xavier Querol, Yulia Sosedova, Nicole Probst-Hensch, Danielle Vienneau, André S.H. Prévôt, Kees de Hoogh, Kaspar R. Daellenbach, Ekaterina Krymova, Imad El Haddad

Swiss Data Science Center
Paul Scherrer Institut
East China Normal University
University of Geneva
Swiss Tropical and Public Health Institute Swiss TPH
University of Basel
National Observatory of Athens
University of Huelva
Umweltbundesamt, Germany
Institute of Environmental Assessment and Water Research (IDAEA-CSIC)
Eötvös Loránd University
University Lille
MRC-PHE Centre for Health and Environment
Datalystica Ltd.
Air Quality Reference Center
Aix-Marseille Université
ISSEP - Institut Scientifique de Service Public
University of Aveiro
U1038
EMPA Materials Science and Technology
NCLA Laser Laboratory
University of Galway
European Commission Joint Research Centre
Environmental Protection Agency of Lombardy (ARPA Lombardia)
University of Genova
Imperial College London
Université Clermont Auvergne-CNRS
Dipartimento di Fisica e Astronomia and National Institute of Nuclear Physics (INFN)
Air Quality Management Department
University of Tartu
German Weather Service
Leibniz Institute for Tropospheric Research
Institut National de l'Environnement Industriel et des Risques (INERIS)
Finnish Meteorological Institute
University of Tampere
The TNO Institute of Applied Physics
Institute of Chemical Process Fundamentals of the CAS
AGH University of Science and Technology
The Cyprus Institute
unité mixte CEA-CNRS-UVSQ
National Institute of Research and Development for Optoelectronics
Institute of Physics
NILU - Norwegian Institute For Air Research
Institute of Biosciences and Applications
University of Granada
CNR
University of Helsinki
Observatoire Pérenne de l’Environnement
Consiglio Nazionale delle Ricerche
Queensland University of Technology
Arpae Emilia-Romagna
Aarhus University
Tsinghua University

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

Abstract

Organic aerosol (OA) is a major component of atmospheric particulate matter (PM), affecting both human health and climate. However, high-resolution estimates of OA exposure needed for exposure analysis remain scarce. Here, we integrate a chemical transport model (CAMx) with a random forest (RF) machine learning approach to bias-correct and downscale daily OA concentrations across Europe. CAMx OA simulations at ∼15 km resolution show moderate agreement with observations (r = 0.55). By combining these outputs with high-resolution land-use data and training the RF model on ∼48,000 daily OA measurements from 137 sites, prediction accuracy improved (r = 0.65), with ∼l5% reduction in root mean square error. The resulting maps provide European daily OA concentrations at ∼250 m resolution for alternate years from 2011 to 2019. The model captures key spatial features, including elevated OA in the Po Valley, Southeastern, and Central Europe, as well as intracity variations due to local hotspots. Seasonal analysis reveals higher concentrations in winter, while long-term trends indicate a general decline in OA levels. Exposure estimates show that half of the European population experiences OA levels above 3 µg/m³, and ∼50 million people are exposed to more than 5 µg/m³, which is the current guideline level recommended by the world health organization for total PM_2.5. These high-resolution OA maps offer vital critical support for epidemiological research and air quality policy.

Original language	English
Article number	110143
Journal	Environment International
Volume	209
DOIs	https://doi.org/10.1016/j.envint.2026.110143
Publication status	Published - Mar 2026
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

CAMX
Chemical transport modelling
Downscaling
Exposure
Machine learning
Organic aerosol
Random forest

Access to Document

10.1016/j.envint.2026.110143

Cite this

Banos, D. T., Upadhyay, A., Cheng, Y., Jiang, J., Vasilakos, P., Nava, A., Ševera, P., Flueckiger, B., Bougiatioti, A., Sanchez De La Campa Verdona, A. M., Schemmel, A., Alastuey, A., Vasanits, A., Font, A., Tobler, A., Bourin, A., Machon, A., Chazeau, B., Bergmans, B., ... Haddad, I. E. (2026). High-resolution modelling of organic aerosol over Europe: exploring spatial and temporal variability and drivers. Environment International, 209, Article 110143. https://doi.org/10.1016/j.envint.2026.110143

@article{b350027e1d1b48b1ab1bf4254fca9b16,

title = "High-resolution modelling of organic aerosol over Europe: exploring spatial and temporal variability and drivers",

abstract = "Organic aerosol (OA) is a major component of atmospheric particulate matter (PM), affecting both human health and climate. However, high-resolution estimates of OA exposure needed for exposure analysis remain scarce. Here, we integrate a chemical transport model (CAMx) with a random forest (RF) machine learning approach to bias-correct and downscale daily OA concentrations across Europe. CAMx OA simulations at ∼15 km resolution show moderate agreement with observations (r = 0.55). By combining these outputs with high-resolution land-use data and training the RF model on ∼48,000 daily OA measurements from 137 sites, prediction accuracy improved (r = 0.65), with ∼l5\% reduction in root mean square error. The resulting maps provide European daily OA concentrations at ∼250 m resolution for alternate years from 2011 to 2019. The model captures key spatial features, including elevated OA in the Po Valley, Southeastern, and Central Europe, as well as intracity variations due to local hotspots. Seasonal analysis reveals higher concentrations in winter, while long-term trends indicate a general decline in OA levels. Exposure estimates show that half of the European population experiences OA levels above 3 µg/m3, and ∼50 million people are exposed to more than 5 µg/m3, which is the current guideline level recommended by the world health organization for total PM2.5. These high-resolution OA maps offer vital critical support for epidemiological research and air quality policy.",

keywords = "CAMX, Chemical transport modelling, Downscaling, Exposure, Machine learning, Organic aerosol, Random forest",

author = "Banos, \{Daniel Trejo\} and Abhishek Upadhyay and Yun Cheng and Jianhui Jiang and Petros Vasilakos and Andrea Nava and Pavol {\v S}evera and Benjamin Flueckiger and Aikaterini Bougiatioti and \{Sanchez De La Campa Verdona\}, \{Ana Maria\} and Andrea Schemmel and Andr{\'e}s Alastuey and Anik{\'o} Vasanits and Anna Font and Anna Tobler and Aude Bourin and Attila Machon and Benjamin Chazeau and Benjamin Bergmans and Alves, \{C{\'e}lia A.\} and C{\'e}line Voiron and Christoph Hueglin and Chunshui Lin and Belis, \{Claudio A.\} and Cristina Colombi and Cristina Reche and \{Sanchezrodas Navarro\}, \{Daniel Alejandro\} and Dario Massab{\`o} and Green, \{David C.\} and Eleonora Cuccia and Evelyn Freney and Fabio Giardi and Francesco Canonaco and Ga{\"e}lle Uzu and Chen, \{Gang I.\} and Hannes Keernik and Harald Flentje and Hartmut Herrmann and Hasna Chebaicheb and Hilkka Timonen and \{van der Gon\}, \{Hugo Denier\} and Iasonas Stavroulas and Imre Salma and Jaroslav Schwarz and Jaroslaw Necki and Jean Sciare and Petit, \{Jean Eudes\} and Jaffrezo, \{Jean Luc\} and Jeni Vasilescu and \{De La Rosa\}, \{Jes{\'u}s D.\} and Julija Pauraite and Jurgita Ovadnevaite and Yttri, \{Karl Espen\} and Konstantinos Eleftheriadis and Laurent Poulain and Livio Belegante and Lucas Alados-Arboledas and Manousakas, \{Manousos Ioannis\} and Marco Paglione and Marek Maasikmets and Minguill{\'o}n, \{Mar{\'i}a Cruz\} and Gini, \{Maria I.\} and Matteo Rinaldi and Michael Pikridas and Minna Aurela and Nicolas Marchand and Olga Zografou and Olivier Favez and Petr Vodi{\v c}ka and Petra Pokorn{\'a} and Radek Lhotka and Samira Atabakhsh and S{\'e}bastien Conil and Sonia Castillo and Stefania Gilardoni and Platt, \{Stephen M.\} and Grange, \{Stuart K.\} and Vanes Poluzzi and Varun Kumar and V{\'e}ronique Riffault and Wenche Aas and Xavier Querol and Yulia Sosedova and Nicole Probst-Hensch and Danielle Vienneau and Pr{\'e}v{\^o}t, \{Andr{\'e} S.H.\} and \{de Hoogh\}, Kees and Daellenbach, \{Kaspar R.\} and Ekaterina Krymova and Haddad, \{Imad El\}",

note = "Publisher Copyright: {\textcopyright} 2026 The Author(s).",

year = "2026",

month = mar,

doi = "10.1016/j.envint.2026.110143",

language = "English",

volume = "209",

journal = "Environment International",

issn = "0160-4120",

publisher = "Elsevier Ltd",

}

Banos, DT, Upadhyay, A, Cheng, Y, Jiang, J, Vasilakos, P, Nava, A, Ševera, P, Flueckiger, B, Bougiatioti, A, Sanchez De La Campa Verdona, AM, Schemmel, A, Alastuey, A, Vasanits, A, Font, A, Tobler, A, Bourin, A, Machon, A, Chazeau, B, Bergmans, B, Alves, CA, Voiron, C, Hueglin, C, Lin, C, Belis, CA, Colombi, C, Reche, C, Sanchezrodas Navarro, DA, Massabò, D, Green, DC, Cuccia, E, Freney, E, Giardi, F, Canonaco, F, Uzu, G, Chen, GI, Keernik, H, Flentje, H, Herrmann, H, Chebaicheb, H, Timonen, H, van der Gon, HD, Stavroulas, I, Salma, I, Schwarz, J, Necki, J, Sciare, J, Petit, JE, Jaffrezo, JL, Vasilescu, J, De La Rosa, JD, Pauraite, J, Ovadnevaite, J, Yttri, KE, Eleftheriadis, K, Poulain, L, Belegante, L, Alados-Arboledas, L, Manousakas, MI, Paglione, M, Maasikmets, M, Minguillón, MC, Gini, MI, Rinaldi, M, Pikridas, M, Aurela, M, Marchand, N, Zografou, O, Favez, O, Vodička, P, Pokorná, P, Lhotka, R, Atabakhsh, S, Conil, S, Castillo, S, Gilardoni, S, Platt, SM, Grange, SK, Poluzzi, V, Kumar, V, Riffault, V, Aas, W, Querol, X, Sosedova, Y, Probst-Hensch, N, Vienneau, D, Prévôt, ASH, de Hoogh, K, Daellenbach, KR, Krymova, E & Haddad, IE 2026, 'High-resolution modelling of organic aerosol over Europe: exploring spatial and temporal variability and drivers', Environment International, vol. 209, 110143. https://doi.org/10.1016/j.envint.2026.110143

TY - JOUR

T1 - High-resolution modelling of organic aerosol over Europe

T2 - exploring spatial and temporal variability and drivers

AU - Banos, Daniel Trejo

AU - Upadhyay, Abhishek

AU - Cheng, Yun

AU - Jiang, Jianhui

AU - Vasilakos, Petros

AU - Nava, Andrea

AU - Ševera, Pavol

AU - Flueckiger, Benjamin

AU - Bougiatioti, Aikaterini

AU - Sanchez De La Campa Verdona, Ana Maria

AU - Schemmel, Andrea

AU - Alastuey, Andrés

AU - Vasanits, Anikó

AU - Font, Anna

AU - Tobler, Anna

AU - Bourin, Aude

AU - Machon, Attila

AU - Chazeau, Benjamin

AU - Bergmans, Benjamin

AU - Alves, Célia A.

AU - Voiron, Céline

AU - Hueglin, Christoph

AU - Lin, Chunshui

AU - Belis, Claudio A.

AU - Colombi, Cristina

AU - Reche, Cristina

AU - Sanchezrodas Navarro, Daniel Alejandro

AU - Massabò, Dario

AU - Green, David C.

AU - Cuccia, Eleonora

AU - Freney, Evelyn

AU - Giardi, Fabio

AU - Canonaco, Francesco

AU - Uzu, Gaëlle

AU - Chen, Gang I.

AU - Keernik, Hannes

AU - Flentje, Harald

AU - Herrmann, Hartmut

AU - Chebaicheb, Hasna

AU - Timonen, Hilkka

AU - van der Gon, Hugo Denier

AU - Stavroulas, Iasonas

AU - Salma, Imre

AU - Schwarz, Jaroslav

AU - Necki, Jaroslaw

AU - Sciare, Jean

AU - Petit, Jean Eudes

AU - Jaffrezo, Jean Luc

AU - Vasilescu, Jeni

AU - De La Rosa, Jesús D.

AU - Pauraite, Julija

AU - Ovadnevaite, Jurgita

AU - Yttri, Karl Espen

AU - Eleftheriadis, Konstantinos

AU - Poulain, Laurent

AU - Belegante, Livio

AU - Alados-Arboledas, Lucas

AU - Manousakas, Manousos Ioannis

AU - Paglione, Marco

AU - Maasikmets, Marek

AU - Minguillón, María Cruz

AU - Gini, Maria I.

AU - Rinaldi, Matteo

AU - Pikridas, Michael

AU - Aurela, Minna

AU - Marchand, Nicolas

AU - Zografou, Olga

AU - Favez, Olivier

AU - Vodička, Petr

AU - Pokorná, Petra

AU - Lhotka, Radek

AU - Atabakhsh, Samira

AU - Conil, Sébastien

AU - Castillo, Sonia

AU - Gilardoni, Stefania

AU - Platt, Stephen M.

AU - Grange, Stuart K.

AU - Poluzzi, Vanes

AU - Kumar, Varun

AU - Riffault, Véronique

AU - Aas, Wenche

AU - Querol, Xavier

AU - Sosedova, Yulia

AU - Probst-Hensch, Nicole

AU - Vienneau, Danielle

AU - Prévôt, André S.H.

AU - de Hoogh, Kees

AU - Daellenbach, Kaspar R.

AU - Krymova, Ekaterina

AU - Haddad, Imad El

PY - 2026/3

Y1 - 2026/3

N2 - Organic aerosol (OA) is a major component of atmospheric particulate matter (PM), affecting both human health and climate. However, high-resolution estimates of OA exposure needed for exposure analysis remain scarce. Here, we integrate a chemical transport model (CAMx) with a random forest (RF) machine learning approach to bias-correct and downscale daily OA concentrations across Europe. CAMx OA simulations at ∼15 km resolution show moderate agreement with observations (r = 0.55). By combining these outputs with high-resolution land-use data and training the RF model on ∼48,000 daily OA measurements from 137 sites, prediction accuracy improved (r = 0.65), with ∼l5% reduction in root mean square error. The resulting maps provide European daily OA concentrations at ∼250 m resolution for alternate years from 2011 to 2019. The model captures key spatial features, including elevated OA in the Po Valley, Southeastern, and Central Europe, as well as intracity variations due to local hotspots. Seasonal analysis reveals higher concentrations in winter, while long-term trends indicate a general decline in OA levels. Exposure estimates show that half of the European population experiences OA levels above 3 µg/m3, and ∼50 million people are exposed to more than 5 µg/m3, which is the current guideline level recommended by the world health organization for total PM2.5. These high-resolution OA maps offer vital critical support for epidemiological research and air quality policy.

AB - Organic aerosol (OA) is a major component of atmospheric particulate matter (PM), affecting both human health and climate. However, high-resolution estimates of OA exposure needed for exposure analysis remain scarce. Here, we integrate a chemical transport model (CAMx) with a random forest (RF) machine learning approach to bias-correct and downscale daily OA concentrations across Europe. CAMx OA simulations at ∼15 km resolution show moderate agreement with observations (r = 0.55). By combining these outputs with high-resolution land-use data and training the RF model on ∼48,000 daily OA measurements from 137 sites, prediction accuracy improved (r = 0.65), with ∼l5% reduction in root mean square error. The resulting maps provide European daily OA concentrations at ∼250 m resolution for alternate years from 2011 to 2019. The model captures key spatial features, including elevated OA in the Po Valley, Southeastern, and Central Europe, as well as intracity variations due to local hotspots. Seasonal analysis reveals higher concentrations in winter, while long-term trends indicate a general decline in OA levels. Exposure estimates show that half of the European population experiences OA levels above 3 µg/m3, and ∼50 million people are exposed to more than 5 µg/m3, which is the current guideline level recommended by the world health organization for total PM2.5. These high-resolution OA maps offer vital critical support for epidemiological research and air quality policy.

KW - CAMX

KW - Chemical transport modelling

KW - Downscaling

KW - Exposure

KW - Machine learning

KW - Organic aerosol

KW - Random forest

UR - https://www.scopus.com/pages/publications/105030345077

U2 - 10.1016/j.envint.2026.110143

DO - 10.1016/j.envint.2026.110143

M3 - Article

C2 - 41713083

AN - SCOPUS:105030345077

SN - 0160-4120

VL - 209

JO - Environment International

JF - Environment International

M1 - 110143

ER -

High-resolution modelling of organic aerosol over Europe: exploring spatial and temporal variability and drivers

Abstract

UN SDGs

Keywords

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