Machine-Learning-Driven Reconstruction of Organic Aerosol Sources across Dense Monitoring Networks in Europe

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985858%3A_____%2F25%3A00640645" target="_blank" >RIV/67985858:_____/25:00640645 - isvavai.cz</a>
Result on the web
<a href="https://pubs.acs.org/doi/pdf/10.1021/acs.estlett.5c00771?ref=article_openPDF" target="_blank" >https://pubs.acs.org/doi/pdf/10.1021/acs.estlett.5c00771?ref=article_openPDF</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acs.estlett.5c00771" target="_blank" >10.1021/acs.estlett.5c00771</a>

Result language
angličtina
Original language name
Machine-Learning-Driven Reconstruction of Organic Aerosol Sources across Dense Monitoring Networks in Europe
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10509 - Meteorology and atmospheric sciences

Project
<a href="/en/project/LM2023030" target="_blank" >LM2023030: ACTRIS – participation of the Czech Republic</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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