Machine-learning-driven reconstruction of organic aerosol sources across dense monitoring networks in Europe
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2025-11-11
Date Acceptance
2025-10-13
Citation
Environmental Science and Technology Letters, 2025, 12 (11), pp.1523-1531
ISSN
2328-8930
Publisher
American Chemical Society
Start Page
1523
End Page
1531
Journal / Book Title
Environmental Science and Technology Letters
Volume
12
Issue
11
Copyright Statement
© 2025 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0.
License URL
Identifier
10.1021/acs.estlett.5c00771
Publication Status
Published
Date Publish Online
2025-10-20