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Machine-learning-driven reconstruction of organic aerosol sources across dense monitoring networks in Europe

Title: Machine-learning-driven reconstruction of organic aerosol sources across dense monitoring networks in Europe
Authors: Jouanny, Adrien; Upadhyay, Abhishek; Jiang, Jianhui; Vasilakos, Petros; Via, Marta; Cheng, Yun; Flueckiger, Benjamin; Uzu, Gaëlle; Jaffrezo, Jean-Luc; Voiron, Céline; Favez, Olivier; Chebaicheb, Hasna; Bourin, Aude; Font, Anna; Riffault, Véronique; Freney, Evelyn; Marchand, Nicolas; Chazeau, Benjamin; Conil, Sébastien; Petit, Jean-Eudes; de la Rosa, Jesús; de la Campa, Ana Sanchez; Navarro, Daniel Sanchez-Rodas; Castillo, Sonia; Alastuey, Andrés; Querol, Xavier; Reche, Cristina; Minguillón, María Cruz; Maasikmets, Marek; Keernik, Hannes; Giardi, Fabio; Colombi, Cristina; Cuccia, Eleonora; Gilardoni, Stefania; Rinaldi, Matteo; Paglione, Marco; Poluzzi, Vanes; Massabò, Dario; Belis, Claudio; Grange, Stuart; Hueglin, Christoph; Canonaco, Francesco; Tobler, Anna; Timonen, Hilkka; Aurela, Minna; Ehn, Mikael; Stavroulas, Iasonas; Bougiatioti, Aikaterini; Eleftheriadis, Konstantinos; Gini, Maria; Zografou, Olga; Manousakas, Manousos-Ioannis; Chen, Gang Ian; Pokorná, Petra; Vodička, Petr; Lhotka, Radek; Schwarz, Jaroslav; Schemmel, Andrea; Atabakhsh, Samira; Herrmann, Hartmut; Poulain, Laurent; Flentje, Harald; Heikkinen, Liine; Kumar, Varun; Denier van der Gon, Hugo Anne; Aas, Wenche; Platt, Stephen; Yttri, Karl Espen; Salma, Imre; Vasanits, Anikó; Bergmans, Benjamin; Sosedova, Yulia; Necki, Jaroslaw; Ovadnevaite, Jurgita; Lin, Chunshui; Pauraite, Julija; Pikridas, Michael; Sciare, Jean; Vasilescu, Jeni; Belegante, Livio; Alves, Célia; Slowik, Jay; Probst-Hensch, Nicole; Vienneau, Danielle; Prévôt, André; Medbouhi, Aniss Aiman; Banos, Daniel Trejo; de Hoogh, Kees; Daellenbach, Kaspar; Krymova, Ekaterina; El Haddad, Imad
Contributors: Paul Scherrer Institut (PSI); Global Institute for Urban and Regional Sustainability, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200231, China; Center for Atmospheric Research, University of Nova Gorica, SI-5000 Nova Gorica, Slovenia; Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology Zürich (ETH Zürich); Swiss Tropical and Public Health Institute, Kreuzstraße 2, 4123 Allschwil, Switzerland; Institut des Géosciences de l’Environnement (IGE); Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Observatoire des Sciences de l'Univers de Grenoble (Fédération OSUG)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); Université Grenoble Alpes (UGA); Institut National de l'Environnement Industriel et des Risques (INERIS); Centre for Energy and Environment (CERI EE - IMT Nord Europe); Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Nord Europe); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); King‘s College London; Imperial College London; Laboratoire de Météorologie Physique (LaMP); Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA); Laboratoire Chimie de l'environnement (LCE); Aix Marseille Université (AMU)-Institut de Chimie - CNRS Chimie (INC-CNRS)-Centre National de la Recherche Scientifique (CNRS); Laboratory of Atmospheric Chemistry Paul Scherrer Institute (LAC); Paul Scherrer Institute (PSI); Agence Nationale pour la Gestion des Déchets Radioactifs (ANDRA); Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE); Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA); Chimie Atmosphérique Expérimentale (CAE); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)); Universidad de Huelva; Universidad de Granada = University of Granada (UGR); Institute of Environmental Assessment and Water Research (IDAEA); Consejo Superior de Investigaciones Cientificas España = Spanish National Research Council Spain (CSIC); Estonian Environmental Research Centre (EKUK); National Institute for Nuclear Physics−Florence Section, Via Sansone 1, 50019 Sesto Fiorentino, Florence, Italy; ENVIRONMENTAL PROTECTION AGENCY OF LOMBARDY ITA; Partenaires IRSTEA; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA); Institute of Polar Sciences Venezia-Mestre (CNR-ISP); National Research Council of Italy; Istituto di Scienze dell'Atmosfera e del Clima Bologna (ISAC); CNR Institute of Atmospheric Sciences and Climate (ISAC); Arpae Emilia-Romagna, Centro Tematico Regionale Qualità dell’Aria, 40139 Bologna, Italy; Università degli studi di Genova = University of Genoa = Université de Gênes (UniGe); European Commission, Joint Research Centre, 21027 Ispra, Italy; School of Earth and Atmospheric Sciences, Queensland University of Technology, Gardens Point, Brisbane, Queensland 4000, Australia; Empa, Swiss Federal Laboratories for Materials Science and Technology, 8600 Dübendorf, Switzerland; Datalystica Ltd.; Atmospheric Composition Research, Finnish Meteorological Institute, 00560 Helsinki, Finland; Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Athens, Greece; Environmental Radioactivity & Aerosol Technology for Atmospheric & Climate Impact Lab, INRaSTES, NCSR “Demokritos”, 15310 Athens, Greece; MRC Centre for Environment and Health, Environmental Research Group, Imperial College London, 86 Wood Lane, London W12 0BZ, United Kingdom; Institute of Chemical Process Fundamentals, Czech Academy of Sciences, 165 00 Prague 6, Czech Republic; Umweltbundesamt, 06844 Dessau-Roßlau, Germany; Leibniz Institute for Tropospheric Research (TROPOS); German Meteorological Service (DWD), 82383 Hohenpeissenberg, Germany; Department of Environmental Science, Stockholm University, 114 18 Stockholm, Sweden; Department of Environmental Sciences, Aarhus University, DK-4000 Aarhus, Denmark; Department of Climate, Air and Sustainability, TNO, 3584 CB Utrecht, Netherlands; Norwegian Institute for Air Research (NILU); Institute of Chemistry, Eötvös Loránd University, 1053 Budapest, Hungary; Institut Scientifique de Service Public (ISSEP), 4000 Liège, Belgium; Datalystica, Limited, 5234 Villigen, Switzerland; Faculty of Physics and Applied Computer Science, AGH University of Krakow, 30-059 Krakow, Poland; University of Galway; 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; SRI Center for Physical Sciences and Technology (FTMC), 10257 Vilnius, Lithuania; 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; National Institute of Research and Development for Optoelectronics (INOE); Centro de Estudos do Ambiente e do Mar = Centre for Environmental and Marine Studies Aveiro (CESAM); Universidade de Aveiro = University of Aveiro; PSI Center for Energy and Environmental Sciences, 5232 Villigen, Switzerland; Department of Intelligent Systems, KTH Royal Institute of Technology, 11428 Stockholm, Sweden; Swiss Data Science Center (SDSC) Collaborative Project Grant (C20-08); Federal Office of Environment of Switzerland; COST Action CA16109 COLOSSAL; Ministry of Education, Youth and Sports of the Czech Republic under Project ACTRIS-CZ LM2023030; Regional Catalan Government (Grant AGAUR 2021SGR00447); Core Program within the Romanian National Research Development and Innovation Plan 2022–2027, carried out with the support of MCID, Project PN 23 05; Hungarian Research, Development and Innovation Office (Contract: Advanced 150835); EPA-Ireland and the Department of Environment, Climate and Communications (AEROSOURCE); Taighde Éireann–Research Ireland (22/FFP-A/10611); Portuguese Foundation for Science and Technology (FCT) for funding CESAM (UIDP/50017/2020, UIDB/50017/2020, and LAP/0094/2020); National Key Research and Development Program of China (2023YFC3710400); National Natural Science Foundation of China (42207122); French Ministry of Environment for the CARA programme; ADEME DECOMBIO, CAMERA, and QAMECS; ACME and MIAI-Airquality (funded by the University Grenoble Alpes); CLIMIBIO and ECRIN Projects (Hauts-de-France Region & European Regional Development Fund); ANR-11-LABX-0005,Cappa,Physiques et Chimie de l'Environnement Atmosphérique(2011); European Project: 101036245,H2020-LC-GD-2020,H2020-LC-GD-2020-6,RI-URBANS(2021); European Project: 856612,H2020-WIDESPREAD-2018-2020,H2020-WIDESPREAD-2018-01,EMME-CARE(2019); European Project: 101081355,HORIZON-MSCA-2021-COFUND-01,HORIZON-MSCA-2021-COFUND-01,SMASH(2023)
Source: ISSN: 2328-8930 ; Environmental Science and Technology Letters ; https://hal.science/hal-05326968 ; Environmental Science and Technology Letters, 2025, 12 (11), pp.1523-1531. ⟨10.1021/acs.estlett.5c00771⟩ ; https://pubs.acs.org/doi/10.1021/acs.estlett.5c00771.
Publisher Information: CCSD
Publication Year: 2025
Collection: Université de Versailles Saint-Quentin-en-Yvelines: HAL-UVSQ
Subject Terms: source apportionment; Europe data set; machine learning; deep learning; organic aerosols; air quality; spatial−temporal analysis; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere
Description: International audience ; 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.
Document Type: article in journal/newspaper
Language: English
Relation: info:eu-repo/grantAgreement//101036245/EU/Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial AreaS (RI-URBANS)/RI-URBANS; info:eu-repo/grantAgreement//856612/EU/Eastern Mediterranean and Middle East – Climate and Atmosphere Research Centre/EMME-CARE; info:eu-repo/grantAgreement//101081355/EU/Machine learning for Sciences and Humanities/SMASH; IRD: fdi:010095433
DOI: 10.1021/acs.estlett.5c00771
Availability: https://hal.science/hal-05326968; https://hal.science/hal-05326968v2/document; https://hal.science/hal-05326968v2/file/machine-learning-driven-reconstruction-of-organic-aerosol-sources-across-dense-monitoring-networks-in-europe.pdf; https://doi.org/10.1021/acs.estlett.5c00771
Rights: https://creativecommons.org/licenses/by-nc/4.0/ ; info:eu-repo/semantics/OpenAccess
Accession Number: edsbas.548BBC93
Database: BASE