Pekel F., Uzu Gaëlle, Weber S., Kranenburg R., Tokaya J., Schaap M., Dominutti P., Favez O., Jaffrezo J. L., Timmermans R. (2025). Comparison of modelled and experimental PM10 source contributions for mapping source-specific oxidative potential. Atmospheric Environment-X, 27, p. 100339 [18 p.].
Titre du document
Comparison of modelled and experimental PM10 source contributions for mapping source-specific oxidative potential
Année de publication
2025
Auteurs
Pekel F., Uzu Gaëlle, Weber S., Kranenburg R., Tokaya J., Schaap M., Dominutti P., Favez O., Jaffrezo J. L., Timmermans R.
Source
Atmospheric Environment-X, 2025,
27, p. 100339 [18 p.]
To effectively reduce the health burden of particulate matter (PM) pollution requires indicators more directly linked to adverse health effects than total PM mass alone. Oxidative potential (OP)-the ability of PM to induce oxidative stress based on its chemical composition-is gaining recognition as a health-relevant metric. Integrating source-specific OP values from field measurements into Chemical Transport Models (CTMs) enables the mapping of source-specific OP with broad spatiotemporal coverage. A critical step is ensuring alignment between CTM-derived and observation-based source contributions. This study evaluates and optimises the consistency between the LOTOS-EUROS CTM and Positive Matrix Factorization (PMF) source profiles, using PM10 data from 15 French sites (2013-2016). While total PM10 shows reasonable correlation with observations (r2 = 0.35-0.66), source-specific comparisons vary across source-types and locations. Promising results are obtained for residential biomass burning (r2 = 0.34-0.75), secondary inorganic aerosols (r2 = 0.30-0.71), and sea salt (r2 = 0.18-0.71), whereas road traffic shows weaker alignment (r2 = 0.01-0.40). Using the optimized source matching, OP maps are generated over France, showing stronger contributions from anthropogenic sources to OP than to PM10 mass. The study highlights key challenges in matching CTM and PMF sources for OP modelling, due to secondary aerosol formation, source mixing within PMF profiles, and spatiotemporal representation differences. Refining emission data, incorporating secondary organic aerosol and aging processes in CTMs, and expanding source-specific OP measurements, particularly for uncharacterized sources like agriculture are identified as essential next steps. Despite current limitations, this approach offers a promising framework for advancing health-oriented air quality management.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Sciences du milieu [021]
Description Géographique
FRANCE
Localisation
Fonds IRD [F B010094293]
Identifiant IRD
fdi:010094293
