Next Article in Journal
A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates
Next Article in Special Issue
Sources and Geographical Origins of PM10 in Metz (France) Using Oxalate as a Marker of Secondary Organic Aerosols by Positive Matrix Factorization Analysis
Previous Article in Journal
Dispersion of a Traffic Related Nanocluster Aerosol Near a Major Road
Previous Article in Special Issue
Evaluation of Tire Wear Contribution to PM2.5 in Urban Environments
Open AccessArticle

Comparison of PM10 Sources Profiles at 15 French Sites Using a Harmonized Constrained Positive Matrix Factorization Approach

Univ. Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), 38000 Grenoble, France
INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
IMT Lille Douai, Univ. Lille, UR SAGE, 59500 Douai, France
Univ. Savoie Mont-Blanc, LCME, 73000 Chambéry, France
Atmo AuRA, 69500 Bron, France
Atmo Sud, 13294 Marseille, France
Atmo Hauts de France, 59044 Lille, France
Atmo Nouvelle Aquitaine, 33692 Merignac, France
Atmo Normandie, 76000 Rouen, France
Atmo Grand Est, 57070 Metz, France
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(6), 310;
Received: 13 May 2019 / Revised: 28 May 2019 / Accepted: 28 May 2019 / Published: 4 June 2019
(This article belongs to the Special Issue Air Quality and Sources Apportionment)
Receptor-oriented models, including positive matrix factorization (PMF) analyses, are now commonly used to elaborate and/or evaluate action plans to improve air quality. In this context, the SOURCES project has been set-up to gather and investigate in a harmonized way 15 datasets of chemical compounds from PM10 collected for PMF studies during a five-year period (2012–2016) in France. The present paper aims at giving an overview of the results obtained within this project, notably illustrating the behavior of key primary sources as well as focusing on their statistical robustness and representativeness. Overall, wood burning for residential heating as well as road transport were confirmed to be the two main primary sources strongly influencing PM10 loadings across the country. While wood burning profiles, as well as those dominated by secondary inorganic aerosols, present a rather good homogeneity among the sites investigated, some significant variabilities were observed for primary traffic factors, illustrating the need to better characterize the diversity of the various vehicle exhaust and non-exhaust emissions. Finally, natural sources, such as sea salts (widely observed in internal mixing with anthropogenic compounds), primary biogenic aerosols and/or terrigenous particles, were also found as non-negligible PM10 components at every investigated site. View Full-Text
Keywords: PM; source apportionment; aerosols; similarity assessment; uncertainties PM; source apportionment; aerosols; similarity assessment; uncertainties
Show Figures

Graphical abstract

MDPI and ACS Style

Weber, S.; Salameh, D.; Albinet, A.; Alleman, L.Y.; Waked, A.; Besombes, J.-L.; Jacob, V.; Guillaud, G.; Meshbah, B.; Rocq, B.; Hulin, A.; Dominik-Sègue, M.; Chrétien, E.; Jaffrezo, J.-L.; Favez, O. Comparison of PM10 Sources Profiles at 15 French Sites Using a Harmonized Constrained Positive Matrix Factorization Approach. Atmosphere 2019, 10, 310.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop