Jouanny A., Upadhyay A., Jiang J. H., Vasilakos P., Via M., Cheng Y., Flueckiger B., Uzu Gaëlle, Jaffrezo J. L., Voiron Céline, Favez O., Chebaicheb H., Bourin A., Font A., Riffault V., Freney E., Marchand N., Chazeau B., Conil S., Petit J. E., de la Rosa J. D., de la Campa A. S., Navarro D. S. R., Castillo S., Alastuey A., Querol X., Reche C., Minguillon M. C., Maasikmets M., Keernik H., Giardi F., Colombi C., Cuccia E., Gilardoni S., Rinaldi M., Paglione M., Poluzzi V., Massabo D., Belis C., Grange S., Hueglin C., Canonaco F., Tobler A., Timonen H. J., Aurela M., Ehn M., Stavroulas I., Bougiatioti A., Eleftheriadis K., Gini M. I., Zografou O., Manousakas M. I., Chen G. I., Green D. C., Pokorna P., Vodicka P., Lhotka R., Schwarz J., Schemmel A., Atabakhsh S., Herrmann H., Poulain L., Flentje H., Heikkinen L., Kumar V., van der Gon H. A. D., Aas W., Platt S. M., Yttri K. E., Salma I., Vasanits A., Bergmans B., Sosedova Y., Necki J., Ovadnevaite J., Lin C. S., Pauraite J., Pikridas M., Sciare J., Vasilescu J., Belegante L., Alves C., Slowik J. G., Probst-Hensch N., Vienneau D., Prevot A. S. H., Medbouhi A. A., Banos D. T., de Hoogh K., Daellenbach K. R., Krymova E., El Haddad I. (2025). Machine-learning-driven reconstruction of organic aerosol sources across dense monitoring networks in Europe. Environmental Science and Technology Letters, 12 (11), 1523-1531. ISSN 2328-8930.
Titre du document
Machine-learning-driven reconstruction of organic aerosol sources across dense monitoring networks in Europe
Année de publication
2025
Auteurs
Jouanny A., Upadhyay A., Jiang J. H., Vasilakos P., Via M., Cheng Y., Flueckiger B., Uzu Gaëlle, Jaffrezo J. L., Voiron Céline, Favez O., Chebaicheb H., Bourin A., Font A., Riffault V., Freney E., Marchand N., Chazeau B., Conil S., Petit J. E., de la Rosa J. D., de la Campa A. S., Navarro D. S. R., Castillo S., Alastuey A., Querol X., Reche C., Minguillon M. C., Maasikmets M., Keernik H., Giardi F., Colombi C., Cuccia E., Gilardoni S., Rinaldi M., Paglione M., Poluzzi V., Massabo D., Belis C., Grange S., Hueglin C., Canonaco F., Tobler A., Timonen H. J., Aurela M., Ehn M., Stavroulas I., Bougiatioti A., Eleftheriadis K., Gini M. I., Zografou O., Manousakas M. I., Chen G. I., Green D. C., Pokorna P., Vodicka P., Lhotka R., Schwarz J., Schemmel A., Atabakhsh S., Herrmann H., Poulain L., Flentje H., Heikkinen L., Kumar V., van der Gon H. A. D., Aas W., Platt S. M., Yttri K. E., Salma I., Vasanits A., Bergmans B., Sosedova Y., Necki J., Ovadnevaite J., Lin C. S., Pauraite J., Pikridas M., Sciare J., Vasilescu J., Belegante L., Alves C., Slowik J. G., Probst-Hensch N., Vienneau D., Prevot A. S. H., Medbouhi A. A., Banos D. T., de Hoogh K., Daellenbach K. R., Krymova E., El Haddad I.
Source
Environmental Science and Technology Letters, 2025,
12 (11), 1523-1531 ISSN 2328-8930
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.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Sciences du milieu [021]
Description Géographique
EUROPE
Localisation
Fonds IRD [F B010095433]
Identifiant IRD
fdi:010095433
