Articles | Volume 21, issue 12
https://doi.org/10.5194/acp-21-9719-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-9719-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
29 Jun 2021
Research article | Highlight paper |
| 29 Jun 2021
| 29 Jun 2021
Disparities in particulate matter (PM10) origins and oxidative potential at a city scale (Grenoble, France) – Part 2: Sources of PM10 oxidative potential using multiple linear regression analysis and the predictive applicability of multilayer perceptron neural network analysis
Lucille Joanna S. Borlaza
CORRESPONDING AUTHOR
University of Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), 38000 Grenoble, France
Samuël Weber
University of Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), 38000 Grenoble, France
Jean-Luc Jaffrezo
University of Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), 38000 Grenoble, France
Stephan Houdier
University of Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), 38000 Grenoble, France
Rémy Slama
University of Grenoble Alpes, Inserm, CNRS, IAB (Institute of Advanced Biosciences), Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
Camille Rieux
Atmo AuRA, 38400 Grenoble, France
Alexandre Albinet
INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
Steve Micallef
Atmo AuRA, 38400 Grenoble, France
Cécile Trébluchon
Atmo AuRA, 38400 Grenoble, France
University of Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), 38000 Grenoble, France
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Latest update: 02 Jul 2025
Short summary
With an enhanced source apportionment obtained in a companion paper, this paper acquires more understanding of the spatiotemporal associations of the sources of PM to oxidative potential (OP), an emerging health-based metric. Multilayer perceptron neural network analysis was used to apportion OP from PM sources. Results showed that such a methodology is as robust as the linear classical inversion and permits an improvement in the OP prediction when local features or non-linear effects occur.
With an enhanced source apportionment obtained in a companion paper, this paper acquires more...
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