Machine-learning-driven reconstruction of organic aerosol sources across dense monitoring networks in Europe
2 Global Institute for Urban and Regional Sustainability, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200231, China
3 Center for Atmospheric Research, University of Nova Gorica, SI-5000 Nova Gorica, Slovenia
4 ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]
5 Swiss Tropical and Public Health Institute, Kreuzstraße 2, 4123 Allschwil, Switzerland
6 IGE - Institut des Géosciences de l’Environnement
7 INERIS - Institut National de l'Environnement Industriel et des Risques
8 CERI EE - IMT Nord Europe - Centre for Energy and Environment
9 King‘s College London
10 Imperial College London
11 LaMP - Laboratoire de Météorologie Physique
12 LCE - Laboratoire Chimie de l'environnement
13 LAC - Laboratory of Atmospheric Chemistry [Paul Scherrer Institute]
14 ANDRA - Agence Nationale pour la Gestion des Déchets Radioactifs
15 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]
16 CAE - Chimie Atmosphérique Expérimentale
17 Universidad de Huelva
18 UGR - Universidad de Granada = University of Granada
19 IDAEA - Institute of Environmental Assessment and Water Research
20 EKUK - Estonian Environmental Research Centre
21 National Institute for Nuclear Physics−Florence Section, Via Sansone 1, 50019 Sesto Fiorentino, Florence, Italy
22 ENVIRONMENTAL PROTECTION AGENCY OF LOMBARDY ITA
23 CNR-ISP - Institute of Polar Sciences [Venezia-Mestre]
24 ISAC - Istituto di Scienze dell'Atmosfera e del Clima [Bologna]
25 ISAC - CNR Institute of Atmospheric Sciences and Climate
26 Arpae Emilia-Romagna, Centro Tematico Regionale Qualità dell’Aria, 40139 Bologna, Italy
27 UniGe - Università degli studi di Genova = University of Genoa = Université de Gênes
28 European Commission, Joint Research Centre, 21027 Ispra, Italy
29 School of Earth and Atmospheric Sciences, Queensland University of Technology, Gardens Point, Brisbane, Queensland 4000, Australia
30 Empa, Swiss Federal Laboratories for Materials Science and Technology, 8600 Dübendorf, Switzerland
31 Datalystica Ltd.
32 Atmospheric Composition Research, Finnish Meteorological Institute, 00560 Helsinki, Finland
33 Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Athens, Greece
34 Environmental Radioactivity & Aerosol Technology for Atmospheric & Climate Impact Lab, INRaSTES, NCSR “Demokritos”, 15310 Athens, Greece
35 MRC Centre for Environment and Health, Environmental Research Group, Imperial College London, 86 Wood Lane, London W12 0BZ, United Kingdom
36 Institute of Chemical Process Fundamentals, Czech Academy of Sciences, 165 00 Prague 6, Czech Republic
37 Umweltbundesamt, 06844 Dessau-Roßlau, Germany
38 TROPOS - Leibniz Institute for Tropospheric Research
39 German Meteorological Service (DWD), 82383 Hohenpeissenberg, Germany
40 Department of Environmental Science, Stockholm University, 114 18 Stockholm, Sweden
41 Department of Environmental Sciences, Aarhus University, DK-4000 Aarhus, Denmark
42 Department of Climate, Air and Sustainability, TNO, 3584 CB Utrecht, Netherlands
43 NILU - Norwegian Institute for Air Research
44 Institute of Chemistry, Eötvös Loránd University, 1053 Budapest, Hungary
45 Institut Scientifique de Service Public (ISSEP), 4000 Liège, Belgium
46 Datalystica, Limited, 5234 Villigen, Switzerland
47 Faculty of Physics and Applied Computer Science, AGH University of Krakow, 30-059 Krakow, Poland
48 University of Galway
49 State Key Laboratory of Loess Sciences, Center for Excellence in Quaternary Science and Global Change, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
50 SRI Center for Physical Sciences and Technology (FTMC), 10257 Vilnius, Lithuania
51 Environmental Chemical Processes Laboratory (ECPL), Chemistry Department, University of Crete, Heraklion, Crete, Greece; Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, 20, Konstantinou Kavafi Str., 2121, Aglantzia, Nicosia, Cyprus
52 INOE - National Institute of Research and Development for Optoelectronics
53 CESAM - Centro de Estudos do Ambiente e do Mar = Centre for Environmental and Marine Studies [Aveiro]
54 PSI Center for Energy and Environmental Sciences, 5232 Villigen, Switzerland
55 Department of Intelligent Systems, KTH Royal Institute of Technology, 11428 Stockholm, Sweden
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Résumé
Fine particulate matter (PM) poses a major threat to public health, with organic aerosol (OA) being a key component. Major OA sources, hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and oxygenated OA (OOA), have distinct health and environmental impacts. However, OA source apportionment via positive matrix factorization (PMF) applied to aerosol mass spectrometry (AMS) or aerosol chemical speciation monitoring (ACSM) data is costly and limited to a few supersites, leaving over 80% of OA data uncategorized in global monitoring networks. To address this gap, we trained machine learning models to predict HOA, BBOA, and OOA using limited OA source apportionment data and widely available organic carbon (OC) measurements across Europe (2010–2019). Our best performing model expanded the OA source data set 4-fold, yielding 85 000 daily apportionment values across 180 sites. Results show that HOA and BBOA peak in winter, particularly in urban areas, while OOA, consistently the dominant fraction, is more regionally distributed with less seasonal variability. This study provides a significantly expanded OA source data set, enabling better identification of pollution hotspots and supporting high-resolution exposure assessments.
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