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.
BANOS Daniel Trejo;
UPADHYAY Abhishek;
CHENG Yun;
JIANG Jianhui;
VASILAKOS Petros;
NAVA Andrea;
SEVERA Pavol;
FLUECKIGER Benjamin;
BOUGIATIOTI Aikaterini;
SANCHEZ DE LA CAMPA VERDONA Ana;
SCHEMMEL Andrea;
ALASTUEY Andres;
VASANITS Anikó;
FONT Anna;
TOBLER Anna;
BOURIN Aude;
MACHON Attila;
CHAZEAU Benjamin;
BERGMANS Benjamin;
ALVES C;
VOIRON Céline;
HUEGLIN Christoph;
LIN Chunshui;
BELIS Claudio;
COLOMBI C.;
RECHE Cristina;
SANCHEZRODAS NAVARRO Daniel Alejandro;
MASSABÒ Dario;
GREEN David;
CUCCIA Eleonora;
FRENEY Evelyn;
GIARDI Fabio;
CANONACO Francesco;
UZU Gaëlle;
CHEN Gang I.;
KEERNIK Hannes;
FLENTJE Harald;
HERRMANN Hartmut;
CHEBAICHEB Hasna;
TIMONEN Hilkka;
DENIER VAN DER GON H.A.C.;
STAVROULAS Iasonas;
SALMA Imre;
SCHWARZ J.;
NECKI Jaroslaw;
SCIARE J;
PETIT Jean-Eudes;
JAFFREZO J.L.;
VASILESCU Jeni;
DE LA ROSA J C;
PAURAITE Julija;
OVADNEVAITE Jurgita;
YTTRI Karl Espen;
ELEFTHERIADIS K.;
POULAIN Laurent;
BELEGANTE Livio;
ALADOS ARBOLEDAS Lucas;
MANOUSAKAS Manousos-Ioannis;
PAGLIONE Marco;
MAASIKMETS Marek;
MINGUILLÓN M. C.;
GINI Maria I.;
RINALDI Matteo;
PIKRIDAS Michael;
AURELA M;
MARCHAND Nicolas;
ZOGRAFOU Olga;
FAVEZ Olivier;
VODIČKA Petr;
POKORNA Petra;
LHOTKA Radek;
ATABAKHSH Samira;
CONIL Sébastien;
CASTILLO Sonia;
GILARDONI S.;
PLATT Stephen M.;
GRANGE Stuart;
POLUZZI Vanes;
KUMAR Varun;
RIFFAULT V.;
AAS Wenche;
QUEROL X.;
SOSEDOVA Yulia;
PROBST-HENSCH Nicole;
VIENNEAU Danielle;
PREVOT Andre S. H.;
DE HOOGH Kees;
DAELLENBACH Kaspar;
KRYMOVA Ekaterina;
EL HADDAD Imad;
2026-07-09
PERGAMON-ELSEVIER SCIENCE LTD
JRC145869
1873-6750 (online),
https://www.sciencedirect.com/science/article/pii/S0160412026001017?via%3Dihub,
https://publications.jrc.ec.europa.eu/repository/handle/JRC145869,
10.1016/j.envint.2026.110143 (online),
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